International Journal of Grid and Utility Computing (69 papers in press)
Research on regression test method based on multiple UML graphic models
by Mingcheng Qu, Xianghu Wu, Yongchao Tao, Guannan Wang, Ziyu Dong
Abstract: Most of the existing graph-based regression testing schemes aim at a given UML graph and are not flexible in regression testing. This paper proposes a method of a universal UML for a variety of graphical model modifications, and obtains a UML graphics module structure modified regression testing that must be retested, determined by the domain of influence analysis on the effect of UML modification on the graphical model test case generated range analysis, finally re auto generate test cases. This method has been proved to have a high logical coverage rate. In order to fully consider all kinds of dependency, it cannot limit the type modification of UML graphics, and has higher openness and comprehensiveness.
Keywords: regression testing; multiple UML graphical models; domain analysis.
A real-time matching algorithm using sparse matrix
by Aomei Li, Wanli Jiang, Po Ma, Jiahui Guo, Weihua Yuan, Dehui Dai
Abstract: Aiming at the shortcomings of the traditional image feature matching algorithm, which is computationally expensive and time-consuming, this paper presents a real-time feature matching algorithm. Firstly, the algorithm constructs sparse matrices by Laplace operator and the Laplace weighting is carried out. Then, the feature points are detected by the FAST feature point detection algorithm. The SURF algorithm is used to assign the direction and descriptor to the feature for rotation invariance, We then use the Gaussian pyramid to make it scalable invariance. Secondly, the match pair is extracted by the violent matching method, and the matching pair is purified by Hamming distance and symmetry method. Finally, the RANSAC algorithm is used to get the optimal matrix, and the affine invariance check is used to match the result. The algorithm is compared with the classical feature point matching algorithm, which proves that the method has high real-time performance under the premise of guaranteeing the matching precision.
Keywords: sparse matrices; Laplace weighted; FAST; SURF; symmetry method; affine invariance check.
How do checkpoint mechanisms and power infrastructure failures impact on cloud applications?
by Guto Leoni Santos, Demis Gomes, Djamel Sadok, Judith Kelner, Elisson Rocha, Patricia Takako Endo
Abstract: With the growth of cloud computing usage by commercial companies, providers of this service are looking for ways to improve and estimate the quality of their services. Failures in the power subsystem represent a major risk of cloud data centre unavailability at the physical level. At same time, software-level mechanisms (such as application checkpointing) can be used to maintain the application consistency after a downtime and also improve its availability. However, understanding how failures at the physical level impact on the application availability, and how software-level mechanisms can improve the data centre availability is a challenge.
This paper analyses the impact of power subsystem failures on cloud application availability, as well as the impact of having checkpoint mechanisms to recover the system from software-level failure. For that, we propose a set of stochastic models to represent the cloud power subsystem, the cloud application, and also the checkpoint-based retrieval mechanisms. To evaluate data centre performance, we also model requests arrival and time to process as a queue, and feed this model with real data acquired from experiments done in a real testbed. To verify which components of the power infrastructure most impact on the data centre availability we perform sensitivity analysis. The results of the stationary analysis show that the selection of a given type of checkpoint mechanism does not present a significant impact on the observed metrics. On the other hand, improving the power infrastructure implies performance and availability gains.
Keywords: cloud data centre; checkpoint mechanisms; availability; performance; stochatic models.
A novel web image retrieval method: bagging weighted hashing based on local structure information
by Li Huanyu
Abstract: Hashing is widely used in ANN searching problems, especially in web image retrieval. A excellent hashing algorithm can help the users to search and retrieve their web images more conveniently, quickly and accurately. In order to conquer several deficiencies of ITQ in image retrieval problem, we use ensemble learning to deal image retrieval problem. A elastic ensemble framework has been proposed to guide the hashing design, and three important principles have been proposed, named high precision, high diversity, and optimal weight prediction. Basing on this, we design a novel hashing method called BWLH. In BWLH, first, the local structure information of the original data is extracted to construct the local structure data, thus to improve the similarity-preserve ability of hash bits. Second, a weighted matrix is used to balance the variance of different bits. Third, bagging is exploited to expand diversity in different hash tables. Sufficient experiments show that BWLH can handle the image retrieval problem effectively, and perform better than several state-of-the-art methods at same hash code length on dataset CIFAR-10 and LabelMe. Finally, search by image, a web-based use case scenario of the proposed hashing BWLH, is given to detail how the proposed method can be used in a web-based environment.
Keywords: web image retrieval; hashing; ensemble learning; local structure information; weighted.
An integrated incentive and trust-based optimal path identification in ad hoc on-demand multipath distance vector routing for MANET
by Abrar Omar Alkhamisi, Seyed M. Buhari, George Tsaramirsis, Mohammed Basheri
Abstract: A Mobile Ad hoc Network (MANET) can exist and work well only when the mobile nodes behave cooperatively in packet routing. To reduce the hazards from malicious nodes and enhance the security of the network, this paper extends an Adhoc On-Demand Multipath Distance Vector (AOMDV) routing protocol, named as An Integrated Incentive and Trust based optimal path identification in AOMDV (IIT-AOMDV) for MANET. To improve the security and reliability of packet forwarding over multiple routes in the presence of potentially malicious nodes, the proposed IIT-AOMDV routing protocol integrates an Intrusion Detection System (IDS) with the Bayesian Network (BN) based trust and payment model. The IDS uses the empirical first- and second-hand trust information of BN, and it underpins the cuckoo search algorithm to map the QoS and trust value into a single fitness metric, tuned according to the presence of malicious nodes in the network. Moreover, the payment system stimulates the nodes to cooperate in routing effectively and improves the routing performance. Finally, the simulation results show that the IIT-AOMDV improves the detection accuracy and throughput by 20% and 16.6%, respectively, more than that of existing AOMDV integrated with the IDS (AID).
Keywords: mobile ad hoc network; intrusion detection system; trust; attack; optimal path identification; isolation.
Detection and mitigation of collusive interest flooding attack on content centric networking
by Tetsuya Shigeyasu, Ayaka Sonoda
Abstract: According to the development of ICT (Information and Communications Technology),
deployments of consumer devices, such as note PC, smartphone, and other information devices, make it easy for users to access to the internet. Users having these devices use services of e-mail and SNS (Social Network Service). NDN (Named Data Networking), which is the most popular network architecture, has been proposed to realise the concept of CCN. However, it has been also reported that the NDN is vulnerable to CIFA (Collusive Interest Flooding Attack). In this paper, we propose a novel distributed algorithm for detecting CIFA for keeping availabilities of NDN. The results of computer simulations confirm that our proposal can detect and mitigate the effects of CIFA, effectively.
Keywords: named data networking; content centric data acquisition; collusive interest flooding attack; malicious prediction.
A new overlay P2P network for efficient routing in group communication with regular topologies
by Abdelhalim Hacini, Mourad Amad
Abstract: This research paper gives a new overlay P2P network to provide a performant
and optimised lookup process. The lookup process of the proposed solution reduces
the overlay hops and consequently the latency for content lookup, between any pair
of nodes. The overlay network is constructed on top of physical networks without any
centralised control and with a hierarchy of two levels. The architecture is based on regular topologies, which are the pancake graphs and the skip graphs. The focus of all topology construction schemes is to reduce the cost of the lookup process (number of hops and delay) and consequently improve the search performance for P2P applications deployed on the overlay network. Performance evaluations of our proposed scheme show that results obtained are globally satisfactory.
Keywords: P2P networking; pancake graphs; skip graphs; routing optimisation.
A smart networking and computing-aware orchestrator to enhance QoS on cloud-based multimedia services
by Rodrigo Moreira, Flavio Silva, Pedro Frosi Rosa, Rui Aguiar
Abstract: Rich-media applications deployed on cloud lead the use of the internet by people and organisations around the world. Networking and computing resource management has become an important requirement to achieve high user QoS. The advent of software-defined networking and networking function virtualisation brings new possibilities to address carrier environment challenges making QoS enhancement possible. The literature does not show a smart and flexible solution that brings scalability with a holistic view of networking and computing resources taking into account different ways to enhance QoS. In this work, we present a smart orchestrator capable of interacting with the network and computing resources and applications hosted on a cloud. By providing support to different ML algorithms, our solution provides better QoS by improvements in aspects such as network resilience, bandwidth allocation based on real-time traffic patterns, and end-to-end QoS mechanism to event-driven scenarios. The solution interacts in an agnostic way with different applications, cloud operating systems, and the network. As a separate control plane entity, the orchestrator is capable of operating across different domains. The solution orchestrates applications, virtual functions, and cloud resources, providing elastic and network enhancing QoS. Our experimental evaluation in a large-scale testbed shows the orchestrator's capability to provide a smart jitter decrease using AI techniques.
Keywords: software-defined networking; network function virtualisation; QoS; machine learning; cloud computing.
Hardware support for thread synchronisation in an experimental manycore system
by Alessandro Cilardo, Mirko Gagliardi, Daniele Passaretti
Abstract: This paper deals with the problem of thread synchronisation in manycore systems. In particular, it considers the open-source GPU-like architecture developed within the MANGO H2020 project. The thread synchronisation hardware relies on a distributed master and on a lightweight control unit to be deployed within the core. It does not rely on memory access for exchanging synchronisation information since it uses hardware-level messages. The solution supports multiple barriers for different application kernels possibly being executed simultaneously. The results for different NoC sizes provide indications about the reduced synchronisation times and the area overheads incurred by our solution.
Keywords: networks on chip; synchronisation; manycore systems.
Identifying journalistically relevant social media texts using human and automatic methodologies
by Nuno Guimaraes, Filipe Miranda, Alvaro Figueira
Abstract: Social networks have provided the means for constant connectivity and fast information dissemination. In addition, real-time posting allowed a new form of citizen journalism, where users can report events from a witness perspective. Therefore, information propagates through the network at a faster pace than traditional media reports it. However, relevant information is a small percentage of all the content shared. Our goal is to develop and evaluate models that can automatically detect journalistic relevance. To do it, we need solid and reliable ground-truth data with a significantly large amount of annotated posts, so that the models can learn to detect relevance in all its spectrum. In this article, we present and confront two different methodologies: an automatic and a human approach. Results on a test dataset labelled by experts show that the models trained with automatic methodology tend to perform better in contrast to the ones trained using human annotated data.
Keywords: relevance detection; machine learning; text mining; crowdsourcing task.
Dijkstra algorithm based ray tracing for tunnel-Like structures
by Kazunori Uchida
Abstract: This paper deals with ray tracing in a closed space, such as tunnel or underground, by using a newly developed simulation method based on the Dijkstra algorithm (DA). The essence of this method is to modify the proximity-node matrix obtained by DA in terms of three procedures, path-selection, path-linearisation and line of sight (LOS) check. The proposed method can be applied to ray tracing in complicated structures ranging from an open space such as random rough surface (RRS) or urban area to a closed space such as tunnel or underground. In case of a closed space, however, more detailed discussions are required than in case of an open space, since especially at a grazing angle of incidence, we have to take account of the effects of floor, ceiling and side walls not only locally but also globally. In this paper we propose an effective procedure for LOS check to solve this difficult situation. Numerical examples are shown for traced rays as well as total link-cost distributions in sinusoidal and cross-type tunnels.
Keywords: Dijkstra algorithm; discrete ray tracing; LOS check; propagation in closed space.
Implementation of a high presence immersive traditional crafting system with remote collaborative work support
by Tomoyuki Ishida, Yangzhicheng Lu, Akihiro Miyakawa, Kaoru Sugita, Yoshitaka Shibata
Abstract: A high presence immersive traditional crafting system was developed to provide users, who interact with the system through head-mounted displays, with a highly realistic traditional crafting presentation experience that allows moving functions, such as free walk-through and teleportation. Users can also interactively operate traditional craft objects in space. In addition, the system supports collaborative work in a virtual space shared by remote users. To evaluate the effectiveness of this system, a questionnaire survey was administered to 124 subjects, who provided overwhelmingly positive responses regarding all functions. However, there is still room for improvement in the operability and relevancy of the system.
Keywords: collaborative virtual environment; head-mounted display; Japanese traditional crafts; interior simulation.
A configurable and executable model of Spark Streaming on Apache YARN
by Jia-Chun Lin, Ming-Chang Lee, Ingrid Chieh Yu, Einar Broch Johnsen
Abstract: Streams of data are produced today at an unprecedented scale. Efficient and stable processing of these streams requires a careful interplay between the parameters of the streaming application and of the underlying stream processing framework. Today, finding these parameters happens by trial and error on the complex, deployed framework. This paper shows that high-level models can help to determine these parameters by predicting and comparing the performance of streaming applications running on stream processing frameworks with different configurations. To demonstrate this approach, this paper considers Spark Streaming, a widely used framework to leverage data streams on the fly and provide real-time stream processing. Technically, we develop a configurable and executable model to simulate both the streaming applications and the underlying Spark stream processing framework. Furthermore, we model the deployment of Spark Streaming on Apache YARN, which is a popular open-source distributed software framework for big data processing. We show that the developed model provides a satisfactory accuracy for predicting performance by means of empirical validation.
Keywords: modelling; simulation; Spark Streaming; Apache YARN; batch processing; stream processing; ABS.
Models for hyper-converged cloud computing infrastructure planning
by Carlos Melo, Jamilson Dantas, Jean Araujo, Paulo Maciel, Rubens Matos, Danilo Oliveira, Iure Fé
Abstract: The data centre concept has evolved, mainly due to the need to reduce expenses with the required physical space to store, provide and maintain large computational infrastructures. The software-defined data centre (SDDC) is a result of this evolution. Through SDDC, any service can be hosted by virtualising more reliable and easier-to-keep hardware resources. Nowadays, many services and resources can be provided in a single rack, or even a single machine, with similar availability, considering the deployment costs of silo-based environments. One of the ways to apply the SDDC into a data centre is through hyper-convergence. Among the main contributions of this paper are the behavioral models developed for availability and capacity-oriented availability evaluation of silo-based, converged and hyper-converged cloud computing infrastructures. The obtained results may help stakeholders to select between converged and hyper-converged environments, owing to their similar availability but with the particularity of having lower deployment costs.
Keywords: Hyper-convergence; Dependability Models; Dynamical Reliability Block Diagrams; SDDC; DRBD; virtualisation; capacity-oriented availability; deployment cost; redundancy; cloud computing; OpenStack.
Architecture for diversity in the implementation of dependable and secure services using the state machine replication approach
by Caio Costa, Eduardo Alchieri
Abstract: The dependability and security properties of a system could be impaired by a system failure or by an opponent that exploits its vulnerabilities, respectively. An alternative to mitigate this risk is the implementation of fault- and intrusion-tolerant systems, in which the system properties are ensured even if some of its components fail (e.g., because a software bug or a failure in the runtime environment) or are compromised by a successful attack. State Machine Replication (SMR) is widely used to implement these systems. In SMR, servers are replicated and client requests are deterministically executed in the same order by all replicas in a way that the system behaviour remains correct even if some of them are compromised since the correct replicas mask the misbehaviour of the faulty ones. Unfortunately, the proposed solutions for SMR do not consider diversity in the implementation and all replicas execute the same software. Consequently, the same attack or software bug could compromise all the system. Trying to circumvent this problem, this work proposes an architecture to allow diversity in the implementation of dependable and secure services using the SMR approach. The goal is not to implement different versions of a SMR library for different programming languages, which demands a lot of resources and is very expensive. Instead, the proposed architecture uses an underlying SMR library and provides means to implement and execute service replicas (the application code) in different programming languages. The main problems addressed by the proposed architecture are twofold: (1) communication among different languages; and (2) data representation. The proposed architecture was integrated in the SMR library BFT-SMaRt and a set of experiments showed its practical feasibility.
Keywords: diversity; security; dependability; state machine replication.
Target exploration by Nomadic Levy walk on unit disk graphs
by Kouichirou Sugihara, Naohiro Hayashibara
Abstract: Random walks play an important role in computer science, covering a wide range of topics in theory and practice, including networking, distributed systems, and
optimisation. Levy walk is a family of random walks whose distance of a walk is chosen
from the power law distribution. There are lots of recent reports of Levy walk in the context of target detection in swarm robotics, analysing human walk patterns, and modelling the behaviour of animal foraging . According to these results, it is known as an efficient method to search in a two-dimensional plane. However, most of the works assume a continuous plane. In this paper, we propose a variant of Homesick Levy walk, called Nomadic Levy walk, and analyse the behaviour of the algorithm regarding the cover ratio on unit disk graphs. We also show the comparison of Nomadic Levy walk and Homesick Levy walk regarding the target search problem. Our simulation results indicate that the proposed algorithm is significantly efficient for sparse target detection on unit disk graphs compared with Homesick Levy walk, and it also improves the cover ratio. Moreover, we analyse the impact of the movement of the sink (home position) on the efficiency on the target exploration.
Keywords: random walk; Levy walk; target search; unit disk graphs; DTN; autonomic computing; bio-inspired algorithms.
On the design and development of emulation platforms for NFV-based infrastructures
by Vinicius Fulber Garcia, Thales Nicolai Tavares, Leonardo Da Cruz Marcuzzo, Carlos Raniery Paula Dos Santos, Giovanni Venancio De Souza, Elias Procopio Duarte Junior, Muriel Figueredo Franco, Lucas Bondan, Lisandro Zambenedetti Granville, Alberto Egon Schaeffer-Filho, Filip De Turck
Abstract: Network Functions Virtualisation (NFV) presents several advantages over traditional network architectures, such as flexibility, security, and reduced CAPEX/OPEX. In traditional middleboxes, network functions are usually executed on specialised hardware (e.g., firewall, DPI). Virtual Network Functions (VNFs) on the other hand, are executed on commodity hardware, employing Software Defined Networking (SDN) technologies (e.g., OpenFlow, P4). Although platforms for prototyping NFV environments have emerged in recent years, they still have limitations that hinder the evaluation of NFV scenarios such as fog computing and heterogeneous networks. In this work, we present NIEP, which is a platform for designing and testing NFV-based infrastructures and VNFs. NIEP consists of a network emulator and a platform for Click-based VNFs development. NIEP provides a complete NFV emulation environment, allowing network operators to test their solutions in a controlled scenario prior to deployment in production networks.
Keywords: NFV; VNF; emulation; platform; infrastructure; Click; Mininet; network.
Evaluation of navigation based on system optimal traffic assignment for connected cars
by Weibin Wang, Minoru Uehara, Haruo Ozaki
Abstract: Recently, many cars have become connected to the internet. In the near future, almost all cars will be connected cars. Such a connected car will automatically drive according to a navigation system. Conventional car navigation systems are based on user equilibrium (UE) traffic assignment. However, system optimal (SO) traffic assignment is better than UE traffic assignment. To realise SO traffic assignment, complete traffic information is required. When connected cars become ubiquitous, all traffic information will be gathered into the cloud. Therefore, a cloud-based navigation system can provide SO-based navigation to connected cars. An SO-based navigation method in which the cloud collects traffic information from connected cars, computes SO traffic assignments, and recommends SO routes to users was recently proposed. In this paper, we evaluate this SO-based navigation method in detail.
Keywords: system optimal traffic assignment; connected cars; intelligent transportation system.
Towards a secure and lightweight network function virtualisation environment
by Marco De Benedictis, Antonio Lioy, Paolo Smiraglia
Abstract: Cloud computing has deeply affected the structure of modern ICT infrastructures. It represents an enabling technology for novel paradigms, such as Network Function Virtualisation (NFV), which proposes the virtualisation of network functions to enhance the flexibility of networks and to reduce the costs of infrastructure management. Besides potential benefits, NFV inherits the limitations of traditional virtualisation where the isolation of resources comes at the cost of a performance overhead. Lightweight forms of virtualisation, such as containers, aim to mitigate this limitation. Furthermore, they allow the agile composition of complex services. These characteristics make containers a suitable technology for NFV environment. A major concern towards the exploitation of containers is security. Since containers provide less isolation than virtual machines, they can expose the whole host to vulnerabilities. In this work, we investigate container-related threats and propose a secure design for a virtual network function deployed in a lightweight NFV environment.
Keywords: security; lightweight virtualisation; container; network function virtualisation; NFV; mandatory access control; selinux; docker.
A spatial access method approach to continuous k-nearest neighbour processing for location-based services
by Wendy Osborn
Abstract: In this paper, two strategies for handing continuous k-nearest neighbour queries for location-based services are proposed. CKNN1 and CKNN2 use a validity (i.e. safe) region approach for minimising the number of query requests that need to be sent to the server. They also use a two-dimensional spatial access method for both validity region selection and in-structure searching. The latter feature ensures that new searches for a validity region are not required to begin from the root. An evaluation and comparison of both strategies is performed against repeated nearest neighbour search. Both random and exponentially distributed point sets are used. Results show that both approaches achieve significant performance gains, especially with respect to reducing the number of queries that must be sent from the client to the server.
Keywords: location-based services; continuous nearest neighbour queries; spatial access methods.
Scheduling communication-intensive applications on Mesos
by Alessandro Di Stefano, Antonella Di Stefano, Giovanni Morana
Abstract: In recent years, the widespread use of container technologies has significantly altered the interactions between cloud service providers and their customers when developing and offering services. The shift away from virtual private server scenarios in infrastructure-as-a-service environments requires drastic changes to the deployment strategies adopted by service providers. This also opens many questions as to what information must be supplied by customers and how to improve the performance of user applications, especially in the case of communication-intensive applications. In this work, the authors propose the adoption of a new framework for Mesos clusters that aims to improve the deployment strategies of communication intensive applications. Coope4M is based on the partitioning of the user application graph via the isolation index parameter obtained through user-knowledge on the degree of the communication between its components.
Keywords: Mesos; cluster placement strategy; containers deployment strategy; containers; isolation index; cloud computing;.
Assessing distributed collaborative recommendations in different opportunistic network scenarios
by Lucas Nunes Barbosa, Jonathan Gemmell, Miller Horvath, Tales Heimfarth
Abstract: Mobile devices are common throughout the world, even in countries with limited internet access and even when natural disasters disrupt access to a centralised infrastructure. This access allows for the exchange of information at an incredible pace and across vast distances. However, this wealth of information can frustrate users as they become inundated with irrelevant or unwanted data. Recommender systems help alleviate this burden. In this work, we propose a recommender system where users share information via an opportunistic network. Each device is responsible for gathering information from nearby users and computing its own recommendations. An exhaustive empirical evaluation was conducted on two different datasets. Scenarios with different node densities, velocities and data exchange parameters were simulated. Our results show that in a relatively short time when a sufficient number of users are present, an opportunistic distributed recommender system achieves results comparable to that of a centralised architecture.
Keywords: opportunistic networks; recommender systems; mobile ad hoc networks.
A methodology for automated penetration testing of cloud applications
by Valentina Casola, Alessandra De Benedictis, Massimiliano Rak, Umberto Villano
Abstract: Security assessment is a very time- and money-consuming activity. It needs specialised security skills and, furthermore, it is not fully integrated into the software development life-cycle. One of the best solutions for the security testing of an application relies on the use of penetration testing techniques. Unfortunately, penetration testing is a typically human-driven procedure that requires a deep knowledge of the possible attacks and of the hacking tools that can be used to launch the tests. In this paper, we present a methodology that enables the automation of penetration testing techniques based on both application-level models, used to represent the application architecture and its security properties in terms of applicable threats, vulnerabilities and weaknesses, and on system-level models, adopted to automatically generate and execute the penetration testing activities. The proposed methodology can be easily integrated into a continuous integration development process and aid software developers in evaluating security.
Keywords: cloud application security assessment; cloud application penetration testing; automated penetration testing modelling; automated penetration testing execution.
Preferential charging for government authorised emergency electrical vehicles
by Raziq Yaqub, Fahd Shifa, Fasih-Ud Din
Abstract: The proliferation of Electrical Vehicles (EVs) is exponential. However, the power grid is not able to provide simultaneous charging of several EVs owing to limited power production capabilities and old distribution infrastructure. Scheduled charging is one of the most advocated solutions. However, it is not viable for emergency vehicles. This paper proposes to provide priority charging service for government authorised emergency EVs. For enablement of this proposal, a complete solution that includes the architecture, as well as the protocols suite, is suggested. To realise such a service, the paper suggests a major functional entity called a Priority Charging Server, i.e. a database server where authorised emergency EVs IDs are registered, and their record is maintained. The paper also proposes modifications in the IEC15118 and IEC 61850 protocol suits. These protocols provide communication between the vehicle and the grid. The solution also includes roaming as well as non-roaming scenarios, i.e. a priority charging request may be originated by an authorised emergency EV from a Home Utility Network, as well as, Visiting Utility Network. The paper is concluded with a MATLAB-based proof-of-concept simulation.
Keywords: priority charging; roaming; non-roaming; electric vehicle; protocols; priority server; AAA server.
Testing of network security systems through DoS, SQL injection, reverse TCP and social engineering attacks
by Arianit Maraj, Ermir Rogova, Genc Jakupi
Abstract: Cyber-attacks are happening with an ever-increasing frequency to organisations with the goal of gaining access to their sensitive information. These attacks can cause huge damage to various governmental, non-governmental, healthcare, financial and other organisations. Nowadays, it is web applications that are being used to access sensitive information, hence they have become a preferred target for attackers through which to try to access sensitive data. Therefore, it has become of a paramount importance for organisations to implement robust security policies in order to protect sensitive data from being compromised. First and foremost, measures should be taken to prevent these attacks. The best way to prevent cyber-attacks is to test security systems before attacks happen. The most frequent types of attack are: SQL (Structured Query Language) injection, DoS (Denial of Service), reverse TCP (Transmission Control Protocol) and social engineering attacks. In this paper, we use penetration testing techniques for testing security issues of computer systems and networks. We analyse firewalls and other protective systems and their role in security. Various scenarios are used for testing security systems through DoS, SQL injection and reverse TCP. Using penetration testing techniques, we try to find out what is the best solution for protecting sensitive data within the governmental network of Kosovo. We also tackle the issue of social engineering attacks on networks.
Keywords: cyber-security; denial of service; SQL injection; reverse TCP; social engineering; penetration testing.
Research on the relationship between geomantic omen and housing choice in the big data era
by Lin Cheng
Abstract: In order to make the optimal decision of housing choice based on geomantic omen, the modern information technology in big data era is applied to confirm the relationship between the geomantic omen and housing choice. Firstly, geomantic theory and residential district planning decision are discussed. The function, core content and goal of geomantic theory are analysed, and the importance of geomantic theory on the site selection, orientation and spacing and indoor environment of residential region is analysed. The indoor environment of an urban residence includes the following elements, which are road, water body, plant and environmental elements. The geomantic theory can make the distribution trend of road system humane based on four principles. The flow direction of water, distribution of dynamic and static water, water area and layout and composition of water body should be designed based on geomantic theory. Secondly, the big data-mining algorithm based on grey relational theory is studied. The linear big data is pretreated, and the grey relational theory is used to construct the big data-mining algorithm. The selection procedure of weight is designed. Thirdly, the big data relational analysis algorithm is put forward. The analysis procedure includes three aspects, which are preprocessing of original data, procession of environmental parameters, and calculation of relational degree. Finally, three residential districts are used as examples to carry out the grey relational analysis for the geomantic theory and housing choice, and the results verify the effectiveness of the big mining algorithm. In addition, geomantic culture is more important for residents' satisfaction than housing choice, and development of good commercialised living population can be achieved based on geomantic theory.
Keywords: big data; housing choice; grey relational analysis.
Research on hardware-in-the-loop simulation of single point suspension system based on fuzzy PID
by Jie Yang, Tao Gao, Shengli Yuan, Heng Shi, Zhenli Zhang
Abstract: The stability control of a maglev train is one of the core problems in the research of maglev train technology, and the realisation of this goal is of great scientific value in the field of magnetic levitation. Based on this, the research on the suspension control strategy of a single point maglev system is founded, and the control strategy is verified by experiments on a magnetic levitation ball system (MLBS). Aiming at the structure of multi-group independent control system of maglev train suspension frame, in order to improve the overall stability control ability of the suspension frame, the coupling relationship between the subsystems is established by introducing the suspension response deviation compensator. Finally, the effect of the single point suspension control system is discussed, and the cooperative control of suspension frame is carried out on MATLAB. Simulation and analysis show that each subsystem has good anti-jamming ability, and the suspension system realises the balanced and stable control under different interference signals, which provides a certain reference value for further study of maglev train suspension control.
Keywords: magnetic suspension; fuzzy PID; maglev train bogie; composite control.
A study on fog computing architectures and energy consumption approaches regarding QoS requirements
by Amel Ksentini, Maha Jebalia, Sami Tabbane
Abstract: The Internet of Things (IoT) promotion is increasing, for both individuals and businesses. Data is gathered for treatment from locals, machines, smart objects, vehicles, healthcare devices, remote surveillance camera, predictive maintenance, real-time customer information, etc. Cloud computing is providing suitable hardware and software for data processing, such as storage and computing. Thus, the integration of IoT with cloud capabilities may offer several benefits for many applications. However, challenges persist for some use-cases, such as for delay-sensitive services, owing to the huge amount of information collected by IoT devices and to be processed by cloud servers. Fog computing has attracted many researchers in past years since it is expected to overcome several limits and challenges in cloud computing concerning the quality of service (QoS) requirements, such as latency, real-time processing, bandwidth and location awareness. This is due to the fact that data processing may be located at the edge of the network when fog computing is invoked instead of sending information for a longer round-trip to the cloud servers. Nevertheless, researchers still have to deal with several issues, namely the architectural level and the energy aspect. In this paper, we investigate fog system architectures and energy consumption reported in the literature, while considering QoS requirements in the synthesis. A system model is then introduced with a potential solution for QoS management for the fog computing environment
Keywords: fog computing; IoT; architecture; QoS; energy consumption.
Study on NVH robustness evaluation method of high mileage automobile based on systematic sampling
by Jianqiang Xiong, Le Yuan, Dingding Liao, Jun Wu
Abstract: At present, automobile riding comfort is primarily focused on the study of the performance of new automobile NVH, and less research on how to analyse and evaluate the NVH characteristics of high mileage automobile. Based on the principle of statistics, this paper presents a robust evaluation method based on systematic sampling for the stability of high mileage automotive NVH characteristics and expounds the method. The basic idea and the main implementation steps focus on the analysis of the NVH characteristics of high mileage automobile and how to evaluate the robustness of high mileage automobile NVH, and provide a new direction for research into automobile riding comfort.
Keywords: automobile vibration and noise; evaluation method; high mileage automobile; systematic sampling.
Success factor analysis for cloud services: a comparative study on software as a service
by Dietmar Nedbal, Mark Stieninger
Abstract: The emergence of cloud computing has been triggering fundamental changes in the information technology landscape for years. The proliferation of cloud services gave rise to novel types of business model, the complexity of which results from numerous different factors critical to a successful adoption. However, when it comes to improvement activities by cloud service providers, owing to their multifacetedness, the challenge lies in figuring out where to start. Furthermore, the acuteness of actions to be taken varies among different settings. Thus, we propose success factor analysis as an approach to prioritise improvement activities according to their acuteness, which is thereby indicated by the gap between the priority and the actual performance of a particular factor. Results show that the factors with the overall highest gap are security and safety, trust, and costs. Overall, the strengths of cloud services are seen in technical features leading to a good ease of use, a positively perceived usefulness, and a broad availability.
Keywords: success factor analysis; cloud computing; software as a service; cloud services; survey.
Data access control algorithm based on fine-grained cloud storage
by Qiaoge Xu
Abstract: With development of network storage and cloud computation, the cloud storage security has become the critical problem of cloud security technology. The data confidentiality of customer should be ensured in unbelievable storage environment, and the legal data of customer should be protected from tampering. In order to ensure the cloud storage security and achieve fine-grained data access control, a new fine-grained data access control algorithm is established based on CP-ABE algorithm. The basic theory of CP-ABE algorithm is studied in depth, the flowchart of CP-ABE algorithm is put forward. Then the fine-grained cloud storage controlling scheme based on digital envelop is put forward. The structure of new fine-grained cloud storage controlling scheme is designed, the trusted third party mainly generates the public parameters and main password of system, the data owner possesses the original plaintext data of client, the normal user can read digital envelopes stored in cloud storage server, and the cloud service provider (CSP) can offer data storage for the user. The new scheme construction process is given, and then the corresponding algorithm is designed. The new scheme can reduce user management complexity of CSP, and the new scheme also keeps the access controlling fine-grained degree and flexible of original scheme. The fine-grained degree access privilege tree is also designed to to improve the robustness of the fine-grained data access control algorithm and to describe the encryption strategy. Simulation analysis is carried out, and results show that the proposed data access control algorithm can effectively improve the searching efficiency of cipher text, and achieve fine-grained access under cloud storage environment.
Keywords: data access control algorithm; fine-grained cloud storage; searching efficiency.
Multi-objective optimisation of traffic signal control based on particle swarm optimisation
by Jian Li
Abstract: In order to relieve traffic jams, an improved particle swarm optimisation is applied in multiple objective optimisation of traffic signal control. Firstly, a multiple objective optimal model of traffic signal is constructed considering the queue length, vehicle delay, and exhaust emission. The multiple optimal function is transferred to single optimal function through three weighted indexes. The vehicle delay and queue length model under the control of traffic signal is constructed through combining the Webster model and the high capacity manual delay model. The vehicle exhaust emission model under the control of traffic signal is also constructed. The objective function and constraint conditions are confirmed. Secondly, the improved particle swarm optimisation algorithm is established through combining the traditional particle swarm algorithm and genetic algorithm. The mathematics of the particle swarm algorithm is studied in depth, and particles are endowed with hybrid probability, which is random and has no fitness degree value. In every iteration, a number of particles are selected based on the hybrid probability to put them into pool. The location of subparticles can be calculated based on the weighted location of the mother particle. The value of the inertia factor can be regulated based on the following nonlinear inertia weight decrement function. Finally, the simulation analysis is carried out using an intersection as the research objective, the flow of straight road ranges from 300 to 450 pcu, the flow of left turn road ranges from 250 to 380 pcu. The optimal performance index is obtained, and the new multiple objective optimisation model can give better optimal results than the traditional multiple objective optimisation algorithm. A better traffic control effect is obtained.
Keywords: particle swarm optimisation; traffic signal control; intersection.
Policies and mechanisms for enhancing the resource management in cloud computing: a performance perspective
by Mitali Bansal, Sanjay Kumar Malik, Sanjay Kumar Dhurandher, Issac Woungang
Abstract: Resource management is among the critical challenges in cloud computing since it can affect its performance, cost, and functionality. In this paper, a survey of the policies and mechanisms for enhancing the resource management in cloud computing is proposed. From a performance perspective, several resource management schemes for cloud computing are investigated and qualitatively compared in terms of various different parameters, such as performance, response time, scalability, pricing factor, throughput, and accuracy, providing a fundamental knowledge base for researchers in the cloud computing area. We also classified various cloud computing techniques based on various policies, such as capacity allocation, admission control, load balancing and energy optimisation. Furthermore, we divided defined techniques on the basis of various parameters, such as low, medium, high time span time techniques, reliability, performance, and availability, to name a few.
Keywords: cloud computing; resource management; load balancing; policies and mechanisms; performance perspective.
Domo Farm 4.0
by Silvia Angeloni
Abstract: The paper explains and discusses an innovative agricultural appliance, based on vertical farming and hydroponics. The innovative and smart model was launched by a brilliant woman, winner of several awards. Applying her engineering skills, the female entrepreneur has set up a modern company, where technology and agriculture are perfectly integrated in a sustainable way to prevent negative and damaging environmental effects. Recently, the company has developed an automatic hydroponic greenhouse appliance for empowering individuals to grow crops at home. The household hydroponic appliance is based on sensors and smart technologies. The environmental and economic benefits and potentiality of the innovative appliance are highlighted.
Keywords: big data; Domo Farm 4.0; internet of things; RobotFarm; sensors; smart energy management; smart farm; sustainability.
A hybrid collaborative filtering recommendation algorithm: integrating content information and matrix factorisation
by Jing Wang, Arun Sangaiah, Wei Liu
Abstract: Matrix factorisation is one of the most popular techniques in recommendation systems. However, matrix factorisation still suffers from the cold start problem. Moreover, there are too many parameters in the matrix factorisation model, producing a complicated computation. In this paper, we present a hybrid recommendation algorithm, that integrates user and item content information and matrix factorisation. First, based on user or item content information, similar user or item neighbour sets can be generated. Through these neighbour sets, user or item rating preference can be evaluated in advance. Incorporating user and item preference into the matrix factorisation model, we obtain the final prediction model. Finally, the momentum stochastic gradient descent method is used to optimize parameter learning. Experimental results on a real dataset have shown our algorithm yield the best performance in terms of MAE and RMSE when compared with other classical matrix factorisation recommendation algorithms.
Keywords: recommender system; collaborative filtering; matrix factorisation; momentum stochastic gradient descent.
Classification of cognitive algorithms for managing services used in cloud computing
by Lidia Ogiela, Makoto Takizawa, Urszula Ogiela
Abstract: This paper presents a new idea of cognitive systems dedicated to cloud computing, especially backgrounds, introduction and description of service management procedures and algorithms dedicated to cloud computing and infrastructure. Cognitive methods are based on semantic description and interpretation procedures. The described idea will be dedicated to secure service management procedures, especially in the cloud and fog stages. The proposed algorithms of cognitive service management will be presented and described by use of semantic aspects. Semantic analysis is used to extract the meaning of the analysed data. Also, in management processes, it is possible to analyse meaning aspects. These kinds of analysis can be used in different application areas. This paper presents services management protocols in the cloud and in the fog. In both of the cloud and fog stages it is possible to realise management procedures by application of secure methods and protocols. This paper presents the sharing techniques for data security in cloud computing.
Keywords: cognitive algorithms; fog and cloud computing; service management protocols; cognitive data security.
Performance analysis of StaaS on IoT devices in fog computing environment using embedded systems
by José Dos Santos Machado, Danilo Souza Silva, Raphael Fontes, Adauto Menezes, Edward Moreno, Admilson Ribeiro
Abstract: This work presents the concept of fog computing, its theoretical contextualisation, and related works, and performs an analysis of fog computing to provide StaaS (Storage as a Service) on IoT devices using embedded systems platforms, in addition to comparing its results with those obtained by a high-performance server. In this article, we use OwnCloud and Benchmark SmashBox (for data transfer analysis). The results showed that the implementation of this service in embedded systems devices can be a good alternative to reduce one of these problems, in this case the storage of data, which currently affects IoT devices.
Keywords: fog computing; cloud computing; IoT; embedded systems; StaaS.
Model for generation of social network considering human mobility and interaction
by Naoto Fukae, Hiroyoshi Miwa, Akihiro Fujihara
Abstract: The structure of an actual network in the real world has often the scale-free property that the degree distribution follows the power law. As for a generation mechanism of a human relations network, it is necessary to consider human mobility and interactions, because, in general, a person moves around, meets another person, and makes human relation stochastically. However, there are few models considering human mobility so far. In this paper, we propose a mathematical model generating a human relations network for the purpose of fundamental research on the usage model for the utility computing. We show by the numerical experiments that a network generated by the proposed model has the scale-free property, the clustering coefficient follows the power law, and the average distance is small. This means that the proposed model can explain the mechanism generating an actual human relations network.
Keywords: human relations network; scale-free; human mobility; human interactions; homesick Levy walk; network generation model.
Algorithmic node classification in AND/OR mobile workflow graph
by Ihtisham Ali, Susmit Bagchi
Abstract: Next-generation data-intensive applications in various fields of science and engineering employ complex workflow graph execution models in dynamic networks. However, in dynamic networks, heterogeneity and the mobility of nodes result in low efficiency owing to end-to-end delay in execution in complex workflow graphs. Supporting such data-intensive workflows and optimising their performance
require analysis of complex workflow graphs in order to reach the objectives such as deadlines and fast execution etc. A major limitation of the current workflow models is the lack of structural stability to visualise a complex workflow graph having the mobility of nodes. In this paper, we address this problem by proposing a hybrid AND/OR mobile workflow graph (MWG) model to visualise a fully conditioned complex workflow graph having the mobility of nodes. Moreover, this paper proposes nodes validity detection (NVD) algorithm for classifying the total number of nodes in the AND/OR MWG. Furthermore, nodes criticality detection (NCD) algorithm is also proposed to identify the set of critical nodes in the AND/OR MWG. The proposed algorithms will enable efficiently analysing, mapping and scheduling of complex workflow graphs in a dynamic network environment. The NVD and NCD algorithms are implemented in Java language and evaluated on the testbed. The regression analysis of projected algorithmic performance is presented. A detailed comparative analysis considering matrix elements is presented in this paper.
Keywords: workflow graph; dynamic networks; mobile node; nodes classification; critical node.
A proposal for a healthcare environment with a real-time approach
by Eliza Helena Areias Gomes, Mario Antonio Ribeiro Dantas, Patricia Della Méa Plentz
Abstract: The increased use of IoT has contributed to the popularisation of environments that monitor the daily activities and health of the elderly, children or people with disabilities. The requirements of these environments, such as low latency and rapid response, corroborate the usefulness of associating fog computing with healthcare environment since one of the advantages of fog is to provide low latency. Because of this, we propose the use of a hardware and software infrastructure capable of storing, processing and presenting monitoring data in real-time, based on the fog computing paradigm. Additionally, we propose the structuring of sensors for the implementation of a simulated healthcare environment, as well as the processing logic for the presentation of results referring to the health of the user.
Keywords: IIoT platform; time constraint; fog computing; healthcare application.
Design and implementation of broadcasting system for selective contents considering interruption time
by Takuro Fujita, Yusuke Gotoh
Abstract: Owing to the recent popularisation of digital broadcasting, selective contents broadcasting has attracted much attention. In selective contents broadcasting, although the server delivers contents based on their preferences, users may experience the interruption time while playing their selected contents. To reduce this interruption time, many researchers have proposed scheduling methods. However, since these scheduling methods evaluated the interruption time in simulation environments, we need to evaluate them in network environments. In this paper, we propose a broadcasting system of selective contents and evaluate its effectiveness in network environments.
Keywords: broadcasting; interruption time; scheduling; selective contents; waiting time.
An algorithm to optimise the energy distribution of data centre electrical infrastructures
by Joao Ferreira, Gustavo Callou, Paulo Maciel, Dietmar Tutsch
Abstract: Owing to the demands of new technologies such as social networks, e-commerce and cloud computing, more energy is being consumed in order to store all the produced data. While these new technologies require high levels of availability, a reduction in the cost and environmental impact is also expected. The present paper proposes a power balancing algorithm (PLDA-D) to optimise the energy distribution of data centre electrical infrastructures. The PLDA-D is based on the Bellman and Ford-Fulkerson flow algorithms that analyse energy-flow models (EFM). EFM computes power efficiency, sustainability, and cost metrics of data centre infrastructures. To demonstrate the applicability of the proposed strategy, we present a case study that analyzed four power infrastructures. The results obtained shows about 3.8% reduction in sustainability impact and operational costs.
Keywords: energy flow model; dependability; sustainability; data centre power architectures; optimisation.
Implementing the software defined management framework
by Maxwell Monteiro, KÃ¡io Simonassi, Rodolfo VillaÃ§a, Renan Tavares, Cassio Reginato
Abstract: Software Defined Infrastructure (SDI) has become a relevant topic for the computing and communications industry. Despite this huge technological movement, network and systems management has been disregarded as one of the main themes in this ecosystem, and SDI has been managed by semi-software-defined management solutions. In order to reduce this gap, this paper presents SDMan, a software defined management framework. The SDMan's proof of concept uses the OpenStack cloud platform and aims to demonstrate the feasibility of the proposed solution.
Keywords: software defined infrastructure; software defined networks; network management; cloud computing.
Predicting students' academic performance: Levy search of cuckoo-based hybrid classification
by Deepali R. Vora, Kamatchi Iyer
Abstract: Nowadays, Educational Data Mining (EDM) exists as a novel trend in the Knowledge Discovery in Databases (KDD) and Data Mining (DM) fields concerned with mining valuable patterns and finding out practical knowledge from educational systems. However, evaluating the educational performance of students is challenging as their academic performance pivots on varied constraints. Hence, this paper intends to predict the educational performance of students based on socio-demographic information. To attain this, performance prediction architecture is introduced with two modules. One module is for handling the big data via MapReduce (MR) framework, whereas the second module is an intelligent module that predicts the performance of the students using intelligent data processing stages. Here, the hybridisation of classifiers such as Support Vector Machine (SVM) and Deep Belief Network (DBN) is adopted to get better results. In DBN, Levy Search of Cuckoo (LC) algorithm is adopted for weight computation. Hence, the proposed prediction model SVM-LCDBN is proposed that makes deep connection with the hybrid classifier to attain more accurate output. Moreover, the adopted scheme is compared with conventional algorithms, and the results are attained.
Keywords: data mining; educational data mining; MapReduce framework; support vector machine; deep belief network; cuckoo search algorithm; Levy flight.
Combined interactive protocol for lattice-based group signature schemes with verifier-local revocation
by Maharage Nisansala Sevwandi Perera, Takeshi Koshiba
Abstract: In group signature schemes the signer is required to prove his validity of generating signatures on behalf of the group to the signature verifier. However, since the signer's identity should be anonymous to the verifier, the proving mechanism should not reveal any information related to the signer. Thus, the signers should follow a zero-knowledge proving system when interacting with the verifiers. In group signature schemes with verifier-local revocation (VLR) mechanism, the group members have another secret attribute called a revocation token other than the secret signing key. Thus, the signer has to prove that his revocation token is not in the revoked member list without revealing his token to the verifier. Even though the first lattice-based group signature scheme with verifier-local revocation (Langlois et al. at PKC2014) proved the validity of the signer using an underlying interactive protocol, their scheme relied on weaker security because revocation tokens are derived from the secret signing keys. This paper discusses situations, where the secret signing keys and revocation tokens are generated separately to achieve strong security, and provides a new combined interactive protocol that passes zero-knowledge to prove the validity of the signer. Moreover, the new interactive protocol covers the situations where the group manager prefers using an explicit tracing algorithm rather than using the implicit tracing algorithm to identify a signer. As a result, this work presents a combined interactive protocol that signer can use to prove his validity of signing, his separately generated revocation token is not in the revocation list, and his index is correctly encrypted to support the explicit tracing algorithm.
Keywords: lattice-based group signatures; verifier-local revocation; zero-knowledge proof; interactive protocol.
Chronological and exponential based Lion optimisation for optimal resource allocation in cloud
by J. Devagnanam, N.M. Elango
Abstract: Cloud computing is a service-oriented architecture, which has prominent importance over the development of the enterprises and markets. The main intention of the cloud computing is to maximise the effectiveness of the shared resources upon the needs and also, to maintain the profit of the cloud provider as well. Accordingly, this paper introduced an optimisation scheme for allocating suitable resources for cloud computing. Previously, a resource allocation was developed by introducing the EWMA based Lion Algorithm (E-Lion). In this work, the previously developed E-Lion algorithm is extended by including the chronological concept to develop a novel algorithm, named Chronological E-Lion. Also, for further refining the resource allocation scheme, the proposed Chronological E-Lion algorithm uses the fitness with parameters, such as cost, profit, CPU allocation, memory allocation, MIPS, and frequency scaling. Implementation of the proposed scheme uses three different problem instances and is evaluated based on metrics, such as profit, CPU allocation rate, and memory allocation rate. From the simulation results, it can be concluded that the proposed Chronological based E-Lion algorithm achieved improved performance with the values of 45.925, 0.1555, and 0.0093, for profit, CPU utilisation rate, and memory utilisation rate.
Keywords: cloud computing; resource allocation; E-Lion; chronological concept; CPU utilisation rate; memory utilisation rate.
An empirical study of alternating least squares collaborative filtering recommendation for MovieLens on Apache Hadoop and Spark
by Jung-Bin Li, Szu-Yin Lin, Yu-Hsiang Hsu, Ying-Chu Huang
Abstract: In recent years, both consumers and businesses have faced the problem of information explosion, and the recommendation system provides a possible solution. This study implements a movie recommendation system that provides recommendations to consumers in an effort to increase consumer spending while reducing the time between film selections. This study is a prototype of collaborative filtering recommendation system based on Alternating Least Squares (ALS) algorithm. The advantage of collaborative filtering is that it can avoid possible violations of the Personal Data Protection Act and reduce the possibility of errors due to poor quality of personal data. Our research improves the ALS's limited scalability by using a platform that combines Spark with Hadoop Yarn and uses this combination to calculate movie recommendations and store data separately. Based on the results of this study, our proposed system architecture provides recommendations with satisfactory accuracy while maintaining acceptable computational time with limited resources.
Keywords: recommendation system; alternating least square; collaborative filtering; MovieLens; Hadoop; Spark; content-based filtering.
Evaluating and modelling solutions for disaster recovery
by Júlio Mendonça, Ricardo Lima, Ermeson Andrade
Abstract: Systems outages can have disastrous effects on businesses, such as data loss, customer dissatisfaction, and subsequent revenue loss. Disaster recovery (DR) solutions have been adopted by companies to minimise the effects of these outages. However, the selection of an optimal DR solution is difficult, since there does not exist a single solution that suits the requirement of every company (e.g., availability and costs). In this paper, we propose an integrated model-experiment approach to evaluate DR solutions. We perform experiments in different real-world DR solutions and propose analytic models to evaluate these solutions regarding DR key metrics: steady-state availability, recovery time objective (RTO), recovery point objective (RPO), downtime, and costs. The results reveal that DR solutions can significantly improve availability and minimise costs. Also, a sensitivity analysis identifies the parameters that most affect the RPO and RTO of the DR adopted solutions.
Keywords: disaster recovery; disaster tolerance; cloud computing; Petri nets.
Big data inconsistencies and incompleteness: a literature review
by Olayinka Johnny, Marcello Trovati
Abstract: The analysis and integration of big data highlight some issues in the identification and resolution of data inconsistencies and knowledge incompleteness. This paper presents an overview of data inconsistencies and a review of approaches to resolve various levels of data inconsistencies. Moreover, we discuss some issues related to incompleteness and stability of known knowledge over specific time periods, and the implication to the decision-making process. In addition, the use of the Bayesian network model in inconsistency resolution in data analysis and knowledge engineering will also be considered.
Keywords: big data; data inconsistencies; NLP; Bayesian networks.
Detection of fatigue on gait using accelerometer data and supervised machine learning
by Dante Arias-Torres, José Adan Hernández-Nolasco, Miguel A. Wister, Pablo Pancardo
Abstract: In this paper, we aim to detect the fatigue based on accelerometer data from human gait using traditional classifiers from machine learning. First, we compare widely used machine learning classifiers to know which classifier can detect fatigue with the fewest errors. We observe that the best results were obtained with a Support Vector Machine (SVM) classifier. Later, we propose a new approach to solve the feature selection problem to know which features are more relevant to detect fatigue in healthy people based on their gait patterns. Finally, we use relevant gait features discovered in a previous step as input in classifiers used previously to know their impact on the classification process. Our results indicate that, using only some gait features selected by our proposed feature selection method, it is possible to improve fatigue detection based on data from human gait. We conclude that it is possible to distinguish between a normal gait person and a fatigued gait person with high accuracy.
Keywords: gait; fatigue; detection; accelerometer; supervised learning.
Towards an encompassing maturity model for the management of higher education institutions
by Rui Humberto Pereira, Joao Vidal Carvalho, Alvaro Rocha
Abstract: Maturity models have been adopted in organisations from different sectors of activity, as guides and references for information systems (IS) management. In the educational field, these models have also been used to deal with the enormous complexity and demand of educational information systems management. Higher education institutions (HEI) IS require different expertise including areas such as academic management and integration, pedagogy, support to research activities and digital technologies associated with education that allow the accurate development and implementation of these systems. This article presents a research project that aims to develop a new comprehensive maturity model for HEI that helps them to address the complexity of their IS, as a useful tool for the demanding role of the management of these systems (and institutions as well). Thus, the maturity models in the Education area are discussed with special insight on those whose focus are the IS and technologies (IST). Those models are identified, as well as the characteristics and gaps that they present in this specific HEI area, justifying the need and the opportunity for the development of a new and comprehensive maturity model. Finally, the methodology is discussed for the development of maturity models that will be adopted for the design of the new model (called HEIMM) and the underlying reasons for its choice. At the moment, we are developing the HEIMM following the chosen methodology.
Keywords: stages of growth; maturity models; higher education institutions; management.
An efficient greedy task scheduling algorithm for heterogeneous inter-dependent tasks on computational grids
by Srinivas D B, Sujay N. Hegde, M.A. Rajan, H.K. Krishnappa
Abstract: Computational grids are interconnected assortments of distributed and heterogeneous resources, coordinately working together to meet the computational needs of different users. Grid services housed in data centres aim to achieve optimal grid utilisation to serve more customers to maximise profits. Similarly, grid service users always want to minimise turnaround time of their applications. Generally, user applications are represented by precedence constrained task graphs. Scheduling these precedence constrained tasks of a task graph on computational grids is a key enabler to achieve the better turnaround time and higher throughput. Many researchers have focused on developing task scheduling algorithms for fully dependent or independent task graphs. These algorithms are based on genetic, game theory, heuristics,bio-inspired approaches, etc. Therefore, designing efficient task scheduling algorithms is still a difficult task owing to its complexity (NP-complete). Thus, there is always scope to design efficient task scheduling algorithms to achieve better turnaround time and grid utilisation for precedence constrained task graphs. In this direction, we propose an efficient greedy task scheduling algorithm for precedence constrained task graphs with varied dependencies (no, partial and fully) on computational grids. Correctness of our proposed task scheduling algorithm is verified by comparing it with proposed backtracking brute force scheduling algorithm. Performance of proposed scheduling algorithm is compared (Turn Around Time (TAT) and grid utilisation) against hungarian, Partial Precedence Constrained Scheduler (P_PCS) and AND scheduling algorithms. Simulation results shows that the performance proposed scheduling algorithm is on par with hungarian, P_PCS and AND scheduling algorithms. Further, running time of proposed algorithm is better than hungarian and is equivalent to P_PCS algorithm.
Keywords: partial dependency; task scheduling; turnaround time; grid utilisation; greedy approach; DAG.
On a user authentication method to realise an authentication system using s-EMG
by Hisaaki Yamaba, Shotaro Usuzaki, Kayoko Takatsuka, Kentaro Aburada, Tetsuro Katayama, Mirang Park, Naonobu Okazaki
Abstract: In our present era, mobile devices such as tablet-type personal computers (PCs) and smartphones have penetrated deeply into our daily lives. This has resulted in situations where malicious strangers have found ways to spy on our touchscreen authentication operations and steal passwords that allow them to access our mobile devices and steal important information and data. In response, designers are working to develop new authentication methods that can prevent this sort of crime, which is commonly called shoulder-surfing. Herein, we report on a new user authentication method for mobile devices that uses surface electromyogram (s-EMG) signals rather than screen-touch operations. These s-EMG signals, which are generated by the electrical activity of muscle fibres during contraction, can be used to identify who generated the signals and which gestures were made. Our method uses a technique called pass-gesture, which refers to a series of hand gestures, to achieve s-EMG-based authentication. However, while human beings can recognise gestures from the s-EMG signals they produce by viewing their charts, it is necessary to introduce computer programs that can do this automatically. In this paper, we propose two methods that can be used to compare s-EMG signals and determine whether they were made by the same gesture. One uses support vector machines (SVMs), and the other uses dynamic time warping (DTW). We also introduce an appropriate method for selecting the validation data used to train SVMs using correlation coefficients and cross-correlation functions, and report on a series of experiments that were carried out to confirm the performance of those proposed methods. From the obtained experimental results, the effectiveness of the two proposed methods was confirmed.
Keywords: user authentication; surface electromyogram; support vector machines; correlation coefficient; cross-correlation; dynamic time warping.
An integrated framework of generating quizzes based on linked data and its application in the medical education field
by Wei Shi, Chenguang Ma, Hikaru Yamamura, Kosuke Kaneko, Yoshihiro Okada
Abstract: E-learning has greatly developed in recent years because of the popularity of smart-phones and tablets. With the improvement of the device performances and the data transmission capability of the internet, more and more complex E-learning materials are provided to learners. Quiz games are a kind of e-learning format which can both test and train learners. How to automatically generate good quizzes is discussed by many researchers. In this paper, we proposed a new framework which can support users to create their own quiz games based on linked data. Compared with other methods, our framework effectively uses the feature of the linked data, which stores both the values and the linkages among values. The quizzes generated by our framework are easy to improve and extend, have more changes, and support the learning analytics of users activities. For obtaining better educational effects, we further extend our framework for supporting the generation of quizzes in 3D environments. Especially, we discuss how to apply our framework in medical education in this paper.
Keywords: linked data; quiz; serious game; e-learning.
Special Issue on: Emergent Peer-to-Peer Network Technologies for Ubiquitous and Wireless Networks
An improved energy efficient multi-hop ACO-based intelligent routing protocol for MANET
by Jeyalaxmi Perumaal, Saravanan R
Abstract: A Mobile Ad-hoc Network (MANET) consists of group of mobile nodes, and the communication among them is done without any supporting centralised structure. Routing in a MANET is a difficult because of its dynamic features, such as high mobility, constrained bandwidth, link failures due to energy loss, etc., The objective of the proposed work is to implement an intelligent routing protocol. Selection of the best hops is mandatory to provide good throughput in the network, therefore Ant Colony Optimisation (ACO) based intelligent routing is proposed. Selecting the best intermediate hop for intelligent routing includes ACO technique, which greatly reduces the network delay and link failures by validating the co-ordinator nodes. Best co-ordinator nodes are selected as good intermediate hops in the intelligent routing path. The performance is evaluated using the simulation tool NS2, and the metrics considered for evaluation are delivery and loss rate of sent data, throughput and lifetime of the network, delay and energy consumption.
Keywords: ant colony optimisation; intelligent routing protocol; best co-ordinator nodes; MANET.
Analysis of spectrum handoff schemes for cognitive radio networks considering secondary user mobility
by K.S. Preetha, S. Kalaivani
Abstract: There has been a gigantic spike in the usage and development of wireless devices since wireless technology came into existence. This has contributed to a very serious problem of spectrum unavailability or spectrum scarcity. The solution to this problem comes in the form of cognitive radio networks, where secondary users (SUs), also known as unlicensed users, make use of the spectrum in an opportunistic manner. The SU uses the spectrum in a manner such that the primary or the licensed user (PU) doesnt face interference above a threshold level of tolerance. Whenever a PU comes back to reclaim its licensed channel, the SU using it needs to perform a spectrum handoff (SHO) to another channel that is free of PU. This way of functioning is termed as spectrum mobility. Spectrum mobility can be achieved by means of SHO. Initially, the SUs continuously sense the channels to identify an idle channel. Errors in the sensing channel are possible. A detection theory is put forth to analyse the spectrum sensing errors with the receiver operating characteristic (ROC) considering false alarm probability, miss detection and detection probability. In this paper, we meticulously investigate and analyse the probability of spectrum handoff (PSHO), and hence the performance of spectrum mobility, with Lognormal-3 and Hyper-Erlang distribution models considering SU call duration and residual time of availability of spectrum holes as measurement metrics designed for tele-traffic analysis.
Keywords: cognitive radio networks; detection probability; probability of a miss; SNR; false alarm probability; primary users; secondary users.
Link survivability rate-based clustering for QoS maximisation in VANET
by D. Kalaivani, P.V.S.S.R. Chandra Mouli Chandra Mouli
Abstract: The clustering technique is used in VANET to manage and stabilise topology
information. The major requirement of this technique is data transfer through
the group of nodes without disconnection, node coordination, minimised interference
between number of nodes, and reduction of hidden terminal problem. The data
communication among each node in the cluster is performed by a cluster head (CH). The
major issues involved in the clustering approaches are improper definition of cluster
structure, maintenance of cluster structure in dynamic network. To overcome these issues in the clustering technique, the link- and weight-based clustering approach is developed along with a distributed dispatching information table (DDIT) to repeatedly use the significant information for avoiding data transmission failure. In this paper, the clustering algorithm is designed on the basis of relative velocity value of two same directional vehicles by forming a cluster with number of nodes in a VANET network. Then, the CH is appropriately selected based on the link survival rate of the vehicle to provide the emergency message towards different vehicles in the cluster, along with the stored data packet information in the DDIT table for fault prediction. Finally, the efficient medium access control (MAC) protocol is used to provide a prioritised message for avoiding spectrum shortage of emergency messages in the cluster. The comparative analysis between the proposed link-based CH selection with DDIT (LCHS-DDIT) with the existing methods, such as clustering-based cognitive MAC (CCMAC), multichannel CR ad-hoc network (MCRAN), and dedicative short range communication (DSRC), proves the effectiveness of LCHS-DDIT regarding the throughput, packet delivery ratio, routing control overhead with minimum transmission delay.
Keywords: vehicular ad-hoc networks; link survival rate; control channel; service channel; medium access control; roadside unit; on-board unit.
Special Issue on: Emerging Scalable Edge Computing Architectures and Intelligent Algorithms for Cloud-of-Things and Edge-of-Things
A survey on fog computing and its research challenges
by Jose Dos Santos Machado, Edward David Moreno, Admilson De Ribamar Lima Ribeiro
Abstract: This paper reviews the new paradigm of distributed computing, which is fog computing, and it presents its concept, characteristics and areas of performance. It performs a literature review on the problem of its implementation and analyses its research challenges, such as security issues, operational issues and their standardisation. We show and discuss that many questions need to be researched in academia so that their implementation will become a reality, but it is clear that their adherence is inevitable for the internet of the future.
Keywords: fog computing; edge computing; cloud computing; IoT; distributed computing; cloud integration to IoT.
Hybrid coherent encryption scheme for multimedia big data management using cryptographic encryption methods
by Stephen Dass, J. Prabhu
Abstract: In todays world of technology, data has been playing an imperative role in many different technical areas. Data confidentiality, integrity and data security over the internet from different media and applications are challenging tasks. Data generation from multimedia and IoT data is another huge source of big data on the internet. When sensitive and confidential data are accessed by attacks this lead to serious countermeasures to security and privacy. Data encryption is the mechanism to forestall this issue. Many encryption techniques are used for multimedia and IoT, but when massive data are developed it there are more computational challenges. This paper designs and proposes a new coherent encryption algorithm that addresses the issue of IoT and multimedia big data. The proposed system can cause a strong cryptographic effect without holding much memory and easy performance analysis. Handling huge data with the help of GPU is included in the proposed system to enhance the data processing more efficiently. The proposed algorithm is compared with other symmetric cryptographic algorithms such as AES,DES,3-DES, RC6 and MARS based on architecture, flexibility, scalability, security level and also based on computational running time, and throughput for both encryption and decryption processes. An avalanche effect is also calculated for the proposed system to be 54.2%. The proposed framework better secures the multimedia against real time attacks when compared with the existing system.
Keywords: big data; symmetric key encryption; analysis; security; GPU; IoT; multimedia big data.
A study on data deduplication schemes in cloud storage
by Priyan Malarvizhi Kumar, Usha Devi G, Shakila Basheer, Parthasarathy P
Abstract: Digital data is growing at immense rates day by day, and finding efficient storage and security mechanisms is a challenge. Cloud storage has already gained popularity because of the huge data storage capacity in storage servers made available to users by the cloud service providers. When lots of users upload data in cloud there can be too many redundant data as well and this can waste storage space as well as affect transmission bandwidth. To promise efficient storage handling of this redundant data is very important, which is done by the concept of deduplication. The major challenge for deduplication is that most users upload data in encrypted form for privacy and security of data. There are many prevailing mechanisms for deduplication, some of which handle encrypted data as well. The purpose of this paper is to conduct a survey of the existing deduplication mechanisms in cloud storage and to analyse the methodologies used by each of them.
Keywords: deduplication; convergent encryption; cloud storage.
Special Issue on: ICTSCI-2019 Swarm Intelligence Techniques for Optimum Utilisation of Resources in Grid and Utility Computing
A vector-based watermarking scheme for 3D models using block rearrangements
by Modigari Narendra, M.L. Valarmathi, L. Jani Anbarasi, L. Prasanna
Abstract: Watermarking schemes help to secure the ownership, authenticity, and copyright-related security issues of the electronic data. An efficient, computationally secure wavelet based watermark is proposed for 3D OBJ models. Digital watermarking is used to enhance the copyright protection of the 3D models and to overcome the copyright and integrity violations. Watermark is embedded into the higher bands of the 3D model vertices resulting in higher invisibility and robustness towards various attacks. The embedding of the watermark on the 3D models were tested to provide high robustness and imperceptibility. Tampering of the 3D obj models was performed to discover the tampered area of the watermarks. Different kinds of geometric and non-geometric attack are analysed, which shows the robustness of the watermarking scheme. Based on the experimental design, the robustness of the watermarking scheme of the 3D models is proved to show a perfect authentication.
Keywords: 3D mesh; mesh watermarking; block rearrangements.
Towards self-optimisation in fog computing environments
by Danilo Silva, Jose Machado, Admilson Ribamar, Edward Moreno
Abstract: In recent years, the number of smart devices (e.g. smartphones, sensors, autonomous vehicles) has grown exponentially. In this scenario, the computational demand for latency-sensitive applications in domains such as IoT, Industry 4.0 and smart cities has grown and the traditional model of cloud computing is no longer able to meet alone all the needs of this type of application. In this direction, a new paradigm of computation was introduced, called fog computing. This paradigm defines the architecture that extends the computational capacity and storage of the cloud to the edge of the network. However, many challenges need to be overcome, especially those regarding issues such as security, power consumption, high latency in communication with critical IoT applications, and need for quality of service (QoS). In this work, we have presented a container migration mechanism between the nodes between the fog and the cloud that supports the implementation of optimisation strategies to achieve different objectives solutions to problems of resources allocation in an environment integrating the IoT and fog computing. In addition, our work emphasises the performance optimisation and latency, through an autonomic architecture based on the MAPE-K control loop and providing a foundation for the analysis and optimisation architectures design to support IoT applications.
Keywords: fog computing; resource management; autonomic; IoT; service migration.
Improved African buffalo optimisation algorithm for petroleum product supply chain management
by Chinwe Peace Igiri, Yudhveer Singh, Deepshikha Bhargava, Samuel Shikaa
Abstract: Designing an efficient supply chain network for a real-world optimisation problem is complex. The complexity is due to the highly constrained large problem size. An optimum petroleum products scheduling would not only influence the distribution cost but also induce scarcity or surplus as the case may be. Unfortunately, the poor masses bear the consequence, especially in case of shortage. Practically speaking, the bio-inspired method is the preferred alternative to conventional or exact algorithms. The latter are gradient based, and therefore require initial guess to obtain a reasonable solution. The African buffalo optimisation (ABO) algorithm belongs to the class of swarm intelligent algorithms and has shown significant performance in the literature. The ABO models the grazing and defending lifestyle of the African buffaloes in the savannah desert. The chaotic ABO and chaotic-Levy ABO are improved variants of the standard ABO. They have also demonstrated outstanding performance in recent studies. Active research methodology approach is employed to carry out this investigation: the PICO (Problem Intervention Comparison Outcome). The present study applies these three algorithms to design an optimum petroleum distribution scheduling system. It further compares the outcomes to identify the best to guide management and logistics to make an informed decision. The result shows that the chaotic-Levy flight ABO, chaotic ABO, and standard ABO reduced the original total cost by 24.79%, 24.85%, and 9.35%, respectively. This performance proves the robustness of CLABO and CABO in real-world optimisation problems.
Keywords: supply chain network; computational intelligence; petroleum product scheduling; bio-inspired algorithm; swarm intelligent; African buffalo optimisation algorithm; chaotic African buffalo optimisation algorithm; chaotic–Levy flight African buffalo optimisation algorithm.
Special Issue on: Current Trends in Ambient Intelligence-Enabled Internet of Things and Web of Things Interface Vehicular Systems
Hybrid energy-efficient and QoS-aware algorithm for intelligent transportation system in internet of things
by N.N. Srinidhi, G.P. Sunitha, S. Raghavendra, S.M. Dilip Kumar, Victor Chang
Abstract: The Internet of Things (IoT) consists of a large number of energy compel devices that are prefigured to progress the effective competence of several industrial applications. It is essential to reduce the energy use of every device deployed in the IoT network without compromising the quality of service (QoS) for intelligent transportation systems. Here, the difficulty of providing the operation between the QoS allocation and the energy competence for the intelligent transportation system is deliberated. To achieve this objective, a multi-objective optimisation problem to accomplish the aim of estimating the outage performance of the clustering process and the network lifetime is devised. Subsequently, a Hybrid Energy-Efficient and QoS-Aware (HEEQA) algorithm that is a combination of quantum particle swarm optimisation (QPSO) along with improved non-dominated sorting genetic algorithm (NGSA) to achieve energy balance among the devices is proposed, and later the MAC layer parameters are tuned to reduce further the energy consumption of the devices. NSGA is applied to solve the problem of multi-objective optimisation and the QPSO algorithm is used to find the optimal cooperative nodes and cluster head in the clusters. The simulation outcome has put forward that the HEEQA algorithm has attained better operation balance between the energy competence and the QoS provisioning in the clustering process by minimising the energy consumption, delay, transmission overhead and maximising network lifetime, throughput and delivery ratio and is best suited for intelligent transportation application.
Keywords: energy efficiency; intelligent transportation system; IoT; network lifetime; QoS.
Analysing control plane scalability issue of software-defined wide area network using simulated annealing technique
by Kshira Sahoo, Somula Ramasubbareddy, B. Balamurugan, B. Vikram Deep
Abstract: In Software Defined Networks (SDN), the decoupling of the control logic from the data plane enables vendor-independent policies, programmability, and provide other numerous advantages. However, since its inception, SDN is a subject of a wide range of criticism mainly related to the scalability issues of the control plane. To address these limitations, recent architectures have supported the implementation of multiple SDN controllers. Usage of multiple controllers in the network arises controller placement problem (CPP). The placement problem is a major issue for wide area networks because, while placing the controllers, significant strategies need to be considered. In most of the placement strategies, authors focused on propagation latency, because it is a critical factor in real networks. In this paper, the placement problem has formulated as an optimisation problem and the Simulated Annealing (SA) technique has been used to analyse the problem. This technique is a probabilistic single-solution-based search method that has influenced the annealing process of metallurgy engineering. Further, we investigate the behaviour of SA with four different neighboring solution techniques. The effectiveness of the algorithms was carried out on TataNld topology and implemented using MATLAB simulator.
Keywords: software-defined networks; scalability; controller placement problem; simulated annealing.
Energy-aware multipath routing protocol for Internet of Things using network coding techniques
by S. Sankar, P. Srinivasan, Somula Ramasubbareddy, B. Balamurugan
Abstract: Energy conservation is a significant challenge in the Internet of Things (IoT), as it connects resource-constrained devices. The routing plays a vital role in transferring the data packets from the source to the destination. In Low Power and Lossy Networks (LLN), the existing routing protocols use the single routing metric, composite routing metric and opportunistic routing technique, to select the parent for the data transfer. However, the packet loss occurs, owing to the bottleneck of nodes nearby the sink and data traffic during the data transfer. In this paper, we propose an energy-aware multipath routing protocol (EAM-RPL) to prolong the network lifetime. The multipath model establishes multiple paths from the source node to the sink. In EAM-RPL, the source node applies the randomised linear network coding to encode the data packets and it transmits the data packets into the next level of cluster nodes. The intermediate nodes receive the encoded data packets from the source node and it forwards to the next cluster of nodes. Finally, the sink node receives the data packets and it decodes the original data packet sent by the source node. The simulation is conducted using COOJA network simulator. The effectiveness of EAM-RPL is compared with the RPL protocol. The simulation result shows that the proposed EAM-RPL improves the packet delivery ratio by 3-5% and prolongs the network lifetime by 5-10%.
Keywords: Internet of Things; network coding; IPv6 routing protocol; low power and lossy networks; multipath routing.
Dynamic group key management scheme for clustered wireless sensor networks
by Vijaya Saraswathi Redrowthu, L. Padma Sree, K. Anuradha
Abstract: Group key management is a technique to establish a shared group key, between the cluster head and sensor nodes, for multiple sessions in a clustered network environment. The common use of this established group key (also termed as conference key) is to permit users to encrypt and decrypt a particular broadcast message that is meant for the total user group. In this work, we propose a cluster-based dynamic group key management protocol that is based on public key cryptography. Cluster head initiates establishment of a group key to the sensor nodes efficiently and achieves secure communication. Later, the computation of the common group key is performed by each sensor node. Group members have functionality to join and leave from particular communication along with this, other nodes, equal to threshold compute new conference key without involvement of cluster head. The proposed protocol is investigated in terms of security and complexity analysis using network simulator NS-2.
Keywords: key management; group key management; wireless networks; privacy; public key cryptography; network simulator.
Intrusion detection technique using coarse Gaussian SVM
by Bhoopesh Singh Bhati, C.S. Rai
Abstract: In the new era of internet technology, everybody is transferring the data from place to place through the internet. As internet technology is improving, different types of attack have also increased. To detect the attacks it is important to protect transmitted information. The role of Intrusion Detection System (IDS) is imperative to detect various types of attack. Researchers have proposed numerous theories and methods in the area of IDS, the research in area of intrusion detection is still going on. In this paper, a Coarse Gaussian Support Vector Machine (CGSVM) based intrusion detection technique is proposed. The proposed method has four major steps, namely data collection, preprocessing and studying data, training and testing using CGSVM, and decisions. In implementation, KDDcup99 datasets are used as a benchmark and MATLAB programming environment is used. The results of the simulation are represented by Receiver Operating Characteristics (ROC) and confusion matrix. Here, the proposed method achieved high detection rates: 99.99%, 99.95%, 99.53%, 99.19%, and 90.57% for DOS, normal, probe, R2L, and U2R, respectively.
Keywords: information security; intrusion detection; machine learning; CGSVM.
Investigation of multi-objective optimisation techniques to minimise the localisation error in wireless sensor networks
by Harriet Puvitha, Saravanan Palani, V. Vijayakumar, Logesh Ravi, V. Subramaniyaswamy
Abstract: Wireless Sensor Networks (WSN) play a major role in remote sensing environments. In recent trends, sensors are used in various wireless technologies owing to their smaller size, cheaper rates and ability to communicate with each other to create a network. The sensor network is the convergent technology of microelectronic and electromechanical technologies. The localisation process can determine the location of each node in the network. Mobility-assisted localisation is an effective technique for node localisation using a mobility anchor. The mobility anchor is also used to optimise the path planning for the location-aware mobile node. In this proposed system, a multi-objective method is proposed to minimise the distance between the source and the target node using the Dijkstra algorithm with obstacle avoidance. The Grasshopper Optimisation Algorithm (GOA), and the Butterfly Optimisation Algorithm (BOA) based multi-objective models are implemented along with obstacle avoidance and path planning. The proposed system maximises the localisation accuracy. Also it minimises the localisation error and the computation time compared with existing systems.
Keywords: localisation models; grasshopper optimisation; butterfly optimisation; Dijkstra; path planning.