International Journal of Web and Grid Services (13 papers in press)
An Adaptive Enhanced Differential Evolution Strategies for Topology Robustness in Internet of Things
by Talha Naeem Qureshi, Nadeem Javaid, Ahmad Almogren, Asad Ullah Khan, Hisham Almajed, Irfan Mohiuddin
Abstract: Internet of Things (IoT) is the backbone of any automation process and a pivot point to lay down the base of smart cities. To effectively increase the robustness of the IoT network without changing the degree distribution of nodes is still a challenging issue. To tackle this problem, we have proposed two algorithms, such as Enhanced Differential Evolution (EDE) and Adaptive EDE (AEDE). Initially, we have generated scale-free topologies according to the characteristics and behavior of IoT networks in real world. Geographic information of IoT sensors are gathered from big data server using a mechanism that saves the IoT sensors from computational overhead of algorithms. Proposed algorithms effectively improve the robustness of the IoT network without changing the degree distribution of nodes. The algorithms are capable to converge the results towards the global optima from solution space along with the fast convergence speed. The AEDE dynamically changes the probabilities of multiple operations of the EDE along with the changing environment. Also, it maintains the balance between the diversity of solution space and the convergence speed through adaptive probabilities. The proposed algorithms show better performance in terms of increasing network's robustness as compared to previous schemes. To validate the proposed concept, both proposed algorithms are compared with well known previous algorithms including the Genetic Algorithm (GA), the Simulating Annealing (SA) and the Hill climbing Algorithm (HA). The proposed algorithms are also compared with benchmark schemes in large scale networks with the changing number of nodes and edge densities as well. The EDE performs 7.13%, 31.6% and 41.8% better as compared to GA, SA and HA, respectively.
Keywords: Data driven; Internet of things; Malicious attacks; Scale-free; Topology robustness.
Resource discovery and scalability-aware routing in cloud federation using distributed meta-brokering paradigm
by Sajid Latif, Mamoona Humayun, Abida Sharif, SEIFEDINE KADRY
Abstract: Resource management in large scale distributed systems is comprises of the various key factors e.g. scheduling, monitoring and discovery of the potential resources. Due to highly dynamic conditions and heterogeneity of resources in emerging distributed paradigm, effective management of resources is a challenging task. Resource discovery is one of the critical and complex process to find the optimal and cost effective resources for efficient allocation. Interconnected cloud (Intercloud) is the emerging variant of large scale distributed computing to cover the geographic footprint with better Service Level Agreement (SLA). In this study, meta-brokering driven distributed resource discovery approach is proposed to find the inevitable resources for applications execution. Core process is based on queries propagation with minimal routing cost in overlay design while maintaining the ontological resource information. Meta-Brokering instances in overlay network takes system dynamics into account to calculate the minimum cost for queries routing. Coordination amongst distributed instances is carried out in P2P mode to enable the distributed brokerage. Such uniform configuration of these instances and resource ontology also realize the interoperability for virtual domains. Moreover, distributed nature of instances are resilient to service outage or link failure as contrary to centralized counterparts. Stack of architectural components in devised approach is classified into different layers for decoupled design setting. In this setup, fine-grained management is performed by splitting the overall functionality into distinct modules. Hence, this paradigm enables distributed resource discovery in seamless way while complementing the scalability factor.
Keywords: Scalability; Meta-Brokering; Monitoring; Discovery; Interconnection.
Adaptive Practical Byzantine Fault Tolerance Consensus Algorithm in Permission Blockchain Network
by G. Indra Navaroj, E. Golden Julie, Y. Harold Robinson
Abstract: In the recent years, blockchain is a prevalent and emerging technology. Blockchain is a distributed ledger or data structure. It is combined with many other technologies like the Internet of Things, Cloud Computing, Artificial Intelligence, Big Data, and Machine Learning. Governments and other industries also have used blockchain technology to overcome many of the issues and challenges of security. Blockchain mainly solves the problem of double-spending and a consensus of distributed systems. Many researchers concentrate on the blockchain consensus algorithm. PBFT is a permission blockchain consensus algorithm; it develops on the concept of state machine replicas. But the efficiency and scalability are low in blockchain networks. Here communication overhead occurs because of many replications. In this paper, we propose an Adaptive Practical Byzantine Fault Tolerance algorithm in the permission blockchain network. In the proposed approach, the node is divided into Trust Node and Faulty Node. Identified faulty nodes have not participated in the voting process because the node reputation reduces. Further, the identified Trust Node is having a high reputation in the consensus process. The Primary Node is elected as the Master Node by a majority of voting value. It is selected as rotation basis and this Adaptive PBFT algorithm has achieved high efficiency, scalability and reduced the communication overhead too. Finally, Adaptive PBFT algorithm performance evaluates with FBFT, ePBFT, RBFT, TPBFT.
Keywords: Blockchain Trust Node; Fault Node; Byzantine; Practical Byzantine.
Cost-aware Edge Server Placement
by Qiyang Zhang, Shangguang Wang, Ao Zhou, Xiao Ma
Abstract: As an urgent problem to be solved, the placement of edge server is the first step in the implementation of mobile edge computing. In practice, service providers always hope to provide services to as many users as they can under the limited funds. In this paper, we aim to study the cost-aware server placement issue for a real-world scenario with access delay guarantee. Considering the network workload, we set an affordable upper limit on the maximum number of base stations. The server placement is then modelled as a mix integer non-linear programming problem, which is proved to be NP-hard. To solve this problem with reduced computation complexity, we propose a biogeography-based optimization algorithm. Extensive simulations on the real-world dataset from Shanghai Telecom show that the proposed algorithm outperforms other benchmarks in terms of the total placement cost and average user access delay.
Keywords: Mobile edge computing; Edge server placement; Cost; Access delay.
Internet of Things based Low Cost Water Meter with Multi Functionality
by Biswaranjan Bhola, Raghvendra Kumar, Brojo Kishore Mishra
Abstract: Water is a very indispensable element for the abidance of human society. Hence it is indeed essential to reduce the wastage of affluent supply drinking water which is further leads to fast diminishing of water day by day. In this paper, aimed an Internet of Things(IoT) based Low cost Water Meter(LWM) used for water usage recording system using Apache server and MySQL database to identify the uses of water in each family. In LWM multi functionality is added using some mathematical function hence the use of extraneous physical sensors are reduced, as a result the cost of Water Meter can be minimized and all the component of this device is scalable in nature hence it is easily assembled. Furthermore it provides a user friendly interface for casual user, the aim is to reduce the wastage of water by using LWM in a minimal cost.
Keywords: Internet of Things (IoT); Water Meter; Network; Flow Sensor; Water distribution system; Water management; Architecture.
A GA-based approach with an interval VIKOR method for solving the constrained QoS-aware service composition in dynamic IoT environments
by Fateh Seghir, Renda Kouachi
Abstract: The QoS-aware service composition (QSC) with global QoS user-constraints in dynamic IoT environments is an NP-hard problem, where the QoS values of the IoT services are often ambiguous in nature due to various reasons such as network topology changes, IoT devices mobility and economic policies. Therefore, motivated by the fact that the interval number is an efficient and a simple model to express the imprecision of the QoS properties; the QSC in uncertain IoT environments is formulated as an interval multi-criteria optimization (INQSC) problem. Furthermore, to solve the modeled INQSC, we provide a GA-based optimization approach, which integrates an interval VIKOR method to deal with feasible solutions ranking, an interval QoS constraint violation sorting to rank infeasible solutions, and a local search operator with an elitism replacement to enhance both the exploitation and the exploration abilities of the provided optimization approach. The experimental comparison of our proposal with a recently provided GAP approach demonstrates the performance and the effectiveness of the proposed GA-based approach.
Keywords: IoT services; Quality of Service (QoS) uncertainty; Interval number; Multi-criteria optimization; VIKOR method; Genetic algorithm.
A Dynamic Load Balancing Mechanism for Fog Computing Environment
by Kamran Sattar Awaisi, Assad Abbas, Hasan Ali Khattak, Abbas Khalid, Hafiz Tayyab Rauf, Seifedine Kadry
Abstract: Fog computing has appeared as an effectively distributed computing paradigm to perform internet of things (IoT) applications. It is an extension of cloud computing that provides cloud-like services at the edge of the network. It overcomes the cloud computing issues and ensures to process of the huge amount of heterogeneous data in minimum time by consuming less network bandwidth. Therefore, dynamic load balancing is necessary to acquire the actual benefits of fog computing. In this paper, we propose a dynamic load balancing mechanism (DLBM) to schedule the number of service requests on fog nodes effectively. Furthermore, the DLBM has three algorithms to dynamically balance the load of fog nodes named appropriate node selection (ANS), effective task distribution (ETD), and global task execution and resource allocation (GTERA). The performance of the proposed mechanism is compared with Cloud only technique, fog-cloud-placement (FCP) algorithm, and self-similarity-based load balancing (SSLB) technique.
Keywords: Internet of Things; Fog Computing; Resource Management; Load Balancing.
Machine learning applications for fog computing in IoT: a survey
by Mitra Mousavi, Javad Rezazadeh, Omid Ameri Sianaki
Abstract: Today, internet of things (IoT) has become an important paradigm. Everyday increasing number of IoT applications and services emerge. Smart devices connected by the IoT generate significant amounts of data. Analysis IoT sensor data using machine learning algorithms is a key to achieve useful information for prediction, classification, data association and data conceptualisation. Offloading input data to cloud servers leads to increased communication costs. Undertaking data analytics at the network edge using fog computing enables the rapid processing of incoming data for real-time response. In this paper, we examine the results of using different machine learning algorithms on fog nodes based on existing research. These results are low latency, high accuracy and low bandwidth. Also, this work presents the current fog computing architecture which consists of different layers that distribute computing, storage, control and networking and finally we investigate the challenges and open issues related to the deployment of machine learning on fog nodes.
Keywords: internet of things; IoT; fog computing; machine learning; fog-based machine learning.
A novel elliptic curve cryptography-based system for smart grid communication
by Ajay Kumar, Kumar Abhishek, Kunjal Shah, Suyel Namasudra, Seifedine Kadry
Abstract: Smart grid describes an electrical grid that has integrated with a fully computerised two-way communication network. Smart grid (SG) communication has recently experienced attention regarding distributed electric power transmission frameworks. Because of the intricate nature of the intelligent grid and different safekeeping prerequisites, structuring a reasonable validation system is a perplexing task. Therefore, validation and data protection of these devices, including lightweight operations with trivial computations, play an exceptional job in fruitful coordination of SG technologies. In this inquiry, a validation system dependent on elliptic curve cryptography (ECC) for securing the smart grid is proposed. The formal verification of this procedure is implemented utilising the ProVerif tool along with Burrows-Abadi-Needham logic (BAN logic), which affirms its security strength within sight of a conceivable trespasser. The proposed system is free from all security attacks. The informal security analysis, performance analysis, and assessment of the proposed approach with several other existing systems demonstrate that it is powerful enough in terms of operation cost, storage cost and transmission cost, efficient and stout against numerous security attacks.
Keywords: validation; elliptic curve cryptography; ECC; security analysis; ProVerif tool; Burrows-Abadi-Needham logic; BAN logic.
Attack resistance-based topology robustness of scale-free internet of things for smart cities
by Talha Naeem Qureshi, Nadeem Javaid, Ahmad Almogren, Zain Abubaker, Hisham Almajed, Irfan Mohiuddin
Abstract: In internet of things (IoTs), the increase in the number of devices is directly proportional to the number of applications. The exponential growth of devices increases both the network complexity and risk against topology robustness. Moreover, the network is also prone to targeted and malicious attacks. In this paper, enhanced angle sum operation ROSE (EASO-ROSE), enhanced ROSE, adaptive genetic algorithm (AGA) and cluster adaptive genetic algorithm (CAGA) are proposed to cater the topology robustness issue for IoT enabled smart cities. In addition, the proposed solutions keep the nodes' initial degree distribution unchanged by maintaining the scale-free nature of the topology. Enhanced ROSE and EASO-ROSE significantly improve the topology robustness by calculating the difference in nodes' degree while rearranging the surrounding angles according to the highest degree node. CAGA and AGA also significantly improve the topology robustness by using adaptive probabilities of crossover and mutation that guide both algorithms to converge towards global optimum solution. Extensive simulations are preformed to evaluate the performance of the proposed strategies. Schneider R is used as a performance metric in the simulations. The results depict that the proposed algorithms perform 61.3%, 48.3%, 45.5% and 34.95%, better than simulating annealing algorithm.
Keywords: internet of things; IoTs; topology robustness; malicious attacks; data driven; scale-free.
Reinforced resource management in vehicular fog computing using deep beacon power control protocol
by T. Ananth Kumar, R. Rajmohan, E. Golden Julie, Y. Harold Robinson, S. Vimal, Seifedine Kadry
Abstract: Vehicular fog computing (VFC) plays a vital role in the mobile ad hoc network. In vehicular fog computing, a deep beacon power control (DBPC) protocol is utilised for the sending of the periodical message in the VANET. This algorithm increases the effectiveness in the coverage of the broadcast of safety and security-related information and satisfies the constraints on both the link state and delay. The induction of deep learning model in beacon power control approach aims to overcome the optimisation issue by improving the amount of a fading multiuser interference channel. VANET is one of the ad hoc network real-life applications for communication between near-by equipment such as roadside equipment and vehicles and between vehicles. The proposed technique leads to optimised data transmission in vehicular fog computing. Unnecessary network overhead and also channel congestion can be minimised using this proposed technique. The proposed deep BPC technique is implemented in both Keras and NS2 simulators. Outcomes of both simulations reveal that when deep learning embedded with BPC protocol, the performance increases rapidly.
Keywords: FOG computing; BPC; vehicular fog computing; VFC; periodic message; broadcast; link state; vehicular ad hoc network; VANET; dedicated short-range communications; DSRC; social internet of vehicles; SIoV.
Special Issue on: Security for Cloud Computing
Searchable Symmetric Encryption Based on the
Inner Product for Cloud Storage
by Jun Yang, Shujuan Li, Xiaodan Yan, Baihui Zhang, Baojiang Cui
Abstract: Searchable encryption enables the data owner to store their own data after
encrypting them in the cloud. Searchable encryption also allows the client to search over
the data without leaking any information about it. In this paper, we rst introduce a
searchable symmetric encryption scheme based on the inner product: it is more ecient
to compute the inner product of two vectors. In our construction, the parties can be Data
Owners, Clients or the Cloud Server. The three parties communicate with each other
through the inner product to achieve the goal that the client can search the data in the
cloud without leaking any information on the data the owner stored in the cloud. We then
perform a security analysis and performance evaluation, which show that our algorithm
and construction are secure and ecient.
Keywords: Searchable Encryption; Searchable Symmetric Encryption; Inner Product;
the Cloud Server; Security.
Special Issue on: Service Platforms in Grid Computing – Recent Advances and Future Trends
An algorithm for online distributed fault-tolerant job scheduling in grid computing
by Jun Zeng
Abstract: In order to solve the problem of various faults in grid computing environment, this paper raises an online distributed fault-tolerant job scheduling algorithm. The algorithm is consisted of two main algorithm modules, which was job schedule algorithm module, and replica management and placement algorithm module, respectively. The former is based on the idea of job replica, which each replica is independently and scheduled at different sites. Those unused resources are used to run the job replica so that at least one of replicas can be successfully completed. The latter makes each remote separate resource manager (SRM) to run a job replica to send jobs at each monitoring interval, which the status of the replica can be told to the original SRM (PSRM). PSRM periodically checks the application status table and queries all remote SRMs to obtain the status of the computing machine and network, and monitors all the running job replicas in the site, so as to achieve the fault tolerance function. The experimental results show that the online distributed fault-tolerant job scheduling algorithm can achieve better job average response time under various failure rates when compared with other grid fault-tolerant scheduling algorithms and non-fault-tolerant scheduling algorithms.
Keywords: grid computing; online distribution model; fault-tolerant; job schedule; fault model.