International Journal of Web and Grid Services (16 papers in press)
A Hybrid Pre-joined Service Network in Graph Database and Memory for Service Composition
by Jing Li, Ming Zhang, Ming Zhu, Lizhen Cui, Yuhong Yan, Bowen Song
Abstract: To make full use of the large space and persistency provided by a database and the fast processing speed of main memory, we propose a Hybrid Pre-joined Service Network method combining in-graph-database and in-memory calculations. This method firstly stores services information and combinations in a graph database. Then, for a users request, it retrieves candidate services from the database and constructs a service network in memory. After that, services are picked and composed to fulfill an optimal solution in memory. We assess the performance of our method by conducting experiments and comparing the approach with other database-based methods. Experiment results indicate the efficiency of this method in comparison, and it can always find solutions and lead to higher users satisfaction.
Keywords: Service Composition; QoS; Graph Database.
A study on data sharing system using ACP-ABE- SE in cloud environment
by YongWoon Hwang, Im Yeong Lee
Abstract: With the development of cloud computing, users can store their data externally and it is convenient to share with other users. However, in a network environment connected to an external cloud, various security threats such as spoofing attacks and collusion attacks may occur, and an attacker's data may be leaked to the external. Therefore, security of data shared in the cloud environment is essential. Among various security technologies, CP-ABE provides data encryption, decryption, and access control. So far, research on CP-ABE methods has continued, but the existing CP-ABE method has a weak security method and an inefficient method. In particular, third parties may violate the privacy of users by inferring the attributes of users accessing data with the access structure contained in the ciphertext. In addition, the cloud server is inefficient because it discovers stored cryptographic statements after users have decrypted them when requesting them, and because the server can identify the decrypted data, confidentiality of the data is not guaranteed. In order to solve this, anonymous CP-ABE methods are being studied to anonymize the access structure of cryptographic statements, and studies are being conducted to apply searchable encryptions to CP-ABE methods. However, problems that can be considered in CP-ABE methods such as increasing ciphertext size and outsourcing servers occur. In this paper, we propose a new ACP-ABE-SE data sharing system that combines anonymous CP-ABE and searchable encryption for secure data sharing in the cloud. In the proposed scheme, a fake access structure is created to protect the privacy of users accessing the ciphertext. In addition, searchable encryption is used to efficiently search for the desired ciphertext among the ciphertext stored on the cloud server. Therefore, it aims to share data safely and efficiently in a cloud environment.
Keywords: Cloud security; ciphertext-policy attribute-based encryption; Attribute-based encryption; Anonymous; Searchable Encryption;.
An Egalitarian Approach of Scheduling Time Restricted Tasks in Mobile Crowdsourcing for Double Auction Environment
by Jaya Mukhopadhyay, Vikash Kumar Singh, Sajal Mukhopadhyay, Anita Pal, Meghana M. D
Abstract: Crowdsourcing with the intelligent agent felicitated with portable smart devices is becoming increasingly popular in recent years as it has opened up meeting an extensive list of real life problems such as supervising pollution control, giving information about the damaged road and so on. In literature this isrnpopularly known as Mobile crowdsourcing (MCS) or participatory sensing (PS).rnHow to motivate the task executors, has been a challenge in MCS environmentrnwhen the tasks are available to be performed. To mitigate this issue several auction based schemes are proposed where agents are motivated by providing incentives. In this paper we have addressed this motivation issue in a double auction environment when the tasks are time restricted (each task has a start time and a finish time) and may be overlapped. Here, we have taken an egalitarian approach so that a balanced allocation of tasks can be established to the participating agents (task executors). In our approach, first the tasks that are imparted by the task providers, are partitioned into several slots in a non-overlapping manner and then allocated to the participating agents through double auction. It is proved that our mechanism is polynomial time solvable (such as to ensure that it can be scaled with varying input sizes) and abide by the economic robustness (i.e. supported with truthful, individually rational, budget balanced properties). It is also exhibited via simulation that our proposed mechanism will perform better when the agents (both task executors and task providers) misreport their valuations.
Keywords: Participatory sensing; Strategic; Truthful; Auction; Scheduling.
An integrated blockchain network with energy router based trading strategy for optimal energy management
by Yunfei Du, Zia Ullah, Xianggen Yin, Jinmu Lai, Zhen Wang
Abstract: The new emerging idea of energy router and energy blockchain network (EBN) transactions gaining remarkable attention in the smart grid and advanced power system due to a variety of applications and innovative solutions for efficient energy trading. However, the implementation of the energy blockchain network (EBN) transactions raises various challenges such as lack of power loss consideration and congestion management, insufficient computing, the storage capacity of trading nodes, and higher transaction costs due to weak central organization low performance of smart meters. This paper introduces a new energy trading approach, focused on the energy router and EBN together, to effectively counter energy trading challenges and perform optimal energy trading. The proposed systematic design of energy trading has divided into four steps which execute (i) forming trading pairs through a two-stage Stackelberg game model, (ii) checking security by using the characteristics of directional power flow of energy router, (iii) congestion management through the minimum loss multi-path power transmission (MLMPT) scheme, (iv) forming smart contracts, and settling transactions. The performance of the proposed framework is verified using 13 nodes EBN implementation, where the results obtained demonstrate the accurate energy trading, balanced trading prices, even in congestion management conditions. Despite congestion prices and trading pairs, the MLMPT scheme in the proposed design also minimizes the power loss to protect sellers' interests.
Keywords: Energy Blockchain Network; Energy Router; Power Transactions; Energy Trading; Stackelberg Master-Slave Game Mode; Congestion Management.
A Sequence-to-Sequence Traffic Predictor on Software-Defined Networking
by Wenchuan Yang, Rui Hua, Qiuhan Zhao
Abstract: Network traffic prediction is very important for load balancing and network planning. Under the current network architecture, it is difficult to realize the collection, prediction and centralized management of traffic. This paper proposes an attention-based traffic predictor (ATP) model to achieve traffic prediction in a software-defined network (SDN) environment. To improve the accuracy and efficiency of a prediction, improvements are made from three aspects: data, model, and evaluation optimization. First, during the traffic data acquisition phase, to reduce the resource consumption of the request caused by acquiring realtime network traffic information and to maintain the accuracy of the prediction, a combination of lower sampling frequency and data augmentation is adopted. Second, based on the long correlation and self-similar characteristics of network traffic, a sequence-to-sequence model with attention (Seq2Seq+Attention) is selected for network traffic prediction. Finally, the traditional mean squared error (MSE) evaluation method sets the same weight for samples of different values, which is not suitable for network traffic prediction. Therefore, this paper proposes an improved weighted MSE evaluation method. Experiments show that the proposed method can maintain the prediction accuracy while reducing the sampling frequency by 50%. The weighted MSE evaluation method can improve the accuracy by 5.37% compared with the original MSE evaluation method. On the basis of highly accurate traffic forecasts, it is possible to further realize intelligent control of network traffic, improve network utilization, reduce network delay, and improve the efficiency of intelligent services.
Keywords: Software defined networking; Network traffic prediction; Data augmentation; Seq2Seq+Attention; Weighted MSE.
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 bandwith. 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, Abhishek Kumar, Kunjal Shah, Suyel Namasudra, Seifedine Kadry
Abstract: Smart Grid describes an electrical grid that has integrated with a fully computerized 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 utilizing the ProVerif tool along with BurrowsAbadiNeedham 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; ProVerif tool; Burrows Abadi Needham logic; Security Analysis.
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: Increase in growth of Internet of Things (IoT) devices leads to an exponential increase in IoT applications. This exponential growth of devices increases the complexity of IoT network. Increase in network complexity intensifies the risks against topology robustness. An IoT network acts as a core enabler for converting conventional cities to smart cities. These devices produce large amount of data related: nodes' geographic location, connected neighbors, etc. Therefore, improving topology robustness of the IoT networks against targeted and malicious attacks is a prime issue. Four algorithms: Enhanced Angle Sum Operation 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 nodes' degree difference along with 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 algorithm to converge towards global optimal solution. Extensive simulations verify that proposed algorithms outperform the ROSE and simulating annealing. Moreover, the Enhanced ROSE and EASO-ROSE are compared with ROSE and simulating annealing. Furthermore, CAGA and AGA algorithms are compared with simulating annealing and hill climbing. Enhanced ROSE, EASO-ROSE, CAGA and AGA perform 61.3%, 48.3%, 45.5% and 34.95%, respectively better as compared to simulating annealing.
Keywords: Internet of things; topology robustness; malicious attacks; data driven; scale-free.
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.
Reinforced Resource Management in Vehicular Fog Computing (VFC) using Deep Beacon Power Control (DBPC) protocol
by T. Ananth Kumar, R. Rajmohan, E. Golden Julie, Y. Harold Robinson, Vimal Shanmuganathan, 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 utilized 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 optimization 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 optimized data transmission in vehicular fog computing. Unnecessary network overhead and also channel congestion can be minimized 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; VFC; Periodic Message; Broadcast; Link State; VANET; DRSC; SLoV.
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, whichthe 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.
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.
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.