International Journal of Communication Networks and Distributed Systems (18 papers in press)
Cloud-fog Computing System for Classification and Scheduling the Information-centric IoT Applications (CSIc_IoTA)
by K. Jairam Naik
Abstract: Cloud users submits their application for processing on the providers infrastructure expecting a diverse set of computational, storage, or communicational resources. Classifying the tasks of these applications based on their needs and scheduling them on to the most suitable resources is a big obstacle. To manage with such issues, a system that supports classification and scheduling them on to the most efficient resource for execution is essential. An information-centric (IC) internet of things (IoT) system for cloud-fog computing environment which supports these two key functionalities through ensured QoS was proposed in this article. The experimental simulations and performance analysis of the proposed approach was prepared by considering a global cloud with three servers, four fogs, and four types of IoT application with a sum of 3,000 tasks collectively. Experimental outcomes states that the anticipated cloud-fog computing architecture reduces the average makespan time and service cost up to a maximum of 11.8%, and 12.63% respectively when compared with other existing approaches. Also, the cost-makespan trade-off (CMT) gains with the proposed CSIc_IoTA is up to 28.5% and can guarantees better QoS requirements of real-time IoT applications.
Keywords: classification; scheduling; IoT applications; resources; cloud-fog architecture; information-centric; utilisation; makespan time.
A Blockchain based ITS framework with Privacy Preserving for Secure and Reliable Communication
by Soumyashree S. Panda, Debashish Jena, Bhabendu Kumar Mohanta, Srikanta Patnaik
Abstract: With the omnipresence of technology, intelligent transportation system (ITS) is no more a distant dream but has become an achievable reality. One of the fundamental challenges in the implementation of an ITS is the proper management of security and privacy issues, especially how the system confirms the validity of its users. Most of the existing security mechanisms are based on a centralized framework and assume the registration authority and roadside units to be trustful. Therefore, a distributed framework using Blockchain and, a very lightweight and privacy-preserving authentication protocol employing an interactive zero-knowledge proof (ZKP) based on elliptic curve cryptography (ECC) is proposed. An in-depth analysis of the protocol demonstrates that it meets all the security and privacy requisites of an ITS. In addition, the suggested protocol is also validated using the widely used AVISPA tool. The reliability and effectiveness of the protocol are analyzed through simulation using NS2 which proves the practicality of the protocol.
Keywords: intelligent transportation system; ITS; zero knowledge proof; blockchain; security and privacy.
Dynamic Workflow Scheduling in Cloud using Neural Network based Multi-Objective Evolutionary Algorithm
by K. Jairam Naik, Siddarth Chandra, Paras Agarwal
Abstract: Workflow is a series of jobs that are executed in order to complete a specific activity where the jobs are often dependent on each other. Data transfer that might occur between such jobs results into the creation of a workflow that aims at utilising resources for workflow tasks by optimising the use of cloud resources. Few of the existing single objectives workflows scheduling solutions have linearly combine multiple objectives to get multi-objective solution, but it might not be able to model the real-world problem efficiently for certain conditions where the environment is dynamic in nature. Hence, a Neural Network based Multi-Objective Evolutionary Algorithm (NN-MOHEFT) that solves the multi-objective workflow scheduling issues in a dynamic environment was proposed in this article. The NN-MOHEFT learns the pattern behind changing Pareto optimal front for successive environment and tries to predict the Pareto optimal front for the next environment from the input Pareto optimal set of the current environment. The proposed NN-MOHEFT algorithm is at par with the original constructs when it comes to the hypervolume of objectives generated. It generates 10% more non-dominated solutions as compared to the original construct.
Keywords: workflow scheduling; cloud computing; resources; multi-objective; makespan; utilisation; hypervolume; EC2 instances.
Real Time Event Area Localization and Estimation in Smart Environments Based on A Realistic Sensing Model
by Srabani Kundu, Nabanita Das, Dibakar Saha
Abstract: The need for early warning systems to design a smart environment is growing rapidly. A lot of works has been reported so far for generating an early warning in case of any disaster like gas leakage, forest fire, etc. using WSNs. In most of the works, it has been assumed that a node is affected whenever it lies within the event region. But in reality, each node does not sense just its point of location but covers a region defined by its sensing range and extracts an aggregated view of the region. So far, no sensing model considers this fact. In this paper, a new realistic sensing model is proposed for continuous event regions. Based on that, a lightweight localised algorithm is developed to identify a minimal set of boundary nodes to locate and estimate the event area in real-time achieving 99% accuracy and 75 to 80 % reduction in message overhead.
Keywords: wireless sensor networks; WSNs; affected area; boundary node; uniform area sensing model; digital-geometry; binary decision predicate.
A reduced hardware complexity algorithm with improved outage probability for 5G communication system
by Ashu Taneja, Nitin Saluja
Abstract: In this paper, a 5G mobile communication system is considered with cooperative environment. The mobile and base-station node communicate with each other through a number of amplify-and-forward (AF) relays. The users have multiple antennas while each relay has a single antenna. The radio-frequency (RF) transceiver chain associated with each antenna element leads to increased hardware complexity. This challenge is reduced by efficient utilisation of RF front ends in the proposed wireless system. The paper proposes an RF chain selection technique which enables the selection of RF front end with maximum received signal-to-noise-ratio (SNR). The outage probability is derived for proposed mobile communication system. Further, system ergodic capacity, energy efficiency and average end-to-end SNR are evaluated. As compared to the conventional random selection approach, this algorithm aims to achieve higher capacity and higher average end-to-end-SNR. At 16 dB SNR, system capacity improves by 52% with proposed algorithm rather than conventional random selection.
Keywords: 5G; RF chain selection; amplify-and-forward; cooperative communication; outage probability; signal-to-noise-ratio; SNR.
Performance Analysis of Peer-to-Peer Networks Based on Two-Phase Service Queuing Theory
by Fengjiao Liu, Zhanyou Ma, Qiannan Si, Miao Yan
Abstract: Peer-to-peer (P2P) network gains enormous popularity in recent years. In order to reduce the cost generated by nodes online, the strategy of multiple synchronous working vacations of partial servers is introduced. Aiming at the service mechanism of nodes in the network, a three dimensional continuous time Markov stochastic model with two phases of service is established. The steady-state analysis of the network model is carried out by using the method of matrix-geometric solution and Gauss-Seidel iterative method based on the observable queuing rules. The expression of the indicators such as the mean sojourn time and expected length of the requesting nodes are obtained. In addition, the online cost of P2P network nodes is analysed. The influences of system parameters on each performance indicator are analysed through numerical experiments. Finally, the benefit function is constructed to obtain the optimal social parameters.
Keywords: P2P networks; synchronous working vacation; matrix-geometric solution; two-phase service.
Implementation of a Constrained Quantum Optimization Method in Resource Distribution Management with Considering Queueing Scenarios
by Sara El Gaily, Sandor Imre
Abstract: Cloud resource demands are rapidly increasing exponentially with the development of cloud computing. For this sake, new modern methods and techniques are needed to improve its quality of service. In this work, the problem of resource distribution management in cloud computing is formulated as an optimisation problem. To this end, we exploit the constrained quantum optimisation algorithm (CQOA) in resource distribution management by considering queueing aspect in order to optimise and improve its performance in terms of computational complexity, accuracy, and energy consumption. Next, we study the implementation and the configuration of the CQOA. After, we investigate the computational complexity of selecting the optimal distribution scenario that corresponds to minimum energy consumption. We also express the lower and upper bounds of the overall possible assignment scenarios within the database. Finally, we validate the efficiency of the proposed implemented CQOA by constructing a simulation environment.
Keywords: constrained quantum optimisation algorithm; CQOA; resource distribution management; task assignment.
Scalable RBAC model for large-scale applications with automatic user-role assignment
by Gurucharansingh Sahani, Chirag Thaker, Sanjay Shah
Abstract: Access control is one of the essential security requirements of any information system. Role-based access control (RBAC) has been the most popular access control model so far. However, in-advance, manual, and time-consuming role assignment process makes it inefficient in large data-sharing applications. Extension to RBAC models using attributes, attribute-based access control (ABAC) model, and combination of access control models with other techniques has become an emerging research area in access control. Recent research either focuses on standardising the ABAC model or making RBAC fine-grained, dynamic, and context-aware. A manual user-role assignment is still an open problem for RBAC that limits it for extending. In large-scale applications, a large number of users makes the manual user-role assignment process complicated and difficult. In this paper, we present an attribute-based framework to make RBACs user-role assignment process automatic by extending it for large-scale applications.
Keywords: access control; automatic user-role assignment; large-scale applications; patient health record; role-based access control; RBAC.
Energy Efficient Clustering and Topology Management Scheme for Wireless Sensor Networks
by Hemantaraj Kelagadi, Priyatamkumar .
Abstract: The performance of wireless sensor networks (WSNs) is influenced by the node energy conditions. Though clustering technique is significantly found to enhance the scalability and energy efficiency of the overall system, conventional clustering algorithms fail to handle critical circumstances. Data collection from every sensor node is important and at the same time duplication of similar data from several nodes has to be bypassed to avoid excess energy dissipation of the cluster heads required to handle indistinguishable data. An algorithm to control the topology by inclusion of clustering scheme and dynamic TDMA scheduling for data aggregation and classification is proposed in this paper. An avenue planning strategy to control the node faults is introduced to provide alternate paths in case of node failures. Fault tolerance and topology control therefore contend to balance the node energies efficiently. Effectiveness of the proposed scheme is analyzed through simulation and the efficacy is verified.
Keywords: fault tolerance; topology control; clustering; avenue planning; primary parent node; PPN; secondary parent node; SPN.
A Survey on Design Challenges in Scheduling Algorithms in Wireless Networks
by Mojtaba Malekpourshahraki, Christopher Desiniotis, Marjan Radi, Behnam Dezfouli
Abstract: Recent advances in wireless technologies open up new avenues toward many newer applications; however, existing wireless networks are not efficient enough to satisfy some new applications requirements such as low energy consumption, high throughput, and low end-to-end delay. Scheduling algorithms are promising for performance improvement by addressing the limitations of standard networking protocols. Due to the widespread deployments and applications of wireless networks, a significant number of scheduling algorithms has been proposed to improve network performance. In this paper, we provide a systematic comparison among proposed scheduling algorithm with an exhaustive, network-independent framework to ease the comparison among different scheduler design and highlight important challenges in the term of algorithmic parameters, computational order, accuracy, and overhead. We also study a set of schedulers in two main categories: low-power, and high-throughput, concerning our framework to extract the weakness and strength of each proposed method compared to other scheduling algorithms.
Keywords: scheduling algorithms; low-power wireless networks; high-throughput networks; MAC.
Light-tree Reconfiguration without Flow Interruption in Sparse Wavelength Converter Network
by Amanvon Ferdinand ATTA, Bernard Cousin, Joël Christian Adépo, Souleymane Oumtanaga
Abstract: Network reconfiguration is an important task in WDM optical networks, which enables the optimisation of network resources. With the growing demand for multicast applications (e.g., distance learning, IPTV), this study focuses on the reconfiguration of the routing of a multicast connection. In this study, the path of the multicast connection is represented by a light-tree. A light-tree reconfiguration consists of migrating an optical flow from a light-tree to a new one. However, it is very difficult to automate light-tree reconfiguration without flow interruption. Flow interruption is undesirable for network operators. Therefore, the problem studied here is to find a sequence of operations in order to migrate an optical flow from a light-tree to a new one without flow interruption in a sparse wavelength converter network. For solving the light-tree reconfiguration problem, we propose a method based on a sub-tree approach. The comprehensive simulation results demonstrated the effectiveness of our method.
Keywords: light-tree reconfiguration; flow interruption; wavelength converter; flow migration; optical network.
Hybrid Ensemble Techniques used for Classifier and Feature selection in Intrusion Detection Systems
by Ankit Kharwar, Devendra Thakor
Abstract: The data security of networks is a universal problem for governments, companies, and persons. The attack rate expanded drastically, and strategies utilised by attackers also gorged ahead periodically. The solution to this problem is intrusion detection, a typical and successful methodology for planning intrusion detection systems (IDS) with machine learning. The proposed method with pre-processing, hybrid feature selection, and hybrid classification for IDS. We remove duplicate data and normalised data in our methods first stage. Sequential forward floating selection (SFFS) with extra-tree use for feature selection removes unwanted features in our methods second stage. LogitBoost with extra-tree classification to use selected features in our method third stage. The proposed method is evaluated on standard datasets KDD CUP99, NSL-KDD, UNSW-NB15, CICIDS2017, and CICIDS2018. The experimental results show that the proposed method outperforms the existing work in terms of accuracy, false alarm rate, and detection rate.
Keywords: intrusion detection; anomaly detection; machine learning; ensemble methods; extra-tree; feature selection; sequential forward floating selection; SFFS; boosting algorithm.
Machine Learning-Based QoS & Traffic-Aware Prediction-Assisted Dynamic Network Slicing
by Naveen Kumar, Anwar Ahmad
Abstract: Since last few years, network slicing has been presented as one of the key ingredients in 5G for efficiently specifying network services as per the heterogeneous quality and functional requirements over common shared resources. Network slices are multiple networks having their own management, requirements, and characteristics, positioned over the same physical network with distinct network functions present in each slice. Thus, multiple independent end-to-end networks are supposed to be deployed in 5G, using parallel network slicing. However, it is not easy to guarantee that the traffic on one slice will not affect the traffic on another slice. To implement independent and intelligent network slicing management, this paper proposes data-driven machine learning-based slicing and allocation model which provides greater flexibility with quality of service (QoS) and traffic-aware reliable dynamic slicing, where resources can be intelligently assigned and redistributed among network slices according to temporal variation of the virtual resource requirements.
Keywords: 5G; machine learning; quality of service; QoS; network slicing; neural network.
Device Fingerprinting using Deep Convolutional Neural Networks
by Sandhya Aneja, Nagender Aneja, Bharat K. Bhargava, Rajarshi Roy Chowdhury
Abstract: Device fingerprinting is a problem of identifying a network device using network traffic data to secure against cyber-attacks. Automated device classification from a large set of network traffic features space is challenging for the devices connected in the cyberspace. In this work, the idea is to define a device-specific unique fingerprint by analysing solely inter-arrival time of packets as a feature to identify a device. Neural networks are the universal function approximation which learn abstract, highlevel, nonlinear representation of training data. Deep convolution neural network is used on images of inter-arrival time signature for device fingerprinting of 58 non-IoT devices of 5 to 11 types. To evaluate the performance, we compared ResNet-50 layer and basic CNN-5 layer architectures. We observed that device type identification models perform better than device identification. We also found that when deep learning models are attacked over device signature, the models identify the change in signature, and classify the device in the wrong class thereby the classification performance of the models degrades. The performance of the models to detect the attacks are significantly different from each other though both models indicate the system under attack.
Keywords: device fingerprinting; deep convolutional neural networks; DCNN; ResNet-50; attack; attack defense.
Network performance optimisation using triple interleaving routing algorithm for oil and gas pipeline network
by MOHAMAD YUSRY L.E.E. IKHWAN LEE, AMIERUL SYAZRUL AZRIL AZMAN, SIVA KUMAR SUBRAMANIAM, FARAH SHAHNAZ FEROZ
Abstract: As demand for oil and gas increases, demands for a safe and efficient system for remote control and pipeline management also increases. The wireless sensor network (WSN) can facilitate real-time data transmission between sensor nodes and centralised monitoring stations. In this application, nodes are distributed in a linear chain configuration to cover long-distance pipelines in the midstream. When the size of the network increases, the performance of WSN is compromised. Consequently, the network has a substantially low throughput, low delivery ratio, high latency, high energy consumption, and unfairness. In this paper, ad-hoc on-demand distance vector triple interleaving (AODVTRI), a reactive protocol that reduced routing inconsistency by splitting the network traffic into three separate routes with the consideration of x-axis is proposed. The proposed routing protocol was evaluated with AODV and DSDV with 20, 40, 60, 80, 100, 120, 140, 160, 180, and 200 node deployments in accordance with IEEE802.11 standard.
Keywords: WSN; linear; oil and gas; pipeline; LWSN; routing algorithm; multi-hop.
A State of Art Security and Attacks Analysis in Blockchain Applications Network
by Sunayana Das, Bhabendu Kumar Mohanta, Debashish Jena
Abstract: In recent years, blockchain has emerged as one of the promising technologies in applications like internet of things, smart city, digital management, healthcare system, supply chain management, real estate business, smart agriculture, smart retail and smart grid. Blockchain is derived from bitcoin cryptocurrency white paper in 2008 where peer to peer transactions are done. Though blockchain is considered a secure platform to store the information in digital ledger format in trustful environment with proper verification and validation process. Distributed architecture of blockchain network required high computation devices for mining and doing the cryptographic operation like digital signature. In a decentralised network challenges are like data validation, transaction integrity, fault tolerance, user anonymity are need to be addressed. In this paper, the state of the art of security attacks and various network architecture challenges are identified. The security attacks in the blockchain are discussed in-depth. The various applications of blockchain and corresponding security challenges are also explained. Lastly, the implementation of blockchain network creation, deployment of smart contracts using the open-source Ethereum platform was demonstrated.
Keywords: blockchain; security; privacy; attacks; risks.
A Novel Privacy Protection Method Based on Node Segmentation for Social Networks
by Zhongli Wang, Aiyun Ju
Abstract: In order to solve the problem of privacy disclosure of weight sequence in weighted social networks privacy protection, this paper proposes an anonymous weighted sequence method based on node segmentation to realise privacy protection of network structure, edge weight and weight sequence. In this method, the anonymity of edge weight sequence is realised mainly through the diameter distance within the group and the relative distance between nodes, which makes up for the privacy disclosure of weight sequence and improves the privacy protection mechanism of weighted social network. Under the premise of privacy security, this method can guarantee the structural features needed for social network analysis, the validity of the published data and the effective resistance to the attack of weight sequence. We also make comparison with other methods in terms of execution time and recognition rate, the results show that the proposed method can obtain shorter time and high node recognition.
Keywords: social networks; privacy protection; node segmentation; diameter distance.
Efficient Sensor Node Connectivity and Target Coverage using Genetic Algorithm with Daubechies 4 Lifting Wavelet Transform
by T. Ganesan, Pothuraju Rajarajeswari
Abstract: Recently, target coverage algorithms are widely used to monitor the target point by dividing sensor nodes into a set of cover groups where each sensor cover group contains the target points. Optimal sensor node placement imposes a critical task when the number of sensors is limited. The quality of maximum target coverage and node connectivity can be improved by deploying sensors in the optimal location. In this paper, a novel genetic algorithm with 2D discrete Daubechies 4 (db4) lifting wavelet transform is proposed for identifying the optimal position of each sensor. Initially, the genetic algorithm identifies the population-based sensor location and 2D discrete db4 lifting adjusts the sensor location into an optimal position where each sensor can cover a maximum number of targets that are connected to another sensor. To further prove that the proposed model is better than the existing algorithm, a set of experiments are carried out with different scenarios by achieving maximum target points covers, node connectivity, and network lifetime with a limited number of sensor nodes.
Keywords: wireless sensor network; target point coverage; node connectivity; sensor deployment; genetic algorithm; two-dimensional db4 lifting; network lifetime.