Showing papers in "International Journal of Communication Systems in 2021"
TL;DR: The dynamic and energy‐efficient clustering for energy hole mitigation (DECEM) is proposed and the simulation experiments reveal that DECEM has enhanced stability period by 5% and 31% as compared to the MEEC and IDHR protocols, respectively.
124 citations
TL;DR: An attention‐based convolution neural network long short‐term memory (CNN‐LSTM), a multistep prediction model, which helps to identify the near term traffic details such as speed that is very important for predicting the future value of flow.
Abstract: Department of Computer Science and Engineering, Ramco Institute of Technology, Rajapalayam, India Department of Electronics and Communication Engineering, Muthayammal Engineering College, Rasipuram, India School of Computer Science and Engineering, SCE, Taylor's University, Subang Jaya, 47500, Malaysia School of Computer Science and Engineering, Lovely Professional University, Phagwara, India Department of IT, National Engineering College, Kovilpatti, India Department of Computer Science, Vanderbilt University, Nashville, Tennessee, USA
71 citations
TL;DR: The optimized black hole algorithm is employed to select an optimal CH from sensor nodes and it outperformed the other four comparative methods in terms of packet delivery rate and the number of transmitted packets to the CH and to the sink.
46 citations
TL;DR: This paper proposes to address the problems associated with the existing models (empirical and deterministic) through the introduction of machine learning algorithms to path loss predictions by developing two machine learning‐based path loss prediction models.
41 citations
TL;DR: A novel 3‐stage methodology using principal component analysis (PCA), total iterative structural modeling (TISM), and Matrice d'Impacts Croisés Multiplication Appliquée à un Classement (MICMAC) analysis is proposed to develop an integrated BC with the food supply chain (FSC).
39 citations
TL;DR: A systematic mapping survey is presented to give a review of IoT architecture and provide a structured overview of research trends and a technical taxonomy is presented for these challenges according to reviewed studies.
38 citations
TL;DR: In this article, a robust hybrid method, including encryption, is used as an efficient approach for resolving the RPL protocol concerns so that the devices are connected securely in the Internet of Things (IoT).
Abstract: Internet of Things (IoT) provides the possibility for milliards of devices throughout the world to communicate with each other, and data is collected autonomously. The big data generated by the devices should be managed securely. Due to security challenges, like malicious nodes, many approaches cannot respond to these concerns. In this paper, a robust hybrid method, including encryption, is used as an efficient approach for resolving the RPL protocol concerns so that the devices are connected securely. Therefore, the proposed DSH-RPL method for securing the RPL protocol comprises the four following phases: The first phase creates a reliable RPL. The second phase detects the sinkhole attack. The third phase quarantines the detected malicious node, and the fourth phase transmits data through encryption. The simulation results show that the DSH-RPL reduces the false-positive rate more than 18.2% and 23.1%, and reduces the false-negative rate more than 16.1% and 22.78%, it also increases the packet delivery rate more than 19.68% and 25.32% and increases the detection rate more than 26% and 31% compared to SecTrust-RPL and IBOOS-RPL.
36 citations
TL;DR: A biometric and elliptic curve cryptography (ECC)‐assisted authentication framework for VCC that obtains most of security features and attributes for secure communication in the presence of active and passive attackers is proposed.
36 citations
TL;DR: Simulation results show that the proposed algorithms outperform current cluster based‐routing protocols using a fuzzy logic algorithm in terms of generating stable clusters and extending the network lifetime.
Abstract: With rapid increase of the advancement in wireless technologies, the world is now moving toward smart wireless devices which require the communication and collaboration of the Internet of Things (IoT) The smart devices, which called sensor nodes, enhance the use of the IoT as the essential components of today's smart world Therefore, the wireless sensor network (WSN) is the most widely used technology enabling IoT scenarios Using the technology, a huge volume of data needs to be tackled and transmitted carefully via the internet Since the lifetime of the network is related to the reduced battery capacity of the connected wireless sensor nodes, extending the network lifetime is the main goal to be achieved Clustering algorithm has a significant effect on the system lifetime for the IoT‐enabled WSN applications In this paper, a fuzzy‐based routing protocol (FRP‐LEACH) and a cross‐layer routing protocol (CLRP‐LEACH) are proposed based on the clustering topology The proposed algorithms are designed for the healthcare IoT applications These algorithms offer several advanced cloud‐based services and facilities to serve patients more effectually and protect the medical and paramedical framework from pandemic illness like the ongoing COVID‐19 pandemic To address the above‐mentioned concerns, an energy efficient routing protocol based on fuzzy logic is proposed to optimize wireless Internet‐of‐Things sensor networks performance Simulation results are presented, and they show that the proposed algorithms outperform current cluster based‐routing protocols using a fuzzy logic algorithm in terms of generating stable clusters and extending the network lifetime [ABSTRACT FROM AUTHOR] Copyright of International Journal of Communication Systems is the property of John Wiley & Sons, Inc and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission However, users may print, download, or email articles for individual use This abstract may be abridged No warranty is given about the accuracy of the copy Users should refer to the original published version of the material for the full abstract (Copyright applies to all Abstracts )
31 citations
TL;DR: A new method of traffic flow forecasting based on quantum particle swarm optimization (QPSO) strategy for intelligent transportation system (ITS) that can reduce prediction errors and get better and more stable prediction results is proposed.
30 citations
TL;DR: A new approach based on the MASs and the concept of agent is proposed, which is named MAS as Metaheuristic (MAMH) method, and the binary version of the proposed method, called Binary MAMH (BMAMH), was implemented on the email spam detection.
TL;DR: The experimental results show that the proposed ETH‐LEACH protocol outperforms the traditional routing protocols and minimizes the energy usage of the sensor nodes and consequently enhances the network's lifetime by extending the duration of node death.
TL;DR: This survey embodies to discuss, explain, and compare various privacy preserving and access control schemes in the context of fog computing for classifying and analyzing similarities and variances with respect to other researchers.
Abstract: To provide reliable data storage and retrieval services to the end users, the cloud service provider implements secure data storage, sharing, and retrieval mechanisms. However, the aforesaid servic ...
TL;DR: A comprehensive review of the machine learning‐based state‐of‐the‐art technologies that can be used to form a three‐tier solution taxonomy and the use of cases of traffic police scheduling that elaborates this review's applicability in various domains.
TL;DR: A weather prediction model for short‐range prediction based on numerical data using the C5.0 algorithm with K‐means clustering is proposed for improving the accuracy and efficiency of forecasting.
TL;DR: This study reviews the security solutions for the vulnerabilities of state‐of‐the‐art SDN controllers and the available countermeasures, and an in‐depth analysis of the SDN features that support security is presented, and some unresolved research issues on SDn controllers are identified.
TL;DR: This paper focuses on summarizing the AMC methoods, comparing between them, presenting the commercial software packages for AMC, and finally considering the new challenges in the implementation of AMC.
TL;DR: This article proposes an environment IoT‐based platform for smart cities that grants interoperability from data capture to knowledge extraction and visualization through the use of Semantic Web Technologies and the definition of an ontology for environment indicators.
TL;DR: Simulation results demonstrate the superiority of the moFIS‐BFO protocol against the existing techniques to control congestion and prolong the network lifetime.
TL;DR: DTCP‐ABE scheme dynamically traces who decrypts the ciphertext during the outsourced decryption process, which helps to find the malicious user who leaks the secret key, and automatically revokes the malicious users once they are identified.
TL;DR: This study presents a multi‐objective virtual machine (VM) placement scheme for ECDCs called TRACTOR, which utilizes an artificial bee colony optimization algorithm for power and network‐aware assignment of VMs onto PMs to minimize the network traffic of the interacting VMs and the power dissipation of the data center's switches and PMs.
Abstract: Technology providers heavily exploit the usage of edge‐cloud data centers (ECDCs) to meet user demand while the ECDCs are large energy consumers. Concerning the decrease of the energy expenditure of ECDCs, task placement is one of the most prominent solutions for effective allocation and consolidation of such tasks onto physical machine (PM). Such allocation must also consider additional optimizations beyond power and must include other objectives, including network‐traffic effectiveness. In this study, we present a multi‐objective virtual machine (VM) placement scheme (considering VMs as fog tasks) for ECDCs called TRACTOR, which utilizes an artificial bee colony optimization algorithm for power and network‐aware assignment of VMs onto PMs. The proposed scheme aims to minimize the network traffic of the interacting VMs and the power dissipation of the data center's switches and PMs. To evaluate the proposed VM placement solution, the Virtual Layer 2 (VL2) and three‐tier network topologies are modeled and integrated into the CloudSim toolkit to justify the effectiveness of the proposed solution in mitigating the network traffic and power consumption of the ECDC. Results indicate that our proposed method is able to reduce power energy consumption by 3.5% while decreasing network traffic and power by 15% and 30%, respectively, without affecting other QoS parameters.
TL;DR: Simulation results show that the proposed approach can accurately detect and isolate selective forwarding attack with improving data delivery ratio of the IoT network.
TL;DR: In this paper, three admission control mechanisms based on Reinforcement Learning, namely Q-learning, Deep Q-Learning, and Regret Matching, are proposed to decide whether to accept or reject network slice requests.
Abstract: Achieving a fair usage of network resources is of vital importance in Slice-ready
5G network. The dilemma of which network slice to accept or to reject is very
challenging for the Infrastructure Provider (InfProv). On one hand, InfProv aims to
maximize the network resources usage by accepting as many network slices as possible;
on the other hand, the network resources are limited, and the network slice
requirements regarding Quality of Service (QoS) need to be fulfilled. In this paper,
we devise three admission control mechanisms based on Reinforcement Learning,
namely Q-Learning, Deep Q-Learning, and Regret Matching, which allow deriving
admission control decisions (policy) to be applied by InfProv to admit or reject
network slice requests. We evaluated the three algorithms using computer simulation,
showing results on each mechanism’s performance in terms of maximizing the
InfProv revenue and their ability to learn offline or online.