Collision detection and mitigation based on optimization and Kronecker recurrent neural network in WSN

IF 1.7 4区 计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC
Akhil Khare, Kannapiran Selvakumar, Raman Dugyala
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Abstract

Nowadays, wireless sensor networks (WSNs) have paid huge attention among researchers due to their wide applications. WSNs possess multiple sensor nodes that transmit data to each other by using constrained energy resources. The sensor nodes are highly affected by collision due to the transmission of packets over the network by one or two nodes at the same time. Collision detection is necessary to increase network security and enhance the lifetime of sensor nodes. In most of the previous research, efficiently implementing collision detection algorithms while minimizing resource usage remains a significant challenge. Thus, a hybrid deep learning model deep Kronecker recurrent neural network (DKRNN) is developed in this research. Here, the cluster head is selected using the chronological skill optimization algorithm (CSOA) algorithmic approach by considering multi-objective parameters like energy, distance, delay, and trust. The network-based parameters are then extracted from the network. Later, the collision is detected using the DKRNN approach and the collision is mitigated finally using a packet pre-scheduling model named Dolphin Ant Lion Optimization (Dolphin ALO). Moreover, the detection performance of CSOA+ DKRNN is validated, and it achieved superior performance with a collision detection rate (CDR) of 0.940, packet delivery ratio (PDR) of 0.660, throughput of 0.850Mbps, and energy consumption of 0.110 J.

Abstract Image

基于优化和 Kronecker 循环神经网络的 WSN 碰撞检测与缓解技术
摘要 如今,无线传感器网络(WSN)因其广泛的应用而受到研究人员的极大关注。WSN 拥有多个传感器节点,它们利用有限的能源资源相互传输数据。由于一个或两个节点同时在网络上传输数据包,因此传感器节点受到碰撞的影响很大。碰撞检测对于提高网络安全性和延长传感器节点的使用寿命非常必要。在以往的大多数研究中,如何在有效实施碰撞检测算法的同时最大限度地减少资源使用仍然是一个重大挑战。因此,本研究开发了一种混合深度学习模型深度克朗克尔递归神经网络(DKRNN)。在这里,通过考虑能量、距离、延迟和信任等多目标参数,使用时序技能优化算法(CSOA)来选择簇头。然后从网络中提取基于网络的参数。之后,使用 DKRNN 方法检测碰撞,最后使用名为海豚蚁狮优化(Dolphin Ant Lion Optimization,Dolphin ALO)的数据包预调度模型缓解碰撞。此外,还验证了 CSOA+ DKRNN 的检测性能,其碰撞检测率(CDR)为 0.940,数据包交付率(PDR)为 0.660,吞吐量为 0.850Mbps,能耗为 0.110 J,性能优越。
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来源期刊
CiteScore
5.90
自引率
9.50%
发文量
323
审稿时长
7.9 months
期刊介绍: The International Journal of Communication Systems provides a forum for R&D, open to researchers from all types of institutions and organisations worldwide, aimed at the increasingly important area of communication technology. The Journal''s emphasis is particularly on the issues impacting behaviour at the system, service and management levels. Published twelve times a year, it provides coverage of advances that have a significant potential to impact the immense technical and commercial opportunities in the communications sector. The International Journal of Communication Systems strives to select a balance of contributions that promotes technical innovation allied to practical relevance across the range of system types and issues. The Journal addresses both public communication systems (Telecommunication, mobile, Internet, and Cable TV) and private systems (Intranets, enterprise networks, LANs, MANs, WANs). The following key areas and issues are regularly covered: -Transmission/Switching/Distribution technologies (ATM, SDH, TCP/IP, routers, DSL, cable modems, VoD, VoIP, WDM, etc.) -System control, network/service management -Network and Internet protocols and standards -Client-server, distributed and Web-based communication systems -Broadband and multimedia systems and applications, with a focus on increased service variety and interactivity -Trials of advanced systems and services; their implementation and evaluation -Novel concepts and improvements in technique; their theoretical basis and performance analysis using measurement/testing, modelling and simulation -Performance evaluation issues and methods.
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