Throughput Analysis of Smart Buildings-oriented Wireless Networks under Jamming Attacks

Wei, Xianglin; Wang, Tongxiang; Tang, Chaogang

doi:10.1007/s11036-019-01481-7

Cited by 3 publications

(2 citation statements)

References 24 publications

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“…Low throughput creates many problems and it can be an indication of a sinkhole attack. During the sinkhole attack, many nodes select the same path which increases traffic and causes latency [68]. As a result, throughput is decreased.…”

Section: E Throughputmentioning

confidence: 99%

A Review on the Security of IoT Networks: From Network Layer’s Perspective

et al. 2023

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Internet of Things (IoT) has revolutionized the world in the last decade. Today millions of devices are connected to each other utilizing IoT technology in one way or the other. With the significant growth in IoT devices, the provision of IoT security is imperative. Routing protocol for low power and lossy networks (RPL) is a network layer protocol, specially designed for routing in IoT devices. RPL protocol faces many attacks such as selective forwarding attacks, blackhole attacks, sybil attacks, wormhole attacks, and sinkhole attacks. All these attacks pose great threats to IoT networks and can substantially affect the performance of the network. In this work, a comprehensive review of internal attacks on the network layer is presented. Specifically, we focus on the literature that considers presenting solutions for the detection and prevention of sinkhole attacks. We reviewed the state-of-the-art works and different performance parameters like energy consumption, scalability, threshold value, packet delivery ratio, and throughput. Moreover, we also present a detailed analysis of machine learning-based algorithms and techniques proposed for the security of RPL protocol against internal attacks.

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Section: E Throughputmentioning

confidence: 99%

A Review on the Security of IoT Networks: From Network Layer’s Perspective

et al. 2023

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“…In [27], a theoretical model of CSMA/CA under a jamming environment was established based on discrete Markov model, and the network performance under malicious jamming environment with different jamming rates and different data streams was analyzed by simulation experiments; the simulation results were verified by a semi-physical simulation platform. In [28], a jamming model based on random geometry was proposed. The influence of this model on the performance of CSMA/CA protocol was analyzed by simulation experiments.…”

Section: Related Workmentioning

confidence: 99%

An Improved CSMA/CA Protocol Anti-Jamming Method Based on Reinforcement Learning

Ming,

Liu,

Yang

et al. 2023

Electronics

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The CSMA/CA algorithm uses the binary backoff mechanism to solve the multi-user channel access problem, but this mechanism is vulnerable to jamming attacks. Existing research uses channel-hopping to avoid jamming, but this method fails when the channel is limited or hard to hop. To address this problem, we first propose a Markov decision process (MDP) model with contention window (CW) as the state, throughput as the reward value, and backoff action as the control variable. Based on this, we design an intelligent CSMA/CA protocol based on distributed reinforcement learning. Specifically, each node adopts distributed learning decision-making, which needs to query and update information from a central status collection equipment (SCE). It improves its anti-jamming ability by learning from different environments and adapting to them. Simulation results show that the proposed algorithm is significantly better than CSMA/CA and SETL algorithms in both jamming and non-jamming environments. And it has little performance difference with the increase in the number of nodes, effectively improving the anti-jamming performance. When the communication node is 10, the normalized throughputs of the proposed algorithm in non-jamming, intermittent jamming, and random jamming are increased by 28.45%, 21.20%, and 17.07%, respectively, and the collision rates are decreased by 83.93%, 95.71%, and 81.58% respectively.

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