A Survey on Wireless Sensor Network Protocols for Transport Layer

Dwivedi, Utkarsh; Agarwal, Ashwani; Roy, A. Thomas Paul; Chakraborty, Adrija; Mukherjee, Amrit

doi:10.1109/iceca.2018.8474610

Cited by 4 publications

(2 citation statements)

References 11 publications

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“…The authors Kumar et al [47] dedicated to the WSN energy-saving protocol based on base station technology, using CH to transmit information, the clustering technology used can achieve effective and real-time decisions in the state of incomplete intelligence. A platform for analyzing the WSN protocol is provided in [48], which significantly assists in designing more efficient protocols.…”

Section: Related Workmentioning

confidence: 99%

Back Propagation Neural Network Based Cluster Head Identification in MIMO Sensor Networks for Intelligent Transportation Systems

et al. 2020

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Wireless Sensor Network (WSN) is an essential technology for the Internet-of-Things (IoT) and intelligence-based applications. In the case of Intelligent Transportation Systems (ITS), the WSNs play an important role in safety and efficient traffic management. Therefore, there is enormous demand for energy efficient WSNs for dynamic resource allocation in vehicles and infrastructures. This work presents a Multi-Input Multi-Output (MIMO) technique model in WSNs, which addresses the Cluster Head (CH) recognition issue for MIMO sensor networks by using Back Propagation Neural Network (BPNN). The conventional CH identification suffers from a lack of location identification due to the dynamic and real-time environment. Thus, to obtain more precise positioning accuracy, the proposed work uses BPNN combined with a distributed gradient drop technique to calculate the position of the unknown CH. This reduces the distance estimation error, and the particle swarm optimization technique is further used to obtain the optimal weight and threshold of the network. The work is validated by using mathematical analysis, simulations, and comparison with existing techniques. The proposed model shows a better performance in terms of energy consumption, error rate, and computation time.

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Section: Related Workmentioning

confidence: 99%

Back Propagation Neural Network Based Cluster Head Identification in MIMO Sensor Networks for Intelligent Transportation Systems

et al. 2020

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“…Then, ubiquitous sensing services to measure surrounding environments based on wireless sensing can be provided through ISAC. Wireless sensing networks (WSNs) [7][8][9][10] will be an important technology for various ISAC application scenarios, including remote sensing, environmental monitoring, smart manufacturing and the smart Internet of things (IoT) [8,[11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Secure Decision Fusion in ISAC-Oriented Distributed Wireless Sensing Networks with Local Multilevel Quantization

Zhang

Sun

2023

Electronics

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Distributed deployment for integrated sensing and communication (ISAC) can improve the sensing accuracy by exploring spatial diversity for covering the target state. However, secure fusion and limited energy consumption are still challenges for wireless-transmission-based distributed ISAC. In this paper, a secure decision-fusion scheme under energy constraint is proposed. First, the local likelihood ratios (LRs) of the local observations at sensing nodes are quantified at multiple levels corresponding to a multiple phase-shift keying (MPSK) constellation, in order to retain more sensing information. Second, an antieavesdropping scheme, which randomly rotates the constellation based on the main channel information between the nodes and ally fusion center (AFC), is proposed to confuse the data fusion of the eavesdropping fusion center (EFC). In addition, the local quantization thresholds and the rotating threshold are optimized to realize the perfect security under energy constraint and maximum rotation angle of π. In addition, the optimized rotation angle is discussed under a relaxed security requirement of the EFC in exchange for reducing the AFC error. Performance evaluation results show that the AFC error probability of the proposed scheme with a two-bit quantization and soft fusion outperforms the single-bit case and three-bit case by above 3 dB and about 0.5 dB at the error probability of 10−2, respectively. The former gain is just contributed by the more local information kept with two-bit against single-bit quantization. However, for the three-bit case, the advantage of more levels of quantization is eliminated by the worse transmission of denser constellation over a noisy channel. Moreover, the proposed scheme outperforms the conventional channel-aware encryption method under a stricter energy constraint and higher signal noise ratio (SNR).

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