CRLB Analysis for Passive Sensor Network Localization Using Intelligent Reconfigurable Surface and Phase Modulation

Liu, Dunge; Chen, Song; Lu, Ziyang; Jin, Shichao; Zhao, Y. B.

doi:10.3390/electronics12010202

Cited by 2 publications

(3 citation statements)

References 19 publications

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“…As shown in the table, we used 100 iterations for all algorithms. Furthermore, the boundary dimension of all algorithms ranged between [1,2,2] and [3,4,6], which indicates the range of [number o f mobileSink, number o f RV, number o f hops].…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…The first solution sol 1 contains two mobile sinks, namely, ms(1,1) and ms (1,2). The second solution sol 2 contains three mobile sinks, namely, ms(1), ms(2), and ms (3). Each mobile sink has its own number of rendezvous points.…”

Section: Examplementioning

confidence: 99%

“…This process is not effective in a large-scale environment due to the increasing cost of deploying a large number of stationary links. Multiple sinks and multiple mobile sinks are more sustainable approaches to managing data transmission to multiple endpoints in a WSN [ 3 ]. However, deploying multisink or multiple mobile sinks in a WSN for data gathering is a mathematically complex process.…”

Section: Introductionmentioning

confidence: 99%

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Variable-Length Multiobjective Social Class Optimization for Trust-Aware Data Gathering in Wireless Sensor Networks

Saad

Jaafar

Chellappan

2023

Sensors

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Data gathering in wireless sensor networks (WSNs) is vital for deploying and enabling WSNs with the Internet of Things (IoTs). In various applications, the network is deployed in a large-scale area, which affects the efficiency of the data collection, and the network is subject to multiple attacks that impact the reliability of the collected data. Hence, data collection should consider trust in sources and routing nodes. This makes trust an additional optimization objective of the data gathering in addition to energy consumption, traveling time, and cost. Joint optimization of the goals requires conducting multiobjective optimization. This article proposes a modified social class multiobjective particle swarm optimization (SC-MOPSO) method. The modified SC-MOPSO method is featured by application-dependent operators named interclass operators. In addition, it includes solution generation, adding and deleting rendezvous points, and moving to the upper and lower class. Considering that SC-MOPSO provides a set of nondominated solutions as a Pareto front, we employed one of the multicriteria decision-making (MCDM) methods, i.e., simple additive sum (SAW), for selecting one of the solutions from the Pareto front. The results show that both SC-MOPSO and SAW are superior in terms of domination. The set coverage of SC-MOPSO is 0.06 dominant over NSGA-II compared with only a mastery of 0.04 of NSGA-II over SC-MOPSO. At the same time, it showed competitive performance with NSGA-III.

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Section: Experiments and Resultsmentioning

confidence: 99%