2-D Coverage Optimization In WSN Using A Novel Variant Of Particle Swarm Optimisation

Anurag, Amulya; Priyadarshi, Rahul; Goel, Amit; Gupta, Bharat

doi:10.1109/spin48934.2020.9070978

Cited by 36 publications

(9 citation statements)

References 11 publications

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“…Bin Yu [8] proposed a periodic hybridization method to enhance the global search capability of the PSO algorithm and obtained higher convergence speed and area coverage rate. Amulya Anurag [3] proposed a negative speed PSO algorithm to improve the local search ability and obtain higher coverage efficiency. Tingli Xiang [19] proposed an optimization based on Cuckoo Search (CS) which divided the algorithm into two stages.…”

Section: Related Workmentioning

confidence: 99%

WSN Coverage Optimization Based on Two-Stage PSO

Fan

et al. 2021

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Wireless Sensor Networks (WSN) coverage perception is an important basis for communication between the cyber world and the physical world in Cyber-Physical Systems (CPS). To address the coverage redundancy, hole caused by initial random deployment and the energy constraint in redeployment, this paper proposes a multi-objective two-stage particle swarm optimization algorithm (MTPSO) based on coverage rate and moving distance deviation to improve coverage efficiency. This algorithm establishes a multi-objective optimization model for above problems, and determines the candidate deployment scheme by reducing its local convergence probability through improved inertia weight, and then introduces virtual force mechanism to adjust the relative position between nodes. This paper mainly analyzes the influence of different initial deployment category and mobile nodes proportion on multi-objective optimization performance, and gives the corresponding algorithm implement. Simulation experiments show that compared with MVFA, SPSO and OPSO algorithms, MTPSO algorithm has a better redeployment coverage performance, which fully demonstrates its effectiveness.

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Section: Related Workmentioning

confidence: 99%

WSN Coverage Optimization Based on Two-Stage PSO

Fan

et al. 2021

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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“…Most often, studies in indoor environments do not consider real constraints like obstacles (walls, appliances, furniture, etc.) [19,28,38]. Frequently, rectangle environment shapes are modelled [20,21].…”

Section: Related Workmentioning

confidence: 99%

“…As can be seen from the Table 2, in terms of used models, most of the studies simplify the problem by using simplified models [16,20,21,26,27,28,39,40,41]. Whereas reducing problem complexity, by neglecting obstacles or using simplified models for sensing and connectivity, leads to inaccurate results.…”

Section: Related Workmentioning

confidence: 99%

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MOONGA: Multi-Objective Optimization of Wireless Network Approach Based on Genetic Algorithm

et al. 2020

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In high-density wireless sensor networks, the quality of service in terms of sensing coverage, connectivity, lifetime, energy consumption and cost is closely linked to the position of the nodes in the network. Consequently, the placement of a large number of nodes while simultaneously optimizing several measurements is considered to be an NP-difficult problem. In this article, we propose a new approach to optimizing the problem of node placement. To achieve this objective, we started by studying the main approaches existing in the literature in order to identify their limits. In order to have accurate solutions, existing physical models are studied, improved, and validated with real measurements. Then, we proposed a new formulation of the deployment optimization problem as a constrained multi-objective optimization problem. This allowed us to develop an optimizer, based on the multi-objective genetic algorithm and the weighted sum optimization method, which we called MOONGA (multi-objective wireless network optimization using the genetic algorithm). This optimizer makes it possible to generate an optimal deployment according to the topology, the environment, the specifications of different applications and the preferences of the network designer users. The algorithms that we have developed and implemented within the framework of experiments carried out on test data in order to prove the effectiveness of our approach. The analysis of the results found confirm well the interest and the superiority of our proposed approach compared to main studied approaches.

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“…Particle Swarm Optimization (PSO) [43] and Ant Colony Optimization (ACO) [7], [8] as swarm intelligence algorithms performed a group of unintelligent or slightly intelligent individuals (agent) through cooperation to show intelligent behaviour, thus providing a new possibility for solving complex problems [32]. In this way, PSO and ACO can be used for optimizing the radio network parameters [10], wireless sensor network path optimization [45], network inference, 2-D coverage optimization in wireless sensor network [3], cellular network spectrum allocation [42], and parameter estimation [46]. These algorithms require a bulk of data set to give feedback to agents.…”

Section: Introductionmentioning

confidence: 99%

DRESIA: Deep Reinforcement Learning-Enabled Gray Box Approach for Large-Scale Dynamic Cyber-Twin System Simulation

Lin

Yang

et al. 2021

IEEE Open J. Comput. Soc.

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The massive data generated by large-scale dynamic systems makes its optimization facing a tough challenge. Traditional White Box-based methods directly model the internal operating mechanism of the system, so massive amounts of measured data need to be handled, which is costly and time-consuming. The poor interpretability of the Black Box-based methods makes it difficult to adapt to the dynamic environment. Thus we propose a novel Gray Box-based approach namely Deep Reinforcement Learning-enabled Constraint Set Inversion Algorithm (DRESIA), which establishes a quantitative model of the nonlinear interoperability effects of system internal states which simplifies the White Box's complex mechanism of reconstruction and prediction and retains the interpretability of the model, therefore improves the prediction efficiency of feasible region while also improving the generalization ability. It further improves the dynamic adaptability of the modeling environment, which provides a new performance balancing scheme for system modeling. Under the premise that the large-scale 5G Cyber-Twin system satisfies the given Quality of Service(QoS) requirements, we perform DRESIA to realize the efficient and dynamic optimal search of feasible region, the results show that the DRESIA reduces the computational cost, and balances the accuracy and robustness of the feasible region, which validate the effectiveness and superiority of Gray Box-based approach.

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2-D Coverage Optimization In WSN Using A Novel Variant Of Particle Swarm Optimisation

Cited by 36 publications

References 11 publications

WSN Coverage Optimization Based on Two-Stage PSO

WSN Coverage Optimization Based on Two-Stage PSO

MOONGA: Multi-Objective Optimization of Wireless Network Approach Based on Genetic Algorithm

DRESIA: Deep Reinforcement Learning-Enabled Gray Box Approach for Large-Scale Dynamic Cyber-Twin System Simulation

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