A coverage optimization strategy for mobile wireless sensor networks based on genetic algorithm

Liang, Chiu-Kuo; Lin, Yuli

doi:10.1109/icasi.2018.8394523

Cited by 17 publications

(5 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, there are a number of related research papers on the WSN coverage optimization problem. Liang and Lin [5] proposed a genetic algorithm based on WSN topological and node mobility requirements with rational crossover and mutation to improve the coverage problem of mobile objects. In [6], the multiobjective optimization problem of maximizing the WAN coverage and the shortest moving distance of the initial node is considered at the same time, and the firefly algorithm is used for optimization.…”

Section: Introductionmentioning

confidence: 99%

Coverage Maximization of WSNs in 3D Space Based on Hybrid Lion Swarm Optimization

Wu,

Huang,

Lin

et al. 2023

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

The wireless sensor network (WSN) collects all the physical information required by the user for the target area and transmits the data to the sink or base station via multihop communication. With a limited number of nodes, it is important to reduce the overlapping area between nodes to improve coverage. However, increasing the coverage rate tends to create isolated points, which reduces the connection efficiency. Therefore, considering coverage and connection efficiency simultaneously is a challenging optimization task. In order to solve the above problems, this paper proposes a spatial configuration optimization strategy based on the hybrid lion swarm optimization (HLSO) algorithm. The proposed algorithm is experimented on a large number of benchmark graphs. The experimental results prove very competitive with and even better than different algorithms. Finally, in order to avoid isolated points in the node deployment process, the optimal deployment scheme of the nodes is found by considering both conditions of node coverage and connection efficiency at the same time.

show abstract

Section: Introductionmentioning

confidence: 99%

Coverage Maximization of WSNs in 3D Space Based on Hybrid Lion Swarm Optimization

Wu,

Huang,

Lin

et al. 2023

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…high storage requirement and low generalization, exhaustive methods or deep learning methods cannot be used in large-scale dynamic system, therefore, heuristic learning is currently one of the mainstream way to deal with this scenario. The Genetic Algorithm(GA) [10], [14], [14], [27] is widely used to quickly search for the optimal solution of combinatorial optimization problems such as gateway placement optimization [1] and coverage optimization of mobile wireless sensor networks [20] etc. However, the Hamming Cliff [11] makes the crossover and mutation steps in the GA hard to cross.…”

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.

View full text Add to dashboard Cite

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.

show abstract

“…Compared with the complex geometric derivation of Voronoi diagram, the intelligent optimization algorithm has the advantages of easy implementation and strong adaptability and is widely used in node optimization deployment. For example, the genetic algorithm (GA) characterized by biological genetic evolution [8][9][10] and the particle swarm optimization (PSO) algorithm for the flocking foraging process. 11,12 Xu and Yao 8 proposed a coverage method of WSN optimized by GA, but this kind of method has the problem of falling into the local optimum, and the convergence speed is slow.…”

Section: Introductionmentioning

confidence: 99%

“…11,12 Xu and Yao 8 proposed a coverage method of WSN optimized by GA, but this kind of method has the problem of falling into the local optimum, and the convergence speed is slow. In Liang and Lin, 9 an adaptive GA for mutation probability and crossover probability was proposed to solve the problem of slow convergence, although this algorithm has a faster convergence rate, it is prone to fall into the problem of local optimum. Cong 11 proposed a PSO algorithm to improve the effective position of computing nodes, this algorithm improved the coverage rate of WSNs to some extent, but it still has a “premature” problem.…”

Section: Introductionmentioning

confidence: 99%

Node coverage optimization algorithm for wireless sensor networks based on improved grey wolf optimizer

Wang

Xie

et al. 2019

Journal of Algorithms & Computational Technology

View full text Add to dashboard Cite

Aiming at the problem of wireless sensor network node coverage optimization with obstacles in the monitoring area, based on the grey wolf optimizer algorithm, this paper proposes an improved grey wolf optimizer (IGWO) algorithm to improve the shortcomings of slow convergence, low search precision, and easy to fall into local optimum. Firstly, the nonlinear convergence factor is designed to balance the relationship between global search and local search. The elite strategy is introduced to protect the excellent individuals from being destroyed as the iteration proceeds. The original weighting strategy is improved, so that the leading wolf can guide the remaining grey wolves to prey in a more reasonable way. The design of the grey wolf's boundary position strategy and the introduction of dynamic variation strategy enrich the population diversity and enhance the ability of the algorithm to jump out of local optimum. Then, the benchmark function is used to test the convergence performance of genetic algorithm, particle swarm optimization, grey wolf optimizer, and IGWO algorithm, which proves that the convergence performance of IGWO algorithm is better than the other three algorithms. Finally, the IGWO algorithm is applied to the deployment of wireless sensor networks with obstacles (rectangular obstacle, trapezoidal obstacle and triangular obstacles). Simulation results show that compared with GWO algorithm, IGWO algorithm can effectively improve the coverage of wireless sensor network nodes and obtain higher coverage rate with fewer nodes, thereby reducing the cost of deploying the network.

show abstract

A coverage optimization strategy for mobile wireless sensor networks based on genetic algorithm

Cited by 17 publications

References 11 publications

Coverage Maximization of WSNs in 3D Space Based on Hybrid Lion Swarm Optimization

Coverage Maximization of WSNs in 3D Space Based on Hybrid Lion Swarm Optimization

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

Node coverage optimization algorithm for wireless sensor networks based on improved grey wolf optimizer

Contact Info

Product

Resources

About