A novel minimal exposure path problem in wireless sensor networks and its solution algorithm

Feng, Hao; Luo, Lei; Wang, Yong; Miao, Yongwei; Dong, Rongsheng

doi:10.1177/1550147716664245

Cited by 9 publications

(11 citation statements)

References 29 publications

(53 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since the Voronoi diagram-based and the grid-based methods posed several disadvantages, as mentioned above, recently, heuristic/metaheuristic methods which were inspired by the process in nature, such as particle swarm optimization (PSO) and the genetic algorithm, were applied to solving the MEP problem [29,[33][34][35][36][37]. These studies converted the MEP problem into the numerical functional extreme (NFE) problem [23] by breaking the penetration path into small enough path intervals and then performed a metaheuristic algorithm to search for the optimal result.…”

Section: Related Workmentioning

confidence: 99%

“…To eliminate this saw-tooth effect of the metaheuristic algorithm and improve the efficiency, the authors in [34] proposed a genetic algorithm with a special crossover operator, a local search scheme and an upside-down operator. Feng et al [37] considered the MEP problem with path constraints. A genetic algorithm was then proposed with a local search operator that could effectively remove the saw-tooth shape in the path solution and increase the convergence speed.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

An Elite Hybrid Particle Swarm Optimization for Solving Minimal Exposure Path Problem in Mobile Wireless Sensor Networks

Binh

Mellouk

Bình

et al. 2020

Sensors

View full text Add to dashboard Cite

Mobile wireless sensor networks (MWSNs), a sub-class of wireless sensor networks (WSNs), have recently been a growing concern among the academic community. MWSNs can improve network coverage quality which reflects how well a region of interest is monitored or tracked by sensors. To evaluate the coverage quality of WSNs, we frequently use the minimal exposure path (MEP) in the sensing field as an effective measurement. MEP refers to the worst covered path along which an intruder can go through the sensor network with the lowest possibility of being detected. It is greatly valuable for network designers to recognize the vulnerabilities of WSNs and to make necessary improvements. Most prior studies focused on this problem under a static sensor network, which may suffer from several drawbacks; i.e., failure in sensor position causes coverage holes in the network. This paper investigates the problem of finding the minimal exposure paths in MWSNs (hereinafter MMEP). First, we formulate the MMEP problem. Then the MMEP problem is converted into a numerical functional extreme problem with high dimensionality, non-differentiation and non-linearity. To efficiently cope with these characteristics, we propose HPSO-MMEP algorithm, which is an integration of genetic algorithm into particle swarm optimization. Besides, we also create a variety of custom-made topologies of MWSNs for experimental simulations. The experimental results indicate that HPSO-MMEP is suitable for the converted MMEP problem and performs much better than existing algorithms.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

An Elite Hybrid Particle Swarm Optimization for Solving Minimal Exposure Path Problem in Mobile Wireless Sensor Networks

Binh

Mellouk

Bình

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…In order to recognize the path among the two points, the overall exposure obtained from the sensors by navigating the path will be reduced, 6 which is explored by the minimal exposure path issue 7 …”

Section: Introductionmentioning

confidence: 99%

A hybrid swarm intelligence based optimization approach for solving minimum exposure problem in wireless sensor networks

Aravinth¹,

Senthilkumar²,

Mohanraj³

et al. 2019

Concurrency and Computation

View full text Add to dashboard Cite

Summary Minimum Exposure Problem (MEP) has become one of the most important issues in WSN as it affects the coverage quality measurement to a greater extent. Recently, MEP issues have been dealt by Physarum Optimization Algorithm (POA). Nevertheless, this method ends with the less scope in precision faults. Along with these issues, the necessity for computation time and memory requirements gets increases statistically. The major aim of the work is to propose a new optimization algorithm to solve the MEP issue. Thus, Hybrid Genetic Particle Swarm Optimization (H‐GPSO) is formulated to give a desirable solution for MEP issue. In the proposed work, energy usage of the sensor node for MEP identification is measured using Hidden Markov Model (HMM) with the intention of prolonging the lifetime of the sensor node. After accomplishing the energy efficiency, MEP is developed and converted to an optimization issue. H‐GPSO is presented to resolve the optimization problem; hence, it gives a desirable solution to MEP issue. Therefore, the pretended answer proves the proposed H‐GPSO related MEP model is desirable for detecting the minimal exposure problem with high energy ratio. The results of the proposed and existing methods are measured in terms of Energy, Throughput, Delay, and Exposure via NS2.

show abstract

“…Aiming at the MEP problem with path constraint conditions, Hao et al divides the path into three parts. The paths of the first part and the last part are calculated by grid-based approach, while the middle part is solved through the hybrid genetic algorithm [37]. Liu uses adaptive cell decomposition to transform the minimal exposure path problem into a discrete problem, and then designs an OMEPS algorithm to search for the obstacle-avoidance minimum exposure path in the grid-based network [38].…”

Section: Related Workmentioning

confidence: 99%

Target guiding self-avoiding random walk with intersection algorithm for minimum exposure path problem in wireless sensor networks

Yang

Jiang

Fang

et al. 2018

J Wireless Com Network

View full text Add to dashboard Cite

To solve minimum exposure path (MEP) problem in wireless sensor networks more efficiently, this work proposes an algorithm called target guiding self-avoiding random walk with intersection (TGSARWI), which mimics the behavior of a group of random walkers that seek path to their destinations in a strange area. Target guiding leads random walkers move toward their end points, while self-avoiding prevents them from taking roundabout routes. Route intersections further accelerate the speed of seeking connected paths. Dijkstra algorithm (DA) is applied to solve MEP problem in a sub-network formed by multiple connected paths that walkers generate (called TGSARWI DA). Simulations show that the path exposure found by TGSARWI DA is very close to that by DA in the global network (Global DA), whereas the time complexity of computation is much lower. Compared with existing heuristic algorithms such as physarum optimization algorithm (POA), our algorithm shows higher generality and efficiency. This algorithm also exhibits good robustness to the fluctuations of parameters. Our algorithm could be very useful for the solution to MEP problem in fields with large-or highdensity sensors.

show abstract

A novel minimal exposure path problem in wireless sensor networks and its solution algorithm

Cited by 9 publications

References 29 publications

An Elite Hybrid Particle Swarm Optimization for Solving Minimal Exposure Path Problem in Mobile Wireless Sensor Networks

An Elite Hybrid Particle Swarm Optimization for Solving Minimal Exposure Path Problem in Mobile Wireless Sensor Networks

A hybrid swarm intelligence based optimization approach for solving minimum exposure problem in wireless sensor networks

Target guiding self-avoiding random walk with intersection algorithm for minimum exposure path problem in wireless sensor networks

Contact Info

Product

Resources

About