Physarum optimization: A biology-inspired algorithm for minimal exposure path problem in wireless sensor networks

Liu, Liang; Song, Yukun; Ma, Huadóng; Zhang, Xi

doi:10.1109/infcom.2012.6195492

Cited by 27 publications

(22 citation statements)

References 18 publications

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“…[40], in the field with scales of 10 × 10, 20 × 20, 50 × 50, and 100 × 100, as well as with the sensor number of 10, 30, and 50, the minimum exposures of paths found by POA and DA are quite close. The relative errors of E(H) of paths got by POA and Fig.…”

Section: Comparison With Poamentioning

confidence: 67%

“…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]. Accordingly, other various improved algorithms based on grid partition also have been proposed [39][40][41][42]. It is noted that, in grid-based approach, the larger the scale of the grid network within a fixed region is, the more accurate the solution to the MEP problem will be.…”

Section: Related Workmentioning

confidence: 99%

“…Some heuristic algorithms have been proposed to simplify the complexity. For example, Liu et al proposed a physarum optimization algorithm (POA) based on the light-avoidance of physarum, which could be used to obtain minimum exposure path with edgecutting method in the grid [40]. The shortage of this method is that, in the simulations, parameters of POA need to be retuned when the grid scale and sensor density change.…”

Section: Related Workmentioning

confidence: 99%

“…The detecting sensitivity ss(s i , Q) of sensor s i to the point Q could be represented as follows: parameters. According to the literature [40], parameter μ denotes the sensitivity of the sensor at the unit distance or the direction of directional sensor, parameter τ denotes the attenuation coefficient of sensor sensitivity on distance, and parameter γ denotes the attenuation coefficient of the directed sensor sensitivity on deviating from the central direction.…”

Section: Minimum Exposure Path Problemmentioning

confidence: 99%

“…As a representative of heuristic algorithms in solving MEP problem, physarum optimization algorithm (POA) has the ability to find the minimum exposure path in the field with various type sensors and reduce the time computation complexity [40]. However, the growth of time computation complexity is still too fast.…”

Section: Comparison With Poamentioning

confidence: 99%

See 4 more Smart Citations

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

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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.

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Section: Comparison With Poamentioning

confidence: 67%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Minimum Exposure Path Problemmentioning

confidence: 99%

Section: Comparison With Poamentioning

confidence: 99%

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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

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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

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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.

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Data Fusion Based Transmission in Multimedia Sensor Networks

Liu

Luo

2021

Multimedia Sensor Networks

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Physarum optimization: A biology-inspired algorithm for minimal exposure path problem in wireless sensor networks

Cited by 27 publications

References 18 publications

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

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

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

Data Fusion Based Transmission in Multimedia Sensor Networks

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