Abstract
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 high-density sensors.
Highlights
Wireless sensor network (WSN) is a kind of distributed sensing networks, whose nodes can detect the surrounding environment [1,2,3]
When the field scale is 900 × 900, ηTFP is only 8.3493 × 10−8. These results suggest that target guiding self-avoiding random walk with intersection (TGSARWI) is efficient to find out the first path for timeliness demand in large-scale field, such as real-time solution and online calculation, the exposure performance of HTFP is not as good as Global Dijkstra algorithm (DA)
The iteration number of physarum optimization algorithm (POA) is far greater than that of TGSARWI DA, no matter what scale the field and the sensor density are. These results fully demonstrate that TGSARWI DA is more appropriate to solve minimum exposure path (MEP) problem with a large-scale field and high-sensor density
Summary
Wireless sensor network (WSN) is a kind of distributed sensing networks, whose nodes can detect the surrounding environment [1,2,3]. When the detecting region is deployed by multiple sensors, the finding of the minimum exposure path, due to the highly ordered nonlinearity of MEP problem, will become rather complex using the Euler-Lagrange equation. 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. Let wij ∈ W denote the weight of the edge eij between two nodes vi and vj, which shows the exposure of eij detected by all sensors in the grid field Since this graph is an undirected network graph, we have wij = wji. After generating a sufficient number of connected paths between vs and ve, Dijkstra algorithm is used to seek minimum exposure path in the sub-network formed by these connected paths
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