Browsing by Author "Li, Yan-Sing"

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A Bat-inspired Algorithm for Router Node Placement with Weighted Clients in Wireless Mesh Networks
(2014)
Lin, Chun-Cheng
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Li, Yan-Sing
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Deng, Der-Jiunn
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工業工程與管理學系
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Department of Industrial Engineering and Management
This paper considers the problem of the dynamic router node placement (dynRNP) with weighted clients in wireless mesh networks (WMNs), in which both mesh clients and mesh router have mobility, and mesh clients can switch on or off their network access at different times. The main objective of the conventional dynRNP problem is to maximize both network connectivity and client coverage, i.e., the size of the greatest subgraph component of the WMN topology and the number of the clients within radio coverage of mesh routers, respectively. Furthermore, this paper considers a more complicated dynRNP problem in WMNs, in which each client is associated with a weighted value (i.e., the mesh clients with higher weights values are served with larger priority), so that the two objective measures are redefined with consideration of clients\' weights. We resolve the weighted problem by a bat-inspired algorithm (BA) with dynamic local search selection scheme, which simulates the echolocation of bats to find the optimal solution. Finally, the performance of the proposed BA is compared with the original BA and PSO, and the effects of weights are analyzed.
Dynamic Weighted Fog Computing Device Placement Using a Bat-Inspired Algorithm with Dynamic Local Search Selection
(1970-01-01)
Lin, Chun-Cheng
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Deng, Der-Jiunn
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Suwatcharachaitiwong, Sirirat
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Li, Yan-Sing
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工業工程與管理學系
This work investigates the dynamical weighted deployment of mobile fog computing devices to support a mobile edge computing environment, in which each edge device is associated with a weight to reflect its importance based on the application. Since edge devices are mobile and could be switched off, it is challenging to dynamically optimize the deployment to adapt to dynamic change. This work further models the problem mathematically and solves it by a bat-inspired algorithm (BA), which searches the optimal solutions by simulating the food-searching behavior of bats via echolocation. Furthermore, three local search methods designed specifically for this problem are integrated into the BA, and a dynamic local search selection mechanism is proposed to adjust the probabilities of choosing the three local search methods iteratively in the BA main loop. Simulation results show outperformance of the proposed BA over the BA without local search and the previous approach.