An Electromagnetism-like mechanism algorithm for the router node placement in wireless mesh networks

Sayad, Lamri; Bouallouche-Medjkoune, Louiza; Aı̈ssani, Djamil

doi:10.1007/s00500-018-3096-y

Cited by 17 publications

(13 citation statements)

References 23 publications

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“…The coordinator communicates with the monitoring center and the terminal serial port, and the terminal node is placed in the warehouse storage. Therefore, when the distance between the terminal node and the coordinator node exceeds the maximum threshold of the communication distance, the router node will continue the communication [25]. The router node does not process arbitrary information.…”

Section: Design Of the Router Nodementioning

confidence: 99%

Artificial Intelligence Monitoring System Using ZigBee Wireless Network Technology in Warehousing and Logistics Innovation and Economic Cost Management

Zhao

2022

Wireless Communications and Mobile Computing

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The study expects to reduce the development cost and energy consumption of the storage environment monitoring and improve the efficiency of the storage monitoring environment. Firstly, the storage environment monitoring system is designed in detail using ZigBee. It includes the design of terminal nodes, router nodes, coordinator nodes, and upper computer software. Here is a detailed description of the process of establishing a communication network for three different types of devices in the ZigBee network. The address allocation mechanism of the ZigBee network is introduced. Then, through the application of activity-based costing, the cost reduction is achieved as expected. Finally, the evaluation model of the storage logistics cost level is established, and the various reasons that affect the storage logistics cost are analyzed. The completed warehouse environment monitoring system is a tree topology network composed of a ZigBee coordinator, several ZigBee routing nodes, and terminal devices through the above design. The coordinator in the network is responsible for establishing the network, and it can communicate with the host computer through the serial port. The terminal node collects environmental parameter information and feeds the data back to the ZigBee coordinator through wireless multihop. The ZigBee routing node is mainly used to expand the range of the network to achieve the purpose of wireless monitoring of the entire storage environment. This provides a reference for the combination of wireless communication technology and sensor technology.

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Section: Design Of the Router Nodementioning

confidence: 99%

Artificial Intelligence Monitoring System Using ZigBee Wireless Network Technology in Warehousing and Logistics Innovation and Economic Cost Management

Zhao

2022

Wireless Communications and Mobile Computing

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“…Setting redundancy angle θ is recorded as the adjustment of the course result of deep learning network, then the boundary [ lo , up ] is determined. Following the process of EM algorithm, 38 the novel total force value F i has been designed to make up for the phenomenon of overflow and great deviation of the total force .…”

Section: The Heuristic Planning Approachmentioning

confidence: 99%

Deep learning structure for collision avoidance planning of unmanned surface vessel

Zheng

2020

Proceedings of the Institution of Mechanical Engineers, Part M:

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The uncertain dynamics and environmental impacts in collision avoidance planning are always the key issues of autonomous navigation for unmanned surface vessel, due to the strong interactive ability and learning ability of deep learning, the combination of CNN and LSTM provide relevance and memory characteristics, which can extract the potential influence features of collision avoidance and correlation of historical strategies, to gain the more general trend of collision avoidance. Meanwhile, to make sure the real-time problem, the heuristic planning based on improved Electromagnetism-like mechanism algorithm is utilized to seek the optimal strategy with the rolling iteration of CNN–LSTM structure, for obtaining a simple and effective on-line scheme of collision avoidance. The complete algorithm has been tested and validated under the different situation comparing with the deterministic algorithm, it is worth mentioning that the algorithm can potentially be developed to advance collision-free planning especially in multiple vessels situation.

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“…In the same context, Sayad et al proposed three new algorithms based on Chemical Reaction Optimization (CRO) algorithm [19], Firefly optimization (FA) algorithm [20], and Electromagnetism-like Mechanism (EM) meta-heuristic [21]. These algorithms were validated using many generated instances with various number of mesh clients and mesh routers, taking into account the coverage and connectivity metrics.…”

Section: Introductionmentioning

confidence: 99%

Solving the Mesh Router Nodes Placement in Wireless Mesh Networks Using Coyote Optimization Algorithm

Taleb

Meraihi

Gabis³

et al. 2022

IEEE Access

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Wireless Mesh Networks (WMNs) have rapid real developments during the last decade due to their simple implementation at low cost, easy network maintenance, and reliable service coverage. Despite these properties, the nodes placement of such networks imposes an important research issue for network operators and influences strongly the WMNs performance. This challenging issue is known to be an NP-hard problem, and solving it using approximate optimization algorithms (i.e. heuristic and metaheuristic) is essential. This motivates our attempts to present an application of the Coyote Optimization Algorithm (COA) to solve the mesh routers placement problem in WMNs in this work. Experiments are conducted on several scenarios under different settings, taking into account two important metrics such as network connectivity and user coverage. Simulation results demonstrate the effectiveness and merits of COA in finding optimal mesh routers locations when compared to other optimization algorithms such as Firefly Algorithm (FA), Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Genetic Algorithm (GA), Bat Algorithm (BA), African Vulture Optimization Algorithm (AVOA), Aquila Optimizer (AO), Bald Eagle Search optimization (BES), Coronavirus herd immunity optimizer (CHIO), and Salp Swarm Algorithm (SSA).

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An Electromagnetism-like mechanism algorithm for the router node placement in wireless mesh networks

Cited by 17 publications

References 23 publications

Artificial Intelligence Monitoring System Using ZigBee Wireless Network Technology in Warehousing and Logistics Innovation and Economic Cost Management

Artificial Intelligence Monitoring System Using ZigBee Wireless Network Technology in Warehousing and Logistics Innovation and Economic Cost Management

Deep learning structure for collision avoidance planning of unmanned surface vessel

Solving the Mesh Router Nodes Placement in Wireless Mesh Networks Using Coyote Optimization Algorithm

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