Impact of redundant sensor deployment over data gathering performance: A model based approach

Chakraborty, Suchetana; Chakraborty, Sandip; Nandi, Sukumar; Karmakar, Sushanta

doi:10.1016/j.jnca.2016.03.012

Cited by 2 publications

(1 citation statement)

References 41 publications

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“…At present, in the deployment of WSNs, many studies have mainly focused on the lifetime [4][5][6], quality of service (QoS) [7,8], and network cost [9][10][11], since they are the most critical issues. Chanak and Banerjee [12] presented a fault node distribution and management approach based on fuzzy rules in WSNs, which implement the efficient route towards the base station to reuse faulty nodes.…”

Section: Introductionmentioning

confidence: 99%

Wireless Sensor Network Deployment in Cyberphysical Machine Tool System Based on Optimal Allocation of Memory Buffers

Zhou

2021

Journal of Sensors

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As an important direction of Industry 4.0, cyberphysical machine tool systems (CPMTS) can realize the deep integration and real-time interaction of physical components and information to optimize manufacturing processes. Wireless sensor network (WSN), an important part of CPMTS, is responsible for data collection and transmission. However, in the process of data transmission, due to memory limitations and noise interference, unreasonable sensor distribution will affect the performance of CPMTS. At the same time, data accuracy will be affected due to the resource constraints of CPMTS. To solve the problems above, this paper firstly presented a single-station transfer model to ensure the layout of sensors in each sink, which can meet the detection capability of fault/monitoring data. Then, by using fuzzy graphs, a multihop-station transfer model and data-collecting model are developed to describe the data flow and memory allocation in the wireless network. Taking noise interference and data position into consideration, a MILP problem is formulated and the optimization solution is obtained by using the “branch and bound” method. Finally, case studies about optimal sensor distribution on the single station and path optimization on the multihop station are presented to illustrate the proposed strategy. The case studies validated that the proposed sensor distribution in a single station can achieve higher detectability with fewer resources, and the optimization path strategy can achieve the best performance in two proposed experiments, compared to the shortest path and noninferior path strategies.

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