Optimized Deployment Strategy of Mobile Agents in Wireless Sensor Networks

Wang, Xue; Wang, Sheng; Jiang, Aiguo

doi:10.1109/isda.2006.253730

Cited by 15 publications

(5 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By employing the principles of GAs, many optimal sensor placement are developed in a complex system to optimize several competing evaluation criteria [26,33,58,[74][75][76][77][78][79]. Ren et al [31] developed a data-mining guided GA to solve the sensor distribution problem to achieve a maximal variance detection capability in a multi-station assembly process.…”

Section: Optimization Algorithmmentioning

confidence: 99%

A Comprehensive Survey of Sensor Deployment Strategies to Diagnose Discrete-Part Manufacturing System

He¹,

Jia²,

Zhao³

et al. 2018

Proceedings of the 2018 International Conference on Mechanical, Electrical, Electronic Engineering &Amp; Science (MEEES 2018)

View full text Add to dashboard Cite

This paper presents a broad overview of the various sensor placement strategies to diagnose discrete-part manufacturing system. Due to the technical complexity, the performance of sensor system would be cursed by any modules of sensor placement strategies. Therefore, the current state of the sensor placement strategies is outlined with three key modules, namely cause-effect relationship model, optimization basis, and optimization algorithm, are surveyed in detail. The challenges faced by industry and academia are discussed and several principle conclusions are drawn, which could create a clear platform for the neophyte researchers for sensor deployment to diagnose discrete-part manufacturing system.

show abstract

Section: Optimization Algorithmmentioning

confidence: 99%

A Comprehensive Survey of Sensor Deployment Strategies to Diagnose Discrete-Part Manufacturing System

He¹,

Jia²,

Zhao³

et al. 2018

Proceedings of the 2018 International Conference on Mechanical, Electrical, Electronic Engineering &Amp; Science (MEEES 2018)

View full text Add to dashboard Cite

show abstract

“…Weighted Averaging Fusion (WAF) is a fusion method to obtain the average detection value of multi-sensor nodes [12]. In a traditional global weighted averaging data fusion algorithm, each cluster has a different but fixed weight.…”

Section: Fuzzy Comprehensive Evaluation Of Fusion Weightsmentioning

confidence: 99%

“…Here, time delay is defined as the interval between the time data packet is taken out from a cluster head and the time the data packet is received by the base station [12]. Moreover, the amount of transmitted data of cluster heads is not fixed.…”

Section: Fuzzy Comprehensive Evaluation Of Fusion Weightsmentioning

confidence: 99%

A Variable Weight Based Fuzzy Data Fusion Algorithm for WSN

Wang

Liao

Wang

et al. 2011

Ubiquitous Intelligence and Computing

View full text Add to dashboard Cite

Abstract. Due to the limited energy, storage space and computing ability, data fusion is very necessary in Wireless Sensor Networks (WSN). In this paper, a new variable weight based fuzzy data fusion algorithm for WSN is proposed to improve the accuracy and reliability of the global data fusion. In this algorithm, the weight of each cluster head node in global fusion is not fixed. Time delay, data amount and trustworthiness of each cluster head will all affect the final fusion weight. We get the fusion weights by variable weight based fuzzy comprehensive evaluation or fuzzy reasoning. In the variable weight based fuzzy comprehensive evaluation, by increasing the weight of the factor with too low value, we can give prominence to deficiency and the clusters with too long time delay or too small amount or too low trustworthiness will get smaller weights in data fusion. And therefore, the cluster head node with deficiency will have a small influence in global fusion. Simulation shows that this algorithm can obtain a more accurate and reliable fusion results especially when there are data undetected or compromised nodes compared with traditional algorithms.

show abstract

“…Wang et al in [11] showed, efficiently, the optimized deployment strategy decrease the energy consumption and time delay in wireless sensor network, and improve the real-time ability. In order to maintain low delays, to support the required data rates and to minimize packet losses under four different topologies Vasos and Charalambos [12] have employed the SenTCP algorithm They used are Simple Diffusion, Constant Placement topologies, R-random placement and Grid placement.…”

Section: Related Workmentioning

confidence: 99%

Time delay performance analysis of sensor allocation strategies on a WSN

Abbasy

Barrantes

Marín

2011

Proceedings of the 1st International Conference on Wireless Technologies for Humanitarian Relief

View full text Add to dashboard Cite

In many of query-based WSN applications, response time factor is an essential issue. Overall performance under different sensor placements depends on the pattern of queries requested from the network, which in turn might depend on the physical segmentation of the field. This paper explores three predefined configurations in order to compare the time delay performance. The experiments showed that the Uniform distribution of the nodes was the strategy that obtains the fastest response time, and as expected, Random is the one with the slowest.

show abstract

Optimized Deployment Strategy of Mobile Agents in Wireless Sensor Networks

Cited by 15 publications

References 16 publications

A Comprehensive Survey of Sensor Deployment Strategies to Diagnose Discrete-Part Manufacturing System

A Comprehensive Survey of Sensor Deployment Strategies to Diagnose Discrete-Part Manufacturing System

A Variable Weight Based Fuzzy Data Fusion Algorithm for WSN

Time delay performance analysis of sensor allocation strategies on a WSN

Contact Info

Product

Resources

About