A Bio-Inspired Algorithm for Route Selection in Wireless Sensor Networks

Gao, Cai; Yan, Chao; Adamatzky, Andrew; Deng, Yong

doi:10.1109/lcomm.2014.2360523

Cited by 13 publications

(6 citation statements)

References 12 publications

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“…Thus, multi-sensor modeling and sensor data fusion are important issues in many real applications [ 1 , 2 , 3 , 4 , 5 , 6 , 7 ]. Driven by real applications, many methods have been proposed for multi-sensor modeling and sensor data fusion [ 8 ], including neural network models [ 1 , 9 ], belief function theory [ 10 , 11 ], Dempster–Shafer evidence theory [ 12 , 13 , 14 ], fuzzy set theory [ 15 ], Z-Numbers [ 16 ], and so on [ 17 ]. Furthermore, accompanied by multi-sensor data fusion, how to measure the uncertain degree or the reliability of sensor reports with heterogeneous sources is still an open issue.…”

Section: Introductionmentioning

confidence: 99%

A Weighted Belief Entropy-Based Uncertainty Measure for Multi-Sensor Data Fusion

Tang

Zhou

et al. 2017

Sensors

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In real applications, how to measure the uncertain degree of sensor reports before applying sensor data fusion is a big challenge. In this paper, in the frame of Dempster–Shafer evidence theory, a weighted belief entropy based on Deng entropy is proposed to quantify the uncertainty of uncertain information. The weight of the proposed belief entropy is based on the relative scale of a proposition with regard to the frame of discernment (FOD). Compared with some other uncertainty measures in Dempster–Shafer framework, the new measure focuses on the uncertain information represented by not only the mass function, but also the scale of the FOD, which means less information loss in information processing. After that, a new multi-sensor data fusion approach based on the weighted belief entropy is proposed. The rationality and superiority of the new multi-sensor data fusion method is verified according to an experiment on artificial data and an application on fault diagnosis of a motor rotor.

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Section: Introductionmentioning

confidence: 99%

A Weighted Belief Entropy-Based Uncertainty Measure for Multi-Sensor Data Fusion

Tang

Zhou

et al. 2017

Sensors

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“…To the best of authors' knowledge, the execution time of the existing routing algorithms for WSNs [40] is depending on the number of the nodes. On the contrary, the execution time of the proposed model is determined by the CA grid dimensions which can be retained while the number of nodes increases.…”

Section: Model Resultsmentioning

confidence: 99%

“…It is noteworthy that despite the increasing density of the sensor nodes, the data tree is drawn after 1500 time steps for all the examples of WSN depicted in Figure 3. To the best of authors’ knowledge, the execution time of the existing routing algorithms for WSNs [40] is depending on the number of the nodes. On the contrary, the execution time of the proposed model is determined by the CA grid dimensions which can be retained while the number of nodes increases.…”

Section: Model Resultsmentioning

confidence: 99%

A Cellular Automata Bioinspired Algorithm Designing Data Trees in Wireless Sensor Networks

Tsompanas

Mayne

Adamatzky

2015

International Journal of Distributed Sensor Networks

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Several studies present methods to economize energy in wireless sensor networks (WSNs) which is one of the most confining resources in these systems. This paper presents a bioinspired, cellular automata (CA) based model for constructing data trees that connect all nodes with a sink node. Nonetheless, the proposed model takes into consideration not only the proximity between two nodes but also their remaining available energy. Consequently, by avoiding nodes with nearly depleted energy sources, the life time of the network can be prolonged. The plasmodium of Physarum polycephalum is the inspiration for the proposed model, as it has proved its robustness in graphically expressed problems. Moreover, CAs are able to encapsulate the parallel dynamics of the model and, thus, achieve a very fast execution.

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“…To suppress control overhead and realize reliable operation even in enough large networks [2], routing protocols relying only on local observables of each router in the network, such as communication delay of data packets that go through each router, attract much attention recently [3][4][5]. Owing to nonrequirement of gathering global information of the whole network, these protocols have been shown to largely reduce communication overhead.…”

Section: Introductionmentioning

confidence: 99%

Distributed multipath routing with packet allocation based on the attractor renewal model

Saitoh

Teramae

Wakamiya

2018

NOLTA

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Information communication networks are rapidly growing recently. Because of increase of communication overhead, traditional routing protocols using global information of the network, such as topology of the whole network or traffic demands between most of pairs of routers, are facing difficulty in reliable routing. To alleviate this, distributed routing protocols relying only on local observables of the network attract much attention recently. The nonrequirement of global knowledge of the whole network largely reduces communication overhead of these protocols. However, the lack of knowledge can also be a significant drawback of them because they cannot promptly respond to traffic changes that occur on out of their local scopes. It means that network resources cannot be utilized sufficiently. To solve the problem, here, by extending an existing distributed routing protocol called ARAS, we propose a novel routing protocol that utilizes multiple paths in parallel. The protocol adaptively modulates packet allocation ratio to paths based only on local observables of the network. Multipath routing, however, easily give flapping of packet allocation due to competition among multiple routers. We study competition between routers and provide ways to suppress the flapping. We show validity of the proposal using a network simulation where prompt response of packet reallocation is required.

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A Bio-Inspired Algorithm for Route Selection in Wireless Sensor Networks

Cited by 13 publications

References 12 publications

A Weighted Belief Entropy-Based Uncertainty Measure for Multi-Sensor Data Fusion

A Weighted Belief Entropy-Based Uncertainty Measure for Multi-Sensor Data Fusion

A Cellular Automata Bioinspired Algorithm Designing Data Trees in Wireless Sensor Networks

Distributed multipath routing with packet allocation based on the attractor renewal model

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