A Cluster Allocation and Routing Algorithm Based on Node Density for Extending the Lifetime of Wireless Sensor Networks

Lee, Bo-Si; Lin, Haowei; Tarng, Wernhuar

doi:10.1109/waina.2012.42

Cited by 7 publications

(4 citation statements)

References 14 publications

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“…As mentioned in [25], the optimal density of nodes is ρ=1.03. In [26], this study proposed increasing the node density near the base station during deployment to compensate for the requirement of high energy consumption. The results show that the optimal node density distribution is 4:2.5:1, and the closer it is to the base station, the greater the sensor node density.…”

Section: Evaluation and Discussionmentioning

confidence: 99%

A New Data Fusion Algorithm for Wireless Sensor Networks Inspired by Hesitant Fuzzy Entropy

Wang

Tawose

Jiang

et al. 2019

Sensors

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The wireless sensor network (WSN) is mainly composed of a large number of sensor nodes that are equipped with limited energy and resources. Therefore, energy consumption in wireless sensor networks is one of the most challenging problems in practice. On the other hand, data fusion can effectively decrease data redundancy, reduce the amount of data transmission and energy consumption in the network, extend the network life cycle, improve the utilization of bandwidth, and thus overcome the bottleneck on energy and bandwidth consumption. This paper proposes a new data fusion algorithm based on Hesitant Fuzzy Entropy (DFHFE). The new algorithm aims to reduce the collection of repeated data on sensor nodes from the source, and strives to utilize the information provided by redundant data to improve the data reliability. Hesitant fuzzy entropy is exploited to fuse the original data from sensor nodes in the cluster at the sink node to obtain higher quality data and make local decisions on the events of interest. The sink nodes periodically send local decisions to the base station that aggregates the local decisions and makes the final judgment, in which process the burden for the base station to process all the data is significantly released. According to our experiments, the proposed data fusion algorithm greatly improves the robustness, accuracy, and real-time performance of the entire network. The simulation results demonstrate that the new algorithm is more efficient than the state-of-the-art in terms of both energy consumption and real-time performance.

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Section: Evaluation and Discussionmentioning

confidence: 99%

A New Data Fusion Algorithm for Wireless Sensor Networks Inspired by Hesitant Fuzzy Entropy

Wang

Tawose

Jiang

et al. 2019

Sensors

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“…(1) procedure start scan() (2) best parent addr = 0XFFFF; min dfs = 255; scan timer = ON (3) wake after(0) ⊳ wakeup now (4) start scan timer(0, SCAN LENGTH) ⊳ scan timer starts now and will fire after SCAN LENGTH (5) end procedure (6) procedure recv beacon(src, dfs, accept) (7) if scan timer then (8) if (dfs < min dfs & accept) then ⊳ replace current parent (9) min dfs = dfs (10) b e s tparent addr = src (11) end if (12) if (my parent addr == best parent addr) then ⊳ restore synchronization with last parent (13) s t o pscan timer() (14) s c a ntimer = OFF (15) end if (16) end if (17) if scan timer then (18) return (19) end if (20) if (not associated) then (21) s leepafter(slot time) (22) wak eafter( frame ) (23) sen dassociate request packet() (24) else if (data available) then (25) s leepafter(slot time) (26) wak eafter(my slot * slot time) ⊳ wake up in my slot to start my frame (27) trysend data() (28) else (29) s leepafter(0) (30) wak eafter(my slot * slot time) (31) end if (32) end procedure (33) procedure scan handler() ⊳ called when scan timer is fired (34) if (best parent addr < 0XFFFF) then (35) m yparent addr = best parent addr (36) m ydfs = min dfs + 1 (37) sca ntimer = OFF (38) else (39) s leepafter(0) (40) s ta rtscan timer(random delay, SCAN LENGTH) (41) end if (42) return (43) end procedure Algorithm 1: Cluster head join algorithm.…”

Section: Journal Of Sensorsmentioning

confidence: 99%

“…LEACH is nonstandard protocol and very well accepted in the sensor network community. A large number of proposed protocols [37][38][39][40][41][42][43][44][45][46] in the literature were compared with it. This motivates us to compare with the same reference protocol.…”

Section: Performance Evaluationmentioning

confidence: 99%

AH-MAC: Adaptive Hierarchical MAC Protocol for Low-Rate Wireless Sensor Network Applications

Al-Sulaifanie

Biswas²,

Al-Sulaifanie

2017

Journal of Sensors

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This paper proposes an adaptive hierarchical MAC protocol (AH-MAC) with cross-layer optimization for low-rate and large-scale wireless sensor networks. The main goal of the proposed protocol is to combine the strengths of LEACH and IEEE 802.15.4 while offsetting their weaknesses. The predetermined cluster heads are supported with an energy harvesting circuit, while the normal nodes are battery-operated. To prolong the network’s operational lifetime, the proposed protocol transfers most of the network’s activities to the cluster heads while minimizing the node’s activity. Some of the main features of this protocol include energy efficiency, self-configurability, scalability, and self-healing. The simulation results showed great improvement of the AH-MAC over LEACH protocol in terms of energy consumption and throughput. AH-MAC consumes eight times less energy while improving throughput via acknowledgment support.

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“…Ref. [10] proposes a cluster allocation and routing algorithm based on node density and study on optimal density proportion for deploying sensor nodes. In Ref.…”

Section: Related Workmentioning

confidence: 99%

A Density-based Energy-efficient Clustering Heterogeneous Algorithm for Wireless Sensor Networks

Xu¹,

Yin²,

Wang³

et al. 2014

IJCA

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Clustering is an efficient method adopted in various routing algorithms for wireless sensor networks. However, most clustering algorithms are not suitable for heterogeneous networks. In this paper, we propose a Density-based Energy-efficient Clustering Heterogeneous Algorithm (DECHA). In DECHA, we define the density of a node and together with its energy condition to adjust the probability for the candidate cluster head selection dynamically. Candidate cluster heads further evaluate the energy level of its neighbors and adjust to find more proper cluster heads. Moreover, we design an intra-cluster algorithm as well as a multi-hop inter-cluster routing algorithm. Simulation results show that cluster heads are properly deployed in a heterogeneous wireless sensor network. Compared with some popular algorithms, in our DECHA, the stability period and network lifetime and prolonged and total energy consumption is prominently reduced.

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A Cluster Allocation and Routing Algorithm Based on Node Density for Extending the Lifetime of Wireless Sensor Networks

Cited by 7 publications

References 14 publications

A New Data Fusion Algorithm for Wireless Sensor Networks Inspired by Hesitant Fuzzy Entropy

A New Data Fusion Algorithm for Wireless Sensor Networks Inspired by Hesitant Fuzzy Entropy

AH-MAC: Adaptive Hierarchical MAC Protocol for Low-Rate Wireless Sensor Network Applications

A Density-based Energy-efficient Clustering Heterogeneous Algorithm for Wireless Sensor Networks

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