Minimum Shared-Link-Count Forwarding for Alleviating Congestion in Wireless Sensor Networks

Thomas, Seemon; Mathew, Thomaskutty

doi:10.1007/978-981-13-1642-5_38

Cited by 1 publication

(2 citation statements)

References 13 publications

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“…The data transmitted via several subsequent intermediate forwarding nodes and consequently the area surrounding the intersection forwarder nodes can be a hotspot for congestion. 39 This congestion happened in tree architecture and is called forwarder congestion.…”

Section: Related Workmentioning

confidence: 99%

“…In the following, the memory usage of the proposed fuzzy-based controller is analyzed. For a triangular fuzzy set, there are four parameters to be stored in the memory, as denoted in Equation (39). For the fuzzy-based cluster head selection case, the memory usage includes nine parameters for the number of neighbors, eight parameters for the remaining energy, nine parameters for the energy cost, and 32 parameters for the chance (output).…”

Section: B Memory Usagementioning

confidence: 99%

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Sink selection and clustering using fuzzy‐based controller for wireless sensor networks

Shahidinejad

Barshandeh

2020

Int J Communication

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Summary Applying multiple sink nodes in a large‐scale wireless sensor networks (WSN) can increase the scalability and lifetime of the network. The current sink selection mechanisms assume an unlimited amount of buffer and bandwidth for the sink nodes. This can be problematic in real‐world applications, especially when many cluster heads select a specific sink node and send their data to the sink at the same time. In this situation, the sink node may not have enough buffer to receive and process data; consequently, some packets are dropped. To mitigate these occasions, a fuzzy‐based controller with reduced rules is proposed for sink selection by considering the capacity of the sink nodes. The capacity of the sink nodes is estimated using the long short‐term memory (LSTM) technique. Then another fuzzy‐based controller with reduced rules is designed to select the cluster head. The fuzzy rules are reduced by employing R‐implications method. Reducing the number of fuzzy rules decreases the complexity of the fuzzy controllers. The results show the efficiency of the proposed sink selection and clustering techniques in terms of consumed energy, remaining energy, first node dead (FND), half nodes dead (HND), last node dead (LND), packet loss, and delay.

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Section: Related Workmentioning

confidence: 99%

Section: B Memory Usagementioning

confidence: 99%