A survey of congestion in Wireless Sensor Networks

Mundada, Monica R; Desai, Pranav B.; Meeradevi,

doi:10.1109/hmi.2016.7449195

Cited by 8 publications

(2 citation statements)

References 7 publications

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“…As a side effect of the sink congestion, the power usage in the nodes around the sink node will be increasing significantly, which cause the sink to be inaccessible to others nodes of the network. 37 Once an event occurred, the sensor nodes nearby the occurrence area will detect it. The nodes which sensed the event would act as source nodes to transmit the sensed data to the cluster head or sink node.…”

Section: Related Workmentioning

confidence: 99%

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%

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

Shahidinejad

Barshandeh

2020

Int J Communication

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“…The node-level congestion is very normal in conventional networks. It is caused by a buffer overflow in the node and may result in packet loss, and also to gain in queuing delay [5]. editor@iaeme.com…”

Section: Node-level Congestionmentioning

confidence: 99%

Wireless Sensor Networks Congestion and Role of Artificial Intelligence

Doibale¹,

Kurundkar²

2019

IJCET

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The occurrence of the congestion has an extremely deleterious impact on the performance of Wireless Sensor Network (WSNs). Some novel blockage control method utilizing computerized reasoning for remote sensor systems. Remote Sensor Systems are a class of remote systems estimated for checking objective and ecological wonders. Our paper provides opening to wireless sensor network as well as for artificial intelligence, which aims to act both proactively, in order to avoid the creation of congestion in WSNs, and reactively, so as to mitigate the diffusion of upcoming congestion through alternative path routing. We make use of Artificial Intelligence (AI) which play an important role in our society, give rise to systems that can manage themselves. Many advanced AI techniques can be utilized to improve the performance and reliability. Investigating the AI algorithm applied to WSN may improve network management, security or routing in WSN which may result in a more reliable network. We will look at AI algorithm applied in WSN and discuss the possible use of these AI in WSN to address the WSN challenge and improve its performance and reliability. And due to the constraint on data processing and power consumptions, use of artificial intelligence has been historically discarded in these kind of network. The use of the AI in WSN makes system error free, fast and efficient in almost many aspects. This paper attempts to encourage the use of artificial intelligence techniques in wireless sensor nodes.

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