Paper Info
Title
Ensuring reliability of high-priority data transport using expected congestion shortfall prediction in wireless sensor networks
Abstract
Sending critical data to sink without any data loss is a challenging task in wireless sensor nodes. Congestion Prediction emerges to carry out this task by overseeing the traffic density and taking preventive measures to control congestion in data routing paths. Existing congestion control algorithms miss an effective and reliable mechanism to send high priority packets to reach sink without any packet drop. As a matter of fact, critical data has to reach sink node without any loss upon event driven triggers. A Reliable High priority data transport using Congestion Prediction (RHpCP) technique is proposed in this paper to handle critical data exchange. There are two types of precedence data reaching sink node namely high priority or critical data and low priority data sensed from the environment. First approach occurs when critical data is encountered and an alternate path is found by pruning the over utilized node and selecting the node with minimal expected congestion shortfall value based on expected shortfall process estimation in economics. The second approach takes place when less priority data is encountered and a simple congestion index is evaluated for selecting the least congested node. The proposed RHpCP method is compared with the existing techniques of priority data-based congestion prediction using a Network Simulator. RHpCP is found to yield greater high priority data to reach sink node in contrast with existing traffic prediction techniques. Energy saving is increased in sensor nodes for maintaining prolonged lifetime during deployment mainly for retrieving alert information in case of critical applications such as medical emergencies and natural disasters.
Year
DOI
Venue
2021
10.1007/s11276-021-02792-y
WIRELESS NETWORKS
Keywords
DocType
Volume
Event trigger, Highly-priority data, Pruning, ECS, Congestion index, Congestion prediction
Journal
27
Issue
ISSN
Citations 
8
1022-0038
1
PageRank 
References 
Authors
0.36
0
2
Name
Order
Citations
PageRank
Sangeetha Ganesan110.36
Vijayalakshmi Muthuswamy272.14