Abstract
Background: Energy-Efficiency is an important issue in Wireless Sensor Networks (WSNs) since sensors are battery powered and are deployed in unattended environments. Long distance transmission is very expensive in WSNs since power consumption is proportional to dα for transmitting over distance d, where α is path loss exponent in the interval [2:6]. Thus, there is a necessary requirement of relay nodes in the network that are capable of performing data sensing, data aggregation and data routing, as described in various patents.
Method: This paper presents a Layered Relay-Medium Access Control (LR-MAC) Protocol for a Personal Area Network. This protocol introduces the concept of multi-layering for the relay (routing) nodes. The protocol is validated by doing extensive simulations in QualNet 6.1 network simulator. The QoS parameters are analyzed for three different deployment strategies viz. random, grid and circular.
Results: The results show that LR-MAC Protocol is an efficient technique in terms of improving network lifetime, average throughput and reducing number of packets dropped due to channel access failure in the network; while LR-MAC gives a neutral performance in terms of end-to-end delay in the network.
Conclusion: LR-MAC protocol can efficiently work for random deployments as well as for wellplanned effective deployments like in smart building monitoring. The network can also be modified for large areas by increasing number of deployed sensor nodes and keeping node density constant.
Keywords: Wireless sensor networks, multi-layering, energy hole, relay/routing node, LR-MAC, energy efficiency, QoS parameters.
Graphical Abstract
Related Books
Challenges and Opportunities for Deep Learning Applications in Industry 4.0
Recent Advances in IoT and Blockchain Technology
Augmented Intelligence: Deep Learning, Machine Learning, Cognitive Computing, Educational Data Mining
Human-Computer Interaction and Beyond: Advances Towards Smart and Interconnected Environments Part II
Machine Learning and Its Application: A Quick Guide for Beginners
Computational Intelligence For Data Analysis
Applied Machine Learning and Multi-Criteria Decision-Making in Healthcare
Advanced Computing Techniques: Implementation, Informatics and Emerging Technologies
How to Design Optimization Algorithms by Applying Natural Behavioral Patterns
Human-Computer Interaction and Beyond: Advances Towards Smart and Interconnected Environments (Part I)
9
2
1
1