An Efficient Distributed TDMA MAC Protocol for Large-Scale and High-Data-Rate Wireless Sensor Networks

2015 ◽  
Author(s):  
Kouhei Moriyama ◽  
Yongbing Zhang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Large Scale ◽  
Mac Protocol ◽  
Data Rate ◽  
High Data Rate ◽  
Wireless Sensor ◽  
High Data ◽  
Tdma Mac

scholarly journals HMC-MAC Protocol for High Data Rate Wireless Sensor Networks

Electronics ◽  
2015 ◽  
Vol 4 (2) ◽  
pp. 359-379 ◽  
Author(s):  
Gerard Chalhoub ◽  
Rana Diab ◽  
Michel Misson
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Mac Protocol ◽  
Data Rate ◽  
High Data Rate ◽  
Wireless Sensor ◽  
High Data

scholarly journals Backpacking: Energy-Efficient Deployment of Heterogeneous Radios in Multi-Radio High-Data-Rate Wireless Sensor Networks

IEEE Access ◽  
2014 ◽  
Vol 2 ◽  
pp. 1281-1306 ◽  
Author(s):  
A. B. M. Alim Al Islam ◽  
Mohammad Sajjad Hossain ◽  
Vijay Raghunathan ◽  
Yu Charlie Hu
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Energy Efficient ◽  
Data Rate ◽  
High Data Rate ◽  
Wireless Sensor ◽  
High Data

TreeMAC: Localized TDMA MAC protocol for real-time high-data-rate sensor networks

2009 ◽  
Vol 5 (6) ◽  
pp. 750-765 ◽  
Author(s):  
Wen-Zhan Song ◽  
Renjie Huang ◽  
Behrooz Shirazi ◽  
Richard LaHusen
Keyword(s):  
Sensor Networks ◽  
Real Time ◽  
Mac Protocol ◽  
Data Rate ◽  
High Data Rate ◽  
High Data ◽  
Rate Sensor ◽  
Tdma Mac

TreeMAC: Localized TDMA MAC protocol for real-time high-data-rate sensor networks

2009 ◽  
Author(s):  
Wen-Zhan Song ◽  
Renjie Huang ◽  
Behrooz Shirazi ◽  
Richard LaHusen
Keyword(s):  
Sensor Networks ◽  
Real Time ◽  
Mac Protocol ◽  
Data Rate ◽  
High Data Rate ◽  
High Data ◽  
Rate Sensor ◽  
Tdma Mac

scholarly journals Optimized Clustering Algorithms for Large Wireless Sensor Networks: A Review

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 322 ◽  
Author(s):  
Damien Wohwe Sambo ◽  
Blaise Yenke ◽  
Anna Förster ◽  
Paul Dayang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Large Scale ◽  
Smart Cities ◽  
Clustering Algorithms ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Environmental Behaviors ◽  
High Data ◽  
High Scalability

During the past few years, Wireless Sensor Networks (WSNs) have become widely used due to their large amount of applications. The use of WSNs is an imperative necessity for future revolutionary areas like ecological fields or smart cities in which more than hundreds or thousands of sensor nodes are deployed. In those large scale WSNs, hierarchical approaches improve the performance of the network and increase its lifetime. Hierarchy inside a WSN consists in cutting the whole network into sub-networks called clusters which are led by Cluster Heads. In spite of the advantages of the clustering on large WSNs, it remains a non-deterministic polynomial hard problem which is not solved efficiently by traditional clustering. The recent researches conducted on Machine Learning, Computational Intelligence, and WSNs bring out the optimized clustering algorithms for WSNs. These kinds of clustering are based on environmental behaviors and outperform the traditional clustering algorithms. However, due to the diversity of WSN applications, the choice of an appropriate paradigm for a clustering solution remains a problem. In this paper, we conduct a wide review of proposed optimized clustering solutions nowadays. In order to evaluate them, we consider 10 parameters. Based on these parameters, we propose a comparison of these optimized clustering approaches. From the analysis, we observe that centralized clustering solutions based on the Swarm Intelligence paradigm are more adapted for applications with low energy consumption, high data delivery rate, or high scalability than algorithms based on the other presented paradigms. Moreover, when an application does not need a large amount of nodes within a field, the Fuzzy Logic based solution are suitable.

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