scholarly journals IEEE 802.15.4 Modifications and Their Impact

2011 ◽  
Vol 7 (1) ◽  
pp. 69-92 ◽  
Author(s):  
M. Goyal ◽  
W. Xie ◽  
H. Hosseini
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Low Power ◽  
Stochastic Modeling ◽  
Ieee 802.15.4 ◽  
Data Rate ◽  
Traffic Load ◽  
Wireless Sensor ◽  
Modeling And Simulations

IEEE 802.15.4 is a popular choice for MAC/PHY protocols in low power and low data rate wireless sensor networks. In this paper, we suggest several modifications to beaconless IEEE 802.15.4 MAC operation and evaluate their impact on the performance via stochastic modeling and simulations. We found that the utility of these modifications is strongly dependent on the traffic load on the network. Accordingly, we make recommendations regarding how these modifications should be used in view of the prevalent traffic load on the network.

scholarly journals The Synchronized Peer-to-Peer Framework and Distributed Contention-Free Medium Access for Multihop Wireless Sensor Networks

2008 ◽  
Vol 2008 ◽  
pp. 1-28 ◽  
Author(s):  
Ahmad Khayyat ◽  
Ahmed Safwat
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Power Consumption ◽  
Low Power ◽  
Ieee 802.15.4 ◽  
Peer To Peer ◽  
Traffic Load ◽  
Wireless Sensor ◽  
Delivery Ratio ◽  
Free Access

IEEE 802.15.4 is a low-power, low-rate MAC/PHY standard that meets most of the stringent requirements of singlehop wireless sensor networks. Sensor networks with nodal populations composed of thousands of devices have been envisioned in conjunction with environmental, vehicular, military applications, and many others. However, such large sensor network deployments necessitate multihop support as well as low power consumption. In the light of the standard's extremely limited joint support of the two aforementioned attributes, this paper presents two essential contributions. First, a framework is proposed to implement a new IEEE 802.15.4 operating mode, namely, thesynchronized peer-to-peermode. This mode is designed to enable the standard's low-power features in peer-to-peer multihop-ready topologies. The second contribution is a distributed GTS(dGTS)management scheme designed to function in the newly devised network mode. This protocol provides reliable contention-free access in peer-to-peer topologies in a completely distributed manner. Assuming optimal routing, our simulation experiments reveal perfect delivery ratios as long as the traffic load does not reach or surpass its saturation threshold. dGTS sustains at least twice the delivery ratio of contention-based access under suboptimal dynamic routing. Moreover, the dGTS scheme exhibits minimum power consumption by eliminating the retransmissions attributed to contention, which, in turn, reduces the number of transmissions to a minimum.

scholarly journals Machine Learning in Wireless Sensor Networks for Smart Cities: A Survey

Electronics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1012
Author(s):  
Himanshu Sharma ◽  
Ahteshamul Haque ◽  
Frede Blaabjerg
Keyword(s):  
Machine Learning ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Low Power ◽  
Smart City ◽  
Ieee 802.15.4 ◽  
Smart Cities ◽  
Traffic Monitoring ◽  
Wireless Sensor ◽  
Ieee 802.15.4 Standard

Artificial intelligence (AI) and machine learning (ML) techniques have huge potential to efficiently manage the automated operation of the internet of things (IoT) nodes deployed in smart cities. In smart cities, the major IoT applications are smart traffic monitoring, smart waste management, smart buildings and patient healthcare monitoring. The small size IoT nodes based on low power Bluetooth (IEEE 802.15.1) standard and wireless sensor networks (WSN) (IEEE 802.15.4) standard are generally used for transmission of data to a remote location using gateways. The WSN based IoT (WSN-IoT) design problems include network coverage and connectivity issues, energy consumption, bandwidth requirement, network lifetime maximization, communication protocols and state of the art infrastructure. In this paper, the authors propose machine learning methods as an optimization tool for regular WSN-IoT nodes deployed in smart city applications. As per the author’s knowledge, this is the first in-depth literature survey of all ML techniques in the field of low power consumption WSN-IoT for smart cities. The results of this unique survey article show that the supervised learning algorithms have been most widely used (61%) as compared to reinforcement learning (27%) and unsupervised learning (12%) for smart city applications.

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