Timing Structure Mechanism of Wireless Sensor Network MAC layer for Monitoring Applications

2016 ◽  
Vol 7 (3) ◽  
pp. 1-20 ◽  
Author(s):  
Basma M. Mohammad El-Basioni ◽  
Abdellatif I. Moustafa ◽  
Sherine M. Abd El-Kader ◽  
Hussein A. Konber
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Wireless Sensor ◽  
Mac Layer ◽  
Node Density ◽  
End To End Delay ◽  
Monitoring Applications ◽  
Logical Topology ◽  
Better Than ◽  
Timing Structure

This paper aims at designing a Timing Structure Mechanism (TSM) for Wireless Sensor Network (WSN) MAC, with specifying its respective logical topology, especially suitable to the monitoring applications, differentiated and characterized from the existing time bounding strategies, paving for a good performance channel access mechanism. The work proposed in this paper is based on classifying the monitoring applications so as to designing efficient setup and benefiting from the node's capabilities in dividing the network into sub-networks. By evaluating TSM against a cluster-tree IEEE802.15.4 in the two cases of one channel and multi-channel clusters, the simulation results showed that with varying the area, the TSM performs better than the two cases of IEEE802.15.4 in terms of lifetime, end-to-end delay, loss percentage by on average 103.44% and 61.84%, 96.59% and 95.37%, and 88.59% and 87.52%, respectively. Also, in case of increasing the node density, TSM is better in terms of the same parameters by on average 446.58% and 356.05%, 98.04% and 95.62%, and 77.62% and 75.2%, respectively.

Wireless Sensor Network for Underground Mining Services Applications

2017 ◽  
pp. 504-530 ◽  
Author(s):  
Pankaj Kumar Mishra ◽  
Subhash Kumar
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Underground Mining ◽  
Underground Mines ◽  
Wireless Sensor ◽  
Maintenance Cost ◽  
Processing Unit ◽  
Central Processing ◽  
Underground Coal Mines ◽  
Logical Topology

Underground mines include a number of challenges due to their hostile milieu. Therefore, geotechnical and environmental monitoring mainly in underground coal mines have always been a critical task to ensure safe working conditions. If the monitoring device is cable based, then it requires an huge amount of cable deployment which can pose not only the high maintenance cost but difficulty in laying out the cable throughout the underground galleries. on the other hand, if it is direct wireless communication between sensing devices and the central processing unit, it is also not so feasible due to the crisscross, uneven and incline path. Therefore, Wireless Sensor Networks grab an opportunity to be deployed in such a hostile environment. Keeping in view, in the present chapter, attempts have been made to discuss the different aspects of wireless sensor network for underground coal mining services applications to overcome the various threats. Further, the best suited logical topology has been identified for the same.

scholarly journals A secure dynamic adaptive routing technique using game theory in wireless sensor network

2018 ◽  
Vol 7 (2) ◽  
pp. 768
Author(s):  
Muruganandam. A ◽  
Anitha. R
Keyword(s):  
Game Theory ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Data Security ◽  
Wireless Sensor ◽  
Wireless Data ◽  
End To End Delay ◽  
And Performance ◽  
Secured Communication ◽  
Significant Concentration

A Wireless Sensor Network (WSNs) is popular developing the field in industrial and other major markets. Wireless data security is the central theme in the WSNs application where security of transmitted data is more concerned. Due to the significant concentration of energy efficiency and performance analysis in WSN, providing secured communication is a challenging issue. To overcome this interdependent problem Game theory can be used. Game theory is applied here to select different routes to transfer the data from source to destination. The performance of the WSNs can be increased by providing security for transmitted data. The graph for throughput, end to end delay, delivery, and packet loss ratio are generated using NS2 simulation tool.

Virtual Clustered MAC Layer Protocol Design of a Wireless Sensor Network: ES-MAC

2014 ◽  
Vol 687-691 ◽  
pp. 1749-1753
Author(s):  
Yong Long Zhuang ◽  
Xiao Lan Weng ◽  
Xiang He Wei
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Data Transfer ◽  
Packet Delay ◽  
Sleep Time ◽  
Wireless Sensor ◽  
Sleep Cycle ◽  
Mac Layer ◽  
Virtual Cluster ◽  
Third Stage

Sensor-MAC (S-MAC), one kind of use common type activities / sleep cycle wireless sensor network MAC layer protocol, we propose a strengthening type Enhanced S-MAC, called ES-MAC, and part of its application in wireless sensor networks than the outer layer of low traffic. ES-MAC is divided into three stages: the first stage is to create a virtual cluster region and preset transmission path; third stage of data transfer; second stage of data collection and sorting. Sensing node virtual cluster in the region, competitive manner using micro slot (Mini-Slot) determine the order of data transmission. In the data transfer phase has been established using the sequential transfer of data, the sense node in order to reduce the unnecessary and excessive collision monitoring, sleep time, and the growth of nodes. Simulation results show that, ES-MAC under different flow and different cycles, compared with S-MAC has lower average packet delay time.

Condition for the Coverage and Connectivity of Wireless Sensor Network

2011 ◽  
Vol 403-408 ◽  
pp. 2589-2592
Author(s):  
Man Tian Xiang ◽  
Li Hong Li ◽  
Li Hua Sun
Keyword(s):  
Spatial Distribution ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Experimental Tests ◽  
Wireless Sensor ◽  
Transmission Range ◽  
Node Density ◽  
Connected Network ◽  
Maximum Range ◽  
Coverage And Connectivity

The topology attributes of both connectivity and coverage in a wireless sensor network depend on the spatial distribution and transmission range of the nodes. This paper proposes an analytical expression of the required critical transmission range of a node, for a given node density, to create an almost surely connected network. Equivalently, if the maximum range of the nodes is given, it can estimate effectively the number of nodes needed to cover a certain connected network. With experimental tests, the method is proved to achieve guaranteed degrees of coverage and connectivity, valuable for researchers in this area.

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