An effective approach for energy aware in wireless sensor network

In wireless communication, energy present very serious problem which could be easily affect the lifetime of all the network. The increasing of the wireless connected object lead to the appearance of a new notion which is the Internet of Thing. The growth of network size produce very important quantity of data as a result which request more quantity of energy to consume. The technology of the IEEE 802.15.4 present very powerfull solution for all application dedicated to low energy consumed which justify our choice for it to study. This paper investigate an approach used to postpone the fault energy of node in the network. So we get the energy consumed by every node then we detect the node with lack of power. After that we propose a specific manner to get both the Superframe Order (SO) and the Beacon Order (BO) for this node in order to more maintain the little power lasted in its battery. We use in our work the topology of tree with the mode of beacon enabled for Wireless Sensor Network (WSN).

[1]  Timur Mirzoev Low Rate Wireless Personal Area Networks (LR-WPAN 802.15.4 standard) , 2014, ArXiv.

[2]  Ahmed Zouinkhi,et al.  USEE: A uniform data dissemination and energy efficient protocol for communicating materials , 2016, Future Gener. Comput. Syst..

[3]  Janne Riihijärvi,et al.  Performance study of IEEE 802.15.4 using measurements and simulations , 2006, IEEE Wireless Communications and Networking Conference, 2006. WCNC 2006..

[4]  Jayalakhsmi Vaithiyanathan,et al.  Performance Evaluation of IEEE 802.15.4 Using Association Process and Channel Measurement , 2011 .

[5]  Kyung Sup Kwak,et al.  A Review of Wireless Body Area Networks for Medical Applications , 2009, Int. J. Commun. Netw. Syst. Sci..

[6]  Habib F. Rashvand,et al.  ADCA: Adaptive Duty Cycle Algorithm for Energy Efficient IEEE 802.15.4 Beacon-Enabled Wireless Sensor Networks , 2014, IEEE Sensors Journal.

[7]  Krishna M. Sivalingam,et al.  On recovery of lost targets in a cluster-based wireless sensor network , 2011, 2011 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops).

[8]  Miguel Garcia,et al.  A wireless sensor network for soccer team monitoring , 2011, 2011 International Conference on Distributed Computing in Sensor Systems and Workshops (DCOSS).

[9]  Jonathan-Christofer Demay,et al.  Practical security overview of IEEE 802.15.4 , 2016, 2016 International Conference on Engineering & MIS (ICEMIS).

[10]  Yacine Ghamri-Doudane,et al.  A duty cycle self-adaptation algorithm for the 802.15.4 wireless sensor networks , 2013, Global Information Infrastructure Symposium - GIIS 2013.

[11]  Pascal Lorenz,et al.  Intra-Mobility Support Solutions for Healthcare Wireless Sensor Networks–Handover Issues , 2013, IEEE Sensors Journal.

[12]  Klaus Kabitzsch,et al.  A new beacon order adaptation algorithm for IEEE 802.15.4 networks , 2005, Proceeedings of the Second European Workshop on Wireless Sensor Networks, 2005..

[13]  Vijay Laxmi,et al.  Comparing the Impact of Black Hole and Gray Hole Attack on LEACH in WSN , 2013, ANT/SEIT.

[14]  Sunil Kumar,et al.  Ubiquitous Computing for Remote Cardiac Patient Monitoring: A Survey , 2008, International journal of telemedicine and applications.

[15]  Miguel Garcia,et al.  A group-based wireless body sensors network using energy harvesting for soccer team monitoring , 2016, Int. J. Sens. Networks.

[16]  Ahmed Zouinkhi,et al.  Decentralized fault detection in Wireless Sensor Network based on function error , 2013, 10th International Multi-Conferences on Systems, Signals & Devices 2013 (SSD13).

[17]  Syed Furqan Qadri,et al.  APPLICATIONS, CHALLENGES, SECURITY OF WIRELESS BODY AREA NETWORKS (WBANS) AND FUNCTIONALITY OF IEEE 802.15.4/ZIGBEE , 2013 .

[18]  Dirk Pesch,et al.  Duty cycle learning algorithm (DCLA) for IEEE 802.15.4 beacon-enabled wireless sensor networks , 2012, Ad Hoc Networks.

[19]  Ahmed Zouinkhi,et al.  A machine learning methods: Outlier detection in WSN , 2015, 2015 16th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA).

[20]  H. T. Mouftah,et al.  Adaptive Sleeping Periods in IEEE 802.15.4 for Efficient Energy Savings: Markov-Based Theoretical Analysis , 2011, 2011 IEEE International Conference on Communications (ICC).

[21]  Joel J. P. C. Rodrigues,et al.  Toward ubiquitous mobility solutions for body sensor networks on healthcare , 2012, IEEE Communications Magazine.

[22]  Robert Simon Sherratt,et al.  Energy-aware distributed routing algorithm to tolerate network failure in wireless sensor networks , 2017, Ad Hoc Networks.

[23]  Alexandros Pantelopoulos,et al.  A survey on wearable biosensor systems for health monitoring , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[24]  Jaime Lloret,et al.  A survey of IEEE 802.15.4 effective system parameters for wireless body sensor networks , 2016, Int. J. Commun. Syst..

[25]  Le Wang,et al.  An improvement of IEEE 802.15.4 MAC protocol in high-density wireless sensor networks , 2015, 2015 IEEE International Conference on Information and Automation.