Link Quality Modeling for Energy Harvesting Wireless Sensor Networks
In energy harvesting wireless sensor networks (EH-WSNs) sensor nodes are capable of harvesting energy from environmental sources. Usually, knowledge about the link quality improves the performance of WSNs. During data routing, selection of good quality li
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Abstract In energy harvesting wireless sensor networks (EH-WSNs) sensor nodes are capable of harvesting energy from environmental sources. Usually, knowledge about the link quality improves the performance of WSNs. During data routing, selection of good quality links are important to maintain stable communication. Thus helps to reduce the unnecessary energy wastage. Obtaining the link state information is more challenging for EH-WSN as different nodes has different energy profiles and state of the node depends on several environmental conditions. In this paper, we have studied different factors affecting the link quality and model it using finite state Markov Model. Energy availability of the harvesting devices through real time traces is considered for modeling the network. This model can significantly provide relevant information which is very effective to improve the routing decisions as the next hop decision will be more accurate. The usefulness and validity of the proposed approach is illustrated through simulations for specific examples. Keywords Energy harvesting
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Energy profile
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Link quality
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Markov chain
1 Introduction In recent past years, the study of WSN has been well exercised. Especially energy harvesting options grab the attention most since it has immense application in present scenario. Often sensor networks fail to meet the design goal in terms of cost,
Moumita Deb (✉) Department of Information Technology, RCCIIT, Kolkata, India e-mail: [email protected] Sarbani Roy (✉) Department of Computer Science and Engineering, Jadavpur University, Kolkata, India e-mail: [email protected] © Springer Science+Business Media Singapore 2017 J.K. Mandal et al. (eds.), Proceedings of the First International Conference on Intelligent Computing and Communication, Advances in Intelligent Systems and Computing 458, DOI 10.1007/978-981-10-2035-3_67
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lifetime, sensing transmission coverage and sensing reliability. One of the primary reasons behind it is sensor nodes are powered by battery. So the lifetime of a network depends solely on the lifetime of the battery installed in the sensor nodes [1]. To overcome this barrier, the concept of energy harvesting, i.e. harvesting energy from the environment or other sources and converting it to electrical energy comes into existence. The researchers and engineers are seeking for strategies to remove batteries and wires so that unlimited and green energy sources can be used to develop autonomous WSNs with theoretically unlimited lifetimes. Moreover these new green sources are based on ambient energies and refer to any available harvesting technology, such as solar cell, thermoelectric, piezo-electric harvester, micro and wind turbine or other transducer [1]. Energy harvesting module in sensor nodes can exploit different sources of energy from environment, such as electromagnetic sources, mechanical vibrations, light, airflow, acoustic, temperature and heat variations etc. Though, energy output of a sensor node varies with tim
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