Energy management model for RFID sensor networks in internet of things (IoT) contexts / Shaik Shabana Anjum - Shaik Shabana, Anjum (2018)

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The Internet of Things (IoT) technology has the capability to encapsulate the identification potential, sensing technology, artificial intelligence, and interconnection of nano - things, ultimately striving towards the objective of developing seamlessly interoperable and securely integrated systems. These integrated communication networks comprise of many interconnected units such as processor, memory, energy storage unit, radio, microcontroller and so on. The energy consumed by these units is very high during communication. Therefore, optimization of this energy consumption is a primary necessity to increase the lifetime of integrated systems. A crucial conduct norm for a sensor network is to avoid network failures and packet drop. One of the other essential requirements is to effectively manage the energy levels of the nodes according to the states of the operation required for an application. This research aims to propose an energy management model with the aim of allowing energy optimization of Radio Frequency (RF)-enabled Sensor Networks (RSN) through Energy Harvesting (EH) and Energy Transfer (ET) techniques. The main aim of this research is twofold – Firstly, to integrate the Wireless Sensor Network (WSN) nodes with Radio Frequency Identification (RFID) technology to enable energy optimization. The focus of this integration is to minimize the burden for the sensor nodes to rely completely on primary energy storage devices such as batteries and capacitors. Currently, these energy storage devices face the drawback of limited lifetime, node failure, energy scarcity, packet loss and poor network performance on the pretext of heavy sensing operations. Therefore, energy harvesting of sensor networks through RF signals is proposed in this research to address the drawback of frequent replacement of batteries, persistent recharge request, dead state of nodes and periodical eradication of batteries. Secondly, this research focuses on mathematical modeling of the RF sensor nodes within the proposed Energy Harvesting RSN (EHRSN) and Energy Transfer RSN (ETRSN) framework where the nodes are characterized using Semi Markov Decision Process (SMDP) and optimal policies are computed for numerically evaluating and analyzing the issue of higher energy consumption. The proposed EH and ET techniques are implemented through simulations and its performance evaluation is carried out in terms of parameters such as throughput, end-to-end delay, latency, network lifetime and residual energy levels. Furthermore, the proposed RSN energy model is validated using real hardware prototype where the results show nearly 80 % of additional energy saving achieved through EH and ET mechanisms of RF-enabled sensors. These proposed mechanisms which are implemented through event trigged approach and enhanced backscattering techniques are further tested and evaluated by comparing it with existing RSN systems in terms of performance and network latency. The proposed system is thereafter applied in IoT context of monitoring air quality levels using temperature, humidity, gas and dust sensors that are energized by RF signals. The empirical values recorded by these sensors that are configured and programmed according to the proposed energy model and energy management techniques are quantified, statistically analyzed and compared with existing systems in use, to validate the efficiency of the proposed system.