Energy Aware Routing Schemes in Solar PoweredWireless Sensor Networks

Abstract

Wireless sensor networks enable inexpensive distributed monitoring systems that are the backbone of smart cities. In this dissertation, we are interested in wireless sensor networks for traffic monitoring and an emergency flood detection to improve the safety of future cities. To achieve real-time traffic monitoring and emergency flood detection, the system has to be continually operational. Accordingly, an energy source is needed to ensure energy availability at all times. The sun provides for the most inexpensive source of energy, and therefore the energy is provided here by a solar panel working in conjunction with a rechargeable battery. Unlike batteries, solar energy fluctuates spatially and temporally due to the panel orientation, seasonal variation and node location, particularly in cities where buildings cast shadows. Especially, it becomes scarce whenever floods are likely to occur, as the weather tends to be cloudy at such times when the emergency detection system is most needed. These considerations lead to the need for the optimization of the energy of the sensor network, to maximize its sensing performance. In this dissertation, we address the challenges associated with long term outdoor deployments along with providing some solutions to overcome part of these challenges. We then introduce the energy optimization problem, as a distributed greedy approach. Motivated by the flood sensing application, our objective is to maximize the energy margin in the solar powered network at the onset of the high rain event, to maximize the network lifetime. The decentralized scheme will achieve this by optimizing the energy over a time horizon T, taking into account the available and predicted energy over the entire routing path. Having a good energy forecasting scheme can significantly enhance the energy optimization in WSN. Thus, this dissertation proposes a new energy forecasting scheme that is compatible with the platform’s capabilities. This proposed prediction scheme was tested on real data and compared with state-of-theart forecasting schemes on on-node WSN platforms. Finally, to establish the relevance of the aforementioned schemes beyond theoretical formulations and simulations, all proposed protocols and schemes are subjected to hardware implementation.