Dehwah, Ahmad H.; Mousa, Mustafa; Claudel, Christian G.(Ad Hoc Networks, Elsevier BV, 2015-05)[Article]
The successful deployment of a large scale solar powered wireless sensor network in an urban, desert environment is a very complex task. Specific cities of such environments cause a variety of operational problems, ranging from hardware faults to operational challenges, for instance due to the high variability of solar energy availability. Even a seemingly functional sensor network created in the lab does not guarantee reliable long term operation, which is absolutely necessary given the cost and difficulty of accessing sensor nodes in urban environments. As part of a larger traffic flow wireless sensor network project, we conducted several deployments in the last two years to evaluate the long-term performance of solar-powered urban wireless sensor networks in a desert area. In this article, we share our experiences in all domains of sensor network operations, from the conception of hardware to post-deployment analysis, including operational constraints that directly impact the software that can be run. We illustrate these experiences using numerous experimental results, and present multiple unexpected operational problems as well as some possible solutions to address them. We also show that current technology is far from meeting all operational constraints for these demanding applications, in which sensor networks are to operate for years to become economically appealing.
Dehwah, Ahmad H.; Shamma, Jeff S.; Claudel, Christian G.(Ad Hoc Networks, Elsevier BV, 2017-10-11)[Article]
Energy management is critical for solar-powered sensor networks. In this article, we consider data routing policies to optimize the energy in solar powered networks. Motivated by multipurpose sensor networks, the objective is to find the best network policy that maximizes the minimal energy among nodes in a sensor network, over a finite time horizon, given uncertain energy input forecasts. First, we derive the optimal policy in certain special cases using forward dynamic programming. We then introduce a greedy policy that is distributed and exhibits significantly lower complexity. When computationally feasible, we compare the performance of the optimal policy with the greedy policy. We also demonstrate the performance and computational complexity of the greedy policy over randomly simulated networks, and show that it yields results that are almost identical to the optimal policy, for greatly reduced worst-case computational costs and memory requirements. Finally, we demonstrate the implementation of the greedy policy on an experimental sensor network.
Canepa, Edward S.; Claudel, Christian G.(Transportation Research Part B: Methodological, Elsevier BV, 2017-06-19)[Article]
Nowadays, traffic management has become a challenge for urban areas, which are covering larger geographic spaces and facing the generation of different kinds of traffic data. This article presents a robust traffic estimation framework for highways modeled by a system of Lighthill Whitham Richards equations that is able to assimilate different sensor data available. We first present an equivalent formulation of the problem using a Hamilton–Jacobi equation. Then, using a semi-analytic formula, we show that the model constraints resulting from the Hamilton–Jacobi equation are linear ones. We then pose the problem of estimating the traffic density given incomplete and inaccurate traffic data as a Mixed Integer Program. We then extend the density estimation framework to highway networks with any available data constraint and modeling junctions. Finally, we present a travel estimation application for a small network using real traffic measurements obtained obtained during Mobile Century traffic experiment, and comparing the results with ground truth data.
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