Ahmad H. Dehwah

Lessons learned on solar powered wireless sensor network deployments in urban, desert environments

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.

A Sensor Network Architecture for Urban Traffic State Estimation with Mixed Eulerian/Lagrangian Sensing Based on Distributed Computing

This article describes a new approach to urban traffic flow sensing using decentralized traffic state estimation. Traffic sensor data is generated both by fixed traffic flow sensor nodes and by probe vehicles equipped with a short range transceiver. The data generated by these sensors is sent to a local coordinator node, that poses the problem of estimating the local state of traffic as a mixed integer linear program (MILP). The resulting optimization program is then solved by the nodes in a distributed manner, using branch-and-bound methods. An optimal amount of noise is then added to the maps before dissemination to a central database. Unlike existing probe-based traffic monitoring systems, this system does not transmit user generated location tracks nor any user presence information to a centralized server, effectively preventing privacy attacks. A simulation of the system performance on computer-generated traffic data shows that the system can be implemented with currently available technology.

UD-WCMA

Energy estimation and forecast represents an important role for energy management in solar-powered wireless sensor networks (WSNs). In general, the energy in such networks is managed over a finite time horizon in the future based on input solar power forecasts to enable continuous operation of the WSNs and achieve the sensing objectives while ensuring that no node runs out of energy. In this article, we propose a dynamic version of the weather conditioned moving average technique (UD-WCMA) to estimate and predict the variations of the solar power in a wireless sensor network. The presented approach combines the information from the real-time measurement data and a set of stored profiles representing the energy patterns in the WSNs location to update the prediction model. The UD-WCMA scheme is based on adaptive weighting parameters depending on the weather changes which makes it flexible compared to the existing estimation schemes without any precalibration. A performance analysis has been performed considering real irradiance profiles to assess the UD-WCMA prediction accuracy. Comparative numerical tests to standard forecasting schemes (EWMA, WCMA, and Pro-Energy) shows the outperformance of the new algorithm. The experimental validation has proven the interesting features of the UD-WCMA in real time low power sensor nodes. Introducing a new dynamical version of the (WCMA) forecast scheme.Proposing a new dynamical forecast scheme with adaptive parameters.The new proposed scheme adapts to the current day dynamics with minimal training.The new proposed scheme is validated using real data (weather station and WSN).Validation shows the outperformance of our scheme compared to existing schemes.

Decentralized Energy and Power Estimation in Solar-Powered Wireless Sensor Networks

Solar powered wireless sensor networks are very adapted to smart city applications, since they can operate for extended durations with minimal installation costs. Nonetheless, they require energy management schemes to operate reliably, unlike their grid-powered counterparts. Such schemes require the forecasting of future solar power inputs for each wireless sensor node, over a time horizon. They also require the determination of battery energy parameters in real time. To address both requirements, we propose a collaborative solar power forecasting framework combined to a real time battery capacity estimation model, which can be used to optimize the node schedules over the corresponding horizon.

Poster abstract: a decentralized routing scheme based on a zero-sum game to optimize energy in solar powered sensor networks

This poster is aimed at solving the problem of maximizing the energy margin of a solar-powered sensor network at a fixed time horizon, to maximize the network performance during an event to monitor. Using a game theoretic approach, the optimal routing maximizing the energy margin of the network at a given time under solar power forcing can be computed in a decentralized way and solved exactly through dynamic programming with a low overall complexity. We also show that this decentralized algorithm is simple enough to be implemented on practical sensor nodes. Such an algorithm would be very useful whenever the energy margin of a solar-powered sensor network has to be maximized at a specific time.

A distributed routing scheme for energy management in solar powered sensor networks

Abstract 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.