Optimization of a Wireless Sensor Network (WSN) downlink with an energy harvesting transmitter (base station) is considered. The base station (BS), which is attached to the central controller of the network, sends control information to the gateways of individual WSNs in the downlink. This paper specifically addresses the case where the BS is supplied with solar energy. Leveraging the daily periodicity inherent in solar energy harvesting, the schedule for delivery of maintenance messages from the BS to the nodes of a distributed network is optimized. Differences in channel gain from the BS to sensor nodes make it a challenge to provide service to each of them while efficiently spending the harvested energy. Based on PTF (Power-Time-Fair), a close-to-optimal solution for fair allocation of harvested energy in a wireless downlink proposed in previous work, we develop an online algorithm, PTF-On, that operates two algorithms in tandem: A prediction algorithm based on a Kalman filter that operates on solar irradiation measurements, and a modified version of PTF. PTF-On can predict the energy arrival profile throughout the day and schedule transmission to nodes to maximize total throughput in a proportionally fair way.
Recently, several works in the field of Industrial WSNs have been conducted. In @cite_5 , a sink resource allocation strategy based on log-utility fairness criteria is proposed. In @cite_18 , authors propose a solution to the problem of maximizing the minimum energy reserve. An algorithm to achieve arbitrarily close to optimal power efficiency while satisfying the desired estimation accuracy of processes over some time is presented in @cite_24 . Authors have expanded their work by introducing a WSN model that includes fairness control among sensors and both energy harvesting batteries and backup battery units. An optimal resource allocation presented in the paper is used for state estimation to provide a desired accuracy constraint by taking advantage of Kalman filter. Even if proposed WSN frame includes energy harvesting batteries modelled as IID random processes, the renewable solar energy may not be modelled as an IID process. Moreover, Kalman filter approach used in @cite_24 is used to estimate the state of the processes to be detected by sensors, not to predict solar irradiations as this paper presents.
@cite_24 @cite_5 @cite_18
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This paper is concerned with the analysis, design and validation of a reliable management strategy for industrial monitoring and control over wireless sensor network (WSN). First, we investigate the interactions between contention resolution and congestion control mechanisms in Wireless Industrial Sensor Network (briefly WISN). An extensive set of simulations are performed in order to quantify the impacts of several network parameters (i.e. buffer, sensors reporting rate) on the overall network performance (i.e. reliability, packet losses). This calls for cross-layer mechanisms for efficient data delivery over WISN. Second, a reliable sink resource allocation strategy based on log-utility fairness criteria is proposed. It is shown that the resource sink manager can plan strategies to better allocate the available resource among competing sensors. Finally, the analysis, design and validation of a reliable sinks cooperative control for WISN are introduced. A sufficient condition for wireless network stability in presence of multiple sinks and heterogeneous sensors with different time delays is given and it is used for network parameters design. The stability condition and the resulting cooperative control performance in terms of fairness, link utilization, packet losses, reliability and latency are validated by Matlab Simulink-based simulator TrueTime, which facilitates co-simulation of controller task execution in real-time kernels and in the wireless network environment. Copyright © 2009 John Wiley & Sons, Ltd. As Cyber-Physical-Systems (CPSs) evolve they will be increasingly relied on to support time-critical and performance-intensive monitoring and control activities. Further, many CPSs that utilize Wireless Sensor Networking (WSN) technologies will require the use of energy harvesting methods to extend their lifetimes. For this application class, there are currently few algorithmic techniques that combine performance sensitive processing and communication with efficient management techniques for energy harvesting. Our paper addresses this problem. We first propose a general purpose, multihop WSN architecture capable of supporting time-critical CPS systems using energy harvesting. We then present a set of Harvesting Aware Speed Selection (HASS) algorithms. Our technique maximizes the minimum energy reserve for all the nodes in the network, thus ensuring highly resilient performance under emergency or fault-driven situations. We present an optimal centralized solution, along with an efficient, distributed solution. We propose a CPS-specific experimental methodology, enabling us to evaluate our approach. Our experiments show that our algorithms yield significantly higher energy reserves than baseline methods.