Environmental wireless sensor network (EWSN) systems are deployed in potentially harsh and remote environments where inevitable node and communication failures must be tolerated. LUSTER—Light Under Shrub Thicket for Environmental Research—is a system that meets the challenges of EWSNs using a hierarchical architecture that includes distributed reliable storage, delay-tolerant networking, and deployment time validation techniques. In LUSTER, a fleet of sensors coordinate communications using LiteTDMA, a low-power cluster-based MAC protocol. They measure the complex light environment in thickets and are open to additional ecological parameters, such as temperature and CO2. LUSTER has been deployed and evaluated in laboratory, forested, and barrier island environments. It includes new sensor hardware designs: (a) “SolarDust, ” a hybrid multichannel energy harvesting and sensing device; (b) “Medusa,” a spatially reconfigurable light sensor; (c) a removable SD card storage node; and, (d) in-situ user interface tool for deployment time validation.

This paper considers the problem of pursuit evasion games (PEGs), where a group of pursuers is required to chase and capture a group of evaders in minimum time with the aid of a sensor network. We assume that a sensor network is previously deployed and provides global observability of the surveillance region, allowing an optimal pursuit policy. While sensor networks provide global observability, they cannot provide high quality measurements in a timely manner due to packet losses, communication delays, and false detections. This has been the main challenge in developing a real-time control system using sensor networks. We address this challenge by developing a real-time hierarchical control system which decouples the estimation of evader states from the control of pursuers via multiple layers of data fusion. While a sensor network generates noisy, inconsistent, and bursty measurements, the multiple layers of data fusion convert them into consistent and high quality measurements and forward them to the controllers of pursuers in a timely manner. For this control system, three new algorithms are developed: multi-sensor fusion, multi-target tracking and multi-agent coordination algorithms. The multi-sensor fusion algorithm converts correlated sensor measurements into position estimates, the multi-target tracking algorithm tracks an unknown number of targets, and the multi-agent coordination algorithm coordinates pursuers to capture all evaders in minimum time using a robust minimum-time feedback controller. The combined system is evaluated in simulation and tested in a sensor network deployment. To our knowledge, this paper presents the first demonstration of multi-target tracking using a sensor network without relying on classification.

Sensornets are becoming more widely adopted for commercial and scientific use and, in settings where battery replacement or recharging is difficult, it is important that sensornets have long and predictable lifetimes. We thus expect

Renewable energy enables sensor networks with the capability to recharge and provide perpetual data services. Due to low recharging rates and the dynamics of renewable energy such as solar and wind power, providing services without interruptions caused by battery runouts is non-trivial. Most environment monitoring applications require data collection from all nodes at a steady rate. The objective of this paper is to design a solution for fair and high throughput data extraction from all nodes in presence of renewable energy sources. Specifically, we seek to compute the lexicographically maximum data collection rate for each node, such that no node will ever run out of energy. We propose a centralized algorithm and an asynchronous distributed algorithm that can compute the optimal lexicographic rate assignment for all nodes. The centralized algorithm jointly computes the optimal data collection rate for all nodes along with the flows on each link, while the distributed algorithm computes the optimal rate when the routes are pre-determined. We prove the optimality for both the centralized and the distributed algorithms, and use a testbed with 155 sensor nodes to validate the distributed algorithm.

This paper describes a systematic approach to building micro-solar power subsystems for wireless sensor network nodes. Our approach composes models of the basic pieces- solar panels, regulators, energy storage elements, and application loads- to appropriately select and size the components. We demonstrate our approach in the context of a microclimate monitoring project through the design of the node, micro-solar subsystem, and network, which is deployed in a challenging, deep forest setting. We evaluate our deployment by analyzing the effects of the range of solar profiles experienced across the network. 1

To ensure sustainable operations of wireless sensor systems, environmental energy harvesting has been regarded as the right solution for long-term applications. In energy-dynamic environments, energy conservation is no longer considered necessarily beneficial, because energy storage units (e.g., batteries or capacitors) are limited in capacity and leakageprone. In contrast to legacy energy conservation approaches, we aim at energy synchronization for wireless sensor devices. The starting point of this work is TwinStar, which uses ultra-capacitor as the only energy storage unit. To efficiently use the harvested energy, we design and implement leakage-aware feedback control techniques to match local and network-wide activity of sensor nodes with the dynamic energy supply from environments. We conduct system evaluation under three typical real-world settings — indoor, outdoor, and mobile backpack under a wide range of system settings. Results indicate our leakage-aware control can effectively utilize energy that could otherwise leak away. Nodes running leakage-aware control can enjoy 70% more energy than the ones running non-leakage-aware control and application performance (e.g., event detection) can be improved significantly.

Abstract—With advances in energy harvesting techniques, it is now feasible to build sustainable sensor networks (SSN) to support long-term applications. Unlike battery-powered sensor networks, the objective of sustainable sensor networks is to effectively utilize a continuous stream of ambient energy. Instead of pushing the limits of energy conservation, we are aiming at energy-synchronized designs 1 to keep energy supplies and demands in balance. Specifically, this work presents the Energy Synchronized Communication (ESC) as a transparent middleware between the network layer and data link layer that controls the amount and timing of RF activity at receiving nodes. In this work, we first derive a delay model for cross-traffic at individual nodes, which reveals an interesting stair effect in low-duty-cycle networks. This effect allows us to design a localized energy synchronization control with O(1) time complexity that shuffles or adjusts the working schedule of a node to optimize crosstraffic delays in the presence of changing duty-cycle budgets. Under different rates of energy fluctuations, shuffle-based and adjustment-based methods have different influences on logical connectivity and cross-traffic delay, due to the inconsistent views of working schedules among neighboring nodes before schedule updates. We study the tradeoff between them and propose methods to update working schedules efficiently. To evaluate our work, ESC is implemented on MicaZ nodes with two state-of-theart routing protocols. Both test-bed experiment and large scale simulation results show significant performance improvements over randomized synchronization controls. I.

Deployment of a wireless sensor network (WSN) system is a critical step because theoretical models and assumptions often differ from real environmental characteristics and performance at the deployment site. In addition, such systems are often located in areas that are difficult to reach or even inaccessible for certain periods of time. Therefore, it is imperative to verify the functionality of the system at the time of the deployment, thus lowering the risk of early failures. Coincidentally, the validation minimizes the expense of revisiting the site in the near future for re-deployment, maintenance, or repairs. In this paper we present a deployment time validation framework SeeDTV that consists of techniques and procedures for WSN status assesment and verification. SeeDTV is supported by a portable, lightweight, and low power in-situ user interface device SeeMote. SeeDTV has demonstrated the potential for early problem detection at three levels of WSN in-situ validation: sensor node devices, wireless network physical and logical integrity, and connectivity to the back-end such as a data server over the Internet. SeeDTV is presented in the context of LUSTER – an environmental sensor network for ecological monitoring under a shrub thicket canopy on islands off the coast of Virginia.

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Abstract. Remote management is essential for wireless sensor networks (WSNs) designed to run perpetually using harvested energy. A natural division of function for managing WSNs is to employ both an in-band data plane to sense, store, process, and forward data, and an out-of-band management plane to remotely control each node and its sensors. This paper presents SRCP, a Simple Remote Control Protocol that forms the core of an out-of-band management plane for WSNs. SRCP is motivated by our target environment: a perpetual deployment of high-power, aggressively duty-cycled nodes capable of handling high-bandwidth sensor data from multiple sensors. The protocol runs on low-power always-on control processors using harvested energy, distills an essential set of primitives, and uses them to control a suite of existing management functions on more powerful main nodes. We demonstrate SRCP’s utility by presenting a case study that (i) uses it to control a broad spectrum of management functions and (ii) quantifies its efficacy and performance. 1