Motivated by navigation and tracking applications within sensor networks, we consider the problem of performing Kalman filtering with intermittent observations. When data travel along unreliable communication channels in a large, wireless, multihop sensor network, the effect of communication delays and loss of information in the control loop cannot be neglected. We address this problem starting from the discrete Kalman filtering formulation, and modeling the arrival of the observation as a random process. We study the statistical convergence properties of the estimation error covariance, showing the existence of a critical value for the arrival rate of the observations, beyond which a transition to an unbounded state error covariance occurs. We also give upper and lower bounds on this expected state error covariance.

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Kalman filtering with intermittent observations

We present Telos, an ultra low power wireless sensor module ("mote") for research and experimentation. Telos is the latest in a line of motes developed by UC Berkeley to enable wireless sensor network (WSN) research. It is a new mote design built from scratch based on experiences with previous mote generations. Telos' new design consists of three major goals to enable experimentation: minimal power consumption, easy to use, and increased software and hardware robustness. We discuss how hardware components are selected and integrated in order to achieve these goals. Using a Texas Instruments MSP430 microcontroller, Chipcon IEEE 802.15.4-compliant radio, and USB, Telos' power profile is almost one-tenth the consumption of previous mote platforms while providing greater performance and throughput. It eliminates programming and support boards, while enabling experimentation with WSNs in both lab, testbed, and deployment settings.

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Telos: enabling ultra-low power wireless research

Sensor networks with battery-powered nodes can seldom simultaneously meet the design goals of lifetime, cost, sensing reliability and sensing and transmission coverage. Energy-harvesting, converting ambient energy to electrical energy, has emerged as an alternative to power sensor nodes. By exploiting recharge opportunities and tuning performance parameters based on current and expected energy levels, energy harvesting sensor nodes have the potential to address the conflicting design goals of lifetime and performance. This paper surveys various aspects of energy harvesting sensor systems- architecture, energy sources and storage technologies and examples of harvesting-based nodes and applications. The study also discusses the implications of recharge opportunities on sensor node operation and design of sensor network solutions.

Energy Harvesting Sensor Nodes: Survey and Implications

Recently, Wireless Sensor Networks (WSNs) have attracted lot of attention due to their pervasive nature and their wide deployment in Internet of Things, Cyber Physical Systems, and other emerging areas. The limited energy associated with WSNs is a major bottleneck of WSN technologies. To overcome this major limitation, the design and development of efficient and high performance energy harvesting systems for WSN environments are being explored. We present a comprehensive taxonomy of the various energy harvesting sources that can be used by WSNs. We also discuss various recently proposed energy prediction models that have the potential to maximize the energy harvested in WSNs. Finally, we identify some of the challenges that still need to be addressed to develop cost-effective, efficient, and reliable energy harvesting systems for the WSN environment.

Energy harvesting in wireless sensor networks: A comprehensive review

Motivated by our experience in building sensor networks for navigation as part of the Networked Embedded Systems Technology (NEST) project at Berkeley, we consider the problem of performing Kalman filtering with intermittent observations. When data travel along unreliable communication channels in a large, wireless, multi-hop sensor network, the effect of communication delays and loss of information in the control loop cannot be neglected. We address this problem starting from the discrete Kalman filtering formulation, and modeling the arrival of the observation as a random process. We study the statistical convergence properties of the estimation error covariance, showing the existence of a critical value for the arrival rate of the observations, beyond which a transition to an unbounded error occurs.

/pdf/kalman-filtering-with-intermittent-observations-2oyhvm3u57.pdf

Sensor networks are gaining a central role in the research community. This paper addresses some of the issues arising from the use of sensor networks in control applications. Classical control theory proves to be insufficient in modeling distributed control problems where issues of communication delay, jitter, and time synchronization between components are not negligible. After discussing our hardware and software platform and our target application, we review useful models of computation and then suggest a mixed model for design, analysis, and synthesis of control algorithms within sensor networks. We present a hierarchical model composed of continuous time-trigger components at the low level and discrete event-triggered components at the high level.

/pdf/distributed-control-applications-within-sensor-networks-40hkgiih96.pdf

Distributed control applications within sensor networks

This paper proposes a neighborhood programming abstraction for sensor networks, wherein a node can identify a subset of nodes around it by a variety of criteria and share state with those nodes. This abstraction allows developers to design distributed algorithms in terms of the neighborhood abstraction itself, instead of decomposing them into component parts such as messaging protocols, data caches, and neighbor lists. In those applications that are already neighborhood-based, this abstraction is shown to facilitate good application design and to reduce algorithmic complexity, inter-component coupling, and total lines of code. The abstraction as defined here has been successfully used to implement several complex applications and is shown to capture the essence of many more existing distributed sensor network algorithms.

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Hood: a neighborhood abstraction for sensor networks

We present the design and implementation of a real-time computer vision system for a rotorcraft unmanned aerial vehicle to land onto a known landing target. This vision system consists of customized software and off-the-shelf hardware which perform image processing, segmentation, feature point extraction, camera pan/tilt control, and motion estimation. We introduce the design of a landing target which significantly simplifies the computer vision tasks such as corner detection and correspondence matching. Customized algorithms are developed to allow for realtime computation at a frame rate of 30 Hz. Such algorithms include certain linear and nonlinear optimization schemes for model-based camera pose estimation. We present results from an actual flight test which show the vision-based state estimates are accurate to within 5 cm in each axis of translation, and 5 degrees in each axis of rotation, making vision a viable sensor to be placed in the control loop of a hierarchical flight management system.

A vision system for landing an unmanned aerial vehicle

Project ExScal (for extreme scale) fielded a 1000+ node wireless sensor network and a 200+ node peer-to-peer ad hoc network of 802.11 devices in a 13km by 300m remote area in Florida, USA during December 2004. In comparison with previous deployments, the ExScal application is relatively complex and its networks are the largest ones of either type fielded to date. In this paper, we overview the key requirements of ExScal, the corresponding design of the hardware/software platform and application, and some results of our experiments.

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ExScal: elements of an extreme scale wireless sensor network

We present the philosophy, design, and initial evaluation of the Trio testbed, a new outdoor sensor network deployment that consists of 557 solar-powered motes, seven gateway nodes, and a root server. The testbed covers an area of approximately 50,000 square meters and was in continuous operation during the last four months of 2005. This new testbed in one of the largest solar-powered outdoor sensor networks ever constructed and it offers a unique platform on which both systems and application software can be tested safely at scale. The testbed is based on Trio, a new mote platform that provides sustainable operation, enables efficient in situ interaction, and supports fail-safe programming. The motivation behind this testbed was to evaluate robust multi-target tracking algorithms at scale. However, using the testbed has stressed the system software, networking protocols, and management tools in ways that have exposed subtle but serious weaknesses that were never discovered using indoor testbeds or smaller deployments. We have been iteratively improving our support software, with the eventual aim of creating a stable hardware-software platform for sustainable, scalable, and flexible testbed deployments.

/pdf/trio-enabling-sustainable-and-scalable-outdoor-wireless-3py0qicl63.pdf

Cory Sharp

Papers

Distributed control applications within sensor networks

Hood: a neighborhood abstraction for sensor networks

A vision system for landing an unmanned aerial vehicle

ExScal: elements of an extreme scale wireless sensor network

Trio: enabling sustainable and scalable outdoor wireless sensor network deployments