Sadaf Zahedi

Power management in energy harvesting sensor networks

Power management is an important concern in sensor networks, because a tethered energy infrastructure is usually not available and an obvious concern is to use the available battery energy efficiently. However, in some of the sensor networking applications, an additional facility is available to ameliorate the energy problem: harvesting energy from the environment. Certain considerations in using an energy harvesting source are fundamentally different from that in using a battery, because, rather than a limit on the maximum energy, it has a limit on the maximum rate at which the energy can be used. Further, the harvested energy availability typically varies with time in a nondeterministic manner. While a deterministic metric, such as residual battery, suffices to characterize the energy availability in the case of batteries, a more sophisticated characterization may be required for a harvesting source. Another issue that becomes important in networked systems with multiple harvesting nodes is that different nodes may have different harvesting opportunity. In a distributed application, the same end-user performance may be achieved using different workload allocations, and resultant energy consumptions at multiple nodes. In this case, it is important to align the workload allocation with the energy availability at the harvesting nodes. We consider the above issues in power management for energy-harvesting sensor networks. We develop abstractions to characterize the complex time varying nature of such sources with analytically tractable models and use them to address key design issues. We also develop distributed methods to efficiently use harvested energy and test these both in simulation and experimentally on an energy-harvesting sensor network, prototyped for this work.

Sensor network data fault types

This tutorial presents a detailed study of sensor faults that occur in deployed sensor networks and a systematic approach to model these faults. We begin by reviewing the fault detection literature for sensor networks. We draw from current literature, our own experience, and data collected from scientific deployments to develop a set of commonly used features useful in detecting and diagnosing sensor faults. We use this feature set to systematically define commonly observed faults, and provide examples of each of these faults from sensor data collected at recent deployments.

Adaptive duty cycling for energy harvesting systems

Harvesting energy from the environment is feasible in many applications to ameliorate the energy limitations in sensor networks. In this paper, we present an adaptive duty cycling algorithm that allows energy harvesting sensor nodes to autonomously adjust their duty cycle according to the energy availability in the environment. The algorithm has three objectives, namely: (a) achieving energy neutral operation, i.e., energy consumption should not be more than the energy provided by the environment; (b) maximizing the system performance based on an application utility model subject to the above energy-neutrality constraint; and (c) adapting to the dynamics of the energy source at run-time. We present a model that enables harvesting sensor nodes to predict future energy opportunities based on historical data. We also derive an upper bound on the maximum achievable performance assuming perfect knowledge about the future behavior of the energy source. Our methods are evaluated using data gathered from a prototype solar energy harvesting platform and we show that our algorithm can utilize up to 58% more environmental energy compared to the case when harvesting-aware power management is not used

Heliomote: enabling long-lived sensor networks through solar energy harvesting

The crucial need for long-lived and autonomous operation has elevated power and energy consumption to primary optimization metrics during wireless sensor network design. While most work in the field of power management and low-power design has focused on optimizing the energy consumer (i.e., the sensor node, including its hardware, software, applications, and network protocols), very little work has targeted the energy supply system itself. A practical approach to alleviating the problem of limited battery resources in sensor nodes is the use of environmental energy harvesting. Solar energy harvesting, in particular, holds significant promise since photovoltaic conversion techniques are now mature enough to permit the development of cheap and small, yet reasonably efficient, solar panels. Our demonstration showcases our recent research in designing solar energy harvesting systems, as well as harvesting aware performance scaling algorithms and network protocols [1].

Compressive Oversampling for Robust Data Transmission in Sensor Networks

Data loss in wireless sensing applications is inevitable and while there have been many attempts at coping with this issue, recent developments in the area of Compressive Sensing (CS) provide a new and attractive perspective. Since many physical signals of interest are known to be sparse or compressible, employing CS, not only compresses the data and reduces effective transmission rate, but also improves the robustness of the system to channel erasures. This is possible because reconstruction algorithms for compressively sampled signals are not hampered by the stochastic nature of wireless link disturbances, which has traditionally plagued attempts at proactively handling the effects of these errors. In this paper, we propose that if CS is employed for source compression, then CS can further be exploited as an application layer erasure coding strategy for recovering missing data. We show that CS erasure encoding (CSEC) with random sampling is efficient for handling missing data in erasure channels, paralleling the performance of BCH codes, with the added benefit of graceful degradation of the reconstruction error even when the amount of missing data far exceeds the designed redundancy. Further, since CSEC is equivalent to nominal oversampling in the incoherent measurement basis, it is computationally cheaper than conventional erasure coding. We support our proposal through extensive performance studies.

Energy efficient sampling for event detection in wireless sensor networks

Compressive Sensing (CS) is a recently developed mechanism that allows signal acquisition and compression to be performed in one inexpensive step so that the sampling process itself produces a compressed version of the signal. This significantly improves systemic energy efficiency because the average sampling rate can be considerably reduced and explicit compression eliminated. In this paper, we introduce a modification to the canonical CS recovery technique that enables even higher gains for event detection applications. We show a practical implementation of this compressive detection with energy constrained wireless sensor nodes and quantify the gains accrued through simulation and experimentation.

Variability-aware duty cycle scheduling in long running embedded sensing systems

Instance and temperature-dependent leakage power variability is already a significant issue in contemporary embedded processors, and one which is expected to increase in importance with scaling of semiconductor technology. We measure and characterize this leakage power variability in current microprocessors, and show that variability aware duty cycle scheduling produces 7.1× improvement in sensing quality for a desired lifetime. In contrast, pessimistic estimations of power consumption leave 61% of the energy untapped, and datasheet power specifications fail to meet required lifetimes by 14%. Finally, we introduce a duty cycle abstraction for TinyOS that allows applications to explicitly specify lifetime and minimum duty cycle requirements for individual tasks, and dynamically adjusts duty cycle rates so that overall quality of service is maximized in the presence of power variability.

Quality Tradeoffs in Object Tracking with Duty-Cycled Sensor Networks

Extending the lifetime of wireless sensor networks requires energy-conserving operations such as duty-cycling. However, such operations may impact the effectiveness of high fidelity real-time sensing tasks, such as object tracking, which require high accuracy and short response times. In this paper, we quantify the influence of different duty-cycle schemes on the efficiency of bearings-only object tracking. Specifically, we use the Maximum Likelihood localization technique to analyze the accuracy limits of object location estimates under different response latencies considering variable network density and duty-cycle parameters. Moreover, we study the tradeoffs between accuracy and response latency under various scenarios and motion patterns of the object. We have also investigated the effects of different duty-cycled schedules on the tracking accuracy using acoustic sensor data collected at Aberdeen Proving Ground, Maryland, by the U.S. Army Research Laboratory (ARL).

Tiered architecture for on-line detection, isolation and repair of faults in wireless sensor networks

Wireless sensor networks fuse data from a multiplicity of sensors of different modalities and spatiotemporal scales to provide information for reconnaissance, surveillance, and situational awareness in many defense applications. For decisions to be based on information returned by sensor networks it is crucial that such information be of sustained high quality. While the Quality of Information (QoI) depends on many factors, perhaps the most crucial is the integrity of the sensor data sources themselves. Even ignoring malicious subversion, sensor data quality may be compromised by non-malicious causes such as noise, drifts, calibration, and faults. On-line detection and isolation of such misbehaviors is crucial not only for assuring QoI delivered to the end-user, but also for efficient operation and management by avoiding wasted energy and bandwidth in carrying poor quality data and enabling timely repair of sensors. We describe a two-tiered system for on-line detection of sensor faults. A local tier running at resource-constrained nodes uses an embedded model of the physical world together with a hypothesis-testing detector to identify potential faults in sensor measurements and notifies a global tier. In turn, the global tier uses these notifications on the one hand during fusion for more robust estimation of physical world events of interest to the user, and on the other hand for consistency checking among notifications from various sensors and generating feedback to update the embedded physical world model at the local nodes. Our system eliminates the undesirable attributes of purely centralized and purely distributed approaches that respectively suffer from high resource consumption from sending all data to a sink, and high false alarms due to lack of global knowledge. We demonstrate the performance of our system on diverse real-life sensor faults by using a modeling framework that permits injection of sensor faults to study their impact on the application QoI.

Adaptive Dynamic Radio Open-source Intelligent Team (ADROIT): Cognitively-controlled Collaboration among SDR Nodes

The ADROIT project is building an open-source software-defined data radio, intended to be controlled by cognitive applications. The goal is to create a system that enables teams of radios, where each radio both has its own cognitive controls and the ability to collaborate with other radios, to create cognitive radio teams. The desire to create cognitive radio teams, and the goal of having an open-source system, requires a rich and carefully architected system that provides great flexibility (enabling cognitive applications to change the radios behavior) and also has a clear structure (both so that others may add or enhance the software, and also so that the system can be clearly modeled for cognitive applications). What follows is a summary of the ADROIT system and the key architectural features intended to enable cognitive radio teams.