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Research paper thumbnail of An environmental energy harvesting framework for sensor networks

Applied Soft Computing, 2003

Energy constrained systems such as sensor networks can increase their usable lifetimes by extract... more Energy constrained systems such as sensor networks can increase their usable lifetimes by extracting energy from their environment. However, environmental energy will typically not be spread homogeneously over the spread of the network. We argue that significant improvements in usable system lifetime can be achieved if the task allocation is aligned with the spatio-temporal characteristics of energy availability. To the best of our knowledge, this problem has not been addressed before. We present a distributed framework for the sensor network to adaptively learn its energy environment and give localized algorithms to use this information for task sharing among nodes. Our framework allows the system to exploit its energy resources more efficiently, thus increasing its lifetime. These gains are in addition to those from utilizing sleep modes and residual energy based scheduling mechanisms. Performance studies for an experimental energy environment show up to 200% improvement in lifetime.

Research paper thumbnail of Intelligent Fluid Infrastructure for Embedded Networks

Computer networks have historically considered support for mobile devices as an extra overhead to... more Computer networks have historically considered support for mobile devices as an extra overhead to be borne by the system. Recently however, researchers have proposed methods by which the network can take advantage of mobile components. We exploit mobility to develop a fluid infrastructure: mobile components are deliberately built into the system infrastructure for enabling specific functionality that is very hard to achieve using other methods. Built-in intelligence helps our system adapt to run time dynamics when pursuing pre-defined performance objectives. Our approach yields significant advantages for energy constrained systems, sparsely deployed networks, delay tolerant networks, and in security sensitive situations. We first show why our approach is advantageous in terms of network lifetime and data fidelity. Second, we present adaptive algorithms that are used to control mobility. Third, we design the communication protocol supporting a fluid infrastructure and long sleep durations on energy-constrained devices. Our algorithms are not based on abstract radio range models or idealized unobstructed environments but founded on real world behavior of wireless devices. We implement a prototype system in which infrastructure components move autonomously to carry out important networking tasks. The prototype is used to validate and evaluate our suggested mobility control methods.

Research paper thumbnail of Adaptive duty cycling for energy harvesting systems

Harvesting energy from the environment is feasible in many applications to ameliorate the energy ... more 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 energyneutrality 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.

Research paper thumbnail of On sensor network lifetime and data distortion

Fidelity is one of the key considerations in data collection schemes for sensor networks. A secon... more Fidelity is one of the key considerations in data collection schemes for sensor networks. A second important consideration is the energy expense of achieving that fidelity. Data from multiple correlated sensors is collected over multihop routes and fused to reproduce the phenomenon. However, the same distortion may be achieved using multiple rate allocations among the correlated sensors. These rate allocations would typically have different energy cost in routing depending on the network topology. We consider the interplay between these two considerations of distortion and energy. First, we describe the various factors that affect this trade-off. Second, we discuss bounds on the achievable performance with respect to this tradeoff. Specifically, we relate the network lifetime Lt to the distortion D of the delivered data. Finally, we present low-complexity approximations for the efficient computation of the Lt(D) bound.

Research paper thumbnail of Wireless Sensor Networks: A Systems Perspective

Research paper thumbnail of Call and response: experiments in sampling the environment

measurements and the relationship among sampling methods, event arrival rate, and sampling perfor... more measurements and the relationship among sampling methods, event arrival rate, and sampling performance are presented.

Research paper thumbnail of An environmental energy harvesting framework for sensor networks

Energy constrained systems such as sensor networks can increase their usable lifetimes by extract... more Energy constrained systems such as sensor networks can increase their usable lifetimes by extracting energy from their environment. However, environmental energy will typically not be spread homogeneously over the spread of the network. We argue that significant improvements in usable system lifetime can be achieved if the task allocation is aligned with the spatio-temporal characteristics of energy availability. To the best of our knowledge, this problem has not been addressed before. We present a distributed framework for the sensor network to adaptively learn its energy environment and give localized algorithms to use this information for task sharing among nodes. Our framework allows the system to exploit its energy resources more efficiently, thus increasing its lifetime. These gains are in addition to those from utilizing sleep modes and residual energy based scheduling mechanisms. Performance studies for an experimental energy environment show up to 200% improvement in lifetime.

Research paper thumbnail of Moments of the ratio of quadratic forms in non-normal variables with econometric examples

Journal of Econometrics, 1994

Journal of Econometrics 62 (1994) 129-141. North-Holland Moments of the ratio of quadratic forms ... more Journal of Econometrics 62 (1994) 129-141. North-Holland Moments of the ratio of quadratic forms in non-normal variables with econometric examples* Aman Ul1ah University of'CaliJ rnia, Riverside, CA 92521, USA Virendra K. Srivastava Lucknow Uniuersitv, Lucknow, ...

Research paper thumbnail of Power management in energy harvesting sensor networks

ACM Transactions in Embedded Computing Systems, 2007

Power management is an important concern in sensor networks, because a tethered energy infrastruc... more 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.

Research paper thumbnail of Heliomote: enabling long-lived sensor networks through solar energy harvesting

Demo Abstract: Heliomote: Enabling Long-Lived Sensor ... Kris Lin, Jennifer Yu, Jason Hsu, Sadaf ... more Demo Abstract: Heliomote: Enabling Long-Lived Sensor ... Kris Lin, Jennifer Yu, Jason Hsu, Sadaf Zahedi, David Lee, Jonathan Friedman, Aman Kansal, Vijay Raghunathan, and Mani Srivastava Networked and Embedded Systems Lab (NESL) Department of Electrical Engineering, ...

Research paper thumbnail of An environmental energy harvesting framework for sensor networks

Applied Soft Computing, 2003

Energy constrained systems such as sensor networks can increase their usable lifetimes by extract... more Energy constrained systems such as sensor networks can increase their usable lifetimes by extracting energy from their environment. However, environmental energy will typically not be spread homogeneously over the spread of the network. We argue that significant improvements in usable system lifetime can be achieved if the task allocation is aligned with the spatio-temporal characteristics of energy availability. To the best of our knowledge, this problem has not been addressed before. We present a distributed framework for the sensor network to adaptively learn its energy environment and give localized algorithms to use this information for task sharing among nodes. Our framework allows the system to exploit its energy resources more efficiently, thus increasing its lifetime. These gains are in addition to those from utilizing sleep modes and residual energy based scheduling mechanisms. Performance studies for an experimental energy environment show up to 200% improvement in lifetime.

Research paper thumbnail of Intelligent Fluid Infrastructure for Embedded Networks

Computer networks have historically considered support for mobile devices as an extra overhead to... more Computer networks have historically considered support for mobile devices as an extra overhead to be borne by the system. Recently however, researchers have proposed methods by which the network can take advantage of mobile components. We exploit mobility to develop a fluid infrastructure: mobile components are deliberately built into the system infrastructure for enabling specific functionality that is very hard to achieve using other methods. Built-in intelligence helps our system adapt to run time dynamics when pursuing pre-defined performance objectives. Our approach yields significant advantages for energy constrained systems, sparsely deployed networks, delay tolerant networks, and in security sensitive situations. We first show why our approach is advantageous in terms of network lifetime and data fidelity. Second, we present adaptive algorithms that are used to control mobility. Third, we design the communication protocol supporting a fluid infrastructure and long sleep durations on energy-constrained devices. Our algorithms are not based on abstract radio range models or idealized unobstructed environments but founded on real world behavior of wireless devices. We implement a prototype system in which infrastructure components move autonomously to carry out important networking tasks. The prototype is used to validate and evaluate our suggested mobility control methods.

Research paper thumbnail of Adaptive duty cycling for energy harvesting systems

Harvesting energy from the environment is feasible in many applications to ameliorate the energy ... more 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 energyneutrality 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.

Research paper thumbnail of On sensor network lifetime and data distortion

Fidelity is one of the key considerations in data collection schemes for sensor networks. A secon... more Fidelity is one of the key considerations in data collection schemes for sensor networks. A second important consideration is the energy expense of achieving that fidelity. Data from multiple correlated sensors is collected over multihop routes and fused to reproduce the phenomenon. However, the same distortion may be achieved using multiple rate allocations among the correlated sensors. These rate allocations would typically have different energy cost in routing depending on the network topology. We consider the interplay between these two considerations of distortion and energy. First, we describe the various factors that affect this trade-off. Second, we discuss bounds on the achievable performance with respect to this tradeoff. Specifically, we relate the network lifetime Lt to the distortion D of the delivered data. Finally, we present low-complexity approximations for the efficient computation of the Lt(D) bound.

Research paper thumbnail of Wireless Sensor Networks: A Systems Perspective

Research paper thumbnail of Call and response: experiments in sampling the environment

measurements and the relationship among sampling methods, event arrival rate, and sampling perfor... more measurements and the relationship among sampling methods, event arrival rate, and sampling performance are presented.

Research paper thumbnail of An environmental energy harvesting framework for sensor networks

Energy constrained systems such as sensor networks can increase their usable lifetimes by extract... more Energy constrained systems such as sensor networks can increase their usable lifetimes by extracting energy from their environment. However, environmental energy will typically not be spread homogeneously over the spread of the network. We argue that significant improvements in usable system lifetime can be achieved if the task allocation is aligned with the spatio-temporal characteristics of energy availability. To the best of our knowledge, this problem has not been addressed before. We present a distributed framework for the sensor network to adaptively learn its energy environment and give localized algorithms to use this information for task sharing among nodes. Our framework allows the system to exploit its energy resources more efficiently, thus increasing its lifetime. These gains are in addition to those from utilizing sleep modes and residual energy based scheduling mechanisms. Performance studies for an experimental energy environment show up to 200% improvement in lifetime.

Research paper thumbnail of Moments of the ratio of quadratic forms in non-normal variables with econometric examples

Journal of Econometrics, 1994

Journal of Econometrics 62 (1994) 129-141. North-Holland Moments of the ratio of quadratic forms ... more Journal of Econometrics 62 (1994) 129-141. North-Holland Moments of the ratio of quadratic forms in non-normal variables with econometric examples* Aman Ul1ah University of'CaliJ rnia, Riverside, CA 92521, USA Virendra K. Srivastava Lucknow Uniuersitv, Lucknow, ...

Research paper thumbnail of Power management in energy harvesting sensor networks

ACM Transactions in Embedded Computing Systems, 2007

Power management is an important concern in sensor networks, because a tethered energy infrastruc... more 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.

Research paper thumbnail of Heliomote: enabling long-lived sensor networks through solar energy harvesting

Demo Abstract: Heliomote: Enabling Long-Lived Sensor ... Kris Lin, Jennifer Yu, Jason Hsu, Sadaf ... more Demo Abstract: Heliomote: Enabling Long-Lived Sensor ... Kris Lin, Jennifer Yu, Jason Hsu, Sadaf Zahedi, David Lee, Jonathan Friedman, Aman Kansal, Vijay Raghunathan, and Mani Srivastava Networked and Embedded Systems Lab (NESL) Department of Electrical Engineering, ...