Power management in energy harvesting sensor networks
TL;DR: In this paper, the authors have developed abstractions to characterize the complex time varying nature of such sources with analytically tractable models and use them to address key design issues.
Abstract: 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.
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01 Sep 2012
TL;DR: A survey of technologies, applications and research challenges for Internetof-Things is presented, in which digital and physical entities can be linked by means of appropriate information and communication technologies to enable a whole new class of applications and services.
Abstract: The term ‘‘Internet-of-Things’’ is used as an umbrella keyword for covering various aspects related to the extension of the Internet and the Web into the physical realm, by means of the widespread deployment of spatially distributed devices with embedded identification, sensing and/or actuation capabilities. Internet-of-Things envisions a future in which digital and physical entities can be linked, by means of appropriate information and communication technologies, to enable a whole new class of applications and services. In this article, we present a survey of technologies, applications and research challenges for Internetof-Things.
3,172 citations
Cites background from "Power management in energy harvesti..."
...A comprehensive take at the technological problem of energy harvesting in real devices is described in [54]....
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TL;DR: Various aspects of energy harvesting sensor systems- architecture, energy sources and storage technologies and examples of harvesting-based nodes and applications are surveyed and the implications of recharge opportunities on sensor node operation and design of sensor network solutions are discussed.
Abstract: 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.
1,870 citations
Cites background or methods from "Power management in energy harvesti..."
...An alternative approach to energy availability prediction uses an Exponentially Weighted Moving-Average (EWMA) filter [20]....
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...Energy sources have different characteristics along the axes of controllability, predictability and magnitude[20]....
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...The condition for energy neutral operation for the two energy harvesting options is as follows[20],...
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...A similar categorization is present in [20]....
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...A thorough and complete mathematical analysis of these conditions is presented in [20]....
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TL;DR: A comprehensive taxonomy of the various energy harvesting sources that can be used by WSNs is presented and some of the challenges still need to be addressed to develop cost-effective, efficient, and reliable energy harvesting systems for the WSN environment are identified.
Abstract: 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.
914 citations
TL;DR: The current state of the art for wireless networks composed of energy harvesting nodes, starting from the information-theoretic performance limits to transmission scheduling policies and resource allocation, medium access, and networking issues are provided.
Abstract: This paper summarizes recent contributions in the broad area of energy harvesting wireless communications. In particular, we provide the current state of the art for wireless networks composed of energy harvesting nodes, starting from the information-theoretic performance limits to transmission scheduling policies and resource allocation, medium access, and networking issues. The emerging related area of energy transfer for self-sustaining energy harvesting wireless networks is considered in detail covering both energy cooperation aspects and simultaneous energy and information transfer. Various potential models with energy harvesting nodes at different network scales are reviewed, as well as models for energy consumption at the nodes.
829 citations
References
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28 Sep 2002
TL;DR: An in-depth study of applying wireless sensor networks to real-world habitat monitoring and an instance of the architecture for monitoring seabird nesting environment and behavior is presented.
Abstract: We provide an in-depth study of applying wireless sensor networks to real-world habitat monitoring. A set of system design requirements are developed that cover the hardware design of the nodes, the design of the sensor network, and the capabilities for remote data access and management. A system architecture is proposed to address these requirements for habitat monitoring in general, and an instance of the architecture for monitoring seabird nesting environment and behavior is presented. The currently deployed network consists of 32 nodes on a small island off the coast of Maine streaming useful live data onto the web. The application-driven design exercise serves to identify important areas of further work in data sampling, communications, network retasking, and health monitoring.
4,623 citations
"Power management in energy harvesti..." refers background or methods in this paper
...achieve much higher duty cycles than those currently used, s uch as 1% used in battery based deployments [Mainwaring et al. 2002]....
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...In addition, these results reiterate the importance of harvesting by showing that environmental energy-harvesting with appropriate power-management can achieve much higher duty cycles than those currently used, such as 1% used in battery-based deployments [Mainwaring et al. 2002]....
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IBM1
TL;DR: Worst-case bounds on delay and backlog are derived for leaky bucket constrained sessions in arbitrary topology networks of generalized processor sharing (GPS) servers and the effectiveness of PGPS in guaranteeing worst-case session delay is demonstrated under certain assignments.
Abstract: Worst-case bounds on delay and backlog are derived for leaky bucket constrained sessions in arbitrary topology networks of generalized processor sharing (GPS) servers. The inherent flexibility of the service discipline is exploited to analyze broad classes of networks. When only a subset of the sessions are leaky bucket constrained, we give succinct per-session bounds that are independent of the behavior of the other sessions and also of the network topology. However, these bounds are only shown to hold for each session that is guaranteed a backlog clearing rate that exceeds the token arrival rate of its leaky bucket. A much broader class of networks, called consistent relative session treatment (CRST) networks is analyzed for the case in which all of the sessions are leaky bucket constrained. First, an algorithm is presented that characterizes the internal traffic in terms of average rate and burstiness, and it is shown that all CRST networks are stable. Next, a method is presented that yields bounds on session delay and backlog given this internal traffic characterization. The links of a route are treated collectively, yielding tighter bounds than those that result from adding the worst-case delays (backlogs) at each of the links in the route. The bounds on delay and backlog for each session are efficiently computed from a universal service curve, and it is shown that these bounds are achieved by "staggered" greedy regimes when an independent sessions relaxation holds. Propagation delay is also incorporated into the model. Finally, the analysis of arbitrary topology GPS networks is related to Packet GPS networks (PGPS). The PGPS scheme was first proposed by Demers, Shenker and Keshav (1991) under the name of weighted fair queueing. For small packet sizes, the behavior of the two schemes is seen to be virtually identical, and the effectiveness of PGPS in guaranteeing worst-case session delay is demonstrated under certain assignments. >
3,967 citations
"Power management in energy harvesti..." refers methods in this paper
...We define the following model which is motivated by leaky bucket Internet traffic models [Cruz 1991a; Parekh and Gallager 1993]....
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...One approach to modeling bursty sources is given by the (r, b) token bucket traf.c regulator [Parekh and Gallager 1993; Parekh 1992; Cruz 1991a; Cruz 1991b] used to model bursty traf.c for QoS in Internet....
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...One approach to modeling bursty sources is given by the (r, b) token bucket traffic regulator [Parekh and Gallager 1993; Parekh 1992; Cruz 1991a; 1991b] used to model bursty traffic for QoS in Internet....
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...We de.ne the following model that is motivated by leaky-bucket Internet traf.c models [Cruz 1991a; Parekh and Gallager 1993]....
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Journal Article•
TL;DR: In this article, the authors proposed a geographical adaptive fidelity (GAF) algorithm that reduces energy consumption in ad hoc wireless networks by identifying nodes that are equivalent from a routing perspective and turning off unnecessary nodes, keeping a constant level of routing fidelity.
Abstract: We introduce a geographical adaptive fidelity (GAF) algorithm that reduces energy consumption in ad hoc wireless networks. GAF conserves energy by identifying nodes that are equivalent from a routing perspective and turning off unnecessary nodes, keeping a constant level of routing fidelity. GAF moderates this policy using application- and system-level information; nodes that source or sink data remain on and intermediate nodes monitor and balance energy use. GAF is independent of the underlying ad hoc routing protocol; we simulate GAF over unmodified AODV and DSR. Analysis and simulation studies of GAF show that it can consume 40% to 60% less energy than an unmodified ad hoc routing protocol. Moreover, simulations of GAF suggest that network lifetime increases proportionally to node density; in one example, a four-fold increase in node density leads to network lifetime increase for 3 to 6 times (depending on the mobility pattern). More generally, GAF is an example of adaptive fidelity, a technique proposed for extending the lifetime of self-configuring systems by exploiting redundancy to conserve energy while maintaining application fidelity.
2,829 citations
16 Jul 2001
TL;DR: A geographical adaptive fidelity algorithm that reduces energy consumption in ad hoc wireless networks by identifying nodes that are equivalent from a routing perspective and then turning off unnecessary nodes, keeping a constant level of routing fidelity.
Abstract: We introduce a geographical adaptive fidelity (GAF) algorithm that reduces energy consumption in ad hoc wireless networks GAF conserves energy by identifying nodes that are equivalent from a routing perspective and then turning off unnecessary nodes, keeping a constant level of routing fidelity GAF moderates this policy using application- and system-level information; nodes that source or sink data remain on and intermediate nodes monitor and balance energy use GAF is independent of the underlying ad hoc routing protocol; we simulate GAF over unmodified AODV and DSR Analysis and simulation studies of GAF show that it can consume 40% to 60% less energy than an unmodified ad hoc routing protocol Moreover, simulations of GAP suggest that network lifetime increases proportionally to node density; in one example, a four-fold increase in node density leads to network lifetime increase for 3 to 6 times (depending on the mobility pattern) More generally, GAF is an example of adaptive fidelity, a technique proposed for extending the lifetime of self-configuring systems by exploiting redundancy to conserve energy while maintaining application fidelity
2,638 citations
TL;DR: A calculus is developed for obtaining bounds on delay and buffering requirements in a communication network operating in a packet switched mode under a fixed routing strategy, and burstiness constraints satisfied by the traffic that exits the element are derived.
Abstract: A calculus is developed for obtaining bounds on delay and buffering requirements in a communication network operating in a packet switched mode under a fixed routing strategy. The theory developed is different from traditional approaches to analyzing delay because the model used to describe the entry of data into the network is nonprobabilistic. It is supposed that the data stream entered into the network by any given user satisfies burstiness constraints. A data stream is said to satisfy a burstiness constraint if the quantity of data from the stream contained in any interval of time is less than a value that depends on the length of the interval. Several network elements are defined that can be used as building blocks to model a wide variety of communication networks. Each type of network element is analyzed by assuming that the traffic entering it satisfies bursting constraints. Under this assumption, bounds are obtained on delay and buffering requirements for the network element; burstiness constraints satisfied by the traffic that exits the element are derived. >
2,049 citations