Control of wireless networks with rechargeable batteries [transactions papers]
TL;DR: A policy with decoupled admission control and power allocation decisions is proposed that achieves asymptotic optimality for sufficiently large battery capacity to maximum transmission power ratio (explicit bounds are provided).
Abstract: We consider the problem of cross-layer resource allocation for wireless networks operating with rechargeable batteries under general arrival, channel state and recharge processes. The objective is to maximize total system utility, defined as a function of the long-term rate achieved per link, while satisfying energy and power constraints. A policy with decoupled admission control and power allocation decisions is proposed that achieves asymptotic optimality for sufficiently large battery capacity to maximum transmission power ratio (explicit bounds are provided). We present first a downlink resource allocation scenario; the analysis is then extended to multihop networks. The policy is evaluated via simulations and is seen to perform very well even in the non-asymptotic regime. This policy is particularly suitable for sensor networks, which typically satisfy the asymptotic conditions required by our methodology.
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TL;DR: In this paper, the authors consider a point-to-point data transmission with an energy harvesting transmitter which has a limited battery capacity, communicating in a wireless fading channel, and they consider two objectives: maximizing the throughput by a deadline, and minimizing the transmission completion time of the communication session.
Abstract: Wireless systems comprised of rechargeable nodes have a significantly prolonged lifetime and are sustainable. A distinct characteristic of these systems is the fact that the nodes can harvest energy throughout the duration in which communication takes place. As such, transmission policies of the nodes need to adapt to these harvested energy arrivals. In this paper, we consider optimization of point-to-point data transmission with an energy harvesting transmitter which has a limited battery capacity, communicating in a wireless fading channel. We consider two objectives: maximizing the throughput by a deadline, and minimizing the transmission completion time of the communication session. We optimize these objectives by controlling the time sequence of transmit powers subject to energy storage capacity and causality constraints. We, first, study optimal offline policies. We introduce a directional water-filling algorithm which provides a simple and concise interpretation of the necessary optimality conditions. We show the optimality of an adaptive directional water-filling algorithm for the throughput maximization problem. We solve the transmission completion time minimization problem by utilizing its equivalence to its throughput maximization counterpart. Next, we consider online policies. We use stochastic dynamic programming to solve for the optimal online policy that maximizes the average number of bits delivered by a deadline under stochastic fading and energy arrival processes with causal channel state feedback. We also propose near-optimal policies with reduced complexity, and numerically study their performances along with the performances of the offline and online optimal policies under various different configurations.
1,130 citations
Posted Content•
TL;DR: This paper considers optimization of point-to-point data transmission with an energy harvesting transmitter which has a limited battery capacity, communicating in a wireless fading channel, and introduces a directional water-filling algorithm which provides a simple and concise interpretation of the necessary optimality conditions.
Abstract: Wireless systems comprised of rechargeable nodes have a significantly prolonged lifetime and are sustainable. A distinct characteristic of these systems is the fact that the nodes can harvest energy throughout the duration in which communication takes place. As such, transmission policies of the nodes need to adapt to these harvested energy arrivals. In this paper, we consider optimization of point-to-point data transmission with an energy harvesting transmitter which has a limited battery capacity, communicating in a wireless fading channel. We consider two objectives: maximizing the throughput by a deadline, and minimizing the transmission completion time of the communication session. We optimize these objectives by controlling the time sequence of transmit powers subject to energy storage capacity and causality constraints. We, first, study optimal offline policies. We introduce a directional water-filling algorithm which provides a simple and concise interpretation of the necessary optimality conditions. We show the optimality of an adaptive directional water-filling algorithm for the throughput maximization problem. We solve the transmission completion time minimization problem by utilizing its equivalence to its throughput maximization counterpart. Next, we consider online policies. We use stochastic dynamic programming to solve for the optimal online policy that maximizes the average number of bits delivered by a deadline under stochastic fading and energy arrival processes with causal channel state feedback. We also propose near-optimal policies with reduced complexity, and numerically study their performances along with the performances of the offline and online optimal policies under various different configurations.
950 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
TL;DR: This work considers transmission policies for rechargeable nodes with energy harvesting battery equipped nodes, and optimum solutions for two related problems are identified, including the transmission policy that maximizes the short term throughput.
Abstract: Wireless networks with energy harvesting battery equipped nodes are quickly emerging as a viable option for future wireless networks with extended lifetime Equally important to their counterpart in the design of energy harvesting radios are the design principles that this new networking paradigm calls for In particular, unlike wireless networks considered to date, the energy replenishment process and the storage constraints of the rechargeable batteries need to be taken into account in designing efficient transmission strategies In this work, such transmission policies for rechargeable nodes are considered, and optimum solutions for two related problems are identified Specifically, the transmission policy that maximizes the short term throughput, ie, the amount of data transmitted in a finite time horizon is found In addition, the relation of this optimization problem to another, namely, the minimization of the transmission completion time for a given amount of data is demonstrated, which leads to the solution of the latter as well The optimum transmission policies are identified under the constraints on energy causality, ie, energy replenishment process, as well as the energy storage, ie, battery capacity For battery replenishment, a model with discrete packets of energy arrivals is considered The necessary conditions that the throughput-optimal allocation satisfies are derived, and then the algorithm that finds the optimal transmission policy with respect to the short-term throughput and the minimum transmission completion time is given Numerical results are presented to confirm the analytical findings
784 citations
Cites background from "Control of wireless networks with r..."
...Systems powered with batteries suffer from a limited lifetime, whereas networks consisting of energy harvesting or rechargeable nodes can survive perpetually [3]....
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Posted Content•
TL;DR: In this paper, the authors considered the problem of minimizing the transmission completion time for a given amount of data in a finite time horizon and derived the necessary conditions that the throughput-optimal allocation satisfies, and then provided the algorithm that finds the optimal transmission policy with respect to the short-term throughput and the minimum transmission completion times.
Abstract: Wireless networks with energy harvesting battery powered nodes are quickly emerging as a viable option for future wireless networks with extended lifetime. Equally important to their counterpart in the design of energy harvesting radios are the design principles that this new networking paradigm calls for. In particular, unlike wireless networks considered up to date, the energy replenishment process and the storage constraints of the rechargeable batteries need to be taken into account in designing efficient transmission strategies. In this work, we consider such transmission policies for rechargeable nodes, and identify the optimum solution for two related problems. Specifically, the transmission policy that maximizes the short term throughput, i.e., the amount of data transmitted in a finite time horizon is found. In addition, we show the relation of this optimization problem to another, namely, the minimization of the transmission completion time for a given amount of data, and solve that as well. The transmission policies are identified under the constraints on energy causality, i.e., energy replenishment process, as well as the energy storage, i.e., battery capacity. The power-rate relationship for this problem is assumed to be an increasing concave function, as dictated by information theory. For battery replenishment, a model with discrete packets of energy arrivals is considered. We derive the necessary conditions that the throughput-optimal allocation satisfies, and then provide the algorithm that finds the optimal transmission policy with respect to the short-term throughput and the minimum transmission completion time. Numerical results are presented to confirm the analytical findings.
659 citations
References
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TL;DR: The concept of sensor networks which has been made viable by the convergence of micro-electro-mechanical systems technology, wireless communications and digital electronics is described.
17,936 citations
"Control of wireless networks with r..." refers background in this paper
...On the other hand, there exist applications where the wireless transmitters can replenish their batteries, two common examples being solar-paneled satellites and sensor networks [1]....
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Book•
01 May 1995TL;DR: The leading and most up-to-date textbook on the far-ranging algorithmic methododogy of Dynamic Programming, which can be used for optimal control, Markovian decision problems, planning and sequential decision making under uncertainty, and discrete/combinatorial optimization.
Abstract: The leading and most up-to-date textbook on the far-ranging algorithmic methododogy of Dynamic Programming, which can be used for optimal control, Markovian decision problems, planning and sequential decision making under uncertainty, and discrete/combinatorial optimization. The treatment focuses on basic unifying themes, and conceptual foundations. It illustrates the versatility, power, and generality of the method with many examples and applications from engineering, operations research, and other fields. It also addresses extensively the practical application of the methodology, possibly through the use of approximations, and provides an extensive treatment of the far-reaching methodology of Neuro-Dynamic Programming/Reinforcement Learning.
10,834 citations
TL;DR: The stability of a queueing network with interdependent servers is considered and a policy is obtained which is optimal in the sense that its Stability Region is a superset of the stability region of every other scheduling policy, and this stability region is characterized.
Abstract: The stability of a queueing network with interdependent servers is considered. The dependency among the servers is described by the definition of their subsets that can be activated simultaneously. Multihop radio networks provide a motivation for the consideration of this system. The problem of scheduling the server activation under the constraints imposed by the dependency among servers is studied. The performance criterion of a scheduling policy is its throughput that is characterized by its stability region, that is, the set of vectors of arrival and service rates for which the system is stable. A policy is obtained which is optimal in the sense that its stability region is a superset of the stability region of every other scheduling policy, and this stability region is characterized. The behavior of the network is studied for arrival rates that lie outside the stability region. Implications of the results in certain types of concurrent database and parallel processing systems are discussed. >
3,018 citations
"Control of wireless networks with r..." refers methods in this paper
...Our model is distinct from the aforementioned works and is directly influenced by the cross-layer stochastic optimization framework of [11], [12], which, in turn, was inspired by [13]....
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Book•
05 Apr 2006TL;DR: In this article, the authors present abstract models that capture the cross-layer interaction from the physical to transport layer in wireless network architectures including cellular, ad-hoc and sensor networks as well as hybrid wireless-wireline.
Abstract: Information flow in a telecommunication network is accomplished through the interaction of mechanisms at various design layers with the end goal of supporting the information exchange needs of the applications. In wireless networks in particular, the different layers interact in a nontrivial manner in order to support information transfer. In this text we will present abstract models that capture the cross-layer interaction from the physical to transport layer in wireless network architectures including cellular, ad-hoc and sensor networks as well as hybrid wireless-wireline. The model allows for arbitrary network topologies as well as traffic forwarding modes, including datagrams and virtual circuits. Furthermore the time varying nature of a wireless network, due either to fading channels or to changing connectivity due to mobility, is adequately captured in our model to allow for state dependent network control policies. Quantitative performance measures that capture the quality of service requirements in these systems depending on the supported applications are discussed, including throughput maximization, energy consumption minimization, rate utility function maximization as well as general performance functionals. Cross-layer control algorithms with optimal or suboptimal performance with respect to the above measures are presented and analyzed. A detailed exposition of the related analysis and design techniques is provided.
1,612 citations
"Control of wireless networks with r..." refers background or methods or result in this paper
...Its difference from the policy of [11] lies in the presence of the term E(t) in the constraint of (12)....
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...As mentioned in Section II-A, this term causes the performance analysis of [11] to be inapplicable to our model....
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...3 of [11], summing the resulting inequalities over l and subtracting the term 2ME [ ∑ l fl (γl(t))∣X(t)] from both sides yields after some algebra (see [16] for details)...
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...The quantity πs p (which is a discrete pdf on the set Ps) is interpreted in [11] as the probability with which a randomized policy selects power p when the current channel state is s....
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...We restrict attention to policies that stabilize the network according to the definition of [11]....
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24 Apr 2005
TL;DR: Prometheus as discussed by the authors is a two-stage energy storage system consisting of supercapacitors (primary buffer) and a lithium rechargeable battery (secondary buffer), which can operate for 43 years under 1% load and 4 years under 10% load.
Abstract: Environmental energy is an attractive power source for low power wireless sensor networks. We present Prometheus, a system that intelligently manages energy transfer for perpetual operation without human intervention or servicing. Combining positive attributes of different energy storage elements and leveraging the intelligence of the microprocessor, we introduce an efficient multi-stage energy transfer system that reduces the common limitations of single energy storage systems to achieve near perpetual operation. We present our design choices, tradeoffs, circuit evaluations, performance analysis, and models. We discuss the relationships between system components and identify optimal hardware choices to meet an application's needs. Finally we present our implementation of a real system that uses solar energy to power Berkeley's Telos Mote. Our analysis predicts the system will operate for 43 years under 1% load, 4 years under 10% load, and 1 year under 100% load. Our implementation uses a two stage storage system consisting of supercapacitors (primary buffer) and a lithium rechargeable battery (secondary buffer). The mote has full knowledge of power levels and intelligently manages energy transfer to maximize lifetime.
803 citations
"Control of wireless networks with r..." refers background in this paper
...Such rechargeable systems are usually regarded as having practically infinite lifetime [2], so that long-term performance metrics become appropriate....
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