Sizing up the Batteries: Modelling of Energy-Harvesting Sensor Nodes in a Delay Tolerant Network (original) (raw)
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Delay Optimal Scheduling for Energy Harvesting Based Communications
Green communications attract increasing research interest recently. Equipped with a rechargeable battery, a source node can harvest energy from ambient environments and rely on this free and regenerative energy supply to transmit packets. Due to the uncertainty of available energy from harvesting, however, intolerably large latency and packet loss could be induced, if the source always waits for harvested energy. To overcome this problem, one Reliable Energy Source (RES) can be resorted to for a prompt delivery of backlogged packets. Naturally, there exists a tradeoff between the packet delivery delay and power consumption from the RES. In this paper, we address the delay optimal scheduling problem for a bursty communication link powered by a capacity-limited battery storing harvested energy together with one RES. The proposed scheduling scheme gives priority to the usage of harvested energy, and resorts to the RES when necessary based on the data and energy queueing processes, with an average power constraint from the RES. Through twodimensional Markov chain modeling and linear programming formulation, we derive the optimal threshold-based scheduling policy together with the corresponding transmission parameters. Our study includes three exemplary cases that capture some important relations between the data packet arrival process and energy harvesting capability. Our theoretical analysis is corroborated by simulation results.
2016
Energy harvesting (EH) provides a means of greatly enhancing the lifetime of wireless sensor nodes. However, the randomness inherent in the EH process may cause significant delay for performing sensing operation and transmitting the sensed information to the sink. Unlike most existing studies on the delay performance of EH sensor networks, where only the energy consumption of transmission is considered, we consider the energy costs of both sensing and transmission. Specifically, we consider an EH sensor that monitors some status environmental property and adopts a harvest-then-use protocol to perform sensing and transmission. To comprehensively study the delay performance, we consider two complementary metrics and analytically derive their statistics: (i) update age - measuring the time taken from when information is obtained by the sensor to when the sensed information is successfully transmitted to the sink, i.e., how timely the updated information at the sink is, and (ii) update ...
Basic Tradeoffs for Energy Management in Rechargeable Sensor Networks
Computing Research Repository, 2010
As many sensor network applications require deployment in remote and hard-to-reach areas, it is critical to ensure that such networks are capable of operating unattended for long durations. Consequently, the concept of using nodes with energy replenishment capabilities has been gaining popularity. However, new techniques and protocols must be developed to maximize the performance of sensor networks with energy replenishment. Here, we analyze limits of the performance of sensor nodes with limited energy, being replenished at a variable rate. We provide a simple localized energy management scheme that achieves a performance close to that with an unlimited energy source, and at the same time keeps the probability of complete battery discharge low. Based on the insights developed, we address the problem of energy management for energyreplenishing nodes with finite battery and finite data buffer capacities. To this end, we give an energy management scheme that achieves the optimal utility asymptotically while keeping both the battery discharge and data loss probabilities low.
Battery recovery aware sensor networks
2009
Many applications of sensor networks require batteries as the energy source, and hence critically rely on energy optimisation of sensor batteries. But as often neglected by the networking community, most batteries are non-ideal energy reservoirs and can exhibit battery recovery effect -the deliverable energy in batteries can be replenished per se, if left idling for sufficient duration. We made several contributions towards harnessing battery recovery effect in sensor networks. First, we empirically examine the gain of battery runtime due to battery recovery effect, and found this effect significant and durationdependent. Second, based on our findings, we model the battery recovery effect in the presence of random sensing activities by a Markov chain model, and study the effect of duty cycling and buffering to harness battery recovery effect. Third, we propose a more energy-efficient duty cycling scheme that is aware of battery recovery effect, and analyse its performance with respect to the latency of data delivery.
Delay-Optimal Resource Scheduling of Energy Harvesting based Devices
IEEE Transactions on Green Communications and Networking
This paper investigates resource scheduling in a wireless communication system operating with Energy Harvesting (EH) based devices and perfect Channel State Information (CSI). The aim is to minimize the packet loss that occurs when the buffer is overflowed or when the queued packet is older than a certain pre-defined threshold. We so consider a strict delay constraint rather than an average delay constraint. The associated optimization problem is modeled as Markov Decision Process (MDP) where the actions are the number of packets sent on the known channel at each slot. The optimal deterministic offline policy is exhibited through dynamic programming techniques, i.e. Value Iteration (VI) algorithm. We show that the gain in the number of transmitted packets and the consumed energy is substantial compared to: i) a naive policy which forces the system to send the maximum number of packets using the available energy in the battery, ii) two variants of the previous policy that take into account the buffer state, and iii) a policy optimized with an average delay constraint. Finally, we evaluate our optimal policy under imperfect CSI scenario where only an estimate of the channel state is available.
Optimal Resource Scheduling for Energy Harvesting Communications under Strict Delay Constraint
2018 IEEE International Conference on Communications (ICC)
This paper investigates the resource scheduling minimizing the packet loss when the wireless communication system operates with Energy Harvesting (EH) based devices. The packet loss occurs when the buffer is overflowed and when the queued packet is older than a certain pre-defined threshold. We so consider a strict delay constraint rather than an average delay constraint. The associated optimization problem can be modeled as Markov Decision Problem (MDP) where the actions are the number of packets sent on the known channel at each slot. The optimal deterministic offline policy is exhibited through dynamic programming techniques, i.e. Value Iteration (VI) algorithm. We show the gain in the number of transmitted packets and the consumed energy is substantial compared to a naive policy which forces the system to send the maximum number of packets using the available energy in the battery.
A Diffusion Model for Energy Harvesting Sensor Nodes
Energy harvesting has recently attracted much interest due to the emergence of the Internet of Things, and the need to operate wireless sensing devices in challenging environments without much human intervention and maintenance. This paper presents a novel approach for modeling the performance of an energy harvesting wireless sensor node, which takes into account fluctuations in the amount of energy extracted from the environment, energy loss due to battery leakage, as well as the energy cost of sensing, data processing and communication. The proposed approach departs from the common queueing-theoretic framework used in the literature, and instead uses Brownian motion to represent more accurately the time evolution of the distribution of the node's energy level. The paper derives some performance measures of interest along with the stationary solution of the system, and discusses possible directions for reducing the number of parameters and states of the model without compromising accuracy.
Transmission policies for energy harvesting sensors with time-correlated energy supply
This paper considers a wireless sensor powered by an energy harvesting device, which reports data of varying importance to its receiver. Modeling the ambient energy supply by a two-state Markov chain ("GOOD" and "BAD"), assuming a finite battery capacity constraint, and associating data transmission with a given energy cost, we propose low-complexity transmission policies, that achieve near-optimal performance in terms of the average long-term importance of the reported data. In particular, we derive the performance of the Balanced Policy (BP), which adapts the transmission probability to the harvesting state, such that energy harvesting and consumption are balanced. Our analysis demonstrates that the performance of the BP largely depends on the power-to-depletion, defined as the power that a fully charged battery can supply on average over a BAD period. Numerical results show that the optimal BP achieves nearoptimal performance and that a BP which avoids energy overflow further reduces the gap with respect to the globally optimal policy. A heuristic BP, based on the analysis of a system with a deterministic and periodic energy supply, is also proposed, and the parallels between the deterministic system and its stochastic counterpart are discussed.
Energy Harvesting Communications With Batteries Having Cycle Constraints
IEEE Transactions on Green Communications and Networking, 2019
Practical energy harvesting (EH) based communication systems typically use a battery to temporarily store the harvested energy prior to its use for communication. The battery capacity can quickly degrade with time if it is subject to repeated shallow charge-discharge cycles. This motivates the cycle constraint which mandates that a battery must be charged only after it is sufficiently discharged and vice versa. We consider a Bernoulli energy arrival model, and a half-duplex battery constraint. In this context, we study EH communication systems with: (a) a single battery with capacity 2B units and (b) dual batteries, each having capacity of B units. The aim is to obtain the best possible long-term average throughputs in point-to-point (P2P) channels and multiple access channels (MAC). For the P2P channel, we obtain an analytical optimal solution in the single battery case, and propose optimal and suboptimal power allocation policies for the dual battery case. We extend these policies to obtain achievable throughput regions in MACs by jointly allocating rates and powers. From numerical simulations, we find that the optimal throughput in the dual battery case can be more than twice of that in the single battery case, although the total energy storage capacity in both cases is 2B units.