Battery recovery aware sensor networks (original) (raw)
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Harnessing Battery Recovery Effect in Wireless Sensor Networks: Experiments and Analysis
2010
Many applications of wireless sensor networks rely on batteries. But most batteries are not simple energy reservoirs, and can exhibit battery recovery effect. That is, the deliverable energy in a battery can be self-replenished, if left idling for sufficient time. As a viable approach for energy optimisation, we made several contributions towards harnessing battery recovery effect in sensor networks. 1) We empirically examine the gain of battery runtime of sensor devices due to battery recovery effect, and affirm its significant benefit in sensor networks. We also observe a saturation threshold, beyond which more idle time will contribute only little to battery recovery. 2) Based on our experiments, we propose a Markov chain model to capture battery recovery considering saturation threshold and random sensing activities, by which we can study the effectiveness of duty cycling and buffering. 3) We devise a simple distributed duty cycle scheme to take advantage of battery recovery using pseudo-random sequences, and analyse its trade-off between the induced latency of data delivery and duty cycle rates.
Recovery Effect in Low-Power Nodes of Wireless Sensor Networks
2014
Energy consumption is a major concern in Wireless Sensor Networks (WSNs) since nodes are powered by batteries. Usually, batteries have low capacity and can not be replaced due to economic and/or logistical issues. In addition, batteries are complex devices as they depend on electrochemical reactions to generate energy. As a result, batteries exhibit non-linear behaviour over time, which makes difficult to estimate their lifetime. Analytical battery models are abstractions that allow estimating the battery lifetime through mathematical equations, taking into account important effects such as rate capacity and charge recovery. The recovery effect is very important since it enables charge gains in the battery after its electrochemical stabilization. Sleep scheduling approaches may take advantage of the recovery effect by adding sleep periods in the node activities in order to extend the network lifetime. This work aims to analyse the recovery effect within WSN context, particularly reg...
Sizing up the Batteries: Modelling of Energy-Harvesting Sensor Nodes in a Delay Tolerant Network
arXiv (Cornell University), 2022
For energy-harvesting sensor nodes, rechargeable batteries play a critical role in sensing and transmissions. By coupling two simple Markovian queue models in a delay-tolerant networking setting, we consider the problem of battery sizing for these sensor nodes to operate effectively: given the intended energy depletion and overflow probabilities, how to decide the minimal battery capacity that is required to ensure opportunistic data exchange despite the inherent intermittency of renewable energy generation.
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.
Towards Achieving Perpetual Operation in Rechargeable Sensor Networks
2010
Energy harvesting sensor platforms have opened up a new dimension to the design of network protocols. In order to sustain the network operation, the energy consumption rate cannot be higher than the energy harvesting rate, otherwise, sensor nodes will eventually deplete their batteries. In contrast to traditional network resource allocation problems where the resources are static, time variations in recharging rate presents a new challenge. In this paper, we first explore the performance of an efficient dual decomposition and subgradient method based algorithm, called QuickFix, for computing the data sampling rate and routes when a DAG routing structure is given. Then, we analytically study the key properties of the optimal DAG(s) and propose a mechanism for constructing a DAG that can support high network utility. Moreover, fluctuations in recharging can happen at a faster timescale than the convergence time of the traditional approach. This leads to battery outage and overflow scenarios, that are both undesirable due to missed samples and lost energy harvesting opportunities respectively. To address such dynamics, a local algorithm, called SnapIt, is designed to adapt the sampling rate with the objective of maintaining the battery at a target level. Our evaluations using the TOSSIM simulator show that QuickFix and SnapIt working in tandem can track the instantaneous optimum network utility while maintaining the battery at a target level. When compared with IFRC, a backpressure-based approach, our solution improves the total data rate by 42% on the average while significantly improving the network utility.
IOSR Journal of Electronics and Communication Engineering, 2014
Battery life extension is the principal driver for energy-efficient wireless sensor network (WSN) design. However, there is growing awareness that in order to truly maximize the operating life of batterypowered systems such as sensor nodes, it is important to discharge the battery in a manner that maximizes the amount of charge extracted from it. In spite of this, there is little published data that quantitatively analyzes the effectiveness with which modern wireless sensor nodes discharge their batteries, under different operating conditions. This paper focuses on discharge profiles of battery under different conditions which could play a vital role in the life time of the wireless sensor networks. Power consumption is the limiting factor for the functionality offered by portable devices that operate on batteries. This power consumption problem is caused by a number of factors. Users are demanding more functionality, more processing, longer battery lifetimes, and smaller form factor and with reduced costs. Battery technology is only progressing slowly; the performance improves just a few percent each year. Mobile devices are also getting smaller and smaller, implying that the amount of space for batteries is also decreasing. Decreasing the size of a mobile device results in smaller batteries, and a need for less power consumption.
A Framework for Battery-Aware Sensor Management
2004
A distributed sensor network (DSN) designed to cover a given region R, is said to be alive if there is at least one subset of sensors that can collectively cover (sense) the region R. When no such subset exists, the network is said to be dead. A key challenge in the design of a DSN is to maximize the operational life of the network. Since sensors are typically powered by batteries, this requires maximizing the battery lifetime. One way to achieve this is to determine the optimal schedule for transitioning sets of sensors between active and inactive states while satisfying user specified performance constraints. This requires identification of feasible subsets (covers) of sensors and a scheme for switching between such subsets. We present an algorithmic solution to compute all the sensor covers in an implicit manner by formulating the problem as unate covering problem (UCP). The representation of all possible sensor sets is extremely efficient and can accommodate very large number of sensor covers. The representation and formulation makes it possible to consider the residual battery charge when switching between covers. We develop algorithms for switching between sensor covers aimed at maximizing the lifetime of the network. The algorithms take into account the transmission/reception costs of sensors, a user specified quality constraint and also utilize a novel battery model that accounts for the rate-dependent capacity effect and charge recovery during idle periods. Our simulation results show that lifetime improvement can be achieved by exploiting the charge recovery process. The work 1 presented here constitutes a framework for battery aware sensor management in which various types of constraints can be incorporated and a range of other communication protocols can be examined.
Battery Recovery-Aware Optimization for Embedded System Communications
Wireless Personal Communications, 2019
In this paper, we consider a point-to-point wireless communications for embedded battery powered systems. We aim to provide an optimal use of all the usable capacity inside the battery before it becomes exhausted. For this purpose, we exploit the recovery effect to extend their lifetime. We consider a stochastic battery model and use both dynamic programming and reinforcement learning approaches to compute optimal transmission policies for wireless sensor networks. The obtained results show that the expected total transmitted data and the battery lifetime are maximized when all the charge units inside the battery are consumed.
Efficient battery management for sensor lifetime
… and Applications Workshops, 2007, 7
It is challenging to design a sensor network if sensors are battery powered. Efficient scheduling and budgeting battery power in sensor networks has become a critical issue in network design. We investigate how energy ratio and the battery ratio, the ratio of initial battery capacities for sensors and cluster heads, affects sensor network lifetime. These results allow the network designer to specify required battery capacities which optimizing energy usage, and therefore leads to reduced total costs for the network which is extremely important in wireless sensor networks.
Joint Energy Management and Resource Allocation in Rechargeable Sensor Networks
2010
Energy harvesting sensor platforms have opened up a new dimension to the design of network protocols. In order to sustain the network operation, the energy consumption rate cannot be higher than the energy harvesting rate, otherwise, sensor nodes will eventually deplete their batteries. In contrast to traditional network resource allocation problems where the resources are static, time variations in recharging rate presents a new challenge. In this paper, we first explore the performance of an efficient dual decomposition and subgradient method based algorithm, called QuickFix, for computing the data sampling rate and routes. However, fluctuations in recharging can happen at a faster time-scale than the convergence time of the traditional approach. This leads to battery outage and overflow scenarios, that are both undesirable due to missed samples and lost energy harvesting opportunities respectively. To address such dynamics, a local algorithm, called SnapIt, is designed to adapt the sampling rate with the objective of maintaining the battery at a target level. Our evaluations using the TOSSIM simulator show that QuickFix and SnapIt working in tandem can track the instantaneous optimum network utility while maintaining the battery at a target level. When compared with IFRC, a backpressure-based approach, our solution improves the total data rate by 42% on the average while significantly improving the network utility.