Recovery Effect in Low-Power Nodes of Wireless Sensor Networks (original) (raw)
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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.
Journal of Sensor and Actuator Networks
The operation of Wireless Sensor Networks (WSNs) is subject to multiple constraints, among which one of the most critical is available energy. Sensor nodes are typically powered by electrochemical batteries. The stored energy in battery devices is easily influenced by the operating temperature and the discharge current values. Therefore, it becomes difficult to estimate their voltage/charge behavior over time, which are relevant variables for the implementation of energy-aware policies. Nowadays, there are hardware and/or software approaches that can provide information about the battery operating conditions. However, this type of hardware-based approach increases the battery production cost, which may impair its use for sensor node implementations. The objective of this work is to propose a software-based approach to estimate both the state of charge and the voltage of batteries in WSN nodes based on the use of a temperature-dependent analytical battery model. The achieved results demonstrate the feasibility of using embedded analytical battery models to estimate the lifetime of batteries, without affecting the tasks performed by the WSN nodes.
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.
Battery recovery aware sensor networks
2009
Abstract 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.
Online estimation of battery lifetime for wireless sensors network
2012
Battery is a major hardware component of wireless sensor networks. Most of them have no power supply and are generally deployed for a long time. Researches have been done on battery physical model and their adaptation for sensors. We present an implementation on a real sensor operating system and how architectural constraints have been assumed. Experiments have been made in order to test the impact of some parameter, as the application throughput, on the battery lifetime.
A Temperature-Dependent Battery Model for Wireless Sensor Networks
Sensors (Basel, Switzerland), 2017
Energy consumption is a major issue in Wireless Sensor Networks (WSNs), as nodes are powered by chemical batteries with an upper bounded lifetime. Estimating the lifetime of batteries is a difficult task, as it depends on several factors, such as operating temperatures and discharge rates. Analytical battery models can be used for estimating both the battery lifetime and the voltage behavior over time. Still, available models usually do not consider the impact of operating temperatures on the battery behavior. The target of this work is to extend the widely-used Kinetic Battery Model (KiBaM) to include the effect of temperature on the battery behavior. The proposed Temperature-Dependent KiBaM (T-KiBaM) is able to handle operating temperatures, providing better estimates for the battery lifetime and voltage behavior. The performed experimental validation shows that T-KiBaM achieves an average accuracy error smaller than 0.33%, when estimating the lifetime of Ni-MH batteries for diffe...
Efficient Battery Management in Wireless Sensor Node: Review Paper
Wireless sensor network is an emerging field in wireless networking. Wireless Sensor Node (WSN) is the key component of wireless sensor networks for data communication inn large networks. WSN is powered with battery as its source of energy. It is usually deployed in hostile. Alkaline batteries are dominantly used in WSN. We need to maximize utilization of battery used in WSN. In this paper we have reviewed various models of the alkaline battery for maximizing its utilization and its lifetime. A brief experiment on relaxation model has been carried out which is most suitable battery model for predicting lifetime of battery in WSN.
mTOSSIM: A simulator that estimates battery lifetime in wireless sensor networks
Simulation Modelling Practice and Theory, 2013
Knowledge of the battery lifetime of the wireless sensor network is important for many situations, such as in evaluation of the location of nodes or the estimation of the connectivity, along time, between devices. However, experimental evaluation is a very time-consuming task. It depends on many factors, such as the use of the radio transceiver or the distance between nodes. Simulations reduce considerably this time. They allow the evaluation of the network behavior before its deployment. This article presents a simulation tool which helps developers to obtain information about battery state. This simulator extends the well-known TOSSIM simulator. Therefore it is possible to evaluate TinyOS applications using an accurate model of the battery consumption and its relation to the radio power transmission. Although an specific indoor scenario is used in testing of simulation, the simulator is not limited to this environment. It is possible to work in outdoor scenarios too. Experimental results validate the proposed model.
An Adaptive Sleep-Time Management Model for Wireless Sensor Networks
2015
The energy consumption of a wireless sensor network affects its lifetime which in turn affects the scope and usefulness of the network. Most existing or proposed MAC protocols enable nodes to specify a duty cycle, so that they can sleep much of the time to save energy. However, only very few models exist to determine the appropriate time and duration of a sleep phase. Existing approaches rely on pre-calculated sleep durations or are difficult to implement on real platforms. We propose a runtime and adaptive model to estimate the sleep time and duration of wireless sensor nodes. Our model takes the statistics of incoming and outgoing packets at a relay node which is then supplied to a general queueing model. The model is lightweight and can be fitted into any existing MAC protocol. We have implemented our model for TelosB platform and TinyOS environment. We integrated our model with two existing protocols (TinyOS LPL MAC and XMAC) and compared the performance of these protocols with ...