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State-of-charge measurement error simulation for power-aware wireless sensor networks
Abstract
Wireless sensor networks (WSNs) are power critical systems, because they are used in application areas without wired infrastructure. Each sensor node needs a dedicated power supply. Today's protocols and applications for WSNs are often power-aware. However, the state-of-charge (SoC) estimation of the energy storage component (e.g. rechargeable battery) influences the decisions of the power-aware software. A measurement error may cause wrong decisions which would reduce the lifetime of the network. This work presents the simulation results of the impact of an SoC measurement error on the network lifetime running a simple and an enhanced power-aware WSN application. A simulation environment written in SystemC-AMS is used, which enables the combined simulation of the sensor node's application software and its hardware. The simulation shows a reduction of the network lifetime of 3.13% caused by an erroneous measurement. However, the enhanced software is able to increase the network lifetime by 1.66%.
... Several battery chemistries respond differently to environmental stimuli, particularly temperature changes and attached loads, and reach an early cut-off stage. As a result, predicting their behaviour over time is critical, particularly when designing energy-efficient algorithms that can extend device lifetime [7][8][9][10]. ...
Wireless sensor networks (WSN) are commonly used in remote environments for monitoring and sensing. These devices are typically powered by batteries, the performance of which varies depending on environmental (such as temperature and humidity) as well as operational conditions (discharge rate and state-of-charge, SOC). As a result, assessing their technical viability for WSN applications requires performance evaluation based on the aforementioned stimuli. This paper proposes an efficient method for examining battery performance parameters such as capacity, open-circuit voltage (OCV) and SOC. Four battery types (lithium-ion, lithium-polymer, nickel-metal hydride and alkaline) were subjected to IEEE 802.15.4 protocol-based discharge rates to record the discharge characteristics. Furthermore, the combined effect of discharge rates on battery surface temperature and OCV variations was investigated. Shorter relaxation times (4-8 h) were observed in lithium-based batteries, resulting in faster energy recovery while maintaining rated capacity. It was observed that nearly 80% of the voltage region was flat, with minor voltage variations during the discharge cycle. Furthermore, lithium-based batteries experienced negligible changes in surface temperatures (approx. 0.03°C) with respect to discharge rates, making them the best battery choice for low-power applications such as WSNs.
... It is well-known that electrochemical cells provide different effective charge capacities according to temperature [3], [4]. It is therefore difficult to estimate their behaviour over time, particularly, the information required to implement energy-aware algorithms, such as the State of Charge (SoC) and voltage levels [5]- [7]. For example, an accurate voltage information is of paramount importance for the Wireless Sensor Network (WSN) lifetime assessment, as sensor nodes stop working when the cut-off voltage level is crossed [8], [9]. ...
Wireless Sensor Networks (WSNs) can be used to support monitoring activities in a wide range of applications and communication environments. Its usage in extreme conditions, in what concerns pressure, temperature, and humidity, must be carefully assessed before the network deployment. In particular, the temperature variations have a direct impact upon the behaviour of WSNs, through the batteries of sensor nodes. These electrochemical devices are highly susceptible to temperature variations, which modifies the offered effective charge capacity. In this context, it is difficult to estimate the behaviour of batteries over time, impairing the extraction of relevant information for energy-aware approaches. Such information, particularly, battery state of charge, voltage, and lifetime, is often used by WSN simulators to predict the communication behaviour in different scenarios. Nevertheless, WSN simulators generally use simplistic battery models, causing significant deviations in simulation results when compared to actual WSN deployments. This paper describes the implementation of the Temperature- Dependent Kinetic Battery Model (T-KiBaM) in the Castalia simulator, which enables a considerable improvement of the accuracy of simulations in communication environments with different temperature conditions. An experimental assessment has been performed with temperature variations over time to validate the usage of the T-KiBaM battery model. The experimental results indicate that the T-KiBaM model is quite accurate when estimating battery behaviour under both different temperature set-points and different temperature variations.
Video distribution is nowadays the dominant source of Internet traffic, and recent studies show that it is expected to reach 90% of the global consumer traffic by the end of 2015. Peer-to-peer assisted solutions have been adopted by many content providers with the aim of improving the scalability and reliability of their distribution network. While many solutions have been proposed, virtually all of them are at the overlay level, and so rely on the standard functionality of the transport layer. The Peer-to-Peer Streaming Protocol workgroup of the IETF has adopted the Libswift transport-layer protocol that is targeted at P2P traffic. In this paper we describe the design features and a first implementation of the Libswift protocol, and a piece-picking protocol that uses the transport features of Libswift in an essential way. We investigate its performance on both high-end and power-constrained low-end devices, comparing it to the state-of-the-art in P2P protocols.
Wireless sensor networks (WSNs) are typically used in application areas without wired infrastructure. WSNs must be energy efficient, because they are powered by batteries or energy harvesting systems. Teaching of practical aspects of energy harvesting enhanced WSNs can be improved by integration of performance-centered tasks. This work presents an educational remote lab concept for energy harvesting enhanced WSNs. Students can learn how WSNs work and how energy harvesting influences their behavior. It is part of the European project Remote-labs Access in Internet-based Performance-centered Learning Environment for Curriculum Support.
Recently there have been numerous re-search results in the area of power efficiency in ad hoc and wireless sensor networks. This paper discusses the effect of power efficient routing algorithms on the lifetime of multihop wireless sensor networks (WSNs). The WSNs considered are special cases of mobile ad hoc networks (MANETs); in particular, we assume that all data and control traffic in the WSN is flowing between the sensor nodes and the base station. This assumption results in a considerably simpler problem and solution than for the more general MANETs. We calculate analytically lifetime bounds of the WSN under specific routing algorithms. The main result of the paper is that, for WSNs, the choice of the routing algorithm has almost no consequence to the lifetime of the network. This result, as well as being obviously useful, is somewhat surprising since this is not true of general MANETs.
We present TinyOS, a flexible, application-specific operating system for sensor networks. Sen-sor networks consist of (potentially) thousands of tiny, low-power nodes, each of which ex-ecute concurrent, reactive programs that must operate with severe memory and power con-straints. The sensor network challenges of limited resources, event-centric concurrent appli-cations, and low-power operation drive the design of TinyOS. Our solution combines flexible, fine-grain components with an execution model that supports complex yet safe concurrent op-erations. TinyOS meets these challenges well and has become the platform of choice for sensor network research; it is in use by over a hundred groups worldwide, and supports a broad range of applications and research topics. We provide a qualitative and quantitative evaluation of the system, showing that it supports complex, concurrent programs with very low memory requirements (many applications fit within 16KB of memory, and the core OS is 400 bytes) and efficient, low-power operation. We present our experiences with TinyOS as a platform for sensor network innovation and applications.
We report on preliminary experiences with deploying a large-scale sensor network (about 100 nodes) for a pilot in precision agriculture. The pilot did not answer the initial research questions, but instead revealed many engineering problems typically overlooked by (computer) scientists evaluating their work by means of simulation. The deployment prompted us to rethink our development process and includes important lessons for the WSN research community as a whole
The paper introduces a model of wireless sensor network (WSN) nodes with SystemC-AMS, a C++ based language for system-level description, then presents model refinements and simulation improvements of the system. Firstly the paper details the components of a WSN node : a sensor, an ADC, a microcontroller and a RF transceiver with some introduced impairements. Secondly, it presents the implementation of RF components refinements, for a designer closer model specifications, such as power gain, noise figure, IIP3 specifications. Finally, the third part shows the simulation improvements introduced by a baseband equivalent implementation made easier with a C++ based language.
This paper presents an easy-to-use battery model applied to dynamic simulation software. The simulation model uses only the battery State-Of-Charge (SOC) as a state variable in order to avoid the algebraic loop problem. It is shown that this model, composed of a controlled voltage source in series with a resistance, can accurately represent four types of battery chemistries. The model's parameters can easily be extracted from the manufacturer's discharge curve, which allows for an easy use of the model. A method is described to extract the model's parameters and to approximate the internal resistance. The model is validated by superimposing the results with the manufacturer's discharge curves. Finally, the battery model is included in the SimPowerSystems (SPS) simulation software and is used in the Hybrid Electric Vehicle (HEV) demo. The results for the battery and for the DC-DC converter are analysed and they show that the model can accurately represent the general behaviour of the battery.
The paper presents a preliminary approach for the modeling and simulation of a complete wireless sensor network with two nodes using SystemC-AMS, an open-source C++ library dedicated to the description of heterogeneous systems containing digital, analog, RF hardware parts as well as embedded software. The WSN node, or mote, detailed herein consists of a physical sensor, a continuous time sigma-delta converter with its associated decimation filter, an ATMEGA128 8-bit microcontroller running the embedded application and a QPSK-based 2.4 GHz RF transceiver. The paper starts with the structural description of the system as a hierarchical set of behavioural modules, then gives an insight on how multi-frequency simulation is handled in SystemC-AMS, and finally presents simulation results that are systematically compared with the Matlab reference in terms of accuracy and simulation time.
The paper presents a system-level approach for the modeling and simulation of a paradigmatic Wireless Sensor Network composed of two nodes using SystemC-AMS, an open-source C++ extension to the OSCI SystemC Standard dedicated to the description of heterogeneous systems containing digital, analog, RF hardware IPs as well as embedded software. The paper is composed of three parts. The first part details the modeled WSN (physical sensor, sigma-delta ADC, ATMEGA128 8-bit microcontroller running the embedded application, QPSK-based 2.4 GHz RF transceiver), presents the corresponding implementation in SystemC-AMS, and gives an insight on how multi-frequency simulation is handled in SystemC-AMS. The second part shows how to introduce several RF designer specifications (noise figure, IIP3, ...) into models and how to express them in SystemC-AMS. The third part proves that the combination of C++ and RF baseband equivalent dramatically reduces simulation time while keeping excellent accuracy and code readability. The paper concludes on the possibilities offered by this approach in terms of validation and optimization of heteregeneous systems through open-source simulation.
This paper presents "SimPlayGround" - a framework for the stimulative evaluation of reactive and proactive power-management approaches in distributed energy harvesting systems. Our simulation framework combines the simulation of wireless communication, the behavior of reversible power supplies and the dynamics of power consumption. It supports models of the environmental power and energy harvesters with an on-line computation of aviable power, so that the control of the simulated system can base on it.
Wireless sensor networks (WSNs) are very complex systems. They are often used in application areas with poor infrastructure and mobility, so each sensor node of a WSN needs its own power supply. Energy harvesting systems (EHSs) can be used to extend the operational lifetime of sensor nodes or even enable perpetual operation. However, the efficiency of the power supply depends not only on the used hardware itself but also on the application running on the sensor node. Therefore, a simulation environment including the complete WSN is needed to be able to understand all dependencies. This work presents the concept and first results of a simulation environment written in SystemC-AMS for the simulation of complete WSN. It includes energy harvesting, communication, power-awareness, location-awareness and dynamic power management. Each node is composed of single modules which can be exchanged easily to test different hardware or software components. The results show the ability to simulate energy harvesting systems using SystemC-AMS.
This paper describes the design and implementation of a battery-less wireless sensor network system that uses the combination of an electric double layer capacitor equipped with a small solar cell as its energy source. Although it is easy to drive sensor nodes with such energy sources on the hardware level, long-interval communication remains an im- portant problem. Since the amount of energy obtained from a solar cell is small, a sensor node should wait until a suf- ficient amount of energy is charged in the capacitor before sensing or wireless communication. Thus, sensor nodes wait for a long period for capacitor charging and then commu- nicate over a short period. This behavior by the node se- riously aects the design of the communication mechanism in battery-less sensor network systems. In order to address this problem, we have designed a communication mecha- nism that is suitable for such behavior of battery-less sensor network nodes based on a practical application scenario of environmental monitoring applications. We also implement the newly designed communication mechanism on custom hardware and evaluate the performance of the mechanism trough several experiments.
This paper presents a SystemC-based system-level modeling framework for wireless sensor networks. Benefit from the advantages of SystemC in the aspect of hardware and software co-simulation, this modeling framework allows the performance evaluation of sensor networks at system-level with elaborate sensor node models. It is used to study an industrial application, in which an IEEE 802.15.4 sensor network based on MICAz motes is deployed to achieve the active control of the vibrations in an automotive system. The simulation results (packet delivery rate, packet delivery latency and power consumption) provide a fundamental understanding of this application for the system designers.
Energy consumption is one of the most important metrics in wireless sensor networks because of the limited power supply in sensor nodes. Many efforts have been taken to reduce the energy consumption of the hardware, software, communication protocols and applications. Simulation is widely used to evaluate the performance of these new designs. Thus, it is necessary to accurately model the energy consumption during the simulation of WSN. In this paper, an energy model for WSN is proposed. It has been implemented in IDEA1, a simulator for WSN developed by SystemC and C++. It enables the energy estimation of both the hardware components of an individual node and the whole sensor network. It can be easily calibrated to different types of node if the electrical characteristics of the hardware components are available. An application based on IEEE 802.15.4 and MICAz motes is studied to demonstrate the capability of the energy model.
Sensor networks are now enabling the monitoring of various environmental phenomena with more accuracy than the previous labour intensive and less technological solutions. This paper is concerned with the application of opportunistic networking techniques to wildlife monitoring, where the sensors are attached to animals moving in their habitat. We present seal-2-seal, a novel protocol for logging of node (i.e., animal) contacts in mobile networks and for dissemination of that information to sinks for further analysis. The protocol utilises an efficient data summary mechanism to reduce the amount of information that needs to be transmitted, thus reducing energy consumption. To evaluate the performance of the protocol, we implemented it for the Contiki operating system on sensor devices and ran simulations based on real-life mobility traces using the Cooja emulator.
Wireless sensor network (WSN) nodes have to cope with severe power supply constraints. Energy harvesting system (EHS) technology is used for prolonging network lifetime. Robust operation of such systems heavily relies on accurate models of EHS efficiency and node power dissipation for calculating sustainable operation modes. A node's energy balance can be described with a power state model (PSM). While for battery operated WSNs PSM measurement errors and battery effects have to be considered, this paper widens the point of view to EHS properties. We analyze varying PSMs at runtime, EHS efficiency and measurement error's impacts on duty cycled WSNs. We show a measurement system setup and results from profiling a PSM for Mica2 nodes with an EHS. We explain important considerations for such systems with showing the variance of a node's energy consumption depending on its supply and we profile EHS efficiency for deducing an energy storage level operating point. The results contribute to problems in the field of low power WSNs and especially EHS supported ones. Robustness of duty cycled WSNs heavily depends on measurement error bounds and is impacted by EHS efficiency. The reader is provided with a methodology for measuring and modeling robust WSNs.
Wireless sensor networks (WSNs) are typically used in application areas without wired infrastructure or mobility. Therefore, each sensor node needs its own energy supply unit. Sustainable WSNs are powered by energy harvesting systems (EHSs). These systems harvest and buffer the energy from the environment into rechargeable batteries or double layer capacitors. Due to the fact that the connectivity of the WSN depends on every single sensor node, it is important to know the state-of-charge (SoC) of the buffer to determine the remaining operating time. Therefore, each node has to measure its SoC, calculate the remaining operating time and populate this information through the network. The measurement is usually done by an integrated analog-to-digital converter (ADC) of the microcontroller. Each ADC has a specified error that has to be taken into account at the calculations of the remaining operating time. This work presents the error analysis of the SoC measurement using different energy storage components.
Power consumption and size are the most important challenges faced when designing radios for distributed wireless sensor networks (WSN). Reducing power consumption requires optimization across all the layers of the communication systems. Although the MAC layer plays a crucial role in the overall energy efficiency, the radio remains one of the bottleneck for implementing ultra low-power WSN. The power consumption of the radios available today does not allow for continuous operation and the radio has to be duty-cycled in order to reach the targeted several years autonomy. This clearly has an impact on how to design a radio for WSN. Reducing the node size can be partly achieved by a high level of integration of all the functions required by one node on a single chip. This leads to systems-on-chip (SoC) that are dedicated to WSN. This paper addresses the different issues in the design of ultra low-power WSN with a particular emphasis on the radio. It reviews the constraints imposed on the transceiver design by the low-power and low-voltage specifications, the duty-cycled operation and the modulation scheme. Several radio architectures that can potentially be used for WSN are discussed in the perspective of a CMOS implementation. As examples, the 1st-and 2nd-generation of WiseNET ultra low-power transceivers are presented. The 1st-generation WiseNET transceiver is integrated in a 0.5 μm standard digital CMOS process. It operates in the 434 MHz band and consumes only 1 mW from a 1 V supply, while achieving a -95 dBm sensitivity for a 24 kb/s data rate with a 10<sup>-3</sup> BER. The 2nd-generation WiseNET transceiver was designed and specifically optimized for the new WiseMAC protocol specially developed for WSN. It runs from a single 1.5 V battery and operates down to 0.9 V while consuming only 1.8 mW in receive mode. It achieves a -104 dBm sensitivity for 25 kb/s data rate with a 10<sup>-3</sup> BER. In addition to this low-power radio, the 2nd-generation WiseNET system-on-chip (SoC) also includes all the functions required for data acquisition, processing and storage of the information provided by the sensor. The WiseNET solution comprising the WiseNET SoC together with the WiseMAC protocol consumes more than 30 times less power than comparable solutions available - today, using for example the IEEE 802.15.4 standard. Finally, some important blocks such as the frequency synthesizer and the ADC are discussed in the perspective of moving to higher data rates, higher operating frequency and phase modulation, taking as example the IEEE 802.15.4 standard. Examples of injection locked oscillators and dividers are given. The conversion of I/Q phase modulated signals to digital are illustrated by an example of an I/Q Δ-Σ ADC and a direct phase ADC.
Sustainable operation of battery powered wireless embedded systems (such as sensor nodes) is a key challenge, and considerable research effort has been devoted to energy optimization of such systems. Environmental energy harvesting, in particular solar based, has emerged as a viable technique to supplement battery supplies. However, designing an efficient solar harvesting system to realize the potential benefits of energy harvesting requires an in-depth understanding of several factors. For example, solar energy supply is highly time varying and may not always be sufficient to power the embedded system. Harvesting components, such as solar panels, and energy storage elements, such as batteries or ultracapacitors, have different voltage-current characteristics, which must be matched to each other as well as the energy requirements of the system to maximize harvesting efficiency. Further, battery non-idealities, such as self-discharge and round trip efficiency, directly affect energy usage and storage decisions. The ability of the system to modulate its power consumption by selectively deactivating its sub-components also impacts the overall power management architecture. This paper describes key issues and tradeoffs which arise in the design of solar energy harvesting, wireless embedded systems and presents the design, implementation, and performance evaluation of Heliomote, our prototype that addresses several of these issues. Experimental results demonstrate that Heliomote, which behaves as a plug-in to the Berkeley/Crossbow motes and autonomously manages energy harvesting and storage, enables near-perpetual, harvesting aware operation of the sensor node.