Wireless sensor networks with energy harvesting: Modeling and simulation based on a practical architecture using real radiation levels

This paper presents a new energy-harvesting model for a network simulator that implements super-capacitor energy storage with solar energy-harvesting recharge. The model is easily extensible, and other energyharvesting systems, or different energy storages, can be further developed. Moreover, code can be conveniently reused as the implementation is entirely uncoupled from the radio and node models. Real radiation data are obtained from available online databases in order to dynamically calculate super-capacitor charge and discharge. Such novelty enables the evaluation of energy evolution on a network of sensor nodes at various physical world locations and during different seasons. The model is validated against a real and fully working prototype, and good result correlation is shown. Furthermore, various experiments using the ns-3 simulator were conducted, demonstrating the utility of the model in assisting the research and development of the deployment of everlasting wireless sensor networks.This work was supported by the CICYT (research projects CTM2011-29691-C02-01 and TIN2011-28435-C03-01) and UPV research project SP20120889.Climent, S.; Sánchez Matías, AM.; Blanc Clavero, S.; Capella Hernández, JV.; Ors Carot, R. (2013). Wireless sensor networks with energy harvesting: Modeling and simulation based on a practical architecture using real radiation levels. Concurrency and Computation: Practice and Experience. 1-19. https://doi.org/10.1002/cpe.3151S119Akyildiz, I. F., & Vuran, M. C. (2010). Wireless Sensor Networks. doi:10.1002/9780470515181Seah, W. K. G., Tan, Y. K., & Chan, A. T. S. (2012). Research in Energy Harvesting Wireless Sensor Networks and the Challenges Ahead. Autonomous Sensor Networks, 73-93. doi:10.1007/5346_2012_27Vullers, R., Schaijk, R., Visser, H., Penders, J., & Hoof, C. (2010). Energy Harvesting for Autonomous Wireless Sensor Networks. IEEE Solid-State Circuits Magazine, 2(2), 29-38. doi:10.1109/mssc.2010.936667Ammar, Y., Buhrig, A., Marzencki, M., Charlot, B., Basrour, S., Matou, K., & Renaudin, M. (2005). Wireless sensor network node with asynchronous architecture and vibration harvesting micro power generator. Proceedings of the 2005 joint conference on Smart objects and ambient intelligence innovative context-aware services: usages and technologies - sOc-EUSAI ’05. doi:10.1145/1107548.1107618Vijayaraghavan, K., & Rajamani, R. (2007). Active Control Based Energy Harvesting for Battery-Less Wireless Traffic Sensors. 2007 American Control Conference. doi:10.1109/acc.2007.4282842Bottner, H., Nurnus, J., Gavrikov, A., Kuhner, G., Jagle, M., Kunzel, C., … Schlereth, K.-H. (2004). New thermoelectric components using microsystem technologies. Journal of Microelectromechanical Systems, 13(3), 414-420. doi:10.1109/jmems.2004.828740Mateu L Codrea C Lucas N Pollak M Spies P Energy harvesting for wireless communication systems using thermogenerators Conference on Design of Circuits and Integrated Systems (DCIS) 2006AEMet Agencia Estatal de Meteorolgía 2013 http//www.aemet.esPANGAEA Data Publisher for Earth & Environmental Science 2013 http://www.pangaea.de/Zeng, K., Ren, K., Lou, W., & Moran, P. J. (2007). Energy aware efficient geographic routing in lossy wireless sensor networks with environmental energy supply. Wireless Networks, 15(1), 39-51. doi:10.1007/s11276-007-0022-0Hasenfratz, D., Meier, A., Moser, C., Chen, J.-J., & Thiele, L. (2010). Analysis, Comparison, and Optimization of Routing Protocols for Energy Harvesting Wireless Sensor Networks. 2010 IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing. doi:10.1109/sutc.2010.35Noh, D. K., & Hur, J. (2012). Using a dynamic backbone for efficient data delivery in solar-powered WSNs. Journal of Network and Computer Applications, 35(4), 1277-1284. doi:10.1016/j.jnca.2012.01.012Lin, L., Shroff, N. B., & Srikant, R. (2007). Asymptotically Optimal Energy-Aware Routing for Multihop Wireless Networks With Renewable Energy Sources. IEEE/ACM Transactions on Networking, 15(5), 1021-1034. doi:10.1109/tnet.2007.896173Ferry, N., Ducloyer, S., Julien, N., & Jutel, D. (2011). Power/Energy Estimator for Designing WSN Nodes with Ambient Energy Harvesting Feature. EURASIP Journal on Embedded Systems, 2011(1), 242386. doi:10.1155/2011/242386Glaser, J., Weber, D., Madani, S., & Mahlknecht, S. (2008). Power Aware Simulation Framework for Wireless Sensor Networks and Nodes. EURASIP Journal on Embedded Systems, 2008(1), 369178. doi:10.1155/2008/369178De Mil, P., Jooris, B., Tytgat, L., Catteeuw, R., Moerman, I., Demeester, P., & Kamerman, A. (2010). Design and Implementation of a Generic Energy-Harvesting Framework Applied to the Evaluation of a Large-Scale Electronic Shelf-Labeling Wireless Sensor Network. EURASIP Journal on Wireless Communications and Networking, 2010(1). doi:10.1155/2010/343690Castagnetti, A., Pegatoquet, A., Belleudy, C., & Auguin, M. (2012). A framework for modeling and simulating energy harvesting WSN nodes with efficient power management policies. EURASIP Journal on Embedded Systems, 2012(1). doi:10.1186/1687-3963-2012-8Alippi, C., & Galperti, C. (2008). An Adaptive System for Optimal Solar Energy Harvesting in Wireless Sensor Network Nodes. IEEE Transactions on Circuits and Systems I: Regular Papers, 55(6), 1742-1750. doi:10.1109/tcsi.2008.922023Xiaofan Jiang, Polastre, J., & Culler, D. (s. f.). Perpetual environmentally powered sensor networks. IPSN 2005. Fourth International Symposium on Information Processing in Sensor Networks, 2005. doi:10.1109/ipsn.2005.1440974Simjee, F., & Chou, P. H. (2006). Everlast. Proceedings of the 2006 international symposium on Low power electronics and design - ISLPED ’06. doi:10.1145/1165573.1165619Sánchez, A., Climent, S., Blanc, S., Capella, J. V., & Piqueras, I. (2011). WSN with energy-harvesting. Proceedings of the 6th ACM workshop on Performance monitoring and measurement of heterogeneous wireless and wired networks - PM2HW2N ’11. doi:10.1145/2069087.2069091Renner C Jessen J Turau V Lifetime prediction for supercapacitor-powered wireless sensor nodes Proc. of the 8th GI/ITG KuVS Fachgesprächİ Drahtlose Sensornetze(FGSN09) 2009TI Analog, Embedded Processing, Semiconductor Company, Texas Instruments 2013 http//www.ti.comWSNVAL Wireless Sensor Networks Valencia 2013 www.wsnval.comSanchez, A., Blanc, S., Yuste, P., & Serrano, J. J. (2011). RFID Based Acoustic Wake-Up System for Underwater Sensor Networks. 2011 IEEE Eighth International Conference on Mobile Ad-Hoc and Sensor Systems. doi:10.1109/mass.2011.103Fan, K.-W., Zheng, Z., & Sinha, P. (2008). Steady and fair rate allocation for rechargeable sensors in perpetual sensor networks. Proceedings of the 6th ACM conference on Embedded network sensor systems - SenSys ’08. doi:10.1145/1460412.1460436Moser, C., Thiele, L., Brunelli, D., & Benini, L. (2010). Adaptive Power Management for Environmentally Powered Systems. IEEE Transactions on Computers, 59(4), 478-491. doi:10.1109/tc.2009.15

Structural health monitoring of bridges using wireless sensor networks

Structural Health Monitoring, damage detection and localization of bridges using Wireless Sensor Networks (WSN) are studied in this thesis. The continuous monitoring of bridges to detect damage is a very useful tools for preventing unnecessary costly and emergent maintenance. The optimal design aims to maximize the lifetime of the system, the accuracy of the sensed data, and the system reliability, and to minimize the system cost and complexity Finite Element Analysis (FEA) is carried out using LUSAS Bridge Plus software to determine sensor locations and measurement types and effectively minimize the number of sensors, data for transmission, and volume of data for processing. In order to verify the computer simulation outputs and evaluate the proposed optimal design and algorithms, a WSN system mounted on a simple reinforced concrete frame model is employed in the lab. A series of tests are carried out on the reinforced concrete frame mounted on the shaking table in order to simulate the existing extreme loading condition. Experimental methods which are based on modal analysis under ambient vibrational excitation are often employed to detect structural damages of mechanical systems, many of such frequency domain methods as first step use a Fast Fourier Transform estimate of the Power Spectral Density (PSD) associated with the response of the system. In this study it is also shown that higher order statistical estimators such as Spectral Kurtosis (SK) and Sample to Model Ratio (SMR) may be successfully employed to more reliably discriminate the response of the system against the ambient noise and better identify and separate contributions from closely spaced individual modes. Subsequently, the identified modal parameters are used for damage detection and Structural Health Monitoring. To evaluate the preliminary results of the project\u27s prototype and quantify the current bridge response as well as demonstrate the ability of the SHM system to successfully perform on a bridge, the deployment of Wireless Sensor Networks in an existing highway bridge in Qatar is implemented. The proposed technique will eventually be applied to the new stadium that State of Qatar will build in preparation for the 2022 World Cup. This monitoring system will help permanently record the vibration levels reached in all substructures during each event to evaluate the actual health state of the stadiums. This offers the opportunity to detect potentially dangerous situations before they become critical

Energy harvesting wireless sensor network edge device simulation tool

Wireless Sensor Networks (WSN) are Internet of Things (IoT) edge devices. They are becoming widely adopted in many industries including health care, building energy management and conditional monitoring. As the scale of WSN deployments increases, the cost and complexity of battery replacement and disposal become more significant and in time may become a barrier to adoption. Harvesting ambient energies provides a pathway to reducing dependence on batteries and in the future may lead to autonomously powered sensors. This work describes a simulation tool that enables the user to predict the battery life of a wireless sensor that utilizes energy harvesting to supplement the battery power. To create this simulator, all aspects of a typical WSN edge device were modelled including, sensors, transceiver and microcontroller as well as the energy source components (batteries, photovoltaic cells, thermoelectric generators (TEG), supercapacitors and DC/DC converters). The tool allows the user to plug and play different pre characterized devices as well as add user defined devices. The goal of this simulation tool is to predict the battery lifetime of a device and determine the scope for extension using ambient energy sources. This work was part of a larger EU multi-partner project titled “Residential Retrofit assessment platform and demonstrations for near zero energy and CO2 emissions with optimum cost, health, comfort and environmental quality” (ReCO2ST). The ReCO2ST project has received funding from the European Union’s “Horizon 2020” research and innovation programme under the grant agreement No. 768576

Coping with spectrum and energy scarcity in Wireless Networks: a Stochastic Optimization approach to Cognitive Radio and Energy Harvesting

In the last decades, we have witnessed an explosion of wireless communications and networking, spurring a great interest in the research community. The design of wireless networks is challenged by the scarcity of resources, especially spectrum and energy. In this thesis, we explore the potential offered by two novel technologies to cope with spectrum and energy scarcity: Cognitive Radio (CR) and Energy Harvesting (EH). CR is a novel paradigm for improving the spectral efficiency in wireless networks, by enabling the coexistence of an incumbent legacy system and an opportunistic system with CR capability. We investigate a technique where the CR system exploits the temporal redundancy introduced by the Hybrid Automatic Retransmission reQuest (HARQ) protocol implemented by the legacy system to perform interference cancellation, thus enhancing its own throughput. Recently, EH has been proposed to cope with energy scarcity in Wireless Sensor Networks (WSNs). Devices with EH capability harvest energy from the environment, e.g., solar, wind, heat or piezo-electric, to power their circuitry and to perform data sensing, processing and communication tasks. Due to the random energy supply, how to best manage the available energy is an open research issue. In the second part of this thesis, we design control policies for EH devices, and investigate the impact of factors such as the finite battery storage, time-correlation in the EH process and battery degradation phenomena on the performance of such systems. We cast both paradigms in a stochastic optimization framework, and investigate techniques to cope with spectrum and energy scarcity by opportunistically leveraging interference and ambient energy, respectively, whose benefits are demonstrated both by theoretical analysis and numerically. As an additional topic, we investigate the issue of channel estimation in UltraWide-Band (UWB) systems. Due to the large transmission bandwidth, the channel has been typically modeled as sparse. However, some propagation phenomena, e.g., scattering from rough surfaces and frequency distortion, are better modeled by a diffuse channel. We propose a novel Hybrid Sparse/Diffuse (HSD) channel model which captures both components, and design channel estimators based on it

SUSTAINABLE ENERGY HARVESTING TECHNOLOGIES – PAST, PRESENT AND FUTURE

Chapter 8: Energy Harvesting Technologies: Thick-Film Piezoelectric Microgenerato

A system-level methodology for the design and deployment of reliable low-power wireless sensor networks

Innovative Internet of Things (IoT) applications with strict performance and energy consumption requirements and where the agile collection of data is paramount are rousing. Wireless sensor networks (WSN) represent a promising solution as they can be easily deployed to sense, process, and forward data. The large number of Sensor Nodes (SNs) composing a WSN are expected to be autonomous, with a node's lifetime dictated by the battery's size. As the form factor of the SN is critical in various use cases such as industrial and building automation, minimizing energy consumption while ensuring availability becomes a priority. Moreover, energy harvesting techniques are increasingly considered as a viable solution for building an entirely green SN and prolonging its lifetime. In the process of building a SN and in the absence of a clear and well-rounded methodology, the designer can easily make unfounded decisions about the right hardware components, their configuration and data reliable data communication techniques such as automatic repeat request (ARQ) and forward error correction (FEC). In this thesis, a methodology to better optimize the design, configuration and deployment of reliable ultra-low power WSNs is proposed. Comprehensive and realistic energy and path-loss (PL) models of the sensor node are also established. Through estimations and measurements, it is shown that following the proposed methodology, the designer can thoroughly explore the design space and make most favorable decisions when choosing commercial off-the-shelf (COTS) components, configuring the node, and deploying a reliable and energy-efficient WSN

Avoin alustakehitys IEEE 802.15.4 -standardin mukaisessa langattomassa automaatiossa

This doctoral dissertation focuses on open source platform development in wireless automation under IEEE 802.15.4 standard. Research method is empirical. A platform based approach, which targets to the design of a generic open source sensor platform, was selected as a design method. The design targets were further focused by interviewing the experts from the academia and industry. Generic and modular sensor platform, the UWASA Node, was developed as an outcome of this process. Based on the implementation results, a wireless sensor and actuator network based on the UWASA Node was a feasible solution for many types of wireless automation applications. It was also possible to interface it with the other parts of the system. The targeted level of sensor platform genericity was achieved. However, it was also observed that the achieved level of genericity increased the software complexity. The development of commercial sensor platforms, which support IEEE 802.15.4 sensor networking, has narrowed down the role of open source sensor platforms, but they are not disappearing. Commercial software is usually closed and connected to a specified platform, which makes it unsuitable for research and development work. Even though there exits many commercial WSN solutions and the market expectations in this area are high, there is still a lot of work to do before the visions about Internet of Things (IoT) are fulfilled, especially in the context of distributed and locally centralized operations in the network. In terms of control engineering, one of the main research issues is to figure out how the well-known control techniques may be applied in wireless automation where WSN is part of the automation system. Open source platforms offer an important tool in this research and development work.Tämä väitöskirja käsittelee avointa alustakehitystä IEEE 802.15.4 -standardin mukaisessa langattomassa automaatiossa. Tutkimusmenetelmä on empiirinen. Työssä sovelletaan alustaperustaista suunnittelutapaa, joka tähtää yleiskäyttöisen avoimen anturialustan kehittämiseen. Suunnittelun tavoitteita tarkennettiin haastattelemalla alan asiantuntijoita teollisuudesta ja yliopistomaailmasta. Tuloksena suunniteltiin ja toteutettiin anturialusta, the UWASA Node. Implementointituloksista voidaan vetää johtopäätös, että anturialustan tavoiteltu yleiskäyttöisyystaso saavutettiin. Toisaalta saavutettu yleiskäyttöisyystaso lisäsi alustan ohjelmistoarkkitehtuurin monimutkaisuutta. Kaupallisten IEEE 802.15.4 -standardia tukevien anturialustojen tulo markkinoille vähentää avointen anturialustojen käyttöä, mutta ne eivät ole katoamassa. Kaupalliset ohjelmistot ovat tyypillisesti suljettuja ja sidoksissa tiettyyn alustaan, mikä tekee niistä sopimattomia tutkimus- ja tuotekehityskäyttöön. Vaikka nykyään on saatavilla useita kaupallisia langattomia anturi- ja toimilaiteverkkoja, vaaditaan vielä paljon työtä ennen kun kaikki esineiden Internetiin (Internet of Things) liittyvät visiot voidaan toteuttaa. Tämä koskee erityisesti langattomassa anturi- ja toimilaiteverkossa hajautetusti tai paikallisesti toteutettavia toimintoja. Säätötekniikan näkökulmasta keskeinen kysymys on, miten tunnettuja säätömenetelmiä tulee soveltaa langattomassa automaatiossa, jossa langaton anturi- ja toimilaiteverkko on osa automaatiojärjestelmää. Avoimet anturialustat ovat tärkeä työkalu sen selvittämisessä.fi=vertaisarvioitu|en=peerReviewed

Radio frequency channel characterization for energy harvesting in factory environments

This thesis presents ambient energy data obtained from a measurement campaign carried out at an automobile plant. At the automobile plant, ambient light, ambient temperature and ambient radio frequency were measured during the day time over two days. The measurement results showed that ambient light generated the highest DC power. For plant and operation managers at the automobile plant, the measurement data can be used in system design considerations for future energy harvesting wireless sensor nodes at the plant. In addition, wideband measurements obtained from a machine workshop are presented in this thesis. The power delay profile of the wireless channel was obtained by using a frequency domain channel sounding technique. The measurements were compared with an equivalent ray tracing model in order to validate the suitability of the commercial propagation software used in this work. Furthermore, a novel technique for mathematically recreating the time dispersion created by factory inventory in a radio frequency channel is discussed. As a wireless receiver design parameter, delay spread characterizes the amplitude and phase response of the radio channel. In wireless sensor devices, this becomes paramount, as it determines the complexity of the receiver. In reality, it is sometimes difficult to obtain full detail floor plans of factories for deterministic modelling or carry out spot measurements during building construction. As a result, radio provision may be suboptimal. The method presented in this thesis is based on 3-D fractal geometry. By employing the fractal overlaying algorithm presented, metallic objects can be placed on a floor plan so as to obtain similar radio frequency channel effects. The environment created using the fractal approach was used to estimate the amount of energy a harvesting device can accumulate in a University machine workshop space

Secure key design approaches using entropy harvesting in wireless sensor network: A survey

Physical layer based security design in wireless sensor networks have gained much importance since the past decade. The various constraints associated with such networks coupled with other factors such as their deployment mainly in remote areas, nature of communication etc. are responsible for development of research works where the focus is secured key generation, extraction, and sharing. Keeping the importance of such works in mind, this survey is undertaken that provides a vivid description of the different mechanisms adopted for securely generating the key as well its randomness extraction and also sharing. This survey work not only concentrates on the more common methods, like received signal strength based but also goes on to describe other uncommon strategies such as accelerometer based. We first discuss the three fundamental steps viz. randomness extraction, key generation and sharing and their importance in physical layer based security design. We then review existing secure key generation, extraction, and sharing mechanisms and also discuss their pros and cons. In addition, we present a comprehensive comparative study of the recent advancements in secure key generation, sharing, and randomness extraction approaches on the basis of adversary, secret bit generation rate, energy efficiency etc. Finally, the survey wraps up with some promising future research directions in this area

Survey Paper Artificial and Computational Intelligence in the Internet of Things and Wireless Sensor Network

In this modern age, Internet of Things (IoT) and Wireless Sensor Network (WSN) as its derivatives have become one of the most popular and important technological advancements. In IoT, all things and services in the real world are digitalized and it continues to grow exponentially every year. This growth in number of IoT device in the end has created a tremendous amount of data and new data services such as big data systems. These new technologies can be managed to produce additional value to the existing business model. It also can provide a forecasting service and is capable to produce decision-making support using computational intelligence methods. In this survey paper, we provide detailed research activities concerning Computational Intelligence methods application in IoT WSN. To build a good understanding, in this paper we also present various challenges and issues for Computational Intelligence in IoT WSN. In the last presentation, we discuss the future direction of Computational Intelligence applications in IoT WSN such as Self-Organizing Network (dynamic network) concept