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. 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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. 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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. 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Procedure for Selecting a Transmission Mode Dependent on the State-of-Charge and State-of-Health of a Lithium-ion Battery in Wireless Sensor Networks with Energy Harvesting Devices

Diverse methods and considerations have been proposed to manage the available energy in an efficient manner in Wireless Sensor Networks. By incorporating Energy Harvesting Devices in these type of networks it is possible to reduce the dependency of the availability of the Energy Storage Devices, particularly the lithium-ion battery. Recently, the State-of-Charge and State-of-Health of the battery have been considered as inputs for the design of the Medium- Access-Control protocols for Wireless Sensor Networks. In this article, different guidelines are proposed for the design of Medium-Access-Control protocols used in Wireless Sensor Networks with Energy Harvesting Devices considering the State-of-Charge and State-of-Health as indicators for the estimation of the transmission time of the sensor node. The proposed guidelines consider different currents used during the transmission to estimate the State-of-Charge and Stateof- Health of the battery. The incorporation of these indicators aim to improve the decision-making process of the sensor node when transmitting.Diverse methods and considerations have been proposed to manage the available energy in an efficient manner in Wireless Sensor Networks. By incorporating Energy Harvesting Devices in these type of networks it is possible to reduce the dependency of the availability of the Energy Storage Devices, particularly the lithium-ion battery. Recently, the State-of-Charge and State-of-Health of the battery have been considered as inputs for the design of the Medium- Access-Control protocols for Wireless Sensor Networks. In this article, different guidelines are proposed for the design of Medium-Access-Control protocols used in Wireless Sensor Networks with Energy Harvesting Devices considering the State-of-Charge and State-of-Health as indicators for the estimation of the transmission time of the sensor node. The proposed guidelines consider different currents used during the transmission to estimate the State-of-Charge and Stateof- Health of the battery. The incorporation of these indicators aim to improve the decision-making process of the sensor node when transmitting

Radio Frequency Energy Harvesting and Management for Wireless Sensor Networks

Radio Frequency (RF) Energy Harvesting holds a promising future for generating a small amount of electrical power to drive partial circuits in wirelessly communicating electronics devices. Reducing power consumption has become a major challenge in wireless sensor networks. As a vital factor affecting system cost and lifetime, energy consumption in wireless sensor networks is an emerging and active research area. This chapter presents a practical approach for RF Energy harvesting and management of the harvested and available energy for wireless sensor networks using the Improved Energy Efficient Ant Based Routing Algorithm (IEEABR) as our proposed algorithm. The chapter looks at measurement of the RF power density, calculation of the received power, storage of the harvested power, and management of the power in wireless sensor networks. The routing uses IEEABR technique for energy management. Practical and real-time implementations of the RF Energy using Powercast harvesters and simulations using the energy model of our Libelium Waspmote to verify the approach were performed. The chapter concludes with performance analysis of the harvested energy, comparison of IEEABR and other traditional energy management techniques, while also looking at open research areas of energy harvesting and management for wireless sensor networks.Comment: 40 pages, 9 figures, 5 tables, Book chapte

Recent advances in wireless sensor networks with environmental energy harvesting

Shu, L.; Liao, W.; Lloret, J.; Wang, L. (2016). Recent advances in wireless sensor networks with environmental energy harvesting. International Journal of Sensor Networks. 21(4):205-207. http://hdl.handle.net/10251/18736720520721

Capacity of Fading Gaussian Channel with an Energy Harvesting Sensor Node

Network life time maximization is becoming an important design goal in wireless sensor networks. Energy harvesting has recently become a preferred choice for achieving this goal as it provides near perpetual operation. We study such a sensor node with an energy harvesting source and compare various architectures by which the harvested energy is used. We find its Shannon capacity when it is transmitting its observations over a fading AWGN channel with perfect/no channel state information provided at the transmitter. We obtain an achievable rate when there are inefficiencies in energy storage and the capacity when energy is spent in activities other than transmission.Comment: 6 Pages, To be presented at IEEE GLOBECOM 201

Self-synchronized duty-cycling for sensor networks with energy harvesting capabilities: Implementation in Wiselib

In this work we present a protocol for a self- synchronized duty-cycling mechanism in wireless sensor net- works with energy harvesting capabilities. The protocol is im- plemented in Wiselib, a library of generic algorithms for sensor networks. Simulations are conducted with the sensor network simulator Shawn. They are based on the specifications of real hardware known as iSense sensor nodes. The experimental results show that the proposed mechanism is able to adapt to changing energy availabilities. Moreover, it is shown that the system is very robust against packet loss.Postprint (published version

Information exchange in randomly deployed dense WSNs with wireless energy harvesting capabilities

©2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.As large-scale dense and often randomly deployed wireless sensor networks (WSNs) become widespread, local information exchange between colocated sets of nodes may play a significant role in handling the excessive traffic volume. Moreover, to account for the limited life-span of the wireless devices, harvesting the energy of the network transmissions provides significant benefits to the lifetime of such networks. In this paper, we study the performance of communication in dense networks with wireless energy harvesting (WEH)-enabled sensor nodes. In particular, we examine two different communication scenarios (direct and cooperative) for data exchange and we provide theoretical expressions for the probability of successful communication. Then, considering the importance of lifetime in WSNs, we employ state-of-the-art WEH techniques and realistic energy converters, quantifying the potential energy gains that can be achieved in the network. Our analytical derivations, which are validated by extensive Monte-Carlo simulations, highlight the importance of WEH in dense networks and identify the tradeoffs between the direct and cooperative communication scenarios.Peer ReviewedPostprint (author's final draft

Optimization of the overall success probability of the energy harvesting cognitive wireless sensor networks

Wireless energy harvesting can improve the performance of cognitive wireless sensor networks (WSNs). This paper considers radio frequency (RF) energy harvesting from transmissions in the primary spectrum for cognitive WSNs. The overall success probability of the energy harvesting cognitive WSN depends on the transmission success probability and energy success probability. Using the tools from stochastic geometry, we show that the overall success probability can be optimized with respect to: 1) transmit power of the sensors; 2) transmit power of the primary transmitters; and 3) spatial density of the primary transmitters. In this context, an optimization algorithm is proposed to maximize the overall success probability of the WSNs. Simulation results show that the overall success probability and the throughput of the WSN can be significantly improved by optimizing the aforementioned three parameters. As RF energy harvesting can also be performed indoors, hence, our solution can be directly applied to the cognitive WSNs that are installed in smart buildings

Energy harvesting over Rician fading channel: A performance analysis for half-duplex bidirectional sensor networks under hardware impairments

In this paper, a rigorous analysis of the performance of time-switching energy harvesting strategy that is applied for a half-duplex bidirectional wireless sensor network with intermediate relay over a Rician fading channel is presented to provide the exact-form expressions of the outage probability, achievable throughput and the symbol-error-rate (SER) of the system under the hardware impairment condition. Using the proposed probabilistic models for wireless channels between mobile nodes as well as for the hardware noises, we derive the outage probability of the system, and then the throughput and SER can be obtained as a result. Both exact analysis and asymptotic analysis at high signal-power-to-noise-ratio regime are provided. Monte Carlo simulation is also conducted to verify the analysis. This work confirms the effectiveness of energy harvesting applied in wireless sensor networks over a Rician fading channel, and can provide an insightful understanding about the effect of various parameters on the system performance.Web of Science186art. no. 1781