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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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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Towards self-powered wireless sensor networks

Ubiquitous computing aims at creating smart environments in which computational and communication capabilities permeate the word at all scales, improving the human experience and quality of life in a totally unobtrusive yet completely reliable manner. According to this vision, an huge variety of smart devices and products (e.g., wireless sensor nodes, mobile phones, cameras, sensors, home appliances and industrial machines) are interconnected to realize a network of distributed agents that continuously collect, process, share and transport information. The impact of such technologies in our everyday life is expected to be massive, as it will enable innovative applications that will profoundly change the world around us. Remotely monitoring the conditions of patients and elderly people inside hospitals and at home, preventing catastrophic failures of buildings and critical structures, realizing smart cities with sustainable management of traffic and automatic monitoring of pollution levels, early detecting earthquake and forest fires, monitoring water quality and detecting water leakages, preventing landslides and avalanches are just some examples of life-enhancing applications made possible by smart ubiquitous computing systems. To turn this vision into a reality, however, new raising challenges have to be addressed, overcoming the limits that currently prevent the pervasive deployment of smart devices that are long lasting, trusted, and fully autonomous. In particular, the most critical factor currently limiting the realization of ubiquitous computing is energy provisioning. In fact, embedded devices are typically powered by short-lived batteries that severely affect their lifespan and reliability, often requiring expensive and invasive maintenance. In this PhD thesis, we investigate the use of energy-harvesting techniques to overcome the energy bottleneck problem suffered by embedded devices, particularly focusing on Wireless Sensor Networks (WSNs), which are one of the key enablers of pervasive computing systems. Energy harvesting allows to use energy readily available from the environment (e.g., from solar light, wind, body movements, etc.) to significantly extend the typical lifetime of low-power devices, enabling ubiquitous computing systems that can last virtually forever. However, the design challenges posed both at the hardware and at the software levels by the design of energy-autonomous devices are many. This thesis addresses some of the most challenging problems of this emerging research area, such as devising mechanisms for energy prediction and management, improving the efficiency of the energy scavenging process, developing protocols for harvesting-aware resource allocation, and providing solutions that enable robust and reliable security support. %, including the design of mechanisms for energy prediction and management, improving the efficiency of the energy harvesting process, the develop of protocols for harvesting-aware resource allocation, and providing solutions that enable robust and reliable security support

Incremental checkpointing of program state to NVRAM for transiently-powered systems

International audienceAs technology improves, it becomes possible to design autonomous, energy-harvesting networked embedded systems, a key building block for the Internet of Things. However, running from harvested energy means frequent and unpredictable power failures. Programming such Transiently Powered Computers will remain an arduous task for the software developer, unless some OS support abstracts energy management away from application design. Various approaches were proposed to address this problem. We focus on checkpointing, i.e. saving and restoring program state to and from non-volatile memory. In this paper, we propose an incremental checkpointing scheme which aims at minimizing the amount of data written to non-volatile memory, while keeping the execution overhead as low as possible

Microwave Antennas for Energy Harvesting Applications

In the last few years, the demand for power has increased; therefore, the need for alternate energy sources has become essential. Sources of fossil fuels are finite, are costly, and causes environmental hazard. Sustainable, environmentally benign energy can be derived from nuclear fission or captured from ambient sources. Large-scale ambient energy is widely available and large-scale technologies are being developed to efficiently capture it. At the other end of the scale, there are small amounts of wasted energy that could be useful if captured. There are various types of external energy sources such as solar, thermal, wind, and RF energy. Energy has been harvested for different purposes in the last few recent years. Energy harvesting from inexhaustible sources with no adverse environmental effect can provide unlimited energy for harvesting in a way of powering an embedded system from the environment. It could be RF energy harvesting by using antennas that can be held on the car glass or building, or in any places. The abundant RF energy is harvested from surrounding sources. This chapter focuses on RF energy harvesting in which the abundant RF energy from surrounding sources, such as nearby mobile phones, wireless LANs (WLANs), Wi-Fi, FM/AM radio signals, and broadcast television signals or DTV, is captured by a receiving antenna and rectified into a usable DC voltage. A practical approach for RF energy harvesting design and management of the harvested and available energy for wireless sensor networks is to improve the energy efficiency and large accepted antenna gain. The emerging self-powered systems challenge and dictate the direction of research in energy harvesting (EH). There are a lot of applications of energy harvesting such as wireless weather stations, car tire pressure monitors, implantable medical devices, traffic alert signs, and mars rover. A lot of researches are done to create several designs of rectenna (antenna and rectifier) that meet various objectives for use in RF energy harvesting, whatever opaque or transparent. However, most of the designed antennas are opaque and prevent the sunlight to pass through, so it is hard to put it on the car glass or window. Thus, there should be a design for transparent antenna that allows the sunlight to pass through. Among various antennas, microstrip patch antennas are widely used because they are low profile, are lightweight, and have planar structure. Microstrip patch-structured rectennas are evaluated and compared with an emphasis on the various methods adopted to obtain a rectenna with harmonic rejection functionality, frequency, and polarization selectivity. Multiple frequency bands are tapped for energy harvesting, and this aspect of the implementation is one of the main focus points. The bands targeted for harvesting in this chapter will be those that are the most readily available to the general population. These include Wi-Fi hotspots, as well as cellular (900/850 MHz band), personal communications services (1800/1900 MHz band), and sources of 2.4 GHz and WiMAX (2.3/3.5 GHz) network transmitters. On the other hand, at high frequency, advances in nanotechnology have led to the development of semiconductor-based solar cells, nanoscale antennas for power harvesting applications, and integration of antennas into solar cells to design low-cost light-weight systems. The role of nanoantenna system is transforming thermal energy provided by the sun to electricity. Nanoantennas target the mid-infrared wavelengths where conventional photo voltaic cells are inefficient. However, the concept of using optical rectenna for harvesting solar energy was first introduced four decades ago. Recently, it has invited a surge of interest, with different laboratories around the world working on various aspects of the technology. The result is a technology that can be efficient and inexpensive, requiring only low-cost materials. Unlike conventional solar cells that harvest energy in visible light frequency range. Since the UV frequency range is much greater than visible light, we consider the quantum mechanical behavior of a driven particle in nanoscale antennas for power harvesting applications

Energy Harvesting and Management for Wireless Autonomous Sensors

Wireless autonomous sensors that harvest ambient energy are attractive solutions, due to their convenience and economic benefits. A number of wireless autonomous sensor platforms which consume less than 100?W under duty-cycled operation are available. Energy harvesting technology (including photovoltaics, vibration harvesters, and thermoelectrics) can be used to power autonomous sensors. A developed system is presented that uses a photovoltaic module to efficiently charge a supercapacitor, which in turn provides energy to a microcontroller-based autonomous sensing platform. The embedded software on the node is structured around a framework in which equal precedent is given to each aspect of the sensor node through the inclusion of distinct software stacks for energy management and sensor processing. This promotes structured and modular design, allowing for efficient code reuse and encourages the standardisation of interchangeable protocols

SIVEH: numerical computing simulation of wireless energy-harvesting sensor nodes

[EN] The paper presents a numerical energy harvesting model for sensor nodes, SIVEH (Simulator I–V for EH), based on I–V hardware tracking. I–V tracking is demonstrated to be more accurate than traditional energy modeling techniques when some of the components present different power dissipation at either different operating voltages or drawn currents. SIVEH numerical computing allows fast simulation of long periods of time—days, weeks, months or years—using real solar radiation curves. Moreover, SIVEH modeling has been enhanced with sleep time rate dynamic adjustment, while seeking energy-neutral operation. This paper presents the model description, a functional verification and a critical comparison with the classic energy approachThe authors gratefully acknowledge financial support from CICYT. ANDREA: Automated Inspection and Remote Performance of Marine Fish Farms (CTM2011-29691-C02-01); and RIDeWAM: Research on Improvement of the Dependability of WSN-based Applications by Developing a Hybrid Monitoring Platform. (TIN2011-28435-C03-01).Sánchez Matías, AM.; Blanc Clavero, S.; Climent, S.; Yuste Pérez, P.; Ors Carot, R. (2013). SIVEH: numerical computing simulation of wireless energy-harvesting sensor nodes. Sensors. 13(9):11750-11771. https://doi.org/10.3390/s130911750S1175011771139Akyildiz, I., Melodia, T., & Chowdury, K. (2007). Wireless multimedia sensor networks: A survey. IEEE Wireless Communications, 14(6), 32-39. doi:10.1109/mwc.2007.4407225Madan, R., Cui, S., Lall, S., & Goldsmith, A. (2006). Cross-Layer Design for Lifetime Maximization in Interference-Limited Wireless Sensor Networks. IEEE Transactions on Wireless Communications, 5(11), 3142-3152. doi:10.1109/twc.2006.04770Wang, Z. L., & Wu, W. (2012). Nanotechnology-Enabled Energy Harvesting for Self-Powered Micro-/Nanosystems. Angewandte Chemie International Edition, 51(47), 11700-11721. doi:10.1002/anie.201201656Riemer, R., & Shapiro, A. (2011). Biomechanical energy harvesting from human motion: theory, state of the art, design guidelines, and future directions. Journal of NeuroEngineering and Rehabilitation, 8(1), 22. doi:10.1186/1743-0003-8-22Sudevalayam, S., & Kulkarni, P. (2011). Energy Harvesting Sensor Nodes: Survey and Implications. IEEE Communications Surveys & Tutorials, 13(3), 443-461. doi:10.1109/surv.2011.060710.00094Alippi, 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.2008.922023Alippi, C., Camplani, R., Galperti, C., & Roveri, M. (2011). A Robust, Adaptive, Solar-Powered WSN Framework for Aquatic Environmental Monitoring. IEEE Sensors Journal, 11(1), 45-55. doi:10.1109/jsen.2010.2051539Lopez-Lapena, O., Penella, M. T., & Gasulla, M. (2010). A New MPPT Method for Low-Power Solar Energy Harvesting. IEEE Transactions on Industrial Electronics, 57(9), 3129-3138. doi:10.1109/tie.2009.2037653Kansal, A., Hsu, J., Zahedi, S., & Srivastava, M. B. (2007). Power management in energy harvesting sensor networks. ACM Transactions on Embedded Computing Systems, 6(4), 32-es. doi:10.1145/1274858.1274870Niyato, D., Hossain, E., Rashid, M., & Bhargava, V. (2007). Wireless sensor networks with energy harvesting technologies: a game-theoretic approach to optimal energy management. IEEE Wireless Communications, 14(4), 90-96. doi:10.1109/mwc.2007.4300988EECS Department of the University of California at Berkleyhttp://bwrc.eecs.berkeley.edu/Classes/IcBook/SPICE/http://www.panasonic.com/industrial/components/pdf/goldcap_tech-guide_052505.pdfAnalog, Embedded Processing, Semiconductor Company, Texas Instrumentshttp://www.ti.comST Microelectronicshttp://www.st.comHome Pagehttp://www.linear.com/ns-3http://www.nsnam.orgSánchez, A., Blanc, S., Yuste, P., Perles, A., & Serrano, J. J. (2012). An Ultra-Low Power and Flexible Acoustic Modem Design to Develop Energy-Efficient Underwater Sensor Networks. Sensors, 12(6), 6837-6856. doi:10.3390/s12060683

Energy challenges for ICT

The energy consumption from the expanding use of information and communications technology (ICT) is unsustainable with present drivers, and it will impact heavily on the future climate change. However, ICT devices have the potential to contribute signi - cantly to the reduction of CO2 emission and enhance resource e ciency in other sectors, e.g., transportation (through intelligent transportation and advanced driver assistance systems and self-driving vehicles), heating (through smart building control), and manu- facturing (through digital automation based on smart autonomous sensors). To address the energy sustainability of ICT and capture the full potential of ICT in resource e - ciency, a multidisciplinary ICT-energy community needs to be brought together cover- ing devices, microarchitectures, ultra large-scale integration (ULSI), high-performance computing (HPC), energy harvesting, energy storage, system design, embedded sys- tems, e cient electronics, static analysis, and computation. In this chapter, we introduce challenges and opportunities in this emerging eld and a common framework to strive towards energy-sustainable ICT