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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Artificial Intelligence for Solar Energy Harvesting in Wireless Sensor Networks

Solar cells have been extensively investigated for wireless sensor networks (WSN). In comparison to other energy harvesting techniques, solar cells are capable of harnessing the highest amount of power density. Furthermore, the energy conversion process does not involve any moving parts and does not require any intermediate energy conversion steps. Their main drawback is the inconsistent amount of energy harvested due to the intermittency and variability of the incoming solar radiation [1]. Consequently, being able to predict the amount of solar radiation is important for making necessary decisions regarding the amount of energy that can be utilized at the sensor node. We demonstrate that artificial intelligence (AI) can be used as an effective technique for predicting the amount of incoming solar radiation at these sensor nodes. We show that a Support Vector Machine (SVM) regression technique can effectively predict the amount of solar radiation for the next 24 hours based on weather data from previous days. We reveal that this technique outperforms other state of the art prediction methods for WSNs. To assess the performance of our proposed solution, we use experimental measurements that were collected for a period of two years from a weather station installed by Beijing Sunda Solar Energy Technology Company [2]. We also demonstrate how the harvested energy can be regulated using an innovative Power Management Unit [3]

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

Resource Aware Sensor Nodes in Wireless Sensor Networks

Wireless sensor networks are continuing to receive considerable research interest due, in part, to the range of possible applications. One of the greatest challenges facing researchers is in overcoming the limited network lifetime inherent in the small locally powered sensor nodes. In this paper, we propose IDEALS, a system to manage a wireless sensor network using a combination of information management, energy harvesting and energy monitoring, which we label resource awareness. Through this, IDEALS is able to extend the network lifetime for important messages, by controlling the degradation of the network to maximise information throughput

Optimal Energy Management Policies for Energy Harvesting Sensor Nodes

We study a sensor node with an energy harvesting source. The generated energy can be stored in a buffer. The sensor node periodically senses a random field and generates a packet. These packets are stored in a queue and transmitted using the energy available at that time. We obtain energy management policies that are throughput optimal, i.e., the data queue stays stable for the largest possible data rate. Next we obtain energy management policies which minimize the mean delay in the queue.We also compare performance of several easily implementable sub-optimal energy management policies. A greedy policy is identified which, in low SNR regime, is throughput optimal and also minimizes mean delay.Comment: Submitted to the IEEE Transactions on Wireless Communications; 22 pages with 10 figure

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