Extending the Energy Framework for Network Simulator 3 (ns-3)

The problem of designing and simulating optimal transmission protocols for energy harvesting wireless networks has recently received considerable attention, thus requiring for an accurate modeling of the energy harvesting process and a consequent redesign of the simulation framework to include it. While the current ns-3 energy framework allows the definition of new energy sources that incorporate the contribution of an energy harvester, the integration of an energy harvester component into an existing energy source is not straightforward using the existing energy framework. In this poster, we propose an extension of the energy framework currently released with ns-3 in order to explicitly introduce the concept of an energy harvester. Starting from the definition of the general interface, we then provide the implementation of two simple models for the energy harvester. In addition, we extend the set of implementations of the current energy framework to include a model for a supercapacitor energy source and a device energy model for the energy consumption of a sensor. Finally, we introduce the concept of an energy predictor, that gathers information from the energy source and harvester and use this information to predict the amount of energy that will be available in the future, and we provide an example implementation. As a result of these efforts, we believe that our contributions to the ns-3 energy framework will provide a useful tool to enhance the quality of simulations of energy-aware wireless networks.Comment: 2 pages, 4 figures. Poster presented at WNS3 2014, Atlanta, G

Energy managed reporting for wireless sensor networks

In this paper, we propose a technique to extend the network lifetime of a wireless sensor network, whereby each sensor node decides its individual network involvement based on its own energy resources and the information contained in each packet. The information content is ascertained through a system of rules describing prospective events in the sensed environment, and how important such events are. While the packets deemed most important are propagated by all sensor nodes, low importance packets are handled by only the nodes with high energy reserves. Results obtained from simulations depicting a wireless sensor network used to monitor pump temperature in an industrial environment have shown that a considerable increase in the network lifetime and network connectivity can be obtained. The results also show that when coupled with a form of energy harvesting, our technique can enable perpetual network operatio

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

HarvWSNet: A co-simulation framework for energy harvesting wireless sensor networks

International audienceRecent advances in energy harvesting (EH) technologies now allow wireless sensor networks (WSNs) to extend their lifetime by scavenging the energy available in their environment. While simulation is the most widely used method to design and evaluate network protocols for WSNs, existing network simulators are not adapted to the simulation of EH-WSNs and most of them provide only a simple linear battery model. Therefore, there is a need for a framework suited to EH-WSN simulation and to lifetime prediction. We propose a co-simulation framework, HarvWSNet, based on WSNet and Matlab, that provides adequate tools for the simulation of the network protocols and the lifetime of EH-WSN. Indeed, the framework allows for the simulation of multi-node network scenarios while including a detailed description of each node's energy harvesting, management subsystem and its time-varying environmental parameters. A simulation case study based on a temperature monitoring application demonstrates HarvWSNet's ability to predict network lifetime while minimally penalizing simulation time

Unlocking the deployment of spectrum sharing with a policy enforcement framework

Spectrum sharing has been proposed as a promising way to increase the efficiency of spectrum usage by allowing incumbent operators (IOs) to share their allocated radio resources with licensee operators (LOs), under a set of agreed rules. The goal is to maximize a common utility, such as the sum rate throughput, while maintaining the level of service required by the IOs. However, this is only guaranteed under the assumption that all “players”respect the agreed sharing rules. In this paper, we propose a comprehensive framework for licensed shared access (LSA) networks that discourages LO misbehavior. Our framework is built around three core functions: misbehavior detection via the employment of a dedicated sensing network; a penalization function; and, a behavior-driven resource allocation. To the best of our knowledge, this is the first time that these components are combined for the monitoring/policing of the spectrum under the LSA framework. Moreover, a novel simulator for LSA is provided as an open access tool, serving the purpose of testing and validating our proposed techniques via a set of extensive system-level simulations in the context of mobile network operators, where IOs and several competing LOs are considered. The results demonstrate that violation of the agreed sharing rules can lead to a great loss of resources for the misbehaving LOs, the amount of which is controlled by the system. Finally, we promote that including a policy enforcement function as part of the spectrum sharing system can be beneficial for the LSA system, since it can guarantee compliance with the spectrum sharing rules and limit the short-term benefits arising from misbehavior

SensEH: From Simulation to Deployment of Energy Harvesting Wireless Sensor Networks

Energy autonomy and system lifetime are critical concerns in wireless sensor networks (WSNs), for which energy harvesting (EH) is emerging as a promising solution. Nevertheless,the tools supporting the design of EH-WSN are limited to a few simulators that require developers to re-implement the application with programming languages different from WSN ones. Further, simulators notoriously provide only a rough approximation of the reality of low-power wireless communication. In this paper we present SENSEH, a software framework that allows developers to move back and forth between the power and speed of a simulated approach and the reality and accuracy of in-field experiments. SENSEH relies on COOJA for emulating the actual, deployment-ready code, and provides two modes of operation that allow the reuse of exactly the same code in realworld WSN deployments. We describe the toolchain and software architecture of SENSEH, and demonstrate its practical use and benefits in the context of a case study where we investigate how the lifetime of a WSN used for adaptive lighting in road tunnels can be extended using harvesters based on photovoltaic panels

Contribution to Research on Underwater Sensor Networks Architectures by Means of Simulation

El concepto de entorno inteligente concibe un mundo donde los diferentes tipos de dispositivos inteligentes colaboran para conseguir un objetivo común. En este concepto, inteligencia hace referencia a la habilidad de adquirir conocimiento y aplicarlo de forma autónoma para conseguir el objetivo común, mientras que entorno hace referencia al mundo físico que nos rodea. Por tanto, un entorno inteligente se puede definir como aquel que adquiere conocimiento de su entorno y aplicándolo permite mejorar la experiencia de sus habitantes. La computación ubicua o generalizada permitirá que este concepto de entorno inteligente se haga realidad. Normalmente, el término de computación ubicua hace referencia al uso de dispositivos distribuidos por el mundo físico, pequeños y de bajo precio, que pueden comunicarse entre ellos y resolver un problema de forma colaborativa. Cuando esta comunicación se lleva a cabo de forma inalámbrica, estos dispositivos forman una red de sensores inalámbrica o en inglés, Wireless Sensor Network (WSN). Estas redes están atrayendo cada vez más atención debido al amplio espectro de aplicaciones que tienen, des de soluciones para el ámbito militar hasta aplicaciones para el gran consumo. Esta tesis se centra en las redes de sensores inalámbricas y subacuáticas o en inglés, Underwater Wireless Sensor Networks (UWSN). Estas redes, a pesar de compartir los mismos principios que las WSN, tienen un medio de transmisión diferente que cambia su forma de comunicación de ondas de radio a ondas acústicas. Este cambio hace que ambas redes sean diferentes en muchos aspectos como el retardo de propagación, el ancho de banda disponible, el consumo de energía, etc. De hecho, las señales acústicas tienen una velocidad de propagación cinco órdenes de magnitud menor que las señales de radio. Por tanto, muchos algoritmos y protocolos necesitan adaptarse o incluso rediseñarse. Como el despliegue de este tipo de redes puede ser bastante complicado y caro, se debe planificar de forma precisa el hardware y los algoritmos que se necesitan. Con esta finalidad, las simulaciones pueden resultar una forma muy conveniente de probar todas las variables necesarias antes del despliegue de la aplicación. A pesar de eso, un nivel de precisión adecuado que permita extraer resultados y conclusiones confiables, solamente se puede conseguir utilizando modelos precisos y parámetros reales. Esta tesis propone un ecosistema para UWSN basado en herramientas libres y de código abierto. Este ecosistema se compone de un modelo de recolección de energía y unmodelo de unmódemde bajo coste y bajo consumo con un sistema de activación remota que, junto con otros modelos ya implementados en las herramientas, permite la realización de simulaciones precisas con datos ambientales del tiempo y de las condiciones marinas del lugar donde la aplicación objeto de estudio va a desplegarse. Seguidamente, este ecosistema se utiliza con éxito en el estudio y evaluación de diferentes protocolos de transmisión aplicados a una aplicación real de monitorización de una piscifactoría en la costa del mar Mediterráneo, que es parte de un proyecto de investigación español (CICYT CTM2011-2961-C02-01). Finalmente, utilizando el modelo de recolección de energía, esta plataforma de simulación se utiliza para medir los requisitos de energía de la aplicación y extraer las necesidades de hardware mínimas.Climent Bayarri, JS. (2014). Contribution to Research on Underwater Sensor Networks Architectures by Means of Simulation [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/3532

Introducing reinforcement learning in the Wi-Fi MAC layer to support sustainable communications in e-Health scenarios

The crisis of energy supplies has led to the need for sustainability in technology, especially in the Internet of Things (IoT) paradigm. One solution is the integration of Energy Harvesting (EH) technologies into IoT systems, which reduces the amount of battery replacement. However, integrating EH technologies within IoT systems is challenging, and it requires adaptations at different layers of the IoT protocol stack, especially at Medium Access Control (MAC) layer due to its energy-hungry features. Since Wi-Fi is a widely used wireless technology in IoT systems, in this paper, we perform an extensive set of simulations in a dense solar-based energy-harvesting Wi-Fi network in an e-Health environment. We introduce optimization algorithms, which benefit from the Reinforcement Learning (RL) methods to efficiently adjust to the complexity and dynamic behaviour of the network. We assume the concept of Access Point (AP) coordination to demonstrate the feasibility of the upcoming Wi-Fi amendment IEEE 802.11bn (Wi-Fi 8). This paper shows that the proposed algorithms reduce the network&amp;#x2019;s energy consumption by up to 25% compared to legacy Wi-Fi while maintaining the required Quality of Service (QoS) for e-Health applications. Moreover, by considering the specific adjustment of MAC layer parameters, up to 37% of the energy of the network can be conserved, which illustrates the viability of reducing the dimensions of solar cells, while concurrently augmenting the flexibility of this EH technique for deployment within the IoT devices. We anticipate this research will shed light on new possibilities for IoT energy harvesting integration, particularly in contexts with restricted QoS environments such as e-Healthcare.</p

An Energy-autonomous Wireless Sensor Network Development Platform

Internet-of-things enabled applications are increasingly popular and are expected to spread even more in the next few years. Energy efficiency is fundamental to support the widespread use of such systems. This paper presents a practical framework for the development and the evaluation of low-power Wireless Sensor Networks equipped with energy harvesting, aiming at energy-autonomous applications. An experimental case study demonstrates the capabilities of the solution