Analytic Conditions for Energy Neutrality in Uniformly-Formed Wireless Sensor Networks

Future deployments of wireless sensor network (WSN) infrastructures for environmental or event monitoring are expected to be equipped with energy harvesters (e.g. piezoelectric, thermal, photovoltaic) in order to substantially increase their autonomy. In this paper we derive conditions for energy neutrality, i.e. perpetual energy autonomy per sensor node, by balancing the node's expected energy consumption with its expected energy harvesting capability. Our analysis assumes a uniformly-formed WSN, i.e. a network comprising identical transmitter sensor nodes and identical receiver/relay sensor nodes with a balanced cluster-tree topology. The proposed framework is parametric to: (i) the duty cycle for the network activation; (ii) the number of nodes in the same tier of the cluster-tree topology; (iii) the consumption rate of the receiver node(s) that collect (and possibly relay) data along with their own; (iv) the marginal probability density function (PDF) characterizing the data transmission rate per node; (v) the expected amount of energy harvested by each node. Based on our analysis, we obtain the number of nodes leading to the minimumenergy harvestingrequirement for each tier of the WSN cluster-tree topology. We also derive closed-form expressions for the difference in the minimum energy harvesting requirements between four transmission rate PDFs in function of the WSN parameters. Our analytic results are validated via experiments using TelosB sensor nodes and an energy measurement testbed. Our framework is useful for feasibility studies on energy harvesting technologies in WSNs and for optimizing the operational settings of hierarchical WSN-based monitoring infrastructures prior to time-consuming testing and deployment within the application environment

Improving Maximum Data Collection Based On Pre-Specified Path Using a Mobile Sink for WSN

Data aggregation is one of the challenging issues which are faced in the wireless sensor network by using Energy Harvesting Sensors. Data collection in a fixed pre-defined path with time varying characteristic forms a major problem in Energy Harvesting Sensor Networks. In the proposed work the Adjustment based allocation method is used to allocate fixed time slots to each sensor nodes in which the network throughput can be increased with less energy consumption. The mobile sink transmits the polling message to all the nodes within the transmission range and makes decision based on the profits gained by the sensor nodes in each timeslot. The NP-Hard problem is defined with the form of reducing the complexity of the sensor nodes where larger number of data can be collected from the environment. The data collection throughput is maximized with the use of optimized path for the mobile sink in the network. This record was migrated from the OpenDepot repository service in June, 2017 before shutting down

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. 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Energy Harvesting-Aware Design for Wireless Nanonetworks

Nanotechnology advancement promises to enable a new era of computing and communication devices by shifting micro scale chip design to nano scale chip design. Nanonetworks are envisioned as artifacts of nanotechnology in the domain of networking and communication. These networks will consist of nodes of nanometer to micrometer in size, with a communication range up to 1 meter. These nodes could be used in various biomedical, industrial, and environmental monitoring applications, where a nanoscale level of sensing, monitoring, control and communication is required. The special characteristics of nanonetworks require the revisiting of network design. More specifically, nanoscale limitations, new paradigms of THz communication, and power supply via energy harvesting are the main issues that are not included in traditional network design methods. In this regard, this dissertation investigates and develops some solutions in the realization of nanonetworks. Particularly, the following major solutions are investigated. (I) The energy harvesting and energy consumption processes are modeled and evaluated simultaneously. This model includes the stochastic nature of energy arrival as well as the pulse-based communication model for energy consumption. The model identifies the effect of various parameters in this joint process. (II) Next, an optimization problem is developed to find the best combination of these parameters. Specifically, optimum values for packet size, code weight, and repetition are found in order to minimize the energy consumption while satisfying some application requirements (i.e., delay and reliability). (III) An optimum policy for energy consumption to achieve the maximum utilization of harvested energy is developed. The goal of this scheme is to take advantage of available harvested energy as much as possible while satisfying defined performance metrics. (IV) A communication scheme that tries to maximize the data throughput via a distributed and scalable coordination while avoiding the collision among neighbors is the last problem to be investigated. The goal is to design an energy harvesting-aware and distributed mechanism that could coordinate data transmission among neighbors. (V) Finally, all these solutions are combined together to create a data link layer model for nanonodes. We believe resolving these issues could be the first step towards an energy harvesting-aware network design for wireless nanosensor networks

Energy Harvesting Networked Nodes: Measurements, Algorithms, and Prototyping

Recent advances in ultra-low-power wireless communications and in energy harvesting will soon enable energetically self-sustainable wireless devices. Networks of such devices will serve as building blocks for different Internet of Things (IoT) applications, such as searching for an object on a network of objects and continuous monitoring of object configurations. Yet, numerous challenges need to be addressed for the IoT vision to be fully realized. This thesis considers several challenges related to ultra-low-power energy harvesting networked nodes: energy source characterization, algorithm design, and node design and prototyping. Additionally, the thesis contributes to engineering education, specifically to project-based learning. We summarize our contributions to light and kinetic (motion) energy characterization for energy harvesting nodes. To characterize light energy, we conducted a first-of-its kind 16 month-long indoor light energy measurements campaign. To characterize energy of motion, we collected over 200 hours of human and object motion traces. We also analyzed traces previously collected in a study with over 40 participants. We summarize our insights, including light and motion energy budgets, variability, and influencing factors. These insights are useful for designing energy harvesting nodes and energy harvesting adaptive algorithms. We shared with the community our light energy traces, which can be used as energy inputs to system and algorithm simulators and emulators. We also discuss resource allocation problems we considered for energy harvesting nodes. Inspired by the needs of tracking and monitoring IoT applications, we formulated and studied resource allocation problems aimed at allocating the nodes' time-varying resources in a uniform way with respect to time. We mainly considered deterministic energy profile and stochastic environmental energy models, and focused on single node and link scenarios. We formulated optimization problems using utility maximization and lexicographic maximization frameworks, and introduced algorithms for solving the formulated problems. For several settings, we provided low-complexity solution algorithms. We also examined many simple policies. We demonstrated, analytically and via simulations, that in many settings simple policies perform well. We also summarize our design and prototyping efforts for a new class of ultra-low-power nodes - Energy Harvesting Active Networked Tags (EnHANTs). Future EnHANTs will be wireless nodes that can be attached to commonplace objects (books, furniture, clothing). We describe the EnHANTs prototypes and the EnHANTs testbed that we developed, in collaboration with other research groups, over the last 4 years in 6 integration phases. The prototypes harvest energy of the indoor light, communicate with each other via ultra-low-power transceivers, form small multihop networks, and adapt their communications and networking to their energy harvesting states. The EnHANTs testbed can expose the prototypes to light conditions based on real-world light energy traces. Using the testbed and our light energy traces, we evaluated some of our energy harvesting adaptive policies. Our insights into node design and performance evaluations may apply beyond EnHANTs to networks of various energy harvesting nodes. Finally, we present our contributions to engineering education. Over the last 4 years, we engaged high school, undergraduate, and M.S. students in more than 100 research projects within the EnHANTs project. We summarize our approaches to facilitating student learning, and discuss the results of evaluation surveys that demonstrate the effectiveness of our approaches