105 research outputs found
The Beauty of the Commons: Optimal Load Sharing by Base Station Hopping in Wireless Sensor Networks
In wireless sensor networks (WSNs), the base station (BS) is a critical
sensor node whose failure causes severe data losses. Deploying multiple fixed
BSs improves the robustness, yet requires all BSs to be installed with large
batteries and large energy-harvesting devices due to the high energy
consumption of BSs. In this paper, we propose a scheme to coordinate the
multiple deployed BSs such that the energy supplies required by individual BSs
can be substantially reduced. In this scheme, only one BS is selected to be
active at a time and the other BSs act as regular sensor nodes. We first
present the basic architecture of our system, including how we keep the network
running with only one active BS and how we manage the handover of the role of
the active BS. Then, we propose an algorithm for adaptively selecting the
active BS under the spatial and temporal variations of energy resources. This
algorithm is simple to implement but is also asymptotically optimal under mild
conditions. Finally, by running simulations and real experiments on an outdoor
testbed, we verify that the proposed scheme is energy-efficient, has low
communication overhead and reacts rapidly to network changes
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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
Quality-Aware Scheduling Algorithms in Renewable Sensor
Wireless sensor network has emerged as a key technology for various applications
such as environmental sensing, structural health monitoring, and area surveillance.
Energy is by far one of the most critical design hurdles that hinders the deployment
of wireless sensor networks. The lifetime of traditional battery-powered sensor
networks is limited by the capacities of batteries. Even many energy conservation
schemes were proposed to address this constraint, the network lifetime is still inherently
restrained, as the consumed energy cannot be replenished easily. Fully
addressing this issue requires energy to be replenished quite often in sensor networks
(renewable sensor networks). One viable solution to energy shortages is enabling
each sensor to harvest renewable energy from its surroundings such as solar energy,
wind energy, and so on. In comparison with their conventional counterparts, the network
lifetime in renewable sensor networks is no longer a main issue, since sensors
can be recharged repeatedly. This results in a research focus shift from the network
lifetime maximization in traditional sensor networks to the network performance optimization
(e.g., monitoring quality). This thesis focuses on these issues and tackles
important problems in renewable sensor networks as follows.
We first study the target coverage optimization in renewable sensor networks
via sensor duty cycle scheduling, where a renewable sensor network consisting of
a set of heterogeneous sensors and a stationary base station need to be scheduled
to monitor a set of targets in a monitoring area (e.g., some critical facilities) for a
specified period, by transmitting their sensing data to the base station through multihop
relays in a real-time manner. We formulate a coverage maximization problem
in a renewable sensor network which is to schedule sensor activities such that the
monitoring quality is maximized, subject to that the communication network induced
by the activated sensors and the base station at each time moment is connected. We
approach the problem for a given monitoring period by adopting a general strategy.
That is, we divide the entire monitoring period into equal numbers of time slots and perform sensor activation or inactivation scheduling in the beginning of each
time slot. As the problem is NP-hard, we devise efficient offline centralized and
distributed algorithms for it, provided that the amount of harvested energy of each
sensor for a given monitoring period can be predicted accurately. Otherwise, we
propose an online adaptive framework to handle energy prediction fluctuation for
this monitoring period. We conduct extensive experiments, and the experimental
results show that the proposed solutions are very promising.
We then investigate the data collection optimization in renewable sensor networks
by exploiting sink mobility, where a mobile sink travels around the sensing field to
collect data from sensors through one-hop transmission. With one-hop transmission,
each sensor could send data directly to the mobile sink without any relay, and thus no
energy are consumed on forwarding packets for others which is more energy efficient
in comparison with multi-hop relays. Moreover, one-hop transmission particularly is
very useful for a disconnected network, which may be due to the error-prone nature
of wireless communication or the physical limit (e.g., some sensors are physically
isolated), while multi-hop transmission is not applicable. In particular, we investigate
two different kinds of mobile sinks, and formulate optimization problems under
different scenarios, for which both centralized and distributed solutions are proposed
accordingly. We study the performance of the proposed solutions and validate their
effectiveness in improving the data quality.
Since the energy harvested often varies over time, we also consider the scenario of
renewable sensor networks by utilizing wireless energy transfer technology, where a
mobile charging vehicle periodically travels inside the sensing field and charges sensors
without any plugs or wires. Specifically, we propose a novel charging paradigm
and formulate an optimization problem with an objective of maximizing the number
of sensors charged per tour. We devise an offline approximation algorithm which
runs in quasi-polynomial time and develop efficient online sensor charging algorithms,
by considering the dynamic behaviors of sensors’ various sensing and transmission
activities. To study the efficiency of the proposed algorithms, we conduct
extensive experiments and the experimental results demonstrate that the proposed
algorithms are very efficient. We finally conclude our work and discuss potential research topics which derive
from the studies of this thesis
Markov fluid queue model of an energy harvesting IoT device with adaptive sensing
Energy management is key in prolonging the lifetime of an energy harvesting Internet of Things (IoT) device with rechargeable batteries. Such an IoT device is required to fulfill its main functionalities, i.e., information sensing and dissemination at an acceptable rate, while keeping the probability that the node first becomes non-operational, i.e., the battery level hits zero the first time within a given finite time horizon, below a desired level. Assuming a finite-state Continuous-Time Markov Chain (CTMC) model for the Energy Harvesting Process (EHP), we propose a risk-theoretic Markov fluid queue model for the computation of first battery outage probabilities in a given finite time horizon. The proposed model enables the performance evaluation of a wide spectrum of energy management policies including those with sensing rates depending on the instantaneous battery level and/or the state of the energy harvesting process. Moreover, an engineering methodology is proposed by which optimal threshold-based adaptive sensing policies are obtained that maximize the information sensing rate of the IoT device while meeting a Quality of Service (QoS) constraint given in terms of first battery outage probabilities. Numerical results are presented for the validation of the analytical model and also the proposed engineering methodology, using a two-state CTMC-based EHP. © 2017 Elsevier B.V
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 MatĂas, 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 MeteorolgĂa 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 FachgesprächÄ° 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
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