8,723 research outputs found
Routing Based Ant Colony Optimization in Wireless Sensor Networks
Wireless Sensor Networks (WSN2019;s) have become an important and challenging issue in last year. Wireless Sensor Networks consist of nodes with limited power are deployed to gather useful information from the field and send the gathered data to the users. In WSNs, it is critical to collect the information in an energy efficient manner. Ant Colony Optimization, a swarm intelligence based optimization technique, is widely used in network routing. In this paper, we introduce a heuristic way to reduce energy consumption in WSNs routing process using Ant Colony Optimization. We introduce three Ant Colony Optimization algorithms, the Ant System, Ant Colony System and improved AS and their application in WSN routing process. The simulation results show that ACO is an effective way to reduce energy consumption and maximize WSN lifetime
Energy-Efficient Multi-Level and Distance-Aware Clustering Mechanism for WSNs
[EN] Most sensor networks are deployed at hostile environments to sense and gather specific information. As
sensor nodes have battery constraints, therefore, the research community is trying to propose energyefficient
solutions for wireless sensor networks (WSNs) to prolong the lifetime of the network. In this paper,
we propose an energy-efficient multi-level and distance-aware clustering (EEMDC) mechanism for WSNs.
In this mechanism, the area of the network is divided into three logical layers, which depends upon the
hop-count-based distance from the base station. The simulation outcomes show that EEMDC is more energy
efficient than other existing conventional approaches.This work has been partially supported by the 'Ministerio de Ciencia e Innovacion', through the 'Plan Nacional de I+D+i 2008-2011' in the 'Subprograma de Proyectos de Investigacion Fundamental', project TEC2011-27516, and by the Polytechnic University of Valencia, through the PAID-15-11 multidisciplinary projectsMehmood, A.; Khan, S.; Shams, B.; Lloret, J. (2015). Energy-Efficient Multi-Level and Distance-Aware Clustering Mechanism for WSNs. International Journal of Communication Systems. 28(5):972-989. https://doi.org/10.1002/dac.2720S972989285Sendra, S., Lloret, J., Garcia, M., & Toledo, J. F. (2011). Power Saving and Energy Optimization Techniques for Wireless Sensor Neworks (Invited Paper). 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A Cluster-Based Architecture to Structure the Topology of Parallel Wireless Sensor Networks. Sensors, 9(12), 10513-10544. doi:10.3390/s91210513LEHSAINI, M., GUYENNET, H., & FEHAM, M. (2010). Cluster-based Energy-efficient k-Coverage for Wireless Sensor Networks. Network Protocols and Algorithms, 2(2). doi:10.5296/npa.v2i2.325Liu, G., Xu, B., & Chen, H. (2011). Decentralized estimation over noisy channels in cluster-based wireless sensor networks. International Journal of Communication Systems, 25(10), 1313-1329. doi:10.1002/dac.1308Cheng, L., Chen, C., Ma, J., & Shu, L. (2011). Contention-based geographic forwarding in asynchronous duty-cycled wireless sensor networks. International Journal of Communication Systems, 25(12), 1585-1602. doi:10.1002/dac.1325Wang, X., & Qian, H. (2011). Hierarchical and low-power IPv6 address configuration for wireless sensor networks. 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COCA: Constructing optimal clustering architecture to maximize sensor network lifetime. Computer Communications, 36(3), 256-268. doi:10.1016/j.comcom.2012.10.006Aslam N Phillips W Robertson W Sivakumar S A multi-criterion optimization technique for energy efficient cluster formation in wireless sensor networks 4th IEEE Consumer Communications and Networking Conference, (CCNC 2007) 2007 650 654Yi, S., Heo, J., Cho, Y., & Hong, J. (2007). PEACH: Power-efficient and adaptive clustering hierarchy protocol for wireless sensor networks. Computer Communications, 30(14-15), 2842-2852. doi:10.1016/j.comcom.2007.05.034Yong, Z., & Pei, Q. (2012). A Energy-Efficient Clustering Routing Algorithm Based on Distance and Residual Energy for Wireless Sensor Networks. Procedia Engineering, 29, 1882-1888. doi:10.1016/j.proeng.2012.01.231Chuan-Chi W A minimum transmission energy consumption routing protocol for user-centric wireless networks 2011 1143 1148Kumar, D., Aseri, T. C., & Patel, R. B. (2009). 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An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications, 1(4), 660-670. doi:10.1109/twc.2002.804190Lai, W. K., Fan, C. S., & Lin, L. Y. (2012). Arranging cluster sizes and transmission ranges for wireless sensor networks. Information Sciences, 183(1), 117-131. doi:10.1016/j.ins.2011.08.029Pantazis, N. A., Vergados, D. J., Vergados, D. D., & Douligeris, C. (2009). Energy efficiency in wireless sensor networks using sleep mode TDMA scheduling. Ad Hoc Networks, 7(2), 322-343. doi:10.1016/j.adhoc.2008.03.006OMNeT++ Community Documentation and Tutorials of omnet++ http://www.omnetpp.org/Castallia Documentation and Tutorials of Castalia Simulator for WSN and BAN http://castalia.research.nicta.com.au/index.php/en/Research Group on Computer Networks and Multimedia Communication UFPA - Brazil Download-Leach-v2-for-Castalia http://www.gercom.ufpa.br/index.php?option=com_filecabinet&view=files&id=1&Itemid=31&lang=p
A comparative study of energy efficient routing strategies based on OLSR
Energy efficiency is a key issue in wireless ad hoc and sensor networks. In order to maximize network lifetime, several directions have been explored, among them energy efficient routing. In this report, we show how to extend the standardized OLSR routing protocol, in order to make it energy efficient. To take into account residual node energy, the native selection of multipoint relays of OLSR is changed. Three selection algorithms based on the minimum residual energy are evaluated and the best one is chosen. The OLSR extension we propose, selects the path minimizing the energy consumed in the end-to-end transmission of a flow packet and avoids nodes with low residual energy. As it has been shown that two-path routing is energy efficient, we compare this extension with a two-path source routing strategy (with different links or different nodes). An extensive performance evaluation allows us to conclude that our proposal maximizes both network lifetime and the amount of data delivered
Potential Game for Energy-Efficient RSS-based Positioning in Wireless Sensor Networks
Positioning is a key aspect for many applications in wireless sensor networks. In order to design practical positioning algorithms, employment of efficient algorithms that maximize the battery lifetime while achieving a high degree of accuracy is crucial. The number of participating anchor nodes and their transmit power have an important impact on the energy consumption of positoning a node. This paper proposes a game theoretical algorithm to optimize resource usage in obtaining location information in a wireless sensor network. The proposed method provides positioning and tracking of nodes using RSS measurements. We use the Geometric Dilution of Precision as an optimization metric for our algorithm, with the aim of minimizing the number and power of anchor nodes that collaborate in positioning, thus saving energy. The algorithm is shown to be a potential game, therefore convergence is guaranteed. A distributed low complexity solution for the implementation is presented. The game is applied to WSN and results show the trade-off between power saving and positioning error
A Coverage Monitoring algorithm based on Learning Automata for Wireless Sensor Networks
To cover a set of targets with known locations within an area with limited or
prohibited ground access using a wireless sensor network, one approach is to
deploy the sensors remotely, from an aircraft. In this approach, the lack of
precise sensor placement is compensated by redundant de-ployment of sensor
nodes. This redundancy can also be used for extending the lifetime of the
network, if a proper scheduling mechanism is available for scheduling the
active and sleep times of sensor nodes in such a way that each node is in
active mode only if it is required to. In this pa-per, we propose an efficient
scheduling method based on learning automata and we called it LAML, in which
each node is equipped with a learning automaton, which helps the node to select
its proper state (active or sleep), at any given time. To study the performance
of the proposed method, computer simulations are conducted. Results of these
simulations show that the pro-posed scheduling method can better prolong the
lifetime of the network in comparison to similar existing method
Power Aware Routing for Sensor Databases
Wireless sensor networks offer the potential to span and monitor large
geographical areas inexpensively. Sensor network databases like TinyDB are the
dominant architectures to extract and manage data in such networks. Since
sensors have significant power constraints (battery life), and high
communication costs, design of energy efficient communication algorithms is of
great importance. The data flow in a sensor database is very different from
data flow in an ordinary network and poses novel challenges in designing
efficient routing algorithms. In this work we explore the problem of energy
efficient routing for various different types of database queries and show that
in general, this problem is NP-complete. We give a constant factor
approximation algorithm for one class of query, and for other queries give
heuristic algorithms. We evaluate the efficiency of the proposed algorithms by
simulation and demonstrate their near optimal performance for various network
sizes
A Non-Cooperative Game Theoretical Approach For Power Control In Virtual MIMO Wireless Sensor Network
Power management is one of the vital issue in wireless sensor networks, where
the lifetime of the network relies on battery powered nodes. Transmitting at
high power reduces the lifetime of both the nodes and the network. One
efficient way of power management is to control the power at which the nodes
transmit. In this paper, a virtual multiple input multiple output wireless
sensor network (VMIMO-WSN)communication architecture is considered and the
power control of sensor nodes based on the approach of game theory is
formulated. The use of game theory has proliferated, with a broad range of
applications in wireless sensor networking. Approaches from game theory can be
used to optimize node level as well as network wide performance. The game here
is categorized as an incomplete information game, in which the nodes do not
have complete information about the strategies taken by other nodes. For
virtual multiple input multiple output wireless sensor network architecture
considered, the Nash equilibrium is used to decide the optimal power level at
which a node needs to transmit, to maximize its utility. Outcome shows that the
game theoretic approach considered for VMIMO-WSN architecture achieves the best
utility, by consuming less power.Comment: 12 pages, 8 figure
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