Current Issue : April - June Volume : 2012 Issue Number : 2 Articles : 5 Articles
Efficient energy consumption is a critical factor for the deployment and operation of wireless sensor networks (WSNs). In general,\r\nWSNs performclustering and routing using localized neighbor information only. Therefore, some studies have used self-organized\r\nsystems and smart mechanisms as research methods. In this paper, we propose a self-organized and smart-adaptive clustering\r\n(SOSAC) and routing method, which performs clustering in WSNs, operates the formed clusters in a smart-adaptive way, and\r\nperforms cluster-based routing. SOSAC is comprised of three mechanisms, which are used to change the fitness value over time, to\r\nback up routing information in preparation for any potential breakdown in WSNs, and to adapt to the changes of the number of\r\nsensor nodes for aWSN.We compared the performance of the proposed SOSAC with that of a well-known clustering and routing\r\nprotocol for WSNs. Our computational experiments demonstrate that the network lifetime, energy consumption, and scalability\r\nof SOSAC are better than those of the compared method....
Flash memory has become a more widespread storage medium for modern wireless devices because of its effective characteristics\r\nlike nonvolatility, small size, light weight, fast access speed, shock resistance, high reliability, and low power consumption. Sensor\r\nnodes are highly resource constrained in terms of limited processing speed, runtime memory, persistent storage, communication\r\nbandwidth, and finite energy. Therefore, for wireless sensor networks supporting sense, store, merge, and send schemes, an energy\r\nefficient and reliable database-based query optimization technique is highly required with consideration of sensor node constraints.\r\nDatabases on hard disk drives perform data storage and retrieval using index structures which are still not practiced for sensor\r\ndevices. In this paper, we evaluate different indices like B-tree, R-tree, and MR-tree by implementing them on log structured\r\nexternal NAND flash memory-based advanced file systems for supporting energy efficient data storage and query optimization\r\nfrom flash based data centric sensor devices in wireless sensor networks. Experimental results show that PIYAS (Rizvi and Chung,\r\n2010) file system along with B-tree indexing deployed on flash memory MLC gives the significant performance in respect of high\r\nquery throughput optimization and less resources consumption for wireless sensor devices....
Neighbor discovery is a component of communication and access protocols for ad hoc networks. Wireless sensor networks often\r\nmust operate under a more severe low-power regimen than do traditional ad hoc networks, notably by turning off radio for extended\r\nperiods. Turning off a radio is problematic for neighbor discovery, and a balance is needed between adequate open communication\r\nfor discovery and silence to conserve power. This paper surveys recent progress on the problems of neighbor discovery for\r\nwireless sensor networks. The basic ideas behind these protocols are explained, which include deterministic schedules of waking\r\nand sleeping, randomized schedules, and combinatorial methods to ensure discovery....
Resource limitations of sensor nodes in wireless sensor networks (WSN) bound the performance on its implementations. Main\r\nconcern becomes utilizing these limited resources (CPU, memory, bandwidth, battery) as efficient as possible. Their efficiency is\r\nmostly affected by the applied routing algorithm, which carries gathered data to inclined/intended destinations. In this paper, a\r\nnovel routing algorithm, stateless weight routing (SWR), is proposed. The SWR differs from other protocols in many ways. Major\r\nfeature of the SWR is its simplicity. It is a completely stateless protocol without requiring any network or neighborhood information\r\nfor routing. This feature decreases packet transmissions and energy consumption dramatically. For reliability, data flows to the\r\nsink node overmultiple paths.Moreover, nodes have the ability of recovering from voids. Nodes process each packet independently\r\nand apply an adaptive approach according to the current conditions. These mechanisms are part of the applied simple routing algorithm,\r\nthe SWR. The resultant of these features assures flexibility and smartness at nodes and in the network. Therefore, topological\r\nchanges have a little effect on data packet transmissions. Performance evaluation of the proposed approach shows that the SWR is\r\nscalable forWSNs whose topology change instantly and frequently as well as remain stationary....
Recently, wireless sensor networks (WSNs) have gained great attention from the research community for various smart grid\napplications, including advanced metering infrastructure (AMI), power outage detection, distribution automation, towers and\npoles monitoring, line fault diagnostics, power fraud detection, and underground cable system monitoring. However, multipath,\nfading, environmental noise, and obstructions in harsh smart grid environments make reliable communication a challenging\ntask for wireless-sensor-network- (WSN-) based smart grid applications. To overcome varying link conditions in smart grid\nenvironments, sensor nodes must be capable of estimating link quality dynamically and reliably. In this paper, the performance of\nthe state-of-the-art link-quality estimation methods is investigated for different smart power grid environments, such as outdoor\nsubstation, underground network transformer vault, and main power control room, in terms of packet delivery ratio, average\nnumber of packet retransmissions, average number of parent changes, average number of hops, and average communication\ndelay. In addition, main smart grid characteristics and potential applications of WSNs in smart grid have been introduced along\nwith the related technical challenges. Overall, our performance evaluations show that the link-quality estimators, called Expected\nTransmission Count (ETX) and four-bit, show the best performance in harsh smart grid environments....
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