Current Issue : October - December Volume : 2012 Issue Number : 4 Articles : 6 Articles
Wireless sensors are battery-limited sensing and computing devices. How to prolong the lifetime of wireless sensors becomes an\r\nimportant issue. In order to reduce the energy consumptions when nodes are in idle listening, duty-cycle-based MAC protocols are\r\nintroduced to let node go into sleep mode periodically or aperiodically. The long duty cycle makes sensors increase the transmission\r\nthroughput but consumes more energy. The short duty cycle makes sensors have low energy consumption rate but increases the\r\ntransmission delay. In this paper, a dynamic traffic-aware MAC protocol for energy conserving in wireless sensor networks is\r\nproposed. The proposed MAC protocol can provide better data transmission rate when sensors are with high traffic loading. On\r\nthe other hand, the proposed MAC protocol can save energy when sensors are with low traffic loading. Simulation results show\r\nthat the proposed protocol has better data throughput than other duty-cycle-based MAC protocols, for example, S-MAC and UMAC.\r\nWe also developed a set of comprehensive experiments based on the well-known OMNET++ simulator and revealed that\r\nour proposed TA-MAC performs significantly outstanding than related schemes under various situations....
Wireless Sensor Networks (WSNs) are composed of small wireless nodes equipped with sensors, a processor, and a radio\r\ncommunication unit, all normally powered by batteries. For most WSN applications, the network is expected to function for\r\nseveral months or years. In the common monitoring application scenario, adjacent nodes in aWSN often sense spatially correlated\r\ndata. Suppressing this correlation can significantly improve the lifetime of the network. The maximum possible network data\r\ncompression can be achieved using distributed source coding (DSC) techniques when nodes encode at Slepian-Wolf rates. This\r\npaper presents contributions to the lifetime optimization problem of WSNs in the form of two algorithms: the Updated-CMAX\r\n(UCMAX) power-aware routing algorithm to optimize the routing tree and the Rate Optimization (RO) algorithm to optimize\r\nthe encoding rates of the nodes. The two algorithms combined offer a solution that maximizes the lifetime of a WSN measuring\r\nspatially correlated data. Simulations show that our proposed approach may significantly extend the lifetime of multihop WSNs\r\nwith nodes that are observing correlated data....
Wakeup scheduling has been widely used in wireless sensor networks (WSNs), for it can reduce the energy wastage caused by the\r\nidle listening state. In a traditional wakeup scheduling, sensor nodes start up numerous times in a period, thus consuming extra\r\nenergy due to state transitions (e.g., from the sleep state to the active state). In this paper, we address a novel interference-free\r\nwakeup scheduling problem called compact wakeup scheduling, in which a node needs to wake up only once to communicate\r\nbidirectionally with all its neighbors. However, not all communication graphs have valid compact wakeup schedulings, and it\r\nis NP-complete to decide whether a valid compact wakeup scheduling exists for an arbitrary graph. In particular, tree and grid\r\ntopologies, which are commonly used inWSNs, have valid compact wakeup schedulings.We propose polynomial-time algorithms\r\nusing the optimum number of time slots in a period for trees and grid graphs. Simulations further validate our theoretical results....
Accurate travel time information acquisition is essential to the effective planning and management of bicycle travel conditions.\r\nTraditionally, video camera data have been used as the primary source for measuring the quality of bicycle travel time. This paper\r\ndeals with an investigation of bicycle travel time estimation on a short corridor, using Bluetooth sensors, based on field survey of\r\ntravel time at one arterial road in Hangzhou. Usually bicycle travel time estimates with Bluetooth sensors contain three types of\r\nerrors: spatial error, temporal error, and sampling error. To avoid these, we introduced filters to ââ?¬Å?purifyââ?¬Â the time series. A median\r\nfiltering algorithm is used to eliminate the outlier observations. The filtering scheme has been applied on Genshan East Road and\r\nMoganshan Road. Test data are used to measure the quality of bicycle travel time data collected by the Bluetooth sensors, and the\r\nresults show that the new technology is a promising method for collecting high-quality travel time data that can be used as ground\r\ntruth for evaluating other sources of travel time and other intelligent transportation system applications...
The problem of environmental monitoring using a wireless network of chemical sensors with a limited energy supply is considered.\r\nSince the conventional chemical sensors in active mode consume vast amounts of energy, an optimisation problem arises in the\r\ncontext of a balance between the energy consumption and the detection capabilities of such a network. A protocol based on\r\nââ?¬Å?dynamic sensor collaborationââ?¬Â is employed: in the absence of any pollutant, the majority of sensors are in the sleep (passive) mode;\r\na sensor is invoked (activated) by wake-up messages from its neighbors only when more information is required. The paper proposes\r\na mathematical model of a network of chemical sensors using this protocol. The model provides valuable insights into the\r\nnetwork behavior and near optimal capacity design (energy consumption against detection). An analytical model of the environment,\r\nusing turbulent mixing to capture chaotic fluctuations, intermittency, and nonhomogeneity of the pollutant distribution, is\r\nemployed in the study. A binary model of a chemical sensor is assumed (a device with threshold detection). The outcome of the\r\nstudy is a set of simple analytical tools for sensor network design, optimisation, and performance analysis...
In existing anomaly detection approaches, sensor node often turns to neighbors to further determine whether the data is normal\r\nwhile the node itself cannot decide. However, previous works consider neighbors� opinions being just normal and anomalous,\r\nand do not consider the uncertainty of neighbors to the data of the node. In this paper, we propose SLAD (subjective logic based\r\nanomaly detection) framework. It redefines opinion deriving from subjective logic theory which takes the uncertainty into account.\r\nFurthermore, it fuses the opinions of neighbors to get the quantitative anomaly score of the data. Simulation results show that\r\nSLAD framework improves the performance of anomaly detection compared with previous works....
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