This paper presents power allocation in nonlinear sensornetworks for Gaussian Mixture (GM) information source. The observations of sensors are transmitted through independent Rayleigh flat fading channels to a fusion...
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ISBN:
(纸本)9781479903566
This paper presents power allocation in nonlinear sensornetworks for Gaussian Mixture (GM) information source. The observations of sensors are transmitted through independent Rayleigh flat fading channels to a fusion centre (FC). Transmit Power is optimally allocated to sensor nodes so as to minimize the mean square error (MSE) of estimate at FC. Bayesian linear and optimal nonlinear estimators are deployed at FC to compare the proposed optimal and uniform power allocation among sensors. Extensive simulations validate that the proposed Bayesian linear estimator with optimized power gains effectively works for GM prior distribution.
Sensing nodes are employed for intelligent ambient monitoring and information dissemination. The primary challenge in making a sensing node autonomous is the ability to power it continuously. The conventional method o...
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Sensing nodes are employed for intelligent ambient monitoring and information dissemination. The primary challenge in making a sensing node autonomous is the ability to power it continuously. The conventional method of powering these nodes through batteries has an associated drawback of periodic maintenance and replacement. Alternate methods of powering sensing nodes are gaining impetus with the advent of low power electronics. The use of piezoelectric harvesters is an alternative approach to power these sensing nodes. These harvesters innately convert the energy from the unused ambient vibration into electrical energy. The energy extractable from vibrations is characterized by the structure of the vibrating surface. We analyze a harvester as a dynamic vibration absorber mounted on a vibrating structure. The influence of mass ratio, damping ratio, and the number of harvesters on the energy transmitted to the harvesters is addressed. We also assess the power levels required by typical sensing nodes. Our analysis addresses the selection of a particular piezoelectric material, categorically, for a given sensing node. We find that the power required by typical sensing nodes can be easily fulfilled by arrays of harvesters. The current work addresses the concept of available energy from vibrations and the selection of appropriate harvesting configuration for a sensing node.
Wireless sensornetworks (WSN) are presently creating scenarios of decentralised architectures where application intelligence is distributed among devices. Decentralised architectures are composed of networks that con...
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ISBN:
(纸本)9783642315510
Wireless sensornetworks (WSN) are presently creating scenarios of decentralised architectures where application intelligence is distributed among devices. Decentralised architectures are composed of networks that contain sensors and actuators. Actuators base their action on the data gathered by sensors. In this paper, a decentralised routing algorithm called DRATC for time critical applications like fire monitoring and extinguishing is proposed that makes use of the Decentralised Threshold Sensitive routing algorithm. The sensing environment consists of many Monitoring Nodes that sense fire and report the data to the Cluster Head. The Cluster Head directs the Extinguishing Node to extinguish the fire before sending the data to the Base Station.
In this work, we propose a decentralized approach for energy efficient data-gathering in a realistic scenario. We address a major limitation of compressed sensing (CS) approaches proposed to data for wireless sensor n...
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ISBN:
(纸本)9789869000604
In this work, we propose a decentralized approach for energy efficient data-gathering in a realistic scenario. We address a major limitation of compressed sensing (CS) approaches proposed to data for wireless sensor network (WSN), namely, that they work only on a regular grid tightly coupled to the sparsity basis. Instead, we assume that sensors are irregularly positioned in the field and do not assume that sparsifying basis is known a priori. Under the assumption that the sensor data is smooth in space, we propose to use a graph-based transform (GBT) to sparsify the sensor data measured at randomly positioned sensors. We first represent the random topology as a graph then construct the GBT as a sparsifying basis. With the GBT, we propose a heuristic design of the data-gathering where aggregations happen at the sensors with fewer neighbors in the graph. In our simulations, our proposed approach shows better performance in terms of total power consumption for a given reconstruction MSE, as compared to other CS approaches proposed for WSN.
Autonomous drones are employed with ever-increasing frequency in applications ranging from search and rescue, detection of forest fires, and battlefield/civilian surveillance. In this paper, we study the effects of li...
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ISBN:
(纸本)9781479925094;9780769550527
Autonomous drones are employed with ever-increasing frequency in applications ranging from search and rescue, detection of forest fires, and battlefield/civilian surveillance. In this paper, we study the effects of limited mobility in such mobile sensor platforms, from the perspective of the effect limited mobility has on coverage effectiveness. We define a problem that we call Exploratory Coverage in Limited Mobility sensornetworks, wherein the objective is to move a number of mobile sensors to fully explore (and hence, sense every point in) a target area in order to detect any critical event that has already occurred in the area. Further, we provide a taxonomy of problems within exploratory coverage as identified by the relationships between sensor range, coverage area, number of sensors, and mobility (range). We then design a purely localized and distributed approximation algorithm for our problem, and provide simulation results to demonstrate the effects of limited mobility on exploratory coverage.
Mass data are usually collected and processed in large and ultra large-scale wireless sensornetworks, and this will greatly affect the life of intelligent sensors and the performance of network. In this paper, we pro...
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ISBN:
(纸本)9780769551593
Mass data are usually collected and processed in large and ultra large-scale wireless sensornetworks, and this will greatly affect the life of intelligent sensors and the performance of network. In this paper, we propose an approach to reduce the collected data from wireless sensornetworks by using compressed sensing method. Compressed sensing is a new sampling method that the data sampling and compressing can be done simultaneously. Compressed sensing can significantly reduce the collected data size by lowering the sampling rates of sensors, but it is non-adaptive and its algorithm has high computational complexity as well. We put forward and achieved the parallel processing of compressed sensing algorithm for improving algorithms execution speed. Experiment results shows that the proposed scheme significantly outperforms existing solutions in terms of reconstruction accuracy.
We consider a multi-input-single-output (MISO) system where a wireless power transmitter sends wireless power to a receiver via beamforming using the estimated channel, and the power receiver uses the harvested energy...
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We consider a multi-input-single-output (MISO) system where a wireless power transmitter sends wireless power to a receiver via beamforming using the estimated channel, and the power receiver uses the harvested energy to power some applications. We consider transmissions based on frames each of which consists of the channel estimation phase, the feedback phase, the wireless power transfer phase, as well as the energy utilization phase. The feedback time is assumed to be negligible. In this paper, we maximize the utility of the harvested energy, by jointly optimizing the time resource allocated for channel estimation, wireless power transfer, as well as the energy utilization. In particular, we consider two typical application scenarios in which the energy-harvesting receiver uses the harvested energy to perform wireless transmission or sensing. The optimal time allocation is derived to maximize the net average information rate or maximize the sensing accuracy. The analysis results are validated by numerical simulations.
Estimating the pose in real-time is a primary function for intelligent vehicle navigation. Whilst different solutions exist, most of them rely on the use of high-end sensors. This paper proposes a solution that exploi...
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ISBN:
(纸本)9781467363587
Estimating the pose in real-time is a primary function for intelligent vehicle navigation. Whilst different solutions exist, most of them rely on the use of high-end sensors. This paper proposes a solution that exploits an automotive type L1-GPS receiver, features extracted by low-cost perception sensors and vehicle proprioceptive information. A key idea is to use the lane detection function of a video camera to retrieve accurate lateral and orientation information with respect to road lane markings. To this end, lane markings are mobile-mapped by the vehicle itself during a first stage by using an accurate localizer. Then, the resulting map allows for the exploitation of camera-detected features for autonomous real-time localization. The results are then combined with GPS estimates and dead-reckoning sensors in order to provide localization information with high availability. As L1-GPS errors can be large and are time correlated, we study in the paper several GPS error models that are experimentally tested with shaping filters. The approach demonstrates that the use of low-cost sensors with adequate data-fusion algorithms should lead to computer-controlled guidance functions in complex road networks.
Tracking of a source (target) which is moving inside a binary Wireless sensor Network (WSN) is a challenging problem particularly when sensors may fail either due to hardware and/or software malfunctions, energy deple...
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ISBN:
(纸本)9781467331227
Tracking of a source (target) which is moving inside a binary Wireless sensor Network (WSN) is a challenging problem particularly when sensors may fail either due to hardware and/or software malfunctions, energy depletion or adversary attacks. Using information from failed sensors to locate and track a target may lead to high estimation errors, therefore, there is a need to develop fault tolerant localization algorithms which perform well even when a percentage of the sensors report erroneous observations. Alternatively, one may fuse information from neighboring sensors in order to determine the health state of each sensor, and subsequently use only healthy sensors in the localization and tracking process. Our contribution is the development of an architecture which combines the sensor health state estimation together with fault tolerant localization algorithms that leads to more robust target tracking in binary WSNs. Simulation results indicate that the proposed approach is resilient to various types of faults.
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