Recently underwater acoustic sensor networks (UASNs) have drawn much attention because of their great value in many underwater applications where human operation is hard to carry out. In this paper, we introduce and c...
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Recently underwater acoustic sensor networks (UASNs) have drawn much attention because of their great value in many underwater applications where human operation is hard to carry out. In this paper, we introduce and compare the performance of four localization algorithms in UASNs, namely, distance vector-hop (DV-hop), a new localization algorithm for underwater acoustic sensor networks (NLA), large-scale hierarchical localization (LSHL), and localization scheme for large scale underwater networks (LSLS). The four algorithms are all suitable for large-scale UASNs. We compare the localization algorithms in terms of localization coverage, localization error, and average energy consumption. Besides, we analyze the impacts of the ranging error and the number of anchor nodes on the performance of the localization algorithms. Simulations show that LSHL and LSLS perform much better than DV-hop and NLA in localization coverage, localization error, and average energy consumption. The performance of NLA is similar to that of the DV-hop. The advantage of DV-hop and NLA is that the localization results do not rely on the number of anchor nodes;that is, only a small number of anchor nodes are needed for localization.
In the study of three-dimensional underwater sensor networks, the nodes would produce changes in perception range under the influence of environmental factors and their own hardware. Requesting all nodes completely is...
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In the study of three-dimensional underwater sensor networks, the nodes would produce changes in perception range under the influence of environmental factors and their own hardware. Requesting all nodes completely isomorphic is unrealistic. Ignoring boundary effects usually causes the coverage effect of the actual deployment of networks to not reach the anticipated result. This paper firstly presents an underwater sensing model with normal distributed node sensing radius. Secondly, it gives the relationship between expected deployment quality and the number of nodes in the premise of considering boundary effects. Then, it deduces nodes' redundancy formula based on the sensing model with normal distributed node sensing radius, making node could determine whether itself is a redundant node only based on the number of its neighbour nodes. Furthermore, this paper proposes a redundancy model and boundary effects based coverage-enhancing algorithm for three-dimensional underwater sensor networks (RBCT). Simulation results show that RBCT, compared to similar algorithms, has certain advantages in saving energy and enhancing coverage rate.
In wireless sensor networks (WSN), the geometric distribution of anchor nodes has a significant influence on the positioning accuracy. Geometric dilution of precision (GDOP) can be used to measure the positioning prec...
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In wireless sensor networks (WSN), the geometric distribution of anchor nodes has a significant influence on the positioning accuracy. Geometric dilution of precision (GDOP) can be used to measure the positioning precision of the localization system. In order to select the optimal node combination, traditional algorithms based on GDOP need to spend much time on calculating every possible combination of nodes. This paper proposes GDOP assisted nodes selection (GANS) algorithm to calculate GDOP value of the current geometric distribution. Sensor node's contribution to the overall GDOP value is adopted as the evaluation criteria. The nodes whose contribution value is greater than the threshold will be selected. The anchor nodes subset, which participates in the positioning, will be real-time determined. Simulation results show that the GANS algorithm can effectively reduce the energy consumption of the system, while the positioning accuracy has no obvious loss. Meanwhile, computational complexity is also obviously decreased.
The network which knows location of all nodes can improve the network capacity and lifetime in location-aware networks. However, localization algorithm of the network has to update its target node location due to mobi...
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The network which knows location of all nodes can improve the network capacity and lifetime in location-aware networks. However, localization algorithm of the network has to update its target node location due to mobility of target nodes and intermittent occurrence of interference. The update procedures cause frequent broadcasting and calculation for relocalization;therefore, maintaining the transmission power control (TPC) in the wireless sensor networks (WSNs) is valuable to the network. This paper proposes a new algorithm of transmission power controlled localization for indoor environment. Firstly, we propose minimum spanning tree (MST) based topology control with location error compensation algorithm to improve location accuracy and prevent target nodes from connecting to unstable links in non-line-of-sight (NLOS) condition. Secondly, we use TPC algorithm to improve the network lifetime. Each target node dynamically adjusts the power and the received signal strength (RSS) target by using the TPC algorithm. Experimental results show that the proposed algorithm compensates for location errors in NLOS condition while reducing the transmission power.
We argue that the current heterogeneous computing environment mimics a complex nonlinear system which needs to borrow the concept of time-scale separation and the delayed difference approach from statistical mechanics...
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We argue that the current heterogeneous computing environment mimics a complex nonlinear system which needs to borrow the concept of time-scale separation and the delayed difference approach from statistical mechanics and nonlinear dynamics. We show that by replacing the usual difference equations approach by a delayed difference equations approach, the sequential fraction of many scientific computing algorithms can be substantially reduced. We also provide a comprehensive theoretical analysis to establish that the error and stability of our scheme is of the same order as existing schemes for a large, well-characterized class of problems.
Cognitive radio (CR) is an efficient way to increase spectrum efficiency for the small low earth orbit (LEO) satellite communication system. Due to the implementation difficulties, we focus on the CR in the uplink tra...
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Cognitive radio (CR) is an efficient way to increase spectrum efficiency for the small low earth orbit (LEO) satellite communication system. Due to the implementation difficulties, we focus on the CR in the uplink transmission. In CR, the cognitive medium access (CMA) is designed to enable the coexistence with the interferences from other systems. However, the CMA schemes designed for the terrestrial system cannot deal well with the global history of interferences in our system. Here, we design the memorized centroid bucket (MCB) scheme that can efficiently utilize the global history of interferences onboard without storing the complete interference samples. With MCB, we can achieve the effective long-term interference prediction to meet the special requirements of the LEO satellite. The key component in MCB is the matching algorithm that can help retrieve the useful historical information. In this paper, we propose three different matching algorithms and the corresponding MCB schemes. The schemes are also compared with the widely used Markovian method and the pair counting-based method. Among all the schemes, the Bayesian scheme MCB-FSNMI-Bayes is the best. The conclusion is validated experimentally with the real data that were collected by an LEO satellite.
We report experimental results on the action of selected local environments on the fidelity of the quantum teleportation protocol, taking into account nonideal, realistic entangled resources. Different working conditi...
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We report experimental results on the action of selected local environments on the fidelity of the quantum teleportation protocol, taking into account nonideal, realistic entangled resources. Different working conditions are theoretically identified, where a noisy protocol can be made almost insensitive to the further addition of noise. We put to test these conditions on a photonic implementation of the quantum teleportation algorithm, where two polarization entangled qubits act as the entangled resource and a path qubit on Alice encodes the state to be teleported. Bob's path qubit is used to implement a local environment, while the environment on Alice's qubit is simulated as a weighted average of different pure states. We obtain a good agreement with the theoretical predictions, experimentally recreate the conditions to obtain a noise-induced enhancement of the protocol fidelity, and identify parameter regions of increased insensibility to interactions with specific noisy environments.
The main focus of this special issue is devoted to a deeper understanding of the algorithms and theories which are developed to build up a robust wireless sensor network. [...]it is to push the theoretical and practi...
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The main focus of this special issue is devoted to a deeper understanding of the algorithms and theories which are developed to build up a robust wireless sensor network. [...]it is to push the theoretical and practical research forward for a deeper understanding in the fundamental algorithm, modeling, and analysis techniques of robust wireless sensor networks.
We present a detailed study on the RSS-based location techniques in wireless sensor networks (WSN). There are two aspects in this paper. On the one hand, the accurate RSSI received from nodes is the premise of accurat...
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We present a detailed study on the RSS-based location techniques in wireless sensor networks (WSN). There are two aspects in this paper. On the one hand, the accurate RSSI received from nodes is the premise of accurate location. Firstly, the distribution trend of RSSI is analyzed in this experiment and determined the loss model of signal propagation by processing experimental data. Secondly, in order to determine the distance between receiving nodes and sending nodes, Gaussian fitting is used to process specific RSSI at different distance. Moreover, the piecewise linear interpolation is introduced to calculate the distance of any RSSI. On the other hand, firstly, the RSSI vector similarity degree (R-VSD) is used to choose anchor nodes. Secondly, we designed a new localization algorithm which is based on the quadrilateral location unit by using more accurate RSSI and range. Particularly, there are two localization mechanisms in our study. In addition, the generalized inverse is introduced to solve the coordinates of nodes. At last, location error of the new algorithm is about 17.6% by simulation experiment.
The effectiveness of sensor networks depends critically on efficient power management of the sensor nodes. Dynamic voltage frequency scaling (DVFS) and dynamic power management (DPM) have been proposed to enable energ...
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The effectiveness of sensor networks depends critically on efficient power management of the sensor nodes. Dynamic voltage frequency scaling (DVFS) and dynamic power management (DPM) have been proposed to enable energy-efficient scheduling for real-time and embedded systems. However, most power-aware scheduling algorithms are designed to deal with only those cases in which the task execution time is determined solely by the clock frequency of the processor. In this study, we propose an extended task execution model that is appropriate for the sensor nodes and an algorithm that determines the optimal clock frequency for a node's processor. We analyze the extended model and verify that our algorithm calculates the clock frequency that optimizes energy savings while satisfying the timing constraints.
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