We design and implement a Bezier-based multipath routing algorithm that allows a given source node to send samples of data to a given sink node in large scale sensor networks. Multipath routing can distribute the ener...
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We design and implement a Bezier-based multipath routing algorithm that allows a given source node to send samples of data to a given sink node in large scale sensor networks. Multipath routing can distribute the energy load onto the multiple routes and thus increase the lifetime and quality of the network. It is important to stress the fact that evenly regulating the routing task among the more nodes of the network can also protect a node from failure considering that a node with heavy duty is likely to deplete its power quickly. On the contrary, all the traffic would be shipped along the shortest path routing, corresponding to the heavily congested path case, which in turn leads to overload of the nodes along the optimal routes between the sink and source pair and finally shortens the lifetime of the network. Also, multipath routing can increase end-to-end throughput and provide load balancing. Our view is that minimizing energy consumption while meeting acceptable latency for applications can lead to significant power savings. Our simulation results demonstrate that Bezier-based multipath routing approach significantly outperforms previous SWEEP, Tributaries and Deltas, and the shortest path approaches in terms of lifetime and latency.
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 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.
Wireless sensor networks (WSNs) consist of a large number of sensor nodes equipped with a diverse number of small and low-cost devices with limited resources, such as a short communication range, a low bandwidth, a sm...
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Wireless sensor networks (WSNs) consist of a large number of sensor nodes equipped with a diverse number of small and low-cost devices with limited resources, such as a short communication range, a low bandwidth, a small memory, and a restricted energy. In particular, among these constraint factors, a sensor node's energy consumption is a very important factor in extending a network's lifetime. Many researchers are focused on the energy efficiency of wireless sensor networks. Many clustering algorithms have been proposed to improve energy efficiency. However, most protocols in previous literature have the problem of not considering the characteristics of real applications, for example, forest fire detection, intruder detection, target tracking, and the like. In this paper, we propose an energy-efficient clustering algorithm that can respond rapidly to unexpected events with increased energy efficiency, because each sensor node detects events individually and creates clusters using a regional competition scheme. Simulation results show improved performance when our algorithm is used.
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.
For large-scale underwater wireless sensor networks (UWSNs) with a minority of anchor nodes, multihop localization is a popular scheme for determining the geographical positions of normal nodes. However, existing mult...
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For large-scale underwater wireless sensor networks (UWSNs) with a minority of anchor nodes, multihop localization is a popular scheme for determining the geographical positions of normal nodes. However, existing multihop localization studies have considered the anchor positions to be free of errors, which is not a valid assumption in practice. In this paper, the problems existing in nonlinear least square-based node self-localization schemes are analyzed, and the biased distribution characteristic of multihop distance estimation errors is pointed out. Then, the orthogonal regression method is employed for the localization of normal nodes in the presence of anchor position errors. In particular, the influences of errors in independent variables and biases in dependent variables on node coordinate estimation are taken into account simultaneously. Extensive simulation results illustrate the robustness and effectiveness of our method.
Due to the limited energy of sensor nodes, it is a research goal that the lifetime of sensor networks is prolonged by transmitting the sensed data to the base station in an energy-savingway. Previous algorithms aim at...
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Due to the limited energy of sensor nodes, it is a research goal that the lifetime of sensor networks is prolonged by transmitting the sensed data to the base station in an energy-savingway. Previous algorithms aim at reducing the average energy consumption rate to extend the network lifetime. However, some nodes sometimes may be served as the cluster-head too many times to conserve their energy, resulting in reduced network lifetime. Thus, the large deviation of network lifetime makes these algorithms impractical. This paper proposes a new clustering algorithm which not only reduces the average energy consumption rate, but also converges the residual energies of all nodes on a small interval. Based on the two-region cluster-heads selection mechanism, the coordinator adaptively adjusts the far-near regions to converge the energies of all nodes on a small interval. With the exclusion-circle of cluster-heads, cluster-heads can be distributed evenly in a spatial respect for each round, resulting in reduced energy consumption. The simulation results show that the proposed algorithm not only makes cluster-heads distribute evenly in a spatial respect but also converges the residual energies of all nodes on a small interval, resulting in extending the network lifetime significantly and stably.
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.
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.
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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