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Since wireless sensor networks are usually used for long-term monitoring in harsh environments, sensor nodes are vulnerable to faults. Function fault can lead to immediate node breakdown, while data fault makes the node generate erroneous sensor data. Faulty data results in incorrect estimation of the environment and causes unnecessary consumption of the network resources. Therefore, it is necessary...
In order to improve the location accuracy in the complicated greenhouse environment, a positioning system is designed in this paper. According to RSSI data in the greenhouse, the parameters of the path-loss model are modified. Besides, least square estimation is used to filter RSSI data to eliminate random disturbance. Based on RSSI, the blind node is positioned by triangle centroid location method...
Data reduction strategy is one of the schemes employed to extend network lifetime. In this paper we present an implementation of a light-weight forecasting algorithm for sensed data which saves packet transmission in the network. The proposed Naive algorithm achieves high energy savings with a limited computational overhead on a node. Simulation results from realistic Building monitoring application...
Data reduction strategy is one of the schemesemployed to extend network lifetime. In this paper we present an implementation of a light-weight forecasting algorithm for sensed data which saves packet transmission in the network. The proposed Naive algorithm achieves high energy savings with a limited computational overhead on a node. Simulation results from realistic Building monitoring application...
The accuracy of radial radio propagation models, e.g. the log-normal path loss model, is severely degraded by the effects of multipath propagation, environmental differences and hardware variability. This has a direct impact on the performance of node localization algorithms that use these models. In this paper, first we study the effect of the environment and hardware variability on the model parameters...
Wireless Body Sensor Networks (WBSNs) have proved to be a suitable technology for supporting the monitoring of physical and physiological activities of the human body. However, avoiding erroneous behavior of WBSN-based systems is an issue of fundamental importance, especially for critical health-care applications. In this regard, proper self-healing techniques should be able to fulfill requirements...
The main purpose of this paper is to construct a data accuracy model for the maximal set of sensor nodes that sense a point event and forms a cluster with fully connected network between them. We determine the minimal set of sensor nodes that are sufficient to give approximately the same data accuracy achieve by the maximal set of sensor nodes.
In Wireless Sensor Networks (WSNs), sensor nodes power consumption is the main challenge. Emerging in-network aggregation techniques are increasingly being sought to overcome this constraint and to save precious energy. WSN applications require spatially dense deployment of sensor nodes to achieve full coverage. As a result, sensors observations have spatial correlation. Rate Distortion theory with...
We present the design of a novel adaptive sampling technique called Exponential Double Smoothing-based Adaptive Sampling (EDSAS), in which the temporal data correlations provide an indication of the prevailing environmental conditions and are used to adapt the sensing rate of a sensor node. EDSAS uses irregular data series prediction to reduce sampling rate in combination with change detection to...
Knowledge discovery and data analysis in resource constrained wireless sensor networks faces different challenges. One of the main challenges is to identify misbehaviors or anomalies with high accuracy while minimizing energy consumption in the network. In this paper, we extend a previous work of us and we present an algorithm for temporal anomalies detection in wireless sensor networks. Our experiments...
The ultimate goal in a multiple classifier system (MCS) is to obtain a global and more accurate model through the combination of several base learners. Among the popular combining rules, averaging has been emphasized as a well qualified option. The averaging rule can be applied with equal (simple averaging) or non-equal (weighted averaging) weights vector for the linear combination. When the formed...
Regression is one of the effective techniques for data analysis in a WSN. Besides distributed data, the limited power supply and bandwidth capacity of nodes makes doing regression difficult in WSNs. Conventional methods, which employ some numerical optimization techniques such as Nelder-Mead simplex and gradient descent, generally work in a pre-established Hamiltonian path among the nodes. Low estimation...
Nonparametric belief propagation (NBP) is the well-known method for cooperative localization in wireless sensor networks. It is capable to provide information about location estimation with appropriate uncertainty and to accommodate non-Gaussian distance measurement errors. However, the accuracy of NBP is questionable in loopy networks. Therefore, in this paper, we propose a novel approach, NBP based...
Wireless sensor networks (WSNs) are data centric networks to which data aggregation is a central mechanism. Nodes in such networks are known to be of low complexity and highly constrained in energy. This requires novel distributed algorithms to data aggregation, where accuracy, complexity and energy need to be optimized in the aggregation of the raw data as well as the communication process of the...
In large-scale object tracking wireless sensor networks, multiple mobile nodes will bring large amount of communication overheads in maintaining the accuracy of localization tracking, which would possibly affect the collection and dissemination of the tracking data, seriously. Existing seminar works purely focus on achieving optimal tracking accuracy and suffer from large amount of protocol overhead,...
Extending the lifetime of wireless sensor networks remains the most challenging and demanding requirement that impedes large-scale deployments. The basic operation in WSNs is the systematic gathering and transmission of sensed data to a base station for further processing. During data gathering, the amount of data can be large sometimes, due to redundant data combined from different sensing nodes...
This positioning process of the wireless sensor network (WSN) nodes is interfered by multipath, multiple access, especially NLOS transmission effect, Basing on TOA/TDOA positioning technology, the article brings forward TOA/TDOA measurement data model, and improves on a location algorithm which bases on least square support vector machine (LS-SVM). On one network with uniformly distributed nodes,...
In a wireless sensor network, data collection algorithm has to be designed to extend the lifetime of sensors to the best extent and at the same time keep the data accuracy to a certain level. This paper presents a novel data gathering algorithm for continuous query in wireless sensor networks. In particular, the problem of adaptive determination of data granularity for QoS-constraint query execution...
In sensor networks, privacy can be addressed in different levels of the network stack and at different points of the information flow. This paper presents an application level scheme for controlling information disclosure at the points of data capture. The scheme includes a trust model for facilitating in-network privacy decisions. The trust model exploits the pre-deployment knowledge on the network...
In object tracking wireless sensor networks, communication overheads affect the accuracy of tracking localization seriously. Existing seminar works suffer from lacking of adaptively on various accuracy requirements, which lead to large amount of network traffic. In this paper, we propose a coordination mechanism based on joint mobility prediction method between sink node and sensor node, as to meet...
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