Dimensional Sensor Networks Research Articles

Estimation of Toeplitz Covariance Matrices in Large Dimensional Regime With Application to Source Detection

In this article, we derive concentration inequalities for the spectral norm of two classical sample estimators of large dimensional Toeplitz covariance matrices, demonstrating in particular their asymptotic almost sure consistence. The consistency is then extended to the case where the aggregated matrix of time samples is corrupted by a rank one (or more generally, low rank) matrix. As an application of the latter, the problem of source detection in the context of large dimensional sensor networks within a temporally correlated noise environment is studied. As opposed to standard procedures, this application is performed online, i.e. without the need to possess a learning set of pure noise samples.

재해정보 확산을 위한 끊김없는 N-스크린 서비스 기술

ABSTRACT A by-pass path in wireless sensor networks is the alternative path which be able to forward data when a routing path is being broken. One reason of depleting energy is occurred by the path. The method for solving prior to addressed the problem is proposed. However, this method may deplete radio resource. The best path has advantage that network lifetime of sensor nodes is prolonged; on the contrary, in order to maintain the best path it have to share their information between the entire nodes. In this paper, we propose the best path searching algorithm in the distributed three dimensional sensor networks. Through the neighboring informations sharing in the proposed method, the proposed algorithm can decide the best k-path as well as the extension of network lifetime. 키워드 : 최적 경로, 충돌 회피점, 무선 센서네트워크, 지역/전역 경로, 확률적 로드맵 Key word : Optimal path, contention avoidance point, wireless sensor network, local/global path, probabilistic road mapJournal of the Korea Institute of Information andCommunication Engineering

IJARCCE - Computer and Communication Engineering

Sensor nodes observing events, occurring in close proximity, gather data that are highly co-related. This work uses a polynomial regression technique to exploit the spatial correlation of the data in a three dimensional sensor network. The sensing nodes, sense the physical attribute and report their position coordinates (x, y, z) and the sensed value to the nearest tree node. Another set of nodes, categorized as the tree nodes, are responsible for generating a polynomial function of the received data and transmit the coefficients of regression to the parent tree node. The approach proceeds from the bottom to the top. The query from the sink, receives a polynomial function which is generated by the root node of the tree in each cluster to compute the attribute value at any location within the boundary. The proposed approach aims to save a lot of energy in the sensor network. Simulations, performed for different tree heights, indicate that a tree with a depth of four gives the best results.

A coverage strategy for wireless sensor networks in a three-dimensional environment

Coverage is one of the fundamental issues in wireless sensor networks (WSNs). The detection area is non–ideal and the terrain of the detection area is more complex in applications of three–dimensional sensor networks. Consequently, many of the existing coverage strategies cannot be directly applied to three–dimensional spaces. This paper presents a new coverage strategy for the three–dimensional sensor networks. Sensor nodes are uniformly distributed. The cost factor is utilised to construct the perceived probability and the classical watershed algorithm after the transformation of points from the three–dimensional space to the two–dimensional plane using the dimensionality reduction method, which can maintain the topology characteristic of the non–linear terrain. The detection probability in the optimal breath path is used as the measure to evaluate the coverage. Simulation results indicate that the proposed strategy can determine the coverage with fewer nodes, while achieving the coverage requirements of the networks.

3차원 무선 센서네트워크에서 최적경로 선정

무선 센서 네트워크에서 우회 경로는 경로 단절시 목적지에 데이터를 전달할 수 있는 대체 경로이다. 또한 이것은 에너지 낭비를 초래할 수 있는 요인이기도 하다. 이러한 문제점을 해결하기 위하여 멀티패스를 사용하는 방법이 제안되었다. 그러나, 이 방법은 센서노드의 무선 자원을 낭비할 수 있다. 최적의 패스는 에너지 소비를 감소시키기 때문에 네트워크의 수명이 길어지지만, 최적의 패스를 유지하기 위해 전체 노드들 간 주기적인 정보 교환이 이루어져야 한다. 본 논문은 분산된 3차원 센서네트워크에서 최적의 경로를 결정 알고리즘을 제안한다. 제안된 방법은 이웃 노드 간 정보 공유를 통하여 최적의 k-경로를 결정할 수 있을 뿐만 아니라 네트워크의 수명을 연장할 수 있다. A by-pass path in wireless sensor networks is the alternative path which be able to forward data when a routing path is being broken. One reason of depleting energy is occurred by the path. The method for solving prior to addressed the problem is proposed. However, this method may deplete radio resource. The best path has advantage that network lifetime of sensor nodes is prolonged; on the contrary, in order to maintain the best path it have to share their information between the entire nodes. In this paper, we propose the best path searching algorithm in the distributed three dimensional sensor networks. Through the neighboring informations sharing in the proposed method, the proposed algorithm can decide the best k-path as well as the extension of network lifetime.

Use of flip ambiguity probabilities in robust sensor network localization

Collinear or near collinear placement of some sensors in a wireless sensor network causes the location estimates of nearby sensors to be sensitive to erroneous distance measurements which leads to large location estimation errors. These errors and the possible propagation of these errors to the entire network or a large portion of it, thereby causing larger estimation errors for some sensors' locations, is a major problem in localization. This phenomenon is well described in rigid graph theory, using the notion of "flip ambiguity". This paper considers arbitrary sensor neighborhoods of two dimensional sensor networks and formulates an analytical expression for the probability of occurrence of the flip ambiguity. Based on the derived probability expression, a methodology is proposed to make the localization algorithms robust by calculating such flip ambiguity probabilities and eliminating potentially poor location estimates as well as assigning confidence factors to the estimated locations to prevent them from ruining the subsequent localization steps. The efficiency of the proposed methodology is demonstrated via a set of simulations.

Constructing low-connectivity and full-coverage three dimensional sensor networks

Low-connectivity and full-coverage three dimensional Wireless Sensor Networks (WSNs) have many real-world applications. By low connectivity, we mean there are at least k disjoint paths between any two sensor nodes in a WSN, where k ≤ 4. In this paper, we design a set of patterns to achieve 1-, 2-, 3- and 4-connectivity and full-coverage, and prove their optimality under any value of the ratio of communication range r <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</sub> over sensing range r <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sub> , among regular lattice deployment patterns. We further investigate the evolutions among all the proposed low-connectivity patterns. Finally, we study the proposed patterns under several practical settings.