Privacy aware publishing of successive location information in sensor networks

2012 ◽  
Vol 28 (6) ◽  
pp. 913-922 ◽  
Author(s):  
Baokang Zhao ◽  
Dan Wang ◽  
Zili Shao ◽  
Jiannong Cao ◽  
Jinshu Su
Keyword(s):  
Sensor Networks ◽  
Location Information ◽  
Successive Location

scholarly journals Fuzzy Logic-Based Geographic Routing Protocol for Dynamic Wireless Sensor Networks

Sensors ◽  
2019 ◽  
Vol 19 (1) ◽  
pp. 196 ◽  
Author(s):  
Xing Hu ◽  
Linhua Ma ◽  
Yongqiang Ding ◽  
Jin Xu ◽  
Yan Li ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Fuzzy Logic ◽  
Sensor Networks ◽  
Routing Protocol ◽  
Geographic Routing ◽  
Wireless Sensor ◽  
Location Information ◽  
Destination Node ◽  
Routing Overhead ◽  
Geographic Routing Protocol

The geographic routing protocol only requires the location information of local nodes for routing decisions, and is considered very efficient in multi-hop wireless sensor networks. However, in dynamic wireless sensor networks, it increases the routing overhead while obtaining the location information of destination nodes by using a location server algorithm. In addition, the routing void problem and location inaccuracy problem also occur in geographic routing. To solve these problems, a novel fuzzy logic-based geographic routing protocol (FLGR) is proposed. The selection criteria and parameters for the assessment of the next forwarding node are also proposed. In FLGR protocol, the next forward node can be selected based on the fuzzy location region of the destination node. Finally, the feasibility of the FLGR forwarding mode is verified and the performance of FLGR protocol is analyzed via simulation. Simulation results show that the proposed FLGR forwarding mode can effectively avoid the routing void problem. Compared with existing protocols, the FLGR protocol has lower routing overhead, and a higher packet delivery rate in a sparse network.

SecRET

2021 ◽  
Vol 15 (1) ◽  
pp. 1-26
Author(s):  
Sudip Misra ◽  
Tamoghna Ojha ◽  
Madhusoodhanan P
Keyword(s):  
Sensor Networks ◽  
Error Rates ◽  
Location Information ◽  
Underwater Sensor Networks ◽  
Node Localization ◽  
Limited Bandwidth ◽  
Secure Localization ◽  
Localization Scheme ◽  
Monitoring Application ◽  
Fundamental Requirement

Node localization is a fundamental requirement in underwater sensor networks (UWSNs) due to the ineptness of GPS and other terrestrial localization techniques in the underwater environment. In any UWSN monitoring application, the sensed information produces a better result when it is tagged with location information. However, the deployed nodes in UWSNs are vulnerable to many attacks, and hence, can be compromised by interested parties to generate incorrect location information. Consequently, using the existing localization schemes, the deployed nodes are unable to autonomously estimate the precise location information. In this regard, similar existing schemes for terrestrial wireless sensor networks are not applicable to UWSNs due to its inherent mobility, limited bandwidth availability, strict energy constraints, and high bit-error rates. In this article, we propose SecRET , a <underline>Sec</underline>ure <underline>R</underline>ange-based localization scheme empowered by <underline>E</underline>vidence <underline>T</underline>heory for UWSNs. With trust-based computations, the proposed scheme, SecRET , enables the unlocalized nodes to select the most reliable set of anchors with low resource consumption. Thus, the proposed scheme is adaptive to many attacks in UWSN environment. NS-3 based performance evaluation indicates that SecRET maintains energy-efficiency of the deployed nodes while ensuring efficient and secure localization, despite the presence of compromised nodes under various attacks.

scholarly journals Distributed Beacon Drifting Detection for Localization in Unstable Environments

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Ming Xia ◽  
Peiliang Sun ◽  
Xiaoyan Wang ◽  
Yan Jin ◽  
Qingzhang Chen
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Detection Algorithm ◽  
Experimental Results ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Location Information ◽  
Detection Accuracy ◽  
Research Issue ◽  
Fundamental Research

Localization is a fundamental research issue in wireless sensor networks (WSNs). In most existing localization schemes, several beacons are used to determine the locations of sensor nodes. These localization mechanisms are frequently based on an assumption that the locations of beacons are known. Nevertheless, for many WSN systems deployed in unstable environments, beacons may be moved unexpectedly; that is, beacons are drifting, and their location information will no longer be reliable. As a result, the accuracy of localization will be greatly affected. In this paper, we propose a distributed beacon drifting detection algorithm to locate those accidentally moved beacons. In the proposed algorithm, we designed both beacon self-scoring and beacon-to-beacon negotiation mechanisms to improve detection accuracy while keeping the algorithm lightweight. Experimental results show that the algorithm achieves its designed goals.

scholarly journals Neighborhood Kalman Estimation for Distributed Localization in Wireless Sensor Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Xiaochu Wang ◽  
Ting Sun ◽  
Chunshi Fan
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Fusion ◽  
Numerical Simulations ◽  
Covariance Matrices ◽  
Wireless Sensor ◽  
Location Information ◽  
Localization Algorithm ◽  
Fast Speed ◽  
Sensor Locations

Accurate location information plays an important role in the performance of wireless sensor networks since many mission applications depend on it. This paper proposes a fully distributed localization algorithm based on the concept of data fusion, allowing the full intranodes information including the correlations among estimates to take part in the algorithm. By properly constructing and updating the estimates as well as the corresponding covariance matrices, the algorithm can fuse intranodes information to generate more accurate estimates on the sensor locations with a fast speed. Finally, numerical simulations are given as examples to demonstrate the effectiveness of the algorithm.

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