Abstract
Wireless Sensor Networks (WSN) are of great current interest in the proliferation of technologies. Since the location of the sensors is one of the most interesting issues in WSN, the process of node localization is crucial for any WSN-based applications. Subsequently, WSN’s node estimation deals with a low-rank matrix which gives rise to the application of the Nuclear Norm Minimization (NNM) method. This paper will focus on the localization of 2-dimensional WSN with objects (obstacles). Recent studies introduce Nuclear Norm Minimization (NNM) for node estimation instead of formulating the rank minimization problem. Common way to tackle this problem is by implementing the Semidefinite Programming (SDP). However, SDP can only handle matrices with a size of less than 100 × 100. Therefore, we introduce the method of Singular Value Thresholding (SVT) which is an iterative algorithm to solve the NNM problem that produces a sequence of matrices { X k , Y k } and executes a soft-thresholding operation on the singular value of the matrix Y k . This algorithm is a user-friendly algorithm which produces a low computational cost with low storage capacity required to give the lowest-rank minimum nuclear norm solution.
Highlights
A Wireless Sensor Network (WSN) is a network of miniature devices known as nodes, which are capable of environment sensing, data computing and information transmission to remote areas via wireless links [1]
WSN has become one of the vital technologies for smart environments, where various kinds of devices are connected to each other according to the principles of the Internet of Things (IoT) [2]
The process of estimating missing entries from the very limited information in a matrix arises in many applications, which are mostly related to engineering and applied sciences, such as computer vision and pattern recognition [3], machine learning [4], Wireless Sensor Network (WSN) and control [5]
Summary
A Wireless Sensor Network (WSN) is a network of miniature devices known as nodes, which are capable of environment sensing, data computing and information transmission to remote areas via wireless links [1]. The range-free localization method focuses on connectivity information and geometric distance, such as a hop count for node localization [22,23]. This method is a well-known cost-effective method that produces poor localization accuracy [22]. The range-based localization method produces approximated results with a higher localization accuracy. This is due to the process of estimating the physical distance between any two sensor nodes [23]. This research will focus on range-based localization for the best localization accuracy
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