Localization Process for WSNs with Various Grid-Based Topology Using Artificial Neural Network

Wireless Sensor Network (WSN) is a technology that can aid human life by providing ubiquitous communication, sensing, and computing capabilities. It allows people to be more able to interact with the environment. The environment contains many nodes to monitor and collect data. Localizing nodes distributed in different locations covering different regions is a challenge in WSN. Localization of accurate and low-cost sensors is an urgent need to deploy WSN in various applications. In this paper, we propose an artificial automatic neural network method for sensor node localization. The proposed method in WSN is implemented with network-based topology in different regions. To demonstrate the accuracy of the proposed method, we compared the estimated locations of the proposed feedforward neural network (FFNN) with the estimated locations of the deep feedforward neural network (DFF) and the weighted centroid localization (WCL) algorithm based on the strength of the received signal index. The proposed FFNN model outperformed alternative methods in terms of its lower average localization error which is 0.056m. Furthermore, it demonstrated its capability to predict sensor locations in wireless sensor networks (WSNs) across various grid-based topologies

Localization Process for WSNs with Various Grid-Based Topology Using Artificial Neural Network

Wireless Sensor Network (WSN) is a technology that can aid human life by providing ubiquitous communication, sensing, and computing capabilities. It allows people to be more able to interact with the environment. The environment contains many nodes to monitor and collect data. Localizing nodes distributed in different locations covering different regions is a challenge in WSN. Localization of accurate and low-cost sensors is an urgent need to deploy WSN in various applications. In this paper, we propose an artificial automatic neural network method for sensor node localization. The proposed method in WSN is implemented with network-based topology in different regions. To demonstrate the accuracy of the proposed method, we compared the estimated locations of the proposed feedforward neural network (FFNN) with the estimated locations of the deep feedforward neural network (DFF) and the weighted centroid localization (WCL) algorithm based on the strength of the received signal index. The proposed FFNN model outperformed alternative methods in terms of its lower average localization error which is 0.056m. Furthermore, it demonstrated its capability to predict sensor locations in wireless sensor networks (WSNs) across various grid-based topologies

Virtual coordinate based techniques for wireless sensor networks: a simulation tool and localization & planarization algorithms

2013 Summer.Includes bibliographical references.Wireless sensor Networks (WSNs) are deployments of smart sensor devices for monitoring environmental or physical phenomena. These sensors have the ability to communicate with other sensors within communication range or with a base station. Each sensor, at a minimum, comprises of sensing, processing, transmission, and power units. This thesis focuses on virtual coordinate based techniques in WSNs. Virtual Coordinates (VCs) characterize each node in a network with the minimum hop distances to a set of anchor nodes, as its coordinates. It provides a compelling alternative to some of the localization applications such as routing. Building a WSN testbed is often infeasible and costly. Running real experiments on WSNs testbeds is time consuming, difficult and sometimes not feasible given the scope and size of applications. Simulation is, therefore, the most common approach for developing and testing new protocols and techniques for sensor networks. Though many general and wireless sensor network specific simulation tools are available, no available tool currently provides an intuitive interface or a tool for virtual coordinate based simulations. A simulator called VCSIM is presented which focuses specifically on Virtual Coordinate Space (VCS) in WSNs. With this simulator, a user can easily create WSNs networks of different sizes, shapes, and distributions. Its graphical user interface (GUI) facilitates placement of anchors and generation of VCs. Localization in WSNs is important for several reasons including identification and correlation of gathered data, node addressing, evaluation of nodes' density and coverage, geographic routing, object tracking, and other geographic algorithms. But due to many constraints, such as limited battery power, processing capabilities, hardware costs, and measurement errors, localization still remains a hard problem in WSNs. In certain applications, such as security sensors for intrusion detection, agriculture, land monitoring, and fire alarm sensors in a building, the sensor nodes are always deployed in an orderly fashion, in contrast to random deployments. In this thesis, a novel transformation is presented to obtain position of nodes from VCs in rectangular, hexagonal and triangular grid topologies. It is shown that with certain specific anchor placements, a location of a node can be accurately approximated, if the length of a shortest path in given topology between a node and anchors is equal to length of a shortest path in full topology (i.e. a topology without any voids) between the same node and anchors. These positions are obtained without the need of any extra localization hardware. The results show that more than 90% nodes were able to identify their position in randomly deployed networks of 80% and 85% node density. These positions can then be used for deterministic routing which seems to have better avg. path length compared to geographic routing scheme called "Greedy Perimeter Stateless Routing (GPSR)". In many real world applications, manual deployment is not possible in exact regular rectangular, triangular or hexagonal grids. Due to placement constraint, nodes are often placed with some deviation from ideal grid positions. Because of placement tolerance and due to non-isotropic radio patterns nodes may communicate with more or less number of neighbors than needed and may form cross-links causing non-planar topologies. Extracting planar graph from network topologies is known as network planarization. Network planarization has been an important technique in numerous sensor network protocols--such as GPSR for efficient routing, topology discovery, localization and data-centric storage. Most of the present planarization algorithms are based on location information. In this thesis, a novel network planarization algorithm is presented for rectangular, hexagonal and triangular topologies which do not use location information. The results presented in this thesis show that with placement errors of up to 30%, 45%, and 30% in rectangular, triangular and hexagonal topologies respectively we can obtain good planar topologies without the need of location information. It is also shown that with obtained planar topology more nodes acquire unique VCs

Developing Energy-Efficient Topologies and Routing for Wireless Sensor Networks

The original publication can be found at www.springerlink.comThe performance of wireless sensor networks (WSNs) is greatly influenced by their topology. WSNs with patterned topologies can efficiently save energy and achieve long networking lifetime. In this paper, we discuss different patterned topologies for constructing WSNs which can provide the required coverage area and the connectivity of all sensor nodes. We compare different performance measures among all patterned topologies, and find that WSNs in strip-based topology can provide the greatest coverage area with the same number of sensor nodes as used for WSNs in other patterned topologies. Strip-based topology also consumes least energy in the routing protocol of flooding. We show that triangle-based topology can provide the highest reliability among all patterned topologies. We also propose several routing protocols for WSNs in patterned topologies, which are based on different parameters when selecting next-hop neighbor. Our protocols require only local information and work in a simple and effective way to achieve energy efficiency.Hui Tian, Hong Shen and Teruo Matsuzaw