Different sensors in different information and their exploitation in intelligent tasks remain a challenge. Given a wireless sensor network consisting of low-power devices, localization is the task of discovering the 2D or 3D positions of the sensor nodes.
According to Räty, T.D., in the“Survey on Contemporary Remote Surveillance Systems for Public Safety,” Sensor data should be decomposed into fundamental blocks and the intelligent components should have the responsibility of composing the deductions from them. He argued that an attempt should be made to construct a multisensor distributed intelligent surveillance system that functions at a relatively high level, capturing alerting situations with a very low false alarm rate. The surveillance personnel should be one of the strongest aspects in the surveillance system and should be retained in the system. Räty concluded that despite advancements in intelligence and awareness, the human being will always be a forerunner in adaptability and deductions.
Therefore, a number of methods have been proposed in the literature and used in practice to locate wireless devices. One method according to Yang et al in “Understanding Node Localizability of Wireless Ad Hoc and Sensor Networks,” is to determine the location of a device is through manual configuration, which may not be feasible for large- scale deployments or mobile systems. Another possibility is Global Positioning System (GPS). Although it is a popular system, it is not suitable for indoor environments and suffers from high hardware cost. In recent years, several approaches have been proposed for in-network localization, in which some special nodes (called beacons or seeds) know their global locations and the rest determine their locations by measuring the Euclidean distances to their neighbours. Based on distance ranging techniques as shown in the diagram below
They argued that localization is essential for many environment monitoring or surveillance applications. And that existing solutions fall into two categories. Range-based approaches assume nodes are able to measure internode distances; while range-free ones merely use neighbourhood information.
Many localization algorithms are range-based adopting distance ranging techniques, such as Received Signal Strength (RSS) and Time Difference of Arrival (TDoA). RSS maps received signal strength to distance according to a signal attenuation model, while TDoA measures the signal propagation time for distance calculation. In practice, RSS-based ranging measurements contain noises on the order of several meters. On the contrast, TDoA is impressively accurate and obtains centimeter accuracy for node separations under several meters in indoor environments. They confirm that result shows that TDoA can further achieve 1-2 cm accuracy within a range of more than ten meters, but it often has a much higher cost. The majority of localization algorithms assume a dense network such that iterative trilateration (or multilateration) can be conducted. Other methods record all possible locations in each positioning step and prune incompatible ones whenever possible, which, in the worst case, can result in an exponential space requirement. Besides, some works and study of the relationship between network localization and rigidity properties of ground truth graphs. Eren et al. proposed the concept of localization in subnetworks, which is weaker than the RR3P condition.
Error analysis and control are critical issues for localization. Due to the hardware limitations and energy constraints of wireless communication devices, range-free approaches are cost-effective alternatives.
Some nodes uniquely localizable under perfect distance ranging may suffer from location ambiguities in a practical scenario of ranging errors. They envisioned at this point in order increasing the robustness of localizability testing.
But with the development in technology and production capabilities, the deployment of large scale wireless sensor networks is becoming increasingly feasible. According to Lili Zhang, Xiwei Zhang, Jie Yang and Guihai Chen, in “Practical node deployment for unique localization in large scale wireless sensor networks“, Wireless sensor networks as a promising emerging technology will connect the internet to the real world, performing tasks in habitat, traffic, home, structural monitoring, exploration, a host of agricultural, security, and military applications. In many of these applications, it is fundamental for sensed data to be associated with a physical position. Knowing the positions of the nodes is useful not only for this purpose, but also for many lower-layer network functionalities such as geographic routing, topology control, coverage and tracking, and controlled mobility. The process by which node locations are determined is called localization
According to the researchers, locations can be achieved in a straight- forward manner by either manually configuring the nodes or equipping each node with a Global Positioning System (GPS). However, there are several potential problems with this approach. First, GPS hardware requirements are excessive for extremely small, resource constrained nodes in large scale networks. Next, GPS requires a line-of-sight with a sufficient number of satellites, and can fail in the presence of obstructions like tall buildings or cliffs, as well as indoors or underground. Another potential shortage in designing a GPS-reliant localization system is that the GPS infrastructure may come under attack; its signals jammed, or are made unavailable by its operators.
Meanwhile, Pedro Antonio, Francesco Grimaccia and Marco Mussetta in Article: “Architecture and Methods for Innovative Heterogeneous Wireless Sensor Network Applications” proposed an evolutionary technique to optimise the WSN lifetime considered in a heterogenous network with mobile nodes that adopts a multihop routine scheme as shown in the two figures below
Finally, in one of the applications of wireless sensor networks Wang Nan and Shen Xue-li, “Research on WSN Nodes Location Technology in Coal Mine” concludes that in the concept of wireless sensor network node they could confirm other nodes’ positions through a few known nodes’ positions. If they don’t know the nodes’ positions, then the sensor node are classified as nodes which are waiting for measuring; while, if they know the nodes’ positions, they will call treat that sensor nodes as anchor node. Anchor node is the node which has its own precise position, and it account for low proportion in the network nodes. Anchor node’s function is that it can help locate unknown node’s position. Except for the anchor node, the other nodes are nodes which are waiting for measuring.
According to different location mechanism, they divided wireless sensor network into two kinds as follows: range-based and range-free method. The former calculates node’s position by measuring the distance between points or by angle information; the later locates only from the network connectivity information. There are many methods which are based on location technology by measuring, such as TOA, TDOA, AOA, and RSSI etc. DV-Hop location algorithm, APIT location algorithm, centroid location algorithm, amorphous location algorithm are the methods, which are not based on location technology by measuring. According to the practical situation, RSSI measuring method and DV-Hop algorithm are used widely in monitoring safety of coal mine. But RSSI algorithm needs a lot of anchor nodes, its cost would increase. In DV-Hop algorithm, there is large inaccuracy in average hop distance which is estimated by hop number between anchor node and other anchor node. In order to make up for the shortage of two algorithms, they added the advantages of the two algorithms in order to improve location accuracy, and reduce cost.
RSSI can estimate distance between nodes through signal’s decay when it transmit. As the signal intensity would decrease in propagation process, they estimated the distance between the sending node and receiving node according to signal strength which is received by receiving node.
Therefore, executing DV-Hop Algorithm and Get Estimating Distance of Anchor Node In the upper step, they realised the distance between unknown node and anchor node in the communication arrange of anchor node. That means the length of some sides required in trilateral location method, and they needed to execute DV-Hop algorithm to obtain other sides. Anchor node sends beacon message and calculate RSSI distance of unknown nodes in communication scope. Next on their agenda was to Use Trilateral Location Algorithm to Estimate Unknown Node Position
For the purpose of enhance the security of poor working in coal mine, it’s important to study on the problem of location and tracking for keeping mining personnel and down hole equipments safe. They put forward wireless sensor network underground location algorithm which is based on the superposition of RSSI and DV-Hop. Their algorithm makes up for the shortage of RSSI algorithm and DV-Hop algorithm.
Their algorithm helps enhance measuring accuracy and reduce const measurement. This technology could also realize location of personnel and equipments underground.
There is need for reseachers in WSN to focus on node localization in hazardous areas. Can the node movement be preemptive in practical scenerio?
Räty, T.D., “Survey on Contemporary Remote Surveillance Systems for Public Safety,” Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on , vol.40, no.5, pp.493,515, Sept. 2010
Yang, Zheng; Liu, Yunhao, “Understanding Node Localizability of Wireless Ad Hoc and Sensor Networks,” Mobile Computing, IEEE Transactions on , vol.11, no.8, pp.1249,1260, Aug. 2012
Lili Zhang; Xiwei Zhang; Jie Yang; Guihai Chen, “Practical node deployment for unique localization in large scale wireless sensor networks,” Wireless Communications and Signal Processing (WCSP), 2010 International Conference on , vol., no., pp.1,6, 21-23 Oct. 2010
Pedro Antonio, Francesco Grimaccia and Marco Mussetta Article: Architecture and Methods for Innovative Heterogeneous Wireless Sensor Network Applications Remote Sens. 2012, 4(5), 1146-1161;doi:10.3390/rs4051146 http://www.mdpi.com/2072-4292/4/5/1146
Wang Nan and Shen Xue-li, “Research on WSN Nodes Location Technology in Coal Mine” Computer Science-Technology and Applications, 2009. IFCSTA ’09. International Forum on , vol.3, no., pp.232,234, 25-27 Dec. 2009