Mobile Ad-hoc Network (MANETs) is an infrastructure less network in which nodes are mobile. In another words, MANET is an autonomous collection of mobile devices (laptops, smart phones, tablets, sensors, etc.) that communicate with each other over wireless links. The entire MANETs network may be mobile, and mobility may be very high so MANETs have varying topology. With the advent of laptops and 802.11/Wi-Fi wireless networking, MANET is getting popular. Integration of Wi-Fi and Bluetooth like technologies in almost all mobile devices such as Mobile phones, PDAs, notebook, etc. is also aiding in the popularity of MANETs. Because of these features, MANET is regarded as a future of ubiquitous networking and computing.

Localization is defined as the phenomenon of obtaining location information of nodes whose positions are not known. Thus, in the context of MANETs, localization means finding location of mobile nodes (MNs). Location awareness is an essential feature to gain full benefits of ubiquitous computing. Thus, several localization algorithms are introduced to estimate location of MNs. Location information obtained from localization algorithms can be useful for accessing various location-based services such as inventory tracking, habitat monitoring, rescue operations, museum tour guide, etc.

Positioning system is a system employed to estimate the location of a mobile node (MN). Positioning system can be both indoor and outdoor. Indoor environments have problems such as multipath effects and reflections of signals due to obstacles such as wall, furniture, people, etc., so accurate location estimation is a challenge. Therefore, localization techniques with good accuracy outdoors may not perform well indoors. The Global Positioning System (GPS) is the basic way of getting location information. However, GPS performs poorly in indoor environments due to its weak signal reception inside the buildings. MANETs are low cost networks which are composed of small, low-power and inexpensive mobile devices so having GPS installed on them are not feasible. Alternate means to GPS are thus introduced in order to detect MN’s location in MANETs. Thus, several positioning techniques have been introduced to make nodes capable of estimating their positions without using GPS.

The localization algorithms are broadly categorized based on some features as mentioned below:

Centralized versus Distributed algorithms:- This classification is based upon either the location server is used or not. Centralized algorithms use a central powerful node called location server [1] . The location server is responsible of computing position of all unknown nodes. On the other hand, in distributed schemes centralized computation is not required. Each node in Distributed algorithms are able to compute its own position if assistance is provided by the network.
Active versus Passive algorithms:- Active algorithms require a certain amount of location traffic to estimate position. Unlike to that passive scheme is a new technique for positioning devices without injecting location traffic.
Range-based versus Range-free algorithms:- Range-based scheme uses point-to-point distance or angle measurements between two nodes by Received Signal Strength Indicator (RSSI), Time of Arrival (TOA), Time Difference of Arrival (TDOA) and Angle of Arrival (AOA) [2]. After obtaining distance or angle measurements, they are used to calculate locations of unknown nodes. Nodes are equipped with radios for capturing the signal strength. TOA and TDOA are based on time where distance can be directly calculated from relation between propagation time and propagation speed. In the case of AOA, node measures the angle of incoming signal by using antenna or compass. Thus, range-based method requires extra hardware, causing rise in cost of the positioning system. High cost and restrictions of hardware discourage use of range-based scheme. In contrast to this in range-free techniques, unknown nodes collect anchor nodes’ or access points’ (nodes whose location is known in advance) location information and estimate their own locations based on these information.
Anchor-based versus Anchor-free algorithms:- MANETs and sensor networks, where few nodes (anchors) know their positions either using GPS or by other means implement Anchor based algorithms. Anchor nodes provide their location information to their neighbors, and then anchor based algorithms use that location information to compute location of unknown nodes. Anchor-free algorithms do not assume availability of prior location information. The algorithm gives relative positioning with the help of coordinate system established by a reference group of nodes.
Fine grained versus Coarse grained algorithms:- This classification is based on precision of the computed location. Fine grained algorithms give more accurate and precise location estimate, whereas, coarse grained localization schemes try to measure a node’s closeness to a reference point of known position. Triangulation is one of the examples of fine grained algorithm. Centroid [3] localization is a proximity based, coarse grained algorithm. Depending upon precision requirement of the system fine grained or coarse grained algorithms can be chosen.
Location fingerprinting:- To mitigate challenges in indoor environments, location fingerprinting technique was proposed. The location fingerprint scheme [4] assumes that each point inside a building has a unique Radio Frequency (RF) signature. Generally, a fingerprint F is labeled with a location information L [5]. The location fingerprints and their labels are maintained in location database and used during on-line phase. The label and fingerprint are generally denoted as a tuple of (L, F).
At present, focus is on developing cost efficient, range-free and passive localization algorithms. However, an inevitable trade-off between cost and precision exists in the location sensing system. Fine-grained algorithms give more accurate location estimation compared with coarse-grained but those systems are more costly. Thus, emphasis is on coarse-grained algorithms as an alternative to expensive algorithms depending upon the precision requirement of the system.


[1] Martin-Escalona, I., Barcelo-Arroyo, F., & Ciurana, M. (2010, oct.). Passive TDOA location in mobile ad-hoc networks. In Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT), 2010 International Congress on (p. 1218-1225).

[2] He, T., Huang, C., Blum, B. M., Stankovic, J.A., & Abdelzaher, T. (2003). Range-free localization schemes for large scale sensor networks. In Proceedings of the 9th annual international conference on mobile computing and networking (p. 81-95). London, UK: ACM.

[3] N. Bulusu, J.Heidemann, D.Estrin, GPS-less low-cost outdoor localization for very small devices, IEEE Personal Communications Magazine 7 (5) (2000) 28-34.

[4] Hossain, A.M. (2009). An Indoor Positioning System Based on Robust Location Fingerprint for Wi-Fi and Bluetooth. Doctoral dissertation, National University of Singapore, Singapore.

[5] Kaemarungsi, K. (2005). Design of Indoor Positioning Systems Based on Location Fingerprinting Technique. Doctoral dissertation, University of Pittsburgh, Pittsburgh.

Er. Sarita Gurung



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