Efficient Localization for Machine Type Communication Nodes

Abstract

Machine to machine (M2M) communication are becoming popular in position based services such as combat zone surveillance, health monitoring, conflagration detection, and wild habitat monitoring. In M2M communication networks, sensors are used as the basic component, as it may be distributed randomly, accessed far-off and configured automatically. They are also known as machine type communication (MTC) devices. M2M communication networks consist of a large number of MTC devices, e.g., sensors, smart meters and RFID tags/readers. M2M communication is recognized as a new form of communications that allows a complete mechanical automation (such as the smart grid and the Internet of Things (IoT) that may modify our styles of living. Unlike sensor networks, where every node is either the network controller (the sink) or a sensor, hence only connections from every device to the network controller are needed. In order to provide the demand of a full mechanization, each device be allowed to play many functions among the sensor, i.e., the action executor and the sink. Visualize, for instance, structure, from room to bridges, that can identify the requirement and contact for their own location-specific maintenance, or self-assist systems that can serve and protect the weak, or disabled, elderly, mechanically turn OFF hotplates, restocking the pantry and insuring food and drink are fresh, and managing expressways that maintain traffic flow; medication timetable; cots that watch sleeping infants; forest that warn guards to wildfires and ``inform" on the interest of their occupant. Consequence of this manner are only a few of the possible outcomes of developments in M2M communications. Hence, the objective of M2M communications is to give scrupulous connections between all MTC devices. As information from MTC devices in the absence of location information are mostly not effective. Whenever the real geometries of some sections of the network are conscious to range errors, as a result invalid local geometric recognition in those sections of the network. Therefore, unique authenticity of distance-based localization will also effect. In sensor network, one such example is flip ambiguity, due to which unique localization is not possible. Motivated by the above mentioned facts, in this thesis, we proposed an efficient closed form localization algorithms for both the cases i.e., when anchor machines have no position error and when they have position error. In the first part of thesis, we proposed a localization algorithm, where we assumed that distance measurement between anchor machines (AMs) and blind machines (BMs) have Gaussian error, furthermore we assumed that there is no position error in AMs. It is verified from simulation that the proposed method is simple and require no initial estimate. In the second part of thesis, we extend our above work by using a more practical approach for measurement error between devices in IoT. Furthermore we derived the theoretical lower bound for our proposed algorithm. Computer simulations shows that instead of the closed form of our proposed algorithm it is more robust even for large measurement noise. In the third part, we investigate a distributed localization problem in noisy networks, where an estimated position of blind MTC machines (BMs) is obtained by using noisy measurements of distance between BM and anchor machines (AMs). We allow positioned BMs also to work as anchors that are referred to virtual AMs (VAMs). VAMs usually have greater position errors than (original) AMs and if used as anchors the error propagates through the whole network. But VAMs are necessary especially when many BMs are distributed in a large area with not a sufficient number of AMs. To overcome the error propagation, we propose a greedy successive anchorization process (GSAP). A round of GSAP consists of consecutive two steps. In the first step, a greedy selection of anchors among AMs and VAMs is done by which GSAP considers only those three anchors that possibly pertain the localization accuracy. In the second step, each BM that can select three anchors in its neighbor determines its location with a proposed distributed localization algorithm. Iterative rounds of GSAP terminate when every BM in the network finds its location. To examine the performance of GSAP, a root mean square error (RMSE) metric is used and the corresponding Cramer-Rao lower bound (CRLB) is provided. By numerical investigation, RMSE performance of GSAP is shown to be better than existing localization methods with and without an anchor selection method and mostly close to the CRLB.