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In military battlefield domain, decision making plays a very important part because safety and protection depends upon the accurate decisions made by the commanders in complex situations. In military and defense applications, there is a need of such technology that helps leaders to take good decisions in the critical situations with information overload. With the help of multi-sensor information fusion, the amount of information can be reduced as well as uncertainties in the information in the decision making of identifying and tracking targets in the military area.   Information fusion refers to the process of getting information from different sources and fusing this information, to supply an enhanced decision support. Decision making is the very core and a vital part in the field of information fusion and better decisions can be obtained by understanding how situation awareness can be enhanced. Situation awareness is about understanding the elements of the situation i.e. circumstances of the surrounding environment, their relations and their future impacts, for better decision making. Efficient situation awareness can be achieved with the effective use of the sensors. Sensors play a very useful role in the multi-sensor fusion technology to collect the data about, for instance, the enemy regarding their movements across the border and finding relationships between different objects in the battlefield that helps the decision makers to enhance situation awareness.   The purpose of this thesis is to understand and analyze the critical issue of uncertainties that results information in overload in military battlefield domain and benefits of using multi-sensor information fusion technology to reduce uncertainties by comparing uncertainty management methods of Bayesian and Dempster Shafer theories to enhance decision making and situation awareness for identifying the targets in battlefield domain.

Advances in Internet-of-Things integrate sensors and actuators in everyday items or even people transforming our society at an accelerated pace. This occurs in areas such as agriculture, logistics, transport, healthcare, and smart cities and has created new ways to interact with and experience entertainment, (serious) games, education, etc. Common to these domains is the challenge to realize and maintain complex relations with any object or individual globally, with the requirement for immediacy in maintaining relations of varying complexity. Existing architectures for maintaining relations on the Internet, e.g., DNS and search engines are insufficient in meeting these challenges. Their deficiencies mandate the research presented in this dissertation enabling the maintenance of dynamic and multi-criteria relationships among entities in real-time in an Internet-of-Things while minimizing the overall cost for maintaining such context-centric relationships. A second challenge is the need to represent nearness in context-centric relationships, since solutions need to build on what is closely related. The dissertation shows that the proximity on relations can be used to bring about the scalability of maintaining relationships across the IoT. It successfully demonstrates the concept and feasibility of self-organizing context-centric overlay networks for maintaining scalable and real-time relationships between endpoints co-located with associated physical entities. This is complemented by an object model for annotating objects and their relationships as derived and defined over the underpinning context interactions. Complementing measures of nearness are added through a non-metric multi-criteria approach to evaluating the notion of context proximity. A query language and an extension to the publish-subscribe approaches achieves distributed support for discovering such relationships; locating entities relative to a defined hyper-sphere of interest. Furthermore, it introduces adaptive algorithms for maintaining such relationships at minimal overall costs. The results demonstrate the feasibility of moving towards context-centric approaches to immersion and that such approaches are realizable over vast and distributed heterogeneous collections of user and their associated context information.