Developing and maintaining context-aware efficient systems in heterogeneous environments is a challenging task. In our research work, we enable context awareness in users, devices, and applications to enable context-based systems in Ambient Networks. We achieve this by proposing a context dissemination system which propagates the fast evolving context information from its sources (e.g., context sensors) to various interested information sinks (e.g., context sensitive clients). The proposed system implements a context aware overlay architecture composed of multi-level overlay networks. This overlay architecture acts as a base (middleware) for the development and maintenance of the application-layer context-specific dissemination protocol (the CSON-D protocol). The protocol's multi-level overlay structure and its intelligence, personalization, and fault tolerance features exhibit adaptive behavior with minimized casting functionality. Our conceptual model is reinforced by means of experimental evaluations. In general, the cost-based overhead for multi-level overlay formation and maintenance is minimal.

Due to the heterogeneity involved in smart interconnected devices, cellular applications, and surrounding (GPS-aware) environments there is a need to develop a realistic approach to track mobile assets. Current tracking systems are costly and inefficient over wireless data transmission systems where cost is based on the rate of data being sent. Our aim is to develop an efficient and improved geographical asset tracking solution and conserve valuable mobile resources by dynamically adapting the tracking scheme by means of context-aware personalized route learning techniques. We intend to perform this tracking by proactively monitoring the context information in a distributed, efficient, and scalable fashion. Context profiles, which indicate the characteristics of a route based on environmental conditions, are utilized to dynamically represent the values of the asset's properties. We designed and implemented an adaptive learning based scheme that makes an optimized judgment of data transmission. This manuscript is complemented with theoretical and practical evaluations that prove that significant costs can be saved and operational efficiency can be achieved.

Presently, smart interconnected heterogeneous devices with built-in GPS, Wi-Fi, and Bluetooth capabilities are more affordable, resulting in numerous novel mobile applications. To adapt applications based on the environment they are executed in, we perform context management by proactively monitoring the (network- and location- based) context information available in such devices and applications. As this requires continuous monitoring, the common fixed data flow based technique for forwarding contexts from multiple context sensors is not energy efficient. In this paper, we propose the design and implementation of an adaptive context monitoring scheme. Alongside context awareness, we employ overlay network, agent, and policy theories. We utilize learning and personalization characteristics to make an optimized judgment of context information in an efficient and scalable fashion. This paper is complemented with protocol evaluations to validate scalability claims based on real logs.

The recent advancements in technologies such as cellular networks, wireless sensor networks (WSN), radio-frequency identification (RFID), and global positioning system (GPS) have lead us to develop a realistic approach to tracking mobile assets. Tracking and managing the dynamic location of mobile assets is critical for many organizations with mobile resources. Current tracking systems are costly and inefficient over wireless data transmission systems where cost is based on the rate of data being sent. Thus our main research goal is to develop efficient and improved asset tracking solutions and consume valuable mobile resources. In addition, we also adapt their routes by means of a novel and efficient geographical tracking approach that performs route adaptation. We focus on tracking GPS-enabled mobile devices mounted on the asset by understanding the behavior of a mobile device for reporting GPS data in various demographics. This paper is complemented with result evaluations based on a simulation environment with real logs.