This thesis studies the issues of building and managing future Internet of Things (IoT)

systems. IoT systems consist of distributed components with services for sensing, processing, and controlling through devices deployed in our living environment as part of the global cyber-physical ecosystem.

Systems with perpetually running IoT devices may use a lot of energy. One challenge is implementing good management policies for energy saving. In addition, a large scale of devices may be deployed in wide geographical areas through low bandwidth wireless communication networks. This brings the challenge of congfiuring a large number of duplicated applications with low latency in a scalable manner. Finally, intelligent IoT applications, such as occupancy prediction and activity recognition, depend on analyzing user and event patterns from historical data. In order to achieve real-time interaction between humans and things, reliable yet real-time analytic support should be included to leverage the interplay and complementary roles of edge and cloud computing.

In this dissertation, I address the above issues from the service oriented point of view. Service oriented architecture (SOA) provides the integration and management flexibility using the abstraction of services deployed on devices. We have designed the WuKong IoT middleware to facilitate connectivity, deployment, and run-time management of IoT applications.

For energy efficient mapping, this thesis presents an energy saving methodology for co-

locating several services on the same physical device in order to reduce the computing and communication energy. In a multi-hop network, the service co-location problem is formulated as a quadratic programming problem. I propose a reduction method that reduces it to the integer programming problem. In a single hop network, the service co-location problem can be modeled as the Maximum Weighted Independent Set (MWIS) problem. I design algorithm to transform a service flow to a co-location graph. Then, known heuristic algorithms to find the maximum independent set, which is the basis for making service co-location decisions, are applied to the co-location graph.

For low latency scalable deployment, I propose a region-based hierarchical management structure. A congestion zone that covers multiple regions is identified. The problem of deploying a large number of copies of a flow-based program (FBP) in a congestion zone is modeled as a network traffic congestion problem. Then, the problem of mapping in a congestion zone is modeled as an Integer Quadratic Constrained Programming (IQCP) problem, which is proved to be a NP-hard problem. Given that, an approximation algorithm based on LP relaxation and an efficient service relocating heuristic algorithm are designed for reducing the computation complexity. For each congestion zone, the algorithm will perform global optimized mapping for multiple regions, and then request multiple deployment delegators for reprogramming individual devices.

Finally, with the growing adoption of IoT applications, dedicated and single-purpose devices are giving way to smart, adaptive devices with rich capabilities using a platform or API, collecting and analyzing data, and making their own decisions. To facilitate building intelligent applications in IoT, I have implemented the edge framework for supporting reliable streaming analytics on edge devices. In addition, a progression framework is built to achieve the self-management capability of applications in IoT. A progressive architecture and a programming paradigm for bridging the service oriented application with the power of big data on the cloud are designed in the framework. In this thesis, I present the detailed design of the progression framework, which incorporates the above features for building scalable management of IoT systems through a

flexible middleware.