Sensors are widely deployed in our environment, and their number is increasing rapidly. In the near future, billions of devices will all be connected to each other, creating an Internet of Things. Furthermore, computational intelligence is needed to make applications involving these devices truly exciting. In IoT, however, the vast amounts of data will not be statically prepared for batch processing, but rather continually produced and streamed live to data consumers and intelligent algorithms. We refer to applications that perform live analysis on live data streams, bringing intelligence to IoT, as the Analysis of Things. However, the Analysis of Things also comes with a new set of challenges. The data sources are not collected in a single, centralized location, but rather distributed widely across the environment. AoT applications need to be able to access (consume, produce, and share with each other) this data in a way that is natural considering its live streaming nature. The data transport mechanism must also allow easy access to sensors, actuators, and analysis results. Furthermore, analysis applications require computational resources on which to run. We claim that system support for AoT can reduce the complexity of developing and executing such applications. To address this, we make the following contributions: - A framework for systems support of Live Streaming Analysis in the Internet of Things, which we refer to as the Analysis of Things (AoT), including a set of requirements for system design - A system implementation that validates the framework by supporting Analysis of Things applications at a local scale, and a design for a federated system that supports AoT on a wide geographical scale - An empirical system evaluation that validates the system design and implementation, including simulation experiments across a wide-area distributed system We present five broad requirements for the Analysis of Things and discuss one set of specific system support features that can satisfy these requirements. We have implemented a system, called \textsubscript{SS}IoTa, that implements these features and supports AoT applications running on local resources. The programming model for the system allows applications to be specified simply as operator graphs, by connecting operator inputs to operator outputs and sensor streams. Operators are code components that run arbitrary continuous analysis algorithms on streaming data. By conforming to a provided interface, operators may be developed that can be composed into operator graphs and executed by the system. The system consists of an Execution Environment, in which a Resource Manager manages the available computational resources and the applications running on them, a Stream Registry, in which available data streams can be registered so that they may be discovered and used by applications, and an Operator Store, which serves as a repository for operator code so that components can be shared and reused. Experimental results for the system implementation validate its performance. Many applications are also widely distributed across a geographic area. To support such applications, \textsubscript{SS}IoTa must be able to run them on infrastructure resources that are also distributed widely. We have designed a system that does so by federating each of the three system components: Operator Store, Stream Registry, and Resource Manager. The Operator Store is distributed using a distributed hast table (DHT), however since temporal locality can be expected and data churn is low, caching may be employed to further improve performance. Since sensors exist at particular locations in physical space, queries on the Stream Registry will be based on location. We also introduce the concept of geographical locality. Therefore, range queries in two dimensions must be supported by the federated Stream Registry, while taking advantage of geographical locality for improved average-case performance. To accomplish these goals, we present a design sketch for SkipCAN, a modification of the SkipNet and Content Addressable Network DHTs. Finally, the fundamental issue in the federated Resource Manager is how to distributed the operators of multiple applications across the geographically distributed sites where computational resources can execute them. To address this, we introduce DistAl, a fully distributed algorithm that assigns operators to sites. DistAl also respects the system resource constraints and application preferences for performance and quality of results (QoR), using application-specific utility functions to allow applications to express their preferences. DistAl is validated by simulation results.