High data rate sensors such as video cameras are becoming ubiquitous in the Internet of Things. This paper describes GigaSight, an Internet-scale repository of crowd-sourced video content, with strong enforcement of privacy preferences and access controls. The GigaSight architecture is a federated system of VM-based cloudlets that perform video analytics at the edge of the Internet, thus reducing demand for ingress bandwidth into the cloud. Denaturing, which is the owner-specific reduction in fidelity of video content to preserve privacy, is one form of analytics on cloudlets. Content-based indexing for search is another form of cloudlet-based analytics.

We propose a scalable Internet system for continuous collection of crowd-sourced video from devices such as Google Glass. Our hybrid cloud architecture, GigaSight, is effectively a Content Delivery Network (CDN) in reverse. It achieves scalability by decentralizing the collection infrastructure using cloudlets based on virtual machines (VMs). Based on time, location, and content, privacy sensitive information is automatically removed from the video. This process, which we refer to as denaturing, is executed in a user-specific VM on the cloudlet. Users can perform content-based searches on the total catalog of denatured videos. Our experiments reveal the bottlenecks for video upload, denaturing, indexing, and contentbased search. They also provide insight on how parameters such as frame rate and resolution impact scalability.

As the popularity of smartphones and tablets increases, the mobile platform is becoming a very important target for application developers. Despite recent advances in mobile hardware, most mobile devices fail to execute complex multimedia applications (such as image processing) with an acceptable level of user experience. Cyber foraging is a well-known computing technique to enhance the capabilities of mobile devices, where the mobile device offloads parts of the application to a nearby discovered server in the network.Although first introduced in 2001, cyber foraging is still not widely adopted in current smartphone platforms or applications. In this respect, two major challenges are to be tackled. First, a suitable adaptive decision engine is needed to determine the optimal offloading decision, that takes into account the potentially high and variable latency between the device and the server. Second, an integrated cyber foraging platform with sufficient support for application developers is not publicly available on popular mobile platforms such as Android.In this paper, we present AIOLOS, a mobile middleware framework for cyber foraging on the Android platform. AIOLOS uses an estimation model that takes into account server resources and network state to decide at runtime whether or not a method call should be offloaded. We also introduce developer tools to integrate the AIOLOS framework in the Android platform, enabling easy development of cyber foraging enabled applications. A prototype implementation is presented and evaluated in detail by means of both a chess application and a newly developed photo editor application.