Edge Computing is a new computing paradigm where applications operate at the network edge, providing low-latency services with augmented user and data privacy. A desirable goal for edge computing is pervasiveness, that is, enabling any capable and authorized entity at the edge to provide desired edge services--pervasive edge computing (PEC). However, efficient access control of users receiving services and edge servers handling user data, without sacrificing performance is a challenge. Current solutions, based on "always-on" authentication servers in the cloud, negate the latency benefits of services at the edge and also do not preserve user and data privacy. In this paper, we present APECS, an advanced access control framework for PEC, which allows legitimate users to utilize any available edge services without need for communication beyond the network edge. The APECS framework leverages multi-authority attribute-based encryption to create a federated authority, which delegates the authentication and authorization tasks to semi-trusted edge servers, thus eliminating the need for an "always-on" authentication server in the cloud. Additionally, APECS prevents access to encrypted content by unauthorized edge servers. We analyze and prove the security of APECS in the Universal Composability framework and provide experimental results on the GENI testbed to demonstrate the scalability and effectiveness of APECS.

A method is demonstrated to evaluate edge detector performance using receiver operating characteristic curves. It involves matching edges to manually specified ground truth to count true positive and false positive detections. Edge detector parameter settings are trained and tested on different images, and aggregate test ROC curves presented for two sets of 10 images. The performance of eight different edge detectors is compared. The Canny and Heitger detectors provide the best performance.

We present a method of evaluating edge detector performance based on empirical receiver-operating characteristic (ROC) curves. The approach is based on using real images, for which the ground truth has been manually specified. The ROC curve for a given edge detector summarizes its range of tradeoffs between true positive and false positive edge pixels, as determined by comparing the detected edge pixels to the specified ground truth. Aggregate ROC curves are constructed for groups of images. To give a fairer evaluation relative to typical application use, a form of "train and test" methodology has been developed.