The Internet of Things (IoT) can only thrive if IoT sensor nodes can be effortlessly deployed and maintained without compromising their general-purpose nature. However, existing low-power sensor systems fail to strike a balance between these two issues, leaving the widespread of IoT sensor nodes as an open problem. In this paper, we propose LEGO+ as a minimalist yet general-purpose sensing edge-end architecture. Instead of running embedded software on a redundant general-purpose microprocessor, LEGO+ can directly construct the desired control functionality for various IoT sensing applications through hardware-level logic orchestration. To achieve this, we first conduct an in-depth analysis of the underlying unit behaviors within IoT sensor systems and, based on this, abstract a uniform logic orchestration model. Next, to enable sensor nodes to comprehend and execute the generated logic, we devise a hierarchical atomic control circuit with negligible overheads. Finally, we develop a task state prediction scheme to further improve the overall operation efficiency among multiple nodes. We prototype LEGO+ for proof-of-concept and conduct comprehensive experiments, and the results demonstrate that LEGO+ can reduce the overall power consumption of sensor nodes by 86% and enhance task efficiency by 49%, thereby facilitating a wider array of IoT sensing applications.

Using a saliency measure based on the global property of contour closure, we have developed a segmentation method which identifies smooth closed contours bounding objects of unknown shape in real images. The saliency measure incorporates the Gestalt principles of proximity and good continuity that previous methods have also exploited. Unlike previous methods, we incorporate contour closure by finding the eigenvector with the largest positive real eigenvalue of a transition matrix for a Markov process where edges from the image serve as states. Element (i, j) of the transition matrix is the conditional probability that a contour which contains edge j will also contain edge i. We show how the saliency measure, defined for individual edges, can be used to derive a saliency relation, defined for pairs of edges, and further show that strongly-connected components of the graph representing the saliency relation correspond to smooth closed contours in the image. Finally, we report for the first time, results on large real images for which segmentation takes an average of about 10 seconds per object on a general-purpose workstation.