This paper proposes two energy-efficient techniques suitable for cooperative spectrum sensing for cognitive radios. Cooperative spectrum sensing collects information from different users to make final decisions. The simplest information to be collected is the decision of each user, and this method is analogous to voting. First of all, we propose a voting scheme based on users' own confidence. Users vote only when they are confident about their spectrum sensing results. Simulation results show that about 10% to 40% of transmission energy can be saved compared to traditional voting schemes. Secondly, we propose a cluster-collect-forward scheme to save energy spent on vote-collection and information exchange. This scheme relies on clustering for vote-collection, and combing and forwarding for information propagation. Our scheme shows significant energy saving from 65% to over 95% compared to the broadcasting scheme. Besides, the processing time is only about half of the broadcasting scheme. Finally, these two proposed techniques can be easily combined to further improve the overall performance.

Distributed smart cameras (DSCs) are real-time distributed embedded systems that perform computer vision using multiple cameras. This new approach has emerged thanks to a confluence of simultaneous advances in four key disciplines: computer vision, image sensors, embedded computing, and sensor networks. Processing images in a network of distributed smart cameras introduces several complications. However, we believe that the problems DSCs solve are much more important than the challenges of designing and building a distributed video system. We argue that distributed smart cameras represent key components for future embedded computer vision systems and that smart cameras will become an enabling technology for many new applications. We summarize smart camera technology and applications, discuss current trends, and identify important research challenges.

Embedded smart camera systems comprise computation- and resource-hungry applications implemented on small, complex but resource-hardy platforms. Efficient implementation of such applications can benefit significantly from parallelization. However, communication between different processing units is a nontrivial task. In addition, new and emerging distributed smart cameras require efficient methods of communication for optimized distributed implementations. In this paper, a novel communication interface, called the signal passing interface (SPI), is presented that attempts to overcome this challenge by integrating relevant properties of two different, yet important, paradigms in this context-dataflow and message passing interface (MPI). Dataflow is a widely used modeling paradigm for signal processing applications, while MPI is an established communication interface in the general-purpose processor community. SPI is targeted toward computation-intensive signal processing applications, and due to its careful specialization, more performance-efficient for embedded implementation in this domain. SPI is also much easier and more intuitive to use. In this paper, successful application of this communication interface to two smart camera applications has been presented in detail to validate a new methodology for efficient distributed implementation for this domain.

This paper describes lessons we have learned over the past several years about teaching the design of modern embedded computing systems. An embedded computing system uses microprocessors to implement parts of the functionality of non-general-purpose computers. Early microprocessor-based design courses, based on simple microprocessors, emphasized input and output (I/O). Modern high-performance embedded processors are capable of a great deal of computation in addition to I/O tasks. Taking advantage of this capability requires a knowledge of fundamental concepts in the analysis and design of concurrent computing systems. We believe that next-generation courses in embedded computing should move away from the discussion of components and toward the discussion of analysis and design of systems.