Radio Frequency Identification (RFID) systems and Wireless Sensor Networks (WSNs) are believed to be the two most important technologies in realizing the ubiquitous computing vision of Future Internet. RFID technology provides much cheaper solution for object identification and tracking based on radio wave. On the other hand, data on various parameters about the physical environment can be acquired using WSNs. Integration of the advantages of both RFID systems and WSNs would benefit many application domains. In RFID system, either an active RFID tag itself or an RFID reader (reading passive or semi-passive tags) consisting of an RF transceiver poses communication capability similar to that for nodes in WSNs. Therefore, instead of using single hop RFID protocol, RFID networks can take advantage of WSN-like multihop communication, and in this regard a number of WSN protocols can be useful for such RFID systems. In this chapter we present possible scenario of the integration of RFID system and WSNs and study a number of wireless sensor network protocols suitable to use in RFID system.
Object analysis using visual sensors is one of the most important and challenging issues in computer vision research due principally to difficulties in object representation, segmentation, and recognition within a general framework. This has motivated researchers to investigate exploiting the potential identification capability of RFID (radio frequency identification) technology for object analysis. RFID however, has a number of fundamental limitations including a short sensing range, missing tag detection, not working for all objects, and some items being just too small to be tagged. This has meant applying RFID alone has not been entirely effective in computer vision applications. To address these restrictions, object analysis approaches based on a combination of visual sensors and RFID have recently been successfully introduced. This chapter presents a contemporary review on these object analysis techniques for localisation, tracking, and object and activity recognition, together with some future research directions in this burgeoning field.