Lighting, heating, and other comports will be adjusted accordingly.... In 2010 a spec of dust on each of your fingernails will continuously transmit fingertip motion to your computer. Your computer will understand when you type, point, click, gesture, sculpt, or play air guitar...\". Although reads like a science fiction, researchers are starting to make progresses in making this come true. For example, the SmartDust project [16] at Berkeley pushes the size limit of sensors to an extreme - a cubic millimeter, such that these sensors can float in the air like dust; the WINS (Wireless Integrated Wireless Sensors) project at UCLA [17] and the WSN (Wireless Sensing Network) project at Rockwell Science Center [22] integrate multi-modality sensing devices and low-level signal processor on the microsensor, making it more intelligent and powerful; the Odyssey developed by the MIT SeaGrant Office is a low cost autonomous underwater vehicle (AUV) with a length less than two meters. It has been used for underwater surveillance and not requiring any special handling equipment for launch and recovery [1]. All these progresses in microsensor research have made it one step closer toward anytime, anywhere sensing which will eventually revolutionize the way we think and live. Despite the exciting potentials presented by sensor networks, their unique characteristics have challenged many aspects of traditional computer networking design. Here, we summarize the advantages of sensor networks as well as the challenges from three points of view: scalability, dynamics, and stringent resource. * Scalability issue and the distributed solution  
As the cost of sensors becomes extremely low, it can be afforded to deploy large number of sensors in the field, e.g. hundreds or thousands. The sheer amount of sensor node causes scalability problem. A centralized approach sends data from each sensor to a fusion center (or processing center) where data processing and fusion are carried out. This approach works fine when the number of sensor nodes is relatively small and the data file needs to be transferred is not large. However, when the amount of sensor nodes goes to hundreds or thousands, the centralized approach will not scale. A natural solution to this problem is the distributed approach. Distributed sensor network (DSN) has been studied extensively since early 80s. [PERSON] et al [24] were among the first to propose the design of DSNs. Since then, several efficient DSN architectures have been presented in the literature, including the hierarchical and committee organization [24], the flat tree network [10, 18], the deBruijn based network [9], and the multi-agent fusion network [11]. All these approaches try to design a network structure such that it facilitates distributed data dissemination. * Dynamics issue and the self-organization solution  
Although DSN research can deal with the scalability issue, it is based on the assumption that the network structure will remain stable since it is constructed. The second unique feature of wireless sensor networks is that it is infrastructureless. Sensors, can be either static or mobile, are usually deployed in large amount which makes it very difficult, if not impossible, to have a pre-designed network structure. Furthermore, because of the limited power supply on each sensor node and the dynamically changing environment, existing sensors may die out, new sensors may be deployed. All of these factors make the sensor network a very dynamic environment. Previous research results from DSN which tend to design efficient network structures to help improve distributed data dissemination could not handle this kind of dynamics. The sensor network has to be self-organizable. * Stringent resource issue and the localized algorithm solution  
Sensor nodes usually compose of four basic units [2]: a sensing unit, a processing unit, a transceiver unit, and a power unit. The power unit supports all the activities on a sensor node, including communication, local data processing, sensing, etc.