Adaptive Sensor-Network Topology Estimating Algorithm Based on the Ant Colony Optimization

Studies on ubiquitous-computing and/or ubiquitous-network systems have recently been very attractive research topics along with interest in rapid developments in information and communications technologies [2]. All kinds of information devices such as computers, cellular phones, electrical appliances, and various sensors will be connected in future ubiquitous-network systems. Consequently, everyone will be able to make use of all kinds of information without stress, anywhere and at any time. However, we need to bring the idea of ”context-awareness” as an essential element into reality to construct ubiquitous-network systems. ”Context” refers to the situation in which ubiquitous-network devices are embedded, and ”awareness” refers to the recognition of ”context” with the ubiquitous network. One goal of context-awareness was to acquire and utilize information about the context to provide services that were appropriate to particular people, places, times, and events. Ubiquitous-network systems were also expected to implement context-awareness by using a sensor network, which was made up of many sensors. Information about adjacent relationships between sensors is necessary [6] to extract human motion in a sensor-network system. Adjacent relationships indicate the physical connectivity between sensors from the point of view of a person’s movement in a sensor network (Fig. 1). This information is usually sent to the system manually [3]. However, investigating and inputting information on the sensor topology becomes much too difficult in proportion to the scale of the sensor-network system. In addition, once the structure of the network changes, e.g., by adding/removing sensor units and by rearranging the network, we have to manually reinvestigate adjacent relationships. Incessantly repetitive investigations and inputs are onerous, and increase the possibility of making mistakes. There is no room for