Selective Reprogramming of Mobile Sensor Networks Through Social Community Detection
The authors target application domains where the behavior of animals or humans is monitored using Wireless Sensor Network (WSN) devices. The code on these devices is updated frequently, as scientists acquire in-field data and refine their hypotheses. Wireless reprogramming is therefore fundamental to avoid the (expensive) re-collection of the devices. Moreover, the code carried by the monitored individuals often depends on their characteristics, e.g., the behavior or preferred habitat. They propose a selective reprogramming approach that simplifies and automates the process of delivering a code update to a target subset of nodes. Target selection is expressed through constraints injected in the WSN, triggering automatic dissemination of code updates whenever verified.