Constructive Interference (CI) phenomenon has been exploited by Glossy, a mechanism for low-latency and reliable network flooding and time synchronization for wireless sensor networks. Recently, CI has also been used for other applications such as data collection and multicasting in static and mobile WSNs. These applications base their working on the high reliability promised by Glossy regardless of the physical conditions of deployment, number of nodes in the network, and unreliable wireless channels that may be detrimental for CI. There are several works that study the working of CI, but they present inconsistent views. We study CI from a receiver's viewpoint, list factors that affect CI and also specify how and why they affect. We validate our arguments with results from extensive and rigorous experimentation in real-world settings. This paper presents comprehensive insights into CI phenomenon. With this understanding, we improve the performance of CI through an energy-efficient and distributed algorithm. We cause destructive interference on a designated byte to provide negative feedback. We leverage this to adapt transmission powers. Compared to Glossy, we achieve 25% lesser packet losses while using only half of its transmission power.@en

Pyroelectric Infra-Red (PIR) sensors are used in many applicationsincluding security. PIRs detect the presence ofhumans and animals from the radiation of their body heat.This could be used to trigger events, e.g., opening doors,recording video, etc. PIRs are used widely because of theirlow power consumption.Hitherto, PIR sensors were used for binary event generation– human/animal present or not-present. At the sametime simple binary output hinders the use of PIR sensors ina wide variety of sophisticated applications. In the literature,we find limited characterization of analog output fromPIR sensors that could provide much more information.We built a simple array of PIR sensors and packaged themin a tower. We used two sets of four PIR sensors and tappedtheir analog signals after amplification. Our major contributionis the characterization of analog signals from the PIRsensors. We describe many interesting aspects obtained fromthe analog signals, which have not been explored until now.We also show their correspondence with the range, speedand size of the moving object. Using the characterization ofPIR sensors analog data as well as simple binary decisionsfrom these PIR sensors, we: (i) classify moving object withhigh precision; and (ii) localize the moving object. The majorincentives are low operating power compared to WSNs.We achieve 30 cm accuracy in 80% of the times, whenranging up to 5 m. Over multiple experiments for differentpersons in the range 1-10 m, we show that the error probabilityfor localization is 0.08 at moderate distances (around5-6 m). Our work will help in designing better detectionand application triggers using PIR sensors in the near future.We believe that this work will open up new avenues inthe development of new applications with PIR sensors.@en