Traffic monitoring using an adaptive sensor power scheduling algorithm

Abstract

Using sensors to monitor surface or subsurface traffic requires sensor placement, detection of traffic changes, and sensor power scheduling for improved efficiency. Of these capabilities, sensor power scheduling is one of the most important as the appropriate sensors must be selected for activation to respond to changes in the traffic. We present an adaptive power scheduling algorithm that uses the homogeneous equilibrium of a potential-field-based dynamical system to determine which sensors should be active. Our algorithm assumes a nearest neighbor topology, which makes additional assumptions about the placement of sensors. We formalize these conditions and construct a sensor placement algorithm to support our scheduling algorithm. To demonstrate the efficacy of our scheduling approach, we provide two distinctive traffic detection algorithms that we combine with our placement and scheduling algorithm to test via simulation. We provide the simulation results that show in both cases, the adaptive scheduling algorithm behaves efficiently as compared to an area coverage approach , as well as an all-active path coverage approach.

Towards a Low-Cost Teacher Orchestration Using Ubiquitous Computing Devices for Detecting Student’s Engagement

“…In terms of encouraging and promoting innovation and modernization in education through mobile and information technology (IT), it also supports traditional lecture-style teaching, convenient information gathering, and information sharing and promotes innovative teaching methods such as cooperative learning [25,26], exploratory learning outside the classroom, and game-based learning [27]. On the flip side, the marvelous expansion of sensor technology in smartphone(s), along with their sensing capabilities for accurate capturing, monitoring, and analysis of information, helps us know about traffic conditions [28][29][30], road conditions [31][32][33], environmental impacts of noise level [34], air quality and pollution level [35][36][37][38][39], humidity and temperature [40], understand patterns of objects movements [41][42][43], alerting and monitoring disaster [44,45], weather information [46], etc.…”