Abstract
Existing context-aware mobile applications directly control sensors in the mobile devices in an uncoordinated and non-optimized manner, which leads to redundant sensor activations and energy waste. Optimal and coordinated sensor usage dictates a comprehensive mobile middleware solution with sensor scheduling on single device to bring together the information from all applications/sensors and intelligently select the best set of sensors to activate. While the widespread use of smartphones, we cooperate the sensors on multiple smartphones and infrastructure sensors to build a novel crowdsensing system. In Chap. 3, we design, implement, and evaluate a novel green sensor management middleware for single device that rigorously makes tradeoffs between energy consumption of sensors and accuracy of inferred contexts. The problem is formulated rigorously as mathematical optimization problems that (i) minimize the total energy consumption while achieving the required accuracy and (ii) maximize the overall accuracy under a given energy budget. Two optimal algorithms for these two optimization problems are proposed, which provide the performance bounds. As they may lead to prohibitively long running time, two efficient heuristic algorithms are then presented, which run in real-time. Extensive trace-driven simulations are conducted using traces from real Android users to evaluate the performance of the proposed middleware and algorithms. The simulation results indicate that the heuristic algorithms: (i) always terminate in real-time, (ii) result in small optimization gap of up to ∼ 2%, and (iii) lead to better performance for larger problems. We also implement and evaluate the proposed middleware and algorithms on real Android smartphones, showing their practicality and efficiency. For the extension, we consider the sensor scheduling on multiple smartphones and infrastructure sensors in Chap. 4. We apply the extensive consideration to crowdsensing system. We present a Smartphone Augmented Infrastructure Sensing (SAIS) system that offers better situation awareness to officials and civilians for minimizing the amount of generated carbon dioxide. The SAIS system minimizes the carbon footprint by solving the task assignment problem. We mathematically formulate the problems and optimally solve it using optimization problem solvers, and we also proposed an efficient task assignment algorithm (ETA) for lower running time. Our tracedriven simulations show the results of our efficient algorithm: (i) saves up to 364 times in carbon footprint, (ii) outperforms by up to 8 times in responding time, and (iii) achieves a small optimization gap of ∼ 2%.