Novel Sampling Algorithm for Human Mobility-Based Mobile Phone Sensing

Smart phones or mobile phones enabled with global positioning system (GPS), different types of sensors, and communication technologies have become ubiquitous application development platform for Internet of Things (IoT) and new sensing technologies. Improving sensing area coverage, reducing overlap of sensing area, and energy consumption are important issues under mobile phone sensing. This paper presents human mobility-based mobile phone sensors sampling algorithm. Human mobility patterns and geographical constraints have an impact on performance of mobile phone sensing applications. The real-outdoor location traces of volunteers, collected using GPS-enabled mobile phones are used for performance analysis of proposed work. The proposed mobile phone sensor sampling algorithm considers velocity of human mobility as an important parameter for improving sensing area coverage and reduction of energy consumption. To an extent overlap between sensing area coverage is allowed to overcome, the reduction of sensor data samples caused by spatial regularities of human mobility. The performance is analyzed and evaluated by considering general regular sampling and proposed sampling method for mobile phone sensing activity. The results show that for normal human walking velocity (