Energy Efficient and Quality-Driven Continuous Sensor Management for Mobile IoT Applications

A novel class of mobile Internet of Things applications falls under the category of mobile crowdsensing, whereby large amounts of sensed data are collected and shared by mobile sensing and computing devices for the purposes of observing phenomena of common interest. Challenges arise with respect to collecting and managing sensor data in an energy- and bandwidth-efficient manner. In this paper we present a cloud-based system architecture centred around a publish/subscribe middleware interfaced with a quality-driven sensor management function, applicable for building mobile IoT applications. The architecture is designed so as to smartly manage and acquire sensor readings in order to satisfy global sensing coverage requirements, while obviating redundant sensor activity and consequently reducing overall system energy consumption. We evaluate the system using a proposed model for calculating bandwidth and energy savings. Model evaluation based on simulation results provides insight into the energy savings for different application requirements and geographical sensor distribution scenarios. Our results show that in certain identified cases, significant energy consumption reductions can be achieved utilizing the proposed architecture and sensor management scheme, while maintaining overall global sensing quality level (in terms of required sensing coverage). Assumptions with regards to user distributions in urban areas are verified using an existing dataset reported in literature.