EasiCAE: A runtime framework for efficient sensor sharing among concurrent IoT applications

Traditional wireless sensor networks (WSNs) can be integrated into Internet and be regarded as its sensing infrastructure, which supports development and running of multiple third-party applications simultaneously. Therefore, due to constrained resource of sensor nodes, it is necessary to establish a runtime framework to improve sensor sharing efficiency for concurrent third-party applications. This paper presents EasiCAE, a concurrent applications runtime framework, to enhance sensor sharing efficiency greatly by incorporating task allocation with redundancy elimination. In brief, EasiCAE decompose the applications into tasks and distributes tasks to the sensors which will bring the least energy to run them. EasiCAE has three salient features. Firstly, we define task-sensor correlation to indicate how many samplings of a sensor can be shared with the new task. Secondly, EasiCAE reduces energy consumption by assigning tasks to a sensor with higher task-sensor correlation. Finally, a light-weight merging algorithm is proposed to eliminate redundant samplings for the assigned sensors. Experimental results show that EasiCAE reduces energy consumption by 31% to 79% compared with existing methods, while introducing tolerable overheads. We also evaluate EasiCAE with various influencing parameters, showing that the performance of EasiCAE increases stably as the network scale and the number of concurrent applications increases.