Green Software-Defined Internet of Things for Big Data Processing in Mobile Edge Networks

Abstract

Mobile Edge Computing (MEC) has recently emerged as a primary candidate for big data processing to reduce the latency and jitter. On the other hand, Software-Defined Internet of Things (SD-IoT) has been proposed to effectively and flexibly collect and process big IoT data. Nevertheless, minimizing the energy consumption in SD-IoT with big data processing (e.g., data aggregation and reconstruction) has not been explored before. In this paper, therefore, we explore the sensor data selection and routing problem in SD-IoT with big data processing. Specifically, given 1) a set of sensors, 2) a set of observed locations, 3) the network topology, and 4) the energy consumption model of big data processing and forwarding, we formulate a new optimization problem, named Sensor Data Selection, Processing, and Routing Problem (SDSPRP), to minimize the total energy consumption in SD-IoT. We prove that the emphasized problem is NP-hard and inapproximable within O(log|K|). To solve the problem, we propose an αlog|K|- approximation algorithm, called Energy Efficient Sensor Selection and Routing (ESR), to minimize the energy consumption by jointly considering the sensor selection and the energy consumption in traffic engineering and data processing. The proposed algorithm is evaluated on two real networks, and the results manifest that the energy consumption in SD- IoT can be reduced by more than 56%.