Abstract
With the development of the smart grid, massive electric Internet of Things (EIoT) devices are deployed to collect data and offload them to edge servers for processing. However, the task of offloading optimization still faces several challenges, such as the differentiated quality of service (QoS) requirements, decision coupling among multiple devices, and the impact of electromagnetic interference. In this paper, a low-complexity delay and energy-efficiency-balanced task offloading algorithm based on many-to-one two-sided matching is proposed for an EIoT. The proposed algorithm shows its novelty in the dynamic tradeoff between energy efficiency and delay as well as in low-complexity and stable task offloading. Specifically, we firstly formulate the minimization problem of weighted difference between delay and energy efficiency to realize the joint optimization of differentiated QoS requirements. Then, the joint optimization problem is transformed into a many-to-one two-sided matching problem. Through continuous iteration, a stable matching between devices and servers is established to cope with decision coupling among multiple devices. Finally, the effectiveness of the proposed algorithm is validated through simulations. Compared with two advanced algorithms, the weighted difference between the energy efficiency and delay of the proposed algorithm is increased by 29.01% and 45.65% when the number of devices is 120, and is increased by 11.57% and 22.25% when the number of gateways is 16.
Highlights
The electric Internet of Things (EIoT) is a kind of industrial-level Internet of Things (IoT) [1,2] applied to the smart grid
The proposed algorithm is compared with two existing task offloading algorithms, i.e., an energy-efficiency-first (EEF) task offloading algorithm [34], which aims to maximize the the energy efficiency without considering the time delay
We addressed the task offloading problem for the EIoT, and proposed a delay and energy-efficiency-balanced task offloading algorithm
Summary
The electric Internet of Things (EIoT) is a kind of industrial-level Internet of Things (IoT) [1,2] applied to the smart grid. In reference [18], Shan et al proposed a matching-based two-step approach aiming at minimizing the energy consumption of the IoT devices by optimizing the task offloading decision and transmission power. In reference [21], a solution to minimize the network delay from a contract-matching integration perspective was provided These previous works only target a single performance metric as either energy efficiency or delay, while the differentiated service demand guarantee oriented to multiple metrics are ignored. In reference [26], Ding et al investigated a decentralized partitioning computation offloading strategy for multiple devices and multiple mobile edge servers with limited resources, where the weighted sum of energy consumption and delay is maximized by optimizing the execution location, CPU frequency, and transmission power.
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