As an essential building block for smart grid, the industrial internet of things (IIoT) plays a significant role in providing powerful sensing capability and ubiquitous connectivity for differentiated power services. The rapid development of smart grid imposes higher data monitoring and transmission requirements in terms of delay and energy efficiency. However, due to the severe electromagnetic interference (EMI) caused by massive electrical equipment, the transmission performance of IIoT becomes inferior. The traditional single-hop transmission mode evolves towards a multihop cooperation mode to satisfy differentiated quality of service (QoS) requirements. In this paper, we propose an upper confidence bound- (UCB-) based joint route and power selection optimization algorithm to support multihop cooperation mode evolution, which adopts a software-defined networking- (SDN-) enabled IIoT network framework to simplify network configuration and management. Compared with existing local-side-information-based route selection (LSI-RS) and random route selection (RRS) algorithms, simulation results demonstrate that the proposed algorithm has superior performances in total delay, energy efficiency, and utility.

The power distribution internet of things (PD-IoT) has the complex network architecture, various emerging services, and the enormous number of terminal devices, which poses rigid requirements on substrate network infrastructure. However, the traditional PD-IoT has the characteristics of single network function, management and maintenance difficulties, and poor service flexibility, which makes it hard to meet the differentiated quality of service (QoS) requirements of different services. In this paper, we propose the software-defined networking (SDN)enabled PD-IoT framework to improve network compatibility and flexibility, and investigate the virtual network function (VNF) embedding problem of service orchestration in PD-IoT. To solve the preference conflicts among different VNFs towards the network function node (NFV) and provide differentiated service for services in various priorities, a matching-based priorityaware VNF embedding (MPVE) algorithm is proposed to reduce energy consumption while minimizing the total task processing delay. Simulation results demonstrate that MPVE significantly outperforms existing matching algorithm and random matching algorithm in terms of delay and energy consumption while ensuring the task processing requirements of high-priority services.