As an enabler for smart industrial Internet of Things, sensor-cloud facilitates data collection, processing, analysis, storage and sharing on demand. However, existing cloud computing model cannot provide direct and effective management for the sensor nodes. To this end, mobile edge nodes with relatively strong computation and storage ability are exploited to provide intelligent trust evaluation and management for sensor nodes. In this paper, an edge computing based intelligent trust evaluation scheme is proposed to comprehensively evaluate the trustworthiness of sensor nodes using probabilistic graphical model. The proposed mechanism evaluates the trustworthiness of sensor nodes from data collection and communication behavior. Moreover, the moving path for the edge nodes is scheduled to improve the probability of direct trust evaluation and decrease the moving distance. An approximation algorithm with provable performance is designed. Extensive experiments validate that our method can effectively ensure the trustworthiness of sensor nodes and decrease the energy consumption.

With the increase of the number of IIOT devices in industrial environments, security threats and quality of service (QoS) issues increase drastically. Internal attack is one type of important security threat that makes service environment worse and less reliable. However, there is no unified and fine-grained trust evaluation mechanism to deal with the threats of internal attack and improve QoS of IIoT. To this end, a unified trustworthy environment based on edge computing is established and maintained, which can timely detect malicious service providers and service consumers, filter unreal information and recommend credible service providers. Moreover, a service selection method is designed to choose the corresponding trustworthy and reliable service providers based on the trust evaluation and the recording criterion, which has distinctive advantages in the succinct trust management, convenient searching service and accurate service matching. Experiments validated the feasibility of the proposed trustworthy environment.

Sensor nodes equipped with various sensory devices can sense a wide range of information regarding human or things, thereby providing a foundation for Internet of Thing (IoT). Fast and energy-efficient data collection to the control center (CC) is of significance yet very challenging. To deal with this challenge, a low redundancy data collection (LRDC) scheme is proposed to reduce delay as well as energy consumption for monitoring network by using matrix completion technique. Due to the correlation of the location-dependent sensing data, some data without being collected can still be recovered by the matrix completion technology, thereby reducing the data amount for data collection and transmission, reducing the network energy consumption, and accelerating the process of data acquisition. Based on matrix completion technique, LRDC scheme can select only part of the nodes to sense data and transmit less data to CC. By doing so, the data collected by the network can be greatly reduced, which can effectively improve the network lifetime. In addition, LRDC scheme also proposes a method for quickly compensate sample data in cases of packet loss, whereby part of redundant data is sent in advance to the area closer to CC. If the data required for matrix completion is lost, these redundant data can be quickly obtained by CC, so the LRDC scheme has low delay characteristics. Simulation results demonstrate that LRDC scheme can achieve better performance than the traditional strategy, and it can reduce the maximum energy consumption of the network by 27.6–57.9% and reduce the delay by 0.7–17.9%.