Anomaly event coverage is usually related to several attributes, among which the primary attribute dominates at the time of improving detection efficiency. In the case of Internet of Things (IoT) devices with complex social-aware relationships, IoT nodes with primary attributes should cooperate with each other through their social-aware interactions, to detect potential event anomalies and further determine the coverage of such anomalies. Existing research has put a lot of effort into designing IoT detection frameworks to discover anomalous sensor data, rarely caring about the social-aware interactions. This paper targets this important efficiency problem, and develops a novel anomaly detection mechanism in collaborative social-edge-cloud architecture. The focus of it is to first construct a vector space based Aggregation Behavior Comparison Detection Model, and quantify the change of monitoring behavior by defining the clustering threshold of vector space. This can quickly judge whether a local social network is abnormal and speed up the abnormal detection rate. If it is, a Social Behavior Correlation Detection Model is further designed based on the correlation of primary attributes derived from the dominating social-aware interaction behavior captured by (primary) edge nodes. This strategy can help detect specific “abnormal” areas managed by one or more edge devices with higher accuracy. In the process of anomaly detection, we also propose a spatial index tree to store the information of IoT nodes, so as to effectively collect and route the perceived data of IoT nodes for anomaly analysis. Experimental results demonstrate that our anomaly detection method promotes the detection efficiency and accuracy in comparison with the state of art’s techniques.

Edge computing is a popular paradigm in solving the problems of long time delay and high energy consumption in Internet of Things (IoT) network, which can effectively realize the IoT task offloading by collaboration of multiple edge servers. Nevertheless, how to choose the appropriate edge servers for offloading the dependent subtasks is still a big challenge, considering the limited resources and computing power of the edge servers as well as the start and end execution time of each subtask. These factors have a great impact on the execution efficiency of the whole task. At present, most of the research works focus on single-hop or multi-hop task offloading, where the edge servers farther away are not considered in the offloading decision. Such task offloading strategy is not optimal, and difficult to achieve high parallel execution of tasks, resulting in some delay-sensitive tasks not being completed within the specified time. In this paper, a two-stage optimization method is proposed to solve the resource allocation problem between edge servers and tasks. In the first stage, we group tasks according to their priorities, and the group with a higher priority is given the preference to resource allocation, thereby ensuring the timeliness of delay-sensitive tasks. Within the same group, resources are competed according to the game theory, and the total delay of all tasks is optimized. In the second stage, we aim to optimize the energy consumption of each task without increasing its completion time by allocating the computing resources to its subtasks based on their maximum completion time. For group resource allocation, we propose a spatial index tree to store the information of all edge servers for optimal server selection. During the selection process, an online learning based double prediction model is utilized to reduce the energy consumption caused by information transmission. We have evaluated the performance of the experiment on iFogSim simulator, and the experimental results show that our proposed method can achieve better performance in terms of time delay and energy consumption.