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Summary A fast rate of progress has allowed the proliferation of smartphones and eased their extensive presence in people's daily life. However, low processing speed and limited battery capacity have hindered improvements in the smartphone's computational capabilities. Offloading computational tasks to the cloud could solve this problem by enabling users to access these services over the Internet. Edge cloud computing has been recognized as an emerging field within the cloud computing paradigm, where computation servers are situated at the edge of the Internet to reduce network delay and traffic. Nevertheless, offloading tasks to the cloud is not always beneficial due to variable network conditions and increased processing costs. In this paper, a deep reinforcement learning‐based offloading framework has been presented that provides smartphones with the ability to make decisions for local processing in the smartphone or to offload processing tasks to the cloud (edge and/or core). Thus, a smartphone can minimize the combination of the processing time, energy consumption, and monetary cost and maximize the accuracy of face recognition as well. Simulation results under synthetic scenarios show that the proposed offloading framework can effectively adapt to the dynamic cloud computing and networking environment.

Community detection is one of the important tasks of social network analysis. It has significant practical importance for achieving cost-effective solutions for problems in the area of search engine optimization, spam detection, viral marketing, counter-terrorism, epidemic modeling, etc. In recent years, there has been an exponential growth of online social platforms such as Twitter, Facebook, Google+, Pinterest and Tumblr, as people can easily connect to each other in the Internet era overcoming geographical barriers. This has brought about new forms of social interaction, dialogue, exchange and collaboration across diverse social networks of unprecedented scales. At the same time, it presents new challenges and demands more effective, as well as scalable, graphmining techniques because the extraction of novel and useful knowledge from massive amount of graph data holds the key to the analysis of social networks in a much larger scale. In this research paper, the problem to find communities within social networks is considered. Existing community detection techniques utilize the topological structure of the social network, but a proper combination of the available attribute data, which represents the properties of the participants or actors, and the structure data of the social network graph is promising for the detection of more accurate and meaningful communities.

The use of the Internet of Things (IoT) is steadily increasing in a wide range of applications. Among these applications, safety and security are some of the prominent applications. Through surveillance systems, we can restrict access to our premises and thus secure our assets. Nowadays face detection and recognition enabled surveillance systems are available in the market, which can detect faces from video frames captured using IP cameras, and then recognize those faces by comparing them with existing databases. However, higher prices and low accuracy are impeding the large scale deployment of those systems. In this paper, we have proposed a generic architecture for face detection and recognition system from real-time video frames that have been captured through IP cameras and processed using low-cost IoT devices by utilizing Cloud computing services. We have selected two IoT platforms: Eclipse Mosquitto IoT broker and Kaa IoT middleware to implement our proposed architecture. The face detection part is deployed in the IoT devices and the computation-intensive task, i.e., face recognition is carried out in backend Cloud servers. We have executed our experiments in two different Cloud infrastructures: Core Cloud and Edge Cloud and measured the total processing time in different scenarios. The experimental results show that the performance of the Mosquitto broker in terms of total processing time is better than Kaa middleware. Total processing time can be further reduced by deploying a face recognition application from Core Cloud to Edge Cloud. Furthermore, the k-nearest neighbor algorithm shows promising results compared to other face recognition algorithms.