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In order to meet people’s demands for intelligent and user-friendly Internet of Things (IoT) services, the amount of computation is increasing rapidly and the requirements of task delay are becoming increasingly more stringent. However, the constrained battery capacity of IoT devices greatly limits the user experience. Energy harvesting technologies enable green energy to provide continuous energy support for devices in the IoT environment. Together with the maturity of the mobile edge computing technology and the development of parallel computing, it provides a strong guarantee for the normal operation of resource-constrained IoT devices. In this article, we design a parallel offloading strategy based on Lyapunov optimization, which is conducive to efficiently finding the optimal decision for delay-sensitive and compute-intensive tasks. We establish a stochastic optimization problem on a discrete-time slot system and propose a green parallel online offloading algorithm (GPOOA). By decoupling the target problem three times, the joint optimization of green energy, task division factor, CPU frequency, and transmission power is realized. Experimental results demonstrate that under the constraints of strict task deadlines and limited server computing resources, GPOOA performs well in terms of system cost and task drop ratio, far superior to several existing offloading algorithms. [ABSTRACT FROM AUTHOR]

Finding semantic communities using network topology and contents together is a hot topic in community detection. Existing methods often use word attributes in an indiscriminate way to help finding communities. Through analysis we find that, words in networked contents often embody a hierarchical semantic structure. Some words reflect a background topic of the whole network with all communities, some imply the high-level general topic covering several topic-related communities, and some imply the high-resolution specialized topic to describe each community. Ignoring such semantic structures often leads to defects in depicting networked contents where deep semantics are not fully utilized. To solve this problem, we propose a new Bayesian probabilistic model. By distinguishing words from either a background topic or some two-level topics (i.e., general and specialized topics), this model not only better utilizes the networked contents to help finding communities, but also provides a clearer multiplex semantic community interpretation. We then give an efficient variational algorithm for model inference. The superiority of this new approach is demonstrated by comparing with ten state-of-the-art methods on nine real networks and an artificial benchmark. A case study is further provided to show its strong ability in deep semantic interpretation of communities. [ABSTRACT FROM AUTHOR]