In multi-tiered fog computing systems, to accelerate the processing of computation-intensive tasks for real-time IoT applications, resource-limited IoT devices can offload part of their workloads to nearby fog nodes, whereafter such workloads may be offloaded to upper-tier fog nodes with greater computation capacities. Such hierarchical offloading, though promising to shorten processing latencies, may also induce excessive power consumptions and latencies for wireless transmissions. With the temporal variation of various system dynamics, such a tradeoff makes it rather challenging to conduct effective and online offloading decision making. Meanwhile, the fundamental benefits of predictive offloading to fog computing systems still remain unexplored. In this paper, we focus on the problem of dynamic offloading and resource allocation with traffic prediction in multitiered fog computing systems. By formulating the problem as a stochastic network optimization problem, we aim to minimize the time-average power consumptions with stability guarantee for all queues in the system. We exploit unique problem structures and propose PORA, an efficient and distributed predictive offloading and resource allocation scheme for multi-tiered fog computing systems. Our theoretical analysis and simulation results show that PORA incurs near-optimal power consumptions with queue stability guarantee. Furthermore, PORA requires only mild-value of predictive information to achieve a notable latency reduction, even with prediction errors.

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As the power consumption of various electronic devices is gradually reduced to milliwatt or even microwatt level, it is possible to achieve self‐powered electronic devices by obtaining weak energy from the environment. In this article, a hybrid nanogenerator that contains two working parts, —the triboelectric nanogenerator (TENG) in sliding independent layer mode and the electromagnetic generator (EMG) in rotating mode, is reported. The hybrid generator can effectively broaden the output voltage range while shortening the voltage boost time. After polishing the surface of nylon film with different mesh sandpaper, the maximum increase of output voltage and current can reach 60% and 80%, respectively. When the wind speed is 9 m s–1, the maximum average output power values of TENG and EMG are 0.33 and 32.87 mW, respectively. Also the hybrid nanogenerator can stably power 200 light‐emitting diodes (LEDs) and hygro‐thermograph after working for 2 s. Compared with the method of supplying power to electronic devices after a long period of energy storage, the triboelectric‐electromagnetic hybrid nanogenerator that is designed can realize real‐time power supply for wireless sensor nodes and Bluetooth modules at a wind speed of 10.5 m s–1, and the voltage at both ends is always maintained dynamic equilibrium.