Fog computing is emerging as a powerful and popular computing paradigm to perform IoT (Internet of Things) applications, which is an extension to the cloud computing paradigm to make it possible to execute the IoT applications in the network of edge. The IoT applications could choose fog or cloud computing nodes for responding to the resource requirements, and load balancing is one of the key factors to achieve resource efficiency and avoid bottlenecks, overload, and low load. However, it is still a challenge to realize the load balance for the computing nodes in the fog environment during the execution of IoT applications. In view of this challenge, a dynamic resource allocation method, named DRAM, for load balancing in fog environment is proposed in this paper. Technically, a system framework for fog computing and the load‐balance analysis for various types of computing nodes are presented first. Then, a corresponding resource allocation method in the fog environment is designed through static resource allocation and dynamic service migration to achieve the load balance for the fog computing systems. Experimental evaluation and comparison analysis are conducted to validate the efficiency and effectiveness of DRAM.

Recently, handwriting recognition approaches has been widely applied to HCI (Human-Computer Interface) applications. The emergence of the novel mobile terminals urges a more man-machine friendly interface mode. The previous airwriting recognition approaches have been accomplished by virtue of cameras and sensors. However, the vision based approaches are susceptible to the light condition and sensor based methods have disadvantages in deployment and highcost. The latest researches have demonstrated that the pervasive wireless signals can be used to identify different gestures. In this paper, we attempt to utilize CSI (channel state information) derived from wireless signals to realize the device-free air-write recognition called Wri-Fi. Compared to the gesture recognition,the increased diversity and complexity of characters of the alphabet make it challenging. The PCA (Principle Component Analysis) is used for denoising effectively and the energy indicator derived from the FFT (Fast Fourier Transform) is to detect action continuously. The unique CSI waveform caused by unique writing patterns of 26 letters serve as feature space. Finally, the HMM (Hidden Markov model) is used for character modeling and classification. We conduct experiments in our laboratory and get the average accuracy of the Wri-Fi are 86.75% and 88.74% in two writing areas, respectively.

In this paper, we propose a novel self-embedding scheme based on the non-uniform watermark sharing mechanism. In order to generate reference bits with high efficiency for content recovery, an algorithm named optimal iterative block truncation coding (OIBTC) is first proposed, which can acquire better visual quality of decoded images than the traditional BTC, and then the OIBTC-encoded compression bits, i.e., binary patterns and reconstruction levels, of the initialized original image are interleaved to produce reference bits through non-uniform sharing mechanism. With the assist of involved redundancy, the extracted reference bits and the re-generated compression bits from intact regions are used to restore the destroyed compression bits, and image contents of tampered regions can be further recovered with OIBTC decoding and spatial correlation of intra-block and inter-block. Experimental results demonstrate that our scheme can achieve better performance of tampering recovery than some of state-of-the-art schemes