With the widespread adoption of deep neural network approaches in mobile devices, the drawbacks of traditional cloud-based or local deployment solutions are becoming apparent. The high latency caused by cloud-based inferred neural networks and the high power consumption caused by local inference of mobile devices greatly reduce the actual user experience of neural network applications. To solve this problem, this paper proposes a hierarchical deployment and inference acceleration model for deep neural networks based on fog computing. Firstly, a search reallocation of the solution space of a deep neural network is performed, and a Solution Space Tree Pruning (SSTP) based deployment algorithm is designed to select the appropriate network layer for deployment in order to reduce the overall inferred delay of a deep neural network. Next, an algorithm for Maximizing Accuracy based on Guaranteed Latency (MAL) is designed. On the pruned solution-space tree of the SSTP algorithm, the appropriate fog computation nodes are selected for mobile devices in different geographic locations to achieve early exit from the inference task according to the operational delay and inference accuracy requirements of real device terminals. The experimental results show that the proposed solution reduces the average latency of the fog computing-based inference acceleration model by 44.79% compared to the traditional cloud-deployed deep neural network inference, and by 28.75% compared to the edge computing acceleration framework in existing studies. The model meets the minimum latency and accuracy of neural network inference in multiple fog computing scenarios. At the same time, it greatly reduces the performance occupation and case cost of the cloud under the traditional cloud computing model.

Hierarchical deployment of deep neural networks based on fog computing inferred acceleration model

The target tracking algorithm of mobile wireless sensor networks involves target motion trend prediction and subsequent node guidance. This study aims to solve the problems of global consistency of node information and significant errors in forecasting fast-moving targets’ trajectories through traditional distributed tracking methods in sensor networks. Initially, the average consistency algorithm is used to average the local measurements of each node to achieve global consistency. Then, semantic moving computing of the Internet of Things calculates and analyzes the node movement to support the subsequent movement guidance of nodes and target movement prediction. Finally, the simulation experiment is carried out to evaluate the commonly used target trajectory prediction model. The simulation results show that the node movement algorithm by average consistency can effectively improve the positioning accuracy of the network for moving targets. Besides, the positioning error decreases with the increase of the sensing radius R, the number of moving nodes nm, and the total number of nodes ns deployed in a particular range in a two-dimensional (2D) space. The positioning error after node movement in 2D space is about 20%-30%R lower than that in a static state. After node movement in a three-dimensional (3D) space, the positioning error is about 40%-50%R lower than in a dormant state. When the target moves at a speed greater than 7m/s, the consistency-based moving computing algorithm’s target loss rate and tracking errors are about 0~10% and 1.5%~2% lower than the target tracking algorithm via Kalman Filter. Therefore, the algorithm reported here can precisely track the high-speed moving target. The existing research on point target tracking has problems of insufficient accuracy and robustness. The algorithm proposed here has stronger robustness, reduced data error in multi-node, and more flexible node movements, providing a reference for the subsequent research on distributed point target tracking.

/pdf/distributed-target-tracking-in-sensor-networks-by-1bj6ivsk.pdf

Distributed Target Tracking in Sensor Networks by Consistency Algorithm and Semantic Moving Computing of Internet of Things

Language is not only a tool for human beings to communicate with each other, but also the main carrier of information and knowledge. In the process of economic globalization and the continuous development of the Internet, international exchanges and cooperation are also extensive, and language differences are the main obstacles to international exchanges and cooperation. To solve this problem, a new interactive intelligent translation system is designed by using cloud computing technology. The hardware of the sys-tem is divided into four layers: user layer, service layer, computing layer and storage layer. The storage structure, translation structure and retrieval structure are designed respectively. The network intelligent translation system is developed and the man-machine interaction is designed in the embedded industrial control master. The calculation outcome show that the system can with advantage achieve network-based intelligent translation and heighten the dependability and intelligence of machine translation.

Overall Design of Interactive Intelligent Translation Platform Based on Cloud Computing Technology

In view of the deficiency of the existing trust model based on recommendation, on the basis of referring to the interpersonal trust research of sociology, combined with the specific characteristics of C2C online auction, using the collaborative filtering technology of mobile Agent system, a personalized trust computing model based on recommendation is established. The model is based on the idea of social network and forms the virtual social network of target users according to the historical transaction data. By examining the trust factors such as trust situation and trust tendency, the trust path is constructed and the trust degree of potential trading partners is calculated. The empirical analysis of the model is carried out by using eBay data, and compared with the beth model, the results show that the trust calculation model is simple and effective, and has good engineering feasibility.

Trust Computing Model of Online Transaction Based on Social Network and Mobile Agent

LPF-IVN: A Lightweight Privacy Enhancing Scheme with Functional Mechanism of Intelligent Vehicle Networking

The use of symbol attributes on the side of symbolic social networks to analyze, understand, and predict the topology, function, and dynamic behaviour of complex networks, and has important theoretical significance for personalized recommendations, attitude prediction, user feature analysis, and clustering and application value. However, due to the huge scale of online social networks, this poses a challenge to traditional symbolic social network analysis methods. Based on the theory of structural equilibrium, this paper studies the evolutionary dynamics of symbolic social networks, proposes the energy function of weak structural equilibrium theory, and uses the evolution of evolutionary algorithms to obtain the weak imbalance of the network. The simulation experiment results show that the calculation method in this paper can get the optimal solution faster. It provides an idea for the study of real and complex social networks.

Evolutionary dynamics modeling of symbolic social network structure equilibrium

In the recent Smart Home (SH) research work, intelligent service recommendation technique based on behavior recognition, it has been extensively preferred by researchers. However, most current research uses the Semantic recognition to construct the user's basic behavior model. This method is usually restricted by environmental factors, the way these models are built makes it impossible for them to dynamically match the services that might be provided in the user environment. To solve this problem, this paper proposes a Semantic behavior assistance (Semantic behavior assistance, SBA). By joining the semantic model on the intelligent gateway, building an SA model, in this way, a logical Internet networks for smart home is established. At the same time, a behavior assistant method based on SBA model is proposed, among them, the user environment-related entities, sensors, devices, and user-related knowledge models exist in the logical interconnection network of the SH system through the semantic model. In this paper, the data simulation experiment is carried out on the method. The experimental results show that the SBA model is better than the knowledge-based pre-defined model.

A new behavior-assisted semantic recognition method for smart home

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Evolutionary dynamics modeling of symbolic social network structure equilibrium

A new behavior-assisted semantic recognition method for smart home