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International Conference on Advanced Cloud and Big Data (CBD)

作者： Yikai Xu Mingkai Chen Zhe Zhang Key Laboratory of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications Nanjing China

With the development of next-generation mobile communication, semantic communication is considered as a novel technology that focuses on conveying the meaning of a message to the intended users. The key of semantic communication is that the receiver and sender should have the similar background knowledge, hence the semantic knowledge base is the corner-stone of semantic communication. However, how to select an appropriate content as the background knowledge from huge amount of knowledge base data is a challenge in realizing a semantic communication system. Caching partial semantic knowledge bases at edge servers in advance is a promising approach to overcome the above issue. In this paper, we propose a novel semantic knowledge base deployment architecture, in which the specific content of the semantic knowledge base cached at the edge server is determined by the user’s personal preferences, fact relevance, and the sender-receiver matching degree. The semantic knowledge base deployment architecture is affected by conditions such as latency and storage capacity of the edge server. Based on preference-based reinforcement learning (PbRL), we propose a policy ranking algorithm to drive the replacement of the categorical cache for semantic knowledge bases to maximize the hit ratio and the sender-receiver matching degree under the constraints of latency and caching capacity. Simulation results show that our algorithm can obtain higher knowledge base hit ratio and matching degree.

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Nonconvex -norm and Laplacian scale mixture with salient map for moving object detection

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Multimedia Tools and Applications 2023年第9期83卷 26159-26182页

作者： Yang, Yongpeng Yang, Zhenzhen Le, Jun Li, Jianlin School of Network and Communication Nanjing Vocational College of Information Technology Nanjing China Key Laboratory of Ministry of Education in Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications Nanjing China

Moving object detection which has attracted wide attention is the critical issue of computer vision. Consequently, the low-rank and sparse decomposition (LRSD) has been a powerful technology for extracting the moving object from videos which has achieved improvement for moving object detection. However, it still has some defaults such as the lower degree for approximating the low-rank and sparsity components, ignoring the spatial information of videos, being sensitive to noise, and so on. To address these problems mentioned above, we propose a new LRSD method which is named nonconvex norm and Laplacian scale mixture with salient map (NNLSMSM). It adopts the nonconvex $$\gamma $$ -norm and the Laplacian scale mixture (LSM) to approximate the low-rank and sparsity components of traditional LRSD model for enhancing the degree of approximating. Meanwhile, a salient map mechanism which can effectively capture the spatial information from videos is introduced to NNLSMSM. In addition, we extend our proposed NNLSMSM method to a robust NNLSMSM (RNNLSMSM) method for enhancing its robustness via introducing a noise item. It can effectively solve the problem of being sensitive to noise. In addition, we adopt the alternating direction method of multipliers (ADMM) to solve our proposed NNLSMSM and RNNLSMSM methods. At last, extensive experiments which are performed on various popular datasets by some state-of-the-art methods demonstrate the effectiveness and superiority of our proposed NNLSMSM and RNNLSMSM methods.

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Cross-Scale Haptic Object Recognition for Intelligent Robot

Cross-Scale Haptic Object Recognition for Intelligent Robot

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International wireless communications and Mobile Computing Conference, IWCMC

作者： Ang Li Wei Lu Haoyu Wang Xin Wei Key Laboratory of Broadband Wireless Communication and Sensor Network Technology (Ministry of Education) Nanjing University of Posts and Telecommunications

Haptic technology enables robots to touch and understand the interactions between objects in the reality. Advanced haptic sensing systems can not only collect pressure, temperature and stiffness of touched objects, but also avoid destructive operations, and assist in navigation and posture control for robots. In order to smoothly interact with different types of objects, in the haptic system, it is necessary to develop haptic object recognition methods for effective haptic perception capability. However, compared to RGB images, haptic images collected by opticallybased haptic sensors are similar in appearance, which makes traditional convolutional neural networks (e.g.,ResNet, VGG, etc.) ineffective. Therefore, in this paper, we are inspired by popular attention mechanism and multi-scale strategies, and propose a cross-scale attention based haptic object recognition network for object-robot interaction. In particular, On the one hand, we design a cross-scale attention module in convolutional neural networks to acquire spatial contextual feature. On the other hand, we design a learnable bilinear fusion strategy to integrate above spatial contextual feature with original haptic feature, so as to effectively discriminate haptic images. Experimental results on ViTac dataset have shown the effectiveness of our approach.

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Mobile Edge Computing Based Task Offloading and Resource Allocation in Smart Grid 13

Mobile Edge Computing Based Task Offloading and Resource All...

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13th International Conference on wireless communications and Signal Processing, WCSP 2021

作者： Dou, Haie Xu, Zhichen Jiang, Xue Cui, Jingwu Zheng, Baoyu Nanjing University of Posts and Telecommunications Ministry of Education Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing China

ISBN: (纸本)9781665407854

Computational offloading, as one of the means to reduce latency and energy consumption in mobile edge computing (MEC), can reduce industrial costs through reasonable offloading decisions. A mixed-integer nonlinear optimization problem that minimizes task completion time is constructed for the smart grid scenario where there are power terminals with insufficient computing power to deal with high latency arising from low latency and high reliability applications, and a joint optimization strategy for task offloading decision and resource allocation is proposed. The strategy first decomposes the problem into two sub-problems, resource allocation and task offloading, and first adopts the Lagrange multiplier method to obtain the optimal solutions for computing resources as well as spectrum resource allocation, and then uses an adaptive genetic algorithm to formulate the offloading decision under the condition of determining the optimal solution for each resource allocation. The simulation results show that the proposed algorithm can minimize the task latency of electric terminals under the condition of limited resources and improve the service experience of end users. © 2021 IEEE.

关键词： Resource allocation

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Dynamic Resource Scheduling Strategy with QoE-aware for the Coexistence of eMBB and URLLC Traffic 13

Dynamic Resource Scheduling Strategy with QoE-aware for the ...

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13th International Conference on wireless communications and Signal Processing, WCSP 2021

作者： Wang, Lei Yuan, Jie Jiang, Xue Cui, Jingwu Zheng, Baoyu Nanjing University of Posts and Telecommunications Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of Education Nanjing China

ISBN: (纸本)9781665407854

The coexistence of URLLC and eMBB services are very important for high data rate and low latency services in 5th Generation(5G) mobile network. But it is a difficult challenge to optimize resource allocation through effective scheduling in this case. Therefore, this paper proposes a coexistence scheme of URLLC and eMBB with the puncturing mechanism. Considering rate demands of different users, we introduce a QoE-aware utility function. Then, we formulate an optimization problem that maximize the average system utility while meeting URLLC requirements. Considering the non-deterministic polynomial(NP) hard nature of the problem, we divide the problem into two parts. In order to solve the problems, we take an iterative way, which further use an effective heuristic algorithm for resource allocation and URLLC puncturing. Simulation results show the advantage of this method compared with other ways. © 2021 IEEE.

关键词： Heuristic algorithms

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Construction method of smart fire control electricity cyber physical system model based on consumption behavior modes 10

Construction method of smart fire control electricity cyber ...

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10th IEEE Joint International Information technology and Artificial Intelligence Conference, ITAIC 2022

作者： Xu, Chenzi Zhu, Leiji Liu, Pingping Xiong, Yong Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Broadband Wireless Communication Technology Laboratory Shanghai200050 China Chinese Academy of Sciences Key Laboratory of Wireless Sensor Network and Communication Shanghai200050 China Shijingshan Beijing100049 China

ISBN: (数字)9781665422079

ISBN: (纸本)9781665422079

With the popularity of smart meters and the development of big data technology, power consumption data is accumulating. In order to help the power sector better understand users and provide better services for users, a variety of user behavior cyber physical system models have been proposed. In order to better construct the cyber physical system model of typical users, this paper proposes a power consumption behavior pattern construction method based on DDTW, and obtains these eight power consumption behavior modes by using the power consumption data of a community in Shanghai. Then, a user cyber physical system model method based on improved K-Modes algorithm is proposed. Combined with the eight power consumption behavior modes, the user cyber physical system models of eight typical users are obtained. The final silhouette coefficient and DBI index are 0.056 and 5.89 respectively. The construction method of smart fire control electricity cyber physical system model based on consumption behavior modes can well extract common power consumption behavior modes from disordered power consumption data, and then through the user cyber physical system model method based on improved K-Modes algorithm, it can well construct the power consumption behavior cyber physical system models of different users, and abstract typical users to provide support for the policy designation of the power department. In addition, the smart fire control electricity cyber physical system model can support the closed-loop control of intelligent fire electrical cyber physical system © 2022 IEEE.

关键词： Cyber Physical System

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Personalized Content Sharing via Mobile Crowdsensing

Personalized Content Sharing via Mobile Crowdsensing

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作者： Zhao, Lindong Wei, Xin Chen, Jianxin Zhou, Liang Guizani, Mohsen Nanjing University of Posts and Telecommunications Key Laboratory of Broadband Wireless Communication and Sensor Network Technology Ministry of Education Nanjing210003 China Qatar University Department of CSE Doha Qatar

Personalized content sharing will inevitably become one of the core applications of mobile Internet of Things. However, the existing strategies for content sharing are far from effective content personalization, since they either fail to protect the diversity of shared content or harm the enthusiasm of users to participate in cooperation. How to optimize the tradeoff between content personalization and sharing efficiency thus becomes an extremely challenging problem. To circumvent this dilemma, we propose a social-aware personalized content-sharing strategy based on mobile crowdsensing (MCS), which specially introduces positive network externalities derived from MCS and the social network. Specifically, we design a two-stage pricing-participation game to model the interactions between mobile users and a profit-making service provider. By solving the subgame-perfect Nash equilibrium (NE) of the proposed game, an efficient participation mechanism and an optimal-pricing strategy are developed. First, users' decision selection of whether to join MCS is modeled as a social-aware MCS participation game (SA-MPG), and two algorithms for solving the Pareto-optimal NE of SA-MPG are designed. Subsequently, the pricing issue for network operators is investigated by exploiting the supermodularity of SA-MPG. Stochastic network model and real-world data set-based simulations corroborate the significant gain of our proposed strategy. © 2014 IEEE.

关键词： Game theory

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An Optimization Method to Maximize the Service Quality of SCMA Grant-Free Access with MPR 13

An Optimization Method to Maximize the Service Quality of SC...

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13th International Conference on wireless communications and Signal Processing, WCSP 2021

作者： Wang, Lei Xu, Jing Qi, Ting Jiang, Xue Cui, Jingwu Zheng, Baoyu Nanjing University of Posts and Telecommunications Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of Education Nanjing China

ISBN: (纸本)9781665407854

Grant-free access successfully reduces the overhead and delay of signaling in the scenario of ultra-reliable low-latency communications (URLLC). However, it is also prone to conflict in the case of high load, resulting the lose of spectrum efficiency, reduced reliability and increased delay. In this paper, we realize K-multipacket reception (K-MPR) with sparse code multiple access (SCMA) technology to improve spectrum efficiency and throughput, and yet its implementation greatly increases the difficulty of ensuring the reliability of data transmission. Therefore, we propose an optimization method to maximize service quality of SCMA grant-free access with MPR. First, we investigate the behaviors of transient system via Markov chain to obtain reliability under delay constraints. Furthermore, we use dichotomy to calculate the minimum MPR ability value K. The simulation results show the validity of theoretical analysis. The proposed scheme maintains the increasing trend of reliability under delay constraints and tradeoff between throughput and reliability under delay constraints is presented, while optimal tradeoff between spectrum efficiency and reliability is achieved. © 2021 IEEE.

关键词： Reliability

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QoE- Driven Tactile Internet: Scenario, Architecture and Analysis 13

QoE- Driven Tactile Internet: Scenario, Architecture and Ana...

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13th International Conference on wireless communications and Signal Processing, WCSP 2021

作者： Li, Jianlong Zhao, Lindong Li, Ang Kang, Bin Chen, Mingkai Nanjing University of Posts and Telecommunications Key Laboratory of Broadband Wireless Communication and Sensor Network Technology Nanjing China

ISBN: (纸本)9781665407854

Tactile Internet, as the advancement of fifth generation (5G) mobile communication, enables users to interact with remote environment and gives users tactile sense across long distances. Tactile Internet achieves the evolution of object perception mode and has broad commercial prospects, such as telesurgery and intelligent transportation. However, it also puts forward to higher requirements, such as lower delay and higher reliability. How to achieve ultra-reliable low-latency transmission has become a significant technical challenge. To get over this dilemma, we first introduce a mobile edge computing (MEC) computation offloading architecture in the Tactile Internet scenario. Then the quality of experience (QoE) maximization problem in the MEC computing offloading architecture under binary computing mode is studied, subject to the constraints of delay and reliability. The closed-form expressions of QoE and end-to-end (E2E) delay for tactile users are derived. Subsequently, an optimal user computing mode selection and a computing resource allocation scheme is proposed. Offloading Decision (OD) and Computing Resource Allocation (CRA) algorithms are, respectively, proposed for solving the formulated problem. The simulation results prove the superiority of our proposed scheme to reduce E2E delay and improve users' QoE while ensuring reliability requirements. © 2021 IEEE.

关键词： Resource allocation

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communication-Efficient Distributed Learning in Massive IoT: A Graph-Based Perspective

Communication-Efficient Distributed Learning in Massive IoT:...

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IEEE Conference on wireless communications and networking

作者： Lindong Zhao Jingyue Tang Mingzhe Chen Liang Zhou Weihua Zhuang Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of Education Nanjing University of Posts and Telecommunications Nanjing China Department of Electrical and Computer Engineering University of Waterloo Waterloo Canada

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Various distributed learning approaches emerge for enabling ubiquitous intelligence in Internet of Things (IoT) without sacrificing data privacy. To improve communication efficiency in frequent knowledge exchange over resource-constrained IoT, different techniques for client selection have been proposed. However, the intractable scalability issues remain to be addressed in massive IoT, since highly-coupled co-channel interference adds exponential complexity to combinatorial client selection. In this work, we develop a client selection framework highly-scalable to large-scale networks with thousands of devices, which exploits the inherent graph structure derived from knowledge exchange and co-channel interference. Specifically, we first model a client selection problem for jointly optimizing learning performance and system cost under volatile network conditions. The formulated problem is encoded into a node classification problem by a directed graph. Subsequently, a general yet simple solver is designed based on graph neural networks, which selects clients by classifying node status with recursive neighborhood aggregation of node representations. Finally, extensive experimental results demonstrate that the proposed approach can perform on par with state-of-the-art methods, while scaling to networks whose size is orders of magnitude larger than they can handle.

关键词： Performance evaluation Knowledge engineering Computer aided instruction Costs Distance learning Scalability Graph neural networks Data models Internet of Things Interchannel interference

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