Smart cities have seen a growing interest among governments, researchers, and industries. Smart cities use digital technologies to enhance the quality of life for residents while promoting sustainability and efficient resource management. By integrating various technologies such as Information and Communication Technologies (ICT), Artificial Intelligence (AI), and Internet of Things (IoT), smart cities can improve the delivery of public services, optimize transportation systems, reduce energy consumption, and enhance public safety, among other benefits. Smart cities focus on automating different disciplines, including smart environment, smart home, smart economy, smart mobility, and smart governance. As a result, multi-agent driven smart cities have received tremendous attention from the research community for obtaining intelligent solutions to complex problems in different disciplines by subdividing responsibilities into multiple agents and empowering agents through AI. In this regard, it is vital to explore the usage of multi-agent systems in different critical application areas of smart cities. In this paper, a detailed description of the multi-agent process for smart city application areas is provided, along with resources and future research directions. Four different application areas: smart home, smart governance, smart environment, and smart mobility are discussed in detail. 

Next Generation of Multi-Agent Driven Smart City Applications and Research Paradigms

The offloading of computationally intensive tasks to edge servers is indispensable in the mobile edge computing (MEC) environment. Once the tasks are offloaded, the subsequent challenges lie in buffering them and assigning them to edge virtual machine (VM) resources to meet the multicriteria requirement. Furthermore, the edge resources’ availability is dynamic in nature and needs a joint prediction and optimal allocation for the efficient usage of resources and fulfillment of the tasks’ requirements. To this end, this work has three contributions. First, a delay sensitivity-based priority scheduling (DSPS) policy is presented to schedule the tasks as per their deadline. Secondly, based on exploratory data analysis and inferred seasonal patterns in the usage of edge CPU resources from the GWA-T-12 Bitbrains VM utilization dataset, the availability of VM resources is predicted by using a Holt–Winters-based univariate algorithm (HWVMR) and a vector autoregression-based multivariate algorithm (VARVMR). Finally, for optimal and fast task assignment, a parallel differential evolution-based task allocation (pDETA) strategy is proposed. The proposed algorithms are evaluated extensively with standard performance metrics, and the results show nearly 22%, 35%, and 69% improvements in cost and 41%, 52%, and 78% improvements in energy when compared with MTSS, DE, and min–min strategies, respectively.

/pdf/time-series-based-edge-resource-prediction-and-parallel-28srks4k.pdf

Time Series-Based Edge Resource Prediction and Parallel Optimal Task Allocation in Mobile Edge Computing Environment

Edge Computing (EC) has gained significant traction in recent years, promising enhanced efficiency by integrating Artificial Intelligence (AI) capabilities at the edge. While the focus has primarily been on the deployment and inference of Machine Learning (ML) models at the edge, the training aspect remains less explored. This survey delves into Edge Learning (EL), specifically the optimization of ML model training at the edge. The objective is to comprehensively explore diverse approaches and methodologies in EL, synthesize existing knowledge, identify challenges, and highlight future trends. Utilizing Scopus' advanced search, relevant literature on EL was identified, revealing a concentration of research efforts in distributed learning methods, particularly Federated Learning (FL). This survey further provides a guideline for comparing techniques used to optimize ML for edge learning, along with an exploration of different frameworks, libraries, and simulation tools available for EL. In doing so, the paper contributes to a holistic understanding of the current landscape and future directions in the intersection of edge computing and machine learning, paving the way for informed comparisons between optimization methods and techniques designed for edge learning.

Training Machine Learning models at the Edge: A Survey

Mine video surveillance has a key role in ensuring the production safety of intelligent mining. However, existing mine intelligent monitoring technology mainly processes the video data in the cloud, which has problems, such as network congestion, large memory consumption, and untimely response to regional emergencies. In this paper, we address these limitations by utilizing the edge-cloud collaborative optimization framework. First, we obtained a coarse model using the edge-cloud collaborative architecture and updated this to realize the continuous improvement of the detection model. Second, we further proposed a target detection model based on the Vision Swin Transformer-YOLOv5(ViST-YOLOv5) algorithm and improved the model for edge device deployment. The experimental results showed that the object detection model based on ViST-YOLOv5, with a model size of only 27.057 MB, improved the average detection accuracy is by 25% compared to the state-of-the-art model, which makes it suitable for edge-end deployment in mining workface. For the actual mine surveillance video, the edge-cloud collaborative architecture can achieve better performance and robustness in typical application scenarios, such as weak lighting and occlusion, which verifies the feasibility of the designed architecture.

/pdf/ecvist-mine-intelligent-monitoring-based-on-edge-computing-9hu48k1i.pdf

ECViST: Mine Intelligent Monitoring Based on Edge Computing and Vision Swin Transformer-YOLOv5

With the vigorous development of computer, two-dimensional data acquisition technology and massive data dynamic visualization information technology, two-dimensional geographic information system (GIS) has gradually matured, and has been transferred from the initial visual expression to thematic application. Because 3D geographic information system has the characteristics of multidimensional data processing, classification, and presentation of information, it breaks through the confinement of monotonously displayed information on the traditional two-dimensional plane and has made breakthroughs in government assisted decision-making, national resource investigation, intelligent transportation, and other fields. E-commerce has been widely used in the field, and it is also very obvious in the field of spatial information socialization, playing an irreplaceable intergenerational effect. The visualization of landscape design is one of the key technologies to construct 3D GIS. Based on the combination of basic research and practical application, this paper deeply analyzes the openness of the garden 3D visualization system using artificial intelligence technology and completes the overall structure design of the 3D visualization system on the basis of studying the practical value and feasibility. Combined with the construction of the system, it is true to determine the acquisition method and construction method of three-dimensional landscape architecture model data, classify the landscape architecture information, and establish geometric model type libraries and texture model libraries. At the same time, the system adopts the object-oriented spatial relational database management mode to organize and manage the three-dimensional landscape building data efficiently and quickly. Determine the corresponding software and hardware configurations, carry out the interface design of the system and the division of functional modules, and establish a three-dimensional terrain visualization system for landscape architecture. In order to improve the operational efficiency of the system, the data is rationally organized and managed while taking optimization measures for the 3D model. Finally, the system adopts AI technology and uses component technology to realize the research of the intelligent terminal application system of landscape architecture.

/pdf/application-of-landscape-architecture-3d-visualization-2gvtt41h.pdf

Application of Landscape Architecture 3D Visualization Design System Based on AI Technology

With the rapid development and wide application of the Internet of Everything, in order to cope with the increasing amount of data and computational scale of mobile terminal processing, and the imbalance of existing scheduling algorithms and low resource utilization, this paper proposes a task scheduling algorithm based on business priority. The algorithm firstly divides the service according to the priority of the service. Secondly, the standard deviation of the computing task group is used to determine the proportion of long and short services, and the dynamic selection model is established. Finally, according to the idea of secondary allocation, the task of heavy load is assigned to the scheduling strategy of light load resources to execute, and the service redistribution model is established. The simulation results show that compared with the typical algorithm, the proposed algorithm achieves the result of comprehensive consideration of Makespan and load balancing to improve system efficiency.

/pdf/edge-computing-task-scheduling-strategy-based-on-load-4vaq6scqa7.pdf

Edge computing task scheduling strategy based on load balancing

Discuss the Application and Presentation of 3D Visualization Technology in Data Center

Network traffic identification is important for traffic engineering, resource allocation, network management, attack detection and improving network QoS. However, with the rapid development of computer network technology, various VPN technologies and applications have emerged, which use encryption and decryption technology, tunneling technology and authentication technology to obfuscate and hide traffic characteristics, making VPN traffic difficult to identify. The recent rise of V2Ray makes up for and completes the shortcomings of previous VPN technologies with a more complete protocol, more robust performance and richer functionality, using V2Ray’s customised VMess protocol, and the VMess protocol supports TLS-based implementations, making it a full-featured and powerful application. These undoubtedly pose a huge challenge for network traffic identification and auditing, as well as a huge risk for network security. Therefore, the identification of VPN traffic is of great importance. In this paper, we propose a VPN traffic identification method based on byte sample entropy and session interaction time difference. We use the byte sample entropy and session interaction time difference of some message sequences in network traffic as feature data, and use Random Forest RF (RF) algorithm to identify V2Ray VMess traffic, TLS-based VMess traffic and ISCX VPN-NonVPN public dataset, achieving 95.97%, 90.32% and 91.78% recognition accuracy, respectively. The experimental results show that the method can be used for the detection and identification of V2Ray traffic, and also supports the detection and identification of the rest of VPN traffic.

Deep learning based on byte sample entropy for VPN encrypted traffic identification

Inﬂuence maximization is the problem of ﬁnding a set of seed nodes in the network that maximizes the inﬂuence spread, which has become an important topic in social network analysis. Conventional inﬂuence maximization algorithms cause “unfair” inﬂuence spread among diﬀerent groups in the population, which could lead to severe bias in public opinion dissemination and viral marketing. To address this issue, we formulate the fair inﬂuence maximization problem concerning the trade-oﬀ between inﬂuence maximization and group fairness. For the purpose of solving the fair inﬂuence maximization problem in large-scale social networks eﬃciently, we propose a novel attribute-based reverse inﬂuence sampling (ABRIS) framework. This framework intends to estimate inﬂuence in speciﬁc groups with guarantee through an attribute-based hypergraph so that we can select seed nodes strategically. Therefore, under the ABRIS framework, we design two diﬀerent node selection algorithms, ABRIS-G and ABRIS-T. ABRIS-G selects nodes in a greedy scheduling way. ABRIS-T adopts a two-phase node selection method. These algorithms run eﬃciently and achieve a good trade-oﬀ between inﬂuence maximization and group fairness. Extensive experiments on six real-world social networks show that our algorithms signiﬁcantly outperform the state-of-the-art approaches.

Fair Inﬂuence Maximization in Large-scale Social Networks Based on Attribute-aware Reverse Inﬂuence Sampling

In the Internet age, information explodes exponentially. As an electric power industry closely related to national life, network public opinion in the electric power field also spreads rapidly. How to fully tap the public opinion information of electric power to promote the improvement of electric power service is an important research topic. According to the research status of power public opinion, this paper defines the problem in fine-grained emotion analysis, collects the corresponding data, and defines the evaluation aspects and evaluation levels. In terms of solution strategy, according to the dimension of the problem, this paper adopts a two-way LSTM model integrating attention mechanism. The experimental results show that this method is effective in the fine-grained emotion analysis task of power public opinion samples.

Fine-grained Emotional Analysis and Intelligent Identification of Electric Power Public Opinion Based on Attention Mechanism

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