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Extended reality (XR) is at the center of attraction in the research community due to the emergence of augmented, mixed, and virtual reality applications. The performance of such applications needs to be uptight to maintain the requirements of latency, energy consumption, and freshness of data. Therefore, a comprehensive performance analysis model is required to assess the effectiveness of an XR application but is challenging to design due to the dependence of the performance metrics on several difficult-to-model parameters, such as computing resources and hardware utilization of XR and edge devices, which are controlled by both their operating systems and the application itself. Moreover, the heterogeneity in devices and wireless access networks brings additional challenges in modeling. In this paper, we propose a novel modeling framework for performance analysis of XR applications considering edge-assisted wireless networks and validate the model with experimental data collected from testbeds designed specifically for XR applications. In addition, we present the challenges associated with performance analysis modeling and present methods to overcome them in detail. Finally, the performance evaluation shows that the proposed analytical model can analyze XR applications' performance with high accuracy compared to the state-of-the-art analytical models., Comment: 12 pages, 4 figures; To appear in Proceedings of IEEE International Conference on Distributed Computing Systems (ICDCS), 2024

Connected and autonomous vehicles (CAVs) rely heavily upon time-sensitive information update services to ensure the safety of people and assets, and satisfactory entertainment applications. Therefore, the freshness of information is a crucial performance metric for CAV services. However, information from roadside sensors and nearby vehicles can get delayed in transmission due to the high mobility of vehicles. Our research shows that a CAV's relative distance and speed play an essential role in determining the Age-of-Information (AoI). With an increase in AoI, incremental service aggregation issues are observed with out-of-sequence information updates, which hampers the performance of low-latency applications in CAVs. In this paper, we propose a novel AoI-based service aggregation method for CAVs, which can process the information updates according to their update cycles. First, the AoI for sensors and vehicles is modeled, and a predictive AoI system is designed. Then, to reduce the overall service aggregation time and computational load, intervals are used for periodic AoI prediction, and information sources are clustered based on the AoI value. Finally, the system aggregates services for CAV applications using the predicted AoI. We evaluate the system performance based on data sequencing success rate (DSSR) and overall system latency. Lastly, we compare the performance of our proposed system with three other state-of-the-art methods. The evaluation and comparison results show that our proposed predictive AoI-based service aggregation system maintains satisfactory latency and DSSR for CAV applications and outperforms other existing methods., Comment: 6 pages, 8 figures, to appear in the Proceedings of IEEE International Conference on Communications (IEEE ICC, 9-13 June 2024, Denver, CO, USA)

Climate change poses one of the most significant challenges to humanity. As a result of these climatic changes, the frequency of weather, climate, and water-related disasters has multiplied fivefold over the past 50 years, resulting in over 2 million deaths and losses exceeding $3.64 trillion USD. Leveraging AI-powered technologies for sustainable development and combating climate change is a promising avenue. Numerous significant publications are dedicated to using AI to improve renewable energy forecasting, enhance waste management, and monitor environmental changes in real time. However, very few research studies focus on making AI itself environmentally sustainable. This oversight regarding the sustainability of AI within the field might be attributed to a mindset gap and the absence of comprehensive energy datasets. In addition, with the ubiquity of edge AI systems and applications, especially on-device learning, there is a pressing need to measure, analyze, and optimize their environmental sustainability, such as energy efficiency. To this end, in this paper, we propose large-scale energy datasets for edge AI, named DeepEn2023, covering a wide range of kernels, state-of-the-art deep neural network models, and popular edge AI applications. We anticipate that DeepEn2023 will improve transparency in sustainability in on-device deep learning across a range of edge AI systems and applications. For more information, including access to the dataset and code, please visit https://amai-gsu.github.io/DeepEn2023., Comment: arXiv admin note: text overlap with arXiv:2310.18329

Today, deep learning optimization is primarily driven by research focused on achieving high inference accuracy and reducing latency. However, the energy efficiency aspect is often overlooked, possibly due to a lack of sustainability mindset in the field and the absence of a holistic energy dataset. In this paper, we conduct a threefold study, including energy measurement, prediction, and efficiency scoring, with an objective to foster transparency in power and energy consumption within deep learning across various edge devices. Firstly, we present a detailed, first-of-its-kind measurement study that uncovers the energy consumption characteristics of on-device deep learning. This study results in the creation of three extensive energy datasets for edge devices, covering a wide range of kernels, state-of-the-art DNN models, and popular AI applications. Secondly, we design and implement the first kernel-level energy predictors for edge devices based on our kernel-level energy dataset. Evaluation results demonstrate the ability of our predictors to provide consistent and accurate energy estimations on unseen DNN models. Lastly, we introduce two scoring metrics, PCS and IECS, developed to convert complex power and energy consumption data of an edge device into an easily understandable manner for edge device end-users. We hope our work can help shift the mindset of both end-users and the research community towards sustainability in edge computing, a principle that drives our research. Find data, code, and more up-to-date information at https://amai-gsu.github.io/DeepEn2023., Comment: This paper has been accepted by ACM/IEEE Symposium on Edge Computing (SEC '23)

The proliferation of network attacks poses a significant threat. Researchers propose datasets for network attacks to support research in related fields. Then, many attack detection methods based on these datasets are proposed. These detection methods, whether two-classification or multi-classification, belong to single-label learning, i.e., only one label is given to each sample. However, we discover that there is a noteworthy phenomenon of behavior attribute overlap between attacks, The presentation of this phenomenon in a dataset is that there are multiple samples with the same features but different labels. In this paper, we verify the phenomenon in well-known datasets(UNSW-NB15, CCCS-CIC-AndMal-2020) and re-label these data. In addition, detecting network attacks in a multi-label manner can obtain more information, providing support for tracing the attack source and building IDS. Therefore, we propose a multi-label detection model based on deep learning, MLD-Model, in which Wasserstein-Generative-Adversarial- Network-with-Gradient-Penalty (WGAN-GP) with improved loss performs data enhancement to alleviate the class imbalance problem, and Auto-Encoder (AE) performs classifier parameter pre-training. Experimental results demonstrate that MLD-Model can achieve excellent classification performance. It can achieve F1=80.06% in UNSW-NB15 and F1=83.63% in CCCS-CIC-AndMal-2020. Especially, MLD-Model is 5.99%-7.97% higher in F1 compared with the related single-label methods., Comment: 6 pages, 26 figures

Malicious communication behavior is the network communication behavior generated by malware (bot-net, spyware, etc.) after victim devices are infected. Experienced adversaries often hide malicious information in HTTP traffic to evade detection. However, related detection methods have inadequate generalization ability because they are usually based on artificial feature engineering and outmoded datasets. In this paper, we propose an HTTP-based Malicious Communication traffic Detection Model (HMCD-Model) based on generated adversarial flows and hierarchical traffic features. HMCD-Model consists of two parts. The first is a generation algorithm based on WGAN-GP to generate HTTP-based malicious communication traffic for data enhancement. The second is a hybrid neural network based on CNN and LSTM to extract hierarchical spatial-temporal features of traffic. In addition, we collect and publish a dataset, HMCT-2020, which consists of large-scale malicious and benign traffic during three years (2018-2020). Taking the data in HMCT-2020(18) as the training set and the data in other datasets as the test set, the experimental results show that the HMCD-Model can effectively detect unknown HTTP-based malicious communication traffic. It can reach F1 = 98.66% in the dataset HMCT-2020(19-20), F1 = 90.69% in the public dataset CIC-IDS-2017, and F1 = 83.66% in the real traffic, which is 20+% higher than other representative methods on average. This validates that HMCD-Model has the ability to discover unknown HTTP-based malicious communication behavior., Comment: 32 pages, 9 figures

HTTP-based Trojan is extremely threatening, and it is difficult to be effectively detected because of its concealment and confusion. Previous detection methods usually are with poor generalization ability due to outdated datasets and reliance on manual feature extraction, which makes these methods always perform well under their private dataset, but poorly or even fail to work in real network environment. In this paper, we propose an HTTP-based Trojan detection model via the Hierarchical Spatio-Temporal Features of traffics (HSTF-Model) based on the formalized description of traffic spatio-temporal behavior from both packet level and flow level. In this model, we employ Convolutional Neural Network (CNN) to extract spatial information and Long Short-Term Memory (LSTM) to extract temporal information. In addition, we present a dataset consisting of Benign and Trojan HTTP Traffic (BTHT-2018). Experimental results show that our model can guarantee high accuracy (the F1 of 98.62%-99.81% and the FPR of 0.34%-0.02% in BTHT-2018). More importantly, our model has a huge advantage over other related methods in generalization ability. HSTF-Model trained with BTHT-2018 can reach the F1 of 93.51% on the public dataset ISCX-2012, which is 20+% better than the best of related machine learning methods., Comment: 31 pages, 11 figures

Trojans are one of the most threatening network attacks currently. HTTP-based Trojan, in particular, accounts for a considerable proportion of them. Moreover, as the network environment becomes more complex, HTTP-based Trojan is more concealed than others. At present, many intrusion detection systems (IDSs) are increasingly difficult to effectively detect such Trojan traffic due to the inherent shortcomings of the methods used and the backwardness of training data. Classical anomaly detection and traditional machine learning-based (TML-based) anomaly detection are highly dependent on expert knowledge to extract features artificially, which is difficult to implement in HTTP-based Trojan traffic detection. Deep learning-based (DL-based) anomaly detection has been locally applied to IDSs, but it cannot be transplanted to HTTP-based Trojan traffic detection directly. To solve this problem, in this paper, we propose a neural network detection model (HSTF-Model) based on hierarchical spatiotemporal features of traffic. Meanwhile, we combine deep learning algorithms with expert knowledge through feature encoders and statistical characteristics to improve the self-learning ability of the model. Experiments indicate that F1 of HSTF-Model can reach 99.4% in real traffic. In addition, we present a dataset BTHT consisting of HTTP-based benign and Trojan traffic to facilitate related research in the field., Comment: 8 pages, 7 figures

Mobile Internet has profoundly reshaped modern lifestyles in various aspects. Encrypted Traffic Classification (ETC) naturally plays a crucial role in managing mobile Internet, especially with the explosive growth of mobile apps using encrypted communication. Despite some existing learning-based ETC methods showing promising results, three-fold limitations still remain in real-world network environments, 1) label bias caused by traffic class imbalance, 2) traffic homogeneity caused by component sharing, and 3) training with reliance on sufficient labeled traffic. None of the existing ETC methods can address all these limitations. In this paper, we propose a novel Pre-trAining Semi-Supervised ETC framework, dubbed PASS. Our key insight is to resample the original train dataset and perform contrastive pre-training without using individual app labels directly to avoid label bias issues caused by class imbalance, while obtaining a robust feature representation to differentiate overlapping homogeneous traffic by pulling positive traffic pairs closer and pushing negative pairs away. Meanwhile, PASS designs a semi-supervised optimization strategy based on pseudo-label iteration and dynamic loss weighting algorithms in order to effectively utilize massive unlabeled traffic data and alleviate manual train dataset annotation workload. PASS outperforms state-of-the-art ETC methods and generic sampling approaches on four public datasets with significant class imbalance and traffic homogeneity, remarkably pushing the F1 of Cross-Platform215 with 1.31%, ISCX-17 with 9.12%. Furthermore, we validate the generality of the contrastive pre-training and pseudo-label iteration components of PASS, which can adaptively benefit ETC methods with diverse feature extractors., Comment: Accepted by 2023 20th Annual IEEE International Conference on Sensing, Communication, and Networking, 9 pages, 6 figures

A Mobility Digital Twin is an emerging implementation of digital twin technology in the transportation domain, which creates digital replicas for various physical mobility entities, such as vehicles, drivers, and pedestrians. Although a few work have investigated the applications of mobility digital twin recently, the extent to which it can facilitate safer autonomous vehicles remains insufficiently explored. In this paper, we first propose visualization of mobility digital twin, which aims to augment the existing perception systems in connected and autonomous vehicles through twinning high-fidelity and manipulable geometry representations for causal traffic participants, such as surrounding pedestrians and vehicles, in the digital space. An end-to-end system framework, including image data crowdsourcing, preprocessing, offloading, and edge-assisted 3D geometry reconstruction, is designed to enable real-world development of the proposed visualization of mobility digital twin. We implement the proposed system framework and conduct a case study to assess the twinning fidelity and physical-to-digital synchronicity within different image sampling scenarios and wireless network conditions. Based on the case study, future challenges of the proposed visualization of mobility digital twin are discussed toward the end of the paper., Comment: The paper has been accepted by The 20th IEEE International Conference on Mobile Ad-Hoc and Smart Systems (MASS 2023)

In recent years, the problem of fuzzy clustering has been widely concerned. The membership iteration of existing methods is mostly considered globally, which has considerable problems in noisy environments, and iterative calculations for clusters with a large number of different sample sizes are not accurate and efficient. In this paper, starting from the strategy of large-scale priority, the data is fuzzy iterated using granular-balls, and the membership degree of data only considers the two granular-balls where it is located, thus improving the efficiency of iteration. The formed fuzzy granular-balls set can use more processing methods in the face of different data scenarios, which enhances the practicability of fuzzy clustering calculations.

Most of the existing clustering methods are based on a single granularity of information, such as the distance and density of each data. This most fine-grained based approach is usually inefficient and susceptible to noise. Therefore, we propose a clustering algorithm that combines multi-granularity Granular-Ball and minimum spanning tree (MST). We construct coarsegrained granular-balls, and then use granular-balls and MST to implement the clustering method based on "large-scale priority", which can greatly avoid the influence of outliers and accelerate the construction process of MST. Experimental results on several data sets demonstrate the power of the algorithm. All codes have been released at https://github.com/xjnine/GBMST.

Artificial intelligence (AI) has enabled a new paradigm of smart applications -- changing our way of living entirely. Many of these AI-enabled applications have very stringent latency requirements, especially for applications on mobile devices (e.g., smartphones, wearable devices, and vehicles). Hence, smaller and quantized deep neural network (DNN) models are developed for mobile devices, which provide faster and more energy-efficient computation for mobile AI applications. However, how AI models consume energy in a mobile device is still unexplored. Predicting the energy consumption of these models, along with their different applications, such as vision and non-vision, requires a thorough investigation of their behavior using various processing sources. In this paper, we introduce a comprehensive study of mobile AI applications considering different DNN models and processing sources, focusing on computational resource utilization, delay, and energy consumption. We measure the latency, energy consumption, and memory usage of all the models using four processing sources through extensive experiments. We explain the challenges in such investigations and how we propose to overcome them. Our study highlights important insights, such as how mobile AI behaves in different applications (vision and non-vision) using CPU, GPU, and NNAPI. Finally, we propose a novel Gaussian process regression-based general predictive energy model based on DNN structures, computation resources, and processors, which can predict the energy for each complete application cycle irrespective of device configuration and application. This study provides crucial facts and an energy prediction mechanism to the AI research community to help bring energy efficiency to mobile AI applications., Comment: Accepted in IEEE International Conference on Communications (ICC); 28 May - 01 June 2023; Rome, Italy; six pages, seven figures, two tables

3GPP has introduced Private 5G to support the next-generation industrial automation system (IAS) due to the versatility and flexibility of 5G architecture. Besides the 3.5GHz CBRS band, unlicensed spectrum bands, like 5GHz, are considered as an additional medium because of their free and abundant nature. However, while utilizing the unlicensed band, industrial equipment must coexist with incumbents, e.g., Wi-Fi, which could introduce new security threats and resuscitate old ones. In this paper, we propose a novel attack strategy conducted by a mobility-enabled malicious Wi-Fi access point (mmAP), namely \textit{PACMAN} attack, to exploit vulnerabilities introduced by heterogeneous coexistence. A mmAP is capable of moving around the physical surface to identify mission-critical devices, hopping through the frequency domain to detect the victim's operating channel, and launching traditional MAC layer-based attacks. The multi-dimensional mobility of the attacker makes it impervious to state-of-the-art detection techniques that assume static adversaries. In addition, we propose a novel Markov Decision Process (MDP) based framework to intelligently design an attacker's multi-dimensional mobility in space and frequency. Mathematical analysis and extensive simulation results exhibit the adverse effect of the proposed mobility-powered attack., Comment: 6 pages, 7 Figures, Accepted in IEEE International Conference on Communications 2023

It is crucial to evaluate the quality and determine the optimal number of clusters in cluster analysis. In this paper, the multi-granularity characterization of the data set is carried out to obtain the hyper-balls. The cluster internal evaluation index based on hyper-balls(HCVI) is defined. Moreover, a general method for determining the optimal number of clusters based on HCVI is proposed. The proposed methods can evaluate the clustering results produced by the several classic methods and determine the optimal cluster number for data sets containing noises and clusters with arbitrary shapes. The experimental results on synthetic and real data sets indicate that the new index outperforms existing ones.

The emerging vehicular connected applications, such as cooperative automated driving and intersection collision warning, show great potentials to improve the driving safety, where vehicles can share the data collected by a variety of on-board sensors with surrounding vehicles and roadside infrastructures. Transmitting and processing this huge amount of sensory data introduces new challenges for automotive edge computing with traditional wireless communication networks. In this work, we address the problem of traditional asymmetrical network resource allocation for uplink and downlink connections that can significantly degrade the performance of vehicular connected applications. An end-to-end automotive edge networking system, FAIR, is proposed to provide fast, scalable, and impartial connected services for intelligent vehicles with edge computing, which can be applied to any traffic scenes and road topology. The core of FAIR is our proposed symmetrical network resource allocation algorithm deployed at edge servers and service adaptation algorithm equipped on intelligent vehicles. Extensive simulations are conducted to validate our proposed FAIR by leveraging real-world traffic dataset. Simulation results demonstrate that FAIR outperforms existing solutions in a variety of traffic scenes and road topology., Comment: This is a personal copy of the authors. Not for redistribution. The final version of this paper was accepted by IEEE Transactions on Intelligent Vehicles

Existing clustering methods are based on a single granularity of information, such as the distance and density of each data. This most fine-grained based approach is usually inefficient and susceptible to noise. Inspired by adaptive process of granular-ball division and differentiation, we present a novel clustering approach that retains the speed and efficiency of K-means clustering while out-performing time-tested density clustering approaches widely used in industry today. Our simple, robust, adaptive granular-ball clustering method can efficiently recognize clusters with unknown and complex shapes without the use of extra parameters. Moreover, the proposed method provides an efficient, adaptive way to depict the world, and will promote the research and development of adaptive and efficient AI technologies, especially density computing models, and improve the efficiency of many existing clustering methods., Comment: 5 pages, 1 figures

Today very few deep learning-based mobile augmented reality (MAR) applications are applied in mobile devices because they are significantly energy-guzzling. In this paper, we design an edge-based energy-aware MAR system that enables MAR devices to dynamically change their configurations, such as CPU frequency, computation model size, and image offloading frequency based on user preferences, camera sampling rates, and available radio resources. Our proposed dynamic MAR configuration adaptations can minimize the per frame energy consumption of multiple MAR clients without degrading their preferred MAR performance metrics, such as latency and detection accuracy. To thoroughly analyze the interactions among MAR configurations, user preferences, camera sampling rate, and energy consumption, we propose, to the best of our knowledge, the first comprehensive analytical energy model for MAR devices. Based on the proposed analytical model, we design a LEAF optimization algorithm to guide the MAR configuration adaptation and server radio resource allocation. An image offloading frequency orchestrator, coordinating with the LEAF, is developed to adaptively regulate the edge-based object detection invocations and to further improve the energy efficiency of MAR devices. Extensive evaluations are conducted to validate the performance of the proposed analytical model and algorithms., Comment: This is a personal copy of the authors. Not for redistribution. The final version of this paper was accepted by IEEE Transactions on Mobile Computing

Applying deep learning to object detection provides the capability to accurately detect and classify complex objects in the real world. However, currently, few mobile applications use deep learning because such technology is computation-intensive and energy-consuming. This paper, to the best of our knowledge, presents the first detailed experimental study of a mobile augmented reality (AR) client's energy consumption and the detection latency of executing Convolutional Neural Networks (CNN) based object detection, either locally on the smartphone or remotely on an edge server. In order to accurately measure the energy consumption on the smartphone and obtain the breakdown of energy consumed by each phase of the object detection processing pipeline, we propose a new measurement strategy. Our detailed measurements refine the energy analysis of mobile AR clients and reveal several interesting perspectives regarding the energy consumption of executing CNN-based object detection. Furthermore, several insights and research opportunities are proposed based on our experimental results. These findings from our experimental study will guide the design of energy-efficient processing pipeline of CNN-based object detection., Comment: This is a personal copy of the authors. Not for redistribution. The final version of this paper was accepted by IEEE Transactions on Green Communications and Networking

As vehicles playing an increasingly important role in people's daily life, requirements on safer and more comfortable driving experience have arisen. Connected vehicles (CVs) can provide enabling technologies to realize these requirements and have attracted widespread attentions from both academia and industry. These requirements ask for a well-designed computing architecture to support the Quality-of-Service (QoS) of CV applications. Computation offloading techniques, such as cloud, edge, and fog computing, can help CVs process computation-intensive and large-scale computing tasks. Additionally, different cloud/edge/fog computing architectures are suitable for supporting different types of CV applications with highly different QoS requirements, which demonstrates the importance of the computing architecture design. However, most of the existing surveys on cloud/edge/fog computing for CVs overlook the computing architecture design, where they (i) only focus on one specific computing architecture and (ii) lack discussions on benefits, research challenges, and system requirements of different architectural alternatives. In this paper, we provide a comprehensive survey on different architectural design alternatives based on cloud/edge/fog computing for CVs. The contributions of this paper are: (i) providing a comprehensive literature survey on existing proposed architectural design alternatives based on cloud/edge/fog computing for CVs, (ii) proposing a new classification of computing architectures based on cloud/edge/fog computing for CVs: computation-aided and computation-enabled architectures, (iii) presenting a holistic comparison among different cloud/edge/fog computing architectures for CVs based on functional requirements of CV systems, including advantages, disadvantages, and research challenges., Comment: This is a personal copy of the authors. Not for redistribution. The final version of this paper is available through the IEEE Xplore Digital Library, at the link: https://ieeexplore.ieee.org/document/9184917, with the DOI: 10.1109/COMST.2020.3020854