Multi-vehicle collaborative perception is fundamental to realizing Level 4+ autonomous driving by enabling connected vehicles to share and integrate sensor data for enhanced situational awareness. Under emerging Internet of Things (IoT) architectures, fleets of vehicles form dynamic, decentralized networks. In practice, however, deployment is hampered by three core challenges: (1) transmission latency in vehicle-to-vehicle (V2V) links, (2) data transmission constrained by limited communication bandwidth, and (3) the complexity of fusing asynchronous, multi-source data streams. To overcome these obstacles, this paper presents STCo, a spatio-temporal context-aware and communication-efficient framework for multi-vehicle collaborative perception. First, a spatio-temporal context modeling mechanism is devised to enhance perceptual continuity and mitigate communication asynchrony in IoT environments. Second, a cross-vehicle sensor perspective disparity mining algorithm leverages distributed observations to extract high-value complementary information. Third, a multi-source data fusion paradigm unifies diverse perception inputs into a unified representation from the ego vehicle’s perspective, thereby strengthening feature correlations. This paper validates STCo on both the real-world V2V4Real dataset and the large-scale simulated OPV2V benchmark. Experimental results demonstrate that STCo outperforms state-of-the-art methods in detection accuracy while substantially reducing communication overhead, highlighting its efficacy and practicality for IoT-enabled autonomous driving systems.

Machine learning platforms offer paid APIs to enable personalized inference services. However, model extraction attacks greatly threaten their intellectual property rights. Malicious users can create query samples using proxy datasets or generative models to train a clone model. Existing defense approaches usually focus on models that return soft-labels, and cannot effectively handle extracting attacks against hard-label models. In this paper, we propose an adaptive defense framework named DAV, which consists of a malicious query detector and an adaptive perturbation mechanism. Two perturbation strategies can be selected based on the detection results and the malicious query rate within the buffer queue, including accuracy-preserving perturbation and maximum-minimum probability inverse perturbation. Comprehensive experimental results show that DAV can significantly reduce the accuracy of the clone model with little impact on the performance of the victim model and benign queries, no matter whether the returned probabilities are for soft-label or hard-label.

The manufacturing environment and activities with a large volume and variety of product data have put forward higher requirements for the proof and verification of identity information. Achieving decentralized digital identity management in the Industrial Internet of Things (IIoT) helps to improve the performance of relevant proofs and authentication. The Decentralized Identity (DID) system serves as a bridge between the physical and digital worlds, assigning digital identities to physical entities to facilitate their participation in online activities. However, faced with the huge number of manufacturing entities accessing the DID system, the number of DID documents in the system has proliferated. It is still a big challenge to improve the scalability of the system while ensuring the efficiency of information access and verification. In this paper, we propose a blockchain-based verifiable decentralized identity system for IIoT. First, we propose a blockchain-based system architecture with a specially designed storage structure for DID documents. Specifically, we design a structure based on Merkle Tree that visually summarises the physical associations of manufacturing entities and reduces access overhead. Second, we design a multiblock storage structure within the blockchain, which establishes inter-block jumps based on the associated DID, effectively improving the query efficiency of the system. Finally, we design a verification scheme that enables users to verify the integrity of the identity data of the proof provider. We implemented the system framework and conducted experiments to evaluate the performance of our system. The experimental results proved the effectiveness of the system.

Abstract Software maintenance and evolution play an important role in the software engineering field, especially when current software becomes more and more complex and powerful. As an entity to implement business processes and gain revenue, valuable software is composed of business logic and corresponding organization role interaction interfaces. With the enterprise development, the organization architecture also evolves, like expanding, cross department cooperation, and so on. However, existing software process adaptive approaches mainly focus on handling the change of the business (program) logic instead of organization structure. Therefore, we propose an adaptive software business process approach that supports organization architecture evolution and automatically migrates the run‐time process instances to the latest version. First, a business process adaptation model is designed, which includes the organization layer, business process layer and event layer that connects the two. Based on the model, the organization changing impact and business process model modification are formalized. Besides, the business process adaptation approach is designed. According to the dependence between the organization architecture and the business process activities, the affected domain detection algorithms for three basic business process structures and the business process instance migration algorithm are developed. Finally, the feasibility and stability of the proposed system are comprehensively evaluated with the synthetic data sets.

Most of the existing personalized recommendation methods predict the probability that one user might interact with the next item by matching their representations in the latent space. However, as a cognitive task, it is essential for an impressive recommender system to acquire the cognitive capacity rather than to decide the users' next steps by learning the pattern from the historical interactions through matching-based objectives. Therefore, in this paper, we propose to model the recommendation as a logical reasoning task which is more in line with an intelligent recommender system. Different from the prior works, we embed each query as a box rather than a single point in the vector space, which is able to model sets of users or items enclosed and logical operators (e.g., intersection) over boxes in a more natural manner. Although modeling the logical query with box embedding significantly improves the previous work of reasoning-based recommendation, there still exist two intractable issues including aggregation of box embeddings and training stalemate in critical point of boxes. To tackle these two limitations, we propose a Contrastive Box learning framework for Collaborative Reasoning (CBox4CR). Specifically, CBox4CR combines a smoothed box volume-based contrastive learning objective with the logical reasoning objective to learn the distinctive box representations for the user's preference and the logical query based on the historical interaction sequence. Extensive experiments conducted on four publicly available datasets demonstrate the superiority of our CBox4CR over the state-of-the-art models in recommendation task.

In modern society, recommendation systems (RSs) already become an indispensable component, especially in smart cities. Their recommendation performance is greatly affected by the available analyzing data, but centralized massive data can cause data privacy issues. Hence, federated learning is applied to achieve a higher recommendation accuracy without sharing raw data. To improve the performance and reliability of traditional federated RSs, we propose HFSA, a semi-asynchronous hierarchical federated RS. First, from the architecture perspective, an edge server layer is involved between the central server and clients, which alleviates the server’s communication pressure and enhances the recommendation model training by configuring the global aggregation frequency. Besides, a semi-asynchronous aggregation mechanism is designed. It collects local parameters as much as possible within the predefined aggregation cycle and allows the slow clients to contribute their model parameters asynchronously. The tolerate round and dynamic participation time weights shield the heterogeneity and instability of edge clients and ensure the convergence of the global model. Compared with several classical baselines, the experimental results show that HFSA can achieve a relatively better recommendation performance with high accuracy and less training time. In addition, the influential factors of HFSA are evaluated as well.

With the development of the Industrial Internet of Things system, the huge amount of devices, services, and continuous data, making it difficult to discover a trusted service in complex scenarios. To better leverage knowledge and historical behavior, recommendation systems are applied. However, the model accuracy closely depends on training data size; there is a great risk of data leaking by collecting from multiple departments. To solve these problems, we propose a graph-convolutional-neural-network-based federated approach, which accurately recommends proper service for participating clients without gathering the raw data. Specifically, each client trains locally and uploads the weights of their model to the server for aggregation. Besides, the potential overlapping services of different clients are leveraged to guide the embedding aggregation and sharing, which, in turn, optimize the local training results. Their sensitive scenarios' embedding is kept locally. Owing to the model aggregation, it also resists the poisoning attack to some degree. In addition, the comprehensive experiments on classic public recommendation datasets evaluate the feasibility, effectiveness, trustworthiness, and potential influences.

AI-empowered 5G/6G networks play a substantial role in taking full advantage of the Internet of Things (IoT) to perform complex computing by offloading tasks to edge services deployed in intelligent transport systems. However, offloading behavior has a certain regularity, and the real-time location of users can easily be inferred by attackers who have historical user data during the data transmission process. To address this problem, a privacy-oriented task offloading method that can resist attacks from privacy attackers with prior knowledge is proposed. First, the local computing model, channel model, and privacy loss model are defined and used to quantify evaluation indicators, such those related to privacy, time, and energy. Among them, privacy loss is formalized as the probability of a successful attack by an attacker with prior knowledge. Second, the process of solving an optimal task offloading decision problem is formalized into a Markov decision process (MDP). Finally, the deep reinforcement learning (DRL) method PPO2 is proposed to solve the planning problem of task offloading with good generalization and convergence speed, where we focus on the location privacy requirement. Experiments show that our method can handle large-scale task offloading and obtain offloading policies with reduced privacy loss, energy consumption and time delays.

Abstract Software testing is an indispensable part of the software engineering industry, which guarantees product reliability and safety. Traditional testing approaches face the testing Oracle problem, they are difficult to construct the expected outputs with the increasing of program complexity. As a result, metamorphic testing, which tests the program by examining the relationship between the execution results, is proposed. However, existing manual metamorphic relation construction requires huge effects of domain experts, and automatic methods are unstable and inefficient due to the insufficient software feature mining. Hence, we proposed a multi‐dimensional program structure‐based metamorphic relation prediction approach, which is composed of feature extraction and prediction model building. In the feature extraction stage, the testing program is converted to multiple intermediate structures (such as control flow graphs and abstract syntax trees) to explore its features. In the prediction model building stage, the extracted feature set is used as the training set, and a novel semi‐supervised support vector machine‐bagging‐K‐nearest neighbors algorithm is designed to train the prediction model. Besides, a two‐phase hybrid granularity search algorithm is proposed to improve the prediction performance by selecting the optimal number of weak classifiers. Compared with existing approaches, our proposed model can improve the accuracy by around 14%.

Traffic context plays an important role in supporting automated driving and intelligent transportation systems. Smart vehicles explore surrounding environments by analyzing sensor data and periodically communicating with neighbors and road infrastructures. The context can be well learned in this way to support driving, but the vehicle trajectory can be also easily exposed under eavesdropping attacks. The pseudonym is proposed to hide the real identity of the vehicles. However, the effectiveness of anonymity, the safety of driving, the convenience of implementation and the utilization of resources in previous approaches have not been well-balanced. Therefore, focusing on efficiently replacing pseudonyms with the premise of ensuring driving safety, we propose a secure dynamic silent mix zone pseudonym changing scheme (TLAS) based on the real-time traffic context prediction for urban regions. It naturally takes the area in front of the red traffic light as a silent mix zone, which avoids the driving security issue caused by signal silence. Besides, the area length is dynamically configured according to the traffic context predicted in the last green light cycle, so the anonymous effect can be improved. In addition, considering the resource utilization and accuracy requirement, the adaptive prediction algorithm is applied. We conduct simulation experiments with real-world traffic history using SUMO and OMNET++, the results show that TLAS strategy can indeed achieve a better anonymous effect (reducing standardized traceability rate by 8.2%) with lower driving speed for safety concern.

Abstract Edge computing perfectly integrates cloud computing centers and edge-end devices together, but there are not many related researches on how the edge-end node devices work to form an edge network and what the protocols used to implement the communication among nodes in the edge network. Aiming at the problem of coordinated communication among edge nodes in the current edge computing network architecture, this paper proposes an edge network routing and forwarding protocol based on target tracking scenarios. This protocol can meet the dynamic changes of node locations, and the elastic expansion of node scale. Individual node failures will not affect the overall network, and the network ensures efficient real-time with less communication overhead. The experimental results display that the protocol can effectively reduce the communications volume of the edge network, improve the overall efficiency of the network, and set the optimal sampling period, so as to ensure that the network delay is minimized.

Vehicular ad hoc networks (VANETs) have been widely used in intelligent transportation systems (ITSs) for purposes such as the control of unmanned aerial vehicles (UAVs) and trajectory prediction. However, an efficient and reliable data routing decision scheme is critical for VANETs due to the feature of self-organizing wireless multi-hop communication. Compared with wireless networks, which are unstable and have limited bandwidth, wired networks normally provide longer transmission distances, higher network speeds and greater reliability. To address this problem, this paper proposes a reliable VANET routing decision scheme based on the Manhattan mobility model, which considers the integration of roadside units (RSUs) into wireless and wired modes for data transmission and routing optimization. First, the problems of frequently moving vehicles and network connectivity are analyzed based on road networks and the motion information of vehicle nodes. Second, an improved greedy algorithm for vehicle wireless communication is used for network optimization, and a wired RSU network is also applied. In addition, routing decision analysis is carried out in accordance with the probabilistic model for various transmission ranges by checking the connectivity among vehicles and RSUs. Finally, comprehensive experiments show that our proposed method can support real-time planning and improve network transmission performance compared with other baseline protocol approaches in terms of several metrics, including package delivery ratio, time delay and wireless hops.

The explosive growth of the Internet of Things and modern networking technologies lay the foundation for the development of intelligent transportation systems and smart cities. To analyzing massive data under the required time for transportation issues, the edge computing paradigm is applied, which pre-processing large amounts of data at the network edge to save bandwidth and improve response time. However, data reliability and security are still facing many challenges in the edge environment. In this article, we propose a multi-authority hybrid attribute-based signature scheme (SDABS). It is composed of four phases: system initialization, signature generation, signature verification, and attribute revocation phases. To better describe frequently changing features in the transportation systems like location, the dynamic attribute is introduced in building the signature. The multi-layer policy tree is applied to support flexible and various access policies, which also naturally form user groups and help data searching. Besides, the multi-authority structure is more suitable for the distributed edge environment. We evaluate SDABS from both theoretical analysis and practical analysis. Compared with two classical signature schemes (MABS and ODMA-ABS), experimental results demonstrate that the proposed SDABS can achieve better performance at an acceptable cost in the terms of attributes and attribute authorities.

As the rapid growth of connected and autonomous vehicles (CAVs) and 5G intensifies, more third-party applications are increasingly being deployed on CAVs. They not only improve user experience but also provide more helpful services, for example, enhancing public safety by recognizing criminals in real-time videos. Current CAVs prefer to process collected data on the vehicle to avoid long transmission latency and extra network cost. However, due to the limitations of the on-board vehicle computing unit (VCU) and increasing use of computing-intensive in-vehicle applications, the burden of on-board VCU has sharply increased, which may affect driving safety. In particular, for existing vehicles on the road, adding more computing devices is a challenge if not impossible due to cost concerns. Inspired by edge computing, we propose a novel platform, MobileEdge, to enhance the computing capability of the unchangeable on-board VCU, which leverages mobile devices as edge nodes, e.g., the passengers' smartphones, by offloading computing tasks to them for collaboratively computing. Moreover, MobileEdge provides the dynamic management of mobile devices, monitoring device status and interfaces for customizable task offloading strategies and eventually achieves optimal task scheduling. We build a prototype to demonstrate the designed platform and evaluate three task offloading strategies which were implemented based on the developed interfaces. The results show that MobileEdge significantly reduces the application response latency. Compared with the baseline which does not employ task offloading, the response latency is almost near real-time when more computing resources are available. In addition, the proposed shortest response latency strategy outperforms the best overall task scheduling among the three strategies.

Driven by the visions of Internet of Things and 5G communications, the edge computing systems integrate computing, storage, and network resources at the edge of the network to provide computing infrastructure, enabling developers to quickly develop and deploy edge applications. At present, the edge computing systems have received widespread attention in both industry and academia. To explore new research opportunities and assist users in selecting suitable edge computing systems for specific applications, this survey paper provides a comprehensive overview of the existing edge computing systems and introduces representative projects. A comparison of open-source tools is presented according to their applicability. Finally, we highlight energy efficiency and deep learning optimization of edge computing systems. Open issues for analyzing and designing an edge computing system are also studied in this paper.

With the development of big data, big data processing systems, such as Hadoop and Spark, are widely used to handle large-scale data. To avoid the complexity and expensiveness of building a self-owned big data processing system, cloud providers tend to deploy big data processing tools as cloud services. Typical examples include Amazon EMR, Azure HDInsight and AliCloud E-MapReduce. However, how to build a cost-efficient system and scale the system is still challenging. In this paper, we have conducted a case study on AliCloud E-MapReduce, and analyzed the system performance upon local and remote file systems. We compared the scalability of Hadoop and Spark by using scaleout and scale-up strategies respectively. Based on the analysis results, we derive several observations and implications, which will contribute to guide the performance optimization.

Servers have different energy efficiency and energy proportionality (EP) due to their hardware configuration (i.e., CPU generation and memory installation) and workload. However, current virtual machine (VM) scheduling in virtualized environments will saturate servers without considering their energy efficiency and EP differences. This article will discuss EASE, the energy efficiency and proportionality aware VM scheduling approach. EASE first executes customized computing intensive, memory intensive, and hybrid benchmarks to calculate a server's energy efficiency and EP. Then it schedules VMs to servers to keep them working at their peak energy efficiency point (or optimal working range). This step improves the overall energy efficiency of the cluster and the data center. For performance guarantee, EASE migrates VMs from servers under highly contending conditions. The experimental results on real clusters show that power consumption can be saved 37.07% ~ 49.98% in the homogeneous cluster. The average completion time of the computing intensive VMs increases only 0.31 % ~ 8.49%. In the heterogeneous nodes, the power consumption of the computing intensive VMs can be reduced by 44.22 %. The job completion time can be saved by 53.80%.

In the big data community, Spark has been widely used for processing interactive queries. Spark employs a query optimizer, called Catalyst, to provides a set of optimization rules and supports Cost-Based Optimization (CBO). In this paper, we investigated the effectiveness of the optimization rules and cost-based optimization in Catalyst. We conducted comprehensive validation experiments by varying the data volume and cluster scale, and found that the execution time of most TPC-H queries were reduced slightly even when query optimizations are applied. We derived some interesting observations on Catalyst, which can help the community better understand and improve the query optimizer of Spark in future.

With the popularity of social networks, Twitter has become one of the dominant providers of massive quantities of information.Exploring the distributions and correlations from Twitter data helps accurate personalized recommendations.Online Analytical Processing, or OLAP, provides an intuitive form that is suitable for exploring Twitter data.Unfortunately, the traditional OLAP approaches can only deal with structured data, not unstructured textual data like tweets.The key to applying OLAP to Twitter data is to mine and build a dimension hierarchy of tweeter interests.However, the current methods can extract tweeter interests from Twitter data on a single level, but fail to obtain a hierarchy of tweeter interests with different granularities.To address this problem, we propose a LDA-based model, called MS-LDA, which combines tweeters' social relationships and tweets to extract and build the tweeters' interest dimension hierarchy.Such a dimension hierarchy can be further employed to apply OLAP techniques to Twitter data.In addition, we employ Word2vec to obtain the linguistic similarity of words in tweets, to improve its effectiveness.The extensive experiments demonstrate that our method can effectively extract the dimension hierarchy of tweeters' interests for multidimensional analysis.

Container enables a new way to run applications by containerizing the application, which provides kinds of services to make them portable, extensible, and easy to be transferred between private data centers and public clouds.Comparing with virtual machines, containers have several advantages in terms of simplicity, low-overhead and lightweight.However, as the OS kernel and resources are shared by all the hosted containers, performance isolation becomes a challenging issue for guaranteeing their SLA.This paper discusses I/O performance isolation issue in container-based clusters.First, we analyze the characteristics of I/O performance isolation from the perspective of the SLA.Then we conduct the observation experiments using multiple containers to obtain the variation trend of the I/O performance parameters and observe the impact of the I/O overload container on the I/O performance isolation of the system.Finally, we propose two algorithms, SLAE and UTE, to improve the I/O performance isolation in container-based systems.These algorithms contribute to decrease the interference caused by the overloaded containers.Experimental results show the feasibility and effectiveness of our proposed algorithms.

Online Analytical Processing, or OLAP, is an approach to answering multidimensional analytical (MDA) queries in an interactive way. However, the traditional OLAP approaches can only deal with structured data, but not unstructured textual data like tweets. To address this problem, we propose a Latent Dirichlet Allocation (LDA)-based model, called Multilayered Semantic LDA (MS-LDA), which detects the hidden layered interests from Twitter data based on LDA. The layered dimension of interests can be further used to apply OLAP techniques to Twitter data. Furthermore, MS-LDA employs the semantic similarity among words of tweets based on word2vec, and also the social relationship among twitters, to improve its effectiveness. The extensive experiments demonstrate that MS-LDA can effectively extract the dimension hierarchy of tweeters' interests for OLAP.

The proliferation of Internet of Things (IoT) and the success of rich cloud services have pushed the horizon of a new computing paradigm, edge computing, which calls for processing the data at the edge of the network. Edge computing has the potential to address the concerns of response time requirement, battery life constraint, bandwidth cost saving, as well as data safety and privacy. In this paper, we introduce the definition of edge computing, followed by several case studies, ranging from cloud offloading to smart home and city, as well as collaborative edge to materialize the concept of edge computing. Finally, we present several challenges and opportunities in the field of edge computing, and hope this paper will gain attention from the community and inspire more research in this direction.