The convergence of Artificial Intelligence (AI) and the Internet of Things (IoT) in future 6G networks (6G-IoT) promises to unlock unprecedented capabilities. However, the continuous collection and analysis of large-scale, low-density data pose significant threats to the reliability and trustworthiness of these systems, leading to high energy consumption and potential decision-making based on stale information. To address these critical challenges, this paper proposes a novel architecture for building reliable and trustworthy 6G-IoT services. Our approach involves three key contributions: 1) We leverage the multi-access edge computing (MEC) paradigm to locally process raw data, filtering redundancy and thereby ensuring that AI models operate on more meaningful information. 2) We design a decoupled, two-level (edge-cloud) decision-making mechanism that explicitly manages the trade-off between data trustworthiness (quantified by information freshness) and system energy consumption, a cornerstone of long-term reliability. 3) We implement these principles in a new, distributed end-edge-cloud framework named Quesada (Query-control-based safe and data-trustworthy acquisition), which coordinates edge and cloud decisions to enhance overall system performance. To validate our approach, we conduct a series of comparative experiments. The results demonstrate that the Quesada framework significantly improves both system reliability and data trustworthiness, making it a viable architecture for future 6G-IoT applications.

Modularized plant factories, characterized by machines executing intelligent control requests to automatically take care of crops, have emerged as a sustainable agricultural paradigm, garnering the attention of Internet-of-Things and agricultural researchers for their production stability and energy efficiency. However, the diversity and pluralism of the plant factory components make it difficult to cooperate and produce crops with better qualities. Therefore, appropriate resource allocation and task scheduling strategies become the key points to optimize the quality of production in the factories by immediately telling which component is more suitable to do what in taking care of the crops. To address this challenge, this paper investigates how the machines of the factory can use their unique services and resource to help improve the crops’ quality and model the machine cooperation as an online decision-making problem. An$\alpha$-competitive approach called$\textsc {OnATS}$is designed based on the transformation of the original problem, and the experiments show that the proposed algorithm is superior to the baselines. Additionally, this paper explores the impact of different system configurations on the proposed method and shows that the proposed approach has broad applicability.

Efficient data collection systems play a crucial role in enabling real-time decision-making in AIoT applications. However, traditional cloud-edge systems encounter challenges such as increased computational load and network transmission, leading to higher costs and latency. To address these issues, Multi-Access Edge Computing (MEC) has emerged as a solution by decentralizing computation and storage, reducing reliance on cloud data transmission. Nonetheless, in scenarios adopting the MEC paradigm, data freshness becomes imperative, necessitating further optimization of data collection. This paper aims to enhance data collection effectiveness from two perspectives: designing an appropriate edge placement strategy before deploying the data collection system, and dynamically adjusting the traffic scheduling strategy to maximize the age of information (AoI) metric while minimizing costs. Experimental results demonstrate that our proposed approach enhances system stability and effectiveness in AIoT applications, outperforming other baseline methods.

Facial recognition technique is used extensively in areas like online payments, education, and social media. Traditionally, these applications relied on powerful cloud-based systems, but advancements in edge computing have changed this, enabling fast and reliable local processing in complex and extreme environment. However, new challenges arise in availability and durability insurance to make the system running 24/7 with acceptable performance. This paper proposes a novel solution to these challenging settings. First, we use edge device for local data processing, reducing the need for cloud communication and enhancing user privacy. Second, we implement an adaptive control strategy to improve energy management in these devices. Lastly, we establish a solar-powered energy system to facilitate long-term device operation. Our approach strikes a balance between performance, quality, and durability, enabling facial recognition systems to work consistently and efficiently in complex environments.

In intelligent transportation systems, data routing in vehicle-to-everything (V2X) networks is key to ensuring efficient information transfer among vehicles, pedestrians, and infrastructure. The quality of data routing directly affects communication efficiency and system performance. However, data routing in V2X networks often faces potential security threats, which may lead to communication interruption, data delay, or information loss. Unreliable routing fails to meet the communication Quality of Service (QoS) requirements for V2X networks. Therefore, this article proposes a joint scheme that combines trust prediction and attack resistance to ensure reliable routing in V2X networks. First, this scheme employs a fuzzy control-based trust evaluation method to provide direct trust indicators. Second, a trust prediction method based on deep belief networks is utilized to evaluate vehicle status. A classification scheme based on the trust levels is used to filter candidate sets for network repair to help the network resist malicious behavior. Finally, a novel routing decision function is introduced to plan reliable routes. Routes planned on the basis of this function not only meet the basic requirements of reliable routing but are also suitable for routing requirements in different scenarios, such as minimizing transmission latency. The experimental results show that, compared with the three baseline schemes, this scheme improves the accuracy and false alarm rate on the UNSW-NB15 dataset by 2.94% and 6.31%, respectively, and this scheme also performs better in terms of the data reception rate and transmission delay rate in actual application scenarios.

The increasing need for communication capabilities in mobile devices has led to the recognition of mobile edge computing (MEC) as a critical solution for addressing computationally intensive and latency-sensitive tasks due to its widespread distribution of resources close to devices. However, in scenarios such as disaster response and emergency rescue, the rapid deployment of edge servers to handle tasks may be challenging. Therefore, unmanned aerial vehicle (UAV)-assisted MEC systems have garnered significant interest due to their ease of deployment and high mobility. Nonetheless, the limited computational resources and sensing radius of UAVs give rise to the challenge of optimizing target area coverage and mission data processing timeliness within a restricted time period. In response to this challenge, we present PCRDAC, a novel reinforcement learning-based mobility management framework for UAVs. This framework periodically instructs UAVs to collaboratively update their decision networks, thus determining their movement patterns. This framework can also control UAVs to support worst-case scenarios. Comprehensive simulation experiments validate the efficacy of our framework. Our framework promotes efficient collaboration among UAVs and significantly reduces data staleness in the system. As a result, edge devices can collect ambient data that is fresh enough.

With the development of multiaccess edge computing (MEC) technology, an increasing number of researchers and developers are deploying their computation-intensive and IO-intensive services (especially AI services) on edge devices. These devices, being close to end users, provide better performance in mobile environments. By constructing a service provisioning system at the network edge, latency is significantly reduced due to short-distance communication with edge servers. However, since the MEC-based service provisioning system is resource-sensitive and the network may be unstable, careful resource allocation and traffic scheduling strategies are essential. This paper investigates and quantifies the cost-effectiveness and robustness of the MEC-based service provisioning system with the applied resource allocation and traffic scheduling strategies. Based on this analysis, acost-effective androbust service provisioningalgorithm, termedCERA, is proposed to minimize deployment costs while maintaining system robustness. Extensive experiments are conducted to compare the proposed approach with well-known baseline algorithms and evaluate factors impacting the results. The findings demonstrate thatCERAachieves at least 15.9% better performance than other baseline algorithms across various instances.

The property of Internet of Things (IoT) applications is their capability to execute tasks through the collaboration of interconnected IoT objects. However, IoT collaborations face significant challenges due to security threats that undermine their reliability. An uncertified task publisher may deceive IoT devices into executing illegal tasks, while malicious attackers may intercept and modify transmitted data. Existing works on IoT trusted management issues tend to concentrate on individual aspects, such as authentication, privacy protection, and access control. However, trusted management for IoT collaboration is a multifaceted and intricate endeavor that necessitates a comprehensive approach. To fill this gap, we propose a lightweight authentication-driven trusted management framework that includes a novel authentication and key agreement scheme to guarantee the validity of task publishers, with greatly reduced overheads compared to recent works. The framework also incorporates a distributed data storage scheme and a fine-grained access control mechanism. We record the interactive messages on the blockchain to ensure behavior traceability. We evaluate the authentication scheme through comparative experiments and formal security analysis, demonstrating its efficiency and effectiveness. The experimental results of data storage and acquisition in real-world IoT environments indicate that the proposed framework is a feasible solution for reliable IoT collaboration.

Multi-server jobs that request multiple computing resources and hold onto them during their execution dominate modern computing clusters. When allocating the multi-type resources to several co-located multi-server jobs simultaneously in online settings, it is difficult to make the tradeoff between the parallel computation gain and the internal communication overhead, apart from the resource contention between jobs. To study the computation-communication tradeoff, we model the computation gain as the speedup on the job completion time when it is executed in parallelism on multiple computing instances, and fit it with utilities of different concavities. Meanwhile, we take the dominant communication overhead as the penalty to be subtracted. To achieve a better gain-overhead tradeoff, we formulate an cumulative reward maximization program and design an online algorithm, namedOgaSched, to schedule multi-server jobs.OgaSchedallocates the multi-type resources to each arrived job in the ascending direction of the reward gradients. It has several parallel sub-procedures to accelerate its computation, which greatly reduces the complexity. We proved that it has a sublinear regret with general concave rewards. We also conduct extensive trace-driven simulations to validate the performance ofOgaSched. The results demonstrate thatOgaSchedoutperforms widely used heuristics by 11.33%, 7.75%, 13.89%, and 13.44%, respectively.

The multi-access edge computing (MEC) technologies have advanced rapidly, bringing the 5G network vision, particularly massive machine type communication (mMTC), closer to people. Computing tasks are offloaded to a widely distributed network edge cluster, enabling efficient and real-time sensing and interaction for mobile devices. However, limited computation and communication resources in edge devices require caution in service deployment and traffic management to maintain overall load balancing, especially during heavy network loads. We explore the performance-cost relationship and transform the optimization problem into a nonlinear integer programming problem (NIP). Our genetic algorithm-based approach, GA4CBST, outperforms baselines in efficiency and effectiveness.

The multi-access edge computing (MEC) paradigm has emerged as a critical solution to address the exponential growth in mobile web services and devices. By implementing an edge-based service provisioning system (EPS) with servers located at the network’s edge, both transmission and computation efficiency can be significantly enhanced. Nevertheless, it is also essential to carefully consider the resource allocation for services, the traffic management of requests, and the path arrangement for data delivery to ensure the cost-effective operation of the EPS. Therefore, we investigate and quantify the relationship between the performance and cost of the EPS in this paper, and model the cost-effective service provisioning problem as a multi-phase convex optimization problem. An online algorithm whose name isRDCbased on the Lyapunov framework is proposed to decompose this problem into several sub-problems.Additionally, a heuristic approach that partitions edge servers into several clusters, calledRDC-NePand based onRDC, has also been proposed to reduce computational complexity. A series of experiments were conducted to evaluate the proposed approach. The results demonstrate thatRDCcan effectively balance expense and performance, whileRDC-NePsignificantly simplifies the processing ofRDCwhen the problem scale increases.

Abstract Recently, the Internet-of-Things technique is believed to play an important role as the foundation of the coming Artificial Intelligence age for its capability to sense and collect real-time context information of the world, and the concept Artificial Intelligence of Things (AIoT) is developed to summarize this vision. However, in typical centralized architecture, the increasing of device links and massive data will bring huge congestion to the network, so that the latency brought by unstable and time-consuming long-distance network transmission limits its development. The multi-access edge computing (MEC) technique is now regarded as the key tool to solve this problem. By establishing a MEC-based AIoT service system at the edge of the network, the latency can be reduced with the help of corresponding AIoT services deployed on nearby edge servers. However, as the edge servers are resource-constrained and energy-intensive, we should be more careful in deploying the related AIoT services, especially when they can be composed to make complex applications. In this paper, we modeled complex AIoT applications using directed acyclic graphs (DAGs), and investigated the relationship between the AIoT application performance and the energy cost in the MEC-based service system by translating it into a multi-objective optimization problem, namely the CA $$^3$$ 3 D problem — the optimization problem was efficiently solved with the help of heuristic algorithm. Besides, with the actual simple or complex workflow data set like the Alibaba Cloud and the Montage project, we conducted comprehensive experiments to evaluate the results of our approach. The results showed that the proposed approach can effectively obtain balanced solutions, and the factors that may impact the results were also adequately explored.

Recommender systems are important approaches for dealing with the information overload problem in the big data era, and various kinds of auxiliary information, including time and sequential information, can help improve the performance of retrieval and recommendation tasks. However, it is still a challenging problem how to fully exploit such information to achieve high-quality recommendation results and improve users' experience. In this work, we present a novel sequential recommendation model, called multivariate Hawkes process embedding with attention (MHPE-a), which combines a temporal point process with the attention mechanism to predict the items that the target user may interact with according to her/his historical records. Specifically, the proposed approach MHPE-a can model users' sequential patterns in their temporal interaction sequences accurately with a multivariate Hawkes process. Then, we perform an accurate sequential recommendation to satisfy target users' real-time requirements based on their preferences obtained with MHPE-a from their historical records. Especially, an attention mechanism is used to leverage users' long/short-term preferences adaptively to achieve an accurate sequential recommendation. Extensive experiments are conducted on two real-world datasets (lastfm and gowalla), and the results show that MHPE-a achieves better performance than state-of-the-art baselines.

Network slicing is the key to enable virtualized resource sharing among vertical industries in the era of 5G communication. Efficient resource allocation is of vital importance to realize network slicing in real-world business scenarios. To deal with the high algorithm complexity, privacy leakage, and unrealistic offline setting of current network slicing algorithms, in this paper we propose a fully decentralized and low-complexity online algorithm, DPoS, for multi-resource slicing. We first formulate the problem as a global social welfare maximization problem. Next, we design the online algorithm DPoS based on the primal-dual approach and posted price mechanism. In DPoS, each tenant is incentivized to make its own decision based on its true preferences without disclosing any private information to the mobile virtual network operator and other tenants. We provide a rigorous theoretical analysis to show that DPoS has the optimal competitive ratio when the cost function of each resource is linear. Extensive simulation experiments are conducted to evaluate the performance of DPoS. The results show that DPoS can not only achieve close-to-offline-optimal performance, but also have low algorithmic overheads.

Edge computing is booming as a promising paradigm to extend service provisioning from the centralized cloud to the network edge. Benefit from the development of serverless computing, an edge server can be configured as a carrier of limited serverless functions, in the way of deploying Docker runtime and Kubernetes engine. Meanwhile, an application generally takes the form of directed acyclic graphs (DAGs), where vertices represent dependent functions and edges represent data traffic. The status quo of minimizing the completion time (a.k.a. makespan) of the application motivates the study on optimal function placement. However, current approaches lose sight of proactively splitting and mapping the traffic to the logical data paths between the heterogeneous edge servers, which could affect the makespan significantly. To remedy that, we propose an algorithm, termed as Dependent Function Embedding (DPE), to get the optimal edge server for each function to execute and the moment it starts executing. DPE finds the best segmentation of each data traffic by exquisitely solving several infinity norm minimization problems. DPE is theoretically verified to achieve the global optimality. Extensive experiments on Alibaba cluster trace show that DPE significantly outperforms two baseline algorithms in makespan by 43.19% and 40.71%, respectively.

The rapid development of the Internet-of-Things (IoT) makes it convenient to sense and collect real-world information with different kinds of widely distributed sensors. With plenty of web services providing diverse functions on the cloud, the collected information can be sufficiently used to complete complex tasks after being uploaded. However, the latency brought by long-distance communication and network congestion limits the development of IoT platforms. A feasible approach to solve this problem is to establish an edge-cloud collaboration (ECC) system based on the multi-access edge computing (MEC) paradigm where the collected information can be refined with the services deployed on nearby edge servers. However, as the edge servers are resource-limited, we should be more careful in allocating the edge resource to services, as well as designing the traffic scheduling strategy. In this paper, we investigated the edge-cloud cooperation mechanism of service provisioning in ECC systems, and to that end, proposed an energy-consumption model for it; we also proposed a performance model and balancing model to quantify the running state of ECC systems. Based on these, we further formulated the energy-effective ECC system optimization problem as a joint optimization problem whose decision variables are the resource allocation strategy and traffic scheduling strategy. With the convexity of this problem proved, we proposed an algorithm to solve it and conducted a series of experiments to evaluate its performance. The results showed that our approach can improve at least 4.3 % of the performance compared with representative baselines.

The dramatically increasing of mobile applications make it convenient for users to complete complex tasks on their mobile devices. However, the latency brought by unstable wireless networks and the computation failures caused by constrained resources limit the development of mobile computing. A popular approach to solve this problem is to establish a mobile service provisioning system based on a mobile edge computing (MEC) paradigm. In the MEC paradigm, plenty of machines are placed at the edge of the network so that the performance of applications can be optimized by using the involved microservice instances deployed on them. In this paper, we explore the deployment problem of microserivce-based applications in the MEC environment and propose an approach to help to optimize the cost of application deployment with the constraints of resources and the requirement of performance. We conduct a series of experiments to evaluate the performance of our approach. The result shows that our approach can improve the average response time of mobile services.

The prosperous development of IoT has created tremendous opportunities to improve people's lives. Essentially, the core property of the IoT applications is the ability to perform collaborative tasks with data supplied by separate IoT managers. However, the fulfillment of collaborative tasks is driven by the participations of the IoT managers. Generally, the willingness can be activated with appropriate profit (or incentive cost). Thus, an efficient incentive mechanism is needed to motivate the IoT managers to participate in the collaboration. In this paper, we present a reverse auction-based incentive mechanism with the goal of minimizing and stabilizing incentive costs while maintaining adequate participants. To prevent the incentive cost explosion, a droppers recruiting scheme is leveraged to attract the inactive participants. A price verification strategy is designed to avoid bid cheating. Furthermore, we introduce blockchain to orchestrate the interactions between collaborative parties, so as to protect their privacy. Finally, we show the feasibility and efficiency of our proposed framework with simulation experiments and theoretical analysis.

The increasing number of mobile web services makes it convenient for users to complete complex tasks on their mobile devices. However, the latency brought by unstable wireless networks and the computation failures caused by constrained resources limit the development of mobile computing. A popular approach to solve this problem is to establish a mobile service provisioning system based on the mobile edge computing (MEC) paradigm, in which the latency can be reduced and the computation can be offloaded with the help of services deployed on nearby edge servers. However, as the edge servers are resource-limited, we should be more careful in allocating the edge resource to services, as well as designing the traffic scheduling strategy. In this paper, we investigate the edge-cloud cooperation mechanism in service provisioning as well as the billing model of it. To minimize the average service response time and make the expense acceptable, we model and formulate the performance-cost service provisioning problem as a joint optimization problem whose decision variables are the resource allocation strategy and traffic scheduling strategy. Then we propose an efficient online algorithm, called PCA- CATS, to decompose this problem into two individual subproblems. We conduct a series of experiments to evaluate the performance of our approach. The results show that PCA- CATS can easily balance the performance and expense with a factor V, and can reduce up to 53.3 % service response time as compared with the baselines.

Abstract Network Function Virtualization (NFV) is an emerging technology to consolidate network functions onto high volume storages, servers and switches located anywhere in the network. Virtual Network Functions (VNFs) are chained together to provide a specific network service, called Service Function Chains (SFCs). Regarding to Quality of Service (QoS) requirements and network features and states, SFCs are served through performing two tasks: VNF placement and link embedding on the substrate networks. Reducing deployment cost is a desired objective for all service providers in cloud/edge environments to increase their profit form demanded services. However, increasing resource utilization in order to decrease deployment cost may lead to increase the service latency and consequently increase SLA violation and decrease user satisfaction. To this end, we formulate a multi-objective optimization model to joint VNF placement and link embedding in order to reduce deployment cost and service latency with respect to a variety of constraints. We, then solve the optimization problem using two heuristic-based algorithms that perform close to optimum for large scale cloud/edge environments. Since the optimization model involves conflicting objectives, we also investigate pareto optimal solution so that it optimizes multiple objectives as much as possible. The efficiency of proposed algorithms is evaluated using both simulation and emulation. The evaluation results show that the proposed optimization approach succeed in minimizing both cost and latency while the results are as accurate as optimal solution obtained by Gurobi (5%).

Edge computing (EC) is now emerging as a key paradigm to handle the increasing Internet-of-Things (IoT) devices connected to the edge of the network. By using the services deployed on the service provisioning system which is made up of edge servers nearby, these IoT devices are enabled to fulfill complex tasks effectively. Nevertheless, it also brings challenges in trustworthiness management. The volatile environment will make it difficult to comply with the service-level agreement (SLA), which is an important index of trustworthiness declared by these IoT services. In this article, by denoting the trustworthiness gain with how well the SLA can comply, we first encode the state of the service provisioning system and the resource allocation scheme and model the adjustment of allocated resources for services as a Markov decision process (MDP). Based on these, we get a trained resource allocating policy with the help of the reinforcement learning (RL) method. The trained policy can always maximize the services' trustworthiness gain by generating appropriate resource allocation schemes dynamically according to the system states. By conducting a series of experiments on the YouTube request dataset, we show that the edge service provisioning system using our approach has 21.72% better performance at least compared to baselines.

Due to the limits of battery capacity of mobile devices, how to select cloud services to invoke in order to reduce energy consumption in mobile environments is becoming a critical issue. This paper addresses the problem of mobile service selection for composition in terms of energy consumption. It formally models this problem and constructs energy consumption computation models. Energy consumption aggregation rules for composite services with different structures are presented. It adopts the genetic algorithm to resolve it. A replanning mechanism is also proposed to deal with the changeable conditions and user behavior. A series of experiments are conducted to evaluate the performance of our method. The results show that our service selection method significantly outperforms traditional methods. Even if the conditions or user behavior is changeable, this method is still effective to recommend services. Moreover, the service selection method performs good scalability as the experimental scale increases.

The increasing number of mobile services makes users confused to select appropriate services among plenty of service icons or links. Current developers always choose to recommend recently or mostly used services to users, but these approaches neglect the relations between user states and environment information and invocations, and the recommendation results will not be accurate when the mobile services are invoked evenly. In this paper, we propose a novel approach to recommend services on mobile devices to user. Firstly, we design a user behavior model by taking advantage of user's mobile context information like time and location to describe the user states. Secondly, we design a generate model to explain how the sequential service invocations are generated by analyzing the collected sequential history record of mobile users. Thirdly, we adopt logistic model tree approach to determine user state according to given mobile context information, and recommend services to user according to his user state. The experiment results show that our approach performs better than baseline approaches.