Large Language Models (LLMs) have revolutionized intelligent interactions, enabling mobile applications such as personal assistants on edge devices for local execution. Speculative decoding (SD) has emerged as a promising paradigm to accelerate LLM inference without compromising generation quality, employing a draft-then-verify manner. However, due to the constrained computing and memory resources on edge devices, existing SD works heavily rely on an auxiliary draft model that incurs additional memory burden and hinders the adaptability, as well as static token trees that yield suboptimal inference performance. To this end, we propose DIAA, a Decoding-efficient Inference Acceleration Approach for on-device LLMs. DIAA achieves plug-and-play and model-agnostic inference speedup with memory and computation efficiency for edge devices. Specifically, a pair of lightweight look-up tables (LUTs) is constructed by Top-K token sampling to cache historical tokens and probabilities for rapid candidate drafting. DIAA integrates a dynamic token tree with prior LUTs enabling paralleled verification, updated during decoding process, to adapt the online context. A computation overlap is then employed to pipeline the update operations of token tree, LUTs, and KV cache to improve the computational efficiency. Finally, through extensive experiments implemented on edge platform NVIDIA Jetson, DIAA outperforms existing baselines in generation speed and inference wall-clock time, while incurring minimal memory overhead.

Ubiquitous, low-latency intelligence at the network edge is central to large-scale Internet of Things (IoT) deployments, yet effectively coordinating communication, computing, and backhaul operations across heterogeneous layers remains challenging. This paper presents a unified cross-layer framework for terrestrial–satellite edge computing IoT systems that integrates Non-Orthogonal Multiple Access (NOMA)-based terrestrial access with local, satellite, and cloud execution. Unlike conventional terrestrial Multi-access Edge Computing (MEC)/edge– cloud scheduling, we jointly optimize partial offloading and path selection over a NOMA-coupled uplink and a multi-hop satellite edge/cloud execution chain under end-to-end latency coupling. The framework jointly determines path selection and partial offloading to minimize a latency–energy objective using accurate end-to-end models. Within this framework, two complementary scheduling algorithms are developed. The Centralized Optimal Cross-Layer Scheduler (COCS) formulates the joint scheduling problem as a mixed-integer nonlinear program (MINLP) with logarithmic and bilinear terms. It employs the spatial branch-and-bound (sBB) method within a commercial solver to obtain a global solution, serving as a performance benchmark. The Decentralized Game-Theoretic Scheduler (DGTS) models user decisions as an ordinal potential game (OPG) and achieves distributed convergence via best-response dynamics (BRD), enabling scalability and adaptability to large networks. Extensive simulations demonstrate that COCS achieves the global optimum while DGTS attains near-optimal performance with much lower complexity. These results validate the effectiveness of the proposed cross-layer framework and highlight the importance of coordinated communication–computation–backhaul design for terrestrial–satellite integrated edge computing.

The rapid development of the Internet of Vehicles (IoV) has led to a surge of computation-intensive and latency-sensitive vehicular applications, which pose significant challenges to resource-constrained vehicles and conventional mobile edge computing (MEC) infrastructures. In dense urban scenarios, the high mobility of vehicles and the spatiotemporally uneven distribution of vehicular workloads often result in severe load imbalance among edge servers, degrading system performance. To address these challenges, this paper investigates a three-tier heterogeneous MEC-enabled IoV architecture that integrates vehicles, terrestrial base stations (BSs), and an unmanned aerial vehicle (UAV). In this architecture, the UAV acts as an elastic computing node that is dynamically deployed over hotspot regions to complement BS-based MEC, while task migration among edge servers is leveraged to alleviate localized overload. We formulate the multi-vehicle computation offloading and migration problem as a distributed game, where each vehicle autonomously selects among local computing, BS offloading, and BS-to-BS migration, while BS-to-UAV migration is available only when its nearest BS lies within the current UAV coverage region. A joint cost function that captures both computation latency and energy consumption is developed. By constructing an appropriate potential function, we prove that the proposed game admits a Nash equilibrium. Building on this property, a Workload-oriented Computation Offloading Game for UAV-assisted MEC (WCOG) is designed to enable scalable and distributed decision-making. Extensive simulation results demonstrate that the proposed algorithm achieves near-optimal system performance, effectively balances server workloads, and significantly reduces the computation cost compared with benchmarks, while maintaining strong scalability under increasing vehicular density or expanding server scales.

The proliferation of mobile edge computing (MEC) enhances its capability to deliver low-latency services, while the geographical distribution and limited resources of edge servers simultaneously make them increasingly susceptible to distributed denial-of-service (DDoS) attacks. Existing edge DDoS mitigation strategies primarily focus on improving serviceability, that is, the number of mitigated requests, or reducing latency, yet often overlook systematic modeling of load fairness among edge servers. This omission can lead to resource imbalance, local overload, and ultimately degrade the system's long-term service performance and responsiveness to attacks. To address this issue, we propose the Fairness-aware Edge DDoS Mitigation (FairEDM) problem, which explicitly incorporates load fairness into the mitigation objective. Specifically, we first formulate FairEDM as a constrained optimization problem and prove its NP-hardness. To obtain a sub-optimal solution, we further model it as a game named FEDMGame, and design a distributed strategy formulation (FEDM-DSF) algorithm, based on multi-player best response dynamics. The resulting Nash equilibrium serves as a feasible mitigation strategy. Both theoretical analysis and experimental results validate the effectiveness and efficiency of the proposed FEDMGame framework.

In Multi-access Edge Computing (MEC) environments, Non-Orthogonal Multiple Access (NOMA) technology significantly improves spectrum efficiency and system access capacity through power-domain multiplexing. However, it also exposes edge servers to more complex resource contention and security risks. In particular, during Distributed Denial-of-Service (DDoS) attacks, conventional mitigation mechanisms often overlook users' quality of experience (QoE), which can lead to prolonged service degradation. To address this challenge, this paper investigates a QoE-guaranteed Edge DDoS Mitigation problem (QEDM) in NOMA-assisted MEC systems by jointly considering request scheduling and power allocation. First, the QEDM problem is modeled as a finite-strategy potential game, and a distributed game-theoretic algorithm, QEDMGame, is proposed. Then, through a multi-player best-response mechanism, the system converges to a Nash equilibrium, thereby achieving joint optimization of service rate and QoE. Next, experimental results on real-world base station deployment data demonstrate that QEDMGame outperforms several representative baseline methods. Additionally, theoretical analysis shows that QEDMGame exhibits strong convergence properties and performance guarantees.

In a collaborative large-small model (CLSM) system, application providers can deploy their small-scale intelligent services at the edge to serve the mobile users. Due to the personalized characteristics of edge small models, an application provider must properly accommodate its users for service and/or data provision. In general, a cost-effective mobile user allocation (MUA) solution must serve the maximum number of users with minimum edge resources by considering mobile users' preferences. However, existing studies of MUA assume that users' and edge servers' available resources are static and neglect the matching between mobile users' preferences and the edge small models' personalization. This is unrealistic in real-world CLSM systems where the mobility of users changes dynamically and the edge servers' available resources differ periodically. Therefore, users must be allocated on the fly to edge servers whose available resources also vary over time. Most existing MUA approaches are impractical in real-world CLSM systems because they are usually designed to address the MUA problem offline, and their performance is usually poor. In this paper, AirMUA, a novel online scheme, is designed, aiming to solve the dynamic MUA (DMUA) problem. AirMUA allocates users to edge servers as they join the system and utilizes system resources dynamically released by user departures. Comprehensive experiments and analyses are conducted to evaluate its performance systematically. Extensive experimental results show the high performance of AirMUA, which ensures its practicality in real-world CLSM systems.

Convolutional Neural Networks (CNN) have been widely adopted in multi-access edge computing system due to their powerful feature extraction and high inference accuracy. Harnessing the heterogeneous computational capacities of end de vices and edge servers, end-edge collaborative inference addresses resource constraints of end devices and latency challenges of real time inference. However, existing studies typically assume that all potential inference models are pre-employed on the server, which may not always align with real-world deployment scenarios. Moreover, the economic incentives of edge service providers are often overlooked in current distributed inference research. To address these issues, we propose an end-edge collaborative inference framework for CNN in multi-access edge computing system, named DisCNN. We first model the interaction between latency-sensitive end devices (EDs) and resource-constrained edge service providers as a Stackelberg game and prove the existence of a Subgame Perfect Equilibrium. Next, we derive the optimal response characteristics of the EDs and develop an efficient algorithm to allocate computation and communication resources to EDs while partitioning the CNN accordingly. To compute the optimal offloading set, we introduce a linear-time algorithm with a bounded approximation ratio. Then, to jointly optimize caching, resource allocation, and pricing, we design an efficient approximate algorithm to compute a near-optimal solution. Finally, extensive simulation results validate the effectiveness of the proposed framework.

Edge computing enables distributed machine learning models to be deployed and trained near the user space. However, the intricate nature of edge computing raises several challenges to distributed machine learning frameworks: 1) inferior convergence arising from non-independent and identically distributed (non-IID) edge data; 2) inefficient structural adaptation, where device dynamism complicates the adjustment of aggregation structure; and 3) reduced training efficiency, as resource heterogeneity and fluctuations create systemic stragglers. To address these issues, a distributed hierarchical model training framework has been proposed by considering the dynamic aggregation structure and frequency in this paper. This framework designs an Edge Aggregation Structure and Frequency method, namely EASF, for distributed model training in heterogeneous edge computing environments. First, a dynamic distributed aggregation structure method is formulated to consider various data distribution patterns. This method constructs and modifies the aggregation structure in a distributed manner to adapt to variations in working edge devices. Second, a self-adapted aggregation frequency method and a timeout abandonment mechanism are proposed to allow each node to update its aggregation frequency adaptively. Lastly, a theoretical analysis demonstrates the convergence property of the EASF method in dynamic environments. Extensive experiments have been conducted on a set of open testbeds. Results show that the EASF significantly improves the efficiency and accuracy of hierarchical model training in heterogeneous edge computing.

As a complementary solution for Mobile Edge Computing (MEC), Unmanned Aerial Vehicles (UAVs) can temporarily provide reliable and flexible offloading services when edge servers are damaged or unavailable. However, existing UAV-assisted MEC systems suffer from issues such as uneven resource allocation, low utilization efficiency, load imbalance, and poor dynamic adaptability, affecting service quality. Moreover, sensitive user equipment (UE) information faces leakage during the computational process of UAVs. How to jointly optimize the scheduling of servers and UAVs for task offloading and resource allocation without compromising UEs' privacy remains a significant challenge. Thus, this paper proposed a privacy-preserving auction framework (namely Prizty) by considering the trajectory of UAVs, their constrained energy and computational capabilities, and the variability in UE distribution. Prizty employs a combinatorial obfuscation method to protect UEs' privacy and links bidding prices to computational resources and energy characteristics. It calls the sub-algorithm WPA to determine the winners by balancing social costs and utility. Theoretical analysis demonstrates that Prizty satisfies truthfulness and individual rationality while maintaining scalability for large-scale resource allocation problems. Extensive experiments on real-world datasets validate Prizty's effectiveness in critical metrics, including offload rate, average service latency, energy consumption, and social cost.

Multi-access edge computing (MEC) brings computational resources closer to end-users by widely distributing the physical edge services, reducing end-to-end service latency at the network edge. Continuous operation of edge servers results in high energy usage and significant carbon footprints. Efficient resource management is essential for MEC sustainability. Recent solutions focus on demand response to reduce energy consumption. However, the reduction in available resources due to server shutdowns forces a degradation in the quality of service (QoS) provided to users, significantly impacting their quality of experience (QoE). Moreover, the non-linear relationship between QoS and QoE further complicates the issue. Therefore, maintaining user's QoE with energy consumption is critical to achieving sustainable edge services. To tackle these challenges, we formulate the QoE-aware energy saving (QoEES) problem and propose QESGame, a game-theoretical algorithm to solve this problem effectively and efficiently with a guaranteed convergence to Nash equilibrium. Through extensive evaluations, we demonstrate that QESGame surpasses the representative approaches by up to 20.21%, 41.62%, and 23.54% in terms of the overall system benefit, energy saving, and QoE.

In edge computing, the Zero-Trust Security Model (ZTSM), as a key enabling technology for next-generation networks, plays a crucial role in providing authentication for addressing data sharing concerns, such as frequent data breaches, data misuse, and cyberattacks. However, due to the complexity and diversity of edge environments, ZTSM struggles to meet the security requirements of data sharing frameworks solely through enhanced authentication. Consequently, such frameworks with ZTSM still face challenges in ensuring data integrity, evaluating various node behaviors, and coping with the increasing complexity of node attributes. To address these issues, we propose a blockchain-enabled secure, fair and scalable data sharing framework in a zero-trust edge-end environment in this paper. Specifically, we first propose a Merkle forest-based data storage model for the classified storage of loosely coupled data, consequently enhancing the scalability of the model. Then, we design a node behavior-based reputation assessment mechanism to ensure fairness during data sharing. Moreover, a data sharing protocol supervised by smart contract is proposed, working with the aforementioned storage and assessment schemes, to ensure the security of data sharing. Finally, comprehensive security analysis validates the security, fairness and scalability of the proposed framework. Extensive experimental results show that, as transaction volume grows, the time cost of data traversal in the storage model becomes progressively more efficient. Additionally, when the size of the smart contract is increased tenfold, the maximum time cost of the data sharing protocol rises by only 4.98 times.

The collaboration of microrobot teams has attracted considerable attention, particularly in the field of micro/nano manipulation. Achieving independent control and motion planning of multiple magnetic microrobots for coordinated movements is one of the most important tasks that is still unsolved. In this paper, a$12\times 12$coil array system is developed to generate a series of localized magnetic fields that enable simultaneous control of multiple identical magnetic microrobots, allowing teams of microrobots to collaborate in parallel for micromanipulation tasks. First, the structure of the microcoil is optimized based on the finite element model to increase the strength and gradient of the magnetic field, which in turn enhances the driving performance of the system. Meanwhile, an improved multi-target tracking algorithm that utilizes kernel correlation filtering (KCF) and image contour detection (ICD) techniques is proposed to improve the tracking accuracy of microrobots. In addition, collaborative planning for multiple magnetic microrobots is also achieved with the combination of the conflict-based search (CBS) algorithm. Finally, the developed system is tested with extensive physical experiments. Especially, experiments on magnetic droplet transport with two microrobots are also conducted. The results impressively demonstrated the effectiveness of the devised system and the proposed methods. Note to Practitioners—This article is motivated by the recent wide interest in magnetic microrobots. Actuated by external magnetic field, magnetic microrobots can wirelessly perform targeted delivery/therapy and other micro-assembly tasks. To facilitate collaboration between microrobots, independent control of each microrobot is desirable. However, due to the interaction between magnetic microrobots and the global magnetic field, the collaboration of multiple microrobots presents great challenges. Therefore, several coil-array-based systems have been developed. In this paper, we develop a magnetic actuation system from both hardware and software aspects for the collaborative motion of multiple magnetic microrobots. The coil structure is optimized to enhance the driving performance of the devised system, and a fused multi-target tracking algorithm is proposed to improve the tracking accuracy. In combination with the CBS algorithm, collision-free paths are planned for multiple identical microrobots. The experimental results show that the constructed system and proposed methods can realize coordinated motion of multiple identical magnetic microrobots, which has enormous potential for some biomedical applications.

Mobile edge computing (MEC) considerably enhances the capabilities and performance of connected autonomous vehicles (CAVs) by deploying edge servers (ESs) on roadside units (RSUs) near CAVs, thereby ensuring low-latency services. Given the constrained and costly nature of ES resources (computing, storage, and bandwidth), equitable ES utilization is critical for CAV operations. However, fairness considerations are often overlooked in current budgeted edge server placement (ESP) strategies, potentially worsening resource imbalances and compromising user experience. This paper investigates the fairness-aware budgeted edge server placement (FESP) problem within RSUs, proving its NP-hardness. To address FESP, we first propose FESP-O, an integer programming-based optimal approach for small-scale problems, followed by FESP-APX, an approximation approach for large-scale scenarios that provides near-optimal solutions. We analyze the time complexity and approximation ratio of our proposed algorithms and validate their efficacy through experiments on real-world datasets. Extensive experimental results demonstrate significant performance improvements over baseline and state-of-the-art methods, indicating practical suitability and efficiency.

Deep Neural Network (DNN) Inference, as a key enabler of intelligent applications, is often computation-intensive and latency-sensitive. Combining the advantages of cloud computing (abundant computing resources) and edge computing (fast transmission), edge-cloud collaborative DNN inference is a powerful solution to these problems. However, in edge-cloud networks with heterogeneous resources, how to obtain reasonable decisions on server selection, model partition and resource allocation for efficient distributed DNN inference is a hard challenge. Furthermore, it is non-trivial to design suitable resource prices to maximize the social welfare. These challenges even escalate in dynamic edge-cloud networks where decisions should be generated as soon as each user arrives without future information. Therefore, we design a combinatorial auction for dynamic distributed DNN inference scheduling, named CADEC. CADEC first constructs a bid set for each user based on convex optimization theory for optimal solution searching. Next, prices of resources in the edge-cloud network are adjusted according to changes in supply-demand relationship, and whether to admit the request of each user is decided. Finally, the dynamic distributed inference scheduling decisions are generated through the primal-dual algorithm to maximize the social welfare. Theoretical analysis shows the good competitive ratio and polynomial time complexity of CADEC. Results of simulation experiments present that CADEC improves social welfare by up to 224% compared with state-of-the-art distributed DNN inference schemes.

Programmable manipulation of fluid-based soft robots has recently attracted considerable attention. Achieving parallel control of large-scale ferrofluid droplet robots (FDRs) is still one of the major challenges that remain unsolved. In this article, we develop a distributed magnetic field control platform to generate a series of localized magnetic fields that enable the simultaneous control of many FDRs, allowing teams of FDRs to collaborate in parallel for multifunctional manipulation tasks. Based on the mathematical model using the finite element method, we first evaluate the distribution properties of the local magnetic fields as well as the gradients generated by individual electromagnets. Meanwhile, the locomotion and deformation behavior of the FDR is also characterized to verify the actuation performance of the developed system. Subsequently, a vision-based closed-loop feedback control strategy is then presented, which aims to achieve path tracking of multiple robot formations. Thermal analysis shows that the system's low output power enables reliable and sustained long-term operation. Finally, the developed system is tested through extensive physical experiments with different numbers of FDRs. The results demonstrate the potential of the designed setup in manipulating dozens of FDRs for digital display, message encoding, and microfluidic logistics. To the best of our knowledge, this is the first attempt that allows independent control of such scale droplet robots (up to 72) for cooperative applications.

The emergence of the mobile edge computing paradigm enables the deployment of microservices on edge servers, which greatly improves the quality of services and reduces network transmission costs. However, due to limited computing and storage resources, an individual edge server can host only a limited number of microservice instances. Moreover, user mobility often results in uneven distribution of service requests in mobile edge computing systems. To this end, it is a key problem to dispatch microservice requests to appropriate edge servers to minimize the average service response time. Current solutions to this problem rely on centralized methods and suffer from serious problems of single point of failure, error-proneness, difficult expansion, low robustness, etc. To resolve these problems, this paper proposes a decentralized game-theoretic approach for dispatching microservice requests effectively and efficiently in mobile edge computing systems. Specifically, we formulate the request dispatching problem as a decentralized non-cooperative game and propose a decentralized request dispatching algorithm that can find the Nash equilibrium through finite iterations. We conduct a series of experiments to demonstrate that our approach beats benchmarking approaches with close-to-optimal performance and high efficiency measured by convergence time.

In the rapidly advancing landscape of technology, the integration of Unmanned Aerial Vehicles (UAVs) with Artificial Intelligence (AI) algorithms holds immense potential, particularly in the realm of house inspections. The burgeoning demand for innovative solutions in the surveillance domain has propelled researchers to explore transformative approaches. However, despite recent strides, significant challenges persist in seamlessly incorporating drones with the Internet of Things (IoT) networks, particularly in addressing response time concerns associated with intricate tasks such as facial recognition and motion detection. This research paper seeks to bridge these existing gaps by proposing an avant-garde architectural framework that directly deploys Large Language Models (LLMs) onto drones. The pivotal motivation stems from the need to not only enhance the performance of AI-enabled drones but also to overcome the limitations tied to centralized AI. While specific quantitative metrics in comparison to existing state-of-the-art methods are not available, this innovative approach demonstrates the potential for significant improvements in efficiency for localized tasks. This highlights the qualitative advancements achieved through our research. By delving into the intricacies of surveillance applications, this research not only contributes to the optimization of house inspections but also charts a path toward the development of more responsive, secure, and efficient AI-enabled drones, thereby shaping the future landscape of UAV and AI integration in surveillance scenarios.

Mobile edge computing (MEC) offers a new computing paradigm that turns computing and storage resources to the network edge to provide minimal service latency compared to cloud computing. Many research works have attempted to help app vendors allocate users to appropriate edge servers for high-performance service provisioning. However, existing edge user allocation (EUA) approaches have ignored fairness in users' data rates caused by interference, which is crucial in service provisioning in the MEC environment. To pursue fairness in EUA, edge users need to be assigned to edge servers so their quality of experience can be ensured at minimum costs without significant service performance differences among them. In this paper, we make the first attempt to address this fair edge user allocation (FEUA) problem. Specifically, we formulate the FEUA problem, prove its N P-hardness, and propose an optimal approach to solve small-scale FEUA problems. To accommodate large-scale FEUA scenarios, we propose a game-theoretic approach called FEUAGame that transforms the FEUA problem into a potential game that admits a Nash equilibrium. FEUA employs a decentralized algorithm to find the Nash equilibrium in the potential game as the solution to the FEUA problem. A widely-used real-world data set is utilised to experimentally compare the performance of FEUAGame to four representative approaches. The numerical outcomes show the effectiveness and efficiency of the proposed approaches in solving the FEUA problem

Mobile edge computing (MEC) raises a variety of new challenges for app vendors, including the Edge User Allocation (EUA) problem. EUA aims to allocate as many app users as possible in an MEC system to minimum edge servers in the system. In non-orthogonal multiple access (NOMA)-based MEC system, multiple app users can be allocated to the same subchannel on an edge server through transmit power allocation based on their intra-cell and inter-cell interference. However, allocating excessive app users to the same subchannel may result in severe interference and consequently impact app users’ data rates. In addition, in an MEC system, app users join and depart randomly, and thus need to be allocated in an online manner. Existing EUA approaches suffer from poor performance in dynamic real-world NOMA-based MEC systems because they allocate app users in an offline manner and do not consider the complication caused by NOMA. In this paper, we propose OL-EUA, an OnLine approach for solving dynamic EUA problems in NOMA-based MEC systems. Its performance is theoretically analyzed and experimentally evaluated on a public dataset.

Similar to cloud servers, edge servers running 24/7 in a mobile edge computing (MEC) system consume a large amount of energy, and thus require demand response management. Demand response has been widely employed to reduce energy consumption at data centers. However, existing demand response approaches for data centers are rendered obsolete by the new and unique characteristics of MEC systems: 1) proximity constraint - mobile users can be served by neighbor edge servers only; 2) latency constraint - mobile users' workloads should be processed by their neighbor edge servers to ensure low latency; and 3) capacity constraint - edge servers have limited computing and communication resources to serve mobile users. Demand response for MEC is further complicated by the non-orthogonal multiple access (NOMA) scheme - the emerging radio access scheme for 5G. Communication resources like channels and transmit power must be systematically considered with computing resources like CPU, memory and storage to fulfil mobile users' resource demands. This paper makes the first attempt to tackle this Edge Demand Response (EDR) problem. We first formulate this problem and prove its NP-hardness. Then, we propose a two-phase game-theoretical approach (EDRGame) to solve the EDR problem. Its performance is theoretically analyzed and experimentally evaluated against state-of-the-art approaches on a widely-used real-world dataset.

Mobile Edge Computing (MEC) has emerged to overcome the inability of cloud computing to offer low latency services. It allows popular data to be cached on edge servers deployed within users' geographic proximity. However, the storage resources on edge servers are constrained due to their limited physical sizes. Existing studies of edge caching have predominantly focused on maximizing caching performance from the mobile network operator's perspective, e.g., maximizing data retrieval success rate, minimizing system energy consumption, balancing the overall caching workload, etc. App vendors, as key stakeholders in MEC systems, need to maximize the caching revenue, considering the cost incurred and the benefit produced. We investigate this novel Mobile Edge Data Caching (MEDC) problem from the app vendor's perspective, and prove its NP-hardness. We then propose Online MEDC (OL-MEDC), an approach that formulates MEDC strategies for app vendors, without requiring future information about data demands. Its performance is theoretically analyzed and experimentally evaluated. The experimental results demonstrate that OL-MEDC outperforms state-of-the-art approaches by at least 20.41\% on average.

With the rollout of the 5G network around the globe, a massive number of edge servers have been deployed to host online applications demanding low service latency for users. These edge servers constitute multi-access edge computing (MEC) systems. Running 24/7, edge servers consume tremendous energy and take up a great part of global carbon emissions. The edge energy-saving (EES) problem is needed to facilitate energy-efficient edge resource provisions. Unfortunately, existing energy-saving approaches designed for data centers are becoming impractical. First, edge servers are used to provide services to a specific geographical area. Its energy utilization is impacted by the temporal distribution of users within its coverage. Second, a user could be accommodated by any of its neighbor edge servers. Third, it is possible to activate and deactivate individual physical machines that facilitate an edge server as needed. Thus, EES is designed to save the system energy of physical machines in a long term by serving the users over time. EES problem has been formulated systematically and its problem hardness has been analyzed theoretically, then we propose EESaver (Edge Energy Saver) for formulating EES strategies dynamically over time to facilitate green MEC. EESaver's superior performance is tested comprehensively.

In this paper, we propose LBS@E, a new architecture for location-based services (LBSs) facilitated by the mobile edge computing paradigm. LBS@E tackles the location privacy problem innovatively by delocalizing LBSs so that mobile users of LBSs implemented based on LBS@E do not have to reveal their locations. They retrieve local information from nearby edge servers around them instead of the cloud. In this way, we resolve the root cause of the conventional location privacy problem. However, LBS@E raises new challenges to location privacy. A mobile user can still be localized to a particular privacy area co-covered by the edge servers accessed by the mobile user. A small privacy area puts the mobile users location at the risk of being approximated. In the meantime, the size of the utility area, which determines the amount of local information retrievable for the mobile user, is positively correlated with the number of edge servers accessed by the mobile user. We model this problem as a constrained optimization problem and propose an optimal approach for solving it based on integer programming. Extensive experiments are conducted on a widely-used real-world dataset to demonstrate effectiveness and efficiency.

Similar to cloud servers which are well-known energy consumers, edge servers running 24/7 jointly consume a tremendous amount of energy and thus require energy-saving management. However, the unique characteristics of edge computing make it a new and challenging problem to manage edge servers in an energy-efficient manner. First, an individual edge server is usually used to serve a specific region. The temporal distribution of end-users in the area impacts the edge server’s energy utilization. Second, multiple base stations may cover an end-user simultaneously and the end-user can be served by the physical machines attached to any of the base stations. Serving the end-users in an area with minimum physical machines can minimize the edge servers’ overall energy consumption. Third, physical machines facilitating an edge server can be powered off individually when not needed to minimize the edge server’s energy consumption. We formulate this Energy-efficient Edge Server Management (EESM) problem and analyze its problem hardness. Next, a game-theoretical approach, i.e., EESM-G, is proposed to address EESM problems efficiently. The superior performance of EESM-G is tested on a public real-world dataset.

Networked edge servers constitute an edge storage system in edge computing (EC). Upon users’ requests, data must be delivered from edge servers in the system or from the cloud to users. Existing studies of edge storage systems have unfortunately neglected the fact that an excessive number of users accessing the same edge server for data may impact users’ data rates seriously due to the wireless interference. Thus, users must first be allocated to edge servers properly for ensuring their data rates. After that, requested data can be delivered to users to minimize their average data delivery latency. In this paper, we formulate this Interference-aware Data Delivery at the network Edge (IDDE) problem, and demonstrate its NP-hardness. To tackle it effectively and efficiently, we propose IDDE-G, a novel approach that first finds a Nash equilibrium as the strategy for allocating users. Then, it finds an approximate strategy for delivering requested data to allocated users. We analyze the performance of IDDE-G theoretically and evaluate its performance experimentally to demonstrate the effectiveness and efficiency of IDDE-G on solving the IDDE problem.

Edge Cloud Computing (ECC) provides a new paradigm for app vendors to serve their users with low latency by deploying their services on edge servers attached to base stations or access points in close proximity to mobile users. From the edge infrastructure provider’s perspective, a cost-effective$k$edge server placement ($k$ESP) aims to place$k$edge servers within a particular geographic area to maximize the number of covered mobile users, i.e., to maximize theuser coverage. However, in the distributed and volatile ECC environment, edge servers are subject to failures due to various reasons, e.g., software exceptions, hardware faults, cyberattacks, etc. Mobile users connected to a failed edge server have to access services in the remote cloud if they are not covered by any other edge servers. This significantly impacts mobile users quality of experience. Thus, the robustness of the edge server network (referred to as network robustness hereafter) in a specific area must be considered in edge server placement. In this article, we formally model this joint user coverage and network robustness oriented$k$edge server placement ($k$ESP-CR) problem, and prove that finding the optimal solution to this problem is$\mathcal {NP}$-hard. To tackle this ESP-CR, we first propose an integer programming based optimal approach (namely ESP-O) for finding optimal solutions to small-scale$k$ESP-CR problems. Then, we propose an approximation approach, namely ESP-A, for solving large-scale$k$ESP-CR problems efficiently and theoretically prove its approximation ratio. Finally, the performance of these two approaches are experimentally evaluated against three representative approaches on a widely-used real-world dataset.

Edge Computing, extending cloud computing, has emerged as a prospective computing paradigm. It allows a SaaS (Software-as-a-Service) vendor to allocate its users to nearby edge servers to minimize network latency and energy consumption on their devices. From the SaaS vendor’s perspective, a cost-effective SaaS user allocation (SUA) aims to allocate maximum SaaS users on minimum edge servers. However, the allocation of excessive SaaS users to an edge server may result in severe interference and consequently impact SaaS users’ data rates. In this article, we formally model this problem and prove that finding the optimal solution to this problem is NP-hard. Thus, we propose ISUAGame, a game-theoretic approach that formulates the interference-aware SUA (ISUA) problem as a potential game. We analyze the game and show that it admits a Nash equilibrium. Then, we design a novel decentralized algorithm for finding a Nash equilibrium in the game as a solution to the ISUA problem. The performance of this algorithm is theoretically analyzed and experimentally evaluated. The results show that the ISUA problem can be solved effectively and efficiently.

Edge computing (EC) is an emerging paradigm that extends cloud computing by pushing computing resources onto edge servers that are attached to base stations or access points at the edge of the cloud in close proximity with end-users. Due to edge servers’ geographic distribution, the EC paradigm is challenged by many new security threats, including the notorious distributed Denial-of-Service (DDoS) attack. In the EC environment, edge servers usually have constrained processing capacities due to their limited sizes. Thus, they are particularly vulnerable to DDoS attacks. DDoS attacks in the EC environment render existing DDoS mitigation approaches obsolete with its new characteristics. In this article, we make the first attempt to tackle the edge DDoS mitigation (EDM) problem. We model it as a constraint optimization problem and prove its$\mathcal {NP}$-hardness. To solve this problem, we propose an optimal approach named EDMOpti and a novel game-theoretical approach named EDMGame for mitigating edge DDoS attacks. EDMGame formulates the EDM problem as a potential EDM Game that admits a Nash equilibrium and employs a decentralized algorithm to find the Nash equilibrium as the solution to the EDM problem. Through theoretical analysis and experimental evaluation, we demonstrate that our approaches can solve the EDM problem effectively and efficiently.

Mobile Edge Computing (MEC), as an emerging and prospective mobile computing paradigm, allows a content provider to serve its users by allocating their mobile devices to nearby edge servers to lower the latency in the delivery of its content to those mobile services. From the content provider's perspective, a cost-effective mobile device allocation (MDA) aims to allocate maximum mobile devices to minimum edge servers. However, the allocation of excessive mobile devices to an edge server may result in severe communication interference and consequently, impact mobile devices data rates. Sometimes, not all users mobile devices can be allocated to edge servers. Unallocated mobile devices can still retrieve content from the remote cloud through base stations, however with high latency. The connection between these mobile devices and the base stations also incur communication interference. We formally model this Interference-aware Mobile Edge Device Allocation (I-MEDA) problem and propose a game-theoretic based approach named I-MEDAGame to formulate the I-MEDA problem as an I-MEDA game. Our theoretical analysis of I-MEDAGame shows that it admits a Nash equilibrium. I-MEDAGame employs a novel decentralized algorithm to find the Nash equilibrium of the IMEDA game. The performance of I-MEDAGame is theoretically analyzed and experimentally evaluated.

Mobile edge computing (MEC) allows edge servers to be placed at cellular base stations. App vendors like Uber and YouTube can rent computing resources and deploy latency-sensitive applications on edge servers for their users to access. Non-orthogonal multiple access (NOMA) is an emerging technique that facilitates the massive connectivity of 5G networks, further enhancing the capability of MEC. The edge user allocation (EUA) problem faces new challenges in 5G NOMA-based MEC systems. In this study, we investigate the EUA problem in a multi-cell multi-channel downlink power-domain NOMA-based MEC system. The main objective is to help mobile app vendors maximize their benefit by allocating maximum users to edge servers in a specific area at the lowest computing resource and transmit power costs. To this end, we introduce a decentralized game-theoretic approach to effectively select a channel and edge server for each user while fulfilling their resource and data rate requirements. We theoretically and experimentally evaluate our solution, which significantly outperforms various state-of-the-art and baseline approaches.

In the multi-access edge computing (MEC) environment, app vendors’ data can be cached on edge servers to ensure low-latency data retrieval. Massive users can simultaneously access edge servers with high data rates through flexible allocations of transmit power. The ability to manage networking resources offers unique opportunities to app vendors but also raises unprecedented challenges. To ensure fast data retrieval for users in the MEC environment, edge data caching must take into account the allocations of data, users, and transmit power jointly. We make the first attempt to study the Data, User, and Power Allocation (DUPA$^3$) problem, aiming to serve the most users and maximize their overall data rate. First, we formulate the DUPA$^3$problem and prove its$\mathcal {NP}$-completeness. Then, we model the DUPA$^3$problem as a potential DUPA$^3$game admitting at least one Nash equilibrium and propose a two-phase game-theoretic decentralized algorithm named DUPA$^3$Game to achieve the Nash equilibrium as the solution to the DUPA$^3$problem. To evaluate DUPA$^3$Game, we analyze its theoretical performance and conduct extensive experiments to demonstrate its effectiveness and efficiency.

Mobile edge computing (MEC), as an emerging technology, allows application vendors to deploy application instances on edge servers to deliver low-latency services to nearby end-users. However, due to hardware faults, software exceptions, or cyberattacks, edge servers are prone to failures in the highly distributed and dynamic MEC environment. Hence service reliability must be ensured when failures occur. This raises a critical and open problem - improving service reliability when deploying application instances in the MEC environment. In this article, we jointly consider both user coverage and service reliability when deploying application instances on edge servers with a given application deployment budget$\mathcal {K}$. We formally define this jointCoverage-Reliability for$\mathcal {K}$-BudgetedEdgeApplicationDeployment (CR-BEAD) problem and model it as a constrained optimization problem. Next, we propose an optimal approach (namedBEAD-O) based on integer programming to find optimal solutions to small-scale CR-BEAD problems. We also propose a greedy approach namedBEAD-Gwith a constant approximation ratio of$1 - 1/e$to solve large-scale CR-BEAD problems efficiently. Extensive experimental evaluation against three representative approaches illustrates the effectiveness and efficiency of our approaches.

Edge computing, as an emerging and prospective distributed computing paradigm, allows a service provider to serve its users by allocating them to nearby edge servers delivering services with low latency. From the service provider’s perspective, a cost-effective service user allocation aims to allocate maximum service users to minimum edge servers. Such an allocation leverages multi-tenancy to reduce the resources hired by the service provider for serving the service users. However, the allocation of excessive service users to an edge server may result in severe interference and consequently impact their data rates. There is a trade-off between multi-tenancy and interference in the pursuit of a cost-effective service user allocation. In this article, we formally model this service user allocation (SUA) problem, and prove that it is NP-hard to find the optimal solution to an SUA problem. To solve the SUA problem effectively and efficiently, we propose a game-theoretic approach, namely MI-SUAGame, to formulate the SUA problem as a potential game. We analyze the game and prove its admission to a Nash equilibrium. Then, a novel decentralized algorithm is designed for finding a Nash equilibrium in the game as the solution to the SUA problem. The performance of MI-SUAGame is theoretically analyzed and experimentally evaluated against the state-of-the-art approach. The results show that it can solve the SUA problem effectively and efficiently.

The new edge computing paradigm extends cloud computing by allowing service vendors to deploy their service instances and data on distributed edge servers to serve their service users in close geographic proximity to those edge servers. Caching edge data on edge servers profoundly reduces the retrieval latency perceived by users. However, these edge data are subject to corruption due to intentional and/or accidental exceptions. This is a major challenge for service vendors but has been overlooked. Thus, verifying the integrity of edge data accurately and efficiently is a critical security problem in the edge computing environment. A unique characteristic of the edge computing environment is that edge servers suffer from constrained computing capacities. Thus, verifying data integrity on massive edge servers individually is computationally expensive and impractical. In this paper, we tackle this Edge Data Integrity (EDI) problem with an inspection and corruption localization scheme for EDI named ICL-EDI. This scheme allows service vendors to inspect data integrity and localize corrupted edge data cached on multiple edge servers accurately and efficiently. To evaluate its performance, we implement ICL-EDI and conduct extensive experiments to demonstrate its effectiveness and efficiency.

In recent years, edge computing has emerged as a prospective distributed computing paradigm that overcomes several limitations of cloud computing. In the edge computing environment, a service provider can deploy its application instances on edge servers at the edge of the network to serve its own users with low latency. Given a limited budget$\mathcal {K}$for deploying applications on the edge servers in a particular geographical area, a number of approaches have been proposed very recently to determine the optimal deployment strategy that achieves various optimization objectives, e.g., to maximize the servers’ coverage, to minimize the average network latency, etc. However, the robustness of the services collectively delivered by the service provider’s applications deployed on the edge servers has not been considered at all. This is a critical issue, especially in the highly distributed, dynamic and volatile edge computing environment. In this article, we make the first attempt to tackle this challenge. Specifically, we formulate thisRobustness-oriented Edge Application Deployment(READ) problem as a constrained optimization problem and prove its$\mathcal {NP}$-hardness. Then, we provide an integer programming based approach named READ-$\mathcal {O}$for solving this problem precisely. We also provide an approximation algorithm, namely READ-$\mathcal {A}$, for finding near-optimal solutions to large-scale READ problems efficiently. We prove its approximation ratio is not worse than$\mathcal {K}/2$, which is a constant regardless of the total number of edge servers. We evaluate our approaches experimentally on a widely-used real-world dataset against five representative approaches. The experiment results demonstrate that our approaches can solve the READ problem effectively and efficiently.

As enterprises around the globe embrace globalization, strategic alliances among enterprises have become an important means to gain competitive advantages. Enterprises cooperate to improve the quality or lower the prices of their services, which introduce quality correlations, i.e., the quality of a service is associated with other services. Existing approaches for service composition have not fully and systematically considered the quality correlations between services. In this paper, we propose a novel approach named Q2C (Query of Quality Correlation) to systematically model quality correlations and enable efficient queries of quality correlations for service compositions. Given a service composition and a set of candidate services, Q2C first preprocesses the quality correlations among the candidate services and then constructs a quality correlation index graph to enable efficient queries for quality correlations. Extensive experiments are conducted on a real-world web service dataset to demonstrate the effectiveness and efficiency of Q2C.

Mobile Edge Computing (MEC) is an emerging and prospective computing paradigm that supports low-latency content delivery. In a MEC environment, edge servers are attached to base stations or access points in closer proximity to endusers to reduce the end-to-end latency in their access to online content. From an edge infrastructure provider's perspective, a cost-effective k edge server placement (kESP) places k edge servers within a particular geographic area to maximize their coverage. However, in the distributed MEC environment, edge servers are often subject to failures due to various reasons, e.g., software exceptions, hardware faults, cyberattacks, etc. End-users connected to a failed edge server have to access online content from the remote cloud if they are not covered by any other edge servers. This significantly jeopardizes endusers' quality of experience. Thus, the robustness of an edge server network must be considered in edge server placement. In this paper, we formally model this Robustness-oriented k Edge Server Placement (RkESP) problem, and prove that finding the optimal solution to this problem is .,Vl'-hard. Thus, we firstly propose an integer programming based optimal approach, namely Opt, to find optimal solutions to small-scale RkESP problems. Then, we propose an approximate approach, namely Approx, for solving large-scale RkESP problems efficiently with an O(k)-approximation ratio. Finally, the performance of the two approaches is experimentally evaluated against five state-of-the-art approaches on a real-world dataset and a large-scale synthesized dataset.

As many applications and services are moving towards a more human-centered design, app vendors are taking the quality of experience (QoE) increasingly seriously. End-to-end latency is a key factor that determines the QoE experienced by users, especially for latency-sensitive applications such as online gaming, health care, critical warning systems and so on. Recently, edge computing has emerged as a promising solution to the high latency problem. In an edge computing environment, edge servers are deployed at cellular base stations, offering processing power and low network latency to users within their geographic proximity. In this paper, we tackle the user allocation problem in edge computing from an app vendor's perspective, where the vendor needs to decide which edge servers to serve which users in a specific area. Also, the vendor must consider the various levels of quality of service (QoS) for its users. Each QoS level results in a different QoE level; thus, the app vendor needs to decide the QoS level for each user so that the overall user experience is maximized. To tackle the NP-hardness of this problem, we formulate it as a potential game then propose QoEGame, an effective and efficient game-theoretic approach that admits a Nash equilibrium as a solution to the user allocation problem. Being a distributed algorithm, QoEGame is able to fully utilize the distributed nature of edge computing. Finally, we theoretically and empirically evaluate the performance of QoEGame, which is illustrated to be significantly better than the state of the art and other baseline approaches.

In mobile edge computing (MEC), edge servers are deployed at base stations to provide highly accessible computational resources and storage capacities to nearby mobile devices. Caching data on edge servers can ensure the service quality and network latency for those mobile devices. However, an app vendor needs to ensure that the data caching cost does not exceed its data caching budget. In this paper, we present the budgeted edge data caching (BEDC) problem as a constrained optimization problem to maximize the overall reduction in data retrieval for all its app users within the budget, and prove that it is NP-hard. Then, we provide an approach named IP-BEDC for solving the BEDC problem optimally based on Integer Programming. We also provide an O(k) -approximation algorithm, namely α-BEDC, to find near-optimal solutions to the BEDC problems efficiently. Our proposed approaches are evaluated on a real-world data set and a synthesized data set. The results demonstrate that our approaches can solve the BEDC problem effectively and efficiently while significantly outperforming five representative approaches.

Edge Computing provides mobile and Internet-of-Things (IoT) app vendors with a new distributed computing paradigm which allows an app vendor to deploy its app at hired edge servers distributed near app users at the edge of the cloud. This way, app users can be allocated to hired edge servers nearby to minimize network latency and energy consumption. A cost-effective edge user allocation (EUA) requires maximum app users to be served with minimum overall system cost. Finding a centralized optimal solution to this EUA problem is NP-hard. Thus, we propose EUAGame, a game-theoretic approach that formulates the EUA problem as a potential game. We analyze the game and show that it admits a Nash equilibrium. Then, we design a novel decentralized algorithm for finding a Nash equilibrium in the game as a solution to the EUA problem. The performance of this algorithm is theoretically analyzed and experimentally evaluated. The results show that the EUA problem can be solved effectively and efficiently.

Along with the progress of the enterprise globalization, alliance and cooperation have become an important means for enterprises to improve their competitiveness in the market. Yet, most current methods for Service Composition Optimization (SCO) fail to address the Alliance Relation (AR) between services and assume that services are independent of each other. To address this issue, this paper presents an alliance-aware service composition method. Firstly, the fundamental properties of the AR are given based on a multi-granularity service composition model. Secondly, alliance relation granularity is coarsened into a relation granulation quotient space and the domain elements are matched reversely with service compositions, thereby reducing the complexity of query and computation of the AR. Finally, a Relation Granularity-aware Particle Swarm Optimization Algorithm (RG-PSO) is proposed based on relation granulation quotient space to solve the alliance-aware SCO prolem. Substantial experimental results show that the proposed model and algorithm are effective and efficient.