With the rapid evolution of artificial intelligence (AI) technologies, supporting computing-intensive and latencysensitive applications in resource-constrained environments has become increasingly challenging. In response, we propose APPOMNLS, a multi-objective optimization approach for Satellite Edge Computing (SEC) that targets application response latency, energy consumption, and on-time completion rates. It integrates a proximal policy optimization (PPO) with a self-attention mechanism under a multi-neighborhood local search framework. The Transformer-based self-attention module enhances the PPO network's representational capability, while multi-neighborhood local search switches flexibly between global and local exploration of the solution space. Experiments based on Iridium-NEXT constellation Two-Line Element (TLE) data demonstrate that our approach clearly outperforms its peers in terms of terminal response speed, energy efficiency and on-time application completion rates. APPO-MNLS brings value to SEC with the capability of guaranteeing reliable and effective global satellite network service.

As massive data is generated by Internet of Things (IoT) devices, user-end devices are required to implement computation-intensive functionalities, including multi-sensory data processing and analysis, sophisticated system control schemes, and artificial intelligence. Mobile Edge Computing (MEC) is a significant technology that has the potential to extend the computation and storage capacities of user-end devices by the decentralization of required resources and contents near users and at the edge. A crucial challenge in this direction is the development of a smart mechanism to effectively cache contents upon Edge Servers (ESs) near users for high effectiveness and low latency of content delivery with the constraints on computational and storage capacities of ESs. This study employs a queuing model for analyzing total request delay and interprets the content caching problem as an adversarial semi-bandits problem. We propose an Online Self-feedback Adversarial Semi-bandits Learning (OSAL) algorithm that incorporates a dual-layer learning architecture for dynamically generating caching strategies and maximizes the long-term reward. Experimental results demonstrate that the proposed method significantly outperforms the state-of-the-art methods across various performance metrics in a real-world multi mobile-user content caching case.

Mobile edge computing(MEC) is an innovative technology that deploys computing resources around the demand side to provide near-request and responsiveness-guaranteed computing and storage services. A major attention paid by related works in this direction is mobility, where mobile traces of both edge users and servers are analyzed and exploited for accommodating offloading and migration requests for computation resources in a highly dynamic MEC environment. Our research in this work suggests that information of user interests, in terms of points of interest (POI), can be exploited in conjunction with mobility as well and proposes a hybrid method for for interest-informed and mobility-aware service migration path selection(HIMS). It synthesizes a trajectory prediction model and user interests prediction one for selecting target servers and reliable service migration paths. Experimental results demonstrate that our approach outperforms traditional methods across multiple performance metrics, especially those with sole input of mobility.

The growing need for applications that require significant computational power and high responsiveness has significantly driven the advancement of multi-access edge computing (MEC), with satellite edge computing (SEC) emerging as a formidable solution for regions where devices are marooned in areas with sparse computational resources. We present a study on enhancing task scheduling and resource allocation efficiency under the SEC framework, introducing a novel system model that simulates a heterogeneous network characterized by variable bandwidths, channel gains, and transmission powers. We propose a tailored SEC architecture that addresses stringent latency requirements and devise a dynamic scheduling method that adjusts task priorities based on urgency. Our experiments, grounded in realistic parameters from the Iridium and OneWeb satellite constellations, demonstrate the efficacy of our algorithm. The findings underscore significant improvements in managing the SEC landscape, providing robust solutions that enhance overall system performance and reliability in global service networks.

Vehicle Edge Computing (VEC) is the deployment of applications close to edge servers to provide low latency and highly responsive services to users. However, due to the complexity and dynamics of the VEC environment, it is prone to errors and failures, and the reliability of edge service migration may be compromised if no measures are taken to cope with different levels of failures. To address this issue, this paper proposes an modified (m, n)-fault tolerance strategy (M-MNFT). Unlike the traditional one, which only considers ES failures, M-MNFT additionally selects redundant edge base stations to ensure task reliability during task migration, and takes into account the fact that the relative distance between the request and the base station is as small as possible when the request is sent, so as to avoid the impact of the edge base station failure on the Quality of Service (QoS) during task migration. In addition, we have performed extensive simulations to show that M-MNFT outperforms existing methods in terms of the number of delayed requests, on-time finish rate, and average waiting time.

Semantic segmentation is one of the most important tasks in remote sensing. In the semantic segmentation of remote sensing images, some regions are repeatedly transformed between multi-classes, which affects the convergence speed and segmentation accuracy. This is because the increased spatial resolution makes the spectral distribution of geographic targets differ from the overall category distribution. Markov random field (MRF) model is widely used for semantic segmentation of remote sensing images because of its outstanding spatial description ability. Some scholars have made improvements on MRF models to extract more information or enhance semantic inference. However, these improvements fail to capture the correlation between the multi-granularity layers and the historical information. In this article, we propose a new MRF-based model, which adopts multi-granularity layers to realize the multi-granularity correlation representation of targets and the spatial-temporal inference of segmentation labels. First, the algorithm constructs a multi-grained layer structure based on remote sensing images to enhance feature extraction for targets of different sizes in images; secondly, for the multi-layer feature field, a cross-layer Gauss-Markov model is constructed based on intra-inter-layer feature correlation constraints; then, for the multi-granularity layer label field, a self-renewing pairwise spatial-temporal potential function with blinking coefficients is constructed based on the newly defined cross-layer augmented neighborhood system, which can accelerate the convergence of segmentation by using the history information and spatial neighborhood information. The proposed method is tested on texture images, SPOT-5, and Gaofen-2 images. Experiments show that the proposed method has better performance compared to other state-of-the-art MRF-based methods.

Mobile edge computing (MEC) is a prospective technology to render services through resources to fulfill the requirements of IoT (Internet of Things) devices at the cloud edge. The highly dynamic and heterogeneous characteristics of IoT devices bring both opportunities and challenges, i.e., a higher-than-usual occurrence rate of failures. Such failures occur at all architectural levels of the IoT applications: IoT sensor and actuator nodes can be missed, network links between IoT nodes can be down, and processing and storage IoT components can fail. In this work, for optimizing service offloading efficiency, energy consumption, and system reliability, a semi-online fault-tolerant offloading method (UDQF) was proposed for countering MEC failures by adopting a semi-online-learning-based service offloading strategy. The proposed strategy leverages a Dueling Deep Q network-based algorithm to determine user offloading behavior and utilizes an adaptive checkpointing mechanism (periodically storing the system state and restarting the system at the last checkpointing) to improve the task reliability. To valid and compare the model, the simulated results indicate that the proposed method outperforms other counterparts in multiple metrics.

With the proliferation of novel Internet of Things (IoT) mobile applications and advanced communication technologies, nowadays we are surrounded by ubiquitous sensors and smart devices. These smart IoT devices generate a large volume of data day and night at the edge of the network, create a huge demand for edge computing resources, and thus, promote the emergence of the multiaccess edge computing (MEC) paradigm. In MEC environments, IoT devices or mobile users are allowed to offload their computational tasks to nearby edge servers to overcome the limitation of local computing resources. Though edge servers could provide low-latency service with high-responsible computing capabilities, they are still facing many challenges posed by the limited hardware resources and diverse offloading requests. However, traditional approaches are usually based on the centralized architecture and batch-processing scheduling mode, which might lead to low efficiency and high communication overhead. Besides, they also lack the consideration of task diversity and priorities, which are crucial in real-world application scenarios. Thus, smart task scheduling and resource provision strategies with a high real-time property are urgently needed for better user experience and higher resource utilization. In this article, we target the online edge IoT task scheduling and resource allocation problem and propose a decentralized approach (DoSRA). The experiments based on real-world edge environments have demonstrated that the proposed approach could achieve at most a 35.34% reduction on the average weighted offloading response time.

An opportunistic link between two mobile devices or nodes can be constructed when they are within each other’s communication range. Typically, cyber–physical environments consist of a number of mobile devices that are potentially able to establish opportunistic contacts and serve mobile applications in a cost-effective way. Opportunistic mobile service computing is a promising paradigm capable of utilizing the pervasive mobile computational resources around the users. Mobile users are thus allowed to exploit nearby mobile services to boost their computing capabilities without investment in their resource pool. Nevertheless, various challenges, especially its quality-of-service and reliability-aware scheduling, are yet to be addressed. Existing studies and related scheduling strategies consider mobile users to be fully stable and available. In this article, we propose a novel method for reliability-aware and deadline-constrained service composition over opportunistic networks. We leverage the Krill–Herd-based algorithm to yield a deadline-constrained, reliability-aware, and well-executable service composition schedule based on the estimation of completion time and reliability of schedule candidates. We carry out extensive case studies based on some well-known mobile service composition templates and a real-world opportunistic contact data set. The comparison results suggest that the proposed approach outperforms existing ones in terms of success rate and completion time of composed services.Note to Practitioners—Recently, the rapid development of mobile devices and mobile communication leads to the prosperity of mobile service computing. Services running on mobile devices within a limited range are allowed to be composed to coordinate through wireless communication technologies and perform complex tasks and business processes. Despite its great potential, mobile service compositions remains a challenge since the mobility of users and devices imposes high unpredictability on the execution of tasks. A careful investigation into existing methods has found their various limitations, e.g., assuming time-invariant availability of mobile services. This article presents a novel reliability-aware and deadline-constrained service composition method for mobile opportunistic networks. Instead of assuming time-invariant availability of mobile nodes, the proposed method is capable of estimating service availability at run-time and leveraging a Krill–Herd-based algorithm to yield the deadline-constrained, reliability-aware, and well-executable service composition schedules. Case studies based on well-known service composition templates and real-world data sets suggest that it outperforms traditional ones in terms of success and completion time of composed services. It can thus aid the design and optimization of composite services as well as their smooth execution in a mobile environment. It can help practitioners better manage the reliability and performance of real-world applications built upon mobile services.

Edge computing is a promising paradigm that can boost the performance of novel mobile applications and energize the real-time governance of Internet-of-Things (IoT) big data. In edge computing, mobile application vendors are allowed to employ edge resources to speed up end-users' applications in an elastic and on-demand manner. However, due to the complex geographical distribution of edge servers and users, how to decide the most appropriate destination edge server to hire and how to decide the corresponding user-server allocation plan with as-low-as-possible monetary cost are the key problems for application vendors. Instead of assuming a simultaneous-batch-arrival pattern of incoming users and considering static optimization of the Edge User Allocation (EUA) problem by most existing studies, in this paper, we consider an online EUA problem where users' arrival and departure follow a general pattern. We take the long-term edge user allocation rate and edge server leasing cost as scheduling targets and propose a decentralized collaborative and fuzzy-control-based approach to yielding real-time user-edge-server allocation schedules. In this approach, edge users are allowed to independently make their own allocation decision only based on local information (i.e., the status of nearby edge servers). Experiments on real-world edge datasets demonstrate our approach outperforms state-of-the-art approaches in terms of long-term allocation rate and system cost.

The rapid development of mobile communication technologies prompts the emergence of mobile edge computing (MEC). As the key technology toward 5th generation (5G) wireless networks, it allows mobile users to offload their computational tasks to nearby servers deployed in base stations to alleviate the shortage of mobile resource. Nevertheless, various challenges, especially the edge-user-allocation problem, are yet to be properly addressed. Traditional studies consider this problem as a static global optimization problem where user positions are considered to be time-invariant and user-mobility-related information is not fully exploited. In reality, however, edge users are usually with high mobility and time-varying positions, which usually result in users reallocations among different base stations and impact on user-perceived quality-of-service (QoS). To overcome the above limitations, we consider the edge user allocation problem as an online decision-making and evolvable process and develop a mobility-aware and migration-enabled approach, named MobMig, for allocating users at real-time. Experiments based on real-world MEC dataset clearly demonstrate that our approach achieves higher user coverage rate and lower reallocations than traditional ones.