Betweenness centrality is one of the key centrality measures in many applications including community detections in biological networks, vulnerability detections in communication networks, misinformation filtering in social networks, etc. The top-K group betweenness centrality problem is to find a group of K nodes from a network so that the total fraction of shortest paths that pass through the K nodes is maximized. Existing studies proposed randomized sampling algorithms for the problem. We notice that the existing studies ensured that, the maximum deviation of the estimated centrality of every group from its expectation is no greater than a small given threshold for all potential groups with no more than K nodes, thereby generating too many samples, as the number of such groups is prohibitively large. In contrast, in this paper we first devise a novel algorithm that enables to estimate the centrality of a tentative group adaptively, and the algorithm immediately stops once the centrality is large enough; otherwise, the algorithm uses more samples to find a better group. We then theoretically show that, even the proposed algorithm uses much less samples, it still can find a performance-guaranteed group with high probability. Experimental results with real-world networks demonstrate that the number of samples used by the proposed algorithm is up to 36 times smaller than the state-of-the-art, while the centrality of the group found by the algorithm is no more than 4.5% smaller than the latter.

Digital twin (DT) technology enables smooth integrations of cyber and physical worlds in alignment with the Industry 4.0 initiative. DTs are virtual presentations of physical objects. Through synchronizations with physical objects in real-time, DTs can reflect the states of their objects with high fidelity. Orthogonal to the DT technology, mobile edge computing (MEC) is a promising computing paradigm that shifts computing power to the edge network, which is appropriate for delay-sensitive intelligent services. In this paper, we study fidelity-aware inference services in a DT-assisted MEC environment, where machine learning-based inference models must be continuously retrained using updated DT data in order to provide high-fidelity services for consumers. To this end, we first formulate two novel optimization problems: the initial DT and model placement problem with the aim of minimizing the total cost of various resources consumed, and the cumulative fidelity maximization problem to maximize the long-term cumulative fidelity of service models while minimizing the cost of resource consumption on service model fidelity enhancements over a given time horizon, through jointly scheduling mobile devices to upload their update data to synchronize with their DTs and determining whether DTs and/or models to be migrated at each time slot. We then develop an efficient algorithm for the initial DT and model placement problem, through a reduction to a series of minimum-cost maximum matching problems in auxiliary graphs. We also devise an online algorithm with a provable competitive ratio for the cumulative fidelity maximization problem, by designing an elegant service request admission strategy. Finally, we evaluate the performance of the proposed algorithms via simulations. Simulation results demonstrate that the proposed algorithms are promising, and outperform their baselines by no less than 28%.

The Internet of Things (IoT) is gathering paces in the new era of Industry 4.0, and the Digital Twin (DT) technology bridges the gap between the bursting amounts of data generated by IoT devices and the user requirements for real-time data processing. DT services maintain living digital models of physical objects, and a DT network enables comprehensive service provisioning with the global knowledge of a group of DTs. On the other hand, exposing serverless computing at network edges, the recent advances in Mobile Edge Computing (MEC) introduce new inspirations to the DT landscape that ensure fine-grained resource management and low network-wide delay of DT services. However, social relationships among IoT devices and DT data privacy impact DT orchestrations. In this paper, we first design a differential privacy-based federated learning framework to build a DT network for DT services in response to user requests in an MEC, thereby enhancing the Quality of Services (QoS). Built upon the proposed framework, we then formulate two novel social-aware DT placement problems: the static social-aware S_DT placement problem, and the dynamic social-aware S_DT placement problem, respectively. We also show the NP-hardness of the defined problems. Then, we formulate an Integer Linear Program (ILP) solution to the static social-aware S_DT placement problem when the problem size is small; otherwise we develop an approximation algorithm with a provable approximation ratio for it. Third, we study the dynamic social-aware S_DT placement problem when requests arrive one by one without the knowledge of future request arrivals over the time horizon, for which we devise an online algorithm with a provable competitive ratio. Finally, we conduct simulations to evaluate the performance of the proposed algorithms. Simulation results show that the proposed algorithms outperform their counterparts, improving the performance compared with their baselines by no less than 14.9%.

Edge intelligence enables mobile users to benefit from real-time inference services based on deep neural networks (DNN). Federated learning (FL) provides a solution for using DNN training while protecting privacy. FL over mobile edge computing (MEC) can aggregate models at the edge and process them in parallel, providing far more real-time results for realworld applications. However, edge nodes have limited computing capacities and bandwidth, and not all user equipments (UEs) can be selected to upload their trained local models. In addition, private information of users can still be leaked while attackers analyze the uploaded model parameters, and users' privacy requirements vary. Thus, we proposed a novel optimization framework - Federated learning with personalized differential privacy over MEC based on deep reinforcement learning. We use deep reinforcement learning (DRL) to maximize the total utility, i.e., the overall accuracy of all global FL models, by choosing UEs for uploading their updated local models due to limited bandwidth on access points (APs) and computing resource capacities on cloudlets (edge servers). Then, we inject differential private noise into local models to enhance privacy and satisfy users' personalized privacy requirements while guaranteeing model accuracy. We finally evaluate the performance of the proposed approach through experiments. Experimental results show that the proposed approach outperforms the comparison counterparts significantly, using public accessible datasets.

Monitoring the Mean Opinion Score (MOS) of video conferencing is critical for Internet Service Providers (ISPs) to ensure user satisfaction. However, a significant technical challenge arises: MOS is a subjective measure, while ISPs primarily rely on deep packet inspection (DPI) data for performance monitoring, making direct mapping between MOS and DPI data nearly impossible. To address this gap, we develop DePI-MOSE, a novel solution that leverages sub-application-level video context clues to build machine-learning models. By inferring the type of end devices and identifying the motion level within video content during a conference session, DePI-MOSE can estimate MOS values from DPI data accurately. We implemented and tested DePI-MOSE in a real-world ISP network, and experimental results show that DePI-MOSE is more accurate than state-of-the-art methods. We also built a network resource management platform for ISPs to dynamically adjust users' network resources by precisely monitoring users' video conferencing QoE.

Existing pretrained models for 3D mesh generation often suffer from data biases and produce low-quality results, while global reinforcement learning (RL) methods rely on object-level rewards that struggle to capture local structure details. To address these challenges, we present $\textbf{Mesh-RFT}$, a novel fine-grained reinforcement fine-tuning framework that employs Masked Direct Preference Optimization (M-DPO) to enable localized refinement via quality-aware face masking. To facilitate efficient quality evaluation, we introduce an objective topology-aware scoring system to evaluate geometric integrity and topological regularity at both object and face levels through two metrics: Boundary Edge Ratio (BER) and Topology Score (TS). By integrating these metrics into a fine-grained RL strategy, Mesh-RFT becomes the first method to optimize mesh quality at the granularity of individual faces, resolving localized errors while preserving global coherence. Experiment results show that our M-DPO approach reduces Hausdorff Distance (HD) by 24.6\% and improves Topology Score (TS) by 3.8\% over pre-trained models, while outperforming global DPO methods with a 17.4\% HD reduction and 4.9\% TS gain. These results demonstrate Mesh-RFT’s ability to improve geometric integrity and topological regularity, achieving new state-of-the-art performance in production-ready mesh generation.

Age of Information (AoI) has been proposed as a new performance metric to capture the freshness of data. At wireless-powered network edge, the source nodes first need to be charged ready for update transmissions, which means the system AoI is not only decided by the scheduling of update transmissions but also by the designing of charging plan. However, the existing works either only focused on the point of scheduling update transmissions or have a rigid assumption that only one source node can be charged per time. Aiming at making the work more practical and general, we investigate the average AoI optimization problem at wireless-powered network edge with directional charging. Firstly, the theoretical bound of the weighted sum of average AoI of the entire network with a directional charger is analyzed, which is proved to be related to nodes' maximum transmitting interval and the charging strategy. An optimal charging time computation algorithm is proposed to obtain the maximum transmitting interval of each source node by considering the overlapped areas of different charging orientations. After then, an AoI-aware periodical charging scheduling algorithm is proposed to compute a periodical charging schedule while the average AoI is bounded, including a charging period$T$and the charging orientations assigned to each time slot within$T$. The proposed algorithm is proved to have an approximation ratio of up to 1.5625. Furthermore, several approximate algorithms are also proposed for average AoI optimization with multiple chargers and network bandwidth constraint. Finally, the extensive simulations demonstrate the high performance of the proposed algorithm in terms of AoI.

Inferring user status at the edge is essential for delivering personalized services, such as detecting emotional states. However, deploying large-scale models directly on user devices is impractical due to substantial computational overhead and the scarcity of labeled data. Conversely, uploading raw data to the cloud for processing raises significant privacy concerns and incurs prohibitive communication costs. To address this challenge, we propose a privacy-preserving multimodal inference framework that leverages large-scale public data while safeguarding sensitive information and optimizing computational efficiency. Specifically, we first train a teacher model in the cloud using publicly available data. Through a feature distillation process, the knowledge from this teacher model is transferred to a lightweight encoder deployed at the user end. This transfer is tailored to the user's data, ensuring that only relevant knowledge is distilled. To accommodate varying communication constraints, we introduce a feature compression mechanism that significantly reduces communication overhead without compromising inference accuracy. Extensive experiments on emotion recognition tasks demonstrate that the proposed framework effectively balances privacy preservation, resource efficiency, and inference accuracy, facilitating seamless collaboration between cloud and edge devices.

In this paper, we study a connected submodular function maximization problem, which arises from many applications including deploying UAV networks to serve users and placing sensors to cover Points of Interest (PoIs). Specifically, given a budget K, the problem is to find a subset S with K nodes from a graph G, so that a given submodular function$f(S)$on S is maximized and the induced subgraph$G[S]$by the nodes in S is connected, where the submodular function f can be used to model many practical application problems, such as the number of users within different service areas of the deployed UAVs in S, the sum of data rates of users served by the UAVs, the number of covered PoIs by placed sensors, etc. We then propose a novel$\frac {1-1/e}{2h+2}$-approximation algorithm for the problem, improving the best approximation ratio$\frac {1-1/e}{2h+3}$for the problem so far, through estimating a novel upper bound on the problem and designing a smart graph decomposition technique, where e is the base of the natural logarithm, h is a parameter that depends on the problem and its typical value is 2. In addition, when$h=2$, the algorithm approximation ratio is at least$\frac {1-1/e}{5}$and may be as large as 1 in some special cases when$K\le 23$, and is no less than$\frac {1-1/e}{6}$when$K\ge 24$, compared with the current best approximation ratio$\frac {1-1/e}{7}\left ({{=\frac {1-1/e}{2h+3}}}\right)$for the problem. Finally, experimental results in the application of deploying a UAV network demonstrate that, the number of users within the service area of the deployed UAV network by the proposed algorithm is up to 7.5% larger than those by existing algorithms, and the throughput of the deployed UAV network by the proposed algorithm is up to 9.7% larger than those by the algorithms. Furthermore, the empirical approximation ratio of the proposed algorithm is between 0.7 and 0.99, which is close to the theoretical maximum value one.

Wireless Power Transfer technology, which can charge IoT devices over the air, has become a promising technology for IoT networks. In wireless-powered IoT networks, broadcasting is a fundamental networking service for disseminating messages to the whole network. To seek a fast and collision-free broadcast schedule, the problem of Minimum Latency Broadcast Scheduling (MLBS) has been well studied when nodes are energy-abundant. However, in wireless-powered networks, a node can only receive or transmit packets after it has harvested enough energy. In such networks, it is of great importance to exploit the divergent harvested energy to reduce the broadcast latency. Unfortunately, existing works always assume a predetermined tree and a fixed transmission power for broadcast scheduling, which greatly limits their performance. Thus, in this article, we investigate the first work for the MLBS problem in wireless-powered networks without relying on predetermined trees. First, the problem is formulated and proved to be NP-hard. Then, two structure-adaptive scheduling algorithms are proposed with a theoretical bound, which can intertwine the construction of broadcast tree with the computation of an energy-aware schedule simultaneously. Furthermore, a power-aware scheduling method is also proposed to take the structure of the broadcast tree, the adjustment of nodes’ transmission powers, and the interference during transmissions into account simultaneously. Additionally, the algorithm for the MLBS problem under the physical interference model is also studied. Finally, the theoretical analysis and simulation results verify that the proposed algorithms have high performance in terms of latency.

In this paper, we study a connected submodular function maximization problem, which arises from many applications including deploying UAV networks to serve users and placing sensors to cover Points of Interest (PoIs). Specifically, given a budget$K$, the problem is to find a subset$S$with$K$nodes from a graph$G$so that a given submodular function$f (S)$on$S$is maximized while the induced subgraph$G[S]$by the nodes in$S$is connected, where the submodular function$f$can be used to model many practical application problems, such as the number of users within different service areas of the deployed UAVs in$S$, the sum of data rates of users served by the UAVs, the number of covered PoIs by placed sensors, etc. We then propose a novel$\frac{1-1/e}{2h+2}$-approximation algorithm for the problem, improving the best approximation ratio$\frac{1-1/e}{2h+3}$for the problem so far, through estimating a novel upper bound on the problem and designing a smart graph decomposition technique, where$e$is the base of the natural logarithm,$h$is a parameter depends on the problem and its typical value is 2. In addition. when$h= 2$, the algorithm approximation ratio is at least$\frac{1-1/e}{5}$and may be as large as 1 in some special cases when$K$≤21, and is no less than$\frac{1-1/e}{6}$when$K$≥ 22, compared with the current best approximation ratio$\frac{1-1/e}{7}(= \frac{1-1/e}{2h+3})$for the problem. We finally evaluate the algorithm performance in the application of deploying a UAV network. Experimental results demonstrate the number of users within the service area of the deployed UAV network by the proposed algorithm is up to 7.5% larger than those by existing algorithms, and its empirical approximation ratio is between 0.7 and 0.99, which is close to the theoretical maximum value one.

The Internet of Things (IoT) is gathering paces in the new era of Industry 4.0, and the Digital Twin (DT) technology bridges the gap between the bursting amounts of data generated by IoT devices and the user requirements for real-time data processing. DT services maintain living digital models of physical objects, and a DT network enables comprehensive service provisioning with the global knowledge of a group of DTs. On the other hand, exposing serverless computing in network edges, the recent advances in Serverless Edge Computing (SEC) introduce new inspirations to the DT landscape that ensure fine-grained resource management and low network-wide delay of DT services. However, social relationships among IoT devices and DT data privacy impact the orchestration of DTs. In this paper, we design a differential privacy-based federated learning framework to build a DT network for DT services in response to user DT service requests in SEC, thereby enhancing the Quality of Services (QoS). To this end, we first formulate a novel social-aware problem for placing DTs in an SEC network, and show its NP-hardness. We then provide an Integer Linear Program (ILP) solution to the problem when the problem size is small; otherwise, we design an approximation algorithm with a provable approximation ratio. We finally evaluate the algorithm performance through simulations. Simulation results demonstrate the proposed algorithm is promising, which improves by no less than 21.1 % of the performance of benchmarks.

Driven by data and models, the digital twin technique presents a new concept of optimizing system design, process monitoring, decision-making and more, through performing comprehensive virtual-reality interaction and continuous mapping. By introducing serverless computing to Mobile Edge Computing (MEC) environments, the emerging serverless edge computing paradigm facilitates the communication-efficient digital twin services and promises agile, fine-grained and cost-efficient provisioning of limited edge resources, where serverless functions are implemented by containers in cloudlets (edge servers). However, the nonnegligible cold start delay of containers deteriorates the responsiveness of digital twin services dramatically and the perceived user service experience. In this paper, we investigate delay-sensitive query service provisioning in digital twin-empowered serverless edge computing by considering user mobility. With digital twins of users deployed in the remote cloud, referred to as primary digital twins, we deploy their digital twin replicas based on serverless functions in cloudlets to mitigate the query service delay while enhancing user service satisfaction that is expressed as a utility function. We study two optimization problems with the aim of maximizing the accumulative utility gain: the digital twin replica placement problem per time slot, and the dynamic digital twin replica placement problem over a finite time horizon. We first formulate an Integer Linear Program (ILP) solution for the digital twin replica placement problem when the problem size is small; otherwise, we propose an approximation algorithm for the problem with a provable approximation ratio. We then design an online algorithm for the dynamic digital twin replica placement problem, and a performance-guaranteed online algorithm for a special case of the problem by assuming each user issues a query at each time slot. Finally, we evaluate the performance of the proposed algorithms for placing digital twin replicas in MEC networks through simulations. The results demonstrate the proposed algorithms are promising, outperforming their counterparts.

As a key technology of 5G, network function virtualization enables each monolithic service to be divided into microservices, facilitating their deployment and management in edge environments. One of the most critical issues in 5G is how to support dynamically arriving mission-critical services with low-latency and high-reliability requirements in distributed edge environments. However, most existing works focus on how to provide reliable services without considering latency, and their heuristics struggle to cope with high-dimensional constraints and complex environments with heterogeneous infrastructure and services. In this paper, we propose a SafeDRL algorithm to resource-efficiently support these dynamically arriving services while meeting their reliability and latency requirements. Specifically, we first formulate the problem as an integer nonlinear programming and prove its NP-hardness. To tackle this problem, our SafeDRL algorithm captures delayed rewards in dynamic environments by reinforcement learning, and corrects constraint violations with high-quality feasible solutions based on expert intervention, and prunes unnecessary backup instances for optimality. The algorithm is proved to have a bounded approximation ratio in general cases. Extensive trace-driven simulations show that, compared with the state-of-the-art solution, SafeDRL can save resource costs by up to 49.32% and improve the service acceptance ratio by up to 55% with acceptable execution time.

By combining edge computing and parallel computing, distributed edge computing has emerged as a new paradigm to exploit the booming IoT devices at the edge. To accelerate computation at the edge,i.e., the inference tasks for DNN-driven applications, the parallelism of both computation and communication needs to be considered for distributed edge computing, and thus, the problem of Minimum Latency joint Communication and Computation Scheduling (MLCCS) is proposed. However, existing works have rigid assumptions that the communication time of each device is fixed and the workload can be split arbitrarily small. Aiming at making the work more practical and general, the MLCCS problem without the above assumptions is studied in this paper. Firstly, the MLCCS problem under a general model is formulated and proved to be NP-hard. Secondly, a pyramid-based computing model is proposed to consider the parallelism of communication and computation jointly, which has an approximation ratio of$1+\delta$, where$\delta$is related to devices' communication rates. An interesting property under such a computing model is identified and proved,i.e., the optimal latency can be obtained under arbitrary scheduling order when all the devices share the same communication rate. When the workload cannot be split arbitrarily, an approximation algorithm with a ratio of at most$2\cdot (1+\delta )$is proposed. Additionally, for handling the dynamically changing network scenarios, several algorithms are also proposed accordingly. Finally, the theoretical analysis and simulation results verify that the proposed algorithm has high performance in terms of latency. Two testbed experiments are also conducted, which show that the proposed method outperforms the existing methods, reducing the latency by up to 29.2% for inference tasks at the edge.

Mobile Edge Computing (MEC) has been identified as a desirable computing paradigm that provides efficient and effective services for various applications, while meeting stringent service delay requirements. Orthogonal to the MEC computing paradigm, Network Function Virtualization (NFV) technology is another enabling technology that provides the network resource management with great flexibility and scalability, where the instances of Virtual Network Functions (VNFs) are deployed in edge servers as Service Function Chains (SFCs) for SFC-enabled services. Although reliable service provisioning in MEC environments is fundamentally important, the deployed VNF instances usually are not reliable, which can be affected by their software implementation, their execution duration, the workload among edge servers, and so on. Empowered by digital twin techniques, the states of VNF instances can be maintained by their digital twins in a real-time manner and their reliability can be accurately predicted through their digital twins. In this paper, we study digital twin-assisted, SFC-enabled reliable service provisioning in MEC networks by exploiting the dynamics of VNF instance reliability. We concentrate on two novel optimization problems of reliable service provisioning: the service cost minimization problem, and the dynamic service admission maximization problem. We first show their NP-hardness. We then formulate an Integer Linear Program (ILP) solution, and devise an approximation algorithm with a constant approximation ratio for the service cost minimization problem. We thirdly provide an ILP solution to the offline version of the dynamic service admission maximization problem. Built upon this offline ILP solution, we also develop an online algorithm with a provable competitive ratio for the problem, by adopting the primal-dual dynamic updating technique. We finally evaluate the performance of the proposed algorithms via simulations. Simulation results demonstrate that the proposed algorithms outperform their comparison benchmarks, and improve the performance of their comparison counterparts by no less than$10.2 \%$.

Digital twins are poised to enter our lives with Industry 4.0. The Digital Twin Network (DTN) paradigm is projected to deliver upon the promise of efficient collaboration among digital twins to enable complicated and systematic services across many domains, through depicting an overall picture of a group of physical objects. To achieve timely data processing of digital twins, Mobile Edge Computing (MEC) shifts the computational power towards the network edge, and network slicing is well-suited to bundle heterogeneous physical resources to build logical networks based on edge servers for accommodating DTNs. In light of this, in this paper we investigate DTN slicing-enabled service provisioning in MEC, where each DTN slice consists of one master digital twin and a set of worker digital twins, and each worker digital twin is synchronized through collecting data from a respective object periodically. The master digital twin aggregates the processed data from worker digital twins to model the DTN continuously for user query services, whilst meeting delay requirements of users. We capture the utility gain of a DTN slicing request based on the DTN model quality at its master digital twin that is impacted by the Age of Information (AoI), and we focus on two novel optimization problems: the utility maximization problem for a single DTN slicing request, and the dynamic utility maximization problem for multiple DTN slicing requests. We propose an approximation algorithm for the former, and an online algorithm with a provable competitive ratio for the latter. We also evaluate the performance of the proposed algorithms through simulations. Experimental results demonstrate that the proposed algorithms are promising, outperforming their counterparts by at least 10.2%.

Propelled by recent advances in Mobile Edge Computing (MEC) and the Internet of Things (IoT), the digital twin technique has been envisioned as a de-facto driving force to bridge the virtual and physical worlds through creating digital portrayals of physical objects. In virtue of the flourishing of edge intelligence and abundant IoT data, data-driven modelling facilitates the implementation and maintenance of digital twins, where simulations of physical objects are usually performed based on Deep Neural Networks (DNNs). A significant advantage of adopting digital twins is to enable decisive prediction on the behaviours of objects in near future without waiting for that really happen. To provide accurate predictions, it is vital to keep each digital twin synchronized with its physical object in real-time. However, it is challenging to maintain the real-time synchronization between a digital twin and its physical object due to the dynamics of physical objects and sensing data drift over time, i.e., the live data from a physical object diverge from the model training data of its digital twin. To address this critical issue, continual learning is a promising solution to retrain models of digital twins incrementally. In this paper, we investigate digital twin synchronization issues via continual learning in an MEC environment, with the aim to maximize the total utility gain, i.e., the enhanced model accuracy. We study two novel optimization problems: the static digital twin synchronization problem per time slot and the dynamic digital twin synchronization problem for a finite time horizon. We first formulate an Integer Linear Program (ILP) solution for the static digital twin synchronization problem when the problem size is small; otherwise, we develop a randomized approximation algorithm at the expense of bounded resource violations for it. We also devise a deterministic approximation algorithm with guaranteed performance for a special case of the static digital twin synchronization problem. We thirdly consider the dynamic digital twin synchronization problem by proposing an efficient online algorithm for it. Finally, we evaluate the performance of the proposed algorithms for continuous digital twin synchronization through simulations. Simulation results show that the proposed algorithms are promising, outperforming counterpart benchmarks by no less than 13.2%, in terms of the total utility gain.

Age of Information, which emerged as a new metric to quantify the freshness of information, has attracted increasing interests recently. To optimize the system AoI, most existing works try to compute an efficient schedule from the point of data transmission. Unfortunately, at wireless-powered network edge, the charging schedule of the source nodes also needs to be decided besides data transmission. Thus, in this paper, we investigate the joint scheduling problem of data transmission and energy replenishment to optimize the maximum peak AoI at network edge with directional chargers. To the best of our knowledge, this is the first work that considers such two problems simultaneously. Firstly, the theoretical bounds of the maximum peak AoI with respect to the charging latency are derived. Secondly, for the minimum peak AoI scheduling problem with a single charger, an optimal scheduling algorithm is proposed to minimize the charging latency, and then a data transmission scheduling strategy is also given to optimize the maximum peak AoI. The proposed algorithm is proved to have a constant approximation ratio of up to 1.5. As for the scenario with multiple chargers, an approximate algorithm is also proposed to minimize the charging latency and the maximum peak AoI. Additionally, when the network bandwidth constraint is considered, the algorithm which considers the parallelism of the charging process and data transmission process is also proposed to reduce the latency and the maximum peak AoI. Finally, the theoretical analysis and simulation results verify that the proposed algorithms have high performance in terms of latency and AoI.

The emerging digital twin technique enhances the network management efficiency and provides comprehensive insights on network performance, through mapping physical objects to their digital twins. The user satisfaction on digital twin-enabled service relies on the freshness of digital twin data, which is measured by the Age of Information (AoI). Due to long service delays, the use of the remote cloud for delay-sensitive service provisioning faces serious challenges. Mobile Edge Computing (MEC), as an ideal paradigm for delay-sensitive services, is able to realize real-time data communication between physical objects and their digital twins at the network edge. However, the mobility of physical objects and dynamics of user query arrivals make seamless service provisioning in MEC become challenging. In this paper, we investigate dynamic digital twin placements for improving user service satisfaction in MEC environments, by introducing a novel metric to measure user service satisfaction based on the AoI concept and formulating two user service satisfaction enhancement problems: the static and dynamic utility maximization problems under static and dynamic digital twin placement schemes. To this end, we first formulate an Integer Linear Programming (ILP) solution to the static utility maximization problem when the problem size is small; otherwise, we propose a performance-guaranteed approximation algorithm. We then propose an online algorithm with a provable competitive ratio for the dynamic utility maximization problem, by considering dynamic user query services. Finally, we evaluate the performance of the proposed algorithms via simulations. Simulation results demonstrate that the proposed algorithms outperform the comparison baseline algorithms, improving the algorithm performance by at least$10.7\%$, compared to the baseline algorithms.

The Mobile Edge Computing (MEC) paradigm gives impetus to the vigorous advancement of the Internet of Things (IoT), through provisioning low-latency computing services at network edges. The emerging digital twin technique has been explosively growing in the IoT community, which bridges the gap between physical objects and their digital representations in an MEC network, enabling real-time monitoring and analysis, simulations on the dynamics of systems, accurate predictions on behaviours of objects, and optimization on network resource allocation. In this paper, we consider AoI-aware query services in an MEC network empowered by digital twin technology for diverse IoT applications. We aim to maximize the weighted sum of the accumulative freshness of query results measured by the Age of Information (AoI) and the total query service delay of admitted requests. To this end, we first formulate a novel minimization problem that explores nontrivial trade-offs between the two conflicting optimization objectives: the freshness of query results and service delays, and we show the NP-hardness of the problem. Then, we propose an approximation algorithm with a provable approximation ratio for the problem, at the expense of bounded computing capacity violations. We also develop a heuristic for the problem without any capacity violations. We finally evaluate the performance of the proposed algorithms via simulations. The simulation results demonstrate that the proposed algorithms are promising, and outperform the comparison benchmarks.

In this paper we study the deployment of an Unmanned Aerial Vehicle (UAV) network that consists of multiple UAVs to provide emergent communication service for people who are trapped in a disaster area, where each UAV is equipped with a base station that has limited computing capacity and power supply, and thus can only serve a limited number of people. Unlike most existing studies that focused on homogeneous UAVs, we consider the deployment of heterogeneous UAVs where different UAVs have different computing capacities. We study a problem of deploying$K$heterogeneous UAVs in the air to form a temporarily connected UAV network such that the network throughput – the number of users served by the UAVs, is maximized, subject to the constraint that the number of people served by each UAV is no greater than its service capacity. We then propose a novel$O(\sqrt{\frac{s}{K}})$-approximation algorithm for the problem, where$s$is a given positive integer with$1 \le s\le K$, e.g.,$s=3$. We also devise an improved heuristic, based on the approximation algorithm. We finally evaluate the performance of the proposed algorithms. Experimental results show that the numbers of users served by UAVs in the solutions delivered by the proposed algorithms are increased by 25% than state-of-the-arts.

Age of Information (AoI) has emerged as a new metric to measure data freshness from the destination's perspective. To optimize the system AoI, most existing works focused on the point of scheduling of update transmissions. While at wireless-powered network edge, the source nodes can only transmit their updates after being charged ready, which means the system AoI is not only determined by the update transmission strategies, but also the charging strategies. Thus, in this paper, we investigate the first work to optimize the weighted peak AoI from the point of charging at wireless-powered network edge. Firstly, the problem of optimizing the weighted sum of average peak AoI with a directional charger is formulated, and then transformed to a charging time optimization problem with respect to the charging orientations and peak AoI, and an approximate algorithm is proposed to obtain the required charging time for each source node. Secondly, an age-based scheduling algorithm is proposed to compute the charging decisions and transmission decisions simultaneously, which can not only optimize the weighted sum of average peak AoI, but also guarantee the maximum peak AoI of each source node is bounded. The proposed algorithm is proved to have an approximation ratio of up to (1+$\varphi$), where$\varphi$is a small value related to the weight of each source node. When there exist multiple chargers, an approximate algorithm is also proposed to minimize the weighted sum of average peak AoI by scheduling the orientations of these chargers cooperatively. Finally, the extensive simulations demonstrate the high performance of the proposed algorithms in terms of peak AoI.

In this paper we study the deployment of a UAV (unmanned aerial vehicle) network that consists of multiple UAVs to provide emergent communication services to people trapped in a disaster area, where each UAV is equipped with a base station that has limited computing capacity and power supply, and thus can only serve a limited number of users. Unlike most existing studies focusing on homogenous UAVs, we consider the deployment of heterogeneous UAVs, where different UAVs have different computing capacities. We study a problem of deploying$K$heterogeneous UAVs in the air to form a connected UAV network such that the number of users served by the UAVs is maximized, subject to the constraint that the number of users served by each UAV is no greater than its service capacity, assuming that the maximum number of users can be served by a UAV is given. We then propose a novel$O(\sqrt{\frac{s}{K}})$-approximation algorithm for the problem, where$s$is a given positive integer, e.g.,$s=3$. We finally evaluate the performance of the approximation algorithm. Experimental results show that the number of users served by all UAVs in the approximate solution is improved by 22% compared with the solutions delivered by state-of-the-arts.

By combing edge computing and parallel computing, distributed edge computing has emerged as a new paradigm to accelerate computation at the edge. Considering the parallelism of both computation and communication, the problem of Minimum Latency joint Communication and Computation Scheduling (MLCCS) is studied recently. However, existing works have rigid assumptions that the communication time of each device is fixed and the workload can be split arbitrarily small. Aiming at making the work more practical and general, the MLCCS problem without the above assumptions is studied in this paper. Firstly, the MLCCS problem under a general model is formulated and proved to be NP-hard. Secondly, a pyramid-based computing model is proposed to consider the parallelism of communication and computation jointly, which has an approximation ratio of 1 + δ, where δ is related to devices’ communication rates. An interesting property under such computing model is identified and proved, i.e., the optimal latency can be obtained under arbitrary scheduling order when all the devices share the same communication rate. When the devices own different communication rates, the optimal scheduling order is also obtained. Additionally, when the workload cannot be split arbitrarily, an approximation algorithm with ratio of at most 2 (1 + δ) is proposed. Finally, the theoretical analysis and simulation results verify that the proposed algorithm has high performance in terms of latency. Two testbed experiments are also conducted, which show that the proposed method outperforms the existing methods, reducing the latency by up to 29.2% in real-world applications.

The emerging digital twin technique enhances the network management efficiency and provides comprehensive insights, through mapping physical objects to their digital twins. The user satisfaction on digital twin-enabled query services relies on the freshness of digital twin data, which is measured by the Age of Information (AoI). Because the remote cloud faces challenges in providing data for users due to long service delays, Mobile Edge Computing (MEC), as a promising technology, offers real-time data communication between physical objects and their digital twins at the edge of the core network. However, the mobility of physical objects and dynamic query arrivals make efficient service provisioning in MEC become challenging. In this paper, we investigate the dynamic digital twin placement for improving user service satisfaction in MEC environments. We focus on two user service satisfaction augmentation problems under both static and dynamic digital twin placement schemes: the static and dynamic utility maximization problems. We first formulate an Integer Linear Programming (ILP) solution to the static utility maximization problem when the problem size is small; otherwise, we propose a performance- guaranteed approximation algorithm for it. We then devise an online algorithm for the dynamic utility maximization problem with a provable competitive ratio. Finally, we evaluate the performance of the proposed algorithms through experimental simulations. Simulation results demonstrate that the proposed algorithms outperform the comparison baseline algorithms, and the performance improvement is no less than 11.6%, compared with the baseline algorithms.

Age of Information, which emerged as a new performance metric to quantify the data freshness, has drawn increasing interests recently. At wireless-powered network edge, the source nodes can only transmit their updates after being charged ready, which means the system AoI is not only determined by the update transmission strategies, but also the charging strategies. However, the existing works either only focused on the point of scheduling of update transmissions or have a rigid assumption that only one source node can be charged per time. Aiming at making the work more practical and general, we investigate the average$A$oI optimization problem at wireless-powered network edge without such limitations in this paper. Firstly, the lower bound of the weighted sum of average$A$oI of the whole network with a directional charger is analyzed, which is proved to be related to nodes' maximum transmitting interval and the charging strategy. An optimal charging time allocation algorithm is proposed to obtain the maximum transmitting interval of each source node by considering the overlapped areas of different charging orientations. After then, an AoI-aware periodical charging scheduling algorithm is proposed, which can obtain a periodical charging schedule including a charging period$T$and the charging orientation assigned to each time slot within$T$, while the average AoI is bounded. The proposed algorithm is proved to have an approximation ratio of up to 1.5625. Finally, the extensive simulations demonstrate the high performance of the proposed algorithm in terms of AoI.

Mobile Edge Computing (MEC) has emerged as a promising paradigm catering to overwhelming explosions of mobile applications, by offloading compute-intensive tasks to MEC networks for processing. The surging of deep learning brings new vigor and vitality to shape the prospect of intelligent Internet of Things (IoT), and edge intelligence arises to provision real-time deep neural network (DNN) inference services for users. To accelerate the processing of the DNN inference of a user request in an MEC network, the DNN inference model usually can be partitioned into two connected parts: one part is processed in the local IoT device of the request, and another part is processed in a cloudlet (edge server) in the MEC network. Also, the DNN inference can be further accelerated by allocating multiple threads of the cloudlet to which the request is assigned. In this paper, we study a novel delay-aware DNN inference throughput maximization problem with the aim to maximize the number of delay-aware DNN service requests admitted, by accelerating each DNN inference through jointly exploring DNN partitioning and multi-thread execution parallelism. Specifically, we consider the problem under both offline and online request arrival settings: a set of DNN inference requests is given in advance, and a sequence of DNN inference requests arrives one by one without the knowledge of future arrivals, respectively. We first show that the defined problems are NP-hard. We then devise a novel constant approximation algorithm for the problem under the offline setting. We also propose an online algorithm with a provable competitive ratio for the problem under the online setting. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results demonstrate that the proposed algorithms are promising

Mobile Edge Computing (MEC) promises to provide mobile users with delay-sensitive services at the edge of network, and each user service request usually is associated with a Service Function Chain (SFC) requirement that consists of Virtualized Network Functions (VNFs) in order. The satisfaction of a user on his requested service is heavily impacted by the service reliability. In this paper, we study user satisfaction on services provided by an MEC network through introducing a submodular function based metric to measure user satisfaction. We first formulate a novel user satisfaction problem with the aim to maximize the accumulative user satisfaction, assuming that all available computing resource in the MEC network can be used for service reliability enhancement. We show that the problem is NP-hard, and devise an approximation algorithm with a provable approximation ratio for it. We then consider the problem under a given computing resource budget constraint, for which we devise an approximation algorithm with a provable approximation ratio, at the expense of moderate budget violations. We finally evaluate the performance of the proposed algorithms through experimental simulations. Simulation results demonstrate that the proposed algorithms outperform the comparison baseline algorithms, improving the performance by more$16.1\%$in comparison with the baseline algorithms.

The surging of deep learning brings new vigor and vitality to shape the prospect of intelligent Internet of Things (IoT), and the rise of edge intelligence enables provisioning real-time deep neural network (DNN) inference services for mobile users. To perform efficient and effective DNN model training in edge computing environments while preserving training data security and privacy of IoT devices, federated learning has been envisioned as an ideal learning paradigm for this purpose. In this article, we study energy-aware DNN model training in edge computing. We first formulate a novel energy-aware, Device-to-Device (D2D) assisted federated learning problem with the aim to minimize the global loss of a training DNN model, subject to bandwidth capacity on an edge server and energy capacity on each IoT device. We then devise a near-optimal learning algorithm for the problem when the training data follows the i.i.d. data distribution. The crux of the proposed algorithm is to explore using the energy of neighboring devices of each device for its local model uploading, by reducing the problem to a series of weighted maximum matching problems in corresponding auxiliary graphs. We also consider the problem without the assumption of the i.i.d. data distribution, for which we propose an efficient heuristic algorithm. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results show that the proposed algorithms are promising.

The real-time communication requirement of the Internet of Things (IoT) applications promotes the convergence of IoT and Mobile Edge Computing (MEC). The MEC paradigm greatly shortens the IoT service delay by leveraging cloudlets (edge servers) of MEC in the proximity of IoT devices. Considering limited computing and storage resources in an MEC network, it is challenging to provide efficient IoT-enabled service provisioning in such a network. In this article, we study the service home identification problem of service provisioning for multi-source IoT applications in an MEC network, by identifying a service home (cloudlet) of each multi-source IoT application for its data processing, querying and storage. Each multi-source IoT application consists of multiple sources located at different geographical locations and each source uploads its data stream via a gateway (its nearby access point) to the MEC network and the uploaded data then is aggregated with the stream data of the other sources of the IoT application at the service home. We here focus on two novel service home identification problems: the service operational cost minimization problem with the aim to minimize the total service operational cost by admitting as many multi-source IoT applications as possible, and the online throughput maximization problem with the aim to maximize the number of multi-source IoT application requests admitted. We first show that both the problems are NP-hard. We then formulate an Integer Linear Programming (ILP) solution to the service operational cost minimization problem, and propose a randomized algorithm with high probability and a deterministic approximation algorithm respectively, at moderate resource capacity violations. We third develop an efficient heuristic algorithm for the problem without any resource violation. Furthermore, we deal with the online throughput maximization problem under an assumption that multi-source IoT application requests arrive one by one without the knowledge of future arrivals, for which we formulate an Integer Linear Programming (ILP) solution to its offline version, followed by devising an online algorithm with competitive ratio. We finally evaluate the performance of the proposed algorithms through experimental simulations. Simulation results demonstrate that the proposed algorithms are promising, and outperform their comparison counterparts.

As a key enabling technology for 5G, network function virtualization abstracts services into software-based service function chains (SFCs), facilitating mission-critical services with high-reliability requirements. However, it is challenging to cost-effectively provide reliable SFCs in dynamic environments due to delayed rewards caused by future SFC requests, limited infrastructure resources, and heterogeneity in hardware and software reliability. Although deep reinforcement learning (DRL) can effectively capture delayed rewards in dynamic environments, its trial-and-error exploration in a vast solution space with massive infeasible solutions may lead to frequent constraint violations and traps in poor local optima. To address these challenges, we propose a RuleDRL algorithm that combines the capability of DRL to capture delayed rewards and the strength of rule-based schemes to explore high-quality solutions without violating constraints. Specifically, we first formulate the reliable SFC provision problem as an integer nonlinear programming problem, which is proven to be NP-hard. Then, we jointly design DRL and rule-based schemes that are coupled to make the final decision and establish a bounded approximation ratio in general cases. Extensive trace-driven simulations show that RuleDRL can save the total cost by up to 65.67% and improve the SFC acceptance ratio by up to 82%, compared to the state-of-the-art solution.

The surging of deep learning brings new vigor and vitality to shape the prospect of intelligent Internet of Things (IoT), and edge intelligence arises to provision real-time deep neural network (DNN) inference services for mobile users. To perform efficient and effective DNN model training in edge environments while preserving training data security and privacy of IoT devices, federated learning has been envisioned as an ideal learning paradigm for this purpose. In this paper we study energy-aware DNN model training in an edge environment. We first formulate a novel energy-aware, device-to-device (D2D) assisted federated learning problem with the aim to minimize the global loss of a training DNN model, subject to bandwidth capacity on an edge server and the energy capacity on each IoT device. We then devise an efficient heuristic algorithm for the problem. The crux of the proposed algorithm is to explore the energy usage of neighboring devices of each device for its local model uploading, by reducing the problem to a series of maximum weight matching problems in corresponding auxiliary graphs. We finally evaluate the performance of the proposed algorithm through experimental simulations. Experimental results show that the proposed algorithm is promising.

Mobile edge computing (MEC) has emerged as a promising technology to push the cloud frontier to the network edge, provisioning network services in proximity of mobile users. Serving users at edge clouds can reduce service latency, lower operational cost, and improve network resource availability. Along with the MEC technology, network function virtualization (NFV) is another promising technique that implements various network service functions as pieces of software in cloudlets (servers or clusters of servers). Providing virtualized network service for mobile users can improve user service experience, simplify network service deployment, and ease network resource management. However, mobile users move in networks arbitrarily, and different users usually request different services with different resource demands and delay requirements. It thus poses a great challenge to providing reliable and seamless virtualized network services for mobile users in an MEC network while meeting their individual delay requirements, subject to resource capacities on the network. In this paper, we focus on the provisioning of virtualized network function services for mobile users in MEC that takes into account user mobility and service delay requirements. We first formulate two novel optimization problems of user service request admissions with the aims to maximize the accumulative network utility and accumulative network throughput for a given time horizon, respectively. We then devise a constant approximation algorithm for the utility maximization problem. We also develop an online algorithm for the accumulative throughput maximization problem. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results demonstrate that the proposed algorithms are promising.

With the integration of Mobile Edge Computing (MEC) and Network Function Virtualization (NFV), service providers are able to provide low-latency services to mobile users for profit. In this paper, we study the online service placement and request assignment problem in an MEC network, where service requests arrive one by one without the knowledge of future arrivals, and each arrived request demands a specific service with a tolerable service delay requirement with the aim to maximize the profit of the service provider, through admitting as many service requests as possible for a given monitoring period. This optimization objective is achieved by assigning service requests to appropriate cloudlets in the MEC network, pre-installing service instances into cloudlets to shorten service delays, and accommodating new services by revoking some idle service instances from cloudlets due to limited computing resources in MEC networks. In this paper, we first show that the problem is NP-hard. We then devise an efficient deep reinforcement learning algorithm for the online service placement and request assignment problem that consists of a deep reinforcement learning-based prediction mechanism for dynamic service placement, followed by a dynamic request assignment procedure to assign requests to cloudlets. We finally evaluate the performance of the proposed algorithms by conducting experiments through simulations. Simulation results demonstrate that the proposed algorithm is promising, improving performance by 46.8% compared with that of the comparison algorithms.

We are embracing an era of Internet of Things (IoT). The latency brought by unstable wireless networks caused by limited resources of IoT devices seriously impacts the quality of services of users, particularly the service delay they experienced. Mobile Edge Computing (MEC) technology provides promising solutions to delay-sensitive IoT applications, where cloudlets (edge servers) are co-located with wireless access points in the proximity of IoT devices. The service response latency for IoT applications can be significantly shortened due to that their data processing can be performed in a local MEC network. Meanwhile, most IoT applications usually impose Service Function Chain (SFC) enforcement on their data transmission, where each data packet from its source gateway of an IoT device to the destination (a cloudlet) of the IoT application must pass through each Virtual Network Function (VNF) in the SFC in an MEC network. However, little attention has been paid on such a service provisioning of multi-source IoT applications in an MEC network with SFC enforcement. In this article, we study service provisioning in an MEC network for multi-source IoT applications with SFC requirements and aiming at minimizing the cost of such service provisioning, where each IoT application has multiple data streams from different sources to be uploaded to a location (cloudlet) in the MEC network for aggregation, processing, and storage purposes. To this end, we first formulate two novel optimization problems: the cost minimization problem of service provisioning for a single multi-source IoT application, and the service provisioning problem for a set of multi-source IoT applications, respectively, and show that both problems are NP-hard. Second, we propose a service provisioning framework in the MEC network for multi-source IoT applications that consists of uploading stream data from multiple sources of the IoT application to the MEC network, data stream aggregation and routing through the VNF instance placement and sharing, and workload balancing among cloudlets. Third, we devise an efficient algorithm for the cost minimization problem built upon the proposed service provisioning framework, and further extend the solution for the service provisioning problem of a set of multi-source IoT applications. We finally evaluate the performance of the proposed algorithms through experimental simulations. Simulation results demonstrate that the proposed algorithms are promising.

The Internet of Things (IoT) technology provisions unprecedented opportunities to evolve the interconnection among human beings. However, the latency brought by unstable wireless networks and computation failures caused by limited resources on IoT devices prevents users from experiencing high efficiency and seamless user experience. To address these shortcomings, the integrated Mobile Edge Computing (MEC) with remote clouds is a promising platform to enable delay-sensitive service provisioning for IoT applications, where edge-clouds (cloudlets) are co-located with wireless access points in the proximity of IoT devices. Thus, computation-intensive and sensing data from IoT devices can be offloaded to the MEC network immediately for processing, and the service response latency can be significantly reduced. In this paper, we first formulate two novel optimization problems for delay-sensitive IoT applications, i.e., the total utility maximization problems under both static and dynamic offloading task request settings, with the aim to maximize the accumulative user satisfaction on the use of the services provided by the MEC, and show the NP-hardness of the defined problems. We then devise efficient approximation and online algorithms with provable performance guarantees for the problems in a special case where the bandwidth capacity constraint is negligible. We also develop efficient heuristic algorithms for the problems with the bandwidth capacity constraint. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results demonstrate that the proposed algorithms are promising in reducing service delays and enhancing user satisfaction, and the proposed algorithms outperform their counterparts by at least 10.8 percent.

Mobile Edge Computing (MEC) has emerged as a promising paradigm catering to overwhelming explosions of mobile applications, by offloading the compute-intensive tasks to an MEC network for processing. The surging of deep learning brings new vigor and vitality to shape the prospect of intelligent Internet of Things (IoT), and edge intelligence arises to provision real-time deep neural network (DNN) inference services for users. To accelerate the processing of the DNN inference of a request in an MEC network, the DNN inference model usually can be partitioned into two connected parts: one part is processed on the local IoT device of the request; and another part is processed on a cloudlet (server) in the MEC network. Also, the DNN inference can be further accelerated by allocating multiple threads of the cloudlet in which the request is assigned.In this paper, we study a novel delay-aware DNN inference throughput maximization problem with the aim to maximize the number of delay-aware DNN service requests admitted, by accelerating each DNN inference through jointly exploring DNN model partitioning and multi-thread parallelism of DNN inference. To this end, we first show that the problem is NP-hard. We then devise a constant approximation algorithm for it. We finally evaluate the performance of the proposed algorithm through experimental simulations. Experimental results demonstrate that the proposed algorithm is promising.

With the integration of Mobile Edge Computing (MEC) and Network Function Virtualization (NFV), service providers are able to provide low-latency services to mobile users for profit. In this paper, we study the problem of service instance placement and request assignment in an MEC network for a given monitoring period, where service requests arrive into the system without the knowledge of future arrivals. Each incoming request requires a specific service with a maximum tolerable service delay requirement. The problem is to maximize the profit of the service provider by admitting service requests for the monitoring period, which can be achieved by preinstalling service instances into cloudlets to shorten service delays, and accommodating new services by removing some idle service instances from cloudlets due to limited computing resources. We then devise an efficient deep-reinforcement-learning-based algorithm for this dynamic online service instance placement problem. We finally evaluate the performance of the proposed algorithm by conducting experiments through simulations. Simulation results demonstrate that the proposed algorithm is promising.

Spelling Error Correction (SEC) that detects and corrects spelling errors in a text has a wide range of applications in human language understanding. Earlier solutions, including statistic-based methods, one-stage, and two-stage machine learning-based methods, cannot build deeply bidirectional models and significantly confine the learning ability. With the recently emerging masked language models, transformer-based networks have achieved remarkable success in SEC. However, current transformer-based Chinese SEC algorithms are all end-to-end methods, which suffer from high false alarm rates because they correct each character of the sentence regardless of its correctness. This issue becomes even more severe when there exist only a small fraction of incorrect characters in the whole sentence. To solve this problem, we propose a cloze-style detector-corrector framework (DCSpell) that firstly detects whether a character is erroneous before correcting it. Specifically, DCSpell employs the discriminator of ELECTRA as the Detector to detect the positions of incorrect characters. The Detector is trained by a sample-efficient replaced token detection pre-training task, and thus allows domain adaption with a small amount of data. After that, a transformer-based Corrector is used to find the correct character for each detected position. It employs sentence pairs as the input, which potentially incorporates the knowledge of phonological and visual similarity. A confusion-set-based post-processing is used to further improve the performance. Experiments show that DCSpell achieves 15.7% improvement on the SIGHAN dataset and 6.6% improvement on a dataset transcribed from a real-world acoustic speech corpus compared to the state-of-the-art methods in terms of the F1 score.

The accelerated development of the Internet of Things (IoT) incurs a great demand for data acquired from Wireless Sensor Networks (WSNs), leading to considerable attention on data collection of WSNs in recent years. With the high agility, mobility and flexibility, the Unmanned Aerial Vehicle (UAV) is widely considered as a promising technology for data collection in WSNs. Along with the Orthogonal Frequency Division Multiple Access (OFDMA) technique, the UAV is capable to collect data from multiple sensors simultaneously within its communication range (referred to as the one-to-many data collection scheme), which improves data collection efficiency significantly. In this paper, we focus on the improvement of the data collection efficiency in WSNs under the one-to-many data collection scheme via the trajectory finding of a UAV for data collection. To this end, we first formulate a novel data collection maximization problem in WSNs via deploying an energy-constrained UAV and show the NP-hardness of the problem. We then devise an efficient algorithm for the problem by investigating the impact of UAV hovering locations on the data collection. We finally evaluate the performance of the devised algorithm through experimental simulations. Simulation results demonstrate that the proposed algorithm is promising, and outperforms the other heuristics significantly.

Mobile Edge Computing (MEC) has emerged as a promising networking paradigm that provides delay-sensitive service for mobile users at the edge of core networks, where mobile users can offload their computing-intensive tasks to MEC networks for processing on no time. Furthermore, with the advance of communication and fabrication technologies, mobile devices now have adequate computing and storage processing capabilities. The device-to-device (D2D) offloading as a new offloading technique enables mobile users to offload their tasks to other mobile devices (referred as helper mobile devices) for processing, thereby alleviating the processing burden on servers in MEC. However, fully utilizing the D2D technique in an MEC network for task offloading service is challenging. Particularly, the mobility of both mobile users and their helper mobile devices makes efficient offloading service placement become difficult. In this paper, we study a novel Mobility-aware Dynamic Offloading Service Placement (MDOSP) problem in a D2D-assisted MEC environment with the aim to minimize the total cost of offloading task services that consists of the computing cost, communication delay cost and migration cost, without the knowledge of future mobility information of mobile users and helper mobile devices. We first formulate an Integer Nonlinear Programming (INP) for the offline setting of the problem. We then prove the NP-hardness and develop an online algorithm with a provable competitive ratio for the problem. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results demonstrate that the proposed algorithms are promising, compared with existing baseline algorithms.

With the advent of Network Function Virtualization (NFV) technology, more and more mobile users make use of virtual network services in Mobile Edge Computing (MEC) networks. Each service request not only requests for a service but also a Service Function Chain (SFC) associated with the request. How to effectively allocate resources in MEC to meet the resource demands of user service requests to maximize the expected profit of the network service provider poses a great challenge. Most existing studies considered resource allocation and scheduling in MEC for user request admissions, under the assumption that the amounts of different resources demanded by each request are givena priorand do not change during the execution of the request. In practice, the resource demands during the implementation of a request are dynamically evolving. This uncertainty of resource demands at different execution stages of the request does impact the service quality and the profit of network service providers. Thus, providing robust services to users against their resource demand uncertainties is a critical issue. In this paper, we study the robust service function chain placement (RSFCP) problem under the uncertainty assumption of both computing resource and data rates demanded by request executions, through the placement of SFCs. We first formulate the RSFCP problem as a Quadratic Integer Programming (QIP) and show that the problem is NP-hard. We then develop a near-optimal approximation algorithm for it, by adopting the Markov approximation technique. We also analyze the proposed approximation algorithm with the optimality gap, and the bounds on the convergence time and perturbation caused by resource demand uncertainties. We finally evaluate the performance of the proposed algorithm through analytical and empirical analyses. Experimental results demonstrate that the proposed algorithm is promising, compared with existing baseline algorithms.

Mobile Edge Computing (MEC) has been envisioning as a promising technology to address limited computing and storage resources in mobile devices. The virtual services provided by the MEC platform are implemented as instances of Virtual Network Functions (VNFs). However, these VNF instances as pieces of software that run in virtual machines (VMs) are not always reliable. To provide reliable services for their users while meeting user service reliability requirements, the service providers of MEC usually adopt the replica policy that deploy a certain number of service replicas for each VNF instance. In this paper, we study reliable service provisioning in an MEC network through redundant placement of instances of VNFs. We assume that each service request consists of a Service Function Chain (SFC) requirement and a service reliability requirement. We formulate a novel reliability-aware service function chain provisioning problem with the aim to maximize the number of requests admitted, while meeting the specified reliability requirement of each admitted request. We first show that the problem is NP-hard, and formulate an ILP solution for the problem when the problem size is small. We then develop a randomized algorithm with a provable approximation ratio and high probability for the problem when the problem size is large, and the achieved approximation ratio is at the expense of moderate computing capacity and reliability constraint violations. We also devise an efficient heuristic for the problem without any resource and requirement constraint violations. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results demonstrate that the proposed algorithms are promising.

We are embracing an era of Internet of Things (IoTs). However, the latency brought by unstable wireless networks and computation failures caused by limited resources on IoT devices seriously impacts the quality of service of user experienced. To address these shortcomings, the Mobile Edge Computing (MEC) platform provides a promising solution for the service provisioning of IoT applications, where edge-clouds (cloudlets) are co-located with wireless access points in the proximity of IoT devices, and the service response latency can be significantly reduced. Meanwhile, each IoT application usually imposes a service function chain enforcement for its data transmission, which consists of different service functions in a specified order, and each data packet transfer in the network from the gateways of IoT devices to the destination must pass through each of the service functions in order.In this paper, we study IoT-driven service provisioning in an MEC network for various IoT applications with service function chain requirements, where an IoT application consists of multiple data streams from different IoT sources that will be uploaded to the MEC network for aggregation, processing, and storage. We first formulate a novel cost minimization problem for IoT-driven service provisioning in MEC networks. We then show that the problem is NP-hard, and propose an IoT-driven service provisioning framework for IoT applications, which consists of streaming data uploading from multiple IoT sources to the MEC network, data stream aggregation and routing, and Virtual Network Function (VNF) instance placement and sharing in cloudlets in the MEC network. In addition, we devise an efficient algorithm for the problem, built upon the proposed service framework. We finally evaluate the performance of the proposed algorithm through experimental simulations. Experimental results demonstrate that the proposed algorithm is promising, compared with the lower bound on the optimal solution of the problem and another comparison heuristic.

The Internet of Things (IoT) technology offers unprecedented opportunities to interconnect human beings. However, the latency brought by unstable wireless networks and computation failures caused by limited resources on IoT devices prevents users from experiencing high efficiency and seamless user experience. To address these shortcomings, the integrated MEC with remote clouds is a promising platform, where edge-clouds (cloudlet) are co-located with wireless access points in the proximity of IoT devices, thus intensive-computation and sensing data from IoT devices can be offloaded to the MEC network for processing, and the service response latency can be significantly reduced. In this paper, we study delay-sensitive service provisioning in an MEC network for IoT applications. We first formulate two novel optimization problems, i.e., the total utility maximization problems under both static and dynamic offloading task request settings, with the aim to maximize the accumulative user satisfaction of using the services provided by the MEC. We then show that the defined problems are NP-hard. We instead devise efficient approximation and online algorithms with provable performance guarantees for the problems. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results demonstrate that the proposed algorithms are promising.

The Mobile Edge-Cloud (MEC) network has emerged as a promising networking paradigm to address the conflict between increasing computing-intensive applications and resource-constrained mobile Internet-of-Thing (IoT) devices with portable size and storage. In MEC environments, Virtualized Network Functions (VNFs) are deployed for provisioning network services to users to reduce the service cost on top of dedicated hardware infrastructures. However, VNFs may suffer from failures and malfunctions while network service providers have to guarantee continuously reliable services to their consumers to meet the ever-growing service demands of users, thereby securing their revenues for the service. In this article, we focus on reliable VNF service provisioning in MECs, by placing primary and backup VNF instances to cloudlets in an MEC network to meet the service reliability requirements of users. We first formulate a novel VNF service reliability problem with the aim to maximize the revenue collected by admitting as many as user requests while meeting their different reliability requirements, assuming that requests arrive into the system one by one without the knowledge of future arrivals, and the admission or rejection decision must be made immediately. We then develop two efficient online algorithms for the problem under two different backup schemes: the on-site (local) and off-site (remote) schemes, by adopting the primal-dual updating technique. Both algorithms achieve provable competitive ratios with bounded moderate resource capacity violations. We finally evaluate the proposed algorithms through experimental simulations. Experimental results demonstrate that the proposed algorithms are promising, compared with existing baseline algorithms.

Mobile Edge Computing (MEC) has emerged as a promising paradigm to address the conflict between increasing computing-intensive applications and resource-constrained mobile Internet-of-Thing (IoT) devices with portable size and storage. In MEC environments, Virtualized Network Functions (VNFs) are deployed for provisioning network services to users to reduce the service cost on top of dedicated hardware infrastructures. However, VNFs may suffer from failures and malfunctions while network service providers have to guarantee continuously reliable services to their users to meet ever-growing service demands of the users. In this paper, we focus on reliable VNF service provisioning in MECs, by provisioning primary and backup VNF instances in order to meet the reliability requirements of mobile users. We first formulate a novel VNF service reliability problem with the aim to maximize the revenue collected by admitting as many as user requests while meeting individual user service reliability requirements. We then develop two efficient on-line scheduling algorithms for the problem under two different backup schemes: on-site (local) and off-site (remote) schemes, by adopting the primal and dual updating technique. Particularly for the on-site scheme, the proposed on-line algorithm achieves a provable competitive ratio with bounded moderate resource violations. We finally evaluate the proposed algorithms through experimental simulations. The experimental results demonstrate that the proposed algorithms are promising, compared with existing baseline algorithms.