With the rapid proliferation of Industrial Internet of Things (IIoT) devices, massive Delay Sensitive and Computation Intensive (DSCI) tasks are generated. Traditional Mobile Edge Computing (MEC) systems face limitations like inter-cell interference at cell edges, degrading Quality of Service (QoS). To address this, Cooperative Access Edge Networks (CAEN) enable dynamic Access Point (AP) clusters for enhanced transmission reliability in IIoT. However, challenges arise from multi-user interference, bandwidth contention, dynamic environments, and heterogeneous resources, complicating joint resource orchestration. This paper proposes EdgeDiffuse, a diffusion-enhanced distributed resource orchestration algorithm, which optimizes task offloading selection, transmission power control, and computational resource allocation to minimize long term task completion time while promoting system load balancing. EdgeDiffuse enables adaptive and hierarchical coordination between user agents and edge servers. It integrates diffusion models under a Multi-Agent Deep Reinforcement Learning (MADRL) framework for improved policy exploration in high dimensional offloading decision spaces, and further uses convex optimization for server side resource allocation. Experimental results demonstrate that EdgeDiffuse achieves 28.17% reduction in task completion time, 7.40% improvement in task transmission rates, and 15.71% enhancement in load balancing compared to advanced baselines, showcasing superior performance in multi-user and resource constrained scenarios.

With the wide use of distributed energy resources, it is important to build efficient and trustworthy coordination among source, grid, load, and storage (SGLS) for the Energy Internet. Blockchain can provide a base of trust, but getting off-chain data through decentralized oracles still faces problems of low efficiency and poor reliability. To address these issues, this paper proposes a four-layer architecture that joins blockchain and oracle services. It also includes a data aggregation and checking algorithm based on threshold signatures and a reputation-based oracle selection method. The main idea of the algorithm is to find reliable nodes more efficiently by using a changing, multi-factor reputation model, and to make the aggregation process faster through preselection and threshold signatures. In this way, it keeps both reliability and efficiency in complex network settings. Simulation results show that the proposed method increases the speed of putting data on the chain and lowers delay, while enhancing the robustness of the oracle network under adverse network conditions. This work providesuseful technical support for building an efficient and dependable distributed-energy coordination infrastructure.

Few-shot knowledge graph completion (few-shot KGC) mines unseen knowledge by leveraging meta-learning and contrastive learning to achieve accurate predictions with limited triples. Recent studies have focused on designing distance or similarity metrics to provide better knowledge representation between entities and relations. However, three issues with negative sampling remain unexplored: 1) the construction of negative queries heavily relies on manual experience in selecting candidate tail entities, 2) the constructed negative queries may mislabel potential true facts, and 3) the varying difficulties of negative queries are ignored. To solve the above issues, in this paper, we introduce curriculum learning into few-shot KGC and propose a novel few-shot KGC framework empowered by an adaptive negative sampling mechanism, which can eliminate the dependence on any additional manual experience, reduce mislabeling, and generate negative queries with appropriate difficulty. Specifically, the proposed framework includes two alternating phases. In the negative sampling phase, we first design a novel positive-unlabeled learning based scoring function with a type-related candidates encoder and then build a variable-speed sliding window based pacing function to select negative queries with appropriate learning difficulty under current training step. In the meta-training phase, we develop an adapted triple-oriented knowledge encoder to provide accurate representation for queries. Experimental results demonstrate that the proposed framework outperforms the state-of-the-art baselines and provides negative queries with appropriate difficulty in few-shot KGC.

Multimodal event extraction task aims to identify event types and arguments from visual and textual representations related to events.Due to the high cost of multimedia training data, previous methods mainly focused on weakly alignment of excellent unimodal encoders.However, they ignore the conflict between event understanding and image recognition, resulting in redundant feature perception affecting the understanding of multimodal events.In this paper, we propose a multimodal event extraction strategy with a multi-level redundant feature selection mechanism, which enhances the event understanding ability of multimodal large language models by leveraging knowledge editing techniques, and requires no additional parameter optimization work.Extensive experiments show that our method outperforms the state-ofthe-art (SOTA) baselines on the M2E2 benchmark.Compared with the highest baseline, we achieve a 34% improvement of Precision on event extraction and a 11% improvement of F1 on argument extraction.

The development of the Internet of Things (IoT) has led to the rapid growth of the types and number of connected devices and has generated large amounts of complex and diverse traffic data. Traffic identification on edge servers solves the real-time and privacy requirements of IoT management and has attracted much attention, but still faces several problems: (1) traditional machine learning (ML) models rely on artificially constructed features, and the existing deep learning (DL) traffic identification models have reached their performance limit; and (2) insufficient computing resources of edge servers limit the possible improvement in the performance of deep learning models by increasing the number of parameters and structural complexity. To address these issues, we propose a lightweight fusion model. First, the Network-in-Network (NiN) model and Random Forest (RF) model are used on the cloud server to construct a traffic identification fusion model. The excellent representation extraction capability of the NiN compensates for the RF’s dependence on manual feature extraction, and its modular structure is suitable for the subsequent model compression operations. Then, the NiN was distilled. We propose Growth-Adaptive Distillation to lightweight the NiN model, which can reduce the operation of manually adjusting the structure of the student model and ensure the efficiency and low power consumption of the fusion model deployment. In addition, both the RF in the cloud and the distilled NiN are deployed on the edge server. Comparisons with multiple algorithms on two network traffic datasets show that the proposed model achieves state-of-the-art performance while ensuring the use of minimal computational resources.

As the development of B5G and 6G continues to progress, higher network bandwidth and increasingly complex vehicle connectivity are driving greater concurrency in highly dynamic and delay-sensitive transportation tasks within the Internet of Vehicles (IoV). Existing resource allocation methods such as Deep Reinforcement Learning (DRL), Graph Neural Network (GNN), Lyapunov and simple Transformer series often result in insufficient individual consideration or unprioritized attention on key resource characteristics, causing high task execution time cost and energy consumption. To overcome above problems, this paper proposes a Dynamic Self-Feedback (DSF) resource allocation approach. First, DSF models task latency and requirements along with diverse computing power to support allocation and dynamically adjusts the dimensions of self-attention heads according to resource consumption prediction in a self-feedback manner. Then, DSF adjusts dimensions of attention embedding to light and heavy tasks as feedback and leads next round of allocation optimization. Therefore, DSF enables individually tailored and energy-efficient allocation of computing resources for high concurrent IoV tasks. Simulations show the proposed mechanism achieves up to 85% tasks execution efficiency and 38% fewer timeout tasks, more than 50% of low energy consumption tasks after allocation, with almost 100% units having a workload lower than 40%.

With the rapid proliferation of the Internet of Things (IoT), network traffic prediction has become crucial for intelligent network management, enabling more reliable and flexible services for a vast array of IoT devices and applications. The heterogeneous and dynamic nature of IoT networks introduces complex spatial relations and underlying periodic dependencies in spatial-temporal graphs that existing methods struggle to model effectively. In this article, we propose multirepresentation spatial-temporal graph convolutional networks (MRSTGCNs), a novel unified framework specifically designed to address these challenges. MRSTGCN integrates a multirepresentation graph convolutional network (MRGCN) module to model node heterogeneity and complex traffic propagation, and two complementary embedding modules—Historical Embedding and Temporal Embedding—to capture and fuse periodic dependencies across different fine-grained temporal cycles. Extensive experiments are conducted on two network traffic datasets, and the results demonstrate that MRSTGCN achieves state-of-the-art performance with obvious improvements in MAE, RMSE and MAPE on three prediction horizons.

Network traffic prediction is crucial for network operation and management, forming the basis for utilizing big data in decision support. Traditional deep learning methods require extensive, assumed independently and identically distributed(IID) data. However, with IoT development, privacy protection gains importance, resulting in distributed data collection with varying distributions. This leads to a significant performance drop when applying a well-trained model to a new dataset, causing domain shift. To tackle domain shift from inconsistent data distributions and meet privacy protection needs, this paper proposes a spatial-temporal feature learning method within the federated domain generalization framework for network traffic prediction. The ultimately trained model effectively generalizes to an unseen domain, as confirmed by experimental results.

The complexity and dynamism of modern networks pose significant challenges to network management services. Existing technologies often exhibit latency in migrating services after encountering problems. However, adopting a proactive approach through anomaly detection before migration enables the early reservation of resources, thereby ensuring overall performance and stability. This paper proposes a Network Management Service Composition (NMSC) migration method leveraging anomaly detection. The method includes an anomaly detection approach using a Transformer model with a time decay mechanism and a multi-agent reinforcement learning algorithm enhanced by a graph attention autoencoder. First, a time decay mechanism is designed to enhance the self-attention mechanism, allowing the model to capture long-term dependencies while maintaining high sensitivity to recent events. Second, a critic network, enhanced by a graph attention autoencoder, enables the multi-agent algorithm to comprehend the interaction dynamics among agents during computation. Experimental results demonstrate that the proposed anomaly detection algorithm significantly outperforms existing algorithms. Furthermore, the service composition migration algorithm exhibits superior performance in terms of reward value and migration delay, thus proving its effectiveness and feasibility.

With the deep integration of a new generation of information technology and physical manufacturing, equipment in all walks of life and fields has transformed to digitalization, networking and intelligence, and the Internet of Things puts forward higher requirements for ubiquitous interconnection, security, reliability, intelligence and efficiency. The data interaction of IoT terminal devices has cross-system, cross-enterprise, and cross-business requirements, but this also leads to many sensitive information in the Internet of Things network such as hidden leakage and difficulty in distinguishing the authenticity of data information. Based on the above challenges, this paper proposes a distributed authentication scheme based on DPoS consensus algorithm and a dynamic reputation evaluation mechanism based on smart contracts, which improves the authentication efficiency and the anti-attack ability of the authentication network and maintains the security and stability of the network. At the same time, the dynamic reputation evaluation results are uploaded to the blockchain storage, which not only ensures the security and immutability of data, but also provides queryable historical reputation records for subsequent terminal access authentication evaluation. Safety analysis and performance simulation experiments show that the proposed scheme has high safety and good performance.

The occurrence of faults increases in edge networks as the service types and component architecture become increasingly complex. Traditional centralized cloud fault prediction technology cannot be directly applied to edge network systems that are typically required to handle real-time data due to their high-computational complexity. Therefore, this article proposes a lightweight fault prediction algorithm for edge networks that realizes fault prediction with cross-layer cooperation. First, for edge devices with limited computation resources, the time feature lightweight extraction method of brain neurology fusion long short term memory (LSTM) based on scene reappearance mechanisms is proposed, which solves the single-step dependency problem of neurons and improves the accuracy. And the LSTM neuron connection method based on pulse dynamics is designed. This method prunes the structure of the LSTM network based on relevant knowledge of biological neurology to realize a lightweight time feature extraction model of fault information. Then, on the edge server side, a spatial feature lightweight extraction method based on a two-way residual structure is proposed. This method uses decomposition convolution to reduce the number of network parameters and realize a lightweight model. Finally, the spatio-temporal correlation features of the extracted fault information are spliced to realize fault prediction. To verify the effectiveness of the proposed model, we compare the improved model with the existing fault prediction model. The experiments show that the algorithm proposed in this article has higher accuracy, lower complexity and lower memory requirements. Therefore, it has high-deployment potential in edge network scenarios.

Deep convolutional neural networks are hindered from empowering resource-constrained devices due to their demanding computational and storage resources. Structured pruning effectively removes the redundant components from neural networks and obtains compact models. Previous pruning methods usually evaluate the importance of filters from a layer-wise perspective, which is deprived of global guidance. We propose an adaptive pruning algorithm based on relevance scores to evaluate the contribution of each channel by calculating its relevance score from the back-propagation of the neural network's output. Our method identifies and removes channels with low contribution from a global perspective. Unlike previous methods that manually set the pruning rate for each pruning iteration, our method adaptively adjusts the pruning rate. In addition, our method performs satisfactorily with limited data for one-shot pruning in the absence of fine-tuning. The ability to obtain compact models through one-shot pruning with limited data is ideally suited for edge computing scenarios. We validate the effectiveness of our method with multiple combinations of convolutional neural networks and datasets. Our approach outperforms existing pruning methods in scenarios with limited data.

Abstract In recent years, Named Data Network (NDN) has become a popular network architecture because of high resource utilization, strong security and high transmission efficiency. Meanwhile, mobile multimedia communication has become the mainstream with the popularization and application of smart terminals. Most of the research on NDN mobility is focused on consumer mobility without taking producer mobility into account. To solve the delay and high cost carried by producer moving, we propose a Double-Lead content search algorithm based on neighbor and proxy and a location prediction algorithm based on traffic features. We use a neural network model to predict a new location of producers and calculate route before switching, which can save the rerouting latency in advance when predicting accurately. In a few cases of inaccurate predictions, we select different search methods according to the distance of the producer’s movement, to complete the Double-Lead search between the producer and the consumer. Experimental results show that DLPNDN can reduce the delay and traffic overhead well in NDN when the producer moves.

The rapid development of digital technology is reshaping the architecture of the Industrial Internet of Things (IIoT). The traditional architecture cannot process vast amounts of data exchanges and provide entities with trust. The future IIoT is expected to be a decentralized architecture in which blockchain and digital twin-driven IIoT can enable trusted data exchanges. However, this architecture cannot obtain huge amounts of external real-time data and isolated data. Moreover, it cannot handle complex industrial computing tasks. Therefore, we combine oracle with decentralized learning to propose a novel IIoT-oriented digital twin architecture. We also propose an effective decentralized collaboration mechanism to support external data and resources exchanges. Moreover, we propose a novel computing collaboration mechanism to expand the learning capabilities of the industrial ecology. Experiments show that our proposed paradigm has less processing time, a more stable process, and better learning ability compared to other paradigms.

Vehicular ad hoc networks (VANETs) have the characteristics of high mobility, frequently changing topology and uneven distribution, which made it a challenge to design an efficient and robust routing protocol with low latency and high packet delivery rate. Currently, intersection-based routing method and full-path based routing method are two popular solutions for the packet routing in VANETs. Although the intersection-based routing method has better real-time performance, it has the problem of local optimization, making the routing results not global optimal. Although the full-path based method can obtain the global optimal solution, it is weak in dealing with the dynamically changing topological network. Aiming at solving the above problems, this paper designs an intersection-based QoS routing (IQRRL) algorithm, which mainly includes two crutial steps: the next intersection selection and the next hop vehicle selection. In the selection of the next intersection, this paper uses an improved intersection-based routing protocol. In addition to considering connectivity and delay, IQRRL also considers the communication quality from the neighbor’s road to the destination node while evaluating the quality of the neighbor’s road, which minimizes the problem of local optimization. When the next intersection is determined, a road is then determined, and then the next hop vehicle within the road should be chosen to relay the packet forward. In the next-hop vehicle selection step, this paper adopts multi-hop evaluation technology based on reinforcement learning. In addition to using “greedy decision-making” to select the next-hop vehicle, it also comprehensively evaluates whether the next-hop vehicle is still optimal in the future, so that the stability and reliability of data forwarding are improved and local optimal problems are avoided. Besides, this article uses a simulation system to compare IQRRL with other routing algorithms. The result reveals that IQRRL outperforms in terms of packet delivery ratio and transmission delay.

Network traffic classification is a key foundation of traffic management and network security. With the development of traffic encryption technologies and more attention given to user privacy, traditional rule-based and payload-based traffic classification methods have become less effective. To address this problem, recent studies have introduced deep learning-based methods. However, most of these studies do not consider both the flow-level and packet-level characteristics, which we believe are significant in network traffic classification. To further improve the accuracy of traffic classification, this paper proposed DM-HNN, a hybrid neural network based on dual-mode features. First, we treat the packet length sequence as the flow-level feature and the initial byte of the packet as the packet-level feature. Then, we diverge into two paths to analyze the dual-mode features using neural networks. Finally, we combine the two-path features and output the final classification results. We have performed the experiments on public datasets, the results comparing to single-mode and dual-mode traffic classifiers indicate that DM-HNN can achieve excellent performance and has certain effectiveness.

Network function virtualization (NFV) is an emerging technology that uses virtualization technology to provide various services in enterprise networks and reduce costs. However, in cloud edge networks, effective virtual network function (VNF) configuration is particularly difficult, and the system design needs to consider the reliability and energy-saving while meeting the requirements of Quality of Service (QoS). This paper uses the binary integer programming (BIP) model to study the service function chain (SFC) orchestration problem, and designs a federated deep reinforcement learning SFC orchestration algorithm (FDOA). With this method, energy consumption can be reduced and the QoS of users can be improved. In addition, considering the limitations of local deep reinforcement learning (DRL) model training, this paper proposes a federated DRL algorithm to help obtain a more robust model, and simultaneously improve the convergence speed of the model. Among them, we introduce reputation theory during model training to evaluate the reliability of the nodes carrying the DRL model, avoiding the influence of unreliable models on the training effect. Finally, the simulation results show that FDOA has better performance in training time and end-to-end delay compared with other existing algorithms.

[J1C2 Presentation Abstract at IEEE SERVICES 2022 for IEEE Transactions on Services Computing DOI 10.1109/TSC.2019.2955486]

Federated learning (FL) is considered to be a promising paradigm to solve data privacy disclosure in large-scale machine learning. To further enhance the privacy protection of federated learning, prior works incorporate the differentially private data perturbation into the federated system. But it is not feasible given the impairment of the model from noise, as adding Gaussian noise to achieve differential privacy (DP) deteriorates the accuracy of the model. In particular, the assumption that the sophisticated system is homogeneous is not realistic for real scenarios. Heterogeneous networks exacerbate noise disruptions. In this paper, we present FedSeC, a novel differential private federated learning (DP-FL) framework which operates with robust convergence and high-accuracy while achieving adequate privacy protection. FedSeC improves upon naive combinations of federated learning and differential privacy approaches with an updates-based optimization of relative-staleness and semi-synchronous approach for fast convergence in heterogeneous networks. Moreover, we propose a valid client selection scheme to trade-off fair resource allocation and discriminatory incentives. Through extensive experimental validation of our method in three different heterogeneities, we show that FedSeC outperforms the previous state-of-the-art method.

Multiaccess edge computing (MEC) provides users with better Quality of Experience (QoE) via offloading tasks to the nearby edge. However, the emergence of new Internet of Things applications with multiple tasks and repeated requests brings redundant computation and transmission to the edge. Meanwhile, the current offloading method based on deep reinforcement learning (DRL) has low sampling efficiency and slow convergence issues for training in a changing environment. Therefore, improving QoE of computation offloading services is still the ultimate challenge. In this article, we devise a collaboration of computing and cache resources among multiple edge nodes, which could reduce redundant computation and transmission. Specifically, we formulate a cache-assisted computation offloading process as a QoE-aware utility maximization problem based on multidimensional indicators. Then, we propose a cache-assisted collaborative task offloading and resource allocation strategy to solve it. This strategy is decomposed into two subproblems. First, to determine and obtain task cache state, we propose a collaborative task caching algorithm, which can improve the hit rate of tasks while balancing network overhead. Second, to acquire offloading and resource allocation decisions efficiently, we propose a metareinforcement learning-based cache-assisted computation offloading method (MCCOM), which can achieve rapid offloading decisions with a few gradient updates and samples. The optimization problem was transformed into multiple Markov decision processes (multiple MDPs). The improved learning process includes metapolicy learning that adapts to multiple Markov decision processes (MDPs) and policy learning for a specific MDP based on metapolicy. Simulation results show that our proposed method outperforms baselines in terms of QoE indicators while achieving rapid convergence and decisions.

With the rapid development of communication networks, there are more stringent requirements for maintenance management. To carry out smart network maintenance automatically and effectively, this paper uses wearable devices, robots, and Unmanned Aerial Vehicles (UAVs) to collect on-site video data and detect defects in real-time. However, the deep learning models deployed in these devices should have fewer model parameters and less storage space. Therefore, we propose a model compression algorithm based on model pruning and model clustering in smart network maintenance. First, model pruning combines channel pruning and layer pruning and uses deep reinforcement learning to determine the pruning ratio of each layer automatically. This method can effectively compress the width and depth of the model while maintaining the accuracy of the model. Second, although the pruning operation greatly reduces the redundancy of the number of weight parameters, the number of bits of floating-point weights is still redundant. We propose an adaptive model clustering method to cluster the remaining nonzero parameter weights and compress the model. It combines advanced balanced iterative reducing and clustering using hierarchies (BIRCH) clustering and K-meansII clustering and takes the result k value of BIRCH clustering as the input of K-meansII. It can avoid limitations of prior knowledge and reduce clustering time. Simulation results show that the target detection model can reduce parameter redundancy, save storage space, and simplify calculation using the proposed algorithm. In addition, it can be better applied in the smart network maintenance environment.

The continuous development of network technology has driven the emergence of smart devices, and the demand for smart devices interconnection has increased sharply, which requires the identity of devices to be authenticated to carry out secure communication. The traditional certificate-based identity authentication scheme can no longer meet the authentication requirements of massive devices. As an authority that issues and manages certificates, Certificate Authority (CA) creates data islands of intra-domain certificates, increasing the complexity of cross-domain authentication. In order to improve the efficiency of cross-domain authentication, this paper introduces blockchain technology, which can establish trust in an untrusted environment. We propose a multi-CA-based authentication architecture to establish distributed trust and share cross-domain certificate information among multiple domains. On this basis, we design a simplified identity authentication scheme to quickly complete cross-domain identity authentication and reduce authentication overhead. To further improve the efficiency of cross-domain authentication, a cross-domain certificate revocation mechanism is designed. The scheme has passed the formal security analysis, and the simulation results show that the cross-domain authentication scheme is efficient.

With the development of Big Data technology and Internet, the surge of data in the network will cause network congestion and untimely task processing. Additionally, caching content in the core network may cause redundant access of content and backhaul bottlenecks. Due to the increasing requirements of users for task processing efficiency, the centralized maintenance system based on traditional cloud computing cannot meet the current computing requirements. In view of these problems, we propose a content collaborative caching mechanism based on joint decision of download delay and energy consumption. By integrating network coding and content caching technology, the work content maintained in the communication network is deployed near the edge of the network in the form of coding to reduce the redundant transmission of content and acquisition time of content. This article establishes a user QoE satisfaction model, which consists of two indexes that measure time delay and energy consumption. This article proposes a$\varepsilon$-hybrid Q-learning algorithm to optimize the placement of cache files, and made the cache action selection based on the combination of improved heuristic greedy algorithm and simulated annealing algorithm. The experimental results show that the proposed cache strategy can reduce the delay of users downloading content and the energy consumption of content cache, so as to improve the quality of field maintenance work in communication network.

With the development of complex networks and with increasing service demands, service use is becoming more complex and the composition of services is becoming more complicated. In the XaaS (X as a Service) environment, users only care about the QoE of a service and do not care about the composition process of the service. Therefore, it is important to evaluate the reliability of the entire service. In this article, we use Petri Net as a basis for modeling the composition of services. In addition, we consider the problems of shared resources and common cause faults. Both of these problems can cause network congestion and regional failures. We use distance to assess the effects of regional faults and queuing theory to simulate the network congestion process. Moreover, in the simulation, we verify the impacts of regional failures and network congestion on service reliability. We choose the Tree-Based Search algorithm and the Semi-Markov Model as comparison algorithms. The results of our algorithm are related to service time. Our algorithm can timely reflect the impact of regional failure or network congestion, and it can feedback different evaluation results according to environmental changes. Therefore, our algorithm is more comprehensive and has better performance.

Blockchain as a new decentralized chain structure can be used in edge computing to solve the security issue caused by edge nodes in model training. However, large amounts of data exchanges in the process of model training of edge computing reduce the performance of blockchain, and meanwhile, the block needed to be saved in the edge node challenges storage capacity of the edge node. Therefore, in the paper we propose a safe and efficient model training mechanism based on onchain and offchain collaboration. In the mechanism, edge nodes train models locally, store the model parameters in offchain and only return identifiers for model aggregation. By the method, the storage pressure of the edge node is reduced and the efficiency of executing consensus algorithms are increased. Moreover, in the mechanism we design a reputation evaluation model based on confidence factors to avoid the uploading of random and wrong data of edge nodes. Evaluation results show that our schemes can reduce the average delay and resources consumption, increase transaction throughput and maintain security compared with a state-of-the-art scheme.

The blockchain system allows various trans-actions and information storage to be executed in a decentralized manner, while smart contracts require multiple nodes to be executed in the local sandbox environment according to preset settings to ensure the consistency of each node, which makes smart contracts unable to proactively obtain data from the outside world. Decentralized oracle can realize the acquisition of off-chain data with a low speed under the premise of ensuring the decentralization of the blockchain. Some oracles use on-chain data storage and maintenance to speed up data acquisition, but this will face higher costs of data storage and maintenance, so current oracles cannot simultaneously ensure privacy and security while taking into account execution cost and processing speed. In this article, we propose Select-Storage, a new oracle design pattern to achieve low operating cost and high processing speed without compromising security. Through experimental analysis, and comparison with other design patterns in processing time and on-chain and off-chain call costs, we have proved the superiority of the Select-Storage design pattern.

The power system is an infrastructure for industrial manufacturing, and its availability is relevant to industrial systems. The smart grid combines communication systems with sensing devices to provide intelligent management tools for fault monitoring and processing of the power grid. Regional failures caused by natural disasters can have a large impact on the power system. To reduce the impact of disasters, an efficient network repair strategy is needed. This article focuses on the emergency repair strategy of a power communication network under disaster conditions and seeks to ensure the operation of the power system with fewer repairs. Combined with the analysis of cascading failures and regional failures, a communication network fast repair algorithm is proposed. The coupled network is constructed in the simulation part to verify the algorithm. The results show that with a limited number of node repairs, the algorithm can ensure the highest percentage of workable nodes.

With the development of Industry 4.0, an increasing number of industrial Internet of Things (IIoT) mobile devices (MD), which constantly transmit data at any time, are working on the production line. However, due to node movement, signal attenuation, or physical obstacles, data must rely on the transmission of relay nodes to finally reach the destination node. Based on this scenario, in this article, we propose a cooperative link prediction model (CLPM) using a stacked denoising autoencoder (SDAE) to predict links of the IIoT-based MDs at the next moment through historical link information. The layer structure of the SDAE model is partitioned so that the local MD and edge servers can cooperatively process the link prediction tasks. Experimental results show that our proposed CLPM outperforms others in terms of prediction performance and execution delay.

With the development of information technology, the network consists of various proprietary hardware devices, and the use of these devices brings problems. To solve problems, network function virtualization is proposed, which decouples the software and hardware in the network, and deploys the existing network function devices to a common physical platform. However, network virtualization needs will inevitably face reliability problems during resource virtualization and service function chain deployment. This article proposes a service function chain reliability evaluation method and reliability optimization algorithm. The composition relationship and reliability influencing factors of service function chain were analyzed, including resource preemption, common cause failure, fault recovery and redundant backup. The service function chain was modeled as a Petri net model, and reliability evaluation results related to execution time were obtained. Based on the reliability assessment results, a VNF migration strategy is designed, with reliability as the optimization goal while considering costs. Simulation results show that, compared with the reliability optimization strategy based on backup, our algorithm costs less and reduces the impact of resource preemption on service reliability.

Cloud computing technologies can not satisfy the requirements of applications on the mobile terminals because of their disadvantages in delay, link load and energy. So Mobile Edge Computing (MEC) is proposed as a kind of novel computing technology. As an important research direction of MEC, service migration methods still have limitations that they cannot learn migration paths and be adaptive in dynamic situation and user movement. In this paper, we propose a novel service migration policy method based on reinforcement learning. We firstly investigate user movement, four different edge network situations and traditional migration policies. Then we formulate the system requirements by Satisfiability Modulo Theory (SMT) logic to acquire the migration policy space. We further propose a dynamic-awareness deep Q-learning algorithm to select paths from the policy space iteratively and conduct dynamic awareness to adjust learning rate adaptively. Meanwhile, the optimal convergence of our algorithm is proved theoretically. Finally, the experimental results highlight the effectiveness as migration successful rate, service interruption time and load balance of our method compared to the other solutions.

Content pre-caching is a kind of significant technology to lower response delay and improve network performance, especially for the delay-sensitive services in dynamic vehicular edge networks. Therefore, in this paper, we propose a zone-based content pre-caching strategy, which aims to implement an active content caching through two algorithms: pre-caching zone selecting algorithm- PCZS and pre-caching node selecting algorithm- PCNS. Firstly, we organize the edge servers (ESs) with a zone-based way at the edge, and assign a Manager node to collect the information of each zone; with the help of road topology and tables information recorded in Manager nodes, PCZS can predict the vehicle motion and zone sojourn time with a high accuracy, and further get a content pre-caching zone by comparing estimated request delay and zone sojourn time; then, PCNS checks whether the content has been cached in the CST of the zone selected by PCZS, if CST hits, the pre-caching process is over, otherwise, by combining ES centrality, load degree with content popularity, we perform PCNS to select a specific ES node to pre-cache the content; simulation results show that our strategy has a higher prediction accuracy and dynamic adaptability, it also outperforms in terms of average response delay and cache hit ratio.

Summary The development of the Internet of Things (IoT) and wearable technology provides an opportunity for the development of maintenance of communication. The use of wearable technology and instant messaging technology of IoT can improve the support capabilities and data interaction ability of on‐site maintenance of the communication network. Existing communication maintenance systems lack real‐time operation and maintenance of data interaction. In the field operation decision‐making and execution process, there are problems of lack of field links and inconvenient information interaction. On‐site maintenance mainly relies on maintenance personnel to actively search for information, and the retrieval results are lacking of personalization, which makes it difficult to meet the needs of on‐site maintenance of the communication network. Edge computing and information push technology can solve these problems to some extent. In this paper, we focus on the current low level in information, complicated scenes, and various information of on‐site maintenance and propose a dynamic context‐aware information push algorithm. The simulation results demonstrate that the algorithm delivers good performance in terms of precision, recall, and F1. Besides, we present a smart wearable maintenance system, an edge computing–assisted IoT platform for the real‐time guidance of technical experts and systems for on‐site maintenance personnel, aiming to improve the efficiency and quality of on‐site maintenance.

With the development of Internet of Vehicles, vehicles establish the social relationships with other vehicles and road side units for exchanging information, which is called Social Internet of Vehicles (SIoV). Making use of the relationships, we propose a QoS-based opportunistic routing mechanism to guarantee the QoS requirement and route reliable of information transmission in this paper. First, we establish a mathematical model for QoS evaluation considering the transmission correct ratio and delay, which can accurately estimate the QoS of road section. Then, we propose the QoS-based opportunistic routing mechanism, which aims to form a reliable and robust route path. Finally, the obtained simulation results validate the accuracy and correctness of our approach.

The scheduling of Virtual Network Functions (VN-Fs) is an important problem for Network Function Virtualization (NFV) resource allocation. In this paper, we investigate how to manage the Network Functions (NFs) efficiently to enhance the utilization of network resources. In the system model, we take into account the VNF transmission delay and processing delay at the same time. Our objective is to minimize the total end-to-end delay for all network services. To reduce the complexity of this issue, we propose a novel algorithm based on genetic algorithms by improving the method of crossover and mutation. The simulation results show that the proposed algorithm can reduce the total end-to-end delay at most 16.74%.

In order to ensure users enjoying the services continuously and steadily, we need an efficient service recovery strategy to quickly recover the failed links and reconstruct the device set. In this paper, we introduce a service recovery strategy based on recovery node which can save and maintain service data flexibly. First, we give the definition of recovery node and the selection mechanism for it. Then we describe our recovery strategy in detail. At last, we make a simulation by NS-3. The effectiveness of the proposed methods is demonstrated by simulation results.

To alleviate the security concerns caused by the openness of the edge computing network and meet the time-sensitive requirements of the edge devices' collaborative tasks, an effective trust evaluation mechanism is needed urgently to resist multi-attacks from various malicious devices. In this work, a light- weight trust mechanism based on multi-source feedback is proposed for edge computing. First, we design a light-weight data-processing algorithm executed in edge brokers and edge devices, which could reduce the data transmission pressure in communication networks effectively and work efficiently in large-scale edge networks. Then, a comprehensive evaluation method is designed for edge brokers based on the Dempster Shafer theory and multi-source feedback mechanism, which makes our mechanism more reliable and pluralistic when resisting various multi-attacks at the same time. At last, we originally develop a neural network in the centralized cloud to update edge brokers' hyper-parameters and weights of the key factors by auditing trust evaluation results uploaded from the edge network according to deep Q-learning algorithm, which are usually weighted manually and subjectively in traditional schemes. The experimental results show the proposed trust mechanism outperforms existing methods in reliability and calculation efficiency when resisting various malicious attacks.

With the development of the edge computing and cloud computing technology, the cloud-edge collaboration framework is proposed as a new effective computing architecture and applied in many fields. However, due to the openness of the edge networks, the security of cloud-edge framework is an unavoidable problem and most recent trust mechanism could not resist mixed malicious attacks at the same time. In this work, a light-weight and reliable trust mechanism based on the improved LightGBM algorithm is originally proposed to evaluate the credibility of edge devices. First, we design a light-weight trust mechanism for edge devices to process raw interaction data and extract trust features, which reduces the amount of data transmission and the pressure on the communication networks. In addition, an evaluation algorithm based on the entropy weight method (EWM) and punishment factors is designed for edge brokers to distinguish the malicious devices from the normal ones, which performs great against mixed malicious attacks. At last, we propose an improved LightGBM algorithm developed in the centralized cloud to learn other researchers' evaluation methods and check the evaluation uploaded from edge brokers, which could make the punishment factors of edge networks weighted adaptively with the change of edge networks. The experimental results show the proposed trust mechanism outperforms existing methods in the accuracy and discriminating speed under mixed malicious attacks.

Smart Grid Communication System (SGCS) needs to meet the QoS requirements of various applications in terms of latency, bandwidth and reliability. However, cloud computing owns significant latency and cannot meet the need of real-time applications, edge computing is gradually applied in SGCS. In this paper, LBPC (edge-based load-balancing algorithm based on popularity and centrality) is proposed based on edge network and achieves the deployment of computing units at the edge nodes. In the aspect of service caching, LBPC measures the popularity of requests and the centrality of nodes, and it also takes the latency needs and the cache cost into consideration. In the aspect of task offloading, LBPC calculates the cost at different neighbor nodes and chooses the best one to finish the calculation when the current node is in high-load condition. Compared with some related works, experimental results show that LBPC can effectively reduce communication latency and balance the network load.

With the wide application of the Internet of Things, the number of network edge devices is increasing rapidly, resulting in huge network traffic that brings huge challenges to the current network. Aiming at the problem of heavy network load, researchers proposed some caching strategies. However, current strategies have some limitations. These existing caching strategies are usually global within the whole network. Most of them are to reduce the network delay and allow users to obtain content more quickly and easily. This paper proposes a network caching strategy based on edge and cloud coordination (ECC).The strategy divides the caching network into two parts (core and edge) to discuss different caching strategies. By choosing reasonable caching strategies in the core network and the edge network to implement different caching goals for different areas. Besides, the paper sets up PN nodes for coordinating and optimizing the cache resources between the edge and the core. Experimental results show that ECC has significant advantages in Server Load Reduction Ratio, Average Hop Reduction Ratio and Cache Redundancy compared with existing methods.

With the rapid development of communication technologies such as 5G, vehicular information and applications are exploding. Mobile edge computing (MEC) as a new technology can transfer the information more quickly and accurately. Providing differentiated services for the information can affect the performance of the applications. In this study, based on 802.11p EDCA protocol, we propose a new differentiated service scheme called DD-EDCA (Differentiating Density Enhanced Distributed Channel Access). Firstly, we use MEC Server to estimate the road density. Secondly, different solutions have been designed according to different vehicle density environment requirements. For example, the displacement trend is considered at a low density, and the multi-hop broadcast information is pre-processed at a high density. And the schemes for dynamically adjusting EDCA parameters are designed. Simulation results show that our method reduces latency and packet loss rate, and improves the throughput.

Due to the limited network resources and onboard processing capacity of the LEO/MEO double-layer satellite communication network, calculating the routing table in advance leads to heavy communication load, and rerouting results in large delay loss. Hence, this paper proposes a Priority- and-Failure-Probability-based Routing (PFPR) algorithm for LEO/MEO double-layer satellite communication networks. We combine virtual node and virtual topology strategies to eliminate satellite mobility, considers service classification and link failure probability to better fulfill the QoS requirements of different services. In addition, we introduce network virtualization technology. By using the method of common mapping of disjoint primary and backup links, it solves the delay problem of rerouting. The simulation results show that the PFPR algorithm proposed can reduce packet dropout rate, and service delay, and improve service throughput for different services, especially delay-sensitive services.

Various problems arise in the maintenance of communication networks. For example, on-site maintenance personnel have insufficient work experience. Devices used for maintenance work have limited computing resources and battery life. Moreover, most maintenance systems still use the centralized single processing mode of traditional cloud computing, which increases the data center computing pressure and slows the data flow. To overcome these problems, we propose a communication network edge maintenance system based on smart wearable technology and introduce computation offloading technology for mobile edge computing (MEC). Before offloading, we propose a multimerged computing sorting segmentation (MCSS) algorithm to divide a part of the task to offload. When making an offloading decision, we access a suitable MEC service node for each user with the lowest transmission cost and establish a related model. We use an improved Kuhn-Munkras (KM) algorithm that considers fairness among users to solve this model. After that, we propose a dynamic energy-efficiency awareness strategy. When tasks are processed locally, we optimize the CPU clock frequency. When tasks are offloaded, we adaptively allocate the transmission power. Finally, we conduct a simulation experiment. The results demonstrate that the proposed scheme can reduce the transmission cost and improve the performance, thereby increasing the level of on-site maintenance work.

With the development of Internet of Things, there are more and more devices and applications at the edge of the smart grid. To enhance the quality of service, further processing of the smart grid to achieve load balancing is regarded as a critical step. The most interesting element in smart grid communications is data itself regardless of the data source. The emergency of Content-Centric network (CCN) just meets the demands and addresses the problems. First we model the smart grid with Content-Centric Network, and concentrate on the edge communication. Then we propose a load-aware potential-based routing (LAPBR) algorithm and evaluate its performances. The simulations results demonstrate the stability and robustness of LAPBR.

With the development of the Internet of Things, more and more devices can access the network through wireless access. And the wireless access of vehicles, which constitutes an edge network, can provide real-time road information and significant traffic state. Thus, it has gradually got the public attention. In order to offer the better quality of service (QoS) in the vehicular network, we propose a multi-priority bionic competition mechanism to implement service differentiation and resource allocation. Firstly, we deduce a context metric (CM) through fuzzy inference, which relates the urgency degree of a vehicle to its environment. Vehicle traffic is re-prioritized into four access categories (ACs) combined with the CM and transmission data types. Then, we propose the bionic competition model based on 802.11e EDCA protocol. This model considers the competition in the same level ACs and the competition among different level ACs, allocates different transmission rate and bandwidth for different ACs, which greatly improve the network throughput and bandwidth utilization. Finally, the simulation results show that our method improves the throughput, reduces the mean delay and packet loss rate.

In the smart grid, to improve the quality of service and to reduce the risk of network much further is the main research direction. How to choose a highly reliable, stable and low-risk routing is the most critical part of the smart grid. The factors considered by most existing algorithms do not sufficiently consider redundancy, so that optimization is not sufficient. Therefore, based on service path pressure and the special factors of the power communication network, this paper proposes a kind of multi-constrained maximally disjoint routing mechanism and states the superiority of this algorithm via experiment which is simulated on the power communication network of a certain province.

This paper studies the multi-layer structure of coupled power network based on the problem of fault cascade and unreasonable network design in the network virtualization environment (NV). Based on the complex network theory, we propose a network optimization algorithm: PNGA (Primary Nodes Group Algorithm). The objective of PNGA is promoting the robustness of the entire network. In the simulation experiment, this paper analyzes the network modeling and topological characteristics of a three-tier power grid in NV. We use the degree sorting algorithm as the control group which is widely used in power grid. Under different attack strategies, we investigate the performance of different algorithms and the state of fault generation. The results of the simulation we performed in this paper have shown that PNGA is superior to the rest of the algorithm in suppressing faults.

To make an accurate evaluation of node importance in the power grid communication network, we propose an algorithm based on the communication topology layer and the power grid layer to evaluate the importance of the nodes. On the basis of the node contraction algorithm [7], the cut point is assigned a higher weight to reflect the difference between the key nodes and the non-key nodes. Simultaneously, combined with the characteristics of the power grid, the power factor, power service and node failure probability are added to evaluate node importance of power grid communication network objectively. Compared with the node contraction algorithm, the results show that the algorithm in this paper can better distinguish the importance of nodes, and has great reference value for the evaluation of node importance in the power grid communication network. Finally, the algorithm is applied to node risk analysis. By optimizing power service routing, average node risk of entire network can reduce significantly. Therefore, the reliability of network is improved.

With the multi-rate transmission and variable bandwidth switching technology, the Elastic Optical Networks (EONs) have many advantages to satisfy current network traffic. Compared with the traditional optical network, the EONs improve the spectrum utilization and increase the network capacity. So the EONs gradually become the key point of next generation optical transport networks. Obviously, the restoration mechanism in EONs has become thefocus of network operators' attention. This paper presents a dynamic restoration scheme based on software defined network (SDN) framework and an improved regional fault-tolerant routing and spectrum allocation algorithm (RSA). Using the SDN framework, we can greatly reduce recovery time and avoid configuration contentions. On this basis, we introduce the improved regional fault-tolerant RSA algorithm. The proposed RSA algorithm can decrease restoration blocking probability and relief effects caused by regional failures. The performance of the proposed dynamic restoration is evaluated in terms of restoration blocking probability and recovery time under different network loads, and compared against other schemes.

Combining VANET with ICN (Information Centric Network), this paper proposes a new FIB (Forwarding Information Base) selecting algorithm-ECRMLET (Efficient Content Routing Model Based on Link Expiration Time). To build stable routings and reduce network traffic, our ECRMLET has the following designs: 1) we modify the structure of PIT (Pending Interest Table) by adding two domains: receive time and tolerance time; 2) we introduce the algorithm of LET (Link Expiration Time) to help with the content routing selection in FIB; 3) ECRMLET also gets the link availability probability to be auxiliary information for our algorithm.

Information Centric Networking (ICN) is a new kind of network architecture centered on content data. The ICN improves the efficiency of data transmission by the longest matching routing mechanism based on the content name prefix of the request interest packets, however, the multipath forwarding performance also resulted in the redundancy of the network content. The existing ICN forwarding strategy does not take into account the selection problem of routings when a content hit multiple Faces. This paper proposes a routing forwarding strategy based on Shapley value. We add a forwarding value table, which is used to calculate the number of content routing and the number of face to forwarding. The table stores the request delay of content routing and the busy degree of the next hop nodes. The content routing number and forwarding nodes of the next hop forwarding are decided by the alliance game. Simulations show that our strategy can improve the cache hit ratio, reduce server load and reduce the average request delay compared with full forwarding strategy, it improves the network performance in total.

In order to meet the requirements of traffic data fusion for real-time urban traffic state estimation, a new kind of fusion structure model is proposed. This fusion model consists of both spatial fusion and temporal fusion. First we use the power average operator as spatial fusion method. Then we propose a temporal correlation based data compression (TCDC) algorithm, based on segment linear regression (SLR) algorithm. Extensive simulation results demonstrate the effectiveness and correctness of TCDC algorithm, as well as TCDC's advantage over SLR on overall performance.

Recent research activities have recognized the essentiality of node mobility for the creation of stable, scalable and adaptive clusters with good performance in mobile ad hoc networks (MANETs). In this paper, we propose a distributed clustering algorithm based on the group mobility and a revised group mobility metric which is derived from the instantaneous speed and direction of nodes. Our dynamic, distributed clustering approach use Gauss Markov group mobility model for mobility prediction that enables each node to anticipate its mobility relative to its neighbors. In particular, it is suitable for reflecting group mobility pattern where group partitions and mergence are prevalent behaviors of mobile groups. We also take the residual energy of nodes and the number of neighbor nodes into consideration. The proposed clustering scheme aims to form stable clusters by reducing the clustering iterations even in a highly dynamic environment. Simulation results show that the performance of the proposed framework is superior to two well-known clustering approaches, the MOBIC and DGMA, in terms of average number of clusterhead changes.

In this paper, we propose a planning mechanism based on telecom user behavior to choose locations of telecom operator's service halls. Telecom service hall network consists of service requirements nodes (RNs) and telecom service hall sites (TSs). Telecom service hall location selection problem mainly focuses on choosing locations of TSs from RNs. With analysis of base station data, we formulate a method based on telecom user distribution model to group users and to find RNs. Then, we propose a theoretical model to obtain telecom operator's greatest economic income with constraints of service satisfaction perceived by telecom users. Finally, a mechanism combined with improved genetic algorithm is put forward to solve it. Our results, supported by extensive experiments using MATLAB, confirm the feasibility and flexibility of our proposed planning mechanism.

Existing cooperative file downloading schemes have some blindness on the selection of collaborative nodes. In this paper we present a cooperative file downloading scheme with genetic algorithm to resolve the collaborative nodes selection problem, especially in the case that we cannot have a prior knowledge about the data rate and position of the mobile nodes. A simple and efficient extended on-demand proxy discovery algorithm will be used to find the potential cooperative nodes. Character of the genetic algorithm (GA) makes it suitable for the selection of cooperative nodes. After this, client uses multiple parallel paths for file downloading. The simulation results show that the cooperative file downloading scheme with genetic algorithm can successfully select collaborative nodes with better performance and effectively reduce file download latency.

In this paper we introduce a new collaboration paradigm to achieve content discovery with infrastructure fixed on many autonomous geographical regions in mobile wireless networks. In this paradigm, mobile users, physically located in the regions, send a request to the infrastructure and obtain contents using their mobile devices. To achieve the paradigm, we design an incentive mechanism with a repeated cooperative game-theoretic approach. This approach stimulates mobile users that are selfish and often reluctant to consume their energy for providing any information or services to make their own contents available to someone else. Additionally, we discuss game ingredients in our approach, including the assessed value, the battery charge level and the signal strength level. We find all Nash equilibria in our approach and obtain the ratio of temporal discounting. The results via our simulations show that our approach can effectively motivate mobile users to share their contents in terms of the ratio.

Mobile social network service (MSNS) provides daily services for the user and can also be used in emergencies, such as natural disasters. How to conduct service composition and recovery among mobile devices quickly and efficiently is one of the important research areas of MSNS. This paper puts forward a comprehensive strategy applied to MSNS during natural disasters. When communication facilities are limited, several devices can work cooperatively to provide users with reliable composite service, also known as the service composition process. In addition, when some of the devices fail and the composite service interrupts, the presented recovery process reconstructs a service path quickly. Composition and recovery cost functions are used in the two processes separately. The goal is to find the service path or the recovery path with minimal cost function value in each process that satisfies the quality-of-service requirement. The simulation results show that the proposed strategy not only reduces the interrupt number and recovery time but also improves the success rate of the service request, making the performance of this strategy better than that of the other similar strategies.

Service search has been introduced to exploit heterogeneous resources of distributed devices on the purpose of supplying ubiquitous services in ubiquitous stub environments, especially in MANETs. However, due to the characteristics of infrastructure-less, devices' limited resources, and dynamic topology caused by the mobility of devices, service search faces great risk of failure. Usually, clusters are the main way of organizing the devices in MANETs. Therefore, an effective clustering algorithm is necessary to ensure the reliability of service search. A maximized reliability clustering algorithm (MRCA) is proposed. We present predicted battery supporting time, CPU computing power, connecting degree and predicted velocity of devices, select the best devices as cluster heads. We combine the four factors together using FAHP algorithm. The simulations show that the MRCA can prolong cluster headers and members' valid time, reduce the consumed energy in the cluster's life cycle. This proves that MRCA can improve the reliability of cluster.

In ubiquitous stub environments, it is a critical challenge to select an optimal set of devices to accomplish a graph-based ubiquitous service and execute it continuously. The mobility of devices, the diverse access technologies and underlying path quality have a great influence on the user experience. Thus, we put forward a hierarchical model and a novel selection function considering the synergetic effect between devices. Then we elaborate a Synergy-aware Selection Mechanism (SSM) which includes three modules: service launch, device selection and service maintenance. We design a distributed core algorithm to integrate the devices and a dynamic updating weight method. The simulation results show that Synergy-aware Selection Mechanism can select a set of executive devices to ensure the service quality, continuity and smoothness. It improves the performance in the perceived experience and the number of service reelections.

A good web selection algorithm can provide the most suitable service for users. However, known for its slow convergence rate and proneness of oscillation in its learning process, the traditional error back propagation neural network algorithm cannot be applied in the service selection scenarios of actual smart distribution grid. In order to meet the requirements of telecommunication technology for smart distribution grid and improve the quality of telecommunication service, this paper proposes an improved error back propagation algorithm, in which the learning factor can be self-adjusted with every iteration. The simulation results show an optimization of the training speed and an oscillation reduction in the learning process with the new algorithm, thus obvious optimizing the web services selection in smart distribution grid.

In ubiquitous stub environment, several mobile devices can work cooperatively to provide efficient and reliable service. But device movement or failure usually causes service interruption. How to recover the service path quickly and enhance the user experience greatly is an important problem. This paper presents a novel recovery strategy to resolve the problem. First of all, the strategy conducts the local service recovery process. If it fails, the strategy will use the global service recovery process. Several key factors affecting recovery process are adopted in the strategy. Compared with other similar strategies in simulation experiment, the failure rate and service time of this strategy is lower than the others. So this novel strategy has a better performance.

Service composition has been introduced to exploit heterogeneous resources of distributed nodes for purpose of supplying ubiquitous services in ubiquitous stub environments, especially in MANETs. However, due to the characteristics of infrastructure-less, the limited resources of the nodes and dynamic topology caused by the mobility of nodes, service composition faces great risk of failure. Therefore, service recovery handling failure is crucial to guarantee composite service's successful execution. In this paper, we propose a novel recovery selection function which incorporates device effective rate, individual capability and cooperative capability. Then we elaborate an original heuristic thought-based backup recovery algorithm: Dynamic Local Backup Recovery Algorithm (DLBRA). Finally, simulation results demonstrate that the proposed strategy ensures high performance, effectively guarantees the sustainability and significantly reduces the response time of the composite service.

Since there is no central management center in the MANETs, nodes need to self-manage service provided by other nodes. To pursue maximal utilities, they should be able to negotiate autonomously for services, e.g., packets transmission, information share. While some selfish mobile nodes in MANETs are unwilling to provide services for other users, it directly leads to serious decline in network performances. Hence an effective negotiation mechanism is required to stimulate them to cooperation. In this paper, we present a service-oriented negotiation model between selfish nodes in view of one-to-many application scenarios, driven by the basic intuition that negotiators tend to maximize their individual payoffs while ensuring that an agreement is reached. We develop the genetic algorithm to make the negotiation more adaptive in the MANETs. The simulation results show that the algorithm reduces energy consumption and communication traffic in deed.

In a clustering-based MANETs, task allocation has posed increasing research challenges because the needs of management and coordination are accentuated by complicated demands of cluster members. A self-adaptive method of task allocation is designed to facilitate self-planning and self-negotiation for nodes during tasks being distributed and executed. The method is composed of two parts: for one part, the cluster head works out an integrated schedule for tasks, including selecting different sets of execution nodes and defining their functions according to task types. Cooperative group towards synergetic task is formed by policies of filtering and voting. Assignment modes based on either polling or mobile agents are also involved, the latter adopts an improved Ant Colony Optimization (ACO) algorithm to plan a migration path. For another, if a cluster member fails to accomplish a task, it could negotiate as a tenderee with other nodes using a revised contract net protocol. In addition, we employ a stimulation mechanism of distributing virtual task experience in connection with QoS guarantees to offer compensation for nodes' energy consumption and extra load. Simulation results demonstrate performance benefits of our self-adaptive method can efficaciously alleviate load of the cluster head, balance loads of nodes in consideration of energy restriction, and prolong the lifecycle of the cluster.