Multiple autonomous underwater vehicles (AUVs) integrating multi-agent reinforcement learning (MARL) have made remarkable achievement and widely utilized for underwater search and rescue missions. However, to perform collaborative multi-AUV search efficiently in harsh and communication-constrained marine environments, challenging issues need to be addressed, such as cold-start problem and poor collaborative information fusion. To deal with these challenges, this paper proposes a MACS scheme which integrates the reasoning capabilities of large language models (LLMs) into the MARL framework to solve the cold-start problem and facilitate efficient collaborative information fusion. In MACS, to alleviate the cold-start problem of MARL caused by the lack of prior knowledge, we design a LEMACS algorithm, which leverages LLMs to infer the initial Target Probability Map (TPM) from search tasks and underwater terrain information to accelerate the search process. Furthermore, to address low efficient data exchange and fusion issue under unstable channel, we propose a LLM-enhanced link selection algorithm LESCL which integrates TPM information and AUV link metrics to optimize the link selection procedure to enhance multi-AUV cooperative search information fusion. To validate the effectiveness of the proposed algorithms, we conduct extensive numerical simulations using open-source regional underwater terrain data, such as coral reef map dataset of Arizona State University (ASU) and the terrain data of the Dongsha Islands, and the simulation results indicate that MACS achieves a search success rate of up to 95% in emergency multi-AUV cooperative search missions. The code is available at https://github.com/SDUST-smartocean/MACS.

With the rapid development of industrial automation, few-shot object detection has emerged as a promising solution for recognizing novel categories using only limited annotated data. However, existing approaches often suffer from high computational complexity and limited adaptability when deployed in resource-constrained industrial environments. To achieve precise detection, efficiency, and security, this paper proposes a collaborative computing framework based on an Edge-Cloud Dual-Prototype Graph Convolutional Network (EC-DP-GCN) for few-shot object detection with hierarchical knowledge embedding. The framework comprises three key components: a device–edge–cloud architecture, a Positive-Negative Prototype (PNP) module, and a Class-Prototype-Sample Hierarchical Graph (CPS-HG) module. Specifically, the PNP module explicitly models intra-class diversity by constructing discriminative positive and negative prototypes from limited support samples, thereby enhancing prototype representativeness. In addition, we further introduce the CPS-HG module, which treats the dual prototypes as class-based prior knowledge and models the relationships among samples through a hierarchical graph structure encompassing class, prototype, and sample levels. This design effectively expands the semantic margins in the embedding space to improve knowledge-guided detection. Extensive experiments on the PASCAL VOC and MS COCO benchmarks demonstrate that EC-DP-GCN significantly outperforms strong baselines and previous state-of-the-art methods, achieving an average improvement of 1.1% in 10-shot detection scenarios.

The direct cross-boundary communication between Unmanned Aerial Vehicles (UAVs) and Autonomous Underwater Vehicles (AUVs) is a pivotal component in establishing the 6G integrated air-sea-space network, holding significant importance for applications such as marine data collection and maritime collaborative search and rescue. Nevertheless, existing solutions exhibit pronounced deficiencies in path planning efficiency, the coverage range of wireless optical communication, and edge computing load balancing, which result in a high Age of Information (AoI), severely compromising the performance of time-sensitive maritime missions. To address these challenges, this paper proposes a maritime data collection scheme called MECOS, which includes LMAR2P algorithm for UAVs path planning and MAPBal algorithm for UAVs to deal with the load balancing issue. In LMAR2P, a multi-agent deep reinforcement learning (MARL) architecture is adopted, in which we leverage the global understanding capability of large language models (LLMs) to provide state representation for MARL, in order to improve path planning efficiency. Furthermore, to solve the unbalanced computational load problem, we design a kullback-leibler (KL) divergence-based reward correction mechanism and propose a distributed adaptive offloading balancing algorithm MAPBal, which enables resource-aware task allocation to ensure load balancing and reduce data processing latency. The simulation results indicate that the MECOS scheme reduces the AoI by 23.6% and 26.4% during the data collection and data processing phases of maritime missions, respectively. This research provides a viable technical solution for practical applications such as maritime monitoring, demonstrating significant scientific value and promising application prospects. The code is available at https://github.com/SDUST-smartocean/MECOS.

In complex underwater environments, multiple autonomous underwater vehicles (AUVs) typically rely on under-water acoustic communication when performing collaborative target search tasks. However, traditional underwater acoustic technology has communication constraints (e.g., high latency and low bandwidth), which leads to poor information sharing and degrade the performance of multi-AUV collaboration target search missions. To address these challenges, this paper proposes a multi-AUV collaborative acoustic-optical communication based target search scheme (AOTSS), which consists of two main components: a particle filter-based path planning algorithm (PFPPA) and a multi-agent reinforcement learning-based multi-AUV Collaborative Search Algorithm (MASA). In PFPPA, to implement efficient multimodal communication among AUVs, we design a navigation algorithm based on particle filter method and deep reinforcement learning. This approach maximizes optical communication to enhance information sharing among AUVs. Furthermore, In MASA we leverage multimodal communication to enhance information sharing among AUVs to obtain precise probability maps, and incorporate pheromones into these maps to guide AUVs performing efficient cooperate search via the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) approach. Through extensive simulations, the results demonstrate that the proposed scheme significantly enhances the multi-AUV collaboration target search efficiency.

The cross-boundary communication between unmanned aerial vehicles (UAVs) and autonomous underwater vehicles (AUVs) constitutes a pivotal component in achieving full coverage of the space-air-ground-sea integrated network of 6G. In such hybrid networks, it is imperative to intelligently plan the trajectory of UAVs by exploiting the deep reinforcement learning (DRL) technique and direct optical wireless communication (OWC) technology to ensure timely data collection. However, the traditional DRL technique suffers from issues such as sparse rewards and low sampling efficiency, which leads to a decrease in the freshness of data. To address these issues, in this paper, we investigate the trajectory planning problem for swarms of UAVs conducting data collection with age of information awareness. Leveraging the prior knowledge of large language models (LLMs) and multi-agent reinforcement learning technique, we propose a novel multi-UAV maritime data collection scheme. Firstly, we extract the prior knowledge strategies of LLMs in the form of expert trajectories through structured prompts. Then, initial strategy models are rapidly generated for UAVs through multi-agent behavior cloning method, which reduce ineffective exploration and accelerates learning. Finally, the MATD3 algorithm is used to fine-tune these strategy models, enhancing their policy learning capability in sparse reward environments. We also conduct simulations to validate the effectiveness of the proposed algorithms.

Accurate estimation of battery health can significantly enhance the safety of electric vehicle (EV) systems. Most machine learning methods employ centralized learning for battery state of health (SOH) estimation. These approaches lead to weak model generalization due to limited samples caused by data privacy, hindering adaptation to varying working conditions. Batteries of the same batch may show manufacturing variances, further constraining model generalization. Additionally, most methods only focus on the temporal nature of battery degradation, they overlook the significant impact of periodic fluctuations in capacity. Existing data-driven methods struggle with low generalization capabilities, manufacturing variances among batteries, and neglect of periodic capacity fluctuations. Therefore, we propose an advanced federated learning framework that combines a contribution-aware federated strategy (CAFS) with battery health predictor (BHP) models to more accurately estimate a battery’s SOH. Specifically, we design the BHP, consisting of a feature-enhanced autoencoder and a temporal period coupled attention mechanism, to effectively learn battery ageing information. Furthermore, we present the CAFS to significantly enhance the model’s generalizability and adaptability to the target battery. The experimental results show that our method achieves an average improvement of 20.18% in SOH estimation accuracy across diverse working conditions. Results from eight federated scenarios demonstrate that our method outperforms conventional federated learning in terms of accuracy, generalization, and training speed.

Predicting interactions between drugs and their targets is vital for drug discovery and repositioning. Conventional techniques are slow and labor-intensive, while deep learning algorithms offer efficient solutions. However, deep learning often focus on single drug representations or simplistic combinations, leading to suboptimal feature representation. Moreover, the prevalent use of convolutional neural networks (CNNs) in drug image representation neglects the necessity for both local and global drug information in Drug-Target Interaction (DTI) tasks. To address these challenges, we propose TriCvT-DTI, a novel approach that combines molecular images, chemical sequence features, and graph representations of drugs to comprehensively capture structural, spatial, and functional aspects. TriCvT-DTI introduces a bidirectional multi-head attention mechanism for interactive feature learning between drugs and targets, enhancing performance by modeling complex relationships. By using Convolutional Vision Transformers (CvTs), TriCvT-DTI can effectively extract structural and spatial features from drug images. We evaluate our model on three datasets: Human, C. elegans, and Davis, and we compare it with state-of-the-art methods. Then we train TriCvT-DTI with uni-modality and bi-modality to compare then extract the impact of each modality on TriCvT-DTI. Experimental results demonstrate that TriCvT-DTI outperforms existing methods on both balanced and unbalanced datasets. Moreover, it presents impressive generalization capabilities on the Drug-Target Interaction (DTI) task.

For a limited scale self-organized multi-agent system operating in environments with unknown task distributions, one challenge is to reduce the task response time via efficiently combining task exploration and allocation, another challenge is to improve the task completion rate via unlocking the potential of network cooperation in task allocation. However, in the existing studies, task allocation is generally regarded as an independent issue for known task distribution environments, rarely combined with task exploration, also hardly solving the conflict between the multi-hop network cooperation and mobility flexibility of agents. In view of this, this paper proposes a network connectivity-aware deep reinforcement learning method for task exploration and allocation in limited scale multi-agent systems (NCADRL4TEA). This method divides the task environment into regions and integrates task exploration with task allocation via two policies: a leaving policy to guide global task exploration among regions according to the distribution of agents and tasks, and a stay policy to guide local task allocation within each region according to the multi-hop network cooperation performance between agents. Further, in the stay policy, a network connectivity-aware task allocation optimization model is provided, which leads agents in the same region to cooperate with each other via multi-hop intermittent network connectivity and flexibly adjust their locations until the optimal multi-hop network cooperation performance is achieved. The experimental results verify that NCADRL4TEA can reduce the task response time in combination of task exploration and allocation, and improve the task completion rate in network cooperation.

In maritime Internet of Things (IoT) systems, leveraging a swarm of Uncrewed Aerial Vehicles (UAVs) and optical communication can achieve a variety of potential maritime missions. However, due to the high directionality of the optical beam and interference from the marine environment, the optical link via UAVs as relays is prone to interruption. To address this challenge, we propose a Joint Link Optimization Scheme (JLOS) that includes Wind Disturbance Resistance (WDR) and Adaptive Beamwidth Adjustment (ABA). In WDR, we first model the problem as a Partially Observed Markov Decision Process (POMDP), and then design a collaborative Multi-Agent Reinforcement Learning (MARL) approach to control a swarm of UAVs in windy conditions, to maintain mechanical stability and prevent link interruption. Furthermore, in ABA, to reduce uncertainties from control activities and environmental factors like sunlight and fog, we design an adaptive algorithm using distributed MARL. It adjusts beamwidth based on historical UAV locations and link Bit Error Ratio (BER) to improve communication reliability. Numerical simulations confirm its effectiveness in enhancing robust data transmission.

Maritime data transmission with unmanned aerial vehicles (UAVs) in maritime Internet of Things (MIoT) systems has received increasing attention due to its flexibility and low cost. To further improve the efficiency of maritime data transmission between the UAVs and the maritime buoys, optical communication is considered as a promising technique because of its low latency and high bandwidth. However, optical communication encounters the challenge of beam pointing alignment, particularly in maritime data transmission involving wave disturbance and drift of buoy, which deteriorates and even interrupts the line-of-sight (LOS) optical transmission. To tackle the challenge, this paper proposes DERLOC, a reliable data transmission solution based on deep reinforcement learning (DRL). We first provide the optimization analysis of reliable data transmission and formulate the data transmission procedure as a Markov decision process (MDP) aiming at maximizing the received signal intensity. Afterwards, we propose a beam pointing adjustment algorithm based on the soft actor-critic (SAC) approach to alleviate the performance deterioration caused by waves. Then, we analyze the drift characteristic of a buoy and develop a method which enables UAV to predict the position and determine an optimal movement control strategy for ensuring the effectiveness of beam pointing and maintaining stable LOS communication. Through extensive simulations and real-time data validation, the results demonstrate that DERLOC is effective and enables a reliable data transmission via optical links.

Vehicular mobile edge computing (vMEC) and non-orthogonal multiple access (NOMA) have emerged as promising technologies for enabling low-latency and high-throughput applications in vehicular networks. In this paper, we propose a novel multi-agent deep deterministic policy gradient (MADDPG) approach for resource allocation in NOMA-based vMEC systems. Our approach leverages deep reinforcement learning (DRL) to enable vehicles to offload computation-intensive tasks to nearby edge servers, optimizing resource allocation decisions while ensuring low-latency communication. We introduce an attention mechanism within the MADDPG model to dynamically focus on relevant information from the input state and joint actions, enhancing the model’s predictive accuracy. Additionally, we propose an attention-based experience replay method to expedite network convergence. The simulation results highlight the effectiveness of multi-agent reinforcement learning (MARL) algorithms, such as MADDPG with attention, in achieving better convergence and performance in various scenarios. The influence of different model parameters, such as input data volumes, task load levels, and resource configurations, on optimization results is also evident. The decision making processes of agents are dynamic and depend on factors specific to the task and environment.

Millions of patients suffer from rare diseases around the world. However, the samples of rare diseases are much smaller than those of common diseases. Hospitals are usually reluctant to share patient information for data fusion due to the sensitivity of medical data. These challenges make it difficult for traditional AI models to extract rare disease features for disease prediction. In this paper, we propose a Dynamic Federated Meta-Learning (DFML) approach to improve rare disease prediction. We design an Inaccuracy-Focused Meta-Learning (IFML) approach that dynamically adjusts the attention to different tasks according to the accuracy of base learners. Additionally, a dynamic weight-based fusion strategy is proposed to further improve federated learning, which dynamically selects clients based on the accuracy of each local model. Experiments on two public datasets show that our approach outperforms the original federated meta-learning algorithm in accuracy and speed with as few as five shots. The average prediction accuracy of the proposed model is improved by 13.28% compared with each hospital's local model.

The production line must continuously adjust to meet the increasing demand for individualized products, such as the unmanned aerial vehicle (UAV) production line. However, the lack of interoperability among equipment hinders the rapid reconfiguration of a production line. The reference architecture model for Industry 4.0 (I4.0) introduces an asset administration shell (AAS) as a new technique to facilitate I4.0 interoperability. AAS is a specification that presents a standard manner to represent assets. To further enhance AAS configuration efficiency, we propose model-based definition (MBD)-enhanced AAS (MBD-AAS) for generic production line design. MBD-AAS optimizes AAS configuration and enables plug-and-play equipment for the production line. We validate the applicability and benefits of MBD-AAS with a case study of a UAV production line design. The experimental results demonstrate that MBD-AAS improves AAS’s interoperability and reduces production line design time.

Gear crack is one of the common failures in transmission systems. With the gradual expansion of cracks, it may cause tooth fracture. Therefore, it is of great significance to study the fault diagnosis of gear cracks. Vibration signals with time sequence are widely used in gear fault diagnosis. Extracting key fault features from vibration signals determines the accuracy of fault diagnosis models. This paper takes spur gears as research objects, and proposes a model for diagnosing gear crack depth based on one-dimensional convolutional neural network (short for CNN4GCDD). In order to identify crack depths, we collect the vibration signals from three gears with various crack depths and a normal gear without cracks. CNN4GCDD uses the original vibration signal as the input, adaptively extracts features, and makes crack depth diagnosis through the convolutional neural network. The experimental results demonstrate that CNN4GCDD can directly use the original time-domain signal for crack depth diagnosis, and make a high accurate prediction.

Traffic congestion is a severe challenge for intelligent transportation system (ITS). So far, if traffic congestion is perceived in a route, a common solution is searching for another congestion-free route. However, it is observed that not all congestions should be tackled with rerouting, since the extra overhead (e.g., extra travel time, extra fuel consumption, and extra CO2 emission) caused by certain congestions might be lower than that of re-routing. Against this backdrop, an intelligent route guidance strategy is proposed, in which vehicles will trade off the extra overhead of re-routing and that of waiting in congestion. Firstly, the perceived congestion is divided into four basic types. Then, the prediction method about the duration of each congestion type is formulated. Finally, the intelligent route planning mechanism is developed. Simulations demonstrate that the proposed strategy can reduce the travel time, fuel consumption, and CO2 emission for vehicles.

With the proliferation of I nternet o f T hings (IoT) devices deployed in edge networks, the functionalities of IoT devices are typically encapsulated in terms of IoT services. Their collaboration is mostly achieved through the composition of functionally complementary and geographically contiguous IoT services, to achieve complex requests. Considering the capacity constraints of IoT devices, newly incoming requests may hardly be satisfied partially (or completely), since these devices are implementing subtasks of previous requests at this moment. Therefore, candidate IoT devices may have no enough remaining capacity to co-host subtasks of these new requests concurrently. To solve this problem, this article proposes a novel r esource a llocation and s ervice co-placement (RaSP) algorithm to address latency-aware online service reconfiguration problem. Specifically, IoT services are reconfigured upon IoT devices in an optimal manner, such that certain IoT services corresponding to subtasks in previous requests should be migrated online from their hosting IoT devices to neighboring ones, and constraints of these requests are still satisfiable. These released resources can be adopted to implement subtasks (or IoT services) of newly incoming requests. A prototype is implemented using anEdgeSimsimulator. The experimental results show that our RaSP algorithm performs better than the state of the art’s techniques in satisfying the latency of newly incoming and previous requests simultaneously, and reducing the energy consumption of edge networks.

Wind farms are typically located at high latitudes, resulting in a high risk of blade icing. Data-driven approaches offer promising solutions for blade icing detection, but they rely on a considerable amount of data. Data exchange between multiple wind farms would improve the performance of detection models, due to the spatio-temporal dependencies capable of reflecting different meteorological conditions. The traditional centralized approach for icing detection faces many challenges, including the requirement of high storage and computational capacity of the server, vulnerability to cyberattacks, and operators’ reluctance of sharing data for commercial reasons. To address these challenges, this article proposes a heterogeneous federated learning (FL) model for wind turbine blade icing detection. The structures of the server and client models in the presented method are different, in contrast to the traditional FL of sharing the same structure. In addition, this article addresses the class imbalance problem in the training data. Last, this article conducts comprehensive experiments to evaluate the proposed method using real-world data from 20 turbines in two wind farms, and compares it with two state-of-the-art FL models and five well-known class imbalance methods. The experimental results verify the effectiveness and superiority of the proposed method.

Real-time intelligent video processing on embedded devices with low power consumption can be useful for applications like drone surveillance, smart cars, and more. However, the limited resources of embedded devices is a challenging issue for effective embedded computing. Most of the existing work on this topic focuses on single device based solutions, without the use of cloud computing mechanisms for parallel processing to boost performance. In this paper, we propose a cloud platform for real-time video processing based on embedded devices. Eight NVIDIA Jetson TX1 and three Jetson TX2 GPUs are used to construct a streaming embedded cloud platform (SECP), on which Apache Storm is deployed as the cloud computing environment for deep learning algorithms (Convolutional Neural Networks - CNNs) to process video streams. Additionally, self-managing services are designed to ensure that this platform can run smoothly and stably, in the form of a metric sensor, a bottleneck detector and a scheduler. This platform is evaluated in terms of processing speed, power consumption, and network throughput by running various deep learning algorithms for object detection. The results show the proposed platform can run deep learning algorithms on embedded devices while meeting the high scalability and fault tolerance required for real-time video processing.

People can post their comments on public events on the Internet, such as their ideas, emotions, and attitudes that can affect others. Online public opinions may affect the stability or security of the country because of the speed and convenience of information disseminating on the Internet. This article proposes the public opinion cellular automata for situation deduction to predict the possible trending of public events. In the cellular automata, online users are represented by cells, and their eigenvalues are calculated from the user's historical comment data. The cell and their neighbors form a cellular space, whose topology is a directed graph. We set the state of each cell based on its attributes and initialize the cellular automata. The automata can deduce public opinion and predict the trend of public opinion by following the set evolutionary rules in advance. Experiments with the real data from Sina Weibo online users show that the cells in the cellular automata can accurately simulate users' behavior, and the deduction results are close to the trend of real historical events. The cellular automata-based prediction of the number of participants and emotional trending are more accurate than other methods.

The development and evolution of modern information and communication technologies is leading us to the fourth industrial revolution, in which the Industrial Internet of Things (IIoT) is assumed to be one of the key aspects to realize Industry 4.0. Federated learning facilitates the implementation of secure platform with consideration on data privacy to support IIoT. Many researchers and practitioners have expressed their interest in this area with the expectation of profound effect in the context of Industry 4.0. However, the topic is quite new and has not been investigated under its different profiles until now. There is a lack of literature from both a theoretical and an empirical point of view. Therefore, this special sector is dedicated to provide cutting-edge technologies and novel studies, which can realize and elevate the effectiveness and advantages of federated learning for advancing industrial IoT. Eleven articles have been accepted by this Special Section based on review, and revision processing.

Traditional character segmentation has low accuracy for container scene text recognition. Convolutional recurrent neural network (CRNN) and connectionist text proposal network (CTPN) methods cannot extract container text features effectively. This paper proposes a novel Container Text Detection and Recognition Network (CTDRNet) for accurately detecting and recognizing container scene text. The CTDRNet consists of three components: (1) CTDRNet text detection enables to improve detection accuracy for single words; (2) CTDRNet text recognition has faster convergence speed and detection accuracy; (3) CTDRNet post-processing improves detection and recognition accuracy. In the end, the CTDRNet is implemented and evaluated with an accuracy of 96% and processing rate of 2.5 fps.

Ship plate recognition is challenging due to variations of plate locations and text types. This paper proposes an effcient Fully Convolutional Network based Plate Recognition approach FCNPR, which uses a CNN (Convolutional Neural Network) to locate ships, then detects plate text lines with the fully convolutional network (FCN). The recognition accuracy is improved with integrating the AIS (Automatic Identification System) information. The actual FCNPR deployment demonstrates that it can work reliably with a high accuracy for satisfying practical usages.

Social data from online social networks is expanding rapidly as the number of users and articles posted increases, making public opinion analysis a greater challenge. Real-time topic detection is a key part of public opinion analysis. The complex data processing involved in traditional clustering and text categorization can lead to time delays in topic detection. In this paper we construct similar networks and detect topics from similar communities that reduces the processing overhead in obtaining real-time topics. The similar communities consist of users with high similarity between them. We collect public topics from the microposts of delegates selected from each similar community. Selecting delegates can reduce the processing time of large amounts of redundant data during topic detection. Obtaining public opinion keywords in real time allows organizations to respond to public opinion security incidents in real time. Experiments showed that our scheme can find public topics faster and more effectively than two traditional algorithms.

Email spam consumes a lot of network resources and threatens many systems because of its unwanted or malicious content. Most existing spam filters only target complete-spam but ignore semispam. This paper proposes a novel and comprehensive CPSFS scheme: Credible Personalized Spam Filtering Scheme, which classifies spam into two categories: complete-spam and semispam, and targets filtering both kinds of spam. Complete-spam is always spam for all users; semispam is an email identified as spam by some users and as regular email by other users. Most existing spam filters target complete-spam but ignore semispam. In CPSFS, Bayesian filtering is deployed at email servers to identify complete-spam, while semispam is identified at client side by crowdsourcing. An email user client can distinguish junk from legitimate emails according to spam reports from credible contacts with the similar interests. Social trust and interest similarity between users and their contacts are calculated so that spam reports are more accurately targeted to similar users. The experimental results show that the proposed CPSFS can improve the accuracy rate of distinguishing spam from legitimate emails compared with that of Bayesian filter alone.

There arises the needs for fast processing of continuous video data using embedded devices, for example the one needed for UAV aerial photography. In this paper, we proposed a distributed embedded platform built with NVIDIA Jetson TX1 using deep learning techniques for real time video processing, mainly for object detection. We design a Storm based distributed real-time computation platform and ran object detection algorithm based on convolutional neural networks. We have evaluated the performance of our platform by conducting real-time object detection on surveillance video. Compared with the high end GPU processing of NVIDIA TITAN X, our platform achieves the same processing speed but a much lower power consumption when doing the same work. At the same time, our platform had a good scalability and fault tolerance, which is suitable for intelligent mobile devices such as unmanned aerial vehicles or self-driving cars.

Smart Home minimizes user's intervention in monitoring home settings and controlling home appliances. This paper presents an approach to the development of Smart Home applications by integrating Internet of Things (IoT) with Web services and Cloud computing. The approach focuses on: (1) embedding intelligence into sensors and actuators using Arduino platform, (2) networking smart things using Zigbee technology, (3) facilitating interactions with smart things using Cloud services, (4) improving data exchange efficiency using JSON data format. Moreover, we implement three use cases to demonstrate the approach's feasibility and efficiency, i.e., measuring home conditions, monitoring home appliances, and controlling home access.

The Internet of Things presents the user with a novel means of communicating with the Web world through ubiquitous object-enabled networks. Cloud Computing enables a convenient, on demand and scalable network access to a shared pool of configurable computing resources. This paper mainly focuses on a common approach to integrate the Internet of Things (IoT) and Cloud Computing under the name of CloudThings architecture. We review the state of the art for integrating Cloud Computing and the Internet of Things. We examine an IoT-enabled smart home scenario to analyze the IoT application requirements. We also propose the CloudThings architecture, a Cloud-based Internet of Things platform which accommodates CloudThings IaaS, PaaS, and SaaS for accelerating IoT application, development, and management. Moreover, we present our progress in developing the CloudThings architecture, followed by a conclusion.

Peer-to-Peer (P2P) technologies have many advantages over traditional client/server technologies, including cost- effectiveness, scalability and robustness, due to their decentralized network structure. However, the performance has traditionally been an issue in P2P systems. Especially the higher lookup latencies, when compared with the traditional client/server systems, have been the bottleneck in many P2P systems. In this paper, we propose a truncated pyramid P2P architecture together with an enhanced model, Vertical Tunneling Model (VTM) for improving the lookup performance. The proposed architecture is built on Peer-to-Peer SIP (P2PSIP) network. VTM builds up vertical tunnels between the upper and lower sub-overlays to speed up the service lookup and decrease the session setup delay. Based on the performance analysis and results, it is shown that VTM has better performance compared with the existing systems in average lookup hops, which is about 1/3 of that of the existing systems, the predominance is more evident as the network scale increases.

Peer-to-peer service-oriented community coordinated multimedia (SCCM) is envisioned as a novel paradigm in which the user consumes multiple media through requesting multimedia-intensive Web services via diversity display devices, converged networks, and heterogeneous platforms within a virtual, open and collaborative community in context of a novel concept of multimedia applications as Web services. A generic P2P SCCM scenario is created and examined first. The SCCM model is designed with respect to service architecture, a tunneled hierarchical P2P model and metamodel framework. The progress with the development of content annotation services is presented.

Reliability-aware software architecture development has recently been gaining growing attention among software architects. This paper tackles the issue by introducing an ontology-based approach, called OntoArch, which is characterized by: 1) integration of software reliability engineering and software architecture design; 2) targeting reliability-aware software architecture development; and 3) OntoArch ontology in the context of software architecture design and software reliability engineering. The OntoArch approach is validated by applying the OntoArch approach to the development of a PIR (Personal Information Repository) system architecture.

Community coordinated multimedia (CCM) envisions the paradigm of consuming multiple media via diversity display devices, converged networks, and heterogeneous platforms within a virtual, open and collaborative community. This paper applies service orientation approach and Web services technology to establish a Service-oriented CCM architecture (SCCM) for tackling requirements of scalability, discoverability, composibility, layered abstraction, QoS and agility in distributed collaborative multimedia management. A generic CCM scenario is examined. CCM service model is designed with service-orientation principles. A prototype of CCM is implemented with supporting multimedia publish, streaming, and viewing within SCCM.

Human experience is being extended and enhanced by collaboratively consuming electronic and networked content and multimedia-intensive services. This technical phenomenon is addressed by our generalized Community Coordinated Multimedia model in this paper. Content-driven and service-driven models are identified and a converged CCM model is introduced. Characteristics of Community Coordinated Multimedia are specified and challenges for developing a technological framework are presented.

Service composition enables service users to develop Web applications by composing services via Internet. Traditional service description is function-centred, but lack of composition information, which hinders service composition with respect to service contexts. In this paper, we examine service composition information (i.e. service contexts and service dependency). The context- aware service management platform that we developed provides support for service users to compose services by managing service dependency.

Current service-oriented architecture (SOA) focuses on service composition for application development, i.e. application composition, which is perceived as volatile (i.e. "compose one time and use one time."). This paper focuses on service composition information (i.e. service dependency) management and dependency-aware service composition for service development (i.e. service composition). This paper explores service composition and service dependency by proposing an extended SOA model, including: 1) establishing a dependency-aware service-oriented architecture (DSOA) that specifies dependency-aware service interactions, i.e. service publication, discovery, composition and binding; 2) developing an upper service dependency ontology for non-volatile DSOA service composition; and 3) demonstrating the validation of DSOA service composition by implementing a DSOA service manager.

Quality-aware service delivery has been receiving increasing attention in both software architecture and service management. Our approach values software and service quality assurance, ranging from quality assessment in software architecting to quality matching in service discovery. This paper proposes an integrated QoS-aware service management method, which examines the 'service as a software' development and the 'software as a service' management against QoS requirements. Moreover, we design an integrated QoS-aware service management infrastructure which promises complete software and service workflow management coupled with a QoS ontology development. We develop the QoS property ontology from the viewpoints of technical quality and managerial quality information management.

Semantic Web services integrate the meaningful content of the semantic Web with the business logic of Web services and thus enable industries and individuals to build, access, deploy and execute services and transactions independently over Internet. This paper surveys semantic Web services from the viewpoints of Web service architectures, service engineering, service description languages, Web service building tools, and also presents a case study

Semantic QoS aware Web services incorporating the emerging Web services in the QoS aware system development are promoting service oriented software engineering (SOSE). To identify the steps toward semantic quality of service (QoS) aware Web services, this paper examines previous studies related to semantic QoS aware Web services, including QoS aware Web service architectures, QoS classification, QoS ontology, QoS specification languages, and Web service creation tools. Moreover, a case study is presented to discuss the gaps between our current quality driven software development approach and the semantic QoS aware Web services