The growth of Electric Vehicles (EVs) places an increasingly heavy burden on the limited charging infrastructure, necessitating an effective charging station recommendation strategy that assists EVs in finding the most suitable charging stations. Deep reinforcement learning is a promising technology that has been applied to optimize EVs’ charging recommendations. However, existing schemes have low scalability and high communication costs as they usually require collecting real-time information on both charging requests and charger availability at various stations during policy training or execution. To address this challenge, we develop a real-time distributed charging station recommendation approach, named ReDirect, to minimize the charging duration experienced by EVs, considering dynamic charging requests of EVs and fluctuating availability at charging stations. ReDirect employs federated meta-reinforcement learning (RL) to empower distributed stations to collaboratively learn effective recommendation strategies and make decisions without sharing their local information, yielding improved scalability, reduced communication overhead, and enhanced data privacy. Furthermore, we conduct a rigorous theoretical analysis of the convergence performance of ReDirect. Extensive experimental results on real-world datasets demonstrate that ReDirect performs closely to the centralized recommendation algorithm and outperforms several state-of-the-art distributed algorithms in EV charging duration while realizing a balanced distribution of charging requests across multiple stations.

Integrating sharded blockchain with IoT presents a solution for trust issues and optimized data flow. Sharding boosts blockchain scalability by dividing its nodes into parallel shards, yet it is vulnerable to the 1% attacks where dishonest nodes target a shard to corrupt the entire blockchain. Balancing security with scalability is pivotal for such systems. Deep Reinforcement Learning (DRL) adeptly handles dynamic, complex systems and multi-dimensional optimization. This paper introduces a Trust-based and DRL-driven (TbDd) framework, crafted to counter collusion attack risks and dynamically adjust node allocation, enhancing throughput while maintaining network security. With a comprehensive trust evaluation mechanism, TbDd discerns node types and performs targeted resharding against potential threats. The TbDd framework maximizes the tolerance for dishonest nodes, optimizes node movement frequency, ensures even node distribution in shards, and balances sharding risks. Extensive evaluations validate TbDd’s superiority over conventional random-, community-, and trust-based sharding methods in shard risk equilibrium and reducing cross-shard transactions.

Web3 is leading a wave of the next generation of web services that even many Web2 applications are keen to ride. However, the lack of Web3 background for Web2 developers hinders easy and effective access and transition. On the other hand, Web3 applications desire encouragement and advertisement from conventional Web2 companies and projects due to their low market shares. In this article, we propose a seamless transition framework that transits Web2 to Web3, named WEBTTCOM [WEBTTCOM stands for Web2 (two)–Web3 (three) Communicator], after exploring the connotation of Web3 and the key differences betweenWeb2 andWeb3 applications.We also provide a full-stack implementation as a use case to support the proposed framework, followed by performance evaluation and surveys with ~1000 participants that show ~80% positive and ~20% neutral responses. We confirm that the proposed framework WEBTTCOM addresses the defined research question, and the implementation well satisfies the framework WEBTTCOM in terms of strong necessity,usability, andcompletenessbased on the survey results.

Electric Vehicle-assisted Multi-access Edge Computing (EV-MEC) is a promising paradigm where EVs share their computation resources at the network edge to perform intensive computing tasks while charging. In EV-MEC, a fundamental problem is to jointly decide the charging power of EVs and computation task allocation to EVs, for meeting both the diverse charging demands of EVs and stringent performance requirements of heterogeneous tasks. To address this challenge, we propose a new joint charging scheduling and computation offloading scheme (OCEAN) for EV-MEC. Specifically, we formulate a cooperative two-timescale optimization problem to minimize the charging load and its variance subject to the performance requirements of computation tasks. We then decompose this sophisticated optimization problem into two sub-problems: charging scheduling and computation offloading. For the former, we develop a novel safe deep reinforcement learning (DRL) algorithm, and theoretically prove the feasibility of learned charging scheduling policy. For the latter, we reformulate it as an integer non-linear programming problem to derive the optimal offloading decisions. Extensive experimental results demonstrate that OCEAN can achieve similar performances as the optimal strategy and realize up to 24% improvement in charging load variance over three state-of-the-art algorithms while satisfying the charging demands of all EVs.

This paper underscores the crucial role of video anonymization in smart ports, were data privacy and security hold critical importance. It introduces the Matrix Platform, specifically designed for smart environments, and highlights its strong video anonymization capabilities. The platform employs advanced Video Anonymization techniques to effectively balance the preservation of data confidentiality with the enhancement of port security. Furthermore, the paper discusses how object detection and anonymization methods are strategically employed to protect sensitive information while still allowing access to critical operational details such as cargo and vessel types. Emphasis is placed on video anonymization's pivotal role in strengthening port security by concealing high-value assets and minimizing the risk of exposing sensitive data. By integrating the Matrix Platform, ports gain the capability to proactively manage security risks, safeguard assets, and secure information. This adaptable platform can be deployed in both Edge and Cloud environments, ensuring alignment with the specific needs of smart ports, with a primary focus on data anonymization. In conclusion, as the port industry continues to evolve, this paper asserts that the adoption of video anonymization techniques is fundamental for future growth and development, providing assurance of privacy and security in this dynamic landscape.

IoT ecosystems are rapidly expanding, and device management is emerging as a key challenge due to the scale, complexity, and dynamism of IoT systems. The adoption of autonomous techniques shows promise to alleviate key issues including maintaining organisational security when large volumes of IoT devices are being added and removed from a telecommunications network. Here we propose a proactive IoT device management approach that addresses the need to control network access in a risk-based manner. The proposed system comprises of two novel core components, a Management Platform for IoT (MP-IoT) component and an Intent-Based Microsegmentation (IBMS) component. The MP-IoT component carries out a risk management role and combines with IBMS to provide risk-based network segmentation. The two components work together to proactively manage risks by migrating devices between isolated network segments according to a dynamic assessment of the risk to the system from an individual device. Self-healing techniques may then be used to mitigate risks associated with a device and consequently change the network segment that it resides in. Here we present the key challenges associated with typical IoT environments and demonstrate how they are addressed by the proposed Proactive IoT Device Management architecture. A prototype implementation is also presented to validate the operation of the proposed architecture.

This paper presents an approach to autonomous IoT device lifecycle management for our developed Matrix IoT platform. We discuss our approach for zero touch onboarding, IoT device failure, device end-of-life offboarding and SLAs to support device lifecycle management. We collected timings on the key stages of our proposed onboarding process. The total onboarding time takes on average 6.4 seconds to onboard a device. Therefore, when scaled to many hundreds of devices, there is a very significant time saving benefit to onboarding devices automatically, along with the benefits of reducing human error.

Federated learning (FL) is a privacy-preserving distributed machine learning paradigm that enables collaborative training among geographically distributed and heterogeneous devices without gathering their data. Extending FL beyond the supervised learning models, federated reinforcement learning (FRL) was proposed to handle sequential decision-making problems in edge computing systems. However, the existing FRL algorithms directly combine model-free RL with FL, thus often leading to high sample complexity and lacking theoretical guarantees. To address the challenges, we propose a novel FRL algorithm that effectively incorporates model-based RL and ensemble knowledge distillation into FL for the first time. Specifically, we utilise FL and knowledge distillation to create an ensemble of dynamics models for clients, and then train the policy by solely using the ensemble model without interacting with the environment. Furthermore, we theoretically prove that the monotonic improvement of the proposed algorithm is guaranteed. The extensive experimental results demonstrate that our algorithm obtains much higher sample efficiency compared to classic model-free FRL algorithms in the challenging continuous control benchmark environments under edge computing settings. The results also highlight the significant impact of heterogeneous client data and local model update steps on the performance of FRL, validating the insights obtained from our theoretical analysis.

Edge computing is an emerging promising computing paradigm that brings computation and storage resources to the network edge, hence significantly reducing the service latency and network traffic. In edge computing, many applications are composed of dependent tasks where the outputs of some are the inputs of others. How to offload these tasks to the network edge is a vital and challenging problem which aims to determine the placement of each running task in order to maximize the Quality-of-Service (QoS). Most of the existing studies either design heuristic algorithms that lack strong adaptivity or learning-based methods but without considering the intrinsic task dependency. Different from the existing work, we propose an intelligent task offloading scheme leveraging off-policy reinforcement learning empowered by a Sequence-to-Sequence (S2S) neural network, where the dependent tasks are represented by a Directed Acyclic Graph (DAG). To improve the training efficiency, we combine a specific off-policy policy gradient algorithm with a clipped surrogate objective. We then conduct extensive simulation experiments using heterogeneous applications modelled by synthetic DAGs. The results demonstrate that: 1) our method converges fast and steadily in training; 2) it outperforms the existing methods and approximates the optimal solution in latency and energy consumption under various scenarios.

Multi-access edge computing (MEC) aims to extend cloud service to the network edge to reduce network traffic and service latency. A fundamental problem in MEC is how to efficiently offload heterogeneous tasks of mobile applications from user equipment (UE) to MEC hosts. Recently, many deep reinforcement learning (DRL)-based methods have been proposed to learn offloading policies through interacting with the MEC environment that consists of UE, wireless channels, and MEC hosts. However, these methods have weak adaptability to new environments because they have low sample efficiency and need full retraining to learn updated policies for new environments. To overcome this weakness, we propose a task offloading method based on meta reinforcement learning, which can adapt fast to new environments with a small number of gradient updates and samples. We model mobile applications as Directed Acyclic Graphs (DAGs) and the offloading policy by a custom sequence-to-sequence (seq2seq) neural network. To efficiently train the seq2seq network, we propose a method that synergizes the first order approximation and clipped surrogate objective. The experimental results demonstrate that this new offloading method can reduce the latency by up to 25 percent compared to three baselines while being able to adapt fast to new environments.

The ever increasing demand for computing resources has led to the creation of hyperscale datacentres with tens of thousands of servers. As demand continues to rise, new technologies must be incorporated to ensure high quality services can be provided without the damaging environmental impact of high energy consumption. Virtualisation technology such as network function virtualisation (NFV) allows for the creation of services by connecting component parts known as virtual network functions (VNFs). By optimising the placement and routing of VNFs this technique can be used to maximally utilise available datacentre resources, to maintain a high quality of service whilst minimising energy consumption. Current research on this problem has focussed on placing VNFs and considered routing as a secondary concern. In this work we argue that the opposite approach, a routing-led approach is preferable. We propose a novel routing-led algorithm and analyse each of the component parts over a range of different topologies on problems with up to 16000 variables and compare its performance against a traditional placement based algorithm. Empirical results show that our routing-led algorithm can produce significantly better solutions to large problem instances on a range of datacentre topologies.

Mobile Cloud Computing (MCC) is regarded as a promising method to increase the data storage and enhance the processing power of mobile devices. Technologies such as Software Defined Networking (SDN) and Network Function Virtualisation (NFV) will be deployed in MCC to simplify the network management and accelerate mobile service deployment. In order to achieve a deeper understanding of future MCC, we developed a comprehensive analytical model to investigate the performance of MCC in the presence of both NFV service chains and SDN networks. The model is capable of capturing the interactions between SDN and NFV when they share the same underlying physical infrastructure. The end-to-end latency is derived for different scales of service deployments and network configurations. Comprehensive simulation experiments are conducted and the results demonstrate that the proposed analytical model corresponds well with the simulation experiments. In addition, we show how the analytical model can be a useful tool to investigate the impact of centralised SDN control on the performance of NFV traffic transmission.

The ten papers in this special section are devoted to the most recent developments and research outcomes addressing the related theoretical and practical aspects of computational intelligence solutions in sustainable computing and aims at presenting latest innovative ideas targeted at the corresponding key challenges, either from a methodological or from an application perspective.

Content caching is a promising approach in edge computing to cope with the explosive growth of mobile data on 5G networks, where contents are typically placed on local caches for fast and repetitive data access. Due to the capacity limit of caches, it is essential to predict the popularity of files and cache those popular ones. However, the fluctuated popularity of files makes the prediction a highly challenging task. To tackle this challenge, many recent works propose learning based approaches which gather the users' data centrally for training, but they bring a significant issue: users may not trust the central server and thus hesitate to upload their private data. In order to address this issue, we propose a Federated learning based Proactive Content Caching (FPCC) scheme, which does not require to gather users' data centrally for training. The FPCC is based on a hierarchical architecture in which the server aggregates the users' updates using federated averaging, and each user performs training on its local data using hybrid filtering on stacked autoencoders. The experimental results demonstrate that, without gathering user's private data, our scheme still outperforms other learning-based caching algorithms such as m-epsilon-greedy and Thompson sampling in terms of cache efficiency.

Information-centric networking (ICN) is an emerging paradigm that decouples content from the host to achieve fast and cost-efficient communication and content distribution in the future Internet. A key feature of ICN is the deployment of ubiquitous in-network caching to speed up service delivery and improve network resource utilisation. ICN caching has been widely studied in terms of caching strategies and caching performance. However, the economic aspect of ICN has received marginal consideration so far, although it is vital to understand the potential cost- efficiency of ICN before its wide deployment in service provider network. To address this issue, we propose a cost-aware caching scheme to study the Quality-of-Service (QoS) and cost of ICN and investigate the inner association between them. Two new models are designed to characterise the cost and QoS of ICN with arbitrary topology under heterogeneous bursty content requests. A multi- objective evolution algorithm is adopted to find the optimal cache resource allocation. Numerical results show the effectiveness of the proposed scheme in achieving cost- efficiency and QoS guarantee in ICN caching.

Human embodiment/representation in virtual environments (VEs) similarly to the human body in real life is endowed with multimodal input/output capabilities that convey multiform messages enabling communication, interaction and collaboration in VEs. This paper assesses how effectively different types of virtual human (VH) artefacts enable smooth communication and interaction in VEs. With special focus on the REal and Virtual Engagement In Realistic Immersive Environments (REVERIE) multi-modal immersive system prototype, a research project funded by the European Commission Seventh Framework Programme (FP7/2007-2013), the paper evaluates the effectiveness of REVERIE VH representation on the foregoing issues based on two specifically designed use cases and through the lens of a set of design guidelines generated by previous extensive empirical user-centred research. The impact of REVERIE VH representations on the quality of user experience (UX) is evaluated through field trials. The output of the current study proposes directions for improving human representation in collaborative virtual environments (CVEs) as an extrapolation of lessons learned by the evaluation of REVERIE VH representation.

Network operators recognise that Content Delivery Networks are essential for meeting user Internet application and content demands. The infrastructure must be tightly integrated to provide request routing, content caching, load balancing, scalability and reliability, whilst minimising deployment time and complexity. A major step towards achieving these goals is to embrace recent Software Defined Network and Network Function Virtualisation objectives and design principles. This paper outlines the OpenCache API; an interface used to define the behaviour and operation of an SDN-based content delivery platform in real-time. We demonstrate the applicability and effectiveness of such an API by implementing load-balancing and fail-over functionalities as part of an experimental deployment.

Abstract In order to achieve agility and shorter concept‐to‐market timescales for new products and services, ICT service providers and their corporate customers alike increasingly adopt a collection of technologies, concepts and capabilities which come under the banner of the Service Oriented Architecture (SOA). The Service Oriented Infrastructure (SOI) approach complements SOA by enabling the optimal use of virtualised infrastructure services and resources via the network, and their integration in tailored solutions that meet customer needs and adapt to their growth pattern. In this paper we focus on the business and technological challenges relating to security and service dependability for SOI. In particular the paper studies challenges in the security areas of (i) identity federation, (ii) distributed usage and access management, (iii) context‐aware secure messaging, routing and transformation and (iv) SOA security governance. It gathers requirements and it proposes an architecture comprising design patterns and a governance framework that address these challenges. An example case‐study presents an implementation of the proposed architecture's SOI security capabilities aiming at the practical validation of the proposed architectural concepts. Copyright © 2010 John Wiley & Sons, Ltd.

Abstract The practical success of pervasive services running in mobile wireless networks relies largely on its flexibility in providing adaptive and cost‐effective services. Service discovery is an essential mechanism to achieve this goal. As an enhancement to our previous work for service discovery, that is, model‐based service discovery (MBSD), this paper proposes a location‐based service advertisement (SA) algorithm named as MBSD‐sa. MBSD‐sa advocates the importance of service location to the service availability and integrates the service location information together with the service semantic information into service information for advertisement. MBSD‐sa utilizes prediction to estimate the service location so as to reduce the number of SA messages (SAMs). Two complementary types of SA mechanisms (Types 1 and 2) are employed by MBSD‐sa to strike the balance between the SAM overhead and the accuracy of service information. The performance of MBSD‐sa is analyzed both numerically and using simulations. Copyright © 2008 John Wiley & Sons, Ltd.

The creation of services is a complex activity that involves several tasks. Furthermore this complexity is augmented by the fact that supporting service creation environments are technology-specific. Consequently a technology-independent approach and framework are required to generate service creation environments and drive service creation. In this paper we present such an approach and a generic framework for supporting service creation. The approach realizes service creation via the phases of: (i) domain specific language definition, (ii) model definition and validation, (iii) model-to-model transformation and (iv) model-to-code generation. Each phase maps to a corresponding phase in service creation starting from service analysis to service implementation. The applicability of the approach and its accompanying framework is demonstrated via an example scenario that illustrates the automatic generation of a service creation environment for an online survey system.

This paper details an application of model-driven development undertaken within the Celtic initiative project Madeira. The objective of Madeira is to apply model-driven approaches to investigate large-scale distribution techniques in network management. So far, the project has concentrated on building a prototype system using the peer-to-peer (P2P) paradigm. For this, one of the major challenges was to provide a model supporting P2P characteristics, such as 1) self-organisation, 2) symmetric communication and 3) distributed control and domain specific concepts for distribution and management as can be found in telecommunications. Our modelling approach had to consider the views of the different participants, such as equipment vendor (Ericsson and Siemens), network operator (BT) and service provider (BT, Telefonica). To minimise the complexity of the model, we have focused on fault and configuration management in a dynamically forming network of transient elements. This paper explains the complexity of the networks we consider and of the management tasks we have to cover. This can be briefly characterized by comprising a multitude of different, sometimes proprietary, technologies and diverse business models. We motivated the application of new paradigms, in our case the P2P paradigm, to simplify management for seamless service provision to customers. Based on this, we provided a case study of how we applied a model-driven approach to capture the complexity of the task and the complexity of the management activities, which ultimately led towards the specification of management information and behaviour of network nodes. The methodology we use is presented in the form of a 'vertical slice', where a logical portion of the project, of limited scope and functionality, is brought from the meta-level right through to the development stage, covering all modelling and architectural work as well as the underlying platform aspects

The Madeira project addresses a novel approach for the management of network elements of increasing number, heterogeneity and transience. As next-generation networks exhibit major challenges for today's centralized network management systems, we investigate the feasibility of a peer-to-peer (P2P) approach, facilitating self-management and dynamic behavior of elements within networks. In this short paper we give an overview of the system architecture developed in Madeira and describe the key concepts, like Madeira platform services, Adaptive Management Components and policies that provide the base for building distributed network management applications.