Edge computing needs to deal with concerns such as load balancing, resource provisioning, and workload placement as optimization problems. Particle Swarm Optimization (PSO) is a nature-inspired stochastic optimization approach that aims at iteratively improving a solution of a problem over a given objective. Utilising PSO in a distributed edge setting would allow the transfer of resource-intensive computational tasks from a central cloud to the edge, this providing a more efficient use of existing resources. However, there are challenges to meet performance and fault tolerance targets caused by the resource-constrained edge environment with a higher probability of faults. We introduce here distributed synchronous and asynchronous variants of the PSO algorithm. These two forms specifically target the performance and fault tolerance requirements in an edge network. The PSO algorithms distribute the load across multiple nodes in order to effectively realize coarse-grained parallelism, resulting in a significant performance increase.

Blockchains are used in many software systems to deal with trusted storage. The selection of the appropriate software architecture stack in distributed systems is generally driven by scalability, security, and decentralization as central qualities. In the blockchain domain, these are known as the blockchain trilemma, as they oppose each other. We select the most popular blockchain platforms based on these trilemma properties and other indicators to provide a platform review. Specific metrics will be derived from the overall goals and applied to the platform options. This serves as a basis to create a Selection framework to facilitate the choice of the best possible platform for a given system architecture. The selection framework is evaluated through a use case.

In many Web and Internet-based systems, sharing Personally Identifiable Information (PII) to identify persons and other entities is common, but centralized systems such as central registries have limitations in terms of control of privacy and identity that a decentralized identity management architecture could address. This study aims to compare the current and potential systems, analyze protocols for decentralized identification and data exchange, propose a protocol selection method, and provide a simple code example. The goal is to assess the feasibility of decentralized processes in software-based business workflows. The methodology involves reviewing protocol materials, including white-papers, articles, and code docs, alongside ontological aspects of identification. Challenges to implementing Decentralized Identifiers (DIDs) include interoperability and the evolving Web/Internet landscape towards more decentralization, openness, and greater user utility.

Computation at the edge or within the Internet-of-Things (IoT) requires the use of controllers to make the management of resources in this setting self-adaptive. Controllers are software that observe a system, analyse its quality and recommend and enact decisions to maintain or improve quality. Today, often reinforcement learning (RL) that operates on a notion of reward is used to construct these controllers. Here, we investigate quality metrics and quality management processes for RL-constructed controllers for edge and IoT settings. We introduce RL and control principles and define a quality-oriented controller reference architecture. This forms the based for the central contribution, a quality analysis metrics framework, embedded into a quality management process.

Self-adaptive systems such as clouds and edge clouds are more and more using Machine Learning (ML) techniques if sufficient data is available to create respective ML models. Self-adaptive systems are built around a controller that, based on monitored system data as input, generate actions to maintain the system in question within expected quality ranges. Machine learning (ML) can help to create controllers for self-adaptive systems such as edge clouds. However, because ML-created controllers are created without a direct full control by expert software developers, quality needs to be specifically looked at, requiring a better understanding of the ML models. Here, we explore a quality-oriented management and governance architecture for self-adaptive edge controllers. The concrete objective here is the validation of a reference governance architecture for edge cloud systems that facilitates ML controller quality management in a feedback loop.

Cooperative Maneuvering is a well-known application in the field of Cooperative, Connected, and Automated Mobility (CCAM). However, extensions are desired to offer more efficient, reliable, and safer solutions. With the introduction of the latest 5G technology, such features can be finally supported with the backup of real-time measurements of multiple vehicles on the road. Projects like the European 5G-CARMEN, address such challenges by designing and implementing edge and cloud based services that take advantage of 5G's high performance and reliability. This paper addresses the simulation of a Cooperative Maneuvering function in the context of the 5G-CARMEN project. Cooperative Maneuvering allows safer and more efficient navigation among multiple vehicles by applying optimal driving strategies. The simulation focuses on the scenario where a vehicle is on the entry lane of a highway and it needs to perform a lane change. Therefore, vehicles depending on their distance and location are advised to apply appropriate actions to make this lane change possible. Additionally, to find the best simulator for a Cooperative Maneuvering implementation a systematic survey of four simulators presents.

Edge clusters consisting of small and affordable single-board devices are used in a range of different applications such as microcontrollers regulating an industrial process or controllers monitoring and managing traffic roadside. We call this wider context of computational infrastructure between the sensor and Internet-of-Things world and centralised cloud data centres the edge or edge computing. Despite the growing hardware capabilities of edge devices, resources are often still limited and need to be used intelligently. This can achieved by providing a self-adaptive scaling component in these clusters that is capable of scaling individual parts of the application running in the cluster. We propose an auto-scalable container-based cluster architecture for lightweight edge devices. A serverless architecture is at the core of the management solution. Our auto-scaler as the key component of this architecture is based on fuzzy logic in order to address challenges arising from an uncertain environment. In this context, it is crucial to evaluate the capabilities and limitations of the application in a real-world context. Our results show that the proposed platform architecture, the implemented application and the scaling functionality meet the set requirements and offer a basis for lightweight edge computing.

Clusters consisting of lightweight single-board devices are used in a variety of use cases: from microcon-trollers regulating the production process of an assembly line to roadside controllers monitoring and managing traffic. Often, data that is accumulated on the devices has to be sent to remote cloud data centers for processing. However, with hardware capabilities of controllers continuously increasing and the need for better performance and security through local processing, directly processing data on a remote cluster, known as Edge Computing, is a favourable solution. Recent trends such as microservices, containerization and server-less technology provide solutions for interconnecting the nodes and deploying software across the created cluster. This paper aims at proposing a serverless architecture for clustered container applications. The architecture relies on the MQTT protocol for communication, Prometheus for monitoring and Docker swarm in conjunction with openFaas for deploying services across a cluster. Using the proposed architecture as a foundation, a concrete traffic management application is implemented as a proof-of-concept. Results show that the proposed architecture meets performance requirements. However, the network setup as well as the network capabilities of the used devices were identified as potential bottlenecks.

Cooperative, Connected, Automated and Autonomous Mobility (CCAM) has a significant effect on improving traffic, lowering pollution and, most significantly, has enormous potential for safer roads. Mobile Edge Computing (MEC) is placing part of the cloud infrastructure at the edge of the network to boost performance and provide context-conscious services. Many of the drawbacks to today's technology could be overcome in tandem with the expected high capacity 5G. Service continuity (SC) across organizational and territorial borders in a highway scenario is one of the main challenges of the EU 5G-CARMEN project. The project tackles this problem by designing and implementing MEC-based services that act as bridges between various realms while taking advantage of 5G's high performance and reliability. This paper addressed the continued introduction of SC frameworks in the context of the 5G-CARMEN project. New high-level prediction algorithms and methods to boost SC have also been proposed.

With telecommunication technology development now moving to a different generation of wireless communication from 4G to 5G, user connections become faster and more reliable. However, from the view of application developers, there is still a testbed environment lacking to evaluate their systems reliability and performance. In this paper, we present an extension for the LTE module collaboration via roaming based on NS-3 simulator, which will provide an ecosystem for on-car service testing as a sample context. We also evaluate the model with specific metrics to check whether the model is adaptable to real-time processes.

In order to leverage the spectrum resources, several forms of wireless duplex have been introduced and investigated in recent years. In Partial Duplex (PD) schemes, part of the band is transmitted in Full-Duplex (FD) and the rest in Half-Duplex (HD); therefore, some transmitted symbols will be characterized, at the receiver, by high SNR (Signal-to-Noise Ratio) and others by low SNR because of the residual self-interference (SI) in the FD part. Combining properly the patterns of these high and low SNR symbols affects the performance of the encoding schemes used in the system; in order to overcome this issue, different encoding and allocation schemes can be adopted for achieving a satisfactory solution. This paper investigates the performance of Low-Density Parity-Check (LDPC), turbo, polar codes for wireless PD. Orthogonal Frequency Division Multiplexing (OFDM) is an efficient multicarrier modulation technique, used in 4G and in the upcoming 5G, and it can be exploited for realizing a proper symbol allocation according to the SNR on each subcarrier. In this context, performance of LDPC, polar, and turbo codes derived from existing specifications has been studied when the system faces a mixture of high and low SNRs on the bits and hence on the symbols coming from the same codeword and this unbalanced SNR distribution is known a-priori at the transmitter, a condition associated with a scheme in which part of the symbols is subject to FD interference.

Low-density parity-check (LDPC) code is one of the fundamental error correction techniques used in wireless networks for achieving low probabilities of error. Wireless full-duplex (FD), according to its inherent nature, faces a considerable self-interference, which cannot be cancelled completely at the FD receivers. LDPC codes are excellent candidates for enabling full duplex in the presence of residual self-interference. This paper studies performance of a class of LDPC codes when the system faces a mixture of high and low bit SNR in the same codeword, condition associated with a scheme in which part of the bits is subject to FD interference (partialduplex scheme). In partial duplex (PD) schemes, part of the band is transmitted in FD and the rest in half-duplex and, consequently, some transmitted bits will be characterized by high SNR and the others by low SNR according to a pattern which is known, a-priori, by the system. Combining properly the patterns of these high and low SNR bits affects the coding performance of the system. In this study, we show the performance achieved by different strategies for allocating low SNR bits in the codeword of LDPC codes derived and adapted by the DVB-S2 standard.

This paper considers the interference mitigation problem in multi-cell networks based on Orthogonal Frequency-Division Multiple Access (OFDMA). The approaches studied here rely on allocation algorithms exploiting base stations cooperation achieved by means of inter-cell interference information sharing. Our emphasis is concentrated on the terminals at the cell edge since, obviously, they are more affected and limited by interference. The paper presents results on several schemes of cooperation (centralized, semi-distributed, coordinated-distributed) combined with allocation algorithms characterized by the awareness of interference relations among users, achieved sharing channel state information among base stations in a typical multi-cell layout. The interference relations are revealed by processing particular graphs, either for partial frequency reuse or soft frequency reuse schemes. So the network realizes, in several forms, a dynamic fractional frequency reuse and the study highlights the main advantages of the best strategies in terms of cooperation and resource allocation.