Distributed ledger technology such as blockchain is considered essential for supporting large numbers of micro-transactions in the Machine Economy, which is envisioned to involve billions of connected heterogeneous and decentralized cyber-physical systems. This stresses the need for performance and scalability of distributed ledger technologies. Addressing this, sharding techniques that divide the blockchain network into multiple committees are a common approach to improve scalability. However, with current sharding approaches, costly cross-shard verification is needed to prevent double-spending. This article proposes a novel and more scalable distributed ledger method named ScaleGraph that implements dynamic sharding by using routing and logical proximity concepts from distributed hash tables. ScaleGraph addresses cybersecurity in terms of integrity and availability to support frequent micro-transactions between autonomous devices. Benefits of ScaleGraph include a total storage space complexity of \(O(t)\) , where \( t \) is the global number of transactions (assuming a constant replication degree). This space is sharded over \( N \) nodes so that each node needs \(O(t/N)\) storage in expectation, which provides a high level of concurrency and data localization as compared to other delegated consensus proposals. ScaleGraph allows for a dynamic grouping of validators that are selected based on a distance metric. We analyze the consensus requirements in such a dynamic setting and show that a synchronous consensus protocol allows shards to be smaller than an asynchronous one, and likely yields better performance. Moreover, we provide an experimental analysis of security aspects regarding the required size of the consensus groups with ScaleGraph. Our analysis shows that dynamic sharding based on proximity concepts brings attractive scalability properties in general, especially when the fraction of corrupt nodes is small.

In servitization cases where customers are offered equipment as a service, it is essential to provide efficient on-site service to resolve multiple issues proactively. Autonomous lawn mowing robots operating in unstructured garden environments face issues, such as potholes, which challenge the delivery of a consistent and reliable service. We investigate a rule-based issue detection system using the inertial measurement unit and position sensors to detect terrain irregularities. Thirteen mowing sessions were conducted using a robotic lawnmower, and issues were detected based on predefined thresholds applied to roll, pitch, and z-axis position data. Two experimental approaches were explored: one aggregating all detected anomalies, and another focusing on mowing direction and spatially intersecting anomalies to identify recurring irregularities. The results indicate that direction influences anomaly detection and helps pinpoint terrain issues likely to affect service delivery. This work contributes to the development of terrain-aware service robots aligned with reliability-driven servitization objectives. The results are discussed in terms of how the detection of terrain issues aligns with reliability goals in servitization frameworks.

Access control in modern digital ecosystems is challenging due to dynamic resources and diverse stakeholders. Traditional mechanisms struggle to adapt, causing inefficiencies and inequities. We propose a novel algorithm that automates access control policy negotiation via objective optimization and utility-based methods. It enables stakeholders to jointly select policies aligned with their preferences, provided a suitable policy exists. Suggested criteria guide the evaluation of predefined policies, and a mathematical formulation quantifies stakeholder preferences with utility functions, using optimization to achieve consensus. The algorithm’s multilinear scalability is demonstrated through time and space complexity analysis. An evaluation tool supports practical testing, and the approach enhances efficiency and trust by ensuring equitable data access within digital ecosystems.

Abstract Authentication and access control for Cyber-Physical Systems (CPSs) are pivotal for protecting systems and their users from problems related to harmful actions and the malicious use of retrieved data. In some situations, making access decisions requires using user information, thereby challenging their privacy. Attribute-based access control (ABAC) supports dynamic and context-aware access decisions that are attractive in cyber-physical system environments. However, privacy preservation for access decisions is an open issue for authorization and is not supported by existing ABAC models. For example, if access decisions need to be made based on private attribute values such as health data, the corresponding access control policies need to be revealed. This paper reviews the ABAC, homomorphic encryption (HE), and zero-knowledge proof (ZKP) approaches, confirming the gap in privacy preservation in ABAC. Based on this observation, we further present the application of a new ZKP-based protocol in which ABAC allows for the privacy-preserving evaluation of attributes. This protocol is implemented and evaluated in terms of its performance and security. The evaluation demonstrates that there is a possibility for privacy-preserving ABAC, which may benefit the use of CPS, e.g., in underground and open-pit mines.

Data sharing is becoming increasingly important as organizations seek to improve their operations and gain a competitive advantage. The data sharing between organizations, stakeholders, and even internal teams requires access control policies that define who can access what data, under what circumstances, and for what purposes. Attribute-based access control (ABAC) provides a flexible and fine-grained mechanism for enforcing such policies, preventing data leakage, and improving security and compliance. A challenge is that these policies should be able to support the agility and adaptability of constantly evolving modern industrial systems, where new data sources, services, and users are frequently added and removed. As the number of data sources and associated policies grows, the manual management of ABAC policies in evolving systems becomes a bottleneck, which prevents the adoption of fine-grained access control mechanisms. This paper presents a model based on tag-matching to automate the process of connecting new data sources and users to existing access control policies. In the proposed model, tag-matching means matching metadata elements to policy attributes to reduce the administrative work of managing access policies. The model takes advantage of the identity abstraction of ABAC policies to connect existing rules between attributes and the target resource or user. We present a proof-of-concept implementation of the tag-matching model in Eclipse Arrowhead and provide an evaluation of the proposed solution.

Many Cyber-Physical Systems are today semiautonomous and powerful enough to perform advanced tasks on their own. This means they can also act as representatives of people or devices that have given them an order. However, traditional access control policies and delegation models do not meet industrial requirements such as support for letting autonomous CPS devices act on their own with certified credentials under the sub authorization by subcontractors, without the need for a separate account per device. In this paper, we analyze and compare power of attorney, proxy signature by warrant, and OAuth to identify the strengths and challenges of each. Based on the comparison, we propose an OAuth grant type based on the power of attorney and inspired by the concept of proxy signature by warrant. Power of Attorney is a generic and self-contained document that a principal signs and directs to an agent, thereby providing it the power to execute actions on behalf of the principal for a predefined time, even if it is offline. One key advantage of the power of attorney is that it can support effective sub-granting on several levels to support industrial scenarios where resource owners bring in authorized contractors that can in their turn authorize and bring in several devices without incurring management overhead to the resource owner. A proof-of-concept and performance evaluation of the proposed model is presented using an industrial use-case scenario with multi-level authorization.

Video conferencing applications help people communicate via the Internet and provide a significant and consistent basis for virtual meetings. However, integrity, security, identification, and authentication problems are still universal. Current video conference technologies typically rely on cloud systems to provide a stable and secure basis for executing tasks and processes. At the same time, video conferencing applications are being migrated from centralized to decentralized solutions for better performance without the need for third-party interactions. This article demonstrates a decentralized smart identification scheme for video conferencing applications based on biometric technology, machine learning, and a decentralized hash table combined with blockchain technology. We store users' information on a distributed hash table and transactional events on the distributed ledger after identifying users by implementing machine learning functions. Furthermore, we leverage distributed ledger technology's immutability and traceability properties and distributed hash table unlimited storage feature to improve the system's storage capacity and immutability by evaluating three possible architectures. The experimental results show that an architecture based on blockchain and distributed hash table has better efficiency but needs a longer time to execute than the two other architectures using a centralized database.

The edge computing paradigm, spurred by the Internet-of-Things, poses new requirements and challenges for distributed application deployment. There is a need for an orchestrator design that leverages characteristics that enable this new paradigm. We present HYDRA, a decentralized and distributed orchestrator for containerized microservice applications. This orchestrator focuses on scalability and resiliency to enable the global manageability of cloud and edge environments. It can manage heterogeneous resources across geographical locations and provide robust application control. Further, HYDRA enables the location-aware deployment of microservice applications via containerization. Thus, an application's services may be deployed to separate locations according to expected needs. In this article, the experiments show the orchestrator scaling to 20000 nodes and simultaneously deploying 30000 applications. Further, empirical results show that location-aware application deployment does not hinder HYDRA's performance, and the random resource search algorithm currently being employed may be used as a baseline to find resources in this decentralized orchestrator. Therefore, we conclude that HYDRA is a viable orchestrator design for the new computing paradigm.

In the industrial automation domain, data integration between heterogeneous systems and architectures is a challenge. Companies and factories are impatient with the selection of technical solutions that are beneficial to local requirements, therefore facing problems of data integration and incompatibility between other systems. There is a need to bridge heterogeneous industrial systems to provide data integration. This article aims to provide guidelines to industrial managers and solution designers to achieve data integration between heterogeneous industrial systems according to their requirements. For this purpose, we have explored the implementation of two data integration models that can be selected based on the state and needs of industrial systems. The first data integration model is based on event-driven architecture (EDA), where Message Queuing Telemetry Transport (MQTT) is deployed as an event broker. The second model is based on the Arrowhead Framework, which is designed by using service-oriented architecture (SOA) principles, where EventHandler provides SOA-based event-driven communication. We conceptually compare and analyze them based on selected criteria essential in an industrial environment. The qualitative analysis of these models is based on proof-of-concept implementations made available for our evaluation.

In truck manufacturing, an assembly line is typically used to produce many models and variations of trucks in any desired order. Thus, stations are fed with specific sets of parts, known as kits, depending on what truck is next in line. This paper focuses on how to automate the layout planning for placing parts on a kitting wagon. Layout planning resembles the pallet loading problem, but differences include that there is no layering, there may be constraints on how each part can be placed (orientation) and there may be predefined layout hints suggesting positions. A layout planner based on constraint programming is presented. The objective is to facilitate a decision support loop where an engineer may add placement hints as constraints (e.g., for enhancing the workflow at assembly stations) and reuse placements from similar kits to provide recognition. The layout planner automatically generates layout proposals. Finally, it is the engineer that approves the layout plan. There may be many acceptable solutions that have different scores (i.e., levels of quality). We show some approaches to reduce the search space to improve performance. The evaluations show the score and time needed for finding results on some problem instances that are optimally solvable. In the general case, however, finding an optimal solution may be unnecessary or even intractable.

Industrial Internet of Things (IIoT) and Industry 4.0 rely heavily on data for reasons such as production follow-up, planning and optimization. Industrial data come in large volumes from production logs and sensors whereof some data carries business and strategic value, sensitive information, or a combination of both. Such data must be protected from unauthorized access, but also be easy to access for authorized users to facilitate work to gain business and operational values from the data. The efficient creation and maintenance of access policies for secure data sharing is hence essential, but unfortunately also challenging in terms of the complexity and administrative effort for fine-grained such. Attribute-based access control (ABAC) such as the Next Generation Access Control (NGAC) provides efficient models for handling access policies. Existing access control models fail however to provide a simple and easy-to-maintain policy language capable of efficiently enforcing fine-grained access control policies for large volumes of time-series data. In this paper, we propose extensions to NGAC based on filter strings that facilitates efficient enforcement of row-level value and time constraint policies for time-series data. We evaluate two approaches for storing and retrieving these filter strings and provide a qualitative and quantitative discussion of the results.

A key aspect of the cloud is its flexibility and abstraction of the underlying hardware. Historically, virtual machines have been the backbone of the cloud industry, allowing cloud providers to offer virtualized multi-tenant solutions. Today, virtualization by lightweight process containers continue to increase in popularity and make up a larger portion of the cloud, often replacing virtual machines, especially in fog and edge computing. Virtualization can enhance flexibility by enabling support for live migration and failover. Live migration is the process of moving a running instance of a virtual machine or container from one host to another and failover ensures that failures will be automatically detected and the instance restarted, possibly on another host.This paper presents an overview of current migration techniques, and metrics that can be used for comparison. We also present a proof-of-concept implementation and description of a system that enables support for both live migration and failover for containers by extending current container migration techniques. It is able to offer this to any OCI-compliant container, and could therefore potentially be integrated into current container frameworks. In addition, measurements are provided and used to compare the proof-of-concept implementation to the pre-copy migration technique. We achieve a downtime equal to, and total migration time lower than that of pre-copy migration at the cost of an increased amount of data needed to be transferred.

In Industry 4.0 and Industrial Internet of Things (IIoT), large amounts of time-series sensor data is collected from devices and machines. Industrial data typically contain sensitive information that may harm the data owner should it leaks. Although such risks exist, selected data frequently needs to be shared in partner eco-systems to take advantage of expertise in analyzing the data and to synchronize between partners collaborating in the production system. Consequently, access control must support efficient data selection and sharing. The access control should be capable of managing and enforcing access policies for different operations and with different levels of granularity, while being simple to properly maintain and potentially automate. In this paper we examine the possible use of Next-Generation Access Control (NGAC) for such access control. NGAC is an attribute-based access control (ABAC) standard based on relations between data elements to create, manage and enforce access control policies. We propose an Access control model that maps the NGAC policy language to the query language of time-series databases to facilitate a secure and efficient data sharing system for IIoT sensor data.

Eclipse Arrowhead, an upcoming open source framework for designing cyber-physical system-of-systems, is starting to become complete enough to be the subject of serious evaluation and prototyping. As a consequence, it has become increasingly pertinent to provide software that makes it straightforward to use and learn it. To address this, we here present Arrowhead Kalix, a Java 11 library designed for the efficient and effective development of correct, concise and relatively performant Arrowhead-compliant systems. In this paper, we outline the philosophy behind the design of the library, its architecture, its fundamental components and give some examples of how it can be used. The purpose of the paper is both to educate and to provide academics using Kalix with a credible point of reference.

Distributed Internet of Things and cyber-physical systems can potentially be used as agents to automatically sign events and transactions on behalf of users. To accomplish this, there is a need for a model that can represent the relationships, credentials and organizational hierarchies of people and devices, facilitating agents acting as signatories in a controlled way. This paper proposes such a model, where people in different positions are entitled to sign on behalf of organizations or departments therein and extend that to representing machines. Central in this model is the Power of Attorney (PoA), which is a self-contained and signed digital document that for a limited time and in a defined context, authorizes a particular agent (whether a person or device) to sign on behalf of a principal. Although such self-contained PoAs can be stored anywhere, we propose a conceptual architecture based on PoAs and include a signatory registry that keeps track of organizational hierarchies in terms of people and devices according to the defined model and stored PoAs in that context.

Evolving traceability requirements increasingly challenge manufacturing supply chain actors to collect tamperproof and auditable evidence about what inputs they process, in what way these inputs are used, and what the resulting process outputs are. Traceability solutions based on blockchain technology have shown ways to satisfy the requirements of creating a tamper-proof and auditable trail of traceability data. However, the existing solutions struggle to meet the increasing storage requirements necessary to create an evidence trail using manufacturing data. In this paper, we show a way to create a tamper-proof and auditable evolving product story that uses a decentralized file system called the InterPlanetary File System (IPFS). We also show how using linked data can help auditors derive a traceable product story from such an accumulating evidence trail. The solution proposed herein can supplement existing blockchain-based traceability solutions and enable traceability in global manufacturing supply chains where forming a consortium incurs prohibitive costs and where storage requirements are high.

Many use cases coming out of initiatives such as Industry 4.0 and Ubiquitous Computing require that systems be able to cooperate by negotiating about and agreeing on the exchange of arbitrary values. While solutions able to facilitate such negotiation exist, they tend to either be domain-specific or lack mechanisms for non-repudiation, which make them unfit for the heterogeneity and scale of many compelling applications. In this paper, we present the Exchange Network, a general-purpose and implementation-independent architecture for digital negotiation and non-repudiable exchanges of tokens, which are symbolic representations of arbitrary values. We consider the implications of implementing the architecture in three different ways, using a common database, a blockchain, and our own Signature Chain data structure, which we also describe. We demonstrate the feasibility of the architecture by outlining our own implementation of it and also describe a supply-chain scenario inspired by one transportation process at Volvo Trucks.

With the increasing number of devices performing Machine-Type Communications (MTC), mobile networks are expected to encounter a high load of burst transmissions. One bottleneck in such cases is the Random Access Channel (RACH) procedure, which is responsible for the attachment of devices, among other things. In this paper, we performed a rich-parameter based simulation on RACH to identify the procedure bottlenecks. A finding from the studied scenarios is that the Physical Downlink Control Channel (PDCCH) capacity for the grant allocation is the main limitation for the RACH capacity rather than the number of Physical Random Access Channel (PRACH) preambles. Guided by our simulation results, we proposed improvements to the RACH procedure and to PDCCH.

This paper outlines the tradeoffs involved in utilizing Information-Centric Networking (ICN) for Internet of Things (IoT) scenarios. It describes contexts and applications where the IoT would benefit from ICN, and where a host-centric approach would be better. Requirements imposed by the heterogeneous nature of IoT networks are discussed in terms of connectivity, power availability, computational and storage capacity. Design choices are then proposed for an IoT architecture to handle these requirements, while providing efficiency and scalability. An objective is to not require any IoT specific changes of the ICN architecture per se, but we do indicate some potential modifications of ICN that would improve efficiency and scalability for IoT and other applications.

This research paper presents CoMA, a Container Monitoring Agent, that oversees resource consumption of operating system level virtualization platforms, primarily targeting container-based platforms ...

We present an algorithm for performing dynamic per-link admission control. It is designed to be suitable for deployment using existing quality of service and router techniques. The rationale for the algorithm is to find a bitrate limit for reserved capacity, which enables efficient capacity utilization and statistical multiplexing gain. By using simple traffic meters configured with respect to defined service levels, a network resource controller estimates an admission limit. The estimated limit reflects the ratio between reserved capacity and the aggregate behavior of current flows. Simulations indicate that reliable admission limits can be estimated already at low reservation levels.

This paper presents a new set of forwarding behaviors that fits rate-adaptive and delay-sensitive applications with limited loss tolerance. We consider an application to have limited loss tolerance if it needs loss-free forwarding of specific packets up to a certain rate. The new set of forwarding behaviors are attractive for developing real-time applications for the Internet. In particular, such applications can be designed to use reserved forwarding capacity efficiently and compete for more bandwidth while being fair to best-effort traffic. To provide the new set of forwarding behaviors, we define a scheduling mechanism that can be implemented efficiently. Through simulations, we show that this mechanism supports the defined forwarding behaviors.

With current Internet protocols, users may experience low and unpredictable forwarding quality at wireless links. This is due to varying link properties caused by changing radio conditions. Decreased forwarding quality can cause severe degradation in utilization. This is undesirable since forwarding capacity often is expensive at wireless links because of the limited radio spectrum. Allowing the application and transport layers to communicate with wireless link layers can improve the forwarding quality and utilization. We propose to enable inter-layer communication by adding hints and notifications (HAN) to the Internet architecture. Hints can be introduced and used without notifications, while notifications need hints or a similar mechanism to operate. By using IP options and ICMP messages to implement HAN, a backward-compatible partial deployment is possible. With HAN, the network layer becomes truly wireless friendly and the radio spectrum can be used efficiently while supporting both real-time and traditional data applications.

When offering loss-rate differentiation in IP networks, the drop strategy used can have a considerable influence on packet loss and delay. In particular, a strategy of dropping packets only as they arrive can cause bursty loss patterns and high jitter. When only arriving packets are dropped, the router may need to wait for low priority packets to arrive before dropping any packet. This results in larger queue oscillation than if low priority packets were dropped immediately from the queue. Queue oscillation gives bursty loss patterns and delay jitter. We present simulations showing that dropping packets from the queue gives smoother loss patterns and less jitter than if packets are dropped only as they arrive. These simulations cover both TCP Sack and TCP Reno. WRED with and without the gentle modification is used to make drop decisions.

This paper evaluates four mechanisms for providing service differentiation in IEEE 802.11 wireless LANs, the point coordinator function (PCF) of IEEE 802.11, the enhanced distributed coordinator function (EDCF) of the proposed IEEE 802.11e extension to IEEE 802.11, distributed fair scheduling (DFS), and Blackburst using the ns-2 simulator. The metrics used in the evaluation are throughput, medium utilization, collision rate, average access delay, and delay distribution for a variable load of real time and background traffic. The PCF performance is comparably low, while the EDCF performs much better. The best performance is achieved by Blackburst. The DFS provides relative differentiation and consequently avoids starvation of low priority traffic.

Many emerging real-time applications generate layered data streams, which can be used effectively to adapt multicast real-time transmissions to heterogeneous bandwidth environments. However, these applications have not changed with regard to being sensitive to transient congestion and cannot wait a full round-trip time for sender-initiated adaptation. In this paper we propose the selective truncating internetwork protocol (STRIP) supporting layered data transfer, and capable of handling congestion at a finer level of granularity by truncating packets, i.e., stripping off less important data. STRIP inter-operates with the traditional IP infrastructure and can be introduced in a step-by-step fashion, starting where the benefits are obvious, for example with routers which are connected to bandwidth constrained links and which have a surplus of processing capacity. We describe the design and architecture of STRIP and compare it with solutions for differentiated forwarding on a per-packet basis. STRIP provides a simple mechanism that can meet the demands for real-time flows effectively by supporting low delay forwarding, avoiding data-unit reordering, and supporting various drop priorities at the same time.

The authors propose an architecture where clients can make advance reservations through agents. For each routing domain in the network there will be an agent responsible for admission control on behalf of the routers in the domain. Requests involving several routing domains are forwarded for admission control with agents along the path for the requested service. Agents maintain hard reservation state using a reliable protocol for agent intercommunication. Agents start allocating resources for advance reservations in the routers by setting up forwarding state shortly before resources are needed for packet fop warding. Resources are made available for advance reservations by means of rejecting further immediate requests and ultimately by preempting some immediate reservations. They have shown that the risk of preemption can be kept very low. Thus, agents can set up packet classifiers and schedulers in their routers, allowing routers to get on with their main task, packet forwarding.