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.

Increasing demands for low latency, cost efficiency, and digital sovereignty are driving organisations beyond centralised cloud models towards decentralised and hybrid computing environments. The Edge-to-Cloud computing continuum aims to unify edge and cloud infrastructures into a seamless execution environment, but achieving this remains complex in practice.ColonyOS is an open-source, ready-to-use meta-operating system that coordinates distributed workloads across diverse computing environments. ColonyOS separates coordination from execution using declarative, intent-based function specifications and a lightweight, broker-based architecture that provides OS-like abstractions. This paper outlines key properties of a meta-operating system and describes how these are realised in ColonyOS. The paper also presents practical experiences from two industrial deployments: real-time seismic monitoring at RockSigma AB and integration with EuroHPC supercomputers. These use cases demonstrate ColonyOS’s capabilities in enabling resilient workload management and hybrid orchestration across HPC and Kubernetes environments. A referenced study on carbon-aware scheduling further illustrates how ColonyOS supports time-shifting of non-urgent workloads to reduce environmental impact. The paper concludes with a discussion of ColonyOS’s architecture and future research directions.

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.

It is paramount for the circular economy that the supply of products, components and materials match the demand for effective re-use and re-manufacturing. In this paper, we present a tool for balancing fairness and environmental impact (e.g., from transport) in trading goods subject to demand-supply matching (DSM) for circularity. The tool addresses the problem of controlling this balance when multiple demanders buy goods from multiple suppliers, which may implement a volume-dependent price model. In such cases, identifying the best possible set of procurements to obtain a desired balance demands a model that provides a predictable output from declarative statements on this balance. We evaluate our tool using simulations to demonstrate its usefulness in evaluating the effects on fairness between trading actors from regulatory policies related to environmental impact. We show that our DSM ranking tool can be used to evaluate such effects using simulations of different market scenarios. Based on these results, we discuss extensions of the model for it to support additional such statements in more complex DSM scenarios that for example also include seller reputation as a matching criterion.

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.

Data is important for the industry to take advantage of digitalization, realize automation, assure quality, and more. Values from data are not only created individually by companies, but also in eco-systems in which data is shared among participating organizations. Secure data sharing is essential in such eco-systems to prevent unauthorized access and use of the data. Usage control extends traditional access control with restrictions concerned with requirements that pertain to data processing contractual obligations, rather than data access provisions only. Thus, usage control is relevant in the context of intellectual property protection, compliance with regulations, and digital rights management. This paper presents a method to negotiate contractual obligations and access provisions, and automatically enforce those provisions with access control. Finalized negotiations establish Ricardian contracts at two levels; a superordinate level with a connected subordinate level. These contracts contain provisions in terms of access control attributes. Using our implementation of a negotiation engine we demonstrate the automatic creation of NIST Next Generation Access Control (NGAC) access control policies. Our negotiation engine uses a lightweight model for the storage of an unforgeable and immutable log of the established contracts based on digital signatures and hashing.

The efficient matching of demanded products, components and materials for re-use and re-manufacturing is paramount for the circular economy. In this paper, we present a model for automated demand-supply matching (DSM) that provides means to rank candidate suppliers according to predefined procurement criteria. Preparing for the acquisition of End-of-life (EoL) products, components, and materials, actors in need of such goods may publish calls for tenders including those criteria. Suppliers can then submit their offerings meeting the needs as stated by one or more calls for tenders. Our automated DSM model evaluates the received offerings based on the procurement criteria to rank suppliers according to each call for tender. Alternatively, suppliers may publish their offerings beforehand, thereby making it possible for demanders to search for matching offerings that meet their needs and to automatically evaluate them using the DSM ranking model. Our basic model takes into account transportation cost, seller reputation and price. We discuss the flexibility of the DSM model in incorporating additional procurement criteria to examine the general applicability of its conceptual design.

In this paper, we define a multi-attribute auctioning system for the circular economy and the trade of products, components and materials subject to recycling. The increasing popularity of auctioning systems for buying and selling goods has led to the adaptation of them to diverse and particular scenarios, many of which require support for attributes like delivery time, quality, etc. Such attributes allow for more explicit and precise negotiations than traditional auctioning systems where only price is taken into account. The circular economy concept replaces end-of-life with the reuse of various goods, aiming to keep as much value as possible of any asset. By allowing users to adjust attributes in multi-step negotiations according to their economic and ecological needs, better deals can be achieved. We address this potential with our multi-attribute, and multi-step auctioning system. The system is based on transparency and fairness principles, and addresses requirements for flexibility in what attributes can be used, and the need for a semi-transparent auctioning procedure. We present a winner determination approach based on scoring protocol based on weights for different input attributes. Our auctioning system uses a signature chain data structure to provide transaction traceability. We demonstrate using a generic example that the proposed system supports simple and flexible multi-attribute auctions.

We propose the AQUILA framework (Adaptive Quality Assurance in Industrial Applications), a concept for digitalization in Industry 4.0 to support the entire industrial manufacturing chain, laying the groundwork for adaptive quality assurance in times of disrupted supply chains and, due to the COVID-19 pandemic, restricted travel possibilities. To that end, our proposed framework allows for the definition and description of industrial processes, quality assurance and testing protocols, and training scenarios in a comprehensive notation based on BPMN, and supports users in task execution, documentation, and evaluation by providing smart glass-based HCI with eye tracking technology, delivering a combination of process documentation, context-sensitive AR visualization, gaze-based interaction schemes, and remote maintenance and assistance functionality.

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.

The construction industry is characterized by its extensive and dynamic collaborations between contractors providing various services and expertise. In such eco-systems, the secure sharing of information, data and equipment challenges the access control needs to be application agnostic. Furthermore, it needs fine-grained access policies including means for abstraction to ease administration, and support for delegated authorization in Service-Oriented Architecture (SOA) based systems. In this paper, we explore the use of delegated access using OAuth 2.0 with Attribute-Based Access Control (ABAC) for the collaborative sharing of equipment at construction sites. In particular, we investigate the use of contextual attributes to capture the dynamic aspects, such as location and urgency, in the booking of construction lifts. Through this study, we propose a solution based on the IoT Application-scoped Access Control as a Service (IAACaaS) architecture model combined with NIST Next Generation Access Control (NGAC). We present an architecture for a general Identity and Access Management (IAM) system for the construction industry, and provide a design and guide for implementation of this architecture in terms how key functionalities should be captured as reusable micro-services. Moreover, we describe how these micro-services can be combined to make the system a general and reusable solution providing access control for collaborative sharing of data, information and equipment at construction sites.

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.

Data collection and annotation is a time consuming and costly process, yet necessary for machine vision. Automation of construction equipment relies on seeing and detecting different objects in the vehicle’s surroundings. Construction equipment is commonly used to perform frequent repetitive tasks, which are interesting to automate. An example of such a task is the short-loading cycle, where the material is moved from a pile into the tipping body of a dump truck for transport. To complete this task, the wheel loader needs to have the capability to locate the tipping body of the dump truck. The machine vision system also allows the vehicle to detect unforeseen dangers such as other vehicles and more importantly human workers. In this work, we investigate the viability to perform semi-automatic annotation of video data using linear interpolation. The data is collected using scale-models mimicking a wheel-loaders approach towards a dump truck during the short-loading cycle. To measure the viability of this type of solution, the workload is compared to the accuracy of the model, YOLOv3. The results indicate that it is possible to maintain the performance while decreasing the annotation workload by about 95%. This is an interesting result for this application domain, as safety is critical and retaining the vision system performance is more important than decreasing the annotation workload. The fact that the performance seems to retain with a large workload decrease is an encouraging sign.

In this paper, we define an auction protocol and implementation of a decentralized and distributed auctioning platform for demand-supply matching of components and materials subject to recycling. Auctioning is the process of buying and selling goods or services by offering them up for bid, taking bids, and then selling the item to a winner according to preset rules. Some auction types relies on sealed-bids while others implement an open-bid procedure that allows for multiple bids before a known or unknown deadline. The English auction and its variants is most common type. It relies on that the current highest bid is always available to potential bidders. Online auction platforms are nowadays used to trade various services and goods. We address the iterative negotiations in form of auction bids aiming at signed contractual agreements stated in legal prose and captured by Ricardian contracts. We evaluate our model towards requirements including privacy, transparency and fairness in terms of acknowledged publishing of highest bids and the ordered arrival of individual bids. In addition, we demonstrate the mechanisms for distributed storage of cryptographically signed Ricardian contracts, omitting the need for trusting the auctioneer or relying on a trusted third party for this storage.

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.

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.

Machine vision is required by autonomous heavy construction equipment to navigate and interact with the environment. Wheel loaders need the ability to identify different objects and other equipment to perform the task of automatically loading and dumping material on dump trucks, which can be achieved using deep neural networks. Training such networks from scratch requires the iterative collection of potentially large amounts of video data, which is challenging at construction sites because of the complexity of safely operating heavy equipment in realistic environments. Transfer learning, for which pretrained neural networks can be retrained for use at construction sites, is thus attractive, especially if data can be acquired without full-scale experiments. We investigate the possibility of using scalemodel data for training and validating two different pretrained networks and use real-world test data to examine their generalization capability. A dataset containing 268 images of a 1:16 scale model of a Volvo A60H dump truck is provided, as well as 64 test images of a full-size Volvo A25G dump truck. The code and dataset are publicly available1. The networks, both pretrained on the MS-COCO dataset, were fine-tuned to the created dataset, and the results indicate that both networks can learn the features of the scale-model dump truck (validation mAP of 0.82 for YOLOv3 and 0.95 for RetinaNet). Both networks can transfer these learned features to detect objects on a full-size dump truck with no additional training (test mAP of 0.70 for YOLOv3 and 0.79 for RetinaNet).

Bucket-filling is a repetitive task in earth-moving operations with wheel-loaders, which needs to be automated to enable efficient remote control and autonomous operation. Ideally, an automated bucket-filling solution should work for different machine-pile environments, with a minimum of manual retraining. It has been shown that for a given machine-pile environment, a time-delay neural network can efficiently fill the bucket after imitation-based learning from 100 examples by one expert operator. Can such a bucket-filling network be automatically adapted to different machine-pile environments without further imitation learning by optimization of a utility or reward function? This paper investigates the use of a deterministic actor-critic reinforcement learning algorithm for automatic adaptation of a neural network in a new pile environment. The algorithm is used to automatically adapt a bucket-filling network for medium coarse gravel to a cobble-gravel pile environment. The experiments presented are performed with a Volvo L180H wheel-loader in a real-world setting. We found that the bucket-weights in the novel pile environment can improve by five to ten percent within one hour of reinforcement learning with less than 40 bucket-filling trials. This result was obtained after investigating two different reward functions motivated by domain knowledge.

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.

Automatic bucket filling is an open problem since three decades. In this paper, we address this problem with supervised machine learning using data collected from manual operation. The range-normalized actuations of lift joystick, tilt joystick and throttle pedal are predicted using information from sensors on the machine and the prediction errors are quantified. We apply linear regression, k-nearest neighbors, neural networks, regression trees and ensemble methods and find that an ensemble of neural networks results in the most accurate predictions. The prediction root-mean-square-error (RMSE) of the lift action exceeds that of the tilt and throttle actions, and we obtain an RMSE below 0.2 for complete bucket fillings after training with as little as 135 bucket filling examples.

Remote operation is a step toward the automation of mobile working machines. Safe and efficient teleremote operation requires good‐quality video feedback. Varying radio conditions make it desirable to adapt the video sending rate of cameras to make the best use of the wireless capacity. The adaptation should be able to prioritize camera feeds in different directions depending on motion, ongoing tasks, and safety concerns. Self‐Clocked Rate Adaptation for Multimedia (SCReAM) provides a rate adaptation algorithm for these needs. SCReAM can control the compression used for multiple video streams using differentiating priorities and thereby provide sufficient congestion control to achieve both low latency and high video throughput. We present results from the testing of prioritized adaptation of four video streams with SCReAM over LTE and discuss how such adaptation can be useful for the teleremote operation of working machines.

HSDPA (high-speed downlink packet access), introduced in WCDMA release 5, provides a high-bandwidth shared channel with short transmission time interval (TTI). The short TTI together with appropriate scheduling enable HSDPA to support efficient multiplexing of traffic. We explain the performance of four scheduling algorithms when transmitting a traffic mix consisting of both conversational (VoIP) traffic and background (Web) traffic over the high-speed downlink shared channel (HS-DSCH) of HSDPA. We consider both cell throughput and user satisfaction. The proportional fair (PF), the maximum rate (MR) scheduler and two extended versions of MR, are tested for different VoIP scheduling delay budgets and varying load. To understand the behaviour of the schedulers, we use the ns-2 simulator extended with a model of HS-DSCH to simulate a mixed traffic scenario. Our results show that a scheduler that gradually increases the VoIP priority and considers the user's current possible rate, performs well. A more drastic increase in VoIP priority is however needed when the delay budget is short. Furthermore, attempting to uphold quality for both VoIP and Web traffic makes the system sensitive to overload situations.

In this paper we investigate the need for admission control for the high-speed downlink shared channel (HS-DSCH) through the evaluation of two admission control mechanisms. One mechanism uses the number of active users in a cell as a metric and the other one uses the mean downlink throughput of a user. We also introduce a model for user behaviour in which the goodput of a completed file download decides if further downloads are made. In order to measure user-experienced quality we use a utility function for transforming per-flow goodput into a user satisfaction index. System performance, measured by total user satisfaction and total goodput, is evaluated for a range of session arrival rates and admission control limits. This evaluation is done using the ns-2 simulator, together with extensions of our own. If the objective is to maximise goodput, our results show that no admission control is needed. Maximising user satisfaction benefits from an admission control. We also note that the impact of user behaviour is not insignificant

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.