Swedish Institute of Computer Science Publications Database
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Architectural Concepts for Federated Embedded Systems
No full textFederated embedded systems (FES) is an approach for systems-of-systems engineering in the domain of cyber-physical systems. It is based on the idea to allow dynamic addition of plug-in software in the embedded system of a product, and through communication between the plug-ins in different products, it becomes possible to build services on the level of a federation of products. In this paper, architectural concerns for FES are elicited, and are used as rationale for a number of decisions in the architecture of products that are enabled for FES, as well as in the application architecture of a federation. A concrete implementation of a FES from the automotive domain is also described, as a validation of the architectural concepts presented
Evocative Balance: Designing for Interactional Empowerment
No full textWe propose an experiential quality called evocative balance as key in designing for affective interaction that aims to empower users in and
through the interaction. Evocative balance draws on the dual meaning of the word “evoke” in characterizing the user’s sense that data and
actions evoke familiar recollections of lived experience, yet are still open enough to evoke multiple interpretations in an ongoing process
of co-constructive making of meaning. Our aim is to capture those experiences that resonate with our lived, everyday, social and bodily
experiences; those experiences that we can recognise in ourselves and, through empathy, in others. We elaborate on and substantiate the
meaning of this quality by means of retrospective reflection on three of our own design projects. This account provides detailed insights
on how to find the balance between openness and familiarity through design
Probabilistic Fault Management in Networked Systems
No full textTechnical advances in network communication systems (e.g. radio access
networks) combined with evolving concepts based on virtualization (e.g.
clouds), require new management algorithms in order to handle the increasing
complexity in the network behavior and variability in the network environment.
Current network management operations are primarily centralized and
deterministic, and are carried out via automated scripts and manual interventions,
which work for mid-sized and fairly static networks. The next generation
of communication networks and systems will be of significantly larger
size and complexity, and will require scalable and autonomous management
algorithms in order to meet operational requirements on reliability, failure
resilience, and resource-efficiency.
A promising approach to address these challenges includes the development
of probabilistic management algorithms, following three main design
goals. The first goal relates to all aspects of scalability, ranging from efficient
usage of network resources to computational efficiency. The second goal relates
to adaptability in maintaining the models up-to-date for the purpose of
accurately reflecting the network state. The third goal relates to reliability in
the algorithm performance in the sense of improved performance predictability
and simplified algorithm control.
This thesis is about probabilistic approaches to fault management that follow
the concepts of probabilistic network management (PNM). An overview
of existing network management algorithms and methods in relation to PNM
is provided. The concepts of PNM and the implications of employing PNMalgorithms
are presented and discussed. Moreover, some of the practical differences
of using a probabilistic fault detection algorithm compared to a deterministic
method are investigated. Further, six probabilistic fault management
algorithms that implement different aspects of PNM are presented.
The algorithms are highly decentralized, adaptive and autonomous, and
cover several problem areas, such as probabilistic fault detection and controllable
detection performance; distributed and decentralized change detection in
modeled link metrics; root-cause analysis in virtual overlays; event-correlation
and pattern mining in data logs; and, probabilistic failure diagnosis. The probabilistic
models (for a large part based on Bayesian parameter estimation)
are memory-efficient and can be used and re-used for multiple purposes, such
as performance monitoring, detection, and self-adjustment of the algorithm
behavior
Poster Abstract: Low-Power Wireless Channel Quality Estimation in the Presence of RF Smog
No full textLow-power wireless networks deployed in indoor
environments inevitably encounter high-power Cross Technology
Interference (CTI) from a wide range of wireless devices operating
in the shared RF spectrum bands. This severely reduces the
performance of such networks and possibly causes loss of connectivity,
which affects their availability and drains their resources.
In this work, to address the channel uncertainty, a consequence
of CTI, we propose a novel channel metric that (i) harnesses
the local knowledge of a node about the wireless channel to
discern the presence of persistent high-power interferers, and
(ii) assists the node in inferring its proximity to the dominant
interference sources in the physical space. In order to motivate
and validate the necessity of such a metric, we empirically
characterize the impact of the interaction between
high/low-power cross technology interferers and IEEE 802.15.4
Poster Abstract: Directional Transmissions and Receptions for Burst Forwarding using Disjoint Paths
No full textExplaining Probabilistic Fault Diagnosis and Classification using Case-based Reasoning
No full textThis paper describes a generic framework for explaining the prediction of a probabilistic classifier using preceding cases. Within the framework, we derive similarity metrics that relate the similarity between two cases to a probability model and propose a novel case-based approach to justifying a classification using the local accuracy of the most similar cases as a confidence measure. As a basis for deriving similarity metrics, we define similarity in terms of the principle of interchangeability that two cases are considered similar or identical if two probability distributions, derived from excluding either one or the other case in the case base, are identical. Thereafter, we evaluate the proposed approach for explaining the probabilistic classification of faults by logistic regression. We show that with the proposed approach, it is possible to find cases for which the used classifier accuracy is very low and uncertain, even though the predicted class has high probability
Gossip-based Algorithms for Information Dissemination and Graph Clustering
No full textDecentralized algorithms are becoming ever more prevalent in almost all real-world applications that are either data intensive, computation intensive or both. This thesis presents a few decentralized solutions for large-scale (i) data dissemination, (ii) graph partitioning, and (iii) data disambiguation. All these solutions are based on gossip, a light weight peer-to-peer data exchange protocol, and thus, appropriate for execution in a distributed environment.
For efficient data dissemination, we make use of the publish/subscribe communication model and provide two distributed solutions, one for topicbased and one for content-based subscriptions, named Vitis and Vinifera respectively. These systems propagate large quantities of data to interested users with a relatively low overhead. Without any central coordinator and only with the use of gossip, we build a novel topology that enables efficient routing in an unstructured overlay. We construct a hybrid system by injecting structure into an otherwise unstructured network. The resulting structure resembles a navigable small-world network that spans along clusters of nodes that have similar subscriptions. The properties of such an overlay make it an ideal platform for efficient data dissemination in large-scale systems. Our solutions significantly outperforms their counterparts on various subscription and churn scenarios, from both synthetic models and real-world traces.
We then investigate how gossiping protocols can be used, not for overlay construction, but for operating on fixed overlay topologies, which resemble graphs. In particular we study the NP-Complete problem of graph partitioning and present a distributed partitioning solution for very large graphs. This solution, called Ja-be-Ja, is based on local search and does not require access to the entire graph simultaneously. It is, therefore, appropriate for graphs that can not even fit into the memory of a single computer. Once again gossip-based algorithms prove efficient as they enable implementing light-weight peer sampling services, which supply graph nodes with partial knowledge about other nodes in the graph. The performance of our partitioning algorithm is comparable to centralized graph partitioning algorithms, and yet it is scalable and can be executed on several machines in parallel or even in a completely distributed peer-to-peer overlay. It can be used for both edge-cut and vertex-cut partitioning of graphs and can produce partition sizes of any given distribution.
We further extend the use of gossiping protocols to find natural clusters in a graph instead of producing a given number of partitions. This problem, known as graph community detection, has extensive application in various fields and communities. We take the use of our community detection algorithm to the realm of linguistics and address a well-known problem of data disambiguation. In particular, we provide a parallel community detection algorithm for cross-document coreference problem. We operate on graphs that we construct by representing documents’ keywords as nodes and the co-location of those keywords in a document as edges. We then exploit the particular nature of such graphs, which is coreferent words are topologically clustered, and thus, can be efficiently discovered by our community detection algorithm
DiffractMe - Using A Skills-Based Approach in Design Practice
No full textThe potential of skills in design is intriguing; as skills open up new perceptions of the world they allow meaning to arise as we engage with the world. Several skills-based techniques that leverage this potential have been developed, and integrated into the Designing in Skills framework. The framework builds on personal engagement of designers in their practice, and promotes them to take a first-person perspective, enabling designs to be enriched with meaning. In this paper, we present the most recent workshop based on this approach, which specifically focuses on employing the Designing in Skills framework as a starting point and catalyst for design practice. We briefly introduce the Designing in Skills framework and present the DiffractMe! project in which we built on this approach to explore its potential for design practice. We conclude with reflections on the process and result by the involved designers. These reflections offer insights into the value of this approach for enriching interactive design with experiential qualities
