Swedish Institute of Computer Science Publications Database
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Demo Abstract: SicsthSense - Dispersing the Cloud
No full text—This demo presents SicsthSense, our open cloud
platform for the Internet of Things. SicsthSense enables low
power devices such as sensor nodes and smartphones to easily
store their generated data streams in the cloud. This allows the
data streams, and their history, to be made permanently available
to users for visualisation, processing and sharing. Moving sensor
data computation and monitoring into the cloud is a promising
avenue to enable centralisation of control and redistribution of
collected data.
We showcase SicsthSense running with real sensor nodes
collecting environmental data and posting it to our datastore.
This live data is then visualised and made available for sharing
between users of the platform. Our Android App will also be
distributed to enable participants to stream their phone sensors
into the system, demonstrating how simple it can be to start
machine-to-machine interactions with SicsthSense
Media Data Protection during Execution on Mobile Platforms – A Review
No full textMultimedia content streaming has become an essential part of digital life. The media-on-demand (e.g., video on demand) service of certain enterprises, such as Netflix, Hulu, and Amazon etc. is changing the equations in which media content were accessed. The days, when one has to buy a bulk of media storage devices, or has to wait for the public broadcasting (e.g., television), to enjoy her preferred media has gone. Such change in the way of entertainment, however, has created new issues of piracy and unauthorized media access. To counter these concerns, the digital rights management (DRM) protection schemes have been adopted.
In this report, we investigate one of the most important aspects of the DRM technology: the problem of protecting the clear text media content when playing licensing protected content on a mobile device. To this end, we first investigate how this problem has been addressed on different platforms and CPU architectures so far, and then discuss how virtualization technologies can be potentially used to protect the media pipe on mobile platforms. Our study will consider both industry-level and academic-level works, and will discuss the hardware-based and software-based approaches
Synchronized sweep algorithms for scalable scheduling constraints
No full textThis paper introduces a family of synchronized sweep-based filtering algorithms for handling scheduling problems involving resource and precedence constraints. The key idea is to filter all constraints of a scheduling problem in a synchronized way in order to scale better. In addition to normal filtering mode, the algorithms can run in greedy mode, in which case they perform a greedy assignment of start and end times. The filtering mode achieves a significant speed-up over the decomposition into independent CUMULATIVE and precedence constraints, while the greedy mode can handle up to 1 million tasks with 64 resource constraints and 2 million precedences. These algorithms were implemented in both CHOCO and SICStus
Energy Savings by Wireless Control of Speed, Scheduling and Travel Times for Hauling Operation
No full textA Quarry and Aggregate production site consist of sequential production processes and activities to process and produce the output products. Compared to a fixed manufacturing plant, the quarry processes involve mobile machines such as wheel loaders, trucks and articulated haulers and a highly dynamic road infrastructure. Today, the mobile machines are generally not synchronized or controlled towards the overall throughput of the site in real time. This indicates a general improvement potential in increased productivity at quarry sites, but also unsolved challenges for the same reason. Assuming a wireless control system that controls speed and throughput of the different processes and activities, there would be a fuel reduction potential in controlling the mobile machines. This optimization requires models of machine fuel consumption for different applications, velocities and travel times. The main contribution of this paper is the presentation of fuel measurements based on different speeds, site application characteristics and travel times for hauling operation. The fuel measures reveal important aspects regarding how different velocities impact fuel consumption. The results of fuel measurements show a potential in fuel savings of up to 42% and a typical improvement of 20-30% depending on machine speeds, travel times, application and site characteristics. Based on this, some of the applications and challenges in wirelessly controlling machines are discussed
Design and Implementation of a Dynamic Component Model for Federated AUTOSAR Systems
No full textThe automotive industry has recently agreed upon the embedded software standard AUTOSAR, which structures an application into reusable components that can be deployed using a configuration scheme. However, this configuration takes place at design time, with no provision for dynamically installing components to reconfigure the system. In this paper, we present the design and implementation of a dynamic component model that extends AUTOSAR with the possibility to add plug-in components at runtime. This opens up for shorter deployment time for new functions; opportunities for vehicles to participate in federated embedded systems; and involvement of third-party software developers
Survey on Combinatorial Register Allocation and Instruction Scheduling
No full textRegister allocation and instruction scheduling are two central compiler back-end problems that are critical for quality. In the last two decades, combinatorial optimization has emerged as an alternative approach to traditional, heuristic algorithms for these problems. Combinatorial approaches are generally slower but more flexible than their heuristic counterparts and have the potential to generate optimal code.
This paper surveys existing literature on combinatorial register allocation and instruction scheduling. The survey covers approaches that solve each problem in isolation as well as approaches that integrate both problems. The latter have the potential to generate code that is globally optimal by capturing the trade-off between conflicting register allocation and instruction scheduling decisions
Networked foresight—The case of EIT ICT Labs
No full textThe objective of this article is to explore the value of networked foresight: foresight conducted in innovation networks for the benefit of the network and its partners with active contributions from the partners. Strategic management, specifically the dynamic capabilities approach and vast literature on corporate and strategic foresight argue that deficiencies like one-dimensionality, narrow-sightedness and myopia of closed corporate processes are remedied by incorporating external sources. A broad knowledge base promises to especially benefit foresight in multiple ways. Thus, created an analytical framework that integrates the dynamic capabilities approach with existing results on potential value contributions of foresight, enriched with existing findings in networked foresight and organizational design in the light increasing importance of inter-organizational networks. We conducted a series of interviews and a survey among foresight practitioners in a network to explore the perceived value proposition of networked foresight for the network partners and the network itself. The analysis is based on data drawn from the EIT ICT Labs network of large industry corporations, small-and-medium sized companies, and academic and research institutes. Our study shows that network partners use the results primarily for sensing activities, i.e. data collection and to a lesser extend activity initiation. More sensitive and fundamental organizational aspects such as strategy and decision-making or path-dependency are less affected. Especially SMEs may benefit substantially from network approaches to foresight whereas MNEs are more confident in their existing corporate foresight processes and results. The value for the network itself is substantial and goes beyond value creation potential for companies as discussed in literature. The development of a shared vision—relatable to organizational learning and reconfiguration capabilities—was identified as particularly valuable for the network
Learning machines for computational epidemiology
No full textResting on our experience of computational epidemiology
in practice and of industrial projects on analytics of
complex networks, we point to an innovation opportunity for
improving the digital services to epidemiologists for monitoring,
modeling, and mitigating the effects of communicable disease.
Artificial intelligence and intelligent analytics of syndromic
surveillance data promise new insights to epidemiologists, but
the real value can only be realized if human assessments are
paired with assessments made by machines. Neither massive
data itself, nor careful analytics will necessarily lead to better
informed decisions. The process producing feedback to humans
on decision making informed by machines can be reversed to
consider feedback to machines on decision making informed by
humans, enabling learning machines. We predict and argue for
the fact that the sensemaking that such machines can perform in
tandem with humans can be of immense value to epidemiologists
in the future
