From missions to systems : generating transparently distributable programs for sensor-oriented systems

Early Wireless Sensor Networks aimed simply to collect as much data as possible for as long as possible. While this remains true in selected cases, the majority of future sensor network applications will demand much more intelligent use of their resources as networks increase in scale and support multiple applications and users. Specifically, we argue that a computational model is needed in which the ways that data flows through networks, and the ways in which decisions are made based on that data, is transparently distributable and relocatable as requirements evolve. In this paper we present an approach to achieving this using high-level mission specifications from which we can automatically derive transparently distributable programs.Postprin

StreamLearner: Distributed Incremental Machine Learning on Event Streams: Grand Challenge

Today, massive amounts of streaming data from smart devices need to be analyzed automatically to realize the Internet of Things. The Complex Event Processing (CEP) paradigm promises low-latency pattern detection on event streams. However, CEP systems need to be extended with Machine Learning (ML) capabilities such as online training and inference in order to be able to detect fuzzy patterns (e.g., outliers) and to improve pattern recognition accuracy during runtime using incremental model training. In this paper, we propose a distributed CEP system denoted as StreamLearner for ML-enabled complex event detection. The proposed programming model and data-parallel system architecture enable a wide range of real-world applications and allow for dynamically scaling up and out system resources for low-latency, high-throughput event processing. We show that the DEBS Grand Challenge 2017 case study (i.e., anomaly detection in smart factories) integrates seamlessly into the StreamLearner API. Our experiments verify scalability and high event throughput of StreamLearner.Comment: Christian Mayer, Ruben Mayer, and Majd Abdo. 2017. StreamLearner: Distributed Incremental Machine Learning on Event Streams: Grand Challenge. In Proceedings of the 11th ACM International Conference on Distributed and Event-based Systems (DEBS '17), 298-30

BRAHMS: Novel middleware for integrated systems computation

Biological computational modellers are becoming increasingly interested in building large, eclectic models, including components on many different computational substrates, both biological and non-biological. At the same time, the rise of the philosophy of embodied modelling is generating a need to deploy biological models as controllers for robots in real-world environments. Finally, robotics engineers are beginning to find value in seconding biomimetic control strategies for use on practical robots. Together with the ubiquitous desire to make good on past software development effort, these trends are throwing up new challenges of intellectual and technological integration (for example across scales, across disciplines, and even across time) - challenges that are unmet by existing software frameworks. Here, we outline these challenges in detail, and go on to describe a newly developed software framework, BRAHMS. that meets them. BRAHMS is a tool for integrating computational process modules into a viable, computable system: its generality and flexibility facilitate integration across barriers, such as those described above, in a coherent and effective way. We go on to describe several cases where BRAHMS has been successfully deployed in practical situations. We also show excellent performance in comparison with a monolithic development approach. Additional benefits of developing in the framework include source code self-documentation, automatic coarse-grained parallelisation, cross-language integration, data logging, performance monitoring, and will include dynamic load-balancing and 'pause and continue' execution. BRAHMS is built on the nascent, and similarly general purpose, model markup language, SystemML. This will, in future, also facilitate repeatability and accountability (same answers ten years from now), transparent automatic software distribution, and interfacing with other SystemML tools. (C) 2009 Elsevier Ltd. All rights reserved

Mobile support in CSCW applications and groupware development frameworks

Computer Supported Cooperative Work (CSCW) is an established subset of the field of Human Computer Interaction that deals with the how people use computing technology to enhance group interaction and collaboration. Mobile CSCW has emerged as a result of the progression from personal desktop computing to the mobile device platforms that are ubiquitous today. CSCW aims to not only connect people and facilitate communication through using computers; it aims to provide conceptual models coupled with technology to manage, mediate, and assist collaborative processes. Mobile CSCW research looks to fulfil these aims through the adoption of mobile technology and consideration for the mobile user. Facilitating collaboration using mobile devices brings new challenges. Some of these challenges are inherent to the nature of the device hardware, while others focus on the understanding of how to engineer software to maximize effectiveness for the end-users. This paper reviews seminal and state-of-the-art cooperative software applications and development frameworks, and their support for mobile devices

An overview to Software Architecture in Intrusion Detection System

Today by growing network systems, security is a key feature of each network infrastructure. Network Intrusion Detection Systems (IDS) provide defense model for all security threats which are harmful to any network. The IDS could detect and block attack-related network traffic. The network control is a complex model. Implementation of an IDS could make delay in the network. Several software-based network intrusion detection systems are developed. However, the model has a problem with high speed traffic. This paper reviews of many type of software architecture in intrusion detection systems and describes the design and implementation of a high-performance network intrusion detection system that combines the use of software-based network intrusion detection sensors and a network processor board. The network processor which is a hardware-based model could acts as a customized load balancing splitter. This model cooperates with a set of modified content-based network intrusion detection sensors rather than IDS in processing network traffic and controls the high-speed.Comment: 8 Pages, International Journal of Soft Computing and Software Engineering [JSCSE]. arXiv admin note: text overlap with arXiv:1101.0241 by other author

Flexibility Support for Homecare Applications Based on Models and Multi-Agent Technology

In developed countries, public health systems are under pressure due to the increasing percentage of population over 65. In this context, homecare based on ambient intelligence technology seems to be a suitable solution to allow elderly people to continue to enjoy the comforts of home and help optimize medical resources. Thus, current technological developments make it possible to build complex homecare applications that demand, among others, flexibility mechanisms for being able to evolve as context does (adaptability), as well as avoiding service disruptions in the case of node failure (availability). The solution proposed in this paper copes with these flexibility requirements through the whole life-cycle of the target applications: from design phase to runtime. The proposed domain modeling approach allows medical staff to design customized applications, taking into account the adaptability needs. It also guides software developers during system implementation. The application execution is managed by a multi-agent based middleware, making it possible to meet adaptation requirements, assuring at the same time the availability of the system even for stateful applications.This work was financed in part by the University of the Basque Country (UPV/EHU) under project UFI 11/28, by the Regional Government of the Basque Country under Project IT719-13, and by the MCYT&FEDER under project DPI 2012-37806-C02-01