Self-Optimizing Architecture in Mobile Machines

Today\u27s machine management systems in off-highway machines are designed to optimize with respect to a target function without integrating the entire machine or considering environmental interactions. For that reason the interdisciplinary project OCOM "Organic Computing in Off-highway Machines" started in February 2009 to design an architecture for an off-highway machine in order to close that gap. Optimization of fuel consumption is exemplarily chosen even though many other goals are reachable. This paper will introduce the generic architecture; first results will be presented

Self-optimizing Machine Management

Today’s machine management systems in off-highway machines are designed to optimize with respect to a target function without integrating the entire machine or considering environmental interactions. For that reason the interdisciplinary project OCOM – “Organic Computing in Off-highway Machines” started in February 2009 to design an architecture for an off-highway machine in order to close that gap. Optimization of fuel consumption is exemplarily chosen even though many other goals are reachable. This paper will introduce the generic architecture; first results will be presented

Simulationsmodell zur Unterstützung von selbstoptimierenden Fähigkeiten eines Traktors

Diese Veröffentlichung thematisiert eine neuartige Steuerungsarchitektur für Traktoren, die sog. Observer/Controller (O/C) Architektur. Unter Einbeziehung adaptiver und lernfähiger Algorithmen und ganzheitlicher Systembetrachtung ist diese fähig, einen Traktor sukzessive zu optimieren. Ziel ist die minimierung des Kraftstoffverbrauchs. Hierzu wurde ein wirkungsgradbehaftetes Modell des Traktors mit reduzierter Dynamik entwickelt um die Architektur anhand einer Model in the Loop- Simulation zu verifizieren. Das Modell wird hier näher vorgestellt. Herausstellungsmerkmal ist die Abbildung aller als wesentlich eingestuften Einflüsse auf den Kraftstoffverbrauch

Organic Computing in Off-highway Machines

Machine management systems in off-highway machines such as tractors or wheel loaders are designed for efficient operation and reduced fuel consumption in some predefined scenarios for which the machine has been developed. In this paper, we outline how concepts from Organic Computing may be used to realize a self-organizing, reliable, adaptive, and robust machine management system that is capable of adjusting to new situations. We propose an architecture for a machine management system based on the generic Observer/Controller architecture and focus on the structure of the Observer. Furthermore, we study the feasibility of working cycle detection by the Observer and analyze real and synthetic data from an off-highway machine. To extract features by which different working cycles of an off-highway machine can be distinguished, we employ principal component analysis

A survey on engineering approaches for self-adaptive systems (extended version)

The complexity of information systems is increasing in recent years, leading to increased effort for maintenance and configuration. Self-adaptive systems (SASs) address this issue. Due to new computing trends, such as pervasive computing, miniaturization of IT leads to mobile devices with the emerging need for context adaptation. Therefore, it is beneficial that devices are able to adapt context. Hence, we propose to extend the definition of SASs and include context adaptation. This paper presents a taxonomy of self-adaptation and a survey on engineering SASs. Based on the taxonomy and the survey, we motivate a new perspective on SAS including context adaptation

Developing a global observer programming model for large-scale networks of autonomic systems

Computing and software intensive systems are now an inextricable part of modern work, life and entertainment fabric. This consequently has increased our reliance on their dependable operation. While much is known regarding software engineering practices of dependable software systems; the extreme scale, complexity and dynamics of modern software has pushed conventional software engineering tools and techniques to their acceptable limits. Consequently, over the last decade, this has accelerated research into non-conventional methods, many of which are inspired by social and/or biological systems model. Exemplar of which are the DARPA-funded Se1f-Regenerative-Systems (SRS) programme, and Autonomic Computing, where a closed-loop feedback control model is essential to delivering the advocated cognitive immunity and self-management capabilities. While much research work has been conducted on vanous aspects of SRS and autonomy, they are typically based on the assumptions that the structural model (organisation) of managed elements is static and exhaustive monitoring and feedback is computationally scalable. In addition, existing federated approaches to distributed computation and control, such as Multi-Agent-Systems fail to satisfactorily address how global control may be enacted upon the whole system and how an individual component may take on specified monitoring duties - although methods of interaction between federated individuals is well understood. Equally, organic-inspired computing looks to deal with event scale and complexity largely from a mining perspective, with observation concerns deferred to a suitably selective abstraction known as the "observation model". However, computing and mathematical science research, along with other fields has developed problem-specific approaches to help manage complexity; abstraction-based approaches can simplify structural organisation allowing the underlying meaning to be better understood. Statistical and graph-based approaches can both provide identifying features along with selectively reducing the size of a modelled structure by selecting specific areas that conform to certain topological criteria. This research studies the engineering concerns relating to observation of large-scale networks of autonomic systems. It examines methods that can be used to manage scale and generalises and formalises them within a software engineering approach; guiding the development of an automated adaptive observation subsystem - the Global Observer Model. This approach uses a model-based representation of the observed system, represented by appropriately attached modelled elements; adapters between the underlying system and the observation subsystem. The concepts of Signature and Technique definitions describe large-scale or complex system characteristics and target selection techniques respectively. Collections of these objects are then utilised throughout the framework along with decision and deployment logic (collectively referred to as the Observer Behaviour Definition - an ECA-like observational control) to provide a runtime-adaptable observation overlay. The evaluation of this research is provided by demonstrations of the observation framework; firstly in experimental form for assessment of the Signature and Technique approach, and then by application to the Email Exploration Tool (EET), a forensic investigation utility

Self-organising smart grid architectures for cyber-security

PhD ThesisCurrent conventional power systems consist of large-scale centralised generation and unidirectional power flow from generation to demand. This vision for power system design is being challenged by the need to satisfy the energy trilemma, as the system is required to be sustainable, available and secure. Emerging technologies are restructuring the power system; the addition of distributed generation, energy storage and active participation of customers are changing the roles and requirements of the distribution network. Increased controllability and monitoring requirements combined with an increase in controllable technologies has played a pivotal role in the transition towards smart grids. The smart grid concept features a large amount of sensing and monitoring equipment sharing large volumes of information. This increased reliance on the ICT infrastructure, raises the importance of cyber-security due to the number of vulnerabilities which can be exploited by an adversary. The aim of this research was to address the issue of cyber-security within a smart grid context through the application of self-organising communication architectures. The work examined the relevance and potential for self-organisation when performing voltage control in the presence of a denial of service attack event. The devised self-organising architecture used techniques adapted from a range of research domains including underwater sensor networks, wireless communications and smart-vehicle tracking applications. These components were redesigned for a smart grid application and supported by the development of a fuzzy based decision making engine. A multi-agent system was selected as the source platform for delivering the self-organising architecture The application of self-organisation for cyber-security within a smart grid context is a novel research area and one which presents a wide range of potential benefits for a future power system. The results indicated that the developed self-organising architecture was able to avoid control deterioration during an attack event involving up to 24% of the customer population. Furthermore, the system also reduces the communication load on the agents involved in the architecture and demonstrated wider reaching benefits beyond performing voltage control