Decentralized Privacy Provision in Dynamically Reconfigurable and Self-Organizing Aero-Physical Cyber Systems

Distributed automation is the highest level of automation and the main trend of distributed automation lies at self-organizing and re-configurable systems by means of distributed intelligence. This paper presents the state of art of self-organizing and re-configurable systems. Then, a new framework is put forward for re-configurable industrial automation, which combines the latest advances of computer science in service-oriented architectures.

In the context of Industry 4.0, production lines as part of Cyber-Physical Production Systems (CPPS) have specific demands for interoperability and flexibility. Machinery, being part of such production lines, has additional requirements in terms of functional safety. The re-configuration of functional safety systems, which is characterized by high manual configuration efforts, leads to time-intensive and expensive downtimes. The concept of self-organizing safety systems reduces the engineering efforts by assisting the system operator in the configuration of changes and consequently reducing machine downtime. The aim of this paper is to present a self-organizing safety system model and discuss the human control of self-organizing safety systems by making use of cybernetic approaches and examining the backgrounds for the need of human control over autonomous systems.

Reconfiguration in Self-Organizing Systems

A new approach of integrating biological microorganisms such as bacteria to an inorganic robot body for propulsion in low velocity or stagnant flow field is proposed in this paper with the ultimate goal of fabricating a few hundreds of micrometer size swimming robots. To show the feasibility of this approach, Serratia marcescens bacteria are attached to microscale objects such as 10 micron polystyrene beads by blotting them in a bacteria swarm plate. Randomly attached bacteria are shown to propel the beads at an average speed of approximately 15 µm/sec stochastically. Using chemical stimuli, bacteria flagellar propulsion is halted by introducing copper ions into the motility medium of the beads, while ethylenediaminetetraacetic acid is used to resume their motion. Thus, repeatable on/off motion control of the bacteria integrated mobile beads was shown. On-board chemical motion control, steering, wireless communication, sensing, and position detection are few of the future challenges for this work. Small or large numbers of these microrobots can potentially enable hardware platforms for self-organization, swarm intelligence, distributed control, and reconfigurable systems in the future.

The introduction of self-organization into a system promises, among other things, to reduce the system's complexity and to increase the system's robustness against failures and its adaptability to changes in its environment. An example for systems that profit from self-organization are resource-flow systems, e.g., production lines. Such systems are characterized by a number of independent agents that process resources by applying capabilities according to a given task. This paper introduces a decentralized reconfiguration mechanism that restructures a part of a resource-flow system in case of a failure. In order to do so, agents coordinate based on local knowledge and combine themselves into groups which are called coalitions. Each coalition then tries to restore the system's functionality, returning a previously consistent and correct system to a new consistent state, thus re-enabling correct processing of resources. As only local coalitions are formed, the parts of the system not involved in the reconfiguration process stay functional, meaning that the overall system does not come to a standstill.

Manufacturing is nowadays facing with markets trends that ask for more customized products, shorter product life-cycles, best quality and shorter prices. Addressing these requirements, manufacturing systems need to be more responsive and reconfigurable, adapting their behaviors to changing conditions. The concepts inherited from biology and nature seem suitable to design reconfigurable manufacturing systems. In this paper, a bio-inspired solution, where self-organization and multi-agent systems play key roles, is described, contributing to achieve an adaptive and evolvable reconfigurable manufacturing control system.

In concert with advances in information and communication technology and their application to manufacturing environments, physical entities in factories are acquiring more intelligence via integration with cyber systems. This integration brings about Cyber-Physical Production Systems and leads to smart manufacturing, the next generation manufacturing paradigm. In the new paradigm, high levels of agility, flexibility, and real-time control make it possible to keep the system running efficiently and self-organized. At the same time, however, it becomes difficult in a self-organized and decentralized system to capture the system's status, evaluate the system's performance, and predict the system's future events. In this article, we suggest improvements to smart manufacturing systems where the intelligence from smart entities could be fully utilized without losing system control. To achieve this goal, a solution for integrating schedule-driven production (push systems) and event-driven production (pull systems) is proposed to optimize both material flow and information flow for manufacturing operations. For each entity in a smart manufacturing system, details of decision making are encapsulated and its status is exposed. The status-based decisions filter out unimportant information and make smart manufacturing systems loosely-coupled and predictable. A simulation case study based on Devices Profile for Web Services [1] is used to illustrate the effectiveness of such an approach. The case study suggests that status-based decisions could be applied to smart manufacturing and that they can be part of an approach that balances the self-organized control with overall performance. Therefore, we can make full use of intelligent entities in lower levels of a factory while keeping the entire system under control.

The NSF CAREER program is a premier program that emphasizes the importance the foundation places on the early development of academic careers solely dedicated to stimulating the discovery process in which the excitement of research enriched by inspired teaching and enthusiastic learning. This paper describes the research and education experiences gained by the principal investigator and his research collaborators and students as a result of a NSF CAREER proposal been awarded by the power, control and adaptive networks (PCAN) program of the electrical, communications and cyber systems division, effective June 1, 2004. In addition, suggestions on writing a winning NSF CAREER proposal are presented.

Software radio and reconfiguration management

Four problems have been addressed on distribution systems monitoring. First, reconfiguring fault diagnostic agent component group is utilized to realize diagnostic system reconfiguration. Second, an evaluating means and an algorithm for the intelligent multi-agent diagnostic system are presented. Third, method of selecting suitable sensors is analyzed for real-time monitoring of different machining processes and reconfigurable machining systems. Finally, the cubic hierarchy Petri Nets framework structure and a computing algorithm of reconfiguring multi-agent diagnosis system are given. This approach can achieve a self-organizing system that is more robust and flexible in dynamic environment and can be self-updated locally.

This paper deals with the method of the optimal configuration of a manipulator based on cell structurization, as one of the methods of determining kinds of cell and order in reconfiguration. The proposed methods can determine the joint location, the degree of freedom and the link length using the characteristics of cell structures of the DRRS. The method takes into account both cases of the fixed-type and the mobile-type of manipulators. The proposed performance index for the selection of both types under given tasks is based on the number of works, the accuracy of the manipulator in positioning, the required torque for keeping the attitude, the displacement between given works and the cost of cells in the configuration. The simulation results are also given to clarify the difference of performance for each task. Thus the DRRS can realize the idea of self-organizing system.

This paper proposes a new approach for the utilization of reconfigurable hardware in a self-organized context. A concept and a system are presented under the form of Reconfigurable Self-Organized Systems (RSS). The goal is to study the impact of FPGA based systems in a self-organized networked environment. Partial Reconfiguration is used to implement hardware accelerators at runtime. The proposed system is designed to study at each level, parameters that influence performance. As results, first timing measurements of partial reconfiguration are proposed. They are seen from the software point of view and show effects of partial reconfiguration technology on the general performance of RSS.

Cyber-physical networks open new possibilities to supply chain configuration. They are based on Cyber-Physical Systems (CPS), which tightly integrate physical systems and cyber (IT) systems based on interaction between these systems in real time. Operation and configuration of CPS require approaches for managing the variability at design time and the dynamics at runtime caused by a multitude of component types and changing application environments. The proposed approach benefits from integration of such technologies as multilevel self-organisation and knowledge fusion. Self-organisation within Cyber-physical networks opens the avenue towards providing new services at runtime, which need to be accommodated in the business models. Adaptable business models are a promising approach to this challenge.

Swarm concepts of various types borrowed from nature have been proposed for multi-agent security approaches. Distributed decision-making in multi-agent systems is of particular interest, and has good application in large networks with end-point agents looking for anomalies and potential threat indications, which in isolation may mean nothing. Quorum sensing (QS) in bacterial systems and Honeybee nest-site selection are two examples of distributed decision making in nature that show promise for reuse in reaching collective conclusions and triggering action in networked cyber systems. This paper examines these two cases of QS in nature and abstracts a generic pattern that qualifies for self-organizing security according to six SAREPH characteristics covered in prior work. The pattern form and qualifying characteristics from this prior work are briefly outlined, and QS in the two different natural systems is shown to reach a tipping point based on the density of independent agents with relevant similarities. The inter-agent signaling mechanisms are shown to be central to the process, and the abstracted core pattern is discussed with the conflicting forces that have to be resolved in any application of the pattern. Illustrative examples of both deployed and proposed security approaches are then shown employing this pattern, along with a pseudo-code model for an appropriate signaling mechanism inspired by a paper on social network quorum achievement.

Resilience is an important property of distributed cyber defence systems operating in complex, adversarial environments. While resilience can be realized through self-management, implementing distributed self-management poses several significant challenges. In this paper, we identify some of these challenges and present our initial work on an approach for cyber resilience called AWaRE. The novelty of AWaRE lies in the use of a conceptual, state-space-based design and reconstitution framework, combined with run-time models and distributed constraint satisfaction/optimization techniques for decision-making and coordination of system re-configurations. The run-time models are generated via an expressive domain-specific language enabling component, constraint and agent modeling as well as constraint problem decomposition and architectural self-organization. We realize AWaRE via a concrete software framework, focusing specifically on autonomic self-properties pertaining to distributed system configuration, deployment and reliable operation. We demonstrate the value of AWaRE in the context of a simple, cloud-based enterprise scenario.