Pervasive and mobile computing call for suitable middleware and programming models to support the activities of complex software systems in dynamic network environments. In this paper we present TOTA (“Tuples On The Air”), a novel middleware and programming approach for supporting adaptive context-aware activities in pervasive and mobile computing scenarios. The key idea in TOTA is to rely on spatially distributed tuples, adaptively propagated across a network on the basis of application-specific rules, for both representing contextual information and supporting uncoupled interactions between application components. TOTA promotes a simple way of programming that facilitates access to distributed information, navigation in complex environments, and achievement of complex coordination tasks in a fully distributed and adaptive way, mostly freeing programmers and system managers form the need to take care of lowlevel issues related to network dynamics. This paper includes both application examples to clarify concepts and performance figures to show the feasibility of the approach.

Natural forming multi-agent systems (aka Swarms) have the ability to grow to enormous sizes without requiring any of the agents to oversee the entire system. The success of these systems comes from the fact that agents are simple and the interaction with the environment and neighboring agents is local in nature. In this paper we look at abstractions in the field of swarms and study their applicability in the context of coordination systems. In particular, we focus on the problematic issue of scalability of Linda systems. 1.

Practitioners of agent-based modeling are often tasked to model or design self-organizing systems while the theoretical foundation of self-organization in science remains to be set. This paper explores self-organization in the context of an agent-based model of ant colony food foraging. We gather specific measures of order-creation and constraint construction particular to leading theories of nonequilibrium thermodynamics that purport to govern self-organizing dynamics. These measures are used to explore three claims: (a) Constraints are constructed from entropy-producing processes in the bootstrapping phase of self-organizing systems; (b) positive feedback loops are critical in the structure formation phase; and (c) constraints tend to decay. The continued presence of far-from-equilibrium boundary conditions are required to reinforce constraints in the maintenance phase.

Abstract. Natural forming multi-agent systems can grow to enormous sizes and perform seemingly complex tasks without the existence of any centralized control. Their success comes from the fact that agents are simple and the interaction with the environment and neighboring agents is local in nature. We describe how swarm intelligence can be used in the implementation of a Linda-based system called SwarmLinda. We argue that SwarmLinda achieves many desired characteristics such as scalability, adaptiveness and even some level of fault-tolerance. 1

Abstract: To cope with increasing internal and external dynamics of production networks, a decentralized and flexible autonomous control approach seems to be promising. This paper presents a dynamic model of a production network with geographically dispersed facilities and fixed transport schedules. It investigates the influence of local autonomous control methods on integrated production and transport processes and shows that the application of autonomous control may improve the handling of internal and external dynamics.

Abstract. Swarm intelligent systems, in which the paths to problem solving emerge as the result of interactions between simple autonomous components (agents or ants) and between them and their environment, appear very promising to develop robust and flexible software application. However, the variety of swarm-based approaches that have been proposed so far still lacks a common modeling and engineering methodology. In the attempt to overcome this problem, this paper presents a general coordination methodology in which swarm’s components are simply driven by abstract computational force fields (Co-Fields), generated either by agents, or by the environment. By having agents be driven in their activities by such fields, globally coordinated behaviors can naturally emerge. Although this model still does not offer a complete engineering methodology, it can provide a unifying abstraction for swarm intelligent systems and it can also be exploited to formalize these systems in terms of dynamical systems whose behavior can be described via differential equations. Several example of swarm systems modeled with Co-Fields are presented to support our thesis. 1

To manage the increasing dynamics inside and outside a production system, a decentralised and autonomous control of shop floor logistics is a promising approach. For developing and benchmarking such autonomous control methods, dynamic models are essential. The paper introduces the idea of autonomous logistic processes and presents a dynamic simulation model of a shop floor with both a conventionally planned and an autonomously controlled scenario. The decentralised and autonomous control strategy bases on autonomous elements that are able to make decisions by themselves using distributed local information. The simulation model is used for analysing the system’s dynamics at varying workloads. The logistic performance is analysed by comparing throughput times for the different logistics situations and during expected and unexpected disturbances. Keywords: Production, Control, Autonomy

Software systems, as currently engineered, are brittle and fragile.

In today's software engineering, the ability to adapt to requirement changes on-the-fly has become a major asset to computer systems. Traditionally, systems were developed following a process consisting of: requirements study, design and implementation phases. Nowadays, requirements are very dynamic -- changing constantly. For the engineering of distributed complex software systems, the frameworks used in the implementation phase need to be able to cope with environment changes -- bandwidth fluctuations, network topology, host down time, etc. This paper looks at the ability of Linda-based systems to adapt to the predictable and unpredictable changes in distributed environments. Several adaptiveness aspects are discussed and a few of Linda-based systems analyzed.

In MANET research the mobility of the nodes is often described using standard synthetic models. Given a particular application, e.g. networks of unmanned aerial vehicles (UAVs) performing a cooperative task, the use of a standard synthetic mobility model can result in incorrect conclusions, as the movement pattern can impact the networking performance of the system. In this paper we present the criteria that characterize desirable mobility properties for the movement of UAVs in a reconnaissance scenario, and provide two mobility models for the scenario. In the first mobility model the UAVs move independently and randomly, and in the second model pheromones guide their movement. The random model is very simple but it achieves mediocre results. The pheromone model has very good scanning properties, but it has problems with respect to network connectivity. The study shows that there in an inherent conflict between the two goals of maximum coverage by frequent rescanning and adequate communication connectivity. 1