Mobile agents offer much promise, but agent mobility and Internet openness make coordination more difficult. Mobile Agent Reactive Spaces, a Linda-like coordination architecture with programming features, can handle a heterogeneous network while still allowing simple and flexible application design.

Internet applications face challenges that mobile agents and the adoption of enhanced coordination models may overcome. To win the race, coordination models have to be inspired by the Linda coordination model and enriched by programmable reactivity.

No abstract

No abstract

No abstract

No abstract

Digital factories comprise a multi-layered integration of various activities along the factories and product lifecycles. A central aspect of a digital factory is that of enabling the product lifecycle stakeholders to collaborate through the use of software solutions. The digital factory thus expands outside the company boundaries and offers the opportunity to collaborate on business processes affecting the whole supply chain.This paper discusses an interoperability architecture for digital factories. To this end, it delves into the issue by analysing the key requirements for enabling a scalable factory architecture characterised by access to services, aggregation of data, and orchestration of production processes. Then, the paper revises the state-of-the-art w.r.t. these requirements and proposes an architectural framework conjugating features of both service-oriented and data-sharing architectures. The framework is exemplified through a case study.vs. icing sugar vs. nothing as topping; (c) yogurt vs. honey vs. 63nothing in the dough. The client can also customize the colors 64 of the baking paper (wrapping the single muffin) as well as the 65 colors of the box. 66The muffin factory collects orders and organizes batches of 67 muffin doughs for production. As an example, if a client asks 68 for 3 boxes of carrot muffins with yogurt, icing sugar on top, 69 pink baking paper, and another client for 2 boxes of carrot 70 muffins with yogurt, nothing on top, yellow baking paper, the 71 same dough can be used for both orders. Clearly, this schedul-72 ing service is based on the number of (and capacity of each) 73 dough mixers, the stream of received orders, etc. The factory 74 has a pool of dough mixers, of different capacity. The fact that 75 the number of different combinations is finite guarantees that 76 such a scheduling can be performed. 77When an order is received, in parallel to the dough prepara-78 tion, the baking paper should be set-up as well. In addition to 79 prepare a set of the requested paper baking cases, a QR-code 80 should be printed on each of them and used as a unique identi-81 fier of the specific order. The identification of the single muffin 82 is crucial for customization. After the dough has been prepared, 83 the muffins are placed in the baking paper cases and sent to the 84 oven (connected to a QR code reader) for cooking. Muffins are 85 cooked in batches of about 1000 items and the length of this 86 step is equal for all of them. 87 After the baking has been performed, the cart is operated 88 in order to route the different muffins to the right boxes, after 89 putting the right topping, and then to the proper delivery sta-90 tion. Depending on the order, different delivery agents can be 91 used. Notably, agility is needed all along the process, e.g., the 92 baking step may overcook some muffins, which therefore are 93 not ready for the delivery and should be prepared again. This 94 imply a communication with the delivery agent in order to skip 95 the planned shipping and to set-up a new on...

Abstract-One of the main issues in mobile services' research (M-service) is supporting M-service availability, regardless of the user's context (physical location, device employed, etc.). However, most scenarios also require the enforcement of context-awareness, to dynamically adapt M-services depending on the context in which they are requested. In this paper, we focus on the problem of adapting M-services depending on the users' location, whether physical (in space) or logical (within a specific distributed group/application). To this end, we propose a framework to model users' location via a multiplicity of local and active service contexts. First, service contexts represent the mean to access to M-services available within a physical locality. This leads to an intrinsic dependency of M-service on the users' physical location. Second, the execution of service contexts can be tuned depending on who is requesting what M-service. This enables adapting M-services to the logical location of users (e.g., a request can lead to different executions for users belonging to different groups/applications). The paper firstly describes the framework in general terms, showing how it can facilitate the design of distributed applications involving mobile users as well as mobile agents. Then, it shows how the MARS coordination middleware, implementing service contexts in terms of programmable tuple spaces, can be used to develop and deploy applications and M-services coherently with the above framework. A case study is introduced and discussed through the paper to clarify our approach and to show its effectiveness.