Large-scale IoT services such as healthcare, smart cities and marine monitoring are pervasive in Cyber-physical environments strongly supported by Internet technologies and Fog computing. Complex IoT services are increasingly composed of sensors, devices, and compute resources within Fog computing infrastructures. The orchestration of such applications can be leveraged to alleviate the difficulties of maintenance and enhance data security and system reliability. However, how to efficiently deal with dynamic variations and transient operational behavior is a crucial challenge within the context of choreographing complex services. Furthermore, with the rapid increase of the scale of IoT deployments, the heterogeneity, dynamicity, and uncertainty within Fog environments and increased computational complexity further dramatically aggravate this challenge. This article provides an overview of the core issues, challenges and future research directions in Fog-enabled orchestration for IoT services. Additionally, we present early experiences of an orchestration scenario, demonstrating the feasibility and initial results of using a distributed genetic algorithm in this context.

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Abstract-Social Media involve many shared items, such as photos, which may concern more than one user. The first challenge we address in this paper is to develop a way for users of such items to take a decision on to whom to share these items. This is not an easy problem, as users' privacy preferences for the same item may conflict, so an approach that just merges in some way the users' privacy preferences may provide unsatisfactory results. We propose a negotiation mechanism for users to agree on a compromise for the conflicts found. The second challenge we address in this paper relates to the exponential complexity of such a negotiation mechanism, which could make it too slow to be used in practice in a Social Media infrastructure. To address this, we propose heuristics that reduce the complexity of the negotiation mechanism and show how substantial benefits can be derived from the use of these heuristics through extensive experimental evaluation that compares the performance of the negotiation mechanism with and without these heuristics. Moreover, we show that one such heuristic makes the negotiation mechanism produce results fast enough to be used in actual Social Media infrastructures with near-optimal results.

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Abstract-Society is moving towards a socio-technical ecosystem in which physical and virtual dimensions of life are intertwined and where people interactions ever more take place with or are mediated by machines. Hybrid Diversity-aware Collective Adaptive Systems (HDA-CAS) is a new generation of sociotechnical systems where humans and machines synergetically complement each other and operate collectively to achieve their goals. HDA-CAS introduce the fundamental properties of hybridity and collectiveness, hiding from the users the complexities associated with managing the collaboration and coordination of machine and human computing elements. In this paper we present an HDA-CAS system called SmartSociety, supporting computations with hybrid human/machine collectives. We describe the platform's architecture and functionality, validate it on two real-world scenarios involving human and machine elements and present a performance evaluation.

This paper presents a novel method for analysing the behaviour of multiagent systems on the basis of the semantically rich information provided by agent communication languages and interaction protocols specified at the knowledge level. More low-level communication mechanisms only allow for a quantitative analysis of the occurrence of message types, the frequency of message sequences, and the empirical distributions of parameter values. Quite differently, the semantics of languages and protocols in multiagent systems can help to extract qualitative properties of observed conversations among agents. This can be achieved by interpreting the logical constraints associated with protocol execution paths or individual messages as the context of an observed interaction, and using them as features of learning samples. The contexts "mined" from such analyses, or context models, can then be used for various tasks, e.g. for predicting others' future responses (useful when trying to make strategic communication decisions to achieve a particular outcome), to support ontological alignment (by comparing the properties of logical constraints attached to messages across participating agents), or to assess the trustworthiness of agents (by verifying the logical coherence of their behaviour). This paper details a formal approach that describes our notion of context models in multiagent conversations, an implementation of this approach in a practical tool for mining qualitative context models, and experimental results to illustrate its use and utility.

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Abstract. This paper presents an argumentation mechanism for reconciling conflicts between planning agents related to plan proposals, which are caused by inconsistencies between basic beliefs regarding the state of the world or the specification of the planning operators. We introduce simple and efficient argument moves that enable discussion about planning steps, and show how these can be integrated into an existing protocol for belief argumentation. The resulting protocol is provably sound with regard to the defeasible semantics of the resulting agreements. We show how argument generation can be treated, for the specific task of argumentation about plans, by replacing the burden of finding proofs in a knowledge base by guided search.