The possibility to have millions of computational devices interconnected across urban environments opens up novel application areas. In such highly distributed scenarios, applications must gain awareness as a result of opportunistic encounters with co-located devices, a departure from traditional reasoning approaches. We envision situated awareness as an emergent property of such networks, where bio-inspired algorithms are employed to coordinate interactions between devices through managing the lifecycle, distribution, and content of data. A congestion-aware, crowd-steering example illustrates this vision.

In large scale networks, agents must use partial knowledge obtained from local interactions to reason about their environment. They require efficient mechanisms to allow them to retrieve and aggregate information beyond their communication range. Even though proposals have been presented for gathering information in large scale wireless sensor networks, it is still a challenge to find an efficient and robust technique for gathering information in large scale mobile wireless networks. In this paper we propose gradients as a multi-path structure for routing and aggregating information across a network of computational mobile nodes. We use simulation to demonstrate that progressive aggregation done on top of a gradient improves the bandwidth usage and memory consumption. We also demonstrate self-* properties of our proposed algorithms including scalability, robustness and adaptability.

The development of communications systems in general, and the Internet in particular, has given billions of people the opportunity to connect and share content with audiences to which they would otherwise never have had access to. Nowadays, anyone can publish and share content, whether personal or not, on the Internet. In addition, the ubiquitous ness of mobile devices makes it possible to access content anywhere, at anytime on different platforms. All of this often leads to situations of potential intentional or unintentional misuse of contents as well as privacy problems. Traditional solutions for these problems such as Digital Rights Management have proven not to be appropriate because they rely heavily on costly and centralized external systems or infrastructure. In this paper, we propose Smart Content, a novel approach for content protection and privacy. Smart Content acts autonomously and embeds with the content the notion of context and policy. This article presents the general model of Smart Content and an example implementation.

Here we present the overall objectives and approach of the SAPERE (“Self-aware Pervasive Service Ecosystems”) project, focussed on the development of a highly-innovative nature-inspired framework, suited for the decentralized deployment, execution, and management, of self-aware and adaptive pervasive services in future network scenarios.

The behaviour of self-* systems is complex to model from an algorithmic point of view.Designing and specifying self-* systems implies a great amount of work that can be sensibly reduced if models can be reused and composed in a modular way.This article discusses a chemicallyinspired architecture and formalisms that facilitate the creation of modular, reusable models based on behavioural patterns inspired by behaviours found in nature.The architecture is based on chemical-like laws ruling the evolution of the system.We show the reuse of general behavioural patterns using three concrete examples of self-* systems from different domains.

Here we present the overall objectives and approach of the SAPERE (Self-aware Pervasive Service Ecosystems) project, focussed on the development of a highly-innovative nature-inspired framework, suited for the decentralized deployment, execution, and management, of self-aware and adaptive pervasive services in future network scenarios. © Selection and peer-review under responsibility of FET11 conference organizers and published by Elsevier B.V. 1. Motivations Pervasive computing technologies promise to notably change the future ICT landscape, letting us envision the emergence of an integrated and very dense socio-technical infrastructure for the provisioning of innovative general- purpose digital services. The infrastructure will be used to ubiquitously access services for better interacting with the surrounding physical world and with the social activities occurring in it. It is also expected that users will be able to deploy customized services, making the overall infrastructure as open as the Web currently is. To support the vision, a great deal of research activity in pervasive computing and service systems has been devoted to solve problems such as: increasing dependability; supporting self-* features; enforcing context-awareness and adaptability; tolerating evolution over time and eventually ensuring that service frameworks can be highly-adaptive and very long-lasting (1). Unfortunately, most of the solutions so far are proposed in terms of add-ons to be integrated in existing frameworks. The result is often an increased complexity of current frameworks and the emergence of contrasting trade-off between different solutions. In our opinion, there is need for tackling the problem at the foundation, answering the following ambitious question: is it possible to conceive a radically new way of modeling integrated pervasive services and their execution environments, such that the apparently diverse issues of context-awareness, dependability, openness, flexible and robust evolution, can all be uniformly addressed once and for all?