Abstract: Ever since the creation of the first human society, people have understood that the only way of sustaining and improving their societies is to rely on each other for exchanging services. This reliance have traditionally built on developing, among them, {\em trust}, a vague, intuitive to a large extend and hard to define concept that brought together people who worked towards the progress we all witness around us today. % Today's society is, however, becoming increasingly massive, collective, and complex and includes not only people, but huge numbers of machines as well. It is no overstatement to say that machines interconnected together into a complex communication fabric as well as communicating directly with people will very soon outnumber people by several orders of magnitude. Thus, trust, being already a difficult concept to define and measure when applied to a few people that form a cooperating group or a set of acquaintances, it is far more difficult to pinpoint when applied to large communities whose members may hardly know each other in person or to interconnected machines employed by these communities. In this paper we attempt to take a pragmatic position with regard to trust definition and measurement. We employ several formalisms, into each of which we define a reasonable notion of trust, and show that inherent weaknesses of these formalisms result in an inability to have a concrete and fully measurable trust concept. % We then argue that trust in the modern intertwined WWW society must, necessarily, incorporate to some degree non-formalizable elements, such as common sense and intuition. Although this may sound pessimistic, our view is that it is not, since by understanding these limitations of formalism with regard to trust, increases self-awareness and caution on people's part and avoid problems that result if one relies only on automation in order to deduce trust.
Abstract: Here we survey various computational models for Wireless Sensor Networks (WSNs). The population protocol model (PP) considers networks of tiny mobile finite-state artifacts that can sense the environment and communicate in pairs to perform a computation. The mediated population protocol model (MPP) enhances the previous model by allowing the communication links to have a constant size buffer, providing more computational power. The graph decision MPP model (GDM) is a special case of MPP that focuses on the MPP's ability to decide graph properties of the network. Another direction towards enhancing the PP is followed by the PALOMA model in which the artifacts are no longer finite-state automata but Turing Machines of logarithmic memory in the population size. A different approach to modeling WSNs is the static synchronous sensor field model (SSSF) which describes devices communicating through a fixed communication graph and interacting with their environment via input and output data streams. In this survey, we present the computational capabilities of each model and provide directions for further research.
