Engineering Resilient Collective Adaptive Systems by Self-Stabilisation

Collective adaptive systems are an emerging class of networked computational systems, particularly suited in application domains such as smart cities, complex sensor networks, and the Internet of Things. These systems tend to feature large scale, heterogeneity of communication model (including opportunistic peer-to-peer wireless interaction), and require inherent self-adaptiveness properties to address unforeseen changes in operating conditions. In this context, it is extremely difficult (if not seemingly intractable) to engineer reusable pieces of distributed behaviour so as to make them provably correct and smoothly composable. Building on the field calculus, a computational model (and associated toolchain) capturing the notion of aggregate network-level computation, we address this problem with an engineering methodology coupling formal theory and computer simulation. On the one hand, functional properties are addressed by identifying the largest-to-date field calculus fragment generating self-stabilising behaviour, guaranteed to eventually attain a correct and stable final state despite any transient perturbation in state or topology, and including highly reusable building blocks for information spreading, aggregation, and time evolution. On the other hand, dynamical properties are addressed by simulation, empirically evaluating the different performances that can be obtained by switching between implementations of building blocks with provably equivalent functional properties. Overall, our methodology sheds light on how to identify core building blocks of collective behaviour, and how to select implementations that improve system performance while leaving overall system function and resiliency properties unchanged.Comment: To appear on ACM Transactions on Modeling and Computer Simulatio

Self-stabilising Priority-Based Multi-Leader Election and Network Partitioning

A common task in situated distributed systems is the self-organising election of leaders. These leaders can be devices or software agents appointed, for instance, to coordinate the activities of other agents or processes. In this work, we focus on the multi-leader election problem in networks of asynchronous message-passing devices, which are a common model in self-organisation approaches like aggregate computing. Specifically, we introduce a novel algorithm for space- and priority-based leader election and compare it with the state of the art. We call the algorithm Bounded Election since it leverages bounding (i.e. minimisation or maximisation) of candidacy messages to drop or promote candidate leaders and ensure stabilisation. The proposed algorithm is formally proven to be self-stabilising, allows for leader prioritisation, and performs on-the-fly network partitioning (namely, as a side effect of the leader election process, the areas regulated by the leaders are also established). Also, we experimentally compare its performance together with the state of the art of leader election in aggregate computing in a variety of synthetic scenarios, showing benefits in terms of convergence time and resilience

Self-stabilising target counting in wireless sensor networks using Euler integration

Target counting is an established challenge for sensor networks: given a set of sensors that can count (but not identify) targets, how many targets are there? The problem is complicated because of the need to disambiguate duplicate observations of the same target by different sensors. A number of approaches have been proposed in the literature, and in this paper we take an existing technique based on Euler integration and develop a fully-distributed, self-stabilising solution. We derive our algorithm within the field calculus from the centralised presentation of the underlying integration technique, and analyse the precision of the counting through simulation of several network configurations.Postprin

ScaFi: A Scala DSL and Toolkit for Aggregate Programming

Supported by current socio-scientific trends, programming the global behaviour of whole computational collectives makes for great opportunities, but also significant challenges. Recently, aggregate computing has emerged as a prominent paradigm for so-called collective adaptive systems programming. To shorten the gap between such research endeavours and mainstream software development and engineering, we present ScaFi, a Scala toolkit providing an internal domain-specific language, libraries, a simulation environment, and runtime support for practical aggregate computing systems development