Affiliation:
1. Università di Bologna
2. Università di Torino, Torino, Italy
3. Raytheon BBN Technologies
Abstract
Collective adaptive systems are an emerging class of networked computational systems particularly suited for 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 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.
Funder
HyVar
Ateneo/CSP project RunVar
Defense Advanced Research Projects Agency
ICT COST Action
IC1402 ARVI
European Union's Horizon 2020 research
United States Air Force
Publisher
Association for Computing Machinery (ACM)
Subject
Computer Science Applications,Modeling and Simulation
Cited by
72 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献