Anytime system level verification via parallel random exhaustive hardware in the loop simulation

System level verification of cyber-physical systems has the goal of verifying that the whole (i.e., software + hardware) system meets the given specifications. Model checkers for hybrid systems cannot handle system level verification of actual systems. Thus, Hardware In the Loop Simulation (HILS) is currently the main workhorse for system level verification. By using model checking driven exhaustive HILS, System Level Formal Verification (SLFV) can be effectively carried out for actual systems. We present a parallel random exhaustive HILS based model checker for hybrid systems that, by simulating all operational scenarios exactly once in a uniform random order, is able to provide, at any time during the verification process, an upper bound to the probability that the System Under Verification exhibits an error in a yet-to-be-simulated scenario (Omission Probability). We show effectiveness of the proposed approach by presenting experimental results on SLFV of the Inverted Pendulum on a Cart and the Fuel Control System examples in the Simulink distribution. To the best of our knowledge, no previously published model checker can exhaustively verify hybrid systems of such a size and provide at any time an upper bound to the Omission Probability

Parallel statistical model checking for safety verification in smart grids

By using small computing devices deployed at user premises, Autonomous Demand Response (ADR) adapts users electricity consumption to given time-dependent electricity tariffs. This allows end-users to save on their electricity bill and Distribution System Operators to optimise (through suitable time-dependent tariffs) management of the electric grid by avoiding demand peaks. Unfortunately, even with ADR, users power consumption may deviate from the expected (minimum cost) one, e.g., because ADR devices fail to correctly forecast energy needs at user premises. As a result, the aggregated power demand may present undesirable peaks. In this paper we address such a problem by presenting methods and a software tool (APD-Analyser) implementing them, enabling Distribution System Operators to effectively verify that a given time-dependent electricity tariff achieves the desired goals even when end-users deviate from their expected behaviour. We show feasibility of the proposed approach through a realistic scenario from a medium voltage Danish distribution network

In silico clinical trials through AI and statistical model checking

A Virtual Patient (VP) is a computational model accounting for individualised (patho-) physiology and Pharmaco-Kinetics/Dynamics of relevant drugs. Availability of VPs is among the enabling technology for In Silico Clinical Trials. Here we shortly outline the state of the art as for VP generation and summarise our recent work on Artificial Intelligence (AI) and Statistical Model Checking based generation of VPs

Optimal fault-tolerant placement of relay nodes in a mission critical wireless network

The operations of many critical infrastructures (e.g., airports) heavily depend on proper functioning of the radio communication network supporting operations. As a result, such a communication network is indeed a mission-critical communication network that needs adequate protection from external electromagnetic interferences. This is usually done through radiogoniometers. Basically, by using at least three suitably deployed radiogoniometers and a gateway gathering information from them, sources of electromagnetic emissions that are not supposed to be present in the monitored area can be localised. Typically, relay nodes are used to connect radiogoniometers to the gateway. As a result, some degree of fault-tolerance for the network of relay nodes is essential in order to offer a reliable monitoring. On the other hand, deployment of relay nodes is typically quite expensive. As a result, we have two conflicting requirements: minimise costs while guaranteeing a given fault-tolerance. In this paper address the problem of computing a deployment for relay nodes that minimises the relay node network cost while at the same time guaranteeing proper working of the network even when some of the relay nodes (up to a given maximum number) become faulty (fault-tolerance). We show that the above problem can be formulated as a Mixed Integer Linear Programming (MILP) as well as a Pseudo-Boolean Satisfiability (PB-SAT) optimisation problem and present experimental results com- paring the two approaches on realistic scenarios