Formal Verification of Synchronisation, Gossip and Environmental Effects for Critical IoT Systems

Abstract

The Internet of Things (IoT) promises a revolution in the monitoring and control of a wide range of applications, from urban water supply networks and precision agriculture food production, to vehicle connectivity and healthcare monitoring. For applications in such critical areas, control software and protocols for IoT systems must be verified to be both robust and reliable. Two of the largest obstacles to robustness and reliability in IoT systems are effects on the hardware caused by environmental conditions, and the choice of parameters used by the protocol. In this paper we use probabilistic model checking to verify that a synchronisation and dissemination protocol for Wireless Sensor Networks (WSNs) is correct with respect to its requirements, and is not adversely affected by the environment. We show how the protocol can be converted into a logical model and then analysed using the probabilistic model-checker, PRISM. Using this approach we prove under which circumstances the protocol is guaranteed to synchronise all nodes and disseminate new information to all nodes. We also examine the bounds on synchronisation as the environment changes the performance of the hardware clock, and investigate the scalability constraints of this approach.

Exploring the effects of environmental conditions and design choices on IoT systems using formal methods

“…• Formal verication of key requirements of the communication protocol related to reliability, time and energy usage, encoded as logical properties that are used during model checking. This work extends [21] via Section 7 on performance evaluation, an extended comparison with the literature in Sections 2 and 5.4, and extended explanations throughout.…”

Multi-scale verification of distributed synchronisation

“…A key difference in our work from that of others analysing PCOs is that we define the oscillation cycle to consist of discrete steps. To the best of our knowledge, with the exception of the paper by Webster et al (including some of the authors of this paper) [49] and our previous work [24,25], there is no other work concerned with PCOs with discrete oscillation cycles. Furthermore, all of these approaches distinguish between single oscillators in the network, while the properties of interest relate to global behaviour.…”

State ordering and classification for analyzing non-sparse large Markov Models

“…Besides, linear time properties are also covered in the specification step of the underlying system. Examples of the application of MDPs in this field includes the verification of hardware systems [2,3], computer networks [4,5], cyberphysical and cyber-security systems [6][7][8], medical sciences [9] robotics and software systems [10][11][12][13]. In reinforcement learning, the main focus is to approximate the optimal expected reward (or cost) before reaching a final state [14].…”