Representing and Reasoning with the Internet of Things: a Modular Rule-Based Model for Ensembles of Context-Aware Smart Things

Abstract

Context-aware smart things are capable of computational behaviour based on sensing the physical world, inferring context from the sensed data, and acting on the sensed context. A collection of such things can form what we call a thing-ensemble, when they have the ability to communicate with one another (over a short range network such as Bluetooth, or the Internet, i.e. the Internet of Things (IoT) concept), sense each other, and when each of them might play certain roles with respect to each other. Each smart thing in a thing-ensemble might have its own context-aware behaviours which when integrated with other smart things yield behaviours that are not straightforward to reason with. We present Sigma, a language of operators, inspired from modular logic programming, for specifying and reasoning with combined behaviours among smart things in a thing-ensemble. We show numerous examples of the use of Sigma for describing a range of behaviours over a diverse range of thing-ensembles, from sensor networks to smart digital frames, demonstrating the versatility of our approach. We contend that our operator approach abstracts away low-level communication and protocol details, and allows systems of context-aware things to be designed and built in a compositional and incremental manner.

Generating random bigraphs with preferential attachment

“…The function Weighted_Random(a: double, b: double) (Line 9) samples a value from the tuple (0, 1) with the given probabilities a and b corresponding to the value 0 and 1 of that tuple, respectively. For example, let a = 0.3 and b = 0.8 then there is a probability of 30 % to create an outer name (0) and a probability of 80 % to create an edge (1). The computed value of that function is evaluated in the if-then construct to determine which link shall be created.…”

“…, 40} so that the node's arities obey a uniform distribution. Then, a node with arity ar i has the probability 1 |ar| to be selected. The results are depicted in Fig.…”