Abstract
In Industry 4.0 scenarios, novel applications are enabled by the capability to gather large amount of data from pervasive sensors and to process them in order to devise the “digital twin” of a physical equipment. The heterogeneity of hardware sensors, communication protocols and data formats constitutes one of the main challenge toward the large-scale adoption of the Internet of Things (IoT) paradigm on industrial environments. To this purpose, the W3C Web of Things (WoT) group is working on the definition of some reference standards intended to describe in a uniform way the software interfaces of IoT devices and services, and hence to achieve the full interoperability among different IoT components regardless of their implementation. At the same time, due also to the recent appearance of the WoT W3C draft, few testbed and real-world deployments of the W3C WoT architecture has been proposed so far in the literature. In this paper, we attempt to fill such gap by describing the realization of a WoT monitoring application of a generic indoor production site: the system is able to orchestrate the sensing operations from three heterogeneous Wireless Sensor Networks (WSNs). We describe how the components of the W3C WoT architecture have been instantiated in our scenario. Moreover, we demonstrate the possibility to decouple the mash-up policies from the network functionalities, and we evaluate the overhead introduced by the WoT approach.
Highlights
The core of the paradigm that justifies its generality and viability on different markets is the concept of Cyber-physical Systems (CBSs), i.e. the strict integration between physical elements and computational data enabled by the recent advances on the Internet of Things (IoT) [1]
All the policies are in charge of dynamically selecting the sensors to query at each instant in order to maximize the policy-specific metric: to this purpose, given the dinamicity of the environment, we employ the Reinforcement Learning (RL) framework [17] to optimally balance the exploration-exploitation tasks. – Third, we report a subset of the experimental results from the Web of Things (WoT) testbed
The experimental analysis is divided in three stages: (i ) first, we characterize the overall performance of different Wireless Sensor Networks (WSNs) and sensors; (ii ) second, we evaluate the four different mash-up policies of Section 4; (ii ) we demonstrate the possibility of dynamic mash-up policy replacement and quantify the overhead introduced by the W3C WoT architecture
Summary
The Industry 4.0 has emerged as a new paradigm able to radically transform the organizations’ production and business in a myriad of sectors beside the smart manufacturing one [1] [2]. We attempt to fill such gap, by describing the design and implementation of a WoT testbed, consisting of a monitoring system of a generic production site that must retrieve and process sensor data from heterogeneous devices using different wireless access technologies (i.e. Wi-Fi, 802.15.4/Zigbee, BLE). The overall goal is to devise mash-up applications able to orchestrate the sensing operations over the target scenario regardless of the network protocols and hardware, decoupling the rationale of the monitoring process (e.g. minimal scenario coverage) from its implementation (i.e. the technology used to query the sensor).
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