Network design and planning of wireless embedded systems for industrial automation

Abstract

The widespread adoption of wireless systems for industrial automation calls for the development of efficient tools for virtual planning of network deployments similarly as done for conventional Fieldbus and wired systems. In industrial sites the radio signal propagation is subject to blockage due to highly dense metallic structures. Network planning should therefore account for the number and the density of the 3D obstructions surrounding each link. In this paper we address the problem of wireless node deployment in wireless industrial networks, with special focus on WirelessHART IEC 62591 and ISA SP100 IEC 62734 standards. The goal is to optimize the network connectivity and develop an effective tool that can work in complex industrial sites characterized by severe obstructions. The proposed node deployment approach is validated through a case study in an oil refinery environment. It includes an ad-hoc simulation environment (RFSim tool) that implements the proposed network planning approach using 2D models of the plant, providing connectivity information based on user-defined deployment configurations. Simulation results obtained using the proposed simulation environment were validated by on-site measurements.

Wireless Coexistence and Spectrum Sensing in Industrial Internet of Things: An Experimental Study

“…In this section, we focus on the problem of PHY layer modeling of the coexistence between WiFi and IEEE 802.15.4 based standards operating in the same unlicensed 2400 ÷ 2490 MHz ISM band. The widely adopted link quality indicator (LQI) or RSSI metric, here denoted as [9] for link , is not enough to estimate the average probability of successful connection in the presence of interference [4,10]. In what follows, we focus on the relevant case (in terms of QoS [11]) of IEEE 802.15.4 devices suffering the interference from one WiFi mobile device.…”

“…where the LQI (or RSSI) is modeled as in [10]. The ratio / indicates the critical value of cochannel disturbance (captured by the receiver) above which the interference has a relevant impact on the connectivity.…”

“…The widely adopted link quality indicator (LQI) or RSSI metric, here denoted as gl [9] for link l , is not enough to estimate the average probability PS of successful connection in the presence of interference [4, 10]. In what follows, we focus on the relevant case (in terms of QoS [11]) of IEEE 802.15.4 devices suffering the interference from one WiFi mobile device.…”

“…The traffic load of the interference is modeled as a Bernoulli process, with probability 0≤Pμ≤1 accounting for the degree of frame collisions. The Signal to Interference Ratio, normalSnormalInormalRl=gl/μl, serves as an additional metric, used jointly with the LQI, for the assessment of the successful connection probability PS (expressed in terms of frame error rate) as where the LQI (or RSSI) gl is modeled as in [10]. The ratio β/βI indicates the critical value of cochannel disturbance μl (captured by the receiver) above which the interference has a relevant impact on the connectivity.…”

Design and Development of a BlueGS Gateway for Bluetooth and GSM Protocols

“…The implementation of an embedded gateway system using wireless technologies, will in time, exploit the recent advances in WPAN, ubiquitous computing, wireless sensors and others areas [3,4,5]. The interoperability between Short-Range Wireless Protocols on embedded systems devices, can give to available to the user several functions that maximize the level of usability.…”