Abstract
This article presents a dataset obtained from the deployment of an IEEE 802.15.4g SUN (Smart Utility Network) single-hop network (11 nodes) in a large industrial scenario (110,044 m 2 ) for a long period of time (99 days). The dataset contains ∼11 M entries with RSSI (Received Signal Strength Indicator), CCA (Clear Channel Assessment), and PDR (Packet Delivery Ratio) values. The analyzed results show a high variability in the average RSSI (i.e., between −82.1 dBm and −101.7 dBm) and CCA (i.e., between −111.2 dBm and −119.9 dBm) values, which is caused by the effects of multi-path propagation and external interference. Despite being above the sensitivity limit for each modulation, these values result in poor average PDR values (i.e., from 65.9% to 87.4%), indicating that additional schemes are needed to meet the link reliability requirements of industrial applications. Hence, the presented dataset will allow researchers and practitioners to propose novel mechanisms and evaluate their performance using realistic conditions, enabling the dependability vision of the RAW (Reliable and Available Wireless) WG (Working Group) at the IETF (Internet Engineering Task Force).
Highlights
Industrial and smart grid applications require dependable transmissions for monitoring and control operations, which means reliable, available, and timely delivery of packets [1]
Wireless communications are subject to propagation and interference effects
Traditional low-power wireless communications in the industrial domain have been based on the IEEE 802.15.4 standard, using the OQPSK-DSSS modulation and operating in the 2.4 GHz ISM
Summary
Industrial and smart grid applications require dependable transmissions for monitoring and control operations, which means reliable, available, and timely delivery of packets [1]. In wired networks, this kind of dependability is achieved using a central path computation entity, which has a complete view of the network topology. This kind of dependability is achieved using a central path computation entity, which has a complete view of the network topology This model cannot be adapted to wireless networks for two main reasons [2]. Wireless communications are subject to propagation (i.e., the Fresnel zone is typically blocked) and interference (i.e., these networks operate in unlicensed bands) effects. Radio conditions may change faster (i.e., mobile nodes or a dynamic environment cause multi-path fading) compared to the adaptability and reprogramming ability of a centralized engine, in particular when the controller is distant or when connectivity is limited.
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