Internet of Things (IoT) is based on data collection, where billions of sensors sample the real world; in other words, the IoT includes a giant distributed measurement system (DMS). A question still requiring an answer is: Are the IoT technologies usable to enhance traditional measurement systems, since they have been developed for a very similar objective? In this paper, the use of a long-range (LoRa) technology, originally developed for IoT, is investigated with the aim of implementing DMSs. After the conclusion that LoRa and LoRa wide area network architectures show a good match with measurement systems, this paper focuses on the characterization of time-related performance indicators that are important for distributed systems. The experimental results show the capability of low-cost transceiver to schedule the transmission of frames with a standard uncertainty less than 3 μs; and an acceptable longterm clock stability (Allan Deviation) of commercial available devices (nodes and packet forwarders) for application such as smart metering, smart building, and process industry.

Information and operational technologies merge into the so-called industrial Internet of Things, which is one of the basic pillars of the Industry 4.0 paradigm. Roughly speaking, yet-to-come services will be offered in the automation scenario by industrial devices having an internet connection for sharing data in the cloud. Currently, most efforts are in the development of protocols able to ensure horizontal interoperability among heterogeneous applications. Consequently, poor attention is devoted to time-related performance. In this paper, a new, full software, platform-independent approach is proposed for experimentally evaluating the delay in transferring information across local and intercontinental routes by applications leveraging on messaging middleware. The application is realized using the node-RED web-based framework, due to its availability on different platforms; the widely accepted message queue telemetry transport protocol has been chosen thanks to its low overhead and complexity. For sake of completeness, five different, private and public, brokers are used. The adopted industrial-grade hardware, complemented by global positioning system time reference, permits an overall synchronization and timestamping accuracy of a few milliseconds. The vast measurement campaign highlighted that, generally, quality of service (QoS) type 1 offers low end-to-end delay (average value less than 0.5 s) with reduced variability (0.1 s). However, the maximum end-to-end one-way delay ranges from 1 s for QoS 0 to less than 1.5 s for fully acknowledged QoS 2.

The LoRaWAN technology, an example of Low Power Wide Area Network, is considered a possible IoT-derived solution for realizing private cellular (single-hop) communications in the industrial domain. However, despite LoRaWAN promises long range and dense environments with many obstacles (as the industrial ones), it may suffer from coverage issue. Additionally, the inverse relationship between data rates and range may be unacceptable for many industrial applications. In this paper, an innovative LoRaWAN range extender (based on an enhanced LoRaWAN node) is described, and its integration in the infrastructure of IIoT-enabled industrial wireless networks is presented. A frame relay strategy is suggested, thus avoiding to trade-off the highest data rates against an increased sensitivity. The feasibility of the solution is formally verified and the features of the realized proof-of-concept prototype, based on commercially available hardware, are discussed. The experimental results, obtained with a purposely designed test bench, show the effectiveness of the proposed approach. In particular, the range extender is transparent for legacy LoRaWAN networks as confirmed by correct relaying of uplink and downlink for different types of LoRaWAN message.

Currently, the applications of the Internet of Things (IoT) generate a large amount of sensor data at a very high pace, making it a challenge to collect and store the data. This scenario brings about the need for effective data compression algorithms to make the data manageable among tiny and battery-powered devices and, more importantly, shareable across the network. Additionally, considering that, very often, wireless communications (e.g., low-power wide-area networks) are adopted to connect field devices, user payload compression can also provide benefits derived from better spectrum usage, which in turn can result in advantages for high-density application scenarios. As a result of this increase in the number of connected devices, a new concept has emerged, called TinyML. It enables the use of machine learning on tiny, computationally restrained devices. This allows intelligent devices to analyze and interpret data locally and in real time. Therefore, this work presents a new data compression solution (algorithm) for the IoT that leverages the TinyML perspective. The new approach is called the Tiny Anomaly Compressor (TAC) and is based on data eccentricity. TAC does not require previously established mathematical models or any assumptions about the underlying data distribution. In order to test the effectiveness of the proposed solution and validate it, a comparative analysis was performed on two real-world datasets with two other algorithms from the literature (namely Swing Door Trending (SDT) and the Discrete Cosine Transform (DCT)). It was found that the TAC algorithm showed promising results, achieving a maximum compression rate of 98.33%. Additionally, it also surpassed the two other models regarding the compression error and peak signal-to-noise ratio in all cases.

The International Electrotechnical Commission (IEC) 61850 standard describes the communication inside the substation automation system (SAS) setting the base for new interoperating devices that share information using the Ethernet network infrastructure. More recently, the request of time synchronization among SAS components for the timestamping of critical events or synchrophasor measurement has generated a very high interest in the IEC 61850 working group for distributed synchronization protocols like IEEE 1588. This paper analyzes the current situation, matching different implementations of the IEEE 1588, and their respective accuracy, with the requirements of typical SAS applications: from the millisecond synchronization accuracy required by fault recorder application to the microsecond required by the sampled values (SV). The main focus of this paper is on the integration of IEEE 1588 with real already existing systems, and the goal is the performance comparison of different architectures. For these reasons, experimental tests have been carried out using a real SAS network infrastructure [redundant gigabit optical fiber Ethernet network managed using rapid spanning tree protocol (RSTP)]. The results show that, under ideal conditions, the IEEE 1588 protocol can achieve synchronization accuracy slightly worse (80 ns) than legacy IRIG-B systems (15 ns). However, IEEE 1588 makes possible a great cabling reduction and a simplified maintenance when synchronization is directly transferred on the SAS system bus. On the other hand, experiments highlight that nodes connected through a legacy RSTP infrastructure, which does not support IEEE 1588 messages, can lose the time synchronization in the case of network traffic or reconfiguration of the network. Therefore, the integration of IEEE 1588 over existing IEC 61850 station bus is basically a tradeoff between installation costs and performance since it requires the replacement of already installed Ethernet infrastructure. In the last part, this paper analyses the challenges for the next SAS generation that has to combine on the same plants the high accuracy IEEE 1588 implementation with high-availability protocols based on IEC 62439. Since the lifetime of an SAS is more than ten years and the adoption of these technologies takes several years, some solutions that introduce synchronization and high-availability protocols guaranteeing compatibility with existing technologies have been analyzed, too. For instance, an adaptation block that (when attached to a high-availability network) reconstructs the interface signals (e.g., synchronization signals) required by traditional bay has been proposed.