IoT/EE

IoT applications rely strongly on the performance of wireless communication networks.
There is a wide variety of wireless IoT technologies and choosing one over another depends on the
specific use case requirements—be they technical, implementation‐related or functional factors.
Among the technical factors, latency, error rate and stability are the main parameters that affect communication reliability. In this work, we present the design, development and validation of a Universal Testbed to experimentally measure these parameters, abstracting them from the wireless IoT technology protocols and hardware platforms. The Testbed setup, which is based on a Raspberry Pi 4, only requires the IoT device under test to have digital inputs. We evaluate the Testbed’s accuracy with a temporal characterisation—accumulated response delay—showing an error less than 290 μs, leading to a relative error around 3% for the latencies of most IoT wireless technologies, the latencies of which are usually on the order of tens of milliseconds. Finally, we validate the Testbed’s performance by comparing the latency, error and stability measurements with those expected for the most common IoT wireless technologies: 6LoWPAN, LoRaWAN, Sigfox, Zigbee, Wi‐Fi, BLE and NB‐IoT.

Sensor devices are becoming omnipresent, supplying data to a wide range of applications. In the building sector, sensors along with other information sources provide the basis for smart building functionalities.
Predicting energy loads and inferring occupancy status of spaces are important tasks that promote energy efficiency and user comfort in buildings. For them, as for many other smart building applications, machine learning modelling utilizing sensor data is commonly applied. This article builds understanding of the environment where this kind of machine learning models have to operate by bringing up properties and quality aspects of the public building data provided by indoor sensor devices. This is done by performing a thorough case study on two real life data sets from university campus buildings located in different climates and applying very different sensor network settings. Outcomes include information about heterogeneity, correlations and temporal patterns present in sensor data, and show the need of the building field for better acknowledging the quality deficiencies that sensor data have. Our results aid in assessing and improving the quality of sensor-based indoor data utilized in machine learning modeling, in evaluating whether a data set is representative enough to build a model that is robust under changing conditions in the building, and in choosing an appropriate number of sensors per space when building an indoor wireless sensor network.

Most smart meters are connected and powered by the electric mains, requiring the service interruption and qualified personnel for their installation. Wireless technologies and energy harvesting techniques have been proved as alternatives for communications and power supply, respectively. In this work, we analyse the energy consumption of the most used IoT wireless technologies nowadays: Sigfox, LoRaWAN, NB-IoT, Wi-Fi, BLE. Smart meters’ energy consumption accounts for metering, standby and communication processes. Experimental measurements show that communication consumption may vary upon the specific characteristics of each wireless communication technology—payload, connection establishment, transmission time. Results show that the selection of a specific technology will depend on the application requirements (message payload, metering period) and location constraints (communication range, infrastructure availability). Besides, we compare the performance of the most suitable energy harvesting (EH) techniques for smart meters: photovoltaic (PV), radiofrequency (RF) and magnetic induction (MIEH). Thus, EH technique selection will depend on the availability of each source at the smart meter’s location. The most appropriate combination of IoT wireless technology and EH technique must be selected accordingly to the very use case requirements and constraints.

In this work, a novel current metering device was presented. This device was intended to bring current metering capabilities to a wide variety of scenarios: Developing countries, rural areas, or any situation with technological constraints. The device was designed to provide a straightforward installation with no intrusion in the electrical panels. This was achieved by applying energy harvesting techniques and wireless communication technology for data transmission. The device was able to exploit the magnetic field inducted around a wire carrying electricity as energy harvesting, thus acquiring the power it needed to work. Since very low power was harvested, an efficient treatment for the incoming power and a minimal power consumption system were essential. Although exploiting the magnetic fields inducted around a wire has been used for years, the combination of this technology for both energy harvesting and current metering in an end-user device was off-center. To work in a wide variety of scenarios, it used Sigfox for communications as this brought wide coverage and out-of-the-box functioning. The theoretical design of the device was validated by verification assessments for the joint performance of the individual parts compounding the device, including metering capabilities and wireless communication test-bench. Finally, the metering device was tested under three distinct real-world scenarios that demonstrated the viability of the system. Results show that, depending on the metering period and the average current value in the mains line, the device could work forever acquiring and sending electricity consumption data. Perpetual working was achieved with an average current of 3.1 A to meter every 15 min, and an average current of 5 A for a 5-min metering period.