# Smart Energy Meters for Smart Grids, an Internet of Things Perspective
## 3 Computational Platforms
Another ARM-based Wi-Fi system is implemented in [203] that augments the meter reading with control functionality. Using an ARM-based processor, a load management and meter reading system is implemented that uses a GPRS network in [204]. YoMo [205] is an Arduino pin compatible power meter which can use Arduino and its eco system for implementing a smart meter. Another Arduino smart meter using GSM as a network is reported in [206]. An Arduino-based meter that uses MATLAB as analysis software is reported in [207]. An IoT server-based smart meter using Arduino as a processor, ACS712 as a current sensor, and ZMPT101B as a voltage sensor is reported in [208]. A smart meter using ESP8266 and built-in Wi-Fi is reported in [209]. The Hall effect-based galvanic isolation sensors are employed for sensing voltage and current in an Arduino Duo-based smart meter in [210]. Non-invasive measurements for smart metering using Arduino or IoT are given in [211]. A Wi-Fi smart meter using ESP8266 and Arduino Uno is reported in [212]. Arduino Yun, Arduino Uno, and ESP8266 are used to deliver real-time measurements of a smart meter to a user, as given in [213]. An edge platform using Jetson Nano for edge implementation communicating with Arduino Uno is reported in [214]. A Raspberry Pi-based real-time energy IoT monitoring system is reported in [215]. A low-voltage smart meter for monitoring power quality disturbances using classification and detection based on a single-board computer is reported in [216]. An IoT-based smart meter for AI-based power measurement and quality assessment is reported in [217]. Table 9 further summarizes the implementations in a structured manner. Though one can find extensive literature on smart meters employing IoT techniques, the state of the art is usually not employed. FPGA and FPGA/SoC systems are seldom used though the complex scenarios of modern day metrology warrant such usage, such as to assess the energy mix, for dynamic pricing engines, for advanced signal processing for power quality, for theft detection, and for multi-protocol conversions, to name only a few. Though industry has developed SoC specifically for the metering, it is not used extensively for research. Some modular platforms have also been reported by the industry, again finding little usage. As the complexity of meteorology is evolving, the smart meters are not employing modular architecture to cope with the increased demands and usually employ single-function execution models, while multiple interacting functionalities might enhance the prospects of smart energy manifold. A modular platform is also discussed for its necessity but has not been implemented so far. Similarly, 5G networks have not been studied for slicing in this domain though it has been reported that smart metering generates a heterogeneous type of data with respect to network functionality. Another issue that is not discussed frequently in the literature is that of the service architecture for smart metering. Finally, the smart meters reported are very much measurement-agnostic deviceswhich will not fare well in a modern AI-driven big data world as measurement techniques might impact the algorithms and the quality of the measurement. For system-oriented implementation, the measurement techniques should be considered while designing the smart meter. Moreover, it will allow for the designing of flexible platforms that could be well suited to a wide variety of metering applications. To sum up the point made in this passage, the Figure 4 presents the \"usage cube\", which color codes the different combinations of technologies used by smart meter research community. To conclude the discussion of the smart meter implementations, we present a brief review of the various applications of smart meters. Table 10 lists some of the smart meter data applications; the list is by no means exhaustive or even numerous. These applications present the use cases of smart meter component usage. Load forecasting predicts the future energy demands based on current meter readings.