As IoT has become more diverse, there has been more research on IoT platforms that can integrate IoT devices and make them compatible [
31]. An IoT platform is a technology that sets the network standards and data sources of various devices, and integrates them regionally and internationally. The goal of an IoT platform is to standardize the devices, to make them compatible with each other, and to integrate them as one. In addition to the international standards organizations, there are various standardization groups and companies that study and develop such technologies.
The international telecommunications union-telecommunication (ITU-T) standardization sector is a major organization in IoT platform research [
32]. This organization develops standards for IoT and expects its extraordinary growth in sensor technology, nanotechnology, and embedded intelligence technology. Specifically, one major research effort involves designing a standard structure for service layers and deciding upon an outline for the API and protocols of each service layer. Further, standards for the requirements and management of identifiers in a mobile or internet environment are being developed. In this area, the development of standards related to numbering, naming, and addressing is very important. Currently, standards are being developed for the analysis of interfaces that manage virtual resources in a cloud computing environment. Another important research field is the development of standards related to test structures and requirements for interoperability, including M2M and IP communications [
33].
Korea electronics technology institute (KETI) [
39] is another major organization in international IoT platform research. KETI unveiled the current IoT platform Mobius 2.0. This organization collaborates with Europe, the United States, and various other countries. Mobius follows the standards of the international standards organization oneM2M, and it supports various applications and services, such as smart cities, smart grids, home automation, and health services. The unique feature of the Mobius IoT platform is that it follows a client–server computing model and is based on the RESTful architecture. The RESTful method is a transmission method derived from the REST method’s typical IoT state. It has the benefit of making it easy for the users to uniformly set up and manage various formats and methods of resources.
This kind of IoT platform research follows international standardization documents and supports various technological infrastructures and setups; however, it makes it difficult for the user to intuitively view data, and its usage convenience and management freedom are very inadequate. Further, many international standards organizations and companies use separate standardized rules; thus, there is a very wide variety of standards. This means there are multiple independent IoT technologies, which leads to great difficulties in integrating them into one. In addition, these IoT standards are inadequate in terms of offering services and features that provide an intensive, well-defined model for home network performance and energy efficiency. The existing IoT platforms can be used profitably in various industrial settings and fields; but unlike these, a smart home is a field that connects directly to people, which makes it very intricate and highly unpredictable. Thus, it is necessary to build an IoT platform service model that optimizes smart home networks and energy.
Intelligent IoT energy efficiency services in smart homes
A smart home uses more home appliances than a normal home and consumes energy accordingly. Furthermore, as the scales of apartment buildings become larger, network efficiency must be considered alongside energy efficiency, if there is a system that manages the buildings’ appliances as one. Thus, various energy and network efficiency services for smart homes are being studied. This research can be considered as divided into two perspectives: physical research and systems research.
On the one hand, there are currently many studies on physical energy efficiency that aim to use clean energy (called green energy) to avoid harming nature and overcome the inadequacies of conventional energy by creating alternative energy [
34].
For example, solar energy has often been used to replace electricity and natural gas used in smart homes. By using solar energy, pollutants that harm the environment are not emitted, and the finiteness of conventional gas and electricity can be overcome. In doing so, the energy used by various IoT applications is provided, and more environment-friendly energy consumption becomes possible [
15‐
24].
On the other hand, among the various studies on systematic energy efficiency, there are many studies being conducted on the concept of a smart grid. A smart grid refers to a next-generation intelligent electrical grid. It is a service that allows the electricity supply to be managed effectively by providing electricity providers and producers with information about the electricity users. The users also receive this information; thus, they can manage their individual electricity use and be provided with a high-quality electrical service [
35,
36].
There are various reasons for the smart grid research to continue. The existing electrical grids are analog and electro-mechanical, and the structure for controlling the grid is limited owing to their centralized systems. Further, if a problem occurs, a large amount of time and manpower are required for repair because of problems related to manual repair systems. A smart grid system is an intelligent and digital approach that enables overcoming these problems. It can meet the consumer’s various needs by understanding them through two-way real-time data exchanges. It avoids a centralized system; thus, its distributed network structure is very flexible, and it has a fast and automatic repair system that works through two-way data exchanges. Currently, smart grid research is being performed in various industries.
Smart grid technology can be very beneficial to use in smart homes as well. Smart home systems that use a smart grid can check the electricity consumed by all the devices used by the user, and this data is collected on the server in real time. By collecting detailed data on electricity use, the user’s electricity usage patterns can be analyzed. Research is being performed on various systems that enable the use of analysis results to provide electricity efficiently [
35,
36].
Building a smart grid requires energy storage systems (ESS), advanced metering infrastructure (AMI), energy management systems, electric car and charging stations, distributed power supply, new-renewable energy, two-way data communication technology, intelligent power transmission-supply systems, and so on. Among these, the ESS is the most essential part of an IoT network. Previously, existing leftover electrical energy was simply wasted. However, ESS helps store large amounts of electricity. This can be used to store energy, adjust consumption, and supply on requirements to reduce energy wastage. The AMI is actively being studied as a method to adjust consumption and supply as needed. The AMI sets electricity usage amounts and discerns each user’s usage patterns based on the usage data from each household. This data can be used to provide optimal electricity, and it can lower electricity costs and prevent wastage. The data is transmitted to an AMI data collector via each household’s Wi-Fi network, and it is gathered together by TropOS mesh radio, which is broadcast throughout each region. However, analyzing energy usage patterns and collecting electricity usage data is a very simple form of data collection and analysis.
Various energy efficiency models that use intelligent learning are being studied. However, these systems still analyze only user patterns and gather electricity using data en masse. This is merely statistical and numerical analysis of the gathered data. Understanding the patterns of each user requires a system that meaningfully processes and analyzes the user’s individual data rather than mass data. It must provide customized service for each user or each device in each household by individually analyzing a variety of collected user data and learning from data which is collected in real time. This is a method for saving and efficiently using energy, and it increases user satisfaction. In the future, more research is needed to study specialized artificial intelligence learning models that are used as smart home energy services which can be individually customized.