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A systematic evaluation of the literature on IoT gateways

The Internet of Things (IoT) relies heavily on gateways to route preprocessed, filtered data to cloud platforms. The necessity for a comprehensive systematic literature review that can expound on the working and functions of IoT gateways was noticed while researching gateways. This study provides a comprehensive assessment of the literature on IoT gateways in general and smart gates in specific. It took into account papers published in the recent ten years, from 2011 to July 2021. In this review, a methodical literature analysis strategy is applied; out of 2347 papers, 67 are chosen for thorough analysis based on pre-defined criteria. The poll begins by classifying IoT gateways into two categories: basic and smart gateways. Passive gateways, semi-automated gateways, and fully-automated gateways are the three types of smart gateways. The survey is conducted using well-defined criteria, including the type of gateways, their needs, tools/platforms, the approach taken, evaluation, and application domain. The functional requirements of IoT gateways are further bifurcated in this study. In this field, research gaps and outstanding issues have been discovered. Future opportunities have been presented as a result to assist academicians in pursuing advanced research.

This article makes the following contributions: 

- A thorough literature review on IoT gateways. 

- State-of-the-art tools/platforms and their impact on IoTgateway 

- For the IoT gateway, state-of-the-art evaluation approaches were applied.

- Classification of IoT gateway functional requirements.

- Examining different parameters that are utilized to establish QoS in the IoTgateway. 

- Identifying existing difficulties and research gaps in the IoT gateway field.

- Future research directions in the realm of IoT gateways, with a focus on self-contained IoT systems.

Smart gateways are a critical component of IoT systems that are self-contained. IoT gateways were divided into two categories in this SLR: Basic and Smart Gateways. Passive, semi-automated, and fully automated smart gates were all classed. The literature study of the research chosen through the methodological review process was divided into two categories based on these smart gate-way classifications. The work presented here has identified the types of smart gateways that are currently being developed, as well as their functional, non-functional, and architectural needs. The survey was conducted using the following criteria: the type of gate-ways, their needs, tools/platforms, strategy taken, including proposed architecture or algorithm, assessment techniques used, such as simulation tools, prototype or real test-beds, and application domain. According to the data gathered while answering the research questions, publication began to pick up speed in 2015, with 48 percent of the peer-reviewed work appearing in reputable journals. The Internet of Things Journal and Future Generation Computer Systems are two of the most well-known periodicals.This research has also identified researchers who are actively working in the subject of IoT gateways. It was discovered that 20% of Raspberry Pi-based implementation and 17% of Arduino-based assessment were completed. According to comparison studies, 60% of Real testbeds were utilized to validate and assess the planned work. Passivesmart gateways were the most heavily researched of the three types of smart gateways. For future study purposes, the functional needs of IoT gateways were divided into two categories. Furthermore, the majority of the existing research focuses on increasing the energy efficiency, dependability, and latency of IoT gateways. Future opportunities in the field include building more autonomous applications in the IoT sector, as well as developing and working on more fully automated gateways. To address the existing difficulties in IoT gateways, a standardized solution should be given.

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Using IoT and an ensemble-based algorithm, smart cities can better estimate parking availability

Smart cities are a result of continual technological advancements aimed at improving the quality of life for their residents. One of the most crucial aspects of smart cities is urban mobility. Urban traffic congestion is becoming increasingly typical in major places as the number of vehicles on the road increases. Furthermore, even in vehicle parks, obtaining a parking spot is difficult for drivers who go in circles. According to studies, cars hunting for parking places cause up to 30% of traffic delays. In this case, drivers must be able to forecast the places available in parking lots where they wish to park. In this research, we propose a new system that combines the Internet of Things with a predictive model based on ensemble methods to improve the forecast of parking space availability in smart parking. The tests we ran on the Birmingham parking data set resulted in an average Mean Absolute Error (MAE) of 0.06 percent using the Bagging Regression technique (BR). As a result, the best existing performance has been enhanced by over 6.6 percent while system complexity has been considerably reduced.

The Internet of Things (IoT) is hastening the pace of transportation innovation, notably in the areas of smart parking and urban mobility. Many smart car parks now have connected systems that allow drivers to check their smartphone apps, set instructions, use roadside assistance, remotely open doors, and find free parking places. As a result, the Internet of Things will bring to the automotive industry novelties about which we have no concept.

Isn't it true that we're already drowning with data? The Internet of Things will generate much more data, complicating current business information management systems. Intelligent parking systems, for example, can generate a lot of data when they're completely functional. These "data-centric" IoTs have concentrated on all areas of data flow, including data collecting, processing, storage, and display (Jin et al., 2014).

The vast amount of data creates issues in terms of data collection, processing, storage, management, and manipulation. To extract useful information from the data supplied by the connected devices, advanced scanning technologies would be required. It will also provide new capabilities for egovernment, supply chain, and urban transportation management, as well as new chances to enhance corporate processes. Data capture, analysis, and delivery will be most effective if they are all done from a cloud-based system. This is the situation with the intelligent parking system's automatic management system. In the following section, we will describe an ensemble-based approach for optimizing space availability prediction in smart parking (Arasteh et al., 2016; Mainetti et al., 2015).

The ability to estimate the availability of parking spaces for city drivers greatly lowers traffic congestion. As a result, there is urban pollution. Several authors have made suggestions. For this forecast, we used methodologies and models that were not ideal. To address this, we presented in this paper an integrated system. Set-based regression models and the Internet of Things We have a data-centric IoT. suggested should allow for the use of all associated resources. items with relation to smart parking lots in order to collect the data, analyze it, and inform the drivers of the findings Our The availability of parking spaces in smart car parks was predicted more accurately using an ensemble-based model for predictive analysis. The tests we ran on the data from Birmingham's parking lot, for example, allowed us to arrive at an average absolute value. With the Bagging algorithm, the average margin of error (MAE) is 0.06 percent. Regression is a term used to describe a (BR).

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Access control and surveillance in a smart home

With the development of a large number of both Internet of Things (IoT) applications and smart devices in recent years, smart home solutions have grown in popularity. The market for home automation and security systems is always expanding, traditional security systems are rapidly developing, and more and more consumers are opting for Smart home solutions. We propose two IoT-based Smart home systems in this paper: qToggle for multiple house automation and MotionEyeOS, a video surveillance OS for single-board computers. ESP8266/ESP8285 chips or Raspberry Pi boards and smart sensors are used in most qToggle devices, while MotionEye uses Raspberry Pi boards.

The most significant issues are safety and home/building security. Integrating security and access control systems into a building automation design ensures the highest level of security while also providing a new level of convenience to the user's lifestyle. Smart solutions provide remote management and control of alarms, access controls, lighting, and cameras, for example, using a smartphone and an app. One of the reasons for the rise of smart technology is to reduce the risk of glare and accidents. Because most people lead such hectic lives, the necessity to remotely monitor and regulate the status of their home has become a major concern.

A number of IoT-based smart home automation systems have been reported in the literature or are commercially available. Each one's combination of technology and setup difficulty differs, as do their benefits and drawbacks in terms of complexity, pricing, and performance. The Internet of Things (IoT) is crucial for introducing security elements in smart buildings. When someone breaks into a home, a traditional security system would activate and sound an alarm. A smart security system is designed to do much more than that: it can send an SMS alert to the owners' smartphones, and the owners can arm and disarm the alarm remotely via a smartphone app. Over time, numerous technologies have been used to construct smart security systems.

In people's lives, technology plays a critical part. Electronics and devices abound in today's homes, necessitating smart solutions. Smart home security has become a prominent topic in recent years, thanks to the growing popularity of smart home systems. Home security is a good example of how the Internet of Things may be utilized to create a low-cost security system for households and, why not, the industrial and commercial sectors as well. Using Raspberry Pi boards and/or ESP8266 chips, we offered two easy home security solutions in this project. Both options are affordable, compact, and simple to use. The proposed systems are simple to install and do not require a large infrastructure, which is a benefit over other systems now in use. Our major goal is to turn qToggle into a smart home prototype with a variety of features, a system that could be used in a variety of situations. Our major goal is to make qToggle a smart home prototype with a variety of functions, a system that can be built, improved, and expanded to include more features. Our main future work will be to integrate a video surveillance system into qToggle, as we believe qToggle is a viable project with many features that might be commercialized in the future.