Future Smart City with less Traffic Congestion Model Enabled by (IoT)

The advancement in wireless telecommunication network has increase the accessibility of more users to wireless connectivity. With the advent of the fifth-generation (5G) wireless network, a seamless connectivity is available for internet users globally. A future smart city is a metropolis that utilizes information and communication technologies (ICT) to grow its functionality effectively to disseminate information among the public and to develop the quality of government facilities and the welfare of the citizen. The Internet of Things (IoT) refer to the interconnection of several systems, devices or physical objects/things which are driven by sensors, software, and other equipment in order to interconnect and interchange data with other devices and systems through the internet. The Internet of things (IoT), is a revolutionary method that allows a diverse number of applications to be interconnected in order to create a single communication architecture. Future smart city has resulted in the increase in population, hence there is need to develop a smart traffic light system to help in managing the problem of future smart city; traffic congestion. The Internet of Things (IoT) a key features necessary for employing a large-scale in IoTS are low-cost sensors, high-speed and error-tolerant data communications, smart computations, and numerous applications which helps in solving these challenges associated with traffic congestion. It enables a smart environment, smart energy, and smart transportation system. In this paper, we shall discuss IoT technology, review some literatures on application area of Internet of Things (IoT), and challenges of IoT. And also discuss the applications of IoT, in smart city development, and traffic congestion management in smart city design, and how it proffers solution to future smart city problem.


INTRODUCTION
According to projections, nearly 70% of the world population will live in urban areas by 2050. While some megacities are already struggling to cope with the current inflow of people, we need to create smart cities in order to make these urban areas more liveable and truly sustainable. Barbara et al. (2013), focused on the quality of life as an essential Smart City mainstay to guarantee the orientation towards a better lifestyle for citizens.
British standards Institutes, (BSI, 2014), a smart city can be define as an urban area (encompassing possibly different areas and scales of the city -street, plaza, neighbor-hood or, ultimately, an entire city) that uses electronic data collection sensors located in infra-structures, buildings, vehicles, institutions, and devices (Io T, Internet of Things) to supply real-time information of the main cities' operating systems. Lee et al. (2014), define a smart city as an inventive, viable region that increases the value of life, produces pleasant surroundings, and the predictions of profitable growth for its resident. Traffic congestion has become a major problem in most urban cities in the world. This is as a result of urbanization problem and the increase in the number of vehicles on the road and poor road infrastructure. In previous days, transportation researchers used Kalman Filter, Hidden Markov and ARIMA models in predicting the traffic volume, by means of conventional techniques through geographical positional sensors. But, due to high costs of installing sensors on roadside, its usage has been limited. The increase in inventive growth of mobile internet, artificial intelligence, movable devices are commonly useful and the ease in retrieving user data (Yin et al. 2016).
Convolution Neural Networks (CNN) can be used to capture spatial features and Recursive Neural Networks (RNN) to capture sequential characteristics. Neural networks comprise of variations and hybrid models, which are used in traffic flow forecasts.
But in now a day's artificial intelligence has been successfully used in several areas of transportation, such as service computing methods, edge computing methods and social networks, (Deng et al. 2016).
Generally, for traffic congestion there are manual traffic control systems, which require a high number of personnel to handle intersections. The manual systems have poor traffic rules and personnel strength, and the establishments cannot efficiently manage the traffic system in cities effectively, due to the large volume of vehicles and population with the manual system. Syed et al. (2021) identified a smart city to be made up of several components as shown in figure 1. The first aspect of smart city applications is data collection; the second is data transmission/reception; the third is data storage, and the fourth is data analysis. Data collection is application-dependent, and it has International Journal for Multidisciplinary Research (IJFMR) been driven majorly by sensor development in a variety of fields. The data transfer from the data gathering units is sent to the cloud for storage and the analysis is the second phase. Many smart city initiatives include city-wide Wi-Fi networks, and3G, 4G and 5G technologies; they are employed in various forms of local networks that transmit data on a local or global basis. Fig.1The components of a smart city. The internet of things (IoT) is core in smart city development; it is the enabling technology that enables ubiquitous digitization that gives rise to smart city development. The internet of things (IoT) refers to the ubiquitous connection of objects to the internet, which allows different devices to communicate data to the cloud and also obtain information for implementation activities. IoT involves the assembly of data and the use of data analytics to extract information which assist in decision-making and policy-making, Ramson et al. (2020). Huang and Nazir (2021), shown that IoT and big data help in the management and analysis of a smart city. They compared the older city and the new smart city concept and the role of IoT devices in smart city development, as shown in Fig. 2.  Figure.2 shows how a smart city is more organized in way when compared with a traditional city. The city is well managed with better road network housing units and near about no vehicle emissions. The port, residential areas, and industrial areas are well planned.

SOME SMART CITY CHALLENGES ARE AS FOLLOWS: Implementation Challenge
Ron and Friedemann (2015), see that some application challenges faced by the Internet of Things, which include the cost of implementation, with the expectation that the technology must be available at a low cost, irrespective of the number of devices implemented.

Expandability
Internet of Things has a vast concept than the conventional Internet of computers; Internet of things is incooperated within an open environment. Basic functionality such as communication and service delivery needs efficient functionality for both small-scale and large-scale environments. The IoT requires a new functional method for efficient operational scalability.

Data Mass
Some application of the internet of things involves up-to-date communication and information gathering from sensor networks. Logistics and large-scale networks collect a huge volume of data from central network nodes or servers. Big data is required to implement large operational technology and to store such information, process, and manage a large volume of data.

Interface
Each smart device in Internet of Things has different information, processing, and communication capabilities. These devices are subjected to different conditions, such as energy availability and communication bandwidth requirements. To facilitate this, communication and cooperation of these devices are required. These common standards help the objects to communicate properly.

Secrecy of Smart City
In any smart city development, security and privacy are of key importance. Any inconsistency in the operations of the city's services would be of great danger to the populaces and put human lives and property in great danger.Smart cities need that the infrastructures must be online so that security difficulties will not be a major issue. In present situation where cybercrime and warfare have become a major global issue, smart cities are increasingly becoming vulnerable to such cyber attacks. Data transmission over the network must be secured. Citizens' trust and involvement are required for smart city programs to succeed. The development of sensors in smart cities helps to collect data from user's activities daily; this exposes residents to daily attacks by hackers, (Ashraf et al. 2020).

EQUIPMENT OF SMART SENSORS
Ashraf and Ahmed, (2020), discovered that sensor equipment shares data, schedules responsibilities between them, and combines data to have an efficient smart city. Expansion and acceptance of open procedures and data layouts is a solution to this difficulty, it allows manufacturers to design equipment that can interact with one another, accelerating the implementation of IoT systems. Standard access point nodes for IoT systems interacts with devices using a variety of communication protocols and interpret the information received. Ashraf and Ahmed, (2020), discovered that different manufacturers have designed their equipment to be interoperable with other protocols. An additional problem with smart sensors is their reliability and robustness. The reliability and accuracy of the IoT system are described as reliability and robustness. The Internet of Things (IoT) is the strength of future smart city, this makes it significant to their working, and IoT system must provide a seamless experience to its consumers. This demands a quick and accurate reply to service demands submitted by app users. Each individual in the smart city needs to have access to good services. Decentralized systems should be used to supply vital services like transportation and energy. The dispersed connection points improve the robustness and dependability of the system. Ashraf et al., (2020), identified different areas of challenges of smart city operations; the operating mechanism involved in the digitalization process necessitates the growth of sensing nodes. With such a large application scope, developing and deploying IoT systems in smart cities presents great difficulties that must be considered. The problems that IoT system designers encounter while making deployments in smart city applications are discussed below. It majorly focuses on the technological problems associated with IoT deployment in smart cities as shown in the figure below. The numerous obstacles that Smart City IoT system implementation faces include: Security and Privacy, smart sensors, Networking, and big data analytical.

Fig. 3 smart city challenges.
Due to growth in road infrastructure and vehicles, handling a traffic and conveyance network has become difficult. The conventional traffic framework has a problem of not detecting the incidence of vehicles transversely on the separate road, and if a road is free, the traffic indicator waste time. The conventional vehicle controlling structure cannot manage traffic congestions efficiently when there is high density of vehicles on the road. Traffic congestion has become a major problem in urban cities, most especially at peak periods (morning and closing hour) at intersections, due to an increase in the number of cars on the road. A lot of time is spent in traffic gridlock and accident do occur at this time, emergency vehicles like ambulances are not given priority to pass, and commuters spent time in traffic gridlock. To solve this problem of traffic congestion problem, we propose a Smart City Smart Transport (SCST), to handle these challenges associated with congestion at intersections during peak hours. In this paper, we designed and implement a Smart City Smart Transport (SCST), enabled by IoT and machine learning technique.

METHODOLOGY
The proposed design is Smart City Smart Transport (SCST). This Design is modular approach. The important and various devices used are cameras, sensors, GPS, actuators and RFID tags. The internet of things make used of the available cloud resources, in establishing processes that integrate and manage the traffic congestion. The SCST utilizes the essential features of IoT, cloud computing, and big data. It helps to enables different devices to communicate using M2M (Machine to Machine). IoT creates a platform for managing traffic-related difficulties (Sodhro, 2019). The system architecture

Working Of System:
Smart City Smart Transport (SCST) Above shown Design, the following means to obtain the traffic light data, vehicle data, and the road data. Then preprocessed. The traffic data sets collected using both inductive loop sensors and video cameras as acquisition systems and some selected parameters, which includes vehicle speed, time of day, traffic volume and number of vehicles on the road to detect congestions at intersections, is then use to monitor the road density for Smart transport, to de-congest vehicles at intersections and also give priority to emergency vehicles. From figure 4 above, the IoT application uses the cloud Node facility to communicate with the IoT devices, and actuators, to send data to the data collection center for pre-processing. It is then stored in the various database, and the traffic dataset is used for Smart transport through the IoT application for Smart City Smart Transport (SCST)

CONCLUSION
To improve the traffic situation, thereby controlling traffic congestions at intersections. The model will regularly update traffic sign programs subject to traffic capacity and projected schedules from neighboring intersections. It will reduce the waiting time by steadily moving vehicles across green signs and also minimize traffic by creating an improved switching time. A case study of Benin City, Edo State is considered, using a Smart City Smart Transport (SCST) based on reinforced learning and IoT technology is considered in this paper. The system will solve the problems associated with more traffic at intersections during peak hours, in areas with high vehicle density, by directing them to alternative routes to eliminate traffic gridlock. Smart City Smart Transport (SCST) will positively impact the smart cities' decision-making process. Intelligent decision-making systems in smart mobility offer many advantages such as saving energy, relaying city traffic, and more efficiently, reducing traffic gridlock and air pollution by offering real-time useful information and imperative knowledge. As the system will be self-learning to adjust to the traffic situation, thereby reducing traffic gridlock by prompting commuters of possible congestion ahead at the intersection, so that it can be avoided. Traffic Mass