## 1 Introduction
Similarly, a unified rerouting framework was proposed in [18] for traffic congestion avoidance by combining the dynamic vehicle rerouting and traffic light control schemes. The authors [19] proposed an intelligent traffic system called CHIMERA (Congestion avoidance througH a trafflc classification MEchanism and a Rerouting Algorithm), which periodically collect information from vehicles and ranks each street according to a congestion level. CHIMERA significantly improves the spatial use of a road network and reduces the average travel costs by avoiding traffic jam. The proposed microscopic congestion detection protocol (MCDP) [20] is aimed to make vehicle-to-vehicle (V2V) communication capable of monitoring vehicle density and to identify traffic jam. By introducing the transportation control domain in the existing network protocol header, each vehicle counts adjacent vehicles and estimates the time spacing among vehicles. MCDP provides an infrastructure-less solution to determine the vehicle density, flow, and average velocity in a microscopical manner [21]. Moreover, the safe speed limit was introduced to enable each vehicle to assess transportation congestion by comparing it with some predefined safety time threshold. The authors concluded that MCDP is an inexpensive transport congestion detection technique by employing V2V communications [20]. The proposed infrastructure-based Vehicular Congestion Detection (IVCD) scheme (using V2I communication) was intended to support congestion detection and speed estimation [22]. IVCD derives the safety time (time headway) between vehicles by using iterative content-oriented communication (COC) contents [23]. On the other hand, the roadside sensor (RSS) provides an infrastructure framework to integrate macroscopic parameters to estimate traffic congestion and vehicle safety speed. The primary responsibilities of RSS in IVCD were to preserve privacy, aggregate data, store information, broadcast routing table, estimate safety speed, detect traffic jams, and generate session ID (S-ID) for vehicles. The V2V and V2I communications in VANETs are used for ad-hoc wireless connectivity of vehicles. While IoV expands VANETs to turn every vehicle into a smart node by adding its computation, storage, and networking capability. Most of the traffic congestion research is based on the conventional transportation rules [4; 9; 18; 24; 25; 26] or either depends on consistent mobile connectivity to GPS map [10; 12]. Presently, ordinary cities are migrating towards \"smart cities\" by using information and communication technologies (ICT). The Internet of Thing (IoT) paradigm plays a vital role in the realization of smart cities [27; 28; 29; 30]. Different IoT-based traffic management schemes (congestion detection) are available in the literature [31; 32; 33; 34]. The IoT-based congestion detection schemes are costly in terms of money and time, as well as challenging to deploy due to its complex structure. MCDP [20] and IVCD [22] are inexpensive and easy deployable schemes for congestion detection. The MCDP and IVCD are network layer protocols, where the congestion detection functions are added to the periodic packet structure without interrupting the physical layer. That is why these protocols are inexpensive and easily deployable. However, these schemes were unable to avoid congestion in real time. Thus, in this context, we propose an infrastructure for a real-time traffic management scheme called the Internet-of-Vehicles (IoV)-based congestion detection and avoidance (IoV-based CDA). The proposed IoV-based CDA uses a parametric approach for congestion detection, while rerouting (using modified EG-Dijkstra [35; 36]) for congestion avoidance in a given area of interest (AOI). In our study, we considered Alwaha district intersection in Riyadh, Saudi Arabia.