# Traffic Congestion Detection System through Connected Vehicles and Big Data
## 2 Related Work
Regarding data privacy and security challenges, several authors have addressed these topics and have implemented schemes and policies to encrypt and protect data source confidentiality [29; 30; 31; 32]. Importantly, [33] proposes a novel approach ensuring privacy by preserving the centralized computation of moving objects. Additionally, the [PERSON] approach utilizes asymmetric cryptography using two keys in conjunction with Shamir's secret. This cryptographic approach guarantees data security and preserves the data source privacy. Figure 1: Big data and the IoT represented by hype cycle curve (source: elaborated by authors, based on [20]). On the other hand, the IoT is presently the object of significant research as it will be implemented on a large scale within the next decade or so. Presently, several investigations have developed IoT applications for natural disasters [34; 35], generalized and specialized industries [36; 37; 38], smart homes [39; 40; 41], medicine [42; 43; 44], agriculture [45; 46; 47], urban planning and intelligent cities [48; 49; 50] and the design of Intelligent Transport Systems (ITS) [51; 52; 53]. In the fields of ITS and smart cities, allowing vehicles to be interconnected, forming vehicular ad-hoc networks (VANETs), has enormous applications related to safety, internet connectivity, entertainment, file transfer and importantly, the detection and avoidance of dangerous or problematic traffic conditions [54]. Although VANETs have several applications, scalability and data aggregation represent two of their major challenges. Their large number of mobile nodes create a high volume of location querying and updating of messages, which often cause network congestion. Additionally, the large volume of data that each node shares with others causes a significant throughput decrease as the network adds new nodes, thus affecting network performance [55]. The authors of [55; 56] concur that the end-to-end throughput available for each node tends to zero as the VANET grows, regardless of the routing protocol. In [55], [PERSON] affirms that data from multiple nodes can be combined using data aggregation techniques to ensure the scalability of protocols, even if the number of nodes significantly increases. Importantly, although data aggregation is often used in static Wireless Sensor Networks applications [57; 58; 59], they are increasingly being applied to VANETs [60; 61; 62]. Data aggregation techniques can be classified as either syntactic or semantic [62]. In the syntactic aggregation approach, vehicular data from several nodes are compressed into one package to reduce overhead. In semantic aggregation, the data from each node is summarized to report vehicular group location, forwarding smaller data packets, although with some accuracy loss. Because traffic congestion represents one of the most important problems in urban environments, several researches have proposed possible solutions through VANETs and ITS. The AUTOPIA system, for example, proposes reducing traffic by implementing a control algorithm based on Fuzzy, which takes into account the information of each vehicle through vehicle-to-infrastructure communication (V2I) [63]. The authors of [64; 65], propose helping solve traffic congestion problems by managing traffic lights, by altering the frequency and timing of the traffic light changes based on the number vehicles on each street. A video camera is used to detect vehicles and count them [66], an algorithm then detects the real-time traffic flow and possible traffic congestion. Other work carried out in [67] addresses the issue of vehicular traffic through prediction based information from cellular networks, proposing models to statistically correlate network information to incidents or events of vehicular traffic. In [68], the authors propose using a unique EPC (Electronic Product Code) to identify vehicles utilizing an RFID (Radio Frequency Identification) reader to read EPC code.