The rapid growth of the Internet of Things (IoT) applications and their interference with our daily life tasks have led to a large number of IoT devices and enormous sizes of IoT-generated data. The resources of IoT devices are limited; therefore, the processing and storing IoT data in these devices are inefficient. Traditional cloud-computing resources are used to partially handle some of the IoT resource-limitation issues; however, using the resources in cloud centers leads to other issues, such as latency in time-critical IoT applications. Therefore, edge-cloud-computing technology has recently evolved. This technology allows for data processing and storage at the edge of the network. This paper studies, in-depth, edge-computing architectures for IoT (ECAs-IoT), and then classifies them according to different factors such as data placement, orchestration services, security, and big data. Besides, the paper studies each architecture in depth and compares them according to various features. Additionally, ECAs-IoT is mapped according to two existing IoT layered models, which helps in identifying the capabilities, features, and gaps of every architecture. Moreover, the paper presents the most important limitations of existing ECAs-IoT and recommends solutions to them. Furthermore, this survey details the IoT applications in the edge-computing domain. Lastly, the paper recommends four different scenarios for using ECAs-IoT by IoT applications.

Ad hoc networks are vulnerable to numerous attacks due to its infrastructure-less nature, one of these attacks is the Sybil attack. Sybil attack is a severe attack on vehicular ad hoc networks (VANET) in which the intruder maliciously claims or steals multiple identities and uses these identities to disturb the functionality of the VANET network by disseminating false identities. Many solutions have been proposed in order to defend the VANET network against the Sybil attack. In this research a hybrid algorithm is proposed by combining footprint and privacy-preserving detection of abuses of pseudonyms (P2DAP) methods. The hybrid detection algorithm is implemented using the ns2 simulator. The proposed algorithm works as follows. P2DAP acts better than the footprint algorithm when the number of vehicles increases. On the other hand, the footprint algorithm acts better when the speed of vehicles increases. The hybrid algorithm depends on encryption, authentication and on the trajectory of the vehicle. The scenarios will be generated using SUMO and MOVE tools.