Summary Internet of Things (IoT) offers various types of application services in different domains, such as “smart infrastructure, health‐care, critical infrastructure, and intelligent transportation system.” The name edge computing signifies a corner or edge in a network at which traffic enters or exits from the network. In edge computing, the data analysis task happens very close to the IoT smart sensors and devices. Edge computing can also speed up the analysis process, which allows decision makers to take action within a short duration of time. However, edge‐based IoT environment has several security and privacy issues similar to those for the cloud‐based IoT environment. Various types of attacks, such as “replay, man‐in‐the middle, impersonation, password guessing, routing attack, and other denial of service attacks” may be possible in edge‐based IoT environment. The routing attacker nodes have the capability to deviate and disrupt the normal flow of traffic. These malicious nodes do not send packets (messages) to the edge node and only send packets to its neighbor collaborator attacker nodes. Therefore, in the presence of such kind of routing attack, edge node does not get the information or sometimes it gets the partial information. This further affects the overall performance of communication of edge‐based IoT environment. In the presence of such an attack, the “throughput of the network” decreases, “end‐to‐end delay” increases, “packet delivery ratio” decreases, and other parameters also get affected. Consequently, it is important to provide solution for such kind of attack. In this paper, we design an intrusion detection scheme for the detection of routing attack in edge‐based IoT environment called as RAD‐EI. We simulate RAD‐EI using the widely used “NS2 simulator” to measure different network parameters. Furthermore, we provide the security analysis of RAD‐EI to prove its resilience against routing attacks. RAD‐EI accomplishes around 95.0% “detection rate” and 1.23% “false positive rate” that are notably better than other related existing schemes. In addition, RAD‐EI is efficient in terms of computation and communication costs. As a result, RAD‐EI is a good match for some critical and sensitive applications, such as smart security and surveillance system.

The increased usage of directional methods of communications (e.g. directional smart antennas lIS], Free Space Optical transceivers lI9], and sector antennas) has prompted research into leveraging directionality in every layer of the network stack. In this paper, we learnt how the concept of directionality can be used in layer 3 to facilitate routing under contexts of I) wireless mesh networks, 2) highly mobile environments, and 3) overlay networks through virtual directions. In the context of wireless mesh networks, we introduce Orthogonal Rendezvous Routing Protocol (ORRP), a lightweight-but-scalable routing protocol utilizing the inherent nature of directional communications to relax information requirements such as coordinate space embedding and node localization. The ORRP source and ORRP destination send route discovery and route dissemination packets respectively in locally chosen orthogonal directions. We show that ORRP achieves connectivity with high probability even in sparse networks with voids. ORRP scales well without imposing DHT-like graph structures (eg: trees, rings, torus etc). We show that MORRP achieves connectivity with high probability even in highly mobile environments while maintaining only probabilistic information about destinations. MORRP scales well without imposing DHT like graph structures (eg: trees, rings, torus etc). We will also show that high connectivity can be achieved without the need to frequently disseminate node position resulting increased scalability even in highly mobile environments. We will also evaluate the metrics of reach ability, state maintenance, path stretch, end-to-end latency and aggregate network good put under conditions of varying network densities, number of interfaces, and TTL values Keywords -Network Simulation (NS2), Mobile Adhoc Networks (MANE1), Orthogonal Rendezvous Routing Protocol (ORRP) 1) Address assumptions made by ORRP including hardware requirements and other cross-layer abstractions.