The Internet of Things (IoT) and the Android operating system have made cutting-edge technology accessible to the general public. These are affordable, easy-to-use, and open-source technology. Android devices connect to different IoT devices such as IoT-enabled cameras, Alexa powered by Amazon, and various other sensors. Due to the escalated growth of Android devices, users are facing cybercrime through their Android devices. This article aims to provide a comprehensive study of the IoT and Android systems. This article classifies different attacks on IoT and Android devices and mitigation strategies proposed by different researchers. The article emphasizes the role of the developer in secure application design. This article attempts to provide a relative analysis of several malware detection methods in the different environments of attacks. This study expands the awareness of certain application-hardening strategies applicable to IoT devices and Android applications and devices. This study will help domain experts and researchers to gain knowledge of IoT systems and Android systems from a security point of view and provide insight into how to design more efficient, robust, and comprehensive solutions. This article discusses different attack vectors and mitigation strategies available to both developers and in the open domain. Certain guidelines are also suggested for application and platform developers, as well as application databases (Google play store), to limit the risk of attack, and users can form their own defense with knowledge regarding keeping hardware and software updated and securing their system with a strong password.

Mobile Ad hoc Networks (MANETs) are intended to work without a fixed framework and provide dependable interchanges to ground vehicles, boats, airplanes, or people and structure a self-mending process that will empower persistent correspondences in any event, when at least one of its nodes are debilitated or briefly expelled from the system. Notwithstanding, MANETs demonstrate themselves to be progressively harder to create for enormous systems with hundreds or thousands more nodes than initially envisioned. In our proposed technique, the node switches its communication mode depending on the connectivity of the adjacent nodes. The transmission power of each node will be calculated with the help of two major scenarios i.e., tree scenario and zone scenario. The autonomous clustering of the nodes among the tree and the zone scenario will be channelized by a comparison of the transmission power (residual energy) among the nodes. The inter and the intra communication of the node is also discussed in the paper. The result will be carried out by the simulation work in various perspectives, such as checking the percentage level of malicious nodes, traffic density, transmission power, and the longevity of nodes.