The Internet-of-drones (IoD) environment is a layered network control architecture designed to maintain, coordinate, access, and control drones (or Unmanned Aerial vehicles UAVs) and facilitate drones' navigation services. The main entities in IoD are drones, ground station, and external user. Before operationalizing a drone in IoD, a control infrastructure is mandatory for securing its open network channel (Flying Ad Hoc Networks FANETs). An attacker can easily capture data from the available network channel and use it for their own purpose. Its protection is challenging, as it guarantees message integrity, non-repudiation, authenticity, and authorization amongst all the participants. Incredibly, without a robust authentication protocol, the task is sensitive and challenging one to solve. This research focus on the security of the communication path between drone and ground station and solving the noted vulnerabilities like stolen-verifier, privileged-insider attacks, and outdated-data-transmission/design flaws often reported in the current authentication protocols for IoD. We proposed a hash message authentication code/secure hash algorithmic (HMACSHA1) based robust, improved and lightweight authentication protocol for securing IoD. Its security has been verified formally using Random Oracle Model (ROM), ProVerif2.02 and informally using assumptions and pragmatic illustration. The performance evaluation proved that the proposed protocol is lightweight compared to prior protocols and recommended for implementation in the real-world IoD environment.
In video surveillance, person tracking is considered as challenging task. Numerous computer vision, machine and deep learning–based techniques have been developed in recent years. Majority of these techniques are based on frontal view images/video sequences. The advancement of convolutional neural network reforms the way of object tracking. The network layers of convolutional neural network models trained on a number of images or video sequences improve speed and accuracy of object tracking. In this work, the generalization performance of existing pre-trained deep learning models have investigated for overhead view person detection and tracking, under different experimental conditions. The object tracking method Generic Object Tracking Using Regression Networks (GOTURN) which has been yielding outstanding tracking results in recent years is explored for person tracking using overhead views. This work mainly focused on overhead view person tracking using Faster region convolutional neural network (Faster-RCNN) in combination with GOTURN architecture. In this way, the person is first identified in overhead view video sequences and then tracked using a GOTURN tracking algorithm. Faster-RCNN detection model achieved the true detection rate ranging from 90% to 93% with a minimum false detection rate up to 0.5%. The GOTURN tracking algorithm achieved similar results with the success rate ranging from 90% to 94%. Finally, the discussion is made on output results along with future direction.
Client-server computing is the analytical development of compatible programming with significant supposition and the detachment of a massive program into its fundamental parts ("modules"), which can create the chance for extra enhancement, inconsiderable improvement, and prominent maintainability. In client-server computing, total extensive modules don't need to be accomplished within the similar memory space totally but can execute independently on a suitable hardware and software platform according to their behavior. The user authentication is the dominant constraint for client-server computing that limits the illegitimate right of entry into the main workstation. This research is mainly focused on the design of a robust authentication scheme for client-server architecture computing. It carries some additional features like security, virtualization, user's programs security, individuality supervision, integrity, control access to server and authentication. The proposed background also delivers the characteristic supervision, mutual authentication, and establishment of secure session key among users and the remote server.
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