The integration of the Internet of Things (IoT) in our lives has become reality during the last decade. Mostly, IoT is considered a system of systems, where sensors make a big chunk of any IoT communication system. Similarly, in this paper, we introduce a notion of the Internet of Underwater Things (IoUT), where we consider a set of sensors deployed in the deep sea to form a communication network for any possible application such as military, environmental, pollution surveillance, etc. Since it is too optimistic to recharge or replace sensor batteries in an underwater environment, efficient management of the available resources can extend the overall network lifetime. To do so, nodes clustering is a potential solution, however, exchange of multiple packets for cluster head selection requires a considerable amount of energy. In this work, we present an Autonomous Unmanned Vehicles (AUVs) assisted Energy-efficient Clustering (AEC) mechanism that introduces wake-up sleep cycle for the underwater nodes. AUV onus includes cluster creation, the cluster head nomination, and creation of a wakeup sleep schedule that relieves the additional burden from energy limited underwater sensor nodes. Due to no additional packets been exchanged, our protocol outperforms traditional clustering schemes and we validate this through simulations.

The Internet of Engineering Task (IETF) developed a lighter application protocol (Constrained Application Protocol (CoAP)) for the constrained IoT devices operating in lossy environments. Based on UDP, CoAP is a lightweight and efficient protocol compared to other IoT protocols such as HTTP, MQTT, etc. CoAP also provides reliable communication among nodes in wireless sensor networks in addition to features such as resource observation, resource discovery, congestion control, etc. These capabilities of CoAP have enabled the implementation of CoAP in various domains ranging from home automation to health management systems. The use of CoAP has highlighted its shortcomings over the time. To overcome shortcomings of CoAP, numerous enhancements have been made in basic CoAP architecture. This survey highlights the shortcomings of basic CoAP architecture and enhancements made in it throughout the time. Furthermore, existing challenges and issue in the current CoAP architecture are also discussed. Finally, some applications with CoAP implementation are mentioned in order to realize the viability of CoAP in real world use cases.

Vehicular Ad-hoc Networks (VANETs) are an essential part of the Intelligent Transportation System (ITS). VANETs promise to offer drivers and passengers safety, traffic efficiency, and infotainment services. To this end, VANETs require vehicles to periodically broadcast unencrypted beacon messages that contain position, velocity, timestamp, and identity. Consequently, VANETs naturally expose users' identities and locations, facilitating tracking by an adversary. To address VANETs location privacy issues, researchers have proposed different pseudonym-based privacy-preserving techniques in the literature. One of the techniques is pseudonym swap. In pseudonym swap techniques, vehicles exchange pseudonyms with each other hence fewer pseudonyms for each vehicle since vehicles can reuse pseudonyms. However, existing pseudonym swap-based schemes either exchange pseudonyms via Roadside Units (RSUs) or ignore VANETs' security requirements by not informing the Certification Authority of the exchanges. Hence, they suffer from high RSUs deployment costs, inflexibility to location privacy preservation, unaccountability, and repudiation attacks. Moreover, the lower level of location privacy provided by the schemes demands further improvements. Therefore, we propose a novel pseudonym swap scheme that uses swappable and non-swappable pseudonyms to attain infrastructure independence while adhering to VANET security requirements. In this scheme, vehicles, independent of RSUs, cooperatively swap and change pseudonyms within mix-contexts having a high level of location privacy. Each exchange is then reported to the authority to enforce accountability and non-repudiation. Besides adhering to VANET security requirements, the simulation experiments show that our scheme outperforms existing pseudonym swap schemes, providing VANET users with a high level of location privacy.

Unmanned Aerial Vehicles (UAVs) are abundantly becoming a part of society, which is a trend that is expected to grow even further. The quadrotor is one of the drone technologies that is applicable in many sectors and in both military and civilian activities, with some applications requiring autonomous flight. However, stability, path planning, and control remain significant challenges in autonomous quadrotor flights. Traditional control algorithms, such as proportional-integral-derivative (PID), have deficiencies, especially in tuning. Recently, machine learning has received great attention in flying UAVs to desired positions autonomously. In this work, we configure the quadrotor to fly autonomously by using agents (the machine learning schemes being used to fly the quadrotor autonomously) to learn about the virtual physical environment. The quadrotor will fly from an initial to a desired position. When the agent brings the quadrotor closer to the desired position, it is rewarded; otherwise, it is punished. Two reinforcement learning models, Q-learning and SARSA, and a deep learning deep Q-network network are used as agents. The simulation is conducted by integrating the robot operating system (ROS) and Gazebo, which allowed for the implementation of the learning algorithms and the physical environment, respectively. The result has shown that the Deep Q-network network with Adadelta optimizer is the best setting to fly the quadrotor from the initial to desired position.