Wireless sensor networks (WSNs) are usually deployed to different areas of interest to sense phenomena, process sensed data, and take actions accordingly. The networks are integrated with many advanced technologies to be able to fulfill their tasks that is becoming more and more complicated. These networks tend to connect to multimedia networks and to process huge data over long distances. Due to the limited resources of static sensor nodes, WSNs need to cooperate with mobile robots such as unmanned ground vehicles (UGVs), or unmanned aerial vehicles (UAVs) in their developments. The mobile devices show their maneuverability, computational and energy-storage abilities to support WSNs in multimedia networks. This paper addresses a comprehensive survey of almost scenarios utilizing UAVs and UGVs with strogly emphasising on UAVs for data collection in WSNs. Either UGVs or UAVs can collect data from static sensor nodes in the monitoring fields. UAVs can either work alone to collect data or can cooperate with other UAVs to increase their coverage in their working fields. Different techniques to support the UAVs are addressed in this survey. Communication links, control algorithms, network structures and different mechanisms are provided and compared. Energy consumption or transportation cost for such scenarios are considered. Opening issues and challenges are provided and suggested for the future developments.

Unmanned aerial vehicles (UAVs) have been widely deployed in many applications such as transportation, data collection, monitoring, or tracking objects. Nowadays, numerous missions require UAVs to operate in a large area or to complete missions in a stringent period of time. Using a single UAV may not meet the performance requirements because of its small size and limited battery. In this situation, multiple Unmanned Aerial Vehicles (UAVs) have emerged as an effective measure that can address these limitations. A group of UAVs cooperatively working together could offer a solution that is more efficient and economical than using a powerful UAV alone. To better utilizing the multiple-UAVs system, control of formation UAVs is a critical challenge that needs to overcome. Therefore, formation control has become an active research topic that gains great attention from researchers. Extensive research efforts have been dedicated to studying the formation control problem with numerous control protocols which have been proposed. This paper reviews the profound studies on formation control in literature. Each approach is investigated based on different criteria, which highlights its distinct merits and demerits. The comparison is provided to facilitate the readers in their future researches in the field of formation control. Finally, some open challenges and research directions are also discussed.

The vehicular network is taking great attention from both academia and industry to enable the intelligent transportation system (ITS), autonomous driving, and smart cities. The system provides extremely dynamic features due to the fast mobile characteristics. While the number of different applications in the vehicular network is growing fast, the quality of service (QoS) in the 5G vehicular network becomes diverse. One of the most stringent requirements in the vehicular network is a safety-critical real-time system. To guarantee low-latency and other diverse QoS requirements, wireless network resources should be effectively utilized and allocated among vehicles, such as computation power in cloud, fog, and edge servers; spectrum at roadside units (RSUs); and base stations (BSs). Historically, optimization problems have mostly been investigated to formulate resource allocation and are solved by mathematical computation methods. However, the optimization problems are usually nonconvex and hard to be solved. Recently, machine learning (ML) is a powerful technique to cope with the complexity in computation and has capability to cope with big data and data analysis in the heterogeneous vehicular network. In this paper, an overview of resource allocation in the 5G vehicular network is represented with the support of traditional optimization and advanced ML approaches, especially a deep reinforcement learning (DRL) method. In addition, a federated deep reinforcement learning- (FDRL-) based vehicular communication is proposed. The challenges, open issues, and future research directions for 5G and toward 6G vehicular networks, are discussed. A multiaccess edge computing assisted by network slicing and a distributed federated learning (FL) technique is analyzed. A FDRL-based UAV-assisted vehicular communication is discussed to point out the future research directions for the networks.

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Recently, unmanned aerial vehicles (UAVs) enhance connectivity and accessibility for civilian and military applications. A group of UAVs with on-board cameras usually monitors or collects information about designated areas. The UAVs can build a distributed network to share/exchange and to process collected sensing data before sending to a data processing center. A huge data transmission among them may cause latency and high-energy consumption. This paper deploys artificial intelligent (AI) techniques to process the video data streaming among the UAVs. Thus, each distributed UAV only needs to send a certain required information to each other. Each UAV processes data utilizing AI and only sends the data that matters to the others. The UAVs, formed as a connected network, communicate within a short communication range and share their own data to each other. Convolution neural network (CNN) technique extracts feature from images automatically that the UAVs only send the moving objects instead of the whole frames. This significantly reduces redundant information for either each UAV or the whole network and saves a huge energy consumption for the network. The UAVs can also save energy for their motion in the sensing field. In addition, a flocking control algorithm is deployed to lead the group of UAVs in the working fields and to avoid obstacles if needed. Simulation and experimental results are provided to verify the proposed algorithms in either AI-based data processing or controlling the UAVs. The results show promising points to save energy for the networks.