## 2 Related Work
Authors in [17] discussed information gathering which is a significant problem in information-centric IoT networks. These networks ensured the Quality of Services (QoS) for network applications including weather monitoring and fog-haze monitoring. As a result, the coverage of nodes enhances the data receiving and sending capabilities. The proposed solution is a cost-effective and better for improving the coverage ratio. The information is acquired randomly in the prior cases and cause of converge reduction. As a result, optimizing coverage via UAVs reduces the cost for information-centric IoT networks. An approach for UAVs was developed to plan the flying routes to achieve greater coverage ranges and decrease the flight duration to save energy consumption by using distance as a priority and time as a second priority. Because of the need of ensuring coverage ratio, and because UAVs consume less energy, the platform benefits from employing them at a lower level. In [18], the authors proposed a T-SloTs \"Trust Data Collections via Vehicles\" solution by using UAVs nodes in smart IoT networks. This scheme was created by using the statistical graphical model to assess the trust completely. It aimed to create a trust-based system for collecting information by combining trusted vehicles and UAVs. The trust evaluation techniques are passive trusted methods where the trust values are computed and the objects interact. By choosing trustworthy vehicles as the next hop, the scheme ensured security. The T-SloTs technique aimed to reduce the traveling trajectories of UAVs to save energy. The placements of static nodes provided trustworthy nodes' mobility. The stored data in those stations were collected by using UAVs. The k-means technique was used to determine the accountable regions of each UAV. Secondly, the routing strategy determined the flight routes of each UAV to gather the data in the accountable regions. The routing strategy reduced the flight routes of each UAV, which minimized the energy usage of UAVs during data collection. The experimental results showed that it not only ensured trust but also saved nodes' energy. In [19], the authors proposed a data collection scheme by using blockchain where the UAVs' nodes securely collected the data related to healthcare. The collected data was related to the health of the users. The sensor nodes were attached to the patient bodies and then the collected data were transmitted to the UAVs' nodes. The acquired data were then stored at the nearest location on the server. In the proposed model, all the users and servers had to be registered with the system and each party must have had a public or private key for corresponding. After key generation, every device should have registered its own key and some basic information with a smart contract which was created by using blockchain. Body sensors encrypted the health data with the help of a shared key which was shared before the transformation of the data to the UAVs. The UAVs then used the same shared key to decrypt healthcare data. The UAVs then validated the health data with the help of hashing and digital signatures. On the arrival of the health data, the server requested other validators to give their acceptance for adding health data to the blockchain. Ad-hoc On-demand Distance Vector (AODV) protocol has also been used for UAV data communication. This protocol uses time slots to avoid congestion in the network. The overhead is observed in the Zone Routing Protocol (ZRP) whereas the Optimized Link State (OLS) routing has good performance [6] because of its proactive characteristics and the lowest delay due to the use of a border-casting mechanism. Dynamic source routing protocol have suffered with high network load issues due to area dimension, speed, and use of several UAVs nodes. The ZRP routing has the lowest network load when the number of UAVs is the same in constant network load. Because of the multi-point relay mechanism, the OLSR has less network load for the whole scenario.