# Internet of Drones: Routing Algorithms, Techniques and Challenges
## 3 Architecture of IoD
### Architecture of IoD  
A WSN is a sensor network is considered as a collection of sensors that is wirelessly controlled by the main network and/or it provides wireless transmission and reception of data from the network nodes called sensor nodes [49]. A WSN can be of any type, ranging from Wi-Fi and WiMAX to optical networks. The main purpose of a WSN is wireless data dissemination. A UAV-based WSN, i.e., IoD, operates on the same principal with the addition of a drone [50]. Figure 3: Application-specific drone models. Cluster-Based IoD Network  
In literature, drones are used as data or ferry nodes whose main job is to collect data across domains through a TCP based network channel [2]. Drones are deployed in payload delivery, traffic monitoring, mobility in dangerous environments, and surveillance. Three-dimensional algorithms are used for path planning of drones. Computational intelligence is used to form paths for drones. Accordingly, people have incorporated the use of drones in their daily lives. In the past few years, the use of drones in wireless sensor networks has begun and is under vast study. New products are formed as the technology improves. The integration of new products with the old system becomes an unavoidable deployment issue. This issue is addressed with a thorough survey of the development and integration of new or familiar solutions by technologies under study. In order to address the UAV routing problems, we must first define the problems and then provide the challenges faced by UAV and WSN environments. Clustering conserves the energy of WSN as a solution to achieve stability, scalability, and effective resource utilisation and allocation. Clustering can make groups of nodes to improve the efficiency of the WSN. The main objectives of a CH are processing and transferring of the member's data. One or more base stations (BS) are deployed as gateways or data processing nodes. The solution to drone data dissemination problems is mainly clustering. Dividing a WSN into 'clusters' is one of the techniques that improve the topology management. Clustering ensures good quality of service (QoS), efficient resource consumption, load balancing, and optimisation. The cluster heads (CHs) belong to each cluster and gather data from other \"members\". The combined data are then sent to the base station directly or indirectly with the help of middlemen nodes. Grouping of the nodes makes clustering an efficient aspect of WSN. This helps to manage resources and configure responsibilities in a fair network. Drones receive a plethora of data from the sensor nodes. How that data are recorded and processed is the backbone work of the BS. The drone identifies the data collection nodes, the paths, and the data to the sink, whereas the BS strives for network connectivity, in addition to every WSN requiring a routing protocol. Drones reach CH and transfer information as required by the sensor nodes. The authors in [51] state that clustering helps to lessen the problems faced by IoDs due to formation of sensor nodes and cluster heads. The CHs communicate with other CHs and also with the sensor nodes. The drones interact with CHs and the BS. The energy of the drones is conserved in this manner and the life span of a drone is increased. Efficient resource allocation is also possible because of clustering. Recent years have seen advancement in smart cities, industries, and IoT applications. Another key challenge faced by IoDs is topology management, important for ad-hoc networks as well. Greater number of nodes poses a problem that can be mitigated by hop-by-hop transmission of data and recognition of best 'neighbours' for transmission. The main role of clustering in an IoD environment is the grouping of nodes done by Voronoi diagrams. Each network is divided into 2D or 3D sections, each having a number of sensor nodes. Figure 4 demonstrates a generic UAV based WSN cluster and the path of a UAV. The UAV path is determined by routing algorithms. Hence computational intelligence is the crux of the UAV path formation.