Heuristics to Secure IoT-Based Edge-Driven UAV

Introduction

The internet of things (IoT) is seen as one of the next Internet revolutions. More and more IoT devices are connecting to the Internet. These devices sense the physical world and perform some tasks in response to events occurring. Such devices are identified through unique IP addresses and often have to send data which might be sensitive and confidential over the network. Thus, the security of the data packets sent by IoT devices over the Internet has to be considered.

In recent times the growth rates of unmanned aerial vehicles (UAV) population have augmented in every sector of the IoT sector. UAV are incorporated into numerous modern IoT applications. This growing number of UAV generates the big data. However processing this large data at several edges becoming a new critical challenge. As per the Cisco report more than 70 billions of applications, organizations and the government sectors get connected by 2025 (Ara,2017) Since past five past five years this new autonomous vehicle almost covered smartly the every industrial sector. Due to its flexibility and portability it attracts various IoT applicatons with its superior features which offers omnipresent IoT applications making sensible to scalable architecture. One such proven case seen in COVID-19 pandemics. UAVs aids in sustaining the modernization such as smart cities, smart farming, smart factories, smart grids, and intelligent transportations. Although the communication protocols still under development for tracking and maneuverability of drones to contribute the critical in communication network which guarantees the reliability (Guizani,2018), primarily they made flexible communication services in remote areas where no interconnections with existing networks or lost the links. These days with the IoT sensors to auto connect UAVs used at the edges in cellular networks . Before the IoT networks enable the collects the data at centralized clouds. Though the centralized systems are easy to deploy but they are constrained to the high failures risks where the data can be easily leaked and also the delays in accessing the data. Due to the latency seen in data processing it causes the latency-sensitive applications. The Decentralized and distributed solutions have emerged to overcome with such issues but the IoT devices should be carefully owned to increase the efficiency .Since the UAVs have been deployed at network edges to store and process the data from IoT ecosystems. Considering the power consumption in fixed architecture UAV proven to be best solution with ultra low latency and flexible support in big data processing improving the scalability and management of massive content caching and delivery (Vouyioukas,2022). This addressed the great advantage by embedding the UAVs into IoT edges and deployed in the unpredictable environments such as in wild, desert, and complex landscapes. Moreover this combination aids the traditional fixed edge system in a cheaper way but the computation would be adapted by distance travelled by UAVs.

But when the IoT hardware devices are attached at the edges there will be arising the edge device hardware security. It is not possible only with software, hardware can’t withstand enough all the security that baked into the edge runtime will still not yield a secure solution. Security is a continuing journey. So, the first step is to analyze the right security approach for the esdge security, but also evolve the approach that require the more and more lexible design and architecture. The blockchain is one of the approach in sustaining the security levels for the IOT driven UAV edges (Fotouhi,2020). The UAV store their data in a chain of blocks so that if any one block hijacked others remains safe. Though the government agencies also now involved with the 5G infrastructure to deploy such architecture where the UAV are under the control of the government agencies following their control. With such an underlying solution UAVs which are at the edges still suffering from the many performance aspects such offloading, energy consumption, resource allocation, computing capacity, and user latency performance under various channel circumstances.