Behaviour and Vulnerability Assessment of Drones-enabled Industrial Internet of Things (IIoT)
Accessibility to industrial processes and direct obtaining of the desired services are the major facilities of Industrial Internet of Things (IIoT). IIoT covers crucial aspects of smart systems such as automation, keenly intellective setups, asset management, and user-industry collaboration. These user-industry setups are facilitated by modern era network technologies, which also include an immense dependency on drones as one of the on-demand components for amending the quality and maximizing the coverage. However, these kinds of network formations require precise operations of drones and their perpetual assessment. The existing studies have highlighted these issues but fail to provide the behavior as well as the vulnerability evaluations of drones-enabled IIoT. In addition, the existing studies are unable to provide state-wise verification of drones and do not recognize anomaly drones based on their behavior over varying properties. Further, the existing solutions lack facilities for including security policies which help in assessing the vulnerabilities with a higher accuracy. This paper fills this gap by using a novel Nlayered Hierarchical Context-Aware Aspect-Oriented Petri Net (HCAPN) model which not only evaluates the drone behaviour but also assesses it for potential vulnerabilities by the utilization of security policies. State-wise verification is performed for the proposed model along with a simulation study, which designates its paramountcy in providing low-complex and low-overheads based solution with a detection rate higher than 95% and accuracy as high as 99.9%. The proposed approach increases the probability of selecting a correct drone by 81.71% even in the case of a high number of failures.
EXISTING SYSTEM :
In this modern age of technology, we have made an attempt to propose an automated system that can be used in multiple applications like disaster management, agriculture fields, defence etc. The above sections give ample proof of the fact that drones acts as sources of both national and international technological up-rise. But all of these are based on single drone only, there is no system of swarm of drones as well as ground bots. The proposed system comprises of several cost-effective ground robots (bots) and UAVs. The bots are connected with each other through wireless technology and utilize the swarm like intelligence to be developed.
They can explore the path avoiding obstacles and move together to gather information from the environment through their associated sensors attached with them. This information is uploaded to the cloud and fetched by the Master Drone on requirement i.e. before issuing the necessary commands for executing a specific job. Similarly, the group of drones in the sky consists of UAVs with GPS and other sensors having different functionalities like capturing photos from different angles, or looking for stuck living body. They can collect information and send it to the Master Drone which can upload the data in the cloud as well.
PROPOSED SYSTEM :
This paper considers the drone-enabled communication between the user-side IoT and IIoT for direct handling of the user requests by the intended equipment of a particular industry. In order to provide trustworthy and secure communications, this article aims at assessing the behaviour of drones and dynamically identifying any potential vulnerability, which may lead to critical attacks in near future. The proposed approach uses N-layered Hierarchical Context- Aware Aspect-Oriented Petri Net (HCAPN) modeling which helps in formally verifying the drone-enabled IIoT through an easy to deploy strategy. The proposed solution supports the dynamic assessment of drone behaviour as well as the implementation of the security policies to identify any potential threats amongst the drones.
This paper proposes a novel N-layered Hierarchical Context- Aware Aspect-Oriented Petri Net (HCAPN) model which helps to evaluate the drone behaviour and identifies any potential vulnerability by the utilization of security policies. The proposed HCAPN model ensures identification of drones which may violate the operational conditions of the network and may expose the entire network to different types of cyberthreats. The evaluations suggest that the proposed approach provides low-complex and low-overheads based behavioural and vulnerability assessment model with a detection rate higher than 95% and accuracy as high as 99.9%.
The proposed approach also increases the probability of selecting a correct drone by 81.71% even in the case of a high number of failures. In addition, the results are presented for resource (memory and energy) extensions while connecting end users to IIoT devices, network offloading delays, state-wise outputs for all the drones, and oscillations of HCAPN behavior conditions. From the methodology and results, it is evident the proposed approach can be used as a benchmark for assessing networks which involve drones as a crucial entity.
In future, the proposed approach will be extended for direct inclusion of security aspects with the HCAPN model and focus will be given to the automation and the inclusion of communication procedures.