A drone-based data management and optimization using metaheuristic algorithms and blockchain smart contracts in a secure fog environment

Abstract

The advent of fog computing as an extension of cloud-enabling technology has moved from the hub of the internet framework to the unmanned aerial vehicle (UAV)-enabled control and management of drone-based data. The key objectives are to improve the computation and processing of drone-based resource constraints and deliver the data to the outsourced computational node to schedule, process, manage, optimize, and preserve it since they are located close to each other through a wireless sensor network. This collaborative technological approach to robustness is the standard of drone system design and implementation, reducing the computational power, resource consumption, and latency for the application that needs a fast response. However, recent research into fog-enabled drone-based data management and optimization poses a serious challenge in terms of privacy, security, and preservation. On the other hand, Blockchain Hyperledger Technology, which is mostly used in Bitcoin Cryptocurrencies, has been applied in a large number of distributed applications due to the ledger integrity, transparency, trustworthiness, provenance, reliability, availability, protection, and security-related distributed features that are provided. Therefore, the distributed application of drone control has received more attention because of its ability to handle collaborative procedures to capture, schedule, process, optimize, manage, and preserve drone-based data using fog nodes and blockchain hyperledger technology. In this paper, we proposed a collaborative approach using blockchain hyperledger fabric and a metaheuristic-enabled genetic algorithm for fog node management called B-Drone. This approach handles drone-based data collection, scheduling, optimizing, processing, managing, and preservation in a secure manner in the fog node. The protection of each transaction between drone and fog node before exchange is private with the utilization of the hash-encryption (SHA-256) algorithm. The blockchain smart contracts are implemented and deployed to auto-manage all the connectivity and communication protocols between drones and fog nodes in the designed private permissioned network. The simulations of the proposed collaborative approach reduce the computing cost down to 12.03% and increase the performance up to 73.11%, thus decreasing the cost of drone-ledger preservation down to 30.13% and the robust usage of the network up to 54.29% as compared to other state-of-the-art methods .

Introduction

Over the last decade, Unmanned Ariel Vehicles (UAVs) with cameras have become a widely adopted technology to monitor geographical areas, environmental and agricultural inspections, and other-related real-time surveys. The capturing of drone-based ariel images is widely used in different fields for different purposes, such as monitoring of land surface changes or for further investigation [1]. The drone-enabling cameras can capture high-quality images, which require large-scale memory consumption. Improving the consumption of resources under constraints has become a key challenge in academic research. However, since the drones are distributed and controlled by fog nodes while capturing ariel images of the urban environment, it leads to an obvious problem in the procedure of high-quality large image (panoramic) capturing, scheduling, processing, optimizing, managing, and preserving in storage, which will be further investigated for land surface changes [1,2]. The role of capturing is a vital objective performed through the different connected drones with the base station, where the fogs are designed to execute multiple processes. For this purpose, a pre-flight preparation strategy is applied, in which a checklist is defined that reduces the risk of crashing and increases the trust in the success of large-scale image capturing during flight. It covers the whole range of land monitoring. As a result, in the latest design, providing a much more efficient and accessible platform for users, particularly those who investigate or dynamic monitor through the drone, is less expensive [3]. capturing and defogging is a key concept that is widely adopted and used in the remote sensing, monitoring, and computing vision domains for extracting more accurate results [4]. During the flight, the impact of natural changes is one of the other challenging aspects, such as cloudy weather, rain, and wind. In this manner, the captured images are prone to the attenuation of the features of collected records; it also damages the integrity and transparency because of the distortion. In image processing, various machine learning and metaheuristic algorithms have been proposed that require images to be processed and optimized under perfect environmental conditions [5,6].

However, the fogs are deployed to process the offload data of drones. The traditional ariel image-based data capture method necessitates the offloading of the drone to begin examination and analysis, which must be repeated until all monitoring areas have been traversed [7]. This procedure consumes more computational power and costs more during data transmission. It is also evaluated that the high delay sensitivity occurs during the data processing. Furthermore, current drone-based monitoring applications necessitate quick response times to generate and handle triggers for land surface monitoring. Thus, the wireless network sensors are designed to manage the sensor nodes' transactions and maintain a bridge between the drones and fog nodes for efficient communication [8,9]. As shown in Fig. 1, the base station is an area where different nodes are connected to schedule drone-based ariel capturing, compute, analysis, optimize, manage, and store, as shown in Fig. 1. It acts as a broker node and is deployed at the edge of the network. The drone-fog-based resource paradigm is designed into three different categories, such as resource allocation and workload distribution, exchanging, and provision [10]. The key assumption is that we exchange available resources effectively and efficiently, which enables services in a low-latency environment for the drones [11].

Through the conventional data collection architectures of fog-enabling drones, the image-based dynamic data is captured, scheduled, and processed in different batches, which increases delay [12,13]. For this reason, there is a need to introduce a distributed architecture for fog-enabled drone configuration in terms of large-scale image capturing, transmission to the base station, processing, optimizing, preserving, and managing in a secure environment in parallel. While the drone collects data on land surface from various perspectives (angles of the surface) and covers (on average a mile) defined urban zones, it dynamically transmits it to a nearby base station via a wireless sensor network. For data scheduling, pre-processing is the initial step to restore the actual pixels and size of the image and continue capturing until it covers the whole. This concurrent process of data capturing, processing, and then preservation reduces the delay that enhances the latency, throughput, response, and duty cycle of applications for urban land surface monitoring.

Recently, the increased number of blockchain distributed ledger adaptations has registered in almost every domain of business/enterprise, such as logistics, supply chain, forecasting, banking, healthcare, etc. The goal of the acquisition is to secure their ledgers while sharing information in the network for real-time processing and delivery. Fog-enabled drone-based data management requires privacy and security; in particular, it must achieve integrity, confidentiality, transparency, provenance, traceability, and availability, along with trustworthiness through the distributed applications [14]. In drone-based land surface data management and monitoring, the blockchain is the solution that aids in the privacy, protection, security, and immutable preservation of fog-enabling monitoring information. Further, it allows a transparent investigation platform to analyze the rate of changes just because of its immutability functionality. However, the blockchain hyperledger technology has been envisioned as being highly adaptive and utilized by different environments for various purposes, such as land monitoring and surface change investigation. To achieve security of the ledger, protected analysis, and transparency in the collected data, the provenance of the ecosystem for real-time processing is required [15]. For the sake of ledger privacy and security, the metaheuristics and artificial intelligence (AI) experts are moving to the blockchain-enabled platform for the sake of ledger privacy and security. Not only that, but they also shifted because of the distributed private permissioned network architecture, protected preservation, and secure channels for data transmission that are provided. One of the most important features of blockchain is to strategy information security and retain its integrity with the help of hyperledger permissioned private channels, which protect the ecosystem against a variety of malicious attacks, especially distributed denial of service (DDoS) [16]. It also improved the nodes' defense capability with the use of the hash-encryption (SHA-256) algorithm and the deployment of intrusion detection.

This paper addresses the detailed design of a secure fog-enabled drone-based data management and optimization using metaheuristic genetic algorithms and blockchain hyperledger Indy. In this distributed architecture, the process of drone-based data collection is defined, in which initially image-based data is captured through the drone's moving cameras and then transmitted towards the base station via a wireless sensor network. Fog nodes receive data and schedule each record for processing; after this, critical features are extracted and optimized with the help of a genetic algorithm, and the complied information is preserved in the immutable ledger for further investigation. This procedure provides an information management facility with complete ledger integrity, provenance, traceability, transparency, and assurance for performing different operations of fog-enabled drone-based data management, especially processing and optimization. Therefore, it creates trust between the drones and fog nodes while data is captured, transmitted, and interpreted among connected stakeholders, such as blockchain hyperledger indy engineers, data analysts, and investigational teams. The comprehensive batch ensures the overall execution of events of node transactions in a secure and protected (using SHA-256) manner.

The major contributions of this research are discussed as follows:

• This paper discusses the detailed design of the current lifecycle of drone-based data management and optimization. Further, we highlight the role of fog nodes for design an efficient processing, networking and cost-effective data preservation solutions.

• In this paper, we proposed a distributed architecture named "B-Drone." An Indy blockchain, called Hyperledger Indy, collaborates with a metaheuristic-enabled modular infrastructure for creating a secure process of fog nodes. And so, the integrated prospect manages and optimizes drone-based data, which are being developed.

• A fog-enabled drone-based data management proof-of-work consensus is designed and created and compared with the built-in/pre-define consensus of blockchain hyperledger Indy. The proposed B-Drone consensus reduces the number of resources that are used in terms of computational power, network bandwidth, and storage by a lot.

• Chain codes (Smart contracts) are designed, created, and deployed to automate the verification and validation of drone registration, authentication, the process of data collection, ledger transmission, adding new records to the immutable storage, and exchanging investigation-related updates.

• Finally, we present the implementation challenges and limitations we faced while working on the real-time deployment. A few solutions to the problems involved are discussed in this paper.

The remainder of this research is organized as follows. In Section 2, various blockchain, hyperledger, metaheuristic, fog node management, and drone control-related literature are studied. And so, we will discuss the current implementation challenges and limitations in fog-enabled drone data management, optimization, preservation, privacy, and security. Section 3 presents a detailed description of the problem and the procedure to control the operations of drones to manage data in the fog node. The detailed design of the proposed collaborative approach is presented along with the working operations and smart contracts in Section 4. Section 5 illustrates the simulations of the proposed approach with the implementation challenges. Finally, we conclude this paper in Section 6.