Drone-based Surveillance Research Articles

From Battlefield to Border: The Evolving Use of Drones in Surveillance Operations

In recent decades, drone technology has undergone rapid advancements, making it a vital tool in various surveillance operations. Initially limited to the battlefield, the use of drones has now expanded to various civilian applications, including border monitoring, law enforcement, and environmental surveillance. This shift is driven by enhancements in drone capabilities, including extended range, endurance, and sensor technology. This study employs a qualitative approach using case studies to analyze the evolution of drone usage in surveillance operations. Data were collected through literature reviews, interviews with industry experts, and analysis of reports from security and defense agencies. The study also compares the effectiveness of drone-based surveillance operations with traditional methods through statistical analysis and field operational evaluations. The findings indicate that drone usage significantly enhances the efficiency and effectiveness of surveillance operations. Drones enable wider area coverage at lower costs and reduced risks compared to conventional methods. Additionally, drones equipped with advanced sensors facilitate more accurate and real-time data collection, which is crucial in critical security situations. The study also identifies major challenges in drone usage, including regulatory issues, privacy concerns, and the integration of technology with existing systems. The evolving use of drones in surveillance operations shows great potential in enhancing security and efficiency across various sectors. However, a balanced approach between technological innovation and regulatory frameworks is necessary to address existing challenges and maximize the benefits of this technology.

Fractal Inspired Hybrid Microstrip Patch Antenna for Surveillance Drone Applications

Drone based surveillance and communication systems are playing a vital role in modern days for both military and civilian applications. Antennas with low profile, smaller size, and light weight are essential for the realization of these systems. A novel fractal-inspired hybrid microstrip patch antenna realized with Sierpinski gasket fractal slots cut in the radiating patch along with corresponding ground structure simultaneously defected with Sierpinski carpet fractal slots is reported for the first time. The antenna resonates at 2.4 GHz with a measured return loss of 17 dB, gain of 7 dB and measured 3 dB beamwidth is of the order of 70 deg. The current approach leads to the size reduction of the antenna to the extent of 55% in comparison to standard radiating patch antenna resonating at the same frequency. The fractal-inspired hybrid microstrip patch antenna is a promising candidate for drone-based surveillance and communication applications owing to its miniaturization and good directional radiation properties.

INTELLIGENT AGRICULTURAL PEST MANAGER DRONE IN PAKISTAN

This paper’s primary goal is to develop an agriculture drone for spraying pesticides. We discuss an architecture based on unmanned aerial vehicles (UAVs) in this study. Pesticides must be used in agriculture if the quality of large-scale output is to be maintained. It is crucial to increase agriculture's production and efficiency by employing cutting-edge technology to replace employees with intelligent equipment like robots. The research suggests a novel method to replace people in a number of agricultural tasks, including the identification of insect presence, the application of pesticides and fertilizers, etc. The created method entails building a prototype that makes use of basic, affordable equipment including a microprocessor, different motors, and terminal equipment to assist farmers in a variety of operations related to crop fields. Design and build an autonomous drone-based surveillance system capable of identifying injured crops and spraying pesticides in specified regions as needed. To combine an image processing and Artificial Intelligence based real time algorithm to determine crop health and evaluate the need for pesticides, as well as a weather monitoring system that can assist anticipate weather conditions

A New Image Encryption Algorithm Based on DNA State Machine for UAV Data Encryption

Drone-based surveillance has become widespread due to its flexibility and ability to access hazardous areas, particularly in industrial complexes. As digital camera capabilities improve, more visual information can be stored in high-resolution images, resulting in larger image sizes. Therefore, algorithms for encrypting digital images sent from drones must be both secure and highly efficient. This paper presents a novel algorithm based on DNA computing and a finite state machine (FSM). DNA and FSM are combined to design a key schedule with high flexibility and statistical randomness. The image encryption algorithm is designed to achieve both confusion and diffusion properties simultaneously. The DNA bases themselves provide diffusion, while the random integers extracted from the DNA bases contribute to confusion. The proposed algorithm underwent a thorough set of statistical analyses to demonstrate its security. Experimental findings show that the proposed algorithm can resist many well-known attacks and encrypt large-sized images at a higher throughput compared to other algorithms. High experimental results for the proposed algorithm include correlation coefficients of 0.0001 and Shannon entropy of 7.999. Overall, the proposed image encryption algorithm meets the requirements for use in drone-based surveillance applications.

Optimizing Forest Fire Prevention: Intelligent Scheduling Algorithms for Drone-Based Surveillance System

Given the importance of forests and their role in maintaining the ecological balance, which directly affects the planet, the climate, and the life on this planet, this research presents the problem of forest fire monitoring using drones. The forest monitoring process is performed continuously to track any changes in the monitored region within the forest. During fires, drones’ capture data is used to increase the follow-up speed and enhance the control process of these fires to prevent their spread. The time factor in such problems determines the success rate of the fire extinguishing process, as appropriate data at the right time may be the decisive factor in controlling fires, preventing their spread, extinguishing them, and limiting their losses. Therefore, this research presented the problem of monitoring task scheduling for drones in the forest monitoring system. This problem is solved by developing several algorithms with the aim of minimizing the total completion time required to carry out all the drones’ assigned tasks. System performance is measured by using 990 instances of three different classes. The performed experimental results indicated the effectiveness of the proposed algorithms and their ability to act efficiently to achieve the desired goal. The algorithm RID achieved the best performance with a percentage rate of up to 90.3% with a time of 0.088 seconds.

Autonomous Vehicles Have Entered the Off-Road Market [Connected and Automated Vehicles

The past year has taught us to expect the unexpected. While digital meetings have gotten increased momentum, the pandemic has put most other platforms for technical development on hold. I have written before in this column about the fact that autonomous vehicles are emerging. A few years ago, there was a sort of mental setback when several companies pioneering autonomous vehicle testing said that the task was more complex than people may think and that fully autonomous vehicles were much further into the future than it may appear from the outside. Based on the stories collected for this issue, however, it is apparent that autonomous off-road vehicles like baggage tractors, drone-based surveillance systems, and vehicles in mines or construction sites are already operational. This may have several different causes. From a researcher's perspective, I would say that it is not until you start to really dig into a topic that you realize its full complexity and how little you actually know. It is therefore likely that the company representatives expressing doubt about fully autonomous vehicles a few years back were behaving just like normal Ph.D. students who generally always doubt their ability before they start to produce sensational results. It is also likely that off-road vehicles are easier to design, in that they operate in a confined area that can be better controlled and defined. This also affects the communication abilities of the vehicles. Basically, all autonomous vehicles, whether they operate off- or on-road, need to be connected. In a confined area, it is possible to deploy Wi-Fi access points enabling reliable connectivity to the autonomous vehicles, coupled with teleoperating abilities for added safety. For on-road vehicles covering a larger geographical area, another type of connectivity is needed, which requires harmonization among technology providers, vehicle manufacturers, legislation, and spectrum authorities. One show-stopper in this respect is the recent spectrum setbacks in the United States, where the entire spectrum previously reserved for dedicated short range communication (DSRC), was given away to Wi-Fi and cellular technology <xref ref-type="bibr" rid="ref1">[1]</xref>. The spectrum authorities in Europe and Australia support a technologically agnostic approach, but the setback in the United States is also affecting these markets as it is unclear what will happen with the huge investments already made in DSRC and its European counterpart called ITS-G5. However, as I wrote in a previous column, the vehicle industry is using this peculiar time to analyze and plan ahead. To this end, Waymo is simulating how the Waymo Driver would behave if involved in a fatal crash reconstructed from statistics obtained from the National Highway Traffic Safety Administration (NHTSA)?it turns out that autonomous vehicles avoid many accidents simply by following the traffic rules. In addition, Volvo Cars has launched a portal making different resources (such as car dashboard data, charge/fuel level, and distance traveled) available to developers and other third parties to build new services. When life gives you lemons, make simulations and share data.

Swachh AI: Real-time Spit Detection using YOLOv3 Darknet Model

In today’s time, all the government authorities are fighting day and night to stop the spread of Coronavirus. Hence, the aim of the system is to focus on using the latest computer technologies to limit the spread of communicable diseases like the Coronavirus by imposing a strict restriction on public spitting, using real-time drone-based surveillance systems, which is responsible for the maximum replication of the virus. To implement this system, CCTV/drone will capture video feed from public places and record live activities of that area. This data will then be sent via Cloud and analyzed using the YOLOv3 Darknet model to detect public spitting. The results will be available to the local authorities using a web/mobile interface. The officials can then penalize the individual. Thus, this system will assist the officials to manage and monitor public spitting in an efficient way.

Comparing distributions of white, bull, and tiger sharks near and away from the surf break using three tech-based methods

Unprovoked shark bites are one of the most recognised human-wildlife conflicts in the marine environment. Historically, management of this threat to public safety largely involved the implementation of lethal strategies. However, there is increasing environmental necessity and social pressure to adopt alternative strategies that minimise harm to sharks and other marine life. While different approaches have been developed that aim to reduce the risk of shark interactions with people, it is difficult to quantify their efficacy due to the rarity of shark bites and a lack of understanding of localised distributions of white (Carcharodon carcharias), bull (Carcharhinus leucas), and tiger (Galeocerdo cuvier) sharks, particularly close to surf breaks. Here, we used a rare opportunity to compare observations of these three ‘target’ shark species nearshore from drone-based surveillance to those caught on SMART (Shark-Management-Alert-in-Real-Time) drumlines and detected from VR4G acoustic receivers, with the latter two both located approximately 500 m offshore. Across 243 operational days, where all three methods were in use off three separate beaches on the east coast of Australia, we found no evidence for nearshore drone-based observations of target shark species to correlate with catches from SMART drumlines or detections from the VR4G acoustic receivers. The absence of correlation was still evident when 2-day, and 3-day moving averages of catches from SMART drumlines and detections from acoustic receivers were considered. While drone-based surveillance can incur sightability errors, it was evident that SMART drumlines do not prevent sharks from entering near the surf zone, and this does not coincide with the portion of sharks detected by the VR4G acoustic receivers. We contend that there is a need for a greater understanding of behavioural processes of large predatory sharks that may pose a threat to ocean users, particularly when considering non-destructive approaches to shark mitigation.

Beach-user perceptions and attitudes towards drone surveillance as a shark-bite mitigation tool

Management of human-wildlife conflict is often challenging and complex, particularly when the conflict involves sharks. New technologies are being trialled in New South Wales, on Australia's east coast, to accommodate the community demand for increased beach-user protection that does not harm marine wildlife. Drones (or unmanned aerial vehicles), are one of a suite of potential tools that can address both these demands. We released an online survey to assess beach-user perceptions and attitudes toward drones on NSW beaches as a shark surveillance tool. From 439 respondents, we found the use of drones on coastal beaches was accepted by the majority of people surveyed (88%) due to perceptions of reduced impact on sharks, and the relatively low cost. Drone surveillance was also the preferred approach for bather protection overall. Arguably the most vulnerable beach-user group for a shark bite incident, surfers, claimed the highest level of awareness of the use of drones for shark surveillance, but also indicated lower confidence in their utility compared to other groups. The study demonstrates an overall social licence regarding the use of drones for shark surveillance purposes, with the levels of support likely to increase with further public education efforts and improvements to the efficacy of drone-based surveillance.