A Hybrid Optimization Solution for UAV Network Routing

The significant advances in Wireless Sensor Networks (WSNs) facilitate many latest applications, such as intelligent battlefield, home automation, traffic control, and more. WSNs comprise small autonomously organized sensor nodes that are powered by batteries. The processes of collecting information and data storage, processing, and transmission deplete the energy of these small devices. Energy efficiency is still a major issue to address in WSN routing. Clustering is the best method that has been developed to reduce node energy consumption. However, current clustering methods are unable to effectively distribute the energy requirements of the nodes without considering energy characteristics, number of nodes, and flexibility. This study proposed a new cluster-based routing model for WSNs and emphasized the need for an improved clustering process with new optimization techniques. In particular, the improved DeepMaxout model was adopted to predict the energy of the nodes. Cluster Head (CH) selection is performed considering the nodes' energy as a prime factor. After choosing the CH, the CIOO algorithm incorporates new link quality and trust evaluations while determining the routing process. Finally, a comparison of energy utilization factors was performed between the suggested and traditional approaches.

Section: Introductionmentioning

confidence: 99%

Combined Osprey-Chimp Optimization for Cluster Based Routing in Wireless Sensor Networks: Improved DeepMaxout for Node Energy Prediction

Vikhyath,

Prasad

2023

“…This concept was initially put into practice towards the conclusion of World War II. These USVs found application in military contexts, including tasks like mine detection, reconnaissance, surveillance, intelligence gathering, surface warfare, and safeguarding strategic installations [9][10][11][12][13]. The potential of ASVs extends to serving as force multipliers in different naval operational domains due to their adaptable structures and capability to transport various payloads.…”

Section: Introductionmentioning

confidence: 99%

“…The potential of ASVs extends to serving as force multipliers in different naval operational domains due to their adaptable structures and capability to transport various payloads. With the integration of Artificial Intelligence (AI) into fully mature ASVs, it is foreseen that by the conclusion of 2025, ASVs will seamlessly operate within naval task groups, collaborating with crewed vessels [12][13][14][15][16]. The development and construction of ASVs poses intricate challenges for builders and developers alike.…”

Section: Introductionmentioning

confidence: 99%

About the Automation of an Autonomous Sail-propelled Search Drone

Cristea,

Popa,

Manea

et al. 2023

The paper presents the prototype of an autonomous sail-propelled vehicle, powered by sails and equipped with mini solar panels, intended for the surveillance of maritime space. Hardware and software aspects regarding the automation of this unmanned boat are addressed. Conceptual solutions have been identified to ensure short manufacturing times, component accessibility (price and availability on the free market), and high system reliability. The paper proposes an original solution to respond to at least two imperative needs: the safety and security of commercial and military navigation in the Black Sea area (faced with a tense geo-political climate) and the increased need to integrate European and global policies on reducing pollution and promoting renewable energy principles.

“…During the recent years, the field of photogrammetry has seen a rise of interest in the use of Unmanned Aerial Vehicles (UAVs), despite that their first uses were for military purposes [1][2]. Over time, the legal system embraced the use of photographs as permissible evidence.…”

Section: Introductionmentioning

confidence: 99%

3D Indoor Crime Scene Reconstruction from Micro UAV Photogrammetry Technique

Sazaly,

Mohd Ariff,

Razali

2023

The application of micro Unmanned Aerial Vehicles (UAVs) in photogrammetry, particularly within the realm of forensic investigation represents a relatively novel approach and has gained increased attention. By measuring the distances and positions of the scene’s components, it is feasible to document and visualize the scene using the photographs that were taken for the purpose of assisting investigators. Capturing accurate crime scene data within a short time frame is always a challenge. Conventionally, photographs were used to document the scene, but the technical qualities of the photographs depended on the skill of the present forensic personnel. The use of 3-Dimensional (3D) photogrammetry enables the production of highly realistic and detailed 3D documentation of a given scene. As this technique involves capturing a series of photographs, it can be a time-consuming process. Therefore, this study aims to explore an alternative approach that enables the rapid acquisition of the scene while preserving the intricate details, thus ensuring efficiency without compromising the accuracy of the resulting documentation. The study employs a methodological approach wherein data are collected from a simulated crime scene situated within a confined and hard-to-reach area. The data collection is facilitated through the utilization of micro UAVs. The acquired data are then processed utilizing photogrammetry software, leading to the generation of a 3D model point cloud. The collected data will be subjected to a comparative analysis with data generated using a Terrestrial Laser Scanner (TLS) as a reference, alongside Vernier Calliper (VC) measurements. The findings indicate that the Root Mean Square Error (RMSE) of the integrated point clouds from TLS and micro UAVs compared to the conventional method is approximately ±0.217 cm. It can be deduced that the integration of data derived from micro UAVs and TLS in forensic photogrammetry within a confined crime scene is viable and yields a high-precision 3D model point cloud.