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Disasters, either manmade or natural, call for rapid and timely actions. Due to disaster, all of the communication infrastructures are destroyed, and there is no way for connection between people in disaster and others outside the disaster range. Drone technology is the critical technology for delivering communication services and guiding people an...
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... drone technology can provide surveillance and formative mapping assessment and allow responders to focus on the tasks that need attention immediately after a disaster. Figure 1 shows the disaster management cycle functions, including preparedness, mitigation, response, and recovery. ...Similar publications
Machine learning, already at the core of increasingly many systems and applications, is set to become even more ubiquitous with the rapid rise of wearable devices and the Internet of Things. In most machine learning applications, the main focus is on the quality of the results achieved (e.g., prediction accuracy), and hence vast amounts of data are...
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... Drones are also often used to help manage various disasters by assessing risks, providing real-time data about emergencies, and helping to understand better the scope of the issues, which in the end, often can result in saving people's lives. Alsamhi et al. [110] presented their research on a robust system for data sharing between intelligent drones and smart wearables used during disasters, highlighting how valuable such drones are in various environmental disasters due to their capabilities to carry out further analysis directly on the device, in an edge-based manner, including running machine learning models. The authors evaluate the impact of network connectivity in the case of such events and highlight that properly optimized transmission is crucial in enabling search-rescue teams to operate efficiently. ...
In recent years, we have been observing the rapid growth and adoption of IoT-based systems, enhancing multiple areas of our lives. Concurrently, the utilization of machine learning techniques has surged, often for similar use cases as those seen in IoT systems. In this survey, we aim to focus on the combination of machine learning and the edge computing paradigm. The presented research commences with the topic of edge computing, its benefits, such as reduced data transmission, improved scalability, and reduced latency, as well as the challenges associated with this computing paradigm, like energy consumption, constrained devices, security, and device fleet management. It then presents the motivations behind the combination of machine learning and edge computing, such as the availability of more powerful edge devices, improving data privacy, reducing latency, or lowering reliance on centralized services. Then, it describes several edge computing platforms, with a focus on their capability to enable edge intelligence workflows. It also reviews the currently available edge intelligence frameworks and libraries, such as TensorFlow Lite or PyTorch Mobile. Afterward, the paper focuses on the existing use cases for edge intelligence in areas like industrial applications, healthcare applications, smart cities, environmental monitoring, or autonomous vehicles.
... The paper [18] investigates how drones with edge intelligence and intelligent wearable devices can efficiently collaborate in disaster management scenarios. Specifically, the focus is on analyzing the network connectivity parameters crucial for enabling real-time data sharing between the two components. ...
... In Eqs. (18)(19)(20), Y t refers to the actual value or label of the training data, while b Y t refers to the predicted value given by the model. The model is trained by adjusting its weights based on the mean square error (MSE) between the predicted values and the actual values. ...
With the assistance of unmanned aerial vehicles (UAVs), wireless communication networks can provide connectivity to edge devices (EDs) even in challenging signal conditions. However, the forthcoming 6th generation mobile networks (6G) will demand increased energy, while EDs typically have limited energy resources. Furthermore, EDs often struggle to accurately track dynamic UAV positions due to time-lag mechanisms, hindering their ability to adapt emission energy dynamically. Additionally, fixed emission power settings on EDs contribute to their limited endurance. To address these challenges, we propose a deep learning-based energy-aware optimization technique (DEO) in this study. DEO dynamically adjusts the emission power of EDs to ensure that the received energy at the mobile relay UAV closely matches the receiver's sensitivity, while minimizing energy consumption. The edge server plays a crucial role by providing computational infrastructure for this task. Our approach employs the enhanced gradient-based graph recurrent neural network (Gradient GRNN) deep learning technique to predict the dynamic locations of relay UAVs. Based on these predictions, the emission energy of EDs is adaptively modified, enabling reliable connections with mobile relay UAVs while conserving energy. Through extensive simulations, we evaluate the effectiveness of various predictive networks under different time-delay conditions (0.4 s, 0.6 s, and 0.8 s). The results demonstrate that, even with communication delays, the algorithm achieves low weighted mean absolute percentage errors (WMAPE) of 0.54%, 0.80%, and 1.15%, respectively.
... Drone applications have recently expanded in fields such as agriculture, transportation, construction, and topographic exploration. As the number of studies in which Internet of Things (IoT) devices collaborate and perform tasks in groups increases [1][2][3][4][5][6][7][8], the development of drone swarms, in which multiple drones collaborate in large teams to accomplish a specific task or set of tasks, also has been actively pursued. The drone swarm can overcome the limitations of individual drones, which generally include constrained resources, such as battery life, flight time, and coverage area. ...
With the advancement of unmanned aerial vehicle (UAV) technology, the market for drones and the cooperation of many drones are expanding. Drone swarms move together in multiple regions to perform their tasks. A Ground Control Server (GCS) located in each region identifies drone swarm members to prevent unauthorized drones from trespassing. Studies on drone identification have been actively conducted, but existing studies did not consider multiple drone identification environments. Thus, developing a secure and effective identification mechanism for drone swarms is necessary. We suggested a novel approach for the remote identification of drone swarms. For an efficient identification process between the drone swarm and the GCS, each Reader drone in the region collects the identification information of the drone swarm and submits it to the GCS for verification. The proposed identification protocol reduces the verification time for a drone swarm by utilizing batch verification to verify numerous drones in a drone swarm simultaneously. To prove the security and correctness of the proposed protocol, we conducted a formal security verification using ProVerif, an automatic cryptographic protocol verifier. We also implemented a non-flying drone swarm prototype using multiple Raspberry Pis to evaluate the proposed protocol’s computational overhead and effectiveness. We showed simulation results regarding various drone simulation scenarios.
... For example, in recent studies, Alsamhi et. al [38] and Fraune et. al [39] incorporated wearable and drones for search and rescue (SAR), while Garge et. ...
... al [40] used UAVs as on-demand QoS (Quality of Service) enablers that support multimedia applications in smart cities. Although [38][39] worked for the same domain, both studies focused on different contexts, namely game and emergency communication. In addition, Fraune et. ...
Many studies, to varying degrees, have confirmed the importance of persuasive approaches in wearable technology. Meanwhile, there are also a growing number of studies in persuasive multimedia, particularly in promoting awareness. Also, many studies reported on wearable multimedia, especially in game-based and VR/AR applications. Given the increasing emergence of these technologies, there is a need to integrate existing diverse research endeavours and consolidate them for improved planned effects on human attitude and behaviour, including one's awareness. However, a similar attempt to incorporate a triad of persuasive, multimedia and wearable design principles toward improved controllability awareness lacks empirical evidence. Here, this study explores the design principles of persuasive, multimedia and wearable technologies that can be leveraged into an integrated design model, especially in promoting controllability awareness of mental health issues. Moreover, this study believes exploring the potential integration of the design principles would significantly impact the application's effectiveness. Therefore, this study conducted a comparative analysis which involved 20 relevant studies pertinent to wearable design principles, persuasive design principles, and multimedia design principles. Furthermore, all identified studies were reviewed regarding the domain, the technology used, target outcomes, and utilisation of the design principles. As a result, this study discovered that many studies were on integrating persuasive and multimedia design principles and persuasive and wearable technologies. Therefore, the outcome of this study could be leveraged to incorporate all three design principles (i.e., wearable, persuasive technology, multimedia) into a conceptual model. The conceptual model is expected to produce a more effective result, especially in enhancing controllability awareness in the mental health domain.
... Overall, UAV images are superior for disease prediction as they offer a comprehensive view of the crop field, facilitate faster and more efficient disease detection, and can provide crucial spectral data for accurate disease diagnosis. Furthermore, UAV can work as edge computing to perform computing tasks closer to smart devices [12], [13] in smart environments, including agriculture, smart city, smart transportation, etc. ...
... This early detection can help to prevent emergencies from becoming more severe and dangerous. • Improved communication [16,[46][47][48]: IoT technology can improve communication among emergency responders and with the public. This can help to coordinate a more effective response and keep people informed and safe during an emergency. ...
The advancement in technology has led to the integration of internet-connected devices and systems into emergency management and response, known as the Internet of Emergency Services (IoES). This integration has the potential to revolutionize the way in which emergency services are provided, by allowing for real-time data collection and analysis, and improving coordination among various agencies involved in emergency response. This paper aims to explore the use of IoES in emergency response and disaster management, with an emphasis on the role of sensors and IoT devices in providing real-time information to emergency responders. We will also examine the challenges and opportunities associated with the implementation of IoES, and discuss the potential impact of this technology on public safety and crisis management. The integration of IoES into emergency management holds great promise for improving the speed and efficiency of emergency response, as well as enhancing the overall safety and well-being of citizens in emergency situations. However, it is important to understand the possible limitations and potential risks associated with this technology, in order to ensure its effective and responsible use. This paper aims to provide a comprehensive understanding of the Internet of Emergency Services and its implications for emergency response and disaster management.
... The authors of [91] highlighted the collaboration of drones and IoT for public safety in smart cities with improving network performance and QoS. While the collaboration of multi-drone and 13 of 36 Search and rescue teams is used for disaster management [16][17][18]. Moreover, blockchain technology is used to manage and decentralize multi-drone collaboration [92], empowering the security of drone swarm in a smart environment [93]. ...
Dataspaces are decentralized and open ecosystems that guarantee trustworthy and secure sharing of data among their participants. In recent years, dataspaces have gained popularity due to their design for managing and sharing heterogeneous data from various sources and domains; and their capability to incrementally solve data integration issues. Leveraging dataspace and advanced technologies plays a vital role in solving many real-world applications effectively and efficiently in real-time. Drone technology is one type of technology that can be deployed to gather data from different resources in harsh or smart environments. Beyond fifth-generation (B5G) communication networks significantly contribute to drones’ development and widespread use by providing low latency and high throughput. Therefore, data sharing among drones in B5G networks offer significant potential to enhance commercial and civilian applications. However, several security issues for collaboration and data sharing, such as data privacy leakage, because of sensitive data and the lack of trustworthy centralized monitoring. Furthermore, sharing data is one of the essential requirements for drone collaboration to achieve their tasks effectively and efficiently in real-time. This conceptual framework presents a novel dataspace in the sky, focusing on securing drone data sharing in B5G for Industry 4.0 toward Industry 5.0. We present how Federated Learning (FL) assists drones in collaboration effectively and efficiently, sharing models instead of raw data. However, because of the fragility of the central curator, the reliability of contribution recording, and the poor quality of shared local models, there are still significant security and privacy issues for drone-assisted smart environments in B5G. Therefore, we present the conceptual framework for leveraging blockchain and FL to secure and manage data sharing of collaborative drones’ dataspace in space in a decentralized fashion. The decentralisation of dataspaces would significantly expand the drive and market for the development of citizen-friendly mobility services.
... They can capture detailed images and data of disaster-affected areas, which can be used to assess the extent of damage and the needs of affected communities. This information can be used to inform disaster response efforts and help authorities make informed decisions about resource allocation [7][8]. ...
... Te cloud computing technology was used in these articles [4,27] and [22]. Te robots/smart wearable devices/sensors/IoT were used in these papers [6,18,22,28,29] and [30]. OPNET was used to evaluate the network performance in these studies [29,31]. ...
... Te robots/smart wearable devices/sensors/IoT were used in these papers [6,18,22,28,29] and [30]. OPNET was used to evaluate the network performance in these studies [29,31]. Tese details are summarized in Table 1. ...
... Wearable Devices/IoT. Te robots/smart wearable devices/sensors/IoT/other devices were used in these papers [6,18,22,[28][29][30]. Tey were given as follows. ...
Drone examination has been overall quickly embraced by NDMM (natural disaster mitigation and management) division to survey the state of impacted regions. Manual video analysis by human observers takes time and is subject to mistakes. The human identification examination of pictures caught by drones will give a practical method for saving lives who are being trapped under debris during quakes or in floods and so on. Drone investigation for research and security and search and rescue (SAR) should involve the drone to filter the impacted area using a camera and a model of unmanned area vehicles (UAVs) to identify specific locations where assistance is required. The existing methods (Balmukund et al. 2020) used were faster-region based convolutional neural networks (F-RCNNs), single shot detector (SSD), and region-based fully convolutional network (R-FCN) for the detection of human and recognition of action. Some of the existing methods used 700 images with six classes only, whereas the proposed model uses 1996 images with eight classes. The proposed model is used YOLOv3 (you only look once) algorithm for the detection and recognition of actions. In this study, we provide the fundamental ideas underlying an object detection model. To find the most effective model for human recognition and detection, we trained the YOLOv3 algorithm on the image dataset and evaluated its performance. We compared the outcomes with the existing algorithms like F-RCNN, SSD, and R-FCN. The accuracies of F-RCNN, SSD, R-FCN (existing algorithms), and YOLOv3 (proposed algorithm) are 53%, 73%, 93%, and 94.9%, respectively. Among these algorithms, the YOLOv3 algorithm gives the highest accuracy of 94.9%. The proposed work shows that existing models are inadequate for critical applications like search and rescue, which convinces us to propose a model raised by a pyramidal component extracting SSD in human localization and action recognition. The suggested model is 94.9% accurate when applied to the proposed dataset, which is an important contribution. Likewise, the suggested model succeeds in helping time for expectation in examination with the cutting-edge identification models with existing strategies. The average time taken by our proposed technique to distinguish a picture is 0.40 milisec which is a lot better than the existing method. The proposed model can likewise distinguish video and can be utilized for real-time recognition. The SSD model can likewise use to anticipate messages if present in the picture.
... In this paper, we will investigate how the cloud-to-edge-to-IoT continuum can support and enable post-disaster SAR operations, and we will propose solutions for the challenges and technological/research questions introduced above. Indeed, distributed applications paired with advanced resource orchestration solutions over the continuum reaching out to the edge of the network can enhance the response time and effective intervention for SAR operation [4][5][6]. The challenges will be discussed, and the possible solutions will be described as part of the Horizon Europe NEPHELE project (NEPHELE project website: https://nephele-project.eu/ (accessed on 19 January 2023)). ...
When a natural or human disaster occurs, time is critical and often of vital importance. Data from the incident area containing the information to guide search and rescue (SAR) operations and improve intervention effectiveness should be collected as quickly as possible and with the highest accuracy possible. Nowadays, rescuers are assisted by different robots able to fly, climb or crawl, and with different sensors and wireless communication means. However, the heterogeneity of devices and data together with the strong low-delay requirements cause these technologies not yet to be used at their highest potential. Cloud and Edge technologies have shown the capability to offer support to the Internet of Things (IoT), complementing it with additional resources and functionalities. Nonetheless, building a continuum from the IoT to the edge and to the cloud is still an open challenge. SAR operations would benefit strongly from such a continuum. Distributed applications and advanced resource orchestration solutions over the continuum in combination with proper software stacks reaching out to the edge of the network may enhance the response time and effective intervention for SAR operation. The challenges for SAR operations, the technologies, and solutions for the cloud-to-edge-to-IoT continuum will be discussed in this paper.
