Disaster Rescue Research Articles

An Improved Lightweight Real-Time Detection Algorithm Based on the Edge Computing Platform for UAV Images

Unmanned aerial vehicle (UAV) image detection algorithms are critical in performing military countermeasures and disaster search and rescue. The state-of-the-art object detection algorithm known as you only look once (YOLO) is widely used for detecting UAV images. However, it faces challenges such as high floating-point operations (FLOPs), redundant parameters, slow inference speed, and poor performance in detecting small objects. To address the above issues, an improved, lightweight, real-time detection algorithm was proposed based on the edge computing platform for UAV images. In the presented method, MobileNetV3 was used as the YOLOv5 backbone network to reduce the numbers of parameters and FLOPs. To enhance the feature extraction ability of MobileNetV3, the efficient channel attention (ECA) attention mechanism was introduced into MobileNetV3. Furthermore, in order to improve the detection ability for small objects, an extra prediction head was introduced into the neck structure, and two kinds of neck structures with different parameter scales were designed to meet the requirements of different embedded devices. Finally, the FocalEIoU loss function was introduced into YOLOv5 to accelerate bounding box regression and improve the localization accuracy of the algorithm. To validate the performance of the proposed improved algorithm, we compared our algorithm with other algorithms in the VisDrone-Det2021 dataset. The results showed that compared with YOLOv5s, MELF-YOLOv5-S achieved a 51.4% reduction in the number of parameters and a 38.6% decrease in the number of FLOPs. MELF-YOLOv5-L had 87.4% and 47.4% fewer parameters and FLOPs, respectively, and achieved higher detection accuracy than YOLOv5l.

Development of Infectious Disease Emergency Response Competencies for Nurses in China: A Delphi Study and an Analytic Hierarchy Process

Aim. To develop a set of infectious disease emergency response competencies specific to frontline nurses in China. Background. Nurses play an important role in the infectious disease emergency response. Competency-based training is the cornerstone of the professionalization of disaster rescue, including the infectious disease emergency response. Accordingly, reaching a consensus on a set of core competencies is essential. However, information regarding the competencies needed for nurses in the infectious disease emergency response is limited. Methods. A literature review and in-depth expert interviews were conducted to establish a draft of competencies, which consisted of 53 items, including 3 first-level index items, 12 second-level index items, and 38 third-level index items. Eighteen experts with the knowledge of infectious disease management and experience with infectious disease emergency rescue from different regions in China were recruited for Delphi consultation. A two-round Delphi survey was conducted via email. Consensus was defined as a mean importance value >4.5 and the coefficient of variation <0.25 among the experts. Finally, the analytic hierarchy process was used to determine the weight of each index on which consensus had been reached. Results. An index system of infectious disease emergency response competencies for nurses was constructed, including 3 first-level indices (knowledge, attitudes, and skills), 10 second-level indices, and 32 third-level indices. The response rates of the two rounds of the Delphi survey were both 100%, and the authority coefficient of the 18 experts was 0.903. The weighted value of each index was established with a consistency ratio <0.1, demonstrating that skill (0.5396) ranked first among the three first-level indices, followed by knowledge (0.2970) and attitudes (0.1634). Conclusion. The study developed a consensus on infectious disease emergency response competencies required for nurses in China, which provides guidance for the assessment and training of nurses on infectious disease emergency response. Implications for Nursing Management. According to the competency index system, nursing managers could develop effective training programs of infectious disease emergency response competency for nurses and select competent nurses for emergency response to infectious diseases.

The JDR Method. Our 20 Years of Experience and Practice in Developing Human Resources for Disaster Medicine.

Introduction:The Japan Disaster Relief Search and Rescue Team (the JDR Rescue Team) Medical Unit consists of EMT (emergency medical team) registered doctors and nurses who provide health care and medical advice to rescue operations. The JDR Medical Unit began 20 years ago when they voluntarily participated in rescue training and is characterized by volunteers who belong to different hospitals across the country. As a result, there were problems due to varied skills, and motivation. Until 2017, all applicants were recruited and trained as provisionally registered members, but only about 30% of them became fully registered members.Method:Since 2018, we have fundamentally changed our personnel training methods, establishing three main pillars. The first is a screening process based on work experience, expertise, and motivation; the second is dedicated guidance through training, textbooks, online study sessions, and training; and finally, we have created abundant training opportunities and visualized the growth process through a ladder to keep them motivated and goal-oriented at all times. Specific trainers are defined as task force members and they analyze each scene of the deployment practically and reflect on training. The task force also receives training abroad and absorbs good practice from other teams.Results:After changing the personnel training methods, the number of participants who dropped out of the training program was significantly reduced, and approximately 90% of the participants became fully registered members. The team members are more motivated and the team's capabilities have improved, leading to IER (INSARAG External Reclassification) certification as a heavy team twice.Conclusion:By selecting experienced and capable members and providing them with sufficient guidance and abundant training opportunities, we succeeded in improving the efficiency and capacity of human resource development. Ideally, victims are handed over to EMTs as patients for the future goal.

The Role of Nurses in the Reclassification Exercise of the Japan Disaster Relief Search and Rescue Team (the JDR Rescue Team)

Introduction:The JDR Rescue Team has successfully completed the INSARAG External Re-Classification (IER) process, which evaluates the operational capability and capacity of Urban Search and Rescue (USAR) teams and has achieved the highest “Heavy” reclassification in November 2022. Two nurses participated in the IER process as part of the medical unit of JDR Rescue Team. In addition, ten registered nurses cooperated as Exercise Control (EXCON).Method:Summarize the JDR Rescue Team and medical unit and make observations on what nurses did in the IER.Results:The JDR Rescue Team is dispatched by the Government of Japan in response to large-scale disasters overseas. The task force team has 75 members from various specialties, including the rescuer, and medical unit. The medical unit consists of one medical manager, 2 doctors, and two nurses. There are currently about 50 registered medical unit members in our team, and of these, a total of 23 nurses are registered. The role of nurses during the IER process, includes a 36-hour non-stop scenario-based exercise. The team nurses are involved in various roles, such as Confined Space Medicine (infusion for patients, assisting on-site amputation), caring or treating injured rescuers and search dogs, providing health and welfare monitoring and operating a decontamination system. The EXCON nurses were involved in managing the simulation. One of their key roles was to play as a victim realistically so as to provide a sense of tension for the simulation.Conclusion:The JDR Rescue Team has more medical unit members than those in other countries. In particular, teams with so many nurses are rare. nurses played a vital role in this IER. The contribution of nurses is identified in order to make the international USAR team more strong and more flexible.

Bioinspired Synergy Strategies Empower Small‐Scale Robots with Higher Performance

Small‐scale robots need to be agile to perform various tasks in complex environments and the design faces great challenges with limited size and power consumption. However, the advent of advanced functional materials, compliant structures/mechanisms, and flexible fabrication techniques has brought new evolution directions to robotics. These technologies enrich the robots' actuation strategies, improving their performance of small robots while simplifying the complexity of the system and thus making it possible to be applied in diverse scenarios, such as disaster rescue, medical, and environmental exploration. In nature, small organisms have shown numerous efficient survival strategies. They can easily achieve locomotion tasks with low energy consumption. For example, dragonflies complete long flights with the help of wind, and stenus (one kind of rove beetle) uses the surface tension of liquids to dodge prey quickly. Insects are usually considered to be agile, efficient, and intelligent, which is the same requirement as for small robots. Yet the corresponding bioinspired design strategies bring new opportunities and challenges to the development of small‐scale robots. Herein, the development trends of small‐scale robots are analyzed and discussed, and several potential bioinspired synergy strategies are induced, which may be useful for the future design of insect‐level soft robots.

QoS-Aware Machine Learning Task Offloading and Power Control in Internet of Drones

Internet of drone (IoD), where drones act as the internet of things (IoT) devices, provides multiple services such as object recognition, traffic monitoring, and disaster rescue. The service response time is limited by drones’ onboard computing power, hence greatly affecting the quality of service (QoS). Machine learning (e.g., image processing) task offloading to the fog node attached to the ground base station can help reduce workload from drones and hence decrease service time. The communication latency between drones and the fog node during task offloading also affects the service time and hence the communication efficiency needs to be considered. Therefore, in this work, we consider the joint optimization of machine learning task offloading and power control in IoD to determine each drone’s number of offloaded images and wireless transmission power. We analyze the energy model of the commonly used convolutional neural network (CNN) for object recognition, and formulate the joint optimization problem as a mixed integer non-linear programming (MINLP) problem to minimize drones’ average service time constrained by drones’ energy budgets. An approximation algorithm with low computational complexity is then designed to address the problem and its performances are compared with the lower bounds and demonstrated via extensive simulations.

Design of a Mixed Reality System for Simulating Indoor Disaster Rescue

Modern buildings are large and complex, and as more time is spent inside them, the risk of indoor disasters such as fires and gas leaks increases. In the event of such a disaster, the success of the rescue operation depends on the ability of the rescue team to navigate and respond to the complex environment. To address this challenge, we designed a mixed reality (MR)-based system simulating indoor disaster rescue. This system uses augmented indoor maps and MR technology to help rescue teams quickly, and effectively respond to unexpected environmental variables and carry out rescue activities. To test the effectiveness of the system, we created a virtual disaster scenario and evaluated the rescue and escape performance within a shortened “golden time” of 2 min, as opposed to 5 min, given the virtual characteristics of the simulation. The results showed that the system is particularly effective at increasing the speed of rescue activities. Additionally, our results indicated the potential for further improvement through additional research. By applying this system for disaster rescue simulations and training, the safety of firefighters and rescuers can be improved by reducing the risk of injury during rescue operations.

Environmental disaster and public rescue: A social media perspective

Resilient city construction requires cities to be able to cope with and resist disasters on their own. With the advent and explosive growth of social media such as microblogs, citizens manage to show strong presence in disaster relief and the construction of resilient cities, which has resulted a huge amount of user-generated data. This paper took China's heavy rain disaster in 2021 as an example, collected 111,248 messages on the Chinese microblog platform, and analyzed the reactions of citizens during emergency rescue as social media users. Results show that social media users play an important role in disseminating disaster information, and the topics discussed by users follow the characteristics of information transmission before emotional expression. By analyzing the emotions exhibited by social media users, we discover that users are particularly concerned about whether information would be accurately and efficiently disseminated. Social media users seldom express emotions when publishing information, and a few users have a negative attitude in the face of disasters. Meanwhile, by examining the influence of different social media on disaster relief, we find that government's official media along with other news media acts as the main information disseminator, while influential individual bloggers serve as the main mediators in the information dissemination process. This paper on the whole offers a new lens for analyzing the role played by social media users in disaster relief, the implications of which could be referred to by the public and government agencies, so as to improve the city's adaptability to disasters through cooperative production and accelerate the construction of resilient cities.

Analysis of UAV Thermal Soaring via Hawk-Inspired Swarm Interaction.

A swarm of unmanned aerial vehicles (UAVs) can be used for many applications, including disaster relief, search and rescue, and establishing communication networks, due to its mobility, scalability, and robustness to failure. However, a UAV swarm's performance is typically limited by each agent's stored energy. Recent works have considered the usage of thermals, or vertical updrafts of warm air, to address this issue. One challenge lies in a swarm of UAVs detecting and taking advantage of these thermals. Inspired by hawks, a swarm could take advantage of thermals better than individuals due to the swarm's distributed sensing abilities. To determine which emergent behaviors increase survival time, simulation software was created to test the behavioral models of UAV gliders around thermals. For simplicity and robustness, agents operate with limited information about other agents. The UAVs' motion was implemented as a Boids model, replicating the behavior of flocking birds through cohesion, separation, and alignment forces. Agents equipped with a modified behavioral model exhibit dynamic flocking behavior, including relative ascension-based cohesion and relative height-based separation and alignment. The simulation results show the agents flocking to thermals and improving swarm survival. These findings present a promising method to extend the flight time of autonomous UAV swarms.

Revealing the generation mechanism of cross-regional emergency cooperation during accidents and disasters rescue

Promoting the development of cross-regional emergency cooperation is critical to emergency management. This paper’s primary goal is to determine the influencing factors and generation mechanism of cross-regional emergency cooperation during accidents and disasters rescue. The qualitative comparative analysis (QCA) method is used to study 26 especially major and major accidents and disasters rescue, aiming to answer the following three questions. First, which factors have the most significant impact on emergency cooperation? Second, how is emergency cooperation generated based on influencing factors? Third, what combination of influencing factors is more likely to produce cross-regional emergency cooperation? Based on the literature review, the influencing factors of cross-regional emergency cooperation include the subjects, objects, environment, methods, regulations, and guarantees of emergency management. There are three path models for cross-regional emergency cooperation in especially major and major accidents and disasters rescue, including the Autonomous regulation path, the System driven path, and the Demand generation path. The possibility of Autonomous regulation cross-regional emergency cooperation is greater than that of the other two paths. Cross-regional emergency cooperation strategies are proposed to build a comprehensive system of cross-regional emergency cooperation environment, regulations, and guarantees.

How Spatial Resolution of Remote Sensing Image Affects Earthquake Triggered Landslide Detection: An Example from 2022 Luding Earthquake, Sichuan, China

The magnitude 6.8 Luding earthquake that occurred on 5 September 2022, triggered multiple large-scale landslides and caused a heavy loss of life and property. The investigation of earthquake-triggered landslides (ETLs) facilitates earthquake disaster assessments, rescue, reconstruction, and other post-disaster recovery efforts. Therefore, it is important to obtain landslide inventories in a timely manner. At present, landslide detection is mainly conducted manually, which is time-consuming and laborious, while a machine-assisted approach helps improve the efficiency and accuracy of landslide detection. This study uses a fully convolutional neural network algorithm with the Adam optimizer to automatically interpret the aerial and satellite data of landslides. However, due to the different resolutions of the remote sensing images, the detected landslides vary in boundary and quantity. In this study, we conducted an assessment in the study area of Wandong village in the earthquake-affected area of Luding. UAV images, GF-6 satellite images, and Landsat 8 satellite images, with a resolution of 0.2 m, 2 m, and 15 m, respectively, were selected to detect ETLs. Then, the accuracy of the results was compared and verified with visual detection results and field survey data. The study indicates that as the resolution decreases, the accuracy of landslide detection also decreases. The overall landslide area detection rate of UAV imagery can reach 82.17%, while that of GF-6 and Landsat 8 imagery is only 52.26% and 48.71%. The landslide quantity detection rate of UAV imagery can reach 99.07%, while that of GF-6 and Landsat 8 images is only 48.71% and 61.05%. In addition, for each landslide detected, little difference is found in large-scale landslides, and it becomes more difficult to correctly detect small-scale landslides as the resolution decreases. For example, landslides under 100 m2 could not be detected from a Landsat 8 satellite image.

Human Detection and Action Recognition for Search and Rescue in Disasters Using YOLOv3 Algorithm

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.

A UAV Navigation Approach Based on Deep Reinforcement Learning in Large Cluttered 3D Environments

This paper is concerned with the problem of using deep reinforcement learning methods to enable safe navigation of UAVs in unknown large-scale environments containing a large number of obstacles. The ability of UAV to navigate autonomously is a precondition for performing disaster rescue and logistics deliveries. In this paper, the perception-constrained navigation of UAV is modeled as a partially observable Markov decision process (POMDP), and a fast recurrent stochastic valued gradient algorithm based on the actot-critic framework is designed for online solution. Compared with traditional SLAM-based and reactive obstacle avoidance-based approaches, the proposed navigation method can map the raw sensing information into navigation signals, and the learned policy deployed to the UAV can avoid the memory occupation and computational consumption required for real-time map building. Through experiments in a newly designed simulation environment, it is demonstrated that the proposed algorithm can enable the UAV to navigate safely in unknown cluttered large-scale environments and outperform the state-of-the-art DRL algorithm in terms of performance.

A Scientometric Research on Applications and Advances of Fire Safety Evacuation in Buildings

Fire safety evacuation has been used in numerous different kinds of buildings. This research conducts a scientometric review of fire safety evacuation applications and advances in the buildings to clarify the research trends of fire evacuation in the future and provide guidance for relevant research. A total of 3312 journals and conference proceedings were analyzed through different dimensions. The result proves that evacuation environments concentrate mainly on residential building, commercial building, school, and railway station. The characteristics of the evacuee have been gradually refined in recent years, including children, the elderly, patients, and vulnerable groups. The main experimental approaches of fire safety evacuation are evacuation drills, site records, and VR/AR experiments. The crowd behavior models mainly consist of six types: a cellular automata model, a social force model, a lattice gas model, a game-theoretic model, an animal agent-based model, and a computer agent-based model. The analysis results in the theoretical method are becoming gradually closer to the behavioral characteristics and movement data of the crowd during the actual evacuation with improvements of practical considerations. The study of evacuation drills, disaster rescue, emergencies, and other external environmental factors will become the forefront of future research, and subway stations, airports, high-rise building, and other personnel places will be the focus of the study of crowd evacuation.

Disaster Rescue via Multi-Robot Collaboration: Development, Control, and Deployment

Aging social infrastructure needs maintenance and inspection for which robot technology is highly effective. It is also effective for disaster rescue and recovery operations. Tunnel disaster rescue is risky for human workers. Robot technology can perform this work easily and accelerate rescue and recovery operations. This paper introduces a framework for the rescue robot development, control, and deployment steps. This framework also proposes automated and hybrid robot control systems in addition to conventional manual control. A multi-robot collaboration system causes the proposed control system to be convenient for deployment, reliable, and accelerate rescue work. In the development phase, controllers and different additional assistance tools for the robot have been developed in addition to the basic robot. As the proposed framework has been cultivated through our development and team collaboration training for the World Robot Summit (WRS) and the actual competition of the WRS, we will also present what we have achieved at the WRS.

Generalized Design, Modeling and Control Methodology for a Snake-like Aerial Robot.

Snake-like robots have been developing in recent decades, and various bio-inspired ideas are deployed in both the mechanical and locomotion aspects. In recent years, several studies have proposed state-of-the-art snake-like aerial robots, which are beyond bio-inspiration. The achievement of snake-like aerial robots benefits both aerial maneuvering and manipulation, thereby having importance in various fields, such as industry surveillance and disaster rescue. In this work, we introduce our development of the modular aerial robot which can be considered a snake-like robot with high maneuverability in flight. To achieve such flight, we first proposed a unique thrust vectoring apparatus equipped with dual rotors to enable three-dimensional thrust force. Then, a generalized modeling method based on dynamics approximation is proposed to allocate the wrench in the center-of-gravity (CoG) frame to thrust forces and vectoring angles. We further developed a generalized control framework that can handle both under-actuated and fully actuated models. Finally, we show the experimental results with two different platforms to evaluate the flight stability of the proposed snake-like aerial robot. We believe that the proposed generalized methods can provide a solid foundation for the snake-like aerial robot and its applications regarding maneuvering and manipulation in midair.

Contribution of Accessibility to Urban Resilience and Evacuation Planning Using Spatial Analysis.

Cities evolve and change with economic development and population growth, and urban planning laws in Taiwan have regulations that should be comprehensively reviewed every six years. Most current government policies aim to add new disaster prevention shelters or rescue stations. An economical way to improve the disaster prevention capabilities of urban planning is through examining or reviewing spatial structures and disaster prevention plans from the perspective of citizens or residents. The UN Office of Disaster Risk Reduction (UNDRR) launched the Making Cities Resilient Campaign policy to enhance disaster-resistant and sustainable urban environments through integrated disaster mitigation, reduction, response, and evacuation plans. This study used space syntax to ascertain evacuation route features using geometric distance analysis. There was 31.61% efficiency in relation to accessible roads on a comprehensive map. We could clearly see that since the areas in the first quadrant were relatively close to accessible roads, and there was an area that was not connected to the existing evacuation channels. The increased number of channels was more accessible and extensive. Such suggestions are helpful for government departments to prepare for disaster management. The spatial characteristics of the physical environment are explained by the accessibility and efficiency of axial maps and visibility analyzed by space syntax. Our findings reveal that space syntax is an important application when examining evacuation maps.

Disaster Preparedness Among Nurses in China: A Cross-Sectional Study.

Increasingly frequent global disasters such as coronavirus disease 2019 pose a threat to human health and life. The World Health Organization has called on countries to formulate detailed plans to prepare for disasters. It is critical to investigate and evaluate the disaster preparedness of nurses. This study was designed to investigate the disaster preparedness and psychological condition of nurses in China and analyze the significant factors influencing their disaster preparedness. A cross-sectional survey was conducted in 2020, and 1,313 nurses were enrolled using convenience sampling. The study questionnaires were distributed and collected via a networking platform equivalent to Amazon Mechanical Turk. The disaster preparedness of the respondents was measured using the Disaster Preparedness Evaluation Tool, the Hospital Anxiety and Depression Scale was used to evaluate anxiety and depression status, and a self-designed questionnaire developed based on a review of the literature was used to explore the potential factors of influence on disaster preparedness. The average score for disaster preparedness among the participants was 186.34 ( SD = 40.80), which corresponded with a moderate level, especially in skill (mean score = 42.01, SD = 12.39). Items with higher scores included support for the government, personal protection, and health education, whereas items with lower scores included nursing leadership in the community, capacity to cope with chemical or biological attacks, and assessment of posttraumatic stress disorder. Disaster preparedness was negatively related with mental health, including depression and anxiety. The main factors affecting disaster preparedness included educational background, nursing specialty, prior disaster training, prior disaster rescue experience, and depression level. The disaster preparedness of Chinese nurses must be improved. More attention should be paid to disaster preparedness in nurses, and future tailored interventions are urgently needed to promote nursing leadership in the community, the ability to cope with chemical or biological attacks, and posttraumatic stress disorder assessments. Moreover, relieving negative emotions to promote the mental health of nurses should receive greater attention.

Real-time fire protection system architecture for building safety

Regardless of the type of disaster a building undergoes, personal safety, property preservation, and attribution of responsibility are the three assessment indicators that summarize the effectiveness of disaster prevention, evacuation, and rescue operations when the area of a building affected by a disaster endangers personal safety. The only possible response under these conditions is for people to immediately leave the dangerous area and escape to a safe and secure area. Therefore, both the timeliness of evacuation and rescue advice directly determine the outcome of the disaster. This study reflects on current fire protection systems and puts forward the approaches of Prevention Level for Deployment in Advance and Spatial Transformation by Human–Machine Collaboration to deal with disasters and promote building safety. A real-time fire protection system architecture for disaster prevention, evacuation, and rescue operations in buildings is proposed and prototyped for a proof of concept. The architecture promotes simulated verification of the effectiveness of disaster prevention, evacuation, and rescue operations at the building planning and design stage.

Trust aware ad hoc routing protocol with key management based mechanism and optimal energy‐efficient cluster head selection in mobile ad hoc networks

SummaryThe implementation of Mobile Ad hoc Networks in numerous domains is necessitated by the fast evolving wireless service requirements as well as deployment demands over the last few decades. The application areas are environmental monitoring, disaster rescue operations, military communications, and other safety‐critical sectors. The underlying routing protocol has a considerable impact on the effectiveness of an ad hoc network deployment in a specific situation. MANETs are vulnerable to major security risks that are difficult to combat with current security measures. As a result, several safe routing protocols have been created to improve MANET security. This work proposes a safe and energy‐efficient routing mechanism using group key management. Initially, Particle Swarm Optimization (PSO) was adopted for efficient selection of cluster heads and malicious node detection, which aims to establish the trust among connected nodes that can enhance security as every participating node will produce as well as propagate authentic, accurate, and trusted content within the network. Two specialized nodes, dubbed Calculator Key (CK) and Distribution Key (DK), are then responsible for producing, verifying, and distributing secret keys among nodes employing Asymmetric key cryptography. The proposed method is named Optimal Cluster_ Trust Asymmetric Key Management Protocol (OptCH_TAKMP), which is considered an excellent energy‐efficient cluster head election process with a securable routing mechanism. It is compared with three state‐of‐art methods. Simulation results show that proposed OptCH_TAKMP achieves 31.4% of routing overhead, 23% of end‐to‐end delay, 78.6% of energy efficiency, 94.8% of throughput, 28.2% of average latency, 91.4% of malicious detection rate, and 92.4% of packet delivery ratio, 85.2% of network lifetime, 19.2% of communication cost and 28.6% of trust computation error.