Pub Date : 2022-12-08DOI: 10.1109/IBSSC56953.2022.10037366
N. Krishnan, Manav Tp, A. N. Geethanjali, Dinesh Kumar, N. Seenu, G. Balaji
A drone is a flying robot that can be remotely controlled or fly autonomously using software-controlled flight plans in its embedded systems, that work in conjunction with onboard sensors and a global positioning system (GPS). UAVs were most often associated with the military. They are used in many applications like Photography and Videography, delivering goods, monitoring change in climate, surveillance and target attacks in military, besides these, drones can also be a very good equipment that can be used in times of a disaster. In case of natural disasters like earthquakes, floods and fire accidents, there will be difficulty in locating and visualizing the disaster-affected area and locating where people are trapped. In such a case, a drone can be used to visualize the condition of the disaster occurred area and locate where the people are trapped. The drone is operated using ROS where the communication between ROS and the drone is made. The drone can also be fed with image-processing algorithms to locate people and also the level of damage can be calculated.
{"title":"Design and Development of Drone","authors":"N. Krishnan, Manav Tp, A. N. Geethanjali, Dinesh Kumar, N. Seenu, G. Balaji","doi":"10.1109/IBSSC56953.2022.10037366","DOIUrl":"https://doi.org/10.1109/IBSSC56953.2022.10037366","url":null,"abstract":"A drone is a flying robot that can be remotely controlled or fly autonomously using software-controlled flight plans in its embedded systems, that work in conjunction with onboard sensors and a global positioning system (GPS). UAVs were most often associated with the military. They are used in many applications like Photography and Videography, delivering goods, monitoring change in climate, surveillance and target attacks in military, besides these, drones can also be a very good equipment that can be used in times of a disaster. In case of natural disasters like earthquakes, floods and fire accidents, there will be difficulty in locating and visualizing the disaster-affected area and locating where people are trapped. In such a case, a drone can be used to visualize the condition of the disaster occurred area and locate where the people are trapped. The drone is operated using ROS where the communication between ROS and the drone is made. The drone can also be fed with image-processing algorithms to locate people and also the level of damage can be calculated.","PeriodicalId":426897,"journal":{"name":"2022 IEEE Bombay Section Signature Conference (IBSSC)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131011510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The potential of the 5G network model to revolutionize Industry and Education is exemplified through 5G technology use cases. Enhanced Mobile Broadband, Massive Machine Type Communication, and Ultra Reliable and Low Latency Communication are the three key components of the 5G concept. The services that 5G provides to users are contained inside these blocks. This study focuses on the concept of a “School on Wheels” as a beneficiary of 5G technologies. Rural Education will benefit from the implementation of 5G-enabled services that are specialized to this industry. We offered frameworks for advancing tools that will accelerate the idea of a Smart Educational System by linking 5G and its disruptive technologies. As a result, this paper provides a thorough examination of 5G technologies, which will facilitate new teaching and learning trends in the educational environment.
{"title":"Implementation of 5G Technology in Rural Education of India","authors":"Unnati Agarwal, Pranjal Dave, Aarushi Tiwari, Vinit Upadhyay, M. Sankhe, Saurav Shrivastav","doi":"10.1109/IBSSC56953.2022.10037466","DOIUrl":"https://doi.org/10.1109/IBSSC56953.2022.10037466","url":null,"abstract":"The potential of the 5G network model to revolutionize Industry and Education is exemplified through 5G technology use cases. Enhanced Mobile Broadband, Massive Machine Type Communication, and Ultra Reliable and Low Latency Communication are the three key components of the 5G concept. The services that 5G provides to users are contained inside these blocks. This study focuses on the concept of a “School on Wheels” as a beneficiary of 5G technologies. Rural Education will benefit from the implementation of 5G-enabled services that are specialized to this industry. We offered frameworks for advancing tools that will accelerate the idea of a Smart Educational System by linking 5G and its disruptive technologies. As a result, this paper provides a thorough examination of 5G technologies, which will facilitate new teaching and learning trends in the educational environment.","PeriodicalId":426897,"journal":{"name":"2022 IEEE Bombay Section Signature Conference (IBSSC)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131381955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-08DOI: 10.1109/IBSSC56953.2022.10037324
Sannidhi Rao, S. Mehta, Shreya Kulkarni, Harshal Dalvi, Neha Katre, M. Narvekar
Autonomous disease prediction systems are the new normal in the health industry today. These systems are used for decision support for medical practitioners and work based on users' health details input. These systems are based on Machine Learning models for generating predictions but at the same time are not capable to explain the rationale behind their prediction as the data size grows exponentially, resulting in the lack of user trust and transparency in the decision-making abilities of these systems. Explainable AI (XAI) can help users understand and interpret such autonomous predictions helping to restore the users' trust as well as making the decision-making process of such systems transparent. The addition of the XAI layer on top of the Machine Learning models in an autonomous system can also work as a decision support system for medical practitioners to aid the diagnosis process. In this research paper, we have analyzed the two most popular model explainers Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) for their applicability in autonomous disease prediction.
{"title":"A Study of LIME and SHAP Model Explainers for Autonomous Disease Predictions","authors":"Sannidhi Rao, S. Mehta, Shreya Kulkarni, Harshal Dalvi, Neha Katre, M. Narvekar","doi":"10.1109/IBSSC56953.2022.10037324","DOIUrl":"https://doi.org/10.1109/IBSSC56953.2022.10037324","url":null,"abstract":"Autonomous disease prediction systems are the new normal in the health industry today. These systems are used for decision support for medical practitioners and work based on users' health details input. These systems are based on Machine Learning models for generating predictions but at the same time are not capable to explain the rationale behind their prediction as the data size grows exponentially, resulting in the lack of user trust and transparency in the decision-making abilities of these systems. Explainable AI (XAI) can help users understand and interpret such autonomous predictions helping to restore the users' trust as well as making the decision-making process of such systems transparent. The addition of the XAI layer on top of the Machine Learning models in an autonomous system can also work as a decision support system for medical practitioners to aid the diagnosis process. In this research paper, we have analyzed the two most popular model explainers Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) for their applicability in autonomous disease prediction.","PeriodicalId":426897,"journal":{"name":"2022 IEEE Bombay Section Signature Conference (IBSSC)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132910859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-08DOI: 10.1109/IBSSC56953.2022.10037297
Akash Ajith, S. Babu, Sangeeth K, S. K, Manoj V. Thomas
The concept of procedural content generation (PCG) in game development has existed for a long time. It is used in games for the generation of unique content which help in making the game re-playable. Procedural content generation can be used in almost all game design areas. From level generation to creating a storyline for the game, the use of PCG helps in decreasing the overall time required to design an interesting game. The only problem with PCG is that it is hard to implement and optimize. This document consists of an algorithm that works on a type of recursion and the concept of snappable meshes. This is done using prefabs and other features that are available in Unity Engine[6]. All the methods mentioned in this document are done using Unity Engine. Unity Engine is one of many famous game engines that are available online. The algorithm mentioned helps in creating a procedural maze. The game levels are generated dynamically, allowing the player to experience new levels and avoid repetition of the same levels as in traditional games. The algorithm and its implementation in Unity Engine are explained in detail. How the meshes are spawned and placed dynamically to generate a level is also discussed.
{"title":"A Novel Method in Procedural Maze Generation","authors":"Akash Ajith, S. Babu, Sangeeth K, S. K, Manoj V. Thomas","doi":"10.1109/IBSSC56953.2022.10037297","DOIUrl":"https://doi.org/10.1109/IBSSC56953.2022.10037297","url":null,"abstract":"The concept of procedural content generation (PCG) in game development has existed for a long time. It is used in games for the generation of unique content which help in making the game re-playable. Procedural content generation can be used in almost all game design areas. From level generation to creating a storyline for the game, the use of PCG helps in decreasing the overall time required to design an interesting game. The only problem with PCG is that it is hard to implement and optimize. This document consists of an algorithm that works on a type of recursion and the concept of snappable meshes. This is done using prefabs and other features that are available in Unity Engine[6]. All the methods mentioned in this document are done using Unity Engine. Unity Engine is one of many famous game engines that are available online. The algorithm mentioned helps in creating a procedural maze. The game levels are generated dynamically, allowing the player to experience new levels and avoid repetition of the same levels as in traditional games. The algorithm and its implementation in Unity Engine are explained in detail. How the meshes are spawned and placed dynamically to generate a level is also discussed.","PeriodicalId":426897,"journal":{"name":"2022 IEEE Bombay Section Signature Conference (IBSSC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128412680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-08DOI: 10.1109/IBSSC56953.2022.10037438
Shivani Pandya, Swati Jain, J. P. Verma
Autism spectrum Disorder(ASD) is a complex neurobehavioral disorder that affects a person's ability to communicate and interact with others. It is also characterized by repetitive behaviors and restricted interests. There is no one-size-fits-all approach to autism, but early intervention and treatment can make a big difference in a person's life. Machine learning and deep learning are two promising areas of research that may help to improve our understanding of autism and lead for better treatments. Machine learning and Deep Learning approaches of artificial intelligence allows computers to learn from data without being explicitly programmed. These models could potentially be used to improve our ability to communicate with, and understand people with autism. Various machine-learning techniques are used to predict autism at an early stage. Support Vector Machine (SVM), Decision tree, Naïve Bayes, Random Forest, Logistic Regression, and K-Nearest Neighbour are some of the machine learning techniques used in this research area. Various advancement in the field of machine learning and Artificial Intelligence (AI) has helped in the development of ASD Detection using Machine learning and Deep Learning. In this research work, the prediction of Autism Spectrum Disorder has been performed on a video dataset. The video dataset contains the video of Autistic and Non-Autistic kids performing four different actions. The video features have been extracted through Convolutional Neural Network(CNN) models such as Inception V3and Resnet50 and are trained through long Short Term Memory(LSTM) based models by using this we get 91 % accuracy.
{"title":"AI based Classification for Autism Spectrum Disorder Detection using Video Analysis","authors":"Shivani Pandya, Swati Jain, J. P. Verma","doi":"10.1109/IBSSC56953.2022.10037438","DOIUrl":"https://doi.org/10.1109/IBSSC56953.2022.10037438","url":null,"abstract":"Autism spectrum Disorder(ASD) is a complex neurobehavioral disorder that affects a person's ability to communicate and interact with others. It is also characterized by repetitive behaviors and restricted interests. There is no one-size-fits-all approach to autism, but early intervention and treatment can make a big difference in a person's life. Machine learning and deep learning are two promising areas of research that may help to improve our understanding of autism and lead for better treatments. Machine learning and Deep Learning approaches of artificial intelligence allows computers to learn from data without being explicitly programmed. These models could potentially be used to improve our ability to communicate with, and understand people with autism. Various machine-learning techniques are used to predict autism at an early stage. Support Vector Machine (SVM), Decision tree, Naïve Bayes, Random Forest, Logistic Regression, and K-Nearest Neighbour are some of the machine learning techniques used in this research area. Various advancement in the field of machine learning and Artificial Intelligence (AI) has helped in the development of ASD Detection using Machine learning and Deep Learning. In this research work, the prediction of Autism Spectrum Disorder has been performed on a video dataset. The video dataset contains the video of Autistic and Non-Autistic kids performing four different actions. The video features have been extracted through Convolutional Neural Network(CNN) models such as Inception V3and Resnet50 and are trained through long Short Term Memory(LSTM) based models by using this we get 91 % accuracy.","PeriodicalId":426897,"journal":{"name":"2022 IEEE Bombay Section Signature Conference (IBSSC)","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134548350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-08DOI: 10.1109/IBSSC56953.2022.10037311
Lokesh Ramesh, Crispin Marie Peter G, Gladwyn K, Sundeep R, T. A, Ramkumar
The AGV's are beginning to change the way of the industries, there are still rooms for development of those AGV's. The hybrid AGV's which can climb walls and move on land for various purposes. The magnetic adhesion plays a major role in deciding the payload of the robot. The distance between the magnet and the iron rail surface embedded in the wall. The analysis was done on the magnet and the metal surface with FEMM software to find the best position to place the magnet in the robot. The distance between the magnet and the iron rail was also analyzed to reduce the friction and avoid magnets sticking to the rail. As it was found that the magnets positioning does play an important role in the overall payload and to give the required data to design the AVG to increase its performance. The design of the AGV is an important factor to consider the payload and the balance of the robot while climbing the wall to make sure that it doesn't fail. The motor modelling has been done with the help of MATLAB and the results are been recorded and is used for further studies and to incorporate the same in the mechanical design and make the AGV work properly. In summarizing the work, the magnets along with a design can improve the overall ability to perform the operations is essential, also the Motor modelling and the analysis done in MATLAB with Simulink will provide the results and data to make the AGV move with more precision.
{"title":"Motor Modelling and Magnetic adhesion Simulation For Hybrid Wall Climbing AGV","authors":"Lokesh Ramesh, Crispin Marie Peter G, Gladwyn K, Sundeep R, T. A, Ramkumar","doi":"10.1109/IBSSC56953.2022.10037311","DOIUrl":"https://doi.org/10.1109/IBSSC56953.2022.10037311","url":null,"abstract":"The AGV's are beginning to change the way of the industries, there are still rooms for development of those AGV's. The hybrid AGV's which can climb walls and move on land for various purposes. The magnetic adhesion plays a major role in deciding the payload of the robot. The distance between the magnet and the iron rail surface embedded in the wall. The analysis was done on the magnet and the metal surface with FEMM software to find the best position to place the magnet in the robot. The distance between the magnet and the iron rail was also analyzed to reduce the friction and avoid magnets sticking to the rail. As it was found that the magnets positioning does play an important role in the overall payload and to give the required data to design the AVG to increase its performance. The design of the AGV is an important factor to consider the payload and the balance of the robot while climbing the wall to make sure that it doesn't fail. The motor modelling has been done with the help of MATLAB and the results are been recorded and is used for further studies and to incorporate the same in the mechanical design and make the AGV work properly. In summarizing the work, the magnets along with a design can improve the overall ability to perform the operations is essential, also the Motor modelling and the analysis done in MATLAB with Simulink will provide the results and data to make the AGV move with more precision.","PeriodicalId":426897,"journal":{"name":"2022 IEEE Bombay Section Signature Conference (IBSSC)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131778708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-08DOI: 10.1109/IBSSC56953.2022.10037377
Siddharth Padhiar, K. Mehta, Juhi Patel, S. Panda
As the outbreak of COVID-19 increased in various countries. India is also majorly affected with the COVID-19 by that education system is affected, and it has transferred the traditional face-to-face teaching to online education platform. Considering student's perspective on both online and offline learning mode in India, we conducted a survey to collect the data. In that survey questionnaire, focus was on the factors and situation which can affect the education system. Using that data, we used Kruskal Wallis test to collect the evidence for which learning mode is better and Naive Bayes Algorithm, we were able to conclude the results.
{"title":"Observation of Online vs Offline Learning Experience","authors":"Siddharth Padhiar, K. Mehta, Juhi Patel, S. Panda","doi":"10.1109/IBSSC56953.2022.10037377","DOIUrl":"https://doi.org/10.1109/IBSSC56953.2022.10037377","url":null,"abstract":"As the outbreak of COVID-19 increased in various countries. India is also majorly affected with the COVID-19 by that education system is affected, and it has transferred the traditional face-to-face teaching to online education platform. Considering student's perspective on both online and offline learning mode in India, we conducted a survey to collect the data. In that survey questionnaire, focus was on the factors and situation which can affect the education system. Using that data, we used Kruskal Wallis test to collect the evidence for which learning mode is better and Naive Bayes Algorithm, we were able to conclude the results.","PeriodicalId":426897,"journal":{"name":"2022 IEEE Bombay Section Signature Conference (IBSSC)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132315459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-08DOI: 10.1109/IBSSC56953.2022.10037436
W. Xiao, Heqing Li, Deying Liu, Yi Zuo, Weiren Zhu, Yuning Yin
In order to promote the innovation of agricultural science and technology and the development of smart agriculture, a non-contact trench quality inspection system, UAV-LiDAR, is developed in this paper. The system uses the unmanned aerial vehicle (UAV) as the mobile flight platform, which can realize the non-contact trench quality data acquisition and automatic evaluation of the trench quality by carrying LiDAR, IMU, router and microcomputer. First of all, point cloud data sets of different areas of the whole farmland were acquired by UAV-LiDAR. Secondly. according to the normal deviation matching algorithm, appropriate frames were chosen to match the point cloud data of furrows in different regions, and the point clouds of each region were registered to the unified coordinate system to obtain the complete point cloud data of the whole farmland furrows. In the endusing the poisson surface reconstruction to realize the reconstruction surface of the complete furrow point cloudmeasuring the trench surface width, trench bottom width and trench depth of the furrow. The experimental results showed that the method proposed in this paper can effectively detect the working performance of the ditching machine. Compared with the manual measurement results, the identification time reduced by $15sim 20$ minutes, and the detection efficiency and accuracy are improved by $50%sim 66.67%$ and $22.96% sim 29.37%$, respectively. It realizes the visual remote appraisal of the performance of the trench machine, and provides an intelligent appraisal means for the performance of agricultural machinery.
{"title":"Non-contact detection of trench quality by UAV-LiDAR system","authors":"W. Xiao, Heqing Li, Deying Liu, Yi Zuo, Weiren Zhu, Yuning Yin","doi":"10.1109/IBSSC56953.2022.10037436","DOIUrl":"https://doi.org/10.1109/IBSSC56953.2022.10037436","url":null,"abstract":"In order to promote the innovation of agricultural science and technology and the development of smart agriculture, a non-contact trench quality inspection system, UAV-LiDAR, is developed in this paper. The system uses the unmanned aerial vehicle (UAV) as the mobile flight platform, which can realize the non-contact trench quality data acquisition and automatic evaluation of the trench quality by carrying LiDAR, IMU, router and microcomputer. First of all, point cloud data sets of different areas of the whole farmland were acquired by UAV-LiDAR. Secondly. according to the normal deviation matching algorithm, appropriate frames were chosen to match the point cloud data of furrows in different regions, and the point clouds of each region were registered to the unified coordinate system to obtain the complete point cloud data of the whole farmland furrows. In the endusing the poisson surface reconstruction to realize the reconstruction surface of the complete furrow point cloudmeasuring the trench surface width, trench bottom width and trench depth of the furrow. The experimental results showed that the method proposed in this paper can effectively detect the working performance of the ditching machine. Compared with the manual measurement results, the identification time reduced by $15sim 20$ minutes, and the detection efficiency and accuracy are improved by $50%sim 66.67%$ and $22.96% sim 29.37%$, respectively. It realizes the visual remote appraisal of the performance of the trench machine, and provides an intelligent appraisal means for the performance of agricultural machinery.","PeriodicalId":426897,"journal":{"name":"2022 IEEE Bombay Section Signature Conference (IBSSC)","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114590509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-08DOI: 10.1109/IBSSC56953.2022.10037546
Noshin Sabuwala, R. Daruwala
Unmanned Aerial Vehicles (UAVs) or drones and Ground Control Station (GCS) frequently use the lightweight Micro Air Vehicle Link (MAVLink) protocol for communication. It describes a series of communications sent back and forth between a GCS and a UAV. The communication provides data regarding the status of the UAV and orders for control sent by the GCS. However, the MAVLink protocol lacks security and is susceptible to several attacks, which poses serious risks to public safety. There is less research that offer remedies for this issue. To fill the gap, we talk about the security flaws in the MAVLink protocol in this paper and examine three security-integrated algorithms - ChaCha20, Encryption by Navid, and DMAV that researchers have proposed for MAVLink to protect the MAVLink messages that are sent back and forth between UAVs and GCSs. Using a simulated environment called Gazebo, a case study examines the methods used by the autopilot system, Ardupilot (a UAV), and QGroundControl (a GCS) to assess how well they perform in terms of packet transfer speed, memory utilisation, and CPU consumption. The results of the experiments demonstrate that ChaCha20 is more effective and performs better than other encryption algorithms. A resource-constrained drone's battery life and message secrecy can both be preserved by integrating ChaCha20 into MAVLink. This can be done without degrading MAVLink's performance and while using similar memory and CPU.
无人驾驶飞行器(uav)或无人机和地面控制站(GCS)经常使用轻型微型飞行器链路(MAVLink)协议进行通信。它描述了在GCS和无人机之间来回发送的一系列通信。通信提供有关无人机状态的数据和由GCS发送的控制命令。但是，MAVLink协议缺乏安全性，容易受到多种攻击，对公共安全构成严重威胁。针对这一问题提供补救措施的研究较少。为了填补这一空白，我们在本文中讨论了MAVLink协议中的安全漏洞，并研究了研究人员为MAVLink提出的三种安全集成算法——ChaCha20、Encryption by Navid和DMAV，以保护无人机和gcs之间来回发送的MAVLink消息。使用名为Gazebo的模拟环境，案例研究检查了自动驾驶系统，Ardupilot(无人机)和QGroundControl (GCS)使用的方法，以评估它们在数据包传输速度，内存利用率和CPU消耗方面的性能。实验结果表明，ChaCha20比其他加密算法更有效，性能更好。通过将ChaCha20集成到MAVLink中，可以保证资源受限无人机的电池寿命和信息保密性。这可以在不降低MAVLink性能的情况下完成，同时使用类似的内存和CPU。
{"title":"Securing Unmanned Aerial Vehicles by Encrypting MAVLink Protocol","authors":"Noshin Sabuwala, R. Daruwala","doi":"10.1109/IBSSC56953.2022.10037546","DOIUrl":"https://doi.org/10.1109/IBSSC56953.2022.10037546","url":null,"abstract":"Unmanned Aerial Vehicles (UAVs) or drones and Ground Control Station (GCS) frequently use the lightweight Micro Air Vehicle Link (MAVLink) protocol for communication. It describes a series of communications sent back and forth between a GCS and a UAV. The communication provides data regarding the status of the UAV and orders for control sent by the GCS. However, the MAVLink protocol lacks security and is susceptible to several attacks, which poses serious risks to public safety. There is less research that offer remedies for this issue. To fill the gap, we talk about the security flaws in the MAVLink protocol in this paper and examine three security-integrated algorithms - ChaCha20, Encryption by Navid, and DMAV that researchers have proposed for MAVLink to protect the MAVLink messages that are sent back and forth between UAVs and GCSs. Using a simulated environment called Gazebo, a case study examines the methods used by the autopilot system, Ardupilot (a UAV), and QGroundControl (a GCS) to assess how well they perform in terms of packet transfer speed, memory utilisation, and CPU consumption. The results of the experiments demonstrate that ChaCha20 is more effective and performs better than other encryption algorithms. A resource-constrained drone's battery life and message secrecy can both be preserved by integrating ChaCha20 into MAVLink. This can be done without degrading MAVLink's performance and while using similar memory and CPU.","PeriodicalId":426897,"journal":{"name":"2022 IEEE Bombay Section Signature Conference (IBSSC)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115414661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Real estate has always been a significant investment sector, but it has a high barrier to entry. Bitland Project aims to use blockchain technology in the real estate market, which could have an impact on market inefficiencies. Bitland is a platform that uses smart contracts to provide services for selling fractional ownership, registering real estate in the property registration and investors to buy the commercial property. This paper discusses existing blockchain application use cases in the real estate market. The study discusses current problems and roadblocks that must be overcome before blockchain technology can completely mature in this business. The goal is to create a blockchain-based digital real estate network for the management of investment properties and to handle the distribution of real estate smart contracts.
{"title":"Bitland-A Decentralized Commercial Real Estate Platform","authors":"Sakshi Sanjay Pande, Shrushti Mandolikar, Sanjay Shitole","doi":"10.1109/IBSSC56953.2022.10037494","DOIUrl":"https://doi.org/10.1109/IBSSC56953.2022.10037494","url":null,"abstract":"Real estate has always been a significant investment sector, but it has a high barrier to entry. Bitland Project aims to use blockchain technology in the real estate market, which could have an impact on market inefficiencies. Bitland is a platform that uses smart contracts to provide services for selling fractional ownership, registering real estate in the property registration and investors to buy the commercial property. This paper discusses existing blockchain application use cases in the real estate market. The study discusses current problems and roadblocks that must be overcome before blockchain technology can completely mature in this business. The goal is to create a blockchain-based digital real estate network for the management of investment properties and to handle the distribution of real estate smart contracts.","PeriodicalId":426897,"journal":{"name":"2022 IEEE Bombay Section Signature Conference (IBSSC)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126867121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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