An AI-Driven Secure and Intelligent Robotic Delivery System

Last-mile delivery has gained much popularity in recent years, it accounts for about half of the whole logistics cost. Unlike container transportation, companies must hire significant number of employees to deliver packages to the customers. Therefore, many companies are studying automated methods such as robotic delivery to complete the delivery work to reduce the cost. It is undeniable that the security issue is a huge challenge in such a system. In this article, we propose an AI-driven robotic delivery system, which consists of two modules. A multilevel cooperative user authentication module for delivering parcel using both PIN code and biometrics verification, i.e., voiceprint and face verification. Another noncooperative user identification module using face verification which detects and verifies the identification of the customer. In this way, the robot can find the correct customer and complete the delivery task automatically. Finally, we implement the proposed system on a Turtlebot3 robot and analyze the performance of the proposed schema. Experimental results show that our proposed system has a high accuracy and can complete the delivery task securely

Performance Comparison Of Weak And Strong Learners In Detecting GPS Spoofing Attacks On Unmanned Aerial Vehicles (uavs)

Unmanned Aerial Vehicle systems (UAVs) are widely used in civil and military applications. These systems rely on trustworthy connections with various nodes in their network to conduct their safe operations and return-to-home. These entities consist of other aircrafts, ground control facilities, air traffic control facilities, and satellite navigation systems. Global positioning systems (GPS) play a significant role in UAV\u27s communication with different nodes, navigation, and positioning tasks. However, due to the unencrypted nature of the GPS signals, these vehicles are prone to several cyberattacks, including GPS meaconing, GPS spoofing, and jamming. Therefore, this thesis aims at conducting a detailed comparison of two widely used machine learning techniques, namely weak and strong learners, to investigate their performance in detecting GPS spoofing attacks that target UAVs. Real data are used to generate training datasets and test the effectiveness of machine learning techniques. Various features are derived from this data. To evaluate the performance of the models, seven different evaluation metrics, including accuracy, probabilities of detection and misdetection, probability of false alarm, processing time, prediction time per sample, and memory size, are implemented. The results show that both types of machine learning algorithms provide high detection and low false alarm probabilities. In addition, despite being structurally weaker than strong learners, weak learner classifiers also, achieve a good detection rate. However, the strong learners slightly outperform the weak learner classifiers in terms of multiple evaluation metrics, including accuracy, probabilities of misdetection and false alarm, while weak learner classifiers outperform in terms of time performance metrics

Remote Sensing for Land Administration 2.0

The reprint “Land Administration 2.0” is an extension of the previous reprint “Remote Sensing for Land Administration”, another Special Issue in Remote Sensing. This reprint unpacks the responsible use and integration of emerging remote sensing techniques into the domain of land administration, including land registration, cadastre, land use planning, land valuation, land taxation, and land development. The title was chosen as “Land Administration 2.0” in reference to both this Special Issue being the second volume on the topic “Land Administration” and the next-generation requirements of land administration including demands for 3D, indoor, underground, real-time, high-accuracy, lower-cost, and interoperable land data and information

UAVs and Blockchain Synergy: Enabling Secure Reputation-based Federated Learning in Smart Cities

Unmanned aerial vehicles (UAVs) can be used as drones’ edge Intelligence to assist with data collection, training models, and communication over wireless networks. UAV use for smart cities is rapidly growing in various industries, including tracking and surveillance, military defense, managing healthcare delivery, wireless communications, and more. In traditional machine learning techniques, an enormous amount of sensor data from UAVs must be shared to central storage to perform model training, which poses serious privacy risks and risks of misuse of information. The federated learning technique (FL), which can be applied to UAVs, is a promising means of collaboratively training a global model while retaining local access to sensitive raw data. Despite this, FL is a significant communication burden for battery-constrained UAVs due to local model training and global synchronization frequency. In this article, we address the major challenges associated with UAV-based FL for smart cities, including single-point failure, privacy leakage, scalability, and global model verification. To tackle these challenges, we present a differentially private federated learning framework based on Accumulative Reputation-based Selection (ARS) for the edge-aided UAV network that utilizes blockchains to prevent single-point failures where we switched from central control to decentralized control, Interplanetary File System (IPFS) for off-chain model storage and their respective hash-keys on-chain to ensure model integrity. Due to IPFS, the size of the blockchain will be reduced, and local differential privacy will be applied to prevent privacy leakages. In the proposed framework, an aggregator will be selected based on its ARS score and model verification by the validators. After most validators approve it, it will be available for use. Several parameters are taken into consideration during evaluation, including accuracy, precision, recall, F1-score, and time consumption. It also evaluates the number of edge computers vs test accuracy, the number of edge computers vs time consumption for global model convergence, and the number of rounds vs test accuracy. This is done by considering two benchmark datasets: MNIST and CIFAR-10. The results show that the proposed work preserves privacy while achieving high accuracy. Moreover, it is scalable to accommodate many participants

Unmanned Aerial Vehicle (UAV)-Enabled Wireless Communications and Networking

The emerging massive density of human-held and machine-type nodes implies larger traffic deviatiolns in the future than we are facing today. In the future, the network will be characterized by a high degree of flexibility, allowing it to adapt smoothly, autonomously, and efficiently to the quickly changing traffic demands both in time and space. This flexibility cannot be achieved when the network’s infrastructure remains static. To this end, the topic of UAVs (unmanned aerial vehicles) have enabled wireless communications, and networking has received increased attention. As mentioned above, the network must serve a massive density of nodes that can be either human-held (user devices) or machine-type nodes (sensors). If we wish to properly serve these nodes and optimize their data, a proper wireless connection is fundamental. This can be achieved by using UAV-enabled communication and networks. This Special Issue addresses the many existing issues that still exist to allow UAV-enabled wireless communications and networking to be properly rolled out

UAS Application in Agriculture: A Review of Technologies Possible to Apply in Portugal

The world population has been significantly growing over the last years. Consequently, also the needs and search of raw materials and goods has been increasing. In this context, the production, in a sustainable way and in the needed quantities, of food is a source of concern and study. At the same time a big evolution in the Unmanned Aerial Vehicles (UAV) has been verified, both in terms at the level of the equipment, and the operational scenarios where they have been used. Portugal presents itself as a country where the agriculture and livestock activities have a very big predominance in the use of the available soil and the economy. However, the lack of studies and implementation of new technologies keeps on being a limiting factor to the increase of productivity, sustainable use of the available resources and of the value that agriculture adds to the national trade balance. The main objective of this dissertation was to show that it is possible to apply several new methods and techniques, more specifically UAVs, to the Portuguese agricultural scenario. For this extensive research was carried out and a set of studies with the potential to be adapted and implement in Portugal were selected, encompassing different cultures and activities associated with them. After the chosen studies had been exposed and carefully analysed it was possible to perceive that the application of UAS’ in Portuguese agriculture would be a great added value insofar as it could lead to savings of several million euros both in increasing productivity, as well as in reducing costs with chemicals and field tests that are becoming obsolete. The saving of limited natural resources, namely water is also a very important factor.A população mundial tem vindo a crescer de forma muito significativa ao longo dos últimos anos. Consequentemente, também as necessidades e a procura de matérias-primas e bens têm aumentado. Neste contexto, a produção, de forma sustentável e nas quantidades necessárias, de bens alimentares é fonte de preocupação e estudo. Paralelamente tem-se verificado uma evolução bastante grande nos Veículos Aéreos Não Tripulados (UAV), quer ao nível do equipamento em si, quer ao dos cenários operacionais nos quais têm vindo a ser empregues. Portugal apresenta-se como um país em que as atividades agropecuárias têm uma predominância muito grande no uso do solo disponível e na economia. No entanto, a falta de estudos e da implementação de novas tecnologias continuam a ser fatores limitativo ao aumento de produtividade, do aproveitamento sustentável dos recursos disponíveis e do valor que a agricultura agrega à balança comercial nacional. O objetivo principal desta dissertação foi mostrar que é possível aplicar diversos novos métodos e técnicas, e mais especificamente UAV, ao cenário agrícola português. Para tal foi efetuada uma extensa pesquisa e selecionado um conjunto de estudos considerados relevantes e com potencial de serem adaptados e implementados em Portugal, englobando diversas culturas e atividades a elas associadas. Com este propósito e depois de os estudos escolhidos terem sido expostos e cuidadosamente analisados foi possível perceber que a aplicação de UAS na agricultura portuguesa seria uma grande mais-valia na medida em que poderia conduzir à poupança de diversos milhões de euros tanto no aumento de produtividade, assim como na redução dos custos em químicos e testes de campo que se estão a tornar obsoletos. Poderá também contribuir para a poupança de recursos naturais, nomeadamente de água

Sustainable Agriculture and Advances of Remote Sensing (Volume 1)

Agriculture, as the main source of alimentation and the most important economic activity globally, is being affected by the impacts of climate change. To maintain and increase our global food system production, to reduce biodiversity loss and preserve our natural ecosystem, new practices and technologies are required. This book focuses on the latest advances in remote sensing technology and agricultural engineering leading to the sustainable agriculture practices. Earth observation data, in situ and proxy-remote sensing data are the main source of information for monitoring and analyzing agriculture activities. Particular attention is given to earth observation satellites and the Internet of Things for data collection, to multispectral and hyperspectral data analysis using machine learning and deep learning, to WebGIS and the Internet of Things for sharing and publishing the results, among others