SOBV-FEF: secure lightweight data offloading base in blockchain technology for internet of vehicles enabled handover UAVs within a Fog-Edge federation.

Due to flexible scheduling, and better transmission channel, unmanned aerial vehicle (UAV) can improve transmit performance of Internet of Things (IoT). In this paper, we propose an UAV-enabled multichannel IoT based on intelligent fog computing, where UAV as a relay forwards IoT's information to the data center under the control of fog computing base station in the case of terrestrial channel fading. The IoT's throughput are maximized by jointly optimizing subcarrier, power of IoT, and UAV, and UAV trajectory, subject to the constraints of information causality, maximum transmit power, and maximum UAV speed. The subcarriers are dynamically allocated according to their channel gains, the water filling algorithm is adopted to optimize the power for UAV, and IoT by fixing UAV trajectory, and the optimal UAV trajectory is achieved with successive convex approximation under the fixed power allocation. Then a jointly iterative optimization on subcarrier, power, and trajectory is presented to get the optimal solution. In addition, we propose a fairness optimization scheme to maximize the minimum transmit rate of IoT nodes. The simulations indicate the IoT with mobile UAV, and dynamic subcarrier allocation may achieve better transmit performance, and the fairness optimization can decrease the rate difference of nodes effectively.

The Internet of Things is defined as a network of identifiable and unique elements that communicate without human interaction using IP connectivity. This new concept covers several domains: energy, smart home, agriculture, healthcare. The integration of IoT in the healthcare sector is giving rise to a new paradigm called the Internet of Medical Things (IoMT). These benefits can be extended when IoMT get integrated to Unmanned Aerial Vehicles (UAVs), since it is very useful in supporting logistics and pave the way to revolutionizing healthcare. This chapter aims to shad the light on coupling different disciplines of IoMT and UAVs in healthcare along with their capabilities, advantages, applications, challenges, and conclude with some recommendations and future work. This contribution is a noticeable shift from exiting work in the literature, where it provides a panoramic view on the proposed integration between IoMT and UAVs, especially in isolated areas with difficult terrines where it could be economically infeasible or may be impossible to provide wireless services using traditional systems.

The Internet of Things (IoT) has many applications, including Industry 4.0. There are a number of challenges when deploying IoT devices in the Industry 4.0 setting, partly due to the low-cost IoT devices/nodes with limited capacity to run/support security solutions. Hence, there is a need for a lightweight and efficient security solution to protect the environment. Thus, in this article, we present a robust, lightweight, and provably secure authentication and key agreement protocol specifically for the IoT environment based on a hierarchical approach. The proposed protocol relies on lightweight operations, such as elliptic curve cryptography, physically unclonable functions, hash functions, concatenation, and XOR operations. We then evaluate the security of the designed protocol, including the widely used automated validation of Internet security protocols and applications (AVISPA), and demonstrate that it supports mutual authentication between IoT nodes and server, and is resilient against a number of common security attacks [denial of service (DoS), replay, spoofing, etc.]. The computational and communication overhead analysis shows that the proposed protocol is comparatively less expensive than three other recently published, competing protocols.

When the Wireless Sensor Network (WSN) is combined with the Internet of Things (IoT), it can be employed in a wide range of applications, such as agriculture, industry 4.0, health care, smart homes, among others. Accessing the big data generated by these applications in Cloud Servers (CSs), requires higher levels of authenticity and confidentiality during communication conducted through the Internet. Signcryption is one of the most promising approaches nowadays for overcoming such obstacles, due to its combined nature, i.e., signature and encryption. A number of researchers have developed schemes to address issues related to access control in the IoT literature, however, the majority of these schemes are based on homogeneous nature. This will be neither adequate nor practical for heterogeneous IoT environments. In addition, these schemes are based on bilinear pairing and elliptic curve cryptography, which further requires additional processing time and more communication overheads that is inappropriate for real-time communication. Consequently, this paper aims to solve the above-discussed issues, we proposed an access control scheme for IoT environments using heterogeneous signcryption scheme with the efficiency and security hardiness of hyperelliptic curve. Besides the security services such as replay attack prevention, confidentiality, integrity, unforgeability, non-repudiations, and forward secrecy, the proposed scheme has very low computational and communication costs, when it is compared to existing schemes. This is primarily because of hyperelliptic curve lighter nature of key and other parameters. The AVISPA tool is used to simulate the security requirements of our proposed scheme and the results were under two backbends (Constraint Logic-based Attack Searcher (CL-b-AtSER) and On-the-Fly Model Checker (ON-t-FL-MCR)) proved to be SAFE when the presented scheme is coded in HLPSL language. This scheme was proven to be capable of preventing a variety of attacks, including confidentiality, integrity, unforgeability, non-repudiation, forward secrecy, and replay attacks.

Recent advancements in technology have led to a great interest in the use of Unmanned Aerial Vehicles (UAVs) for a vast array of applications such as real time site monitoring, target search and destroy and UAVs being used as mobile sinks to collect data from Internet of Things (IoT) devices. This is mainly due to their autonomy, high mobility, ease of deployment and affordable nature. A group of UAVs can be used collectively to bring a coordinated effort in task execution, allowing more tasks to be completed in a wider area and in the shortest possible time. However, using multiple UAVs presents some challenges for efficient cooperation. UAVs are resource constrained due to being battery powered and this limits the permissible flight time. Therefore, it is necessary to intelligently manage their operation given the limited resources and other constraints associated with the mission. In this paper, we propose a multi-objective UAV task assignment model to support spatio-temporally distributed events raised by static IoT devices, using a discrete Non- Dominated Sorting Genetic Algorithm II (NSGA-II). This model assigns the most suitable UAV(s) to serve at the different event locations ensuring that none of the constraints are violated. The performance of the algorithm was evaluated through numerical simulations and compared to a similar implementation using Mixed Integer Linear Programming (MILP). Results show an improvement of 7.9% in the total energy consumption for all UAVs while ensuring that all the temporal constraints are not violated.

At present, unmanned aerial vehicles (UAVs) have been widely used in civilian fields (such as smart cities). However, the external environment of the UAV network is complex and computing resources are limited, so it is vulnerable to serious security threats, such as replay attacks, forgery attacks, man-in-the-middle attacks. Seriously, it will cause great damage to the work of the UAV in smart cities. Aiming at the problem of transmission instruction data leakage caused by malicious UAV in communication between road base stations and UAVs, this paper proposes a lightweight identity authentication scheme based on elliptic curve cryptography (ECC). The purpose is to ensure the identity authentication of the UAV and the road base station, to ensure that the mission instructions received by the UAV are authentic and reliable, and to ensure the privacy of the UAV's identity information. The algorithm mainly includes the system initialization phase, initializing the UAV and road base station, and the identity authentication phase. Compared with the traditional identity authentication method, this method has the characteristics of low computational cost, short key and high security, and is more suitable for UAV communication.

To enhance the service quality of the unmanned aerial vehicle (UAV), the UAV-aided Internet of Things (IoT) systems could deploy a Deep Neural Network (DNN) for performance prediction for the users. Non-orthogonal multiple access (NOMA) is applied to such networks in order to improve spectrum efficiency, and results in improved quality of service at the ground users under the mobility of UAV. The outage and ergodic capacity requirements of the IoT users may not be satisfied due to some imperfect system parameters such as hardware noise. A DNN-based algorithm for performance prediction and the design of multiple antennas at the UAV under hardware noise is proposed. In this DNN-based UAV-NOMA, the central controller (server) collects system parameters periodically based on observing the state of IoT system and performs adjustments to the dynamic environment. The closed-form expressions for the outage probability and the ergodic capacity are derived to evaluate the performance of a group of IoT users. Our numerical results demonstrate that: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i) In contrast to the traditional UAV-NOMA system, the UAV cannot know the performance at each IoT user in order to adjust the parameters (i.e. power allocation factors) before transmitting the signals to the devices; while the proposed DNN-based IoT system is capable of predicting the performance; ii) The performance of the IoT users can be significantly improved by integrating more antennas at the UAV and limiting levels of hardware noise; iii) By designing NOMA, the UAV-NOMA-based IoT system can increase the throughput to the tune of 40% compared with the benchmark (the orthogonal multiple access (OMA)-based IoT)</i> .

A lightweight key management scheme for key-escrow-free ECC-based CP-ABE for IoT healthcare systems

In many emerging Internet of Things (IoT) applications, the freshness of the is an important design criterion. Age of Information (AoI) quantifies the freshness of the received information or status update. This work considers a setup of deployed IoT devices in an IoT network; multiple unmanned aerial vehicles (UAVs) serve as mobile relay nodes between the sensors and the base station. We formulate an optimization problem to jointly plan the UAVs' trajectory, while minimizing the AoI of the received messages and the devices' energy consumption. The solution accounts for the UAVs' battery lifetime and flight time to recharging depots to ensure the UAVs' green operation. The complex optimization problem is efficiently solved using a deep reinforcement learning algorithm. In particular, we propose a deep Q-network, which works as a function approximation to estimate the state-action value function. The proposed scheme is quick to converge and results in a lower ergodic age and ergodic energy consumption when compared with benchmark algorithms such as greedy algorithm (GA), nearest neighbour (NN), and random-walk (RW).

Energy optimization for full-duplex wireless-powered IoT networks using rotary-wing UAV with multiple antennas

Internet of Things (IoT) authentication for resource-constrained devices thrives under lightweight solutions. The requirements of the lightweight solutions are that, they have to meet the processing, storage and limited resource base of the resource-constrained devices. There are a number of lightweight solutions advanced for IoT under different domains. To provide feasible authentication solutions for smart home security calls for focus on key attributes that suit the domain in question. This paper is positioned to give a review of some existing lightweight authentication schemes, guide the selection and design of best possible solutions that can be applied to smart home environments. From the costing of randomly selected lightweight authentication techniques, the least costly solution is recommended for adoption.

The data everywhere was being transferred for each second using domain of Internet of Things (IoT). To secure data using the Internet of Things (IoT) is a tedious task. When the data is being transferred using Internet of Things (IoT), more security can be provided using elliptic curve cryptography. Asymmetric cryptography is used by most of the applications for providing secure communication between two parties (Weber in Comput Law Secur Rev 26:23–30, 2010). The purpose of this type of cryptography is the requirement of huge amount of computation and storage. This is where the use of elliptic curve cryptography comes into picture, as it needs less storage and can be used in small computational devices. ECC needs smaller key sizes and provides stronger encryption compared to various asymmetric cryptographic algorithms like RSA. The usage of power required is low but the performance of the devices using ECC is high for different types of devices like IoT, sensors, etc. This paper shows how ECC must be implemented strongly for providing communication securely to encode the data on an elliptic curve, in IoT devices. The encryption of the data must be done securely while mapping the data on to the elliptic curve. This paper shows how the data is encrypted and mapped on to the curve securely. The work shows how the data can be encrypted with ECC and how it can be visible to only authorized users. In the field of cryptography, ECC is a method for asymmetric cryptography which is dependent on the algebraic structure of an elliptic curve on the finite field.

Abstract: This review paper offers a comprehensive exploration of the multifaceted applications of Unmanned Aerial Vehicles (UAVs) in various domains, showcasing their transformative impact in addressing complex challenges. The evaluation of cloud-based UAV systems' stability reveals their robustness and reliability, underlining their significance in numerous industries. Additionally, their role in enhancing robot navigation in intricate environments signifies a substantial advancement in robotics and automation. The integration of blockchain technology for secure Internet of Things (IoT) data transfer emphasizes the critical importance of data integrity and confidentiality in the IoT era. Furthermore, the optimization of energy-efficient data collection in IoT networks through UAVs demonstrates their potential to revolutionize data-driven decision-making processes, particularly in fields reliant on data accuracy and timeliness. The paper also highlights the application of deep reinforcement learning to enhance UAV-assisted IoT data collection, showcasing the synergy between advanced machine learning techniques and UAV technology. Finally, the discussion underscores the pivotal role of UAVs in precision agriculture, where they facilitate ecological farming practices and monitor environmental conditions, contributing to the pursuit of sustainable and efficient agriculture. This review reaffirms UAVs' status as transformative tools, reshaping industries and unlocking new frontiers of innovation and problem-solving. With ongoing technological advancements, UAVs are poised to play an increasingly central role in a wide range of applications, promising a future marked by ground breaking possibilities. Key findings include the dominance of the United States and China in the field, exploration of characteristics such as crop production, and innovative UAV-based methods for grassland mapping, maize growth assessment, and Arctic plant species monitoring. The research underscores the potential of UAVs in bridging field data and satellite mapping, providing valuable insights into diverse applications, from soil analysis to yield predictions, highlighting their transformative role in environmental monitoring and precision agriculture

This paper presents a novel mutual identity authentication scheme which can be applied securely in Internet of Things. Based on secure hash algorithm(SHA), feature extraction and elliptic curve cryptography(ECC), we propose an asymmetric mutual authentication scheme between the platform and the terminal node, which imposes light computation and communication cost, through security analysis it is also shown that the proposed scheme is secure and feasible for applications in the Internet of Things.

The internet of things (IoT) is a rapidly developing area that consists of a globally linked network architecture based on the Internet. The internet of healthcare things (IoHT) is a subset of IoT that comprises of smart healthcare devices that are critical in monitoring, processing, storing, and transferring sensitive data. It is confronted with new issues in terms of data privacy protection. To safeguard healthcare information, this work proposes hybrid lightweight ciphers (PRESENT and TEA) that leverage elliptic curve cryptography (ECC) in the key generation phase. The proposed system evaluated using the main network evaluation parameters as throughput in Kbps, delay in ms, packet loss rate (%). The proposed approach provides secure data transmission of IoT devices based on the used lightweight security algorithms, in addition it provides conserving network performance, improving channel resource usage, network latency is increased due to the security level added by PRESENT and TEA with ECC, and decrease number of loss packets compared without security case study.