Wireless Powering Internet of Things with UAVs: Challenges and Opportunities

Unmanned aerial vehicles (UAVs) have the potential to overcome the deployment constraint of Internet of Things (IoT) in remote or rural area. Wirelessly powered communications (WPC) can address the battery limitation of IoT devices through transferring wireless power to IoT devices. The integration of UAVs and WPC, namely UAV-enabled Wireless Powering IoT (Ue-WPIoT) can greatly extend the IoT applications from cities to remote or rural areas. In this article, we present a state-of-the-art overview of Ue-WPIoT by first illustrating the working flow of Ue-WPIoT and discussing the challenges. We then introduce the enabling technologies in realizing Ue-WPIoT. Simulation results validate the effectiveness of the enabling technologies in Ue-WPIoT. We finally outline the future directions and open issues.Comment: 7 pages, 4 figure

Federated Learning with Privacy-Preserving Incentives for Aerial Computing Networks

With the help of artificial intelligence (AI) model, aerial computing can help analyze and predict the network dynamics and support intelligent decision-making to improve the performance of 6G space-air-ground integrated networks. Federated learning has been proposed to tackle the challenges of limited energy and data shortage for the application of AI models in aerial computing networks. A critical problem of FL for aerial computing is the lack of incentives due to privacy concerns. On the one hand, the information needed to measure users’ learning quality may be eavesdropped. On the other hand, users’ real costs for determining payments may also undertake inference attacks. In this paper, we design a privacy-preserving and learning quality-aware incentive mechanism for federated learning in aerial computing networks. We propose differential privacy based scheme to protect the privacy of the real cost. In addition, utilize Combinatorial Multi-Armed Bandit (CMAB) algorithm to evaluate the user learning quality without any participant information. Simulation results demonstrate that our scheme can significantly motivate high-quality participants with guaranteed privacy preservation and achieve effective federated learning under the constraint of the limited budget

Unmanned Aerial Vehicle for Internet of Everything: Opportunities and Challenges

The recent advances in information and communication technology (ICT) have further extended Internet of Things (IoT) from the sole "things" aspect to the omnipotent role of "intelligent connection of things". Meanwhile, the concept of internet of everything (IoE) is presented as such an omnipotent extension of IoT. However, the IoE realization meets critical challenges including the restricted network coverage and the limited resource of existing network technologies. Recently, Unmanned Aerial Vehicles (UAVs) have attracted significant attentions attributed to their high mobility, low cost, and flexible deployment. Thus, UAVs may potentially overcome the challenges of IoE. This article presents a comprehensive survey on opportunities and challenges of UAV-enabled IoE. We first present three critical expectations of IoE: 1) scalability requiring a scalable network architecture with ubiquitous coverage, 2) intelligence requiring a global computing plane enabling intelligent things, 3) diversity requiring provisions of diverse applications. Thereafter, we review the enabling technologies to achieve these expectations and discuss four intrinsic constraints of IoE (i.e., coverage constraint, battery constraint, computing constraint, and security issues). We then present an overview of UAVs. We next discuss the opportunities brought by UAV to IoE. Additionally, we introduce a UAV-enabled IoE (Ue-IoE) solution by exploiting UAVs's mobility, in which we show that Ue-IoE can greatly enhance the scalability, intelligence and diversity of IoE. Finally, we outline the future directions in Ue-IoE.Comment: 21 pages, 9 figure

Securing internet of medical things with friendly-jamming schemes

The Internet of Medical Things (IoMT)-enabled e-healthcare can complement traditional medical treatments in a flexible and convenient manner. However, security and privacy become the main concerns of IoMT due to the limited computational capability, memory space and energy constraint of medical sensors, leading to the in-feasibility for conventional cryptographic approaches, which are often computationally-complicated. In contrast to cryptographic approaches, friendly jamming (Fri-jam) schemes will not cause extra computing cost to medical sensors, thereby becoming potential countermeasures to ensure security of IoMT. In this paper, we present a study on using Fri-jam schemes in IoMT. We first analyze the data security in IoMT and discuss the challenges. We then propose using Fri-jam schemes to protect the confidential medical data of patients collected by medical sensors from being eavesdropped. We also discuss the integration of Fri-jam schemes with various communication technologies, including beamforming, Simultaneous Wireless Information and Power Transfer (SWIPT) and full duplexity. Moreover, we present two case studies of Fri-jam schemes in IoMT. The results of these two case studies indicate that the Fri-jam method will significantly decrease the eavesdropping risk while leading to no significant influence on legitimate transmission

Connectivity of underwater cognitive acoustic networks under spectrum constraint

There is an extensive attention on underwater cognitive acoustic networks (UCANs) since acoustic spectrum becomes deficient owning to the proliferation of human activity in ocean. This paper presents an overview of our recent progress in investigating the connectivity of secondary users (SUs) in UCANs. In particular, this model takes both topological connectivity and spectrum availability into account. Simulation results verify the accuracy of the proposed model

Artificial noise aided scheme to secure UAV-assisted internet of things with wireless power transfer

The proliferation of massive Internet of Things (IoT) devices poses research challenges especially in unmanned aerial vehicles(UAV)-assisted IoT. In particular, the limited battery capacity not only restricts the life time of UAV-assisted IoT but also brings security vulnerabilities since computation-complex cryptographic algorithms cannot be adopted in UAV-assisted IoT systems. In this paper, artificial noise and wireless power transfer technologies are integrated to secure communications in UAV-assisted IoT (particularly in secret key distribution). We present the artificial noise aided scheme to secure UAV-assisted IoT communications by letting UAV gateway transfer energy to a number of helpers who will generate artificial noise to interfere with the eavesdroppers while the legitimate nodes can decode the information by canceling additive artificial noise. We introduce the eavesdropping probability and the security rate to validate the effectiveness of our proposed scheme. We further formulate an eavesdropping probability constrained security rate maximization problem to investigate the optimal power allocation. Moreover, analytical and numerical results are provided to obtain some useful insights, and to demonstrate the effect of crucial parameters (e.g., the transmit power, the main channel gain) on the eavesdropping probability, the security rate, and the optimal power allocation. © 2020 Elsevier B.V

UAV-enabled friendly jamming scheme to secure industrial Internet of Things

Eavesdropping is a critical threat to the security of industrial Internet of things (IIoT) since many malicious attacks often follow eavesdropping activities. In this paper, we present an anti-eavesdropping scheme based on multiple unmanned aerial vehicles (UAVs) who emit jamming signals to disturb eavesdropping activities. We name such friendly UAV-enabled jamming scheme as Fri-UJ scheme. In particular, UAV-enabled jammers (UJs) emit artificial noise to mitigate the signal to interference plus noise ratio (SINR) at eavesdroppers consequently reducing the eavesdropping probability. In order to evaluate the performance of the proposed Fri-UJ scheme, we establish a theoretical framework to analyze both the local eavesdropping probability and the overall eavesdropping probability. Our analytical results show that the Fri-UJ scheme can significantly reduce the eavesdropping risk while having nearly no impact on legitimate communications. Meanwhile, the simulation results also agree with the analytical results, verifying the accuracy of the proposed model. The merits of Fri-UJ scheme include the deployment flexibility and no impact on legitimate communications

Ear in the sky : terrestrial mobile jamming to prevent aerial eavesdropping

The emerging unmanned aerial vehicles (UAVs) pose a potential security threat for terrestrial communications when UAVs can be maliciously employed as UAV-eavesdroppers to wiretap confidential communications. To address such an aerial security threat, we present a friendly jamming scheme named terrestrial mobile jamming (TMJ) to protect terrestrial confidential communications from UAV eavesdropping. In our TMJ scheme, a jammer moving along the protection area can emit jamming signals toward the UAV-eavesdropper so as to reduce the eavesdropping risk. We evaluate the performance of our scheme by analyzing a secrecy-capacity maximization problem subject to the legitimate connectivity and eavesdropping probability. In addition, we investigate the optimized position for the jammer as well as its jamming power. Simulation results verify the effectiveness of the proposed scheme. © 2021 IEEE