Lightweight blockchain assisted secure routing of swarm UAS networking

Over the last few years, several researchers have been developing protocols and applications in order to autonomously land unmanned aerial vehicles (UAVs). However, most of the proposed protocols rely on expensive equipment or do not satisfy the high precision needs of some UAV applications such as package retrieval and delivery or the compact landing of UAV swarms. Therefore, in this work, a solution for high precision landing based on the use of ArUco markers is presented. In the proposed solution, a UAV equipped with a low-cost camera is able to detect ArUco markers sized 56 × 56 cm from an altitude of up to 30 m. Once the marker is detected, the UAV changes its flight behavior in order to land on the exact position where the marker is located. The proposal was evaluated and validated using both the ArduSim simulation platform and real UAV flights. The results show an average offset of only 11 cm from the target position, which vastly improves the landing accuracy compared to the traditional GPS-based landing, which typically deviates from the intended target by 1 to 3 m.

https://www.mdpi.com/2079-9292/8/12/1532/pdf

Accurate Landing of Unmanned Aerial Vehicles Using Ground Pattern Recognition

Rapid evolution of 5G mobile network has prompted the design of more reliable service assurance mechanism for radio and optical wireless networks. It has been a crucial issue of network operation that once multiple failures occur simultaneously, more users will be affected and the transmission of real-time services cannot be guaranteed. Therefore, rapid locating of faults is the premise for network to recover quickly. However, current faults location methods can’t satisfy the requirement due to the expansion of network scale and the complexity of topological connection in 5G and beyond scene. In this paper, we propose an efficient hybrid multi-faults location algorithm based on Hopfield Neural Network (HNN) in radio and optical wireless networks. We make full use of the information of network topology and the services transmitted to model the relationship between fault set and alarm set. HNN is used as an optimization method to analyze the uncertainty of faults and alarms and to find where the faults most likely occur by constructing a proper energy function. It has been proved by simulations that this method has a fast convergence and can achieve real-time faults location while ensuring positioning accuracy.

Efficient Hybrid Multi-Faults Location Based on Hopfield Neural Network in 5G Coexisting Radio and Optical Wireless Networks

Blockchain is a modern technology that has revolutionized the way society interacts and trades. It could be defined as a chain of blocks that stores information with digital signatures in a distributed and decentralized network. This technique was first adopted for the creation of digital cryptocurrencies, such as Bitcoin and Ethereum. However, research and industrial studies have recently focused on the opportunities that blockchain provides in various other application domains to take advantage of the main features of this technology, such as: decentralization, persistency, anonymity, and auditability. This paper reviews the use of blockchain in several interesting fields, namely: finance, healthcare, information systems, wireless networks, Internet of Things, smart grids, governmental services, and military/defense. In addition, our paper identifies the challenges to overcome, to guarantee better use of this technology.

/pdf/blockchain-for-modern-applications-a-survey-2xziiwpb.pdf

Blockchain for Modern Applications: A Survey

Unmanned aerial vehicle (UAV) communications have attracted significant attention from both academia and industry. To facilitate the large-scale usage of UAVs for various applications in practice, we provide a comprehensive survey on the prototype and experiment for UAV communications. To this end, we first provide an overview on the general architecture of the prototype and experiment for UAV communications, and then present experimental verification for air-to-ground channel models and UAV energy consumption models. Next, we discuss measurement experiments on two promising paradigms of UAV communications, namely cellular-connected UAVs and UAV-enabled aerial communication platforms. For the former, we focus on the feasibility study and address the interference mitigation issue. For UAV-enabled aerial communication platforms, we present three scenarios, namely UAV-enabled aerial base stations, UAV-enabled aerial relays and UAV-enabled aerial data collection/dissemination. Finally, we point out some promising future directions for prototype and experimental measurements of UAV communications.

A survey of prototype and experiment for UAV communications

One of the core issues in routing packets within Mobile Ad hoc Networks (MANETs) is the lack of trust and reputation of the participating nodes, which often leads to unreliable packet delivery. We use a fraction of nodes to validate routing actions taken by other nodes and leverage the distributed consensus mechanism in Blockchain networks to accrue the reputation of each node. Specifically, we employ heterogeneous difficulty for Proof of Work to represent the credibility of validation and design a scoring system to isolate malicious nodes via distributed consensus. The reputation of a node is then based on the combination of the difficulty level and the score. This reputation is incorporated in a novel routing metric to calculate the shortest, most reputed path between a source and destination node. The goal is to discourage malicious nodes by excluding those from participating in routing packets. A joint simulation of the Blockchain and routing algorithm reveal ≈12% improvement in overall packet delivery in the presence of routing attacks, compared to conventional routing algorithms in MANETs.

Reputation based Routing in MANET using Blockchain

Distributed enforcement of spectrum policies require fusion of sensing results from a set of spatially scattered sensors to detect anomalous behavior with the highest possible accuracy. Central to this problem is the lack of trust or reputation of the participating sensors, which often leads to incorrect and biased inferences. In SenseChain, we leverage the distributed consensus mechanism employed in Blockchain networks to capture the reputation of the sensors, leading to a highly reliable and accurate enforcement system. Specifically, we define and analyze a detection mechanism to identify falsifying sensors using a distributed anomaly detection system and use the Blockchain to record the individual’s behavior. The reputation is then based on the combination of the difficulty level of the consensus method and the degree of falsehood in the reported sensor values. We evaluate SenseChain using an integrated Blockchain and anomaly detection simulator to show that DLTs can be used to track reputation of distributed sensors for distributed enforcement of spectrum policies.

SenseChain: Blockchain based Reputation System for Distributed Spectrum Enforcement

Modern wireless systems are increasingly dense and dynamic that makes the channel highly non-stationary, rendering conventional receivers sub-optimal in practice. Predicting the channel characteristics for non-stationary channels has the distinct advantage of pre-conditioning the waveform at the transmitter to match the expected fading profile. The difficulty lies in extracting an accurate model for the channel, especially if the underlying variables are uncorrelated, unobserved and immeasurable. Our work implements this prescience by assimilating the Channel State Information (CSI), obtained as feedback from the receiver, over time and space to adjust the modulation vectors such that the channel impairments are significantly diminished at the receiver, improving the Bit Error Rate (BER). We design a channel recommender, in which an adaptive smoother is used to filter the noise in CSI, while a tensor factorization & completion approach is used to track the ephemeral changes in non-stationary channel statistics by observing the changes in certain measurable parameters. V2X communication is used as an example of non-stationary channels to shows the efficacy of this approach. Overall, the system is shown to operate with a prediction accuracy of 10−3 MSE even in dense scattering environments over space and time, improving the BER at the receiver by 90% for higher-order modulations.

Real-time Prediction of Non-stationary Wireless Channels

Recommending channel characteristics for V2X communication has the distinct advantage of pre-conditioning the waveform at the transmitter to match the expected fading profile. The difficulty lies in extracting an accurate model for the channel, especially if the underlying variables are uncorrelated, unobserved and immeasurable. Our work implements this prescience by assimilating the Channel State Information (CSI), obtained as a feedback from vehicles, over time and space to adjust the modulation vectors such that the channel impairments are significantly diminished at the receiver, improving the Bit Error Rate (BER) by 96% for higher order modulations. To account for the multivariate, non-stationary V2X channel, a tensor decomposition and completion approach is used to mitigate the effects of sparsity and noise in the CSI measurements. Overall, the system is shown to operate with a prediction accuracy of 10−3 MSE even in dense scattering environments over space and time.

Spatio-Temporal Recommender for V2X Channels

There is a growing interest towards enabling practical, dynamic and agile wireless applications by systems of independent or cooperative mobile agents such as Unmanned Aerial Vehicles (UAVs). Such mobile UAVs are often constrained on resources like storage, power and radio capabilities and require accurate position information to facilitate many of these wireless applications. In this paper, we introduce HiPER-V, which is a generalized UAV prototype platform to enable a broad range of applications in wireless communications using a single UAV or can be extended to a swarm of UAVs. We implement HiPER-V by using an UAV, equipped with resource constrained radio devices, and high precision position information available via RTK-GPS modules, achieving a median position accuracy of 3.8 cm. The details of implementation of HiPER-V and its applicability to a wide variety of applications in wireless communications are presented in this paper. With minimal payload and simple software modification, our solution can be ported to any UAV platform and extended to multiple UAV testbeds that enable an array of research in wireless applications using UAVs.

Maqsood Ahamed Abdul Careem

Papers

Reputation based Routing in MANET using Blockchain

SenseChain: Blockchain based Reputation System for Distributed Spectrum Enforcement

Real-time Prediction of Non-stationary Wireless Channels

Spatio-Temporal Recommender for V2X Channels

HiPER-V: A High Precision Radio Frequency Vehicle for Aerial Measurements