The path planning of Unmanned Aerial Vehicle (UAV) is a critical issue in emergency communication and rescue operations, especially in adversarial urban environments. Due to the continuity of the flying space, complex building obstacles, and the aircraft's high dynamics, traditional algorithms cannot find the optimal collision-free flying path between the UAV station and the destination. Accordingly, in this paper, we study the fast UAV path planning problem in a 3D urban environment from a source point to a target point and propose a Three-Step Experience Buffer Deep Deterministic Policy Gradient (TSEB-DDPG) algorithm. We first build the 3D model of a complex urban environment with buildings and project the 3D building surface into many 2D geometric shapes. After transformation, we propose the Hierarchical Learning Particle Swarm Optimization (HL-PSO) to obtain the empirical path. Then, to ensure the accuracy of the obtained paths, the empirical path, the collision information and fast transition information are stored in the three experience buffers of the TSEB-DDPG algorithm as dynamic guidance information. The sampling ratio of each buffer is dynamically adapted to the training stages. Moreover, we designed a reward mechanism to improve the convergence speed of the DDPG algorithm for UAV path planning. The proposed TSEB-DDPG algorithm has also been compared to three widely used competitors experimentally, and the results show that the TSEB-DDPG algorithm can archive the fastest convergence speed and the highest accuracy. We also conduct experiments in real scenarios and compare the real path planning obtained by the HL-PSO algorithm, DDPG algorithm, and TSEB-DDPG algorithm. The results show that the TSEB-DDPG algorithm can archive almost the best in terms of accuracy, the average time of actual path planning, and the success rate.

Unmanned aerial vehicle (UAV)-enabled edge computing is emerging as a potential enabler for Artificial Intelligence of Things (AIoT) in the forthcoming sixth-generation (6G) communication networks. With the use of flexible UAVs, massive sensing data is gathered and processed promptly without considering geographical locations. Deep neural networks (DNNs) are becoming a driving force to extract valuable information from sensing data. However, the lightweight servers installed on UAVs are not able to meet the extremely high requirements of inference tasks due to the limited battery capacities of UAVs. In this work, we investigate a DNN model placement problem for AIoT applications, where the trained DNN models are selected and placed on UAVs to execute inference tasks locally. It is impractical to obtain future DNN model request profiles and system operation states in UAV-enabled edge computing. The Lyapunov optimization technique is leveraged for the proposed DNN model placement problem. Based on the observed system overview, an advanced online placement (AOP) algorithm is developed to solve the transformed problem in each time slot, which can reduce DNN model transmission delay and disk I/O energy cost simultaneously while keeping the input data queues stable. Finally, extensive simulations are provided to depict the effectiveness of the AOP algorithm. The numerical results demonstrate that the AOP algorithm can reduce 18.14% of the model placement cost and 29.89% of the input data queue backlog on average by comparing it with benchmark algorithms.

Unmanned and aerial systems as interactors among different system components for communications, have opened up great opportunities for truth data discovery in Mobile Crowd Sensing (MCS) which has not been properly solved in the literature. In this paper, an Unmanned Aerial Vehicles-supported Intelligent Truth Discovery (UAV-ITD) scheme is proposed to obtain truth data at low-cost communications for MCS. The main innovations of the UAV-ITD scheme are as follows: (1) UAV-ITD scheme takes the first step in employing UAV joint Deep Matrix Factorization (DMF) to discover truth data based on the trust mechanism for an Information Elicitation Without Verification (IEWV) problem in MCS. (2) This paper introduces a truth data discovery scheme for the first time that only needs to collect a part of n data samples to infer the data of the entire network with high accuracy, which saves more communication costs than most previous data collection schemes, where they collect n or kn data samples. Finally, we conducted extensive experiments to evaluate the UAV-ITD scheme. The results show that compared with previous schemes, our scheme can reduce estimated truth error by 52.25%-96.09%, increase the accuracy of workers’ trust evaluation by 0.68-61.82 times, and save recruitment costs by 24.08%-54.15% in truth data discovery.

In recent years, the Industrial Internet and Industry 4.0 came into being. With the development of modern industrial intelligent manufacturing technology, digital twins, Web3 and many other digital entity applications are also proposed. These applications apply architectures such as distributed learning, resource sharing, and arithmetic trading, which make high demands on identity authentication, asset authentication, resource addressing, and service location. Therefore, an efficient, secure, and trustworthy Industrial Internet identity resolution system is needed. However, most of the traditional identity resolution systems follow DNS architecture or tree structure, which has the risk of a single point of failure and DDoS attack. And they cannot guarantee the security and privacy of digital identity, personal assets, and device information. So we consider a decentralized approach for identity management, identity authentication, and asset verification. In this paper, we propose a distributed trusted active identity resolution system based on the inter-planetary file system (IPFS) and non-fungible token (NFT), which can provide distributed identity resolution services. And we have designed the system architecture, identity service process, load balancing strategy and smart contract service. In addition, we use Jmeter to verify the performance of the system, and the results show that the system has good high concurrent performance and robustness.

Industrial Internet combines the industrial system with Internet connectivity to build a new manufacturing and service system covering the entire industry chain and value chain. Its highly heterogeneous network structure and diversified application requirements call for the applying of network slicing technology. Guaranteeing robust network slicing is essential for Industrial Internet, but it faces the challenge of complex slice topologies caused by the intricate interaction relationships among Network Functions (NFs) composing the slice. Existing works have not concerned the strengthening problem of industrial network slicing regarding its complex network properties. Towards this end, we aim to study this issue by intelligently selecting a subset of most valuable NFs with the minimum cost to satisfy the strengthening requirements. State-of-the-art AlphaGo series of algorithms and the advanced graph neural network technology are combined to build the solution. Simulation results demonstrate the superior performance of our scheme compared to the benchmark schemes.

Unmanned Aerial Vehicles (UAVs) are gaining increasing attention in many fields, such as military, logistics, and hazardous site mapping. Utilizing UAVs to assist communications is one of the promising applications and research directions. The future Industrial Internet places higher demands on communication quality. The easy deployment, dynamic mobility, and low cost of UAVs make them a viable tool for wireless communication in the Industrial Internet. Therefore, UAVs are considered as an integral part of Industry 4.0. In this article, three typical use cases of UAVs-assisted communications in Industrial Internet are first summarized. Then, the state-of-the-art technologies for drone-assisted communication in support of the Industrial Internet are presented. According to the current research, it can be assumed that UAV-assisted communication can support the future Industrial Internet to a certain extent. Finally, the potential research directions and open challenges in UAV-assisted communications in the upcoming future Industrial Internet are discussed.

Green and low-carbon is a new development model that seeks balance between environmental sustainability and high economic growth. If explainable and available carbon emission data can be accurately obtained, it will help policy regulators and enterprise managers to more accurately implement this development strategy. A lot of research has been carried out, but it is still a difficult problem that how to accommodate and adapt the complex carbon emission data computing models and factor libraries developed by different regions, different industries and different enterprises. Meanwhile, with the rapid development of the Industrial Internet, it has not only been used for the supply chain optimization and intelligent scheduling of the manufacturing industry, but also been used by more and more industries as an important way of digital transformation. Especially in China, the Industrial Internet identification and resolution system is becoming an important digital infrastructure to uniquely identify objects and share data. Hence, a compatible carbon efficiency information service framework based on the Industrial Internet Identification is proposed in this paper to address the problem of computing and querying multi-source heterogeneous carbon emission data. We have defined a multi cooperation carbon emission data interaction model consisting of three roles and three basic operations. Further, the implementation of the framework includes carbon emission data identification, modeling, calculation, query and sharing. The practice results show that its capability and effectiveness in improving the responsiveness, accuracy, and credibility of compatible carbon efficiency data query and sharing services.

Nowadays, Internet of Things (IoT) is widely deployed and brings great opportunities to change people's daily life. To realize more effective human-computer interaction in the IoT applications, the Question Answering (QA) systems implanted in the IoT services are supposed to improve the ability to understand natural language. Therefore, the distributed representation of words, which contains more semantic or syntactic information, has been playing a more and more important role in the QA systems. However, learning high-quality distributed word vectors requires lots of storage and computing resources, hence it cannot be deployed on the resource-constrained IoT devices. It is a good choice to outsource the data and computation to the cloud servers. Nevertheless, it could cause privacy risks to directly upload private data to the untrusted cloud. Therefore, realizing the word vector learning process over untrusted cloud servers without privacy leakage is an urgent and challenging task. In this paper, we present a novel efficient word vector learning scheme over encrypted data. We first design a series of arithmetic computation protocols. Then we use two non-colluding cloud servers to implement high-quality word vectors learning over encrypted data. The proposed scheme allows us to perform training word vectors on the remote cloud servers while protecting privacy. Security analysis and experiments over real data sets demonstrate that our scheme is more secure and efficient than existing privacy-preserving word vector learning schemes.

In this paper, we analyze a hybrid Heterogeneous Cellular Network (HCNet) framework by deploying millimeter Wave (mmWave) small cells with coexisting traditional sub-6GHz macro cells to achieve improved coverage and high data rate. We consider randomly-deployed macro base stations throughout the network whereas mmWave Small Base Stations (SBSs) are deployed in the areas with high User Equipment (UE) density. Such user centric deployment of mmWave SBSs inevitably incurs correlation between UE and SBSs. For a realistic scenario where the UEs are distributed according to Poisson cluster process and directional beamforming with line-of-sight and non-line-of-sight transmissions is adopted for mmWave communication. By using tools from stochastic geometry, we develop an analytical framework to analyze various performance metrics in the downlink hybrid HCNets under biased received power association. For UE clustering we considered Thomas cluster process and derive expressions for the association probability, coverage probability, area spectral efficiency, and energy efficiency. We also provide Monte Carlo simulation results to validate the accuracy of the derived expressions. Furthermore, we analyze the impact of mmWave operating frequency, antenna gain, small cell biasing, and BSs density to get useful engineering insights into the performance of hybrid mmWave HCNets. Our results show that network performance is significantly improved by deploying millimeter wave SBS instead of microwave BS in hot spots.

We consider the problem of energy efficiency aware dynamic adaptation of data transmission rate and transmission power of the users in carrier sensing based Wireless Local Area Networks (WLANs) in the presence of path loss, Rayleigh fading and log-normal shadowing. For a data packet transmission, we formulate an optimization problem, solve the problem, and propose a rate and transmission power adaptation scheme with a restriction methodology of data packet transmission for achieving the optimal energy efficiency. In the restriction methodology of data packet transmission, a user does not transmit a data packet if the instantaneous channel gain of the user is lower than a threshold. To evaluate the performance of the proposed scheme, we develop analytical models for computing the throughput and energy efficiency of WLANs under the proposed scheme considering a saturation traffic condition. We then validate the analytical models via simulation. We find that the proposed scheme provides better throughput and energy efficiency with acceptable throughput fairness if the restriction methodology of data packet transmission is included. By means of the analytical models and simulations, we demonstrate that the proposed scheme provides significantly higher throughput, energy efficiency and fairness index than a traditional non-adaptive scheme and an existing most relevant adaptive scheme. Throughput and energy efficiency gains obtained by the proposed scheme with respect to the existing adapting scheme are about 75% and 103%, respectively, for a fairness index of 0.8. We also study the effect of various system parameters on throughput and energy efficiency and provide various engineering insights.

With the boom of the communication systems on some independent platforms (such as satellites, space stations, airplanes, and vessels), co-site interference is becoming prominent. The adaptive interference cancellation method has been adopted to solve the co-site interference problem. But the broadband interference cancellation performance of traditional Adaptive Co-site Interference Cancellation System (ACICS) with large delay mismatching and antenna sway is relatively poor. This study put forward an Adaptive Co-site Broadband Interference Cancellation System With Two Auxiliary Channels (ACBICS-2A). The system model was established, and the steady state weights and Interference Cancellation Ratio (ICR) were deduced by solving a time-varying differential equation. The relationship of ICR, system gain, modulation factor, interference signal bandwidth and delay mismatching degree was acquired through an in-depth analysis. Compared with traditional adaptive interference cancellation system, the proposed ACBICS-2A can improve broadband interference cancellation ability remarkably with large delay mismatching and antenna sway for the effect of auxiliary channel. The maximum improved ICR is more than 25 ​dB. Finally, the theoretical and simulation results were verified by experiments.

In this paper, efficient signal detectors are designed for Orthogonal Time Frequency Space (OTFS) modulation with Index Modulation (IM) systems. Firstly, the Minimum Mean Squared Error (MMSE) based linear equalizer and its corresponding soft-aided decision are studied for OTFS-IM. To further improve the performance, a Vector-by-Vector-aided Message Passing (VV-MP) detector and its associated soft-decision are proposed, where each IM symbol is considered an entire vector utilized for message calculation and passing. Simulation results are shown that the OTFS-IM system relying on the proposed detectors is capable of providing considerable Bit Error Rate (BER) performance gains over the OTFS and Orthogonal Frequency Division Multiplex (OFDM) with IM systems.

Normally, in the downlink Network-Coded Multiple Access (NCMA) system, the same power is allocated to different users. However, equal power allocation is unsuitable for some scenarios, such as when user devices have different Quality of Service (QoS) requirements. Hence, we study the power allocation in the downlink NCMA system in this paper, and propose a downlink Network-Coded Multiple Access with Diverse Power (NCMA-DP), wherein different amounts of power are allocated to different users. In terms of the Bit Error Rate (BER) of the multi-user decoder, and the number of packets required to correctly decode the message, the performance of the user with more allocated power is greatly improved compared to the Conventional NCMA (NCMA-C). Meanwhile, the performance of the user with less allocated power is still much better than NCMA-C. Furthermore, the overall throughput of NCMA-DP is greatly improved compared to that of NCMA-C. The simulation results demonstrate the remarkable performance of the proposed NCMA-DP.

Threshold signature has been widely used in electronic wills, electronic elections, cloud computing, secure multi-party computation and other fields. Until now, certificateless threshold signature schemes are all based on traditional mathematic theory, so they cannot resist quantum computing attacks. In view of this, we combine the advantages of lattice-based cryptosystem and certificateless cryptosystem to construct a certificateless threshold signature from lattice (LCLTS) that is efficient and resistant to quantum algorithm attacks. LCLTS has the threshold characteristics and can resist the quantum computing attacks, and the analysis shows that it is unforgeable against the adaptive Chosen-Message Attacks (UF-CMA) with the difficulty of Inhomogeneous Small Integer Solution (ISIS) problem. In addition, LCLTS solves the problems of the certificate management through key escrow.

The malicious mining pool can sacrifice part of its revenue to employ the computing power of blockchain network. The employed computing power carries out the pool mining attacks on the attacked mining pool. To realize the win-win game between the malicious mining pool and the employee, the paper proposes an Employment Attack Pricing Algorithm (EAPA) of mining pools in blockchain based on game theory. In the EAPA, the paper uses mathematical formulas to express the revenue of malicious mining pools under the employment attack, the revenue increment of malicious mining pools, and the revenue of the employee. It establishes a game model between the malicious mining pool and the employee under the employment attack. Then, the paper proposes an optimal computing power price selection strategy of employment attack based on model derivation. In the strategy, the malicious mining pool analyzes the conditions for the employment attack, and uses the derivative method to find the optimal utilization value of computing power, employees analyze the conditions for accepting employment, and use the derivative method to find the optimal reward value of computing power. Finally, the strategy finds the optimal employment computing power price to realize Nash equilibrium between the malicious mining pool and the employee under the current computing power allocation. The simulation results show that the EAPA could find the employment computing power price that realizes the win-win game between the malicious mining pool and the employee. The EAPA also maximizes the unit computing power revenue of employment and the unit computing power revenue of honest mining in malicious mining pool at the same time. The EAPA outperforms the state-of-the-art methods such as SPSUCP, DPSACP, and FPSUCP.

The Internet of Things (IoT) has taken the interconnected world by storm. Due to their immense applicability, IoT devices are being scaled at exponential proportions worldwide. But, very little focus has been given to securing such devices. As these devices are constrained in numerous aspects, it leaves network designers and administrators with no choice but to deploy them with minimal or no security at all. We have seen distributed denial-of-service attacks being raised using such devices during the infamous Mirai botnet attack in 2016. Therefore we propose a lightweight authentication protocol to provide proper access to such devices. We have considered several aspects while designing our authentication protocol, such as scalability, movement, user registration, device registration, etc. To define the architecture we used a three-layered model consisting of cloud, fog, and edge devices. We have also proposed several pre-existing cipher suites based on post-quantum cryptography for evaluation and usage. We also provide a fail-safe mechanism for a situation where an authenticating server might fail, and the deployed IoT devices can self-organize to keep providing services with no human intervention. We find that our protocol works the fastest when using ring learning with errors. We prove the safety of our authentication protocol using the automated validation of Internet security protocols and applications tool. In conclusion, we propose a safe, hybrid, and fast authentication protocol for authenticating IoT devices in a fog computing environment.

In order to improve the Physical Layer Security (PLS) perspective, this paper aims to empower function of PLS by considering a backhaul Non-Orthogonal Multiple Access (NOMA) system in two practical situations. In the proposed schemes, the untrusted user intercepts information transmitted to the far user, or the external eavesdropper overhears confidential information sent to the far user in the context of NOMA technique. Unlike the conventional NOMA systems, this paper emphasizes the actual situations of the existence of actual illegal users and legitimate users, especially the reasonable use of relay selection architecture to improve the confidentiality performance. To evaluate the security properties of the proposed scheme, a comprehensive analysis of the Security Outage Probability (SOP) performance is first performed, and then the corresponding SOP asymptotic expressions are derived for real scenarios related to eavesdroppers and untrusted users. Numerical results are performed to verify the analysis in terms of the secure performance metric.

Package delivery via ridesharing provides appealing benefits of lower delivery cost and efficient vehicle usage. Most existing ridesharing systems operate the matching of ridesharing in a centralized manner, which may result in the single point of failure once the controller breaks down or is under attack. To tackle such problems, our goal in this paper is to develop a blockchain-based package delivery ridesharing system, where decentralization is adopted to remove intermediaries and direct transactions between the providers and the requestors are allowed. To complete the matching process under decentralized structure, an Event-Triggered Distributed Deep Reinforcement Learning (ETDDRL) algorithm is proposed to generate/update the real-time ridesharing orders for the new coming ridesharing requests from a local view. Simulation results reveal the vast potential of the ETDDRL matching algorithm under the blockchain framework for the promotion of the ridesharing profits. Finally, we develop an application for Android-based terminals to verify the ETDDRL matching algorithm.

The development of the Internet of Things (IoT) has brought great convenience to people. However, some information security problems such as privacy leakage are caused by communicating with risky users. It is a challenge to choose reliable users with which to interact in the IoT. Therefore, trust plays a crucial role in the IoT because trust may avoid some risks. Agents usually choose reliable users with high trust to maximize their own interests based on reinforcement learning. However, trust propagation is time-consuming, and trust changes with the interaction process in social networks. To track the dynamic changes in trust values, a dynamic trust inference algorithm named Dynamic Double DQN Trust (Dy-DDQNTrust) is proposed to predict the indirect trust values of two users without direct contact with each other. The proposed algorithm simulates the interactions among users by double DQN. Firstly, CurrentNet and TargetNet networks are used to select users for interaction. The users with high trust are chosen to interact in future iterations. Secondly, the trust value is updated dynamically until a reliable trust path is found according to the result of the interaction. Finally, the trust value between indirect users is inferred by aggregating the opinions from multiple users through a Modified Collaborative Filtering Average-based Similarity (SMCFAvg) aggregation strategy. Experiments are carried out on the FilmTrust and the Epinions datasets. Compared with TidalTrust, MoleTrust, DDQNTrust, DyTrust and Dynamic Weighted Heuristic trust path Search algorithm (DWHS), our dynamic trust inference algorithm has higher prediction accuracy and better scalability.

In Internet of Vehicles (IoV), the security-threat information of various traffic elements can be exploited by hackers to attack vehicles, resulting in accidents, privacy leakage. Consequently, it is necessary to establish security-threat assessment architectures to evaluate risks of traffic elements by managing and sharing security-threat information. Unfortunately, most assessment architectures process data in a centralized manner, causing delays in query services. To address this issue, in this paper, a Hierarchical Blockchain-enabled Security threat Assessment Architecture (HBSAA) is proposed, utilizing edge chains and global chains to share data. In addition, data virtualization technology is introduced to manage multi-source heterogeneous data, and a metadata association model based on attribute graph is designed to deal with complex data relationships. In order to provide high-speed query service, the ant colony optimization of key nodes is designed, and the HBSAA prototype is also developed and the performance is tested. Experimental results on the large-scale vulnerabilities data gathered from NVD demonstrate that the HBSAA not only shields data heterogeneity, but also reduces service response time.

Space-Time Block Coded (STBC) Orthogonal Frequency Division Multiplexing (OFDM) satisfies higher data-rate requirements while maintaining signal quality in a multipath fading channel. However, conventional STBCs, including Orthogonal STBCs (OSTBCs), Non-Orthogonal (NOSTBCs), and Quasi-Orthogonal STBCs (QOSTBCs), do not provide both maximal diversity order and unity code rate simultaneously for more than two transmit antennas. This paper targets this problem and applies Maximum Rank Distance (MRD) codes in designing STBC-OFDM systems. By following the direct-matrix construction method, we can construct binary extended finite field MRD-STBCs for any number of transmitting antennas. Work uses MRD-STBCs built over Phase-Shift Keying (PSK) modulation to develop an MRD-based STBC-OFDM system. The MRD-based STBC-OFDM system sacrifices minor error performance compared to traditional OSTBC-OFDM but shows improved results against NOSTBC and QOSTBC-OFDM. It also provides 25% higher data-rates than OSTBC-OFDM in configurations that use more than two transmit antennas. The tradeoffs are minor increases in computational complexity and processing delays.

The advent of Network Function Virtualization (NFV) and Service Function Chains (SFCs) unleashes the power of dynamic creation of network services using Virtual Network Functions (VNFs). This is of great interest to network operators since poor service quality and resource wastage can potentially hurt their revenue in the long term. However, the study shows with a set of test-bed experiments that packet loss at certain positions (i.e., different VNFs) in an SFC can cause various degrees of resource wastage and performance degradation because of repeated upstream processing and transmission of retransmitted packets.
To overcome this challenge, this study focuses on resource scheduling and deployment of SFCs while considering packet loss positions. This study developed a novel SFC packet dropping cost model and formulated an SFC scheduling problem that aims to minimize overall packet dropping cost as a Mixed-Integer Linear Programming (MILP) and proved that it is NP-hard. In this study, Palos is proposed as an efficient scheme in exploiting the functional characteristics of VNFs and their positions in SFCs for scheduling resources and deployment to optimize packet dropping cost. Extensive experiment results show that Palos can achieve up to 42.73% improvement on packet dropping cost and up to 33.03% reduction on average SFC latency when compared with two other state-of-the-art schemes.

To ensure the safe operation of industrial digital twins network and avoid the harm to the system caused by hacker invasion, a series of discussions on network security issues are carried out based on game theory. From the perspective of the life cycle of network vulnerabilities, mining and repairing vulnerabilities are analyzed by applying evolutionary game theory. The evolution process of knowledge sharing among white hats under various conditions is simulated, and a game model of the vulnerability patch cooperative development strategy among manufacturers is constructed. On this basis, the differential evolution is introduced into the update mechanism of the Wolf Colony Algorithm (WCA) to produce better replacement individuals with greater probability from the perspective of both attack and defense. Through the simulation experiment, it is found that the convergence speed of the probability (X) of white Hat 1 choosing the knowledge sharing policy is related to the probability (x₀) of white Hat 2 choosing the knowledge sharing policy initially, and the probability (y₀) of white hat 2 choosing the knowledge sharing policy initially. When y₀ ​= ​0.9, X converges rapidly in a relatively short time. When y₀ is constant and x₀ is small, the probability curve of the “cooperative development” strategy converges to 0. It is concluded that the higher the trust among the white hat members in the temporary team, the stronger their willingness to share knowledge, which is conducive to the mining of loopholes in the system. The greater the probability of a hacker attacking the vulnerability before it is fully disclosed, the lower the willingness of manufacturers to choose the "cooperative development" of vulnerability patches. Applying the improved wolf colony-co-evolution algorithm can obtain the equilibrium solution of the "attack and defense game model", and allocate the security protection resources according to the importance of nodes. This study can provide an effective solution to protect the network security for digital twins in the industry.

The working of a Mobile Ad hoc NETwork (MANET) relies on the supportive cooperation among the network nodes. But due to its intrinsic features, a misbehaving node can easily lead to a routing disorder. This paper presents two trust-based routing schemes, namely Trust-based Self-Detection Routing (TSDR) and Trust-based Co-operative Routing (TCOR) designed with an Ad hoc On-demand Distance Vector (AODV) protocol. The proposed work covers a wide range of security challenges, including malicious node identification and prevention, accurate trust quantification, secure trust data sharing, and trusted route maintenance. This brings a prominent solution for mitigating misbehaving nodes and establishing efficient communication in MANET. It is empirically validated based on a performance comparison with the current Evolutionary Self-Cooperative Trust (ESCT) scheme, Generalized Trust Model (GTM), and the conventional AODV protocol. The extensive simulations are conducted against three different varying network scenarios. The results affirm the improved values of eight popular performance metrics overcoming the existing routing schemes. Among the two proposed works, TCOR is more suitable for highly scalable networks; TSDR suits, however, the MANET application better with its small size. This work thus makes a significant contribution to the research community, in contrast to many previous works focusing solely on specific security aspects, and results in a trade-off in the expected values of evaluation parameters and asserts their efficiency.

Cooperative communication through energy harvested relays in Cognitive Internet of Things (CIoT) has been envisioned as a promising solution to support massive connectivity of Cognitive Radio (CR) based IoT devices and to achieve maximal energy and spectral efficiency in upcoming wireless systems. In this work, a cooperative CIoT system is contemplated, in which a source acts as a satellite, communicating with multiple CIoT devices over numerous relays. Unmanned Aerial Vehicles (UAVs) are used as relays, which are equipped with onboard Energy Harvesting (EH) facility. We adopted a Power Splitting (PS) method for EH at relays, which are harvested from the Radio frequency (RF) signals. In conjunction with this, the Decode and Forward (DF) relaying strategy is used at UAV relays to transmit the messages from the satellite source to the CIoT devices. We developed a Multi-ObjectiveOptimization (MOO) framework for joint optimization of source power allocation, CIoT device selection, UAV relay assignment, and PS ratio determination. We formulated three objectives: maximizing the sum rate and the number of admitted CIoT in the network and minimizing the carbon dioxide emission. The MOO formulation is a Mixed-Integer Non-Linear Programming (MINLP) problem, which is challenging to solve. To address the joint optimization problem for an epsilon optimal solution, an Outer Approximation Algorithm (OAA) is proposed with reduced complexity. The simulation results show that the proposed OAA is superior in terms of CIoT device selection and network utility maximization when compared to those obtained using the Nonlinear Optimization with Mesh Adaptive Direct-search (NOMAD) algorithm.

When the ground communication base stations in the target area are severely destroyed, the deployment of Unmanned Aerial Vehicle (UAV) ad hoc networks can provide people with temporary communication services. Therefore, it is necessary to design a multi-UAVs cooperative control strategy to achieve better communication coverage and lower energy consumption. In this paper, we propose a multi-UAVs coverage model based on Adaptive Virtual Force-directed Particle Swarm Optimization (AVF-PSO) strategy. In particular, we first introduce the gravity model into the traditional Particle Swarm Optimization (PSO) algorithm so as to increase the probability of full coverage. Then, the energy consumption is included in the calculation of the fitness function so that maximum coverage and energy consumption can be balanced. Finally, in order to reduce the communication interference between UAVs, we design an adaptive lift control strategy based on the repulsion model to reduce the repeated coverage of multi-UAVs. Experimental results show that the proposed coverage strategy based on gravity model outperforms the existing state-of-the-art approaches. For example, in the target area of any size, the coverage rate and the repeated coverage rate of the proposed multi-UAVs scheduling are improved by 6.9-29.1%, and 2.0-56.1%, respectively. Moreover, the proposed scheduling algorithm is high adaptable to diverse execution environments.© 2022 Published by Elsevier Ltd.

How to use a few defect samples to complete the defect classification is a key challenge in the production of mobile phone screens. An attention-relation network for the mobile phone screen defect classification is proposed in this paper. The architecture of the attention-relation network contains two modules: a feature extract module and a feature metric module. Different from other few-shot models, an attention mechanism is applied to metric learning in our model to measure the distance between features, so as to pay attention to the correlation between features and suppress unwanted information. Besides, we combine dilated convolution and skip connection to extract more feature information for follow-up processing. We validate attention-relation network on the mobile phone screen defect dataset. The experimental results show that the classification accuracy of the attention-relation network is 0.9486 under the 5-way 1-shot training strategy and 0.9039 under the 5-way 5-shot setting. It achieves the excellent effect of classification for mobile phone screen defects and outperforms with dominant advantages.

The power system frequency fluctuations could be captured by digital recordings and extracted to compare with a reference database for forensic timestamp verification. It is known as the Electric Network Frequency (ENF) criterion, enabled by the properties of random fluctuations and intra-grid consistency. In essence, this is a task of matching a short random sequence within a long reference, whose accuracy is mainly concerned with whether this match could be uniquely correct. In this paper, we comprehensively analyze the factors affecting the reliability of ENF matching, including the length of test recording, length of reference, temporal resolution, and Signal-to-Noise Ratio (SNR). For synthetic analysis, we incorporate the first-order AutoRegressive (AR) ENF model and propose an efficient Time-Frequency Domain noisy ENF synthesis method. Then, the reliability analysis schemes for both synthetic and real-world data are respectively proposed. Through a comprehensive study, we quantitatively reveal that while the SNR is an important external factor to determine whether timestamp verification is viable, the length of test recording is the most important inherent factor, followed by the length of reference. However, the temporal resolution has little impact on performance. Finally, a practical workflow of the ENF-based audio timestamp verification system is proposed, incorporating the discovered results.

This paper studies a dual-hop Simultaneous Wireless Information and Power Transfer (SWIPT)-based multi-relay network with a direct link. To achieve high throughput in the network, a novel protocol is first developed, in which the network can switch between a direct transmission mode and a Single-Relay-Selection-based Cooperative Transmission (SRS-CT) mode that employs dynamic decode-and-forward relaying accomplished with Rateless Codes (RCs). Then, under this protocol, an optimization problem is formulated to jointly optimize the network operation mode and the resource allocation in the SRS-CT mode. The formulated problem is difficult to solve because not only does the noncausal Channel State Information (CSI) cause the problem to be stochastic, but also the energy state evolution at each relay is complicated by network operation mode decision and resource allocation. Assuming that noncausal CSI is available, the stochastic optimization issue is first to be addressed by solving an involved deterministic optimization problem via dynamic programming, where the complicated energy state evolution issue is addressed by a layered optimization method. Then, based on a finite-state Markov channel model and assuming that CSI statistical properties are known, the stochastic optimization problem is solved by extending the result derived for the noncausal CSI case to the causal CSI case. Finally, a myopic strategy is proposed to achieve a tradeoff between complexity and performance without the knowledge of CSI statistical properties. The simulation results verify that our proposed SRS-and-RC-based design can achieve a maximum of approximately 40% throughput gain over a simple SRS-and-RC-based baseline scheme in SWIPT-based multi-relay networks.

Orbital Angular Momentum (OAM) waves are characterized by helical wave fronts and orthogonality between different modes. Therefore, OAM waves have huge potential in improving wireless communications' channel capacity and radar imaging's resolution. Consequently, the generation and application of OAM waves have attracted a lot of attention. And many methods are proposed to generate OAM waves. Although antenna array is the most popular method of generating OAM waves, OAM waves generated by antenna array have redundant modes. However, all advantages of OAM waves are closely related to infinite OAM modes. Thus, to better apply OAM waves to wireless communications and radar, it is very important to reduce unnecessary OAM modes and improve the OAM mode purity. In order to improve the OAM mode purity, two combined antenna arrays composed of X direction antenna and Y direction antenna array are proposed in this paper. The X direction antenna array and the Y direction antenna array are supplied by the excitations with the same amplitude and fixed phase shift. The overall phase shift of the X direction antenna array is π/2 more or less than that of the Y direction antenna array. The results of formulas and antenna models in CST show that the combined antenna arrays can generate OAM waves with less redundant modes in x component, y component and z component. Besides, the z component carries pure OAM modes.

Federated Learning (FL), a burgeoning technology, has received increasing attention due to its privacy protection capability. However, the base algorithm FedAvg is vulnerable when it suffers from so-called backdoor attacks. Former researchers proposed several robust aggregation methods. Unfortunately, due to the hidden characteristic of backdoor attacks, many of these aggregation methods are unable to defend against backdoor attacks. What's more, the attackers recently have proposed some hiding methods that further improve backdoor attacks' stealthiness, making all the existing robust aggregation methods fail.
To tackle the threat of backdoor attacks, we propose a new aggregation method, X-raying Models with A Matrix (XMAM), to reveal the malicious local model updates submitted by the backdoor attackers. Since we observe that the output of the Softmax layer exhibits distinguishable patterns between malicious and benign updates, unlike the existing aggregation algorithms, we focus on the Softmax layer's output in which the backdoor attackers are difficult to hide their malicious behavior. Specifically, like medical X-ray examinations, we investigate the collected local model updates by using a matrix as an input to get their Softmax layer's outputs. Then, we preclude updates whose outputs are abnormal by clustering. Without any training dataset in the server, the extensive evaluations show that our XMAM can effectively distinguish malicious local model updates from benign ones. For instance, when other methods fail to defend against the backdoor attacks at no more than 20% malicious clients, our method can tolerate 45% malicious clients in the black-box mode and about 30% in Projected Gradient Descent (PGD) mode. Besides, under adaptive attacks, the results demonstrate that XMAM can still complete the global model training task even when there are 40% malicious clients. Finally, we analyze our method's screening complexity and compare the real screening time with other methods. The results show that XMAM is about 10-10000 times faster than the existing methods.

Hybrid precoding is considered as a promising low-cost technique for millimeter wave (mm-wave) massive Multi-Input Multi-Output (MIMO) systems. In this work, referring to the time-varying propagation circumstances, with semi-supervised Incremental Learning (IL), we propose an online hybrid beamforming scheme. Firstly, given the constraint of constant modulus on analog beamformer and combiner, we propose a new broad-network-based structure for the design model of hybrid beamforming. Compared with the existing network structure, the proposed network structure can achieve better transmission performance and lower complexity. Moreover, to enhance the efficiency of IL further, by combining the semi-supervised graph with IL, we propose a hybrid beamforming scheme based on chunk-by-chunk semi-supervised learning, where only few transmissions are required to calculate the label and all other unlabelled transmissions would also be put into a training data chunk. Unlike the existing single-by-single approach where transmissions during the model update are not taken into the consideration of model update, all transmissions, even the ones during the model update, would make contributions to model update in the proposed method. During the model update, the amount of unlabelled transmissions is very large and they also carry some information, the prediction performance can be enhanced to some extent by these unlabelled channel data. Simulation results demonstrate the spectral efficiency of the proposed method outperforms that of the existing single-by-single approach. Besides, we prove the general complexity of the proposed method is lower than that of the existing approach and give the condition under which its absolute complexity outperforms that of the existing approach.

Distinguishing between web traffic generated by bots and humans is an important task in the evaluation of online marketing campaigns. One of the main challenges is related to only partial availability of the performance metrics: although some users can be unambiguously classified as bots, the correct label is uncertain in many cases. This calls for the use of classifiers capable of explaining their decisions. This paper demonstrates two such mechanisms based on features carefully engineered from web logs. The first is a man-made rule-based system. The second is a hierarchical model that first performs clustering and next classification using human-centred, interpretable methods. The stability of the proposed methods is analyzed and a minimal set of features that convey the class-discriminating information is selected. The proposed data processing and analysis methodology are successfully applied to real-world data sets from online publishers.

Utilizing multi-band and multi-carrier techniques enhances throughput and capacity in Long-Term Evolution (LTE)-Advanced and 5G New Radio (NR) mobile networks. However, these techniques introduce Passive Inter-Modulation (PIM) interference in Frequency-Division Duplexing (FDD) systems. In this paper, a novel multi-band Wiener-Hammerstein model is presented to digitally reconstruct PIM interference signals, thereby achieving effective PIM Cancellation (PIMC) in multi-band scenarios. In the model, transmitted signals are independently processed to simulate Inter-Modulation Distortions (IMDs) and Cross-Modulation Distortions (CMDs). Furthermore, the Finite Impulse Response (FIR) filter, basis function generation, and B-spline function are applied for precise PIM product estimation and generation in multi-band scenarios. Simulations involving 4 carrier components from diverse NR frequency bands at varying transmitting powers validate the feasibility of the model for multi-band PIMC, achieving up to 19 dB in PIMC performance. Compared to other models, this approach offers superior PIMC performance, exceeding them by more than 5 dB in high transmitting power scenarios. Additionally, its lower sampling rate requirement reduces the hardware complexity associated with implementing multi-band PIMC.

L With the ever-increasing popularity of Internet of Things (IoT), massive enterprises are attempting to encapsulate their developed outcomes into various lightweight Web Application Programming Interfaces (APIs) that can be accessible remotely. In this context, finding and writing a list of existing Web APIs that can collectively meet the functional needs of software developers has become a promising approach to economically and easily develop successful mobile applications. However, the number and diversity of candidate IoT Web APIs places an additional burden on application developers’ Web API selection decisions, as it is often a challenging task to simultaneously ensure the diversity and compatibility of the final set of Web APIs selected. Considering this challenge and latest successful applications of game theory in IoT, a Diversified and Compatible Web APIs Recommendation approach, namely DivCAR, is put forward in this paper. First of all, to achieve API diversity, DivCAR employs random walk sampling technique on a pre-built “API-API” correlation graph to generate diverse “API-API” correlation subgraphs. Afterwards, with the diverse “API-API” correlation subgraphs, the compatible Web APIs recommendation problem is modeled as a minimum group Steiner tree search problem. A sorted set of multiple compatible and diverse Web APIs are returned to the application developer by solving the minimum group Steiner tree search problem. At last, a set of experiments are designed and implemented on a real dataset crawled from www.programmableweb.com. Experimental results validate the effectiveness and efficiency of our proposed DivCAR approach in balancing the Web APIs recommendation diversity and compatibility.