The development of CubeSat is currently experiencing a rapid increase. One of the growing missions is the space-based Internet of Things (IoT) network using LoRa protocol. The antenna installed on the spacecraft is essential for receiving data from sensors. Compact and adaptable microstrip antennas are commonly used in this design. This study has investigated a microstrip antenna design that incorporated the truncated corner and defected ground structure (DGS) method to improve the antenna performance. Its shape was designed to fit the CubeSat form factor. The antenna is working at 922 MHz of frequency. The experiment result showed that the antenna has return loss of 12.746 dB, 12 MHz of bandwidth, and 1.8 of VSWR. It produces bi-directional beam pattern that has 1.14 dBi of gain with elliptical polarization. The antenna has been integrated with a LoRa module and a range test has been conducted to validate the antenna performance. The result was the antenna was able to communicate with the other LoRa module.
This paper proposes overloaded multiuser multiple-input-multiple-output (MU-MIMO) that is able to increase network throughput by increasing the number of the signal streams to more than that of receive antennas. The spatial filter is employed at each user for such spatial multiplexing. In addition, the proposed overloaded MU-MIMO applies the non-linear precoding with the lattice reduction for improving the transmission performance with low computational complexity. The use of the non-linear precoding can simplify the receiver configuration where amplifiers and the spatial filters are only needed. Computer simulation shows that the proposed overloaded MU-MIMO can double not only the user throughput but also the network throughput compared with the conventional MU-MIMO based on the block diagonalization (BD). In spite of the superior throughput enhancement, the proposed overloaded MU-MIMO achieves 13 dB better transmission performance than the conventional MUMIMO at the BER of $10^{-4}$.
Semantic communications rely on deep neural networks (DNNs) to reduce the amount of transmitted data by only transmitting the semantics of data rather than the whole data, showing the potential on image transmission even in low signal-to-noise-ratio (SNR) conditions. However, the performance deficiency happens once the real-time data do not follow the independent identical distribution (i.i.d) with the training dataset, which results from the poor generalization of DNNs. To tackle this problem, a promising solution is to align the real-time data to follow the similar distribution with the training dataset at the feature level. Thus, we propose a one-shot data adaptive semantic communication (ODASC), where domain adaptation is incorporated as a pre-processing module to cope with domain shift by aligning the distribution between the real-time data and the training dataset. Image transmission is considered as a case study to demonstrate the big plus of ODASC on data recovery and task accuracy.
The utilisation of median filtering (MF), a nonlinear signal processing technique, offers distinct advantages within picture anti-forensics. Consequently, there has been an increased focus on the forensic investigation of MF. However, due to lossy compression, identifying MF in the compressed domain is challenging. Towards this, research presents a novel approach for forensic analysis of MF in compressed images based on utilising deep noise residuals. In this framework, median filtering residuals (MFR) are employed to preprocess the images by passing through two streams. After that, the MFR output is extended to encompass two parallel blocks with different dilation rates to form a fusion feature vector. Further, the MFeRNet framework incorporates convolution, specifically developed to enhance information integration from several streams compared to conventional techniques. The proposed method, MFeRNet, aims to effectively integrate the three-level information of an image and comprehensively extract forensic clues in a compressed scenario. In addition, the experimental results demonstrate that the proposed methodology exhibits superior performance and reduced training time compared to the early reported techniques with equivalent convolution depth.
This paper presents PuFLo, a publish/subscribe forwarding strategy, to deliver content to interested nodes in opportunistic networks. Most of the existing schemes typically use temporal or social knowledge when building routing tables. Instead, PuFLo considers node mobility information when making forwarding decisions. Initially, a node stores the ID of each location it visits in a sequence list. When a node contact occurs, PuFLo selects suitable relays based on two forwarding metrics: mobility similarity and centrality. The former evaluates the mobility diversity of two nodes based on dynamic time warping. The latter assesses node activeness based on Shannon entropy, which reflects the variation of previously visited places. Finally, the two metrics are integrated using the entropy weight method to define the PuFLo forwarding utility. Extensive simulations using the ONE simulator show that PuFLo consistently outperforms Epidemic and SimBet routing in terms of dissemination efficiency, latency, and cost.
Sub-1 GHz (920 MHz) frequency bands for LPWAN (Low Power Wide Area Network) wireless communications systems are attracting attention from various IoT applications. Environmental and infrastructure monitoring systems, such as smart meter, ground inclinometer, and bridge sensor, are widely deployed. AsLPWAN systems operating on Sub-1 GHz bands can provide long distance communications,, a large number of network devices can connect to networks. Although these networks can be configured in a star configuration for a relatively small area, the mesh configuration has been emerging recently. IEEE 802.15.4g-FSK PHY/OFDM PHY is a typical PHY technology in mesh networks for the purpose of transferring IoT application data over a wider area. To distribute the same data such as firmware to LPWAN devices during network operation, improving distribution efficiency becomes critical. On the one hand, using broadcast transmission, the delivery confirmation cannot be performed. On the other hand, unicast transmission is very time consuming if the number of IoT devices is large. Therefore, we proposed a novel firmware distribution method using erasure code for large scale IoT networks. Our ns-3 simulations demonstrate that the proposed method can improve the distribution efficiency by up to 1.8 times compared to conventional methods and achieve higher spectrum efficiency for IEEE 802.15.4g-OFDM PHY.
An essential aspect of Industrial Internet of Things (IIoT) systems lies in their reliability and resilience against failures. Fault detection serves as a crucial method for mitigating errors, leading to reduced downtime. Previous studies have predominantly focused on fault detection using centralized Artificial Intelligence (AI) approaches, wherein participant information is centralized and forwarded to a central server. However, Federated Learning (FL) offers a solution to these issues, enhancing the system’s reliability. In this study, we propose a Decentralized FL (DFL) approach for collaborative learning in bearing fault detection. DFL is preferred over centralized FL due to its elimination of a single point of failure. By leveraging the decentralized FL concept, the vulnerability of the collaborative framework to attacks can be minimized. Our proposed DFL integrates continual learning techniques to reduce communication overhead. The results demonstrate that decentralized collaborative learning achieves satisfactory performance, with an accuracy rate of 96.08% and a learning time reduction of up to 37.52%.
Urban areas globally face escalating challenges in traffic congestion, air pollution, and inefficient transportation systems. Traditional traffic management strategies are increasingly inadequate for handling the growing complexity of urban mobility. To address these issues, this paper presents the development of an ontology for Intelligent Transportation Systems (ITS) aimed at enhancing traffic management and safety through sustainable practices. The research problem focuses on the lack of comprehensive and standardized ontologies that integrate diverse data sources, support real-time decision-making, and incorporate sustainability considerations. Utilizing SUMO Eclipse for simulations and tools like Protege and OwlReady2 for ontology development, the project integrates environmental considerations and vehicular communication protocols. The ontology's structure encompasses five main concepts: Environment, Communication, System, WeatherState, and ValuePartition.
In the realm of vehicular communication, ensuring robust and extensive coverage in high-speed highway environments is paramount. This paper introduces an innovative approach that employs Multi-Agent Reinforcement Learning (MARL) to optimize the selection of relay entities, aiming to enhance V2V communication coverage. Also, we propose a novel resource reservation scheme based on 5G New Radio (NR) Mode 2, tailored for relaying entities to ensure adherence to the latest 3rd Generation Partnership Project (3GPP) protocols. Simulation results reveal that the implementation of our resource reservation strategy markedly boosts the Packet Reception Ratio (PRR), confirming the effectiveness of the proposed method. By juxtaposing scenarios with and without our resource allocation technique, we demonstrate an enhancement in PRR performance, thereby validating the benefits of our approach.
Sub-Terahertz (sub-THz) communication is a potential technology to enable 6G wireless communication with extreme experience due to its rich spectrum resources. Working on sub-THz frequency, radio frequency (RF) impairments including phase noise (PN) and non-linear power amplifier (PA) become more severe and will heavily degrade communication performance. Considering the new characteristics in such high frequency, various waveform schemes have been proposed to achieve better performance under RF impairments, among which the enhanced waveforms based on discrete Fourier transform spreading orthogonal frequency diversion (DFT-s-OFDM) are highly regarded for the low peak to average power ratio (PAPR) characteristic. This paper focuses on 6G candidate waveforms of orthogonal frequency division multiplexing with cyclic prefix (CP-OFDM), DFT-s-OFDM and enhanced DFT-s-OFDM including unified non-orthogonal waveform (uNOW), DFT-s-OFDM with frequency-domain spectral shaping (DFT-s-OFDM with FDSS), and DFT-s-OFDM with FDSS and spectral extension (DFT-s-OFDM with FDSS and SE). In this paper, 6G candidate waveforms are evaluated for different sub-carrier spacing (SCS) considering PN and non-linear PA in sub-THz. Specially, waveforms are compared and analyzed from various aspects including PN robustness without compensation, PN compensation performance based on phase tracking reference signal (PT-RS), and output power back-off (OBO). In addition, under non-linear PA, this work has in-depth discussions on the changing trend of each RF requirement as OBO increases for different 6G candidate waveforms. The results indicate that uNOW outperforms other waveforms for BLER performance considering both PN compensation and OBO, making it the promising 6G candidate waveform.
Unauthorized UAVs present a major concern for public safety, and their detection poses challenges in terms of technical solution and integration with existing aviation regulatory systems. This paper presents an effective detection system setup in this regard and its integration with the UTM system for monitoring the airspace for UAVs. The detection mechanism relies on measuring received RF power at the detector units emanating from the UAV and using machine learning techniques on these measured powers to estimate the location of the unauthorized UAV. The proposed system has been tested for both location estimation and tracking of the flight trajectory of the UAV. With a three-detector system, the R-squared values for detection achieve 0.99 in both LOS and NLOS propagation environments while the accuracy performance in distance yields percentage error of (4.13%, 4.62%) and (1.14% and 1.59%) in (line-of-sight (LOS) and non-LOS (NLOS)) environments for three and four detector system, respectively. The detector system includes an auxiliary visual detection system based on 120 degrees sectored camera system for supporting law enforcement steps after the successful detection stage. Lab measurements for estimating the distance of a test object for visual detection has been performed and reported in this paper. Additionally, the neutralization of such UAVs by the proposed detector units has been presented. The integration of the proposed detection system with an existing UTM system has been discussed in detail in this paper.
In most Internet of Vehicles (IoV) scenarios, intelligent vehicle terminals are required to cope with a multitude of heterogeneous tasks, each of which is subject to increasingly strict constraints on delay and energy consumption. Task offloading is an efficient way to tackle this issue. However, due to performance constraints, a single or two-tier offloading strategy can not enable fine-grained task allocation and flexible service deployment. To address the above problems, we propose a collaborative cloud-edge-end task offloading scheme for IoV scenarios. Since traditional single-agent Deep Reinforcement Learning (DRL) makes it difficult to coordinate multiple objectives of dynamic services simultaneously, we propose a task offloading strategy based on multiagent Deep Deterministic Policy Gradient (DDPG), to jointly consider service delay and energy consumption. We further introduce attentive experience replay (AER) to mitigate the issue of insufficient experience sampling in the DDPG algorithm caused by the catastrophic forgetting problem. Through simulation of IoV scenarios, our proposed model significantly enhances task offloading effectiveness and concurrently reduces delay by 10.6% and energy consumption by 8.1% compared to other state-of-the-art baseline algorithms.
Biometric verification is essential for secure identity verification and authentication during banking transactions using fingerprints, facial features, irises, and voices. Among these methods, voice biometrics is a promising alternative owing to its potential for robust and convenient user authentication. However, their effectiveness is significantly challenged by variations in the voice caused by different device configurations and environmental conditions. These variations can reduce the effectiveness of speaker identification and undermine the reliability of voice-based systems for securing online transactions. For an effective comparative solution, this study addresses these challenges by focusing on the difficulties posed by voice variations due to differences in device hardware, microphone quality, and environmental noise. Our approach employs machine-learning techniques using advanced speech enhancement methods to improve the consistency and accuracy of voice biometric verification across diverse devices. Specifically, we employ an adaptive filter model that enhances signal extraction, noise suppression, and predictive precision. Furthermore, our empirical demonstration showed that the adaptive filter significantly improved the accuracy of voice biometric systems by mitigating the impact of device-induced voice variations. In addition, we evaluate the performance of this model using a range of metrics.
Mobile edge computing (MEC) supported by Low Earth Orbit (LEO) satellite communication systems is a promising approach to improve the Quality of Service (QoS) of terrestrial users. Most existing offloading methods neglect the downlink connectivity of satellites during task offloading, which may bring in extra delay and affect the offloading efficiency. This paper considers satellite-terrestrial-MEC networks in which the tasks are generated by source devices, and the computing services are provided by destination devices or the LEO satellites. Furthermore, we propose a joint task offloading and beam hopping optimization method, which aims to minimize the average time delay of all computation tasks. We formulate the joint optimization problem as a zero-one integer programming problem, which is NP-hard, and provide a multi-agent deep reinforcement learning (MADRL) framework for the intelligent task offloading scheme. In this framework, each agent is responsible for either task offloading or beam hopping, with shared rewards to enhance cooperation between agents. Additionally, to reduce the training time of traditional reinforcement learning algorithms, an attention mechanism is incorporated into MADRL. Simulation results demonstrate that our method effectively reduces the average offloading delay compared with other baseline schemes.
NR-V2X sidelink (SL) broadcast is used for the real-time exchange of position and other information between adjacent vehicles. However, its reliability degrades much in non-line-of-sight environments, where Blind ReTransmission (BReTX) does not work well. Relay can effectively address this issue, and a relaybased SL broadcast method was proposed in previous work. However, in this method, each vehicle must detect communication Link Quality (LQ) and exchange LQ Indicators (LQIs) with its neighbors to select a proper relay, which causes much overhead. This paper aims to improve the relay-based SL broadcast from two aspects. (i) To reduce the overhead, we suggest aggregating LQIs per direction, making the overhead of sharing LQIs irrelevant to the number of vehicles. (ii) To further improve reliability, we use an explicit ACK to deal with the potential failure of the transmission from the source vehicle to the relay. Using explicit ACKs allows the source vehicle to retransmit the packet until the relay vehicle correctly receives it, after which the relay vehicle handles the remaining retransmissions. Simulation results confirm that the proposed method, RReTX-ACK, improves the packet dissemination rate by approximately 8.30% within a 200 m distance from the source vehicle in an intersection scenario, compared to BReTX without using relay vehicles.
In this study, we investigate minimization of energy consumption in multi-UAV assisted networks. We formulate an energy minimization optimization problem with UAV trajectory design, content fetching, power allocation and content placement constraints. The problem is a mixed integer nonlinear programming (MINLP); therefore, we convert the formulated problem into semi-Markov decision process (SMDP). To tackle this SMDP optimization challenge, we introduce an option-based hierarchical deep reinforcement learning (OHDRL) approach. We designate UAV trajectory planning and power allocation as the low level action space, and content placement and content fetching as the high level option space. Through simulations, we demonstrate the effectiveness of the proposed OHDRL method.
In future space-ground integrated networks, a satellite-based core network can reduce frequent signaling interactions between satellites and ground stations, thereby enhancing network architecture and supporting global communications. Users can achieve end-to-end communication through the satellitebased User Plane Function (UPF). However, the high dynamics of Low Earth Orbit (LEO) satellites result in frequent inter-satellite handovers, significantly affecting user service continuity. Existing satellite handover strategies are overly simplistic and fail to ensure the Quality of Service (QoS). Additionally, ground users compete for satellite links based on limited observations, leading to network congestion. This paper proposes a loadbalanced, distributed, multi-agent deep reinforcement learning method for satellite handover. We formulate a combinatorial optimization problem to maximize the total utility of user-satellite associations across various service types. Each user acts based on local information and engages in distributed matching with satellites. Simulation results indicate that our method ensures QoS for various service types, optimizes load balancing, and outperforms basic handover strategies in terms of handover success rate and frequency.
Integrated sensing and communication (ISAC) is an important new research topic for sixth-generation (6G), where the waveform design should be studied. The chirp-based waveform is widely used in radar systems due to its low peak-to-average power ratio (PAPR) and good autocorrelation properties and is a candidate waveform for ISAC systems. To realize the chirp-based waveform in ISAC systems without additional hardware cost, an orthogonal frequency division multiplexing (OFDM) compatible sensing waveform is proposed in this paper. Firstly, a frequency domain processing (FDP) module is proposed to realize chirp signal under the OFDM transmitter structure. Then, to satisfy different communication requirements, the sensing symbol pattern is designed on top of the FDP module to realize multiple orthogonal chirps with flexible patterns. Evaluation results demonstrate that the proposed chirp-based waveform based on the unified waveform structure can achieve 4 dB and 13 dB signal-to-noise ratio (SNR) gain over traditional OFDM waveform with Zadoff-Chu (ZC) sequence or random payloads under the same range and velocity estimation accuracy.
The performance of mean square error (MSE) in channel estimation for multi-cell massive multiple-input multiple-output (MIMO) systems with Rician fading is studied. In this report, we initially derive the closed-form expressions of the probability distribution function and cumulative distribution function of MSE, which are applicable for any number of base-station antennas M and any Rician K-factor. Furthermore, we perform an asymptotic analysis for both strong line-of-sight (LOS) and Rayleigh fading scenarios. Subsequently, we present closed-form expressions for the expectation of MSE (Exp(mse)) and the variance of MSE. Next, utilizing maximal-ratio combining detector, we investigate the relationship between achievable downlink spectral efficiency and Exp(mse). It is observed that as Exp(mse) increases, the achievable downlink spectral efficiency constantly reduces, eventually reaches a given constant. Finally, Monte-Carlo simulations are performed to corroborate the results discussed earlier.
This research introduces PISTON, a novel protocol designed to enhance the security, efficiency, and performance of Internet of Vehicles (IoV) networks. PISTON integrates advanced authentication mechanisms utilizing Physically Unclonable Functions (PUFs) and multifactor authentication with dynamic challenges and zero-knowledge proof-based authentication to ensure robust security and mitigate various cyber threats, including Denial-of-Service (DoS) attacks. The protocol further incorporates sleep-wake scheduling, priority-based scheduling, and adaptive modulation and coding to optimize network performance. The communication overhead in PISTON is derived through a formula that incorporates latency, energy consumption, and throughput, demonstrating the protocol’s efficiency in dynamic vehicular environments. Comparative analysis against existing protocols highlights PISTON’s superiority in seamless handover, provable security, and DoS attack resilience. Experimental results show that PISTON reduces energy consumption by 30% and achieves 20% higher data throughput while maintaining low latency, essential for real-time IoV applications. The empirical findings underscore PISTON’s advancements in establishing a new benchmark for future IoV deployments, ensuring secure, energy-efficient, low-latency, and high-throughput communication.
