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See detailNOMA-Enabled Optimization Framework for Next-Generation Small-Cell IoV Networks Under Imperfect SIC Decoding
Khan, Wali Ullah UL; Li, Xingwang; Ihsan, Asim et al

in IEEE Transactions on Intelligent Transportation Systems (2022)

To meet the demands of massive connections, diverse quality of services (QoS), ultra-reliable and low latency in the future sixth-generation (6G) Internet-of-vehicle (IoV) communications, we propose non ... [more ▼]

To meet the demands of massive connections, diverse quality of services (QoS), ultra-reliable and low latency in the future sixth-generation (6G) Internet-of-vehicle (IoV) communications, we propose non-orthogonal multiple access (NOMA)-enabled small-cell IoV network (SVNet). We aim to investigate the trade-off between system capacity and energy efficiency through a joint power optimization framework. In particular, we formulate a nonlinear multi-objective optimization problem under imperfect successive interference cancellation (SIC) detecting. Thus, the objective is to simultaneously maximize the sum-capacity and minimize the total transmit power of NOMA-enabled SVNet subject to individual IoV QoS, maximum transmit power and efficient signal detecting. To solve the nonlinear problem, we first exploit a weighted-sum method to handle the multi-objective optimization and then adopt a new iterative Sequential Quadratic Programming (SQP)-based approach to obtain the optimal solution. The proposed optimization framework is compared with Karush-Kuhn-Tucker (KKT)-based NOMA framework, average power NOMA framework, and conventional OMA framework. Monte Carlo simulation results unveil the validness of our derivations. The presented results also show the superiority of the proposed optimization framework over other benchmark frameworks in terms of system sum-capacity and total energy efficiency. [less ▲]

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See detailOn reliability in the performance analysis of cognitive radio networks
Khan, Abd Ullah; Tanveer, Muhammad; Khan, Wali Ullah UL

in Journal of King Saud University - Computer and Information Sciences (2022)

Satisfying the quality of service (QoS) requirements of users in the form of channel availability and service retainability within the resource limited environment has been a major problem in cognitive ... [more ▼]

Satisfying the quality of service (QoS) requirements of users in the form of channel availability and service retainability within the resource limited environment has been a major problem in cognitive radio networks. In this connection, several research studies have been carried out in the literature to improve the QoS of users by proposing dynamic channel reservation algorithms. However, the studies have a number of limitations in their conceptual and mathematical modeling of channel availability and service retainability, which render their performance evaluation unreliable. In this paper, we address these limitations for leading to more realistic, reliable, and practically valid modeling. For conceptual modeling, we use connection availability in place of channel availability, motivated by the fact that the latter does not necessarily lead to a successful establishment of connection and, thus, is not a suitable performance indicator. For instance, obtaining a channel for transmission is of no avail if the intended receiver is inaccessible. Similarly, we consider service retainability with accessibility/inaccessibility of the intended receiver incorporated. For mathematical modeling, we use CTMC and the resultant closed from expressions to include all the required states of channel availability yet unsuccessful connection establishment. Additionally, we derive closed form equations for channel availability and service retainability that are in exact conformance with the CTMC model. Results confirm that considering the impact of receiver’s accessibility leads to performance difference for the channel availability and service retainability presented in the sate-of-the-art. [less ▲]

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See detailA model-based framework for inter-app Vulnerability analysis of Android applications
Nirumand, Atefeh; Zamani, Bahman; Tork-Ladani, Behrouz et al

in Software: Practice and Experience (2022)

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See detailUnclonable human-invisible machine vision markers leveraging the omnidirectional chiral Bragg diffraction of cholesteric spherical reflectors
Agha, Hakam UL; Geng, Yong UL; Ma, Xu UL et al

in Light: Science and Applications (2022), 11(309), 10103841377-022-01002-4

The seemingly simple step of molding a cholesteric liquid crystal into spherical shape, yielding a Cholesteric Spherical Reflector (CSR), has profound optical consequences that open a range of ... [more ▼]

The seemingly simple step of molding a cholesteric liquid crystal into spherical shape, yielding a Cholesteric Spherical Reflector (CSR), has profound optical consequences that open a range of opportunities for potentially transformative technologies. The chiral Bragg diffraction resulting from the helical self-assembly of cholesterics becomes omnidirectional in CSRs. This turns them into selective retroreflectors that are exceptionally easy to distinguish— regardless of background—by simple and low-cost machine vision, while at the same time they can be made largely imperceptible to human vision. This allows them to be distributed in human-populated environments, laid out in the form of QR-code-like markers that help robots and Augmented Reality (AR) devices to operate reliably, and to identify items in their surroundings. At the scale of individual CSRs, unpredictable features within each marker turn them into Physical Unclonable Functions (PUFs), of great value for secure authentication. Via the machines reading them, CSR markers can thus act as trustworthy yet unobtrusive links between the physical world (buildings, vehicles, packaging,...) and its digital twin computer representation. This opens opportunities to address pressing challenges in logistics and supply chain management, recycling and the circular economy, sustainable construction of the built environment, and many other fields of individual, societal and commercial importance. [less ▲]

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See detailEnergy-Efficient Backscatter-Assisted Coded Cooperative-NOMA for B5G Wireless Communications
Asif, Muhammad; Ihsan, Asim; Khan, Wali Ullah UL et al

in IEEE Transactions on Green Communications and Networking (2022)

In this manuscript, we propose an alternating optimization framework to maximize the energy efficiency of a backscatter-enabled cooperative Non-orthogonal multiple access (NOMA) system by optimizing the ... [more ▼]

In this manuscript, we propose an alternating optimization framework to maximize the energy efficiency of a backscatter-enabled cooperative Non-orthogonal multiple access (NOMA) system by optimizing the transmit power of the source, power allocation coefficients (PAC), and power of the relay node under imperfect successive interference cancellation (SIC) decoding. A three-stage low-complexity energy-efficient alternating optimization algorithm is introduced which optimizes the transmit power, PAC, and relay power by considering the quality of service (QoS), power budget, and cooperation constraints. Subsequently, a joint channel coding framework is introduced to enhance the performance of far user which has no direct communication link with the base station (BS) and has bad channel conditions. In the destination node, the far user data is jointly decoded using a Sum-product algorithm (SPA) based joint iterative decoder realized by jointly-designed Quasi-cyclic Low-density parity-check (QC-LDPC) codes. Simulation results evince that the proposed backscatter-enabled cooperative NOMA system outperforms its counterpart by providing an efficient performance in terms of energy efficiency. Also, proposed jointly-designed QC-LDPC codes provide an excellent bit-error-rate (BER) performance by jointly decoding the far user data for considered BSC cooperative NOMA system with only a few decoding iterations. [less ▲]

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See detailMARL based resource allocation scheme leveraging vehicular cloudlet in automotive-industry 5.0
Ahmed, Manzoor; Liu, Jinshi; Mirza, Muhammad Ayzed et al

in Journal of King Saud University - Computer and Information Sciences (2022)

Automotive-Industry 5.0 will use Beyond Fifth-Generation (B5G) communications to provide robust, abundant computation resources and energy-efficient data sharing among various Intelligent Transportation ... [more ▼]

Automotive-Industry 5.0 will use Beyond Fifth-Generation (B5G) communications to provide robust, abundant computation resources and energy-efficient data sharing among various Intelligent Transportation System (ITS) entities. Based on the vehicle communication network, the Internet of Vehicles (IoV) is created, where vehicles’ resources, including processing, storage, sensing, and communication units, can be leveraged to construct Vehicular Cloudlet (VC) to realize resource sharing. As Connected and Autonomous Vehicles (CAV) onboard computing is becoming more potent, VC resources (comprising stationary and moving vehicles’ idle resources) seems a promising solution to tackle the incessant computing requirements of vehicles. Furthermore, such spare computing resources can significantly reduce task requests’ delay and transmission costs. In order to maximize the utility of task requests in the system under the maximum time constraint, this paper proposes a Secondary Resource Allocation (SRA) mechanism based on a dual time scale. The request service process is regarded as M/M/1 queuing model and considers each task request in the same time slot as an agent. A Partially Observable Markov Decision Process (POMDP) is constructed and combined with the Multi-Agent Reinforcement Learning (MARL) algorithm known as QMix, which exploits the overall vehicle state and queue state to reach effective computing resource allocation decisions. There are two main performance metrics: the system’s total utility and task completion rate. Simulation results reveal that the task completion rate is increased by 13%. Furthermore, compared with the deep deterministic policy optimization method, our proposed algorithm can improve the overall utility value by 70% and the task completion rate by 6%. [less ▲]

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See detailSwarm of UAVs for Network Management in 6G: A Technical Review
Khan, Muhammad Asghar; Kumar, Neeraj; Mohsan, Syed Agha Hassnain et al

in IEEE Transactions on Network and Service Management (2022)

Fifth-generation (5G) cellular networks have led to the implementation of beyond 5G (B5G) networks, which are capable of incorporating autonomous services to swarm of unmanned aerial vehicles (UAVs). They ... [more ▼]

Fifth-generation (5G) cellular networks have led to the implementation of beyond 5G (B5G) networks, which are capable of incorporating autonomous services to swarm of unmanned aerial vehicles (UAVs). They provide capacity expansion strategies to address massive connectivity issues and guarantee ultra-high throughput and low latency, especially in extreme or emergency situations where network density, bandwidth, and traffic patterns fluctuate. On the one hand, 6G technology integrates AI/ML, IoT, and blockchain to establish ultra-reliable, intelligent, secure, and ubiquitous UAV networks. 6G networks, on the other hand, rely on new enabling technologies such as air interface and transmission technologies, as well as a unique network design, posing new challenges for the swarm of UAVs.Keeping these challenges in mind, this article focuses on the security and privacy, intelligence, and energy-efficiency issues faced by swarms of UAVs operating in 6G mobile network. In this state-of-the-art review, we integrated blockchain and AI/ML with UAV networks utilizing the 6G ecosystem. The key findings are then presented, and potential research challenges are identified. We conclude the review by shedding light on future research in this emerging field of research. [less ▲]

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See detailNon-stationary 3-D GBSM channel model for V2Vcommunications
Xu, Yan; Saleem, Asad; Asif, Muhammad et al

in IET Communications (2022)

The spatial characteristics of the propagation channel have a considerable impact onthe applicability of multi-antenna systems. In this paper, a non-stationary 3-D GBSMvehicle-to-vehicle channel model is ... [more ▼]

The spatial characteristics of the propagation channel have a considerable impact onthe applicability of multi-antenna systems. In this paper, a non-stationary 3-D GBSMvehicle-to-vehicle channel model is proposed in the tunnel environment based on massivemultiple-input multiple-output antenna arrays. Instead of the plane wavefront assumptionsutilized in traditional multiple-input multiple-output systems, the proposed channel modelfor vehicle-to-vehicle communications uses spherical wavefront assumptions. Initially, thechannel impulse response and closed-form expression for the probability density func-tion of angle-of-departure and angle-of-arrival are derived in the elevation and azimuthplanes. Following that, due to the mobility of transmitting and receiving antenna arrays,expressions for the delay spread (DS), Doppler power spectrum density, temporal cross-correlation function, and channel capacity are extracted by examining line of sight andthe non line of sight propagation paths. The influence of numerous model parameterson the temporal cross-correlation function is also investigated, including antenna arrayspacing,K-factor, movement velocity, and time separation. The proposed 3-D model’s sta-tistical characteristics are verified through measurements, simulations, and analytical results,revealing its adaptability and effectiveness in the high-speed-train environment. [less ▲]

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See detailPost-quantum Remote Device Authentication and Data Analysis Protocols for IoT
Liu, Bowen UL

Doctoral thesis (2022)

Advances in networking and hardware technology have made the design and deployment of Internet of Things (IoTs) and decentralised applications a trend. For example, the fog computing concept and its ... [more ▼]

Advances in networking and hardware technology have made the design and deployment of Internet of Things (IoTs) and decentralised applications a trend. For example, the fog computing concept and its associated edge computing technologies are pushing computations to the edge so that data aggregation can be avoided to some extent. This naturally brings benefits such as efficiency and privacy, but on the other hand, it forces data analysis tasks to be carried out in a distributed manner. Hence, we will focus on establishing a secure channel between an edge device and a server and performing data analysis with privacy protection. In this thesis, we first studied the state-of-art Key Exchange (KE) and Authenticated Key Exchange (AKE) protocols in the literature, including security properties, security models for various security properties, existing KE and AKE schemes of pre-quantum and post-quantum era with varied authentication factors. As a result of the above research, a novel IoT-oriented security model for AKE protocol is introduced. In addition to the general security properties satisfaction, we also define several detailed security games for the desired security properties of perfect forward secrecy, key compromise impersonation resilience and server compromise impersonation resilience. Furthermore, by studying the current multi-factor AKE protocols in the literature, we are inspired by the usage of bigdata in the IoT setting for the authentication and session key establishment propose. With this in mind, we proposed a bigdata-facilitated two-party AKE protocol for IoT systems that uses the bigdata as one of the authentication factors. Moreover, we also proposed a modular framework for constructing IoT-server AKE in post-quantum setting. It is flexible that it can integrate with a public key encryption and a KE component. In addition to this, we notice that as IoT generates and collects more and more data, the need to perform data analysis increases at the same time. In order to avoid the performance limitations of IoT devices, ease the burden of the server, and also guarantee the quality of service of IoT applications, we presented a privacy-preserving decentralised Singular Value Decomposition (SVD) for fog architecture, which could be considered as a multi-IoT and multi-server setting and provides protection for the bigdata set. Next, we would like to further integrate the SVD results from different subsets using a federated learning mechanism. Privacy protection is always a fundamental requirement we need to consider; with this in mind, we proposed a privacy-preserving federated SVD scheme with secure aggregation. The results from the different edge devices are securely aggregated with the server and returned to the individual devices for further applications. [less ▲]

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See detailExperimental and Theoretical Investigations for the improvement of AGMD Energy Efficiency
Dalle, Marie-Alix UL

Doctoral thesis (2022)

Water and power related resources (energy sources and required material) are both critical and crucial resources that have become even more and more strategic as a result of climate change and geopolitics ... [more ▼]

Water and power related resources (energy sources and required material) are both critical and crucial resources that have become even more and more strategic as a result of climate change and geopolitics. By making a large store of salty water available, desalination appears to be a viable solution to the water crisis already affecting 40% of the population today. However, because existing desalination procedures are power intensive and rely on non-renewable energy resources, their power use at large scale is unsustainable. Alternative techniques exist that are promising in terms of environmental impact, but not yet competitive in terms of fresh water outflow and energy efficiency. The focus of this work is on one of these alternatives, Air-Gap Membrane Distillation (AGMD), which was chosen because it relies on low-grade heat that is easy to collect from solar radiations or from industrial waste heat. This technique mimics the water cycle, thanks to the use of a membrane, allowing to bring the hot and cold water streams closer together. As a result, the temperature difference that drives evaporation is strengthened and the process accelerated. However, the development of a boundary layer at the membrane interface reduces this temperature difference and thus decreases the overall performance of the process. Thus this technique still requires some improvements to become industrially attractive, in terms of fresh water outflow per kWh and energy use. The goal of this thesis work is to contribute to AGMD energy efficiency and output flow enhancement by leveraging both experimental and theoretical considerations. A test facility characterizing the boundary layer based on a Schlieren method as well as an adapted AGMD module were designed and built. By interacting with the boundary layer, the laser allows the observation of the continuous temperature profile in the hot water channel of a at sheet AGMD module. The measurement can be performed in close proximity to the membrane and under a variety of operational conditions (inlet hot and cold temperatures, inlet velocities). In parallel, the fresh water outflow corresponding to these experimental conditions can be measured. Moreover, the experimental layout opens the way for further observations of the AGMD process from a different angle - such as concentration profiles or experimentation in the air-gap - with very little addition. The overall experimental set-up has eventually been used to produce a first set of data over a range of temperature (60-75◦C), which is then interpreted thanks to a custom algorithm deriving the temperature profiles and boundary layer thicknesses. A three dimensional heat and mass transfer model for AGMD (3DH&MT) - previously developed in the research team - has been used to numerically reproduce the experimental conditions and compare the results. The comparison showed promising results as the temperature gradients at the membrane interface and fresh water outflows present similar orders of magnitude and trends. The accuracy of the experiment can be further increased through several adaptations in the set-up. This 3DH&MT model could be used to simulate more complex AGMD module designs, such as spiral modules in order to optimize the operating conditions and the overall shape of the AGMD module to enhance its performances. Finally, in the aim of improving the energy efficiency and fresh water outflow of the AGMD process, spacers are usually added in the individual channels to boost mixing and thus reduce the boundary layer thickness, which improves evaporation flux. Two novel spacer geometries inspired by current industrial mixing state of the art and nature have been proposed and investigated, yielding interesting results for two distinct applications. One is particularly well-suited to maximizing mixing regardless of the energy used, hence improving the energy efficiency of the process. The second is optimal for minimizing energy consumption while maintaining a decent mixing result, thus enhancing the fresh water outflow of the process. A couple of indicators have also been proposed to assess the mixing performance of more complex 3D geometries. Overall this work broadens the current AGMD research by providing an experimental test-bench enabling the continuous temperature profile measurement, and the validation of a 3D heat and mass transfer model. Moreover, interesting tracks for improving the design of spacers are proposed in order to minimize the AGMD process's energy efficiency resistance. AGMD is an extremely promising water treatment technique since it is applicable to a broader range of waters than just seawater. The test equipment described in this work is sufficiently adaptable to investigate this potential as well as variants of AGMD processes that might boost its attractiveness. As it is based on readily available materials and technologies, it may be used anywhere and its reliance on a naturally available energy flow (solar radiation) makes it attractive in isolated regions. [less ▲]

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See detailA Deep Learning Approach for Reconstruction in Millimeter-Wave Imaging Systems
Rostami Abendansari, Peyman UL

in IEEE Transactions on Antennas and Propagation (2022)

In millimeter-wave (MMW) imaging, the objects of interest are oftentimes modeled as 2D binary (black and white) shapes with white pixels representing the reflecting interior of the object. However, due to ... [more ▼]

In millimeter-wave (MMW) imaging, the objects of interest are oftentimes modeled as 2D binary (black and white) shapes with white pixels representing the reflecting interior of the object. However, due to propagation of the scattered waves, the continuous-domain binary images are convolved with a so-called point-spread function (PSF) before being digitized by means of sampling. As the 2D PSF is both non-separable and non-vanishing in the case of MMW imaging, exact recovery is quite complicated. In this paper, we propose a deep learning approach for image reconstruction. We should highlight that the wave scatterings are suitably represented with complex-valued quantities, while standard deep neural networks (DNN) accept real-valued inputs. To overcome this challenge, we separate the real and imaginary parts as if we had two imaging modalities and concatenate them to form a real-valued input with larger size. Fortunately, the network automatically learns how to combine the mutual information between these modalities to reconstruct the final image. Among the advantages of the proposed method are improved robustness against additive noise and mismatch errors of imaging frequency and object to antenna distance; indeed, the method works well in wide-band imaging scenarios over a wide range of object to antenna distances even in presence of high noise levels without requiring a separate calibration stage. We test the method with synthetic data simulated with software as well as real recordings in the laboratory. [less ▲]

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See detailOpportunities for Intelligent Reflecting Surfaces in 6G-Empowered V2X Communications
Khan, Wali Ullah UL; Mahmood, Asad UL; Bozorgchenani, Arash et al

in Bulletin. Cornell University Libraries (2022)

The applications of upcoming sixth generation (6G)-empowered vehicle-to-everything (V2X) communications depend heavily on large-scale data exchange with high throughput and ultra-low latency to ensure ... [more ▼]

The applications of upcoming sixth generation (6G)-empowered vehicle-to-everything (V2X) communications depend heavily on large-scale data exchange with high throughput and ultra-low latency to ensure system reliability and passenger safety. However, in urban and suburban areas, signals can be easily blocked by various objects. Moreover, the propagation of signals with ultra-high frequencies such as millimeter waves and terahertz communication is severely affected by obstacles. To address these issues, the Intelligent Reflecting Surface (IRS), which consists of nearly passive elements, has gained popularity because of its ability to intelligently reconfigure signal propagation in an energy-efficient manner. Due to the promise of ease of deployment and low cost, IRS has been widely acknowledged as a key technology for both terrestrial and non-terrestrial networks to improve signal strength, physical layer security, positioning accuracy, and reduce latency. This paper first describes the introduction of 6G-empowered V2X communications and IRS technology. Then it discusses different use case scenarios of IRS enabled V2X communications and reports recent advances in the existing literature. Next, we focus our attention on the scenario of vehicular edge computing involving IRS enabled drone communications in order to reduce vehicle computational time via optimal computational and communication resource allocation. At the end, this paper highlights current challenges and discusses future perspectives of IRS enabled V2X communications in order to improve current work and spark new ideas. [less ▲]