Optimal Throughput Research Articles

Secrecy Throughput Optimization and Precoding Design in Adaptive Transmit Antenna Selection Systems With Limited Feedback

We analyze the secrecy performance of a wireless system with limited feedback, where the channel state information (CSI) is imperfectly known at a multi-antenna source. In order to mitigate the adverse effects of limited feedback and eavesdropper attack on secrecy transmission, we propose the quantized secrecy precoding (QSP) oriented adaptive transmit antenna selection (ATAS) scheme denoted as QSP-ATAS, where the transmit antennas with poor channel quality are inactivated by adjusting the channel gain threshold. Besides, the conventional single transmit antenna selection (STAS) scheme without secrecy precoding (NSP-STAS), where only the optimal transmit antenna has the chance to be selected for transmission, is considered as a baseline. Since that both the number of active transmit antennas and the number of quantization bits affect the quantization error, we define the effective secrecy throughput as the difference between the secrecy data rate and feedback rate to evaluate the system performance. We conduct an effective secrecy throughput analysis for both QSP-ATAS and NSP-STAS schemes and show the existence of the maximal effective secrecy throughput of proposed QSP-ATAS scheme. Furthermore, an optimization analysis of the QSP-ATAS scheme is carried out for the sake of further improving the effective secrecy throughput with regard to the channel gain threshold. Numerical simulation results demonstrate that our proposed QSP-ATAS scheme performs better than the conventional NSP-STAS scheme in terms of the effective secrecy throughput.

Intelligence Reflecting Surface-Aided Integrated Data and Energy Networking Coexisting D2D Communications

In this paper, we consider an integrated data and energy network and D2D communication coexistence (DED2D) system. The DED2D system allows a base station (BS) to transfer data to information-demanded users (IUs) and energy to energy-demanded users (EUs), i.e., using a time-fraction-based information and energy transfer (TFIET) scheme. Furthermore, the DED2D system enables D2D communications to share spectrum with the BS. Therefore, the DED2D system addresses the growth of energy and spectrum demands of the next generation networks. However, the interference caused by the D2D communications and propagation loss of wireless links can significantly degrade the data throughput of IUs. To deal with the issues, we propose to deploy an intelligent reflecting surface (IRS) in the DED2D system. Then, we formulate an optimization problem that aims to optimize the information beamformer for the IUs, energy beamformer for EUs, time fractions of the TFIET, transmit power of D2D transmitters, and reflection coefficients of the IRS to maximize IUs’ worse throughput while satisfying the harvested energy requirement of EUs and D2D rate threshold. The max-min throughput optimization problem is computationally intractable, and we develop an alternating descent algorithm to resolve it with low computational complexity. The simulation results demonstrate the effectiveness of the proposed algorithm.

VSAT Packet Security Performance Enhancement Using Modified Interlock Protocol

This study has focused on developing the Interlock protocol and on implementing it on the VSAT system to overcome the man-in-the-middle attack in the double hob topology. A new cryptography protocol has been modified into Interlock protocol by using the concept of Cipher Block Chinning mode and AES, RSA, SAH-256 algorithms. The TCP protocol is flexible and has the ability to tune its parameters depending on the environment under which it works. In this study the TCP protocol parameters had been readjusted to reach the optimum throughput when the packet was encrypted and transmitted in the VSAT environment. The results indicate an increase in the throughput of up to about 30%.

When Aloha and CSMA Coexist: Modeling, Fairness, and Throughput Optimization

With the emerging unlicensed spectrum sharing and Machine-to-Machine communications, the coexistence performance of multiple devices with different access schemes operating at the same unlicensed bands has received significant research interests. As the two most representative random-access schemes, Aloha and Carrier Sense Multiple Access (CSMA) both found wide applications in unlicensed bands. Yet most of the studies have focused on the coexistence of CSMA-based networks, leaving the coexistence of Aloha-based and CSMA-based networks largely unexplored. The challenge originates from the lack of a coexistence model of slotted Aloha and CSMA. In this paper, the throughput performance of coexisting Aloha and CSMA networks is characterized and optimized by extending a unified analytical framework proposed for random-access networks. The analysis shows that different from the single-network case where the maximum throughput of CSMA is much higher thanks to carrier sensing, when Aloha and CSMA coexist, Aloha would significantly outperform CSMA if each network optimizes its own throughput performance without cooperation, leading to poor throughput performance for CSMA and severe unfairness. To achieve fair coexistence, inter-network cooperation is crucial. The optimal transmission probabilities of Aloha and CSMA for maximizing the total network throughput under a given throughput ratio are further derived, and applied to coexisting LTE Unlicensed and WiFi networks to optimize their coexistence performance.

Secure over-the-air software update for connected vehicles

Current trends forecast that Over-the-Air (OTA) software updates will be highly significant for future connected vehicles. The OTA software updates will enable upgrading vehicle functionalities or bug fixations in embedded software installed on electronic control units remotely. However, in terms of security, OTA updates are highly critical as they need complete access to the in-vehicle communication network. Furthermore, scheduling OTA software updates at a massive scale over a cellular network is highly challenging. This paper proposes STRIDE, a novel technique for secure and scalable software updates using cloud through cellular network. STRIDE ensures end-to-end security using ciphertext-policy attribute-based encryption. To enable fast and reliable distribution of update package, we then propose a software update scheduling algorithm to serve dynamic traffic flow. Particularly, we integrate dynamic traffic flow with the Lyapunov-drift analysis framework, and establish throughput optimality of our proposed scheduling algorithm. We evaluate the performance of STRIDE through extensive experiments. Experimental results show that STRIDE reduces more than 52% computation and storage overheads, 60% propagation delay and increases throughput by 35% than the state-of-the-art solutions, in addition to enjoying the stronger security properties.

Variable Data Rate Architectures in Optical LEO Direct-to-Earth Links: Design Aspects and System Analysis

In the frame of ongoing efforts between space agencies to define an on-off-keying-based optical low-Earth-orbit (LEO) direct-to-Earth (DTE) waveform, this paper offers an in-depth analysis of the Variable Data Rate (VDR) technique. VDR, in contrast to the currently adopted Constant Data Rate (CDR) approach, enables the optimization of the average throughput during a LEO pass over the optical ground station (OGS). The analysis addresses both critical link level aspects, such as receiver (time, frame, and amplitude) synchronization, as well as demonstrates the benefits stemming from employing VDR at system level.

What is the added value of liquid chromatography-tandem mass spectrometry for routine urine drug screening in clinical situations?

Replacing the common two-step process of urine drug screening by a single-step methodology based on selective and quantitative liquid chromatography-tandem mass spectrometry (LC-MSMS) seems quite attractive. With this approach, it may be more appropriate to use the limit of analytical quantification instead of a cutoff concentration to report positive results. In this study, we want to evaluate the impact of this modification on the results of urine samples received in clinical situations. Results of all urine drug screens and associated confirmations performed between July 2021 and March 2022 for amphetamine, benzodiazepine and opiate groups, cannabis and cocaine metabolites and methadone were retrieved from the laboratory information system of the Medical Biology Department of Laboratoire National de Santé in Luxembourg. The samples came from hospitals, addiction centers, prisons and occupational health clinics. They were analyzed by an automated immunoassays, Cobas (Roche Diagnostics) and by a targeted quantitative screening assay, MassTox Drugs of Abuse testing, (Chromsystems) on a 6500QTRAP LC-MSMS (Sciex). We calculated the number of samples where results were discordant. Cases where no drugs were identified by LC-MSMS but results of the immunoassay were above the cut-off concentration (based on the screen test cut-off concentration of European guidelines) were considered as false positive (FP) results. Whereas cases where drugs were quantified by LC-MSMS but results of the immunoassay were below the cut-off concentration were considered as false negative (FN) results. The FP and FN results were 1 and 5% for cannabis metabolite ( n = 477), 0 and 14% for cocaine metabolite ( n = 189), 2 and 20% for methadone ( n = 211), 1 and 10% for benzodiazepines ( n = 489), 1 and 20% for opiates ( n = 149) and 48 and 2% for Amphetamines ( n = 92). The median concentrations of FN were 18 ng/ml (range from 15 to 31) for THC-COOH, 62 ng/ml (range from 24 to 121) for benzoylecgonin, 221 ng/ml (from 63 to 785) for methadone and 367 ng/ml (from 66 to 3174) for EDDP. In the benzodiazepine FN results (median concentration 69 ng/ml, range from 26-318), oxazepam, lorazepam and OH-alprazolam were the most frequently detected drugs. Among the opiate FN results, morphine associated or not with codeine was the most frequently quantified drug (median 81 ng/ml, range from 27 to 519). Four out of the 29 FN samples contained 6-monoacetylmorphine (from 17 to 136 ng/ml) in the absence of morphine, 2 samples were positive for pholcodine and one sample contained high concentration of meperidine. The two FN samples for amphetamines contained amphetamine (500 and 800 ng/ml). Nine out of the 42 amphetamines FP samples contained ritalinic acid (from 10,000 to 130,000 ng/ml). This targeted method improves the sensitivity but is restricted to a pre-defined list of drugs, although it covers a wider panel than commercial immunoassays, some relevant molecules may be missing. The hydrolysis protocol used before LC-MSMS analysis also explains the increase in sensitivity for benzodiazepines and opiates (allowing the detection of conjugated metabolites). Decreasing the threshold of detection for urine drug screening allowed for the extension of the drug detection window, improving the detection of drugs administered at low dose or those reacting poorly in immunoassay. This single step approach bypasses the main pitfalls of the immunoassays, namely the lack of analytical specificity (mainly for amphetamines) and a screening limited to well-established (parent) drugs, with consequences on patient management still only partially objectified. This targeted quantitative screening LC-MSMS method with its high sensitivity, accurate quantification, and associated internal quality control, achieves the required analytical performances expected in a clinical setting. The implementation of this approach will largely be conditioned by the optimization of the sample throughput to ensure a turn-around-times compatible with various clinical situations and the availability of the equipment and highly trained staff.

Modified Vector Quantization for Small-Cell Access Point Placement With Inter-Cell Interference

In this paper, we explore the small-cell uplink access point (AP) placement problem in the context of throughput optimality and provide solutions while taking into consideration inter-cell interference (ICI). First, we briefly review the vector quantization (VQ) approach and related single user throughput-optimal formulations for AP placement. Then, we investigate the small-cell case with multiple users and expose the limitations of mean squared error based VQ for solving this problem. While the Lloyd algorithm from the VQ approach is found not to strictly solve the small-cell case, based on the tractability and quality of the resulting AP placement, we deem it suitable as a simple and appropriate framework to solve more complicated problems. Accordingly, to minimize ICI and consequently enhance achievable throughput, we design two Lloyd-type algorithms, namely the Interference Lloyd algorithm and the Inter-AP Lloyd algorithm, both of which incorporate ICI in their distortion functions. Simulation results show that both of the proposed algorithms provide superior 95%-likely rate over the traditional Lloyd algorithm and the Inter-AP Lloyd algorithm yields a significant increase of up to 36.34% in achievable rate over the Lloyd algorithm.

Performance Analysis of Hybrid Cryptography System for High Security and Cloud-Based Storage

Now a day, the security of the data is playing a major part in communication systems due to further bushwhackers between channels media. The security position depends on cache crucial and as per literature, advanced the bit size of the key, advanced the security and also larger data size comes major challenge task for the further process. Thus, the generation of crucial with the further size is a major grueling task and at present, the Advanced Encryption Standard (AES) is a better cryptography system where the encryption and decryption can perform with a fixed key. The literature says the holomorphic function is well-suitable for data size reduction. To address this issue, new Holomorphic grounded encryption and decryption and AES are combined to increase the security position. The alternate novelty is that variable crucial generation using Elliptic Wind Cryptography (ECC) due to its enlarged proportion of consideration in assiduity and experimenters. The ECC uses point addition and point doubling to induce 256 values and addition operations can be avoided. After the generation of the matrix, each matrix value is translated and decrypted using a Holomorphic algorithm. The proposed work has been designed using MATLAB 2017a, dissembled, and validated with different datasets in real decors. Cloud computing is expected to be considered one of the primary computing platforms in the field of storage and security as it possesses many advantages such as profitability, efficiency as well as lower implementation overheads. Contemporary cloud computing security algorithms are enhanced extensions of cryptography. Data privacy, as well as data protection, are the major areas of concern in Cloud computing. The cryptographic with holomorphic based data encryption and interchange of information is exchanged and then accumulated in the cloud through holomorphic encryption which uses point addition and doubling operation to ensure data confidentiality of owners as well as users. Proposed work novel hybrid algorithm based on the context of encryption and decryption and thus integrates cryptography hybrid techniques include modified 126-bit AES and ElGamal based ECC through splitting algorithm. The advantage of splitting the larger data in size into binary form and then processing for encryption and decryption leads to optimization of latency, increase throughput, and security. The proposed hybrid approach has better security towards information sharing as well as cloud storage intrusions. Based on obtained results in MATLAB 2017a software tool, the obtained results show that 43% improvement in throughput and 12% reduction in latency, and a 21% improvement in security level.

Network throughput optimization for grouping based NB-CR-IoT wireless body area network for healthcare monitoring system

Network throughput optimization for grouping based NB-CR-IoT wireless body area network for healthcare monitoring system

Near optimal scheduling for opportunistic spectrum access over block fading channels in cognitive radio assisted vehicular network

With the increasing use of cognitive radio technology in vehicular communications, vehicles will be enabled with cognitive radio in the future. Cognitive radio assisted vehicular networks make cognitive radio enabled vehicles utilize licensed spectrum on highways opportunistically. This work tackles cognitive radio assisted vehicular networks including M primary users (transmitter), M primary receivers, a secondary user (transmitter) with K channels and K secondary receivers. A channel is assigned to each primary user for data transmission whenever they need to use. The secondary user has data backlog. It knows neither the states (good or bad) of M channels, or statistics of channel evolution processes. It selects K of M channels to send data at each time slot. If it senses that a selected channel is in good state, it uses that channel for data packet transmission in same time slot. If not, it chooses another channel at the next time slot. We tackle this problem under average reward criteria for finite and infinite time horizon. This work presents Uniforming Random Ordered Policy (UROP) and shows UROP achieves near-optimal throughput under block fading assumption for generic channel evolution process. Numerical results show that our policy achieves near-optimal throughput (more than 80% of optimal throughput for scarce number of available channels) under average reward criteria whereas MP may achieve less than even 25% of optimal throughput for scarce number of available channels.

RIS-Aided Zero-Forcing and Regularized Zero-Forcing Beamforming in Integrated Information and Energy Delivery

This paper considers a network of a multi-antenna array base station (BS) and a reconfigurable intelligent surface (RIS) to deliver both information to information users (IUs) and power to energy users (EUs). The RIS links the connection between the IUs and the BS as there is no direct path between the former and the latter. The EUs are located nearby the BS in order to effectively harvest energy from the high-power signal from the BS, while the much weaker signal reflected from the RIS hardly contributes to the EUs’ harvested energy. To provide reliable links for all users over the same time-slot, we adopt the transmit time-switching (transmit-TS) approach, under which information and energy are delivered over different time-slot fractions. This allows us to rely on conjugate beamforming for energy links and zero-forcing/regularized zero-forcing beamforming (ZFB/RZFB) and on the programmable reflecting coefficients (PRCs) of the RIS for information links. We show that ZFB/RZFB and PRCs can be still separately optimized in their joint design, where PRC optimization is based on iterative closed-form expressions. We then develop a path-following algorithm for solving the max-min IU throughput optimization problem subject to a realistic constraint on the quality-of-energy-service in terms of the EUs’ harvested energy thresholds. We also propose a new RZFB for substantially improving the IUs’ throughput.

Search-Free Inference Acceleration for Sparse Convolutional Neural Networks

Sparse convolution neural networks (CNNs) are promising in reducing both memory usage and computational complexity while still preserving high inference accuracy. State-of-the-art sparse CNN accelerators can deliver high throughput by skipping zero weights and/or activations. To operate on only nonzero weights and activations, sparse accelerators typically search pairs of nonzero weights and activations for multiplication-accumulation (MAC) operations. However, the conventional search operation results in a severe limitation in the processing element (PE) array scale because of the enormous demands of internal interconnection and memory bandwidth. In this article, we first provide a design principle to free the search process of sparse CNN accelerations. Specifically, the indexes of the static compressed weights access the dynamic activations directly to avoid the search process for MAC operations. We then develop two search-free inference accelerators, called Swan and Swan-flexible, for sparse CNN accelerations. Swan supports search-free sparse convolution accelerations for interconnection and bandwidth saving. Compared with Swan, Swan-flexible not only has the search-free capability but also comprises a configurable architecture for optimum throughput. We formulate a mathematical optimization problem by combining the configurable characterization with the compressive dataflow to optimize the overall throughput. Evaluations based on a place-and-route process show that the proposed designs, in a compact factor of 4096 PEs, achieve 1.5– <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.7\times $ </tex-math></inline-formula> higher speedup and 6.0– <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$13.6\times $ </tex-math></inline-formula> better energy efficiency than representative accelerator baselines with the same PE array scale.

AI-federated novel delay-aware link-scheduling for Industry 4.0 applications in IoT networks

Purpose With the advent of AI-federated technologies, it is feasible to perform complex tasks in industrial Internet of Things (IIoT) environment by enhancing throughput of the network and by reducing the latency of transmitted data. The communications in IIoT and Industry 4.0 requires handshaking of multiple technologies for supporting heterogeneous networks and diverse protocols. IIoT applications may gather and analyse sensor data, allowing operators to monitor and manage production systems, resulting in considerable performance gains in automated processes. All IIoT applications are responsible for generating a vast set of data based on diverse characteristics. To obtain an optimum throughput in an IIoT environment requires efficiently processing of IIoT applications over communication channels. Because computing resources in the IIoT are limited, equitable resource allocation with the least amount of delay is the need of the IIoT applications. Although some existing scheduling strategies address delay concerns, faster transmission of data and optimal throughput should also be addressed along with the handling of transmission delay. Hence, this study aims to focus on a fair mechanism to handle throughput, transmission delay and faster transmission of data. The proposed work provides a link-scheduling algorithm termed as delay-aware resource allocation that allocates computing resources to computational-sensitive tasks by reducing overall latency and by increasing the overall throughput of the network. First of all, a multi-hop delay model is developed with multistep delay prediction using AI-federated neural network long–short-term memory (LSTM), which serves as a foundation for future design. Then, link-scheduling algorithm is designed for data routing in an efficient manner. The extensive experimental results reveal that the average end-to-end delay by considering processing, propagation, queueing and transmission delays is minimized with the proposed strategy. Experiments show that advances in machine learning have led to developing a smart, collaborative link scheduling algorithm for fairness-driven resource allocation with minimal delay and optimal throughput. The prediction performance of AI-federated LSTM is compared with the existing approaches and it outperforms over other techniques by achieving 98.2% accuracy. Design/methodology/approach With an increase of IoT devices, the demand for more IoT gateways has increased, which increases the cost of network infrastructure. As a result, the proposed system uses low-cost intermediate gateways in this study. Each gateway may use a different communication technology for data transmission within an IoT network. As a result, gateways are heterogeneous, with hardware support limited to the technologies associated with the wireless sensor networks. Data communication fairness at each gateway is achieved in an IoT network by considering dynamic IoT traffic and link-scheduling problems to achieve effective resource allocation in an IoT network. The two-phased solution is provided to solve these problems for improved data communication in heterogeneous networks achieving fairness. In the first phase, traffic is predicted using the LSTM network model to predict the dynamic traffic. In the second phase, efficient link selection per technology and link scheduling are achieved based on predicted load, the distance between gateways, link capacity and time required as per different technologies supported such as Bluetooth, Wi-Fi and Zigbee. It enhances data transmission fairness for all gateways, resulting in more data transmission achieving maximum throughput. Our proposed approach outperforms by achieving maximum network throughput, and less packet delay is demonstrated using simulation. Findings Our proposed approach outperforms by achieving maximum network throughput, and less packet delay is demonstrated using simulation. It also shows that AI- and IoT-federated devices can communicate seamlessly over IoT networks in Industry 4.0. Originality/value The concept is a part of the original research work and can be adopted by Industry 4.0 for easy and seamless connectivity of AI and IoT-federated devices.

Free2Shard

In this paper, we formulate and study a new, but basic, distributed resource allocation problem arising in scaling blockchain performance. While distributed resource allocation is a well-studied problem in networking, the blockchain setting additionally requires the solution to be resilient to adversarial behavior from a fraction of nodes. Scaling blockchain performance is a basic research topic; a plethora of solutions (under the umbrella of sharding) have been proposed in recent years. Although the various sharding solutions share a common thread (they cryptographically stitch together multiple parallel chains), architectural differences lead to differing resource allocation problems. In this paper we make three main contributions: (a) we categorize the different sharding proposals under a common architectural framework, allowing for the emergence of a new, uniformly improved, uni-consensus sharding architecture. (b) We formulate and exactly solve a core resource allocation problem in the uni-consensus sharding architecture -- our solution, Free2shard, is adversary-resistant and achieves optimal throughput. The key technical contribution is a mathematical connection to the classical work of Blackwell approachability in dynamic game theory. (c) We implement the sharding architecture atop a full-stack blockchain in 3000 lines of code in Rust -- we achieve a throughput of more than 250,000 transactions per second with 6 shards, a vast improvement over state-of-the-art.

Genetic algorithm based bi-objective optimization of sigmoidal utility and throughput in ad-hoc wireless networks

Genetic algorithm based bi-objective optimization of sigmoidal utility and throughput in ad-hoc wireless networks

VQ-CSMA: Throughput-Optimal Low-Delay Random Access

Queue based carrier-sense multiple access (Q-CSMA) is designed to ensure throughput optimality in wireless networks, where the access probability depends on the local queue length information. However, some links may be long-term blocked if their neighbors continuously occupy the channel, which is called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">starvation</i> and leads to poor delay performance. In this letter, to deal with the starvation problem, we construct a neighbor-aware virtual queue system, which considers not only the local queue lengths but also the neighbors’ queue lengths. In the virtual queue system, we design a new Lyapunov function and derive a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">virtual queue based CSMA (VQ-CSMA)</i> algorithm. With the presence of negative virtual queues, we discuss four cases of virtual queues and prove that VQ-CSMA achieves throughput optimality by analyzing the corresponding maximum-weight scheduling. The simulation results demonstrate that VQ-CSMA alleviates the starvation problem and consequently leads to a better delay performance compared to existing Q-CSMA.

Throughput optimization of interference limited cognitive radio-based Internet of Things (CR-IoT) network

The Internet of Things (IoT) technology allows massive devices to connect to the internet for data exchange. It is anticipated that in near future, trillions of IoT devices will be connected to the internet. To deploy these devices, the requirement of the spectrum is increasing day by day. Most of these devices transmit data over unlicensed frequency bands that cause severe interference to each other while exchanging the data as these bands are becoming overcrowded. Therefore, to overcome spectrum scarcity and interference problems among these devices, a novel communication paradigm called cognitive radio-based internet of things (CR-IoT) is evolving at a very fast pace that integrates cognitive radio technology into the IoT devices. The technology has the potential to overcome the spectrum scarcity and interference problem by allowing dynamic spectrum access to conventional IoT networks. Such devices continuously monitor spectrum availability to transmit the data by incorporating an intelligent sensing mechanism into the devices. However, the performance of the sensing unit in terms of the probability of detection and the probability of false alarm, significantly deteriorates due to the noise uncertainties, especially in low signal-to-noise ratio environments. For the efficient utilization of the spectrum, the probability of detection and the probability of false alarm of the spectrum sensor should be high and low, respectively. Both sensing parameters are greatly influenced by the selection of the sensing threshold. Moreover, these IoT devices deal with short packet transmissions, the optimum sensing time is another crucial parameter that governs the performance of these devices.While addressing these two important issues, the convex optimization problem is formed over sensing time and sensing threshold, and the concavity on sensing threshold is proved. Further, an iterative algorithm is proposed for the CR-IoT system that intelligently adapts the sensing threshold to meet desired sensing performance in terms of Pd and Pf especially, in low SNR regions, and also optimize the sensing time to overcome the sensing throughput tradeoff. The simulation results are presented to validate the effectiveness of the proposed algorithm. It is demonstrated that the proposed algorithm increases the CR-IoT system throughput by 95% as compared to the conventional scheme at the received signal to noise ratio equals −20 dB while satisfying the requirement of Pd and Pf as per IEEE 802.22 standard.

Secure Throughput Optimization for Cache-Enabled Multi-UAVs Networks

This article considers an ultradense heterogeneous network (UDHN) consisting of cache-enabled unmanned aerial vehicles (UAVs) and Internet of Things mobile devices (IMDs) receiving their requested contents via the power domain nonorthogonal multiple access (PD-NOMA) protocol. Employing the fast global <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> -means (FGKMs) algorithm, IMDs are partitioned into several clusters connecting to either other IMDs or UAV belonging to the same cluster in the presence of untrusted users, known as nonlegitimate eavesdroppers. It is assumed that users located in the cluster edge area communicate with multiple UAVs to obtain their requested contents. The main goal for such a network is to jointly optimize the number of UAVs, their 3-D placements, and the cache placement probability of contents stored in UAVs and IMDs by maximizing the secure cache throughput. Toward this goal, we prove that the objective function is nonconcave. Therefore, we decompose the optimization problem into multiple subproblems. We first employ the FGKM algorithm to optimally determine the number of employed UAVs and their horizontal placements. Then, the UAVs’ altitudes are optimized by employing the interior-point method (IPM), while the convex approximation for the objective function and its constraints are substituted. Then, we optimize the secure cache throughput of IMDs by proposing a caching placement strategy for contents stored in UAVs and IMDs via Device to Device (D2D) and UAV to Device (U2D) links, given illegal eavesdroppers’ presence. Then, we propose an iterative algorithm to achieve the near-optimal solution for the cache throughput of IMDs. Different from existing works, the closed-form expressions for the achievable secrecy rate and the successful probability of D2D communications and U2D transmissions, as well as the secure cache throughput are derived. Finally, simulation results are presented to validate the proposed caching placement strategy. It is shown that the proposed scheme outperforms the conventional most popular caching (MPC) strategy substantially.

Resource Selection from Edge-Cloud for IIoT and Blockchain-Based Applications in Industry 4.0/5.0

Industrial Internet of Things (IIoT) is attempting to integrate the real world into the digital world through smart devices, information technology, and Internet. IIoT is connecting enormous devices in industry 4.0/5.0 which may be heterogeneous in nature. With the evolution of IoT, diverse technologies have been employed to deliver the quality of services to the end users in a seamless manner. Cloud computing has considerably boosted the growth of IIoT by serving the computational and data storage needs of IIoT- and blockchain-based applications in industry 4.0/5.0. However, cloud is providing services to IIoT users, but still, there is a need to improve the latency rate of delivery of services, the transmission rate of end-to-end delivery, and overall throughput of the network channels in industry 4.0 and blockchain-based distributed systems. The cloud servers those are located at remote locations are not capable to offer the quality of services to users who require real-time responses, minimum network latency, and optimum throughput. The advancements in Edge computing are making Edge-Cloud more suitable for end users in blockchain-based transactions and industry 4.0 to serve the requirements of IIoT-based applications. This paper aims at providing the resources of Edge-Cloud to the end users by proposing a soft-computing technique for selecting the most suitable resources from the pool of available resources at Edge-Cloud. This paper is proposing a multicriteria statistical approach for resource selection to exploit the benefits of Edge-Cloud and to suffice the needs of IIoT and blockchain-based applications in industry 4.0. The results obtained from the proposed research assist in enhancing the service providing rate, minimizing the delay in transmission, and optimizing the throughput of Edge-Cloud servers.