Minimum Throughput Research Articles

Joint Resource Management and Trajectory Optimization for UAV-Enabled Maritime Network.

Due to the lack of places to employ communication infrastructures, there are many coverage blind zones in maritime communication networks. Benefiting from the high flexibility and maneuverability, unmanned aerial vehicles (UAVs) have been proposed as a promising method to provide broadband maritime coverage for these blind zones. In this paper, a multi-UAV-enabled maritime communication model is proposed, where UAVs are deployed to provide the transmission service for maritime users. To improve the performance of the maritime communication systems, an optimization problem is formulated to maximize the minimum average throughput among all users by jointly optimizing the user association, power allocation, and UAV trajectory. To derive the solutions with a low computational complexity, we decompose this problem into three subproblems, namely user association optimization, power allocation optimization, and UAV trajectory optimization. Then, a joint iterative algorithm is developed to achieve the solutions based on the successive convex approximation and interior-point methods. Extensive simulation results validate the effectiveness of the proposed algorithm and demonstrate that UAVs can be used to enhance the maritime coverage.

A digital twin based framework for detection, diagnosis, and improvement of throughput bottlenecks

Digitalization through Industry 4.0 technologies is one of the essential steps for the complete collaboration, communication, and integration of heterogeneous resources in a manufacturing organization towards improving manufacturing performance. One of the ways is to measure the effective utilization of critical resources, also known as bottlenecks. Finding such critical resources in a manufacturing system has been a significant focus of manufacturing research for several decades. However, finding a bottleneck in a complex manufacturing system is difficult due to the interdependencies and interactions of many resources. In this work, a digital twin framework is developed to detect, diagnose, and improve bottleneck resources using utilization-based bottleneck analysis, process mining, and diagnostic analytics. Unlike existing bottleneck detection methods, this novel approach is capable of directly utilizing enterprise data from multiple levels, namely production planning, process execution, and asset monitoring, to generate event-log which can be fed into a digital twin. This enables not only the detection and diagnosis of bottleneck resources, but also validation of various what-if improvement scenarios. The digital twin itself is generated through process mining techniques, which can extract the main process map from a complex system. The results show that the utilization can detect both sole and shifting bottlenecks in a complex manufacturing system. Diagnosing and managing bottleneck resources through the proposed approach yielded a minimum throughput improvement of 10% in a real factory setting. The concept of a custom digital twin for a specific context and goal opens many new possibilities for studying the strong interaction of multi-source data and decision-making in a manufacturing system. This methodology also has the potential to be exploited for multi-objective optimization of bottleneck resources.

The maximal secondary throughput in cognitive radio networks with energy harvesting

In terms of spectrum reuse and lifetime prolongation, the energy-harvesting cognitive radio networks (EH-CRNs) have been a hot issue in the wireless networking research community. While satisfying the minimal throughput demand of primary users (PUs), it is aimed to maximize the throughput of secondary users (SUs) in the EH-CRN with multiple SUs. Specifically, the problem of secondary throughput maximization (STM) is first formulated as a non-linear optimization problem, then its convexity is proven, and finally an efficient algorithm is proposed that jointly uses the Fibonacci search method and equal interval search method to obtain the optimal time allocation among primary transmitter (PT)'s energy transfer and each SU's packet transmission, and the optimal transmit power of PT. Furthermore, for the scenarios where the circuit power is negligible, the convex problem is first proven, and a more efficient algorithm is presented for the problem of STM. Simulation results demonstrate that, with the increase of the minimal throughput demand of PUs, the reduction rate of the maximal secondary throughput increases.

Access Control and Deployment Design for Multi-UAV Assisted Wireless Networks

In this letter, we investigate the ground user (GU) access control and unmanned aerial vehicle base station (UAV-BS) deployment design in a multiple UAV-BSs assisted wireless network. The objective of each GU is to maximize its own throughput, and the objective of each UAV-BS is to maximize the minimum throughput of GUs served by it. Firstly, we optimize the GU access control by game theory. The access control game is proved to be an exact potential game, and we use the best response algorithm to solve the Nash equilibrium (NE). Then, we optimize the UAV-BS deployment design through convex optimization theory. The simulation results demonstrate that the proposed approach outperforms other five baselines.

Hybrid Task Allocation of an AGV System for Task Groups of an Assembly Line

An AGV system can be used to transport different-size materials in an assembly line. The hybrid task allocation problem is involved in the assembly line, where both single-AGV tasks and multi-AGV tasks exist. However, there is little research on this problem. The goal of solving this problem is to obtain a task allocation scheme with minimum idle time and maximum system throughput. Since all necessary materials must be delivered to the assembly station before the operation can start, the delivery tasks are not independent of each other in a task group serving the operation. To solve the problem above, a hybrid task allocation method based on a task binding strategy and an improved particle swarm optimization (IPSO) is proposed. Firstly, a mathematical model considering the punctuality of material delivery and the cooperative relationship between tasks is established. Secondly, a task binding strategy and four heuristic rules are devised to improve the quality of randomly- and heuristic-generated individuals in the initial population for model optimization. Thirdly, an IPSO is developed to help the optimization algorithm jump out of local optimums. Finally, a simulation is performed to verify the effectiveness of the proposed methods. The simulation results show that a better scheme can be obtained by our hybrid task allocation method, compared to conventional Genetic Algorithms and PSO algorithms.

Efficient User Grouping for MU-MIMO Visible Light Communications Based on Block Diagonalization

This paper investigates efficient user grouping methods for multi-user multi-input multi-output (MU-MIMO) visible light communication (VLC) systems. Block diagonalization (BD) precoding is considered for interference avoidance. In addition, time division multiplexing (TDM) is applied to perform user grouping when the number of users exceeds the limit of BD precoding based on the number of light emitting diode (LED) transmitters and the total number of users' photodiodes (PDs). User grouping methods are proposed based on pairwise interference considerations among users in the same group. The proposed methods can be implemented through integer linear programming (ILP), which requires less computation than exhaustive search. The numerical results on the average minimum user throughputs over random scenarios indicate that the proposed hybrid method can significantly outperform random user grouping and performs reasonably well compared to exhaustive search. Finally, this study demonstrates that, when BD precoding greatly attenuates the desired user signals, user grouping can help improve minimum user throughputs even though BD precoding can support all users as a single group.

Multiobjective Optimization for Improving Throughput and Energy Efficiency in UAV-Enabled IoT

Unmanned-aerial-vehicle (UAV)-aided wireless communication in Internet of Things (IoT) applications is becoming the focus of attention of researchers. This article investigates a UAV-assisted communication system for serving IoT, in which multiple rotary-wing UAVs are employed to communicate with multiple terrestrial IoT devices. Specifically, we formulate a UAV deployment multiobjective optimization problem (UAVDMOP) to simultaneously maximize the minimum throughput of the UAV–device pairs, maximize the total throughput of the whole system, and minimize the total energy consumptions of UAVs via a joint optimization of the locations of UAVs, transmission power of UAVs, and association relationship between the UAVs and IoT devices. UAVDMOP consists of both discrete and continuous solution spaces, which is difficult to be solved. Thus, we propose an improved discrete and continuous multiobjective evolutionary algorithm based on decomposition (IDCMOEA/D) with a hybrid solution initialization operation and a hybrid solution reproduction operation for increasing the performance of the algorithm so that making it more suitable for dealing with the UAVDMOP. Simulation results demonstrate that the proposed method is effective to enhance the throughput and energy efficiency of the system and it has superior performance compared to other methods.

Energy-Efficient UAV-Aided Ocean Monitoring Networks: Joint Resource Allocation and Trajectory Design

The Internet of Underwater Things (IoUT) plays a key role in maritime monitoring systems, but energy-efficient data-uploading has been a challenging task owing to energy-constrained and expensive facilities, such as buoys and underwater sensors. In this article, we present an energy-efficient data collection scheme for unmanned aerial vehicle (UAV)-aided ocean monitoring networks (OMNs), where underwater acoustic and aerial radio frequency (RF) links are considered collaboratively. Our goal is to maximize energy efficiency (EE) of the entire OMN by jointly optimizing the transmit power of buoys and sensors, scheduling their transmissions, as well as designing the UAV's trajectory; the objective function is constrained by minimum throughput thresholds, power consumption budgets, and the UAV's kinematic conditions. Furthermore, we introduce a tradeoff between the energy consumption of buoys and sensors to bridge the gap between acoustic and RF links. The formulated problem is decomposed into three subproblems and they are solved alternatively. In each iteration, we leverage Dinkelbach's method and successive convex approximation (SCA) technique to tackle the fractional program (FP) and transform an original subproblem into a convex form, respectively. Extensive simulations confirm the convergence of our proposed scheme, reveal the influence of the tradeoff on EE, and show that our scheme outweighs other benchmarks in different scenarios.

UAV-Enabled Mobile Edge Computing with Binary Computation Offloading and Energy Constraints

<p>Mobile edge computing (MEC) has been considered to provide computation services near the edge of mobile networks, while the unmanned aerial vehicle (UAV) is becoming an important integrated component to extend service coverage. In this paper, we consider a UAV-enabled MEC with binary computation offloading and energy constraints, where an energy-limited UAV is employed as an aerial edge server and each task of devices is either executing locally or offloading to the aerial edge server as a whole. To provide fairness among different ground devices, we aim to maximize the minimum computation throughput among all devices via the joint design of computing mode selection and UAV trajectory as well as resource allocation. The optimization problem is formulated as a mixed-integer non-linear problem consisting of binary variables, which is difficult to tackle. By employing deductive penalty function to penalize the effect of non-binary solution, we develop an efficient iterative algorithm to obtain a suboptimal solution via leveraging the penalty successive convex approximation (P-SCA) method and difference of two convex (D.C.) optimization framework, where the algorithm is guaranteed to converge. Extensive simulations are conducted and the results with different system parameters show that the proposed joint design algorithm can improve the computation throughput by about 40% compared to other benchmark schemes.</p> <p> </p>

Energy-Efficient Resource Allocation in Cognitive Wireless-Powered Hybrid Active-Passive Communications

Integrating hybrid active-passive communications into cognitive radio can achieve a spectrum- and energy-efficiency information transmission, while the resource allocation has not been well studied particularly for the network with multiple secondary users (also termed as the Internet of Things (IoT) users). In this article, we formulate an optimization problem to maximize the energy efficiency of all the IoT nodes in a cognitive wireless-powered hybrid active-passive communication network by taking the interference from the IoT node to the primary link, the energy causality constraint, and the minimum throughput constraint per IoT node. By using the Dinkelbach method and introducing auxiliary variables, we devise an iterative algorithm to optimally solve the formulated problem. Computer simulations are provided to validate the quick convergence of the iterative algorithm and the advantages of the proposed scheme in terms of the energy efficiency.

Method of ensuring the reliability of connecting nodes to the information and communication system of the enterprise based on 5G

The object of research is the process of building a cloud information and communication system for automation of production processes. The subject of the research is the method of ensuring the reliability of connection of nodes to the information and communication system of the enterprise on the basis of 5G. The purpose of this work is to increase the reliability of connecting nodes to the information and communication system of the enterprise based on 5G. to optimize production processes. As a result of the research, the concepts characterizing the reliability of distributed 5G information and communication networks were considered. The synthesis of functionally stable structure of the distributed information and communication network of 5G of the enterprise was formalized. The method of planning of 5G network for automation of production processes of the enterprise was developed. on the way of distribution taking into account restrictions on the minimum throughput, number of connections and reliability and construction of a communication transport segment with definition of an optimum location of cross premises. Conclusions. The developed method allows planning the optimal structure of the 5G cellular network to optimize production processes, evaluate and reduce the total cost of building the network, while providing the necessary indicators of service quality of network nodes and its reliability.

QoS Enhancement of Wireless Campus Network Using Throughput Parameter

In recent years, broadband wireless systems developed themselves as one of the fastest growing and emerging sector in the field of telecommunications. It is a network of multiple interconnected local area networks in a limited geographical area. Quality of Service (QoS) is considered as one of the key issue in Wireless Campus Network due to the bottleneck and the competition among different kinds of traffic flow on the network. In this paper a Short-Term and Long-Term models are developed to enhancements QoS performance for Wireless Campus Network using throughput parameter. The models were designed and simulated using Optimized Network Engineering Tool, OPNET IT GURU Academic Edition with linkages between wireless workstations suitable for implementation to determine the performance of the network throughput. The two versions contain the same number of wireless workstations, but with some additional access points and multiple processors in the long-term model, a point-to - point protocol (PPP) was used to link the access points to the routers and the cloud while using 100BaseT cable to connect. In each case, the wireless network throughput was obtained. The findings obtained from these models showed that, relative to the short-term model, the Long-Term model achieves higher wireless network performance. That may be due to the fact that while in both models the total number of wireless workstations is the same, the long-term model topology includes additional access points and double processors resulting in the observed minimum throughput.

Container Elasticity: Based on Response Time using Docker

Objective:: Cloud computing is an approach to provide the computing resources (machine) to end-users for running their application over the Internet. The computing resources consist of various things (e.g. RAM, Memory, CORE, etc.). These resources are allocated to an application without human intervention for managing the fluctuating workload. To manage the real-time fluctuating workload, cloud providers use VM based or Container-based virtualization to host the client services. Adding/removing resources dynamically as per the demand of application through cloud is known as elasticity. Cloud providers use the auto-scaling mechanism to implement elasticity. A machine that hosts an application can be either overloaded or under-loaded due to the real-time fluctuating workload. The cloud providers use an auto-scaling mechanism to automatically scale up or down the computing resources at the right moment for managing the real-time fluctuating workload. The failure of allocation/de-allocation of resources at right time leads to SLA violation, service unavailability, customers lost, more power consumption, minimum throughput and maximum response time. Hence, the allocation/de-allocation of resources at right moment becomes critical for successful completion of task in dynamic environment efficiently. Methods:: Resource provisioning for managing dynamic and fluctuating workload has been achieved through an algorithm (PID with dynamic HAProxy) which is based on decision-making approach that depends on response time of container using mechanism of control theory. Results:: The proposed work has improved performance of the system in terms of resource utilization and response time to manage the fluctuating workload. Conclusion:: The addition/removal of containers dynamically to manage fluctuating workload can be achieved more efficiently.

Energy Efficiency Optimization for SWIPT-Based D2D-Underlaid Cellular Networks Using Multiagent Deep Reinforcement Learning

In this article, we study the optimization of energy efficiency in wireless device-to-device (D2D-underlaid cellular networks where multiple D2D pairs adopt simultaneous wireless information and power transfer functionality. We formulate the optimization problem, which is a NP-hard combinatorial problem with nonlinear constraints. First, we use optimization-based-iterative techniques such as exhaustive search (ES) and gradient search (GS) with barrier, which are generally used to obtain the global optimum and local optimum of the nonconvex optimization problem, respectively. Considering that these techniques require a centralized unit to share information with each other, we propose multiagent deep reinforcement learning to solve this optimization problem in a distributed manner, which provides optimal decision making together with efficient deep network training under inequality constraints including transmit power, power splitting ratio, and minimum requirement data rate for D2D and cellular users. In this proposed method, we consider the virtual environment in which each agent can train their model according to shared information, and then, we deploy the trained model into the actual environment where each agent can only know their channel gain, interference power, and required minimum throughput. Simulation results show that the proposed algorithm can afford a near-global-optimum solution with much lower computation complexity than ES and outperforms the GS.

Hybrid Puncturing and Superposition Scheme for Joint Scheduling of URLLC and eMBB Traffic

Recent modifications to the air interface of 5G and beyond networks have improved the support for mixed enhanced mobile broadband (eMBB) and ultra-reliable and low-latency (URLLC) traffic. In particular, the concept of mini-slots has been introduced, and URLLC traffic is served at the mini-slot boundaries using superposition or puncturing of eMBB resources. This approach enables low latency for URLLC traffic, but it can incur a rate loss for eMBB traffic. To alleviate the latter, we propose a novel hybrid puncturing and superposition policy that jointly maximizes the minimum average throughput of eMBB traffic and the number of admitted URLLC users. Using sequential convex programming, a joint access technology selection, resource block, and power allocation problem is solved, where eMBB and URLLC user pairing and the decoding order in case of superposition are optimized based on channel state information. Our numerical results confirm that our proposed policy achieves a better trade-off between eMBB throughput and the number of admissible URLLC users when compared with existing solutions.

Power-Efficient Passive Beamforming and Resource Allocation for IRS-Aided WPCNs

This paper studies an intelligent reflecting surface (IRS)-assisted wireless-powered communication network (WPCN), where a hybrid access point (HAP) broadcasts energy signals to multiple devices for their energy harvesting in the downlink (DL) and then the devices use the harvested energy to transmit information signals to the HAP in the uplink (UL) with the help of an IRS. In particular, we propose three types of IRS beamforming configurations, namely <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fully dynamic IRS beamforming (FDBF)</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">partially dynamic IRS beamforming (PDBF)</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">static IRS beamforming (SBF)</i> , to strike a balance between the system performance and signaling overhead as well as implementation complexity. Moreover, we adopt a practical non-linear energy harvesting (EH) model, and leverage a power-splitting (PS) EH receiver architecture with multiple rectifiers to avoid the input radio frequency power to get stuck into the saturation regime. We aim to minimize the transmit energy consumption at the HAP by jointly optimizing the DL/UL time allocation, the HAP/devices transmit power, the PS factor, and IRS phase shifts, subject to a set of minimum throughput requirements for individual devices. To address the resulting non-convex optimization problems, a successive convex approximation (SCA) based alternating optimization algorithm is proposed. Moreover, we study the case with the ideal linear EH model and two algorithms, namely SCA-based algorithm and semidefinite relaxation (SDR) algorithm, are proposed. Simulation results demonstrate the effectiveness of our proposed designs over various benchmark schemes and also unveil the importance of the joint design of IRS beamforming and PS rectifiers for achieving energy efficient WPCNs in practice.

Fair Downlink Communications for RIS-UAV Enabled Mobile Vehicles

This letter considers the sub-6GHz downlink communications from base station (BS) to mobile vehicles (MVs), where reconfigurable intelligent surface enabled unmanned aerial vehicle (RIS-UAV) serves as a mobile passive relay. To achieve communication fairness, we aim to maximize the minimum throughput among all the MVs via jointly optimizing RIS passive beamforming, MV scheduling, UAV trajectory and power allocation. The formulated problem is a mixed-integer non-convex optimization problem which is intractable to solve. Hence, the original problem is divided into three subproblems, joint passive beamforming and MV scheduling optimization, power allocation optimization, and trajectory optimization, which can be solved by the successive convex approximation (SCA) method. An alternating iterative optimization algorithm of the three subproblems is proposed to obtain the suboptimal solutions. Numerical results demonstrate the remarkable throughput gain of the proposed algorithm against other benchmarks.

Encryption with DNA: The Manipulation of DNA to Deal with the Cyber Security Attacks

The witnessed technological developments in wireless communications have made an individual’s dependency on online transactions. As is the nature of wireless, the wireless networks are more prone to man-in-middle attacks, which are intended for crucial information theft, modification called active and passive attacks. Recent investigations of the application of biology DNA (deoxribonucleic acid) to encrypt the data have shown improved security results. The proposed technique is an advanced version of DNA-based cryptography, is a hybrid technique of Improved Pig-Pen Cipher and DNA encryption techniques, uses a 256-bit length key to encrypt the message with prescribed shapes. This algorithm takes less processing time and it will work faster for encryption and decryption with minimum throughput. The DNA-based cryptographic algorithms were defined by using the biological properties of DNA strands.

DDPG-Based Throughput Optimization with AoI Constraint in Ambient Backscatter-Assisted Overlay CRN.

The combination of ambient backscatter (AB) communications (ABCs) and RF-powered cognitive radio networks (CRNs) deals with challenges of both energy supply and spectrum shortage, and improves the network performances. With the expansion of wireless networks, many applications raise requirements for both high-throughput and timely data. Driven by these facts, we study the long-term throughput optimization of the secondary network in the AB-assisted overlay CRN (ABO-CRN), ABCs, and CRNs with the age of information (AoI) constraint, which is a novel metric for measuring the freshness of data received by receivers. Due to the dynamic environment, complete knowledge of the environment could not be obtained. Then, the deep deterministic policy gradient (DDPG), a deep reinforcement learning (DRL) method that addresses decision issues in both continuous and discrete spaces, is deployed to address the throughput optimization. We consider the impacts of time and energy allocation on the reward when the AoI constraint can not be satisfied, and develop the corresponding reward functions. Furthermore, we analyze the impacts of the minimum throughput requirement and maximum allowable AoI on the throughput and AoI of the secondary networks in the ABO-CRN, ABCs, and CRNs. We compare the throughput optimization scheme under the AoI constraint with two baseline schemes (i.e., throughput-optimal (T-O) and AoI-optimal (A-O) baseline schemes), and the simulation results show that the throughput of the ABO-CRN is close to the optimal throughput of the T-O baseline scheme, and the AoI of the ABO-CRN is close to the optimal AoI of the A-O baseline scheme.

Wireless Multimedia Sensor Network QoS Bottleneck Alert Mechanism Based on Fuzzy Logic

Wireless Multimedia Sensor Networks (WMSNs) are mostly affected by bottleneck issues, high packet loss, increased delay, and minimum throughput. One of the most effective schemes towards controlling the bottleneck on the web is traffic control. The WMSNs handle different types of data, hence QoS is essential to afford trustworthy as well as reasonable services towards these kinds of data. The existing congestion control methods, FTLP and FEWPBRC consider the frequency of packet transmission and decide on the output transmission rate of the sink. In the Fuzzy-Based QoS Alert Bottleneck Mechanism, the probability of congestion is predicted by using a fuzzy inference system with three special congestion indicators, and the traffic rate is adjusted based on the priority of the real-time and non-real-time applications. The FBQACC is simulated using the NS2 simulator and it gives an improvement in the average throughput FTLP and FEWPBRC by 7.1499% and 6.3327%, respectively. Similarly, FBQACC reduces average delay compared to FTLP and FEWPBRC by 11.074% and 7.8128%, respectively. The proposed work also gives a minimized average packet loss percentage compared to the existing congestion control methods.