Joint Optimization Of Performance Research Articles

Joint optimization of communication and mission performance for multi-UAV collaboration network: A multi-agent reinforcement learning method

In emergency rescue, target search and other mission scenarios with Unmanned Aerial Vehicles (UAVs), the Relay UAVs (RUs) and Mission UAVs (MUs) can collaborate to accomplish tasks in unknown environments. In this paper, we investigate the problem of trajectory planning and power control for the MU and RU collaboration. Firstly, considering the characteristics of multi-hop data transmission between the MU and Ground Control Station, a multi-UAV collaborative coverage model is designed. Meanwhile, a UAV control algorithm named MUTTO is proposed based on multi-agent reinforcement learning. In order to solve the problem of the unknown information about the number and locations of targets, the geographic coverage rate is used to replace the target coverage rate for decision making. Then, the reward functions of two types of UAVs are designed separately for the purpose of better cooperation. By simultaneously planning the trajectory and transmission power of the RU and MU, the mission target coverage rate and network transmission rate are maximized while the energy consumption of the UAV is minimized. Finally, numerical simulations results show that MUTTO can solve the UAV network control problem in an efficient way and has better performance than the benchmark method.

Resource-aware multi-criteria vehicle participation for federated learning in Internet of vehicles

Federated learning (FL), as a safe distributed training mode, provides strong support for the edge intelligence of the Internet of Vehicles (IoV) to realize efficient collaborative control and safe data sharing. However, due to the resource limitation and the instability of training environment in the complex IoV, ideal performance of FL cannot be achieved. Since considering the actual resource constraints and federated task requirements, the diversified device selection criteria make the resource-aware vehicle selection problem become a multi-criteria selection problem. To effectively support FL for IoV, the resource-aware multi-criteria vehicle selection problem was described as a many-objective optimization problem, and a resource-aware many-objective vehicle selection model (RA-MaOVSM) is proposed to optimize resource efficiency. The RA-MaOVSM considering heterogeneous resources (like computation resources, communication resources, energy resources and data resources) of on-board devices in IoV, and realizes the joint optimization of learning efficiency, energy cost and global performance. Additionally, a novel probability distribution combination game strategy is applied to many-objective evolutionary algorithm (MaOEA) for improving the model solving performance. Simulation results demonstrate that RA-MaOVSM can effectively optimize the IoV resources and FL model performance, and the designed algorithm exhibits good convergence and distribution, achieving a good balance among multiple device selection criteria.

Joint Radar-Communication Optimization of Distributed Airborne Radar for AOA Localization

Compared to the distributed ground-based radar (DGBR), the distributed airborne radar (DAR) has been widely applied due to its stronger anti-damage ability, more degrees of freedom, and better detection view of targets. However, unlike DGBR, the premise for the normal operation of DAR is to maintain stable wireless communication between unmanned aerial vehicles (UAVs). This requires each UAV to make reasonable use of its electromagnetic domain resources. That is, to maximize radar detection performance while ensuring communication performance constraints. However, current research in the field of radar resource allocation has not taken this into account, which greatly limits the practical application of optimization algorithms. Moreover, the current research tends to adopt centralized optimization algorithms. When the baseline of the UAV swarm is long, applying multi-relay methods directly results in heavy communications overhead and long-time delay. Based on the above background, this article aimed to develop a fully distributed algorithm for the joint optimization of radar detection performance and communication transmission performance. This study first took the measurement angle of arrival (AOA) as an example to provide a system model with communication constraints. This model considers the impact of factors such as the UAV location error, UAV communication coverage, and dynamic communication topology of the UAV on joint optimization. A formal representation of the joint optimization is presented. Then, we proposed a joint radar-communication optimization (JRCO) algorithm to fully utilize the electromagnetic domain resources of each UAV. Finally, numerical simulations verified the effectiveness of the proposed JRCO algorithm to traditional radar resource allocation methods.

A Novel Epidemic-Based Video Diffusion Strategy Using Awareness of Sociality and Mobility in Wireless Networks

Social networks open up a new channel of video sharing and promote the scale and efficiency of video diffusion. Adjustable and scalable video diffusion is significant for the quality and performance of the service of video systems. In this paper, we propose a novel epidemic-based video diffusion strategy using awareness of sociality and mobility in wireless networks (EVDSM). EVDSM constructs a video diffusion model with the consideration of interest preference, social influence, and user mobility according to the roles and the propagation process of the Epidemic model. EVDSM designs an estimation method of interest preference according to content similarity and preference discrimination between videos; EVDSM designs an estimation method of user roles by investigation of interest preference and social influence to identify the video sharing behaviors of users and define the roles of users; EVDSM designs an estimation method of user mobility in terms of data transmission time and path structure stability. EVDSM proposes a control strategy of video diffusion, which formulates priority-based pairing between infectors and candidate infectors to achieve joint optimization of pairing success rate and delivery performance. The simulation results show how EVDSM achieves much better performance results in comparison with other state-of-the-art solutions.

Joint Optimization of Radar and Communications Performance in 6G Cellular Systems

Dual functional radar communication (DFRC) is a promising approach that provides a viable solution for the problem of spectrum sharing between communication and radar applications. This paper studies a DFRC system with multiple communication users (CUs) and a radar target. The goal is to devise beamforming vectors at the DFRC transmitter in such a way that the radar received signal-to-clutter-plus-noise-ratio (SCNR) is maximized while satisfying the minimum data rate requirements of the individual CUs. With regard to clutter, we consider two scenarios based on the possibility of clutter removal. Even though the formulated optimization problems are non-convex, we present efficient algorithms to solve them using convex optimization techniques. Specifically, we use duality theory and Karush-Kuhn-Tucker conditions to show the underlying structure of optimal transmit precoders. In the proposed solution, it is observed that there is no need to transmit separate probing signal for the radar detection in both the considered scenarios. This results in reduction in the number of optimization variables in the problem. Moreover, we make use of the asymptotic equivalence between Toeplitz matrices and Circulant matrices to further reduce the complexity of the proposed algorithm. Finally, numerical results are presented to demonstrate the effectiveness of the proposed algorithms.

Joint Optimization for Mobile Edge Computing-Enabled Blockchain Systems: A Deep Reinforcement Learning Approach.

A mobile edge computing (MEC)-enabled blockchain system is proposed in this study for secure data storage and sharing in internet of things (IoT) networks, with the MEC acting as an overlay system to provide dynamic computation offloading services. Considering latency-critical, resource-limited, and dynamic IoT scenarios, an adaptive system resource allocation and computation offloading scheme is designed to optimize the scalability performance for MEC-enabled blockchain systems, wherein the scalability is quantified as MEC computational efficiency and blockchain system throughput. Specifically, we jointly optimize the computation offloading policy and block generation strategy to maximize the scalability of MEC-enabled blockchain systems and meanwhile guarantee data security and system efficiency. In contrast to existing works that ignore frequent user movement and dynamic task requirements in IoT networks, the joint performance optimization scheme is formulated as a Markov decision process (MDP). Furthermore, we design a deep deterministic policy gradient (DDPG)-based algorithm to solve the MDP problem and define the multiple and variable number of consecutive time slots as a decision epoch to conduct model training. Specifically, DDPG can solve an MDP problem with a continuous action space and it only requires a straightforward actor–critic architecture, making it suitable for tackling the dynamics and complexity of the MEC-enabled blockchain system. As demonstrated by simulations, the proposed scheme can achieve performance improvements over the deep Q network (DQN)-based scheme and some other greedy schemes in terms of long-term transactional throughput.

Application of response surface methodology for the joint optimization of performance and emission characteristics of a diesel engine

ABSTRACT Multi-injection is an effective strategy to control the NOx emission while maintaining the performance of combustion engines, however, it is difficult to balance emission and performance in practical operations. In this study, the Response Surface Methodology (RSM) was proposed to optimize them simultaneously. The combustion and performance of engine were firstly characterized by varying the multi-injection parameters, including fuel quantity distribution, injection dwell time and injection pressure. Experiments were conducted on a 3.856 L diesel engine, through which the cylinder pressure, torque output, and main emissions were measured, with brake specific fuel consumption (BSFC) and effective thermal efficiency (ηet) being analyzed. The experimental results showed that the fuel injection properties had a significant impact on in-cylinder combustion and emissions. Based on the RSM analysis, the coupling effects between engine performance and NOx emission were then optimized, which were then verified and tested by experimental measurements. The optimal results were obtained when the fuel share ratio, dwell time, and injection pressure were 5, 10.56 °CA and 95 MPa, respectively. Through the verification tests, NOx emission could substantially reduce as much as 31.8% with torque slightly reduced by 3.3%.

Learning Content-Weighted Deep Image Compression.

Learning-based lossy image compression usually involves the joint optimization of rate-distortion performance, and requires to cope with the spatial variation of image content and contextual dependence among learned codes. Traditional entropy models can spatially adapt the local bit rate based on the image content, but usually are limited in exploiting context in code space. On the other hand, most deep context models are computationally very expensive and cannot efficiently perform decoding over the symbols in parallel. In this paper, we present a content-weighted encoder-decoder model, where the channel-wise multi-valued quantization is deployed for the discretization of the encoder features, and an importance map subnet is introduced to generate the importance masks for spatially varying code pruning. Consequently, the summation of importance masks can serve as an upper bound of the length of bitstream. Furthermore, the quantized representations of the learned code and importance map are still spatially dependent, which can be losslessly compressed using arithmetic coding. To compress the codes effectively and efficiently, we propose an upper-triangular masked convolutional network (triuMCN) for large context modeling. Experiments show that the proposed method can produce visually much better results, and performs favorably against deep and traditional lossy image compression approaches.

A multi-staged niched evolutionary approach for allocating parallel tasks with joint optimization of performance, energy, and temperature

This paper presents a multi-stage multi-objective evolutionary approach (MS-MOEA) for allocating parallel computations on multi-core processors by joint optimizing performance (P), energy (E), and temperature (T). Evolutionary techniques have been shown to be effective for solving optimization problems, including our own previous work on solving the PET-optimized scheduling (PETOS) problem. There have long been a great many debates and rivalries between various evolutionary approaches, such as the SPEA or NSGA, with regard to their relative matters. The novelty of the proposed MS-MOEA approach is its amalgamation of the basic evolutionary algorithms that are already shown to be highly effective, thereby creating a niche of these techniques. The niche takes advantages of the strengths of each baseline technique for achieving additional enhancement in the precision of the optimization. We propose six multi-stage hybrids, each designed with either niched fitness assignment strategy, or combining populations from multiple MOEAs, or incorporating the problem knowledge into the conventional technique. The experimental results measure the quality of resulting Pareto fronts and demonstrate that the proposed MS-MOEAs yield better optimization for the PETOS problem in achieving three-objective in parallel task-to-core mapping.

Modelling and characterization of mechanical properties of optimized honeycomb structure

The honeycomb structure can be applied to the design of new lightweight composites materials due to its excellent properties such as high strength, strong energy absorption ability, low thermal conductivity, etc. In recent years, researchers have noticed that cell joint performance optimization can improve the performance of honeycomb structures. In this study, an optimized honeycomb structure with cell joint thickened was obtained by a theoretical analysis in which two principal geometrical parameters were adjusted, and then fabricated with additive manufacturing technology for compression testing. The results were obtained through experimentation and qualitative simulation, and then compared with those of a traditional honeycomb structure with uniform wall thickness. Finally, the mechanism of the optimized thickened-joint honeycomb structure was investigated, which could be a design guideline for the application of honeycomb structures in various engineering fields.

Characterization of keyhole gas tungsten arc welded AISI 430 steel and joint performance optimization

Keyhole gas tungsten arc welding (K-TIG) is a recently developed welding process which enables single-pass welding without filler metal addition or joint preparation. Using K-TIG, 8-mm-thick AISI 430 ferritic stainless steel was successfully welded. The fusion zone of the weld was composed of columnar ferrite grains. Retained austenite was distributed along grain boundaries. The mechanical properties and corrosion resistance of the joint were tested. Three methods to enhance joint performance were trialed; high-frequency pulse arc welding (HFP-AW), austenite interlayer addition, and post-welding heat treatment (PWHT). Heat input was decreased by 42.24% when using HFP-AW. The addition of an austenite interlayer led to primary columnar austenite generation. Carbon precipitated and martensite diffused evenly into the matrix when using PWHT. The mechanical properties and corrosion resistance of the enhanced joints improved. In particular, the tensile properties of the PWHT joint and the corrosion resistance of the joint with the austenite interlayer were better than those of the base metal.

Joint Optimization of Wireless Network Energy Consumption and Control System Performance in Wireless Networked Control Systems

Communication system design for wireless networked control systems requires satisfying the high reliability and strict delay constraints of control systems for guaranteed stability, with the limited battery resources of sensor nodes, despite the wireless networking induced non-idealities. These include non-zero packet error probability caused by the unreliability of wireless transmissions and non-zero delay resulting from packet transmission and shared wireless medium. In this paper, we study the joint optimization of control and communication systems incorporating their efficient abstractions practically used in real-world scenarios. The proposed framework allows including any non-decreasing function of the power consumption of the nodes as the objective, any modulation scheme and any scheduling algorithm. We first introduce an exact solution method based on the analysis of the optimality conditions and smart enumeration techniques. Then, we propose two polynomial-time heuristic algorithms based on intelligent search space reduction and smart searching techniques. Extensive simulations demonstrate that the proposed algorithms perform very close to optimal and much better than previous algorithms at much smaller runtime for various scenarios.

Energy efficiency based joint cell selection and power allocation scheme for HetNets

Heterogeneous networks (HetNets) composed of overlapped cells with different sizes are expected to improve the transmission performance of data service significantly. User equipments (UEs) in the overlapped area of multiple cells might be able to access various base stations (BSs) of the cells, resulting in various transmission performances due to cell heterogeneity. Hence, designing optimal cell selection scheme is of particular importance for it may affect user quality of service (QoS) and network performance significantly. In this paper, we jointly consider cell selection and transmit power allocation problem in a HetNet consisting of multiple cells. For a single UE case, we formulate the energy efficiency of the UE, and propose an energy efficient optimization scheme which selects the optimal cell corresponding to the maximum energy efficiency of the UE. The problem is then extended to multiple UEs case. To achieve joint performance optimization of all the UEs, we formulate an optimization problem with the objective of maximizing the sum energy efficiency of UEs subject to QoS and power constraints. The formulated nonlinear fractional optimization problem is equivalently transformed into two subproblems, i.e., power allocation subproblem of each UE-cell pair, and cell selection subproblem of UEs. The two subproblems are solved respectively through applying Lagrange dual method and Kuhn–Munkres (K-M) algorithm. Numerical results demonstrate the efficiency of the proposed algorithm.

Distributed optimization of lifetime and throughput with power consumption balance opportunistic routing in dynamic wireless sensor networks

This article studies a joint performance optimization on the network lifetime and network throughput for an energy-constrained dynamic wireless sensor network. We propose a fully distributed power consumption balance opportunistic routing scheme to cope with the dynamic network that is not considered in the classic low-energy adaptive clustering hierarchy routing and evenly allocate power consumption among the sensor nodes for obtaining longer network lifetime. Moreover, a fully distributed optimization solution, whose distinctive feature to the Lagrange dual approach is capable of handling the changing network, is developed to achieve joint performance optimization of objectives. We mathematically prove the convergence of the proposed solution and analyze its computational complexity. Extensive simulation results illustrate the effective measures to deal with the varying network of power consumption balance opportunistic routing and the best tradeoff performance achieved by the proposed solution and evaluate the more positive impact on the network lifetime of power consumption balance opportunistic routing than the existing routings and better ability to adapt the dynamic network of the proposed solution than the Lagrange dual approach.

Sixteen Heuristics for Joint Optimization of Performance, Energy, and Temperature in Allocating Tasks to Multi-Cores

Three-way joint optimization of performance ( P ), energy ( E ), and temperature ( T ) in scheduling parallel tasks to multiple cores poses a challenge that is staggering in its computational complexity. The goal of the PET optimized scheduling ( PETOS ) problem is to minimize three quantities: the completion time of a task graph, the total energy consumption, and the peak temperature of the system. Algorithms based on conventional multi-objective optimization techniques can be designed for solving the PETOS problem. But their execution times are exceedingly high and hence their applicability is restricted merely to problems of modest size. Exacerbating the problem is the solution space that is typically a Pareto front since no single solution can be strictly best along all three objectives. Thus, not only is the absolute quality of the solutions important but “the spread of the solutions” along each objective and the distribution of solutions within the generated tradeoff front are also desired. A natural alternative is to design efficient heuristic algorithms that can generate good solutions as well as good spreads -- note that most of the prior work in energy-efficient task allocation is predominantly single- or dual-objective oriented. Given a directed acyclic graph (DAG) representing a parallel program, a heuristic encompasses policies as to what tasks should go to what cores and at what frequency should that core operate. Various policies, such as greedy, iterative, and probabilistic, can be employed. However, the choice and usage of these policies can influence a heuristic towards a particular objective and can also profoundly impact its performance. This article proposes 16 heuristics that utilize various methods for task-to-core allocation and frequency selection. This article also presents a methodical classification scheme which not only categorizes the proposed heuristics but can also accommodate additional heuristics. Extensive simulation experiments compare these algorithms while shedding light on their strengths and tradeoffs.

Cognitive Network Management Framework and Approach for Video Streaming Optimization in Heterogeneous Networks

The future Internet will be highly heterogeneous in supporting a multitude of access technologies and networks with overlapping coverages. Optimization of network operations like management of resources, mobility or Quality of Service in order to ensure smooth network operation and high user satisfaction will be very challenging in the future networks. Cognitive network management can provide a solution of managing such complex systems. This paper studies cognitive network management in the context of optimizing video streaming performance in heterogeneous multi-access networks. The paper proposes a network management framework that relies on cognitive decision techniques in the joint optimization of network and video service performance. The proposed solution is also implemented and validated in part in a testbed environment. The results attest the feasibility of the solution as well as the benefits of cognitive decision techniques over non-learning or non-adaptive approaches.

Distributed routing for signal detection in wireless sensor networks

We study the problem of energy-efficient routing for signal detection in wireless sensor networks. Generic routing protocols use networking-centric measures such as minimum hop or minimum energy to establish routes. These schemes do not take into account the performance of application-specific algorithms that is achievable from the data collected by the nodes along the routes. Routing protocols for signal detection have recently been proposed to facilitate joint optimization of detection performance and energy efficiency by developing metrics that connect detection performance with energy consumption of each link along the routes. In existing routing for signal detection (RSD) schemes, however, the routes are computed centrally requiring complex optimization algorithms and global information such as locations and observation coefficients of all nodes in the network. Clearly, for large-scale networks, or networks with dynamically changing topologies, distributed routing schemes are more practical due to their better flexibility and scalability. We present a distributed RSD protocol where each node, based on locally available information, selects its next-hop with the goal of maximizing the detection performance associated with unit energy expenditure. We show that the proposed protocol is readily implementable in ZigBee networks, and present simulation results that reveal its significant improvements in detection performance and energy efficiency over generic routing protocols.

Lifetime and Distortion Optimization With Joint Source/Channel Rate Adaptation and Network Coding-Based Error Control in Wireless Video Sensor Networks

In this paper, we study joint performance optimization on network lifetime and video distortion for an energy-constrained wireless video sensor network (WVSN). To seek an appropriate tradeoff between maximum network lifetime and minimum video distortion, a framework for joint source/channel rate adaptation is proposed, where the video encoding rate, link rate, and power consumption are jointly considered, formulating a weighted convex optimization problem. In the context of lossy wireless channels, an efficient error control scheme that couples network coding and multipath routing is explored. Moreover, an integrated power consumption model, including power dissipation on video compression, error control, and data communication, is specifically developed for the video sensor node. By primal decomposition, the original problem is decomposed into a two-level optimization procedure, with the high-level procedure for source adaptation (source rate optimization) and the low-level procedure for channel adaptation (network resource allocation). Finally, a fully decentralized iterative algorithm is developed to resolve the target optimization problem. Extensive simulation results evaluate the convergence performance of the proposed algorithm and demonstrate the best tradeoff performance.

The disparity between P2P overlays and ISP underlays: issues, existing solutions, and challenges

The proliferation of peer-to-peer applications has generated tremendous traffic in the Internet backbone and has also posed unprecedented pressure on Internet Service Providers. Many P2P applications, oblivious of underlying ISP networks, can lead to inefficient utilization of Internet resources and a significant amount of costly inter-ISP traffic. Recently, increasing efforts have been directed toward mitigating ISP costs from P2P applications while preserving the user perceived service quality. In this article we review state-of-the-art research on P2P applications with particular focus on their topological properties and navigating algorithms with awareness of ISP costs and performances. We discuss ISP-friendly designs in P2P applications by identifying their respective benefits and deficiencies. Based on the lessons we have learned, we further highlight future research challenges and issues regarding the joint design and optimization of P2P system performance and ISP traffic and costs.

Self piercing riveting for metal-polymer joints

Self-Piercing Riveting (SPR) is a sheet metal joining technique based on the insertion of a rivet into two or more sheets, with no preparatory hole. This process has gained wide diffusion in the automotive industry, due to the increasing use of materials alternative to steel, that are difficult or impossible to join with traditional techniques. In particular, polymeric materials are becoming increasingly used, due to their favorable weight/strength ratio. This paper reports the results of experimental investigations, aimed at identifying the variables affecting the mechanical characteristics of mixed metal-plastic joints. A statistic model for the optimization of the geometrical parameters has been computed. The paper demonstrates that self-piercing riveting appears competitive for metal/polymer junction. The results analyzed in light of statistical techniques show that some geometrical parameters affect joint performance more than others and can therefore be used as independent variables for joint performance optimization.