Maximization Scheme Research Articles

Incentive mechanism-based influential maximisation scheme for social cloud service networks

The integration of social network services (SNS) and cloud computing (CC) is a technological revolution that presents the future of connectivity and reachability. This paper explores the novel paradigm for future internet of things (IoT). Although there have been some studies in social-driven IoT, they merely consider the CC technique to improve service qualities and influences. In this study, we develop a novel social cloud control scheme based on the incentive mechanism (IM) approach. To maximise the total SNS system performance, SNSs have been adaptively executed in the CC system while taking into account the social welfare. In addition, we protect each user's privacy according to the differential privacy (DP) method. Therefore, our study can capture the properties of SNS and CC, and provides an effective solution for the social cloud system while ensuring privacy. The experimental results demonstrate the efficiency and effectiveness of the proposed scheme.

Incentive mechanism-based influential maximisation scheme for social cloud service networks

The integration of social network services (SNS) and cloud computing (CC) is a technological revolution that presents the future of connectivity and reachability. This paper explores the novel paradigm for future internet of things (IoT). Although there have been some studies in social-driven IoT, they merely consider the CC technique to improve service qualities and influences. In this study, we develop a novel social cloud control scheme based on the incentive mechanism (IM) approach. To maximise the total SNS system performance, SNSs have been adaptively executed in the CC system while taking into account the social welfare. In addition, we protect each user's privacy according to the differential privacy (DP) method. Therefore, our study can capture the properties of SNS and CC, and provides an effective solution for the social cloud system while ensuring privacy. The experimental results demonstrate the efficiency and effectiveness of the proposed scheme.

A Profit Maximization Scheme in Cloud Computing With Deadline Constraints

Cloud computing has attracting more and more attention for its flexibility and economic benefits. To maintain the supply-demand relationship among different participants in cloud computing environment, the exchange of value is the inner drive. From the perspective of cloud service provider, its primary concern is to earn the profit, which can be obtained by finishing the tasks published from customers. In this paper, we consider each task consists of numbers of sub-tasks in the logical order, each sub-task corresponds to a type of service requests, which can be served in unique multi-server system. On this basis, we propose a profit maximization problem in the multistage multi-server queue systems, in which customers are served at more than one stage, arranged in a series structure. Moreover, a deadline constraint is taken into consideration, which demonstrates the maximum tolerance degree that the customers can wait. Therefore, how to configure the parameters in multistage multi-server queue systems to maximize profit on the premise of reducing the waiting times of customers is a critical issue for cloud service provider. To address this problem, we first discuss the probability distribution function of the waiting time for single multi-server system and multistage multi-server queue systems respectively, and then propose a profit maximization model under the deadline constraint. Due to the complexity of this model, the analytical solution can hardly be obtained, we study a heuristic method to search for the optimal solution. At last, a series of numerical simulations are implemented to describe the performance of the proposed profit maximization scheme, the results show that not only the profit can be maximized, but also the waiting time of customers have been reduced effectively.

Coverage Maximization in Wireless Sensor Networks Using Minimal Exposure Path and Particle Swarm Optimization

The fundamental objective of deploying wireless sensors is to monitor a physical condition(s) within the desired region and relay the measured data to the interested parties through a gateway. However, achieving maximum coverage over the desired area after the initial random deployment of nodes is likely impossible. Arbitrary paths become exposed, uncovered by any of the sensor nodes questioning the quality of service of the network. In our proposed coverage maximization scheme, exposure profiles of sensor nodes were used by the sink node to compute the minimal exposure path. A combination of the computed minimal exposed path and the ratio of covered to uncovered grids were used as the function fitness in our particle swarm optimization algorithm. Bearing in mind the energy constraints of sensor nodes, randomly deployed nodes were only repositioned in the optimization process when their energy levels are above the predefined threshold. The accuracy and efficiency of our scheme are established via simulations. We compared the results of our scheme with a similar optimization scheme. The results showed our scheme achieved a higher coverage degree with the same number of deployed nodes over a region of interest of the same size.

Robust congestion control in cognitive radio network using dynamic event‐triggered sliding mode

SummaryThe ever‐increasing service demand from wireless users under restricted bandwidth necessitates the development of suitable congestion control scheme for throughput maximization. A robust congestion controller design has been addressed in this work to sustain necessary Quality of Service in Cognitive Radio Network. The goal is to reduce packet loss while ensuring optimum utilization of available resources. Sliding mode controller proclaimed for its disturbance rejection capabilities and robustness has been used to design the proposed controller. Using the benefits of sliding mode control, dynamic event‐triggering strategies have been combined for better utilization in resource‐constrained environments. Control execution requires minimal updates while guaranteeing the desired closed‐loop system behavior. The controller efficacy has been validated in simulations.

Pricing-Based Semi-Distributed Clustering and Beamforming for User-Centric MIMO Networks

User-centric dynamic base station (BS) clustering can efficiently avoid cell edge effect, which results in overlapped clusters and introduces tremendous complexity in beamforming optimizations. However, existing works mainly focus on addressing the problem in a centralized manner. In this letter, a semi-distributed user-centric clustering and beamforming scheme for sum-rate maximization is proposed, where the optimization problem is split by introducing interference pricing and solved subsequently at the anchor of each cluster. Specifically, based on interference pricing, the problem is decomposed into parallel subproblems by decoupling interference constraints and power constraints, where beamforming matrices are optimized at the anchor of each cluster through generalized iterative minimization of mean-square error; besides, cluster size is controlled by taking power spent as penalizing term and minimum rate constraints are taken into consideration to guarantee the throughput of edge users. Simulation results show about $3\times $ gains in sum-rate criteria and $2\times $ gains in proportional fair criteria of the proposed scheme in terms of 5%-tile user throughput compared to BS-centric and centralized schemes.

Joint Power Allocation and Splitting Control for SWIPT-Enabled NOMA Systems

Transmission rate and harvested energy are well-known conflictive optimization objectives in simultaneous wireless information and power transfer (SWIPT) systems, and thus their trade-off and joint optimization are important problems to be studied. In this paper, we investigate joint power allocation and splitting control in a SWIPT-enabled non-orthogonal multiple access (NOMA) system with the power splitting (PS) technique, with an aim to optimize the total transmission rate and harvested energy simultaneously whilst satisfying the minimum rate and the harvested energy requirements of each user. These two conflicting objectives make the formulated problem a constrained multi-objective optimization problem. Since the harvested power is usually stored in the battery and used to support the reverse link transmission, we transform the harvested energy into throughput and define a new objective function by summing the weighted values of the transmission rate achieved by information decoding and transformed throughput from energy harvesting, defined as equivalent-sum-rate (ESR). As a result, the original problem is transformed into a single-objective optimization problem. The considered ESR maximization problem which involves joint optimization of power allocation and PS ratio is nonconvex, and hence challenging to solve. In order to tackle it, we decouple the original nonconvex problem into two convex subproblems and solve them iteratively. In addition, both equal PS ratio case and independent PS ratio case are considered to further explore the performance. Numerical results validate the theoretical findings and demonstrate that significant performance gain over the traditional rate maximization scheme can be achieved by the proposed algorithms in a SWIPT-enabled NOMA system.

Energy efficiency and throughput maximization scheme based on joint antenna selection and beamforming optimization in FD-SWIPT bidirectional relay systems

FD-SWIPT two-way relay is used to provide cell coverage in mobile cellular networks and improve the throughput and energy efficiency of the system. To address the problem in FD-SWIPT bidirectional relay research, an efficient and throughput maximization scheme based on joint optimization antenna selection and beamforming in the FD-SWIPT bidirectional relay system is proposed in this study. First, a sub-optimal antenna grouping algorithm with low complexity is presented. The strategy of antenna grouping is used at the relay receiver to transmit the received signal with great channel CSI to the destination node, and then the remaining signal is used for energy harvest. Second, the beamforming scheme is designed at the relay transmitter, and the maximum generalized eigenvalue method is used to optimize the beamforming vector and enhance the signal-to-interference-plus-noise ratio of the system. The simulation results show that the proposed joint optimization scheme can effectively improve the throughput and energy efficiency of the system, and the antenna selection scheme plays an important role in the system.

Power Allocation Scheme for Energy Efficiency Maximization in Distributed Antenna System with Discrete-Rate Adaptive Modulation

Power Allocation Scheme for Energy Efficiency Maximization in Distributed Antenna System with Discrete-Rate Adaptive Modulation

Exploiting Dispersive Power Gain and Delay Spread for Sybil Detection in Industrial WSNs: A Multi-Kernel Approach

Industrial wireless sensor networks (IWSNs) promote innovations in the industry such as structural status mapping, instrument fault diagnosing, and oriented automation system associating. However, due to the shared nature of the wireless propagation environment, the emerging sensor nodes (SNs) with wireless properties are vulnerable to external malicious attacks. The security threats, especially Sybil attacks, impose great difficulties in fulfilling quality requirements of industrial applications. What is more complicated is that the harsh industrial environment brings about new challenges which can degrade the accuracy of detecting Sybil threats. In this paper, we focus on how to detect the malicious packets transmitted from Sybil attackers without adding extra authentication overhead into the transmission frame. We develop a multi-Kernel-based expectation maximization (MKEM) scheme to detect Sybil attacks in IWSNs. Instead of directly investigating the radio resource of SNs, we produce channel-vectors which are extracted from the power gain and delay spread of the channel impulse response obtained from the received packets to represent each SN. Specifically, a kernel-oriented method is designed to discriminate the malicious packets from benign ones without establishing a pre-defined database of channel features of all SNs. Meanwhile, we allocate different kernel weights to the proposed kernels and combine them to improve the discrimination ability of the scheme. Moreover, a kernel parameter optimization method is developed to regulate each kernel weight and parameter to reduce the effects of transmission impairments in IWSNs. To avoid poor detection accuracy when the number of Sybil attackers increases, we use the gap statistical analysis method to verify and EM method to summarize the detection results. The simulation results show that the proposed MKEM scheme can achieve high accuracy on detecting malicious packets transmitted from Sybil attackers from benign ones, and tolerate the effects of transmission impairments in the industrial environment. Moreover, the MKEM scheme can guarantee the detection accuracy even if the number of Sybil attackers increases.

Partial volume correction for arterial spin labeling using the inherent perfusion information of multiple measurements

BackgroundArterial spin labeling (ASL) provides a noninvasive way to measure cerebral blood flow (CBF). The CBF estimation from ASL is heavily contaminated by noise and the partial volume (PV) effect. The multiple measurements of perfusion signals in the ASL sequence are generally acquired and were averaged to suppress the noise. To correct the PV effect, several methods were proposed, but they were all performed directly on the averaged image, thereby ignoring the inherent perfusion information of mixed tissues that are embedded in multiple measurements. The aim of the present study is to correct the PV effect of ASL sequence using the inherent perfusion information in the multiple measurements.MethodsIn this study, we first proposed a statistical perfusion model of mixed tissues based on the distribution of multiple measurements. Based on the tissue mixture that was obtained from the high-resolution structural image, a structure-based expectation maximization (sEM) scheme was developed to estimate the perfusion contributions of different tissues in a mixed voxel from its multiple measurements. Finally, the performance of the proposed method was evaluated using both computer simulations and in vivo data.ResultsCompared to the widely used linear regression (LR) method, the proposed sEM-based method performs better on edge preservation, noise suppression, and lesion detection, and demonstrates a potential to estimate the CBF within a shorter scanning time. For in vivo data, the corrected CBF values of gray matter (GM) were independent of the GM probability, thereby indicating the effectiveness of the sEM-based method for the PV correction of the ASL sequence.ConclusionsThis study validates the proposed sEM scheme for the statistical perfusion model of mixed tissues and demonstrates the effectiveness of using inherent perfusion information in the multiple measurements for PV correction of the ASL sequence.

Maximum Entropy Based Non-Negative Optoacoustic Tomographic Image Reconstruction.

Optoacoustic (photoacoustic) tomography is aimed at reconstructing maps of the initial pressure rise induced by the absorption of light pulses in tissue. In practice, due to inaccurate assumptions in the forward model, noise, and other experimental factors, the images are often afflicted by artifacts, occasionally manifested as negative values. The aim of this work is to develop an inversion method which reduces the occurrence of negative values and improves the quantitative performance of optoacoustic imaging. We present a novel method for optoacoustic tomography based on an entropy maximization algorithm, which uses logarithmic regularization for attaining non-negative reconstructions. The reconstruction image quality is further improved using structural prior-based fluence correction. We report the performance achieved by the entropy maximization scheme on numerical simulation, experimental phantoms, and in-vivo samples. The proposed algorithm demonstrates superior reconstruction performance by delivering non-negative pixel values with no visible distortion of anatomical structures. Our method can enable quantitative optoacoustic imaging, and has the potential to improve preclinical and translational imaging applications.

Secrecy Throughput Optimization for the WPCNs With Non-Linear EH Model

This paper investigates secure communication from a wireless-powered transmitter to a desired receiver with multiple eavesdroppers in the wireless powered communication networks (WPCNs). Considering the non-linear energy harvesting (EH) model, we propose a secure two-phase communication protocol with the help of a hybrid base station (HBS). First, in the power transfer (PT) phase, the HBS transfers wireless power to the transmitter. Then in the subsequent secure information transmission (SIT) phase, the transmitter sends the secret information using the energy harvested in the PT, under the protection of artificial noise (AN) generated by the HBS. First, based on this communication protocol, we maximize the secrecy throughput with perfect channel information state (CSI) under the transmit power constraint at the HBS. The secrecy throughput maximization (STM) problem is non-convex, and hence we reformulate it by exploiting the primal decomposition method (PDM) to obtain tractable forms. The PDM-based transmit scheme (PDM-TS) is proposed for the STM. In addition, considering the imperfect CSI of wiretap channel, we further design the robust transmit scheme for the worst-case secrecy throughput maximization (wSTM) problem. Since the wSTM shows high non-convexity, we extend the PDM by combining it with the S -procedure, and the PDM-based robust transmit scheme (PDM-RTS) is proposed for the wSTM. Finally, the numerical simulations are provided to show the effectiveness of the proposed transmit schemes.

IEEE Access Special Section Editorial: Recent Advances on Radio Access and Security Methods in 5G Networks

Serviceability is the ability of a network to serve user equipments (UEs) within desired requirements (e.g., throughput, delay, and packet loss). High serviceability is considered as one of the key foundational criteria towards a successful fog radio access infrastructure satisfying the Internet of Things paradigm in the 5G era. In the article by Dao et al. , Adaptive resource balancing for serviceability maximization in fog radio access networks, the authors propose an adaptive resource balancing (ARB) scheme for serviceability maximization in fog radio access networks wherein the resource block (RB) utilization among remote radio heads (RRHs) is balanced using the backpressure algorithm with respect to a time-varying network topology issued by potential RRH motilities. The optimal UE selection for service migration from a high-RB-utilization RRH to its neighboring low RB-utilization RRHs is determined by the Hungarian method to minimize RB occupation after moving the service. Analytical results reveal that the proposed ARB scheme provides substantial gains compared to the standalone capacity-aware, max-rate, and cache-aware UE association approaches in terms of serviceability, availability, and throughput.

Cost-based constrained task scheduling in cloud environment

In the cloud environment, all the tasks are allocated and executed by using the available services in order to achieve higher performance, least total time for computation, shortest period of response time and utilisation of resources and so on. There are some serious challenges that the existing models could allocate the task to the resources without the profit maximisation scheme. The main motivation that is behind this paper is to design and develop a Cloud Manager (CM) to efficiently manage the cloud resources and also completing the jobs for the allocated resources for the given task in the cloud environment. It is implemented using a prior resource optimisation (PRO)-based allocation algorithm that takes into account execution time, transmission time and cost. The desirable feature of this paper is that the resources are allocated as per the cloud consumer request. The potency of this is presented in the cloud environment by finding the optimal and suboptimal allocation scheme of resources which maximise the profit of cloud provider and also improve the quality of scheduling solution.

Aerial Small Cells Using Coordinated Multiple UAVs: An Energy Efficiency Optimization Perspective

Recently, unmanned aerial vehicle (UAV) communications have attracted great research interest. Due to the limited on-board energy, the optimization of energy efficiency (EE) is critical for UAV communications. In this paper, we propose an EE maximization scheme for UAV swarm-enabled small cell networks using large-scale channel state information at the transmitter (CSIT). The proposed scheme provides an agile coordination strategy for the UAVs in a swarm under energy constraints. We first formulate the EE maximization problem, where the objective function is defined as the ratio of the ergodic total data size to the total energy consumption. After that, an accurate approximation is derived to remove the intractable expectation operator in the objective function. As the newly formulated problem is non-convex, we decompose it into two subproblems to optimize the transmit power and the hovering time in an iterative way. Further by leveraging the max-min and linear optimization tools, both subproblems are efficiently solved. Simulation results demonstrate the superiority of our EE maximization scheme.

Secrecy Energy Efficiency Optimization for Artificial Noise Aided Physical-Layer Security in OFDM-Based Cognitive Radio Networks

In this paper, we investigate the power allocation of primary base station (PBS) and cognitive base station (CBS) across different orthogonal frequency division multiplexing (OFDM) subcarriers for energy-efficient secure downlink communication in OFDM-based cognitive radio networks (CRNs) with the existence of an eavesdropper having multiple antennas. We propose a secrecy energy efficiency maximization (SEEM) scheme by exploiting the instantaneous channel state information (ICSI) of the eavesdropper, called ICSI based SEEM (ICSI-SEEM) scheme with a given total transmit power budget for different OFDM subcarriers of both PBS and CBS. As for the case when the eavesdropper's ICSI is unknown, we also propose an SEEM scheme through using the statistical CSI (SCSI) of the eavesdropper, namely SCSI based SEEM (SCSI-SEEM) scheme. Since the ICSI-SEEM and SCSI-SEEM problems are fractional and non-convex, we first transform them into equivalent subtractive problems, and then achieve approximate convex problems through employing the difference of two-convex functions approximation method. Finally, new two-tier power allocation algorithms are proposed to achieve $\varepsilon$-optimal solutions of our formulated ICSI-SEEM and SCSI-SEEM problems. Simulation results illustrate that the ICSI-SEEM has a better secrecy energy efficiency (SEE) performance than SCSI-SEEM, and moreover, the proposed ICSI-SEEM and SCSI-SEEM schemes outperform conventional SR maximization and EE maximization approaches in terms of their SEE performance.

Developing User Perceived Value Based Pricing Models for Cloud Markets

With the rapid deployment of cloud computing infrastructures, understanding the economics of cloud computing has become a pressing issue for cloud service providers. However, existing pricing models rarely consider the dynamic interactions between user requests and the cloud service provider. Thus, the law of supply and demand in marketing is not fully explored in these pricing models. In this paper, we propose a dynamic pricing model based on the concept of user perceived value that accurately captures the real supply and demand relationship in the cloud service market. Subsequently, a profit maximization scheme is designed based on the dynamic pricing model that optimizes profit of the cloud service provider without violating service-level agreement. Finally, a dynamic closed loop control scheme is developed to adjust the cloud service price and multiserver configurations according to the dynamics of the cloud computing environment such as fluctuating electricity and rental fees. Extensive simulations using the data extracted from real-world applications validate the effectiveness of the proposed user perceived value-based pricing model and the dynamic profit maximization scheme. Our algorithm can achieve up to 31.32 percent profit improvement compared to a state-of-the-art approach.

Data Center Demand Response With On-Site Renewable Generation: A Bargaining Approach

The rapid growth of cloud computing and data centers with skyrocketing energy consumption, together with the accelerating penetration of renewable energy sources, is creating both severe challenges and tremendous opportunities. Data centers offering large flexible loads in the grid, opens up a unique opportunity to smooth out the significant fluctuation and uncertainty of renewable generation and hence enable seamless integration. To take the market power of data centers into consideration, this paper proposes a bargaining solution to the market program for data center demand response when the load serving entity (LSE) has power supply deficiency. Specifically, due to the uncertainty of load flexibility of data centers incurred by the intermittent on-site renewable generation and dynamic service requests, there exists information asymmetry between the LSE and the data center, which complicates the design of the bargaining solution. Making use of the log-concavity of the (expected) utility functions, a computationally efficient method to implement the best response updates in the bargaining procedure is presented. Furthermore, it is shown analytically that the bid sequences of the LSE and the data center are guaranteed to converge and the final price clinched by the bargaining algorithm is indeed the Nash bargaining solution, which is proportionally fair. In addition, the proposed bargaining solution is compared with two other schemes, namely the Stackelberg game and the social welfare maximization schemes. Finally, extensive numerical experiments are conducted to validate the theoretical guarantees of the bargaining and to examine the impact of various model parameters. Empirical comparison indicates the fairness advantage of the bargaining approach over the other two schemes, especially when the load of the data center is not very flexible, highlighting the importance of information feedback embodied by the bargaining procedure.

A Fund-Constrained Investment Scheme for Profit Maximization in Cloud Computing

Cloud computing is becoming more and more popular and has received considerable attention recently. As a new kind of Information Technology (IT) commercial model, understanding the economics of cloud computing becomes critically important. From the cloud service providers’ perspective, profit maximization is the top issue for them. Because a multi-server system is devoted to serving one type of service requests and application, service providers should build multiple multi-server systems to satisfy the market requirements of different application domains. Because available funding for a service provider is generally limited, it cannot afford to invest in all application domains. Hence, how to select appropriate application domains for investment and allocate funding such that the total profit is maximized are important issues for service providers. To address this problem, a fund-constrained profit maximization model is proposed. However, the exact solution of this optimization model is very difficult to formulate due to its complexity. Hence, this paper presents a heuristic strategy to search for a high quality solution. In our strategy, the optimization problem is solved in four stages, and the solution is optimized gradually. Through the proposed heuristic investment strategy, an appropriate investment scheme can be developed that synthesizes the market requirement, the fund constraint, the service level agreement, and so forth. A series of numerical calculations is executed to assess the performance of the proposed strategy. Then, six other investment strategies are compared to our strategy. Our results show that the investment scheme designed using our strategy can produce much more profit than these six other strategies.