Optimal Network Resource Allocation Research Articles

Enhancing 5G Core Network Performance through Optimal Network Fragmentation and Resource Allocation

Enhancing 5G Core Network Performance through Optimal Network Fragmentation and Resource Allocation

An expert system for hybrid edge to cloud computational offloading in heterogeneous MEC–MCC environments

Mobile Cloud Computing (MCC) integrates cloud computing into the mobile environment, effectively tackling performance, environmental, and security constraints. However, the proliferation of clouds and applications introduces complexities in offloading decisions. Mobile Edge Computing (MEC) has emerged as a solution to bolster MCC performance, capitalizing on the proximity to edge devices. However, optimizing computation offloading remains paramount for heterogeneous smart (mobile phones, wearables, and IoT) devices, necessitating efficient utilization of network resources and computational offloading. To address these challenges, this paper introduces NSANNOM (Network and Device Resources Utilization through Smart ANN-based Offloading Mechanism), an expert system designed for optimal computational offloading decision-making and efficient network resource allocation mechanisms. NSANNOM employs Artificial Neural Networks (ANN) for precise decision-making by validating real-world datasets, underscoring ANN’s superiority over existing algorithms, and showcasing enhanced energy savings, cost efficiency, and latency response. Experimental evaluations demonstrate that the proposed ANN model for the offloading decision-making algorithm achieves a training accuracy of 97% and a validation accuracy of 99%. The system consumes minimal energy (10 MJ) for task scheduling and exhibits remarkable accuracy in resource utilization across multiple tasks (10–50) ranging in size (from 1 to 16 GB). Additionally, it minimizes time delays (in milliseconds) during the offloading process.

Predictive Modeling of Delay in an LTE Network by Optimizing the Number of Predictors Using Dimensionality Reduction Techniques

Delay in data transmission is one of the key performance indicators (KPIs) of a network. The planning and design value of delay in network management is of crucial importance for the optimal allocation of network resources and their performance focuses. To create optimal solutions, predictive models, which are currently most often based on machine learning (ML), are used. This paper aims to investigate the training, testing and selection of the best predictive delay model for a VoIP service in a Long Term Evolution (LTE) network using three ML techniques: Multilayer Perceptron (MLP), Support Vector Machines (SVM) and k-Nearest Neighbors (k-NN). The space of model input variables is optimized by dimensionality reduction techniques: RReliefF algorithm, Backward selection via the recursive feature elimination algorithm and the Pareto 80/20 rule. A three-segment road in the geo-space between the cities of Banja Luka (BL) and Doboj (Db) in the Republic of Srpska (RS), Bosnia and Herzegovina (BiH), covered by the cellular network (LTE) of the M:tel BL operator was chosen for the case study. The results show that the k-NN model has been selected as the best solution in all three optimization approaches. For the RReliefF optimization algorithm, the best model has six inputs and the minimum relative error (RE) RE = 0.109. For the Backward selection via the recursive feature elimination algorithm, the best model has four inputs and RE = 0.041. Finally, for the Pareto 80/20 rule, the best model has 11 inputs and RE = 0.049. The comparative analysis of the results concludes that, according to observed criteria for the selection of the final model, the best solution is an approach to optimizing the number of predictors based on the Backward selection via the recursive feature elimination algorithm.

Energy Efficiency of Mobile Devices Using Fuzzy Logic Control by Exponential Weight with Priority-Based Rate Control in Multi-Radio Opportunistic Networks

The rapid development of mobile devices and wireless network technologies have made them indispensable. This has created a demand for faster networks and longer battery life. The assurance of a stable network service and the enhancement of network experiences for mobile devices is equally crucial to meeting these demands. To address these challenges, mobile devices employ various techniques to decrease power consumption when they connect to wireless networks. Moreover, enhancing the endurance of mobile devices to maintain stable network services is critical when using wireless networks. In this paper, we propose a dual-radio opportunistic network for energy efficiency (DRONEE)–exponential weight with priority-based on rate control (WPRC) method which can extend and enhance the DRONEE hybrid network. By leveraging fuzzy logic control (FLC) and quality of service (QoS), our proposed method effectively solves the weighting problem in the DRONEE–weight (DRONEE–W) method. Through efficient allocation of network resources within each cluster, we minimize resource wastage and maximize resource utilization. Simulation results demonstrate the superior performance of our DRONEE–WPRC method in terms of throughput, buffer size, delay time, and power consumption compared to other methods. Therefore, our proposed method achieves optimal network resource allocation and energy efficiency.

A Study on Two-stage Selection Model of Tourism Destination at the Scale of Urban Agglomerations

Considering that the demand of tourism destination is variable on the scale of urban agglomeration, the selection process of travel destination is divided into two stages. The traditional transportation combination model based on the multinomial Logit cannot reflect this characteristic. And it is the lack of consideration of the influence of travel distribution and the dynamic transfer of passenger flow between various transport routes. Therefore, this thesis established a combination model of travel demand distribution and transportation assignment with two-stage terminal selection characteristics based on the nested Logit. Based on the analysis of tourists' trip process on the scale of urban agglomeration, a tourist flow transport network with travel destination nest structure is constructed. The generalized cost impedance function of transportation route is constructed based on the direct cost of transportation mode and the indirect cost of travel time. Based on the characteristics of two-stage destination selection of tourists, the form of travel distribution function of tourist flow is given. Through the first-order optimization conditions, it proved that the volume of travel distribution and tourism passenger transport assignment can meet the two-stage equilibrium conditions in the equilibrium state. Based on the idea of MSA algorithm, it designed the solution algorithm of the model and verified the feasibility of the model and algorithm in a simplified example. The calculation results show that the two-stage equilibrium assignment model proposed in this paper can obtain the volume of travel distribution and transportation assignment at the same time, meanwhile compared with the multinomial logit model, the nested Logit structure fully considers the attraction measure of the city destination and the scenic spot destination, which is more in line with the choice behavior of the tourists when choosing the transportation route. Thus, it provides a new comparable method for the optimal allocation of tourism passenger flow transport network resources on the scale of urban agglomeration, and can provide data support for the transportation organization plans of government decision-making departments and tourism transport enterprises.

A novel bi-level programming model for structure sustainability optimization of passenger transport corridor

The transportation industry has entered a new stage from quantity expanding to structure optimization, quality and efficiency improvement, and from respective governance to integrative development. This indicates that the traditional corridor mode allocation dominated by quantity equilibrium can no longer meet the requirements of the new stage. In this paper, we propose a multi-entity programming model based on the economic equilibrium between supply and demand. It not only ensures the economic equilibrium in the market, but also maximizes the social benefits of the whole system, thereby realizing the sustainable development of the transportation system. Also, the Globalsearch algorithm and intlinprog algorithm are designed to solve the problem. The actual case of Beijing-Shanghai corridor shows that the model and algorithms are effective, providing decision support for the optimal allocation of regional transport network resources.

Optimal Transmission Switching Based on Analytical Target Cascading Algorithm

The penetration of a high proportion of renewable energy sources (RES) into the power grid intensifies the source–load imbalance, which greatly weakens the network transmission performance and power supply quality, and the effect of relying only on individual regulation within the region is negligible. To enhance the capacity of interconnection and coordination among different areas of power systems and improve the accommodation level of RES and low-carbon efficiency, an optimal transmission switching model based on the bus tearing method is proposed in this article. Firstly, the complex power system is decomposed based on the bus tearing method, and thus, the interconnected power grid structure of the multiarea system is constructed. Secondly, the optimal model of interconnected power grid decomposition and coordination structure considering renewable energy generation is constructed, based on exquisite modeling, to reduce the difficulty of unified analysis and decision-making of the multiarea interconnected power system, and the expression of the model is simplified in the form of the matrix. Then, the analytical target cascading (ATC) method is used to decouple the complex model from the main problem and subproblem and solve the distributed parallel problem, to understand the optimization of the decomposition and coordination structure of the interconnected power grid with source–load coordination. Finally, based on the case studies of the IEEE 14-bus system and IEEE 118-bus system, the effectiveness of the proposed model and method is verified, the coordinated operation of the interconnected power grid and the optimal allocation of network resources are achieved, and the economy of power system operation is improved.

Analysis of Legal Issues of Personal Information Protection in the Field of Big Data.

In the era of big data, while every citizen enjoys the convenience of the Internet, all kinds of information closely related to their personal and property are in a state of “streaking” for a long time. Personal information is frequently leaked, illegally misappropriated, and used. There are necessary requirements of network security. The risks and challenges faced by personal information in the context of big data increase. In the context of the rapid development of big data, the information is circulated in virtual cyberspace in the form of electronic data, and data sharing has become an important flow form of information circulation. It is an important link to realize the optimal allocation of network resources and realize the value of data. The great progress of the digital economy and artificial intelligence technology has greatly increased the demand for data, the speed of circulation has been further accelerated, and the potential value of data has been fully reflected. At present, the scope of personal information protected by Chinese laws is relatively narrow and is basically limited to the scope of privacy rights, which makes a large amount of information owned by individuals that do not belong to the category of personal privacy rights lack legal protection. Personal information is different from the characteristics of privacy rights. It is decided that the protection of personal information should be different from the protection of traditional personal privacy. To sum up, in view of this, this article attempts to analyze the new challenges faced by personal information protection in the era of big data from the perspective of law, combined with the background of the era of big data, from the legislative model, system design, supervision mechanism, industry self-discipline, and other aspects. Exploration of the legislative framework for the protection of personal information in the era of big data in our country is done in order to find a realistic path that balances the development of big data technology and the protection of personal information.

Method for optimal allocation of network resources based on discrete probability model

Method for optimal allocation of network resources based on discrete probability model

Deployment of Hybrid Evolutionary Algorithm-Artificial Neural Network for Optimum Real Time Frequency Allocation in a Heterogeneous Frequency Requisition Network

In heterogeneous frequency demand cellular networks, the frequency demand varies across the cells involved, in contrast to the homogeneous scenario, where the demand is the same. This paper reports the deployment of hybridized evolutionary algorithm and artificial neural network for optimum frequency allocation in heterogeneous frequency requisition mobile networks to ensure interference control. Frequency sharing and reuse among cells, due to scarcity, are fundamental in a communication network for optimum frequency utilization. Primarily, efficient frequency sharing allows cells of adequate reuse distance the utilization of the same channels, with minimized inter-cell interference. The degree of freedom from interference and efficient frequency allocation mechanism dictate the grade of service, GoS, of a communication network. A real-time frequency allocation is defined by some degree of randomness. Thus, frequency allotment problem is mostly expressed as a constrained optimization formulation. The optimal allocations are achieved at points of minimum cost metric. In this paper, the frequency allotment issue is expressed as a two-objective optimization challenge, using Key Performance Indicators (KPIs) data acquired via Drive Test as input parameters. NSGA-II, an evolutionary algorithm was first deployed on the formulated problem, then in combination with SOM, an artificial neural network technique. The hybrid algorithm was implemented in MATLAB for a heterogeneous frequency demand scenario. The results obtained from the hybrid technique show performance improvements of between 6% and 28% in terms of fitness indices for interference cost function and, between 3% and 65% for demand infringement cost function. The algorithm could be embedded in the operating system of Base Station Controllers for enhanced real-time optimal allocation of network resources.

An Edge Computing-Based Photo Crowdsourcing Framework for Real-Time 3D Reconstruction

Image-based three-dimensional (3D) reconstruction utilizes a set of photos to build 3D model and can be widely used in many emerging applications such as augmented reality (AR) and disaster recovery. Most of existing 3D reconstruction methods require a mobile user to walk around the target area and reconstruct objectives with a hand-held camera, which is inefficient and time-consuming. To meet the requirements of delay intensive and resource hungry applications in 5G, we propose an edge computing-based photo crowdsourcing (EC-PCS) framework in this paper. The main objective is to collect a set of representative photos from ubiquitous mobile and Internet of Things (IoT) devices at the network edge for real-time 3D model reconstruction, with network resource and monetary cost considerations. Specifically, we first propose a photo pricing mechanism by jointly considering their freshness, resolution and data size. Then, we design a novel photo selection scheme to dynamically select a set of photos with the required target coverage and the minimum monetary cost. We prove the NP-hardness of such problem, and develop an efficient greedy-based approximation algorithm to obtain a near-optimal solution. Moreover, an optimal network resource allocation scheme is presented, in order to minimize the maximum uploading delay of the selected photos to the edge server. Finally, a 3D reconstruction algorithm and a 3D model caching scheme are performed by the edge server in real time. Extensive experimental results based on real-world datasets demonstrate the superior performance of our EC-PCS system over the existing mechanisms.

Congestion-Aware and Energy-Aware Virtual Network Embedding

Network virtualization is an inherent component of future internet architectures. Network resources are virtualized from the underlying substrate and elastically provisioned and offered to customers on-demand. Optimal allocation of network resources in terms of utilization, quality of service, and energy consumption has been a challenge. Existing solutions consider congestion control in a single-objective virtual network embedding (VNE) problem. This paper defines a multiple-objective VNE problem called the congestion-aware, energy-aware VNE (CEVNE). The aim is to seek a solution that saves cost, saves energy and avoids network congestion simultaneously. CEVNE modelling techniques and solution approaches apply both the weighting method and the constraint method to search for pareto-optimal solutions that produce the best compromised solutions for all three objectives. Solving VNE problem is, however, NP-hard. A heuristic solution is proposed involving a two-stage coordinated CEVNE. The node-mapping algorithm searches for the sub-optimal solutions for three objectives. The link mapping process is an SDN-based heuristic algorithm that deploys a path service and a resource monitoring application on an SDN controller. The solution is realized using SDN, Segment Routing, and open network operating system platform (ONOS) technologies. The energy minimization is implemented with a registry that keeps track of active nodes and sets inactive nodes to sleep mode. The evaluation results showed that the multiple-objective CEVNE approach is feasible and achieves its goals of optimizing the resource allocation, improving the runtime, saving the energy consumption and controlling the network congestion.

Anti-jamming method for vehicle communication network based on internet of vehicles technology

In order to solve the problems of poor signal anti-interference ability, high error rate and low network coverage in traditional vehicle communication network and improve the communication quality of vehicle communication network, an anti-interference method of vehicle communication network based on Internet of Vehicle (IoV) technology is proposed. The maximum cellular rate resource reuse algorithm (MCRRA) is used to optimise the link resources of vehicle communication network, so as to realise the optimal allocation of vehicle communication network resources. Then, the wavelet denoising method is used to filter the signal noise after resource allocation in vehicle communication network. Finally, the improved threshold function method of wavelet transform is used to compensate the pseudo-Gibbs phenomenon and signal loss in vehicle communication network. Experiments show that this method can effectively suppress the interference of vehicle communication network. The error rate of the vehicle communication network using this method is only 10%, and the coverage rate is as high as 98.7%.

Anti-jamming method for vehicle communication network based on internet of vehicles technology

In order to solve the problems of poor signal anti-interference ability, high error rate and low network coverage in traditional vehicle communication network and improve the communication quality of vehicle communication network, an anti-interference method of vehicle communication network based on Internet of Vehicle (IoV) technology is proposed. The maximum cellular rate resource reuse algorithm (MCRRA) is used to optimise the link resources of vehicle communication network, so as to realise the optimal allocation of vehicle communication network resources. Then, the wavelet denoising method is used to filter the signal noise after resource allocation in vehicle communication network. Finally, the improved threshold function method of wavelet transform is used to compensate the pseudo-Gibbs phenomenon and signal loss in vehicle communication network. Experiments show that this method can effectively suppress the interference of vehicle communication network. The error rate of the vehicle communication network using this method is only 10%, and the coverage rate is as high as 98.7%.

Optimal Resource Allocation for Green and Clustered Video Sensor Networks

Wireless video sensor networks (WVSNs) are opening the door for many applications, such as industrial surveillance, environmental tracking, border security, and infrastructure health monitoring. In WVSN, energy conservation is very essential because: 1) sensors are usually battery-operated and 2) each sensor node needs to compress the video prior to transmission, which consumes more power than conventional wireless sensor networks. In this paper, we study the problem of minimizing the total power consumption in a cluster-based WVSN, leveraging cross-layer design to optimize the encoding power, the transmission power, and the source rate at each sensor node. To realize this problem, we devise a resource optimization framework, which takes into account the video signal distortion due to compression, in addition to packet loss in the wireless channel while trying to allocate network resources among multiple video sensors. Leveraging duality theory, we design a proximal minimization algorithm that is capable of achieving the optimal allocation of network resources, source rates, and encoding and communication powers, while providing application-level quality of service represented by video distortion. The algorithm is extended for sensor nodes with hybrid energy sources, leveraging energy harvesting to minimize the aggregate power, while addressing the tradeoff between renewable versus grid energy sources.

Dual Methods for Optimal Allocation of Telecommunication Network Resources with Several Classes of Users

We consider a general problem of optimal allocation of limited resources in a wireless telecommunication network. The network users are divided into several different groups (or classes), which correspond to different levels of service. The network manager must satisfy these different users’ requirements. This approach leads to a convex optimization problem with balance and capacity constraints. We present several decomposition type methods to find a solution to this problem, which exploit its special features. We suggest applying first the dual Lagrangian method with respect to the total capacity constraint, which gives the one-dimensional dual problem. However, calculation of the value of the dual cost function requires solving several optimization problems. Our methods differ in approaches for solving these auxiliary problems. We consider three basic methods: Dual Multi Layer (DML), Conditional Gradient Dual Multilayer (CGDM) and Bisection (BS). Besides these methods we consider their modifications adjusted to different kind of cost functions. Our comparison of the performance of the suggested methods on several series of test problems show satisfactory convergence. Nevertheless, proper decomposition techniques enhance the convergence essentially.

Optimal allocation of limited and random network resources to discrete stochastic demands for standardized cargo transportation networks

We consider the resource allocation problem with discrete random demands and discrete random resource capacities for standardized cargo transportation networks, in which a freight operator needs to determine the integral quantity of booking requests to be accepted for each product to maximize the expected profit. We formulate the problem as a stochastic integer programming model and provide theoretical results that completely characterize the optimal solution to the stochastic model under a special case. We present a progressive augmentation algorithm and a sampling based method for solving the stochastic model under a general case. We also offer numerical experiments to test the two methods and shed light on their performances.

On the Network Sharing of Mixed Network Coding and Routing Data Flows in Congestion Networks

In this paper, we study the congestion game for a network where mutliple network coding (NC) and routing users sharing a single common congestion link to transmit their information. The data flows using NC and routing will compete network resources, and we need to determine the optimal allocation of network resources between NC and routing data flows to maximize the network payoff. To facilitate the design, we formulate this process using a cost-sharing game model. A novel average-cost-sharing (ACS) pricing mechanism is developed to maximize the overall network payoff. We analyze the performance of ACS in terms of price of anarchy (PoA). We formulate an analytical expression to compute PoA under the ACS mechanism. In contrast to the previous affine marginal cost (AMC) mechanism, where the overall network payoff decreases when NC is applied, the proposed ACS mechanism can considerably improve the overall network payoff by optimizing the number and the spectral resource allocation of NC and routing data flows sharing the network link.

Network Behavior Analysis and Resource Consumption Evaluation for Android IM Applications in GPRS

2G data service is still an important component in mobile communication system. GPRS network bears a wide variety of data business, especially IM communication business which has a large number of users. QQ, Weixin, Fetion are beyond any doubt the most popular IM applications in the current Mobile Internet applications spectrum. Their wide use led to signaling storm, which drawn the attention of telecom operators and the research community, both interested in knowing IM applications' internal mechanisms, charactering traffic and understanding the applications' network behavior. In this paper, we dissect the network behavior and traffic consumption of QQ, Weixin and Fetion for Android in GPRS. We use a traffic sniffer to get data packets of each application in different scenarios. Furthermore, leveraging on the use of the network protocol analyzer-Wireshark and our knowledge of network communication, we study each application’s behavior characteristics. Understanding the characteristics of these applications makes for a better business promotion, a more optimal network resource allocation, a positive guiding significance for business income and resources investment income.

Rate control-based framework and algorithm for optimal provisioning

Ideally, networks should be designed to accommodate a variety of different traffic types, while at the same time maximizing its efficiency and utility. Network utility maximization (NUM) serves as an effective approach for solving the problem of network resource allocation (NRA) in network analysis and design. In existing literature, the NUM model has been used to achieve optimal network resource allocation such that the network utility is maximized. This is important, since network resources are at premium with the exponential increase in Internet traffic. However, most research work considering network resource allocation does not take into consideration key issues, such as routing and delay. A good routing policy is the key to efficient network utility, and without considering the delay requirements of the different traffic, the network will fail to meet the user's quality of service (QoS) constraints. In this paper, we propose a new NUM framework that achieves improved network utility while taking into routing and delay requirements of the traffic. Then, we propose a decomposition technique-based algorithm, D-NUM, for solving this model efficiently. We compare our approach with existing approaches via simulations and show that our approach performs well.