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We develop a model for contagion risks and optimal security investment in a directed network of interconnected agents with heterogeneous degrees, loss functions and security profiles. Our model generalizes much of contagion models in the literature; in particular the independent cascade model and the linear threshold model. We state various limit theorems on the final size of infected agents in the case of random networks with given vertex degrees for finite and infinite variance degree distributions. The results allow us to derive a resilience condition of the network to the infection of a large group of agents and quantify how contagion amplifies small shocks to the network. We show that when the degree distribution has infinite variance and, highly correlated in- and out-degrees, then even when agents have high thresholds, a sub-linear fraction of initially infected agents is enough to trigger the infection of a positive fraction. We also show how these results are sensitive to vertex and edge percolation (immunization). We then study the asymptotic Nash equilibrium and socially optimal security investment. In the asymptotic limit, agents' risk depends on all other agents' investment through an aggregate quantity, that we call network vulnerability. The limit theorems allow us to capture the impact of one class of agents' decision on the overall network vulnerability. Based on our results, the vulnerability is semi-analytic allowing for a tractable Nash equilibrium. We give sufficient conditions for investment in equilibrium to be monotone in the network vulnerability. When investment is monotone, we show that the (asymptotic) Nash equilibrium is unique. In the particular example of two types core-periphery agents, we exhibit a strong effect of the cost heterogeneity and in particular non-monotonous investment as a function of costs.

In B5G heterogeneous cellular networks, a rapid increase in the number of small cell base stations (SBSs) to support a massive number of devices tends to waste a considerable amount of energy. Therefore, intelligent management of SBSs’ power consumption is one of the most important research issues. We herein propose quasi-distributed Q-learning-based cell breathing (QD-QCB) considering full and partial SBS collaborations for maximizing network energy efficiency. Also, the concept of an aggregated active SBS set based on regional user distributions is proposed for computing- and energy-efficient operation. Through intensive simulations, we show that the proposed QD-QCB algorithm can achieve optimal energy efficiency, and improve the network energy efficiency significantly compared with conventional algorithms such as no transmit power control, random cell breathing, and greedy cell breathing algorithms.

We consider a distributed computing framework where the distributed nodes have different communication capabilities, motivated by the heterogeneous networks in data centers and mobile edge computing systems. Following the structure of MapReduce, this framework consists of Map computation phase, Shuffle phase, and Reduce computation phase. The Shuffle phase allows distributed nodes to exchange intermediate values, in the presence of heterogeneous communication bottlenecks for different nodes (heterogeneous communication load constraints). For this setting, we characterize the minimum total computation load and the minimum worst-case computation load in some cases, under the heterogeneous communication load constraints. While the total computation load depends on the sum of the computation loads of all the nodes, the worst-case computation load depends on the computation load of a node with the heaviest job. We show an interesting insight that, for some cases, there is a tradeoff between the minimum total computation load and the minimum worst-case computation load, in the sense that both cannot be achieved at the same time. The achievability schemes are proposed with careful design on the file assignment and data shuffling. Beyond the cut-set bound, a novel converse is proposed using the proof by contradiction. For the general case, we identify two extreme regimes in which the scheme with coding and the scheme without coding are optimal, respectively.

Heterogeneous Networks (HetNets) and cell densification represent promising solutions for the surging data traffic demand in wireless networks. In dense HetNets, user traffic is steered toward the Low-Power Node (LPN) when possible to enhance the user throughput and system capacity by increasing the area spectral efficiency. However, because of the transmit power differences in different tiers of HetNets and irregular service demand, a load imbalance typically exists among different serving nodes. To offload more traffic to LPNs and coordinate the Inter-Cell Interference (ICI), Third-Generation Partnership Project (3GPP) has facilitated the development of the Cell Range Expansion (CRE), enhanced Inter-Cell Interference Coordination (eICIC) and Further enhanced ICIC (FeICIC). In this paper, we develop a cell clustering-based load-aware offsetting and an adaptive Low-Power Subframe (LPS) approach. Our solution allows the separation of User Association (UA) functions at the User Equipement (UE) and network server such that users can make a simple cell-selection decision similar to that in the maximum Received Signal Strength (max-RSS) based UA scheme, where the network server computes the load-aware offsetting and required LPS periods based on the load conditions of the system. The proposed solution is evaluated using system level simulations wherein the results correspond to performance changes in different service regions. Results show that our method effectively solves the offloading and interference coordination problems in dense HetNets.

Since real-world objects and their interactions are often multi-modal and multi-typed, heterogeneous networks have been widely used as a more powerful, realistic, and generic superclass of traditional homogeneous networks (graphs). Meanwhile, representation learning (\aka~embedding) has recently been intensively studied and shown effective for various network mining and analytical tasks. In this work, we aim to provide a unified framework to deeply summarize and evaluate existing research on heterogeneous network embedding (HNE), which includes but goes beyond a normal survey. Since there has already been a broad body of HNE algorithms, as the first contribution of this work, we provide a generic paradigm for the systematic categorization and analysis over the merits of various existing HNE algorithms. Moreover, existing HNE algorithms, though mostly claimed generic, are often evaluated on different datasets. Understandable due to the application favor of HNE, such indirect comparisons largely hinder the proper attribution of improved task performance towards effective data preprocessing and novel technical design, especially considering the various ways possible to construct a heterogeneous network from real-world application data. Therefore, as the second contribution, we create four benchmark datasets with various properties regarding scale, structure, attribute/label availability, and \etc.~from different sources, towards handy and fair evaluations of HNE algorithms. As the third contribution, we carefully refactor and amend the implementations and create friendly interfaces for 13 popular HNE algorithms, and provide all-around comparisons among them over multiple tasks and experimental settings., Accepted by IEEE TKDE. All code and data available at https://github.com/yangji9181/HNE

Wireless virtualization has emerged as one of the efficient ways to overcome numerous wireless networking challenges such as RF resource sharing. RF spectrum sharing in wireless networks focuses on increasing the usage efficiency of radio frequency (RF) spectrum with its increasing demand. To reduce uncertainties that accompany the spectrum sharing process caused by RF sensing, database-based spectrum sharing allows opportunistic spectrum access through occupancy information query of a spectrum database. In this paper, we propose a 2-level hierarchical spectrum allocation framework for multiple service-based virtual networks (VN) by leasing wireless resources from Wireless Infrastructure Providers (WIPs). The software defined network (SDN)-enabled virtualization configures the operating environment on the fly. This paper focuses on the competition between multiple VNs to get best RF channels to serve their users while eliminating the need for one-to-one contracts between VNs and WIPs. We develop a mathematical model for the spectrum allocation scheme which allows heterogeneous network configurations with better economic benefit. A trust-based security mechanism is also proposed for early detection and prevention of security attacks to maintain the availability attribute of the resource allocation process. Simulation results show that spectrum sharing is efficient, and assures service availability to perceived legitimate VN users.

Humankinds urbanization and luxurious need increase the number of vehicles day by day. Due to the resource constraints for obtaining a high processing rate in performing the intelligent tasks, Vehicular Edge Computing (VEC) has been introduced. However, a delay occurs in transferring the information due to the traits of automotive tasks. In this paper, a Federated Learning empowered computation Offloading and Resource management (FLOR) is proposed. The FLOR framework operates over heterogeneous networks that include Vehicle-to-Everything (V2X) communication, Dedicated Short Range Communication (DSRC), 5G millimeter-wave (5G-mmWave), and 6G Vehicle-to-Vehicle (6G-V2V) communication. FLOR employs the stochastic network calculus mechanism for computing the upper bound delay of the heterogeneous communication and obtains the probability of offloading mechanism. Upon the detected probability value, the FLOR offloads the computation task to the optimal network. Further, to avoid the increase in resource cost and utilize the available resources efficiently, an effective Radio Resource Management (e-RRM) based on Federated Q-Learning is proposed in FLOR, for allocating the available resources optimally. The simulation result shows that the proposed FLOR framework effectively offloads the computation between the available heterogeneous networks with optimal resource allocation by 20.69% than the existing solutions.

With numerous nodes on online heterogeneous networks, how to reach and extract target nodes of our specic interests is a pressing problem. In this paper, we propose a novel heterogeneous network crawler, MCrawl. It addresses the problem via iterative online heterogeneous network crawling by navigating its available APIs, starting from a set of target nodes, i.e., seed nodes. We are facing two challenges towards addressing the problem. First, to navigate within a vast network, how do we start from a small set of target nodes In other words, which nodes in the current frontier and which direction shall we expand, to reach promising target nodes quickly We propose motif-based crawling to exploit the complex structures and rich semantics of heterogeneous networks. Second, in many scenarios, we do not have a classier to assess the quality of the harvested nodes and thus the motifs to expand. We develop a probabilistic inference framework to estimate the yield and harvest rates of motifs, achieving principled bootstrapping for crawling. Our experiment on real networks of MCrawl achieves signicant margins over baselines.

The sixth-generation (6G) communication technology provides a high level of interoperability through terahertz data transfer and latency-less service sharing. Due to its interoperable nature, the integration of heterogeneous networks such as the Internet of Things (IoT) and Cloud Radio Access Networks (CRAN) is performed at ease. This integration is managed using Software Defined Networks (SDNs) for managing the Quality of Service (QoS) experience of the users, irrespective of the application. This manuscript proposes the Service Virtualization and Flow Management Framework (SVFMF) for the reliable utilization of resources in the 6G-cloud environment. The imbalance in a service request and response due to overloaded and idle virtual resources is addressed in this framework. For this purpose, this framework endorses service virtualization and user allocation modules for mitigating the drawbacks of imbalanced service allocations. Linear decision-making of the service virtualization process helps to reduce the computation and service discovery by identifying overloaded services and performing a reallocation. The purpose of user allocation is to distribute the service requests to the idle service providers to reduce the prolonged wait time of the increasing user requests. The performance of the proposed framework is verified using experimental analyses, for the metrics service discovery and computation time, service failure ratio, and flows. The reliability of SVFMF is proved by varying the density of users, virtual machines, service requests, and user allocation per virtual machine, respectively.

With the promising advances in the telecommunication and automotive industries, there are increasing concerns on the reliability of Intelligent Transportation Systems (ITS) applications and standardization efforts. Nowadays, vehicles are empowered with innovative communications and sensing capabilities to accelerate next-generation Connected and Autonomous Vehicles (CAV) or Vehicle-to-Everything (V2X) communications. Furthermore, V2X is based on many radio access technologies including Dedicated Short-Range Communications (DSRC), Cellular-V2X, and millimeter-wave. It is expected that a single technology is incapable to support the diverse QoS and reliability requirements of vehicular safety and non-safety applications. Therefore, recent research works have investigated and developed different approaches to enable multi-radio access technologies (Multi-RAT) in V2X networks to support these multitudes of requirements. In addition, the heterogeneity of diverse radio access technologies and rigidity in their deployment increases the complexity of the network management. Complications may also arise from inefficient resource utilization due to the complexity of an optimum RAT selection or multi-link establishment through Multi-RATs. Therefore, a global monitoring paradigm such as Software-Defined Vehicular Network (SDVN) has been identified as a solution to reduce this bottleneck, by adding flexibility and agility to the vehicular network. This paper extensively reviews recent related works on Multi-RAT V2X networks focusing on the opportunistic selection mechanism of RAT, allocation of radio resources, and simultaneous establishment of multi-link over a heterogeneous network. Consequently, we have outlined the SDVN architecture, layers, features, and significance towards enhancing the performance of such Multi-RAT networks. Finally, several potential challenges with probable solutions and opportunities are discussed.

The distribution of large-size data block in the Internet of Vehicles (IoV), especially in the urban IoV with dense vehicles, is still a challenge issue. Though the methods based on 5G-cellular network commonly can efficiently distribute the large-size file in IoVs, they also have obvious defects, such as occupying scarce 5G resources, generating communication fees and limited service coverage. This paper focuses on distributing large-size data block in IoVs based on dedicated vehicular ad hoc network to reduce the relying on cellular (5G/6G) resources. A heterogeneous vehicular network (HetVNET), in which a short-range OFDM wideband communication (SOWC) with ultra-high data rate and the original IEEE 802.11p protocol are included, is first proposed. To match with the proposed HetVNET, a content-centric data distribution scheme based on edge caching is designed. The content-centric data distribution architecture for distributing large-size file, the cache node selection and the process of data distribution and control based on the HetVNET are studied. The 5G/6G communication can be enabled in the scheme to further enhance the engineering stability of the massive infrastructure-to-vehicle (I2V) broadcast in IoVs. The evaluation results show that the proposed approach has low delivery delay and high penetration ratio.

Vehicular ad hoc networks (VANETs) have attracted increasing attention in cross-regional communication. The interconnection between VANETs and satellite networks expands their communication range. To improve the transmission performance of heterogeneous networks composed of VANETs and satellite networks, this paper proposes an end-to-end transmission control scheme for satellite network-assisted VANETs, analyzes the bandwidth delay product of multihop VANETs through heterogeneous networks, and establishes a transmission control model applicable to cross-regional environments. To address the long delay and high bit error rate in heterogeneous networks, the model designs the congestion window in the slow start and congestion avoidance of the transmission scheme. The data transmission volume is increased in the slow start, and various packet losses are distinguished in congestion avoidance. The experimental results show that the scheme can achieve good transmission performance in heterogeneous networks composed of VANETs and satellite networks. The results show that VANETs data can be effectively transmitted in heterogeneous networks. Compared with the traditional transmission scheme, this simple and practical end-to-end transmission control scheme can improve speed by 83.49 kbps and reduce transmission duration time of FTP server by 4.72 x 10³ s. The results of this study provide a reference for massive data transmission in heterogeneous networks of VANETs and satellite networks.

The densified deployment of heterogeneous networks coexisting with cells with overlapping coverage has emerged as a viable solution for next generation wireless networks. Despite numerous advantages, heterogeneity and denseness also raise complicated handover management issue. Nonetheless, most existing handover methods generally depend on one or more objective attributes, and rarely consider subjective demands of personalized users and specific applications that demand differentiated services. Through decomposing control plane and data plane, SDN (Software Defined Network) offers a flexible architectural paradigm to overcome these challenges. In this paper, we first develop an SDN-driven handover architecture that perceives global network status and requirements from various perspectives, including physical layer, users, and applications. Then, a context-aware multi-criteria handover mechanism is developed in SDN edge to provide differentiated services. Considering numerous complicated factors, the handover decision is made based on a hierarchical fuzzy inference system to process diverse attributes and vague requirements described in natural language. Finally, we evaluate the performance of our proposed scheme through a combination of extensive simulations and real-world experiments. The results demonstrate that our solution outperforms baseline handover schemes, more efficiently providing differentiated services with respect to throughput, bandwidth cost, and application satisfaction, and is efficient and feasible in practice.

Deploying small cell base stations (SBS) under the coverage area of a macro base station (MBS), and caching popular contents at the SBSs in advance, are effective means to provide high-speed and low-latency services in next generation mobile communication networks. In this paper, we investigate the problem of content caching (CC) and user association (UA) for edge computing. A joint CC and UA optimization problem is formulated to minimize the content download latency. We prove that the joint CC and UA optimization problem is NP-hard. Then, we propose a CC and UA algorithm (JCC-UA) to reduce the content download latency. JCC-UA includes a smart content caching policy (SCCP) and dynamic user association (DUA). SCCP utilizes the exponential smoothing method to predict content popularity and cache contents according to prediction results. DUA includes a rapid association (RA) method and a delayed association (DA) method. Simulation results demonstrate that the proposed JCC-UA algorithm can effectively reduce the latency of user content downloading and improve the hit rates of contents cached at the BSs as compared to several baseline schemes.

These days, social media users tend to express their feelings through sharing images online. Capturing the emotions embedded in these social images involves great research challenges and practical values. Most existing works concentrate on extracting the visual feature from a global view, while ignoring the fact that visual objects are also rich in emotion. How to leverage the multilevel visual features to improve the sentiment analysis performance is important yet challenging. Besides, existing works view each social image as an independent sample while ignoring the rich correlations among social images, which may be helpful in detecting visual emotion. In this article, we propose a novel model called social relations-guided multiattention networks (SRGMANs) to incorporate both the multilevel (region-level and object-level) visual features of a single image and the correlations among multiple social images to conduct visual sentiment analysis. Specifically, we first construct a heterogeneous network consisting of various types of social relations and introduce a heterogeneous network embedding method to learn the network representation for each image. Then, two visual attention branches (region attention network and object attention network) are devised to extract emotional and discriminative visual features. For each branch, we design a self-attention module to capture the emotional dependencies among visual parts. Besides, a network-guided attention module is also designed in each branch to focus on more network-related emotional visual parts with the guidance of the topology information. Finally, the attended visual features from the two attention models, together with network representation features, are combined within a holistic framework to predict the sentiment of social images. Extensive experiments demonstrate the superiority of our model on three benchmark datasets.

To support more Internet of Things devices, we present a novel blockchain-enabled heterogeneous cellular network (HetNet). In this network, devices store block data to the cloud service provider, offload the proof-of-work mining tasks to base stations (BSs), and associate with the macro-cell BS or small-cell BSs. Then, we analyze the user association problem, computation offloading problem, and block storage problem in the blockchain-enabled HetNet. We also design corresponding algorithms to solve these problems. Furthermore, to tackle the challenge of data congestion of BSs, based on the obtained computation offloading and block storage strategies, we propose a modified user association algorithm. The analysis shows that the proposed blockchain-enabled HetNet can effectively attain computing offloading, block storage, and user association strategies, and more devices can access the blockchain network. Analytical results show that the proposed modified user association algorithm can greatly avoid data congestion of BSs. Numerical results demonstrate the effectiveness of our proposed algorithms for computation offloading and block storage, and the proposed modified user association algorithm has a significantly great advantage compared with the traditional nearest BS association algorithm in terms of avoiding data congestion.

In this work, we propose a globally optimal joint successive interference cancellation (SIC) ordering and power allocation (JSPA) algorithm for the sum-rate maximization problem in downlink multi-cell non-orthogonal multiple access (NOMA) systems. The proposed algorithm is based on the exploration of base stations (BSs) power consumption, and closed-form of optimal powers obtained for each cell. Although the optimal JSPA algorithm scales well with larger number of users, it is still exponential in the number of cells. For any suboptimal decoding order, we propose a low-complexity near-optimal joint rate and power allocation (JRPA) strategy in which the complete rate region of users is exploited. Furthermore, we design a near-optimal semi-centralized JSPA framework for a two-tier heterogeneous network such that it scales well with larger number of small-BSs and users. Numerical results show that JRPA highly outperforms the case that the users are enforced to achieve their channel capacity by imposing the well-known SIC necessary condition on power allocation. Moreover, the proposed semi-centralized JSPA framework significantly outperforms the fully distributed framework, where all the BSs operate in their maximum power budget. Therefore, the centralized JRPA and semi-centralized JSPA algorithms with near-optimal performances are good choices for larger number of cells and users., Comment: 55 pages, 33 figures

The heterogeneity of today’s Web sources requires information retrieval (IR) systems to handle multi-modal queries. Such queries define a user’s information needs by different data modalities, such as keywords, hashtags, user profiles, and other media. Recent IR systems answer such a multi-modal query by considering it as a set of separate uni-modal queries. However, depending on the chosen operationalisation, such an approach is inefficient or ineffective. It either requires multiple passes over the data or leads to inaccuracies since the relations between data modalities are neglected in the relevance assessment. To mitigate these challenges, we present an IR system that has been designed to answer genuine multi-modal queries. It relies on a heterogeneous network embedding, so that features from diverse modalities can be incorporated when representing both, a query and the data over which it shall be evaluated. By embedding a query and the data in the same vector space, the relations across modalities are made explicit and exploited for more accurate query evaluation. At the same time, multi-modal queries are answered with a single pass over the data. An experimental evaluation using diverse real-world and synthetic datasets illustrates that our approach returns twice the amount of relevant information compared to baseline techniques, while scaling to large multi-modal databases.

This paper considers an Ultra-Dense Heterogeneous Network (UDHN) consisting of cache-enabled Unmanned Aerial Vehicles (UAVs) and Internet of things Mobile Devices (IMDs) receiving their requested contents via the Power Domain Non-Orthogonal Multiple Access (PD-NOMA) protocol. Employing the Fast Global K-Means (FGKM) algorithm, IMDs are partitioned into several clusters connecting to either other IMDs or UAV belonging to the same cluster in the presence of untrusted users, known as non-legitimate eavesdroppers. It is assumed that users located in the cluster edge area communicate with multiple UAVs to obtain their requested contents. The main goal for such a network is to jointly optimize the number of UAVs, their 3D placements, and the cache placement probability of contents stored in UAVs and IMDs by maximizing the secure cache throughput. Toward this goal, we prove that the objective function is non-concave. Therefore, we decompose the optimization problem into multiple subproblems. We first employ the FGKM algorithm to optimally determine the number of employed UAVs and their horizontal placements. Then, the UAVs’ altitudes are optimized by employing the Interior-Point Method (IPM), while the convex approximation for the objective function and its constraints are substituted. Then, we optimize the secure cache throughput of IMDs by proposing a caching placement strategy for contents stored in UAVs and IMDs via Device to Device (D2D) and UAV to Device (U2D) links, given illegal eavesdroppers’ presence. Then, we propose an iterative algorithm to achieve the near-optimal solution for the cache throughput of IMDs. Different from existing works, the closed-form expressions for the achievable secrecy rate and the successful probability of D2D communications and U2D transmissions, as well as the secure cache throughput are derived. Finally, simulation results are presented to validate the proposed caching placement strategy. It is shown that the proposed scheme outperforms the conventional Most Popular caching (MPC) strategy substantially.

Energy consumption accounts for a significant portion of OPEX in 5G networks, which will be further increased when millimeter-wave (mmWave) base stations are deployed. To reduce the cost, renewable energy has been introduced to provide complementary power supply. Nevertheless, how to cost-efficiently utilize radio resources and renewable energy remains a challenge. In this paper, we jointly investigate the beamwidth management and resource allocation in mmWave backhaul HetNets with hybrid energy supply aiming to maximize long-term cost efficiency. This requires solving a mixed stochastic combinatorial optimization problem, characterizing the causal property of renewable energy harvesting process, stochastic channel conditions, and imperfect antenna alignment. Considering the complexity and dynamic environment, a learning system is established instead of using traditional optimization methods which typically experience exponential worst-case complexity and require complete information. Specifically, we propose a Learning-based Cost-efficient Resource Allocation (LCRA) algorithm that employs deep neural network to learn policies from experiences to ensure system performance while achieving cost-efficiency. To enhance the sampling efficiency and stability of the conventional deep reinforcement learning methods for our problem, an improved proximal policy optimization method is proposed to reuse the history samples. Specifically, a modified clip function is designed to deal with the hard restrictions between current and old policies. Furthermore, random network distillation is introduced to enhance the exploration capability. Numerical results reveal the convergence performance of the LCRA and the superiority in improving the cost efficiency in a hybrid energy powered mmWave backhaul HetNet compared with state-of-the-arts

The latest Heterogeneous Network (HetNet) environments, supported by 5th generation (5G) network solutions, include small cells deployed to increase the traditional macrocell network performance. In HetNet environments, before data transmission starts, there is a user association (UA) process with a specific base station (BS). Additionally, during data transmission, diverse resource allocation (RA) schemes are employed. UA-RA solutions play a critical role in improving network load balancing, spectral performance, and energy efficiency. Although several studies have examined the joint UA-RA problem, there is no optimal strategy to address it with low complexity while also reducing the time overhead. We propose two different versions of simulated annealing (SA): Reduced Search Space SA (RS3A) and Performance-Improved Reduced Search Space SA (P IRS3A), algorithms for solving UA-RA problem in HetNets. First, the UA-RA problem is formulated as a multiple knapsack problem (MKP) with constraints on the maximum BS capacity and transport block size (TBS) index. Second, the proposed RS3A and P IRS3A are used to solve the formulated MKP. Simulation results show that the proposed scheme P IRS3A outperforms RS3A and other existing schemes such as Default Simulated Annealing (DSA), and Default Genetic Algorithm (DGA) in terms of variability and DSA and RS3A in terms of Quality of Service (QoS) metrics, including throughput, packet loss ratio (PLR), delay and jitter. Simulation results show that P IRS3A generates solutions that are very close to the optimal solution.

Industrial Internet of things (IIoT) is facing significant scalability and security challenges in sharing massive device data across heterogeneous network domains. Traditional cloud-based sharing solutions for IIoT applications that are getting closer to devices are uneconomical and inefficient because of their centralized architecture. We propose a blockchain-based data-sharing framework called EdgeShare for edge data-sharing services among heterogeneous network domains by introducing a two-layer overlay network topology and an edge computing paradigm that can dramatically alleviate system pressure and reduce communication delays to improve data-sharing efficiency. Under such framework, a novel smart contract is designed to construct access control strategies with dissimilar granularities to achieve different levels of data access control in an undeniable and automated manner. All IIoT data-sharing activities are recorded in a blockchain for secured transaction logging and auditing. We implement an EdgeShare prototype and conduct extensive experiments and analyses to evaluate the feasibility of our proposals. In contrast with centralized sharing methods, results demonstrate that the proposed framework can effectively improve the reliability, efficiency, and security of massive data sharing while introducing reasonable and acceptable overheads.

The Internet of Vehicles (IoV) can offer safe and comfortable driving experience, by the enhanced advantages of space-air-ground integrated networks (SAGINs), i.e., global seamless access, wide-area coverage and flexible traffic scheduling. However, due to the huge popular traffic volume, the limited cache/power resources and the heterogeneous network infrastructures, the burden of backhaul link will be seriously enlarged, degrading the energy efficient of the IoV in SAGIN. In this paper, to implement the popular content severing multiple vehicle users (VUs), we consider a Cache-enabled Satellite-UAV-Vehicle Integrated Network (CSUVIN), where geosynchronous earth orbit (GEO) satellite is regard as a cloud server, unmanned aerial vehicles (UAVs) are deployed as edge caching servers. Then, we propose an energy-aware coded caching strategy employed in our system model to provide more multicast opportunities, and to reduce the backhaul transmission volume, considering the effects of file popularity, cache size, request frequency, and mobility in different road sections (RSs). Furthermore, we derive the closed-form expressions of total energy consumption both in single-RS and multi-RSs scenarios with asynchronous and synchronous services schemes, respectively. An optimization problem is formulated to minimize the total energy consumption, and the optimal content placement matrix, power allocation vector and coverage deployment vector are obtained by well-designed algorithms. We finally show, numerically, our coded caching strategy can greatly improve energy efficient performance in CSUVINs, compared with other benchmarked caching schemes under the heterogeneous network conditions.

Cloud of Things (CoT) is an integration of Internet of Things (IoT) and cloud computing, where Network Function Virtualization (NFV) can dynamically provide Virtualized Network Functions (VNFs) for IoT devices based on service-specific requirements. The provisioning of VNFs in CoT is formulated as an online decision-making problem, but widely-used methods mostly focus on modeling the environment simply to obtain the optimal solution. Valuable historical experience on provisioning is ignored and Quality of Service (QoS) requirements for different types of CoT services are also not considered, which leads to inefficient and coarse-gained provisioning. In this paper, an intelligent provisioning framework of VNFs is proposed for adaptive CoT resource scheduling according to traffic identification of heterogeneous network services. The framework leverages a Deep Reinforcement Learning (DRL) based model to make decisions based on the complexity of network environments and traffic variances. In this model, a policy gradient DRL algorithm, namely, Policy Optimization using Kronecker-Factored Trust Region (POKTR) is adopted to obtain the stable performance by a novel surrogate objective function. Experimental results verify that our framework improves the QoS in CoT by real-time VNFs provisioning. The DRL-based model with POKTR algorithm reduces network congestion and achieves higher throughput than other DRL algorithms.

The target of this paper is studying the problem of quasi-synchronization of heterogeneous Lur’e networks with uncertain parameters and impulsive effect. Considering the heterogeneity of Lur’e networks, quasi-synchronization instead of synchronization is discussed. Noting that the uncertain factors exist extensively in reality, uncertain parameters are introduced to the system in this paper. By using the Schur complement lemma, stability theory and the principle of comparison, the sufficient criteria for the heterogeneous networks to achieve quasi-synchronization are derived, and the upper bound of exponential quasi-synchronization errors is calculated. In addition, two corollaries are presented in consideration of homogeneous systems and weighted undirected graphs. Finally, three numerical simulations are given to illustrate the effectiveness of the control protocol and theoretical results.

Network data traffic has expanded exponentially over the past decade, resulting in massive congestion in heterogeneous networks. Nevertheless, it is nearly impossible to run latency-intensive applications to the end-users local processing unit due to limited system resources. Recently, fog computing has come up with a solution to reduce such data congestion by offloading some or the whole part of the task to the nearby fog nodes or the clouds. But this offloading policy becomes more complex when the fog node is unable to process the task and further offload it to another neighboring fog node or the cloud. In this context, in this study, we have analyzed the offloading strategy in a hierarchical fog-cloud network consisting of several heterogeneous fog devices along with a helping fog and a centralized cloud server. We have considered the most possible practical situation where the fog nodes are equipped with different CPU frequencies and hence the power consumption is also different. The total system cost is formulated as a mixed-integer non-linear problem that aims to reduce the overall delay in the proposed network. To solve the NP-hard problem, we transform it into Quadratically Constrained Quadratic Programming (QCQP) formation and further solve it by the separable semidefinite relaxation (SDR) method. Finally, by adopting several benchmark data, we conduct comprehensive simulations to test the efficiency of the proposed offloading profile. The simulation results depict that the proposed strategy outperforms in many aspects when compared to various baseline algorithms.

Cloud-edge heterogeneous network is an emerging technique built on edge infrastructure, which is based on the core of cloud computing technology and edge computing capabilities. The joint problem of computation offloading, cache decision, and resource allocation for cloud-edge heterogeneous network system is a challenging issue. In this paper, we investigate the joint problem of computation offloading, cache decision, transmission power allocation, and CPU frequency allocation for cloud-edge heterogeneous network system with multiple independent tasks. The goal is to minimize the weighted sum cost of the execution delay and energy consumption while guaranteeing the transmission power and CPU frequency constraint of the tasks. The constraint of computing resource and cache capacity of each Access Point (AP) are considered as well. The formulated problem is a mixed integer non-linear optimization problem. In order to solve the formulated problem, we propose a two-level alternation method framework based on Reinforcement Learning (RL) and Sequential Quadratic Programming (SQP). In the upper level, given the allocated transmission power and CPU frequency, the task offloading decision and cache decision problem is solved using Deep Q-Network method. In the lower level, the optimal transmission power and CPU frequency allocation with the offloading decision and cache decision is obtained by using SQP technique. Simulation results demonstrate that the proposed scheme achieves significant reduction on the sum cost compared to other baselines.

With the massive amount of data generated from mobile devices and the increase of computing power of edge devices, the paradigm of Federated Learning has attracted great momentum. In federated learning, distributed and heterogeneous nodes collaborate to learn model parameters. However, while providing benefits such as privacy by design and reduced latency, the heterogeneous network present challenges to the synchronisation methods, or barrier control methods, used in training, regarding system progress and model convergence etc. The design of these barrier mechanisms is critical for the performance and scalability of federated learning systems. We propose a new barrier control technique called Probabilistic Synchronous Parallel (PSP). In contrast to existing mechanisms, it introduces a sampling primitive that composes with existing barrier control mechanisms to produce a family of mechanisms with improved convergence speed and scalability. Our proposal is supported with a convergence analysis of PSP-based SGD algorithm. In practice, we also propose heuristic techniques that further improve the efficiency of PSP. We evaluate the performance of proposed methods using the federated learning specific FEMNSIT dataset. The evaluation results show that PSP can effectively achieve good balance between system efficiency and model accuracy, mitigating the challenge of heterogeneity in federated learning.

In virtual power plants (VPPs), distributed devices (such as residential appliances, energy storage, and electric vehicles) communicate with control centers via wireless access points (APs). Nevertheless, with a massive number of devices connected to APs, VPPs would be adversely affected by packet loss and consequently suffer from revenue reduction. Therefore, devices’ access connections need to be delicately designated to mitigate these negative impacts. This paper studies the device access optimization problem from the perspective of VPP operations. An optimization model to minimize the revenue reduction induced by packet loss is first formulated to designate APs for devices in heterogeneous communication networks. The significance of various device data is determined by revenue reduction as a result of packet loss. Additionally, the heterogeneous access network model is established to characterize different types of APs. To make the model implementable, this paper further proposes a layered decomposition solution algorithm, which decomposes optimization tasks into small-scale sub-problems and successively assigns them to different layers of APs. Sub-problems are further decomposed for APs to greedily cut revenue reduction by evaluating devices’ marginal contributions to revenue reduction. Numerical case studies are also conducted to verify the benefits of device access optimization.

Numerous microbes inhabit human body, making a vast difference in human health and disease. Therefore, understanding associations between microbes and diseases is beneficial to disease prevention and treatment. In this study, we develop a prediction method called HNGFL by Learning global graph feature on the heterogeneous network. Firstly, a heterogeneous network is integrated by known microbe-disease associations and multiple similarities. For microbe similarity network, on the basis of Gaussian interaction profile (GIP) kernel similarity, we consider different effects of these microbes on organs in the human body to further improve microbe similarity. For disease similarity network, we combine GIP kernel similarity, disease semantic similarity and disease-symptom similarity. And then, we use an embedding algorithm called GraRep to learn global structural information for this network. According to vector feature of every node, we utilize Support Vector Machine (SVM) classifier to calculate the relevance for each microbe-disease pair. HNGFL achieves a reliable performance in cross validation, outperforming the compared methods. In addition, we carry out case studies of three diseases. Results show that HNGFL can be considered as a reliable method for microbe-disease association prediction.

The upcoming 5G heterogeneous networks (HetNets) have attracted much attention worldwide. Large amounts of high velocity data can be transported by using the bandwidth spectrum of HetNets, yielding both great benefits and several concerning issues. In particular, great harm to our community could occur if the main visual information channels, such as images and videos, are maliciously attacked and uploaded to the internet, where they can be spread quickly. Therefore, we propose a novel framework as a digital forensics tool to protect end users. It is built based on deep learning and can realize the detection of attacks via classification. Compared with the conventional methods and justified by our experiments, the data collection efficiency, robustness, and detection performance of the proposed model are all refined. In addition, assisted by 5G HetNets, our proposed framework makes it possible to provide high-quality real-time forensics services on edge consumer devices (ECE) such as cell phones and laptops, which brings colossal practical value. Some discussions are also carried out to outline potential future threats.

As the fast development of vehicular network, the layout of the roadside unit (RSU) is indispensable. Due to the shack-les of factors such as coverage and cost, there is an urgent need for effective solution to solve the contradiction that RSU cannot be de-ployed on large scale. Parked cars provide a feasible solution for replacing RSUs and effectively reducing the arrangement of edge nodes. Inspired by this, parked cars as RSUs (P-RSUs) is leveraged to support cities’ vehicular network in this paper. We firstly con-struct the P-RSU based cellular-V2V heterogeneous networks (C-V2V HetNets) system model, and then formulate an optimization problem to maximize the energy efficiency (EE) of C-V2V HetNets with parked cars. Since the proposed issue is a NP-hard mixed in-teger nonlinear programming (MINLP) problem coupled with P-RSU incentive, we reformulate it into two subproblems, which are the P-RSU recruitment and the joint resource allocation. For the first subproblem, an effective reverse auction based mechanism is given to encourage parked cars participate and become P-RSUs. For the second subproblem, nonlinear fractional programming is used to optimize transmission power, and many-to-one matching is utilized to effectively obtain channel reusing scheme constrained by QoS. Moreover, a multi-hop based transmission strategy is given to further expand vehicular network coverage. Algorithms are evalu-ated based on real world scenarios using SUMO. Numerical results demonstrate that the proposed approach can both effectively re-cruit P-RSUs with low cost and achieve excellent system perfor-mance in terms of EE, spectrum efficiency and network coverage compared to other benchmark algorithms.

The problem of distributed fusion and random observation loss for mobile sensor networks is investigated herein. In view of the fact that the measured values, sampling frequency and noise of various sensors are different, the observation model of a heterogeneous network is constructed. A binary random variable is introduced to describe the drop of observation component and the topology switching problem caused by complete observation loss is also considered. A cubature information filtering algorithm is adopted to design local filters for each observer to suppress the negative effects of measurement noise. To derive a consistent and accurate estimation result, a novel weighted average consensus-based filtering approach is put forward. For the sensor that suffers from observation loss, its local prediction information vector is fused with the information contribution vectors of the neighbors to obtain the local estimation. Then the consensus weight matrix is designed for consensus-based distributed collaborative information fusion. The boundness of the estimation errors is proved by employing the stochastic stability theory. In the end, two numerical examples are offered to assert the validity of the presented method.

Epistasis between single nucleotide polymorphisms (SNPs) plays an important role in elucidating the missing heritability of complex diseases. Diverse approaches have been invented for detecting SNP interactions, but they canonically neglect the important and useful connections between SNPs and other bio-molecules (i.e., miRNAs and lncRNAs). To comprehensively model these disease related molecules, a heterogeneous bio-molecular network based solution EpiHNet is introduced for high-order SNP interactions detection. EpiHNet firstly uses case/control data to construct an SNP statistical network, and meta-path based similarity on the heterogeneous network composed with SNPs, genes, lncRNAs, miRNAs and diseases to define another SNP relational network. The SNP relational network can explore and exploit different associations between molecules and diseases to complement the SNP statistical network and search the significantly associated SNPs. Next, EpiHNet integrates these two networks into a composite network, applies the modularity based clustering with fast search strategy to divide SNP nodes into different clusters. After that, it detects SNP interactions based on SNP combinations derived from each cluster. Synthetic experiments on diverse two-locus and three-locus disease models manifest that EpiHNet outperforms competitive baselines, even without the heterogeneous network. For real WTCCC breast cancer data, EpiHNet also demonstrates expressive results on detecting high-order SNP interactions.

This paper investigates a new medium access control (MAC) protocol for multi-channel heterogeneous networks (HetNets) based on deep reinforcement learning (DRL), referred to as multi-channel deep-reinforcement learning multiple access (MC-DLMA). Specifically, we consider a HetNet where different radio networks adopt different MAC protocols to transmit data packets to a common access point on different wireless channels. Three key challenges for the MC-DLMA node are (i) no environmental knowledge is known in advance; (ii) the channels in HetNets are allocated to nodes using different MAC protocols; (iii) the capacities of different channels may be different. The main goal of MC-DLMA is to find an optimal access policy to transmit on those pre-allocated channels and expedite more efficient spectrum utilization. Due to the complex temporal correlation of spectrum states in HetNets, the traditional DRL technique, e.g., original deep Q-network (DQN) algorithm, is no longer applicable to our problem. In our MC-DLMA design, an advanced class of recurrent neural network, termed as Gated Recurrent Unit (GRU), is embedded into the original DQN technique to aggregate observations over time and reason the underlying temporal feature in multi-channel HetNets. Furthermore, we analytically give the optimal spectrum access patterns and derive the optimal throughputs in various HetNet scenarios. With judicious definitions of the state, action, and reward function in the parlance of the DRL framework, simulation results show that MC-DLMA can (i) find the optimal spectrum access strategies in various HetNets, (ii) outperform the random access policy, the whittle index policy, and the original DQN, (iii) perform cooperative transmission in a fully distributed manner in the presence of multiple agents, and (iv) adapt well to the environmental changes.

Over the past decades, the successful invention and employment of multiple sensors have marked the advent of a new era in multisensor remote-sensing (RS) images acquisition. To effectively utilize the massive multisensor images for RS scene understanding, we expect that a scene classification model learned with particular sensor data can generalize well to other sensor data. However, this is a very challenging task due to the cross-sensor data differences. In the deep learning (DL) pipeline, a common way to handle this challenging task is to fine-tune the models pretrained on source sensor data with limited labeled data from the target sensor. Unfortunately, fine-tune technique is usually applied between homogeneous networks, which may not be the best choice if the source and target data are largely different. To address these issues, we formulate the cross-sensor RS scene understanding problem as a heterogeneous network-oriented transfer learning problem, in which the source and the target networks are different and data-oriented selected. Afterward, the knowledge between heterogeneous networks is transferred using the pseudo-label recursive propagation mechanism inspired by the concept of knowledge distillation. To the best of our knowledge, this is the first time to investigate the cross-sensor scene classification problem by constructing such a heterogeneous networks' transfer scheme in RS fields. Our experiments using two cross-sensor RS datasets [aerial images → multispectral images (MSIs) and aerial images → hyper-spectral images (HSIs)] demonstrated that the proposed transfer learning strategy based on heterogeneous networks outperforms the supervised learning (SL) and fine-tune scheme for cross-sensor scene classification.

In 5G scenarios, network slicing and multi-tier heterogeneous networks are critical to guarantee the service level agreement (SLA) of various services. In this paper, a dynamic radio resource slicing framework considering joint bandwidth slicing ratios and base station (BS)-user association is presented for a two-tier heterogeneous wireless network. This framework maximizes the spectrum reuse ratio through a two-step deep reinforcement learning (DRL) method, and guarantees the SLA of network slices simultaneously. Specially, a distributed agent (D-Agent) is deployed at each BS for acquiring the slicing resource in a single BS level. Meanwhile, a centralized agent (C-Agent) manages radio resource allocation and user association among heterogeneous BSs to guarantee the SLA.

The Industrial Internet of Things (IIoT) is one of the important applications under the 5G massive machine type of communication (mMTC) scenario. To ensure the high reliability of IIoT services, it is necessary to apply an efficient resource allocation method under the dynamic and complex environment. In view of the absence of energy-efficient resource management architecture for the entire network, this article proposes an intelligent-driven green resource allocation mechanism for the IIoT under 5G heterogeneous networks. First, an intelligent end-to-end self-organizing resource allocation framework for IIoT service is given. Next, an energy-efficient resource allocation model within the framework is proposed. It is then solved by an intelligent mechanism with the asynchronous advantage actor critic driven deep reinforcement learning algorithm. Through the comparison analysis of different methods and rewards under IIoT scenarios with proper parameters setting, the proposed method can achieve better performance than other traditional deep learning (DL) methods and maintain service quality above accepted levels as well.

The concept of Multi-access Edge Computing (MEC) extends the provisioning of computing and storage capabilities from remote Cloud Data Centers (DC) to the proximity of end users via heterogeneous networks. By augmenting User Equipment (UE) with external computing power under the local coverage, Cloudlet-based offloading performs as a critical enabler to boost application execution performance and to prolong battery lifespan in the mobile devices. However, the mobility of UEs introduces intra-Cloudlet intermittent connections and inter-Cloudlet unbalanced load distributions in the MEC environment, which consequently leads to offloading failures and service downgrading. In this paper, we propose a novel MEC-based mobility-aware offloading model to solve the intra-Cloudlet offloading scheduling issue and inter-Cloudlet load-aware heterogeneous resource allocation issue in terms of concerning the offloading execution efficiency, task processing time constraints, and energy efficiency. A priority-based queue model is designed to formulate the intra-Cloudlet mobility-aware offloading scheduling problem, resolved by the adoption of the Particle Swarm heuristic. The energy-aware inter-Cloudlet resource selection procedure is formalized in a mobility-aware multi-site resource allocation model, which is further solved by lightweight dynamic load balancing.

Device-to-device (D2D) communication as direct communication technology has many application scenarios and plays a very important role in the fifth-generation (5G) era. Using D2D communication in third generation partnership project (3GPP) 5G Heterogeneous Network (HetNet) can effectively relieve the network traffic pressure and reduce the energy consumption of the base station. However, there are numerous security threats in D2D applications since the D2D communication remains in the early stage. The existing standards and solutions rarely consider device discovery, efficient authentication, mutual authentication, and key negotiation with privacy protection for D2D user equipments (UE) in heterogeneous access scenarios. In this paper, we present a unified efficient anonymity proximity device discovery and batch authentication mechanism for heterogeneous D2D UEs based on a new proposed efficient pairing-free certificateless batch signature (CLBS), the identity-based prefix encryption and Chinese remainder theorem (CRT). Our proposed scheme can be applied to all the 5G heterogeneous access scenarios of D2D communication. The security analysis and performance results show that our scheme can achieve mutual authentication, key agreement, identity privacy protection, batch verification, and resist several protocol attacks with ideal efficiency.

The next generation of sustainable vehicular networks is expected to have a wide range of access technologies, multi-homing capabilities, and traffic demand and pattern heterogeneity. In order to deliver massive data loads from various services and applications to end-users, network softwarization is a key contributor that mitigates challenges in heterogeneous networks by providing a shared interface platform. SDN-enabled vehicular networks provides global bird's eye view of the network's status and connectivity. However, using a centralized control center, brings many difficulties, including bottlenecks and densification issues. A distributed control plane is an alternative, but it raises questions about where to deploy the control units and how many controllers are needed in a given network structure. In this article, we propose an energy-efficient adaptive controller management strategy for distributed software-defined vehicular networks using vehicles' mobility densities and communication latencies between switch-enabled access points. To reduce the number of controllers, the proposed method employs a split-and-merge clustering technique. The performance of the clustering solution was evaluated using realistic mobility traces and compared to a number of benchmark clustering solutions and variations. The results indicate the efficiency of the proposed scheme in terms of energy consumption, latency, and load on network entities.

In this study, we present a novel framework called Community-based sentiment Extraction and NeTwork Embedding for POI Recommendation (CENTER) in order to suggesting impressive POIs in an effective and efficient fashion. The CENTER framework integrates two essential techniques: (1) a latent probabilistic generative model called Community-based Sentiment Extraction (CSE), which can accurately capture the review contents in LBSNs by taking into consideration the characteristics of social communities. The parameters of the CSE model are inferred effectively by the Gibbs sampling method. The primary sentiments are obtained based on the probability distribution of sentiments; (2) a network embedding model called Sentiment-aware Nework Embedding for POI Recommendation (SNER) is employed to learn the representation of important factors including POIs, users and textual sentiments in a low-dimensional embedding space. The joint training is utilized to alternatively sample all sets of edges in a heterogeneous network. Extensive experiments were conducted on two large-scale real datasets, in order to evaluate the performance of CENTER. The results demonstrate that CENTER outperforms the state-of-the-art baseline methods in the effectiveness and efficiency of POI suggestion.

Long-term evolution advanced (LTE-A) heterogeneous networks have been observed to offer reliable and service-differentiated communication, thereby enabling numerous mobile applications such as smart meters, remote sensors, and vehicular applications. This fact envisions the trend of Internet of things (IoT) underlaying heterogeneous small cell networks. On this basis, this paper proposes an energy-efficient framework for such a scenario, where multitier heterogeneous small cell networks provide wireless connection and seamless coverage for mobile users and IoT nodes. In our proposed framework, an elastic cell-zooming algorithm based on the quality of service and traffic loads of end-users is performed by adaptively adjusting the transmission power of small cells in order to reduce energy consumption. In addition, aiming at the high energy efficiency of IoT underlaying small cell networks, a clustering-based IoT structure is used, where a SWIPT-CH selection algorithm is proposed to maximize the average residual energy of IoT nodes and to mitigate resource competition between IoT nodes and mobile users.

The computational prediction of novel drug-target interactions (DTIs) may effectively speed up the process of drug repositioning and reduce its costs. Most previous methods integrated multiple kinds of connections about drugs and targets by constructing shallow prediction models. These methods failed to deeply learn the low-dimension feature vectors for drugs and targets and ignored the distribution of these feature vectors. We proposed a graph convolutional autoencoder and generative adversarial network (GAN)-based method, GANDTI, to predict DTIs. We constructed a drug-target heterogeneous network to integrate various connections related to drugs and targets, i.e., the similarities and interactions between drugs or between targets and the interactions between drugs and targets. A graph convolutional autoencoder was established to learn the network embeddings of the drug and target nodes in a low-dimensional feature space, and the autoencoder deeply integrated different kinds of connections within the network. A GAN was introduced to regularize the feature vectors of nodes into a Gaussian distribution. Severe class imbalance exists between known and unknown DTIs. Thus, we constructed a classifier based on an ensemble learning model, LightGBM, to estimate the interaction propensities of drugs and targets. This classifier completely exploited all unknown DTIs and counteracted the negative effect of class imbalance. The experimental results indicated that GANDTI outperforms several state-of-the-art methods for DTI prediction. Additionally, case studies of five drugs demonstrated the ability of GANDTI to discover the potential targets for drugs.

With the rapidly growing traffic demand, the cellular industry is moving toward heterogeneity to fulfill the heavy traffic requirement. The existing heterogeneous networks are comprised of the Long-Term Evolution (LTE), LTE-Advanced (LTE-A), and other compatible access technologies. It has been identified that due to the shortage of capacity, cell performance can deteriorate. To deal with users’ requirements, the 5G heterogeneous network architectures comprising LTE-advanced access technology with the combination of comparatively low configured small base stations, and macro eNodeBs (MeNB) have been extended as a solution. Therefore, operators have introduced the 5G with the existing LTE Advanced Heterogeneous Network (5GLHN) where small cells such as Home eNodeBs (HeNBs) are deployed overlapping with the conventional macro eNodeB (MeNB). However, in highly dense and closely compacted 5GLHNs, the cell capacity can still be lower than what is on-demand, thus affecting the system throughput. This article proposes a framework to maximize the throughput in 5GLHNs through a Constriction Factor Particle Swarm Optimization (CFPSO) technique of cell association and load-balancing algorithm to enhance the throughput of the 5GLHN. The proposed approach is designed to offload the traffic of MeNB Users (MUEs) to the small cells (HeNBs). The convergence, cumulative distribution function of the UEs rate, average throughput, and allocation time are analyzed to evaluate the performance of the proposed CFPSO approach. Simulation results reveal that the throughput of the proposed approach is improved by up to 44.08% of the existing index-based approach and by 94.20 \% of the existing Matching with Minimum Quota (MMQ) approach.