With the rapid expansion of large-bandwidth grid services in recent years, efficient resource allocation has become a critical challenge. This paper explores an optimized resource allocation strategy that integrates satellite communication technology to ensure efficient and stable grid communication services in high-bandwidth scenarios. The main contributions of this study are as follows: we propose a two-stage prediction–allocation closed-loop framework for high-throughput-satellite (HTS)–enabled smart-grid communications. The framework comprises an attention-based traffic-prediction module and a dynamic bandwidth-allocation module, which respectively provide accurate forecasts of future node traffic and priority-aware multibeam bandwidth optimization, thereby offering end-to-end decision support for satellite resource scheduling. Experimental results show that the proposed scheme enhances the grid’s adaptability to future traffic variations at communication nodes, addresses bandwidth provisioning under uncertain high-bandwidth conditions, improves priority-aligned bandwidth utilization and priority efficiency, and ensures both the stability of large-bandwidth grid communications and the performance of critical services.

In the Internet of Vehicles (IoV), developing accurate road information models is essential for analyzing perception data gathered from multiple vehicles. However, traditional centralized data-sharing methods can compromise the privacy and security of data providers. federated learning (FL) presents a promising solution as a distributed machine learning approach that balances data privacy protection with efficient utilization by keeping data localized and sharing only model updates. Nevertheless, conventional FL strategies often fail to adequately address differences in resource investment and data quality among participating vehicles while aggregating local training results. This oversight can lead to inequitable model aggregation and distribution, reducing the motivation for vehicles to share their data. This article proposes a reputation evaluation-based, fair, and secure FL scheme for the IoV to address these challenges. In this scheme, the aggregation node utilizes fuzzy comprehensive evaluation to assess the training outcomes of participating vehicles and assigns aggregation weights accordingly. It also calculates reputation values for each vehicle using periodic averaging methods. Subsequently, the node implements differentiated global model compression and distribution based on these reputation scores. Experimental results indicate that the proposed scheme performs comparably to established algorithms while effectively evaluating vehicle reputations. It achieves model compression and equitable distribution, demonstrating an ability to identify and counteract malicious client attacks. Consequently, this approach enhances fairness and security in FL systems designed for the IoV.

Low-Earth orbit (LEO) satellite networks can achieve global network coverage without geographical restrictions and are essential to the future communication network. In this article, we study the computing offloading problem in a satellite-terrestrial integrated network for the Internet of Remote Things (IoRT), which aims to reduce the total cost (weighted sum of energy consumption and delay), and jointly offload node selection, offloading ratio, and computational resource allocation to achieve the dynamic management of network resources. First, we propose a hybrid cloud and satellite multilayer multiaccess edge computing (MEC) network architecture that can provide heterogeneous computing resources to terrestrial users. Subsequently, since the problem under consideration is a mixed-integer nonlinear programming problem, we propose a computing offloading algorithm for multiagent reinforcement learning, which is an integration of double deep Q learning (DDQN) and deep deterministic policy gradient (DDPG). The algorithm can learn the optimal policy for actions containing a mixture of discrete and continuous variables. Finally, an optimal computational resource allocation scheme is proposed to improve the task computation efficiency. Simulation results show that the proposed task offloading and resource allocation scheme can achieve reasonable scheduling of computational tasks and optimal allocation of computational resources, reducing the cost of task computation.

In intelligent connected vehicle applications, tasks such as path planning and health management involve numerous matrix operations, particularly matrix multiplication. Due to limited resources, these tasks are often outsourced to the edge server. However, outsourcing these tasks involving matrix multiplication might incur potential risks, such as returning incorrect results to expedite processing or even exposing sensitive data during the computation. Privacy-preserving verifiable matrix multiplication schemes address these concerns. However, it is meaningful in practice only if the verification and decoding time is lower than that of local computation. In this paper, we propose a privacy-preserving verifiable matrix multiplication for intelligent connected vehicles that further reduces the verification and decoding time. To achieve this, we first reduce the length of the ciphertext of linearly homomorphic encryption when encrypting a group of messages. Subsequently, we construct our verifiable matrix multiplication scheme based on the improved linearly homomorphic encryption. It has a lower critical dimension than the state-of-the-art scheme with a similar security level, since the shorter ciphertext and the simpler linearly homomorphic encryption algorithm. Performance analysis and experimental results demonstrate that the critical dimensions of our improved scheme are reduced by 23.3%, while the communication cost is reduced by 68.3%, making it particularly suitable for intelligent connected vehicle applications.

The introduction of satellite Internet resource-slicing technology can efficiently allocate satellite network resources and meet the personalized needs of different users. This article proposes a trust-based satellite Internet resource-slicing access authentication scheme, which solves the efficient and secure access requirements in situations where satellite communication and service resources are relatively limited. The working idea of this article is to provide users with access authentication protocols with different efficiencies through trust as a standard. Firstly, The user’s trust value is calculated by establishing a trust metric model based on Beta function, communication byte fluctuations, and centralized trend measurements. Drawing on the requirements of the security policy function in the resource slicing technology standard, assigning different security policies to users can both improve the fast access ability of high-trust users and reduce the priority of low trust users’ access. After that, based on the results of trust metrics, this paper proposes a two-factor-based no certificate satellite Internet slicing access authentication protocol for users with moderate trust levels. This protocol achieves the ability for users to access slicing services anonymously and efficiently through the use of resource-slicing credentials and managers. Final, this article verify the correctness and security of the protocol. Through communication cost comparison, it is shown that this protocol has fewer costs. Through trust simulation, the effectiveness of the trust scheme is analyzed and compared.

This article considers a two-hop wireless-powered relay network consisting of multiple sources, multiple destinations, and one relay. The relay can receive energy from the sources and forward data to the destinations. We focus on the source selection problem during the energy transfer process and the resource allocation problem during the data transmission process. First, the relay can choose among all sources based on the transferred energy from the sources. A credit mechanism is introduced for the relay to achieve optimal selection. Second, a Stackelberg differential game-based model is adopted for the resource allocation problem in the data transmission process, using the differential equation to describe the dynamic variation of energy, and the Stackelberg game to describe the relationships between the sources and the relay. In the proposed approach, both sources and relays consider energy consumption and energy revenue. To find the optimal solutions, an adaptive dynamic programming-based algorithm is utilized. The Lyapunov-based stability analysis shows that the system has uniform ultimate boundedness and convergence. Finally, the trained neural networks can achieve optimal resource allocation strategies. Through extensive simulation experiments, the effectiveness of the proposed algorithm is verified.

Unmanned aerial vehicle (UAV) communications have become a prominent technology for emergency communications to enhance network services. This article investigates computing offloading and resource allocation in nonorthogonal multiple access (NOMA)-based UAV emergency communication scenarios. To minimize the computational overhead of the terminal device, a joint task offloading and resource allocation problem is investigated, where the computation overhead of the marine Internet of Things (IoT) device is measured as a weighting of the task completion time and the energy consumption of the device. The optimization of the transmission of IoT devices, the allocation of computing resources to UAVs, task offloading, and carrier allocation are formulated in the considered problem, which is an NP-hard mixed integer nonlinear programming problem. To reduce the complexity, we decompose it into two parts from the property of the problem: 1) the resource optimization problem and 2) the task offloading problem. To solve the resource allocation problem, we first decouple the problem and then use the proposed quasi-convex and convex optimization methods. Meanwhile, a low-complexity task offloading algorithm is designed to achieve a Nash-stable solution by introducing a coalition game approach based on this. Numerical results verify the algorithm’s effectiveness and are compared with other schemes in the literature.

The rapid development of the acrlong IoV has placed a heavy burden on the edge server. Conversely, many idle vehicles parked near the vehicle in demand are not utilized. Opportunistic computing of vehicles can organize these idle vehicles to provide computing services, but this also requires a secure data-sharing scheme to offer support. Although the existing ciphertext policy attribute-based encryption (CP-ABE) can provide a secure fine-grained data sharing, they either map the data to an algebra element that cannot be applied in practice due to the limited length, or they are hybrid schemes that cannot satisfy the forward security. In addition, they require a trusted authority (TA), which may be unrealistic in implementation. To cope with these issues, we propose a dual hybrid CP-ABE scheme without a TA for vehicular opportunistic computing (VOC) in this article. We exploit the dual hybrid mechanism to solve the problem of forward security in hybrid schemes and eliminate the TA by combining the characteristics of VOC. Then, we prove the acrlong IND-sCPA and forward security of the scheme. Finally, we evaluate the computation, storage, and communication cost from theoretical and simulation perspectives and compare them with other typical schemes. These results illustrate that the proposed scheme has better efficiency and lower storage and communication cost in general.

Dense low-power small cell base station (SBS)-based heterogeneous wireless cellular networks (HetNets) have attracted much attention to achieving high-traffic density and peak rate performance. However, the serious energy consumption problem is still a challenge for HetNets. The use of renewable energy (RE) has been considered as one promising solution for the above problem. This article proposes an energy trading scheme among base stations in RE-based HetNets. All SBSs in HetNets are considered as either the energy demander (SBS-ED) or the energy supplier (SBS-ES) based on their abilities in producing RE, and the macro base station (MBS) works as the energy trading manager to control the trading price. A dynamic evolutionary game-based energy trading model between SBS-ES and SBS-ED is established to achieve cooperative energy trading, and the evolutionary stable strategy (ESS) of the proposed model is analyzed. The pricing mechanism of MBS is also investigated, which can effectually affect the EES performance of the proposed model. It is concluded that the MBS’s strategy in the trading price can affect the energy trading strategies of the SBSs. An energy transmission model is proposed, and the minimum energy loss is considered as the goal to obtain the optimal solutions. Numerical results are given to prove the validity and correctness of our proposed method.

A combination of Industrial Internet of Things (IIoT) and federated learning (FL) is deemed as a promising solution to realize Industry 4.0 and beyond. However, scheduling more IIoT devices engaged in FL contributes to accelerated learning speed, but resulting in increased learning cost in terms of energy consumption and model accuracy reduction. In this article, we investigate the tradeoff between learning speed and cost in a three-layer FL-enabled IIoT system. Particularly, a weighted learning utility function is designed by capturing such a tradeoff. We aim to maximize the weighted learning utility in an FL training round by jointly optimizing the edge association as well as the allocations of resource block, computation capacity, and transmit power of the IIoT device. The resulting problem is a nonconvex and mixed-integer optimization problem, and consequently, it is difficult to solve. We thereby decompose the original problem into three subproblems, and then propose an overall alternating optimization algorithm to solve the subproblems iteratively until convergence. Via experimental results, it is demonstrated that the proposed scheme significantly improves the system-wide learning utility as compared to other baseline schemes. It is also shown that the proposed scheme can achieve the optimized tradeoff between learning speed and learning cost.

With the fierce global competition on satellite networks, the building of satellite constellations grows explosively. Sharply increasing on-orbit data will face the challenge of satellite-ground data transmission. GEO satellites become the top choice for satellite data relay due to their stable satellite-ground link. Most existing spectrum resource management for GEO relays is equipment-oriented and benefit priority, which may lead to a waste of spectrum resources. In this paper, we propose a real-time task-oriented resource allocation strategy for GEO relay systems. We model the spectrum allocation problem as a distributed non-cooperative Stackelberg game process. We prove that when both sides of the game pursue the maximization of personal revenue, the system will enter a Nash equilibrium state, whereas spectrum resources are not fully used. Based on the maximization of individual utilities (U-prior) and spectrum utilization (S-prior) methods, we design an adaptive dual-mode pricing mode to maximize the spectrum resources within a certain loss of revenue. The simulation results show that the S-prior and U-prior have better performance than the baseline method and existing optimization methods. Our proposed dual-mode strategy is making more throughputs and has less delay with little loss of utility values than that of individual utility maximization.

In this paper, we study the architectures of space-air-ground integration network (SAGIN) proposed by domestic scientific research institutes, and put forward an collaborative federal learning architecture suitable for SAGIN to solve the problems of insecurity and low timeliness caused by traffic backhaul. An anomaly traffic detection method is proposed based on the requirements and characteristics of SAGIN. The problem that it is difficult to manually label and extract features in the traffic of SAGIN is solved through the improvement of deep learning algorithm. The challenge of lack of professionals labeling training set is solved by studying the method of semi supervision. The problem of artificial feature engineering is solved by studying the end-to-end anomaly traffic detection algorithm. Finally, we design a simulation environment for the anomaly traffic detection in SAGIN, and verify the feasibility and advanced nature of the proposed methods.

In this paper, we propose a multi-leader multi-follower Stackelberg game model for the resource allocation problem between edge nodes and terminal users in the multi-access edge computing system. In the proposed model, the edge nodes can set the price of edge computing resources according to the strategies of other nodes and predict users’ behaviors. Subsequently, the terminal users can choose their optimal strategies based on the price strategies of edge nodes. A game theory based algorithm is proposed to find the optimal pricing strategies and optimal resource allocation solutions by solving the Nash equilibriums, so that the benefits of both edge nodes and terminal users are optimally satisfied. Simulation results show that the proposed approach yields a high utility at the equilibrium.

Mobile crowdsensing (MCS) is an effective perception paradigm for large-scale tasks, driven by the proliferation of mobile devices with more powerful sensing and computing capabilities. An effective incentive mechanism is critical to the operation of an MCS system in promoting public engagement. However, the great majority of works discuss fixed task pricing, while the inherent inequality of the supply–demand relationship of the tasks exists. Therefore, it is essential to study the dynamic task pricing problem in the peer-to-peer data sharing MCS system. In this article, we formulate the interactions between the requester and the sensors as a two-stage Stackelberg differential game model, while considering the average behavior of sensors to solve the dynamic task pricing problem. Specifically, in the game model, the requester is the leader who first announces the issued task rate and provides decisive state-changing task pricing dynamics to the sensors. Then, the sensors are the followers who decide the rate of tasks completed noncooperatively based on requesters’ observed strategy, using the level of effort as the state dynamics. The requester and the sensors interact through a mean-field term included in the dynamic state functions, which catches the average behavior of all users. By solving the model, the optimal strategies for the users and the optimal tasks pricing trends in the dynamic environment are obtained. Furthermore, the effectiveness and feasibility of the scheme are verified by a series of numerical simulation experiments.

The ubiquitous mobile portable devices have accelerated the rise of mobile crowdsensing (MCS), a distributed perception paradigm. In MCS, multiple requesters issue their sensing tasks on a server platform, and then the platform distributes the tasks to multiple workers. In this process, requesters typically specify the tasks and requirements; meanwhile, the needed task pricing to workers is also specified, which is used to offsetting worker’s efforts by completing the tasks. However, for different application scenarios, the task requirements, the time periods, and the resource consumptions for completing the tasks are varied, which results in a challenge to raise an appropriate task pricing to diverse workers. Therefore, we study the dynamic task pricing problem in the MCS network system with diverse factors (e.g., multiple requester queueing competitions, dynamic task requirements, and distinct waiting time costs). To solve the problem, we resort to the theory of Age of Information (AoI). Specifically, we leverage the AoI timeliness metric in modeling the requester’s waiting time costs, and then we use queueing game theory to build the dynamic task pricing model. The model analysis has shown the existence of the optimal pricing strategies under the first-come–first-served (FCFS) queueing rule and the last-come–first-served with preemptive in waiting (LCFSW) queueing rule. Finally, numerical simulations are conducted to validate the existence of the optimal task pricing.

The combination of Internet of Things (IoT) and artificial intelligence (AI) technology plays an important role in many fields, especially in the field of psychology and medical treatment. This work is mainly to study an affective robotics that can serve humans emotionally based on the IoT and AI technology. The design of affective robotics is important to understand the underlying mechanisms of human behaviors in real life. These mechanisms mainly include human nonverbal behaviors and affective states, which are important but difficult to be precisely modeled. To address this challenge, we introduce a human–robot interaction (HRI) architecture, including emotion recognition, affective computing, emotion diagnosis, and emotion control. First, we propose a system model based on HRI between affective robotics and human, in order to enhance the emotional service. Then, we develop a dynamical model with affective computing and control, where we provide a mathematical formulation method based on stochastic differential equations to quantify the emotional state. Furthermore, we perform the dynamic behavior analysis of the existence, boundedness, and stability of the model solution comprehensively. Numerical results are provided to demonstrate the validity and feasibility of the proposed design techniques.

Recently, just-in-time (JIT) defect prediction technique attracted a lot of attention. In JIT defect prediction, all branches and omitting changes outside the main branch should be considered which can significantly affect the performance of JIT defect prediction. However, there are many duplicate changes among all the branches, which are referred to as a pair of changes with identical implementation in different branches. Such changes can influence the calculation of developer experience metrics and are considered as the noisy data for JIT defect prediction. In this article, the impact of duplicate changes on JIT defect prediction is explored. An empirical study on a total of 105 828 changes from eight Apache open-source projects is given. We find that 13% of changes from different branches are duplicate among the studied projects. The duplicate changes have a great influence on the model metrics for JIT defect prediction. For 50% of the changes, removing duplicate changes decreases the experience metrics with an average of 6–55. In addition, the duplicate changes have a significant impact on the evaluation and interpretation of JIT defect prediction models. Removing duplicate changes among the studied projects can significantly improve the performance of JIT defect prediction models ranging from 1 to 125% concerning various performance measures (i.e., area under the curve, Matthews correlation coefficient, and F1). Given the impact of duplicate changes, we suggest that researchers should remove duplicate changes from the original historical changes of software repository when evaluating the performance of JIT defect prediction models in future work.

The Space-Air-Ground Integrated Networks (SAGINs) converges the rapidly developing ground, aerial and satellite communication networks to provide users with efficient and personalized services through the multi-layer network architecture. The complex structure, inconsistent communication protocol, and incompatible equipment significantly affect the information transmission efficiency in SAGINs. The application of Software Defined Network (SDN) technology promotes global deployment of the SAGIN resources. Aiming at the information transmission bottleneck between controller and switch nodes in the software-defined SAGINs, a dynamic handover transmission control scheme based on the queuing game model is proposed. Considering long transmission delay links in the SAGINs, the traditional queuing game model is improved to reduce the number of information interactions. The users’ service value are defined as the random distribution to describe the various requirements in the network. In addition, the relationship between the social welfare and the arrival rate of the transmission control system is discussed under two modes of observable and unobservable controller queues. Through theoretical analysis and calculation, the unique handover arrival rate is obtained to make the social welfare equal in the two modes. When the arrival rate is less than the handover threshold, the controller queue is set to unobservable mode, and otherwise to observable mode. Then, the system can operate in an optimal way to obtain the maximum benefit. At last, numerical and system simulation verify the effectiveness of the scheme.

Industrial Internet of Things (IIoT) is a new stage for traditional industry to achieve intelligent development. However, the problems of limited resource, communication congestion, and capacity-constrained batteries have emerged due to massive wireless sensing devices (WSDs). With the recent advent of the wireless power transfer (WPT) technique and the development of edge computing technology, the IIoT pays much attention to the interconnected, instant, and high-intelligent system. In this article, we first consider a three-layer architecture to describe the IIoT environment and we propose a resource allocation strategy aiming at maximizing the residual energy of WSDs. The optimization of residual energy is formulated as a mixed-integer nonconvex programming NP hard problem, by constraining the offloading decision, power allocation, computing resource allocation, and time allocation. In addition, an improved hybrid whale optimization algorithm (IHWOA) is proposed to search for the approximate optimal solution. Rapid convergent and stable efficient solution is obtained through simulation experiments. Finally, numerical results prove that the proposed solution achieves good performance.

Mobile edge computing is becoming a major trend in providing computation capacities at the edge of mobile networks. Meanwhile, unmanned aerial vehicles (UAVs) have been considered as distinctly important integrated components to extend services coverage. In order to provide users with higher and satisfied quality of services, edge computing resources need to be allocated between edge computing stations (ECSs) and UAVs in mobile networks. However, there are significant security and privacy problems due to the open environments of ECSs and UAVs. In this paper, we propose a resource pricing and trading scheme based on Stackelberg dynamic game to optimally allocate edge computing resources between ECSs and UAVs, and blockchain technology is applied to record the entire resources trading process to protect the security and privacy. The ECSs control the resources price of the allocated edge computing resources, where the UAVs follow the price announced by the ECSs and make optimal decisions on the edge computing resources demands. Blockchain is integrated in the resource trading process to ensure the security and privacy. Numerical simulations are given to show the effectiveness of the proposed scheme.

In this paper, a satellite-aerial integrated computing (SAIC) architecture in disasters is proposed, where the computation tasks from two-tier users, i.e., ground/aerial user equipments, are either locally executed at the high-altitude platforms (HAPs), or offloaded to and computed by the Low Earth Orbit (LEO) satellite. With the SAIC architecture, we study the problem of joint two-tier user association and offloading decision aiming at the maximization of the sum rate. The problem is formulated as a 0-1 integer linear programming problem which is NP-complete. A weighted 3-uniform hypergraph model is obtained to solve this problem by capturing the 3D mapping relation for two-tier users, HAPs, and the LEO satellite. Then, a 3D hypergraph matching algorithm using the local search is developed to find a maximum-weight subset of vertex-disjoint hyperedges. Simulation results show that the proposed algorithm has improved the sum rate when compared with the conventional greedy algorithm.

With the development of wisdom network, this paper assumes that intelligent devices become more and more intelligent, which can easily collect and provide a variety of context awareness data. The research goal is to design a dynamic conflict resolution strategy for context-aware resource allocation. The limited availability of resources inevitably leads to conflicts. Considering the characteristics of wisdom network, the quality of service when solving conflicts, a mechanism is proposed to improve the quality of services and to solve the resources allocation conflicts. This paper constructs the optimal model of context-aware based on a differential game and optimizes the resource allocation of context-aware based on the priority of scenarios. Fog computing is used to provide enough computing resources for the control of resource allocation of context-aware. The Bellman dynamic programming is introduced to solve the feedback Nash equilibrium solution of the proposed differential game model, to obtain the optimal allocation of service resources and solve the effectiveness of resource allocation.

In this paper, the energy efficient virtual machine (VM) placement and virtual resource allocation problem for the mobile edge computing (MEC) system is explored. Particularly, we develop an optimization framework of energy consumption minimization for computing and offloading by jointly optimizing the VM placement matrix and the number of physical machines (PMs). To resolve this problem, we transform the optimization problem into a non-uniform weighted hypergraph model. In this model, the weight of hyperedge is defined as the negative of the accumulated energy consumption for computing at VM instances hosted by one PM. Based on the hypergraph model, a hypergraph matching algorithm by utilizing the local search policy is proposed for finding the maximum-weight subset of vertex-disjoint hyperedges, aiming to obtain an optimal VM placement, i.e., (M*)-perfect matching. Furthermore, the virtualized resources are further allocated to user equipments (UEs) in the form of multiple VM instances via the optimal VM placement to meet the requirement of workloads. Simulation results are presented to demonstrate the effectiveness of the proposed hypergraph matching algorithm over the alternative benchmark algorithm.

The application of drone small cells (DSCs) which are unmanned aerial vehicles (UAVs) carrying communication payload to complete the construction of the high-altitude base stations, is playing an increasingly important role for providing emergent wireless services in different scenarios. In order to coordinate interference and reduce huge backhaul overhead among static ultra-dense DSCs on the low-altitude platform, the paper studies a DSC cluster precoding network with massive multiple-input multiple-output (mMIMO). Considering the disadvantage of the energy-constrained unmanned aerial base station (UABS), we investigate the problem of designing precoding at cluster DSCs to minimize the transmission power of UABSs. A modified cluster scheme based on the Euclidean distance is adopted to cluster the DSCs. We eliminate the intra-cluster interference via performing the modified zero-forcing method and coordinate the inter-cluster interference to achieve our target of reducing transmit power. A non-cooperative game among the DSC clusters is formulated, and the existence and uniqueness of the Nash equilibrium of the proposed game are proved. The non-convex optimization problem is solved via the iterative methods and the numerical results show the effectiveness of our proposed scheme.

In wireless networks, introducing small cells into the macro cell can increase the system capacity, but may bring interferences into the original wireless networks. In this paper, we design an approach on the power control problems in the small cell networks, to control the interferences through control the transmission power. The interferences between the macro base station (MBS) and the small cell base stations (SBSs) are constructed as a Stackelberg game, where the MBS and SBSs act as the leader and followers respectively. Meanwhile, the status of the MBS and SBSs are described through two differential equations, to show the dynamic characteristics of the energy. Then the optimal control strategies for both the leader and followers can be given based on the open-loop Stackelberg equilibriums to the differential game. Numerical simulations and results show the effectiveness and advantages of the proposed algorithms.

Network virtualization (NV) allows multiple heterogeneous virtual networks (VNs) to coexist and operate over the same physical network (PN) infrastructures. Some of the benefits of this advancement include flexibility in VN topologies, heterogeneity in VN technologies, and modularity of network operations. However, there are a few areas, such as resource allocation and economics, which challenge the implementation of NV. In this paper, we first introduce some NV parameters that influence the resource allocation and economics of an NV system. Next, we formulate an economic model for NV using the prey-predator food chain model. This model takes into account the dynamics in an NV system, such as the service, payoff, failure, and competition rates within each VN and PN. The solution point to this model represents the resource strategy of the service provider (SP) given the number of users trying to use its VN, as well as the resource strategy of the infrastructure provider (InP) given the strategy of the VN leasing its PN. In addition, we establish economic models that relate the capacities of the end users, the SP, and the InP. Finally, we provided simulations that show how the prey-predator food chain model fits well on an NV system.