This paper aims to enhance the performance of Vehicular Platooning (VP) systems integrated with Wireless Federated Learning (WFL). In highly dynamic environments, vehicular platoons experience frequent communication changes and resource constraints, which significantly affect information exchange and learning model synchronization. To address these challenges, we first formulate WFL in VP as a joint optimization problem that simultaneously considers Age of Information (AoI) and Federated Learning Model Drift (FLMD) to ensure timely and accurate control. Through theoretical analysis, we examine the impact of FLMD on convergence performance and develop a two-stage Resource-Aware Control framEwork (RACE). The first stage employs a Lagrangian dual decomposition method for resource configuration, while the second stage implements a multi-agent deep reinforcement learning approach for vehicle selection. The approach integrates Multi-Head Self-Attention and Long Short-Term Memory networks to capture spatiotemporal correlations in communication states. Experimental results demonstrate that, compared to baseline methods, the proposed framework improves AoI optimization by up to 45%, accelerates learning convergence, and adapts more effectively to dynamic VP environments on the AI4MARS dataset.

Knowledge diversity and knowledge forgetting are two major issues in sustaining collaborative learning within heterogeneous vehicle networks. These issues become especially severe when vehicles possess varying sensing capabilities, computational resources, and domain expertise, leading to fragmented learning and unstable knowledge retention over time. To address these challenges, we propose a dual-level game-theoretic approach. We first formulate a new metric, Utility-of-Information (UoI), to characterize the features of knowledge learning, retention, and consolidation. Based on this metric, we design a game-theoretic dual-level approach, which comprises a lower-level coalition formation game where vehicles self-organize into “teacher-student” coalitions based on their UoI profiles, and an upper-level UAV resource allocation game where vehicle coalitions compete for limited communication resources. To optimize both levels of the game, we design a unified reinforcement learning-based framework that enables adaptive searching for optimization under dynamic network conditions. Experimental results demonstrate that our approach effectively addresses knowledge diversity and significantly mitigates the effects of knowledge forgetting in UAV-assisted heterogeneous vehicle networks.

Reconfigurable Intelligent Surface (RIS)-assisted uncrewed Aerial Vehicle (UAV) communications have been realized as essential to space-air-group system integration in the 6 G technology landscape. Trajectory planning plays a crucial role in RIS-assisted UAV communications to face the challenges of UAV’s limited power capacities and dynamic wireless channels. Existing solutions assume complete channel state information, focus on single-rotor UAVs, and rely heavily on time-consuming training processes for machine learning; thus, they lack applicability to deal with highly dynamic real-world scenarios. To fill these research gaps, we aim to characterize RIS-assisted UAV communications and design responsive and accurate UAV trajectory planning algorithms in this paper. We first develop a communication model with incomplete information and an energy consumption model for quadrotor UAVs. We then formulate UAV trajectory planning as an optimization problem to minimize UAV’s energy consumption while maintaining communication throughput. To solve this problem, we design an acceleration framework,FedX, for reinforcement learning (RL) solvers and present two fast trajectory planning algorithms, FedSAC and FedPPO, as instantiations of theFedXframework. Our evaluation results indicate that the proposed framework is effective and efficient–more than 3 times faster with 5 agents and 7 times faster with 10 agents than standard RL algorithms, making it suitable for using RL solvers within wireless networks and mobile computing environments. We also discuss and identify the pros and cons of our proposed framework.

Currently, treatment options for patients with treatment-resistant depression (TRD) are extremely limited. Although the rapid antidepressant effects of nitrous oxide have been preliminarily validated, the underlying neurophysiological mechanisms remain unclear. In this study, we intended to explore impact on brain modules induced by nitrous oxide through a randomized controlled study. A total of 44 patients with TRD were recruited. Subjects were randomly allocated to nitrous oxide-intervention group (1-hour inhalation of 50% nitrous oxide/50% oxygen) or placebo-control group (1-hour inhalation of 50% oxygen/50% air). The eye-closed resting state EEG signals were recorded. Our findings reveal that nitrous oxide treatment significantly altered the segregation of interconnected brain functional modules. The result of General Linear Model (repeated measurements) demonstrates superior antidepressant efficacy of nitrous oxide compared to placebo, as evidenced by reductions in both the participation coefficient and connector hub after treatment. Furthermore, changes in modular metrics moderately correlated with reductions in depressive symptoms. These findings offer valuable insights into mechanism underlying the treatment of TRD using nitrous oxide from a modular perspective.

We investigate how to provide Quality-of-Service (QoS) for diversified video flows. We design a fine-grained video traffic control mechanism that integrates traffic classification with path selection for video flows within the framework of SDN. For the design, we present a category-theoretic ontology log (olog) diagram model, which provides a novel perspective on the interdependency among various system components. For the video traffic classification, we first evaluate various machine learning classifiers in terms of their performance and then chose the most effective one to be the first module. For the path selection, we devise a multi-constrained QoS routing strategy by restructuring a state-of-the-art graph algorithm, combine it with the${k}$-shortest path algorithm, and deploy this strategy as another video traffic control module. We implemented a prototype of the proposed mechanism on the SDN emulator Mininet, and we evaluate its effectiveness using the performance results obtained.

Multi-access edge computing (MEC) and ultra-dense networking (UDN) are recognized as two promising paradigms for future mobile networks that can be utilized to improve the spectrum efficiency and the quality of computational experience (QoCE) . In this paper, we study the task offloading problem in an MEC-enabled UDN architecture with the aim to minimize the task duration while satisfying the energy budget constraints. Due to the dynamics associated with the environment and parameter uncertainty, designing an optimal task offloading algorithm is highly challenging. Consequently, we propose an online task offloading algorithm based on a state-of-the-art deep reinforcement learning (DRL) technique: asynchronous advantage actor-critic (A3C) . It is worthy of remark that the proposed method requires neither instantaneous channel state information (CSI) nor prior knowledge of the computational capabilities of the base stations. Simulations show that the our method is able to learn a good offloading policy to obtain a near-optimal task allocation while meeting energy budget constraints of mobile devices in the UDN environment.

Millimeter wave (mmWave) communication is a promising New Radio in Unlicensed (NR-U) technology to meet with the ever-increasing data rate and connectivity requirements in future wireless networks. However, the development of NR-U networks should consider the coexistence with the incumbent Wireless Gigabit (WiGig) networks. In this paper, we introduce a novel multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) based mmWave NR-U and WiGig coexistence network for uplink transmission. Our aim for the proposed coexistence network is to maximize the spectral efficiency while ensuring the strict NR-U delay requirement and the WiGig transmission performance in real time environments. A joint user grouping, hybrid beam coordination and power control strategy is proposed, which is formulated as a Lyapunov optimization based mixed-integer nonlinear programming (MINLP) with unit-modulus and nonconvex coupling constraints. Hence, we introduce a penalty dual decomposition (PDD) framework, which first transfers the formulated MINLP into a tractable augmented Lagrangian (AL) problem. Thereafter, we integrate both convex-concave procedure (CCCP) and inexact block coordinate update (BCU) methods to approximately decompose the AL problem into multiple nested convex subproblems, which can be iteratively solved under the PDD framework. Numerical results illustrate the performance improvement ability of the proposed strategy, as well as demonstrating the effectiveness to guarantee the NR-U traffic delay and WiGig network performance.

Energy efficiency plays a vital role in device-to-device communications, which has been recognized as a key challenge. In this article, we study the implications of the peak power constraints and processing cost on the energy scheduling policy, and find that the peak power should be used around the time slots of each energy arrival, and the only time slot in which the intermittent communication may occur is the last time slot. A new optimal energy scheduling algorithm is devised based on these observations. Also, we propose a near-optimal energy scheduling algorithm that simultaneously takes into account the peak power constraints and the processing cost. Our simulation results demonstrate the effectiveness of the proposed energy scheduling algorithm and the validity of the mathematical analyses.

Recently, Internet of Vehicles (IoV) has become one of the most active research fields in both academic and industry, which exploits resources of vehicles and Road Side Units (RSUs) to execute various vehicular applications. Due to the increasing number of vehicles and the asymmetrical distribution of traffic flows, it is essential for the network operator to design intelligent offloading strategies to improve network performance and provide high-quality services for users. However, the lack of global information and the time-variety of IoVs make it challenging to perform effective offloading and caching decisions under long-term energy constraints of RSUs. Since Artificial Intelligence (AI) and machine learning can greatly enhance the intelligence and the performance of IoVs, we push AI inspired computing, caching and communication resources to the proximity of smart vehicles, which jointly enable RSU peer offloading, vehicle-to-RSU offloading and content caching in the IoV framework. A Mix Integer Non-Linear Programming (MINLP) problem is formulated to minimize total network delay, consisting of communication delay, computation delay, network congestion delay and content downloading delay of all users. Then, we develop an online multi-decision making scheme (named OMEN) by leveraging Lyapunov optimization method to solve the formulated problem, and prove that OMEN achieves near-optimal performance. Leveraging strong cognition of AI, we put forward an imitation learning enabled branch-and-bound solution in edge intelligent IoVs to speed up the problem solving process with few training samples. Experimental results based on real-world traffic data demonstrate that our proposed method outperforms other methods from various aspects.

One-to-many Device-to-Device (D2D) communications, also refer to as D2D multicast communications, has been realized as an effective paradigm various practical settings. With the energy issue of mobile devices becomes more prominent, the energy efficiency of D2D multicast communication must be enhanced. In this paper, we propose POET, an energy-efficient resource management mechanism with joint Power cOntrol and channEl allocaTion for one-to-many D2D communications to address this issue. To be specific, by formulating the problem of energy efficiency maximization with QoS constraints on both cellular and D2D communications, we decompose this NP-hard problem into power control and channel allocation sub-problems. We present a gradient projection method for the first along with an iterative combinatorial auction algorithm for the second. Our preliminary results demonstrate that POET is lightweight and cost-effective in improving the energy efficiency of D2D multicast communications.

The emerging 5G-enabled vehicular networks can satisfy various requirements of vehicles by traffic offloading. However, limited cellular spectrum and energy supplies restrict the development of 5G-enabled applications in vehicular networks. In this article, we construct an intelligent offloading framework for 5G-enabled vehicular networks, by jointly utilizing licensed cellular spectrum and unlicensed channels. A cost minimization problem is formulated by considering the latency constraint of users and is further decomposed into two subproblems due to its complexity. For the first subproblem, a two-sided matching algorithm is proposed to schedule the unlicensed spectrum. Then, a deep-reinforcement-learning-based method is investigated for the second one, where the system state is simplified to realize distributed traffic offloading. Real-world traces of taxies are leveraged to illustrate the effectiveness of our solution.

Power allocation plays a vital role in coordinating interference between Device-to-Device (D2D) and cellular communications, and when power allocation meets simultaneous wireless information and power transfer (SWIPT), the energy efficiency of D2D communications can be significantly improved. While numerous research studies have been conducted on D2D power allocation, most of these studies do not take the presence of SWIPT into consideration. Toward a remedy for this issue, we investigate the problem of D2D power allocation with SWIPT power-splitting architecture, and address it by establishing a novel game-theoretic model. Two power allocation mechanisms are proposed to simultaneously allocate transmit power and choose power splitting ratio for D2D communications. We also develop two pricing strategies for the proposed power allocation mechanisms based on the social utility (sum utility of both D2D and cellular communications) maximization. Simulation results validate theoretical analyses and the effectiveness of the proposed mechanisms. In particular, we find through performance comparisons that our developed pricing strategies are light-weighted and energy-efficient, and the distributed power allocation mechanism is responsive to the mobility of D2D users.

This paper addresses the energy efficiency issue for content sharing with collaborative mobile clouds (CMC). We start by maximizing the data rate of cellular transmissions under the maximum transmit power constraint of the cellular users, to obtain the optimal beamforming vectors. Using these vectors, we propose a water filling based data segmentation approach for content distribution. Furthermore, within the CMC, we design cost-effective resource allocation and power control mechanisms for device-to-device communications. Through performance comparisons, we disclose that our proposed scheme outperforms some previous study in terms of delay and energy consumption per mobile terminal, which further validates the effectiveness of our design.

The ubiquitous sensor-rich mobile devices (e.g., smartphones, wearable devices, and smart vehicles) have been playing a vital role in the evolution of the Internet of Things (IoT), which bridges the gap between digital and physical spaces. The powerful computing/communication capacities, huge population, and inherent mobility make mobile device networks a much more flexible and cost-effective IoT solution than traditional wireless sensor networks. However, the energy issue of mobile terminals poses significant challenges to the widespread use of IoT: not only the mobile terminals have short lifetime with the proliferation of mobile applications but also the current networking and communication technologies are not adequately taking the energy efficiency into account. Therefore, the sustainable issue of IoT has attracted considerable attention from both academia and industry. Wireless energy harvesting (EH), and transfer technology was recently proposed as an effective mean to address this issue. It enables the mobile terminals to harvest energy from the ambient environment to prolong its battery. Although some forms of EH have been applied to WSNs, networking and communication solutions must be redesigned for wireless powered IoT with massive number of mobile terminals.

The crucial role of networking in cloud computing calls for federated management of both computing and networking resources for end-to-end service provisioning. Application of the Service-Oriented Architecture (SOA) in both cloud computing and networking enables a convergence of network and cloud service provisioning. One of the key challenges to high performance converged network-cloud service provisioning lies in composition of network and cloud services with end-to-end performance guarantee. In this paper, we propose a QoS-aware service composition approach to tackling this challenging issue. We first present a system model for network-cloud service composition and formulate the service composition problem as a variant of Multi-Constrained Optimal Path (MCOP) problem. We then propose an approximation algorithm to solve the problem and give theoretical analysis on properties of the algorithm to show its effectiveness and efficiency for QoS-aware network-cloud service composition. Performance of the proposed algorithm is evaluated through extensive experiments and the obtained results indicate that the proposed method achieves better performance in service composition than the best current MCOP approaches.

Cloud computing has emerged as a new computing paradigm, with provisioning model generally consisting of cloud service providers (CSPs), network service providers (NSPs), and end users. The associated economics has opened up a new research area; and with the expansion of the cloud computing market, the relationship between CSPs and NSPs, is changing profoundly. In addition to providing the default network services, traditional NSPs, in attempt to compete with CSPs, have started offering cloud services to end users. Though much progress has been made toward addressing competitions among CSPs themselves or among NSPs themselves, few studies have focused on the competition between CSPs and NSPs. In this paper, we investigate the problem of insufficient studies on the competition between CSPs and NSPs by presenting a new economic model to characterize the competition between CSPs and NSPs, and by conducting thorough theoretical analysis as well as numeric experiments to validate the proposed model. We believe, based on results, that the proposed economic model is general and feasible, and thus is applicable to modeling the competition among service providers in cloud computing market.

One grand challenge in software defined networking is to select appropriate locations for controllers to shorten the latency between controllers and switches in wide area networks. In the literature, the majority of approaches are focused on the reduction of packet propagation latency, but propagation latency is only one of the contributors of the overall latency between controllers and their associated switches. In this paper, we explore and investigate more possible contributors of the latency, including the end-to-end latency and the queuing latency of controllers. In order to decrease the end-to-end latency, the concept of network partition is introduced and a clustering-based network partition algorithm (CNPA) is then proposed to partition the network. The CNPA can guarantee that each partition is able to shorten the maximum end-to-end latency between controllers and switches. To further decrease the queuing latency of controllers, appropriate multiple controllers are then placed in the subnetworks. Extensive simulations are conducted under two real network topologies from the Internet Topology Zoo. The results verify that the proposed algorithm can remarkably reduce the maximum latency between controllers and their associated switches.

Machine-to-machine (M2M) communications and applications are expected to be a significant part of the Internet of Things (IoT). However, conventional network gateways reported in the literature are unable to provide sustainable solutions to the challenges posted by the massive amounts of M2M communications requests, especially in the context of the IoT for smart cities. In this paper, we present an admission control model for M2M communications. The model differentiates all M2M requests into delay-sensitive and delay-tolerant first, and then aggregates all delay-tolerant requests by routing them into one low-priority queue, aiming to reduce the number of requests from various devices to the access point in the IoT for smart cities. Also, an admission control algorithm is devised on the basis of this model to prevent access collision and to improve the quality of service. Performance evaluations by network calculus, numerical experiments, and simulations show that the proposed model is feasible and effective.

The widespread deployment of web services and rapid development of big data applications bring in new challenges to web service compositions in the context of big data. The large number of web services processing a huge amount of diverse data together with the complex and dynamic relationships among the services require automatic composition of semantic web services to be performed quickly, thereby demanding more efficient service composition algorithms. In this paper, we investigate the issue of web service composition in big data environments by proposing novel composition algorithms with low time-complexity. Specifically, we decompose the service composition into three stages - construction of parameter expansion graphs, transformation of service dependence graphs, and backtracking search for service compositions. Based on the parameter expansion strategies, we then propose two efficient semantic web service composition algorithms and analyze their time complexity. We also conduct comparison experimentally to evaluate the efficiency of the algorithms and validate their effectiveness using a big data (service composition) set.

With the explosive increase of mobile traffic in recent years, cellular networks face enormous challenges in high quality of service (QoS) provisioning for mobile users. Mobile data offloading is a promising way to address this issue, through which a cellular system can reduce its traffic burden by offloading some portion of data to other networks, such as Wi-Fi. However, when the Wi-Fi networks are deployed by different operators, an efficient incentive mechanism is needed to encourage the participation of these networks. However, most of the existing studies on the incentive mechanism design focus on the amount of data offloading from the cellular network, rather than the diverse data patterns and features of different applications. In this paper, we propose a QoS-based incentive mechanism, termed QBIM, to promote the cooperation of multiple offloading networks while achieving high QoS level for mobile users with different applications. Through this mechanism, the cellular network chooses the Wi-Fi access points to offload data traffic of mobile users by jointly considering access point operators' bid vectors and the mobile user utilities of different services. Meanwhile, the corresponding payments are designed as the compensation to the involved Wi-Fi systems. The proposed incentive mechanism can not only achieve the maximum social welfare, but satisfy desirable properties of individual rationality and truthfulness as well. Simulation results show that the proposed mechanism achieves a higher utility with a smaller cost compared to the other counterparts.

Machine-to-machine (M2M) communications and applications are expected to be a significant part of the next generation 5G networks. While there have been a large amount of research studies with respect to radio resource management, load balancing, and devices grouping for M2M communications, few of them has addressed the issue of admission control. In this paper, we propose a new admission control model for M2M communications, which classifies all M2M requests into delay-sensitive and delay-tolerant first, and then aggregates all delay-tolerant requests, aiming to reduce the number of requests from devices to base stations. An admission control algorithm based on this model is devised to prevent congestion and to improve the quality of services, and a network calculus based performance-analyzing technique is developed for this model. Both theoretical analyses and simulation results show that the proposed model is feasible and valid.

A smart meter Neighborhood Area Network (NAN) is a significant component for smart grid. The delay- sensitive communication in NAN, such as the interaction of power system control signal, usually requires the maximum allowed delay in the order of a few milliseconds. Therefore, it is crucial to investigate how to shorten the latency and guarantee real-time communications. Since the location of Data Aggregation Points (DAPs) significantly affects the propagation latency between DAPs and their associated smart meters, in this paper, we aim at tackling the DAP placement problem in a delay-sensitive smart meter NAN. Specifically, the DAP placement problem is formulated first. Then, a network partition approach, termed Clustering-based DAP Placement (CDP), is proposed to solve the problem. Extensive simulations are conducted based on an actual neighborhood topology. The simulation results demonstrate that the proposed CDP is able to remarkably reduce the maximum propagation latency of data between DAP and their associated smart meters.

Multicast routing that meets multiple quality of service constraints is important for supporting multimedia communications in the Internet of Things (IoT). Existing multicast routing technologies for IoT mainly focus on ad hoc sensor networking scenarios; thus, are not responsive and robust enough for supporting multimedia applications in an IoT environment. In order to tackle the challenging problem of multicast routing for multimedia communications in IoT, in this paper, we propose two algorithms for the establishing multicast routing tree for multimedia data transmissions. The proposed algorithms leverage an entropy-based process to aggregate all weights into a comprehensive metric, and then uses it to search a multicast tree on the basis of the spanning tree and shortest path tree algorithms. We conduct theoretical analysis and extensive simulations for evaluating the proposed algorithms. Both analytical and experimental results demonstrate that one of the proposed algorithms is more efficient than a representative multiconstrained multicast routing algorithm in terms of both speed and accuracy; thus, is able to support multimedia communications in an IoT environment. We believe that our results are able to provide in-depth insight into the multicast routing algorithm design for multimedia communications in IoT.

Recent development in Cloud and networking technologies have stimulated unification of network and Cloud service provisioning, in which service composition plays a crucial role. While encouraging progress has been made toward network-Cloud service composition, the impact of correlated network and Cloud services on the QoS of composite services, however, has not been sufficiently studied. In this paper, we address the challenging problem of QoS correlation-aware network and Cloud service composition. Specifically, we formulate this problem as a multi-constraint optimal path problem and propose a novel algorithm to solve it. We also evaluate the performance of the proposed algorithm with extensive simulations. The experimental results show that the proposed algorithm is effective and efficient and it is able to yield service composition solutions with better QoS guarantees through considering QoS correlations among different services.

Software Defined Networking (SDN), the novel paradigm of decoupling the control logic from packet forwarding devices, has been drawing considerable attention from both academia and industry. As the latency between a controller and switches is a significant factor for SDN, selecting appropriate locations for controllers to shorten the latency becomes one grand challenge. In this paper, we investigate multi-controller placement problem from the perspective of latency minimization. Distinct from previous works, the network partition technique is introduced to simplify the problem. Specifically, the network partition problem and the controller placement problem are first formulated. An optimized K-means algorithm is then proposed to address the problem. Extensive simulations are conducted and results demonstrate that the proposed algorithm can remarkably reduce the maximum latency between centroid and their nodes compared with the standard K-means. Specifically, the maximum latency can reach 2.437 times shorter than the average latency achieved by the standard K-means.

Spectrum prediction plays a critical role in cognitive radio networks because it is promising to significantly speed up the sensing process and hence save energy as well as improve resource utilization. However, most existing spectrum prediction models are not able to fully explore the hidden correlation among adjacent observations or appropriately describe the channel behavior. In this paper, we propose a novel prediction approach termed high-order hidden bivariate Markov model (H^2BMM), by leveraging the advantages of both HBMM and high-order. H^2BMM applies two dimensional parameters, i.e., hidden process and underlying process, to more accurately describe the channel behavior. In addition, the current channel state is predicted by observing multiple previous states. Extensive simulations are conducted and results verify that the prediction accuracy is significantly improved using the proposed H^2BMM compared with traditional Hidden Markov Model (HMM) and Hidden Bivariate Markov Model (HBMM).

Cloud computing has emerged as a new computing paradigm and its economics has opened up a new research area. Though progress has been made toward address competitions among Cloud service providers (CSPs) or among network service providers (NSPs), few studies have focused on the relationship between CSPs and NSPs. In this paper, we investigate this problem and present a new economic model to characterize the competition between CSPs and NSPs. We then conduct thorough theoretical analysis and numeric experiments to validate the proposed model. The results show that the replacement coefficient, connection rate, service coefficient, the equilibrium will affect the market share and the profit of CSPs and NSPs. Through this study, we believe that the developed economic model is general and practical, thus it is applicable to model the Cloud computing market.

Resource allocation plays a critical role in implementing D2D communications underlaying a cellular network. Game-based approaches are recently proposed to address the resource allocation issue. Most existing approaches employ deterministic game models while implicitly assuming that each player in the game is completely willing to exchange transmission parameters with other players. Thus each player knows the complete information of all others. However, this assumption may not be satisfied in practice. For example, users may be reluctant to disclose all their parameters to peers. In this paper, we fully consider this scenario, i.e., players have incomplete information of others, and investigate the resource allocation problem for multicell D2D communications where a D2D link utilizes common resources of multiple cells. To attack this problem, a game-theoretic approach under the incomplete information condition is proposed. Specifically, we characterize the Base Stations (BSs) as players competing for resource allocation quota from the D2D demand, formulate the utility of each player as payoff from both cellular and D2D communications leasing the resources, and design the strategy for each player that is determined based on prior probabilistic payoff information of other players. We conduct extensive simulations to examine the proposed approach and the results demonstrate that the utility, sum rate, and sum rate gain of each player under the incomplete information condition are surprisingly higher than the counterparts under the complete information condition.

The proliferation of the mobile Internet and social networks reshapes the proportion of uploaded data in the entire Internet traffic. IFOM (IP Flow Mobility) technology, which offloads the cellular data to the WiFi or Femtocells or other complementary networks, plays a crucial role in improving the throughput of cellular systems. Although there have been many studies on the IFOM technology, most of them are done from a technical perspective, and the issues related the dissemination and utilization of the IFOM technology are largely overlooked. Unlike prior research works, this paper addresses issues involved with the IFOM technology from an economic perspective. Specifically, we model the competitions among multiple service providers supporting or not supporting the IFOM technology by leveraging the Game Theory, and then analyze the Nash Equilibrium for the ensuing game model. We also conduct extensive simulations to determine the factors that affect the market share and profit of the service providers. We believe that this research work will provide valuable guidance to service providers for the promotion and utilization of the IFOM technology.

In this work, we try to collect useful emotional stress information from electrocardiogram (ECG) signals via a real-time wearable Wireless Body Area Network (WBAN). Discrete Wavelet Transform (DWT) is applied on collected ECG signals for feature extraction, which carries important information for stress level identification. After the stress level is classified using K-Nearest Neighboring (KNN), adaptive convolutional coding is considered for ECG signal protection during transmission according to their various stress levels, which is able to provide an acceptable low Bit Error Rate (BER) and efficient energy consumption at the same time.

Device-to-device (D2D) communication has recently emerged as a promising technology to improve the capacity and coverage of cellular systems. Coordinating interference between D2D and cellular users plays a crucial role in realizing D2D communications underlaying cellular networks successfully. While most of prior mechanisms for D2D have focused on mitigating interference within a single-cell system, they fail to address intercell interference with multiple cell settings. In this paper, we investigate the intercell interference issue in a cellular network where a D2D link reuses the available spectrum resources of multiple cells. We propose a game-theoretic resource allocation scheme, termed GALLERY, to address this problem. Unlike existing works that typically treat D2D users as players, we characterize base stations (BSs) as players competing for resources allocation quota of D2D demand, and define the utility of each player as the payoff gained from both cellular and D2D. We also propose a resource allocation protocol based on the equilibrium derivation. Extensive simulations are conducted to verify the proposed scheme and the results show that it can considerably enhance the system performance in terms of sum rate and sum rate gain. It is expected that GALLERY provides systematical insights into resource configurations of multiple cells for D2D communications.

Software Defined Network (SDN) may significantly enhance network and service management by enabling separated control and data planes. The centralized OpenFlow controller with a global vision of network states offers a promising approach to realizing flow-based admission control for supporting Quality of Service (QoS) provisioning in SDN. However, per-flow process brings in challenges to scalability of OpenFlow-based SDN. Flow aggregation has been explored as an effective method to address this issue. In this paper, we investigate admission control with flow aggregation for QoS provisioning in SDN. Specifically we propose a model for admission control with flow aggregation and develop the analysis techniques for determining the required amounts of bandwidth and buffer space at OpenFlow-enabled switches for meeting performance requirements in delay and packet loss. Network calculus is applied in our modeling and analysis; which makes our method applicable to general OpenFlow-based SDNs with various implementations. Numerical experiment results are also provided to evaluate effectiveness of the developed modeling and analysis techniques.

Spectrum sensing is an essential process in cognitive radio networks. The majority of existing sensing approaches aim to detect the existence of a signal on a busy channel without differentiating whether a signal originates from a primary user or not. In this paper, we address this issue and propose to employ an Angle of Arrival (AoA) based sensing approach to effectively distinct a primary user's signal from a secondary user' signal. Multiple Signal Classification (MUSIC), a classical AoA algorithm, is selected due to easy implementation and high resolution. Unlike the previous works on AoA based sensing, we thoroughly investigate its sensing performance based on a practical model which captures typical characteristics in cognitive radio networks. Two performance metrics named false alarm probability and miss detection probability are theoretically analyzed. Closed-form analytical expressions are derived for both metrics. Extensive simulations are carried out under various scenarios to evaluate AoA based sensing approach.

The large amount of data collected in the Internet of Things (IoT) need to be transmitted to servers for processing in order to provide various services. Due to the limited amount of resources in IoT, including network bandwidth, node processing abilities, and server capacities, congestion control in IoT plays a crucial role for meeting service performance requirements. In this paper, we propose a model for congestion control in IoT with an improved Random Early Discard (IRED) algorithm. We employ queueing theory to analyze the performance of the proposed control mechanism. We also conduct extensive simulations to evaluate performance of the proposed control and compare it with regular RED algorithm. Our analysis and simulation results show that the proposed control achieves comparable delay performance and better throughput performance compared to standard RED. The simple control mechanism of IRED makes it more suitable to be implemented in IoT.

Device-to-Device (D2D) communication is envisioned as a promising technology to significantly improve the performance of current cellular infrastructures. Allocating resources to the D2D link, however, raises an enormous challenge to the co-existing D2D and cellular communications due to mutual interference. While there have been many resource allocation solutions proposed for D2D underlaying cellular network, they have primarily focused on the intracell scenario while leaving the intercell settings untouched. In this paper, we investigate the resource allocation problem for intercell D2D communications underlaying cellular networks, where D2D link is located in the overlapping area of two neighboring cells. We present three inter-cell D2D scenarios regarding the resource allocation problem. To address this problem, we develop a repeated game model under these scenarios. Distinct from existing works, we characterize the communication infrastructure, namely Base Stations (BSs), as players competing resource allocation quota for D2D demand, and define the utility of each player as the payoff from both cellular and D2D communications using radio resources. We also propose a resource allocation algorithm and protocol based on the equilibrium derivations. Numerical results indicate that the developed model not only significantly enhances the system performance including sum rate and sum rate gain, but also sheds lights on resource configurations for intercell D2D scenarios.

Expanding our knowledge about human mobility is essential for building efficient wireless protocols and mobile applications. Previous human mobility studies have typically been built upon empirical single-source data (e.g., cellphone or transit data), which inevitably introduces a bias against residents not contributing this type of data, e.g., call detail records cannot be obtained from the residents without cellphone activities, and transit data cannot cover the residents who walk or ride private vehicles. To address this issue, we propose and implement a novel architecture mPat to explore human mobility using multi-source data. A reference implementation of mPat was developed at an unprecedented scale upon the urban infrastructures of Shenzhen, China. The novelty and uniqueness of mPat lie in its three layers: (i) a data feed layer consisting of real-time data feeds from 24 thousand vehicles, 16 million smart cards and 10 million cellphones; (ii) a mobility abstraction layer exploring the correlation and divergence among the multi-source data to analyze and infer human mobility; and (iii) an application layer to improve urban efficiency based on the human mobility findings of the study. The evaluation shows that mPat achieves a 75% inference accuracy, and that its real-world application reduces passenger travel time by 36%.

The Internet of Things (IoT) has been realized as one of the most promising networking paradigms that bridge the gap between the cyber and physical world. Developing green deployment schemes for IoT is a challenging issue since IoT achieves a larger scale and becomes more complex so that most of the current schemes for deploying wireless sensor networks (WSNs) cannot be transplanted directly in IoT. This paper addresses this challenging issue by proposing a deployment scheme to achieve green networked IoT. The contributions made in this paper include: 1) a hierarchical system framework for a general IoT deployment, 2) an optimization model on the basis of proposed system framework to realize green IoT, and 3) a minimal energy consumption algorithm for solving the presented optimization model. The numerical results on minimal energy consumption and network lifetime of the system indicate that the deployment scheme proposed in this paper is more flexible and energy efficient compared to typical WSN deployment scheme; thus is applicable to the green IoT deployment.

Network Calculus (NC) is a powerful means to provide deep insight for flow problems in network performance analysis. Multi-priority scheduling as one of the fundamental models in NC has become an active research topic recently. However, existing works consider neither the arrival interval of the flows nor their arrival orders; thus limiting their applications to only a few delicate scenarios. In this paper, we address these two issues and propose a novel multi-priority model based on the non-preemptive priority scheduling. We derive the theoretical formulation for the service curve under this model, and then obtain the upper bounds of delay and backlog for multi-priority scheduling. We also use two representative case studies to show the correctness and effectiveness of the proposed model. The theoretical analysis is further validated by the numerical experiments. In addition, we discuss the factors that may affect the delay and backlog bounds according to the numerical results.

Cloud computing is one of the most significant latest efforts in the field of information technology, which may change the way how information services are provisioned. In a Cloud environment, different types of resources need to be virtualized as a collection of Cloud services using virtualization technology. End-users in the Cloud are usually provided with customized Cloud services that involve not only different kinds of computing services but also the networks interconnecting those computing services. Therefore, a set of Cloud computing services and the networking services should be modeled as a composite customized Cloud service. In this paper, we present an improved model for Cloud service provisioning based on our previous Network-Cloud proposal, and propose a procedure with several QoS-aware service selection algorithms for composing different services offered by a Cloud. Our analysis with numerical experiments show that the presented algorithms can select services appropriately that deal with different requirements of service provisioning.

Cloud computing is an emerging computing paradigm that may change the way how information services are provisioned. Network virtualization plays a crucial role in a Cloud environment for abstracting and virtualizing various network infrastructures as services. Therefore virtual network services should be integrated with Cloud service to form the composite Cloud service. However, little research has focused on modeling and optimization of network virtualization in Cloud service provisioning to end users. In this paper, we model the Cloud service provisioning feature in virtualization-based Cloud computing, and propose an exact algorithm for QoS-aware service composition to optimize user's experiences for Cloud service access. Our theoretical analysis indicates that the proposed algorithm is light-weighted and cost-effective. We also compare the proposed algorithm against a variant of the best-known QoS routing algorithm experimentally. The results show that the proposed algorithm has better performance both in execution time and finding solution. We believe that the modeling technique and the algorithm presented in this paper are general and effective, thus are applicable to practical Cloud computing systems.

Routing with end-to-end Quality-of-Service (QoS) guarantees is a key to the widespread deployment of recent emerged services. Developing QoS routing algorithm in the network is an important open topic. This paper investigates the QoS routing related problems and proposes a Fully Polynomial Time Approximation Scheme (FPTAS) for QoS routing. In the proposed FPTAS, a graph-extending based dynamic programming approach is developed, and an extended version of the proposed algorithm is studied. The theoretical analyses show that the proposed algorithms outperform the previous best-known studies.

Finding a path that satisfies multiple Quality-of-Service (QoS) requirements is vital to the deployment of current emerged services. However, existing QoS routing algorithms are not very efficient and effective at finding such path. Moreover, few works focus on two or more QoS constraints. In this paper, we propose an effective fully polynomial approximation scheme (FPAS) for multiconstrainted path optimal problem based on the technique of auxiliary graph construction. By employing the nonlinear definition of the path constraint and limited iteration of the FPAS itself, our FPAS can not only achieve the complexity reduction but generate a preferable path as well. We further analyze the Markov properties of the entire network and obtain some key parameters to reflect the routing characteristic. We experiment with different scale of random networks and compare our FPAS against previous well known studies. Our results show that FPAS can find path with lower complexity and better quality.