Immutability, decentralization, and linear promoted scalability make sharded blockchain a promising solution, which can effectively address the trust issue in the large-scale Internet of Things (IoT). However, currently, the throughput of sharded blockchains is still limited when it comes to high proportions of cross-shard transactions (CST). On the other hand, assemblage characteristics of collaborative computing in IoT have not been received attention. Therefore, in this paper, we present a clustering-based sharded blockchain strategy for collaborative computing in the IoT, where the sharding of the blockchain system is implemented in two steps: k-means clustering-based user grouping and the assignment of consensus nodes. In this framework, how to reasonably group the IoT users while simultaneously guaranteeing the system performance is the key point. Specifically, we describe the data transactions among IoT devices by data transaction flow graph (DTFG) based on a dynamic stochastic block model. Then, formed as a Markov decision process (MDP), the optimization of the cluster number (shard number) and the adjustment of consensus parameters are jointly trained by deep reinforcement learning (DRL). Simulation results show that the proposed scheme improves the scalability of the sharded blockchain in the IoT application.

Driven by numerous emerging mobile devices and various Quality-of-Service (QoS) requirements, mobile-edge computing (MEC) has been recognized as a prospective paradigm to promote the computation capability of mobile devices, as well as reduce energy overhead and service latency of applications for the Internet of Things (IoT). However, there are still some open issues in the existing research works: 1) limited network and computing resource; 2) simple or nonintelligent resource management; and 3) ignored security and reliability. In order to cope with these issues, in this article, 6G and blockchain technology are considered to improve network performance and ensure the authenticity of data sharing for the MEC-enabled IoT. Meanwhile, a novel intelligent optimization method named as collective reinforcement learning (CRL) is proposed and introduced, to realize intelligent resource allocation, meet distributed training results sharing, and avoid excessive consumption of system resources. Based on the designed network model, a cloud–edge collaborative resource allocation framework is formulated. By joint optimizing the offloading decision, block interval, and transmission power, it aims to minimize the consumption overheads of system energy and latency. Then, the formulated problem is designed as a Markov decision process, and the optimal strategy can be obtained by the CRL. Some evaluation results reveal that the system performance based on the proposed scheme outperforms other existing schemes obviously.

Immutability, decentralization, and linear promoted scalability make the sharded blockchain a promising solution, which can effectively address the trust issue in the large-scale Internet of Things (IoT). However, currently, the throughput of sharded blockchains is still limited when it comes to high proportion of cross-shard transactions (CSTs). On the other hand, the assemblage characteristic of the collaborative computing in IoT has not been received attention. Therefore, in this article, we present a clustering-based sharded blockchain strategy for collaborative computing in the IoT, where the sharding of the blockchain system is implemented in two steps:K-means-clustering-based user grouping and the assignment of consensus nodes. In this framework, how to reasonably group the IoT users while simultaneously guaranteeing the system performance is the key point. Specifically, we describe the data transactions among IoT devices by data transaction flow graph (DTFG) based on a dynamic stochastic block model. Then, formed as a Markov decision process (MDP), the optimization of the cluster number (shard number) and the adjustment of consensus parameters are jointly trained by deep reinforcement learning (DRL). Simulation results show that the proposed scheme improves the scalability of the sharded blockchain in the IoT application.

For group signature (GS) supporting membership revocation, verifier-local revocation (VLR) mechanism seems to be a more flexible choice, because it requires only that verifiers download up-to-date revocation information for signature verification, and the signers are not involved. As a post-quantum secure cryptographic counterpart of classical number-theoretic cryptographic constructions, the first lattice-based VLR group signature (VLR-GS) was introduced by Langlois et al. (2014). However, none of the contemporary lattice-based VLR-GS schemes provide backward unlinkability (BU), which is an important property to ensure that previously issued signatures remain anonymous and unlinkable even after the corresponding signer (i.e., member) is revoked. In this study, we introduce the first lattice-based VLR-GS scheme with BU security (VLR-GS-BU), and thus resolve a prominent open problem posed by previous works. Our new scheme enjoys an $${\cal O}\left( {\log \,N} \right)$$ factor saving for bit-sizes of the group public-key (GPK) and the member’s signing secret-key, and it is free of any public-key encryption. In the random oracle model, our scheme is proven secure under two well-known hardness assumptions of the short integer solution (SIS) problem and learning with errors (LWE) problem.

Heterogeneous networks (HetNets) with end-to-end (E2E) network slicing are regarded as effective approaches to meet diverse service requirements from vertical industries. Due to the dense deployment of base stations (BSs) and the complicated associations between BSs and E2E network slices (NSs) in the scenario, the handoff problem faces challenges of the huge system state space and handoff action space and the considerable communication overhead. In this paper, we take these issues into account and consider a distributed E2E NS handoff decision framework in the HetNet. A decentralized Markov decision process (DEC-MDP)-based model is formulated for the distributed E2E NS handoff problem, and the jointly observable and random characteristics of the DEC-MDP are analyzed. To obtain a theoretical performance reference, the original distributed E2E NS handoff problem is simplified, and a Nash equilibrium-based performance bound is given. More practically, the multi-agent double deep Q-network-based distributed handoff (MA-DDQN-DH) algorithm with the centralized training and decentralized executing framework is proposed. Simulation results show that the Nash equilibrium-based performance bound is reasonable, and the proposed MA-DDQN-DH algorithm performs well in the comparison.

Nowadays, the rise of the Internet of Vehicles (IoV) has led to the rapid development of smart transportation. To increase the computing capacity of mobile vehicles and decrease the content delivery latency of suppliers, mobile edge computing (MEC) is considered as an indispensable solution. However, there are some essential issues to be considered: 1) security and privacy of data transmission, and 2) reasonable resource allocation for collaborative computing and caching. In this paper, to solve above issues, blockchain technology is adopted to ensure reliable transmission and interaction of data. Meanwhile, we develop an intelligent resource framework about computing and caching for blockchain-enabled MEC systems in IoV. Through jointly considering and optimizing offloading decision of computation task carried by vehicle, caching decision, the number of offloaded consensus nodes, block interval and block size, the energy consumption and computation overheads can be decreased, and the data throughput of the blockchain can be increased significantly. Moreover, the proposed optimization problem is modeled and formulated as a Markov decision process. Facing the complexity and dynamic of resource allocation, the asynchronous advantage actor-critic approach is considered and applied to solve the optimization problem. Experiment results demonstrate that the advantages of the proposed optimization scheme are obvious compared with other existing schemes.

Recently, electric vehicles (EVs) have been widely used under the call of green travel and environmental protection, and diverse requirements for charging are also increasing gradually. In order to ensure the authenticity and privacy of charging information interaction, blockchain technology is proposed and applied in charging station billing systems. However, there are some issues in blockchain itself, including lower computing efficiency of the nodes and higher energy consumption in the consensus process. To handle the above issues, in this paper, combining blockchain and mobile edge computing, we develop a reliable billing data transmission scheme to improve the computing capacity of nodes and reduce the energy consumption of the consensus process. By jointly optimizing the primary and replica nodes offloading decisions, block size and block interval, the transaction throughput of the blockchain system is maximized, as well as the consumption costs of latency and energy consumption is minimized. Moreover, we formulate the joint optimization problem as Markov decision process (MDP). To tackle this dynamic and continuity of the system state, the actor–critic reinforcement learning is introduced to solve the MDP problem. Finally, simulation results demonstrate that the performance improvement of the proposed scheme through comparison with other existing schemes.

In this paper, a super-resolution enhancement model based on skip residual dense net(SRDN) is proposed. We design a model with a two-channel skip residual dense nets to extract deeper feature information. The two channels have the same network structure and are connected by skip connections. The model first divide the input image into two components by a guided filtering. The features of two components is learned by one convolution layer and a three layers double-channel SRDN network respectively. Then model uses the concatenation operation to combine two channels’ feature. Finally, the super-resolution image is obtained by the up-sampling network and one convolution operation. Different to the classical loss function, we define a joint loss functions for training, which is consisted of content loss, perceptual loss and color discrimination loss. The experimental results show that the proposed algorithm achieves better super-resolution visual results ans objective evaluation indicators.

Accurate and efficient ship detection in remote sensing images still remains a challenging task due to the large variations of scales, orientations and distributions. In this paper, we propose an anchor-free ship detector that directly regresses ship localization parameters, offering a simpler pipeline over the previous methods. The detection network is then trained in a multi-task fashion which contains not only the ship center-point maps and oriented bounding boxes but the ship masks. Instead of fixing the weights among the multiple task losses, we adopt a curriculum learning strategy which gradually adapts the loss weights during the training process so that the network can learn the discriminative ship features at the early stage and obtain more localization information while training continues. Experimental results on real dataset demonstrate the effectiveness and efficiency of our proposed method.

End-to-end network slicing, as a key technology in 5G and B5G mobile communication systems, is to enable traditional wireless networks to support different services in vertical industries. In a heterogeneous cellular network (HetNets) with network slicing functions, due to the dense deployment of base stations (BSs) and the mobility of user equipments (UEs), dynamically switching network slices (NSs) is necessary for better system performance. This paper models the handoff problem of end-to-end NS as a Markov decision process (MDP) maximizing the utility related to the UE's profit of being served, the handoff cost and the outage penalty. Both the states of the radio access network resources and the core network resources of each end-to-end NS are considered. The deep reinforcement learning (DRL) is adopted as the solution, and a double deep Q network (DQN) based NS handoff algorithm is designed. Numerical results confirm the convergence of the DQN used to make handoff decisions and show that compared with typical handoff algorithms, the algorithm we proposed performs the best from the aspect of the cumulative reward designed in this paper.

Industrial Internet of Things (IIoT) has emerged with the developments of various communication technologies. In order to guarantee the security and privacy of massive IIoT data, blockchain is widely considered as a promising technology and applied into IIoT. However, there are still several issues in the existing blockchain-enabled IIoT: 1) unbearable energy consumption for computation tasks; 2) poor efficiency of consensus mechanism in blockchain; and 3) serious computation overhead of network systems. To handle the above issues and challenges, in this article, we integrate mobile-edge computing (MEC) into blockchain-enabled IIoT systems to promote the computation capability of IIoT devices and improve the efficiency of the consensus process. Meanwhile, the weighted system cost, including the energy consumption and the computation overhead, are jointly considered. Moreover, we propose an optimization framework for blockchain-enabled IIoT systems to decrease consumption, and formulate the proposed problem as a Markov decision process (MDP). The master controller, offloading decision, block size, and computing server can be dynamically selected and adjusted to optimize the devices energy allocation and reduce the weighted system cost. Accordingly, due to the high-dynamic and large-dimensional characteristics, deep reinforcement learning (DRL) is introduced to solve the formulated problem. Simulation results demonstrate that our proposed scheme can improve system performance significantly compared to other existing schemes.

Recently, the development of the Internet of Things (IoT) provides plenty of opportunities and challenges in various fields. As an essential part of IoT, machine-to-machine (M2M) communications open a novel way that the machine-type communication devices (MTCDs) are connected and communicated without any human intervention. Meanwhile, delay-tolerant data play an important role in M2M communications-based IoT, and it puts more emphasis on powerful data caching, computing, and processing, as well as the security and stability of data transmission. To meet these requirements in M2M communications networks, in this article, we introduce some promising technologies, such as edge computing and blockchain, and propose a joint optimization framework about caching, computation, and security for delay-tolerant data in M2M communications networks based on dueling deep Q-network (DQN). According to the dynamic decision process by DQN, the optimal selection and decision of caching servers, computing servers, and blockchain systems can be made to achieve maximum system rewards, which includes higher efficiency of data processing, lower network costs, and better security of data interaction. Extensive simulation results with different system parameters show that our proposed framework can effectively improve the system performance for blockchain-enabled M2M communications compared to the existing schemes.

Attribute-based encryption (ABE) can support a fine-grained access control to encrypted data. When the user’s secret-key is compromised, the ABE system has to revoke its decryption privileges to prevent the leakage of encrypted data. Although there are many constructions about revocable ABE from bilinear maps, the situation with lattice-based constructions is less satisfactory, and a few efforts were made to close this gap. In this work, we propose the first lattice-based server-aided revocable attribute-based encryption (SR-ABE) scheme and thus the first such construction that is believed to be quantum resistant. In the standard model, our scheme is proved to be secure based on the hardness of the Learning With Errors (LWE) problem.

In data center networks based on SDN, mice-flows are latency-sensitive and packet loss sensitive. Meanwhile, they account for the majority of traffic in the network, most flow rules are installed to direct the forwarding of mice-flows. According to the characteristics mentioned above, this paper proposes a joint balancing flow table and reducing delay (BFTRTD) scheme for mice-flows in data center networks to efficiently utilize limited flow tables and minimize the delay for mice-flows. In this scheme, a novel evaluation index for table balance is proposed to balance flow tables, combining with the delay of the path to initialize routes. In addition, this paper also adopts the uptodate flow rules installation mechanism to further guarantee the transmission quality and delay of mice-flows. We evaluated the proposed BFTRTD in terms of average packet loss rate and average delay of mice-flows. Simulation results show that, compared with ECMP and DIFF- Mice, the proposed BFTRTD scheme reduces the average packet loss rate by an average of 4.5% and 5.9%, while decreases the average delay by an average of 4.1% and 4.7%, when the flow arrival rate is between 120 Mbit/min and 280 Mbit/min where network load goes from low to high.

Recently, mobile applications such as augmented reality, virtual reality and face recognition have become more and more ubiquitous. These delay- sensitive applications demand intensive computation and high energy consumption. However, mobile devices have limited battery power and computation resources that influence the quality of experience (QoE) of mobile users. Mobile edge computing (MEC) has become a key technology to meet these demands. The crucial challenges regarding MEC paradigm are computation offloading decision, spectrum and computation resource allocation for offloading users. In order to deal with these challenges, this paper formulates a joint delay-energy optimization problem by jointly considering spectrum resource Allocation, computation resource Allocation and computation Offloading decision (AAO). Further, we transform the original problem into convex optimization and solve the optimization problem through an alternating direction method of multipliers (ADMM) algorithm in a distributed way. Finally, simulation results validate the efficiency of the proposed AAO in saving delay and energy consumption.

Recent advances in Internet of Things (IoT) provide plenty of opportunities for various areas. Nevertheless, the machine-to-machine (M2M) communications-based IoT develops rapidly but suffers from extra energy consumption, large data transmission latency as well as overmuch network cost, because various of machine-type communication devices (MTCDs) are deployed in the network. To meet the requirements of energy efficient M2M communications, in this paper, we introduce a promising technology named as mobile edge computing (MEC), and propose a performance optimization framework with MEC for M2M communications network based on deep reinforcement learning (DRL). According to dynamic decision process by DRL, the appropriate access networks and the computing servers can be determined and selected with the minimum system cost, which includes lower network cost, time cost and energy consumption for data transmission and computing tasks execution. Extensive simulation results with different system parameters show that our proposed framework can effectively improve the system performance for M2M communications compared to the existing schemes.

In this paper, an optimal control algorithm for connected vehicle systems is proposed in order to ensure the vehicular platoon stable as well as reduce the transmission power consumptions in the presence of the network-induced delays. First, the vehicle dynamic modeling and power consumption analysis are addressed based on a typical 3-vehicle platoon. With the objective of minimizing the deviations of vehicle's headway and velocity as well as reducing power consumption, an optimization problem is formulated using a quadratic cost function. Then, the design of the optimal control strategy with minimum power consumption for connected vehicle systems can be divided into two steps: first the minimal hop routings for human- driven vehicles are obtained based on the network topology, and then the optimal control strategy is derived based on the determined transmission routing.

In most cases, the batteries of sensor nodes in the Internet of Things (IoT) are usually constrained by size and weight, and are difficult to recharge or replace. In traditional wireless sensor networks, data is transmitted in a multi-hop manner, which may cause the high data transmission delay and unbalanced traffic load. In this paper, an Unmanned Aerial Vehicle (UAV)-assisted IoT architecture is introduced, in which UAV is utilized to achieve low-latency and seamless-coverage acquisition of the sensing data. Furthermore, based on the recent advances on deep reinforcement learning algorithms, considering both data delay requirements and network energy consumption, a real-time flight path planning scheme of the UAV in the dynamic IoT sensor networks has been proposed based on dueling deep Q-network (DQN). Besides, the grid-based method is used to handle the network state modeling, which effectively reduces the complexity of the proposed scheme. Simulation results show that the proposed scheme significantly improves the network performance.

With an increasing number of machine-type communication devices (MTCDs), machine-to-machine (M2M) communications have attracted great attentions from both academia and industry. Different from traditional communication networks, the data connections with M2M communications are typically small-sized but with high frequency, necessitating the efficiency optimization of both energy consumption and computation. In this paper, we introduce mobile edge computing (MEC) into virtualized cellular networks with M2M communications, to decrease the energy consumption and optimize the computing resource allocation as well as improve computing capability. Moreover, based on different functions and quality of service (QoS) requirements, the physical network can be virtualized into several virtual networks, and then each MTCD selects the corresponding virtual network to access through the embedded-SIM (eSIM) technology. Meanwhile, the random access process of MTCDs is formulated as a partially observable Markov decision process (POMDP) to minimize the system cost, which consists of both the energy consumption and execution time of computing tasks. Furthermore, to facilitate the network architecture integration, software-defined networking (SDN) is introduced to deal with the diverse protocols and standards in the networks. Extensive simulation results with different system parameters reveal that the proposed scheme could significantly improve the system performance compared to the existing schemes.

In this paper, we propose a joint dynamic pilot and data power allocation scheme for time division duplex (TDD) massive multiple-input multiple-output (MIMO) systems, so as to both adaptively mitigate pilot contamination and balance the mutual interference. Due to the unknown of instant channel state information before pilots, we exploit the Gauss-Markov process of temporally-correlated channels and use the Kalman filter to not only filter out the pilot contamination but also provide the priori estimation values. Subsequently, the deterministic approximation of the rate is derived as a function of the priori channel estimation and the priori estimate errors, and accordingly the rate-profile maximization to achieve max-min fairness is formulated. To deal with this optimization coupled across the pilot power and data power as well as the users, we give an iterative alternating rate-suboptimal algorithm composed of two sub-problems, both of which are further solved by introducing the successive convex approximation (SCA) methods and slack variables. Numerical results confirm the improved rate provided by the proposed scheme.

With the explosion in the number of connected devices and Internet of Things (IoT) services in smart city, the challenges to meet the demands from both data traffic delivery and information processing are increasingly prominent. Meanwhile, the connected vehicle networks have become an essential part in smart city, bringing massive data traffic as well as significant networking, caching and computing resources. In this paper, we propose a novel vehicle network architecture, mitigating the network congestion with the joint optimization of networking, caching and computing. Cloud computing at the data centers as well as mobile edge computing (MEC) at the evolved node Bs (eNodeBs) and on-board units (OBUs) are taken as the paradigms to provide caching and computing resources. The programmable control principle originated from software-defined networking (SDN) paradigm has been introduced to facilitate the system architecture and resource integration. With the careful modeling of the services, the vehicle mobility and the system state, a joint resource management scheme is proposed and formulated as a partially observable Markov decision process (POMDP) to minimize system cost, which consists of both network overhead and execution time of computing tasks. Extensive simulation results with different system parameters reveal that the proposed scheme could significantly improve the system performance compared to the existing schemes.

Recent advances in networking, caching and computing technologies can have great impacts on the developments of green heterogeneous wireless networks, where different sizes of cells co-exist. Nevertheless, these important enabling technologies have traditionally been studied separately in the existing works on wireless networks. In this paper, we propose an integrated framework that can enable dynamic orchestration of networking, caching and computing resources to improve the performance of green heterogeneous wireless networks. We use an energy-efficient caching strategy based on storing maximum-distance separable (MDS) encoded packets. The resource allocation strategy in this framework is formulated as a joint optimization problem. The decision on how to allocate the dynamic resources is very complicated when considering networking, caching and computing. Therefore, we propose a novel deep reinforcement learning approach, which can effectively handle systems with large complexity. In addition, we use Google TensorFlow to implement deep reinforcement learning. Simulation results with different system parameters are presented to show the effectiveness of the proposed scheme.

The Fourth Industrial Revolution we are experiencing currently is reshaping the world by facilitating factories with intelligence and significantly improved manufacturing efficiency and flexibility. Among the key technologies to achieve Industrie 4.0, industrial wireless networking enables convenient and reliable connections among the machines, network devices, cloud servers and humans for both delay-sensitive traffic and delay-tolerant data delivery. In this paper, the Cognitive radio and Edge computing based Industrial wireless Network (CEIN) is introduced. In CEIN, edge computing handles the processing requirements of the data during its transmission, and is deployed close to the machines for immediate response to delay-sensitive industrial data that requires real-time processing. Cognitive radio technologies are also adopted to ensure efficient spectrum resource utilization for big delay- tolerant data transmission that contributes mostly to the industrial data traffic. Besides, we propose an optimal networking and computing resource management scheme for CEIN. The harvested spectrum bands are allocated to the network devices taking into account of the computing requirements of industrial data. Stochastic optimization is adopted to find the optimal allocation actions with low on-line computational complexity. Extensive simulation results are also presented to demonstrate the significant system performance improvement.

As an important part of the Internet-of-Things (IoT), machine-to-machine (M2M) communications have attracted great attention. In this paper, we introduce mobile edge computing (MEC) into virtualized cellular networks with M2M communications, to decrease the energy consumption and optimize the computing resource allocation as well as improve computing capability. Moreover, based on different functions and quality of service (QoS) requirements, the physical network can be virtualized into several virtual networks, and then each MTCD selects the corresponding virtual network to access. Meanwhile, the random access process of MTCDs is formulated as a partially observable Markov decision process (POMDP) to minimize the system cost, which consists of both the energy consumption and execution time of computing tasks. Furthermore, to facilitate the network architecture integration, software-defined networking (SDN) is introduced to deal with the diverse protocols and standards in the networks. Extensive simulation results with different system parameters reveal that the proposed scheme could significantly improve the system performance compared to the existing schemes.

Recent advances in networking, caching and computing have significant impacts on the developments of vehicular networks. Nevertheless, these important enabling technologies have traditionally been studied separately in the existing works on vehicular networks. In this paper, we propose an integrated framework that can enable dynamic orchestration of networking, caching and computing resources to improve the performance of next generation vehicular networks. We formulate the resource allocation strategy in this framework as a joint optimization problem. The complexity of the system is very high when we jointly consider these three technologies. Therefore, we propose a novel deep reinforcement learning approach in this paper. Simulation results are presented to show the effectiveness of the proposed scheme.

Indoor localization has attracted much attention recently, due to its wide applications in location-based services(LBSs). Localization accuracy and system costs are the key issues while designing indoor localization schemes. In this paper, an edge big data-enabled indoor localization scheme is proposed. We use the radio signal strength (RSS) information that is always available wherever WiFi coverage is available, to avoid the costs on deploying and maintaining specific devices for indoor localization. Bayesian theory and edge computing are adopted in our system, so that big localization data is collected and utilized to update the prior location probabilities. A testbed, BJUTLocate, is built to evaluate the performance of the proposed scheme, and the evaluation results show its significant performance improvement.

Machine-to-machine (M2M) communications have attracted great attention from both academia and industry. In this paper, with recent advances in wireless network virtualization and software- defined networking (SDN), we propose a novel framework for M2M communications in software- defined cellular networks with wireless network virtualization. In the proposed framework, according to different functions and quality of service (QoS) requirements of machine-type communication devices (MTCDs), a hypervisor enables the virtualization of the physical M2M network, which is abstracted and sliced into multiple virtual M2M networks. Moreover, we formulate a decision-theoretic approach to optimize the random access process of M2M communications. In addition, we develop a feedback and control loop to dynamically adjust the number of resource blocks (RBs) that are used in the random access phase in a virtual M2M network by the SDN controller. Extensive simulation results with different system parameters are presented to show the performance of the proposed scheme.

To deal with the rapid growth of mobile data traffic and the user interest shift from peer-to-peer communications to content dissemination-based services, such as video streaming, information-centric networking has emerged as a promising architecture and has been increasingly used for wireless and mobile networks. In this paper, we focus on video dissemination in information-centric cognitive radio networks (IC-CRNs) and investigate the use of harvested bands for proactively caching video contents at the locations close to the interested users to improve the performance of video distribution. With consideration of the dynamic and unobservable nature of some parameters, we formulate the allocation of harvested bands as a Markov decision process with hidden and dynamic parameters and transform it into a partially observable Markov decision process and a multi-armed bandit formulation. Based on them, we develop a new spectrum management mechanism, which maximizes the benefit of proactive video caching as well as the efficiency of spectrum utilization in the IC-CRNs. Extensive simulation results demonstrate the significant performance improvement of the proposed scheme for video streaming.

Abstract Cyber‐physical system has been widely used in various areas as the integration of computing and physical system. As a typical application of cyber‐physical system, cyber‐physical surveillance system for transportation (CPSST) allows real‐time video monitoring to facilitate the deploying of smart transportation systems. For video streaming in CPSST, dynamic radio spectrum management is a key technology dealing with the current situation that the spectrum resource is almost used up. In this paper, taking into account the application layer quality of service and wireless link security, a novel dynamic spectrum management scheme has been proposed to minimize the system cost of CPSST. Video distortion is considered as the application layer quality of service metric, and the system cost is defined as a combination of distortion and security cost. We use intra‐refreshing rate in video coding to minimize the distortion. Furthermore, the problem is formulated as a restless bandit system, which uses current and historical information to optimize the action, with the objective of maximizing the total discounted system reward. We also describe the two spectrum management processes. Extensive simulation results are presented to demonstrate the significant performance improvement of the proposed scheme compared with the existing one that ignores video distortion and subband security optimization. Copyright © 2014 John Wiley & Sons, Ltd.

Abstract Two ring signature schemes over number theory research unit (NTRU) lattices are presented. The first scheme constructed in the random oracle model is an extension of Ducas, Lyubashevsky, and Prest's identity‐based encryption scheme over NTRU lattices (in Asiacrypt 2014). Moreover, motivated by Boyen's lattice mixing and vanishing trapdoors (in PKC 2010), the second scheme in the standard model is achieved. Under the chosen‐message attack, our new constructions are proved strongly existentially unforgeable, and the security can be reduced to the hardness of NTRU lattices. Compared with the existing lattice‐based ring signatures, our schemes are more efficient and with shorter signature length. Copyright © 2016 John Wiley & Sons, Ltd.

Recently, unmanned aerial vehicle (UAV) cooperation networks have attached much attention due to their successful applications in complex military and civilian missions. In this paper, we propose a navigation data-assisted optimal opportunistic spectrum access scheme for wireless communications in heterogeneous UAV networks, to achieve maximized data rate by flexibly scheduling the spectrum subbands. The system architecture is introduced, and thanks to the navigation data that is always available locally at the entities in the network, prediction of wireless link quality and routing information can be obtained to assist subband allocations. Furthermore, the spectrum allocation process is formulated as an optimization problem. Simulation results are also presented to demonstrate the significant performance improvement of the proposed scheme compared to the existing one.

This paper is concerned with the challenging problem of joint channel estimation and data detection for high mobility multiple-input multiple-output orthogonal frequency division multiplexing systems. We propose a new iterative channel estimation and detection scheme, which reduces the unexpected effects of both detection errors and channel estimation errors. Detection errors in channel estimation are analyzed and transformed as part of the noise. To filter this equivalent noise by Kalman estimator, we derive the covariance of both the channels and data errors in detection. Besides, we propose a new detection algorithm with an optimized weight to minimize the detection error caused by channel estimation errors. To obtain this optimized weight, the error covariance of the estimated channels is derived from the error estimate covariance matrix in Kalman estimator. Simulation results are presented to demonstrate the significant performance improvement in joint channel estimation and data detection with the proposed iterative scheme.

In mobile cloud computing (MCC) systems, the resource in both the cloud and the mobile network should be carefully managed. Cloud resource management and radio resource management have traditionally been addressed separately in previous works. In this paper, we propose to jointly study dynamic cloud and radio resource management so as to improve end-to-end performance of adaptive video transmissions in MCC systems. Video application quality of service performance, distortion, is adopted as the performance measure. An important video application layer parameter, intra-refreshing rate, is optimized to improve the video distortion performance. We formulate the problem as a stochastic restless bandits optimization problem, which facilitates the distributed MCC architecture and simplifies the computation and implementation due to its “indexibility” property. Simulation results are presented to show the effectivenes of the proposed scheme.

Cyber-physical system (CPS) has been widely used in various areas as the integration of computing and physical system. As a typical application of CPS, cyber-physical surveillance system (CPSS) allows real-time video monitoring for various fields such as smart transportation and warehouse management systems. For video streaming in CPSS, dynamic radio spectrum management is a key technology dealing with the current situation that the spectrum resource is almost used up. In this paper, taking into account the application layer quality-of-service (QoS) and wireless link security, a novel dynamic spectrum management scheme is proposed to minimize the system cost of CPSS. Video distortion is considered as the application layer QoS metric, and the system cost is defined as a combination of distortion and security cost. We use intra-refreshing rate in video coding to minimize the distortion. Furthermore, the problem is formulated as a restless bandit system, which uses current and historical information to optimize the action, with the objective of maximizing the total discounted system reward. We also describe the spectrum management operation processes. Extensive simulation results are presented to demonstrate the significant performance improvement of the proposed scheme compared with the existing one that ignores video distortion and subband security optimization.

In cognitive radio (CR) networks, there is a common assumption that the secondary devices always obey the spectrum access rules and are under full control. However, this may become unrealistic for future CR networks composed of intelligent, complicated and autonomous devices. To solve this problem, the concept of “proactive-optimization” cognitive radio (POCR) is proposed in this paper, in which the highly-intelligent secondary users proactively optimize their own behavior decisions according to the available information including device state and network condition to maximize their long-term reward. Furthermore, we propose an optimal transmission behavior decision scheme for secondary users in POCR networks considering imperfect spectrum channel sensing results. Specifically, we formulate the system as a partially-observable Markov decision process (POMDP) problem. With this formulation, a low complexity dynamic programming framework is introduced to obtain the optimal behavior policy. Extensive simulation results are presented to illustrate the significant performance improvement of the proposed scheme compared with the existing one that ignores the secondary user behavior optimization.

In Cognitive Radio (CR) networks, there could be a spectrum market which operates in real time with primary users (PU) as manager, where the secondary users (SU) pay the PUs for spectrum resource usage. Multi-carrier systems such as OFDM are best candidates for applying in CR networks because of the spectrum shaping and high adaptive capabilities. In this paper, an optimal resource allocation scheme aims at maximizing PU's reward in OFDM-based CR networks is proposed. Both the interference limit and BER requirements of SUs are considered by the PU to allocate its spectrum resources. The scheme is modeled as restless bandits problem, which can dramatically simplify the computation and implementation. Furthermore, extensive simulation results show that our proposed scheme can improve the reward of PU significantly compared to the existing random scheme and greedy scheme.

The phenomenon of Triangle inequality violations (TIVs) is very common in the Internet today. And it will remain as a property of the Internet for the foreseeable future. Though TIVs may bring some problems to the research on the Internet, they can also be used by us. We suggest that TIVs can help to find the relay hosts in order to get the faster paths. We show that TIVs can be detected by using their clustering feature and then we can use this. At last, we simulate such a distributed methodology to show that it is efficient.

Spectrum pooling, which allows the secondary networks to utilize the available spectrum bands from different licensed networks, is one of the most promising technologies for dynamic spectrum sharing in cognitive radio systems. Most previous work on spectrum pooling concentrates on the system architecture and the design of flexible access algorithms and schemes. In this paper, a distributed scheme for optimal internetwork spectrum sharing among multiple cognitive radio systems is proposed. Besides, the spectrum access price and spectrum efficiency are considered as the design criteria in the proposed scheme. The spectrum sharing problem is formulated as a restless bandits system, which dramatically reduces the computational complexity by simply allocating the new available band to the secondary network with the lowest index. Furthermore, extensive simulation results illustrate the significant performance improvement of the proposed scheme improves compared to the existing scheme.

A bit error rate (BER) analysis of an asynchronous DS-BPSK UWB system in dense multipath channels by means of standard Gaussian approximation (SGA) and simplified improved Gaussian approximation (SIGA) is presented. The standard UWB channel (proposed by IEEE 802.15.3a task group) is employed in the analysis to reflect the actual channel conditions. Several key parameters that affect the BER performance are obtained by means of SGA. On the basis of SGA, the validity of improved Gaussian approximation (IGA) method is established in our systems and the accuracy of the bit error rate expression obtained via SIGA is demonstrated with Monte-Carlo simulations. Besides, the impacts of several different kinds of transmitted pulses on BER performance are investigated with numerical examples.

Performance evaluations of an asynchronous DS-BPSK UWB system in standard UWB channels by means of characteristic function (CF) method, standard Gaussian approximation (SGA) and simplified improved Gaussian approximation (SIGA) are presented. The standard UWB channel (proposed by IEEE 802.15.3a task group) is employed in the analysis to reflect the actual channel conditions. Firstly, a bit error rate (BER) expression obtained via CF method is presented and the availability of it is analyzed. Then, several key parameters that affect the BER performance are obtained by means of SGA. On the basis of SGA, the validity of IGA method is established in our systems and the accuracy of the BER expression obtained via SIGA is demonstrated with Monte-Carlo simulations. Besides, the impacts of several different kinds of transmitted pulses on BER performance are investigated.