Unmanned Aerial Vehicles (UAVs) have emerged as relay nodes to alleviate multi-user traffic congestion in wireless communication networks due to their flexibility, fast deployment and line-of-sight links. However, the selfish attribute of both users and UAVs presents significant challenges to the efficient participation of UAVs in multiple relay tasks, particularly with conflicting profit objectives. In this article, we propose a double auction-based incentive mechanism that incentivizes UAVs to engage in relay tasks while achieving a strategic trade-off between network throughput and market fairness. Specifically, UAVs (sellers) provide relay services to ground users (buyers), with base station acting as an auctioneer responsible for matching user and UAV, charging fees from users, and giving payments to UAVs. The presence of heterogeneous users and UAVs complicates the relay tasks processing. Additionally, designing an effective payment strategy that meets economic properties further exacerbates the difficulty. To address these challenges, we propose a throughput maximization algorithm to enhance network communication efficiency and design a winner and pricing determination algorithm to establish a reasonable market. The simulation results demonstrate that our mechanism achieves higher transaction quantity than benchmark schemes (McAfee, Candidate critical pair, and Vickrey-Clarke-Groves (VCG)), motivating UAVs to participate in relay tasks for users.

Summary In postdisaster rescue scenario, unmanned aerial vehicles (UAVs) are effective tools to help ground users in disaster areas to transmit rescue‐critical data to the relief center in time due to their flexible mobilities and fast deployment. However, how UAVs choose disaster areas to take part in traffic offloading with limited bandwidth resources is challenging. This article investigates UAVs' access selection and disaster areas' bandwidth allocation scheme by jointly optimizing network throughput, bandwidth and UAVs' energy cost. The UAVs form coalitions to participate in traffic offloading cooperatively, where each UAV selects a disaster area independently. Specifically, the bandwidth resources are dynamically allocated to all disaster areas when a new coalition partition forms. The UAVs adjust their access choices for disaster areas when the bandwidth allocation changes. When no UAVs migrate to other disaster areas, the network achieves a stable state. The UAVs fly to the final selected disaster area and provide traffic offloading services. To resolve traffic offloading competitions yet enable cooperations among UAVs, we address the UAVs' access selection issue by coalition formation game. A gradient projection method is then proposed to allocate bandwidth resources which maximizes the benefit of the network. We demonstrate that the UAVs' access selection and disaster areas' bandwidth allocation algorithms are convergent. The simulation results demonstrate that our proposed cooperation order is better than the pareto and selfish orders, thereby increasing the benefit of the network.

Internet of Things (IoT) devices can reduce their energy consumption by computation offloading. However, IoT devices located in areas without deployed ground communication facilities face significant challenges in computation offloading. For this reason, we propose the space-air-terrestrial integrated networks (SATINs) and design a three-tier computing framework for providing computing services to IoT devices. In the computing framework, device's task can be computed locally, on mobile edge computing (MEC) servers in the air layer, or on cloud servers in the ground. In this article, we jointly optimize the computation offloading decisions of tasks and computing resource allocation of MEC servers. We aim at minimizing the total cost of executing tasks while satisfying both the time constraints of tasks and capacity constraints of MEC servers. And the total cost includes the energy cost of IoT devices and the usage cost of servers. Since the computation offloading decisions are binary variables, the joint optimization problem is a mixed integer nonlinear programming (MINLP) problem and is NP-hard. To tackle this problem, we use relaxation technique and Majorize-minimize (MM) method to transform the optimization problem into a series of convex problems for solving. Moreover, we propose a distributed algorithm based on Lagrange dual decomposition method with low time complexity. Experimental results demonstrate that our proposed distributed algorithm can effectively reduce the total cost of the system compared with other benchmark algorithms.

The Satellite-Terrestrial Integrated Networks (STINs) is considered as a reliable and agile next-generation communication network scheme, because it is flexible to deploy and robust to disasters (e.g., earthquakes, floods, and volcanic eruptions). The routing and resource allocation directly affect the power consumption of satellites in the STINs. However, most of existing research works study these two issues separately, and neglect the impact of path selection on the satellite power consumption. This paper investigates the joint optimization problem of routing, bandwidth allocation, user association, channel allocation, and power allocation of the STINs. We aim at minimizing satellites’ power consumption while satisfying user's Quality of Service (QoS). Because the power consumption of satellites for routing in the space segment is involved with the data rate through satellite gateways in the user segment, this optimization problem is a Multi-Objective Optimization (MOO) problem. To this end, we convert the MOO problem to a Single-Objective Optimization (SOO) problem by linear weighted method. Considering that the SOO problem is a Mixed Integer Nonlinear Programming (MINLP) problem, we decompose it into the resource allocation sub-problem in the user segment and the routing optimization sub-problem in the space segment. And we solve these two sub-problems iteratively. Specifically, we convert the first sub-problem into a convex optimization problem and obtain the current user association and channel allocation. Then we formulate the second sub-problem into a multi-commodity flow problem and solve it based on the obtained user association and channel allocation. The SOO problem is solved by iteratively optimizing these two sub-problems. Experimental results demonstrate that our proposed algorithm is able to get an approximate optimal solution quickly and effectively reduce the power consumption of satellites compared with benchmark algorithms in the STINs.

The energy harvesting of unmanned aerial vehicle (UAV) has been researched extensively in recent years. However, the existing energy harvesting between the base station and UAVs does not consider the information asymmetry factor, which means the base station provides the radio frequency (RF) energy for UAVs in the context of UAVs’ partial private information. In order to maximize the base station’s utility or payoff, it is crucial for the base station to motivate more UAVs to harvest RF energy. In the paper, we propose an effective incentive energy harvesting mechanism in UAV communication network, which is a challenging problem since there exist interest conflicts that the base station and UAVs are rational individuals who maximize their utilities. Our objective is to make the base station’s utility maximum via balancing the tradeoff between transmit power cost and charged price benefit, while incentivizing UAVs to purchase transmit power. We design a series of optimal energy harvesting contract with different price discounts targeting different types of UAVs by contract theory. Owing to information asymmetry, we analyze two different information scenarios: complete and incomplete information. We suppose the base station knows each UAV’s type in complete information, then we analyze the practical case that the base station is aware of incomplete information of UAV’s private information. The base station aims to maximize its utility by providing contract. The UAVs choose the contract meeting the individual rationality (IR) and incentive compatibility (IC) rules while maximizing their utilities. Our simulation shows that the energy harvesting mechanism maximizes the base station’s utility and stimulates UAVs to purchase RF energy transmit power in different scenarios. Compared with other methods, our proposed optimal contract can improve the utility of the base station while maximizing the utility of UAVs.

For the cloud market, we proposed a Dynamic Combinatorial Double Auction (DCDA) model to improve the social welfare and resource utilization. In the model, cloud-agents represent cloud service providers, and user-agents represent cloud users. They bid for various combinations of resources in a dynamic environment. To overcome the computational complexity of combinatorial auctions, we employed a greedy approximation method to solve the winner determination problem together with a truthful payment scheme. The proposed model is proven to be approximately efficient, incentive compatible, individually rational, and budget-balanced. Considering both parties' interests and the relative scarcity of cloud resources, this model also ensures fairness and balances resource allocation.

Nowadays, Internet usage has become prevalent, primarily because of high-quality heterogeneous multimedia content expectations from the subscriber (consumer), which puts tremendous pressure on the publisher (producer) in the networks. Information-Centric Networking (ICN) is a future internet architecture that optimizes data resources through content-based forwarding and caching, making it well-suited for multimedia content and video streaming (VS) scenarios. However, real-time data delivery is challenging in the current ICN-based publish–subscribe (pub-sub) mechanism, which pushes the existing pub-sub studies to prioritize more on the forwarding information base (FIB) rather than the pending interest table (PIT). This leads to issues such as inefficient caching and forwarding mechanisms, high overhead, and communication costs. To address these challenges, in this paper, we present a novel forwarding and caching solution named VS-ICSDN, integrating the combined principles of ICN-based pub-sub scheme and software-defined networking (SDN) in order to utilize the network resources more efficiently. We design a clean-slate caching strategy and name-based forwarding method to support both on-path and off-path caching on ICN nodes to coordinate flow entries among the SDN controller and clean-slate ICN nodes to maximize PIT utilization. In addition, the framework allows the content to be stored and searched in chunks with a single request to access the desired content, reducing the communication overhead and significantly improving overall performance. A simulation-based testbed and experimental result analysis validate our proposed work’s effectiveness in ensuring efficient network resource usage with low communication overhead and computational cost compared to other baseline methods.

In a post-pandemic era with personal precautions and vaccination, the emergence of COVID-19 variants with higher transmissibility and the socio-economic reopening have raised new challenges to existing human-to-human digital contact tracing systems, where privacy, efficiency, and energy-consumption issues are major concerns. In this article, we propose a novel blockchain-based human-to-infrastructure contact tracing framework for the post-pandemic era. Specifically, our approach collects and records the interaction information between persons and predeployed anchor nodes to trace the possible contacts with confirmed patients, so as to capture the indirect contacts and reduces the energy consumption of users. To address the privacy leakage and reliability issues in contact tracing, we introduce a self-sovereign identity (SSI) model-based blockchain which enables users to gain full control of their own identities and eliminate the linkage between the identity and location information in interaction records. To further preserve the privacy of confirmed patients, we introduce the private set intersection cardinality (PSI-CA) protocol to estimate the risk of infection by only counting the number of encounters between users and confirmed patients. Two self-executed smart contracts are deployed on the SSI blockchain to perform contact tracing, which guarantees the robustness of the system. The performance analysis validates the effectiveness of our approach.

Blockchain-based trust management has attracted great attention for vehicular networks due to its decentralized, transparent, and tamper-proof natures. However, the highly dynamic vehicular environment challenges the reliability of trust evaluation as well as the privacy preservation of vehicles against tracking attacks. In this paper, we propose a privacy-preserving trust management system to evaluate the trustworthiness of vehicles by exploiting the recent advanced blockchain techniques. Specifically, we build up a trust evaluation blockchain, where the trustworthiness of an involved vehicle is evaluated by distributed road-side units (RSUs) based on the rating feedback from neighboring vehicles. To enable efficient and privacy-preserving trust evaluation, we deploy the feedback messages aggregation and trust evaluation on two smart contracts, which are executed and verified by distributed RSUs automatically. In particular, identity authentication based on Elliptic Curve Cryptography (ECC) cryptosystem is introduced to prevent privacy leakage of vehicles. Security analysis and performance evaluation reveal that our system is secure and efficient to manage the trust evaluation while guaranteeing privacy-preservation for vehicular networks.

Traditional health insurance schemes make contracts with periodic premiums according to the health conditions of customers. Insurance companies are thus seeking an incentive for a healthy lifestyle of customers. Nowadays, more and more people want to gather metrics associated with physical activity. High quality and quantity information introduced by wearable devices may indicate the potential risks of acute conditions. Therefore, wearable technology brings new opportunities for insurance companies to change the interaction way with customers and to improve risk management for users. In this article, we investigate the problem of incentive mechanism design for the insurance market boosted by wearable technology. Designing an effective incentive mechanism is challenging since both the insurer and the users are selfish and rational parties who try to maximize their own utility. There exists an information asymmetry, i.e., the insurer is unaware of the intrinsic physical fitness of each user (type of users). Inspired by the contract theory, we design a series of optimal insurance contracts with different premium discounts and recommended exercise levels targeting different types of users. We first analyze the scenario where the insurer knows the type of each individual, then move to the more practical case where the insurer has incomplete information of user types. We theoretically prove that the proposed contracts satisfy individual rationality and incentive compatibility, which enables the insurer to achieve utility maximization and each type of user to choose the most appropriate contract. We have conducted extensive simulations to provide insight into the performance under different scenarios. The numerical results verify that our contract-based insurance incentive mechanism maximizes insurer's utility and motivates users to exercise more.

With the ever-increasing demands on decentralization and transparency of cloud storage, CP-ABE (Ciphertext Policy-Attribute-Based Encryption) has become a promising technology for blockchain-enabled data sharing methods due to its flexibility. However, real-world blockchain applications usually have some special requirements like time restrictions or power limitations. Thus, decryption outsourcing is widely used in data sharing scenarios and also causes concerns about data security. In this paper, we proposed a secure access control scheme based on CP-ABE, which could share contents during a particular time slot in blockchain-enabled data sharing systems. Specifically, we bind the time period with both ciphertexts and the keys to archive the goal of only users who have the required attributes in a particular time slot can decrypt the content. Besides, we use time slots as a token to protect the data and access control scheme when users want to outsource the decryption phase. The security analysis shows that our scheme can provide collusion resistance ability under a time restriction, and performance evaluations indicate that our scheme uses less time in decryption compared to other schemes while ensuring security.

The fifth-generation (5G) and beyond networks are designed to efficiently utilize the spectrum resources to meet various quality of service (QoS) requirements. The unlicensed frequency bands used by WiFi are mainly deployed for indoor applications and are not always fully occupied. The cellular industry has been working to enable cellular and WiFi coexistence. In particular, 5G New Radio in unlicensed channel spectrum (NR-U) supports the uplink and downlink transmission on the maximum channel occupation time (MCOT) duration. In this paper, we consider maximizing the total throughput of both downlink and uplink in NR-U by jointly optimizing the time and power allocation during MCOT while ensuring fair coexistence with WiFi. Fairness is guaranteed in two steps: 1) tuning the access related parameters of NR-U to achieve proportional fairness, and 2) including 3GPP fairness from the throughput perspective as a constraint in NR-U throughput maximization. Numerical analysis and simulation have demonstrated the superior performance of the proposed resource allocation algorithm compared to conventional deployment strategies.

Cellular vehicle‐to‐everything‐ (C‐V2X‐) based communications can support various content‐oriented applications and have gained significant progress in recent years. However, the limited backhaul bandwidth and dynamic topology make it difficult to obtain the multimedia service with high‐reliability and low‐latency communication in C‐V2X networks, which may degrade the quality of experience (QoE). In this paper, we propose a novel cluster‐based cooperative cache deployment and coded delivery strategy for C‐V2X networks to improve the cache hit ratio and response time, reduce the request‐response delay, and improve the bandwidth efficiency. To begin with, we design an effective vehicle cluster method. Based on the constructed cluster, we propose a two‐level cooperative cache deployment approach to cache the frequently requested files on the edge nodes, LTE evolved NodeB (eNodeB) and cluster head (CH), to maximize the overall cache hit ratio. Furthermore, we propose an effective coded delivery strategy to minimize the network load and the ratio of redundant files. Simulation results demonstrate that our proposed method can effectively reduce the average response delay and network load and improve both the hit ratio and the ratio of redundant files.

As vehicles are becoming more and more intelligent, mobile data traffic in vehicular ad hoc network (VANET) has been increasing dramatically. This makes the communication capacity of VANET systems and the computing resources of vehicles insufficient. In the meantime, location-aware large-scale distributed services with very low latency and high reliability are demanded by most of the novel functions, such as accident alarming, and congestion warning, in the intelligent transportation system. To meet these claimed characteristics of VANET, we first present a novel architecture that integrates vehicular fog computing and vehicle-to-vehicle (V2V) communication technologies. Lower latency and higher quality services can be supplied to vehicles by nearby fog servers, which are virtualized from vehicles that locate close enough and communicate using the V2V link. However, like all collaborative systems, computing reliability is vital to collaborative VANET. In this article, we design a novel energy-efficient proactive replication mechanism. Follower vehicles calculate with a lazy rate act as backups of host vehicles to ensure the reliability of the system. Considering the time sensitivity of computing requirements in VANET, the upper bound on the total number of failures is proposed through theoretical analysis. Then, the lower bound on the lazy calculating rate of followers is derived by balancing the tradeoffs between delay and energy. A fast algorithm for searching this lower bound based on the discrete Newton method is also proposed. Results of numerical experiments show that our new mechanism is effective in energy saving and reliability enhancing.

Cloud computing integrates a large number of resources through virtualization technology, and then provides users with personalized services on an on-demand basis. In response to this service model, this paper draws on the economic theories and proposed a game-based combinatorial double auction model for cloud resource allocation. Firstly, through Harsanyi transformation, the incomplete information game for cloud resource allocation is converted into a complete but imperfect information game, and the Bayesian Nash equilibrium solution is obtained. Then, we designed the resource allocation model supporting multiple infrastructure providers and service providers bidding on various combinations of resources. Considering both parties' interests, this model ensures fairness and high resource utilization. Simulation results show that this method not only forms a fair incentive mechanism for all parties in the transaction, but also optimizes the social welfare.

In this paper, we study the channel assignment problem in cellular-VANET heterogeneous wireless networks. The D2D communication technology can be applied to VANET. Vehicular device-to-device (D2D) network as an underlying network to the cellular network can share the uplink channel resources of the cellular network. Interference as a critical element has an impact on the utilization in channel assignment. To minimize the interference when allocating channels, we present a novel channel assignment algorithm based on reuse distance. Essentially we have limited spectrum resources that can be shared by vehicular transmitters and cellular users in an area, to assign the minimal number of channels to vehicles in a prescribed area is our first concern. Since the interference between co-channel devices is related to their distance, we divide the area to small hexagon regions then use Region-based Channel Assignment Algorithm to assign different channel sets to each region. In this case, three sets of resources can fulfill the channel assignment requirements to all vehicles. We also prove the theoretical guarantee as approximation factor of 3 for the minimal channel assignment problem. To improve the system throughput with limited channel resources in the HetNets, we propose a Local Search Throughput Maximization algorithm to find the vehicular transmitters and cellular users combinations. We prove the optimal approximation factor is (1-ε) and the complexity of our algorithm in each small region. We show the effectiveness and efficiency of proposed algorithm in experiments.

Summary In view of the huge scale of cloud computing, cloud computing systems face many problems in terms of scalability and high availability. In view of the distributed data storage in the cloud computing environment, a recursion based N boundary network model is constructed. A data management model is given on the basis of the data center network structure. A replica distribution strategy and a copy selection strategy are designed to improve the data availability and load balance. On the premise of guaranteeing data availability, a data migration algorithm based on coverage set is proposed, which uses node selection strategy to reduce the migration cost as much as possible. Data migration makes more machines dormant and reduces energy consumption. By comparing and analyzing the experimental data, the correctness and effectiveness of the proposed network topology, data management model, and data migration technology are verified. In the edge cloud computing architecture, in order to ensure the response time of the cloud computing service and minimize the data redundancy in the system, this paper proposes and analyzes the data migration strategy and the pre stored data migration strategy for the network transmission, and gives the suitable application scene. On this basis, in order to control the balance of access network transmission and storage flexibly, a data migration strategy based on network performance is proposed, which ensures the real‐time response of the service.

Due to the dynamically changing topology of Internet of Vehicles (IoV), it is a challenging issue to achieve efficient data dissemination in IoV. This paper considers strongly connected IoV with a number of heterogenous vehicular nodes to disseminate information and studies distributed replication-based data dissemination algorithms to improve the performance of data dissemination. Accordingly, two data replication algorithms, a deterministic algorithm and a distributed randomised algorithm, are proposed. In the proposed algorithms, the number of message copies spread in the network is limited and the network will be balanced after a series of average operations among the nodes. The number of communication stages needed for network balance shows the complexity of network convergence as well as network convergence speed. It is proved that the network can achieve a balanced status after a finite number of communication stages. Meanwhile, the upper and lower bounds of the time complexity are derived when the distributed randomised algorithm is applied. Detailed mathematical results show that the network can be balanced quickly in complete graph; thus highly efficient data dissemination can be guaranteed in dense IoV. Simulation results present that the proposed randomised algorithm outperforms the present schemes in terms of transmissions and dissemination delay.

In Vehicular Ad Hoc Networks (VANETs), using multihop broadcast to achieve fast and reliable dissemination of event-driven emergency messages among vehicles has positive significance. Most existing solutions make relay selection by either senders or receivers. However, the former would fall into invalidation due to the unstable links, while the latter would generate extra delay. In this paper, we propose a new mechanism named MBM-EMD (i.e., multihop broadcast mechanism for emergency messages dissemination) to solve these problems. Our contributions are two-fold. i) We integrate multiple influencing factors such as vehicle density, relative movement, and channel quality into a new metric named ETGPH (i.e., the expected transmission gain per hop), and use it to select the optimal relays. ii) After acquiring the candidate set for further rebroadcast, the current sender firstly appoints an optimal relay from it to rebroadcast immediately. Other candidates will assist in forwarding according to our rules if this relay fails. The simulation results demonstrate the feasibility and effectiveness of the proposed solution.

In vehicular ad hoc networks, it is common for vehicles to inform their current moving states which include a vehicle's position, velocity and moving direction to their neighbors by using beacon messages periodically. However, high frequency of beacon broadcasting may lead to heavy channel load and congestion in certain scenarios. In this paper, a mobility prediction based beacon rate adaptation (MPBR) scheme is proposed to decrease beacon frequency and alleviate bandwidth consumption. By tracking their neighbors using the predicted moving states instead of using periodic beacon broadcasting, vehicles decrease the beacon rate significantly. Whenthe prediction error is higher than the threshold due to accumulation of tiny errors, a beacon broadcasting will be triggered. In order to further reduce the channel load and congestion,we classify the road traffic status into three categories to determine the value of error threshold. The simulation results clearly demonstrate the efficiency of our proposed approach.

GPU cloud computing is emerging as a new type of cloud service that drives computation-extensive jobs, such as big data analytics and distributed machine learning. The introduction of GPU brings parallel processing power at the cost of excessive energy consumption. Dynamic Voltage and Frequency Scaling (DVFS) is a promising method to control energy consumption of GPU VMs. This work focuses on using DVFS to reduce energy of cloud computing in datacenter demand response. We first consider an online demand response scenario where users arrive stochastically, aiming at maximizing social welfare and meeting energy reduction goals by employing DVFS. We address the challenge posed by DVFS through a new technique of compact infinite optimization. A more practical scenario where both energy and resource limitations present is further studied. We design a primal-dual approximation algorithm that can compute a feasible solution in polynomial time with guaranteed approximation ratio, and a payment scheme that works in concert to form a truthful cloud job auction.

Network function virtualization (NFV) represents the latest technology advancement in network service provisioning. Traditional hardware middleboxes are replaced by software programs running on industry standard servers and virtual machines, for service agility, flexibility, and cost reduction. NFV users are provisioned with service chains composed of virtual network functions (VNFs). A fundamental problem in NFV service chain provisioning is to satisfy user demands with minimum system-wide cost. We jointly consider two types of cost in this work: nodal resource cost and link delay cost, and formulate the service chain provisioning problem using nonlinear optimization. Through the method of auxiliary variables, we transform the optimization problem into its separable form, and then apply the alternating direction method of multipliers (ADMM) to design scalable and fully distributed solutions. Through simulation studies, we verify the convergence and efficacy of our distributed algorithm design.

With the emerging demand for road safety and entertainment applications, efficient information exchanges among vehicles in Vehicular Ad-hoc Networks (VANETs) have attracted considerable attention. Since VANETs usually suffer from intermittent connectivity, long delay, and packet loss, Road-Side Unit (RSU) placement has been introduced to improve communication performance recently. Although some researchers have focused on scheduling and routing of buses, efficient communication in public transportation systems with infrastructure has not been well studied. In this paper, we use a space-time graph to model topology changes in Bus Ad-hoc Networks (BANETs), and then propose an effective greedy algorithm to minimize the number of RSUs, such that the communication delay between any two buses is within a given delay bound in BANETs. To evaluate the proposed scheme, we conduct simulations and analyze the performance. The simulation results show that our algorithm can significantly reduce the number of installed RSUs and the average end-to-end delay in the entire network.

Mobile crowdsensing (MCS) is emerging as a new paradigm to perform crowdsourcing tasks. While incentive mechanisms for mobile users in the MCS market are well studied, there is a lack of study on the competition of crowdsourcers for limited sensing resources and data processing capacity. In this paper, we consider a MCS market, where multiple crowdsourcers bid for sensing resources provided by an MCS platform to perform crowdsourcing tasks. The MCS platform allocates sensing resources to selected crowdsensing tasks and charge crowdsourcers corresponding prices. To begin with, we formulate the sensing resource allocation as a social welfare maximization problem, and propose a primal-dual approximation algorithm that achieves a feasible solution in polynomial time with guaranteed approximation ratio. Then, we design a payment scheme to ensure the truthfulness and individual rationality of the auction. Both theoretical analysis and simulation results verify the efficiency and effectiveness of our proposed auction mechanism for resource provisioning in the MCS market.

Propagation delay has an important impact on system performance in long-distance wireless networks. While there is not much attention paid to taking the advantage of the differential propagation delays. In this paper, we propose a cross-layer delay-aware random access (DARA) for applying for time slots to transmit data in long-distance wireless networks. Based on Irregular Repetition Slotted ALOHA (IRSA), DARA takes propagation delay into consideration and makes a better time slot selection strategy. Simulation results show that DARA could achieve 30% improvement at most compared to IRSA in respect to the rate of successful decoding. Meanwhile, we try to optimize the rate of successful decoding with minimal cost, but find it is hard to formulate the relationship among the number of mobile hosts M, the number of slots N and the rate of successful decoding. At last, curve-fitting is utilized to get an approximative result.

Collective I/O is a widely used middleware technique that exploits I/O access correlation among multiple processes to improve I/O system performance. However, most existing implementations of collective I/O strategies are designed and optimized for homogeneous I/O systems. In practice, the homogeneity assumptions do not hold in heterogeneous parallel I/O systems, which consist of multiple HDD and SSD-based servers and become increasingly promising. In this paper, we propose a heterogeneity-aware collective-I/O (HACIO) strategy to enhance the performance of conventional collective I/O operations. HACIO reorganizes the order of I/O requests for each aggregator with awareness of the storage performance of heterogeneous servers, so that the hardware of the systems can be better utilized. We have implemented HACIO in ROMIO, a widely used MPI-IO library. Experimental results show that HACIO can significantly increase the I/O throughputs of heterogeneous I/O systems.

Auction mechanisms have recently been studied as an efficient approach for dynamic resource allocation in a cloud market. Existing mechanisms are mostly limited to the offline setting or execute jobs in continuous time slots. This work focuses on a practical case of online auction design, where users bid for future cloud resources for executing their batch processing jobs with hard deadline constraints. We design an online primal-dual auction framework for Virtual Machine (VM) allocation with social welfare maximization, which is truthful, computationally efficient, and guarantees a small competitive ratio. We leverage the framework of post price auctions to design our online primal-dual algorithm, where a bid is accepted if its expected execution cost in future time slots is smaller than its bidding price. We interpret the dual variables as marginal prices per unit of resource, and iteratively update it according to the allocated amount of resource. Theoretical analysis and trace-driven simulation studies validate the efficacy of the online auction framework, including both its computational efficiency and economic efficiency.

Software Defined Networking (SDN) has emerged as a new paradigm that offers the programmability required to dynamically configure and control a network. In this paper, we present a path assignment method based on load balancing(PA-LB) for a centralized controller to calculate optimal end-to-end virtual paths over the underlying network infrastructure, considering multiple requests and load balance simultaneously. In the paper, theoretical analysis shows that the path selection is NP-hard, and strongly polynomial time inapproximable under the assumption P not equal to NP. We also give a constant-factor polynomial time approximation algorithm for this problem under some bounded parameters. Extensive simulation results, over a wide range of underlying network topologies and input parameters, demonstrate that the proposed method outperforms traditional shortest path first (SPF) approaches not only in paths mapping number, but also in network load balance. Under the condition of limited resources, such as bandwidth, the proposed method has a better performance with the increase of the number of nodes. Moreover, optimal load balance can be kept in the network, in order to avoid network congestion and minimize the average packet transmission latency.

Network Function Virtualization (NFV) is emerging as a new paradigm for providing elastic network functions through flexible virtual network function (VNF) instances executed on virtualized computing platforms exemplified by cloud datacenters. In the new NFV market, well defined VNF instances each realize an atomic function that can be chained to meet user demands in practice. This work studies the dynamic market mechanism design for the transaction of VNF service chains in the NFV market, to help relinquish the full power of NFV. Combining the techniques of primal-dual approximation algorithm design with Myerson's characterization of truthful mechanisms, we design a VNF chain auction that runs efficiently in polynomial time, guarantees truthfulness, and achieves near-optimal social welfare in the NFV eco-system. Extensive simulation studies verify the efficacy of our auction mechanism.

To protect cloud data against corruption, enabling data integrity checking along with error recovery becomes quite critical. Recently, regenerating code has caught researchers' attention due to low repair traffic while preserving fault tolerance. Existing integrity checking schemes for regenerating code storage only support static data and have high auditing cost, which are not suitable for practical application. In this work, We propose a public auditing scheme for regenerating code based cloud storage supporting data dynamics. Our scheme allows the auditor to check the integrity of data stored across multiple servers and identify the location of corrupted data when data corruption is detected within one round. To support data dynamics, we design a novel update method based on matrix and index mechanism. The update method support multiple update requests performed in batch way without downloading and re-coding the data file and re-computing the data tags, such that the communication and computation cost can be greatly reduced. Theoretic analysis and experimental results show the security and efficiency of our proposed scheme.

Abstract Ciphertext‐policy attribute‐based encryption is becoming a cryptographic solution to realize fine‐grained access control. However, some important problems have not been effectively solved yet such as (i) low efficiency of dynamic change of a user or his attribute and (ii) high computation cost at user end. In this paper, we propose an access control scheme with dynamic user management using a version key and especially realize direct cloud‐aided attribute revocation without updating another user's key or re‐encrypting ciphertexts. We present a cloud‐aided decryption method with which most of decrypting work can be transferred to cloud. Compared with the existing schemes, our scheme causes less computation cost at user end and supports efficient dynamic change of a user or his attribute. Simulation indicates that our direct cloud‐aided attribute revocation method takes less time. Copyright © 2017 John Wiley & Sons, Ltd.

This paper addresses the delay-bounded minimal cost roadside units (RSUs) placement problem in vehicular ad hoc networks. There are two types of RSUs: cable connected RSU (c-RSU) and wireless RSU (w-RSU). c-RSUs are interconnected through wired lines, and they form the backbone of VANETs. They also usually have a larger communication range due to the availability of power source and more powerful devices. Despite the benefit of fast information dissemination, c-RSUs are often associated with high cost. On the other hand, w-RSUs connect to other RSUs through wireless communication and typically have a smaller transmission range. Given a set of candidate sites in a region and a delay bound, the problem is how to find the optimal placement of c-RSUs and w-RSUs, such that the total cost is minimized, while all of the vehicles in the region can receive the message sent out from c-RSUs within the delay bound. We first prove that the problem is NP-hard. Then, we propose a greedy algorithm and a two-phase algorithm to solve the problem. Simulation results show our proposed algorithms can significantly reduce the total cost, compared with other methods.

Cloud storage enables users to remotely store their data and share the data through the cloud. Existing integrity auditing schemes for shared data are often not identity-preserving and have high auditing cost, and hence are still far from practical application. In this work, we propose a public auditing scheme for shared data in cloud storage with identity privacy preservation. To preserve identity privacy against the auditor, we convert signatures computed by different users into signatures computed by the challenge user with proxy re-signature. Our scheme supports user revocation without re-signing signatures computed by revoked users, while the integrity of shared data can still be correctly checked. Furthermore, the auditing is efficient in the sense that the number of pairing operations during auditing is independent of the number of challenged blocks and users. We further present a batch auditing supporting multiple auditing delegations from different groups instead of only the same group. Security analysis demonstrates that our scheme is provably secure. Numeric analysis and simulation results show that both computation and communication costs of our scheme are lower than in existing schemes.

Wireless data broadcast is an efficient way to disseminate data to a large number of users in mobile communication environments. In many applications, such as stock quotes, flight schedules and traffic news, the users may want to download multiple data items at one time and the applications may require the support of multi-channel architectures. This paper studies the problem of retrieving a set of data items from multiple wireless broadcasting channels such that all the requested data items can be downloaded with the least access time, namely Least Time Data Retrieval (LTDR). On one hand, we find that it is NP-hard to approximate this problem to within any non-trivial factor even if there are only two channels. On the other hand, we find that there exists a polynomial time (1+1/1-α)-approximation solution for an LTDR instance L if the optimal solution of L requirers τ time slots and (1-α)kT ≤ τ ≤ kT, where k is a positive integer, α is an arbitrary constant between 0 and 1, and T is the least common multiple of cycle lengths for all channels. We also derive a lower bound of polynomial time approximation for L matching that upper bound under the well known assumption P ≠ NP. To improve the practical efficiency of the approximation solution, a heuristic is exhibited and tested by simulations.

In this paper, we investigate the problem of optimal road side units (RSUs) placement in Vehicular Ad Hoc Network (VANET) on a highway, which enables the VANET maintain a good connectivity. Our goal is to find out minimal number of road side units, such that the vehicles could communicate with RSUs. These road side units are connected by wire. We develop a randomized algorithm to deploy road side units in the VANET. It gives an approximation to the optimal distance to guarantee the information can be passed to RSUs from the accident site via the VANET. Simulations are conducted to show the performance of our proposed method.

Wireless interference can make a significant impact on the performance of a wireless network. The paper studies TDMA-based timeslot allocation to schedule links efficiently under RTS/CTS interference model of wireless networks, and presents an efficient centralized timeslot assignment algorithm. In addition, the paper presents an effective heuristic algorithm for calculating end-to-end bandwidth on a path, which is used together with AODV to setup QoS routes. Simulation results show that compared with the shortest path routing, our interference-aware QoS routing algorithm increases success ratio of finding a route with required bandwidth to destination node.

This paper investigates topology control and seeks to find a distributed solution with low interference, high performance. The paper proposes a distributed topology control algorithm called CBDTG by using the method of adjusting transmission power and the achievements of computational geometry, especially Delaunay triangulation (DT). Each node in the network invokes the neighbor discovery process by using an initial power and adds the discovered neighbors into its neighbor set. Each node finds DT neighbors in order to generate a triangulation with good properties such as planarity and regularity. The experimental result shows that the average node degree is between that of basic CBTC and the condensed CBTC with several edges removed, and the resulting topology will not suffer from high interference due to high node degree, nor will it suffer from low connectivity because of extremely low node degree. CBDTG also outperforms the basic CBTC in throughput.

In this paper, we investigate the issues of QoS multicast routing in ad hoc wireless networks. Due to limited bandwidth of a wireless node, a QoS multicast call could often be blocked if there does not exist a single multicast tree that has the requested bandwidth, even though there is enough bandwidth in the system to support the call. In this paper we propose a multicast routing scheme by using multiple paths or multiple trees to meet the bandwidth requirement of a call. Three multicast routing strategies are studied, SPT (shortest path tree) based multiple-paths (SPTM), least cost tree based multiple-paths (LCTM) and multiple least cost trees (MLCT). The final routing tree(s) can meet the user's QoS requirements such that the delay from the source node to the furthest destination node shall not exceed the bound and the aggregate bandwidth of the paths or trees shall meet the bandwidth requirement of the call. Extensive simulations have been conducted to evaluate the performance. The simulation results show that the new scheme has three major advantages: 1) it greatly reduces the system blockings; 2) multicast routing is in a fully distributed fashion; 3) the proposed routing protocol follows the format of existing on-demand multicast routing protocols for ad hoc networks, which makes it easy to be incorporated into the existing on-demand routing protocols