Operating system in intelligent transportation systems (ITSs) is a complex software system whose correctness and security are not obvious. There are advances in formal description and verification of operating systems in ITSs recently and they mainly focus on bottom-up proofs in which the source codes satisfy certain expected properties expressed by logic formulae. In this paper, we propose a layered object model for operating systems in ITSs. This model includes functionality layer, refinement layer and concrete layer. We consider the operating system object model as a logic system ($L$) with variables representing the objects of$L$, and a series of logic formulae for security and functional configurations in security of ITSs. We establish a mathematical structure as a domain of discourse for operating system in ITSs and accordingly, construct a mapping from operating system objects to the domain. In this way, we propose a formal method to verify the operating system security properties and configurations in ITSs. We use the virtual memory management part of our self-designed operating system VSOS as an example to illustrate the model and show that the claimed security properties can be rigorously proven for ITSs. The evaluation and verification of VSOS indicate that the proposed model implementation is feasible and achieves the security goals.

Abstract As a powerful economic theory, auction mechanism has been extensively studied in dynamic spectrum allocation for cognitive radio networks (CRNs) recently. Different from most of existing works that focused on the mechanism design from the spectrum owner's side, we study from a new perspective on profit maximization of the secondary users (SUs). Because the spectrum auction mechanism has already been designed by the spectrum owner, we derive SUs' optimal bid strategies, which maximize their profits. First, we relax the limitation of SU's value on spectrum band, which is formerly defined as the transmission rate on channel, and introduce the affiliated value considering the impacts from other SUs. Further, the optimal value determination function is derived, which maximizes SU's expected profit; second, we analyze the auctioneer cheating issue, which has great influence on SU's profit, and the Nash equilibrium strategies for both spectrum owner and SUs are derived. Moreover, the repeated auction game mechanism is proposed that resists the auctioneer cheating effectively. Copyright © 2013 John Wiley & Sons, Ltd.

We study the relay-based communication schemes for cooperative spectrum sharing among multiple primary users (PUs) and multiple secondary users (SUs) with incomplete information. Inspired by the matching theory, we model the network as a matching market. In this market, each PU proposes a certain proposal representing a combination of relay power and spectrum access time to attract the SUs, while each SU maximizes its utility by selecting the most suitable PU. We derive the sufficient and necessary conditions for a stable matching in which none of the PUs or SUs would like to change its decision. We further establish a distributed matching algorithm (DMA) and a DMA with utility increasing (DMA-UI) to achieve the equilibria in partially incomplete and incomplete information scenarios, respectively. Moreover, we provide detailed discussions on the implementation of the distributed algorithms in practical networks. Simulation results show that the losses of PUs' total utilities caused by incomplete information are diminished when the number of SUs increases. Specifically, the effects of the incomplete information are reduced as the competition among SUs (PUs) is more intensive than that among PUs (SUs).

Recently, many dynamic spectrum allocation schemes based on economics are proposed to improve spectrum utilization in cognitive radio networks (CRNs). However, existing mechanisms do not take into account the economic efficiency and the spatial reusability simultaneously, which leaves room to further enhance the spectrum efficiency. In this paper, we introduce the coalition double auction for efficient spectrum allocation in CRNs, where secondary users (SUs) are partitioned into several coalitions and the spectrum reusability can be executed within each coalition. The partition formation process is not only related to the interference condition between SUs, but also the expected economic goals. Therefore, we propose a fully-economic spatial spectrum allocation mechanism by incorporating the coalition formation approach with auction theory. With the proposed scheme, the primary operator acts as an auctioneer, who performs multiple virtual auctions to form a stable partition of SUs and conducts a final auction to decide the winning SUs. Moreover, we propose a possible operation rules for the primary operator to iteratively change the partition, and prove that the virtual auctions could converge in finite time. Comprehensive theoretical analysis and simulation results are presented to show that our scheme can satisfy the crucial economic robustness properties of double auction, and outperform existing mechanisms.

Intra-cell interference management is one of the technical challenges for device-to-device (D2D) communication underlaying cellular networks. In this paper, we propose two superposition coding-based cooperative relaying schemes to exploit the transmission opportunities for the D2D users without deteriorating the performance of the cellular users. In the first scheme, the D2D transmitter (DT) is enabled to decode and regenerate the cellular signal, and transmit the cellular signal by superposing it with its own signal. In this way, the interference from the D2D pair to the cellular pair can be canceled by properly allocating time and power. To further exploit the transmission opportunity for the D2D pair, in the second scheme the cellular transmitter splits its signal into two parts and broadcasts these two parts in a superposition signal. DT relays only one part of the cellular signal. Analytic and numerical results confirm the efficiency of the proposed schemes.

In cognitive radio network, cooperation between primary users and secondary users can improve their data rate and achieve dynamic spectrum sharing. This paper proposes a novel spectrum sharing model to solve the opportunistic access problem in cooperative cognitive radio network (CCRN). In this model, primary users select sets of secondary users as their relays and allocate channel resource to secondary users according to their cooperative transmitting power. Secondary users have their own data rate requirement, and they have to determine their optimal relay power to satisfy their requirement. Primary users have to select the best relay group and maximize their throughput. We model this problem as a Stackelberg game and prove the existence and uniqueness of Nash Equilibrium. A low-complexity algorithm is proposed to realize the cooperative transmission mechanism. Simulation results show significant throughput enhancement in the primary network and more opportunities for secondary users to get access into the spectrum.

Spectrum sensing is a key approach for the realization of cognitive radio and needs to efficiently detect the presence of the licensed users to eliminate interference and find the spectrum holes for transmission. Even though the available holes have been successfully detected, how to allocate remains a challenge. In this paper, we model a scheme combining spectrum sensing and spectrum allocation to overcome these challenges. Specifically, we frame a training period and introduce Linear Discriminant Analysis (LDA) aiming at improving the accuracy of the spectrum sensing in the data fusion process. Also a channel allocation scheme is carefully designed to guarantee the fairness among secondary users (SUs) and improve the sensing performance. Besides, with this information, fusion center can balance sensing performance and spectrum utilization so as to maximize global utility. Simulations results illustrate a significant improvement in detect accuracy and the utility of fusion center and secondary users ensured by the proposed scheme.

The concept of femtocell access points underlying existing macrocell network has recently emerged as a key technology that provides better coverage and improves wireless system capacity. Among all three access control mechanism adopted by Femtocell Access Points (FAPs), Hybrid access is regarded as the most desirable because this mechanism makes it possible to allow both femto and macro end users to access femtocell network, which significantly enhances overall network performance. In this paper, we propose an incentive mechanism for the two-tier macro-femtocell network under hybrid access control, in which FAPs expect to lease licensed spectrum from the coexisting Macrocell Service Provider (MSP) to serve their end users (FUs), in return FAPs grant the MSP a fraction of Access Permission (ACP) so that macro end users (MUs) can access FAPs to increase the Quality of Service and macrocell network capacity. We introduce the concept of contract to specify the relationship between FAPs and the MSP. We study how the MSP can obtain the optimal contract by designing different ACP-spectrum combinations (i.e., the amount of spectrum sold from the MSP to FAPs and the corresponding quantity of ACP sold from FAPs to the MSP) for different types of FAPs. Numerical results show that both MSP and FAPs can benefit from this incentive mechanism in term of their utilities.

The existence of under-utilized spectrum and the congestion of certain spectrum give rise to development on cognitive radios as a promising technology to address these problems. However, the general precondition that secondary users should evacuate from the channel which primary users are willing to use, makes the cognitive ad hoc networks vulnerable and unstable since network partition may occur since the links affected by primary users may form a cut set of the network. In this paper, we address this problem through a hybrid channel assignment scheme, where channels are carefully assigned to each link to guarantee the connectivity and the capacity based on spanning trees. Since the optimal solution is NP-hard, we propose an effective approximation algorithm. Also, dynamic channel assignment is proposed to further improve the network performance.

Cognitive radio is a promising paradigm to achieve efficient utilization of spectrum resource by allowing the unlicensed users to access the licensed spectrum. Designing mechanism with proper economic incentives is essential for the success of dynamic spectrum sharing. In this paper, we study both the long-term market (i.e. contract-theoretic model) and short-term market (i.e. Stackelberg game model) between a single primary spectrum owner (PO) and multiple unlicensed secondary users (SUs) in a Market-driven secondary spectrum trading. In long-term market, we design optimal contracts, which are offered by PO. Then SUs choose whether to accept the contract based on both their demand and their types. We can show these optimal contracts maximize both PO and SUs profit. After long-term market, secondary spectrum trading enters into short-term market, where SUs can buy some amount of licensed spectrum at each time slot. We model and analyze the interactions between PO and SUs as a Stackelberg game. Finally, our simulation result demonstrates that this integrated design mechanism is effective to improve spectrum utilization and address profit maximization problem in both PO and SUs' side.

In cognitive radio networks, secondary users (SUs) are considered as selfish spectrum users, thus how to maximize the spectrum efficiency by these selfish users becomes an endless research topic. Recent studies mostly focus on the competition analysis between SUs using economic mechanism, such as game theory and auction, but the spectrum owner can hardly increase the spectrum efficiency directly when SUs apply the distributed manner. In this paper, we consider the slotted uplink scenario where several SUs have data transmitted to secondary access point (AP) under distributed random access manner. The AP decides how to divide its spectrum which maximizes the whole throughput, then SUs select the channels which would maximize their own profit. Our results show that SUs' channel selection process leads to a Nash Equilibrium, and the AP derives the proper number of channels based on the properties of NE. Moreover, we derive a rule for AP to decide which SUs should access the spectrum and lead to the increment in the whole throughput.

Cognitive radio networks (CRN) is a promising technology for the efficient utilization of spectrum resource. However, how to dynamically allocate the spectrum combining with the characteristics of CRN is a crucial challenge for the mechanism design. In this paper, from the perspective of frequency-time characteristics of the spectrum opportunity in CRN and the secondary users' flexible requirements, we model the dynamic spectrum allocation problem as a knapsack problem and prove that maximizing the social welfare is NP-hard. Therefore, a spectrum auction mechanism is developed to approximate the optimal social welfare, and we prove its approximate ratio. Further, to guarantee the truthfulness, we devise a corresponding payment scheme. In simulation results, we show that no matter how many SUs are in the CRN, our scheme can approximate the maximal social welfare perfectly, and the spectrum utilization can be improved to over 96.5% or even larger.

In Cognitive Radio (CR) networks, Primary Users (PUs) can share their idle spectrums with Secondary Users (SUs) under certain mechanisms. Centralized schemes and market-driven mechanisms are two major considerations in solving the spectrum sharing problems in the multiple-PU scenario. Centralized schemes can achieve the social optimal spectrum allocation among multiple PUs and multiple SUs but require a central control entity, which is impractical in the CR environment due to its distributed nature. Market-driven mechanisms can be implemented in a distributed manner but are hard to obtain the social optimal spectrum utilization in the multiple-PU scenario. Furthermore, many market-driven mechanisms are not economic-robust (Incentive Compatible, Individual Rational, etc). In this paper, we propose a distributed-centralized spectrum sharing scheme based on contracts for the multiple-PU scenario, which introduces a Random Leader who is elected randomly among SUs or PUs. The distributed aspect of our scheme lies in that our scheme requires no central control entities, which can be independently implemented within a distributed spectrum market. The centralized aspect is that the leader draws up and assigns the social optimal spectrum allocation contracts as well as the payment contracts for all PUs and SUs in a centralized manner. The former contracts maximize the spectrum utilization of the whole network, and the latter contracts help attain the economic robustness of our scheme. Under the leadership of the Random Leader, those non-cooperative SUs and PUs behave in a cooperative manner, which mitigates the complexity and time overhead needed for information exchange.

Energy efficiency is the core issue in wireless sensor network's scheduling algorithm design, which calls up wide attention from researchers. However, few of the previous works notice the constraints on sensors caused by limited buffer size. It challenges the system's meeting deadlines performance. To alleviate this concern, we propose a scheduling policy: the least energy and free storage remaining first, which extends wireless sensor network's lifetime and provides a high real-time service's quality. To make our strategy more practical, we consider the user's non-cooperative behavior out of self-interests. It increases the system's packet lost in buffer overflow and reduces the energy efficiency. Based on a mixed strategy game model, we show that the non-cooperative game converges to an inefficient Nash Equilibrium, where the spectrum resource is significantly wasted. In order to eliminate user's selfish manner, we design a punishment scheme via a repeated game, which considers the time-variant character of network and can detect non-cooperative action precisely and rapidly.

In this paper, we tackle the spectrum allocation problem in cognitive radio (CR) networks with time-frequency flexibility consideration using combinatorial auction. Different from all the previous works using auction mechanisms, we model the spectrum opportunity in a time-frequency division manner. This model caters to much more flexible requirements from secondary users (SUs) and has very clear application meaning. The additional flexibility also brings theoretical and computational difficulties. We model the spectrum allocation as a combinatorial auction and show that under the time-frequency flexible model, reaching the social welfare maximal is NP hard and the upper bound of worst-case approximation ratio is √m, m is the number of time-frequency slots. Therefore, we design an auction mechanism with near-optimal winner determination algorithm, whose worst-case approximation ratio reaches the upper bound √m. Further we devise a truthful payment scheme under the approximation winner determination algorithm to guarantee that all the bids submitted by SUs reflect their true valuation of the spectrum. To further address the issue and reach optimality, we simplify the general model to that only frequency flexibility is allowed, which is still useful, and propose a truthful, optimal and computationally efficient auction mechanism under modified model. Extensive simulation results show that all the proposed algorithms generate high social welfare as well as high spectrum utilization ratio. What's more, the actual approximation ratio of near-optimal algorithm is much higher than the worst-case approximation ratio.

Market based spectrum trading has been extensively studied to realize efficient spectrum utilization in cognitive radio networks (CRNs). In this paper, we utilize the concept of insurance in spectrum trading so as to improve spectrum efficiency in CRNs. We show that by additionally purchasing a specifically designed insurance contract from a PU, an SU can improve its utility since it will be insured against the potential accident, i.e., transmission failure incurred by excessively low SINR. Therefore insurance provides SUs more incentive to purchase PUs' channels and spectrum utilization in CRNs can be improved. In this paper, the original spectrum market including multiple PUs and multiple SUs are modeled as a hybrid market consisting of a spectrum market and an insurance market. In this hybrid market PUs serve as spectrum sellers as well as insurers and SUs act as spectrum buyers as well as insureds. We further model the hybrid market game as a four-stage Bayesian game between PUs and SUs. We characterize the second-best Pareto optimal (SBPO) market allocations and players' perfect Bayesian equilibrium (PBE) strategies. Furthermore, through extensive simulation, we have demonstrated that at the PBE, high risk and low risk SUs will respectively experience improvement in their utilities for approximately 23.5% and 4.6%.

Cognitive radio is promising in improving spectrum efficiency by enabling unlicensed users to access to the licensed spectrum. Spectrum auction is perceived as a potential way to realize it. Primary users (PUs) act as sellers selling unused spectrum bands and secondary users (SUs) act as buyers who intend to get spectrum bands from PUs. In situations where multiple PUs and SUs exist, double auction is a paradigm to assign spectrum. Efficiency and economic robustness are considered two essential properties in the model. Previous work often employ bid-independent uniform pricing to maintain economic robust at the substantial cost of efficiency. In this paper, we investigate the tradeoff between efficiency and robustness. We propose DIPA, a DIscriminatory Pricing double Auction for spectrum, in which bidders are charged of varying prices for the same item they purchase. We demonstrate that DIPA is robust and improves efficiency largely over the previous design.

In Cognitive Radio Networks (CRNs), Primary Users (PUs) can share their idle spectra with Secondary Users (SUs) under certain mechanisms. In this paper, we propose a distributed-centralized and incentive-aware spectrum sharing scheme for the multiple-PU scenario, which introduces a Random Leader who is elected randomly from SUs or PUs. The distributed aspect of our scheme lies in that it requires no central control entities, which can be independently implemented within a distributed spectrum market. The centralized aspect is that the leader draws up and assigns the socially optimal contracts for all PUs and SUs in a centralized manner, which maximizes the throughput of the whole network and attains the economic robustness (including Incentive Compatibility and Individual Rationality). Analysis shows that the proposed scheme takes in the advantages of both centralized and distributed schemes but overcomes their weaknesses. We use the proposed scheme to study two sharing scenarios: the short-term and the long-term spectrum sharing. The Short-Term Sharing (STS) focuses on distributing PUs' idle spectra within one time slot while the Long-Term Sharing (LTS) considers multiple slots, where the spectrum mobility must be investigated. As an integrated design of both STS and LTS, our scheme not only fulfils SUs' heterogeneous spectrum requirements but also obtains the socially optimal throughput while accounting for both SUs' and PUs' incentives.

In this paper, we deal with possible data transmission congestion on the sink node in wireless sensor networks (WSNs). We consider a scenario in which all the sensor nodes have a certain amount of storage space and acquire data from the surroundings at heterogeneous speed. Because receiving bandwidth of the sink node is limited, a proper bandwidth allocation mechanism should be implemented to avoid possible congestion or data loss due to the overflow of some sensor nodes. To address this problem, we firstly design a novel bandwidth allocation mechanism, SWM, that can maximize the social utility, an indicator of every sensor node's satisfaction degree and the social fairness. Furthermore, we model the allocation process under the SWM as a noncooperative game and figure out the unique Nash Equilibrium. The uniqueness of the equilibrium demonstrates that this network will actually approach to a fair and stable state.

In this paper, we deal with the scenario that a single secondary user (SU) attempts to opportunistically access the 3G wireless CDMA networks licensed to primary users (PUs). Different to earlier studies in which consider the channel as two state Markov process, here, we expand the channel's availability into a multiple-state Markov process according to the transmission power of the SU. We propose a cyclical, but not periodic sensing and transmission strategy, which can be divided into two steps: 1) the SU acquires the estimation about how many PUs are in this network during a sensing period Ts; 2) Based on the estimation, the SU determines how much transmission power and how long it can transmit. Further, we derive the optimal transmission strategy which consists of different transmission power and time corresponding to different number of PUs in service. Finally, simulation results indicate that our optimal strategy is superior to the empirical strategy such as the maximum power transmission strategy and the maximum time transmission strategy, and achieves the highest throughput of SUs in the long run.

Cell selection and resource allocation (CS-RA) are processes of determining cell and radio resource which provide service to mobile station (MS). Optimizing these processes is an important step towards maximizing the utilization of current and future networks. In this paper, we investigate the problem of CS-RA in heterogeneous wireless networks. Specifically, we propose a distributed cell selection and resource allocation mechanism, in which the CS-RA processes are performed by MSs independently. We formulate the problem as a two-tier game named as inter-cell game and intra-cell game, respectively. In the first tier, i.e. the inter-cell game, MSs select the best cell according to an optimal cell selection strategy derived from the expected payoff. In the second tier, i.e., the intra-cell game, MSs choose the proper radio resource in the serving cell to achieve maximum payoff. We analyze the existence of Nash equilibria of both games, the structure of which suggests the interesting property that we can achieve automatic load balance through the two-tier games. Furthermore, we propose distributed algorithms named as CS-Algorithm and RA-Algorithm to enable the independent MSs converge to Nash equilibria. Simulation results show that the proposed algorithms converge effectively to Nash equilibria and that the proposed CS-RA mechanism achieves better performance in terms of throughput and payoff compared to conventional mechanisms.