Researches on cognitive networks and heterogeneous networks with different communication technologies, such as 5G/4G cellular networks, wireless LANs, are very popular nowadays. However, existing researches do not consider users' satisfaction with social cooperative behaviours. Therefore, SHaaS (Sharing framework for Heterogeneous network services as a Service) has been proposed in our previous work. In the SHaaS framework, cooperation among users and service providers is considered to improve users' satisfaction and service providers' profitability. In this paper, an evaluation model of SHaaS is proposed in the case of normal situation and network failure situation. The evaluation results show that SHaaS can improve users' satisfaction and service providers' profitability.

In order to prevent potential accidents between pedestrians and vehicles, it will be effective to introduce a vehicle-to-pedestrian warning system. The system detects potential accidents between pedestrians and vehicles in advance and sends warning to pedestrians and vehicles ahead of time so that potential accidents can be avoided. The time ahead of the potential accidents is named prior notification time and it is set in advance in the system. However, there is a difference between the prior notification time and the actual notification time due to system delay and inaccuracy of GPS localization. This difference is named warning error. The location estimation error and the speed of pedestrians and vehicles will affect the appropriate estimation of prior notification time. In this paper, how warning error is changed with vehicle speed is investigated through simulation. The simulation results show that the warning error increases as the vehicle speed increases. The maximum warning error is 2 seconds regardless of the vehicle speed. Thus, notification time delay can be reduced by estimating the maximum value of the warning error and increasing the prior notification time in advance.

The telecommunication services market has been greatly changed because of the significantly changing of telecommunication network technologies and corresponding users' environment. The application services on the network have become more important. As the application service platform, the telecommunication network with suitable pricing and high utility are required from users. In this paper, we aim to provide telecommunication network service with quality of service that satisfies users and social welfare. The impact of reliability of telecommunication service on users' utility is studied. The failure rate of service is important, but few studies have focused on this point. The relation between the failure rate and willingness to pay (WTP) as well as willingness to accept (WTA) are clarified through questionnaire survey. The survey results show the differences regarding the failure rate between mass and business users. In addition, we propose the method to improve the sum and average of users' utility with the auction based trading among the users when the failure occurred, and show the effectiveness of the method by the simulation.

The past few years have witnessed a huge acceleration in global Internet traffic. Users' demand for contents is also rising accordingly. Therefore, content providers (CPs) that provide contents for users get high revenue from the traffic growth. There are generally two ways for CPs to get revenue: (i) charge users for the contents they view or download; (ii) get revenue from advertisers. On the other hand, Internet service providers (ISPs) are investing in network infrastructure to provide better quality of service (QoS), but they do not benefit directly from the content traffic. One option for ISPs to compensate their investment cost is sharing CPs' revenue by side payment from CPs to ISPs. Then ISPs will be motivated to keep on investing in developing new network technology and enlarging the capacity to improve QoS. However, it is important to evaluate how each player is affected by this kind of side payment. Our previous work has studied this problem by assuming that CPs charged users for the contents they view or download, in this paper it is considered that CP does not directly charge end users, but charges advertisers for revenue. Stackelberg game is utilized to study the interactions among ISP, CP, end users and advertisers. A unique Nash equilibrium is established and numerical analysis has validated our theoretic results. It shows that side payment from CP to ISP impairs the CP's investment of contents, and ISP can benefit from charging CP, while CP's payoff is impaired.

In this paper, we consider the problem of spectrum sharing in a Cognitive Radio Network (CRN) with spectrum holder, two secondary operators and secondary users (SUs). In the system model under consideration, the spectrum allocated to the two secondary operators can be shared by SUs, which means that secondary operators buy spectrum from spectrum holder and then sell spectrum access service to SUs. We model the relationship between secondary operators and SUs as a two-stage stackelberg game, where secondary operators make spectrum channel quality and price decisions in the first stage, and then the SUs make their spectrum demands decisions. The backward induction method is employed to solve the stackelberg game. Numerical results are performed to evaluate our analysis.

Nowadays, mobile network operators (MNOs) are trying to deploy wireless local area network (LAN) to offload mobile data from their cellular networks to complementary wireless LAN for congestion relief and cost savings. However, these network-centric methods do not take into consideration mobile user's (MU's) interests of monetary cost, energy consumption, and applications' deadlines. How the MU decides whether to offload their traffic to a complementary wireless LAN is non-trivial and important issue. Previous studies assume that MNO adopts usage-based pricing for mobile data, which only cares about how much a MU consumes data but not when a MU consumes data. In this paper, we study the MU's policy to minimize his monetary cost and energy consumption under time-dependent pricing (TDP). We formulate MU's wireless LAN offloading problem as a finite-horizon discrete-time Markov decision process (MDP) and establish an optimal policy by a dynamic programming based algorithm. Extensive simulations are conducted to validate our proposed offloading algorithm.

In order to improve the quality of service (QoS) for mobile users (MUs) and save investment cost for deploying new cellular base station, mobile network operators (MNOs) are deploying wireless local area network (LAN) access points (APs) to offload MU's traffic from cellular network to wireless LAN. However, offloading too much traffic from cellular network may impair MNO's profit since the cellular network price is higher than that of wireless LAN, whose price is low or even zero. Therefore, how to deploy wireless LAN APs to offload traffic without impairing MNO's profit is a critical problem for MNOs. As far as the authors understand, existing studies about deployment of wireless LAN APs do not consider MNO's profit and are usually in a heuristic manner. In this paper, we study the location-based advertising (LBA) leveraged wireless LAN deployment, where MNO may also collect revenue by selling LBA service in different locations to advertisers. We formulate MNO's profit maximization problem by considering different MU's demand in different locations, wireless LAN price for MUs, and revenue from LBA service. Extensive simulations are conducted to validate our analytical results.

Owing to proliferation of smart phones, communication services such as a video streaming service are common in a mobile situation. For these services, quality evaluation and communication control based on Quality of Experience (QoE), which is the degree of a user's subjective satisfaction, is very important because the final goal of delivering high-quality service is improving user's satisfaction. QoE tends to be affected by several factors including Quality of Service (QoS). Therefore, collecting QoS data from the users who use the mobile application has become one of promising schemes to meet the QoE, which is called the crowd sourcing data. The crowd sourcing data are, however, apt to be affected by sensing errors and low accuracy. In this study, we propose to estimate some densities just from the records of application use and its location information to remove the sensing errors and low accuracy. The Kernel density estimator is used to derive a continuous density function from discrete distributed sample data such as the collected QoS data. From the viewpoint of QoS, it is important to extract high-density fields of the application use to find out QoS degradation. After estimating the Kernel density estimator, we determine the borderline between the high-density field and the other field by using the reference value that can be determined from the observed data. Simulated experiments verify effectiveness of the proposed method.

The ongoing enhancement of mobile devices capabilities and advanced technologies has largely contributed to the huge increase of wireless traffic demand. Mobile network operators around the world are struggling to manage this new era of higher generation networks by finding new means and strategies to implement in order to meet the explosion in traffic demand. One of the current and most efficient strategies to handle this issue is the femtocell technology, which now has drawn the interest of main concerned actors as for manufacturers, operators, and researchers. In this paper, our main motivation was to address the macro-femto heterogeneous networks deployment issue from the economic side. Thus, we aimed to propose a pricing strategy for macro-femto networks with a user centric vision where users would have the choice to access one of the networks based on the proposed service price when he/she is accessing it. Based on our strategy, a wireless service provider's (WSP's) revenue optimization is then performed and evaluated in order to show the efficiency of our proposed pricing scheme.

With rapid increases in demand for mobile data, mobile network operators are trying to expand wireless network capacity by deploying WiFi hotspots to offload their mobile traffic. However, these network-centric methods usually do not fulfill interests of mobile users (MUs). MUs consider many problems to decide whether to offload their traffic to a complementary WiFi network. In this paper, we study the WiFi offloading problem from MU's perspective by considering delay-tolerance of traffic, monetary cost, energy consumption as well as the availability of MU's mobility pattern. We first formulate the WiFi offloading problem as a finite-horizon discrete-time Markov decision process (FDTMDP) with known MU's mobility pattern and propose a dynamic programming based offloading algorithm. Since MU's mobility pattern may not be known in advance, we then propose a reinforcement learning based offloading algorithm, which can work well with unknown MU's mobility pattern. Extensive simulations are conducted to validate our proposed offloading algorithms.

Network traffic load usually differs significantly at different times of a day due to users' different time preference. Network congestion may happen in traffic peak times. In order to prevent this from happening, network service providers (NSPs) can either over-provision capacity for demand at peak times of the day, or use dynamic time-dependent pricing (TDP) scheme to reduce the demand at traffic peak times. Since over-provisioning network capacity is costly, many researchers have proposed TDP schemes to control congestion as well as to improve the revenue of NSPs. To the best of our knowledge, all these studies consider only the monopoly NSP case. In our previous work, the duopoly and oligopoly NSP cases have been studied. NSPs try to maximize their overall revenue by setting time-dependent prices, while users choose NSPs by considering their own time preference, congestion statuses in the networks and the prices set by the NSPs. One assumption that has been made is that Quality of Service (QoS) function of each NSP is linear, which means that the level of QoS degradation is proportional to the number of users in the network. However, in reality, the level of QoS may degrade rapidly after a certain point, which is not reflected through linear QoS functions. Therefore, concave QoS function is a better choice. In this paper, the case of concave QoS function is considered. TDP is evaluated under different QoS functions. The results shows that TDP is also effective under concave QoS functions.

Pricing cloud resources plays an important role in leading to the success of cloud computing. Cloud services are priced at different levels in infrastructure-as-a-service (IaaS) cloud market. For example, Amazon EC2 offers its cloud resources with three pricing schemes, the subscription model, pay-as-you-go model and spot pricing model. With more and more IaaS cloud service providers (CSPs) beginning to provide cloud services, they form a competitive market to compete for cloud users. Therefore, how to set optimal prices in order to maximize their revenue in a competitive IaaS cloud computing market while at the same time meeting the cloud users' demand satisfaction is a problem that CSPs should consider. Towards this end, in this paper, we study subscription pricing competition in a duopoly IaaS cloud computing market. First, we analyze whether or not the cloud users choose to use cloud service. Then, we present a game theoretic analysis of a cloud market with two CSPs competing non-cooperatively for cloud users.

Notion of the user-centered design (UCD) is requisite to improve user satisfaction. Matching the concept of UCD, Quality of Experience (QoE) is expected to become a paradigm shift from the existing operator-initiative network services. There are, however, several difficulties to realize practical communication services based on QoE. One difficulty is how to bridge QoE and Quality of Service (QoS) parameters relevant to system performance. Moreover, since QoE is based on the end-user subjective evaluation, flexible modeling corresponding to the end-user psychological status is needed. In this paper, an interactive Web service is considered and a QoE model with end-user psychological effect is derived by use of the Weber-Fechner law. Data of QoE and QoS parameter are collected through experiments and the mapping functions between QoE and QoS are estimated from the data.

In this paper, we consider a price-based connection admission control (CAC) for wireless random access networks. In particular, a network operator determines sequential prices to dynamically maintain the traffic admitted into the network below the channel capacity. The CAC tries to ensure quality of service (QoS) guarantees to users and hence maximize the overall revenue. We find that the revenue maximization problem over all sequential prices is NP-hard. Therefore, a greedy algorithm is employed for obtaining a simple, easy-to-implement solution.

Public WLANs based on IEEE802.11 standard are now widely deployed. In an environment offering several APs (Access Points), a UTE (User Terminal Equipment) needs to choose which AP to associate with. Conventionally, the UTE associates with the AP with the strongest RSSI (Received Signal Strength Indicator). This simple approach, however, can degrade the efficiency of network utilization since the UTE is likely choose a heavily loaded AP, even though choosing a more lightly loaded AP would be more effective. So far, some AP association schemes have been proposed. To the best of our knowledge, however, these existing schemes do not take into account of individual difference among UTEs in terms of user utility, physical data rate, traffic characteristics such as traffic demand and frame length. In this paper, we propose a distributed user association control scheme that does considering such UTE characteristics. The proposed scheme needs to estimate uplink and downlink throughput, so that we introduce a throughput estimation method that considers UTE diversities. Through numerical simulations, we confirm that our distributed control scheme can improve user utility and fairness.

Both of Quality of Service (QoS) and Quality of Experience (QoE) are defined to specify the degree of service quality. Although they are dealt with in different layers in multi-layered models, collaboration of these is necessary to improve the user satisfaction for telecommunication services. In this paper, after sorting out the concepts and specification of QoS and QoE, a service quality coordination model combining these is proposed. The model is applied to a video-sharing service and its coordination model is derived based on subjective experiments. The structural equation modeling is used to compute the user satisfaction from QoS and QoE.

Recent simulation-based networking research prefers the use of real packet traces to traditional model-based traffic generation. Part of this demand comes from the fact that synthetic traffic is only remotely similar to that found in real life. Now, the biggest problem with replaying real packet traces in simulation is that the majority of publicly available traces are bilateral. Splitting traces into two subtraces for each direction of the link is a challenging task and has already been tackled by several research works and software tools with imperfect outcome. This research proposes a new method for two-side splitting of packet traces.

Mobile users want to be connected to a network at any time for mobile computing applications, such as thin-client communications and network games, even while they are moving toward their destinations. They could take an alternate route (called a “longcut” route) if the route is geographically longer than a shortcut route to their destinations but gives more wireless network resources, such as bandwidth and throughput. In the case of a single user, the longcut routes are shown to be effective when they are optimally selected within a specified cost restriction. In the case of multiple users, users should be diversified in their route to avoid competing with each other. Optimization should be achieved by the appropriate arrangement of routes an d, in addition, by incentives to relax users' cost restrictions.

In this paper, a simplified practical method for dimensioning asynchronous transfer mode (ATM) systems is proposed. Packet switching services are classified into two categories: connection-oriented packet switching (COPS) with quality of service (QoS) guaranteed and connection-less packet switching (CLPS) on a best effort basis. A new control scheme is proposed for maintaining the cell-level QoS requirement for the COPS even in overload conditions. A dimensioning method is also proposed for the system where the COPS is served with nonpreemptive priority over the CLPS. The methods proposed are expected to contribute to the realization of robust and economical ATM systems.