Wireless body area network (WBAN) is an emerging technology that has enormous potentials for continuous health monitoring of various diseases. For different medical and healthcare applications, the collected physiological data from multiple WBANs are further transmitted to the remote medical cloud servers. However, several technical issues and challenges are associated with the integration of WBANs and cloud computing services. In this study, we develop a new cloud-assisted WBAN control scheme to effectively use the limited system resource. By employing the main ideas of Generalized Gini and Choquet bargaining solutions, our approach unfolds into dual stages of bargaining processes while increasing the flexibility and adaptability. In particular, we consider the unique features of cloud-assisted WBAN applications and provide a generalized fair-efficient solution for the resource allocation problem. Numerical simulation results demonstrate that we can verify the superiority of our proposed scheme over the existing protocols. Lastly, major further challenges and future research directions about the cloud-assisted WBAN paradigm are summarized and discussed.

In the last few years, we have seen an exponential increase in the number of computation-intensive applications, which have resulted in the popularity of fog and cloud computing paradigms among smart-chip-embedded mobile devices. These devices can partially offload computation tasks either using the fog system or using the cloud system. In this study, we design a new task offloading scheme by considering the challenges of future edge, fog and cloud computing paradigms. To provide an effective solution toward an appropriate task offloading problem, we focus on two cooperative bargaining game solutions—Tempered Aspirations Bargaining Solution (TABS) and Gupta-Livne Bargaining Solution (GLBS). To maximize the application service quality, a proper bargaining solution should be properly selected. In the proposed scheme, the TABS method is used for time-sensitive offloading services, and the GLBS method is applied to ensure computation-oriented offloading services. The primary advantage of our bargaining-based approach is to provide an axiom-based strategic solution for the task offloading problem while dynamically responding to the current network environments. Extensive simulation studies are conducted to demonstrate the effectiveness of the proposed scheme, and the superior performance over existing schemes is observed. Finally, we show prime directions for future work and potential research issues.

With the explosion in the number of wireless services, the unprecedented growth of mobile date traffic has brought a heavy burden on the traditional cellular networks. To meet the explosive traffic services, the potential of network virtualization and multicasting device-to-device (MD2D) technology have been proposed as a promising solution for next-generation networks. In this paper, we propose a novel MD2D control scheme for virtualized cellular networks, which enables device clustering for local MD2D services to obtain the finest system performance. By taking into consideration dynamic situations and competitive environments, we formulate our control algorithms as a game model with imperfect system information. Inspired by the incentive mechanism and evolutionary decision process, the proposed game approach can guide selfish mobile devices toward honest behaviors, and the MD2D services are provided based on the step-by-step interactive feedback process. Through numerical evaluation and simulation analysis, we not only quantify the outcome of our proposed scheme’s system throughput, bandwidth utilization, and MD2D service efficiency, but also provide the performance comparison with existing schemes. Finally, we provide further challenges and various opportunities in the research area of MD2D-enabled cellular network operations.

Social Internet of Things (SIoT) is a control paradigm by the integration of social networking concepts into the Internet of Things, and Fog Computing (FC) is an emerging technology that is aimed at moving the cloud computing facilities to the access network. Recently, the SIoT and FC models are combined by using complementary features, and a new Social Fog IoT (SFIoT) paradigm has been developed. In this paper, we design novel resource allocation algorithms for the SFIoT system. Considering the social relationship and each preference, mobile devices in the SFIoT effectively share the limited computation and communication resources of FC operators. To formulate the interaction among mobile devices and FC operator, we adopt the basic concept of two game models: voting and bargaining games. Bicooperative voting approach can make control decisions for the resource allocation method, and Nash bargaining solution is used to effectively distribute the computation resource to different application tasks. Based on the two-phase game model, the proposed scheme takes various benefits in a fair-efficient way. Through the extensive simulation experiments, we can validate the superiority of our proposed approach by the fact that it produces a mutually acceptable agreement among game players and significantly outperforms the existing protocols. Last, we point out the further challenges and future research issues about the SFIoT paradigm opportunities.

With the rapid growth of network of devices with embedded technology, mobile crowdsensing (MCS) has been gaining increasing popularity. The development of 5G network services is prompting further growth in crowdsensing applications. However, MCS participants risk their privacy when reporting data with their actual sensing positions. To address this issue, the concept of differential privacy (DP) can be adopted to provide a theoretical guarantee for participants’ privacy in MCS services. In this study, we design a new DP crowdsensing scheme with game theory. Based on the multilevel interactive game model, MCS server, DP controllers, and mobile devices are regarded as rational individual decision makers that aim to maximize their own payoffs. For these decision makers, the proposed game approach analyzes suitably the competitive and coordinative MCS environments. The main novelty possessed by our control scheme is to capture the dynamics of MCS system operations with the privacy consideration. Compared with other existing protocols, performance evaluation shows the advantages of our proposed scheme in terms of the sensing task success ratio, MCS participating ratio, and normalized payoff of participating devices. Finally, we provide the guidance on the future research direction of MCS services including other issues.

In this paper, we propose an Internet management scheme that includes rate adjustment algorithm. This algorithm is designed by adaptive online approach to resolve conflicting QoS criteria while ensuring efficient network performance. Our online scheme exhibits dynamic adaptability to the current traffic conditions in multimedia networks. The results of a simulation study demonstrate the excellent performance of our proposed scheme under widely varying diverse traffic loads.