Kohei Sugiyama

Bio: Kohei Sugiyama is an academic researcher from KDDI. The author has contributed to research in topics: Information-centric networking & Routing protocol. The author has an hindex of 8, co-authored 21 publications receiving 542 citations. Previous affiliations of Kohei Sugiyama include The Chinese University of Hong Kong.

Incentive mechanisms for smartphone collaboration in data acquisition and distributed computing

Abstract: This paper analyzes and compares different incentive mechanisms for a client to motivate the collaboration of smartphone users on both data acquisition and distributed computing applications.

Motivating Smartphone Collaboration in Data Acquisition and Distributed Computing

Abstract: This paper analyzes and compares different incentive mechanisms for a master to motivate the collaboration of smartphone users on both data acquisition and distributed computing applications. To collect massive sensitive data from users, we propose a reward-based collaboration mechanism, where the master announces a total reward to be shared among collaborators, and the collaboration is successful if there are enough users wanting to collaborate. We show that if the master knows the users' collaboration costs, then he can choose to involve only users with the lowest costs. However, without knowing users' private information, then he needs to offer a larger total reward to attract enough collaborators. Users will benefit from knowing their costs before the data acquisition. Perhaps surprisingly, the master may benefit as the variance of users' cost distribution increases. To utilize smartphones' computation resources to solve complex computing problems, we study how the master can design an optimal contract by specifying different task-reward combinations for different user types. Under complete information, we show that the master involves a user type as long as the master's preference characteristic outweighs that type's unit cost. All collaborators achieve a zero payoff in this case. If the master does not know users' private cost information, however, he will conservatively target at a smaller group of users with small costs, and has to give most benefits to the collaborators.

Motivating Smartphone Collaboration in Data Acquisition and Distributed Computing

Abstract: This paper analyzes and compares different incentive mechanisms for a master to motivate the collaboration of smartphone users on both data acquisition and distributed computing applications. To collect massive sensitive data from users, we propose a reward-based collaboration mechanism, where the master announces a total reward to be shared among collaborators, and the collaboration is successful if there are enough users wanting to collaborate. We show that if the master knows the users' collaboration costs, then he can choose to involve only users with the lowest costs. However, without knowing users' private information, then he needs to offer a larger total reward to attract enough collaborators. Users will benefit from knowing their costs before the data acquisition. Perhaps surprisingly, the master may benefit as the variance of users' cost distribution increases. To utilize smartphones' computation resources to solve complex computing problems, we study how the master can design an optimal contract by specifying different task-reward combinations for different user types. Under complete information, we show that the master involves a user type as long as the master's preference characteristic outweighs that type's unit cost. All collaborators achieve a zero payoff in this case. If the master does not know users' private cost information, however, he will conservatively target at a smaller group of users with small costs, and has to give most benefits to the collaborators.

Congestion price for cache management in information-centric networking

Abstract: We tightly integrate caching and congestion control into a unified approach to enhance user-centric performance in information-centric networking. To do that, we take advantage of the available congestion price fed back by congestion control to guide caching decision at each content router. The point is to let caching work in a longer time scale to alleviate congestion retrieving expensive content in terms of congestion cost. As a consequence, not only does caching increase throughput for users who request the cached content, but also it contributes to a large extent towards reducing network congestion, which benefits all other users who share the same congested parts of the network. Our use of existing congestion feedback signals eliminates the need for out-of-band congestion measurement such as bandwidth share computation which in reality might not reflect the true congestion state of the delivery paths. The proposed method yields positive performance gain in simulations compared with existing cache schemes.

Proposal on routing-based mobility architecture for ICN-based cellular networks

Abstract: Forthcoming 5G networks raise an important research issue of seamless mobility management of cellular and non-cellular networks. Adopting Information Centric Networking (ICN) architecture as common mobility management is promising because existing mobility management mechanisms are complicated and incur large signaling overhead. This paper designs a routing-based mobility architecture to provide seamless mobility for the both networks. The architecture consists of routing during longer duration and anchor-less forwarding during shorter duration. A main contribution of the paper is that the architecture is designed by carefully considering how to port it to cellular networks with as few modifications to them as possible.

Incentives for Mobile Crowd Sensing: A Survey

Abstract: Recent years have witnessed the fast proliferation of mobile devices (e.g., smartphones and wearable devices) in people's lives. In addition, these devices possess powerful computation and communication capabilities and are equipped with various built-in functional sensors. The large quantity and advanced functionalities of mobile devices have created a new interface between human beings and environments. Many mobile crowd sensing applications have thus been designed which recruit normal users to contribute their resources for sensing tasks. To guarantee good performance of such applications, it's essential to recruit sufficient participants. Thus, how to effectively and efficiently motivate normal users draws growing attention in the research community. This paper surveys diverse strategies that are proposed in the literature to provide incentives for stimulating users to participate in mobile crowd sensing applications. The incentives are divided into three categories: entertainment, service, and money. Entertainment means that sensing tasks are turned into playable games to attract participants. Incentives of service exchanging are inspired by the principle of mutual benefits. Monetary incentives give participants payments for their contributions. We describe literature works of each type comprehensively and summarize them in a compact form. Further challenges and promising future directions concerning incentive mechanism design are also discussed.

Incentive mechanisms for crowdsensing: crowdsourcing with smartphones

Abstract: Smartphones are programmable and equipped with a set of cheap but powerful embedded sensors, such as accelerometer, digital compass, gyroscope, GPS, microphone, and camera. These sensors can collectively monitor a diverse range of human activities and the surrounding environment. Crowdsensing is a new paradigm which takes advantage of the pervasive smartphones to sense, collect, and analyze data beyond the scale of what was previously possible. With the crowdsensing system, a crowdsourcer can recruit smartphone users to provide sensing service. Existing crowdsensing applications and systems lack good incentive mechanisms that can attract more user participation. To address this issue, we design incentive mechanisms for crowdsensing. We consider two system models: the crowdsourcer-centric model where the crowdsourcer provides a reward shared by participating users, and the user-centric model where users have more control over the payment they will receive. For the crowdsourcer-centric model, we design an incentive mechanism using a Stackelberg game, where the crowdsourcer is the leader while the users are the followers. We show how to compute the unique Stackelberg Equilibrium, at which the utility of the crowdsourcer is maximized, and none of the users can improve its utility by unilaterally deviating from its current strategy. For the user-centric model, we design an auction-based incentive mechanism, which is computationally efficient, individually rational, profitable, and truthful. Through extensive simulations, we evaluate the performance and validate the theoretical properties of our incentive mechanisms.

How to crowdsource tasks truthfully without sacrificing utility: Online incentive mechanisms with budget constraint

Abstract: Mobile crowdsourced sensing (MCS) is a new paradigm which takes advantage of pervasive smartphones to efficiently collect data, enabling numerous novel applications. To achieve good service quality for a MCS application, incentive mechanisms are necessary to attract more user participation. Most of existing mechanisms apply only for the offline scenario where all users' information are known a priori. On the contrary, we focus on a more realistic scenario where users arrive one by one online in a random order. Based on the online auction model, we investigate the problem that users submit their private types to the crowdsourcer when arrive, and the crowdsourcer aims at selecting a subset of users before a specified deadline for maximizing the value of services (assumed to be a non-negative monotone submodular function) provided by selected users under a budget constraint. We design two online mechanisms, OMZ and OMG, satisfying the computational efficiency, individual rationality, budget feasibility, truthfulness, consumer sovereignty and constant competitiveness under the zero arrival-departure interval case and a more general case, respectively. Through extensive simulations, we evaluate the performance and validate the theoretical properties of our online mechanisms.

Game-theoretic resource allocation methods for device-to-device communication

Abstract: Device-to-device communication underlaying cellular networks allows mobile devices such as smartphones and tablets to use the licensed spectrum allocated to cellular services for direct peer-to-peer transmission. D2D communication can use either one-hop transmission (i.e. D2D direct communication) or multi-hop clusterbased transmission (i.e. in D2D local area networks). The D2D devices can compete or cooperate with each other to reuse the radio resources in D2D networks. Therefore, resource allocation and access for D2D communication can be treated as games. The theories behind these games provide a variety of mathematical tools to effectively model and analyze the individual or group behaviors of D2D users. In addition, game models can provide distributed solutions to the resource allocation problems for D2D communication. The aim of this article is to demonstrate the applications of game-theoretic models to study the radio resource allocation issues in D2D communication. The article also outlines several key open research directions.