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During the last years, researche'rs have proposed solutions to help smartphones improve execution time and reduce energy consumption by offloading heavy tasks to remote entities. Lately, inspired by the promising results of message forwarding in opportunistic networks, many researchers have proposed strategies for task offloading towards nearby mobile devices, giving birth to the Device-to-Device offloading paradigm. None of these strategies, though, offers any mechanism that considers selfish users and, most importantly, that motivates and defrays the participating devices who spend their resources. In this paper, we address these problems and propose the design of a framework that integrates an incentive scheme and a reputation mechanism. Our proposal follows the principles of the Hidden Market Design approach, which allows users to specify the amount of resources they are willing to sacrifice when participating in the offloading system. The underlying algorithm, that users are not aware of, is based on a truthful auction strategy and a peer-to-peer reputation exchange scheme. Extensive simulations on real traces depict how our designed mechanism achieves higher offloading rate and produces less traffic compared to three benchmark algorithms. Finally, we show how collaborating devices get rewarded for their contribution, while selfish ones get sidelined by others.

During the last years, researche'rs have proposed solutions to help smartphones improve execution time and reduce energy consumption by offloading heavy tasks to remote entities. Lately, inspired by the promising results of message forwarding in opportunistic networks, many researchers have proposed strategies for task offloading towards nearby mobile devices, giving birth to the Device-to-Device offloading paradigm. None of these strategies, though, offers any mechanism that considers selfish users and, most importantly, that motivates and defrays the participating devices who spend their resources. In this paper, we address these problems and propose the design of a framework that integrates an incentive scheme and a reputation mechanism. Our proposal follows the principles of the Hidden Market Design approach, which allows users to specify the amount of resources they are willing to sacrifice when participating in the offloading system. The underlying algorithm, that users are not aware of, is based on a truthful auction strategy and a peer-to-peer reputation exchange scheme. Extensive simulations on real traces depict how our designed mechanism achieves higher offloading rate and produces less traffic compared to three benchmark algorithms. Finally, we show how collaborating devices get rewarded for their contribution, while selfish ones get sidelined by others.

During the last years, researchers have proposed solutions to help smartphones offload heavy tasks to remote entities in order to improve execution time and reduce energy consumption. Lately, inspired by the promising results of message forwarding in opportunistic networks, many researchers have proposed strategies for task offloading towards nearby mobile devices. None of these strategies, though, proposes any mechanism that considers selfish users and, most importantly, that motivates and defrays the participating devices who spend their resources. In this paper, we address these problems and propose the design of a framework that integrates an incentive scheme and a reputation mechanism. Our proposal follows the principles of the Hidden Market Design approach, which allows users to specify the amount of resources they are willing to sacrifice when participating in the offloading system. The underlying algorithm, that users are not aware of, is based on a truthful auction strategy and a peer-to-peer reputation exchange scheme. Extensive simulations on real traces depict how our designed mechanism achieves higher offloading rate and produces less traffic compared to three benchmark algorithms. Finally, we show how collaborating devices get rewarded for their contribution, while selfish ones get sidelined by others. © 2016 IEEE.

The concepts of wisdom of crowd and collective intelligence have been utilized by mobile application developers to achieve large-scale distributed computation, known as crowd computing. The profitability of this method heavily depends on users' social interactions and their willingness to share resources. Thus, different crowd computing applications need to adopt mechanisms that motivate peers to collaborate and defray the costs of participating ones who share their resources. In this article, we propose OPENRP, a novel, lightweight, and scalable system middleware that provides a unified interface to crowd computing and opportunistic networking applications. When an application wants to perform a device-to-device task, it delegates the task to the middleware, which takes care of choosing the best peers with whom to collaborate and sending the task to these peers. OPENRP evaluates and updates the reputation of participating peers based on their mutual opportunistic interactions. To show the benefits of the middleware, we simulated the behavior of two representative crowdsourcing applications: message forwarding and task offloading. Through extensive simulations on real human mobility traces, we show that the traffic generated by the applications is lower compared to two benchmark strategies. As a consequence, we show that when using our middleware, the energy consumed by the nodes is reduced. Finally, we show that when dividing the nodes into selfish and altruistic, the reputation scores of the altruistic peers increase with time, while those of the selfish ones decrease.

The concepts of wisdom of crowd and collective intelligence have been utilized by mobile application developers to achieve large-scale distributed computation, known as crowd computing. The profitability of this method heavily depends on users' social interactions and their willingness to share resources. Thus, different crowd computing applications need to adopt mechanisms that motivate peers to collaborate and defray the costs of participating ones who share their resources. In this article, we propose OPENRP, a novel, lightweight, and scalable system middleware that provides a unified interface to crowd computing and opportunistic networking applications. When an application wants to perform a device-to-device task, it delegates the task to the middleware, which takes care of choosing the best peers with whom to collaborate and sending the task to these peers. OPENRP evaluates and updates the reputation of participating peers based on their mutual opportunistic interactions. To show the benefits of the middleware, we simulated the behavior of two representative crowdsourcing applications: message forwarding and task offloading. Through extensive simulations on real human mobility traces, we show that the traffic generated by the applications is lower compared to two benchmark strategies. As a consequence, we show that when using our middleware, the energy consumed by the nodes is reduced. Finally, we show that when dividing the nodes into selfish and altruistic, the reputation scores of the altruistic peers increase with time, while those of the selfish ones decrease.

During the last years, researchers have proposed solutions to help smartphones offload heavy tasks to remote entities in order to improve execution time and reduce energy consumption. Lately, inspired by the promising results of message forwarding in opportunistic networks, many researchers have proposed strategies for task offloading towards nearby mobile devices. None of these strategies, though, proposes any mechanism that considers selfish users and, most importantly, that motivates and defrays the participating devices who spend their resources. In this paper, we address these problems and propose the design of a framework that integrates an incentive scheme and a reputation mechanism. Our proposal follows the principles of the Hidden Market Design approach, which allows users to specify the amount of resources they are willing to sacrifice when participating in the offloading system. The underlying algorithm, that users are not aware of, is based on a truthful auction strategy and a peer-to-peer reputation exchange scheme. Extensive simulations on real traces depict how our designed mechanism achieves higher offloading rate and produces less traffic compared to three benchmark algorithms. Finally, we show how collaborating devices get rewarded for their contribution, while selfish ones get sidelined by others.