- مبلغ: ۸۶,۰۰۰ تومان
- مبلغ: ۹۱,۰۰۰ تومان
Opportunistic networking (a.k.a. device-to-device communication) is considered a feasible means for offloading mobile data traffic. Since mobile nodes are battery-powered, opportunistic networks must be expected to satisfy the user demand without greatly affecting battery lifetime. To address this requirement, this work introduces progressive selfishness, an adaptive and scalable energy-aware algorithm for opportunistic networks used in the context of mobile data offloading. The paper evaluates the performance of progressive selfishness in terms of both application throughput and energy consumption via extensive trace-driven simulations of realistic pedestrian behavior. The evaluation considers two modes of nodal cooperation: full and limited, with respect to the percentage of nodes in the system that adopt progressive selfishness. The paper demonstrates that under full cooperation the proposed algorithm is robust against the distributions of node density and initial content availability. The results show that in certain scenarios progressive selfishness achieves up to 85% energy savings during opportunistic downloads while sacrificing less than 1% in application throughput. Furthermore, the study demonstrates that in terms of total energy consumption (by both cellular and opportunistic downloads) in dense environments the performance of progressive selfishness is comparable to downloading contents directly from a mobile network. Finally, the paper shows that progressive selfishness is robust against the presence of non-cooperative nodes in the system, and that in certain scenarios the system-level performance does not deteriorate significantly under limited cooperation even when 50% of the nodes in the system do not adhere to the specifics of the algorithm.
In this paper we propose progressive selfishness, an adaptive and scalable energy-aware algorithm for improving energy-efficiency in mobile devices in the context of mobile data offloading via opportunistic communication. Previous work in the area of mobile data offloading via opportunistic communication focuses mainly on maximizing the data delivery to end users. However, since mobile nodes are battery-powered, opportunistic networking can only be considered a viable mechanism for offloading data if it delivers high content volumes at a low energy cost. Thus, we evaluated the performance of the proposed progressive selfishness algorithm both in terms of application throughput (i.e. goodput) and energy consumption via extensive trace-driven simulations of realistic pedestrian mobility. We introduced two modes of nodal cooperation: full and limited, with respect to the percentage of nodes in the system that adopt progressive selfishness. We showed that under full cooperation the algorithm decreases energy consumption of participating nodes during the opportunistic downloads with up to 85% across different node densities in the observed area without significantly compromising goodput. We then investigated the effects of initial content availability, and observed that the performance of progressive selfishness is robust to it across different node densities. We also showed that at higher densities the energy spent by nodes that adopt progressive selfishness becomes comparable to the energy nodes would spend for downloading the same data directly from the cellular network. Thus, progressive selfishness not only offloads up to 85% of the mobile data traf- fic, but does the offloading at a similar price in terms of energy. We also demonstrated that progressive selfishness scales with the injection probability of content carriers in the observed area. Finally, we investigated the performance of progressive selfishness under limited cooperation across different node densities, content availabilities and injection probabilities, and showed that progressive selfishness is robust against the presence of non-cooperative nodes in the system in terms of offloaded traffic volumes. In dense scenarios progressive selfishness could tolerate up to 50% of non-cooperative nodes without significantly deteriorating neither the goodput nor the energy consumption observed at a system level. Thus, by simply evaluating average performance metrics it is impossible to determine the presence of non-cooperative nodes in the system. However examining individual energy consumption patterns may provide better insights as non-cooperative nodes are able to further reduce their energy consumption compared to nodes that adhere to the progressive selfishness algorithm. Mobile operators may thus be required to provide economic incentives in order to prevent nodes from intentional non-cooperation.