ABSTRACT
By using device-to-device communication, opportunistic networks promise to fill the gap left by infrastructure-based networks in remote areas, to support communication in disaster and emergency situations, as well as to enable new local social networking applications. Yet, to become feasible in practice and accepted by the users, it is crucial that opportunistic communication is energy-efficient. In this paper, we measure and analyze the energy consumption of today's device-to-device communication technologies: Wi-Fi Direct, Bluetooth and WLAN-Opp (a solution based on the WLAN access point mode). We compare the energy consumption of individual operations such as neighbor discovery and connection establishment/maintenance across the different standards. We find that all of these technologies suffer from two problems. First, neighbor discovery is expensive and can quickly drain the battery if implemented carelessly. We analyze this by measuring the impact of scanning frequency on battery lifetime for the different technologies. Second, all technologies suffer from unfairness issues once a connection is established. The ``host'' of a connection consumes two to five times the energy of a "client". We propose strategies to increase fairness by alternating the hosting role among the peers. We compute the frequency of switching roles based on the distribution of the residual connection time, to achieve a good trade-off between fairness and switching cost.
- Chaintreau A, Hui P et al. Pocket switched networks: real-world mobility and its consequences for opportunistic forwarding. Tech. rep., University of Cambridge Computer Laboratory, 2005.Google Scholar
- Lenders V, May M et al. Wireless ad hoc podcasting. ACM SIGMOBILE Mob Comput Commun Rev, 2008. Google ScholarDigital Library
- Guo S, Falaki MH et al. Very low-cost internet access using KioskNet. ACM SIGCOMM Comput Commun Rev, 2007. Google ScholarDigital Library
- Hossmann T, Carta P et al. Twitter in disaster mode: security architecture. SWID. 2011. Google ScholarDigital Library
- Han B, Hui P et al. Cellular traffic offloading through opportunistic communications: a case study. CHANTS. 2010. Google ScholarDigital Library
- Pietil\"ainen AK, Oliver E et al. Mobiclique: middleware for mobile social networking. WOSN. 2009. Google ScholarDigital Library
- Wang W, Srinivasan V et al. Adaptive contact probing mechanisms for delay tolerant applications. MobiCom. 2007. Google ScholarDigital Library
- Wang Y, Krishnamachari B et al. Markov-optimal sensing policy for user state estimation in mobile devices. IPSN. 2010. Google ScholarDigital Library
- IEEE-SA. IEEE 802.11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) specifications, 2007.Google Scholar
- Trifunovic S, Distl B et al. WiFi-Opp: ad-hoc-less opportunistic networking. CHANTS. 2011. Google ScholarDigital Library
- Rachuri KK, Mascolo C et al. Sociablesense: exploring the trade-offs of adaptive sampling and computation offloading for social sensing. MobiCom. 2011. Google ScholarDigital Library
- Balasubramanian N, Balasubramanian A et al. Energy consumption in mobile phones: a measurement study and implications for network applications. IMC. 2009. Google ScholarDigital Library
- Carroll A and Heiser G. An analysis of power consumption in a smartphone. USENIX. 2010. Google ScholarDigital Library
- Friedman R, Kogan A et al. On power and throughput tradeoffs of WiFi and Bluetooth in smartphones. INFOCOM. 2011.Google Scholar
- Dong M and Zhong L. Self-constructive high-rate system energy modeling for battery-powered mobile systems. MobiSys. 2011. Google ScholarDigital Library
- Monsoon Power Monitor. prefixhttp://www.msoon.com/LabEquipment/PowerMonitor/.Google Scholar
- Camps-Mur D, Garcia-Saavedra A et al. Device to device communications with WiFi Direct: overview and experimentation. IEEE Wireless Communications Magazine, 2013.Google Scholar
- Chaintreau A, Hui P et al. Impact of Human Mobility on Opportunistic Forwarding Algorithms. IEEE TMC, 2007. Google ScholarDigital Library
- Lenders V, Wagner J et al. Measurements from an 802.11b mobile ad hoc network. IEEE EXPONWIRELESS. 2006. Google ScholarDigital Library
- Clauset A, Shalizi C et al. Power-law distributions in empirical data. SIAM Review 51, 2009. Google ScholarDigital Library
Index Terms
- Slicing the battery pie: fair and efficient energy usage in device-to-device communication via role switching
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