The problem of overbuffering in the current Internet (termed as bufferbloat) has drawn the attention of the research community in recent years. Cellular networks keep large buffers at base stations to smooth out the bursty data traffic over the time-varying channels and are hence apt to bufferbloat. However, despite their growing importance due to the boom of smart phones, we still lack a comprehensive study of bufferbloat in cellular networks and its impact on TCP performance. In this paper, we conducted extensive measurement of the 3G/4G networks of the four major U.S. carriers and the largest carrier in Korea. We revealed the severity of bufferbloat in current cellular networks and discovered some ad-hoc tricks adopted by smart phone vendors, which mitigate the impact of bufferbloat but result in performance degradation under various practical scenarios. To address the problem, we propose dynamic receiver window adjustment (DRWA) that requires slight TCP modification only in smart phones, thus guarantees quick deployment via over-the-air updates. Our extensive real-world tests confirm that DRWA reduces the latency of TCP flows by 25-49 percent and increase TCP throughput by up to 51 percent in certain scenarios. It is further verified that DRWA has significant effect on the latency of Voice-over-IP traffic and on the traffic going through TCP split scenarios with performance enhancing proxy.

Locality-aware P2P file sharing systems have drawn the attention of the research community as a promising technique to alleviate the tussle between P2P traffic and ISPs. However, existing locality-based schemes mainly focus on how to distinguish whether a peer is local or not and pay less attention to how to utilize the locality information. Typically, they simply bias the peer selection towards local peers in the hope that it will reduce cross-ISP traffic. In this paper, we argue that such coarse-grained control is inadequate and propose a fine-grained mechanism called Swarm-over-Swarm (SOS). Through explicit coordination on piece selection among local peers, SOS is much more effective in reducing redundant cross-ISP traffic than existing schemes. We implement SOS in both NS-2 and a real BitTorrent client and demonstrate the effectiveness of our solution via both simulations and Internet experiments.

The problem of overbuffering in the current Internet (termed as bufferbloat) has drawn the attention of the research community in recent years. Cellular networks keep large buffers at base stations to smooth out the bursty data traffic over the time-varying channels and are hence apt to bufferbloat. However, despite their growing importance due to the boom of smart phones, we still lack a comprehensive study of bufferbloat in cellular networks and its impact on TCP performance. In this paper, we conducted extensive measurement of the 3G/4G networks of the four major U.S. carriers and the largest carrier in Korea. We revealed the severity of bufferbloat in current cellular networks and discovered some ad-hoc tricks adopted by smart phone vendors to mitigate its impact. Our experiments show that, due to their static nature, these ad-hoc solutions may result in performance degradation under various scenarios. Hence, a dynamic scheme which requires only receiver-side modification and can be easily deployed via over-the-air (OTA) updates is proposed. According to our extensive real-world tests, our proposal may reduce the latency experienced by TCP flows by 25% ~ 49% and increase TCP throughput by up to 51% in certain scenarios.

Recent years, researchers have proposed numerous positioning algorithms for wireless sensor networks, in which range-free schemes are more attractive because of their low-cost advantage. However, in practical circumstance, the raw range-free information (neighboring set and hop-counts et al.) is usually unreliable due to combined factors such as irregular signal patterns, environment noise and so on. To make matters worse, the unreliable localization information will lead to the reduction of localization accuracy. In order to tackle this problem, we propose a Reputation-based Revising Scheme (RRS) to preprocess the raw localization information before applying any positioning algorithm. In RRS, we reevaluate the reliability of raw information by utilizing the neighboring relationship. After being processed through RRS, the raw information is close to the information collected in ideal environment. According to our detailed simulations, we demonstrate that the revised localization information has 27% lower average error rate than the raw one. Based on the revised information, the traditional range-free localization algorithms are capable to achieve higher accuracy.