The availability of “always-on” communications has tremendous implications for how people interact socially. In particular, sociologists are interested in the question if such pervasive access increases or decreases face-to-face interactions. Unlike triangulation which seeks to precisely define position, the question of face-to-face interaction reduces to one of proximity, i.e., are the individuals within a certain distance? Moreover, the problem of proximity estimation is complicated by the fact that the measurement must be quite precise (1-1.5 m) and can cover a wide variety of environments. Existing approaches such as GPS and Wi-Fi triangulation are insufficient to meet the requirements of accuracy and flexibility. In contrast, Bluetooth, which is commonly available on most smartphones, provides a compelling alternative for proximity estimation. In this paper, we demonstrate through experimental studies the efficacy of Bluetooth for this exact purpose. We propose a proximity estimation model to determine the distance based on the RSSI values of Bluetooth and light sensor data in different environments. We present several real world scenarios and explore Bluetooth proximity estimation on Android with respect to accuracy and power consumption.

Due to fading and node mobility, packet loss in wireless networks is prevalent and unavoidable. Significant research has been conducted to improve the quality of wireless networks. Compared to other wireless networks, the backbone of a wireless mesh network (WMN) tends to be a relatively static topology and thus link error is likely to be transient rather than permanent. This paper proposes a technique, Save For Later (SFL), that leverages such transience to improve the performance of WMNs. SFL is a link-layer enhancement that allows an intermediate mesh router to temporarily save failed transmissions due to the high probability that a later successful transmission will convey that the failure is transient in nature. The paper develops a stochastic model to analyze the steady state throughput of a TCP bulk data transfer under SFL. Finally, the paper shows through simulation and analytical results that SFL elegantly improves the performance of WMNs in terms of throughput and flow fairness.

Over the last decade, numerous admission control schemes have been studied to allocate network resources. Although per-flow control schemes can provide guaranteed QoS, such schemes face scalability issues in large networks due to the tremendous number of flows present. While aggregation-based approaches such as Differentiated Services relieve the storage of state in the core, admission control of flows, especially short-lived flows, is still a serious bottleneck. To that end, we propose an admission control scheme, Fast Admission for Short Flows (FASF), that enables accelerated admission control at the edge rather than via centralized or in-path mechanisms. FASF not only reduces the burden on admission control by largely distributing the dominant resource requests (i.e. short-lived flows), but also improves flow completion time and hence network goodput.

Despite the significant body of research that has been conducted on quality of service (QoS), the notion of a dominant approach to end-to-end (E2E) QoS remains elusive. Beyond the ever present issue of deployment, the complexity and limited speed of resource negotiation arising from per-hop or per-domain interactions limit the general utility of the existing approaches. In this paper, we propose a novel approach to make significant strides in practical end-to-end QoS by leveraging the over-provisioned nature of the core and cooperative interactions amongst end autonomous systems. Our approach, E3AC (End-wise delegation for End-to-End Admission Control), provides a framework for catalyzing QoS amongst end ISPs while dramatically reducing the setup latency for QoS negotiations

Real-time media servers are becoming increasingly important as the Internet supports more and more multimedia applications. In order to meet these ever increasing demands, real-time media servers will be responsible for supporting a large number of clients with a wide range of QoS requirements. While techniques to aggregate state information for scalability have been proposed in the literature such as with Differentiated Services; the per-stream effects of such aggregation are poorly understood. Based on the (m,k)-firm model to schedule loss-tolerant streams, we explore the effects of aggregated state information in this paper and describe our scheme, called granularity aware (m,k) queue management (GAQM). GAQM improves control over the tradeoff between scalability and per-stream QoS performance. Specifically, we identify the necessity of balancing aggregation groups according to characteristics such as relative deadlines. Another key finding of this work is that with proper biasing, the inaccuracy of aggregate state lends itself to burst scheduling rather than simply extending traditional scheduling mechanisms. This finding is profound in that the result is counterintuitive: less frequent scheduling leads to increased per-stream performance. We present detailed examples of GAQM and evaluate our work through simulation studies and Markov chain analysis.

Wireless network access has become an integral part of computing both at home and at the workplace. The convenience of wireless network access at work may be extremely beneficial to employees, but can be a burden to network security personnel. This burden is magnified by the threat of inexpensive wireless access points being installed in a network without the knowledge of network administrators. These devices, termed Rogue Wireless Access Points , may allow a malicious outsider to access valuable network resources, including confidential communication and other stored data. For this reason, wireless connectivity detection is an essential capability, but remains a difficult problem. We present a method of detecting wireless hosts using a local RTT metric and a novel packet payload slicing technique. The local RTT metric provides the means to identify physical transmission media while packet payload slicing conditions network traffic to enhance the accuracy of the detections. Most importantly, the packet payload slicing method is transparent to both clients and servers and does not require direct communication between the monitoring system and monitored hosts.

The emergence of popular video sharing sites such as YouTube has created a tremendous content shift towards timely, medium-sized media together with placing significant demands on the network. While techniques such as Application Layer Multicast and P2P streaming offer the potential to reduce the impact of general streaming content, the difficulty in imposing synchronization and the heavy asymmetry of clients nullify the majority of the respective benefits of the techniques. In this paper, we propose a method, transparent tail synchronization, that discovers latent opportunities for synchronization from the tail of the content to take advantage of efficient distribution techniques. Our approach maximizes savings at the content provider while operating in a straightforward and easily deployable manner. We describe how tail synchronized media distribution can offer savings across a wide range of asymmetry at the end clients.