Mobile communication is often frustratingly slow. When a user encounters poor performance, and perhaps even "confirms" the same by running a speed test, the tendency is to ascribe blame to the user's last-mile provider. However, as we argue in this paper, a more nuanced approach is needed to identify the location of the bottleneck responsible for the poor performance. Specifically, we focus on the question of whether the bottleneck lies in the cellular last hop (3G or LTE link) or elsewhere in the WAN path.

Real-time, interactive streaming for applications such as audio-video conferencing (e.g., Skype) and cloud-based gaming depends critically on the network providing low latency, jitter, and packet loss, much more so than on-demand streaming (e.g., YouTube) does. However, WiFi networks pose a challenge; our analysis of data from a large VoIP provider and from our own measurements shows that the WiFi access link is a significant cause of poor streaming experience.

To minimize battery drain due to background communication in cellular-connected devices such as smartphones, the duration for which the cellular radio is kept active should be minimized. This, in turn, calls for scheduling the background communication so as to maximize the throughput. It has been recognized in prior work that a key determinant of throughput is the wireless link quality. However, as we show here, another key factor is the load in the cell, arising from the communication of other nodes. Unlike link quality, the only way, thus far, for a cellular client to obtain a measure of load has been to perform active probing, which defeats the goal of minimizing the active duration of the radio.

Many networked applications that run in the background on a mobile device incur significant energy drains when using the cellular radio interface for communication. This is mainly due to the radio-tail, where the cellular radio remaining in a high energy state for up to 20s after each communication spurt. In order to cut down energy consumption, many recent devices employ fast dormancy, a feature that forces the client radio to quickly go into a low energy state after a fixed short idle period. However, aggressive idle timer values for fast dormancy can increase signaling overhead due to frequent state transitions, which negatively impacts the network. In this work, we have designed and implemented RadioJockey, a system that uses program execution traces to predict the end of communication spurts, thereby accurately invoking fast dormancy without increasing network signaling load. We evaluate RadioJockey on a broad range of background applications and show that it achieves 20-40\% energy savings with negligible increase in signaling overhead compared to fixed idle timer-based approaches.

Cellular radios consume more power and suffer reduced data rate when the signal is weak. According to our measurements, the communication energy per bit can be as much as 6x higher when the signal is weak than when it is strong. To realize energy savings, applications must preferentially communicate when the signal is strong, either by deferring non-urgent communication or by advancing anticipated communication to coincide with periods of strong signal. Allowing applications to perform such scheduling requires predicting signal strength, so that opportunities for energy-efficient communication can be anticipated. Furthermore, such prediction must be performed at little energy cost.

WiFi radios in smart-phones consume a significant amount of power when active. The 802.11 standard allows these devices to save power through an energy-conserving Power Save Mode (PSM). However, depending on the PSM implementation strategies used by the clients/Access Points (APs), we find competing background traffic results in one or more of the following negative consequences: a significant increase, up to 300%, in a client's energy consumption, a decrease in wireless network capacity due to unnecessary retransmissions, and unfairness.

Cellular radio communication is a significant contributor to battery energy drain on smartphones, in some cases inflating the energy cost by a factor of 5 or more compared to the energy cost of the base device. Stratus is a system to reduce this energy consumption by leveraging cloud resources to make data communication on smartphones more efficient. Using a cloud-based proxy, Stratus employs optimizations that adapt an application's incoming and outgoing traffic to better match the energy characteristics of the radio interface. The optimizations include (a) aggregation to bunch up sporadic transmissions, (b) asymmetric dictionary-based compression to reduce the number of bits transmitted over the air, and (c) opportunistic scheduling to avoid communication during periods of poor signal reception. These optimizations can be used individually, or in combination, subject to an application's delay tolerance. For example, using our Stratus prototype, the aggregation and compression optimizations together achieve up to 50% energy savings for web browsing, while the aggregation and scheduling optimizations together achieve up to 35% energy savings for a media streaming application.

We consider the problem of providing ubiquitous yet affordable Internet connectivity to devices at home, at work, and on the move. In this context, we take advantage of two significant technology trends: the commoditization of WiFi WLAN technology and the rapid growth of cellular data services. We propose an architecture called Cool-Tether that harnesses the cellular radio links of one or more mobile smartphones in the vicinity, builds a WiFi hotspot on-the-fly, and provides energy-efficient, affordable connectivity.

IP multicast applications such as live lecture broadcasts are being increasingly used in enterprise and campus networks. In many cases, end hosts access these multicast streams using Wi-Fi networks. However, multicast over Wi-Fi suffers from several well-known problems such as low data rate, high losses and unfairness vis-a-vis other contending unicast transmissions. In this paper we present DirCast, a system to solve many of these problems. DirCast requires no changes to the 802.11 MAC protocol or the wireless access points. Software changes are required on clients only if they wish to participate in multicast sessions. The aim of DirCast system is to minimize the airtime consumed by the multicast traffic, while simultaneously improving client experience. To meet these goals, the DirCast converts multicast packets to unicast packets targeted to certain selected clients; other clients receive these packets by listening in promiscuous mode. The target clients are carefully selected to minimize loss rate experienced by the non-targeted clients. If necessary, clients are forced to change the AP they are associated with. In addition, DirCast uses proactive adaptive FEC to further reduce the loss rate and implements a novel virtual multicast interface in order to be compatible with the security needs of the enterprise. We demonstrate the effectiveness of DirCast using extensive experiments in a Wi-Fi prototype implementation and through large-scale simulations.

In this article, we design techniques that exploit data correlations in sensor data to minimize communication costs (and hence, energy costs) incurred during data gathering in a sensor network. Our proposed approach is to select a small subset of sensor nodes that may be sufficient to reconstruct data for the entire sensor network. Then, during data gathering only the selected sensors need to be involved in communication. The selected set of sensors must also be connected, since they need to relay data to the data-gathering node. We define the problem of selecting such a set of sensors as the connected correlation-dominating set problem, and formulate it in terms of an appropriately defined correlation structure that captures general data correlations in a sensor network. We develop a set of energy-efficient distributed algorithms and competitive centralized heuristics to select a connected correlation-dominating set of small size. The designed distributed algorithms can be implemented in an asynchronous communication model, and can tolerate message losses. We also design an exponential (but nonexhaustive) centralized approximation algorithm that returns a solution within O (log n ) of the optimal size. Based on the approximation algorithm, we design a class of centralized heuristics that are empirically shown to return near-optimal solutions. Simulation results over randomly generated sensor networks with both artificially and naturally generated data sets demonstrate the efficiency of the designed algorithms and the viability of our technique—even in dynamic conditions.

802.11a, b, and g standards were designed for deployment in cooperative environments, and hence do not include mechanisms to protect from jamming attacks. In this paper, we explore how to protect 802.11 networks from jamming attacks by having the legitimate transmission hop among channels to hide the transmission from the jammer. Using a combination of mathematical analysis and prototype experimentation in an 802.11a environment, we explore how much throughput can be maintained in comparison to the maintainable throughput in a cooperative, jam-free environment. Our experimental and analytical results show that in today's conventional 802.11a networks, we can achieve up to 60% of the original throughput. Our mathematical analysis allows us to extrapolate the throughput that can be maintained when the constraint on the number of orthogonal channels used for both legitimate communication and for jamming is relaxed.

In this work, we investigate the use of directional antennas and beam steering techniques to improve performance of 802.11 links in the context of communication between amoving vehicle and roadside APs. To this end, we develop a framework called MobiSteer that provides practical approaches to perform beam steering. MobiSteer can operate in two modes - cached mode - where it uses prior radiosurvey data collected during "idle" drives, and online mode, where it uses probing. The goal is to select the best AP and beam combination at each point along the drive given the available information, so that the throughput can be maximized. For the cached mode, an optimal algorithm for AP and beam selection is developed that factors in all overheads.

This paper presents first results of experiments in vehicular-to-roadside communication using directional antennas. With directional antennas on one side, the duration of connection to a fixed access point or a road side communication unit can be extended and on the other side the interference caused to others can be reduced. In this work results of experiments with electronical steerable directional antennas mounted on a car communicating with stationary access points are presented. The measurements show the benefit of using directional antennas in different environments typical for vehicular communications. The duration of potential 802.11b connections have been compared using directional and omnidirectional antenna patterns when driving through suburban environment. This comparison is based on passive scanning for access points in order to validate the approach in realistic scenarios. The results clearly prove a substantial potential improvement when using directional antennas.

In this paper we design and evaluate Ganges, a wireless mesh network architecture that can efficiently transport real-time video streams from multiple sources to a central monitoring station. Video quality suffers from deterioration in the presence of bursty network losses and due to packets missing their playback deadline. Ganges spatially separates the paths to reduce inter-flow contention. It finds out a fair rate allocation for the different video sources. The wireless routers in the mesh network implement several optimizations in order to reduce the end-to-end delay variation. Ganges improves the network capacity by over 10-15% over a shortest path tree, and improves the video picture quality by 7-10 dB

In the recent past, there has been a tremendous increase in the popularity of VoIP services as a result of huge growth in broadband access. The same voice-over-Internet protocol (VoIP) service poses new challenges when deployed over a wireless mesh network, while enabling users to make voice calls using WiFi phones. Packet losses and delay due to interference in a multiple-hop mesh network with limited capacity can significantly degrade the end-to-end VoIP call quality. In this work, we discuss the basic requirements for efficient deployment of VoIP services over a mesh network. We present and evaluate practical optimizing techniques that can enhance the network capacity, maintain the VoIP quality and handle user mobility efficiently. Extensive experiments conducted on a real testbed and ns-2 provide insights into the performance issues and demonstrate the level of improvement that can be obtained by the proposed techniques. Specifically, we find that packet aggregation along with header compression can increase the number of supported VoIP calls in a multihop network by 2-3 times. The proposed fast path switching is highly effective in maintaining the VoIP quality. Our fast handoff scheme achieves almost negligible disruption during calls to roaming clients

In this paper, we design techniques that exploit data correlations in sensor data to minimize communication costs (and hence, energy costs) incurred during data gathering in a sensor network. Our proposed approach is to select a small subset of sensor nodes that may be sufficient to reconstruct data for the entire sensor network. Then, during data gathering only the selected sensors need to be involved in communication. The selected set of sensors must also be connected, since they need to relay data to the data-gathering node. We define the problem of selecting such a set of sensors as the connected correlation-dominating set problem, and formulate it in terms of an appropriately defined correlation structure that captures general data correlations in a sensor network.We develop a set of energy-efficient distributed algorithms and competitive centralized heuristics to select a connected correlation-dominating set of small size. The designed distributed algorithms can be implemented in an asynchronous communication model, and can tolerate message losses. We also design an exponential (but non-exhaustive) centralized approximation algorithm that returns a solution within O(log n) of the optimal size. Based on the approximation algorithm, we design a class of efficient centralized heuristics that are empirically shown to return near-optimal solutions. Simulation results over randomly generated sensor networks with both artificially and naturally generated data sets demonstrate the efficiency of the designed algorithms and the viability of our technique -- even in dynamic conditions.

We have designed and evaluated iMesh, an infrastructure-mode 802.11-based mesh network. IEEE 802.11 access points double as routers making the network architecture completely transparent to mobile clients, who view the network as a conventional wireless LAN. Layer-2 handoffs between access points trigger routing activities inside the network, which can be thought of as layer-3 handoffs. We describe the design rationale and a testbed implementation of iMesh. We present results related to the handoff performance. The results demonstrate excellent handoff performance, the overall latency varying between 50-100 ms, depending on different layer-2 techniques, even when a five-hop long route update is needed. Various performance measurements also demonstrate the clear superiority of a flat routing scheme relative to a more traditional, Mobile IP-like scheme to handle layer-3 handoff.

In most existing systems, the authorization check for system resource access is based on the user ID of the running processes. Such systems are vulnerable to password stealing/cracking attacks. Considering that remote attackers usually do not have physical access to local machines, we propose a security architecture called NPTrace (network-wide process tracing), which requires a user to know the root password and to prove that he is within some physical proximity in order to exercise the root privilege. More specifically, NPTrace attaches a privilege-level attribute to every process, and propagates this attribute across machines on demand. The privilege-level attribute of a process is set to rootable if the system can trace back its origin to a process started by a user that has physically logged on from a specific set of hosts on the network. Only a root process with this privilege-level attribute set to rootable, is allowed to perform privileged operations. The NPTrace architecture essentially exploits physical security to strengthen password-based security. This paper describes the design and implementation of the NPTrace prototype, which features a distributed mechanism to identify the entry point of a user into a network. The prototype is implemented under Linux and has been tested under many attack scenarios. The system shows correct behavior in these tests with negligible performance overhead.