The growing popularity of live online classes, even in remote areas, stresses the need for a good and seamless quality of experience to enhance learning. However, these bandwidth-hungry applications challenge the current cellular networks to maintain consistent bandwidth and latency. In this work, we, therefore, propose using the collaboration of multiple devices with their individual cellular networks to support such live video streaming. We design a content-aware system Compact that splits video into foreground and background using video tiles (independently encoded spatial blocks) and streams them over different paths. Compact depends on its scheduler, which exhaustively searches for the best quality based on the network estimates. We extensively evaluate our system using network traces while walking and traveling on the bus or car. Compared to the single path, Compact manages to reduce the median stall and E2E lag by 70.6% and 28.57%, and the tail stall and lag by 83.9% and ≈ 80% on a bus trace. Furthermore, we performed a live experiment to test Compact on the actual cellular network.

Smartphone app usage has steeply risen in India in the past decade. But limited efforts in the past assess the privacy aspects of these smartphone apps. Many of these are used for common utilities and handle sensitive user data. Such sensitive data leaks can have a wide variety of consequences when exposed to untrusted players (e.g., repressive governments, data/content hosting companies, and other third parties). These could range from mere embar-rassment to personal targeting and surveillance. This paper presents a measurement study on the data collection and privacy considerations of some of the most popular apps on the Indian Google Play Store. We selected 24 apps, and analyzed their data collection behavior on phones as well as the security of the servers to whom they send the data. We also obfuscated the data being collected and sent to the backend servers. Interestingly, for the non-government apps we found that extensive (mostly personally identifiable information), often “unnecessary”, data collection is being performed. In other words, we observed that a lot of these apps work fine even without these pieces of sensitive information. We then classified the data collected as necessary or not necessary based on this information. Furthermore, we found that often such sensitive data may be available in plaintext to the intermediate players managing/deploying the hosting infrastructure. We also found that while the governmental services-based apps collect fewer such unnecessary data points, they often store the data on web-fronted back-end databases with little to no user authentication mechanisms enabled. We expect our study to enable better understanding among both users and app developers about the privacy implications of these data collection practices.

Smart factories have data packets with a mix of stringent and non-stringent deadlines with varying levels of importance that need to be delivered via a wireless network. However, the scheduling of packets in the wireless network is crucial to satisfy the deadlines. In this work, we propose a technique of utilizing IEEE 802.11ax, popularly known as WiFi 6, for such applications. IEEE 802.11ax has a few unique characteristics, such as specific configurations of dividing the channels into resource units (RU) for packet transmission and synchronized parallel transmissions. We model the problem of scheduling packets by assigning profit to each packet and then maximizing the sum of profits. We first show that this problem is strongly NP-Hard, and then propose an approximation algorithm with a 12-approximate algorithm. Our approximation algorithm uses a variant of local search to associate the right RU configuration to each packet and identify the duration of each parallel transmission. Finally, we extensively simulate different scenarios to show that our algorithm works better than other benchmarks.

A well-known drawback of spectrally-spatially elastic optical networks (SS-EONs) is crosstalk, which increases blocking probability; evaluating blocking probability analytically is challenging due to additional constraints. Not surprisingly, all the existing studies at this time mainly use simulation-based techniques to quantify blocking probabilities in multi-core and multi-mode fibers-based SS-EONs. This paper proposes AnalyticalBP, an exact analytical continuous-time Markov chain model for blocking probabilities considering the crosstalk-avoided approach in SS-EONs. AnalyticalBP generates all the feasible states and their transitions while avoiding inter-core and inter-mode crosstalks for both single and multi-class requests. Single-class requests use the same number of slots, whereas multi-class requests adopt different numbers of slots to satisfy clients’ requirements. When AnalyticalBP is not tractable due to scalability, we introduce an iterative approximate model for a single-hop link. We further extend the single-hop model for a multi-hop network. We compare AnalyticalBP, the iterative approximate model, and Monte Carlo simulation studies for a single-hop link. Numerical results indicate that the performances obtained by AnalyticalBP and the simulation studies, in terms of blocking probabilities and resource utilization, are comparable, and the accuracy of the iterative approximate model is also acceptable.

With the push towards HTTP/3, most modern browsers have started supporting it. HTTP/3 uses QUIC, which runs on top of UDP. However, a few Internet middle-boxes tend to block or rate-limit UDP traffic; therefore, the browsers ensure compatibility by enabling connection racing via simultaneously initiating a TCP connection with the QUIC one. Each time the QUIC protocol suffers, connection racing is activated, and whichever protocol wins the race is further used for the application. In this paper, we study how browsers implement this connection racing mechanism and analyze its impact on applications that require a long-lived Internet connection, such as video streaming. We perform a large-scale measurement study across different browsers (Chrome/Chromium and Firefox), which helps to analyze why and how the repeated connection racing between protocols affects adaptive streaming QoE over 6013 YouTube sessions covering 5474 hours of streaming. Interestingly, we observe that YouTube QoE over an HTTP/3 supported browser suffers many times, and repeated connection racing is one of the major reasons that hinder the performance. We modified the Chromium browser source code to disable the connection racing altogether and observed that it improves the QoE for YouTube streaming over this modified browser. We then design and implement a solution that dynamically decides when to enable connection racing. We observe that it improves the QoE compared to the original browser. The analysis presented in this paper highlights the requirement of revisiting how browsers handle and switch between protocols through connection racing to ensure compatibility with middleboxes.

HTTP/3 is a new application layer protocol supported by most browsers. It uses QUIC as an underlying transport protocol. QUIC provides multiple benefits, like faster connection establishment, reduced latency, and improved connection migration. Hence, most popular browsers like Chrome/Chromium, Microsoft Edge, Apple Safari, and Mozilla Firefox have started supporting it. In this paper, we present an HTTP/3-supported browser dataset collection tool named H3B. It collects the application and network-level logs during YouTube streaming. We consider YouTube, as it the most popular video streaming application supporting QUIC. Using this tool, we collected a dataset of over 5936 YouTube sessions covering 5464 hours of streaming over 5 different geographical locations and 5 different bandwidth patterns. We believe our tool and as well as the dataset could be used in multiple applications such as a better configuration of application/transport protocols based on the network conditions, intelligent integration of network and application, predicting YouTube's QoE etc. We analyze the dataset and observe that during an HTTP/3 streaming not all requests are served by HTTP/3. Instead whenever the network condition is not favorable the browser chooses to fallback, and the application requests are transmitted using HTTP/2 over the old-standing transport protocol TCP. We observe that such switching of protocols impacts the performance of video streaming applications.

Traditional censorship revolves around blocking access to some websites (or services) over the Internet. However, recently there has been a rise in the events of an extreme form of censorship viz., deliberate Internet shutdown, leading to complete Internet disconnection, severely impacting lives in such regions. Naturally, these shutdowns render all existing circumvention schemes unusable. Thus, we present Dolphin, a first of its kind system that can provide access to lightweight and delay tolerant Internet applications (email, tweets, news snippets, etc.) during Internet shutdowns. Dolphin uses the cellular voice channel to transmit data bits. A user in the shutdown region (who wishes to access these applications) requires a peer in non-shutdown region to send and retrieve content on its behalf. The data bits between the peers are sent by first encoding them into audio and then transmitting them over a cellular voice call. We overcome multiple challenges while designing and implementing Dolphin. E.g., the cellular voice channel is inherently lossy and unreliable. But the Internet applications need reliable transfers. Thus, in Dolphin we develop a TCP-style reliability layer to overcome the losses that works atop any underlying encoding and modulation scheme. Further, to evade eavesdroppers over the insecure voice channel, we provide end-to-end confidentiality. Also, Dolphin can function even without human intervention, by using cellular voice automation services. We experimentally show that Dolphin works for Internet applications, by testing it for sending email, tweets and accessing news snippets. All these applications take a few minutes to be accessed (e.g., a 500 character email was received in under 2 minutes).

IEEE 802.11ax introduces OFDMA where multiple users transmit concurrently. This is particularly useful in dense scenarios where the density of WiFi connected devices is very high in settings such as stadiums, airports, classrooms, etc. The effectiveness of such a system relies heavily upon an efficient Scheduling and Resource Allocation (SRA) algorithm. One approach towards designing such an algorithm is to model this problem as a constrained optimization problem that maximizes a linear utility function. Such a framework attempts to adapt several standard SRA rules, like maximizing the Sum Rate, Proportional Fair, to the OFDMA settings. State-of-the-art (SOTA) SRA heuristics lack theoretical guarantees. In this paper, we show a reduction of the SRA problem in OFDMA to a budgeted variant of the maximum weight matching problem on bipartite graphs. We observe that this problem is NP-hard. Hence, we design an efficient approximation algorithm, FAIRSPLIT, with provable guarantees. We demonstrate the effectiveness of our algorithm through a variety of simulations. We see that FAIRSPLIT outperforms SOTA by 11-48% in terms of aggregate throughput while utilizing the bandwidth more efficiently.

Nowadays, quantum-key-distribution (QKD) gains popularity for providing high-security in high-capacity spectrally-spatially elastic optical network (SS-EONs). Considering QKD in SS-EONs requires addition resources for classical and quantum channels and introduces addition noises. The key challenge during resource allocation in QKD-enabled SS-EONs is to deal with the background noises generated by classical and quantum channels along with inter-core and inter-mode XT while reducing the highest utilized spectrum slot index. For the first time, this paper proposes a routing, spectrum, core, and mode allocation (RSCMA) model in QKD-enabled SS-EONs while avoiding intercore and inter-mode XT and the noises generated by the classical and quantum channels. The proposed model allocates spectrum for the data and classical channels for each request maintaining the spectrum continuity and contiguity constraints. It selects a dedicated mode for the quantum channels along the routing path for each request. We model the QKD-enabled resource allocation as an integer linear programming (ILP) to minimize the highest utilized spectrum slot index. We introduce a heuristic when the optimization problem is not tractable for large networks. Numerical results indicate that the values of the highest utilized spectrum slot index using the ILP and heuristic approaches are comparable and the computation time of the heuristic approach is much smaller than that of the ILP approach.

Signal transmission using counter-propagation nowadays is adopted to enhance resource utilization in spectrally-spatially elastic optical networks (SS-EONs), where inter-mode and inter-core crosstalks always degrade signal quality and become bottlenecks for high transport capacity. In this paper, we propose BPRIA for the first time, a Bi-Partitioning-based crosstalk-avoided Resource Identification and Allocation scheme in SS-EONs to enhance resource utilization while suppressing both inter-mode and inter-core crosstalks. We introduce a bi-partitioning optimization problem with vertex elimination to maximize the number of non-adjacent cores and modes in each partition of the bipartite graph; two distinct sets of cores and modes are used in a counter-propagation manner for lightpath allocation. The optimization problem is formulated as an integer linear programming (ILP) problem, and we prove that the optimization problem is NP-complete. An algorithm for core-mode-spectrum allocation is developed considering two distinct sets of cores and modes obtained by ILP to serve lightpath requests while avoiding inter-mode and inter-core crosstalks. For dynamic scenarios, we present core-mode-spectrum allocation. Numerical results reveal that the blocking probability is reduced by BPRIA in SS-EONs, and it enhances traffic admissibility in the network.

We address the problem of connecting IoT nodes to the Internet via challenged networks, which are characterized by minimal or intermittent network association. Examples of such networks can be found in forests with distant access points or in railway stations with overburdened access points. In such places, it is difficult for a node to establish and maintain an association with the access point. There exist several solutions that demonstrate the feasibility of data transfer using low bit-rate broadcast of discovery frames that do not necessitate association. We categorize them into a representative protocol called ViFi. However, the low bit-rate broadcast traffic hampers the performance of heavilyutilized networks by consuming significant airtime and resulting in a severe drop in goodput. We come up with a novel adaptive version of ViFi, Adaptive ViFi, that adapts to network conditions and intelligently selects a channel and an interval for the data transmission. In principle, our proposed solution is agnostic to WiFi standard, client, or AP in use. We develop an end-to-end working prototype on Raspberry Pi and hostapd-based access points. With a set of extensive experiments, we find that Adaptive ViFi gracefully addresses the limitations of ViFi in a heavily utilized network, at the same time it achieves up to 10.7x higher goodput than the ViFi. It co-exists well with other WiFi-based non-IoT clients with minimal impact on their goodput (4%). It scales well with a delivery rate of 77% and 50% with 150 and 300 nodes, respectively.

IEEE 802.11ax introduces OFDMA (Orthogonal Frequency Division Multiple Access) that allows multiple users to transmit or receive frames concurrently. The OFDMA transmission duration is decided by the client with maximum transmission duration. We focus on minimizing the maximum transmission duration. The standard restricts assignment of at max one RU to a client and provides a specific way of splitting the channel into smaller RUs. In this paper, we come up with a generic framework, MMRU-Alloc (Min Max Resource Unit Allocation) to allocate RUs to clients under the given constraints for a single OFDMA transmission. The framework allows one to define any non-negative cost function such as data transmission time or the padding length required for synchronized end time of all clients for a client-RU pair and we believe this can capture a wide variety of scenarios. We design provably efficient and optimal algorithms for this general problem. We demonstrate the applicability of our framework for two specific problems pertaining to transmission of one OFDMA frame. (1) Minimizing the transmission duration of one OFDMA frame for a given set of clients. (2) Minimizing the maximum padding length required by any client to ensure synchronized end time. We implemented (1) in NS-3 and evaluated its performance. We compare it with two popular scheduling and resource allocation algorithms: Max Rate (MR) and Proportional Fairness (PF). We find that it outperforms both MR and PF by upto 91.3% in terms of frame transmission time and upto 91.1% terms of throughput achieved.

Anonymous VoIP calls over the Internet holds great significance for privacy-conscious users, whistle-blowers and political activists alike. Prior research deems popular anonymization systems like Tor unsuitable for providing the requisite performance guarantees that real-time applications like VoIP need. Their claims are backed by studies that may no longer be valid due to constant advancements in Tor. Moreover, we believe that these studies lacked the requisite diversity and comprehensiveness. Thus, conclusions from these studies, led them to propose novel and tailored solutions. However, no such system is available for immediate use. Additionally, operating such new systems would incur significant costs for recruiting users and volunteered relays, to provide the necessary anonymity guarantees.

We address the problem of automating the process of network troubleshooting for a large-scale WiFi network. Specifically, we target identifying the causes of unnecessary active scans in WiFi networks, that are known to degrade the WiFi performance. We collect 340 hours worth of data with several thousands of episodes of active scans to train various machine learning models. Data is collected with 27 devices across vendors in varied network setups under a controlled setting. We study unsupervised and supervised machine learning techniques to conclude that a multilayer perceptron is the best model to detect the causes of active scanning. Further, we perform an in-vivo model validation in an uncontrolled real-world WiFi network. We also compare our models with a static rule-based approach. Our model improves the mean F1-score accuracy of cause detection from 0.78 to 0.99. Our proposed mechanism has the potential of being incorporated in the existing WiFi controllers, such as that of Cisco and Aruba.

Internet-of-Things (IoT) nodes in dense WiFi deployments struggle to maintain their associations with the access points (APs) and efficiently transmit their data. In the process, they waste their energy and airtime, which affects their battery-life and throughput. We present Elixir, a protocol for WiFi-based IoT nodes that enables them to transmit data without the need of a WiFi association. Elixir is implemented in the user space, which makes it easy to port the implementation to any Linux-based client and AP. We demonstrate its end-to-end operation on a Raspberry Pi module in good [signal strengths '40 to '50 dBm and no losses] and bad network conditions [signal strengths below '80 dBm and losses present]. We measure benefits of Elixir in terms of (a) the ability to transmit when the WiFi association cannot be maintained [in bad network condition], (b) lesser number of frames transmitted [50x - good network condition, 100x ? bad network condition], (c) reduced airtime consumption [10x - bad network condition], and (d) reduced battery expenditure [18% for both network conditions].

In recent years, WiFi has grown in capacity as well as deployment demand. WiFi system administrators (sysads) want a simple answer to the question “Is my WiFi network healthy?”, and a possible follow-up “What is wrong with it?”, if it is reported as “unhealthy”. But we are far from having such an interface today. It is this gap that this work attempts to fill. We present Witals, a system for WiFi performance diagnosis. We first design a causal diagnosis graph, that extensively identifies the underlying cause(s) of WiFi performance problems. Next, we identify a set of metrics corresponding to nodes in this causal graph. These metrics are measured in real-time by an operational AP, and help in quantifying the effect of each cause. We design a diagnosis algorithm based on the causal graph and the metrics, which ultimately presents a sanitized view of WiFi network health to the sysad. We have implemented a prototype of Witals on an enterprise grade 802.11n AP platform. Using a variety of controlled as well as real-life measurements, we show that our diagnosis framework follows ground truth accurately. Witals has also helped the sysads uncover some unexpected diagnoses.

How does a dense WiFi network perform, specifically for the common case of TCP download? While the empirical answer to this question is `poor', analysis and experimentation in prior work has indicated that TCP clocks itself quite well, avoiding contention-driven WiFi overload in dense settings. This paper focuses on measurements from a real-life use of WiFi in a dense scenario: a classroom where several students use the network to download quizzes and instruction material. We find that the TCP download performance is poor, contrary to that suggested by prior work. Through careful analysis, we explain the complex interaction of various phenomena which leads to this poor performance. Specifically, we observe that a small amount of upload traffic generated when downloading data upsets the TCP clocking, and increases contention on the channel. Further, contention losses lead to a vicious cycle of poor interaction with autorate adaptation and TCP's timeout mechanism. To reduce channel contention and improve performance, we propose a modification to the AP scheduling policy to improve the performance of large TCP downloads. Our solution, WiFiRR, picks only a subset of clients to be served by the AP during any instant, and varies this set of “active” clients periodically in a round-robin fashion over all clients to ensure that no client starves. We have done extensive evaluation of WiFiRR in simulation and in real settings. By reducing the number of contending nodes at any point of time, WiFiRR improves the download time of large TCP flows upto 3.5x of our classroom scenario. We also compare WiFiRR with state-of-the-art prior work WiFox, WiFiRR improves download time by 2.25× over WiFox.

We present Witals, a system for WiFi performance diagnosis. In Witals, a live access point diagnoses the health of the WiFi network by examining the health of the air around it. Such diagnosis is critical functionality for sysads, and is an important addition to the state of the art. In the demo we will show WiFi performance diagnosis by Witals live at MobiCom venue and also from past collected traces in other settings & for controlled experiments.