Even as the COVID-19 pandemic drove advances in contact tracing and exposure notification systems, user privacy challenges continue to plague otherwise promising approaches to contain contagions. We propose a novel, scalable approach to address privacy in contact tracing that improves utility. We apply passive WiFi scan data using two metrics suitable for estimating contact between users. We support this with real world experimental data captured across a range of environments relevant to contact tracing. To preserve privacy, we leverage properties of truncated cryptographic hashes in an adaptation unique to contact tracing. This hash collision filter allows users to share information about potential contacts with a central server without revealing sensitive information. Using an aggressive threat model, including adversarial users and a malicious server, we share how this technique can improve utility while still providing strong security protections compared to other approaches using, for example, only Bluetooth (BT) or global navigation satellite systems (GNSSs). Finally, we discuss a capability of this approach that allows notification for asynchronous co-location from past contacts.

Transmitter localization remains a challenging problem in large-scale outdoor environments, especially when transmitters and receivers are allowed to be mobile. We consider localization in the context of a Radio Dynamic Zone (RDZ), a proposed experimental platform where researchers can deploy experimental devices, waveforms, or wireless networks. Wireless users outside an RDZ must be protected from harmful interference coming from sources inside the RDZ. In this setting, localizing transmitters that are causing interference is critical. One notable obstacle for developing data-driven methods for localization is the lack of large-scale training datasets. As our first contribution, we present a new dataset for localization, captured at 462.7 MHz in a 4 sq. km outdoor area with 29 different receivers and over 4,500 unique transmitter locations. Receivers are both mobile and stationary, and heterogeneous in terms of hardware, placement, and gain settings. Next, we propose a new machine learning-based localization method that can handle inputs from uncalibrated, heterogeneous receivers. Finally, we leverage our new dataset to study the robustness of our technique and others against “out of distribution” (OOD) inputs that are common in most real life applications. We show that our technique, CUTL (Calibrated U-Net Transmitter Localization), is 49% more accurate on in-distribution data, and more robust than previous methods on OOD data.

Software defined radios (SDRs) are often used in the experimental evaluation of next-generation wireless technologies. While crowdsourced spectrum monitoring is an important component of future spectrum-agile technologies, there is no clear way to test it in the real world, i.e., with hundreds of users each carrying an SDR while uploading data to a cloud-based controller. Current fully functional SDRs are bulky, with components connected via wires, and last at most hours on a single battery charge. To address these needs, we design and develop a compact, portable, untethered, and inexpensive SDR we callSitara. Our SDR interfaces with a mobile device over Bluetooth 5 and can function standalone or as a client to a central command and control server. It transmits and receives common waveforms, uploads IQ samples or processed receiver data through a mobile device to a server for remote processing and performs spectrum sensing functions. We present results from a user study involving more than 100 participants to evaluate Sitara in a hypothetical large-scale crowdsourced spectrum monitoring application. We also present a comparative analysis of Sitara to related crowdsensing systems with a particular emphasis on the role of incentives and user participation.

Wireless systems are an integral part of aviation. Apart from their apparent use in air-to-ground communication, wireless systems play a crucial role in avionic functions including navigation and landing. An interference-free wireless environment is therefore critical for the uninterrupted operation and safety of an aircraft. Hence, there is an urgency for airport facilities to acquire the capability to continuously monitor aviation frequency bands for real-time detection of interference and anomalies. To meet this critical need, we design and build AviSense, an SDR-based real-time , versatile system for monitoring aviation bands. AviSense detects and characterizes signal activities to enable practical and effective anomaly detection. We identify and tackle the challenges posed by a diverse set of critical aviation bands and technologies. We evaluate our methodology with real-world aviation signal measurements and two custom datasets of anomalous signals. We find that our signal classification capability achieves a true positive rate of ∼99%, with few exceptions, and a false positive rate of less than 4%. We also demonstrate that AviSense can effectively distinguish between different types of anomalies. We build and evaluate a prototype implementation of AviSense that supports distributed monitoring.

We propose using Carrier Sensing (CS) for distributed interference management in millimeter-wave (mmWave) cellular networks where spectrum is shared by multiple operators that do not coordinate among themselves. In addition, even the base station sites can be shared by the operators. We describe important challenges in using traditional CS in this setting and propose enhanced CS protocols to address these challenges. Using stochastic geometry, we develop a general framework for downlink coverage probability analysis of our shared mmWave network in the presence of CS and derive the downlink coverage probability expressions for several CS protocols. Our work is the first to investigate and analyze (using stochastic geometry) CS for mmWave networks with spectrum and BS sites shared among non-coordinating operators. We evaluate the downlink coverage probability of our shared mmWave network using simulations as well as numerical examples based on our analysis. Our evaluations show that our proposed approach leads to an improvement in coverage probability, compared to the coverage probability with no CS, for higher values of signal-to-interference and noise ratio (SINR). Interestingly, our evaluations also reveal that for lower values of SINR, not using any CS is the best strategy in terms of the downlink coverage probability.

This paper studies the problem of distributed beam scheduling for 5G millimeter-Wave (mm-Wave) cellular networks where base stations (BSs) belonging to different operators share the same spectrum without centralized coordination among them. Our goal is to design efficient distributed scheduling algorithms to maximize the network utility, which is a function of the achieved throughput by the user equipment (UEs), subject to the average and instantaneous power consumption constraints of the BSs. We propose a Media Access Control (MAC) and a power allocation/adaptation mechanism utilizing the Lyapunov stochastic optimization framework and non-cooperative games. In particular, we first decompose the original utility maximization problem into two sub-optimization problems for each time frame, which are a convex optimization problem and a non-convex optimization problem, respectively. By formulating the distributed scheduling problem as a non-cooperative game where each BS is a player attempting to optimize its own utility, we provide a distributed solution to the non-convex sub-optimization problem via finding the Nash Equilibrium (NE) of the game whose weights are determined optimally by the Lyapunov optimization framework. Finally, we conduct simulation under various network settings to show the effectiveness of the proposed game-based beam scheduling algorithm in comparison to that of several reference schemes.

We consider the radio window attack, a privacy threat in which an attacker monitors the wireless link over a period of time, recording the channel state information (CSI) across multiple packets and uses a model to detect, estimate, or classify human movements. To prevent such privacy attacks on a wireless channel, we propose modifying radio training (MoRTr), a novel system for Wi-Fi MIMO-OFDM devices that alters transmitted symbols over time, space and frequency via a pseudo-random process that mimics the changes due to human activity, particularly the training symbols that are used to measure the wireless channel by the receiver. We perform extensive experiments to demonstrate that an attacker is thwarted by the approach. At the same time, we demonstrate that any receiver is able to use its measured CSI to demodulate the data without any significant degradation in performance, despite the fact that the receiver is not measuring the true CSI.

We present DeepRadar, a novel deep-learning-based environmental sensing capability system for detecting radar signals and estimating their spectral occupancy. DeepRadar makes decisions in real-time and maintains continuous operability by adapting its computations based on the available computing resources. We thoroughly evaluate DeepRadar using a variety of test data at different signal-to-interference ratio (SIR) levels. Our evaluation results show that at 20 dB peak-to-average SIR, per MHz, DeepRadar detects radar signals with 99% accuracy and misses only less than 2 MHz, on average, while estimating their spectral occupancy. Our implementation of DeepRadar using a commercial-off-the-shelf software-defined radio also achieves a similarly high detection accuracy.

POWDER is a highly flexible, deeply programmable, and city-scale scientific instrument that enables cutting-edge research in wireless technologies. Researchers interact with the POWDER platform via the Internet to conduct their experiments, with zero penalty for remote access. In this two-part demonstration, the POWDER implementers show how to use the platform. First, they present the workflow that researchers follow to conduct experiments. Second, they highlight some of the hardware and software building blocks available through POWDER, including components related to over-the-air wireless and mobile networking, 5G, and massive MIMO.

A malicious attacker could, by taking control of internet-of-things devices, use them to capture received signal strength (RSS) measurements and perform surveillance on a person's vital signs, activities, and sound in their environment. This article considers an attacker who looks for subtle changes in the RSS in order to eavesdrop sound vibrations. The challenge to the adversary is that sound vibrations cause very low amplitude changes in RSS, and RSS is typically quantized with a significantly larger step size. This article contributes a lower bound on an attacker's monitoring performance as a function of the RSS step size and sampling frequency so that a designer can understand their relationship. Our bound considers the little-known and counter-intuitive fact that an adversary can improve their sinusoidal parameter estimates by making some devices transmit to add interference power into the RSS measurements. We demonstrate this capability experimentally. As we show, for typical transceivers, the RSS surveillance attacker can monitor sound vibrations with remarkable accuracy. New mitigation strategies will be required to prevent RSS surveillance attacks.

Today, Cloud providers offer simplistic scaling policies that rely on thresholds that force tenants to have a priori knowledge of their workloads. We develop a new method for scaling memory-intensive workloads that needs no thresholds. This makes it worry-free for tenants, and it adapts even as workloads evolve. This is especially hard for memory-bound applications where even a small decrease in the amount of memory available can have a dramatic, almost unbounded impact on performance. Hence, sizing a machine's physical memory correctly is critical to application performance and operating cost. To determine a natural threshold for memory-intensive applications, our approach automatically analyzes an application's miss ratio curve (MRC) and models it as a hyperbola. Intuitively, a memory scaling policy should operate at the point where the curve flattens: that is, at its intersection with its latus rectum (LR). Our system uses a new approach to constructing and analyzing MRCs at run time that captures memory references from a slice of any scalable application as it executes on standard virtual machines from any major Cloud provider. We demonstrate with multiple applications running on Amazon Web Services (AWS) and Microsoft Azure. Our implementation and evaluation show that, though the LR doesn't require tenants to set thresholds, it is effective in scaling memory-intensive workloads to save on operating costs while avoiding queuing, thrashing, or collapse. It increases throughput by 1.5× and reduces queuing delay by 2× in our evaluation.

We make a case for using the emerging blockchain technology for spectrum sharing in Citizen's Broadband Radio Service (CBRS). We show that blockchain can dynamically allocate channels in a transparent and fair manner by achieving distributed channel de-confliction. Furthermore, blockchain helps automate communication between the Spectrum Access Systems (SASs) belonging to competing, non-trusting organizations by providing consensus, while at the same time preserving confidentiality of sensitive information. We implement our CBRS blockchain architecture using the Hyperledger Fabric platform. In order to support high channel request rates, we also develop a novel feature that adaptively pre-processes channel allocation requests at the SAS before sending those to the blockchain network. We evaluate our blockchain solution under different settings and show that it meets the FCC latency requirements. We also analyze the scalability of our system and show that the latency and throughput guarantees can be maintained in scenarios involving multiple organizations.

We present LLOCUS, a novel learning-based system that uses mobile crowdsourced RF sensing to estimate the location and power of unknown mobile transmitters in real time, while allowing unrestricted mobility of the crowdsourcing participants. We carefully identify and tackle several challenges in learning and localizing, based on RSS, in such a dynamic environment. We decouple the problem of localizing a transmitter with unknown transmit power into two problems, 1) predicting the power of a transmitter at an unknown location, and 2) localizing a transmitter with known transmit power. LLOCUS first estimates the power of the unknown transmitter and then scales the reported RSS values such that the unknown transmit power problem is transparent to the method of localization. We evaluate LLOCUS using three experiments in different indoor and outdoor environments. We find that LLOCUS reduces the localization error by 17-68% compared to several non-learning methods.

The large scale deployment of multi-antenna wireless networks in homes and office buildings introduces new privacy concerns for people residing in these spaces. By measuring the signal strength using receivers placed outside the premises, an attacker can track the movement of people inside. One way to defend against such an attack is to have the signal strengths of the transmitters vary (sometimes reducing to zero) according to some randomized schedule. We show that the question of finding the schedule that minimizes the worst-case "privacy loss" can be formulated as a constant-sum Stackelberg game between an attacker, whose goal is to place receivers in order to learn the movement of users, and a defender who tries to prevent the attacker while maintaining the connectivity and QoS requirements of the network. We introduce a flexible framework that enables us to capture the constraints of the attacker and the defender. The framework allows us to capture features of modern wireless systems such as directional antennas and also allows us to plug in different path-loss models with minimal changes to the setup. We then formulate the problem of finding the optimal defender strategy as a linear program and show that it can be solved efficiently. We also perform numerical evaluations on how the payoffs are affected as the requirements of the defender and the resources the attacker can afford to exhaust change.

With the tremendous increase in the use of wireless devices, understanding the surrounding wireless/RF environment is becoming essential for many application areas. In this work, we develop the technical building blocks needed for a spectrum monitoring system that can incrementally learn about the signals present in a deployed environment. We achieve incremental learning (IL) by identifying and grouping the new/unknown signals and, automatically building new machine learning (ML) models for detecting them. A thorough evaluation of our approach demonstrates its adaptability and high accuracy with signal data from several over-the-air scenarios.

Software-defined radios (SDRs) are often used in the experimental evaluation of next-generation wireless technologies. While crowd-sourced spectrum monitoring is an important component of future spectrum-agile technologies, there is no clear way to test it in the real world, i.e., with hundreds of users each carrying an SDR while uploading data to a cloud-based controller. Current fully functional SDRs are bulky, with components connected via wires, and last at most hours on a single battery charge. To address the needs of such experiments, we design and develop a compact, portable, untethered, and inexpensive SDR we call Sitara. Our SDR interfaces with a mobile device over Bluetooth 5 and can function standalone or as a client to a central command and control server. The Sitara offers true portability: it operates up to one week on battery power, requires no external wired connections and occupies a footprint smaller than a credit card. It transmits and receives common waveforms, uploads IQ samples or processed receiver data through a mobile device to a server for remote processing and performs spectrum sensing functions. Multiple Sitaras form a distributed system capable of conducting experiments in wireless networking and communication in addition to RF monitoring and sensing activities. In this paper, we describe our design, evaluate our solution, present experimental results from multi-sensor deployments and discuss the value of this system in future experimentation.

We design and build a protocol called on-off noise power communication (ONPC), which modifies the software in commodity packet radios to allow communication, independent of their standard protocol, at a very slow rate at long range. To achieve this long range, we use the transmitter as an RF power source that can be on or off if it does or does not send a packet, respectively, and a receiver that repeatedly measures the noise and interference power level. We use spread spectrum techniques on top of the basic on/off mechanism to overcome the interference caused by other devices' channel access to provide long ranges at a much lower data rate. We implement the protocol on top of commodity WiFi hardware. We discuss our design and how we overcome key challenges such as non-stationary interference, carrier sensing and hardware timing delays. We test ONPC in several situations to show that it achieves significantly longer range than standard WiFi.

With advancements in network technology and cost-efficient hardware, developers have begun placing servers throughout the world. These servers reside at the edge of cloud network infrastructures and vastly improved network quality. However, many locations in the world are still distant when accessing these edge servers. Further, massively multiplayer online games can strain edge server resources with additional hardware not available at the desired location.

When several internet-of-things devices are required to be installed in a smart home, significant effort is required to provide each device with the association information for the home's wireless router. We design and build a novel protocol called Secure Transfer of Association Protocol (STRAP), which securely bootstraps connectivity between a set of deployed WiFi devices and a home's wireless router. We show that STRAP works in a variety of environments and is faster than conventional methods for connecting WiFi devices to home wireless routers.

Many networked users through their devices are interested in participating in distributed sensing and data collection for the purpose of betterment of human society or for earning rewards. Preservation of their location privacy is an important requirement for users participating and contributing to the data collection. We develop a novel privacy preserving approach for collecting noise-free data from vehicular users. Collection of noise-free, "pure" data, enhances its utility in the applications that use it. Location privacy must be preserved from the entity that we call a central controller, that collects all the vehicular data, and is assumed to be adversarial. We collect the data in a noise-free form by introducing temporal and spatial variations using Random Delays and Indirections. We run simulations using network and vehicle simulators driven by a real-world traffic scenario from the city of Luxembourg to evaluate our approach. Our simulation results show that the adversary cannot localize the uploaders within the thresholds of the number of streets and the length of the region of interest chosen by them.

The current mechanisms for locating spectrum offenders are time consuming, human-intensive, and expensive. In this paper, we propose a novel approach to locate spectrum offenders using crowdsourcing. In such a participatory sensing system, privacy and bandwidth concerns preclude distributed sensing devices from reporting raw signal samples to a central agency; instead, devices would be limited to measurements of received power. However, this limitation enables a smart attacker to evade localization by simultaneously transmitting from multiple infected devices. Existing localization methods are insufficient or incapable of locating multiple sources when the powers from each source cannot be separated at the receivers. In this paper, we first propose a simple and efficient method that simultaneously locates multiple transmitters using the received power measurements from the selected devices. Second, we build sampling approaches to select sensing devices required for localization. Next, we enhance our sampling to also take into account incentives for participation in crowdsourcing. We experimentally evaluate our localization framework under a variety of settings and find that we are able to localize multiple sources transmitting simultaneously with reasonably high accuracy in a timely manner.

Growing demand for data and increasing number of devices are drastically changing the scale of operation in mobile networks. Future services and business models require efficient provisioning with enhanced traffic management. It is hard to meet these requirements on today's mobile networks that are deployed over specialized hardware. While operators are keen to adopt NFV (Network Function Virtualization) to virtualize their networks, virtualized mobile network deployments face a few technical barriers. To address these challenges, we design SCOPE that effectively applies concepts from SDN and distributed systems to realize NFV-based LTE core networks. Using centralized allocation, SCOPE effectively manages the resources across multiple telecom data-centers in a way to meet the traffic requirements. To enforce the computed al- locations, SCOPE includes flexible and efficient mechanisms to configure the data-plane. With full compliance to 3GPP- based protocols, SCOPE ensures faster and cost-effective deployments. The efficacy of SCOPE is shown using a prototype implementation and large-scale simulations.

High performance edge networks, such as fiber-to-the-premises (FTTP), are increasingly being deployed by municipalities and communities to support advanced services and applications. The complexity of operating these networks often means that their full potential is not being reached and they are relegated to being fast access pipes to the Internet. In this paper, we present our work on OpenEdge, a dynamic and secure open service edge network architecture. OpenEdge provides a control architecture that automates the configuration of the edge network in a cloud-like manner to simplify the introduction of new network services and applications.

Network management tasks remain tedious and error-prone, and often require complex reasoning on the part of the network administrator. With KnowNet we address the challenge of reasoning about network management by approaching it as a set of cooperating applications executing over a knowledge graph which captures data and information about the network and the applications that manage and reason over it. We apply our approach to enterprise network management by developing a suite of cooperating applications that deals with security and application performance management in an enterprise network.

In addition to growth of data traffic, mobile networks are bracing for a significant rise in the control-plane signaling. While a complete re-design of the network to overcome inefficiencies may help alleviate the effects of signaling, our goal is to improve the design of the current platform to better manage the signaling. To meet our goal, we combine two key trends. Firstly, mobile operators are keen to transform their networks with the adoption of Network Function Virtualization (NFV) to ensure economies of scales. Secondly, growing popularity of cloud computing has led to advances in distributed systems. In bringing these trends together, we solve several challenges specific to the context of telecom networks. We present SCALE - A framework for effectively virtualizing the MME (Mobility Management Entity), a key control-plane element in LTE. SCALE is fully compatible with the 3GPP protocols, ensuring that it can be readily deployed in today's networks. SCALE enables (i) computational scaling with load and number of devices, and (ii) computational multiplexing across data centers, thereby reducing both, the latencies for control-plane processing, and the VM provisioning costs. Using an LTE prototype implementation and large-scale simulations, we show the efficacy of SCALE.

When traffic arrives from the network for an idled mobile device, the network executes device activation procedures to wake the device up. Current device activation mechanisms are ill suited to support the expected growth of machine-to-machine (M2M) devices and traffic. We propose an adaptive device activation architecture for LTE/EPC cellular networks that adapts to network conditions and M2M application requirements to realize scalable device activation without increasing the resources used for this purpose. Our evaluation shows that our adaptive approach enables the network to handle M2M applications with a large number of devices without negatively impacting existing human-to-human (H2H) and human-to-machine (H2M) traffic.

Abstract—In this paper, our goal is to develop approaches to reduce the energy consumption in Radio Tomographic Imaging (RTI)-based methods for device free localization without giving up localization accuracy. Our key idea is to only measure those links that are near the current location of the moving object being tracked. We propose two approaches to find the most effective links near the tracked object. In our first approach, we only consider links that are in an ellipse around the current velocity vector of the moving object. In our second approach, we only consider links that cross through a circle with radius r from the current position of the moving object. Thus, rather than creating an attenuation image of the whole area in RTI, we only create the attenuation image for effective links in a small area close to the current location of the moving object. We also develop an adaptive algorithm for determining r. We evaluate the proposed approaches in terms of energy consumption and localization error in three different test areas. Our experimental results show that using our approach, we are able to save 50 % to 80 % of energy. Interestingly, we find that our radius-based approach actually increases the accuracy of localization. I.

Bluetooth has found widespread adoption in phones, wireless headsets, stethoscopes, glucose monitors, and oximeters for communication of, at times, very critical information. However, the link keys and encryption keys in Bluetooth are ultimately generated from a short 4 digit PIN, which can be cracked off-line. We develop an alternative for secure communication between Bluetooth devices using the symmetric wireless channel characteristics. Existing approaches to secret key extraction primarily use measurements from a fixed, single channel (e.g., a 20 MHz WiFi channel); however in the presence of heavy WiFi traffic, the packet exchange rate in such approaches can reduce as much as 200 x. We build and evaluate a new method, which is robust to heavy WiFi traffic, using a very wide bandwidth (B >> 20 MHz) in conjunction with random frequency hopping. We implement our secret key extraction on two Google Nexus One smartphones and conduct numerous experiments in indoor-hallway and outdoor settings. Using extensive real-world measurements, we show that outdoor settings are best suited for secret key extraction using Bluetooth. We also show that even in the absence of heavy WiFi traffic, the performance of secret key generation using Bluetooth is comparable to that of WiFi while using much lower transmit power.

We investigate the ability of an attacker to passively use an otherwise secure wireless network to detect moving people through walls. We call this attack on privacy of people a "monitoring radio windows" (MRW) attack. We design and implement the MRW attack methodology to reliably detect when a person crosses the link lines between the legitimate transmitters and the attack receivers, by using physical layer measurements. We also develop a method to estimate the direction of movement of a person from the sequence of link lines crossed during a short time interval. Additionally, we describe how an attacker may estimate any artificial changes in transmit power (used as a countermeasure), compensate for these power changes using measurements from sufficient number of links, and still detect line crossings. We implement our methodology on WiFi and ZigBee nodes and experimentally evaluate the MRW attack by passively monitoring human movements through external walls in two real-world settings. We find that %our methods an attacker may achieve close to 100% accuracy in detecting line crossings and determining direction of motion, even through reinforced concrete walls.

This paper shows experimentally that standard wireless networks which measure received signal strength (RSS) can be used to reliably detect human breathing and estimate the breathing rate, an application we call “BreathTaking”. We present analysis showing that, as a first order approximation, breathing induces sinusoidal variation in the measured RSS on a link, with amplitude a function of the relative amplitude and phase of the breathing-affected multipath. We show that although an individual link may not reliably detect breathing, the collective spectral content of a network of devices reliably indicates the presence and rate of breathing. We present a maximum likelihood estimator (MLE) of breathing rate, amplitude, and phase, which uses the RSS data from many links simultaneously. We show experimental results which demonstrate that reliable detection and frequency estimation is possible with 30 seconds of data, within 0.07 to 0.42 breaths per minute (bpm) RMS error in several experiments. The experiments also indicate that the use of directional antennas may improve the systems robustness to external motion.

Secret key establishment is a fundamental requirement for private communication between two entities. In this article, we propose and evaluate a new approach for secret key extraction where multiple sensors collaborate in exchanging probe packets and collecting channel measurements. Essentially, measurements from multiple channels have a substantially higher differential entropy compared to the measurements from a single channel, thereby resulting in more randomness in the information source for key extraction, and this in turn produces stronger secret keys. We also explore the fundamental trade-off between the quadratic increase in the number of measurements of the channels due to multiple nodes per group versus a linear reduction in the sampling rate and a linear increase in the time gap between bidirectional measurements. To experimentally evaluate collaborative secret key extraction in wireless sensor networks, we first build a simple yet flexible testbed with multiple TelosB sensor nodes. Next, we perform large-scale experiments with different configurations of collaboration. Our experiments show that in comparison to the 1 × 1 configuration, collaboration among sensor nodes significantly increases the secret bit extraction per second, per probe, as well as per millijoule of transmission energy. In addition, we show that the collaborating nodes can improve the performance further when they exploit both space and frequency diversities.

Configuration errors are the most significant cause of failure in networks. Little research has been devoted to preventing network configuration errors using device fingerprints. We demonstrate how they can be used to prevent information from being incorrectly routed in an IEEE 802.15.4 beacon-enabled wireless sensor network with multiple coordinators. To determine if they are appropriate for this application, we investigate the number of unique fingerprints that clock skew and radio frequency characteristics provide.

We evaluate the effectiveness of secret key extraction, for private communication between two wireless devices, from the received signal strength (RSS) variations on the wireless channel between the two devices. We use real world measurements of RSS in a variety of environments and settings. The results from our experiments with 802.11-based laptops show that in certain environments, due to lack of variations in the wireless channel, the extracted bits have very low entropy making these bits unsuitable for a secret key, an adversary can cause predictable key generation in these static environments, and in dynamic scenarios where the two devices are mobile, and/or where there is a significant movement in the environment, high entropy bits are obtained fairly quickly. Building on the strengths of existing secret key extraction approaches, we develop an environment adaptive secret key generation scheme that uses an adaptive lossy quantizer in conjunction with Cascade-based information reconciliation and privacy amplification. Our measurements show that our scheme, in comparison to the existing ones that we evaluate, performs the best in terms of generating high entropy bits at a high bit rate. The secret key bit streams generated by our scheme also pass the randomness tests of the NIST test suite that we conduct. We also build and evaluate the performance of secret key extraction using small, low-power, hand-held devices-Google Nexus One phones-that are equipped 802.11 wireless network cards. Last, we evaluate secret key extraction in a multiple input multiple output (MIMO)-like sensor network testbed that we create using multiple TelosB sensor nodes. We find that our MIMO-like sensor environment produces prohibitively high bit mismatch, which we address using an iterative distillation stage that we add to the key extraction process. Ultimately, we show that the secret key generation rate is increased when multiple sensors are involved in the key extraction process.

Orthogonal frequency division multiplexing (OFDM), widely recommended for sharing the spectrum among different nodes in a dynamic spectrum access network, imposes tight timing and frequency synchronization requirements. We examine the use of filterbank multicarrier (FBMC), a somewhat lesser known and understood alternative, for dynamic spectrum access. FBMC promises very low out-of-band energy of each subcarrier signal when compared to OFDM. In order to fully understand and evaluate the promise of FBMC, we first examine the use of special pulse-shaping filters of the FBMC PHY layer in reliably transmitting data packets at a very high rate. Next, to understand the impact of FBMC beyond the PHY layer, we devise a distributed and adaptive medium access control (MAC) protocol that coordinates data packet traffic among the different nodes in the network in a best-effort manner. Using extensive simulations, we show that FBMC consistently achieves at least an order of magnitude performance improvement over OFDM in several aspects including packet transmission delays, channel access delays, and effective data transmission rate available to each node in static, indoor settings. Using measurements of power spectral density and high data rate transmissions from a transceiver that we build using our National Instruments hardware platform, we show that while FBMC can decode/distinguish all the received symbols without any errors, OFDM cannot. Finally, we also examine the use of FBMC in a vehicular network setup. We find that FBMC achieves an order of magnitude performance improvement over large distances in this setup as well. Furthermore, in the case of multihop vehicular networks, FBMC can achieve about 20 × smaller end-to-end data packet delivery delays and relatively low packet drop probabilities. In summary, FBMC offers a much higher performing alternative to OFDM for networks that dynamically share the spectrum among multiple nodes.

Variance-based Radio Tomographic Imaging (VRTI) is an emerging technology that locates moving objects in areas surrounded by simple and inexpensive wireless sensor nodes. VRTI uses human motion induced variation in RSS and spatial correlation between link variations to locate and track people. An artificially induced power variations in the deployed network by an adversary can introduce unprecedented errors in localization process of VRTI and, given the critical applications of VRTI, can potentially lead to serious consequences including loss of human lives. In this paper, we tackle the problem of detecting malicious receivers that report false RSS values to induce artificial power variations in a VRTI system. We use the term “Receiver Attack” to refer to such malicious power changes. We use a combination of statistical hypothesis testing and heuristics to develop real-time methods to detect receiver attack in a VRTI system. Our results show that we can detect receiver attacks of reasonable intensity and identify the source(s) of malicious activity with very high accuracy.

New techniques in cross-layer wireless networks are building demand for ubiquitous channel sounding, that is, the capability to measure channel impulse response (CIR) with any standard wireless network and node. Towards that goal, we present a software-defined IEEE 802.11b receiver and CIR measurement system with little additional computational complexity compared to 802.11b reception alone. The system implementation, using the universal software radio peripheral (USRP) and GNU Radio, is described and compared to previous work. We validate the CIR measurement system and present the results of a measurement campaign which measures millions of CIRs between WiFi access points and a mobile receiver in urban and suburban areas.

Perimeter distinction in a wireless network is the ability to distinguish locations belonging to different perimeters. It is complementary to existing localization techniques. A draw-back of the localization method is that when a transmitter is at the edge of an area, an algorithm with isotropic error will estimate its location in the wrong area at least half of the time. In contrast, perimeter distinction classifies the location as being in one area or the adjacent regardless of the transmitter position within the area. In this paper, we use the naturally different wireless fading conditions to accurately distinguish locations across perimeters. We examine the use of two types of wireless measurements: received signal strength (RSS) and wireless link signature (WLS), and propose multiple methods to retain good distinction rates even when the receiver faces power manipulation by malicious transmitters. Using extensive measurements of indoor and outdoor perimeters, we find that WLS outperforms RSS in various fading conditions. Even without using signal power WLS can achieve accurate perimeter distinction up to 80%. When we train our perimeter distinction method with multiple measurements within the same perimeter, we show that we are able to improve the accuracy of perimeter distinction, up to 98%.

Measurements of received signal strength (RSS) on wireless links provide position information in various localization systems, including multilateration-based and fingerprint-based positioning systems, and device-free localization systems. Existing localization schemes assume a fixed or known transmit power. Therefore, any variation in transmit power can result in error in location estimate. In this paper, we present a generic framework for detecting power attacks and identifying the source of such transmit power variation. Our results show that we can achieve close to zero missed detections and false alarms with RSS measurements of only 50 transmissions. We also present an analysis of trade-off between accuracy and latency of detection for our method.

We investigate location distinction, the ability of a receiver to determine when a transmitter has changed location, which has application for energy conservation in wireless sensor networks, for physical security of radio-tagged objects, and for wireless network security in detection of replication attacks. In this paper, we investigate using a measured temporal link signature to uniquely identify the link between a transmitter (TX) and a receiver (RX). When the TX changes location, or if an attacker at a different location assumes the identity of the TX, the proposed location distinction algorithm reliably detects the change in the physical channel. This detection can be performed at a single RX or collaboratively by multiple receivers. We use 9,000 link signatures recorded at different locations and over time to demonstrate that our method significantly increases the detection rate and reduces the false alarm rate, in comparison to existing methods. We present a procedure to estimate the mutual information in link and link signature using the Edgeworth approximation. For the measured data set, we show that approximately 66 bits of link information is contained in each measured link signature.

In this paper, we shed light on the cross-layer interactions between the PHY, link and routing layers in networks with MIMO links operating in the diversity mode. Many previous studies assume an overly simplistic PHY layer model that does not sufficiently capture these interactions. We show that the use of simplistic models can in fact lead to misleading conclusions with regards to the higher layer performance with MIMO diversity. Towards understanding the impact of various PHY layer features on MIMO diversity, we begin with a simple but widely-used model and progressively incorporate these features to create new models. We examine the goodness of these models by comparing the simulated performance results with each, with measurements on an indoor 802.11 n testbed. Our work reveals several interesting cross-layer dependencies that affect the gains due to MIMO diversity. In particular, we observe that relative to SISO links: (a) PHY layer gains due to MIMO diversity do not always carry over to the higher layers, (b) the use of other PHY layer features such as FEC codes significantly influence the gains due to MIMO diversity, and (c) the choice of the routing metric can impact the gains possible with MIMO.

Social networks can serve as an effective mechanism for distribution of vulnerability patches and other malware immunization code. We propose a novel approach - SocialSwarm - by which peers exploit distances to their social peers to approximate levels of altruism and to collaborate on flash distribution of large files. SocialSwarm supports heterogeneous BitTorrent swarms of mixed social and non-social peers. We implement SocialSwarm as an extension to the Rasterbar libtorrent library - widely used by BitTorrent clients - and evaluate it on a testbed of 500 independent clients with social distances extracted from Facebook. We show that SocialSwarm can significantly reduce the average file distribution time, not only among socially connected peers, but also among other swarm participants.

We propose an approach where wireless devices, interested in establishing a secret key, sample the channel impulse response (CIR) space in a physical area to collect and combine uncorrelated CIR measurements to generate the secret key. We study the impact of mobility patterns in obtaining uncorrelated measurements. Using extensive measurements in both indoor and outdoor settings, we find that (i) when movement step size is larger than one foot the measured CIRs are mostly uncorrelated, and (ii) more diffusion in the mobility results in less correlation in the measured CIRs. We develop efficient mechanisms to encode CIRs and reconcile the differences in the bits extracted between the two devices. Our results show that our scheme generates very high entropy secret bits and that too at a high bit rate. The secret bits, that we generate using our approach, also pass the 8 randomness tests of the NIST test suite.

This paper presents novel methodologies which allow robust secret key extraction from radio channel measurements which suffer from real-world non-reciprocities and a priori unknown fading statistics. These methodologies have low computational complexity, automatically adapt to differences in transmitter and receiver hardware, fading distribution and temporal correlations of the fading signal to produce secret keys with uncorrelated bits. Moreover, the introduced method produces secret key bits at a higher rate than has previously been reported. We validate the method using extensive measurements between TelosB wireless sensors.

We explore the use of clock skew of a wireless local area network access point (AP) as its fingerprint to detect unauthorized APs quickly and accurately. The main goal behind using clock skews is to overcome one of the major limitations of existing solutions - the inability to effectively detect Medium Access Control (MAC) address spoofing. We calculate the clock skew of an AP from the IEEE 802.11 Time Synchronization Function (TSF) time stamps sent out in the beacon/probe response frames. We use two different methods for this purpose - one based on linear programming and the other based on least-square fit. We supplement these methods with a heuristic for differentiating original packets from those sent by the fake APs. We collect TSF time stamp data from several APs in three different residential settings. Using our measurement data as well as data obtained from a large conference setting, we find that clock skews remain consistent over time for the same AP but vary significantly across APs. Furthermore, we improve the resolution of received time stamp of the frames and show that with this enhancement, our methodology can find clock skews very quickly, using 50-100 packets in most of the cases. We also discuss and quantify the impact of various external factors including temperature variation, virtualization, clock source selection, and NTP synchronization on clock skews. Our results indicate that the use of clock skews appears to be an efficient and robust method for detecting fake APs in wireless local area networks.

Secret keys can be generated and shared between two wireless nodes by measuring and encoding radio channel characteristics without ever revealing the secret key to an eavesdropper at a third location. This paper addresses bit extraction, i.e., the extraction of secret key bits from noisy radio channel measurements at two nodes such that the two secret keys reliably agree. Problems include 1) nonsimultaneous directional measurements, 2) correlated bit streams, and 3) low bit rate of secret key generation. This paper introduces high-rate uncorrelated bit extraction (HRUBE), a framework for interpolating, transforming for decorrelation, and encoding channel measurements using a multibit adaptive quantization scheme which allows multiple bits per component. We present an analysis of the probability of bit disagreement in generated secret keys, and we use experimental data to demonstrate the HRUBE scheme and to quantify its experimental performance. As two examples, the implemented HRUBE system can achieve 22 bits per second at a bit disagreement rate of 2.2 percent, or 10 bits per second at a bit disagreement rate of 0.54 percent.

In this paper, to improve the performance of multihop wireless networks, we explore a cross layer multirate adaptation scheme (we call it CROMA) that uses the phenomenon of physical capture at the physical layer for effectively distinguishing losses due to collisions from those due to channel-error. We first estimate the number of packets dropped due to collisions, at each node by counting the number of packets that are not successfully retrieved by physical capture. Next, using a simple algorithm, we assign this collision loss to neighboring sources of packets that might have generated the colliding packets. Using extensive ns-2 simulations, we show that our multirate adaptation scheme consistently outperforms the existing schemes.

We evaluate the effectiveness of secret key extraction, for private communication between two wireless devices, from the received signal strength (RSS) variations on the wireless channel between the two devices. We use real world measurements of RSS in a variety of environments and settings. Our experimental results show that (i) in certain environments, due to lack of variations in the wireless channel, the extracted bits have very low entropy making these bits unsuitable for a secret key, (ii) an adversary can cause predictable key generation in these static environments, and (iii) in dynamic scenarios where the two devices are mobile, and/or where there is a significant movement in the environment, high entropy bits are obtained fairly quickly. Building on the strengths of existing secret key extraction approaches, we develop an environment adaptive secret key generation scheme that uses an adaptive lossy quantizer in conjunction with Cascade-based information reconciliation [7] and privacy amplification [14]. Our measurements show that our scheme, in comparison to the existing ones that we evaluate, performs the best in terms of generating high entropy bits at a high bit rate. The secret key bit streams generated by our scheme also pass the randomness tests of the NIST test suite [21] that we conduct.

We explore the use of clock skew of a wireless local area network access point (AP) as its fingerprint to detect unauthorized APs quickly and accurately. The main goal behind using clock skews is to overcome one of the major limitations of existing solutions - the inability to effectively detect Medium Access Control (MAC) address spoofing. We calculate the clock skew of an AP from the IEEE 802.11 Time Synchronization Function (TSF) timestamps sent out in the beacon/probe response frames. We use two different methods for this purpose - one based on linear programming and the other based on least square fit. We supplement these methods with a heuristic for differentiating original packets from those sent by the fake APs. We collect TSF timestamp data from several APs in two different residential settings. Using our measurement data as well as data obtained from a large conference setting, we find that clock skews remain consistent over time for the same AP but vary significantly across APs. Furthermore, we improve the resolution of received timestamp of the frames and show that with this enhancement our methodology can find clock skews very quickly, using 50-100 packets in most of the cases. We also discuss and quantify the impact of various external factors including temperature variation, virtualization, and NTP synchronization on clock skews. Our results indicate that the use of clock skews appears to be an efficient and robust method for detecting fake APs in wireless local area networks.

Location distinction is the ability to determine when a device has changed its position. We explore the opportunity to use sophisticated PHY-layer measurements in wireless networking systems for location distinction. We first compare two existing location distinction methods - one based on channel gains of multi-tonal probes, and another on channel impulse response. Next, we combine the benefits of these two methods to develop a new link measurement that we call the complex temporal signature. We use a 2.4 GHz link measurement data set, obtained from CRAWDAD [10], to evaluate the three location distinction methods. We find that the complex temporal signature method performs significantly better compared to the existing methods. We also perform new measurements to understand and model the temporal behavior of link signatures over time. We integrate our model in our location distinction mechanism and significantly reduce the probability of false alarms due to temporal variations of link signatures.

Congestion caused by a large number of interacting TCP flows at a bottleneck network link is different from that caused by a lesser number of flows sending large amounts of data -the former would require cutting down the number of competing flows, while cutting down the data sending rate is sufficient for the latter. However, since existing congestion control schemes view congestion only from a packet-level perspective, they treat both to be the same, resulting in suboptimal performance. We propose two best effort, search-based, session (or flow) level congestion control strategies for the Internet, to complement existing packet-level congestion control schemes. Our strategies control the number of competing flows to optimize for the flow completion rate and the flow completion time. Furthermore, our session control mechanisms do not require any per-flow state or computation at the routers, make no assumption about input traffic characteristics and requirements, avoid starvation of new flows when existing flows do not leave the system, and do not require any end host TCP modifications. Using evaluations under a wide variety of static and varying traffic load conditions, we demonstrate the significant performance and fairness gains that our session control mechanisms provide.

We present a new distributed approach that establishes reputation-based trust among sensor nodes in order to identify malfunctioning and malicious sensor nodes and minimize their impact on applications. Our method adapts well to the special characteristics of wireless sensor networks, the most important being their resource limitations. Our methodology computes statistical trust and a confidence interval around the trust based on direct and indirect experiences of sensor node behavior. By considering the trust confidence interval, we are able to study the tradeoff between the tightness of the trust confidence interval with the resources used in collecting experiences. Furthermore, our approach allows dynamic scaling of redundancy levels based on the trust relationship between the nodes of a wireless sensor network. Using extensive simulations we demonstrate the benefits of our approach over an approach that uses static redundancy levels in terms of reduced energy consumption and longer life of the network. We also find that high confidence trust can be computed on each node with a relatively small memory overhead and used to determine the level of redundancy operations among nodes in the system.

To aid security in the Internet, we propose a new connection oriented architecture to restrict reachability in the Internet to only those end hosts that explicitly request it. We first describe the various components of our architecture. Next, using qualitative arguments, and some preliminary computations, we show the benefits of our architecture. We also present viable strategies for minimizing connection state at routers, and discuss relevant security issues.

The ability of a receiver to determine when a transmitter has changed location is important for energy conservation in wireless sensor networks, for physical security of radio-tagged objects, and for wireless network security in detection of replication attacks. In this paper, we propose using a measured temporal link signature to uniquely identify the link between a transmitter and a receiver. When the transmitter changes location, or if an attacker at a different location assumes the identity of the transmitter, the proposed link distinction algorithm reliably detects the change in the physical channel. This detection can be performed at a single receiver or collaboratively by multiple receivers. We record over 9,000 link signatures at different locations and over time to demonstrate that our method significantly increases the detection rate and reduces the false alarm rate, in comparison to existing methods.

The authors propose to secure ad hoc networks against data injection attacks by placing firewall functionality at strategic locations in the ad hoc network. The authors first show that, given the locations of attackers and victims, the problem of placement of firewall functionality at a fixed number of ad hoc nodes while minimizing the impact of the data injection attack is identical to the k-coverage problem, this problem is known to be NP-hard. Then, the authors develop a near-optimal approximate algorithm for placing firewall functions. The authors also incorporate the loss behavior of wireless links in our algorithm. Next, the authors develop an architecture to determine the location of the attackers. Our architecture uses a separate control network (a cellular network in this paper) in conjunction with ad hoc networks to provide a provable attack detection mechanism. The authors evaluate our firewall placement algorithm for various topologies obtained from ns-2 simulations. Our results show that our algorithm can find near-optimal solutions. Based on a simple analysis and measurement results, the authors also find that the overhead of our provable attack detection mechanism is low.

We identify two typical problems in WLAN hotspots that result in unbounded unfairness between upstream and downstream flows. The first unfairness problem arises due to the uniformity of the MAC layer protocol at the access point (AP) and user nodes that result in equal share to the AP and the user nodes but not to the individual flows. The second unfairness problem arises due to the inability of the physical layer to distinguish frame errors due to hidden terminal based collisions and frame errors due to poor signal strength. We present FairMAC, a deployable solution that addresses these unfairness problems without requiring a change to the 802.11 protocol. Thus, our solution is immediately deployable in the millions of currently operational hotspots. We evaluate the performance of our protocol using simulations and a prototype implementation. We show that FairMAC provides fair access to all the flows regardless whether they are originating at the AP or a host.

We propose multi-class signaling overload control algorithms, for telecommunication switches, that are robust against different input traffic patterns and system upgrades. In order to appropriately measure the system load when several classes of signaling traffic are present, we first introduce the concept of equivalent system load measure that converts the multiple system measures associated with different classes of traffic into a single measure with respect to a pre-defined base class. We use this measure to develop three multi-class overload detection and measurement algorithms. Next, we develop a new algorithm for partitioning the allowable equivalent system load across multiple traffic classes, using a strict priority scheme. Using simulations of call flows from mobile telecommunications standards, we compare different multi-class overload algorithms under a variety of overload conditions. Our simulation results indicate that our algorithm that measures system load using a combination of request acceptance rate and processor occupancy provides highly reactive and robust overload control. Last, for the purpose of making the overload control algorithms more robust, we propose a measurement-based simple regression technique to dynamically estimate key system parameters. We find that estimates derived in this manner converge rapidly to their true values.

We present a new metric, Expected Data Rate (EDR), for accurately finding high-throughput paths in multihop ad hoc wireless networks. Our metric is based upon a new model for transmission interference which is a critical factor in determining path throughput. We construct a realistic and practical transmission interference model by (1) determining transmission contention degree of each link as a function of the wireless link loss, (2) quantifying the impact of the wireless link loss on medium access backoff, and (3) considering possible concurrent transmissions when two links do not interfere with each other. Our transmission interference model also takes the non-optimality of IEEE 802.11 medium access scheduling into account. Using extensive ns-2 simulations of IEEE 802.11 ad hoc networks, we find that EDR can accurately determine the achievable data rates of ad hoc paths, thereby significantly outperforming the other existing metrics.

Although multicast communication is well-suited to shared wireless links, receiver heterogeneity impedes the use of multicast in wireless networks. In this paper, we examine an approach that addresses the receiver heterogeneity problem in cellular multicast with the help of an additional IEEE 802.11 ad hoc network. The basic idea is to allow the cellular receivers experiencing poor channel conditions to use the ad hoc network to connect to those cellular receivers that are experiencing good cellular channel conditions. The good receivers (called proxies) relay multicast data to the poor receivers through the ad hoc network. We specifically consider the third generation cellular high data rate (HDR) broadcast/multicast services (BCMCS). We develop a new routing algorithm to find efficient ad hoc paths from the proxies to the cellular multicast receivers. Unlike existing algorithms (Luo et al. (2003)), our routing algorithm considers the effect of ad hoc path interference. Using simulations of an HDR BCMCS network in conjunction with an IEEE ad hoc network, we show that our algorithm improves the receiver goodput by up to 280% compared to that obtained without using ad hoc paths. We also show that our algorithm achieves up to 98% higher receiver goodput in comparison to the greedy algorithm proposed in Luo.

As CDMA-based cellular networks mature, the current point-to-point links used in connecting base stations to network controllers evolve to an IP-based radio access network (RAN) for reasons of lower cost due to statistical multiplexing gains, better scalability and reliability, and the projected growth in data applications. In this paper, we study the impact of congestion in a best-effort IP RAN on CDMA cellular voice networks. We propose and evaluate three congestion control mechanisms, admission control, diversity control, and router control, to maximize network capacity while maintaining good voice quality. We first propose two new enhancements to CDMA call admission control that consider a unified view of both IP RAN and air interface resources. Next, we introduce a novel technique called diversity control that exploits the soft-handoff feature of CDMA networks and drops selected frames belonging to multiple soft-handoff legs to gracefully degrade-voice quality during congestion. Finally, we study the impact of router control where an active queue management technique is used to reduce delay and minimize correlated losses. Using simulations of a large mobile network, we show that the three different control mechanisms can help gracefully manage 10-40 percent congestion overload in the IP RAN.

As CDMA-based cellular networks mature, the current point-to-point links used in connecting base stations to network controllers will evolve to an IP-based radio access network (RAN) for reasons of lower cost due to statistical multiplexing gains, better scalability and reliability, and the projected growth in data applications. In this paper, we study the impact of congestion in a best-effort IP RAN on CDMA cellular voice networks. We propose and evaluate three congestion control mechanisms, admission control, diversity control, and router control, to maximize network capacity while maintaining good voice quality. We first propose two new enhancements to CDMA call admission control that consider a unified view of both IP RAN and air interface resources. Next, we introduce a novel technique called diversity control that exploits the soft-handoff feature of CDMA networks and drops selected frames belonging to multiple soft-handoff legs to gracefully degrade voice quality during congestion. Finally, we study the impact of router control where an active queue management technique is used to reduce delay and minimize correlated losses. Using simulations of a large mobile network, we show that the three different control mechanisms can help gracefully manage 10-40% congestion overload in the IP RAN.

Telecommunication switches implement overload controls to maintain call throughput and delay at acceptable levels during periods of high load. Existing work has mostly focused on controls under sustained overload-they do not meet the demands of modern telecommunication systems where the increased number of services and mobile subscribers often creates fast changing hot spots. We introduce new algorithms that are designed to be highly reactive to sudden bursts of load. One algorithm is a modified version of RED for signaling traffic that measures the queue size. The second algorithm uses two measures: call acceptance rate and processor occupancy. Using simulations of realistic system models, we compare these new algorithms with each other and an existing algorithm that uses processor occupancy only. Our simulation results and qualitative arguments show that the combination of acceptance rate and processor occupancy results in a highly reactive and robust signaling overload control.

In wireless networks, mobile hosts must update the network with their current location in order to get packets delivered. Paging facilitates efficient power management at the mobile host by allowing the host to update the networkless frequently at the cost of providing the network with only approximate location information. The network determines the exact location of a mobile host through paging before delivering packets destined to the mobile host. In this paper, we propose the concept of paging as an IP service. IP paging enables a common infrastructure and protocol to support the different wireless interfaces such as CDMA, GPRS, wireless LAN, avoiding the duplication of several application layer paging implementations and the inter-operability issues that exists today. We present the design, implementation, and detailed qualitative and quantitative evaluation, using measurements and simulation, of three IP-based paging protocols for mobile hosts.

We examine an approach for providing reliable, scalable multicast communication, involving the use of multiple multicast channels for reducing receiver processing costs and reducing network bandwidth consumption in a multicast session. In this approach a single multicast channel is used for the original transmission of packets. Retransmissions of packets are done on separate multicast channels, which receivers dynamically join and leave. We first show that protocols using an infinite number of multicast channels incur much less processing overhead at the receivers compared to protocols that use only a single multicast channel. This is due to the fact that receivers do not receive retransmissions of packets they have already received correctly. Next, we derive the number of unwanted redundant packets at a receiver due to using only a finite number of multicast channels, for a specific negative acknowledgment (NAK)-based protocol. We then explore the minimum number of multicast channels required to keep the cost of processing unwanted packets to a sufficiently low value. For an application consisting of a single sender transmitting reliably to many receivers we find that only a small number of multicast channels are required for a wide range of system parameters. In the case of an application where all participants simultaneously act as both senders and receivers a moderate number of multicast channels is needed. Finally, we present two mechanisms for implementing multiple multicast channels, one using multiple IP multicast groups and the other using additional router support for selective packet forwarding. We discuss the impact of both mechanisms on performance in terms of end-host and network resources.

Multicast is an efficient paradigm for transmitting data from a sender to a group of receivers. In this paper, we focus on multicast in single channel multi-access wireless local area networks (LANs) comprising several small cells. In such a system, a receiver cannot correctly receive a packet if two or more packets are sent to it at the same time, because the packets "collide". Therefore, one has to ensure that only one node sends at a time. We look at two important issues. First, we consider the problem of the sender acquiring the multi-access channel for multicast transmission. Second, for reliable multicast in each cell of the wireless LAN, we examine ARQ-based approaches. The second issue is important because the wireless link error rates can be very high. We present a new approach to overcome the problem of feedback collision in single channel multi-access wireless LANs, both for the purpose of acquiring the channel and for reliability. Our approach involves the election of one of the multicast group members (receivers) as a "leader" or representative for the purpose of sending feedback to the sender. For reliable multicast, on erroneous reception of a packet, the leader does not send an acknowledgement, prompting a retransmission. On erroneous reception of the packet at receivers other than the leader, our protocol allows negative acknowledgements from these receivers to collide with the acknowledgement from the leader thus destroying the acknowledgement and prompting the sender to retransmit the packet. Using analytical models, we demonstrate that the leader-based protocol exhibits higher throughput in comparison to two other protocols which use traditional delayed feedback-based probabilistic methods. Last, we present a simple scheme for leader election.

We propose and evaluate novel reliable multicast protocols that combine active repair service (a.k.a. local recovery) and parity encoding (a.k.a. forward error correction or FEC) techniques. We show that, compared to other repair service protocols, our protocols require less buffer inside the network, maintain the low bandwidth requirements of previously proposed repair service/FEC combination protocols, and reduce the amount of FEC processing at repair servers, moving more of this processing to the end-hosts. We also examine repair service/FEC combination protocols in an environment where loss rates differ across domains within the network. We find that repair services are more effective than FEC at reducing bandwidth utilization in such environments. Furthermore, adding FEC to a repair services protocol not only reduces buffer requirements at repair servers, but also reduces bandwidth utilization in domains with high loss, or in domains with large populations of receivers.

Local recovery approaches for reliable multicast have the potential to provide significant performance gains in terms of reduced bandwidth and delay, and higher system throughput. In this paper we examine two local recovery approaches-one server-based, and the other receiver-based, and compare their performance. The server-based approach makes use of specially designated hosts, called repair servers, co-located with routers inside the network. In the receiver-based approach, only the end hosts (sender and receivers) are involved in error recovery. Using analytical models, we first show that the two local recovery approaches yield significantly higher protocol throughput and lower bandwidth usage than an approach that does not use local recovery. Next, we demonstrate that server-based local recovery yields higher protocol throughput and lower bandwidth usage than receiver-based local recovery when the repair servers have processing power slightly higher than that of a receiver and several hundred kilobytes of buffer per multicast session.

We examine an approach for providing reliable, scalable multicast communication, using multiple multicast groups for reducing receiver processing costs in a multicast session. In this approach a single multicast group is used for the original transmission of packets. Retransmissions of packets are done to separate multicast groups, which receivers dynamically join or leave. We first show that by using an infinite number of multicast groups, processing overhead at the receivers are substantially reduced. Next, we show that, for a specific negative acknowledgment (NAK)-based protocol, most of this reduction can be obtained by using only a small number of multicast groups for a wide range of system parameters. Finally, we present a local filtering scheme for minimizing join/leave signaling when multiple multicast groups are used.