We present methods and applications for the development of digital twins (DT) for urban traffic management. While the majority of studies on the DT focus on its “eyes,” which is the emerging sensing and perception like object detection and tracking, what really distinguishes the DT from a traditional simulator lies in its “brain,” the prediction and decision making capabilities of extracting patterns and making informed decisions from what has been seen and perceived. In order to add value to urban transportation management, DTs need to be powered by artificial intelligence and complement with low-latency high-bandwidth sensing and networking technologies, in other words, cyberphysical systems. This paper can be a pointer to help researchers and practitioners identify challenges and opportunities for the development of DTs; a bridge to initiate conversations across disciplines; and a road map to exploiting potentials of DTs for diverse urban transportation applications.

We propose a framework for adaptive data collection aimed at robust learning in multi-distribution scenarios under a fixed data collection budget. In each round, the algorithm selects a distribution source to sample from for data collection and updates the model parameters accordingly. The objective is to find the model parameters that minimize the expected loss across all the data sources. Our approach integrates upper-confidence-bound (UCB) sampling with online gradient descent (OGD) to dynamically collect and annotate data from multiple sources. By bridging online optimization and multi-armed bandits, we provide theoretical guarantees for our UCB-OGD approach, demonstrating that it achieves a minimax regret of $O(T^{\frac{1}{2}}(K\ln T)^{\frac{1}{2}})$ over $K$ data sources after $T$ rounds. We further provide a lower bound showing that the result is optimal up to a $\ln T$ factor. Extensive evaluations on standard datasets and a real-world testbed for object detection in smart-city intersections validate the consistent performance improvements of our method compared to baselines such as random sampling and various active learning methods.

This paper introduces PAVE (Pedestrian Awareness Via Edge analytics), a scalable real-time video analytics system that uses street cameras to enhance pedestrian safety while preserving their privacy. PAVE processes live camera streams on an edge server to track pedestrians and vehicles in real-time, predict vehicles' trajectories, and identify danger zones where pedestrians are present. The coordinates of these zones are sent to pedestrians' mobile devices via a custom iOS app, which locally determines if they are at risk without sharing any data with the edge server, hence preserving privacy. Moreover, anonymized metadata, including real-time location and speed/direction of pedestrians and vehicles, are visualized on a public map. PAVE's effectiveness was validated through deployment on the NSF COSMOS testbed, processing live video from cameras in diverse urban environments. Live field tests show that PAVE can alert at-risk pedestrians ~0.9 s before a vehicle reaches them. Through extensive profiling, we show that optimizing memory/compute configuration per pipeline stage can reduce latency by up to 10× compared to the default operating system configurations.

We showcase the workflow of PAVE (Pedestrian Awareness Via Edge analytics), a scalable system for real-time video analytics that leverages street cameras to improve pedestrians' safety while maintaining their privacy. PAVE distributes computation across edge servers and end-user mobile devices. Cameras' live streams are processed at the edge to forecast vehicles' trajectories and detect danger zones. Pedestrians' mobile devices then locally determine if the user is inside a danger zone and trigger timely alerts via a custom iOS app. In addition, anonymized metadata, such as pedestrian and vehicle positions, speeds, and directions, are aggregated and displayed on a public map for broader situational awareness. We evaluated PAVE's performance through implementation on the NSF COSMOS testbed's edge server while processing real-time video stream from cameras in diverse urban environments. Live field tests at an intersection in New York City show that PAVE can alert at-risk pedestrians about 0.9 s before a vehicle reaches them. With low-latency cameras, this lead time extends to around 1.6 s which is within the 1–2 s window pedestrians typically need to react.

Given the increasing residential Internet use, a thorough understanding of what services are used and how they are delivered to residential networks is crucial. However, access to residential traces is limited due to their proprietary nature. Most prior work used campus datasets from academic buildings and undergraduate dorms, and the few studies with residential traces are often outdated or use data unavailable to other researchers. In our SIGMETRICS 2025 publication, we introduced a new residential dataset---we have been collecting traffic from ~1000 off-campus residences that house faculty, postdocs, graduate students, and their families. Although our residents are university affiliates, our dataset captures their activity at home, and we show that this dataset offers a distinct perspective from the campus and dorm traffic. We also investigate the serving infrastructures and services accessed by the residences. Extending this published work, since May 2025, we have improved our pipeline efficiency to enable continuous 24/7 data collection and scale up to approximately 1500 residences, providing a more complete view of the residential network. We also make the dataset available for research use upon request, with the goal of motivating and supporting future research.

The management of RF spectrum resources between heterogeneous RF devices has become more challenging with the advent of$5 \mathrm{G}, 6 \mathrm{G}$and the desire to enable more spectrum sharing interactions in different bands. Most of the research on Dynamic Spectrum Access (DSA) algorithms considers non-cooperative scenarios with RF devices using omnidirectional antennas. In this paper, we study the effects of antenna directionality on cooperative DSA. Specifically, we develop a custom simulator for large-scale DSA networks that leverages IEEE 1900.5.2 Spectrum Consumption Models (SCMs) to enable coordination and computation of aggregate interference to deconflict spectrum use in large scale scenarios. SCMs offer a mechanism for RF devices to describe the characteristics of their use of spectrum and their needs in terms of interference protection. We create SCMs for RF systems with directional antennas based on measurements from a directional mmWave antenna and from the operational characteristics defined by the European Telecommunications Standards Institute (ETSI). We leverage these SCMs to perform a comparative analysis of spectrum use efficiency in cooperative DSA networks with up-to 300 links of transmitter-receiver RF devices using omnidirectional antennas vs similar networks using directional antennas with different half-power beam widths. The simulation results show the benefits to spectrum use efficiency that can be achieved with directional antennas and how largescale DSA methods can be studied and designed with the use of SCMs that incorporate detailed characteristics of directional antennas.

We consider a decentralized wireless network with several source-destination pairs sharing a limited number of orthogonal frequency bands. Sources learn to adapt their transmissions (specifically, their band selection strategy) over time, in a decentralized manner, without sharing information with each other. Sources can only observe the outcome of their own transmissions (i.e., success or collision), having no prior knowledge of the network size or of the transmission strategy of other sources. The goal of each source is to maximize their own throughput while striving for network-wide fairness. We propose a novel fully decentralized Reinforcement Learning (RL)-based solution that achieves fairness without coordination. The proposed Fair Share RL (FSRL) solution combines: (i) state augmentation with a semi-adaptive time reference; (ii) an architecture that leverages risk control and time difference likelihood; and (iii) a fairness-driven reward structure. We evaluate FSRL in several network settings. Simulation results suggest that, when we compare FSRL with a common baseline RL algorithm from the literature, FSRL can be up to 89.0 % fairer (as measured by Jain's fairness index) in stringent settings with several sources and a single frequency band, and 48.1 % fairer on average.

Dynamic Spectrum Access (DSA) is a key mechanism for meeting the ever-increasing demand for emerging wireless services. DSA involves managing and assigning available spectrum resources in a way that minimizes interference and allows RF coexistence between heterogeneous devices and systems. Such co-existence mechanisms, if they are to succeed when heterogeneous RF devices managed by different entities need to operate in a given area and frequency band (licensed and/or unlicensed), require a common mechanism for expressing the boundaries of spectrum use of each device so that spectrum use deconfliction methods can be built and verified. Spectrum Consumption Models (SCMs) – defined in the IEEE 1900.5.2 standard – offer a mechanism for RF devices to: (i) declare the characteristics of their intended spectrum use and their interference protection needs; and (ii) determine compatibility (non-interference) with existing devices. In this paper, we propose a novel SCM-based Spectrum Deconfliction (SD) algorithm that dynamically configures RF operational parameters (e.g., center frequency and transmission power) of a target transmitter-receiver pair aiming to minimize interference with existing devices/systems. We also propose sequential and distributed DSA methods that use the SD algorithm for assigning spectrum in large-scale networks. To evaluate the performance of our methods in terms of computation time, spectrum assignment efficiency, and overhead, we use two custom-made simulation platforms. Finally, to experimentally demonstrate the feasibility of our methods, we build a proof-of-concept implementation in the NSF PAWR COSMOS wireless testbed. The results reveal the advantages of using SCMs and their capabilities to conduct spectrum assignments in dynamic and congested communication environments.

Full-duplex (FD) wireless can significantly enhance spectrum efficiency but requires effective self-interference (SI) cancellers. RF SI cancellation (SIC) via frequency-domain equalization (FDE), where bandpass filters channelize the SI, is suited for integrated circuits (ICs). In this paper, we explore the limits and higher layer challenges associated with using such cancellers. We evaluate the performance of a custom FDE-based canceller using two testbeds; one with mobile FD radios and the other with upgraded, static FD radios in the PAWR COSMOS testbed. The latter is a lasting artifact for the research community, alongside a dataset containing baseband waveforms captured on the COSMOS FD radios, facilitating FD-related experimentation at the higher networking layers. We evaluate the performance of the FDE-based FD radios in both testbeds, with experiments showing 95 dB overall achieved SIC (52 dB from RF SIC) across 20 MHz bandwidth. We conduct network-level experiments for (i) uplink-downlink networks with inter-user interference, and (ii) heterogeneous networks with half-duplex and FD users, showing FD gains of$1.14\times $–$1.25\times $and$1.25\times $–$1.73\times $, respectively, confirming analytical results. We also evaluate the performance of an FD jammer-receiver, demonstrating a strong dependence on relative transmit power levels and modulation schemes.

Ensuring the safety of Vulnerable Road Users (VRUs) at intersections is crucial to enhancing urban traffic systems. This paper introduces a novel intelligent warning system specifically designed to increase the safety of VRUs crossing intersections. The proposed system leverages the COSMOS testbed to obtain real time vehicle information and employs Message Queuing Telemetry Transport (MQTT) as a standards-based messaging protocol for device communication and data transmission and utilizes a transformer model and Time To Collision (TTC) method to predict the collision. To validate the effectiveness and reliability of our intelligent alert system, we conducted comprehensive tests using the CARLA simulator, incorporating hardware in the loop simulation approach. The results demonstrate the potential for increased situational awareness and reduced risk factors associated with VRUs at intersections. Our work supports the integration of this intelligent alert system as a viable solution for reducing accidents and enhancing the overall safety of urban intersections in real time.

A microwave radiometer (MWR) at NOAA-CESSRST in Manhattan, NYC has experienced interference from nearby sources operating in the 5G FR2 n257 band (26.50--29.50 GHz). In this poster, we produce interference using a mobile 28 GHz IBM Phased Array Antenna Module (PAAM). The mobile PAAM leverages a software-defined radio which offers flexibility in varying center frequency, modulation, gain, bandwidth, time schedule, and more. In this poster, we show preliminary experiments which successfully created controlled interference to a MWR's 28 GHz channel which lead to distortion in some of the MWR final products, such as water vapor profile. We transmitted a 10 MHz bandwidth OFDM signal with varying amplitude, observing the highly sensitive MWR voltage response to fractional dB increments of the transmitter gain. The mobile PAAM is characterized in an anechoic chamber and MWR measurements are taken at various azimuth angles to help estimate the MWR antenna pattern. Future work will develop a Spectrum Consumption Model to help enable coexistence of MWRs and Beyond-5G networks.

Recent advances in Visual Language Models (VLMs) have significantly enhanced video analytics. VLMs capture complex visual and textual connections. While Convolutional Neural Networks (CNNs) excel in spatial pattern recognition, VLMs provide a global context, making them ideal for tasks like complex incidents and anomaly detection. However, VLMs are much more computationally intensive, posing challenges for large-scale and real-time applications. This paper introduces EdgeCloudAI, a scalable system integrating VLMs and CNNs through edge-cloud computing. Edge-CloudAI performs initial video processing (e.g., CNN) on edge devices and offloads deeper analysis (e.g., VLM) to the cloud, optimizing resource use and reducing latency. We have deployed EdgeCloudAI on the NSF COSMOS testbed in NYC. In this demo, we will demonstrate EdgeCloudAI's performance in detecting user-defined incidents in real-time.

In order to enable the simultaneous transmission and reception of wireless signals on the same frequency, a full-duplex (FD) radio must be capable of suppressing the powerful self-interference (SI) signal emitted from the transmitter and picked up by the receiver. Critically, a major bottleneck in wideband FD deployments is the need for adaptive SI cancellation (SIC) that would allow the FD wireless system to achieve strong cancellation across different settings with distinct electromagnetic environments. In this work, we evaluate the performance of an adaptive wideband FD radio in three different locations and demonstrate that it achieves strong SIC in every location across different bandwidths.

We present experiments using mobile 28 GHz Phased Array Antenna Modules (PAAMs), demonstrating their ability to perform beam steering with high granularity. The mobile node contains a 64-element IBM 28 GHz PAAM along with a USRP software defined radio, allowing for configuration of the transmit/receive (TX/RX) parameters. These parameters include beam shape, beam steering, and duty cycling. We demonstrate the capabilities of the mobile PAAMs by forming a wireless OFDM link between two mobile PAAMs. We then showcase the beam steering capabilities of the PAAM by performing beam sweeping on the RX PAAM to find the angle of arrival from the TX PAAM. A simple graphical user interface is presented for configuring the PAAMs. A tutorial is available online for users interested in experimentation with 28 GHz PAAMs*.

Blind and low-vision (BLV) people rely on GPS-based systems for outdoor navigation. GPS’s inaccuracy, however, causes them to veer off track, run into obstacles, and struggle to reach precise destinations. While prior work has made precise navigation possible indoors via hardware installations, enabling this outdoors remains a challenge. Interestingly, many outdoor environments are already instrumented with hardware such as street cameras. In this work, we explore the idea of repurposing existing street cameras for outdoor navigation. Our community-driven approach considers both technical and sociotechnical concerns through engagements with various stakeholders: BLV users, residents, business owners, and Community Board leadership. The resulting system, StreetNav, processes a camera’s video feed using computer vision and gives BLV pedestrians real-time navigation assistance. Our evaluations show that StreetNav guides users more precisely than GPS, but its technical performance is sensitive to environmental occlusions and distance from the camera. We discuss future implications for deploying such systems at scale.

Social distancing is an effective public health tool to reduce the spread of respiratory pandemics such as COVID-19. To analyze compliance with social distancing policies, we design two video-based pipelines for social distancing analysis, namely, automated video-based social distancing analyzer (Auto-SDA) and bird’s eye view social distancing analyzer (B-SDA). Auto-SDA is designed to measure social distancing using street-level cameras. To avoid privacy concerns of using street-level cameras, we further develop B-SDA, which uses bird’s eye view cameras, thereby preserving pedestrian’s privacy. We used the COSMOS testbed deployed in West Harlem, New York City (NYC), to evaluate both pipelines. In particular, Auto-SDA and B-SDA are applied on videos recorded by two of COSMOS cameras deployed on the 2nd floor (street-level) and 12th floor (bird’s eye view) of Columbia University’s Mudd building, looking at 120th St. and Amsterdam Ave. intersection, NYC. Videos are recorded before and during the peak of the pandemic, as well as after the vaccines became broadly available. The results represent the impact of social distancing policies on pedestrians’ social behavior. For example, the analysis shows that after the lockdown, less than 55% of the pedestrians failed to adhere to the social distancing policies, whereas this percentage increased to 65% after the vaccines’ availability. Moreover, after the lockdown, 0%–20% of the pedestrians were affiliated with a social group, compared to 10%–45% once the vaccines became available. The results also show that the percentage of face-to-face failures has decreased from 42.3% (prepandemic) to 20.7% (after the lockdown).

The relentless demand for data in our society has driven the continuous evolution of wireless technologies to enhance network capacity. While current deployments of 5G have made strides in this direction using massive multiple-input-multiple-output (MIMO) and millimeter-wave (mmWave) bands, all existing wireless systems operate in a half-duplex (HD) mode. Full-duplex (FD) wireless communication, on the other hand, enables simultaneous transmission and reception (STAR) of signals at the same frequency, offering advantages such as enhanced spectrum efficiency, improved data rates, and reduced latency. This article presents a comprehensive review of FD wireless systems, with a focus on hardware design, implementation, cross-layered considerations, and applications. The major bottleneck in achieving FD communication is the presence of self-interference (SI) signals from the transmitter (TX) to the receiver, and achieving SI cancellation (SIC) with real-time adaption is critical for FD deployment. The review starts by establishing a system-level understanding of FD wireless systems, followed by a review of the architectures of antenna interfaces and integrated RF and baseband (BB) SI cancellers, which show promise in enabling low-cost, small-form-factor, portable FD systems. We then discuss digital cancellation techniques, including digital signal processing (DSP)- and learning-based algorithms. The challenges presented by FD phased-array and MIMO systems are discussed, followed by system-level aspects, including optimization algorithms, opportunities in the higher layers of the networking protocol stack, and testbed integration. Finally, the relevance of FD systems in applications such as next-generation (xG) wireless, mmWave repeaters, radars, and noncommunication domains is highlighted. Overall, this comprehensive review provides valuable insights into the design, implementation, and applications of FD wireless systems while opening up new directions for future research.

Outdoor-to-indoor signal propagation poses significant challenges to millimeter-wave link budgets. To gain insight into outdoor-to-indoor millimeter-wave at 28GHz, we conducted an extensive measurement campaign consisting of over 2,200 link measurements in West Harlem, New York City, covering seven highly diverse buildings. A path loss model constructed over all measured links shows an average of 30dB excess loss over free space at distances beyond 50m. We find the type of glass to be the dominant factor in outdoor-to-indoor loss, with 20dB observed difference between grouped scenarios with low-and high-loss glass. Other factors such as the presence of scaffolding, tree foliage, or elevated subway tracks, as well as difference in floor height are also found to have a 5–10dB impact. We show that for urban buildings with high-loss glass, outdoor-to-indoor downlink capacity up to 400Mb/s is supported for 90% of indoor customer premises equipment by a base station up to 40m away. For buildings with low-loss glass, such as our case study covering multiple classrooms of a public school, downlink capacity over 2.8/1.4Gb/s is possible from a base station 57/133m away within line-of-sight. We expect these results to help inform the planning of millimeter-wave networks targeting outdoor-to-indoor deployments in dense urban environments, as well as provide insight into the development of scheduling and beam management algorithms.

Blind and low-vision (BLV) people use GPS-based systems for outdoor navigation assistance, which provide instructions to get from one place to another. However, such systems do not provide users with real-time, precise information about their location and surroundings which is crucial for safe navigation. In this work, we investigate whether street cameras can be used to address aspects of navigation that BLV people still find challenging with existing GPS-based assistive technologies. We conducted formative interviews with six BLV participants to identify specific challenges they face in outdoor navigation. We discovered three main challenges: anticipating environment layouts, avoiding obstacles while following directions, and crossing noisy street intersections. To address these challenges, we are currently developing a street camera-based navigation system that provides real-time auditory feedback to help BLV users avoid obstacles, know exactly when to cross the street, and understand the overall layout of the environment. We close by discussing our evaluation plan.

We present a set of experiments utilizing wideband real-time adaptive full-duplex (FD) radios, demonstrating simultaneous transmission and reception on the same frequency channel. Each FD radio consists of a circulator-based antenna interface, a switched-capacitor delay-line-based configurable Radio-Frequency Integrated Circuit (RFIC) that implements Self-Interference Cancellation (SIC), an FPGA that optimizes the RFIC configuration in under 1.1 sec and can adapt to environmental changes in under 0.3 sec, and a Software-Defined Radio (SDR) transmitting OFDM-like packets. We demonstrate a real-time adaptive FD radio that achieves the SIC necessary to reach the noise floor across a wide bandwidth of 50 MHz. Then, we use two FD radios to create a wireless link and showcase the superior FD throughput.

Next generation wireless services and applications, including Augmented Reality, Internet-of-Things, and Smart-Cities, will increasingly rely on Dynamic Spectrum Access (DSA) methods that can manage spectrum resources rapidly and efficiently. Advances in regulatory policies, standardization, networking, and wireless technology are enabling DSA methods on a more granular basis in terms of time, frequency, and geographical location which are key for the operation of 5G and beyond-5G networks. In this context, this paper proposes a novel DSA algorithm that leverages IEEE 1900.5.2 Spectrum Consumption Models (SCMs) which offer a mechanism for RF devices to: (i) "announce" or "declare" their intention to use the spectrum and their needs in terms of interference protection; and (ii) determine compatibility (i.e., non-interference) with existing devices. In this paper, we develop an SCM-based DSA algorithm for spectrum deconfliction in large-scale wireless network environments and evaluate this algorithm in terms of computation time, efficiency of spectrum allocation, and number of device reconfigurations due to interference using a custom simulation platform. The results demonstrate the benefits of using SCMs and their capabilities to perform fine grained spectrum assignments in dynamic and dense communication environments.

Outdoor-to-indoor (OtI) signal propagation further challenges link budgets at millimeter-wave (mmWave). To gain insight into OtI mmWave at 28 GHz, we conducted an extensive measurement campaign consisting of over 2,000 link measurements in West Harlem, New York City, covering seven highly diverse buildings. A path loss model constructed over all links shows an average of 30 dB excess loss over free space at distances beyond 50 m. We find the type of glass to be the dominant factor in OtI loss, with 20 dB observed difference between clustered scenarios with low- and high-loss glass. Other factors, such as difference in floor height, are found to have an impact between 5--10 dB. We show that for urban buildings with high-loss glass, OtI data rates up to 400 Mb/s are supported for 90% of indoor users by a base station (BS) up to 49 m away. For buildings with low-loss glass, such as our case study covering multiple classrooms of a public school, data rates over 2.8/1.4 Gb/s are possible from a BS 68/175 m away when a line-of-sight path is available. We expect these results to be useful for the deployment of OtI mmWave networks in dense urban environments and the development of scheduling and beam management algorithms.

Crowded metropolises present unique challenges to the potential deployment of autonomous vehicles. Safety of pedestrians cannot be compromised and personal privacy must be preserved. Smart city intersections will be at the core of Artificial Intelligence (AI)-powered citizen-friendly traffic management systems for such metropolises. Hence, the main objective of this work is to develop an experimentation framework for designing applications in support of secure and efficient traffic intersections in urban areas. We integrated a camera and a programmable edge computing node, deployed within the COSMOS testbed in New York City, with an Eclipse sensiNact data platform provided by Kentyou. We use this pipeline to collect and analyze video streams in real-time to support smart city applications. In this demo, we present a video analytics pipeline that analyzes the video stream from a COSMOS' street-level camera to extract traffic/crowd-related information and sends it to a dedicated dashboard for real-time visualization and further assessment. This is done without sending the raw video, in order to avoid violating pedestrians' privacy.

Social distancing can reduce infection rates in respiratory pandemics such as COVID-19, especially in dense urban areas. Hence, we used the PAWR COSMOS wireless edge-cloud testbed in New York City to design and evaluate two different approaches for social distancing analysis. The first, \textbf{Auto}mated video-based \textbf{S}ocial \textbf{D}istancing \textbf{A}nalyzer (\textbf{Auto-SDA}), was designed to measure pedestrians compliance with social distancing protocols using street-level cameras. However, since using street-level cameras can raise privacy concerns, we also developed the \textbf{B}ird's eye view \textbf{S}ocial \textbf{D}istancing \textbf{A}nalyzer (\textbf{B-SDA}) which uses bird's eye view cameras, thereby preserving pedestrians' privacy. Both Auto-SDA and B-SDA consist of multiple modules. This demonstration illustrates the roles of these modules and their overall performance in evaluating the compliance of pedestrians with social distancing protocols. Moreover, we demonstrate applying Auto-SDA and B-SDA on videos recorded from cameras deployed on the 2nd and 12th floor of Columbia's Mudd building, respectively.

Full-duplex (FD) wireless and phased arrays are both promising techniques that can significantly improve data rates in future wireless networks. However, integrating FD with transmit (Tx) and receive (Rx) phased arrays is extremely challenging, due to the large number of self-interference (SI) channels. Previous work relies on either RF canceller hardware or on analog/digital Tx beamforming (TxBF) to achieve SI cancellation (SIC). However, Rx beamforming (RxBF) and the data rate gain introduced by FD nodes employing beamforming have not been considered yet. We study FD phased arrays with joint TxBF and RxBF with the objective of achieving improved FD data rates. The key idea is to carefully select the TxBF and RxBF weights to achieve wideband RF SIC in the spatial domain with minimal TxBF and RxBF gain losses. Essentially, TxBF and RxBF are repurposed, thereby not requiring specialized RF canceller circuitry. We formulate the corresponding optimization problem and develop an iterative algorithm to obtain an approximate solution with provable performance guarantees. Using SI channel measurements and datasets, we extensively evaluate the performance of the proposed approach in different use cases under various network settings. The results show that an FD phased array with 9/36/72 elements can cancel the total SI power to below the noise floor with sum TxBF and RxBF gain losses of 10.6/7.2/6.9dB, even at Tx power level of 30dBm. Moreover, the corresponding FD rate gains are at least 1.33/1.66/1.68 ×.

As video traffic continues to dominate the Internet, interest in near-second low-latency streaming has increased. Existing low-latency streaming platforms rely on using tens of seconds of video in the buffer to offer a seamless experience. Striving for near-second latency requires the receiver to make quick decisions regarding the download bitrate and the playback speed. To cope with the challenges, we design a new adaptive bitrate (ABR) scheme, Stallion, for STAndard Low-LAtency vIdeo cONtrol. Stallion uses a sliding window to measure the mean and standard deviation of both the bandwidth and latency. We evaluate Stallion and compare it to the standard DASH DYNAMIC algorithm over a variety of networking conditions. Stallion shows 1.8x increase in bitrate, and 4.3x reduction in the number of stalls.

To support experimentation with full-duplex (FD) wireless, we recently integrated two FlexICoN Gen-2 wideband FD radios in the open-access, city-scale NSF PAWR COSMOS testbed. Each integrated FD radio consists of an antenna, a customized Gen-2 RF self-interference (SI) canceller box, a USRP software-defined radio, and a remotely accessible compute node. The RF SI canceller box includes an RF canceller printed circuit board which emulates an integrated circuit implementation based on the technique of frequency-domain equalization. The Gen-2 canceller box can achieve up to 50 dB RF SI cancellation across 20MHz bandwidth. In this demo, we present the design and implementation of the open-acccess, remotely accessible FD radios that are integrated in the indoor COSMOS Sandbox 2 at Columbia University. We also demonstrate example experiments that are available to researchers, where demo participants can observe the visualized performance of the open-access FD radios.

This paper focuses on COSMOS - Cloud enhanced Open Software defined MObile wireless testbed for city-Scale deployment. The COSMOS testbed is being deployed in West Harlem (New York City) as part of the NSF Platforms for Advanced Wireless Research (PAWR) program. It will enable researchers to explore the technology "sweet spot" of ultra-high bandwidth and ultra-low latency in the most demanding real-world environment. We describe the testbed's architecture, the design and deployment challenges, and the experience gained during the design and pilot deployment. Specifically, we describe COSMOS' computing and network architectures, the critical building blocks, and its programmability at different layers. The building blocks include software-defined radios, 28 GHz millimeter-wave phased array modules, optical transport network, core and edge cloud, and control and management software. We describe COSMOS' deployment phases in a dense urban environment, the research areas that could be studied in the testbed, and specific example experiments. Finally, we discuss our experience with using COSMOS as an educational tool.

We introduce and study the doubly balanced connected graph partitioning problem: Let G =( V , E ) be a connected graph with a weight (supply/demand) function p : V → {−1, +1} satisfying p ( V )=∑ j &isin V p ( j ) = 0. The objective is to partition G into ( V 1 , V 2 ) such that G [ V 1 ] and G [ V 2 ] are connected, ∣ p ( V 1 )∣,∣ p ( V 2 )∣≤ c p , and max{ ∣ V 1 / V 2 ∣,∣ V 2 / V 1 ∣} ≤ c s , for some constants c p and c s . When G is 2-connected, we show that a solution with c p =1 and c s =2 always exists and can be found in randomized polynomial time. Moreover, when G is 3-connected, we show that there is always a “perfect” solution (a partition with p ( V 1 )= p ( V 2 )=0 and ∣ V 1 ∣=∣ V 2 ∣, if ∣ V ∣≡ 0 (mod 4)), and it can be found in randomized polynomial time. Our techniques can be extended, with similar results, to the case in which the weights are arbitrary (not necessarily ±1), and to the case that p ( V )≠ 0 and the excess supply/demand should be split evenly. They also apply to the problem of partitioning a graph with two types of nodes into two large connected subgraphs that preserve approximately the proportion of the two types.

As video traffic dominates the Internet, it is important for operators to detect video quality of experience (QoE) to ensure adequate support for video traffic. With wide deployment of end-to-end encryption, traditional deep packet inspection--based traffic monitoring approaches are becoming ineffective. This poses a challenge for network operators to monitor user QoE and improve upon their experience. To resolve this issue, we develop and present a system for RE al-time QU ality of experience metric detection for E ncrypted T raffic— Requet —which is suitable for network middlebox deployment. Requet uses a detection algorithm that we develop to identify video and audio chunks from the IP headers of encrypted traffic. Features extracted from the chunk statistics are used as input to a machine learning algorithm to predict QoE metrics, specifically buffer warning (low buffer, high buffer), video state (buffer increase, buffer decay, steady, stall), and video resolution. We collect a large YouTube dataset consisting of diverse video assets delivered over various WiFi and LTE network conditions to evaluate the performance. We compare Requet with a baseline system based on previous work and show that Requet outperforms the baseline system in accuracy of predicting buffer low warning, video state, and video resolution by 1.12×, 1.53×, and 3.14×, respectively.

Full-duplex (FD) wireless is an attractive communication paradigm with high potential for improving network capacity and reducing delay in wireless networks. Despite significant progress on the physical layer development, the challenges associated with developing medium access control (MAC) protocols for heterogeneous networks composed of both legacy half-duplex (HD) and emerging FD devices have not been fully addressed. Therefore, we focus on the design and performance evaluation of scheduling algorithms for infrastructure-based heterogeneous HD-FD networks (composed of HD and FD users). We first show that centralized Greedy Maximal Scheduling (GMS) is throughput-optimal in heterogeneous HD-FD networks. We propose the Hybrid-GMS (H-GMS) algorithm, a distributed implementation of GMS that combines GMS and a queue-based random-access mechanism. We prove that H-GMS is throughput-optimal. Moreover, we analyze the delay performance of H-GMS by deriving lower bounds on the average queue length. We further demonstrate the benefits of upgrading HD nodes to FD nodes in terms of throughput gains for individual nodes and the whole network. Finally, we evaluate the performance of H-GMS and its variants in terms of throughput, delay, and fairness between FD and HD users via extensive simulations. We show that in heterogeneous HD-FD networks, H-GMS achieves 16-$30\times $ better delay performance and improves fairness between HD and FD users by up to 50% compared with the fully decentralized Q-CSMA algorithm.

In order to support experimentation with full-duplex (FD) wireless, we integrated the FlexICoN Gen-2 wideband FD radio with the city-scale PAWR COSMOS testbed [1]. In particular, the implemented FD radio consists of an antenna, a customized Gen-2 RF self-interference (SI) canceller box, a USRP software-defined radio (SDR), and a compute node. The RF canceller box includes an RF SI canceller implemented using discrete components on a printed circuit board (PCB), which emulates its RFIC canceller counterpart. The Gen-2 RF SI canceller achieves 50dB RF SI cancellation across 20MHz bandwidth using the technique of frequency-domain equalization (FDE) [2]. In this abstract, we present the design and implementation of the remotely accessible Gen-2 wideband FD radio integrated with the COSMOS sandbox at Columbia University. We also present an example real-time wideband FD wireless link demonstration using the GNU Radio software.

The Cloud-Enhanced Open Software Defined Mobile Wireless Testbed for City-Scale Deployment (COSMOS) platform is a programmable city-scale shared multi-user advanced wireless testbed that is being deployed in West Harlem of New York City [1]. To keep pace with the significantly increased wireless link bandwidth and to effectively integrate the emerging C-RANs, COSMOS is designed to incorporate a fast programmable core network for providing connections across different computing layers. A key feature of COSMOS is its dark fiber based optical x-haul network that enables both highly flexible, user defined network topologies and experimentation directly in the optical physical layer. The optical architecture of COSMOS was presented in [2]. In this abstract, we present the tools and services designed to configure and monitor the performance of optical paths and topologies of the COSMOS testbed. In particular, we present the SDN framework that allows testbed users to implement experiments with application-driven control of optical and data networking functionalities.

As video traffic dominates the Internet, it is important for operators to detect video Quality of Experience (QoE) in order to ensure adequate support for video traffic. With wide deployment of end-to-end encryption, traditional deep packet inspection based traffic monitoring approaches are becoming ineffective. This poses a challenge for network operators to monitor user QoE and improve upon their experience. To resolve this issue, we develop and present a system for REal-time QUality of experience metric detection for Encrypted Traffic, Requet. Requet uses a detection algorithm we develop to identify video and audio chunks from the IP headers of encrypted traffic. Features extracted from the chunk statistics are used as input to a Machine Learning (ML) algorithm to predict QoE metrics, specifically, buffer warning (low buffer, high buffer), video state (buffer increase, buffer decay, steady, stall), and video resolution. We collect a large YouTube dataset consisting of diverse video assets delivered over various WiFi network conditions to evaluate the performance. We compare Requet with a baseline system based on previous work and show that Requet outperforms the baseline system in accuracy of predicting buffer low warning, video state, and video resolution by 1.12X, 1.53X, and 3.14X, respectively.

Full-duplex (FD) wireless can significantly enhance spectrum efficiency but requires tremendous amount of self-interference (SI) cancellation. Recent advances in the RFIC community enabled wideband RF SI cancellation (SIC) in integrated circuits (ICs) via frequency-domain equalization (FDE), where reconfigurable RF filters are used to channelize the SI signal path. In [2], we designed and implemented an FDE-based RF canceller on a printed circuit board (PCB). We also presented an optimized canceller configuration scheme based on the derived canceller model, and extensively evaluated the performance of the FDE-based FD radios in a software-defined radio (SDR) testbed in different network settings.

Full-duplex (FD) wireless can significantly enhance spectrum efficiency but requires tremendous amount of self-interference (SI) cancellation. Recent advances in the RFIC community enabled wideband RF SI cancellation (SIC) in integrated circuits (ICs) via frequency-domain equalization (FDE), where RF filters channelize the SI signal path. Unlike other FD implementations, that mostly rely on delay lines, FDE-based cancellers can be realized in small-form-factor devices. However, the fundamental limits and higher layer challenges associated with these cancellers were not explored yet. Therefore, and in order to support the integration with a software-defined radio (SDR) and to facilitate experimentation in a testbed with several nodes, we design and implement an FDE-based RF canceller on a printed circuit board (PCB). We derive and experimentally validate the PCB canceller model and present a canceller configuration scheme based on an optimization problem. We then extensively evaluate the performance of the FDE-based FD radio in the SDR testbed. Experiments show that it achieves 95dB overall SIC (52dB from RF SIC) across 20MHz bandwidth, and an average link-level FD gain of 1.87x. We also conduct experiments in: (i) uplink-downlink networks with inter-user interference, and (ii) heterogeneous networks with half-duplex and FD users. The experimental FD gains in the two types of networks confirm previous analytical results. They depend on the users' SNR values and the number of FD users, and are 1.14x-1.25x and 1.25x-1.73x, respectively. Finally, we numerically evaluate and compare the RFIC and PCB implementations and study various design tradeoffs.

Full-duplex (FD) wireless and phased arrays are both promising techniques that can significantly improve data rates in future wireless networks. However, integrating FD with transmit (Tx) and receive (Rx) phased arrays is extremely challenging, due to the large number of self-interference (SI) channels. Previous work relies on either RF canceller hardware or on analog/digital Tx beamforming (TxBF) to achieve SI cancellation (SIC). However, Rx beamforming (RxBF) and the data rate gain introduced by FD nodes employing beamforming have not been considered yet. We study FD phased arrays with joint TxBF and RxBF with the objective of achieving improved FD data rates. The key idea is to carefully select the TxBF and RxBF weights to achieve wideband RF SIC in the spatial domain with minimal TxBF and RxBF gain losses. Essentially, TxBF and RxBF are repurposed, thereby not requiring specialized RF canceller circuitry. We formulate the corresponding optimization problem and develop an iterative algorithm to obtain an approximate solution with provable performance guarantees. Using SI channel measurements and datasets, we extensively evaluate the performance of the proposed approach in different use cases under various network settings. The results show that an FD phased array with 9/36/72 elements can cancel the total SI power to below the noise floor with sum TxBF and RxBF gain losses of 10.6/7.2/6.9 dB, even at Tx power level of 30 dBm. Moreover, the corresponding FD rate gains are at least 1.33/1.66/1.68X.

Network slicing will allow 5G network operators to offer a diverse set of services over a shared physical infrastructure. We focus on supporting the operation of the Radio Access Network (RAN) slice broker, which maps slice requirements into allocation of Physical Resource Blocks (PRBs). We first develop a new metric, REVA, based on the number of PRBs available to a single Very Active bearer. REVA is independent of channel conditions and allows easy derivation of an individual wireless link's throughput. In order for the slice broker to efficiently utilize the RAN, there is a need for reliable and short term prediction of resource usage by a slice. To support such prediction, we construct an LTE testbed and develop custom additions to the scheduler. Using data collected from the testbed, we compute REVA and develop a realistic time series prediction model for REVA. Specifically, we present the X-LSTM prediction model, based upon Long Short-Term Memory (LSTM) neural networks. Evaluated with data collected in the testbed, X-LSTM outperforms Autoregressive Integrated Moving Average Model (ARIMA) and LSTM neural networks by up to 31%. X-LSTM also achieves over 91% accuracy in predicting REVA. By using X-LSTM to predict future usage, a slice broker is more adept to provision a slice and reduce over-provisioning and SLA violation costs by more than 10% in comparison to LSTM and ARIMA.

Photovoltaic (PV) panels have become a significant source of electric power generation. These panels are considered to be one of the cleanest energy production systems available, so their spread is expected to increase in the following years, especially because recent technologies have reduced the cost of these panels. Unlike classic energy production methodologies that are connected to the high-voltage transmission power lines, many PV panels are connected directly to the lower voltage distribution networks of the electric power grid, making the management of the grid an ongoing challenge. In this letter, we address this challenge and show that the irradiance field that is required to calculate the expected power output of the PV panels can be estimated in a simplistic methodology, using partial observability of the solar radiation. We validate our proposed methodology by conducting an empirical study that uses real data of the solar radiation taken from satellites, and we show that even when the observability of the solar radiation is as low as 10% (meaning that only one in ten points of interest in a regular grid is observable), the irradiance field can be accurately estimated.

LTE evolved Multimedia Broadcast/Multicast Service (eMBMS) is an attractive solution for video delivery to very large groups in crowded venues. However, the deployment and management of eMBMS systems are challenging, due to the lack of real-time feedback from the user equipments (TIEs). Therefore, we present the Dynamic Monitoring (DyMo) system for low-overhead feedback collection. DyMo leverages eMBMS for broadcasting stochastic group instructions to all UEs. These instructions indicate the reporting rates as a function of the observed quality of service (QoS). This simple feedback mechanism collects very limited QoS reports from the TIEs. The reports are used for network optimization, thereby ensuring high QoS to the TIEs. We present the design aspects of DyMo and evaluate its performance analytically and via extensive simulations. Specifically, we show that DyMo infers the optimal eMBMS settings with extremely low overhead while meeting strict QoS requirements under different TIE mobility patterns and presence of network component failures. For instance, DyMo can detect the eMBMS signal-to-noise ratio experienced by the 0.1th percentile of the TIEs with a root mean square error of 0.05% with only 5 to 10 reports per second regardless of the number of TIEs.

Full-duplex (FD) wireless is an attractive communication paradigm with high potential for improving network capacity and reducing delay in wireless networks. Despite significant progress on the physical layer development, the challenges associated with developing medium access control (MAC) protocols for heterogeneous networks composed of both legacy half-duplex (UD) and emerging FD devices have not been fully addressed. Therefore, we focus on the design and performance evaluation of scheduling algorithms for infrastructure-based heterogeneous networks (composed of UD and FD users). We develop the hybrid Greedy Maximal Scheduling (U-GMS) algorithm, which is tailored to the special characteristics of such heterogeneous networks and combines both centralized GMS and decentralized Q-CSMA mechanisms. Moreover, we prove that H-GMS is throughput-optimal. We then demonstrate by simple examples the benefits of adding FD nodes to a network. Finally, we evaluate the performance of U-GMS and its variants in terms of throughput, delay, and fairness between FD and UD users via extensive simulations. We show that in heterogeneous UD-FD networks, U-GMS achieves 5-10x better delay performance and improves fairness between HD and FD users by up to 50% compared with the fully decentralized Q-CSMA algorithm.

No abstract available.

We study the achievable rate regions of full-duplex links in the single- and multi-channel cases (in the latter case, the channels are assumed to be orthogonal, e.g., OFDM). We present analytical results that characterize the uplink and downlink rate region and efficient algorithms for computing rate pairs at the region's boundary. We also provide near-optimal and heuristic algorithms that “convexify” the rate region when it is not convex. The convexified region corresponds to a combination of a few full-duplex rates (i.e., to time sharing between different operation modes). The algorithms can be used for theoretical characterization of the rate region as well as for resource (time, power, and channel) allocation with the objective of maximizing the sum of the rates when one of them (uplink or downlink) must be guaranteed (e.g., due to QoS considerations). We numerically illustrate the rate regions and the rate gains (compared with time division duplex) for various channel and cancellation scenarios. The analytical results provide insights into the properties of the full-duplex rate region and are essential for future development of scheduling, channel allocation, and power control algorithms.

WiFi multicast to very large groups has gained attention as a solution for multimedia delivery in crowded areas. Yet, the most recently proposed schemes do not provide performance guarantees and none have been tested at scale. To address the issue of providing high multicast throughput with performance guarantees, we present the design and experimental evaluation of the multicast dynamic rate adaptation (MuDRA) algorithm. MuDRA balances fast adaptation to channel conditions and stability, which is essential for multimedia applications. MuDRA relies on feedback from some nodes collected via a light-weight protocol and dynamically adjusts the RA response time. Our experimental evaluation of MuDRA on the ORBIT testbed with over 150 nodes shows that MuDRA outperforms other schemes and supports high throughput multicast flows to hundreds of receivers while meeting quality requirements. MuDRA can support multiple high-quality video streams, where 90% of the nodes report excellent or very good video quality.

LTE evolved Multimedia Broadcast/Multicast Service (eMBMS) is an attractive solution for video delivery to very large groups in crowded venues. However, deployment and management of eMBMS systems is challenging, due to the lack of realtime feedback from the User Equipment (UEs). Therefore, we present the Dynamic Monitoring (DyMo) system for low-overhead feedback collection. DyMo leverages eMBMS for broadcasting Stochastic Group Instructions to all UEs. These instructions indicate the reporting rates as a function of the observed Quality of Service (QoS). This simple feedback mechanism collects very limited QoS reports from the UEs. The reports are used for network optimization, thereby ensuring high QoS to the UEs. We present the design aspects of DyMo and evaluate its performance analytically and via extensive simulations. Specifically, we show that DyMo infers the optimal eMBMS settings with extremely low overhead, while meeting strict QoS requirements under different UE mobility patterns and presence of network component failures. For instance, DyMo can detect the eMBMS Signal-to-Noise Ratio (SNR) experienced by the 0.1% percentile of the UEs with Root Mean Square Error (RMSE) of 0.05% with only 5 to 10 reports per second regardless of the number of UEs.

We introduce and study the Doubly Balanced Connected graph Partitioning (DBCP) problem: Let G=(V, E) be a connected graph with a weight (supply/demand) function p:V→{-1, +1} satisfying P(V)=∑j∊v p(j)=0· The objective is to partition G into (V1, V2) such that G[V1] and G[V2] are connected, |p(V1)|, |p(V2)|≤cp, and for some constants cp and cs · When G is 2-connected, we show that a solution with cp=1 and cs=3 always exists and can be found in polynomial time. Moreover, when G is 3-connected, we show that there is always a ‘perfect’ solution (a partition with p(V1)=p(V2)=0 and |V1| = |V2|, if |V|≡0(mod 4)), and it can be found in polynomial time. Our techniques can be extended, with similar results, to the case in which the weights are arbitrary (not necessarily ±1), and to the case that p(V)=0 and the excess supply/demand should be split evenly. They also apply to the problem of partitioning a graph with two types of nodes into two large connected subgraphs that preserve approximately the proportion of the two types.

Full-duplex (FD) communication has the potential to substantially increase the throughput in wireless networks. However, the benefits of FD are still not well understood. In this paper, we characterize the FD rate gains in both single-channel and multi-channel use cases. For the single-channel case, we quantify the rate gain as a function of the remaining self-interference (SI) and signal-to-noise ratio values. We also provide a sufficient condition under which the sum of uplink and downlink rates on an FD channel is biconcave in the transmission power levels. Building on these results, we consider the multi-channel case. For that case, we introduce a new realistic model of a compact (e.g., smartphone) FD receiver and demonstrate its accuracy via measurements. We study the problem of jointly allocating power levels to different channels and selecting the frequency of maximum SI suppression, where the objective is to maximize the sum of the rates over uplink and downlink orthogonal frequency division multiplexing channels. We develop a polynomial time algorithm, which is nearly optimal, in practice, under very mild restrictions. To reduce the running time, we develop an efficient nearly optimal algorithm under the high SINR approximation. Finally, we demonstrate via numerical evaluations the capacity gains in different use cases and obtain insights into the impact of the remaining SI and wireless channel states on the performance.

Wireless object tracking applications are gaining popularity and will soon utilize emerging ultra-low-power device-to-device communication. However, severe energy constraints require much more careful accounting of energy usage than what prior art provides. In particular, the available energy, the differing power consumption levels for listening, receiving, and transmitting, as well as the limited control bandwidth must all be considered. Therefore, we formulate the problem of maximizing the throughput among a set of heterogeneous broadcasting nodes with differing power consumption levels, each subject to a strict ultra-low-power budget. We obtain the oracle throughput (i.e., maximum throughput achieved by an oracle) and use Lagrangian methods to design EconCast - a simple asynchronous distributed protocol in which nodes transition between sleep, listen, and transmit states, and dynamically change the transition rates. We also show that EconCast approaches the oracle throughput. The performance is evaluated numerically and via extensive simulations and it is shown that EconCast outperforms prior art by 6x - 17x under realistic assumptions. Finally, we implement EconCast using the TI eZ430-RF2500-SEH energy harvesting nodes and experimentally show that in realistic environments it obtains 57% - 77% of the achievable throughput.

Over the past two decades, fair resource allocation problems have received considerable attention in a variety of application areas. However, little progress has been made in the design of distributed algorithms with convergence guarantees for general and commonly used $α$-fair allocations. In this paper, we study weighted $α$-fair packing problems, that is, the problems of maximizing the objective functions (i) $\sum_j w_j x_j^{1-α}/(1-α)$ when $α> 0$, $α\neq 1$ and (ii) $\sum_j w_j \ln x_j$ when $α= 1$, over linear constraints $Ax \leq b$, $x\geq 0$, where $w_j$ are positive weights and $A$ and $b$ are non-negative. We consider the distributed computation model that was used for packing linear programs and network utility maximization problems. Under this model, we provide a distributed algorithm for general $α$ that converges to an $\varepsilon-$approximate solution in time (number of distributed iterations) that has an inverse polynomial dependence on the approximation parameter $\varepsilon$ and poly-logarithmic dependence on the problem size. This is the first distributed algorithm for weighted $α-$fair packing with poly-logarithmic convergence in the input size. The algorithm uses simple local update rules and is stateless (namely, it allows asynchronous updates, is self-stabilizing, and allows incremental and local adjustments). We also obtain a number of structural results that characterize $α-$fair allocations as the value of $α$ is varied. These results deepen our understanding of fairness guarantees in $α-$fair packing allocations, and also provide insight into the behavior of $α-$fair allocations in the asymptotic cases $α\rightarrow 0$, $α\rightarrow 1$, and $α\rightarrow \infty$.

WiFi multicast to very large groups has gained attention as a solution for multimedia delivery in crowded areas. Yet, most recently proposed approaches do not provide performance guarantees. In this paper, we describe the AMuSe system, whose objective is to enable scalable and adaptive WiFi multicast services. AMuSe includes a lightweight feedback mechanism that allows monitoring channel quality of a large number of users. This feedback allows the system to dynamically optimize the multicast transmission rate at the AP. We implemented AMuSe on the ORBIT testbed and evaluated its performance in large groups with approximately 200 WiFi devices in different scenarios. We show that AMuSe supports high throughput multicast flows to hundreds of receivers while meeting quality requirements and that it outperforms other systems.

WiFi multicast to very large groups has gained attention as a solution for multimedia delivery in crowded areas. Yet, most recently proposed schemes do not provide performance guarantees and none have been tested at scale. To address the issue of providing high multicast throughput with performance guarantees, we present the design and experimental evaluation of the Multicast Dynamic Rate Adaptation (MuDRA) algorithm. MuDRA balances fast adaptation to channel conditions and stability, which is essential for multimedia applications. MuDRA relies on feedback from some nodes collected via a light-weight protocol and dynamically adjusts the rate adaptation response time. Our experimental evaluation of MuDRA on the ORBIT testbed with over 150 nodes shows that MuDRA outperforms other schemes and supports high throughput multicast flows to hundreds of receivers while meeting quality requirements. MuDRA can support multiple high quality video streams, where 90% of the nodes report excellent or very good video quality.

Object tracking applications are gaining popularity and will soon utilize Energy Harvesting (EH) low-power nodes that will consume power mostly for Neighbor Discovery (ND) (i.e., identifying nodes within communication range). Although ND protocols were developed for sensor networks, the challenges posed by emerging EH low-power transceivers were not addressed. Therefore, we design an ND protocol tailored for the characteristics of a representative EH prototype: the TI eZ430-RF2500-SEH. We present a generalized model of ND accounting for unique prototype characteristics (i.e., energy costs for transmission/reception, and transceiver state switching times/costs). Then, we present the Power Aware Neighbor Discovery Asynchronously (Panda) protocol in which nodes transition between the sleep, receive, and transmit states. We analyze Panda and select its parameters to maximize the ND rate subject to a homogeneous power budget. We also present Panda-D, designed for non-homogeneous EH nodes. We perform extensive testbed evaluations using the prototypes and study various design tradeoffs. We demonstrate a small difference (less then 2%) between experimental and analytical results, thereby confirming the modeling assumptions. Moreover, we show that Panda improves the ND rate by up to 3x compared to related protocols. Finally, we show that Panda-D operates well under non-homogeneous power harvesting.

A demonstration of a real-time full-duplex wireless link is presented, in which a pair of full-duplex transceivers perform simultaneous transmission and reception on the same frequency channel. A full-duplex transceiver is composed of a custom-designed small-form-factor analog self-interference canceller, and a digital self-interference cancellation implementation is integrated with the National Instruments Universal Software Radio Peripheral (USRP). An adaptive analog self-interference canceller tuning mechanism adjusts to environmental changes. We demonstrate the practicality and robustness of the full-duplex wireless link through the National Instruments LabVIEW interface.

We study the achievable capacity regions of fall-duplex links in the single- and multi-channel cases (in the latter case, the channels are assumed to be orthogonal - e.g., OFDM). We present analytical results that characterize the uplink and downlink capacity region and efficient algorithms for computing rate pairs at the region's boundary. We also provide near-optimal and heuristic algorithms that "convexify" the capacity region when it is not convex. The convexified region corresponds to a combination of a few full-duplex rates (i.e., to time sharing between different operation modes). The algorithms can be used for theoretical characterization of the capacity region as well as for resource (time, power, and channel) allocation with the objective of maximizing the sum of the rates when one of them (uplink or downlink) must be guaranteed (e.g., due to QoS considerations). We numerically illustrate the capacity regions and the rate gains (compared to time division duplex) for various channel and cancellation scenarios. The analytical results provide insights into the properties of the full-duplex capacity region and are essential for future development of scheduling, channel allocation, and power control algorithms.

Object tracking applications are gaining popularity and will soon utilize energy harvesting low-power wireless nodes where power is mostly consumed for neighbor discovery. Such applications require the design and experimentation with low-power neighbor discovery protocols. We demonstrate the Panda protocol [4, 5] implementation using commercial off-the-shelf energy harvesting devices, based on the TI eZ430-RF2500-SEH prototype. The prototypes harvest indoor light energy to perform power-aware neighbor discovery, while maintaining a power budget. A custom-designed online monitoring system interactively demonstrates the network dynamics, including the energy storage levels of the devices, the neighbor discovery events, and aggregate discovery statistics.

Object tracking applications are gaining popularity and will soon utilize energy harvesting (EH) low-power nodes that will consume power mostly for neighbor discovery (ND) (i.e., identifying nodes within communication range). Although ND protocols were developed for sensor networks, the challenges posed by emerging EH low-power transceivers were not addressed. Therefore, we design an ND protocol tailoredfor the characteristics of a representative EH prototype: the TI eZ430-RF2500-SEH. We present a generalized model of ND accounting for unique prototype characteristics (i.e., energy costs for transmission/reception, and transceiver state switching times/costs). Then, we present the Power Aware ND Asynchronously (Panda) protocol, in which nodes transition between the sleep, receive, and transmit states. We analyze Panda and select its parameters to maximize the ND rate subject to a homogeneous power budget. We also present Panda-D, designed for non-homogeneous EH nodes. We perform extensive testbed evaluations using the prototypes and study various design tradeoffs. We demonstrate a small difference (less than 2%) between experimental and analytical results, thereby confirming the modeling assumptions. Moreover, we show that Panda improves the ND rate by up to 3× compared with related protocols. Finally, we show that Panda-D operates well under non-homogeneous power harvesting.

WiFi networks have been globally deployed and most mobile devices are currently WiFi-enabled. While WiFi has been proposed for multimedia content distribution, its lack of adequate support for multicast services hinders its ability to provide multimedia content distribution to a large number of devices. In this paper, we present the AMuSe system, whose objective is to enable scalable and adaptive WiFi multicast services. AMuSe is based on accurate receiver feedback and incurs a small control overhead. In particular, we develop an algorithm for dynamic selection of a subset of the multicast receivers as feedback nodes, which periodically send information about the channel quality to the multicast sender. This feedback information can be used by the multicast sender to optimize multicast service quality, e.g., by dynamically adjusting transmission bitrate. AMuSe does not require any changes to the standards or any modifications to the WiFi devices. We implemented AMuSe on the ORBIT testbed and evaluated its performance in large groups with approximately 200 WiFi devices, both with and without interference sources. Our extensive experiments demonstrate that AMuSe can provide accurate feedback in a dense multicast environment. It outperforms several alternatives even in the case of external interference and changing network conditions.

Proportional Fair (PF) scheduling algorithms are the de facto standard in cellular networks. They exploit the users' channel state diversity (induced by fast-fading) and are optimal for stationary channel state distributions and an infinite time-horizon. However, mobile users experience a nonstationary channel, due to slow-fading (on the order of seconds), and are associated with base stations for short periods. Hence, we develop the Predictive Finite-horizon PF Scheduling ((PF)2S) Framework that exploits mobility. We present extensive channel measurement results from a 3G network and characterize mobility-induced channel state trends. We show that a user's channel state is highly reproducible and leverage that to develop a data rate prediction mechanism. We then present a few channel allocation estimation algorithms that exploit the prediction mechanism. Our trace-based simulations consider instances of the (PF)2S Framework composed of combinations of prediction and channel allocation estimation algorithms. They indicate that the framework can increase the throughput by 15%-55% compared to traditional PF schedulers, while improving fairness.

Full-duplex communication has the potential to substantially increase the throughput in wireless networks. However, the benefits of full-duplex are still not well understood. In this paper, we characterize the full-duplex rate gains in both single-channel and multi-channel use cases. For the single-channel case, we quantify the rate gain as a function of the remaining self-interference and SNR values. We also provide a sufficient condition under which the sum of uplink and downlink rates on a full-duplex channel is concave in the transmission power levels. Building on these results, we consider the multi-channel case. For that case, we introduce a new realistic model of a small form-factor (e.g., smartphone) full-duplex receiver and demonstrate its accuracy via measurements. We study the problem of jointly allocating power levels to different channels and selecting the frequency of maximum self-interference suppression, where the objective is maximizing the sum of the rates over uplink and downlink OFDM channels. We develop a polynomial time algorithm which is nearly optimal under very mild restrictions. To reduce the running time, we develop an efficient nearly-optimal algorithm under the high SINR approximation. Finally, we demonstrate via numerical evaluations the capacity gains in the different use cases and obtain insights into the impact of the remaining self-interference and wireless channel states on the performance.

Recent events demonstrated the vulnerability of power grids to cyber attacks and to physical attacks. Therefore, we focus on joint cyber and physical attacks and develop methods to retrieve the grid state information following such an attack. We consider a model in which an adversary attacks a zone by physically disconnecting some of its power lines and blocking the information flow from the zone to the grid's control center. We use tools from linear algebra and graph theory and leverage the properties of the power flow DC approximation to develop methods for information recovery. Using information observed outside the attacked zone, these methods recover information about the disconnected lines and the phase angles at the buses. We identify sufficient conditions on the zone structure and constraints on the attack characteristics such that these methods can recover the information. We also show that it is NP-hard to find an approximate solution to the problem of partitioning the power grid into the minimum number of attack-resilient zones. However, since power grids can often be represented by planar graphs, we develop a constant approximation partitioning algorithm for these graphs. Finally, we numerically study the relationships between the grid's resilience and its structural properties, and demonstrate the partitioning algorithm on real power grids. The results can provide insights into the design of a secure control network for the smart grid.

Numerous energy harvesting wireless devices that will serve as building blocks for the Internet of Things (IoT) are currently under development. However, there is still only limited understanding of the properties of various energy sources and their impact on energy harvesting adaptive algorithms. Hence, we focus on characterizing the kinetic (motion) energy that can be harvested by a wireless node with an IoT form factor and on developing energy allocation algorithms for such nodes. In this paper, we describe methods for estimating harvested energy from acceleration traces. To characterize the energy availability associated with specific human activities (e.g., relaxing, walking, cycling), we analyze a motion dataset with over 40 participants. Based on acceleration measurements that we collected for over 200 hours, we study energy generation processes associated with day-long human routines. We also briefly summarize our experiments with moving objects. We develop energy allocation algorithms that take into account practical IoT node design considerations, and evaluate the algorithms using the collected measurements. Our observations provide insights into the design of motion energy harvesters, IoT nodes, and energy harvesting adaptive algorithms.

This article focuses on a new type of wireless devices in the domain between RFIDs and sensor networks—Energy-Harvesting Active Networked Tags (EnHANTs). Future EnHANTs will be small, flexible, and self-powered devices that can be attached to objects that are traditionally not networked (e.g., books, furniture, toys, produce, and clothing). Therefore, they will provide the infrastructure for various tracking applications and can serve as one of the enablers for the Internet of Things. We present the design considerations for the EnHANT prototypes, developed over the past 4 years. The prototypes harvest indoor light energy using custom organic solar cells, communicate and form multihop networks using ultra-low-power Ultra-Wideband Impulse Radio (UWB-IR) transceivers, and dynamically adapt their communications and networking patterns to the energy harvesting and battery states. We describe a small-scale testbed that uniquely allows evaluating different algorithms with trace-based light energy inputs. Then, we experimentally evaluate the performance of different energy-harvesting adaptive policies with organic solar cells and UWB-IR transceivers. Finally, we discuss the lessons learned during the prototype and testbed design process.

We consider line outages in the transmission network of the power grid, and specifically those caused by natural disasters or large-scale physical attacks. In such networks, an outage of a line may lead to overload on other lines, thereby leading to their outage. Such a cascade may have devastating effects not only on the power grid but also on the interconnected communication networks. We study a model of such failures and show that it differs from other models used to analyze cascades (e.g., epidemic/percolation-based models). Inspired by methods developed for network-survivability analysis, we show how to identify the most vulnerable locations in the network. We also perform extensive numerical experiments with real grid data to estimate the effects of geographically correlated outages and briefly discuss mitigation methods. The developed techniques can indicate potential locations for grid monitoring, and hence, will have impact on the deployment of the smart-grid networking infrastructure.

Proportional Fair (PF) scheduling algorithms are the de-facto standard in cellular networks. They exploit the users' channel state diversity (induced by fast-fading), and are optimal for stationary channel state distributions and an infinite time-horizon. However, mobile users experience a non-stationary channel, due to slow-fading (on the order of seconds), and are associated with basestations for short periods. Hence, we develop the Predictive Finite-horizon PF Scheduling ((PF)2S) Framework that exploits mobility. We present extensive channel measurement results from a 3G network and characterize mobility-induced channel state trends. We show that a user's channel state is highly reproducible and leverage that to develop a data rate prediction mechanism. We then present a few channel allocation estimation algorithms that rely on the prediction mechanism. Our trace-based simulations consider instances of the PF2S Framework composed of combinations of prediction and channel allocation estimation algorithms. They indicate that the framework can increase the throughput by 15%–55% compared to traditional PF schedulers, while improving fairness.

This paper considers max-min fair rate allocation and routing in energy harvesting networks where fairness is required among both the nodes and the time slots. Unlike most previous work on fairness, we focus on multihop topologies and consider different routing methods. We assume a predictable energy profile and focus on the design of efficient and optimal algorithms that can serve as benchmarks for distributed and approximate algorithms. We first develop an algorithm that obtains a max-min fair rate assignment for any given (time-variable or time-invariable) unsplittable routing or a routing tree. For time-invariable unsplittable routing, we also develop an algorithm that finds routes that maximize the minimum rate assigned to any node in any slot. For fractional routing, we study the joint routing and rate assignment problem. We develop an algorithm for the time-invariable case with constant rates. We show that the time-variable case is at least as hard as the 2-commodity feasible flow problem and design an FPTAS to combat the high running time. Finally, we show that finding a max-min fair unsplittable routing or a routing tree is NP-hard, even for a time horizon of a single slot. Our analysis provides insights into the problem structure and can be applied to other related fairness problems.

We present a control plane architecture to accelerate multicast and incast traffic delivery for data-intensive applications in cluster-computing interconnection networks. The architecture is experimentally examined by enabling physical layer optical multicasting on-demand for the application layer to achieve non-blocking performance.

Numerous energy harvesting wireless devices that will serve as building blocks for the Internet of Things (IoT) are currently under development. However, there is still only limited understanding of the properties of various energy sources and their impact on energy harvesting adaptive algorithms. Hence, we focus on characterizing the kinetic (motion) energy that can be harvested by a wireless node with an IoT form factor and on developing energy allocation algorithms for such nodes. In this paper, we describe methods for estimating harvested energy from acceleration traces. To characterize the energy availability associated with specific human activities (e.g., relaxing, walking, cycling), we analyze a motion dataset with over 40 participants. Based on acceleration measurements that we collected for over 200 hours, we study energy generation processes associated with day-long human routines. We also briefly summarize our experiments with moving objects. We develop energy allocation algorithms that take into account practical IoT node design considerations, and evaluate the algorithms using the collected measurements. Our observations provide insights into the design of motion energy harvesters, IoT nodes, and energy harvesting adaptive algorithms.

Core and aggregation optical networks are remarkably static, despite the emerging dynamic capabilities of the individual optical devices. This stems from the inability to address optical impairments in real-time. As a result, tasks such as adding and removing wavelengths take a substantial amount of time, and therefore, optical networks are over-provisioned and inefficient in terms of capacity and energy. Optical Performance Monitors (OPMs) that assess the Quality of Transmission (QoT) in real-time can be used to overcome these inefficiencies. However, prior work mostly focused on the single link level. In this paper, we present a network-wide optimization algorithm that leverages OPM measurements to dynamically control the wavelengths' power levels. Hence, it allows adding and dropping wavelengths quickly while mitigating the impacts of impairments caused by these actions, thereby facilitating efficient operation of higher layer protocols. We evaluate the algorithm's performance using a network-scale optical simulator under real-world scenarios and show that the ability to add and drop wavelengths dynamically can lead to significant power savings. Moreover, we experimentally evaluate the algorithm in an optical testbed and discuss the practical implementation issues. To the best of our knowledge, this paper is the first attempt at providing a global power control algorithm that uses live OPM measurements to enable dynamic optical networking.

IEEE 802.11-based wireless local area networks, referred to as WiFi, have been globally deployed and the vast majority of mobile devices are currently WiFi-enabled. While WiFi has been proposed for multimedia content distribution, its lack of adequate support for multicast services hinders its ability to provide multimedia content distribution to a large number of devices. We propose AMuSe, a scalable and adaptive interference mitigation solution for WiFi multicast services which is based on accurate receiver feedback and that incurs a small control overhead. Specifically, we develop a scheme for dynamic selection of a subset of the multicast receivers as feedback nodes, which periodically send information, such as channel quality or received packet statistics, to the multicast sender. This feedback information is used by the multicast sender to optimize the multicast service quality, e.g., by dynamically adjusting the transmission bit-rate. Our proposed solution does not require any changes to the standards or any modifications to the WiFi devices. We have implemented the proposed solution in the ORBIT testbed and evaluated its performance in large groups with approximately 250 receivers, both with and without interference sources. Our online experiments demonstrate that our system provides practical multicast services that can accommodate hundreds of receivers.

Core and aggregation optical networks are remarkably static, despite the emerging dynamic capabilities of the individual optical devices. This stems from the inability to address optical impairments in real-time. As a result, tasks such as adding and removing wavelengths take a substantial amount of time, and therefore, optical networks are over-provisioned and inefficient in terms of capacity and energy. Optical Performance Monitors (OPMs) that assess the Quality of Transmission (QoT) in real-time can be used to overcome these inefficiencies. However, prior work mostly focused on the single link level. In this paper, we present a network-wide optimization algorithm that leverages OPM measurements to dynamically control the wavelengths' power levels. Hence, it allows adding and dropping wavelengths quickly while mitigating the impacts of impairments caused by these actions, thereby facilitating efficient operation of higher layer protocols. We evaluate the algorithm's performance using a network-scale optical simulator under real-world scenarios and show that the ability to add and drop wavelengths dynamically can lead to significant power savings. Moreover, we experimentally evaluate the algorithm in an optical testbed and discuss the practical implementation issues. To the best of our knowledge, this paper is the first attempt at providing a global power control algorithm that uses live OPM measurements to enable dynamic optical networking.

This paper focuses on a new type of wireless devices in the domain between RFIDs and sensor networks - Energy Harvesting Active Networked Tags (EnHANTs). Future EnHANTs will be small, flexible, and self-powered devices that can be attached to objects that are traditionally not networked (e.g., books, toys, clothing), thereby providing the infrastructure for novel tracking applications. We present the design considerations for the EnHANT prototypes, developed over the past 3 years. The prototypes harvest indoor light energy using custom organic solar cells, communicate and form multihop networks using ultralow-power Ultra-Wideband Impulse Radio (UWB-IR) transceivers, and adapt their communications and networking patterns to the energy harvesting and battery states. We also describe a small scale EnHANTs testbed that uniquely allows evaluating different algorithms with trace-based light energy inputs.

The modern computing landscape increasingly requires a range of skills to successfully integrate complex systems. Project-based learning is used to help students build professional skills. However, it is typically applied to small teams and small efforts. In this paper, we describe our experience in engaging a large number of students in research projects within a multi-year interdisciplinary research effort. The projects expose the students to various disciplines in Electrical Engineering (circuit design, wireless communications, hardware prototyping), Computer Science (embedded systems, algorithm design, networking) and Applied Physics (thin-film battery design, solar cell fabrication). While a student project is usually focused on one discipline area, it requires interaction with at least two other areas. Over 4 years, 115 semester-long projects have been completed. The students were a diverse group of high school, undergraduate, and M.S. Computer Science, Computer Engineering, and Electrical Engineering students. Some of the approaches we have taken to facilitate student learning are real-world system development constraints, regular cross-group meetings, and extensive involvement of Ph.D. students in student mentorship and knowledge transfer. To assess our approaches, we conducted a survey among the participating students. The results demonstrate the effectiveness of our methods. For example, 70% of the students surveyed indicated that working on their research project improved their ability to function on multidisciplinary teams more than coursework, internships, or any other activity.

This paper focuses on cascading line failures in the transmission system of the power grid. Such a cascade may have a devastating effect not only on the power grid but also on the interconnected communication networks. Recent large-scale power outages demonstrated the limitations of epidemic- and percolation-based tools in modeling the cascade evolution. Hence, based on a linearized power flow model (that substantially differs from the classical packet flow models), we obtain results regarding the various properties of a cascade. Specifically, we consider performance metrics such as the the distance between failures, the length of the cascade, and the fraction of demand (load) satisfied after the cascade. We show, for example, that due to the unique properties of the model: (i) the distance between subsequent failures can be arbitrarily large and the cascade may be arbitrarily long, (ii) a large set of initial line failures may have a smaller effect than a failure of one of the lines in the set, and (iii) minor changes to the network parameters may have a significant impact. Moreover, we show that finding the set of lines whose removal has the most significant impact (under various metrics) is NP-Hard. Moreover, we develop a fast algorithm to recompute the flows at each step of the cascade. The results can provide insight into the design of smart grid measurement and control algorithms that can mitigate a cascade.

Recent advances in energy harvesting materials and ultra-low-power communications will soon enable the realization of networks composed of energy harvesting devices. These devices will operate using very low ambient energy, such as energy harvested from indoor lights. We focus on characterizing the light energy availability in indoor environments and on developing energy allocation algorithms for energy harvesting devices. First, we present results of our long-term indoor radiant energy measurements, which provide important inputs required for algorithm and system design (e.g., determining the required battery sizes). Then, we focus on algorithm development, which requires nontraditional approaches, since energy harvesting shifts the nature of energy-aware protocols from minimizing energy expenditure to optimizing it. Moreover, in many cases, different energy storage types (rechargeable battery and a capacitor) require different algorithms. We develop algorithms for calculating time fair energy allocation in systems with deterministic energy inputs, as well as in systems where energy inputs are stochastic.

Telecommunications networks, and in particular optical WDM networks, are vulnerable to large-scale failures in their physical infrastructure, resulting from physical attacks (such as an electromagnetic pulse attack) or natural disasters (such as solar flares, earthquakes, and floods). Such events happen at specific geographical locations and disrupt specific parts of the network, but their effects cannot be determined exactly in advance. Therefore, we provide a unified framework to model network vulnerability when the event has a probabilistic nature, defined by an arbitrary probability density function. Our framework captures scenarios with a number of simultaneous attacks, when network components consist of several dependent subcomponents, and in which either a 1+1 or a 1:1 protection plan is in place. We use computational geometric tools to provide efficient algorithms to identify vulnerable points within the network under various metrics. Then, we obtain numerical results for specific backbone networks, demonstrating the applicability of our algorithms to real-world scenarios. Our novel approach allows to identify locations that require additional protection efforts (e.g., equipment shielding). Overall, the paper demonstrates that using computational geometric techniques can significantly contribute to our understanding of network resilience.

Motivated by applications in Dynamic Spectrum Access Networks, we focus on a system in which a few agents are engaged in a costly individual exploration process where each agent's benefit is determined according to the minimum obtained value. Such an

We consider a dynamic spectrum access system in which Secondary Users (SUs) choose to either acquire dedicated spectrum or to use spectrum-holes (white spaces) which belong to Primary Users (PUs). The trade-off incorporated in this decision is between immediate yet costly transmission and free but delayed transmission (a consequence of both the possible appearance of PUs and sharing the spectrum holes with multiple SUs). We first consider a system with a single PU band, in which the SU decisions are fixed. Employing queueing-theoretic methods, we obtain explicit expressions for the expected delays associated with using the PU band. Based on that, we then consider self-interested SUs and study the interaction between them as a non-cooperative game. We prove the existence and uniqueness of a symmetric Nash equilibrium, and characterize the equilibrium behavior explicitly. Using our equilibrium results, we show how to maximize revenue from renting dedicated bands to SUs and briefly discuss the extension of our model to multiple PUs. Finally, since spectrum sensing can be resource-consuming, we characterize the gains provided by this capability.

We explore distributed mechanisms for maintaining the physical layer connectivity of a mobile wireless network while still permitting significant area coverage. Moreover, we require that these mechanisms maintain connectivity despite the unpredictable wireless propagation behavior found in complex real-world environments. To this end, we propose the Spreadable Connected Autonomic Network (SCAN) algorithm, a fully distributed, on-line, low overhead mechanism for maintaining the connectivity of a mobile wireless network. SCAN leverages knowledge of the local (2-hop) network topology to enable each node to intelligently halt its own movement and thereby avoid network partitioning events. By relying on topology data instead of locality information and deterministic connectivity models, SCAN can be applied in a wide range of realistic operational environments. We believe it is for precisely this reason that, to our best knowledge, SCAN was the first such approach to be implemented in hardware. Here, we present results from our implementation of SCAN, finding that our mobile robotic testbed maintains full connectivity over 99% of the time. Moreover, SCAN achieves this in a complex indoor environment, while still allowing testbed nodes to cover a significant area.

Efficient operation of wireless networks and switches requires using simple (and in some cases distributed) scheduling algorithms. In general, simple greedy algorithms (known as Greedy Maximal Scheduling, or GMS) are guaranteed to achieve only a fraction of the maximum possible throughput (e.g., 50% throughput in switches). However, it was recently shown that in networks in which the Local Pooling conditions are satisfied, GMS achieves 100% throughput. Moreover, in networks in which the σ-Local Pooling conditions hold, GMS achieves σ% throughput. In this paper, we focus on identifying the specific network topologies that satisfy these conditions. In particular, we provide the first characterization of all the network graphs in which Local Pooling holds under primary interference constraints (in these networks, GMS achieves 100% throughput). This leads to a linear-time algorithm for identifying Local-Pooling-satisfying graphs. Moreover, by using similar graph-theoretical methods, we show that in all bipartite graphs (i.e., input-queued switches) of size up to 7 ×n, GMS is guaranteed to achieve 66% throughput, thereby improving upon the previously known 50% lower bound. Finally, we study the performance of GMS in interference graphs and show that in certain specific topologies, its performance could be very bad. Overall, the paper demonstrates that using graph-theoretical techniques can significantly contribute to our understanding of greedy scheduling algorithms.

Telecommunications networks, and in particular optical WDM networks, are vulnerable to large-scale failures of their physical infrastructure, resulting from physical attacks (such as an Electromagnetic Pulse attack) or natural disasters (such as solar flares, earthquakes, and floods). Such events happen at specific geographical locations and disrupt specific parts of the network but their effects are not deterministic. Therefore, we provide a unified framework to model the network vulnerability when the event has a probabilistic nature, defined by an arbitrary probability density function. Our framework captures scenarios with a number of simultaneous attacks, in which network components consist of several dependent subcomponents, and in which either a 1+1 or a 1:1 protection plan is in place. We use computational geometric tools to provide efficient algorithms to identify vulnerable points within the network under various metrics. Then, we obtain numerical results for specific backbone networks, thereby demonstrating the applicability of our algorithms to real-world scenarios. Our novel approach allows for identifying locations which require additional protection efforts (e.g., equipment shielding). Overall, the paper demonstrates that using computational geometric techniques can significantly contribute to our understanding of network resilience.

Recent advances in energy harvesting materials and ultra-low-power communications will soon enable the realization of networks composed of energy harvesting devices. These devices will operate using very low ambient energy, such as indoor light energy. We focus on characterizing the energy availability in indoor environments and on developing energy allocation algorithms for energy harvesting devices. First, we present results of our long-term indoor radiant energy measurements, which provide important inputs required for algorithm and system design (e.g., determining the required battery sizes). Then, we focus on algorithm development, which requires nontraditional approaches, since energy harvesting shifts the nature of energy-aware protocols from minimizing energy expenditure to optimizing it. Moreover, in many cases, different energy storage types (rechargeable battery and a capacitor) require different algorithms. We develop algorithms for determining time fair energy allocation in systems with predictable energy inputs, as well as in systems where energy inputs are stochastic.

We explore distributed mechanisms for maintaining the physical layer connectivity of a mobile wireless network while still permitting significant area coverage. Moreover, we require that these mechanisms maintain connectivity despite the unpredictable wireless propagation behavior found in complex real-world environments. To this end, we propose the Spreadable Connected Autonomic Network (SCAN) algorithm, a fully distributed, on-line, low overhead mechanism for maintaining the connectivity of a mobile wireless network. SCAN leverages knowledge of the local (2-hop) network topology to enable each node to intelligently halt its own movement and thereby avoid network partitioning events. By relying on topology data instead of locality information and deterministic connectivity models, SCAN can be applied in a wide range of realistic operational environments. We believe it is for precisely this reason that, to our best knowledge, SCAN was the first such approach to be implemented in hardware. Here, we present results from our implementation of SCAN, finding that our mobile robotic testbed maintains full connectivity over 99% of the time. Moreover, SCAN achieves this in a complex indoor environment, while still allowing testbed nodes to cover a significant area.

The performance of many algorithms in dynamic networks depends on the properties of the underlying graph representing the network. Since such a graph is inherently time-varying, quantifying the change in its structure is important for understanding the behavior of higher-layer network algorithms. In this paper, we study change in the dynamic graph structure of mobile wireless networks that evolve over time due to node mobility. We define several graph evolution metrics and evaluate them through extensive numerical simulations under Levy Walk mobility, which has been shown previously to have similarities to human mobility patterns. Based on the mean and distribution of these metrics, we obtain important insights into the properties of the evolving graph generated by Levy Walk mobility, and then compare the results to the Random Waypoint mobility model. Finally, we discuss the effects of the rate of graph change on the performance of network applications such as data routing and flooding. Our results suggest that the proposed metrics are viable for quantitatively measuring the magnitude of change in a sequence of evolving graphs.

We consider a dynamic spectrum access system in which Secondary Users (SUs) choose to either acquire dedicated spectrum or to use spectrum-holes (white spaces) which belong to Primary Users (PUs). The tradeoff incorporated in this decision is between immediate yet costly transmission and free but delayed transmission (a consequence of both the possible appearance of PUs and sharing the spectrum holes with multiple SUs). We first consider a system with a single PU band, in which the SU decisions are fixed. Employing queueing-theoretic methods, we obtain explicit expressions for the expected delays associated with using the PU band. Based on that, we then consider self-interested SUs and study the interaction between them as a noncooperative game. We prove the existence and uniqueness of a symmetric Nash equilibrium, and characterize the equilibrium behavior explicitly. Using our equilibrium results, we show how to maximize revenue from renting dedicated bands to SUs. Finally, we extend the scope to a scenario with multiple PUs, show that the band-pricing analysis can be applied to some special cases, and provide numerical examples.

Energy Harvesting Active Networked Tags (EnHANTs) are a new class of devices in the domain between RFIDs and sensor networks. EnHANTs will be small, flexible, and energetically self-reliant. Their development is enabled by advances in ultra-low-power ultra-wideband (UWB) communications and in organic semiconductor-based energy harvesting materials. In this demo, we present UWB-enabled EnHANT prototypes. Each prototype is based on a MICA2 mote integrated with a UWB Transceiver and an energy harvesting module (EHM) that allows demonstrating energy harvesting-adaptive communications. Additional information about EnHANTs is available at [2] and http://enhants.ee.columbia.edu.

Energy Harvesting Active Networked Tags (EnHANTs) will be a new class of devices in the domain between RFIDs and sensor networks. Small, flexible, and energetically self-reliant, EnHANTs will be attached to objects that are traditionally not networked, such as books, furniture, toys, produce, and clothing. More information about the EnHANTs project is available at http://enhants.ee.columbia.edu. In this demo we present a small network of EnHANT prototypes. The current EnHANT prototypes are integrated with novel custom in-house-developed energy harvesting and communications hardware, namely organic solar cells and ultra-wide-band impulse radio (UWB-IR) transceivers. The demo showcases prototypes communicating using the novel UWB-IR transceivers and adapting their communications and networking parameters to the available environmental energy harvested by the organic solar cells.

We focus on resource allocation for energy harvesting devices. We analytically and numerically evaluate the performance of algorithms that determine time fair energy allocation in systems with predictable and stochastic energy inputs. To gain insight into the performance of networks of devices, we obtain results for the simple cases of a single node and a link. Due to the need for low complexity algorithms, we focus on simple policies (some of which proposed in the past as heuristics) and analytically derive performance guarantees. We also evaluate the performance via simulation, using real-world energy traces that we collected for over a year, and in a testbed of energy harvesting devices developed within the EnHANTs project.

Communication networks are vulnerable to natural disasters, such as earthquakes or floods, as well as to physical attacks, such as an electromagnetic pulse (EMP) attack. Such real-world events happen in specific geographical locations and disrupt specific parts of the network. Therefore, the geographical layout of the network determines the impact of such events on the network's connectivity. In this paper, we focus on assessing the vulnerability of (geographical) networks to such disasters. In particular, we aim to identify the most vulnerable parts of the network. That is, the locations of disasters that would have the maximum disruptive effect on the network in terms of capacity and connectivity. We consider graph models in which nodes and links are geographically located on a plane. First, we consider a simplistic bipartite graph model and present a polynomial-time algorithm for finding a worst-case vertical line segment cut. We then generalize the network model to graphs with nodes at arbitrary locations. We model the disaster event as a line segment or a disk and develop polynomial-time algorithms that find a worst-case line segment cut and a worst-case circular cut. Finally, we obtain numerical results for a specific backbone network, thereby demonstrating the applicability of our algorithms to real-world networks. Our novel approach provides a promising new direction for network design to avert geographical disasters or attacks.

Efficient operation of wireless networks and switches requires using simple (and in some cases distributed) scheduling algorithms. In general, simple greedy algorithms (known as Greedy Maximal Scheduling - GMS) are guaranteed to achieve only a fraction of the maximum possible throughput (e.g., 50% throughput in switches). However, it was recently shown that in networks in which the Local Pooling conditions are satisfied, GMS achieves 100% throughput. Moreover, in networks in which the ¿-Local Pooling conditions hold, GMS achieves ¿% throughput. In this paper, we focus on identifying the specific network topologies that satisfy these conditions. In particular, we provide the first characterization of all the network graphs in which Local Pooling holds under primary interference constraints (in these networks GMS achieves 100% throughput). This leads to a linear time algorithm for identifying Local Pooling-satisfying graphs. Moreover, by using similar graph theoretical methods, we show that in all bipartite graphs (i.e., input-queued switches) of size up to 7 × n, GMS is guaranteed to achieve 66% throughput, thereby improving upon the previously known 50% lower bound. Finally, we study the performance of GMS in interference graphs and show that in certain specific topologies its performance could be very bad. Overall, the paper demonstrates that using graph theoretical techniques can significantly contribute to our understanding of greedy scheduling algorithms.

With the convergence of ultra-low-power communications and energy-harvesting technologies, networking self-sustainable ubiquitous devices is becoming feasible. Hence, we have been recently developing new devices, referred to as Energy Harvesting Active Networked Tags (EnHANTs). These small, flexible, and energetically self-reliant tags can be seen as a new class of devices in the domain between RFIDs and sensor networks. EnHANTs are made possible by advances in ultra-lowpower ultra-wideband (UWB) communications and in organic semiconductor-based energy harvesting materials. They will enable novel tracking applications, such as continuous monitoring of objects and locating misplaced items. In this demo, we present phase I EnHANT prototypes. These prototypes are much larger than the envisioned EnHANTs and do not include custom-made UWB and organic electronic components. Yet, they serve as platforms for preliminary experiments and allow demonstrating energy harvesting-adaptive EnHANT communications. Each prototype is based on a MICA2 mote and includes a custom-designed sensor board with a light sensor and a solar cell, which are used to determine the light energy received from the environment. We have also designed a monitoring system which is used in the demo to show how the EnHANT prototypes adjust their communications patterns based on their energy harvesting parameters.

Dynamic Spectrum Access systems exploit temporarily available spectrum ('white spaces') and can spread transmissions over a number of non-contiguous sub-channels. Such methods are highly beneficial in terms of spectrum utilization. However, excessive fragmentation degrades performance and hence off-sets the benefits. Thus, there is a need to study these processes so as to determine how to ensure acceptable levels of fragmentation. Hence, we present experimental and analytical results derived from a mathematical model. We model a system operating at capacity serving requests for bandwidth by assigning a collection of gaps (sub-channels) with no limitations on the fragment size. Our main theoretical result shows that even if fragments can be arbitrarily small, the system does not degrade with time. Namely, the average total number of fragments remains bounded. Within the very difficult class of dynamic fragmentation models (including models of storage fragmentation), this result appears to be the first of its kind. Extensive experimental results describe behavior, at times unexpected, of fragmentation under different algorithms. Our model also applies to dynamic linked-list storage allocation, and provides a novel analysis in that domain. We prove that, interestingly, the 50% rule of the classical (non-fragmented) allocation model carries over to our model. Overall, the paper provides insights into the potential behavior of practical fragmentation algorithms.

The physical layer of future wireless networks will be based on novel radio technologies such as UWB and MIMO. One of the important capabilities of such technologies is the ability to capture a few packets simultaneously. This capability has the potential to improve the performance of the MAC layer. However, we show that in networks with spatially distributed nodes, reusing backoff mechanisms originally designed for narrow-band systems (e.g., CSMA/CA) is inefficient. It is well known that when networks with spatially distributed nodes operate with such MAC protocols, the channel may be captured by nodes that are near the destination, leading to unfairness. We show that when the physical layer enables multipacket reception, the negative implications of reusing the legacy protocols include not only such unfairness, but also a significant throughput reduction. We present alternative backoff mechanisms and evaluate their performance via Markovian analysis, approximations, and simulation. We show that our alternative backoff mechanisms can improve both overall throughput and fairness.

Communication networks are vulnerable to natural disasters, such as earthquakes or floods, as well as to physical attacks, such as an Electromagnetic Pulse (EMP) attack. Such real- world events happen in specific geographical locations and disrupt specific parts of the network. Therefore, the geographical layout of the network determines the impact of such events on the network's connectivity. In this paper, we focus on assessing the vulnerability of (geographical) networks to such disasters. In particular, we aim to identify the most vulnerable parts of the network. That is, the locations of disasters that would have the maximum disruptive effect on the network in terms of capacity and connectivity. We consider graph models in which nodes and links are geographically located on a plane, and model the disaster event as a line segment or a circular cut. We develop algorithms that find a worst- case line segment cut and a worst-case circular cut. Then, we obtain numerical results for a specific backbone network, thereby demonstrating the applicability of our algorithms to real-world networks. Our novel approach provides a promising new direction for network design to avert geographical disasters or attacks.

This paper presents the design challenges posed by a new class of ultra-low-power devices referred to as Energy-Harvesting Active Networked Tags (EnHANTs). EnHANTs are small, flexible, and self-reliant (in terms of energy devices that can be attached to objects that are traditionally not networked (e.g., books, clothing, and produce), thereby providing the infrastructure for various novel tracking applications. Examples of these applications include locating misplaced items, continuous monitoring of objects (items in a store, boxes in transit), and determining locations of disaster survivors. Recent advances in ultra-low-power wireless communications, ultra-wideband (UWB) circuit design, and organic electronic harvesting techniques will enable the realization of EnHANTs in the near future. In order for EnHANTs to rely on harvested energy, they have to spend significantly less energy than Bluetooth, Zigbee, and IEEE 802.15.4a devices. Moreover, the harvesting components and the ultra-low-power physical layer have special characteristics whose implications on the higher layers have yet to be studied (e.g., when using ultra-low-power circuits, the energy required to receive a bit is an order of magnitude higher than the energy required to transmit a bit). These special characteristics pose several new cross-layer research problems. In this paper, we describe the design challenges at the layers above the physical layer, point out relevant research directions, and outline possible starting points for solutions.

We study a novel hierarchical wireless networking approach in which some of the nodes are more capable than others. In such networks, the more capable nodes can serve as mobile backbone nodes and provide a backbone over which end-to-end communication can take place. Our approach consists of controlling the mobility of the backbone nodes in order to maintain connectivity. We formulate the problem of minimizing the number of backbone nodes and refer to it as the Connected Disk Cover (CDC) problem. We show that it can be decomposed into the Geometric Disk Cover (GDC) problem and the Steiner Tree Problem with Minimum Number of Steiner Points (STP-MSP). We prove that if these subproblems are solved separately by gamma- and delta-approximation algorithms, the approximation ratio of the joint solution is gamma+delta. Then, we focus on the two subproblems and present a number of distributed approximation algorithms that maintain a solution to the GDC problem under mobility. A new approach to the solution of the STP-MSP is also described. We show that this approach can be extended in order to obtain a joint approximate solution to the CDC problem. Finally, we evaluate the performance of the algorithms via simulation and show that the proposed GDC algorithms perform very well under mobility and that the new approach for the joint solution can significantly reduce the number of mobile backbone nodes.

The physical layer of future wireless networks will be based on novel radio technologies such as UWB and MIMO. One of the important capabilities of such technologies is the ability to capture a few packets simultaneously. This capability has the potential to improve the performance of the MAC layer. However, we show that in networks with spatially distributed nodes, reusing backoff mechanisms originally designed for narrow-band systems (e.g. CSMA/CA) is inefficient. It is well known that when networks with spatially distributed nodes operate with such MAC protocols, the channel may be captured by nodes that are near the destination, leading to unfairness. We show that when the physical layer enables multipacket reception, the negative implications of reusing the legacy protocols include not only such unfairness but also a significant throughput reduction. We present alternative backoff mechanisms and evaluate their performance via Markovian analysis and simulation. We show that our alternative backoff mechanisms can improve both overall throughput and fairness.

Efficient operation of wireless networks requires distributed routing and scheduling algorithms that take into account interference constraints. Recently, a few algorithms for networks with primary- or secondary-interference constraints have been developed. Due to their distributed operation, these algorithms can achieve only a guaranteed fraction of the maximum possible throughput. It was also recently shown that if a set of conditions (known as Local Pooling) is satisfied, simple distributed scheduling algorithms achieve 100% throughput. However, previous work regarding Local Pooling focused mostly on obtaining abstract conditions and on networks with single-hop interference or single-hop traffic. In this paper, we identify several graph classes that satisfy the Local Pooling conditions, thereby enabling the use of such graphs in network design algorithms. Then, we study the multihop implications of Local Pooling. We show that in many cases, as the interference degree increases, the Local Pooling conditions are more likely to hold. Consequently, although increased interference reduces the maximum achievable throughput of the network, it tends to enable distributed algorithms to achieve 100% of this throughput. Regarding multihop traffic, we show that if the network satisfies only the single-hop Local Pooling conditions, distributed joint routing and scheduling algorithms are not guaranteed to achieve maximum throughput. Therefore, we present new conditions for Multihop Local Pooling, under which distributed algorithms achieve 100% throughout. Finally, we identify network topologies in which the conditions hold and discuss the algorithmic implications of the results.

Efficient communication in Bluetooth networks requires design of intra and inter-piconet scheduling algorithms, and therefore, numerous algorithms have been proposed. However, due to complexities of the Bluetooth MAC, the performance of these algorithms has been analyzed mostly via simulation. We present analytic results regarding the exhaustive, gated, and limited (pure round robin) scheduling algorithms in piconets with bidirectional and unidirectional traffic. We show that a piconet operated according to the limited scheduling algorithm is equivalent to a 1-limited polling system and present exact results regarding symmetric piconets with bidirectional traffic. Then, the difficulties in analyzing the performance of the exhaustive and gated algorithms in a piconet with bidirectional traffic are demonstrated. In addition, we present exact analytic results for piconets with unidirectional traffic. We show that, surprisingly, in symmetrical piconets with only uplink traffic, the mean waiting time is the same for the exhaustive and limited algorithms. This observation results from the differences between piconets and traditional polling systems and can be extended for time-division-duplex systems with arbitrary packet lengths. Furthermore, we show that the mean waiting time in a piconet with only uplink traffic is significantly higher than its corresponding value in a piconet with only downlink traffic. Finally, we numerically compare the exact results to approximate results, presented in the past.

This paper considers the interaction between channel assignment and distributed scheduling in multi-channel multiradio Wireless Mesh Networks (WMNs). Recently, a number of distributed scheduling algorithms for wireless networks have emerged. Due to their distributed operation, these algorithms can achieve only a fraction of the maximum possible throughput. As an alternative to increasing the throughput fraction by designing new algorithms, in this paper we present a novel approach that takes advantage of the inherent multi-radio capability of WMNs. We show that this capability can enable partitioning of the network into subnetworks in which simple distributed scheduling algorithms can achieve 100% throughput. The partitioning is based on the recently introduced notion of Local Pooling. Using this notion, we characterize topologies in which 100% throughput can be achieved distributedly. These topologies are used in order to develop a number of channel assignment algorithms that are based on a matroid intersection algorithm. These algorithms partition a network in a manner that not only expands the capacity regions of the subnetworks but also allows distributed algorithms to achieve these capacity regions. Finally, we evaluate the performance of the algorithms via simulation and show that they significantly increase the distributedly achievable capacity region.

We study a novel hierarchical wireless networking approach in which some of the nodes are more capable than others.In such networks,the more capable nodes can serve as Mobile Backbone Nodes and provide a backbone over which end-to-end communication can take place. Our approac consists of controlling the mobility of the Backbone Nodes in order to maintain connectivity. We formulate the problem of minimizing the number of backbone nodes and refer to it as the Connected Disk Cover problem.We show that it can be decomposed into the Geometric Disk Cover (GDC)problem and the Steiner Tree Problem wit Minimum Number of Steiner Points (STP-MSP). We prove that if these sub-problems are solved separately by γ- and δ- approximation algorithms, the approximation ratio of t e joint solution is γ + δ. Then, we focus on the two subproblems and present a number of distributed approximation algorithms that maintain a solution to the GDC problem under mobility A new approach to the solution of the STP-MSP is also described. We show that this approach can be extended in order to obtain a joint approximate solution to the Connected Disk Cover problem. Finally, we evaluate the performance of the algorithms via simulation and show that the proposed GDC algorithms perform very well under mobility and that the new approac for the joint solution can significantly reduce the number of required Mobile Backbone Nodes.

This paper introduces a novel concept that enables transmitting wireless communication over CATV networks. We present the architecture of a system for Cellular Communication over CATV (CeTV) and review the required modifications to the cable network. These modifications affect only the cable network, thereby enabling the system to operate with unmodified cellular phones. In addition to improving in-building coverage, the CeTV system significantly increases the capacity of the cellular network. We also present the architecture of a Wi-Fi (IEEE 802.11) over CATV (Ca-Fi) system. The implementation of the Ca-Fi system requires improving the MAC protocol used by the Access Points that are deployed within the cable network. However, it does not require modifying the users' devices. We present a few alternative MAC protocols which aim at polling 802.11 stations using the Distributed Coordination Function (DCF). These protocols deal with, and take advantage of the special characteristics of the CATV network. The performance of the proposed protocols is evaluated analytically and via simulation.

Efficient communication in Bluetooth networks requires design of intra and inter-piconet scheduling algorithms, and therefore numerous algorithms have been proposed. However, due to complexities of the Bluetooth MAC, the performance of these algorithms has been analyzed mostly via simulation. We present exact analytic results regarding the exhaustive, gated, and limited (pure round robin) scheduling algorithms in piconets with unidirectional traffic. We show that, surprisingly, in symmetrical piconets with only uplink traffic, the mean waiting time is the same for the exhaustive and limited algorithms. This observation is extended for time-division-duplex systems with arbitrary packet lengths. Furthermore, we show that the mean waiting time in a piconet with only uplink traffic is significantly higher than its corresponding value in a piconet with only downlink traffic. We then demonstrate the difficulties in analyzing the performance of the exhaustive and gated algorithms in a piconet with bi-directional traffic. Finally, we numerically compare the exact results to approximate results, presented in the past.

The terrorist attacks on September 11, 2001 have drawn attention to the use of wireless technology in order to locate survivors of structural collapse. We propose to construct an ad hoc network of wireless smart badges in order to acquire information from trapped survivors. We investigate the energy efficient routing problem that arises in such a network and show that since smart badges have very limited power sources and very low data rates, which may be inadequate in an emergency situation, the solution of the routing problem requires new protocols. The problem is formulated as an anycast routing problem in which the objective is to maximize the time until the first battery drains-out. We present iterative algorithms for obtaining the optimal solution of the problem. Then, we derive an upper bound on the network lifetime for specific topologies. Finally, a polynomial algorithm for obtaining the optimal solution in such topologies is described.