Gossip Algorithms for Distributed Signal Processing

Gossip algorithms are attractive for in-network processing in sensor networks because they do not require any specialized routing, there is no bottleneck or single point of failure, and they are robust to unreliable wireless network conditions. Recently, there has been a surge of activity in the computer science, control, signal processing, and information theory communities, developing faster and more robust gossip algorithms and deriving theoretical performance guarantees. This article presents an overview of recent work in the area. We describe convergence rate results, which are related to the number of transmitted messages and thus the amount of energy consumed in the network for gossiping. We discuss issues related to gossiping over wireless links, including the effects of quantization and noise, and we illustrate the use of gossip algorithms for canonical signal processing tasks including distributed estimation, source localization, and compression.Comment: Submitted to Proceedings of the IEEE, 29 page

Throughput-Optimal Topology Design for Cross-Silo Federated Learning

NeurIPS 2020International audienceFederated learning usually employs a client-server architecture where an orchestrator iteratively aggregates model updates from remote clients and pushes them back a refined model. This approach may be inefficient in cross-silo settings, as close-by data silos with high-speed access links may exchange information faster than with the orchestrator, and the orchestrator may become a communication bottleneck. In this paper we define the problem of topology design for cross-silo federated learning using the theory of max-plus linear systems to compute the system throughput---number of communication rounds per time unit. We also propose practical algorithms that, under the knowledge of measurable network characteristics, find a topology with the largest throughput or with provable throughput guarantees. In realistic Internet networks with 10 Gbps access links for silos, our algorithms speed up training by a factor 9 and 1.5 in comparison to the master-slave architecture and to state-of-the-art MATCHA, respectively. Speedups are even larger with slower access links

Resource Allocation in Networked and Distributed Environments

A central challenge in networked and distributed systems is resource management: how can we partition the available resources in the system across competing users, such that individual users are satisfied and certain system-wide objectives of interest are optimized? In this thesis, we deal with many such fundamental and practical resource allocation problems that arise in networked and distributed environments. We invoke two sophisticated paradigms -- linear programming and probabilistic methods -- and develop provably-good approximation algorithms for a diverse collection of applications. Our main contributions are as follows. Assignment problems: An assignment problem involves a collection of objects and locations, and a load value associated with each object-location pair. Our goal is to assign the objects to locations while minimizing various cost functions of the assignment. This setting models many applications in manufacturing, parallel processing, distributed storage, and wireless networks. We present a single algorithm for assignment which generalizes many classical assignment schemes known in the literature. Our scheme is derived through a fusion of linear algebra and randomization. In conjunction with other ideas, it leads to novel guarantees for multi-criteria parallel scheduling, broadcast scheduling, and social network modeling. Precedence constrained scheduling: We consider two precedence constrained scheduling problems, namely sweep scheduling and tree scheduling, which are inspired by emerging applications in high performance computing. Through a careful use of randomization, we devise the first approximation algorithms for these problems with near-optimal performance guarantees. Wireless communication: Wireless networks are prone to interference. This prohibits proximate network nodes from transmitting simultaneously, and introduces fundamental challenges in the design of wireless communication protocols. We develop fresh geometric insights for characterizing wireless interference. We combine our geometric analysis with linear programming and randomization, to derive near-optimal algorithms for latency minimization and throughput capacity estimation in wireless networks. In summary, the innovative use of linear programming and probabilistic techniques for resource allocation, and the novel ways of connecting them with application-specific ideas is the pivotal theme and the focal point of this thesis

Cooperative Diversity and Partner Selection in Wireless Networks

Next generation wireless communication systems are expected to provide a variety of services including voice, data and video. The rapidly growing demand for these services needs high data rate wireless communication systems with reliability and high user capacity. Recently, it has been shown that reliability and achievable data rate of wireless communication systems increases dramatically by employing multiple transmit and receive antennas. Transmit diversity is a powerful technique for combating multipath fading in wireless communications. However, employing multiple antennas in a mobile terminal to achieve the transmit diversity in the uplink is not feasible due to the limited size of the mobile unit. In order to overcome this problem, a new mode of transmit diversity called cooperative diversity (CD) based on user cooperation, was proposed very recently. By user cooperation, it is meant that the sender transmits to the destination and copies to other users, called partners, for relaying to the destination. The antennas of the sender and the partners together form a multiple antenna situation. CD systems are immuned not only against small scale channel fading but also against large scale channel fading. On the other hand, CD systems are more sensitive to interuser (between sender and partner) transmission errors and user mobility. In this dissertation, we propose a bandwidth and power efficient CD system which could be accommodated with minimal modifications in the currently available direct or point-to-point communication systems. The proposed CD system is based on quadrature signaling (QS). With quadrature signaling, both sender’s and partners’ information symbols are transmitted simultaneously in his/her multiple access channels. It also reduces the synchronization as well as the interference problems that occur in the schemes reported in the literature. The performance of the proposed QS-CD system is analyzed at different layers. First, we study the bit error probability (BEP) of the QS-CD system for both fixed and adaptive relaying at the partner. It is shown from the BEP performance that the QS-CD system can achieve diversity order of two. Then, a cross-layer communication system is developed by combing the proposed QS-CD system at the physical layer and the truncated stop-and- wait automatic repeat request (ARQ) at the data link layer. The performance of the cross-layer system is analyzed and compared with existing schemes in the literature for performance metrics at the data link layer and upper layers, i.e., frame error rate, packet loss rate, average packet delay, throughput, etc. In addition, the studies show that the proposed QS-CD-ARQ system outperforms existing schemes when it has a good partner. In this respect, the proposed system is fully utilizing the communication channel and less complex in terms of implementation when compared with the existing systems. Since the partner selection gives significant impact on the performance of the CD systems, partner selection algorithms (PSAs) are extensively analyzed for both static and mobile user network. In this case, each individual user would like to take advantage of cooperation by choosing a suitable partner. The objective of an individual user may conflict with the objective of the network. In this regard, we would like to introduce a PSA which tries to balance both users and network objectives by taking user mobility into consideration. The proposed PSA referred to as worst link first (WLF), to choose the best partner in cooperative communication systems. The WLF algorithm gives priority to the worst link user to choose its partner and to maximize the energy gain of the radio cell. It is easy to implement not only in centralized networks but also in distributed networks with or without the global knowledge of users in the network. The proposed WLF matching algorithm, being less complex than the optimal maximum weighted (MW) matching and the heuristic based Greedy matching algorithms, yields performance characteristics close to those of MW matching algorithm and better than the Greedy matching algorithm in both static and mobile user networks. Furthermore, the proposed matching algorithm provides around 10dB energy gain with optimal power allocation over a non-cooperative system which is equivalent to prolonging the cell phone battery recharge time by about ten times

Cooperative Diversity and Partner Selection in Wireless Networks

Next generation wireless communication systems are expected to provide a variety of services including voice, data and video. The rapidly growing demand for these services needs high data rate wireless communication systems with reliability and high user capacity. Recently, it has been shown that reliability and achievable data rate of wireless communication systems increases dramatically by employing multiple transmit and receive antennas. Transmit diversity is a powerful technique for combating multipath fading in wireless communications. However, employing multiple antennas in a mobile terminal to achieve the transmit diversity in the uplink is not feasible due to the limited size of the mobile unit. In order to overcome this problem, a new mode of transmit diversity called cooperative diversity (CD) based on user cooperation, was proposed very recently. By user cooperation, it is meant that the sender transmits to the destination and copies to other users, called partners, for relaying to the destination. The antennas of the sender and the partners together form a multiple antenna situation. CD systems are immuned not only against small scale channel fading but also against large scale channel fading. On the other hand, CD systems are more sensitive to interuser (between sender and partner) transmission errors and user mobility. In this dissertation, we propose a bandwidth and power efficient CD system which could be accommodated with minimal modifications in the currently available direct or point-to-point communication systems. The proposed CD system is based on quadrature signaling (QS). With quadrature signaling, both sender’s and partners’ information symbols are transmitted simultaneously in his/her multiple access channels. It also reduces the synchronization as well as the interference problems that occur in the schemes reported in the literature. The performance of the proposed QS-CD system is analyzed at different layers. First, we study the bit error probability (BEP) of the QS-CD system for both fixed and adaptive relaying at the partner. It is shown from the BEP performance that the QS-CD system can achieve diversity order of two. Then, a cross-layer communication system is developed by combing the proposed QS-CD system at the physical layer and the truncated stop-and- wait automatic repeat request (ARQ) at the data link layer. The performance of the cross-layer system is analyzed and compared with existing schemes in the literature for performance metrics at the data link layer and upper layers, i.e., frame error rate, packet loss rate, average packet delay, throughput, etc. In addition, the studies show that the proposed QS-CD-ARQ system outperforms existing schemes when it has a good partner. In this respect, the proposed system is fully utilizing the communication channel and less complex in terms of implementation when compared with the existing systems. Since the partner selection gives significant impact on the performance of the CD systems, partner selection algorithms (PSAs) are extensively analyzed for both static and mobile user network. In this case, each individual user would like to take advantage of cooperation by choosing a suitable partner. The objective of an individual user may conflict with the objective of the network. In this regard, we would like to introduce a PSA which tries to balance both users and network objectives by taking user mobility into consideration. The proposed PSA referred to as worst link first (WLF), to choose the best partner in cooperative communication systems. The WLF algorithm gives priority to the worst link user to choose its partner and to maximize the energy gain of the radio cell. It is easy to implement not only in centralized networks but also in distributed networks with or without the global knowledge of users in the network. The proposed WLF matching algorithm, being less complex than the optimal maximum weighted (MW) matching and the heuristic based Greedy matching algorithms, yields performance characteristics close to those of MW matching algorithm and better than the Greedy matching algorithm in both static and mobile user networks. Furthermore, the proposed matching algorithm provides around 10dB energy gain with optimal power allocation over a non-cooperative system which is equivalent to prolonging the cell phone battery recharge time by about ten times

Data-driven Methodologies and Applications in Urban Mobility

The world is urbanizing at an unprecedented rate where urbanization goes from 39% in 1980 to 58% in 2019 (World Bank, 2019). This poses more and more transportation demand and pressure on the already at or over-capacity old transport infrastructure, especially in urban areas. Along the same timeline, more data generated as a byproduct of daily activity are being collected via the advancement of the internet of things, and computers are getting more and more powerful. These are shown by the statistics such as 90% of the world’s data is generated within the last two years and IBM’s computer is now processing at the speed of 120,000 GPS points per second. Thus, this dissertation discusses the challenges and opportunities arising from the growing demand for urban mobility, particularly in cities with outdated infrastructure, and how to capitalize on the unprecedented growth in data in solving these problems by ways of data-driven transportation-specific methodologies. The dissertation identifies three primary challenges and/or opportunities, which are (1) optimally locating dynamic wireless charging to promote the adoption of electric vehicles, (2) predicting dynamic traffic state using an enormously large dataset of taxi trips, and (3) improving the ride-hailing system with carpooling, smart dispatching, and preemptive repositioning. The dissertation presents potential solutions/methodologies that have become available only recently thanks to the extraordinary growth of data and computers with explosive power, and these methodologies are (1) bi-level optimization planning frameworks for locating dynamic wireless charging facilities, (2) Traffic Graph Convolutional Network for dynamic urban traffic state estimation, and (3) Graph Matching and Reinforcement Learning for the operation and management of mixed autonomous electric taxi fleets. These methodologies are then carefully calibrated, methodically scrutinized under various performance metrics and procedures, and validated with previous research and ground truth data, which is gathered directly from the real world. In order to bridge the gap between scientific discoveries and practical applications, the three methodologies are applied to the case study of (1) Montgomery County, MD, (2) the City of New York, and (3) the City of Chicago and from which, real-world implementation are suggested. This dissertation’s contribution via the provided methodologies, along with the continual increase in data, have the potential to significantly benefit urban mobility and work toward a sustainable transportation system

Spatial-temporal domain charging optimization and charging scenario iteration for EV

Environmental problems have become increasingly serious around the world. With lower carbon emissions, Electric Vehicles (EVs) have been utilized on a large scale over the past few years. However, EVs are limited by battery capacity and require frequent charging. Currently, EVs suffer from long charging time and charging congestion. Therefore, EV charging optimization is vital to ensure drivers’ mobility. This study first presents a literature analysis of the current charging modes taxonomy to elucidate the advantages and disadvantages of different charging modes. In specific optimization, under plug-in charging mode, an Urgency First Charging (UFC) scheduling policy is proposed with collaborative optimization of the spatialtemporal domain. The UFC policy allows those EVs with charging urgency to get preempted charging services. As conventional plug-in charging mode is limited by the deployment of Charging Stations (CSs), this study further introduces and optimizes Vehicle-to-Vehicle (V2V) charging. This is aim to maximize the utilization of charging infrastructures and to balance the grid load. This proposed reservation-based V2V charging scheme optimizes pair matching of EVs based on minimized distance. Meanwhile, this V2V scheme allows more EVs get fully charged via minimized waiting time based parking lot allocation. Constrained by shortcomings (rigid location of CSs and slow charging power under V2V converters), a single charging mode can hardly meet a large number of parallel charging requests. Thus, this study further proposes a hybrid charging mode. This mode is to utilize the advantages of plug-in and V2V modes to alleviate the pressure on the grid. Finally, this study addresses the potential problems of EV charging with a view to further optimizing EV charging in subsequent studies