158 research outputs found
Optimal Reverse Carpooling Over Wireless Networks - A Distributed Optimization Approach
We focus on a particular form of network coding, reverse carpooling, in a
wireless network where the potentially coded transmitted messages are to be
decoded immediately upon reception. The network is fixed and known, and the
system performance is measured in terms of the number of wireless broadcasts
required to meet multiple unicast demands. Motivated by the structure of the
coding scheme, we formulate the problem as a linear program by introducing a
flow variable for each triple of connected nodes. This allows us to have a
formulation polynomial in the number of nodes. Using dual decomposition and
projected subgradient method, we present a decentralized algorithm to obtain
optimal routing schemes in presence of coding opportunities. We show that the
primal sub-problem can be expressed as a shortest path problem on an
\emph{edge-graph}, and the proposed algorithm requires each node to exchange
information only with its neighbors.Comment: submitted to CISS 201
Distributed design of network codes for wireless multiple unicasts
Previous results on network coding for low-power
wireless transmissions of multiple unicasts rely on opportunistic
coding or centralized optimization to reduce the power
consumption. This paper proposes a distributed strategy for
reducing the power consumption in a network coded wireless
network with multiple unicasts. We apply a simple network
coding strategy called âreverse carpooling,â which uses only
XOR and forwarding operations. In this paper, we use the
rectangular grid as a simple network model and attempt to
increase network coding opportunities without the overhead
required for centralized design or coordination. The proposed
technique designates âreverse carpooling linesâ analogous to
a collection of bus routes in a crowded city. Each individual
unicast then chooses a route from its source to its destination
independently but in a manner that maximizes the fraction
of its path spent on reverse carpooling lines. Intermediate
nodes apply reverse carpooling opportunistically along these
routes. Our network optimization attempts to choose the reverse
carpooling lines in a manner that maximizes the expected power
savings with respect to the random choice of sources and sinks
Wireless Inter-Session Network Coding - An Approach Using Virtual Multicasts
This paper addresses the problem of inter-session network coding to maximize throughput for multiple communication sessions in wireless networks. We introduce virtual multicast connections which can extract packets from original sessions and code them together. Random linear network codes can be used for these virtual multicasts. The problem can be stated as a flow-based convex optimization problem with side constraints. The proposed formulation provides a rate region which is at least as large as the region without inter-session network coding. We show the benefits of our technique for several scenarios by means of simulation.United States. Defense Advanced Research Projects Agency (Subcontract 18870740-37362-C
ROUTING IN MOBILE AD-HOC NETWORKS: SCALABILITY AND EFFICIENCY
Mobile Ad-hoc Networks (MANETs) have received considerable research interest in recent years. Because of dynamic topology and limited resources, it is challenging to design routing protocols for MANETs. In this dissertation, we focus on the scalability and efficiency problems in designing routing protocols for MANETs. We design the Way Point Routing (WPR) model for medium to large networks. WPR selects a number of nodes on a route as waypoints and divides the route into segments at the waypoints. Waypoint nodes run a high-level inter-segment routing protocol, and nodes on each segment run a low-level intra-segment routing protocol. We use DSR and AODV as the inter-segment and the intra-segment routing protocols, respectively. We term this instantiation the DSR Over AODV (DOA) routing protocol. We develop Salvaging Route Reply (SRR) to salvage undeliverable route reply (RREP) messages. We propose two SRR schemes: SRR1 and SRR2. In SRR1, a salvor actively broadcasts a one-hop salvage request to find an alternative path to the source. In SRR2, nodes passively learn an alternative path from duplicate route request (RREQ) packets. A salvor uses the alternative path to forward a RREP when the original path is broken. We propose Multiple-Target Route Discovery (MTRD) to aggregate multiple route requests into one RREQ message and to discover multiple targets simultaneously. When a source initiates a route discovery, it first tries to attach its request to existing RREQ packets that it relays. MTRD improves routing performance by reducing the number of regular route discoveries. We develop a new scheme called Bilateral Route Discovery (BRD), in which both source and destination actively participate in a route discovery process. BRD consists of two halves: a source route discovery and a destination route discovery, each searching for the other. BRD has the potential to reduce control overhead by one half. We propose an efficient and generalized approach called Accumulated Path Metric (APM) to support High-Throughput Metrics (HTMs). APM finds the shortest path without collecting topology information and without running a shortest-path algorithm. Moreover, we develop the Broadcast Ordering (BO) technique to suppress unnecessary RREQ transmissions
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 worldâs data is generated within the last two years and IBMâs 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 dissertationâs 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
The Merits of Sharing a Ride
The culture of sharing instead of ownership is sharply increasing in
individuals behaviors. Particularly in transportation, concepts of sharing a
ride in either carpooling or ridesharing have been recently adopted. An
efficient optimization approach to match passengers in real-time is the core of
any ridesharing system. In this paper, we model ridesharing as an online
matching problem on general graphs such that passengers do not drive private
cars and use shared taxis. We propose an optimization algorithm to solve it.
The outlined algorithm calculates the optimal waiting time when a passenger
arrives. This leads to a matching with minimal overall overheads while
maximizing the number of partnerships. To evaluate the behavior of our
algorithm, we used NYC taxi real-life data set. Results represent a substantial
reduction in overall overheads
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