4,710 research outputs found
A state of the art of sensor location, flow observability, estimation, and prediction problems in traffic networks
A state-of-the-art review of flow observability, estimation, and prediction problems in traffic networks is performed. Since mathematical optimization provides a general framework for all of them, an integrated approach is used to perform the analysis of these problems and consider them as different optimization problems whose data, variables, constraints, and objective functions are the main elements that characterize the problems proposed by different authors. For example, counted, scanned or “a priori” data are the most common data sources; conservation laws, flow nonnegativity, link capacity, flow definition, observation, flow propagation, and specific model requirements form the most common constraints; and least squares, likelihood, possible relative error, mean absolute relative error, and so forth constitute the bases for the objective functions or metrics. The high number of possible combinations of these elements justifies the existence of a wide collection of methods for analyzing static and dynamic situations
Optimization with interval data: new problems, algorithms, and applications.
The parameters of real-world optimization problems are often uncertain due to the failure of exact estimation of data entries. Throughout the years, several approaches have been developed to cope with uncertainty in the input parameters of optimization problems, such as robust optimization, stochastic optimization, fuzzy programming, parametric programming, and interval optimization. Each of these approaches tackles the uncertainty in the input data with different assumptions on the source of uncertainty and imposes different requirements on the returned solutions. In this dissertation, the approach we take is that of interval optimization, and more specifically, interval linear programming. The two main problems we consider in this context are, considering all realizations of the interval data, the problems of finding the range of the optimal values and determining the set of all possible optimal solutions. While the theoretical aspects of these problems are well-studied, the algorithmic aspects and the engineering implications have not been explored. In this dissertation, we partially fill these voids. In the first part of the dissertation, we present and test three heuristics to find bounds on the worst optimal value of the equality-constrained interval linear program, which is known to be an intractable problem. In the second part of the dissertation, motivated by a real-case problem in the healthcare context, we define and analyze a new problem, the outcome range problem, in interval linear programming. The solution to the problem would help decision-makers quantify unintended/further consequences of optimal decisions made under uncertainty. Basically, the problem finds the range of an extra function of interest (different from the objective function) over all possible optimal solutions of an interval linear program. We analyze the problem from the theoretical and algorithmic perspectives. We evaluate the performance of our algorithms on simulated problem instances and on a real-world healthcare application. In the third part of the dissertation, we extend our analysis of the outcome range problem, exploring different theoretical properties and designing several new solution algorithms. We also test our solution approaches on different datasets, highlighting the strengths and weaknesses of each approach. Finally, in the last part of the dissertation, we discuss an application of interval optimization in the sensor location problem in the traffic management context. Particularly, we propose an optimization approach to handle the measurement errors in the full link flow observability problem. We show the applicability of our approach on several real-world traffic networks
Carreau: CARrier REsource Access for mUle, DTN applied to hybrid WSN / satellite system
International audienceBoth WSNs (Wireless Sensor Networks) and observation satellites are able to get measurements from a geographic area. To interconnect these technologies, we propose to use a store-carry-and-forward architecture relying on the DTN (Disruption and Delay Tolerant Networking) Bundle Protocol. This architecture aims at being generic, so it is application-agnostic and suits a wide range of scenarios. WSN may collect sporadically large data volume while terrestrial stations communicating with Low Earth Orbit (LEO) satellites have to endure long link disruptions when the satellite is not in the line of sight. These sporadic growths within the WSN coupled with the large latency on satellite links require to schedule data to provide quality of service to several flows. We propose a scheduling policy based on deadline of Bundles and compare it with classical DTN solutions
Data Collection in Two-Tier IoT Networks with Radio Frequency (RF) Energy Harvesting Devices and Tags
The Internet of things (IoT) is expected to connect physical objects and end-users using technologies such as wireless sensor networks and radio frequency identification (RFID). In addition, it will employ a wireless multi-hop backhaul to transfer data collected by a myriad of devices to users or applications such as digital twins operating in a Metaverse. A critical issue is that the number of packets collected and transferred to the Internet is bounded by limited network resources such as bandwidth and energy. In this respect, IoT networks have adopted technologies such as time division multiple access (TDMA), signal interference cancellation (SIC) and multiple-input multiple-output (MIMO) in order to increase network capacity. Another fundamental issue is energy. To this end, researchers have exploited radio frequency (RF) energy-harvesting technologies to prolong the lifetime of energy constrained sensors and smart devices. Specifically, devices with RF energy harvesting capabilities can rely on ambient RF sources such as access points, television towers, and base stations. Further, an operator may deploy dedicated power beacons that serve as RF-energy sources. Apart from that, in order to reduce energy consumption, devices can adopt ambient backscattering communication technologies. Advantageously, backscattering allows devices to communicate using negligible amount of energy by modulating ambient RF signals.
To address the aforementioned issues, this thesis first considers data collection in a two-tier MIMO ambient RF energy-harvesting network. The first tier consists of routers with MIMO capability and a set of source-destination pairs/flows. The second tier consists of energy harvesting devices that rely on RF transmissions from routers for energy supply. The problem is to determine a minimum-length TDMA link schedule that satisfies the traffic demand of source-destination pairs and energy demand of energy harvesting devices. It formulates the problem as a linear program (LP), and outlines a heuristic to construct transmission sets that are then used by the said LP. In addition, it outlines a new routing metric that considers the energy demand of energy harvesting devices to cope with routing requirements of IoT networks. The simulation results show that the proposed algorithm on average achieves 31.25% shorter schedules as compared to competing schemes. In addition, the said routing metric results in link schedules that are at most 24.75% longer than those computed by the LP
Distributed Detection and Estimation in Wireless Sensor Networks
In this article we consider the problems of distributed detection and
estimation in wireless sensor networks. In the first part, we provide a general
framework aimed to show how an efficient design of a sensor network requires a
joint organization of in-network processing and communication. Then, we recall
the basic features of consensus algorithm, which is a basic tool to reach
globally optimal decisions through a distributed approach. The main part of the
paper starts addressing the distributed estimation problem. We show first an
entirely decentralized approach, where observations and estimations are
performed without the intervention of a fusion center. Then, we consider the
case where the estimation is performed at a fusion center, showing how to
allocate quantization bits and transmit powers in the links between the nodes
and the fusion center, in order to accommodate the requirement on the maximum
estimation variance, under a constraint on the global transmit power. We extend
the approach to the detection problem. Also in this case, we consider the
distributed approach, where every node can achieve a globally optimal decision,
and the case where the decision is taken at a central node. In the latter case,
we show how to allocate coding bits and transmit power in order to maximize the
detection probability, under constraints on the false alarm rate and the global
transmit power. Then, we generalize consensus algorithms illustrating a
distributed procedure that converges to the projection of the observation
vector onto a signal subspace. We then address the issue of energy consumption
in sensor networks, thus showing how to optimize the network topology in order
to minimize the energy necessary to achieve a global consensus. Finally, we
address the problem of matching the topology of the network to the graph
describing the statistical dependencies among the observed variables.Comment: 92 pages, 24 figures. To appear in E-Reference Signal Processing, R.
Chellapa and S. Theodoridis, Eds., Elsevier, 201
In-network computation in sensor networks
Sensor networks are an important emerging class of networks that have many
applications. A sink in these networks acts as a bridge between the sensor nodes
and the end-user (which may be automated and/or part of the sink). Typically,
convergecast is performed in which all the data collected by the sensors is
relayed to the sink, which in turn presents the relevant information to the
end-user. Interestingly, some applications require the sink to relay just a
function of the data collected by the sensors. For instance, in a fire alarm
system, the sinks needs to monitor the maximum of the temperature readings of
all the sensors. For these applications, instead of performing convergecast, we
can let the intermediate nodes process the data they receive, to significantly
reduce the volume of traffic transmitted and increase the rate at which
the data is collected and processed at the sink: this is known as in-network
computation.
Most of the current literature on this novel technique focuses on asymptotic
results for large networks and for very elementary functions. In this
dissertation, we study a new class of functions for which we want to compute
explicit solutions for networks of practical size.
We consider the applications where the sink is interested in the first
M statistical moments of the data collected at a certain time.
The k-th statistical moment is
defined as the expectation of the k-th power of the data. The M=1 case represents the
elementary functions like MAX, MIN, MEAN, etc. that are commonly considered in
the literature. For this class of functions, we are interested in explicitly
computing the maximum achievable throughput including routing, scheduling and
queue management for any given network when in-network computation is allowed.
Flow models have been routinely used to solve optimal joint routing and scheduling
problems when there is no in-network computation and they are typically
tractable for relatively large networks. However, deriving such models is not
obvious when in-network computation is allowed. Considering a single rate wireless network
and the physical model of interference, we develop a discrete-time model for
the real-time network operation and perform two transformations to obtain a flow
model that keeps the essence of in-network computation. This model gives an
upper bound on the maximum achievable throughput. To show the tightness of that
upper bound, we derive a numerical lower bound by computing a feasible solution
to the discrete-time model. This lower bound turns out to be
close to the upper bound proving that the flow model is an excellent
approximation to the discrete-time model.
We then adapt the flow model to a
wired multi-rate network with asynchronous transmissions on links with different
capacities. To compute the lower bound for wired networks, we propose a
heuristic strategy involving the generation of multiple trees and effective
queue management that achieves a throughput close to the one computed by the
flow model. This cross validates the tightness of the upper bound and the
goodness of our heuristic strategy. Finally, we provide several engineering
insights on what in-network computation can achieve in both types of networks
Cross-layer design of multi-hop wireless networks
MULTI -hop wireless networks are usually defined as a collection of nodes
equipped with radio transmitters, which not only have the capability to
communicate each other in a multi-hop fashion, but also to route each others’ data
packets. The distributed nature of such networks makes them suitable for a variety of
applications where there are no assumed reliable central entities, or controllers, and
may significantly improve the scalability issues of conventional single-hop wireless
networks.
This Ph.D. dissertation mainly investigates two aspects of the research issues
related to the efficient multi-hop wireless networks design, namely: (a) network
protocols and (b) network management, both in cross-layer design paradigms to
ensure the notion of service quality, such as quality of service (QoS) in wireless mesh
networks (WMNs) for backhaul applications and quality of information (QoI) in
wireless sensor networks (WSNs) for sensing tasks. Throughout the presentation of
this Ph.D. dissertation, different network settings are used as illustrative examples,
however the proposed algorithms, methodologies, protocols, and models are not
restricted in the considered networks, but rather have wide applicability.
First, this dissertation proposes a cross-layer design framework integrating
a distributed proportional-fair scheduler and a QoS routing algorithm, while using
WMNs as an illustrative example. The proposed approach has significant performance
gain compared with other network protocols. Second, this dissertation proposes
a generic admission control methodology for any packet network, wired and
wireless, by modeling the network as a black box, and using a generic mathematical
0. Abstract 3
function and Taylor expansion to capture the admission impact. Third, this dissertation
further enhances the previous designs by proposing a negotiation process,
to bridge the applications’ service quality demands and the resource management,
while using WSNs as an illustrative example. This approach allows the negotiation
among different service classes and WSN resource allocations to reach the optimal
operational status. Finally, the guarantees of the service quality are extended to
the environment of multiple, disconnected, mobile subnetworks, where the question
of how to maintain communications using dynamically controlled, unmanned data
ferries is investigated
Quantitative Performance Comparison of Various Traffic Shapers in Time-Sensitive Networking
Owning to the sub-standards being developed by IEEE Time-Sensitive Networking
(TSN) Task Group, the traditional IEEE 802.1 Ethernet is enhanced to support
real-time dependable communications for future time- and safety-critical
applications. Several sub-standards have been recently proposed that introduce
various traffic shapers (e.g., Time-Aware Shaper (TAS), Asynchronous Traffic
Shaper (ATS), Credit-Based Shaper (CBS), Strict Priority (SP)) for flow control
mechanisms of queuing and scheduling, targeting different application
requirements. These shapers can be used in isolation or in combination and
there is limited work that analyzes, evaluates and compares their performance,
which makes it challenging for end-users to choose the right combination for
their applications. This paper aims at (i) quantitatively comparing various
traffic shapers and their combinations, (ii) summarizing, classifying and
extending the architectures of individual and combined traffic shapers and
their Network calculus (NC)-based performance analysis methods and (iii)
filling the gap in the timing analysis research on handling two novel hybrid
architectures of combined traffic shapers, i.e., TAS+ATS+SP and TAS+ATS+CBS. A
large number of experiments, using both synthetic and realistic test cases, are
carried out for quantitative performance comparisons of various individual and
combined traffic shapers, from the perspective of upper bounds of delay,
backlog and jitter. To the best of our knowledge, we are the first to
quantitatively compare the performance of the main traffic shapers in TSN. The
paper aims at supporting the researchers and practitioners in the selection of
suitable TSN sub-protocols for their use cases
Towards Realistic Urban Traffic Experiments Using DFROUTER: Heuristic, Validation and Extensions
[EN] Traffic congestion is an important problem faced by Intelligent Transportation Systems (ITS), requiring models that allow predicting the impact of different solutions on urban traffic flow. Such an approach typically requires the use of simulations, which should be as realistic as possible. However, achieving high degrees of realism can be complex when the actual traffic patterns, defined through an Origin/Destination (O-D) matrix for the vehicles in a city, remain unknown. Thus, the main contribution of this paper is a heuristic for improving traffic congestion modeling. In particular, we propose a procedure that, starting from real induction loop measurements made available by traffic authorities, iteratively refines the output of DFROUTER, which is a module provided by the SUMO (Simulation of Urban MObility) tool. This way, it is able to generate an O-D matrix for traffic that resembles the real traffic distribution and that can be directly imported by SUMO. We apply our technique to the city of Valencia, and we then compare the obtained results against other existing traffic mobility data for the cities of Cologne (Germany) and Bologna (Italy), thereby validating our approach. We also use our technique to determine what degree of congestion is expectable if certain conditions cause additional traffic to circulate in the city, adopting both a uniform pattern and a hotspot-based pattern for traffic injection to demonstrate how to regulate the overall number of vehicles in the city. This study allows evaluating the impact of vehicle flow changes on the overall traffic congestion levels.This work was partially supported by Valencia’s Traffic Management Department and by the “Ministerio de EconomĂa y Competitividad, Programa Estatal de InvestigaciĂłn, Desarrollo e InnovaciĂłn Orientada a los Retos de la Sociedad, Proyectos I+D+I 2014”, Spain, under Grant TEC2014-52690-R, and the “Programa de Becas SENESCYT”de la RepĂşblica del Ecuador.Zambrano-Martinez, J.; Tavares De Araujo Cesariny Calafate, CM.; Soler Fernández, D.; Cano, J. (2017). Towards Realistic Urban Traffic Experiments Using DFROUTER: Heuristic, Validation and Extensions. Sensors. 17(12):1-29. doi:10.3390/s17122921S129171
- …