Emerging Technologies for Urban Traffic Management

Nowadays, the number of vehicles on the road and the need of transporting people grow fast. Road transportation has become the backbone of industrialized countries. Nevertheless, the road network system in cities is not sufficient to cope with the current demands due to the size of roads available. Building additional or extending existing roads do not solve the traffic congestion problem due to the high costs and the environmental and geographical limitations. As a consequence, the modern society is facing more traffic jams, higher fuel bills and high levels of CO2 emissions

A RELIABILITY-BASED ROUTING PROTOCOL FOR VEHICULAR AD-HOC NETWORKS

Vehicular Ad hoc NETworks (VANETs), an emerging technology, would allow vehicles to form a self-organized network without the aid of a permanent infrastructure. As a prerequisite to communication in VANETs, an efficient route between communicating nodes in the network must be established, and the routing protocol must adapt to the rapidly changing topology of vehicles in motion. This is one of the goals of VANET routing protocols. In this thesis, we present an efficient routing protocol for VANETs, called the Reliable Inter-VEhicular Routing (RIVER) protocol. RIVER utilizes an undirected graph that represents the surrounding street layout where the vertices of the graph are points at which streets curve or intersect, and the graph edges represent the street segments between those vertices. Unlike existing protocols, RIVER performs real-time, active traffic monitoring and uses this data and other data gathered through passive mechanisms to assign a reliability rating to each street edge. The protocol then uses these reliability ratings to select the most reliable route. Control messages are used to identify a node’s neighbors, determine the reliability of street edges, and to share street edge reliability information with other nodes

FRIEND: A Cyber-Physical System for Traffic Flow Related Information Aggregation and Dissemination

The major contribution of this thesis is to lay the theoretical foundations of FRIEND — A cyber-physical system for traffic Flow-Related Information aggrEgatioN and Dissemination. By integrating resources and capabilities at the nexus between the cyber and physical worlds, FRIEND will contribute to aggregating traffic flow data collected by the huge fleet of vehicles on our roads into a comprehensive, near real-time synopsis of traffic flow conditions. We anticipate providing drivers with a meaningful, color-coded, at-a-glance view of flow conditions ahead, alerting them to congested traffic. FRIEND can be used to provide accurate information about traffic flow and can be used to propagate this information. The workhorse of FRIEND is the ubiquitous lane delimiters (a.k.a. cat\u27s eyes) on our roadways that, at the moment, are used simply as dumb reflectors. Our main vision is that by endowing cat\u27s eyes with a modest power source, detection and communication capabilities they will play an important role in collecting, aggregating and disseminating traffic flow conditions to the driving public. We envision the cat\u27s eyes system to be supplemented by road-side units (RSU) deployed at regular intervals (e.g. every kilometer or so). The RSUs placed on opposite sides of the roadway constitute a logical unit and are connected by optical fiber under the median. Unlike inductive loop detectors, adjacent RSUs along the roadway are not connected with each other, thus avoiding the huge cost of optical fiber. Each RSU contains a GPS device (for time synchronization), an active Radio Frequency Identification (RFID) tag for communication with passing cars, a radio transceiver for RSU to RSU communication and a laptop-class computing device. The physical components of FRIEND collect traffic flow-related data from passing vehicles. The collected data is used by FRIEND\u27s inference engine to build beliefs about the state of the traffic, to detect traffic trends, and to disseminate relevant traffic flow-related information along the roadway. The second contribution of this thesis is the development of an incident classification and detection algorithm that can be used to classify different types of traffic incident Then, it can notify the necessary target of the incident. We also compare our incident detection technique with other VANET techniques. Our third contribution is a novel strategy for information dissemination on highways. First, we aim to prevent secondary accidents. Second, we notify drivers far away from the accident of an expected delay that gives them the option to continue or exit before reaching the incident location. A new mechanism tracks the source of the incident while notifying drivers away from the accident. The more time the incident stays, the further the information needs to be propagated. Furthermore, the denser the traffic, the faster it will backup. In high density highways, an incident may form a backup of vehicles faster than low density highways. In order to satisfy this point, we need to propagate information as a function of density and time

Towards efficacy and efficiency in sparse delay tolerant networks

The ubiquitous adoption of portable smart devices has enabled a new way of communication via Delay Tolerant Networks (DTNs), whereby messages are routed by the personal devices carried by ever-moving people. Although a DTN is a type of Mobile Ad Hoc Network (MANET), traditional MANET solutions are ill-equipped to accommodate message delivery in DTNs due to the dynamic and unpredictable nature of people\u27s movements and their spatio-temporal sparsity. More so, such DTNs are susceptible to catastrophic congestion and are inherently chaotic and arduous. This manuscript proposes approaches to handle message delivery in notably sparse DTNs. First, the ChitChat system [69] employs the social interests of individuals participating in a DTN to accurately model multi-hop relationships and to make opportunistic routing decisions for interest-annotated messages. Second, the ChitChat system is hybridized [70] to consider both social context and geographic information for learning the social semantics of locations so as to identify worthwhile routing opportunities to destinations and areas of interest. Network density analyses of five real-world datasets is conducted to identify sparse datasets on which to conduct simulations, finding that commonly-used datasets in past DTN research are notably dense and well connected, and suggests two rarely used datasets are appropriate for research into sparse DTNs. Finally, the Catora system is proposed to address congestive-driven degradation of service in DTNs by accomplishing two simultaneous tasks: (i) expedite the delivery of higher quality messages by uniquely ordering messages for transfer and delivery, and (ii) avoid congestion through strategic buffer management and message removal. Through dataset-driven simulations, these systems are found to outperform the state-of-the-art, with ChitChat facilitating delivery in sparse DTNs and Catora unencumbered by congestive conditions --Abstract, page iv

Development of an efficient Ad Hoc broadcasting scheme for critical networking environments

Mobile ad hoc network has been widely deployed in support of the communications in hostile environment without conventional networking infrastructure, especially in the environments with critical conditions such as emergency rescue activities in burning building or earth quick evacuation. However, most of the existing ad hoc based broadcasting schemes either rely on GPS location or topology information or angle-of-arrival (AoA) calculation or combination of some or all to achieve high reachability. Therefore, these broadcasting schemes cannot be directly used in critical environments such as battlefield, sensor networks and natural disasters due to lack of node location and topology information in such critical environments. This research work first begins by analyzing the broadcast coverage problem and node displacement form ideal locations problem in ad hoc networks using theoretical analysis. Then, this research work proposes an efficient broadcast relaying scheme, called Random Directional Broadcasting Relay (RDBR), which greatly reduces the number of retransmitting nodes and end-to-end delay while achieving high reachability. This is done by selecting a subset of neighboring nodes to relay the packet using directional antennas without relying on node location, network topology and complex angle-of-arrival (AoA) calculations. To further improve the performance of the RDBR scheme in complex environments with high node density, high node mobility and high traffic rate, an improved RDBR scheme is proposed. The improved RDBR scheme utilizes the concept of gaps between neighboring sectors to minimize the overlap between selected relaying nodes in high density environments. The concept of gaps greatly reduces both contention and collision and at the same time achieves high reachability. The performance of the proposed RDBR schemes has been evaluated by comparing them against flooding and Distance-based schemes. Simulation results show that both proposed RDBR schemes achieve high reachability while reducing the number of retransmitting nodes and end-to-end delay especially in high density environments. Furthermore, the improved RDBR scheme achieves better performance than RDBR in high density and high traffic environment in terms of reachability, end-to-end delay and the number of retransmitting nodes

Road-based routing in vehicular ad hoc networks

Vehicular ad hoc networks (VANETs) can provide scalable and cost-effective solutions for applications such as traffic safety, dynamic route planning, and context-aware advertisement using short-range wireless communication. To function properly, these applications require efficient routing protocols. However, existing mobile ad hoc network routing and forwarding approaches have limited performance in VANETs. This dissertation shows that routing protocols which account for VANET-specific characteristics in their designs, such as high density and constrained mobility, can provide good performance for a large spectrum of applications. This work proposes a novel class of routing protocols as well as three forwarding optimizations for VANETs. The Road-Based using Vehicular Traffic (RBVT) routing is a novel class of routing protocols for VANETs. RBVT protocols leverage real-time vehicular traffic information to create stable road-based paths consisting of successions of road intersections that have, with high probability, network connectivity among them. Evaluations of RBVT protocols working in conjunction with geographical forwarding show delivery rate increases as much as 40% and delay decreases as much as 85% when compared with existing protocols. Three optimizations are proposed to increase forwarding performance. First, one- hop geographical forwarding is improved using a distributed receiver-based election of next hops, which leads to as much as 3 times higher delivery rates in highly congested networks. Second, theoretical analysis and simulation results demonstrate that the delay in highly congested networks can be reduced by half by switching from traditional FIFO with Taildrop queuing to LIFO with Frontdrop queuing. Third, nodes can determine suitable times to transmit data across RBVT paths or proactively replace routes before they break using analytical models that accurately predict the expected road-based path durations in VANETs

Self-organized backpressure routing for the wireless mesh backhaul of small cells

The ever increasing demand for wireless data services has given a starring role to dense small cell (SC) deployments for mobile networks, as increasing frequency re-use by reducing cell size has historically been the most effective and simple way to increase capacity. Such densification entails challenges at the Transport Network Layer (TNL), which carries packets throughout the network, since hard-wired deployments of small cells prove to be cost-unfeasible and inflexible in some scenarios. The goal of this thesis is, precisely, to provide cost-effective and dynamic solutions for the TNL that drastically improve the performance of dense and semi-planned SC deployments. One approach to decrease costs and augment the dynamicity at the TNL is the creation of a wireless mesh backhaul amongst SCs to carry control and data plane traffic towards/from the core network. Unfortunately, these lowcost SC deployments preclude the use of current TNL routing approaches such as Multiprotocol Label Switching Traffic Profile (MPLS-TP), which was originally designed for hard-wired SC deployments. In particular, one of the main problems is that these schemes are unable to provide an even network resource consumption, which in wireless environments can lead to a substantial degradation of key network performance metrics for Mobile Network Operators. The equivalent of distributing load across resources in SC deployments is making better use of available paths, and so exploiting the capacity offered by the wireless mesh backhaul formed amongst SCs. To tackle such uneven consumption of network resources, this thesis presents the design, implementation, and extensive evaluation of a self-organized backpressure routing protocol explicitly designed for the wireless mesh backhaul formed amongst the wireless links of SCs. Whilst backpressure routing in theory promises throughput optimality, its implementation complexity introduces several concerns, such as scalability, large end-to-end latencies, and centralization of all the network state. To address these issues, we present a throughput suboptimal yet scalable, decentralized, low-overhead, and low-complexity backpressure routing scheme. More specifically, the contributions in this thesis can be summarized as follows: We formulate the routing problem for the wireless mesh backhaul from a stochastic network optimization perspective, and solve the network optimization problem using the Lyapunov-driftplus-penalty method. The Lyapunov drift refers to the difference of queue backlogs in the network between different time instants, whereas the penalty refers to the routing cost incurred by some network utility parameter to optimize. In our case, this parameter is based on minimizing the length of the path taken by packets to reach their intended destination. Rather than building routing tables, we leverage geolocation information as a key component to complement the minimization of the Lyapunov drift in a decentralized way. In fact, we observed that the combination of both components helps to mitigate backpressure limitations (e.g., scalability,centralization, and large end-to-end latencies). The drift-plus-penalty method uses a tunable optimization parameter that weight the relative importance of queue drift and routing cost. We find evidence that, in fact, this optimization parameter impacts the overall network performance. In light of this observation, we propose a self-organized controller based on locally available information and in the current packet being routed to tune such an optimization parameter under dynamic traffic demands. Thus, the goal of this heuristically built controller is to maintain the best trade-off between the Lyapunov drift and the penalty function to take into account the dynamic nature of semi-planned SC deployments. We propose low complexity heuristics to address problems that appear under different wireless mesh backhaul scenarios and conditions..