Novel Fuzzy and Game Theory Based Clustering and Decision Making for VANETs

Different studies have recently emphasized the importance of deploying clustering schemes in Vehicular ad hoc Network (VANET) to overcome challenging problems related to scalability, frequent topology changes, scarcity of spectrum resources, maintaining clusters stability, and rational spectrum management. However, most of these studies addressed the clustering problem using conventional performance metrics while spectrum shortage, and the combination of spectrum trading and VANET architecture have not been tackled so far. Thus, this paper presents a new fuzzy logic based clustering control scheme to support scalability, enhance the stability of the network topology, motivate spectrum owners to share spectrum and provide efficient and cost-effective use of spectrum. Unlike existing studies, our context-aware scheme is based on multi-criteria decision making where fuzzy logic is adopted to rank the multi-attribute candidate nodes for optimizing the selection of cluster heads (CH)s. Criteria related to each candidate node include: received signal strength, speed of vehicle, vehicle location, spectrum price, reachability, and stability of node. Our model performs efficiently, exhibits faster recovery in response to topology changes and enhances the network efficiency life time

The vocal behaviour of long-tailed manakins (Chiroxiphia linearis): The role of vocalizations in mate attraction and male-male interactions

Tropical birds are little studied relative to temperate birds, despite the fact that tropical species often exhibit unique behaviours, not seen in the temperate-zone. I explore the link between male vocal behaviour and female movement in neotropical lekking Long-tailed Manakins (Chiroxiphia linearis) using acoustic recordings to monitor male vocalizations and a novel telemetry system to monitor female visitation behaviour. I report surprisingly few relationships between male vocalizations and female visitation behaviour, in contrast to results from another long-term study population in a different habitat. I examine the timing of male calling behaviour, showing that males avoid overlapping known neighbours, but actively overlap unknown intruders simulated through playback. Overall, my thesis provides insight into acoustic communication in manakins, revealing that male vocalizations play less of a role than expected in female mate choice, and that males modify the timing of their calls in response to the vocalizations of other nearby individuals

Smart Wireless Sensor Networks

The recent development of communication and sensor technology results in the growth of a new attractive and challenging area - wireless sensor networks (WSNs). A wireless sensor network which consists of a large number of sensor nodes is deployed in environmental fields to serve various applications. Facilitated with the ability of wireless communication and intelligent computation, these nodes become smart sensors which do not only perceive ambient physical parameters but also be able to process information, cooperate with each other and self-organize into the network. These new features assist the sensor nodes as well as the network to operate more efficiently in terms of both data acquisition and energy consumption. Special purposes of the applications require design and operation of WSNs different from conventional networks such as the internet. The network design must take into account of the objectives of specific applications. The nature of deployed environment must be considered. The limited of sensor nodes� resources such as memory, computational ability, communication bandwidth and energy source are the challenges in network design. A smart wireless sensor network must be able to deal with these constraints as well as to guarantee the connectivity, coverage, reliability and security of network's operation for a maximized lifetime. This book discusses various aspects of designing such smart wireless sensor networks. Main topics includes: design methodologies, network protocols and algorithms, quality of service management, coverage optimization, time synchronization and security techniques for sensor networks

Strategic and Tactical Guidance for the Connected and Autonomous Vehicle Future

Autonomous vehicle (AV) and Connected vehicle (CV) technologies are rapidly maturing and the timeline for their wider deployment is currently uncertain. These technologies are expected to have a number of significant societal benefits: traffic safety, improved mobility, improved road efficiency, reduced cost of congestion, reduced energy use, and reduced fuel emissions. State and local transportation agencies need to understand what this means for them and what they need to do now and in the next few years to prepare for the AV/CV future. In this context, the objectives of this research are as follows: Synthesize the existing state of practice and how other state agencies are addressing the pending transition to AV/CV environment Estimate the impacts of AV/CV environment within the context of (a) traffic operations—impact of headway distribution and traffic signal coordination; (b) traffic control devices; (c) roadway safety in terms of intersection crashes Provide a strategic roadmap for INDOT in preparing for and responding to potential issues This research is divided into two parts. The first part is a synthesis study of existing state of practice in the AV/CV context by conducting an extensive literature review and interviews with other transportation agencies. Based on this, we develop a roadmap for INDOT and similar agencies clearly delineating how they should invest in AV/CV technologies in the short, medium, and long term. The second part assesses the impacts of AV/CVs on mobility and safety via modeling in microsimulation software Vissim

Vehicle-to-vehicle communication: design, performance, and disruption mitigation in real-world environment

This thesis investigates the performance of 802.11p-based V2V communication in real-life scenarios, and explores potential practical applications such as GNSS correction data broadcasting to improve the positioning accuracy of nearby vehicles, and enhancing communication robustness by preemptively predicting potential disruptions with the assistance of Machine Learning (ML) models. A custom V2V On-board Unit (OBU) hardware platform was developed, and real- world multi-vehicle outdoor experiments were planned and carried out. The collected data was examined and used to train a number of ML models, and their performance was compared. The experiments revealed that the custom OBU was fully functional, and signal quality and communication range were observed to be affected by real-world imperfections. The GNSS correction data broadcasting was shown to notably increase the positioning accuracy of nearby vehicles, and the ML models trained from Key Performance Indicators (KPIs) demonstrated excellent prediction accuracy, allowing pre-emptive actions to be taken to reduce the downtime from communication disruption

Techniques to enhance the lifetime of mobile ad hoc networks

Devices in Mobile Ad Hoc Networks (MANETs) are mostly powered by battery. Since the battery capacity is fixed, some techniques to save energy at the device level or at the protocol stack should be applied to enhance the MANETs lifetime. In this thesis, we have proposed a few energy saving approaches at the network layer, and MAC layer. First, we proposed a routing technique, to which the following metrics are built into: (i) node lifetime, (ii) maximum limit on the number of connections to a destination, and (iii) variable transmission power. In this technique, we consider a new cost metric which takes into account the residual battery power and energy consumption rate in computing the lifetime of a node. To minimize the overutilization of a node, an upper bound is set on the number of connections that can be established to a destination. The proposed technique is compared with AODV [1] and LER [2]. It outperforms AODV and LER in terms of network lifetime. Next, a technique called Location Based Topology Control with Sleep Scheduling (LBTC) is proposed. It uses the feature of both topology control approach in which the transmission power of a node is reduced, and power management approach in which nodes are put to sleep state. In LBTC the transmission power of a node is determined from the neighborhood location information. A node goes to sleep state only when: (i) it has no traffic to participate, and (ii) its absence does not create a local partition. LBTC is compared with LFTC [3] and ANTC [4]. We observed that the network lifetime in LBTC is substantially enhanced. A framework for post-disaster communication using wireless ad hoc networks is proposed. This framework includes: (i) a multi-channel MAC protocol, (ii) a node-disjoint multipath routing, and (iii) a distributed topology aware scheme. Multi-channel MAC protocol minimizes the congestion in the network by transmitting data through multiple channels. Multipath routing overcomes the higher energy depletion rate at nodes associated with shortest path routing. Topology aware scheme minimizes the maximum power used at node level. Above proposals, taken together intend to increase the network throughput, reduce the end-to-end delay, and enhance the network lifetime of an ad hoc network deployed for disaster response

Vehicle-to-vehicle communication: design, performance, and disruption mitigation in real-world environment

This thesis investigates the performance of 802.11p-based V2V communication in real-life scenarios, and explores potential practical applications such as GNSS correction data broadcasting to improve the positioning accuracy of nearby vehicles, and enhancing communication robustness by preemptively predicting potential disruptions with the assistance of Machine Learning (ML) models. A custom V2V On-board Unit (OBU) hardware platform was developed, and real- world multi-vehicle outdoor experiments were planned and carried out. The collected data was examined and used to train a number of ML models, and their performance was compared. The experiments revealed that the custom OBU was fully functional, and signal quality and communication range were observed to be affected by real-world imperfections. The GNSS correction data broadcasting was shown to notably increase the positioning accuracy of nearby vehicles, and the ML models trained from Key Performance Indicators (KPIs) demonstrated excellent prediction accuracy, allowing pre-emptive actions to be taken to reduce the downtime from communication disruption