FOCUS Series : Bio-Inspired Routing Protocols for Vehicular Ad-Hoc Networks.

By: Bitam, SalimContributor(s): Mellouk, AbdelhamidMaterial type: TextTextSeries: FOCUS SeriesPublisher: Somerset : John Wiley & Sons, Incorporated, 2014Copyright date: ©2014Edition: 1st edDescription: 1 online resource (154 pages)Content type: text Media type: computer Carrier type: online resourceISBN: 9781119008125Subject(s): Routing protocols (Computer network protocols) | Vehicular ad hoc networks (Computer networks)Genre/Form: Electronic books.Additional physical formats: Print version:: FOCUS Series : Bio-Inspired Routing Protocols for Vehicular Ad-Hoc NetworksDDC classification: 388.3124 LOC classification: TE228.37 -- .B583 2014ebOnline resources: Click to View
Contents:
Cover page -- Half-Title page -- Title page -- Copyright page -- Contents -- Preface -- Introduction -- Acronyms and Notations -- 1: Vehicular Ad Hoc Networks -- 1.1. VANET definition, characteristics and applications -- 1.1.1. Definition of vehicular ad hoc network -- 1.1.2. Characteristics of vehicular ad hoc networks -- 1.1.2.1. Vehicle velocity -- 1.1.2.2. VANET density -- 1.1.2.3. Node heterogeneity -- 1.1.2.4. Mobility model -- 1.1.3. Applications of vehicular ad hoc networks -- 1.1.3.1. Road safety applications -- 1.1.3.2. Vehicular authority services -- 1.1.3.3. Enhanced driving -- 1.1.3.4. Business and entertainment services -- 1.2. VANET architectures -- 1.2.1. Vehicular WLAN/cellular architecture -- 1.2.2. Pure ad hoc architecture -- 1.2.3. Hybrid architecture -- 1.3. Mobility models -- 1.3.1. Random-based mobility models -- 1.3.1.1. Random waypoint mobility model -- 1.3.1.2. Random walk mobility model -- 1.3.1.3. Limitations of random-based mobility models -- 1.3.2. Geographic map-based mobility models -- 1.3.2.1. Manhattan grid mobility model -- 1.3.2.2. City section mobility model -- 1.3.2.3. Freeway mobility model -- 1.3.2.4. Limitations of geographic map-based mobility models -- 1.3.3. Group-based mobility -- 1.3.3.1. Reference point group mobility model -- 1.3.3.2. Virtual track mobility model -- 1.3.3.3. Limitations of group-based mobility model -- 1.3.4. Prediction-based mobility models -- 1.3.4.1. Gauss-Markov based mobility model -- 1.3.4.2. Markov-History based mobility model -- 1.3.4.3. Discussion of prediction-based mobility models -- 1.3.5. Software-tools-based mobility models -- 1.3.5.1. SUMO framework -- 1.3.5.2. VanetMobiSim framework -- 1.3.5.3. MOVE framework -- 1.3.5.4. Discussion of software-tools-based mobility models -- 1.4. VANET challenges and issues -- 1.4.1. VANET routing.
1.4.2. Vehicular network scalability -- 1.4.3. Computational complexity in VANET networking -- 1.4.4. Routing robustness and self-organization in vehicular networks -- 1.4.5. Vehicular network security -- 1.5. Bibliography -- 2: Routing for Vehicular Ad Hoc Networks -- 2.1. Basic concepts -- 2.1.1. Single-hop versus multi-hop beaconing in VANETs -- 2.1.1.1. Single-hop beaconing -- 2.1.1.2. Multi-hop beaconing -- 2.1.2. Routing classification of VANETs -- 2.1.2.1. Topology-based routing -- 2.1.2.1.1. Proactive routing -- 2.1.2.1.2. Reactive routing -- 2.1.2.1.3. Hybrid routing -- 2.1.2.2. Geography-based routing -- 2.1.2.3. Cluster-based routing -- 2.2. Quality-of-service of VANET routing -- 2.2.1. Quality-of-service definition -- 2.2.2. Quality-of-service criteria -- 2.2.2.1. Average end-to-end delay (measured in milliseconds) -- 2.2.2.2. Average jitter (measured in milliseconds) -- 2.2.2.3. Average available bandwidth (measured in KB/s) -- 2.2.2.4. Packet delivery ratio -- 2.2.2.5. Normalized overhead load -- 2.3. VANET routing standards -- 2.3.1. Dedicated short range communication -- 2.3.2. Standards for wireless access in vehicular environments (WAVE) -- 2.3.3. VANET standards related to routing layers -- 2.3.3.1. Controller area network (ISO 11898) -- 2.3.3.2. Local interconnect network (ISO 9141) -- 2.3.4. Other VANET routing standards -- 2.4. VANET routing challenges and issues -- 2.4.1. Dynamics nature of VANETs (mobility pattern and vehicles' velocity) -- 2.4.2. Vehicular network density and scalability -- 2.4.3. Safety improvement and quality-of-service -- 2.5. Bibliography -- 3: Conventional Routing Protocols for VANETs -- 3.1. Topology-based routing -- 3.1.1. Reactive routing protocols -- 3.1.1.1. Ad hoc on-demand distance vector routing -- 3.1.1.2. Prediction-based routing -- 3.1.1.3. Multi-hop routing protocol for urban VANETs.
3.1.2. Proactive routing protocols -- 3.1.2.1. Optimized link state routing protocol -- 3.1.2.2. Road-based using vehicular traffic proactive routing -- 3.1.3. Hybrid routing protocols -- 3.1.3.1. Hybrid location-based ad-hoc routing -- 3.1.4. Critics of topology-based routing -- 3.2. Geography-based routing -- 3.2.1. Geography-based routing principle -- 3.2.2. Geography-based routing protocols -- 3.2.2.1. Greedy perimeter stateless routing -- 3.2.2.2. Greedy perimeter coordinator routing -- 3.2.2.3. Distance routing effect algorithm for mobility protocol -- 3.2.2.4. Connectivity-aware routing -- 3.2.2.5. Vehicle-assisted data delivery protocols -- 3.2.2.6. Adaptive connectivity aware routing -- 3.2.2.7. Geographic cross protocol -- 3.2.2.8. Connectivity-aware minimum-delay geographic routing -- 3.2.2.9. Intersection-based geographical routing protocol -- 3.2.2.10. Reliable inter-vehiclar routing -- 3.2.3. Critics of geography-based routing -- 3.3. Cluster-based routing -- 3.3.1. Cluster-based routing principle -- 3.3.2. Cluster-based routing protocols -- 3.3.2.1. Clustering algorithm for open inter-vehicle networks -- 3.3.2.2. Receive on most stable group-path routing -- 3.3.2.3. Passive clustering aided routing -- 3.3.2.4. Location-based vehicular service discovery protocol -- 3.3.3. Critics of cluster-based routing -- 3.4. Bibliography -- 4: Bio-inspired Routing Protocols for VANETs -- 4.1. Motivations for using bio-inspired approaches in VANET routing -- 4.1.1. Network scalability -- 4.1.2. Computational complexity -- 4.1.3. Self-organization and adaptability -- 4.1.4. Routing robustness -- 4.2. Fundamental concepts and operations of bio-inspired VANET routing -- 4.2.1. Optimization problem definition -- 4.2.2. Search space (SSp) -- 4.2.3. Objective function -- 4.2.4. Population -- 4.2.5. Individual encoding -- 4.2.6. Initialization.
4.2.7. Stopping criterion -- 4.3. Basic bio-inspired algorithms used in VANET routing literature -- 4.3.1. Genetic algorithm -- 4.3.1.1. Selection strategy -- 4.3.1.2. Crossover operation -- 4.3.1.3. Mutation operation -- 4.3.2. Ant colony optimization -- 4.3.3. Particle swarm optimization -- 4.3.4. Bees life algorithm -- 4.3.5. Bacterial foraging optimization -- 4.4. Evolutionary algorithms for VANET routing -- 4.4.1. Sequential genetic algorithms for VANET routing -- 4.4.2. Parallel genetic algorithms for VANET routing -- 4.5. Swarm intelligence for VANET routing -- 4.5.1. Ant colony optimization for VANET routing -- 4.5.2. Particle swarm optimization for VANET routing -- 4.5.3. Bee colony optimization for VANET routing -- 4.5.4. Bacterial foraging optimization for VANET routing -- 4.6. Another bio-inspired approach for VANET routing -- 4.7. Bibliography -- Conclusion -- C.1. Summary -- C.2. Opportunities and future trends -- C.2.1. Vehicular network scalability -- C.2.2. Self-organized control of VANET's dynamic nature -- C.2.3. Reducing resource requirements and complexity of message exchanges -- C.2.4. Robustness -- Index.
Summary: Vehicular Ad-Hoc Networks (VANETs) play a key role to develop Intelligent Transportation Systems (ITS) aiming to achieve road safety and to guaranty needs of drivers and passengers, in addition to improve the transportation productivity. One of the most important challenges of this kind of networks is the data routing between VANET nodes which should be routed with high level of Quality of Service (QoS) to ensure receiving messages in the time. Then, the driver can take the appropriate decision to improve the road safety. In the literature, there are several routing protocols for VANETs which are more or less reliable to reach safety requirements. In this book, we start by describing all VANET basic concepts such as VANET definition, VANET versus Mobile ad-Hoc Network (MANET), architectures, routing definition and steps, Quality of Service (QoS) for VANET Routing, Metrics of evaluation, Experimentation, and simulation of VANETs, mobility patterns of VANET etc. Moreover, different routing protocols for routing in VANETs will be described. We propose two main categories to be presented: classical routing and bio-inspired routing. Concerning classical VANET, main principles and all phases will be overviewed, as well as, their two sub-categories which are topological and geographical protocols. After that, we propose a new category called bio-inspired routing which is inspired by natural phenomenon such as Ant colony, Bee life, Genetic operators etc. We present also, some referential protocols as example of each category. In this book, we focus on the idea of how to apply bio-inspired principle into VANET routing to improve road safety, and to ensure QoS of vehicular applications.
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Cover page -- Half-Title page -- Title page -- Copyright page -- Contents -- Preface -- Introduction -- Acronyms and Notations -- 1: Vehicular Ad Hoc Networks -- 1.1. VANET definition, characteristics and applications -- 1.1.1. Definition of vehicular ad hoc network -- 1.1.2. Characteristics of vehicular ad hoc networks -- 1.1.2.1. Vehicle velocity -- 1.1.2.2. VANET density -- 1.1.2.3. Node heterogeneity -- 1.1.2.4. Mobility model -- 1.1.3. Applications of vehicular ad hoc networks -- 1.1.3.1. Road safety applications -- 1.1.3.2. Vehicular authority services -- 1.1.3.3. Enhanced driving -- 1.1.3.4. Business and entertainment services -- 1.2. VANET architectures -- 1.2.1. Vehicular WLAN/cellular architecture -- 1.2.2. Pure ad hoc architecture -- 1.2.3. Hybrid architecture -- 1.3. Mobility models -- 1.3.1. Random-based mobility models -- 1.3.1.1. Random waypoint mobility model -- 1.3.1.2. Random walk mobility model -- 1.3.1.3. Limitations of random-based mobility models -- 1.3.2. Geographic map-based mobility models -- 1.3.2.1. Manhattan grid mobility model -- 1.3.2.2. City section mobility model -- 1.3.2.3. Freeway mobility model -- 1.3.2.4. Limitations of geographic map-based mobility models -- 1.3.3. Group-based mobility -- 1.3.3.1. Reference point group mobility model -- 1.3.3.2. Virtual track mobility model -- 1.3.3.3. Limitations of group-based mobility model -- 1.3.4. Prediction-based mobility models -- 1.3.4.1. Gauss-Markov based mobility model -- 1.3.4.2. Markov-History based mobility model -- 1.3.4.3. Discussion of prediction-based mobility models -- 1.3.5. Software-tools-based mobility models -- 1.3.5.1. SUMO framework -- 1.3.5.2. VanetMobiSim framework -- 1.3.5.3. MOVE framework -- 1.3.5.4. Discussion of software-tools-based mobility models -- 1.4. VANET challenges and issues -- 1.4.1. VANET routing.

1.4.2. Vehicular network scalability -- 1.4.3. Computational complexity in VANET networking -- 1.4.4. Routing robustness and self-organization in vehicular networks -- 1.4.5. Vehicular network security -- 1.5. Bibliography -- 2: Routing for Vehicular Ad Hoc Networks -- 2.1. Basic concepts -- 2.1.1. Single-hop versus multi-hop beaconing in VANETs -- 2.1.1.1. Single-hop beaconing -- 2.1.1.2. Multi-hop beaconing -- 2.1.2. Routing classification of VANETs -- 2.1.2.1. Topology-based routing -- 2.1.2.1.1. Proactive routing -- 2.1.2.1.2. Reactive routing -- 2.1.2.1.3. Hybrid routing -- 2.1.2.2. Geography-based routing -- 2.1.2.3. Cluster-based routing -- 2.2. Quality-of-service of VANET routing -- 2.2.1. Quality-of-service definition -- 2.2.2. Quality-of-service criteria -- 2.2.2.1. Average end-to-end delay (measured in milliseconds) -- 2.2.2.2. Average jitter (measured in milliseconds) -- 2.2.2.3. Average available bandwidth (measured in KB/s) -- 2.2.2.4. Packet delivery ratio -- 2.2.2.5. Normalized overhead load -- 2.3. VANET routing standards -- 2.3.1. Dedicated short range communication -- 2.3.2. Standards for wireless access in vehicular environments (WAVE) -- 2.3.3. VANET standards related to routing layers -- 2.3.3.1. Controller area network (ISO 11898) -- 2.3.3.2. Local interconnect network (ISO 9141) -- 2.3.4. Other VANET routing standards -- 2.4. VANET routing challenges and issues -- 2.4.1. Dynamics nature of VANETs (mobility pattern and vehicles' velocity) -- 2.4.2. Vehicular network density and scalability -- 2.4.3. Safety improvement and quality-of-service -- 2.5. Bibliography -- 3: Conventional Routing Protocols for VANETs -- 3.1. Topology-based routing -- 3.1.1. Reactive routing protocols -- 3.1.1.1. Ad hoc on-demand distance vector routing -- 3.1.1.2. Prediction-based routing -- 3.1.1.3. Multi-hop routing protocol for urban VANETs.

3.1.2. Proactive routing protocols -- 3.1.2.1. Optimized link state routing protocol -- 3.1.2.2. Road-based using vehicular traffic proactive routing -- 3.1.3. Hybrid routing protocols -- 3.1.3.1. Hybrid location-based ad-hoc routing -- 3.1.4. Critics of topology-based routing -- 3.2. Geography-based routing -- 3.2.1. Geography-based routing principle -- 3.2.2. Geography-based routing protocols -- 3.2.2.1. Greedy perimeter stateless routing -- 3.2.2.2. Greedy perimeter coordinator routing -- 3.2.2.3. Distance routing effect algorithm for mobility protocol -- 3.2.2.4. Connectivity-aware routing -- 3.2.2.5. Vehicle-assisted data delivery protocols -- 3.2.2.6. Adaptive connectivity aware routing -- 3.2.2.7. Geographic cross protocol -- 3.2.2.8. Connectivity-aware minimum-delay geographic routing -- 3.2.2.9. Intersection-based geographical routing protocol -- 3.2.2.10. Reliable inter-vehiclar routing -- 3.2.3. Critics of geography-based routing -- 3.3. Cluster-based routing -- 3.3.1. Cluster-based routing principle -- 3.3.2. Cluster-based routing protocols -- 3.3.2.1. Clustering algorithm for open inter-vehicle networks -- 3.3.2.2. Receive on most stable group-path routing -- 3.3.2.3. Passive clustering aided routing -- 3.3.2.4. Location-based vehicular service discovery protocol -- 3.3.3. Critics of cluster-based routing -- 3.4. Bibliography -- 4: Bio-inspired Routing Protocols for VANETs -- 4.1. Motivations for using bio-inspired approaches in VANET routing -- 4.1.1. Network scalability -- 4.1.2. Computational complexity -- 4.1.3. Self-organization and adaptability -- 4.1.4. Routing robustness -- 4.2. Fundamental concepts and operations of bio-inspired VANET routing -- 4.2.1. Optimization problem definition -- 4.2.2. Search space (SSp) -- 4.2.3. Objective function -- 4.2.4. Population -- 4.2.5. Individual encoding -- 4.2.6. Initialization.

4.2.7. Stopping criterion -- 4.3. Basic bio-inspired algorithms used in VANET routing literature -- 4.3.1. Genetic algorithm -- 4.3.1.1. Selection strategy -- 4.3.1.2. Crossover operation -- 4.3.1.3. Mutation operation -- 4.3.2. Ant colony optimization -- 4.3.3. Particle swarm optimization -- 4.3.4. Bees life algorithm -- 4.3.5. Bacterial foraging optimization -- 4.4. Evolutionary algorithms for VANET routing -- 4.4.1. Sequential genetic algorithms for VANET routing -- 4.4.2. Parallel genetic algorithms for VANET routing -- 4.5. Swarm intelligence for VANET routing -- 4.5.1. Ant colony optimization for VANET routing -- 4.5.2. Particle swarm optimization for VANET routing -- 4.5.3. Bee colony optimization for VANET routing -- 4.5.4. Bacterial foraging optimization for VANET routing -- 4.6. Another bio-inspired approach for VANET routing -- 4.7. Bibliography -- Conclusion -- C.1. Summary -- C.2. Opportunities and future trends -- C.2.1. Vehicular network scalability -- C.2.2. Self-organized control of VANET's dynamic nature -- C.2.3. Reducing resource requirements and complexity of message exchanges -- C.2.4. Robustness -- Index.

Vehicular Ad-Hoc Networks (VANETs) play a key role to develop Intelligent Transportation Systems (ITS) aiming to achieve road safety and to guaranty needs of drivers and passengers, in addition to improve the transportation productivity. One of the most important challenges of this kind of networks is the data routing between VANET nodes which should be routed with high level of Quality of Service (QoS) to ensure receiving messages in the time. Then, the driver can take the appropriate decision to improve the road safety. In the literature, there are several routing protocols for VANETs which are more or less reliable to reach safety requirements. In this book, we start by describing all VANET basic concepts such as VANET definition, VANET versus Mobile ad-Hoc Network (MANET), architectures, routing definition and steps, Quality of Service (QoS) for VANET Routing, Metrics of evaluation, Experimentation, and simulation of VANETs, mobility patterns of VANET etc. Moreover, different routing protocols for routing in VANETs will be described. We propose two main categories to be presented: classical routing and bio-inspired routing. Concerning classical VANET, main principles and all phases will be overviewed, as well as, their two sub-categories which are topological and geographical protocols. After that, we propose a new category called bio-inspired routing which is inspired by natural phenomenon such as Ant colony, Bee life, Genetic operators etc. We present also, some referential protocols as example of each category. In this book, we focus on the idea of how to apply bio-inspired principle into VANET routing to improve road safety, and to ensure QoS of vehicular applications.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2018. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.

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