Analytical modeling of wireless communication systems / / Carla-Fabiana Chiasserini, Marco Gribaudo, Daniele Manini

• Record Nr.: UNINA9910821465503321
• Autore: Chiasserini Carla-Fabiana
• Pubbl/distr/stampa: London, England ; ; Hoboken, New Jersey : , : ISTE : , : Wiley, , 2016; ©2016
• ISBN: 1-119-30774-0; 1-119-30773-2; 1-119-30772-4
• Edizione: [1st edition]
• Descrizione fisica: 1 online resource (155 p.)
• Collana: Stochastic Models in Computer Science and Telecommunication Networks Set ; ; Number 1
• Disciplina: 621.384
• Soggetti: Wireless communication systems; Wireless communication systems - Design and construction
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Cover; Title Page; Copyright; Contents; Preface; Introduction; List of Acronyms; PART 1: Sensor Networks; PART 2: Vehicular Networks; PART 3: Cellular Networks; Bibliography; Index; Other titles from ISTE in Networks and Telecommunications; EULA; 1: Fluid Models and Energy Issues; 2: Hybrid Automata for Transient Delay Analysis; 3: Safety Message Broadcasting; 4: Modeling Information Sharing; 5: Multi-RAT Algorithms; 1.1. The fluid-based approach; 1.2. Network scenario; 1.3. The sensor network model; 1.4. Results; 2.1. Event detection in WSNs; 2.2. Model for single-hop network topologies; 2.3. Solution technique2.4. Model for multi-hop network topologies; 2.5. Model validation and exploitation results; 2.6. Discussion; 3.1. System description; 3.2. Dissemination of safety messages; 3.3. Assumptions and notations; 3.4. Model outline; 3.5. Computation of the block probability; 3.6. Computation of the probability of first reception; 3.7. Performance evaluation; 4.1. System scenario; 4.2. Modeling information exchange in IVN; 4.3. Computation of the probability of successful information retrieval; 4.4. Model validation and exploitation; 5.1. RAT network; 5.2. Network model; 5.3. Model solution5.4. Performance evaluation; 1.1.1. Sensor density and traffic generation; 1.1.2. Data routing; 1.1.3. Local and relay traffic rates; 1.1.4. Channel contention and data transmission; 1.1.5. Mean packet delivery delay; 1.1.6. Sensor active/sleep behavior; 1.3.1. A minimum energy routing strategy: computing u(r'|r); 1.3.2. Channel contention and data transmission: computing s(r) and PR(r); 1.3.3. Mean packet delivery delay: computing q(r); 1.4.1. Model validation; 1.4.2. Model exploitation; 1.4.3. Model solution complexity and accuracy; 2.1.1. The 802.15.4 MAC protocol; 2.2.1. Single message transfer2.2.2. Multiple message transfers; 2.3.1. Time discretization; 2.3.2. Transient solution; 2.3.3. Performance metrics computation; 3.2.1. The spatial differentiation approach; 3.2.2. The safety application; 3.6.1. A Gaussian approximation to the transient system behavior; 3.7.1. The impact of power capture; 3.7.2. The case of occupation probability ρ = 1; 3.7.3. The case of homogeneous occupation probability ρ < 1; 3.7.4. The case of inhomogeneous occupation probability; 3.7.5. The impact of the forwarding policy; 4.2.1. Model description; 5.1.1. Scenario; 5.1.2. RAT selection strategy5.2.1. Functional rates; 5.3.1. Analytical approach; 5.3.2. Computation of performance metrics; 5.4.1. Setting and results; 1.3.1.1. Computing єm(r, r')|; 1.3.1.2. Computing FkmE(e|r); 1.3.1.3. Computation of the minimum energy path (equation [1.9]); 1.3.1.4. Computing FmE(e|r); 1.3.1.5. Computing ps; r(r'|e); 1.3.1.6. Computing u(r'|r); 1.3.2.1. Computation of the mean number of transmissions freezing the backoff counter; 1.3.3.1. Computing q(r) for always active sensors; 1.3.3.2. Computing q(r) for active and sleeping sensors; 5.1.1.1. Network scenario; 5.2.1.1. Rates derivation
• Sommario/riassunto: Wireless networks represent an inexpensive and convenient way to connect to the Internet. However, despite their applications across several technologies, one challenge still remains: to understand the behavior of wireless sensor networks and assess their performance in large-scale scenarios. When a large number of network nodes need to interact, developing suitable analytical models is essential to ensure the appropriate coverage and throughput of these networks and to enhance user mobility. This is intrinsically difficult due to the size and number of different network nodes and users. This book highlights some examples which show how this problem can be overcome with the use of different techniques. An intensive parameter analysis shows the reader how to the exploit analytical models for an effective development and management of different types of wireless networks.