Farooq, Junaid,
Resource management for on-demand mission-critical internet of things applications / Junaid Farooq, University of Michigan-Dearborn, Quanyan Zhu, New York University. - First edition. - 1 online resource
Includes bibliographical references and index.
Preface xiii -- Acknowledgments xvii -- Acronyms xix -- Part I Introduction 1 -- 1 Internet of Things-Enabled Systems and Infrastructure 3 -- 1.1 Cyber-Physical Realm of IoT 3 -- 1.2 IoT in Mission-Critical Applications 4 -- 1.3 Overview of the Book 4 -- 1.3.1 Main Topics 5 -- 1.3.1.1 Dynamic Reservation ofWireless Spectrum Resources 5 -- 1.3.1.2 Dynamic Cross-Layer Connectivity Using Aerial Networks 5 -- 1.3.1.3 Dynamic Processes Over Multiplex Spatial Networks and -- Reconfigurable Design 6 -- 1.3.1.4 Sequential Resource Allocation Under Spatio-Temporal -- Uncertainties 7 -- 1.3.2 Notations 8 -- 2 Resource Management in IoT-Enabled Interdependent -- Infrastructure 9 -- 2.1 System Complexity and Scale 9 -- 2.2 Network Geometry and Dynamics 10 -- 2.3 On-Demand MC-IoT Services and Decision Avenues 11 -- 2.4 Performance Metrics 12 -- 2.5 Overview of Scientific Methodologies 12 -- Trim Size: 6in x 9in Single Column Farooq716099 ftoc.tex V1 - 06/02/2021 12:07pm Page viii -- _ -- _ _ -- _ -- viii Contents -- Part II Design Challenges in MC-IoT 15 -- 3 Wireless Connectivity Challenges 17 -- 3.1 Spectrum Scarcity and Reservation Based Access 17 -- 3.2 Connectivity in Remote Environments 19 -- 3.3 IoT Networks in Adversarial Environments 22 -- 4 Resource and Service Provisioning Challenges 25 -- 4.1 Efficient Allocation of Cloud Computing Resources 25 -- 4.2 Dynamic Pricing in the Cloud 27 -- 4.3 Spatio-Temporal Urban Service Provisioning 31 -- Part III Wireless Connectivity Mechanisms for MC-IoT 35 -- 5 Reservation-Based Spectrum Access Contracts 37 -- 5.1 Reservation of Time-Frequency Blocks in the Spectrum 37 -- 5.1.1 Network Model 38 -- 5.1.2 Utility of Spectrum Reservation 39 -- 5.2 Dynamic Contract Formulation 39 -- 5.2.1 Objective of Network Operator 40 -- 5.2.2 Spectrum Reservation Contract 40 -- 5.2.2.1 Operator Profitability 40 -- 5.2.2.2 IC and IR Constraints 41 -- 5.2.3 Optimal Contracting Problem 41 -- 5.2.4 Solution to the Optimization Problem 42 -- 5.3 Mission-Oriented Pricing and Refund Policies 44 -- 5.4 Summary and Conclusion 47 -- 6 Resilient Connectivity of IoT Using Aerial Networks 49 -- 6.1 Connectivity in the Absence of Backhaul Networks 49 -- 6.2 Aerial Base Station Modeling 50 -- 6.3 Dynamic Coverage and ConnectivityMechanism 52 -- 6.3.1 MAP-MSD Matching 53 -- 6.3.2 MAP Dynamics and Objective 54 -- 6.3.3 Controller Design 55 -- 6.3.3.1 Attractive and Repulsive Function 55 -- 6.3.3.2 Velocity Consensus Function 56 -- 6.3.4 Individual Goal Function 56 -- 6.3.5 Cluster Centers 57 -- 6.4 Performance Evaluation and Simulation Results 58 -- 6.4.1 Results and Discussion 59 -- 6.4.1.1 Simulation Parameters 59 -- Trim Size: 6in x 9in Single Column Farooq716099 ftoc.tex V1 - 06/02/2021 12:07pm Page ix -- _ -- _ _ -- _ -- Contents ix -- 6.4.1.2 Resilience 61 -- 6.4.1.3 Comparison 64 -- 6.5 Summary and Conclusion 68 -- Part IV Secure Network DesignMechanisms 69 -- 7 Wireless IoT Network Design in Adversarial -- Environments 71 -- 7.1 Adversarial Network Scenarios 71 -- 7.2 Modeling Device Capabilities and Network Heterogeneity 71 -- 7.2.1 Network Geometry 72 -- 7.2.2 Network Connectivity 73 -- 7.2.2.1 Intra-layer Connectivity 73 -- 7.2.2.2 Network-wide Connectivity 74 -- 7.3 Information Dissemination Under Attacks 76 -- 7.3.1 Information Dynamics 77 -- 7.3.1.1 Single Message Propagation 78 -- 7.3.1.2 MultipleMessage Propagation 79 -- 7.3.2 Steady State Analysis 80 -- 7.4 Mission-Specific Network Optimization 81 -- 7.4.1 Equilibrium Solution 81 -- 7.4.2 Secure and Reconfigurable Network Design 87 -- 7.5 Simulation Results and Validation 91 -- 7.5.1 Mission Scenarios 92 -- 7.5.1.1 Intelligence 92 -- 7.5.1.2 Encounter Battle 93 -- 7.6 Summary and Conclusion 96 -- 8 Network DefenseMechanisms Against Malware -- Infiltration 97 -- 8.1 Malware Infiltration and Botnets 97 -- 8.1.1 Network Model 97 -- 8.1.2 Threat Model 99 -- 8.2 PropagationModeling and Analysis 101 -- 8.2.1 Modeling of Malware and Information Evolution 101 -- 8.2.2 State Space Representation and Dynamics 102 -- 8.2.3 Analysis of Equilibrium State 104 -- 8.3 Patching Mechanism for Network Defense 109 -- 8.3.1 Simulation Results 115 -- 8.3.2 Simulation and Validation 120 -- 8.4 Summary and Conclusion 124 -- Trim Size: 6in x 9in Single Column Farooq716099 ftoc.tex V1 - 06/02/2021 12:07pm Page x -- _ -- _ _ -- _ -- x Contents -- Part V Resource ProvisioningMechanisms 125 -- 9 Revenue Maximizing Cloud Resource Allocation 127 -- 9.1 Cloud Service Provider Resource Allocation Problem 127 -- 9.2 Allocation and Pricing Rule 128 -- 9.3 Dynamic Revenue Maximization 129 -- 9.3.1 Adaptive and Resilient Allocation and Pricing Policy 134 -- 9.4 Numerical Results and Discussions 135 -- 9.5 Summary and Conclusion 139 -- 10 Dynamic Pricing of Fog-Enabled MC-IoT Applications 141 -- 10.1 Edge Computing and Delay Modeling 142 -- 10.2 Allocation Efficiency and Quality of Experience 143 -- 10.2.1 Allocation Policy 144 -- 10.2.2 Pricing Policy 145 -- 10.3 Optimal Allocation and Pricing Rules 146 -- 10.3.1 Single VMI Case 146 -- 10.3.2 Multiple VMI Case 149 -- 10.3.3 Expected Revenue 155 -- 10.3.4 Implementation of Dynamic VMI Allocation and -- Pricing 156 -- 10.4 Numerical Experiments and Discussion 158 -- 10.4.1 Experiment Setup 158 -- 10.4.2 Simulation Results 158 -- 10.4.3 Comparison with Other Approaches 160 -- 10.5 Summary and Conclusion 164 -- 11 Resource Provisioning to Spatio-Temporal Urban -- Services 165 -- 11.1 Spatio-TemporalModeling of Urban Service Requests 165 -- 11.1.1 Characterization of Service Requests 166 -- 11.1.2 Utility of Resource Allocation 167 -- 11.1.3 Problem Definition 169 -- 11.2 Optimal Dynamic Allocation Mechanism 169 -- 11.2.1 Dynamic Programming Solution 170 -- 11.2.2 Computation and Implementation 172 -- 11.3 Numerical Results and Discussion 174 -- 11.3.1 Special Cases 174 -- 11.3.1.1 Power Law Utility 174 -- 11.3.1.2 Exponential Utility 176 -- 11.3.2 Performance Evaluation and Comparison 178 -- 11.4 Summary and Conclusions 180 -- Trim Size: 6in x 9in Single Column Farooq716099 ftoc.tex V1 - 06/02/2021 12:07pm Page xi -- _ -- _ _ -- _ -- Contents xi -- Part VI Conclusion 183 -- 12 Challenges and Opportunities in the IoT Space 185 -- 12.1 Broader Insights and Future Directions 185 -- 12.1.1 Distributed Cross-Layer Intelligence for Mission-Critical IoT -- Services 185 -- 12.1.1.1 Secure and Resilient Networking for Massive IoT Networks 185 -- 12.1.1.2 Autonomic Networked CPS: From Military to Civilian -- Applications 186 -- 12.1.1.3 Strategic Resource Provisioning for Mission-Critical IoT -- Services 187 -- 12.2 Future Research Directions 187 -- 12.2.1 Distributed Learning and Data Fusion for Security and Resilience in -- IoT-Driven Urban Applications 188 -- 12.2.1.1 Data-Driven Learning and Decision-Making for Smart City Service -- Provisioning 188 -- 12.2.1.2 Market Design for On-Demand and Managed IoT-Enabled Urban -- Services 189 -- 12.2.1.3 Proactive Resiliency Planning and Learning for Disaster -- Management in Cities 190 -- 12.2.2 Supply Chain Security and Resilience of IoT 190 -- 12.3 Concluding Remarks 191 -- Bibliography 193 -- Index 207 -- _.
"The Internet of things (IoT) is an emerging paradigm that allows the interconnection of devices, which are equipped with electronic sensors and actuators. There is a plethora of resources, at each stage of the IoT ecosystem, which need to be managed effectively to cater for the demands of potentially mission-critical (MC) applications. Service requests often appear randomly over time and space with varying intensity. Resource provisioning decisions need to be made strategically in real-time, particularly when there is incomplete information about the time, location, and intensity of future requests. Resource management has traditionally been focused on dealing with objectives such as efficiency, capacity, throughput, etc., in mind. However, often the underlying incentives and economic aspects have been ignored"--
9781119716112 9781119716129 1119716128 9781119716105 1119716101 111971611X
10.1002/9781119716112 doi
9536218 IEEE
2021031983
Internet of things.
Radio resource management (Wireless communications)
Fault-tolerant computing.
Fault-tolerant computing.
Internet of things.
Radio resource management (Wireless communications)
Electronic books.
TK5105.8857
004.67/8
Resource management for on-demand mission-critical internet of things applications / Junaid Farooq, University of Michigan-Dearborn, Quanyan Zhu, New York University. - First edition. - 1 online resource
Includes bibliographical references and index.
Preface xiii -- Acknowledgments xvii -- Acronyms xix -- Part I Introduction 1 -- 1 Internet of Things-Enabled Systems and Infrastructure 3 -- 1.1 Cyber-Physical Realm of IoT 3 -- 1.2 IoT in Mission-Critical Applications 4 -- 1.3 Overview of the Book 4 -- 1.3.1 Main Topics 5 -- 1.3.1.1 Dynamic Reservation ofWireless Spectrum Resources 5 -- 1.3.1.2 Dynamic Cross-Layer Connectivity Using Aerial Networks 5 -- 1.3.1.3 Dynamic Processes Over Multiplex Spatial Networks and -- Reconfigurable Design 6 -- 1.3.1.4 Sequential Resource Allocation Under Spatio-Temporal -- Uncertainties 7 -- 1.3.2 Notations 8 -- 2 Resource Management in IoT-Enabled Interdependent -- Infrastructure 9 -- 2.1 System Complexity and Scale 9 -- 2.2 Network Geometry and Dynamics 10 -- 2.3 On-Demand MC-IoT Services and Decision Avenues 11 -- 2.4 Performance Metrics 12 -- 2.5 Overview of Scientific Methodologies 12 -- Trim Size: 6in x 9in Single Column Farooq716099 ftoc.tex V1 - 06/02/2021 12:07pm Page viii -- _ -- _ _ -- _ -- viii Contents -- Part II Design Challenges in MC-IoT 15 -- 3 Wireless Connectivity Challenges 17 -- 3.1 Spectrum Scarcity and Reservation Based Access 17 -- 3.2 Connectivity in Remote Environments 19 -- 3.3 IoT Networks in Adversarial Environments 22 -- 4 Resource and Service Provisioning Challenges 25 -- 4.1 Efficient Allocation of Cloud Computing Resources 25 -- 4.2 Dynamic Pricing in the Cloud 27 -- 4.3 Spatio-Temporal Urban Service Provisioning 31 -- Part III Wireless Connectivity Mechanisms for MC-IoT 35 -- 5 Reservation-Based Spectrum Access Contracts 37 -- 5.1 Reservation of Time-Frequency Blocks in the Spectrum 37 -- 5.1.1 Network Model 38 -- 5.1.2 Utility of Spectrum Reservation 39 -- 5.2 Dynamic Contract Formulation 39 -- 5.2.1 Objective of Network Operator 40 -- 5.2.2 Spectrum Reservation Contract 40 -- 5.2.2.1 Operator Profitability 40 -- 5.2.2.2 IC and IR Constraints 41 -- 5.2.3 Optimal Contracting Problem 41 -- 5.2.4 Solution to the Optimization Problem 42 -- 5.3 Mission-Oriented Pricing and Refund Policies 44 -- 5.4 Summary and Conclusion 47 -- 6 Resilient Connectivity of IoT Using Aerial Networks 49 -- 6.1 Connectivity in the Absence of Backhaul Networks 49 -- 6.2 Aerial Base Station Modeling 50 -- 6.3 Dynamic Coverage and ConnectivityMechanism 52 -- 6.3.1 MAP-MSD Matching 53 -- 6.3.2 MAP Dynamics and Objective 54 -- 6.3.3 Controller Design 55 -- 6.3.3.1 Attractive and Repulsive Function 55 -- 6.3.3.2 Velocity Consensus Function 56 -- 6.3.4 Individual Goal Function 56 -- 6.3.5 Cluster Centers 57 -- 6.4 Performance Evaluation and Simulation Results 58 -- 6.4.1 Results and Discussion 59 -- 6.4.1.1 Simulation Parameters 59 -- Trim Size: 6in x 9in Single Column Farooq716099 ftoc.tex V1 - 06/02/2021 12:07pm Page ix -- _ -- _ _ -- _ -- Contents ix -- 6.4.1.2 Resilience 61 -- 6.4.1.3 Comparison 64 -- 6.5 Summary and Conclusion 68 -- Part IV Secure Network DesignMechanisms 69 -- 7 Wireless IoT Network Design in Adversarial -- Environments 71 -- 7.1 Adversarial Network Scenarios 71 -- 7.2 Modeling Device Capabilities and Network Heterogeneity 71 -- 7.2.1 Network Geometry 72 -- 7.2.2 Network Connectivity 73 -- 7.2.2.1 Intra-layer Connectivity 73 -- 7.2.2.2 Network-wide Connectivity 74 -- 7.3 Information Dissemination Under Attacks 76 -- 7.3.1 Information Dynamics 77 -- 7.3.1.1 Single Message Propagation 78 -- 7.3.1.2 MultipleMessage Propagation 79 -- 7.3.2 Steady State Analysis 80 -- 7.4 Mission-Specific Network Optimization 81 -- 7.4.1 Equilibrium Solution 81 -- 7.4.2 Secure and Reconfigurable Network Design 87 -- 7.5 Simulation Results and Validation 91 -- 7.5.1 Mission Scenarios 92 -- 7.5.1.1 Intelligence 92 -- 7.5.1.2 Encounter Battle 93 -- 7.6 Summary and Conclusion 96 -- 8 Network DefenseMechanisms Against Malware -- Infiltration 97 -- 8.1 Malware Infiltration and Botnets 97 -- 8.1.1 Network Model 97 -- 8.1.2 Threat Model 99 -- 8.2 PropagationModeling and Analysis 101 -- 8.2.1 Modeling of Malware and Information Evolution 101 -- 8.2.2 State Space Representation and Dynamics 102 -- 8.2.3 Analysis of Equilibrium State 104 -- 8.3 Patching Mechanism for Network Defense 109 -- 8.3.1 Simulation Results 115 -- 8.3.2 Simulation and Validation 120 -- 8.4 Summary and Conclusion 124 -- Trim Size: 6in x 9in Single Column Farooq716099 ftoc.tex V1 - 06/02/2021 12:07pm Page x -- _ -- _ _ -- _ -- x Contents -- Part V Resource ProvisioningMechanisms 125 -- 9 Revenue Maximizing Cloud Resource Allocation 127 -- 9.1 Cloud Service Provider Resource Allocation Problem 127 -- 9.2 Allocation and Pricing Rule 128 -- 9.3 Dynamic Revenue Maximization 129 -- 9.3.1 Adaptive and Resilient Allocation and Pricing Policy 134 -- 9.4 Numerical Results and Discussions 135 -- 9.5 Summary and Conclusion 139 -- 10 Dynamic Pricing of Fog-Enabled MC-IoT Applications 141 -- 10.1 Edge Computing and Delay Modeling 142 -- 10.2 Allocation Efficiency and Quality of Experience 143 -- 10.2.1 Allocation Policy 144 -- 10.2.2 Pricing Policy 145 -- 10.3 Optimal Allocation and Pricing Rules 146 -- 10.3.1 Single VMI Case 146 -- 10.3.2 Multiple VMI Case 149 -- 10.3.3 Expected Revenue 155 -- 10.3.4 Implementation of Dynamic VMI Allocation and -- Pricing 156 -- 10.4 Numerical Experiments and Discussion 158 -- 10.4.1 Experiment Setup 158 -- 10.4.2 Simulation Results 158 -- 10.4.3 Comparison with Other Approaches 160 -- 10.5 Summary and Conclusion 164 -- 11 Resource Provisioning to Spatio-Temporal Urban -- Services 165 -- 11.1 Spatio-TemporalModeling of Urban Service Requests 165 -- 11.1.1 Characterization of Service Requests 166 -- 11.1.2 Utility of Resource Allocation 167 -- 11.1.3 Problem Definition 169 -- 11.2 Optimal Dynamic Allocation Mechanism 169 -- 11.2.1 Dynamic Programming Solution 170 -- 11.2.2 Computation and Implementation 172 -- 11.3 Numerical Results and Discussion 174 -- 11.3.1 Special Cases 174 -- 11.3.1.1 Power Law Utility 174 -- 11.3.1.2 Exponential Utility 176 -- 11.3.2 Performance Evaluation and Comparison 178 -- 11.4 Summary and Conclusions 180 -- Trim Size: 6in x 9in Single Column Farooq716099 ftoc.tex V1 - 06/02/2021 12:07pm Page xi -- _ -- _ _ -- _ -- Contents xi -- Part VI Conclusion 183 -- 12 Challenges and Opportunities in the IoT Space 185 -- 12.1 Broader Insights and Future Directions 185 -- 12.1.1 Distributed Cross-Layer Intelligence for Mission-Critical IoT -- Services 185 -- 12.1.1.1 Secure and Resilient Networking for Massive IoT Networks 185 -- 12.1.1.2 Autonomic Networked CPS: From Military to Civilian -- Applications 186 -- 12.1.1.3 Strategic Resource Provisioning for Mission-Critical IoT -- Services 187 -- 12.2 Future Research Directions 187 -- 12.2.1 Distributed Learning and Data Fusion for Security and Resilience in -- IoT-Driven Urban Applications 188 -- 12.2.1.1 Data-Driven Learning and Decision-Making for Smart City Service -- Provisioning 188 -- 12.2.1.2 Market Design for On-Demand and Managed IoT-Enabled Urban -- Services 189 -- 12.2.1.3 Proactive Resiliency Planning and Learning for Disaster -- Management in Cities 190 -- 12.2.2 Supply Chain Security and Resilience of IoT 190 -- 12.3 Concluding Remarks 191 -- Bibliography 193 -- Index 207 -- _.
"The Internet of things (IoT) is an emerging paradigm that allows the interconnection of devices, which are equipped with electronic sensors and actuators. There is a plethora of resources, at each stage of the IoT ecosystem, which need to be managed effectively to cater for the demands of potentially mission-critical (MC) applications. Service requests often appear randomly over time and space with varying intensity. Resource provisioning decisions need to be made strategically in real-time, particularly when there is incomplete information about the time, location, and intensity of future requests. Resource management has traditionally been focused on dealing with objectives such as efficiency, capacity, throughput, etc., in mind. However, often the underlying incentives and economic aspects have been ignored"--
9781119716112 9781119716129 1119716128 9781119716105 1119716101 111971611X
10.1002/9781119716112 doi
9536218 IEEE
2021031983
Internet of things.
Radio resource management (Wireless communications)
Fault-tolerant computing.
Fault-tolerant computing.
Internet of things.
Radio resource management (Wireless communications)
Electronic books.
TK5105.8857
004.67/8