Resource Management for On-Demand Mission-Critical Internet of Things Applications

Junaid Farooq is an Assistant Professor with the Department of Electrical and Computer Engineering at the University of Michigan-Dearborn. His research focus is on system level modeling, analysis, and the optimization of wireless communication networks.Quanyan Zhu, PhD, is Associate Professor with the Department of Electrical and Computer Engineering at New York University.

• Preface xiiiAcknowledgments xviiAcronyms xixPart I Introduction 11 Internet of Things-Enabled Systems and Infrastructure 31.1 Cyber–Physical Realm of IoT 31.2 IoT in Mission-Critical Applications 41.3 Overview of the Book 41.3.1 Main Topics 51.3.1.1 Dynamic Reservation ofWireless Spectrum Resources 51.3.1.2 Dynamic Cross-Layer Connectivity Using Aerial Networks 51.3.1.3 Dynamic Processes Over Multiplex Spatial Networks andReconfigurable Design 61.3.1.4 Sequential Resource Allocation Under Spatio-TemporalUncertainties 71.3.2 Notations 82 Resource Management in IoT-Enabled InterdependentInfrastructure 92.1 System Complexity and Scale 92.2 Network Geometry and Dynamics 102.3 On-Demand MC-IoT Services and Decision Avenues 112.4 Performance Metrics 122.5 Overview of Scientific Methodologies 12Trim Size: 6in x 9in Single Column Farooq716099 ftoc.tex V1 - 06/02/2021 12:07pm Page viii__ __viii ContentsPart II Design Challenges in MC-IoT 153 Wireless Connectivity Challenges 173.1 Spectrum Scarcity and Reservation Based Access 173.2 Connectivity in Remote Environments 193.3 IoT Networks in Adversarial Environments 224 Resource and Service Provisioning Challenges 254.1 Efficient Allocation of Cloud Computing Resources 254.2 Dynamic Pricing in the Cloud 274.3 Spatio-Temporal Urban Service Provisioning 31Part III Wireless Connectivity Mechanisms for MC-IoT 355 Reservation-Based Spectrum Access Contracts 375.1 Reservation of Time–Frequency Blocks in the Spectrum 375.1.1 Network Model 385.1.2 Utility of Spectrum Reservation 395.2 Dynamic Contract Formulation 395.2.1 Objective of Network Operator 405.2.2 Spectrum Reservation Contract 405.2.2.1 Operator Profitability 405.2.2.2 IC and IR Constraints 415.2.3 Optimal Contracting Problem 415.2.4 Solution to the Optimization Problem 425.3 Mission-Oriented Pricing and Refund Policies 445.4 Summary and Conclusion 476 Resilient Connectivity of IoT Using Aerial Networks 496.1 Connectivity in the Absence of Backhaul Networks 496.2 Aerial Base Station Modeling 506.3 Dynamic Coverage and ConnectivityMechanism 526.3.1 MAP–MSD Matching 536.3.2 MAP Dynamics and Objective 546.3.3 Controller Design 556.3.3.1 Attractive and Repulsive Function 556.3.3.2 Velocity Consensus Function 566.3.4 Individual Goal Function 566.3.5 Cluster Centers 576.4 Performance Evaluation and Simulation Results 586.4.1 Results and Discussion 596.4.1.1 Simulation Parameters 59Trim Size: 6in x 9in Single Column Farooq716099 ftoc.tex V1 - 06/02/2021 12:07pm Page ix__ __Contents ix6.4.1.2 Resilience 616.4.1.3 Comparison 646.5 Summary and Conclusion 68Part IV Secure Network DesignMechanisms 697 Wireless IoT Network Design in AdversarialEnvironments 717.1 Adversarial Network Scenarios 717.2 Modeling Device Capabilities and Network Heterogeneity 717.2.1 Network Geometry 727.2.2 Network Connectivity 737.2.2.1 Intra-layer Connectivity 737.2.2.2 Network-wide Connectivity 747.3 Information Dissemination Under Attacks 767.3.1 Information Dynamics 777.3.1.1 Single Message Propagation 787.3.1.2 MultipleMessage Propagation 797.3.2 Steady State Analysis 807.4 Mission-Specific Network Optimization 817.4.1 Equilibrium Solution 817.4.2 Secure and Reconfigurable Network Design 877.5 Simulation Results and Validation 917.5.1 Mission Scenarios 927.5.1.1 Intelligence 927.5.1.2 Encounter Battle 937.6 Summary and Conclusion 968 Network DefenseMechanisms Against MalwareInfiltration 978.1 Malware Infiltration and Botnets 978.1.1 Network Model 978.1.2 Threat Model 998.2 PropagationModeling and Analysis 1018.2.1 Modeling of Malware and Information Evolution 1018.2.2 State Space Representation and Dynamics 1028.2.3 Analysis of Equilibrium State 1048.3 Patching Mechanism for Network Defense 1098.3.1 Simulation Results 1158.3.2 Simulation and Validation 1208.4 Summary and Conclusion 124Trim Size: 6in x 9in Single Column Farooq716099 ftoc.tex V1 - 06/02/2021 12:07pm Page x__ __x ContentsPart V Resource ProvisioningMechanisms 1259 Revenue Maximizing Cloud Resource Allocation 1279.1 Cloud Service Provider Resource Allocation Problem 1279.2 Allocation and Pricing Rule 1289.3 Dynamic Revenue Maximization 1299.3.1 Adaptive and Resilient Allocation and Pricing Policy 1349.4 Numerical Results and Discussions 1359.5 Summary and Conclusion 13910 Dynamic Pricing of Fog-Enabled MC-IoT Applications 14110.1 Edge Computing and Delay Modeling 14210.2 Allocation Efficiency and Quality of Experience 14310.2.1 Allocation Policy 14410.2.2 Pricing Policy 14510.3 Optimal Allocation and Pricing Rules 14610.3.1 Single VMI Case 14610.3.2 Multiple VMI Case 14910.3.3 Expected Revenue 15510.3.4 Implementation of Dynamic VMI Allocation andPricing 15610.4 Numerical Experiments and Discussion 15810.4.1 Experiment Setup 15810.4.2 Simulation Results 15810.4.3 Comparison with Other Approaches 16010.5 Summary and Conclusion 16411 Resource Provisioning to Spatio-Temporal UrbanServices 16511.1 Spatio-TemporalModeling of Urban Service Requests 16511.1.1 Characterization of Service Requests 16611.1.2 Utility of Resource Allocation 16711.1.3 Problem Definition 16911.2 Optimal Dynamic Allocation Mechanism 16911.2.1 Dynamic Programming Solution 17011.2.2 Computation and Implementation 17211.3 Numerical Results and Discussion 17411.3.1 Special Cases 17411.3.1.1 Power Law Utility 17411.3.1.2 Exponential Utility 17611.3.2 Performance Evaluation and Comparison 17811.4 Summary and Conclusions 180Trim Size: 6in x 9in Single Column Farooq716099 ftoc.tex V1 - 06/02/2021 12:07pm Page xi__ __Contents xiPart VI Conclusion 18312 Challenges and Opportunities in the IoT Space 18512.1 Broader Insights and Future Directions 18512.1.1 Distributed Cross-Layer Intelligence for Mission-Critical IoTServices 18512.1.1.1 Secure and Resilient Networking for Massive IoT Networks 18512.1.1.2 Autonomic Networked CPS: From Military to CivilianApplications 18612.1.1.3 Strategic Resource Provisioning for Mission-Critical IoTServices 18712.2 Future Research Directions 18712.2.1 Distributed Learning and Data Fusion for Security and Resilience inIoT-Driven Urban Applications 18812.2.1.1 Data-Driven Learning and Decision-Making for Smart City ServiceProvisioning 18812.2.1.2 Market Design for On-Demand and Managed IoT-Enabled UrbanServices 18912.2.1.3 Proactive Resiliency Planning and Learning for DisasterManagement in Cities 19012.2.2 Supply Chain Security and Resilience of IoT 19012.3 Concluding Remarks 191Bibliography 193Index 207_