Resource Management for On-Demand Mission-Critical Internet of Things Applications E-Book
Junaid Farooq
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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Serie: Wiley - IEEE
- Sprache: Englisch
RESOURCE MANAGEMENT FOR ON-DEMAND MISSION-CRITICAL INTERNET OF THINGS APPLICATIONS Discover an insightful and up-to-date treatment of resource management in Internet of Things technology In Resource Management for On-Demand Mission-Critical Internet of Things -Applications, an expert team of engineers delivers an insightful analytical perspective on modeling and decision support for mission-critical Internet of Things applications. The authors dissect the complex IoT ecosystem and provide a cross-layer perspective on the design and operation of IoT, especially in the context of smart and connected communities. The book offers an economic perspective on resource management in IoT systems with a particular emphasis on three main areas: spectrum management via reservation, allocation of cloud/fog resources to IoT applications, and resource provisioning to smart city service requests. It leverages theories from dynamic mechanism design, optimal control theory, and spatial point processes, providing an overview of integrated decision-making frameworks. Finally, the authors discuss future directions and relevant problems on the economics of resource management from new perspectives, like security and resilience. Readers will also enjoy the inclusion of: * A thorough introduction and overview of IoT applications in smart cities, mission critical IoT services and requirements, and key metrics and research challenges * A comprehensive exploration of the allocation of spectrum resources to mission critical IoT applications, including the massive surge of IoT and spectrum scarcity problem * Practical discussions of the provisioning of cloud/fog computing resources to IoT applications, including allocation policy * In-depth examinations of resource provisioning to spatio-temporal service requests in smart cities Perfect for engineers working on Internet of Things and cyber-physical systems, Resource Management for On-Demand Mission-Critical Internet of Things Applications is also an indispensable reference for graduate students, researchers, and professors with an interest in IoT resource management.
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Table of Contents
Cover
Title Page
Copyright
Dedication
Preface
Acknowledgments
Acronyms
Part I: Introduction
1 Internet of Things-Enabled Systems and Infrastructure
1.1 Cyber–Physical Realm of IoT
1.2 IoT in Mission-Critical Applications
1.3 Overview of the Book
2 Resource Management in IoT-Enabled Interdependent Infrastructure
2.1 System Complexity and Scale
2.2 Network Geometry and Dynamics
2.3 On-Demand MC-IoT Services and Decision Avenues
2.4 Performance Metrics
2.5 Overview of Scientific Methodologies
Part II: Design Challenges in MC-IoT
3 Wireless Connectivity Challenges
3.1 Spectrum Scarcity and Reservation Based Access
3.2 Connectivity in Remote Environments
3.3 IoT Networks in Adversarial Environments
Note
4 Resource and Service Provisioning Challenges
4.1 Efficient Allocation of Cloud Computing Resources
4.2 Dynamic Pricing in the Cloud
4.3 Spatio-Temporal Urban Service Provisioning
Notes
Part III: Wireless Connectivity Mechanisms for MC-IoT
5 Reservation-Based Spectrum Access Contracts
5.1 Reservation of Time–Frequency Blocks in the Spectrum
5.2 Dynamic Contract Formulation
5.3 Mission-Oriented Pricing and Refund Policies
5.4 Summary and Conclusion
Note
6 Resilient Connectivity of IoT Using Aerial Networks
6.1 Connectivity in the Absence of Backhaul Networks
6.2 Aerial Base Station Modeling
6.3 Dynamic Coverage and Connectivity Mechanism
6.4 Performance Evaluation and Simulation Results
6.5 Summary and Conclusion
Notes
Part IV: Secure Network Design Mechanisms
7 Wireless IoT Network Design in Adversarial Environments
7.1 Adversarial Network Scenarios
7.2 Modeling Device Capabilities and Network Heterogeneity
7.3 Information Dissemination Under Attacks
7.4 Mission-Specific Network Optimization
7.5 Simulation Results and Validation
7.6 Summary and Conclusion
Notes
8 Network Defense Mechanisms Against Malware Infiltration
8.1 Malware Infiltration and Botnets
8.2 Propagation Modeling and Analysis
8.3 Patching Mechanism for Network Defense
8.4 Summary and Conclusion
Notes
Part V: Resource Provisioning Mechanisms
9 Revenue Maximizing Cloud Resource Allocation
9.1 Cloud Service Provider Resource Allocation Problem
9.2 Allocation and Pricing Rule
9.3 Dynamic Revenue Maximization
9.4 Numerical Results and Discussions
9.5 Summary and Conclusion
Notes
10 Dynamic Pricing of Fog-Enabled MC-IoT Applications
10.1 Edge Computing and Delay Modeling
10.2 Allocation Efficiency and Quality of Experience
10.3 Optimal Allocation and Pricing Rules
10.4 Numerical Experiments and Discussion
10.5 Summary and Conclusion
Notes
11 Resource Provisioning to Spatio-Temporal Urban Services
11.1 Spatio-Temporal Modeling of Urban Service Requests
11.2 Optimal Dynamic Allocation Mechanism
11.3 Numerical Results and Discussion
11.4 Summary and Conclusions
Notes
Part VI: Conclusion
12 Challenges and Opportunities in the IoT Space
12.1 Broader Insights and Future Directions
12.2 Future Research Directions
12.3 Concluding Remarks
Bibliography
Index
End User License Agreement
List of Tables
Chapter 6
Table 6.1 Simulation parameters.
List of Illustrations
Chapter 1
Figure 1.1 IoT technology stack describing the different actors involved in ...
Chapter 2
Figure 2.1 IoT-enabled infrastructure systems.
Figure 2.2 Geometry of heterogeneous wireless network and degree profile of ...
Figure 2.3 Key goals and objectives in IoT system design.
Chapter 3
Figure 3.1 A spectrum reservation based UNB-IoT system where the IoT devices...
Figure 3.2 Stages of spectrum reservation contract. In the first stage, the ...
Figure 3.3 Example scenario of spatially clustered mobile smart devices inte...
Chapter 4
Figure 4.1 Cloud service provider allocating available VMs to sequentially a...
Figure 4.2 Tolerable end-to-end delays of some typical mission-critical IoT ...
Figure 4.3 Architecture of a fog-enabled IoT ecosystem in which the MC-IoT d...
Figure 4.4 Expected revenue of the CSP for a single available VMI with varyi...
Figure 4.5 Illustration of the centralized resource allocation problem durin...
Chapter 5
Figure 5.1 Available spectrum divided into time–frequency blocks for reserva...
Figure 5.2 Effect of varying proportion of MC applications in the network.
Figure 5.3 Effect of decreasing average valuation of MC applications.
Figure 5.4 Effect of average channel cost on the reservation contract.
Chapter 6
Figure 6.1 Illustration of the strength of communication link function in (1...
Figure 6.2 An example of two connected MAPs serving the underlying MSDs. The...
Figure 6.3 Feedback cognitive loop for the proposed resilient connectivity f...
Figure 6.4 Example run of the cognitive algorithm showing snapshots of the M...
Figure 6.5 A random MAP failure event is induced at seconds, making % of ...
Figure 6.6 Proportion of MSDs covered by the MAPs.
Figure 6.7 Probability of information penetration in the D2D enabled MAP net...
Figure 6.8 Reachability of MAPs determined by the algebraic connectivity.
Figure 6.9 Top view of final configurations of MAPs in comparison with the p...
Figure 6.10 Comparison of coverage of MSDs with baseline algorithms.
Figure 6.11 Comparison of reachability of MAPs with baseline algorithms.
Figure 6.12 Comparison of probability of information dissemination in the MA...
Chapter 7
Figure 7.1 Heterogeneous IoBT network decomposed into virtual connectivity l...
Figure 7.2 Combined degree of a typical device located at the origin.
Figure 7.3 State transition diagram for the simultaneous diffusion of two di...
Figure 7.4 Accuracy of Jensen's lower bound.
Figure 7.5 Information dissemination profiles for varying average degree of ...
Figure 7.6 Optimal network parameters for the intelligence mission. (a) Tran...
Figure 7.7 Optimal network parameters for the encounter mission. (a) Transmi...
Figure 7.8 Network parameter variation with changes in combined-layer inform...
Figure 7.9 Network parameter variation with changes in intra-layer informati...
Chapter 8
Figure 8.1 Network model: A typical IoT device, referred to as device , is ...
Figure 8.2 Analyzing potential connectivity of WiFi hotspots in NYC.
Figure 8.3 State evolution diagram for a typical device. Un-compromised devi...
Figure 8.4 Approximation accuracy of link probabilities.
Figure 8.5 Curvature analysis of equilibrium population processes for differ...
Figure 8.6 Relative impact of unit patching rate of a degree device on net...
Figure 8.7 Impact of varying un-compromised bot proportion threshold and i...
Figure 8.8 Expected total cost of patching against varying system parameters...
Figure 8.9 Snapshot of network states at equilibrium in a PPP network.
Figure 8.10 Time evolution of the proportion of un-compromised devices. (a) ...
Figure 8.11 Snapshot of network states at equilibrium in the LinkNYC network...
Chapter 9
Figure 9.1 Flow diagram of the adaptive and resilient resource allocation an...
Figure 9.2 Optimal allocation thresholds for low and high arrival rates of c...
Figure 9.3 Optimal allocation threshold for varying number of available VMs....
Figure 9.4 Expected revenue of the CSP over time under variations in the num...
Chapter 10
Figure 10.1 Resource allocation and pricing flow diagram.
Figure 10.2 Optimal cutoff curves for (a) exponentially distributed and (b) ...
Figure 10.3 Effect of arrival rate on the expected revenue of the CSP (a, c)...
Figure 10.4 Effect of the mean characteristic on the expected revenue of the...
Figure 10.5 Comparison of expected QoE for exponentially distributed (a, b) ...
Chapter 11
Figure 11.1 Decision tree when allocation periods are remaining and reso...
Figure 11.2 Light gray dots indicate the boundary cases for the pair (). Da...
Figure 11.3 Spatial requests in one allocation period. The bold arrow repres...
Figure 11.4 Resource allocation thresholds for exponential magnitude of serv...
Figure 11.5 Total expected utility against varying spatio-temporal density o...
Figure 11.6 Total expected utility against varying magnitude of requests.
Figure 11.7 The shaded region represents the region of integration . (a) Sh...
Chapter 12
Figure 12.1 Autonomic CPS/IoT systems.
Figure 12.2 Overview of future research and approach.
Figure 12.3 Examples of urban on-demand services.
Guide
Cover Page
Title Page
Copyright
Dedication
Preface
Acknowledgments
Acronyms
Table of Contents
Begin Reading
Bibliography
Index
WILEY END USER LICENSE AGREEMENT
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Resource Management for On-Demand Mission-Critical Internet of Things Applications
Junaid Farooq
University of Michigan-Dearborn
Quanyan Zhu
New York University
This edition first published 2021
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To our parents
Preface
Modern infrastructure is becoming increasingly connected and autonomous, giving rise to new paradigms such as smart homes, offices, factories, and cities, etc. These utilize recent advancements in wireless communication technologies, embedded systems, and artificial intelligence-based software to create an ecosystem of networked electronic devices, referred to an Internet of things (IoT). The cyber-physical integration, enabled by the IoT, is resulting in a myriad of applications and services. However, the IoT itself is not a standalone system. Instead, it is composed of a variety of different systems and components such as endpoint devices, communication networks, cloud computing systems, and user devices, etc. Furthermore, the footprint of the IoT is massive and the constituent components are often operated and controlled by completely different entities. Therefore, effective management and operation of the resources in the IoT ecosystem requires the development of policies, decision-making frameworks, and practical mechanisms.
At each level of interactions between the systems in the IoT, there are decision problems that arise for achieving various objectives such as enhancing the efficiency, quality of service and experience, economics, security, and resilience, etc. This book takes a holistic cyber-physical view towards decision-making, when applied to large scale IoT systems and networks that may also be highly dynamic in nature. For instance, at the device level, it is important to ensure wireless connectivity and communication efficacy. If the communication infrastructure such as wireless base stations are deployed, there is a need for regulating the spectrum usage. However, in the absence of such infrastructure, the connectivity might have to be achieved using alternative techniques such as an overlay network of aerial base stations. Decisions have to be made on how to allocate the spectrum to users and how to configure and place aerial base stations to provide adequate coverage and connectivity. This book proposes a reservation-based spectrum usage mechanism that provides performance guarantees to enable real-time and mission-critical applications. In the absence of backhaul wireless connectivity, a novel approach based on flocking of unmanned aerial vehicles is proposed for providing uninterrupted wireless services to ground users and communities.
Once the connectivity of devices has been achieved, networks are used for information dissemination. The emphasis is on designing networks that achieve the desired information propagation and are reconfigurable in the case of adversarial attacks. Furthermore, there is a need to design security mechanisms against stealthy adversarial threats that may be using the same communication networks to infiltrate and sabotage network operation. This book proposes a novel modeling and analysis approach based on spatial point processes and mathematical epidemiology to analytically characterize the propagation of information and/or malware over wireless communication networks. This is then used to propose decision support in designing networks that are geared towards achieving the desired objective such as information dissemination or network security. The developed theoretical foundations have widespread applications in military and civilian scenarios.
The next frontier in the IoT ecosystem is the resource allocation and service provisioning, which appears in a variety of scenarios such as the use of cloud computing resources by smart devices or the use of city resources in an urban setting. Service requests may appear randomly over time and space with varying service requirements. It is important to efficiently allocate and price the available resources in order to provide a high quality of experience to the users and to generate high revenues for the cloud service provider. This book proposes revenue maximizing approaches to filter the requests dynamically and sets rules for allocation and pricing of available resources. The decisions are based on statistical modeling of the service requests and predictive analysis to schedule and assign resources to incoming requests.
The overarching goal of this book is to lay the theoretical foundations of decision and management science in IoT network design and operation. It leverages tools and theories from a diverse range of systems sciences such as mathematical epidemiology, spatial point processes, stochastic processes, optimal control theory, and optimization to address the challenges and problems at multiple levels across the IoT stack. In a nutshell, this book aims to close the gap between the theory of dynamic mechanism design and wireless and IoT systems. It also paves the way for the development of a comprehensive science for decision-making in the IoT networks. The proposed mechanisms in this book are envisioned to enable and support the development of smart and connected cities with features including resilient communications infrastructure, next generation emergency response, critical infrastructure security, sensing and data markets, etc. We hope that this book will provide a broad understanding of the multiple facets of resource management in the IoT ecosystem and will enable the readers to delve deeper in this interesting and rapidly evolving area of research.
Dearborn, MI, March 2021
Brooklyn, NY, March 2021
Junaid Farooq
Quanyan Zhu
Acknowledgments
We would like to acknowledge the support that we receive from our institutions: the University of Michigan-Dearborn and New York University (NYU). We also thank many of our friends and colleagues for their inputs and suggestions. Special thanks go to the members of the Laboratory of Agile and Resilient Complex Systems (LARX) at NYU, including Jeffrey Pawlick, Juntao Chen, Rui Zhang, Tao Zhang, Linan Huang, Yunhan Huang, and Guanze Peng. Their encouragement and support has provided an exciting intellectual environment for us where the major part of the work presented in this book was completed. We would also like to acknowledge support from several funding agencies, including the National Science Foundation (NSF), Army Research Office (ARO), and the Critical Infrastructure Resilience Institute (CIRI) at the University of Illinois at Urbana-Champaign for making this work possible.
Junaid Farooq and Quanyan Zhu
Acronyms
AI
Artificial Intelligence
AP
Access Point
AR
Augmented Reality
BS
Base Station
ISR
Command, Control, Communications, Computers, Intelligence, Surveillance, and Reconnaissance
CDF
Cumulative Distribution Function
CI
Critical Infrastructure
CPS
Cyber-Physical Systems
CRN
Cognitive Radio Networks
CSMA
Carrier Sense Multiple Access
CSP
Cloud Service Provider
D2D
Device-to-Device
DDoS
Distributed Denial of Service
FSD
First-Order Stochastic Dominance
HVAC
Heating Ventilation and Air Conditioning
IC
Incentive Compatibility
IoBT
Internet of Battlefield Things
IoT
Internet of Things
IR
Individual Rationality
LPWA
Low-Power Wide-Area
MAC
Medium Access Control
MAP
Mobile Access Point
MC-IoT
Mission-Critical Internet of Things
MD
Mechanism Design
MSD
Mobile Smart Device
Probability Density Function
PMF
Probability Mass Function
PPP
Poisson Point Process
QoE
Quality of Experience
QoS
Quality of Service
RAN
Radio Access Network
RGG
Random Geometric Graph
RTT
Round Trip Time
SG
Stochastic Geometry
SINR
Signal-to-Noise-Plus Interference-Ratio
SIS
Susceptible-Infected-Susceptible
TF
Time-Frequency
ToI
Things of Internet
UAV
Unmanned Aerial Vehicle
VM
Virtual Machine
VMI
Virtual Machine Instance
VR
Virtual Reality
WAN
Wide Area Network
WDoS
Wireless Denial of Service
WPAN
Wireless Personal Area Network
Part IIntroduction
1Internet of Things-Enabled Systems and Infrastructure
1.1 Cyber–Physical Realm of IoT
Network-connected electronic devices are becoming an essential part of modern infrastructure systems to automate manual processes resulting in improved efficiency and productivity. The Internet of Things (IoT) is an interconnection of different types of devices (classified as sensors and actuators) using communication networks and computing systems to achieve such automated operation. The difference in IoT from traditional computing systems is their interaction with the physical world as opposed to just the cyber world. For instance, we have electronic devices controlling the temperature in smart buildings by sensing the environment and operating the heating, ventilation, and air conditioning (HVAC) systems. The IoT is in fact a massive network of cyber–physical systems (CPSs). Therefore, the cyber and physical components are an integral part of the emerging IoT ecosystem. The cyber and physical systems are coupled together in an intricate fashion where the cyber world influences decisions in the physical world and vice versa.
Figure 1.1 shows the structure of a typical IoT system. In essence, there are several actors involved in setting up the IoT ecosystem that includes sensing/actuating devices, firmware, radio access network (RAN), cloud server, mobile apps, and end user devices. The endpoint devices are made of embedded hardware that interact with the physical environment and are driven by software processes referred to as firmware or operating system. They make use of communication infrastructure, which is composed of access points, gateways, and core IP networks to connect to cloud servers, that in turn host applications and services, which are operated by users via computing devices, such as smart phones, smart watches, and voice assistants, etc.
Figure 1.1 IoT technology stack describing the different actors involved in setting up the IoT ecosystem.
1.2 IoT in Mission-Critical Applications
IoT systems have a wide variety of application areas. Some of the IoT applications are highly delay-sensitive, e.g. real-time systems such as those involving artificial intelligence (AI), virtual reality (VR) and augmented reality (AR), real-time control loops, streaming analytics, etc. [121]. Such applications are referred to as mission-critical [158] not only due to conventional “life risk” interpretation but also pertaining to the risks of interruption of public services interruption, perturbing public order, jeopardizing enterprise operation and causing losses to businesses, etc. In mission-critical IoT (MC-IoT) applications, often a delay in communication may in fact fail the initial objective of the application. For instance, in a surveillance system where an unusual activity needs to be reported promptly to avoid any potential damage or loss of property and a report beyond a certain delay may be futile. Nevertheless, the traditional MC definition still holds and more so since IoT is also being rapidly integrated into these systems such as in public safety systems or other emergency networks [38] requiring dedicated resources at all times due to the unpredictability of unforeseen events.
1.3 Overview of the Book
The book is organized into six main parts. Part I provides a high level description of the IoT ecosystem and its main features; Part II provides an overview of the main design challenges facing the IoT systems and networks across different layers; Part III investigates and addresses the wireless connectivity challenge faced by the endpoint devices; Part IV tackles the networking layer challenges including information dissemination and message propagation over networks. Part V deals with the service provisioning and resource allocation problems in IoT for mission-critical service delivery. Finally, Part VI provides a broader view of the impact of this work along with a vision governing future research in this domain. Each part is further organized into multiple chapters. The main contributions of the book along with references to the relevant chapters are summarized in the following subsection.
1.3.1 Main Topics
This book takes a clean slate approach toward the design of IoT-enabled systems and networks. It uses a cross-layer perspective in decision-making across various avenues in the IoT ecosystem. Some of the main topics are presented in the following subsections.
1.3.1.1 Dynamic Reservation of Wireless Spectrum Resources
In Chapter 5, a dynamic mechanism for spectrum reservation considering the uncertainty in available spectrum at each time and the uncertainty in the requirement for spectrum access is developed. We make use of tools from sequential screening [26] and mechanism design literature to establish a dynamic menu of contracts which comprise of an advanced payment for spectrum reservation in the future along with a rebate policy if the spectrum is released before the time of spectrum access. This allows the network operator to discriminate the unknown application types and generate higher profits than the traditional auction mechanisms where every application is completely aware of its true utility. A two-type categorization of IoT applications is considered where they are classified as either MC or non-MC and consequently an optimal binary contract is designed by the service provider. Based on assumptions on the distribution of utility of the MC and non-MC applications, closed form results for the optimal contracts are derived and the effect of system parameters is analyzed to gain insights.
1.3.1.2 Dynamic Cross-Layer Connectivity Using Aerial Networks
In Chapter 6, a dynamic approach is used to configure robotic network nodes such as unmanned aerial vehicles (UAVs) to provide connectivity to IoT devices. Although the existing methodologies provide optimization based approaches to the UAV placement problem, this problem is dynamic in nature and hence a more holistic approach is required to obtain an efficient placement of the UAVs in real-time. In addition to effective initial deployment of UAVs, there is a need for an autonomic, self-organizing, and self-healing overlay network that can continuously adapt and reconfigure according to the constantly changing network conditions [23]. Therefore, a distributed and dynamic approach to providing resilient connectivity is essential to cope with the growing scale of the networks toward a massive IoT [44]. To this end, this book develops a feedback based distributed cognitive framework that maintains connectivity of the network and is resilient to the mobility of ground users and/or failures of the UAVs. The continuous feedback enables the framework to actively react to network changes and appropriately reconfigure the network in response to a failure event that has resulted in loss of connectivity. Simulation results demonstrate that if sufficient UAVs are available, they can be arranged into a desired configuration from arbitrary initial positions and the configuration continuously adapts according to the movement of the ground users as well as recovers connectivity under varying levels of a random UAV failure event.
1.3.1.3 Dynamic Processes Over Multiplex Spatial Networks and Reconfigurable Design
In Chapter 7, a stochastic geometry (SG) based model is used to characterize the connectivity of wireless networks in adversarial environments such as battlefields. We then use an epidemic spreading model to capture the dynamic diffusion of multiple messages within the network of devices at the equilibrium state. A novel multiplex network model for Internet of battlefield things (IoBT) networks is proposed that helps in characterizing the intra-layer and network-wide connectivity of heterogeneous battlefield devices by considering the spatial randomness in their locations. A tractable framework is developed for quantification of simultaneous information dissemination in the multiplex IoBT network based on mathematical epidemiology that considers the perceived level of threat to the network from cyber–physical attacks. Approximate closed form results relating the proportion of informed devices at equilibrium and the network parameters are provided. The resulting integrated open-loop system model is used as a basis for reconfiguring the network parameters to ensure a mission-driven information spreading profile in the network. An optimization problem is formulated that can assist military commanders in identifying the physical network parameters that are required in order to sufficiently secure the network from the perceived attacks. It can also help in reconfiguring existing networks to achieve a desired level of communication reliability. A detailed investigation of the developed integrated framework is provided for particular battlefield missions, and the effect of threat level and performance thresholds is studied. This book bridges the gap between the spatial stochastic models for wireless networks and the dynamic diffusion models in contact-based biological networks to derive new insights that aid in the planning and design of secure and reliable IoBT networks for mission critical information dissemination. The developed framework, with some modifications, is also applicable to the more general class of heterogeneous ad-hoc networks.
In Chapter 8, novel methodologies are proposed to overcome the unique challenges of modeling and analyzing the crucial interplay between malware infection, control commands propagation, and device patching in wireless IoT networks. We leverage ideas from the theories of dynamic population processes [70] and point processes to setup a mean field dynamical system that captures the evolution of malware infected devices and control command aware devices over time. In general, obtaining tractable characterizations of the equilibrium state in such population processes is theoretically involved due to the self-consistent nature of the equations involved and the complex connectivity profile of the network. However, we propose a variation of the mean field population process model based on a customized state space that allows us to analyze the formation of botnets in wireless IoT networks and helps in making decisions to control its impact.
1.3.1.4 Sequential Resource Allocation Under Spatio-Temporal Uncertainties
In Chapter 9, an adaptive and resilient dynamic resource allocation and pricing framework is developed for the context of cloud-enabled IoT systems. We present an optimal dynamic policy to filter incoming service requests by IoT applications based on the complexity of the tasks. The qualification threshold for tasks is adaptive to the number of available virtual machines (VMs), the arrival rate of requests, and their average complexity. The optimal policy can be dynamically updated in order to maintain high expected revenues of the cloud service provider (CSP). Furthermore, the proposed framework is also able to adapt according to the changing availability of the VMs due to reprovisioning of resources for other applications or due to the effect of malicious attacks.
In Chapter 10, a revenue maximizing perspective toward allocation and pricing in fog based systems designed for mission critical IoT applications is proposed. The quality-of-experience (QoE) resulting from the pairing of fog resources with computation requests is used as a basis for pricing. We develop a dynamic policy framework leveraging the literature in economics, mechanism design [51], and dynamic revenue maximization [52] to provide an implementable mechanism for dynamic allocation and pricing of sequentially arriving IoT requests that maximizes the expected revenue of the CSP. The developed optimal policy framework assists in both determining which fog node to allocate an incoming task to and the price that should be charged for it for revenue maximization. The proposed policy is statistically optimal, dynamic, i.e. adapts with time, and is implementable in real-time as opposed to other static matching schemes in the literature. The dynamically optimal solution can be computed offline and implemented in real-time for sequentially arriving computation requests.
In Chapter 11, the spatio-temporal aspect is combined with incomplete information about resource requests to devise an integrated resource provisioning framework. Ideas from the stochastic assignment of sequentially arriving tasks to workers [32] are enriched to encompass a more generic utility function that also incorporates the spatial dimension of the sequentially arriving requests. Statistical properties of utility maximizing spatial service requests are characterized using spatio-temporal Poisson processes and extreme value analysis. Analysis for a generalized utility function is done based on the distance from the source as well as the magnitude of the request. Special cases of the utility function are considered for numerical evaluations. An integrated and holistic policy framework is developed that is dynamically optimal and can act as the foundation for allocation and pricing in a wide variety of applications in the context of smart city applications. Finally, a comparison of the performance of the proposed resource provisioning framework is provided with benchmark allocation strategies.
1.3.2 Notations
The book has used a breadth of different notations for studying various theoretical models. In general, there has been an attempt to keep the notations consistent unless otherwise highlighted individually on a case-by-case basis. Some common notations used are presented as follows:
Double-struck symbols, e.g. , , etc. generally indicate sets or spaces, except , which represents the probability measure, and , which represents the expectation operator. The associated density and distribution functions are denoted by and , respectively. Upper case alphabets, e.g. etc. are generally used to represent random variables, while lower case alphabets indicate the value assumed by the random variables. The operator represents the cardinality of the set, while denotes the Euclidean norm. First derivative and second derivatives are denoted by and , respectively. Other notations apply to the chapters within which they are defined.
2Resource Management in IoT-Enabled Interdependent Infrastructure
The Internet of Things (IoT) is becoming indispensable in the critical infrastructure (CI) systems such as in energy, transportation, communications, emergency services, public administration, defense, etc., due to their burgeoning scale and complexity. Furthermore, CIs may be interdependent, for instance, most CIs depend on energy systems and hence the energy systems may have a significant influence on the operation of other CIs. An illustration of the IoT-enabled infrastructure is provided in Figure 2.1
