This chapter initially explains how dependencies are established when at least a part of an infrastructure system requires the provision of the service to function. Although the focus is on functional dependencies, this chapter also explores physical and conditional dependencies. Resilience metrics presented in previous chapters are broadened in order to represent the effect of dependencies on resilience levels. Dependencies established within an infrastructure system are also explained. The concept of buffer as a local storage of the resources related to the depending service is defined as part of these expanded metrics, and then it is exemplified by examining a practical application of such buffers: power plants for information and communication network (ICN) sites. After introducing the main concepts and ideas related to dependencies, this chapter takes a broader view by discussing interdependencies when those are established both directly and indirectly. The study of interdependencies for electric power grids and ICN also explores the relationship with other infrastructures, such as transportation networks and water distribution systems, and with community social systems.

The increased interest that the topic of critical infrastructure resilience is attracting in academia, government, commerce, services, and industry is creating an alternative engineering field that could be called resilience engineering. However, the views of the meaning of resilience have varied, and even in some very relevant world languages, an exact translation of the word “resilience” has only recently been introduced – for example, the word “resiliencia” was added to the dictionary of the Royal Academy of Spanish Language in 2014 – or it still does not exist, as happens in Japanese. Thus, this chapter introduces the main concepts associated with the study of resilience engineering applicable to critical infrastructure systems with a focus on electric power grids and information and communication networks (ICNs) because these are the infrastructures that are identified as “uniquely critical” in US Presidential Policy Directive 21, which is the source for the definition of resilience that is used in this book.

Although today’s power grids have their own sensing and control communications infrastructure in dedicated networks operating separate from the publicly used information and communication networks (ICNs), technological advances may lead to more integrated electric power and ICN infrastructures. Some of the motivating technological changes that may act as catalysts for such increased integration of both infrastructures include the need for much higher power supply resilience for ICN sites, development of an “Internet of Things,” and the increased communication needs for electric power devices at users’ homes or at the power distribution level of the grid as part of power systems’ evolution into “smarter” grids. Hence, this chapter explores the implications in terms of resilience of integrated electric power and ICN infrastructures. In particular, the use of integrated power management to facilitate the use of renewable energy sources is discussed. Fundamental concepts about cybersecurity are also presented.

As was introduced in Chapter 1, adaptation is a fundamental attribute of resilient systems. Adaptation could occur by identifying changes in the physical and social environment with the potential to affect a community system operation or by reacting after a disruptive event happens. Part of a positive reaction in the latter of these adaptation mechanisms involves learning about which factors contributed to improving resilience and which factors caused a lower resilience. This chapter focuses on an important tool that is part of such a learning process for improved resilience: disaster forensics. Disaster forensics are based on a postdisaster investigation, in which field investigations and postevent data collection are important components. Hence, the first part of this chapter will focus on explaining the steps and procedures involved with a disaster forensic investigation, including a description of how to perform field investigations. This chapter then describes power grids’ and information and communication networks’ performance in recent natural disasters based on lessons obtained during past forensic investigations.

This chapter is dedicated to examining technologies and strategies for improved resilience of information and communications networks. Initially, this chapter describes typical service requirements for information and communications networks by discussing services provision expectations. These expectations are presented in context by describing typical regulatory environments observed in the United States and other countries, placing special attention on emergency 911 regulations. The second part of this chapter provides an overview of most commonly observed strategies and technologies used to improve resilience. These strategies and technologies include resources management approaches, as well as hardware- and software-based technologies.

This chapter provides an overview of the main infrastructure systems that are the focus of this book as well as describing fundamental concepts and information about network theory, reliability and availability, and disruptive events that are also applicable to the rest of this book.

This chapter is divided into two main parts. The first part presents various resilience modeling approaches for critical infrastructures, with a focus on power grids and communication networks. However, as is explained, a main modeling framework relying on graph theory is applicable to most other critical infrastructure systems. The second part discusses various resilience metric approaches, with special attention to those applied to power grids. Metrics for concepts related to resilience that have also been used in the literature are also discussed in this chapter. Discussion of both resilience modeling and metrics is expanded in later chapters, particularly in Chapter 4, where dependencies and interdependencies are taken into consideration.

This chapter explains problems associated with planning infrastructure systems in order to improve resilience. Understanding the concept and basic methods for planning infrastructure investments is an important aspect for studying resilience because planning is a key process that contributes to resilience preparedness and adaptation attributes. Initially, the chapter discusses the fundamental problems and issues found when making decisions about investment allocations amid uncertain conditions. Then, probabilistic risk assessment (PRA) as still the main tool used in industry in planning processes is explained. Because characterizing intensity and other relevant attributes of disruptive events is an important component of planning processes for enhancing resilience, this chapter continues by exploring how these events – and especially hurricanes – can be characterized in order to obtain information that can be used as input for the planning process. Finally, the chapter concludes by discussing economic concepts and tools related to infrastructure resilience enhancement planning processes.

This chapter is dedicated to examining strategies and technologies for improving power grids’ resilience. The first part of this chapter focuses on traditional power grids by presenting technologies and management approaches for improved resilience at the power generation, transmission, and distribution levels and by discussing strategies for enhanced withstanding capability or reduced restoration speed. The second part of this chapter explores the effect that the evolution of power grids into “smart” grids may likely have in the future. Advanced technologies that have already been implemented at all levels of power grids are discussed. Alternative power distribution approaches implemented at the load level, such as microgrids, able to significantly improve resilience with respect to traditional power grids, are also described in this chapter.

The design of wireless devices and networks present unique design challenges due to the combined effects of physical constraints, such as bandwidth and power, and communication errors introduced through channel fading and noise. The limited bandwidth has to be addressed through a compression operation that removes redundant information from the source. Unfortunately, the removal of redundancy makes the transmitted data not only more important but also more sensitive to channel errors. Therefore, it is necessary to complement the source compression operation with the application of an error control code to do channel coding. The goal of the channel coding operation is to add redundancy back to the source coded data that has been designed to efficiently detect and correct errors introduced in the channel. In this chapter we will study the use of cooperation to transmit multimedia traffic (such as voice or video conferencing). This involves studying the performance of schemes with strict delay constraint where user cooperation is combined with source and channel coding.

Although the source and channel codecs complement each other, historically their design had been approached independently of each other. The basic assumptions in this design method are that the source encoder will present to the channel encoder a bit stream where all source redundancy have been removed and that the channel decoder will be able to present to the source decoder a bit stream where all channel errors have been corrected.

Despite the promised gains of cooperative communication demonstrated in many previous works, the impact of cooperation at higher network levels is not yet completely understood. In the previous chapters, it was assumed that the user always has a packet to transmit, which is not generally true in a wireless network. For example, in a network, most of the sources are bursty in nature, which leads to periods of silence in which the users may have no data to transmit. Such a phenomenon may affect important system parameters that are relevant to higher network layers, for example, buffer stability and packet delivery delay. We focus on the multiple access layer in this chapter. One can ask many important questions now. Can we design cooperation protocols that take these higher layer network features into account? Can the gains promised by cooperation at the physical layer be applied to the multiple access layer? More specifically, what is the impact of cooperation on important multiple access performance metrics such as stable throughput region and packet delivery delay?

In this chapter, we try to address all of these important questions to demonstrate the possible gains of cooperation at the multiple access layer. A slotted time division multiple access (TDMA) framework in which each time slot is assigned only to one terminal, i.e., orthogonal multiple access is considered. If a user does not have a packet to transmit in his time slot, then this time slot is not utilized.

In the previous chapter, the symbol error rate performance of single-relay cooperative communications was analyzed for both the decode-and-forward and amplify-and-forward relaying strategies. This chapter builds upon the results in the previous chapter and generalizes the symbol error rate performance analysis to the multi-relay scenario.

Decode-and-forward relaying will be considered first, followed by the amplify-and-forward case. In both scenarios, exact and approximate expressions for the symbol error rate will be derived. The symbol error rate expressions are then used to characterize an optimal power allocation strategy among the relays and the source node.

Multi-node decode-and-forward protocol

We begin by presenting a class of cooperative decode-and-forward protocols for arbitrary N-relay wireless networks, in which each relay can combine the signal received from the source along with one or more of the signals transmitted by previous relays. Then, we focus on the performance of a general cooperation scenario and present an exact symbol error rate (SER) expressions for both M-ary phase shift keying (PSK) and quadrature amplitude modulation (QAM) signalling. We also consider an approximate expression for the SER of a general cooperation scenario that is shown to be tight at high enough SNR. Finally, we study optimal power allocation for the class of cooperative diversity schemes, where the optimality is determined in terms of minimizing the SER of the system.