This chapter introduces the definition of network tomography and the three branches of network tomography and provides an overview of the main issues addressed in the subsequent chapters.

Boolean network tomography is another well-studied branch of network tomography, which addresses the inference of binary performance indicators (e.g., normal vs. failed, or uncongested vs. congested) of internal network elements from the corresponding binary performance indicators on measurement paths. Boolean network tomography fundamentally differs from additive network tomography in that it is a Boolean linear system inversion problem in which each measurement path only provides one bit of information and hence deserves a separate discussion. This chapter introduces a series of identifiability measures (e.g., k-identifiability, maximum identifiability index) to quantify the capability of Boolean network tomography in uniquely detecting and localizing failed/congested network elements. As the definitions of these identifiability measures are combinatorial in nature and hard to verify for large networks, the discussion focuses on polynomial-time verifiable conditions and computable bounds, as well as the associated algorithms.

Based on the conditions for identifying additive link metrics presented in Chapter 2, this chapter addresses two network design questions: (1) Given an unbounded number of monitors, where should they be placed in the network to identify the metrics of all the links using a minimum number of monitors? (2) Given a bounded number of monitors, where should they be placed in the network to identify the metrics of the largest subset of links? The focus here is on the design of intelligent algorithms that can efficiently compute the optimal monitor locations without enumerating all possible monitor placements, achieved through strategic decomposition of the network topology based on the required identifiability conditions. Variations of these algorithms are also given to address cases with predictable or unpredictable topology changes and limited links of interest. In addition to theoretical analysis, empirical results are given to demonstrate the capability of selected algorithms for which such results are available.

In contrast to unicast measurements considered in Chapter 7, this chapter focuses on stochastic network tomography based on multicast measurements, where each probe is sent along a multicast tree from one source to multiple destinations, duplicated at each intermediate node with at least two outgoing links. Using loss tomography as an example, the chapter details how the correlations between the measurements at different destinations sharing links in the multicast tree can be utilized to infer link loss rates, while briefly discussing how this approach applies to other performance metrics. Moreover, this chapter further illustrates how correlated loss observations obtained from multicast probes can be used to reliably infer the topology of the multicast tree, which belongs to another branch of network tomography (network topology tomography) that will be formally introduced in Chapter 9, and complements the high-level discussions there.

This chapter covers preliminary materials required to understand the presentation in the following chapters, including selected definitions from graph theory, linear algebra, and parameter estimation. We also introduce a classification of routing mechanisms based on the controllability of the routing of probes by monitors generating the probes, which will facilitate the discussion in the following chapters.

Based on the identifiability measures for Boolean network tomography presented in Chapter 5, this chapter addresses the follow-up question of how to design the measurement system to optimize the identifiability measure of interest, with a focus on the placement of monitoring nodes.Depending on the mechanism to collect measurements, the problem is divided into (1) monitor placement, (2) beacon placement, and (3) monitoring-aware service placement, where the first approach requires monitoring nodes at both endpoints of each measurement path, the second approach requires a monitoring node only at one of the endpoints of each measurement path, and the third approach requires each measurement path to be the default routing path between a client and a server. As many of such problems are NP-hard, the focus is put on establishing the hardness of the optimal solution and developing polynomial-time suboptimal algorithms with performance guarantees. The chapter also covers a suite of path construction problems addressing how to construct or select measurement paths to optimize the tradeoff between identifiability and probing cost.

In this chapter, we introduce a Software-Defined Active Synchronous Detection (SDASD) approach to defending against both the cyberattacks on SDN network and power bot attack on inverters. First, an effective defense strategy is devised and implemented in the SDN network controller to identify and mitigate cyberattacks on the SDN network of networked microgrids. Second, with a secured SDN network, an active synchronous detection method is devised to accurately detect and localize power bot attacks on networked microgrids by sending a probe signal to inverter controllers in networked microgrids and checking the responses.

This chapter discusses an SDN-enabled architecture that transforms isolated local microgrids into integrated networked microgrids capable of achieving the desired resiliency, elasticity, and efficiency. It provides an overview of SDN architecture, OpenFlow protocol, and SDN-based microgrid communication architecture. A distributed power-sharing scheme is developed on the SDN architecture. Event-triggered communication and control are introduced to networked microgrids to allow for unprecedentedly resiliene and efficient control and plug-and-play in networked microgrids.

This chapter introduces the concepts of microgrid and networked microgrids, describes challenges in building networked microgrids, and provides an overview of the topics of this book.

This chapter introduces a generalized power flow approach to networked microgrids situational awareness. Techniques such as copositional power flow, power flow incorporating droop control, and power flow incorporting hierachical control are provided as solutions to the steady-state analysis of networked microgrids.

This chapter envisions a game-changing way for distributing power: the software-defined distribution network (SD2N), a novel gigabit urban infrastructure that integrates software-defined networking (SDN), real-time computing, Internet of Things (IoT) techniques, and distributed control and optimization algorithms for urban distribution networks.