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During the last one and a half decades, wireless sensor networks have witnessed significant growth and tremendous development in both academia and industry. A large number of researchers, including computer scientists and engineers, have been interested in solving challenging problems that span all the layers of the protocol stack of sensor networking systems. Several venues, such as journals, conferences, and workshops, have been launched to cover innovative research and practice in this promising and rapidly advancing field. Because of these trends, I thought it would be beneficial to provide our sensor networks community with a comprehensive reference on as much of the findings as possible on a variety of topics in wireless sensor networks. As this area of research is in continuous progress, it does not seem to be a reasonable solution to keep delaying the publication of such reference any more.

This book relates to the second volume and focuses on the advanced topics and applications of wireless sensor networks. Our rationale is that the second volume has all application-specific and non-conventional sensor networks, emerging techniques and advanced topics that are not as matured as what is covered in the first volume. Thus, the second volume deals with three-dimensional, underground, underwater, body-mounted, and societal networks. Following Donald E. Knuth’s above-quoted elegant strategy to focus on several important fields (The Art of Computer Programming: Fundamental Algorithms, 1997), all the book chapters in this volume include up-to-date research work spanning various topics, such as stochastic modeling, barrier and spatiotemporal coverage, tracking, estimation, counting, coverage and localization in three-dimensional sensor networks, topology control and routing in three-dimensional sensor networks, underground and underwater sensor networks, multimedia and body sensor networks, and social sensing. Most of these major topics can be covered in an advanced course on wireless sensor networks. This book will be an excellent source of information for graduate students majoring in computer science, computer engineering, electrical engineering, or any related discipline. Furthermore, computer scientists, researchers, and practitioners in both academia and industry will find this book useful and interesting.



Introduction and Stochastic Modeling


Chapter 1. Introduction

Nowadays, the design and development of wireless sensor networks for various real-world applications, such as environmental monitoring, health monitoring, industrial process automation, battlefields surveillance, and seism monitoring, has become possible owing to the rapid advances in both of wireless communications and sensor technology.
Habib M. Ammari

Chapter 2. Stochastic Modeling of Delay, Energy Consumption, and Lifetime

Emerging applications of wireless sensor networks (WSNs) require real-time quality of service (QoS) guarantees to be provided by the network. Due to the non-deterministic impacts of the wireless channel and queuing state, probabilistic analysis of QoS is essential. For most WSNs applications, the end-to-end delay for packet delivery and the energy consumption are the most important QoS metrics. In this chapter, a comprehensive cross-layer probabilistic analysis framework is presented to investigate the probabilistic evaluation of QoS performance provided by WSNs. In particular, the QoS performance is evaluated in two levels. In the node level, using a Discrete-Time Markov queueing model, the distribution of single-hop delay and single-node energy consumption and lifetime are analyzed. In the network level, based on the node level analysis, the distributions of end-to-end delay, the network lifetime, and the event detection delay are then analyzed. Fluid models are utilized in the network level analysis. The framework also considers a realistic channel environments. Compared to the first-order QoS statistics, such as the mean and the variance, the distribution of QoS metrics reveals the relationship between the performance and reliability with QoS-based operations in WSNs. Using the framework, effective network development can be performed.
Yunbo Wang, Mehmet C. Vuran, Steve Goddard

Barrier and Spatiotemporal Coverage


Chapter 3. Barrier Coverage: Foundations and Design

The coverage of a wireless sensor network (WSN) characterizes the quality of surveillance that the WSN can provide. A deep understanding of the coverage is of great importance for the deployment, design, and planning of wireless sensor networks. Barrier coverage measures the capability of a wireless sensor network to detect intruders that attempt to cross the deployed region. The goal is to prevent intruders from sneaking through the network undetected. It is a critical issue for many military and homeland security applications. In this chapter we provide a comprehensive survey on the barrier coverage of wireless sensor networks. The main topics include the critical conditions and construction of barrier coverage in a 2-dimensional WSN, the barrier coverage under a line-based sensor deployment scheme, the effect of sensor mobility on barrier coverage, and the barrier coverage for a 3-dimensional underwater sensor network. For each topic we discuss the challenges, fundamental limits, and the solution for the construction of sensor barriers.
Anwar Saipulla, Jun-Hong Cui, Xinwen Fu, Benyuan Liu, Jie Wang

Chapter 4. Spatiotemporal Coverage in Fusion-Based Sensor Networks

Wireless sensor networks (WSNs) have been increasingly available for critical applications such as security surveillance and environmental monitoring. As a fundamental performance measure of WSNs, coverage characterizes how well a sensing field is monitored by a network. Two facets of coverage, i.e., spatial coverage and temporal coverage, quantify the percentage of area that is well monitored by the network and the timeliness of the network in detecting targets appearing in the sensing field, respectively. Although advanced collaborative signal processing algorithms have been adopted by many existing WSNs, most previous analytical studies on spatiotemporal coverage of WSNs are conducted based on overly simplistic sensing models (e.g., the disc model) that do not capture the stochastic nature of sensing. In this chapter, we attempt to bridge this gap by exploring the fundamental limits of spatiotemporal coverage based on stochastic data fusion models that fuse noisy measurements of multiple sensors. We derive the scaling laws between spatiotemporal coverage, network density, and signal-to-noise ratio (SNR). We show that data fusion can significantly improve spatiotemporal coverage by exploiting the collaboration among sensors when several physical properties of the target signal are known. In particular, for signal path loss exponent of \(k\) (typically between \(2.0\) and \(5.0\)), we prove that \(\rho _f{/}\rho _d = {\mathcal {O}}(\delta ^{2/k})\), where \(\rho _f\) and \(\rho _d\) are the densities of uniformly deployed sensors that achieve full spatial coverage or minimum detection delay under the fusion and disc models, respectively, and \(\delta \) is SNR. Our results help understand the limitations of the previous analytical results based on the disc model and provide key insights into the design of WSNs that adopt data fusion algorithms. Our analyses are verified through extensive simulations based on both synthetic data sets and data traces collected in a real deployment for vehicle detection.
Rui Tan, Guoliang Xing

Tracking, Estimation, and Counting


Chapter 5. Probabilistic Indoor Tracking of Mobile Wireless Nodes Relative to Landmarks

The profile-based approach is known to be advantageous when it comes to inferring positions of mobile wireless devices in complex indoor environments. The past decade has seen a significant body of work that explores different implementations of this approach, with varying degrees of success. Here, we cast the profile-based approach in a probabilistic framework. Launching from the theoretical basis that this framework provides, we provide a suite of carefully designed methods that make use of sophisticated computations in pursuit of high localization accuracy with low hardware investment and moderate set-up cost. More specifically, we use full distributional information on signal measurements at a set of discrete locations, termed landmarks. Positioning of a mobile node is done relative to the resulting landmark graph and the node can be found near a landmark or in the area between two landmarks. Key elements of our approach include profiling the signal measurement distributions over the coverage area using a special interpolation technique; a two-tier statistical positioning scheme that improves efficiency by adding movement detection; and joint clusterhead placement optimization for both localization and movement detection. The proposed system is practical and has been implemented using standard wireless sensor network hardware. Experimentally, our system achieved an accuracy equivalent to less than \(5\) m with a \(95\,\%\) success probability and less than \(3\) m with an \(87\,\%\) success probability. This performance is superior to well-known contemporary systems that use similar low-cost hardware.
Ioannis Ch. Paschalidis, Keyong Li, Dong Guo, Yingwei Lin

Chapter 6. Protocol Design for Real-Time Estimation Using Wireless Sensors

This chapter discusses how a wireless sensor network can be built for real-time estimation purposes. The finite capacity of a wireless network in delivering information means that a real-time estimation process has finite accuracy too. Improving accuracy requires faster sampling and more communication; however, faster communication is not always a possibility due to scalability issues and the limited capacity of a network. In fact, in networks where the wireless medium is shared amongst many nodes, the increase in the amount of communication may even have a negative impact on the capacity of wireless medium, and in turn on the quality of real-time estimation (e.g., loss of network capacity as seen in CSMA/CA—Carrier Sense Multiple Access/Collision Avoidance- networks). In this chapter we describe methods and protocols that can be employed to control the behavior of nodes to allow maximal use of the shared medium for the purpose of real-time estimation. We describe how transmission control protocols (adapting rate and range of communication) should be applied in a wireless network of mobile sensors, such as vehicles, to allow the highest possible accuracy given the limitations of the medium. Such protocols have been evaluated in the form of transmission rate and range control schemes in wireless vehicular networks, with the purpose of real-time vehicle position tracking.
Yaser P. Fallah, Raja Sengupta

Chapter 7. Target Counting in Wireless Sensor Networks

Target counting in wireless sensor networks has attracted a lot of attention in recent years from both academia and industry. In this chapter, we review various problem formulations and technical approaches proposed in recent literature for target counting. Major existing works are classified into the following four categories: binary counting, numeric counting, energy counting, and compressive counting, based on the sensing capabilities of the network and the underlying theoretical foundations of the technical approaches. Within each category, we summarize the representative works according to their objectives, technical methods, performances, and advantages and disadvantages. Comparative evaluations are provided to illustrate the influence of different sensor network settings on the target counting accuracy. The applicable environments of these algorithms are also discussed at the end of the chapter.
Dengyuan Wu, Bowu Zhang, Hongjuan Li, Xiuzhen Cheng

Coverage and Localization in Three-Dimensional Wireless Sensor Networks


Chapter 8. Coverage and Connectivity in 3D Wireless Sensor Networks

A wireless sensor network (WSN) is categorized as three-dimensional (3D) when variation in the height of deployed sensor nodes is not negligible as compared to length and breadth of deployment field. The fundamental problem in such 3D networks is to find an optimal way to deploy sensor nodes needed to maintain full (or targeted degree of) coverage of monitored volume and reliable connectivity as desired by network designers. The solution should yield lower bound on number of nodes needed to achieve full coverage and connectivity. However, optimizing coverage and connectivity in 3D WSNs comes with its inherent complexities and intrinsic design challenges. 3D WSNs are not only difficult to visualize but their analysis is also computationally intensive. This literature summarizes major work conducted in the domain of coverage and connectivity in 3D WSNs. It studies different placement strategies, fundamental characteristics, modeling schemes, analytical methods, limiting factors, and practical constraints dealing with coverage and connectivity in 3D WSNs.
Usman Mansoor, Habib M. Ammari

Chapter 9. Localization in Three-Dimensional Wireless Sensor Networks

A wireless sensor network (WSN) is categorized as three-dimensional (3D) when the variation in the height of deployed sensor nodes is not negligible as compared to length and breadth of deployment field. Localization is one of the fundamental components of any wireless sensor application. A localization algorithm estimates the position of a node by using information provided/inferred from anchor beacons, reference nodes or neighbors connectivity. The effectiveness of a localization algorithm is usually determined in terms of accuracy, resilience to node failure, computational cost, messaging overhead, hardware constraints and deployment practicality. This survey overviews the major recent work done in the field of localization in 3D WSNs. The major contribution of this work is to present all the major 3D (generic, airborne, terrestrial and submerged) localization schemes in a single literature along with their relative strengths and weaknesses.
Usman Mansoor, Habib M. Ammari

Topology Control and Routing in Three-Dimensional Wireless Sensor Networks


Chapter 10. Three-Dimensional Wireless Sensor Networks: Geometric Approaches for Topology and Routing Design

Three-dimensional (3D) wireless sensor networks have attracted a lot of attention due to their great potential usages in both commercial and civilian applications, such as environmental data collection, pollution monitoring, space exploration, disaster prevention, and tactical surveillance. Unfortunately, the design of 3D networks is surprisingly more difficult than the design in two-dimensional (2D) networks. Many properties of the network require additional computational complexity, and many problems cannot be solved by extensions or generalizations of 2D methods. In addressing these challenges, there have been new network protocols and algorithms designed for 3D wireless sensor networks using geometric approaches. In this chapter, we review the most recent advances in 3D topology control and 3D geographic routing for 3D wireless sensor networks.
Yu Wang

Chapter 11. Routing in Three-Dimensional Wireless Sensor Networks

Advances in wireless sensor networks (WSNs) technology have been undergoing a revolution that promises a significant impact on society. Most existing wire-less systems and protocols are based on two-dimensional design, where all wire-less nodes are distributed in a two-dimensional (2D) plane. However, 2D assumption may no longer be valid if a wireless network is deployed in space, atmosphere, or ocean, where nodes of a network are distributed over a three-dimensional (3D) space and the differences in the third dimension are too large to be ignored. In fact, recent interest in wireless sensor networks hints at the strong need to design 3D wireless networks. The characteristics of 3D wireless sensor networks require more effective methods to ensure routing and data dissemination protocols in these networks. In this chapter, we present a survey of the state-of-the-art routing techniques in 3D WSNs.
Anne Paule Yao, Habib M. Ammari

Underground and Underwater Sensor Networks


Chapter 12. The Future of Wireless Underground Sensing Networks Considering Physical Layer Aspects

The design of a WSN for the underground environment is typically characterized by an excess of either pessimism or optimism. For many years, underground communication has been considered infeasible. Rather, we should neither abandon the hope of designing a functioning underground WSN, nor expect things to automatically work in an underground setting by simply importing technologies from existing WSNs, the majority of which are developed for aboveground environment. Besides energy challenges (more critical compared to typical WSNs), the design of an underground WSN is governed by the characteristics of the underground communication channel. Compared to over-the-air (OTA) radio frequency communication, signal attenuation in soil can be 20–300 times worse. For instance, a typical communication range of 300 m for a radio transceiver can decrease to less than 1m in soil. Moreover, while OTA transceivers and underwater communication have been available for many years, the same cannot be said for underground communication. The mining industry has been looking for a long-range, low-power, wireless communication solution for rescue missions in the event of trapped miners due to a collapse, and has so far not been very successful. These facts highlight the challenges in realizing wireless underground communication. Recent innovations based on relatively short-range communication and high density of nodes can potentially lead to the proliferation of wireless underground sensor networks (WUSNs) in the near future. In this chapter, we present in detail the traditional challenges faced by WUSN researchers, the perceived limitations, and recent technological advances that are beginning to change the outlook. Through this discussion, we show that a generic solution for WUSNs cannot be expected. Instead, the design must be tailored to the application. For instance, the features and techniques to be exploited in designing a WUSN to detect oil pipeline leakage are distinctly different from that of a WUSN for agricultural draught or landslide monitoring.
Agnelo Rocha da Silva, Mahta Moghaddam, Mingyan Liu

Chapter 13. A Communication Framework for Networked Autonomous Underwater Vehicles

Underwater acoustic communications consume a significant amount of energy due to the high transmission power (10–50 \(\mathrm {W}\)) and long data packet transmission duration (0.1–1 \(\mathrm {s}\)). Mobile Autonomous Underwater Vehicles (AUVs) can conserve energy by waiting for the ‘best’ network topology configuration, e.g., a favorable alignment, before starting to communicate. Due to the frequency-selective underwater acoustic ambient noise and high medium power absorption—which increases exponentially with distance—a shorter distance between AUVs translates into a lower transmission loss and a higher available bandwidth. By leveraging the predictability of AUV trajectories, a novel solution is proposed that optimizes communications by delaying packet transmissions in order to wait for a favorable network topology (thus trading end-to-end delay for energy and/or throughput). In addition, the proposed solution exploits the frequency-dependent radiation pattern of underwater acoustic transducers to reduce communication energy consumption. Our solution is implemented and evaluated through emulations, showing improved performance over some well-known geographic routing solutions and delay-tolerant networking solutions.
Baozhi Chen, Dario Pompili

Multimedia and Body Sensor Networks


Chapter 14. Low-Complexity Video Streaming for Wireless Multimedia Sensor Networks

In recent years, there has been intense research and considerable progress in solving numerous wireless sensor networking challenges. However, the key problem of enabling real-time quality-aware multimedia transmission over wireless sensor networks is largely unexplored. The large amount of data generated by most multimedia applications (compared to traditional scalar sensor networks), along with the higher QoS requirements make it difficult to meet the low energy use requirements of practical sensor networks. We explore the use of compressed sensing (aka “compressive sampling”) to reduce the energy required to encode and transmit high quality video in a severely resource-constrained environment. In this chapter, we will examine some of the major challenges of wireless multimedia sensor network (WMSN) implementation. Specifically, we examine what it would take to develop a WMSN that has similar performance (and restrictions) as a traditional scalar wireless sensor network (WSN). We then examine how we can use the new paradigm of compressed sensing (CS) to solve many of these problems.
Scott Pudlewski, Tommaso Melodia

Chapter 15. Body Sensor Networks for Activity and Gesture Recognition

The last decade has witnessed a rapid surge of interest in new sensing and monitoring devices for health care applications. An important development in this area is that of Body Sensor Networks (BSN) that operate in a pervasive manner for on-body applications. Intelligent processing of the sensor streams from BSN is key to the success of applications that rely on this framework. In this chapter we dwell upon one application of BSN that involved processing of wearable accelerometer data for recognizing ambulatory or simple activities and activity gestures. We elaborate on the different steps such as feature extraction and classification involved in the processing of raw sensor data for detecting activities and gestures. We also discuss various aspects associated with a real-time simple activity recognition system such as computational complexity and factors that emerge considering that the sensors are worn by humans. While some of these factors are common to wireless sensor networks in general, the discussion of the chapter is focused on the BSN system developed by us for recognizing simple activities and activity gestures.
Narayanan C. Krishnan, Sethuraman Panchanathan

Social Sensing


Chapter 16. Analytic Challenges in Social Sensing

Social sensing applications refer to those where individuals play an important role in data collection. They can act as sensor carriers (e.g., carrying GPS devices that share location data), sensor operators (e.g., taking pictures with smart phones), or as sensors themselves (e.g., sharing their observations on Twitter). The proliferation of sensors in the possession of the average individual, together with the popularity of social networks that allow massive information dissemination, heralds an era of social sensing that brings about new research challenges reviewed in this chapter.
Tarek Abdelzaher, Dong Wang

Chapter 17. Behavior-Aware Mobile Social Networking

The next frontier in sensor networks is sensing the human society. Human interaction, with technology and within mobile communities provides enormous opportunities to provide new paradigms of user communication. Traditionally, communication in computer networks has focused on delivering messages to machine identities. Each host is uniquely addressed, and network protocols aim to find routes to a given machine identity efficiently. While this framework has been proven successful in the past, it is questionable whether it will be sufficient in the era of social networking and mobility. As we envision the emergence of mobile terminals tightly coupled with their users and thus reflect the behavior and preferences of the users, it is beneficial to consider an alternative (and complementary) framework: Could user behavior be collected and summarized as a representation of the user’s interest, and be leveraged as a way to guide message delivery? In this chapter, we elaborate on this possibility, discussing user behavior trace collection, representation, and pioneering works on behavior-aware mobile network protocols.
Wei-Jen Hsu, Ahmed Helmy

Chapter 18. Emerging Applications of Wireless Sensing in Entertainment, Arts and Culture

This chapter provides an overview of cultural applications of wireless sensing systems from four perspectives: the “Internet of Things”, the “Smart Grid”, “participatory sensing” on mobile phones, and an event-based point of view. The challenges and unique requirements of these applications are examined, and future opportunities for research are suggested in three technical areas: Machine Learning, Networking and Privacy. The need for more advanced authoring tools—enabling creators of cultural applications with less technical backgrounds to develop complex systems that use wireless sensing—also motivates work on these three technical topics.
Jeffrey A. Burke


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