Guide to Wireless Sensor Networks

Wireless sensor networks (WSN), which normally consist of hundreds or thousands of sensor nodes each capable of sensing, processing, and transmitting environmental information, are deployed to monitor certain physical phenomena or to detect and track certain objects in an area of interests. Since the sensor nodes are equipped with battery only with limited energy, energy efficient information processing is of critical importance to operate the deployed networks as long as possible. This chapter presents how some classical information processing problems, mainly focusing on estimation and classification, need to be reexamined in such energy constrained WSNs. We first present the basics of estimation and classification and certain typical solutions. We then introduce the requirements for supporting their counterparts in WSNs. Some recent energy efficient information processing algorithms are then reviewed to illustrate how to enforce energy efficient information processing in WSNs. Examples, questions, and solutions are also provided to help the understanding of the topic in this chapter.

Topology management is a key component of network management of wireless sensor networks. The primary goal of topology management is to conserve energy while maintaining network connectivity. Topology management consists of knowing the physical connections and logical relationships among the sensors. Another important concept of topology management is to have only a subset of nodes actively participating in the network, thus creating less communication and conserving energy in nodes. This chapter provides a detailed survey of existing topology management algorithms proposed for wireless sensor networks in three categories: topology discovery (learning the layout of the nodes), sleep cycle management (allowing some nodes to sleep to conserve energy), and clustering (grouping nodes to conserve energy).

Ad-hoc networks of devices and sensors with (limited) sensing and wireless communication capabilities are becoming increasingly available for commercial and military applications. The first step in deploying these wireless sensor networks is to determine, with respect to application-specific performance criteria, (i) in the case that the sensors are static, where to deploy or activate them; and (ii) in the case that (a subset of) the sensors are mobile, how to plan the trajectory of the mobile sensors. These two cases are collectively termed as the coverage problem in wireless sensor networks. In this chapter, we give a comprehensive treatment of the coverage problem. Specifically, we first introduce several fundamental properties of coverage that have been derived in the literature and the corresponding algorithms that will realize these properties. While giving insights on how optimal operations can be devised, most of the properties are derived (and hence their corresponding algorithms are constructed) under the perfect disk assumption. Hence, we consider in the second part of the chapter coverage in a more realistic setting, and allow (i) the sensing area of a sensor to be anisotropic and of arbitrary shape, depending on the terrain and the meteorological conditions, and (ii) the utilities of coverage in different parts of the monitoring area to be nonuniform, to account for the impact of a threat on the population, or the likelihood of a threat taking place at certain locations. Finally, in the third part of the chapter, we consider mobile sensor coverage, and study how mobile sensors may navigate in a deployment area to maximize threat-based coverage.

Wireless sensor networks are formed by small sensor nodes communicating over wireless links without using a fixed network infrastructure. Sensor nodes have a limited transmission range, and their processing and storage capabilities as well as their energy resources are also limited. Routing protocols for wireless sensor networks have to ensure reliable multi-hop communication under these conditions. We describe design challenges for routing protocols in sensor networks and illustrate the key techniques to achieve desired characteristics, such as energy efficiency and delivery guarantees. We give a survey of state-of-the-art routing techniques with a focus on geographic routing, a paradigm that enables a reactive message-efficient routing without prior route discovery or knowledge of the network topology. Different geographic routing strategies are described as well as beaconless routing techniques. We also show the physical layer impact on routing and outline further research directions.

This chapter surveys routing algorithms for wireless sensor networks that use geometric ideas and abstractions. Wireless sensor networks have a unique geometric character as the sensor nodes are embedded in, and designed to monitor, the physical space. Thus the geometric embedding of the network can be exploited for scalable and efficient routing algorithm design. This chapter starts with geographical routing that use nodes’ geographical locations to guide the choice of the next hop node on the routing path. The scalability of geographical routing motivates more work on the design of virtual coordinates with which greedy routing algorithms are developed and applied to route messages in the network. The last section is concerned about data-centric routing, in which a query is routed to reach the sensor node holding data of interest. Thus the challenge is to discover the “source node” that possess the data as well as route the message there.

Cooperative relaying has been shown to be an effective method to significantly improve the error-rate performance in wireless networks. This technique combats fading by exploiting the spatial diversity made available through cooperating nodes that relay signals for each other. In the context of wireless sensor networks, cooperative relaying can be applied to reduce the energy consumption in sensor nodes and thus extend the network lifetime. Realizing this benefit, however, requires a careful incorporation of this technique into the routing process to exploit diversity gains. In this chapter, we introduce the basic concepts required to understand cooperative relaying and review current state of the art energy-efficient routing protocols that realize cooperative relaying.

Data-centricity is a feature of wireless sensor networks that distinguishes them from other wireless data networks. Establishing the data as the centre of operation in sensor networks provides better usage of the limited resources available in such networks. In addition, data-centricity matches well the nature of wireless sensor networks. This chapter reviews a number of emerging topics collectively constituting a data-centric view of wireless sensor networks. These topics include data-centric routing, data aggregation, and data-centric storage.

Wireless sensor networks (WSNs) present a range of unique challenges to protocol designers due to their communication pattern, poor, and unpredictable performance of their low-power wireless radios, wireless interference, and resource constrains of individual sensor nodes. One of the challenges is how to address congestion control and reliable data delivery in such environments: the nature of WSN applications (data centric, prone to redundancy due to multiple sensors reporting a single event) and infrastructure (sensor capabilities, and deployment density and strategy) invite significantly different solutions from those present in conventional networks. In this chapter, we present a survey of existing congestion control approaches and classify them based on various parameters such as mechanisms used for congestion detection and control, support for application specific design, target data delivery model, and support for fairness and reliability. Since, WSN applications exhibit a wide variety of communication patterns, existing literature has focused on three types of applications with regards to communication among sensors: one-to-one, one-to-many, and many-to-one. Reliability and congestion management approaches in the case of one-to-one (unicast) and one-to-many (multicast or broadcast) communication have been studied extensively in wired as well as wireless ad hoc networks, providing significant experience to draw on. However, many-to-one communication pattern involves various opportunities (e.g., loss-tolerance) as well as challenges (e.g., congestion management), thereby gaining major attention from the research community. Thus, the main focus of this chapter is on congestion and flow control approaches for many-to-one traffic pattern in WSNs.

Dynamics of wireless communication, resource constraints, and application diversity pose significant challenges to data transport control in wireless sensor networks. In this chapter, we examine the issue of data transport control in the context of two typical communication patterns in wireless sensor networks: convergecast and broadcast. We study the similarity and differences of data transport control in convergecast and broadcast; we discuss existing convergecast and broadcast protocols, and we present open issues for data transport control in wireless sensor networks.

Wireless sensor networks (WSNs) have wide variety of applications and provide limitless future potentials. Nodes in WSNs are prone to be failure due to energy depletion, hardware failure, communication link errors, malicious attack, and so on. Therefore, fault tolerance is one of the critical issues in WSNs. The chapter investigates current research work on fault tolerance in WSNs. We study how fault tolerance is addressed in different applications of WSNs. Five categories of applications are discussed: node placement, topology control, target and event detection, data gathering and aggregation, and sensor surveillance. In each category, we focus on the representative research works that presented algorithms and approaches in application layer to achieve fault tolerance.

The basis of sensor networks is the process of sensing, data processing, and information communication [1]. In this chapter we investigate methods for computing self-healing and self-configuring strategies that can be encoded into a sensor unit prior to deployment. A strategy is a set of rules that assigns actions to specific states; specifically, a sensor unit periodically checks whether its current state matches some rule, and in affirmative the corresponding action (or actions) is executed. Ideally, an optimization method evaluates the rules, refines them, and converges to a temporarily optimal strategy. We do not address reliability of the sensor node or the correctness of the data collected through the sensing unit. The design of self-organization is different among the wireless networks models (e.g., MANET, cellular networks, WSN) due to the performance objective of the network Clare et al. (Proc SPIE 3713: 229–237, 1999); (Sohrabi et al. IEEE Pers Comm Mag 7: 16–27, 2000); Sohrabi and Pottie (Proc IEEE Vehicular Tech Conf, 1222–1226, 1999).

Although well studied for traditional computer networks, quality of service (QoS) concepts have not been applied to wireless sensor networks (WSNs) until recently. QoS support is challenging due to severe energy and computational resource constrains of wireless sensors. Moreover, certain service properties such as the delay, reliability, network lifetime, and quality of data may conflict by nature. Multi-path routing, for example, can improve the reliability; however, it can increase the energy consumption and delay due to duplicate transmissions. Also, high resolution sensor readings incur more energy consumptions and delays. Modeling such relationships, measuring the provided quality, and providing means to control the balance is essential for QoS support. In this context, this chapter discusses existing approaches for QoS support in WSNs and suggests directions for further research.

Several operating systems (OS) for wireless sensor networks (WSNs) have been designed, implemented and are in the process of enhancement. However, early before implementation, designers face an important decision to make. The designer of an embedded operating system (EOS) has to conform to one of two completely different design philosophies and build his system according to that philosophy. This decision is crucial in the sense that the behavior and performance of each model differs, and those will be reflected on the WSN since the EOS is the core of the system, and any protocol built on top of it will drag with it the characteristics of the design model. Both models are investigated in this chapter by looking at the design and architectures of several EOSs built for WSNs.

A major research challenge in the field of sensor networks is the distributed resource allocation problem, which concerns how the limited resources in a sensor network should be allocated or scheduled to minimize costs and maximize the network capability. We survey the existing work on the distributed resource allocation problem. To address the drawbacks in the existing work, we propose the adaptive distributed resource allocation (ADRA) scheme, which specifies relatively simple local actions to be performed by individual sensor nodes in a sensor network for mode management. Each node adapts its operation over time in response to the status and feedback of its neighboring nodes. Desirable global behavior results from the local interactions between nodes.

We study the effectiveness of the general ADRA scheme for a realistic application scenario, namely, the sensor mode management for an acoustic wireless sensor network (WSN) to track vehicle movement. An enhanced version of ADRA, ADRA with node density compensator, is also proposed to improve the performance of the algorithm for randomly distributed sensor fields. We evaluated these algorithms via simulations and also prototyped the acoustic WSN scenario using the Crossbow MICA2 motes. Our simulation and hardware implementation results indicate that the ADRA scheme and its enhanced variant provide good trade-off between performance objectives such as coverage area, power consumption, and network lifetime.

We investigate scheduling activities in sensor networks; the materials covered are far beyond medium access control (MAC) protocols and the purpose is not to review specific or general purpose MAC approaches. Our purpose is more generic and we investigate scheduling strategies and techniques that could be applied to avoid interference, to prolong the network lifetime by reducing energy consumption, to optimize network performance by taking into account the underlying application communication patterns, to guarantee sensing coverage in monitoring tasks, and to achieve good levels of QoS. We examine scheduling under various interference models, including the traditional channel separation constraints model, the protocol model, and the physical Signal-to-Interference-plus-Noise-Ratio model. For each topic covered in this chapter, we survey the results and one or two representative works are examined in details as examples.

Medium access control for wireless sensor networks has been an active research area in the past decade. This chapter discusses a set of important medium access control (MAC) attributes and possible design trade-offs in protocol design, with an emphasis on energy efficiency. Then we categorize existing MAC protocols into five groups, outline the representative protocols, and compare their advantages and disadvantages in the context of wireless sensor network. Finally, thoughts for practitioners are presented and open research issues are also discussed.

Devices in a wireless sensor network are typically powered by limited and sometimes unchargeable batteries, which are supposed to sustain for months or even years. To enhance the lifetime of a sensor network, highly efficient energy management techniques are mandatory, in order to successfully achieve the missions of the network. These techniques, however, involve all levels of the sensor system hierarchy in data processing and transmitting. Thus, energy awareness should be incorporated into every level of the system design and operation to maximize the lifetime and connectivity as much as possible. In this chapter, state-of-the-art techniques at each layer for optimizing the energy usage proposed in literature are introduced. To illustrate the efficacies of the approaches, design examples in reducing energy expenditure are also given as well. In addition, new thoughts in energy conservation by exploiting the interactions between different layers are presented. These new ideas could be effective in reducing the energy consumption.

Communication is usually the most energy-consuming event on mobile ad hoc networks. Hence, medium access control (MAC) protocols use techniques tomitigate energy consumption on the transmission of data. This chapter presents one such technique, which consists of adjusting the transmission power of the packets. We discuss the fundamentals of transmission power control (TPC), showing their effects in wireless communication and the requirements of TPC solutions. Next, we examine the issues associated with their implementation and show the difficulties of implementing those techniques on real hardware, based on our experience with TPC-aware MAC protocols for wireless sensor networks. We close this chapter with a glimpse of future challenges of TPC-aware MAC protocols on MANETs and WSNs.

Small, inexpensive, battery-powered wireless sensors can be easily deployed in places where human access could be difficult, dangerous, expensive, or even intrusive to the subject of sensing such as wild animals. Deployed wireless sensors can cooperate with each other to enhance the efficiency of, for example, scientific research, manufacturing, construction, transportation, or military operations. However, the open cooperative nature can expose wireless sensors to various attacks from a malicious adversary. No physical security is available to a wireless sensor network (WSN) deployed in an open environment. Most existing security solutions developed for wired networks are computationally too expensive for wireless sensors with limited energy, computational power, and communication bandwidth. Because of the cooperative nature, many sensor nodes can be affected even when a single node is compromised. This chapter discusses security challenges and vulnerabilities in WSNs. It gives a survey of representative security mechanisms designed to address known vulnerabilities. Finally, it highlights key research issues that remain to be tackled.

In wireless sensor networks, cryptography is the means to achieve data confidentiality, integrity, and authentication. To use cryptography effectively, however, the cryptographic keys need to be managed properly. First of all, the necessary keys need to be distributed to the sensor nodes before the nodes are deployed in the field, in such a way that any two or more nodes that need to communicate securely can establish a session key. Then, the session keys need to be refreshed from time to time to prevent birthday attacks. Finally, in case any of the nodes is found to be compromised, the key ring of the compromised node needs to be revoked and some or all of the compromised keys might need to be replaced. These processes, together with the policies and techniques needed to support them, are called key management. In this chapter, we explore different key management schemes with their respective advantages and disadvantages.

The biggest advantage of building “intelligence” into a sensor is that the sensor can process data before sending them to a data consumer. The kind of processing that is often needed is to aggregate the data into a more compact representation called an aggregate, and send the aggregate to the data consumer instead. The main security challenges to such a process are (1) to prevent Byzantine-corrupted data from rendering the final aggregate totally meaningless and (2) to provide end-to-end confidentiality between the data providers and the data consumer. This chapter surveys the state of the art in techniques for addressing these challenges.

The emergence of low-cost and mature technologies in wireless communication, visual sensor devices, and digital signal processing facilitate of wireless multimedia sensor networks (WMSN). Like sensor networks which respond to sensory information such as temperature and humidity, WMSN interconnects autonomous devices for capturing and processing video and audio sensory information. This survey highlights the following topics (1) a summary of applications and challenges of WMSN; (2) an overview of advanced coding techniques for WMSN, including video and audio source coding, and distributed coding techniques; (3) a survey of WMSN communication protocols, including routing techniques and physical layer standards; and (4) a summary of Quality-of-Service (QoS) and security aspects of WMSN.

Application development for wireless sensor networks (WSNs) demands for expertise in distributed as well as embedded programming. To ease the task of application development and make this area more accessible to nonexperts, middleware abstractions are commonly employed. Middleware is defined as software which is located in between software applications. Similar to operating systems, middleware systems provide applications with additional services to implement their functionality in a more abstract manner. Since devices forming a WSN have only little capabilities in terms of processing power and memory, their corresponding operating systems only provide very basic support for application development. At the same time various kinds of applications do have additional requirements to simplify their implementation. A multitude of middleware approaches are available to fill in this gap, thus provide support for comfortable application development. We will discuss common application building blocks in this domain, discuss a selection of middleware approaches available, and provide an evaluation of their applicability by mapping application needs to middleware services.

In the last few years, tremendous efforts have been made to enhance the performance of stationary wireless sensor networks (WSNs). However, such improvements are constrained by the limitations of being a stationary network. Recent advances in robotic and the potential usage of naturally moving objects such as vehicle, animal, and even human, enable some of the sensors in the network to be mobile, and such a network is so called a Mobile WSN (MWSN). In this chapter, we study how mobility can improve the network performance such as the network lifetime, coverage, and connectivity. For example, the lifetime of a WSN can be improved by additionally deploying some mobile sensors in the hot spot around the Base Stations (BSs). The coverage is further enhanced by allowing some or all sensors to reposition themselves or move continuously. Furthermore, high connectivity along with coverage is maintained by replacing the broken links or adding extra sensors to reconnect the partitioned networks through the use of mobile relay units. To provide a complete understanding of these aspects, we perform a comprehensive examination of existing approaches in designing a MWSN.

Sensor networks are complex systems incorporating a variety of different devices. As with any system, simulation of the system, or key components, reduces design time. With simulation, a designer can investigate performance and system correctness without having to build a device and test-bed. As a result, simulation is well suited to sensor networks, saving time and money, because to construct a test-bed hundred or even thousands of devices may be required to be produced and deployed. However, simulating sensor networks involves conflicting tradeoffs in the development and running of a simulation. This chapter explores the two different methods, discrete event simulation and analytical modeling, available to simulate sensor networks and pros and cons of each method. The chapter concludes with a comparison of the two methods.

Wireless-sensor networks (WSN) are expected to enable connection between physical world and the Internet to provide access to vast amount of information from anywhere and anytime through any kind of communication devices and services. However, this vision poses significant challenges for WSN. Due to the pervasion in its nature, centralized control of WSN is not a practical solution. Instead, WSN and its communication protocols must have the capabilities of scalability, self-organization, self-adaptation, and survivability. In nature, the biological systems intrinsically have these capabilities such that billions of blood cells, which constitute the immune system, can protect the organism from the pathogens without any central control of the brain. Similarly, in the insect colonies insects can collaboratively allocate certain tasks according to the sensed information from the environment without any central controller. Therefore, the natural biological systems may give great inspiration to develop the communication network models and techniques for WSN. In this chapter, we introduce potential solution avenues from the biological systems toward addressing the challenges of WSN such as scalability, self-organization, self-adaptation, and survivability. After the existing biological models are first investigated, biologically inspired communication approaches are introduced for WSN. The objective of these communication approaches is to serve as a roadmap for the development of efficient scalable, adaptive, and self-organizing bioinspired communication techniques for WSN.

Communications infrastructures are a critical asset in today’s information society. However, legacy telecommunication systems easily collapse in case of disruptions that may occur due to security incidents or crises. In this chapter, we first elaborate on the major shortcomings of the current communications networks for security applications to identify the key missing requirements for such networks. Then, we show that the ad hoc networking technologies, coupled with disruptive-tolerant techniques, are the best suited paradigm to build the next generation of dependable, secure, and rapidly deployable communications infrastructures. In particular, we focus on mesh, opportunistic, vehicular, and sensor networks giving an overview of the most recent advances and summarizing the challenges facing the design and the deployment of these networks. Finally, we conclude this chapter presenting the open research issues to realize the vision of a dependable communications infrastructure, with special attention to aspects such as interoperability among multiple heterogeneous networks, autonomic network management, and QoS protection.