The Art of Wireless Sensor Networks

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.

The popularity of low-power wireless sensors increased significantly in the last decade, triggering a golden era for wireless sensor network research and development. During the early years of the twenty-first century, wireless sensor network applications have evolved from small demonstrations with a lifetime of only a few hours to complete systems made up of hundreds of tiny wireless nodes deployed in a wide variety of settings, ranging from harsh and remote environments to residential buildings and clinical units. This survey gives an overview of the most relevant applications of wireless sensor network applications deployed during the last ten years, and classifies them using a novel taxonomy that aims to help identifying relevant programming constructs and run-time services. With more than 60 applications reviewed, ranging from military and civilian surveillance to tracking systems, from environmental and structural monitoring to home and building automation, from agriculture and industrial settings to health care, this survey will serve as a reference to guide researchers and system designers.

The majority of low-cost and off-the-shelf Wireless Sensor Networks (WSNs) solutions cannot adequately address issues related to an unattended deployment in a harsh environment, especially if the network needs to scale and achieve high density or high coverage or both. This is usually the case in environmental applications. In this chapter, this problem is investigated and extensive discussion on the pros and cons of a specific WSN design is presented. However, before moving from generic and well-established WSN solutions to customization, a detailed analysis of the gains of having a tailored design is necessary. Accordingly, a case study involving sparse deployments in outdoors is used to illustrate the process.

Wireless Sensor Networks (WSNs) are widely used in applications such as event detection, environment monitoring, target tracking, and home automation. Typically, manually deployed sensors measure environmental information, e.g., temperature, image, and wind speed, etc., and transmit noisy versions of these measurements over wireless fading channels to the local intelligent control center (ICC), in which an estimation of these state measurements is obtained and thus related control operations are conducted.

Network coding is a technique where relay nodes mix packets using mathematical operations, which can increase the throughput. Network coding was first proposed for wired networks to solve the bottleneck in a single multicast session problem and to increase the throughput. However, the broadcast nature of wireless networks and the diversity of the links make network coding more attractive in wireless networks. Network coding can be classified as either inter or intra-session. Inter-session network coding allows the packets from different sessions (sources) to be mixed to increase the throughput. In contrast, intra-session network coding, which can be used to address the packet loss problem, uses the diversity of the wireless links and mixes packets from the same sessions. In this chapter, we survey the recent works on network coding in both general wireless networks and wireless sensor networks. We present various network coding techniques, their assumptions, applications, as well as an overview of the proposed methods.

Sensors have limited resources such as energy, computational power and bandwidth, and thus they require protocols and techniques that are resource aware and energy efficient. As energy waste through idle listening, retransmissions and overhearing are some of the primary causes of reduced lifetime in wireless sensor networks, sensor sleeping is critically important. Sleeping techniques prolong the network lifetime by placing components of the sensor node into a sleep mode while aiming to minimize the impact on application performance. Sensor sleeping can be applied to different layers of the protocol stack, and a cross-layer sleep manager can orchestrate sleeping in multiple layers simultaneously. In this chapter, the importance of sensor sleeping, the various sleeping techniques proposed and the applications using these approaches are discussed.

Wireless sensor networks have attracted much attention due to their ability to collect data from areas of interest. The limited energy capacity along with the difficulty of charging batteries of deployed sensors render energy-aware routing essential for sustained operation of wireless sensor networks. In this chapter, we classify energy-aware routing algorithms into five categories according to their network architecture: flat multi-hop routing that finds paths to minimize energy consumption or increase sensor network lifetime, hierarchical routing that creates a hierarchy and applies data-aggregation to reduce energy consumption, hybrid multi-hop routing that is a combination of the former two and mitigates the energy hole problem, data-centric routing that performs in-network data-aggregation to eliminate wasteful transmissions, and location-based routing that uses location information to reduce the energy consumption of the wireless sensor network. Furthermore, we present a cross-cutting discussion which addresses data-aggregation, network lifetime definition, routing overhead, the energy hole phenomenon, and collisions/interferences.

In Wireless Sensor Networks (WSNs) sensor nodes often operate unattended in a collaborative manner to perform some tasks. In many applications, the network is deployed in harsh environments such as battlefield where the nodes are susceptible to damage. In addition, nodes may fail due to energy depletion and breakdown in the onboard electronics. The failure of nodes may have major consequences. First, some areas may be left uncovered. Second, the fidelity of the collected data gets degraded. And finally, the network may get partitioned into disjoint segments. In particular, losing network connectivity has a very negative effect on the applications since it prevents data exchange and hinders coordination among some nodes. Therefore, restoring the overall network connectivity with the least resource overhead and performance impact is very crucial. This chapter focuses on network topology management techniques for tolerating node failures. It analyzes the effects of node failure on network connectivity in WSNs, categorizes recently published recovery schemes, and outlines related open issues.

Mobility management is a major challenge in mobile ad hoc networks (MANETs), due in part to the dynamically changing network topologies. For mobile wireless sensor networks (WSNs) that are deployed for surveillance applications, it is important to use a mobility management scheme that can empower nodes to make better decisions regarding their positions such that strategic tasks such as target tracking can benefit from node movement. In this chapter, we describe a purposeful and distributed mobility management scheme for mobile sensor networks. The proposed scheme considers node movement decisions as part of a distributed optimization problem, which integrates mobility-enhanced improvement in the quality of target tracking data with the associated negative consequences of increased energy consumption due to locomotion, potential loss of network connectivity, and loss of sensing coverage.

In wireless sensor networks, sensors are often deployed without a priori knowledge of their locations or sensor node locations can change during the lifetime of a network. However, location information is essential for a variety of reasons. Sensors monitor phenomena in the physical world and given the location of the sensors, it is then possible to estimate the location of the observed phenomenon. For example, chemical and humidity sensors deployed on a farm can provide information about soil moisture, crop health, and animal movement if the sensor locations are known. Accurate location information is also needed for various sensor network management tasks such as routing based on geographic information, object tracking, and providing location-aware services. Frequently, sensor node localization is performed using ranging techniques, where the distances between a sensor device and several known reference points are determined to derive the position of a sensor. However, the cost and limitations of the hardware needed for range-based localization schemes often make them poor choices for WSNs. Therefore, a variety of localization protocols have been proposed that attempt to avoid the use of ranging techniques with the goal to provide more cost-effective and simpler alternatives. These range-free localization techniques estimate a node’s position using either neighborhood information, hop counts from well-known anchor points, or information derived from the area a node is believed to reside in. This chapter introduces the basic concepts of range-free localization, surveys a variety of state-of-the-art localization techniques, compares qualitatively the characteristics of these protocols, and discusses current research directions in range-free localization.

In numerous applications of wireless sensor networks, the reliability of the data collected by sensors is cast as specific QoS requirements expressed in terms of the minimum number of sensors needed to perform various tasks. Designing a long-lived sensor network with reliable performance has always been challenging due to the modest non-renewable energy budget of individual sensors. In order to promote network longevity, this chapter looks at two energy-aware task management protocols: the first protocol is centralized, while the second one is fully distributed. Both protocols assign sensors to tasks based on their remaining energy so that energy expenditure among neighboring sensors is as even as possible. We compare the network longevity, i.e., the functional lifetime of the sensor network, achieved by assigning tasks to sensors using the proposed protocols against an optimal task assignment and also against energy-oblivious protocols. Extensive simulation results have revealed that the performance of the proposed protocols is very close to that of the optimal task assignment. Furthermore, our simulations have shown that the proposed protocols can increase the functional longevity of the network by about 16 %.

In this chapter, we provide a data management perspective on large-scale sensor environments applications posing non-functional requirements to meet the underlying timeliness, reliability and accuracy needs in addition to the functional needs of data collection. Due to the large-scale regional spread, we need methods that will allow scaling of today’s systems to large-scale deployments. Our data management techniques have solved a fundamental challenge in such situations, that is the ability to handle the explosion of sensor data in sensor networks, either due to scaling of the network or due to increased data generation by highly capable and “media-rich” nodes.

Sensor networks collect data from their environment. Locations of the sensors are an important attribute of that information and provide a context useful to understand, and use sensor data. In this chapter, we will discuss geometric ideas to organize sensor data by using their locations. We will consider distributed methods for managing queries about isolated events, queries about mobile objects, and queries for general signal fields. Location-based methods often require suitable simple geometric domains to operate, and we will discuss how they can be adapted to networks with complex shapes.

In this chapter we give an overview of the different components needed in an end-to-end wireless sensor network monitoring system. Using two case studies with vastly different data throughput we show selected examples of components commonly found in data collection networks and use actual deployments as motivating examples. Specifically, the Life Under Your Feet soil monitoring project focus on extreme duty-cycling and low data rate communications while the data center monitoring network RACNet emphasizes high throughput and efficient channel utilization.

Data gathering is one of the primary operations carried out in Wireless Sensor Networks (WSNs). It involves data collection with aggregation and data collection without aggregation, referred to as data aggregation and data collection respectively. In the last decade, many techniques for these two applications are proposed, with different focuses, such as accuracy, reliability, time complexity, and so on. This chapter reviews the state of the art of data aggregation and data collection techniques in order to present a comprehensive guidance on how to choose a more appropriate approach for different applications. The definitions of data aggregation and data collection are firstly introduced. Subsequently, the challenges of designing effective data aggregation and data collection methods are discussed. Then some typical data aggregation techniques and their classifications are presented. Particularly, a latest distributed data aggregation algorithm (DAS) is illustrated in details. For data collection, we begin with some new advances and then introduce several new tree-based and cell-based data collection algorithms. Finally, this chapter is ended by pointing out some possible future research directions.

Recent advances in MEMS hardware have enabled small-footprint and inexpensive sensors to be deployed in hard-to-access locations and to form wireless sensor networks (WSNs). WSNs are typically mission-oriented networks and offer appealing solutions to a range of practical problems. However, due to the characteristics of WSN, their design principles differ from other types of networks. For instance, the severe limitations of computational and energy resources in the network nodes restrict their ability to process and communicate information. These characteristics, particular to WSNs, dictate new security challenges and require new approaches to implementation of security protocols. In this chapter, we present some of the WSNs security challenges and discuss a number of selected solutions presented in the technical literature. The structure of the chapter is as follows. In Sect. 1, we provide background material on WSN security; in particular, we present the security goals, implementation constraints, potential attacks and defenses, and evaluation benchmarks. In Sect. 2, we discuss basic security challenges and approaches, including cryptography schemes, key management schemes, and attack detection and prevention mechanisms. Then, in Sects. 3, 4, and 5, we discuss secure routing, secure localization, and secure data aggregation, respectively. Finally, we conclude the survey in Sect. 6.

Since wireless sensor networks (WSNs) are vulnerable to malicious attacks due to their characteristics, privacy is a critical issue in many WSN applications. In this chapter, we discuss existing privacy enhancing technologies designed for protecting system privacy, data privacy and context privacy in wireless sensor networks (WSNs). The privacy-preserving techniques for the system privacy hide the information about the location of source nodes and the location of receiver nodes. The data privacy techniques mainly protect the privacy of data content and in-network data aggregation. The context privacy refers to location privacy of users and the temporal privacy of events. For each of these three kinds of privacy in WSNs, we describe its threats and illustrate its existing privacy-preserving techniques. More importantly, we make comparisons between different techniques and indicate their strengths and weaknesses. We also discuss possible improvement, thus highlighting some research trends in this area.

In this chapter we examine the rapid advances that have occurred recently in the wireless sensor networks (WSNs) domain and argue that intelligent middleware is needed to tackle the challenges brought by these changes. We present an overview on existing design approaches for WSN middleware, as well as the most common middleware services and programming abstractions. We describe key features that must be incorporated in middleware for the current generation wireless sensor networks and conclude the chapter with a discussion on new issues and future trends in the design of WSN middleware.

In this chapter we present an emerging approach to develop systems for WSN, named Service-Oriented Middleware (SOM), in which the WSN is logically viewed as a service provider for consumer applications. SOM provides abstractions for the complex underlying WSN through a set of generic and/or application-specific services. Services as data aggregation, adaptation, security, self-organization, resource management as well as other advanced services can be designed, implemented, and integrated in an SOM framework to provide a flexible and easy environment to develop effective WSN applications. Moreover, the intrinsically decoupled nature of the various components involved in a service-oriented architecture promotes interoperability between service providers and consumers. The adoption of service oriented approach provides WSN users with a unified protocol to access and communicate with the WSN components and developers with a flexible programing model to build efficient and scalable WSN systems. Besides presenting the basic concepts of SOM development and discussing the potential benefits of such approach in building WSM middleware systems, in this chapter we also present a concrete example of a WSN SOM to further illustrate the described features.

The emergence of resource constrained embedded systems such as sensor networks have introduced unique challenges for the design and implementation of operating systems. In OS designs for these systems, only partial functionality is required compared to conventional ones, as their code is running on a much more restricted and homogeneous platform. In fact, as illustrated by microcontrollers, most hardware platforms in wireless sensor networks (WSNs) simply do not have the required resources to support a full-fledged operating system. Instead, operating systems for WSNs should adapt to their unique properties, which motivate the design and development of a range of unique operating systems for WSNs in recent years. In this chapter, we systematically survey these operating systems, compare them in their unique designs, and provide our insights on their strengths and weaknesses. We hope that such an approach is helpful for the reader to get a clear view of recent developments of wireless sensor network operating systems.

Wireless sensor networks offer many advantages in different application areas with its ease of deployment, low-cost, low-power capabilities. With the increased interest to the wireless sensor networks, the research community have started to carry out network simulations to better analyze the network’s behavior and performance since they provide significant reduction in cost and simulate the different types of sceneries in tolerable time intervals. This chapter introduces the network simulators, i.e., NS-2, OMNET++, J-Sim, OPNET and TOSSIM, respectively with some of the major network programming languages, e.g., nesC and Mate.

Wireless sensor network is composed of a collection of sensor nodes that sense the physical phenomena for further analysis purposes. ZigBee, WirelessHART, 6LoWPAN and ISA.100.11a are some of the wireless sensor network technologies that are presented throughout this chapter with the details of their network structure, protocol layers, key characteristics and application areas.