Gathering data concerning the physical world is at many levels a progressive exercise in uncertainty reduction. There is uncertainty in the signal propagation model, the calibration of the sensor, the model of the phenomenon, and in the communications process. A system that can add resources where uncertainty is greatest can potentially achieve a similar level of uncertainty reduction as a network where nodes are uniformly deployed at high density. The more varied the environment and the less certain the initial models the greater the potential benefit of mobility of sensing and communication resources. This chapter is concerned with the interaction of mobile and static nodes, with mobility of three types considered:

• articulation of elements such as directional antennas, photovoltaic panels, or sensors to gain better position;

• use of freely mobile nodes that may act to supply a static network or provide mobile communication or sensing means;

• infrastructure to support mobility, communications, and energy distribution and the implications for static or freely mobile elements.

Our scope does not include details of how mobile elements can actually be made to work in an autonomous fashion whether individually or in teams; this requires far more detail than can be provided here. Our focus is rather on how the fact of mobility in some combination of the above forms can fundamentally change the sensor network problem set.

Articulation

Articulation of a mechanical device consists of some combination of rotation and extension/retraction.

Spatio-temporal relationships for physical phenomena are critical observation features, whether for point or distributed sources. To determine the location and time of events, and how they evolve in space and time, the sensors must know their own position and the time. Many techniques for determining location in turn depend on having precise time references. Thus the two topics of localization and synchronization are closely connected. This chapter begins with an overview of techniques for determining position, assuming synchronism is available. The next section explores how synchronism can be obtained in a network, with the following section discussing how position can be determined in a network. The chapter concludes with a brief discussion of sources of error and how they can be mitigated.

Principles of location

Location, the computation of position, has historically been considered as a component of surveying or navigation. In either case, known reference points are used to compute the present position. In surveying, this allows new reference points to be constructed, enabling map-making. In navigation, the objective is to chart a course using references or a map. Both celestial and land references have been used, and more recently electronic beacons and satellites have been constructed to aid both tasks.

Triangulation

References are required for orientation (to set up the coordinate axes) and position. Traditional survey instruments establish the direction of gravity, and measure angles in azimuth (the horizontal plane), elevation, or both.

The paramount logistical issues in the deployment of sensor networks over extended time periods are the establishment of reliable communication networks and the provision of energy to operate the system. This chapter is concerned with energy issues: sources, energy consumption for particular operations, and strategies for maximizing the network usefulness subject to energy resource constraints. Without careful attention to energy issues at the levels of both nodes and networks, deployment scales and lifetimes can be sharply limited. In battery-powered devices, each bit communicated or processed brings a node closer to its death. In other situations the power supply may be limited, motivating the choice of lower-energy means of accomplishing the network objectives.

Energy sources

Many different types of energy source are available to networks of embedded devices. Table 10.1 lists some of the possibilities, and their electrical power generation potential. Table 10.2 compares available electrical energy per unit mass of batteries with the chemical energy of various fuels.

These tables show that batteries are quite good power supplies: the power density they can supply is within a factor of 1000 that of nuclear reactions and within a factor of 3–10 of fuel cells depending on the technology. Moreover, while methanol's energy density is 30 times that of the best batteries, clearly energy conversion efficiencies considerably lower the gap particularly if low-temperature operation is required and the weight or volume of an energy conversion device is considered.

The basic idea of multicarrier modulation is to divide the transmitted bitstream into many different substreams and send these over many different subchannels. Typically the subchannels are orthogonal under ideal propagation conditions. The data rate on each of the subchannels is much less than the total data rate, and the corresponding subchannel bandwidth is much less than the total system bandwidth. The number of substreams is chosen to ensure that each subchannel has a bandwidth less than the coherence bandwidth of the channel, so the subchannels experience relatively flat fading. Thus, the intersymbol interference on each subchannel is small. The subchannels in multicarrier modulation need not be contiguous, so a large continuous block of spectrum is not needed for high-rate multicarrier communications. Moreover, multicarrier modulation is efficiently implemented digitally. In this discrete implementation, called orthogonal frequency division multiplexing (OFDM), the ISI can be completely eliminated through the use of a cyclic prefix.

Multicarrier modulation is currently used in many wireless systems. However, it is not a new technique: it was first used for military HF radios in the late 1950s and early 1960s. Starting around 1990, multicarrier modulation has been used in many diverse wired and wireless applications, including digital audio and video broadcasting in Europe, digital subscriber lines (DSL) using discrete multitone, and the most recent generation of wireless LANs.

In multiuser systems the system resources must be divided among multiple users. This chapter develops techniques to allocate resources among multiple users and examines the fundamental capacity limits of multiuser systems. We know from Section 5.1.2 that signals of bandwidth B and time duration T occupy a signal space of dimension 2BT. In order to support multiple users, the signal space dimensions of a multiuser system must be allocated to the different users. Allocation of signaling dimensions to specific users is called multiple access. Multiple access methods perform differently in different multiuser channels, and we will apply these methods to the two basic multiuser channels: downlink channels and uplink channels. Because signaling dimensions can be allocated to different users in an infinite number of different ways, multiuser channel capacity is defined by a rate region rather than a single number. This region describes all user rates that can be simultaneously supported by the channel with arbitrarily small error probability. We will discuss multiuser channel capacity regions for both the uplink and the downlink. We also consider random access techniques, whereby signaling dimensions are allocated only to active users, as well as power control, which ensures that users maintain the SINR required for acceptable performance. The performance benefits of multiuser diversity, which exploits the time-varying nature of the users' channels, is also described. We conclude with a discussion of the performance gains and signaling techniques associated with multiple antennas in multiuser systems.

Many signals in communication systems are real bandpass signals with a frequency response that occupies a narrow bandwidth 2B centered around a carrier frequency fc with 2B « fc, as shown in Figure A.1. Since bandpass signals are real, their frequency response has conjugate symmetry: a bandpass signal s(t) has |S(f)| = |S(−f)| and ∠S(f) = −∠S(−f). However, bandpass signals are not necessarily conjugate symmetric within the signal bandwidth about the carrier frequency fc; that is, we may have |S(fc + f)| ≠ |S(fc − f)| or ∠S(fc + f) ≠ −∠S(fc − f) for some f : 0 < f ≤ B. This asymmetry in |S(f)| about fc (i.e., |S(fc + f)| ≠ |S(fc − f)| for some f < B) is illustrated in the figure. Bandpass signals result from modulation of a baseband signal by a carrier, or from filtering a deterministic or random signal with a bandpass filter. The bandwidth 2B of a bandpass signal is roughly equal to the range of frequencies around fc where the signal has nonnegligible amplitude. Bandpass signals are commonly used to model transmitted and received signals in communication systems. These are real signals because the transmitter circuitry can only generate real sinusoids (not complex exponentials), and the channel simply introduces an amplitude and phase change at each frequency of the real transmitted signal.

Although bandwidth is a valuable commodity in wireless systems, increasing the transmit signal bandwidth can sometimes improve performance. Spread spectrum is a technique that increases signal bandwidth beyond the minimum necessary for data communication. There are many reasons for doing this. Spread-spectrum techniques can hide a signal below the noise floor, making it difficult to detect. Spread spectrum also mitigates the performance degradation due to intersymbol and narrowband interference. In conjunction with a RAKE receiver, spread spectrum can provide coherent combining of different multipath components. Spread spectrum also allows multiple users to share the same signal bandwidth, since spread signals can be superimposed on top of each other and demodulated with minimal interference between them. Finally, the wide bandwidth of spread-spectrum signals is useful for location and timing acquisition.

Spread spectrum first achieved widespread use in military applications because of its inherent property of hiding the spread signal below the noise floor during transmission, its resistance to narrowband jamming and interference, and its low probability of detection and interception. For commercial applications, the narrowband interference resistance has made spread spectrum common in cordless phones. The ISI rejection and bandwidth-sharing capabilities of spread spectrum are very desirable in cellular systems and wireless LANs. As a result, spread spectrum is the basis for both second- and third-generation cellular systems as well as second-generation wireless LANs.

The growing demand for wireless communication makes it important to determine the capacity limits of the underlying channels for these systems. These capacity limits dictate the maximum data rates that can be transmitted over wireless channels with asymptotically small error probability, assuming no constraints on delay or complexity of the encoder and decoder. The mathematical theory of communication underlying channel capacity was pioneered by Claude Shannon in the late 1940s. This theory is based on the notion of mutual information between the input and output of a channel. In particular, Shannon defined channel capacity as the channel's mutual information maximized over all possible input distributions. The significance of this mathematical construct was Shannon's coding theorem and its converse. The coding theorem proved that a code did exist that could achieve a data rate close to capacity with negligible probability of error. The converse proved that any data rate higher than capacity could not be achieved without an error probability bounded away from zero. Shannon's ideas were quite revolutionary at the time: the high data rates he predicted for telephone channels, and his notion that coding could reduce error probability without reducing data rate or causing bandwidth expansion. In time, sophisticated modulation and coding technology validated Shannon's theory and so, on telephone lines today, we achieve data rates very close to Shannon capacity with very low probability of error.

The wireless radio channel poses a severe challenge as a medium for reliable high-speed communication. Not only is it susceptible to noise, interference, and other channel impediments, but these impediments change over time in unpredictable ways as a result of user movement and environment dynamics. In this chapter we characterize the variation in received signal power over distance due to path loss and shadowing. Path loss is caused by dissipation of the power radiated by the transmitter as well as by effects of the propagation channel. Path-loss models generally assume that path loss is the same at a given transmit–receive distance (assuming that the path-loss model does not include shadowing effects). Shadowing is caused by obstacles between the transmitter and receiver that attenuate signal power through absorption, reflection, scattering, and diffraction. When the attenuation is strong, the signal is blocked. Received power variation due to path loss occurs over long distances (100–1000 m), whereas variation due to shadowing occurs over distances that are proportional to the length of the obstructing object (10–100 m in outdoor environments and less in indoor environments). Since variations in received power due to path loss and shadowing occur over relatively large distances, these variations are sometimes referred to as large-scale propagation effects. Chapter 3 will deal with received power variations due to the constructive and destructive addition of multipath signal components.

The advances over the last several decades in hardware and digital signal processing have made digital transceivers much cheaper, faster, and more power efficient than analog transceivers. More importantly, digital modulation offers a number of other advantages over analog modulation, including higher spectral efficiency, powerful error correction techniques, resistance to channel impairments, more efficient multiple access strategies, and better security and privacy. Specifically, high-level digital modulation techniques such as MQAM allow much more efficient use of spectrum than is possible with analog modulation. Advances in coding and coded modulation applied to digital signaling make the signal much less susceptible to noise and fading, and equalization or multicarrier techniques can be used to mitigate intersymbol interference (ISI). Spread-spectrum techniques applied to digital modulation can simultaneously remove or combine multipath, resist interference, and detect multiple users. Finally, digital modulation is much easier to encrypt, resulting in a higher level of security and privacy for digital systems. For all these reasons, systems currently being built or proposed for wireless applications are all digital systems.

Digital modulation and detection consist of transferring information in the form of bits over a communication channel. The bits are binary digits taking on the values of either 1 or 0. These information bits are derived from the information source, which may be a digital source or an analog source that has been passed through an A/D converter.

This chapter summarizes the technical details associated with the two most prevalent wireless systems in operation today: cellular phones and wireless LANs. It also summarizes the specifications for three short range wireless network standards that have emerged to support a broad range of applications. More details on wireless standards can be found in.

Cellular Phone Standards

First-Generation Analog Systems

In this section we summarize cellular phone standards. We begin with the standards for first-generation (1G) analog cellular phones, whose main characteristics are summarized in Table D.1. Systems based on these standards were widely deployed in the 1980s. While many of these systems have been replaced by digital cellular systems, there are many places throughout the world where these analog systems are still in use. The best known standard is the Advanced Mobile Phone Service (AMPS), developed by Bell Labs in the 1970s and first used commercially in the United States in 1983. After its U.S. deployment, many other countries adopted AMPS as well. This system has a narrowband version, narrowband AMPS (N-AMPS), with voice channels that are one third the bandwidth of regular AMPS. Japan deployed the first commercial cellular phone system in 1979 with the NTT (MCS-L1) standard based on AMPS, but at a higher frequency and with voice channels of slightly lower bandwidth. Europe also developed a similar standard to AMPS called the Total Access Communication System (TACS), which operates at a higher frequency and with smaller bandwidth channels than AMPS.

In this chapter we examine fading models for the constructive and destructive addition of different multipath components introduced by the channel. Although these multipath effects are captured in the ray-tracing models from Chapter 2 for deterministic channels, in practice deterministic channel models are rarely available and so we must characterize multipath channels statistically. In this chapter we model the multipath channel by a random time-varying impulse response. We will develop a statistical characterization of this channel model and describe its important properties.

If a single pulse is transmitted over a multipath channel then the received signal will appear as a pulse train, with each pulse in the train corresponding to the line-of-sight component or a distinct multipath component associated with a distinct scatterer or cluster of scatterers. The time delay spread of a multipath channel can result in significant distortion of the received signal. This delay spread equals the time delay between the arrival of the first received signal component (LOS or multipath) and the last received signal component associated with a single transmitted pulse. If the delay spread is small compared to the inverse of the signal bandwidth, then there is little time spreading in the received signal. However, if the delay spread is relatively large then there is significant time spreading of the received signal, which can lead to substantial signal distortion.