Beginnings

What is an algorithm? The word is derived from the name of the Persian scholar Mohammad Al-Khowarizmi (see B.5.1 and Fig. 5.1). In the introduction to his classic book Algorithmics: The Spirit of Computing, computer scientist David Harel gives the following definition:

Thus an algorithm can be regarded as a “recipe” detailing the mathematical steps to follow to do a particular task. This could be a numerical algorithm for solving a differential equation or an algorithm for completing a more abstract task, such as sorting a list of items according to some specified property. The word algorithmics was introduced by J. F. Traub in a textbook in 1964 and popularized as a key field of study in computer science by Donald Knuth (B.5.2) and David Harel (B.5.3). When the steps to define an algorithm to carry out a particular task have been identified, the programmer chooses a programming language to express the algorithm in a form that the computer can understand.

In 2012 the U.S. National Research Council published the report “Continuing Innovation in Information Technology.” The report contained an updated version of the Tire Tracks figure, first published in 1995. Figure A.2 gives examples of how computer science research, in universities and in industry, has directly led to the introduction of entirely new categories of products that have ultimately provided the basis for new billion-dollar industries. Most of the university-based research has been federally funded.

The bottom row of the figure shows specific computer science research areas where major investments have resulted in the different information technology industries and companies shown at the top of the figure. The vertical red tracks represent university-based research and the blue tracks represent industry research and development. The dashed black lines indicate periods following the introduction of significant commercial products resulting from this research, and the green lines represent the establishment of billion-dollar industries with the thick green lines showing the achievement of multibillion-dollar markets by some of these industries.

Early visions

The British science fiction writer, Brian Aldiss, traces the origin of science fiction to Mary Shelley’s Frankenstein in 1818. In her book, the unwise scientist, Victor Frankenstein, deliberately makes use of his knowledge of anatomy, chemistry, electricity, and physiology to create a living creature. An alternative starting point dates back to the second half of the nineteenth century with the writing of Jules Verne (B.17.1) and Herbert George (H. G.) Wells (B.17.2). This was a very exciting time for science – in 1859 Charles Darwin had published the Origin of Species; in 1864 James Clerk Maxwell had unified the theories of electricity and magnetism; and in 1869 Mendeleev had brought some order to chemistry with his Periodic Table of the Elements, and Joule and Kelvin were laying the foundations of thermodynamics. Verne had the idea of combining modern science with an adventure story to create a new type of fiction. After publishing his first such story “Five Weeks in a Balloon” in 1863, he wrote:

Inspirations

There are many “popular” books on science that provide accessible accounts of the recent developments of modern science for the general reader. However, there are very few popular books about computer science – arguably the “science” that has changed the world the most in the last half century. This book is an attempt to address this imbalance and to present an accessible account of the origins and foundations of computer science. In brief, the goal of this book is to explain how computers work, how we arrived at where we are now, and where we are likely to be going in the future.

The key inspiration for writing this book came from Physics Nobel Prize recipient Richard Feynman. In his lifetime, Feynman was one of the few physicists well known to a more general public. There were three main reasons for this recognition. First, there were some wonderful British television programs of Feynman talking about his love for physics. Second, there was his best-selling book “Surely You’re Joking, Mr. Feynman!”: Adventures of a Curious Character, an entertaining collection of stories about his life in physics – from his experiences at Los Alamos and the Manhattan atomic bomb project, to his days as a professor at Cornell and Caltech. And third, when he was battling the cancer that eventually took his life, was his participation in the enquiry following the Challenger space shuttle disaster. His live demonstration, at a televised press conference, of the effects of freezing water on the rubber O-rings of the space shuttle booster rockets was a wonderfully understandable explanation of the origin of the disaster.

The first computer games

Since the earliest days, computers have been used for serious purposes and for fun. When computing resources were scarce and expensive, using computers for games was frowned upon and was typically an illicit occupation of graduate students late at night. Yet from these first clandestine experiments, computer video games are now big business. In 2012, global video game sales grew by more than 10 percent to more than $65 billion. In the United States, a 2011 survey found that more than 90 percent of children aged between two and seventeen played video games. In addition, the Entertainment Software Association in the United States estimated that 40 percent of all game players are now women and that women over the age of eighteen make up a third of the total game-playing population. In this chapter we take a look at how this multibillion-dollar industry began and how video games have evolved from male-dominated “shoot ’em up” arcade games to more family-friendly casual games on smart phones and tablets.

One of the first computer games was written for the EDSAC computer at Cambridge University in 1952. Graduate student Alexander Douglas used a computer game as an illustration for his PhD dissertation on human-computer interaction. The game was based on the game called tic-tac-toe in the United States and noughts and crosses in the United Kingdom. Although Douglas did not name his game, computer historian Martin Campbell-Kelly saved the game in a file called OXO for his simulator program, and this name now seems to have escaped into the wild. The player competed against the computer, and output was programmed to appear on the computer’s cathode ray tube (CRT) as a display screen. The source code was short and, predictably, the computer could play a perfect game of tic-tac-toe (Fig. 9.1).

The network is the computer

Today, with the Internet and World Wide Web, it seems very obvious that computers become much more powerful in all sorts of ways if they are connected together. In the 1970s this result was not so obvious. This chapter is about how the Internet of today came about. As we can see from Licklider’s (B.10.1) quotation beginning this chapter, in addition to arguing for the importance of interactive computing in his 1960 paper on “Man-Computer Symbiosis,” Lick also envisaged linking computers together, a practice we now call computer networking. Larry Roberts, Bob Taylor’s hand-picked successor at the Department of Defense’s Advanced Research Projects Agency (ARPA), was the person responsible for funding and overseeing the construction of the ARPANET, the first North American wide area network (WAN). A WAN links together computers over a large geographic area, such as a state or country, enabling the linked computers to share resources and exchange information.

Performance evaluation of Wireless Sensor Networks (WSNs), like any communications network system, can be conducted by using simulation analysis, analytic modeling, and measurement/testing techniques. Evaluation of WSN systems is needed at every stage in their life. There is no point in designing and implementing a new system that does not have competitive performance, and does not meet the objectives and performance evaluation and quality of service requirements. Performance evaluation of an existing system is also important since it helps to determine how well it is performing and whether any improvements are needed in order to enhance the performance [1].

After a system has been built and is running, its performance can be assessed by using the measurement technique. In order to evaluate the performance of a component or subsystem that cannot be measured, for example, during the design and development phases, it is necessary to use analytic and/or simulation modeling so as to predict the performance [1–46].

The objective of this chapter is to provide an up-to-date treatment of the techniques that can be used to evaluate the performance of WSN systems.

Background information

Wireless sensor networks (WSNs) are unique in certain aspects that make them different from other wireless networks. These aspects include: