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In case of both SeRLoc as well as HiRLoc, an attacker can try to compromise a threshold number of anchors As a result an attacker can successfully cause faulty estimation of the locations of the system nodes In addition an attacker can also interfere with the localization process in both SeRLoc and HiRLoc by selectively jamming transmissions of locators Jamming resistance has been added to HiRLoc and the resulting proposal is explained in Section 735 Jamming resistance is achieved by using more complex hardware, which is capable of nanosecond precision 734 Transmission Range Variation-Based Schemes

In this chapter we have seen how the Wonder Troops created a new front end for the system they have been developing They took the look and feel of the ASP based web site and added it to their system They also added new functionality that did not exist in their original system, namely the ability to order custom-made pizzas and beverages over the internet In the next chapter, we will introduce the Java Message Service (JMS) This will be used to improve the communication of orders between the Pizza2Go centers and the pizza outlets Currently Pizza2Go just sends a fax to the outlet and E-Pizza sends an e-mail message These mechanisms will be enhanced using JMS point-to-point messaging Plus JMS publish-and-subscribe will give us the opportunity to create an interesting example of a loyalty scheme

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In this chapter we will be reviewing the basic concepts of messaging and the Java Message Service (JMS) API We will then review two examples that use JMS and enhance the application that has been developed in the previous chapters

10 G A Korn, Model replication techniques for parameter-influence studies and Monte Carlo simulation with random parameters, Math and Computers in Simulation, 67: No6, 2004, pp 501 513 11 G A Korn, Fast Monte Carlo simulation of noisy dynamic systems on small digital computers, Simulation News Europe, December 2002 12 G A Korn, Real statistical experiments can use simulation-package software, Simulation Practice and Theory, 13, 2005, pp 39 54

Messaging is generally considered to be a mechanism that allows communication between two or more computer programs Under this definition, pretty much anything can be considered a messaging system, from a simple exchange of TCP/IP packets to the communication between a client program and a database Rather than creating a specific messaging mechanism for every situation that arises, a number of software vendors have created systems that allow passing messages between different applications in a generic fashion These systems are called Message Oriented Middleware (MOM) They define a message structure and the protocol by which the applications send and receive messages One of the advantages of a MOM system is that it decouples the message producers from the message consumers; in this way they can operate independently of each other One side effect of this is that this architecture can scale more than one where producers and consumers are communicating directly Another important feature offered by MOM systems is the concept of Guaranteed Message Delivery (GMD) where, at the price of decreased performance, the messages are stored in a database before being delivered to the consumers This way, if the consumer is not available for whatever reason, they can receive the messages when they become available There are two different models in which MOM systems can handle messages

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COMPUTER MODELS OF NOISE PROCESSES 5-1 Noise in DYNAMIC Program Segments 4 described how experiment-protocol scripts use the library function ran() to produce random parameters and random initial conditions More general random-process models also need random functions of the time in DYNAMIC program segments Modeling continuous noise on digital computers raises problems because (1) pseudorandom noise is inherently discontinuous, and (2) models of wideband noise need very large numbers of pseudorandom-noise samples We will first study sampled-data processes and then go on to continuous-noise models 5-2 Sampled-data Random Processes

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