Part V Advanced Programming Techniques in Java

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thread. The advent of second-generation wireless microsensor nodes such as the MICA2 motes, which have 4 kB of SRAM, has enabled the development of complete multithreaded systems such as MOS capable of supporting four to eight application threads simultaneously. The RAM in current systems is suf cient to support multiple stacks, with the expectation that from this point forward that Moore s law as applied to RAM will make multithreading increasingly affordable on microsensor nodes. Context switch time will continue to remain an issue, though at present our experience has not found this to be a signi cant problem in the sensing and forwarding applications that have been developed on MOS. Multithreaded systems additionally must be adapted to be energy-ef cient. MOS implements power management such that when there are no more threads with meaningful execution, the scheduler sleeps the system rather than have the idle thread spin in a continuous loop. The duration of sleep is determined from the hints provided by each application thread, namely, the argument it provided to the sleep( ) call. The complexity of power managmenet is largely hidden from the user except through the sleep( ) API. Both multithreaded and event-driven systems also must handle synchronization issues introduced by concurrency. When there is concurrent execution in a multithreaded system, it is important that code be synchronized in a thread-safe manner. MOS provides mutual exclusion primitives, which can be used to build thread-safe code. Provided the programmer is furnished with reentrant libraries, then synchronization complexity can be reduced for the application programmer. However, the designer in charge of the multithreaded operating system must then assume the burden and complexity of realizing thread safety while avoiding deadlock and race conditions. An event-driven system such as TinyOS also faces similar synchronization issues. TinyOS introduced atomic operations and the async keyword to address race conditions [31]. This is limited to hardware event handlers. These operations seek to guarantee correct concurrency in an event-driven model. As sensor networks evolve, we expect to see increasing heterogeneity, with nodes of different capabilities; for example, aggregators differ from leaf nodes in purpose and capabilities. Thus, future sensor networks may consist of mixtures of nodes with multithreaded and event-driven sensor operating systems. Recent research has also investigated combining the best features of multithreaded and event-driven systems in a new sensor operating system [32]. 6.9 CONCLUSION
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You can t, however, perform any manipulation on dates that are actually text. For example, you can t change its formatting, you can t determine which day of the week this date occurred on, and you can t calculate the date that occurs seven days later.
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In ref. [14], the following framework and general algorithm were established for reliable broadcasting. The algorithm is based on two concepts: CDS as the particular type of backbone that provides reliability, and neighbor-elimination scheme. Backbone formation was already discussed in Section 11.2. Connectivity provides propagation through the whole network, while domination assures reachability by all nodes. Excess messages in any protocol affect node power and the bandwidth available; thus, the main goal is to describe a reliable broadcast protocol with a minimal number of retransmissions, that is, to construct a connected dominating set of minimal size. The neighbor-elimination scheme was independently proposed in three papers in August 2000 [33 35]. In this scheme, a node does not need to rebroadcast a message if all its neighbors have been covered by previous transmissions. After each copy of the same message is received, a node eliminates, from its rebroadcast list, neighbors that are assumed to have correctly received the same message (based on one-hop positional or 2-hop topological knowledge that the node has about its neighbors). If the list becomes empty before the node decides to rebroadcast, the rebroadcasting is canceled. The general algorithm [14] for intelligent ooding is then the following one. The source node transmits the packet. Upon receiving the rst copy of the transmitted packet intended for broadcasting, the node will not retransmit it if it is not in the CDS. If it is in the CDS, it will select a time-out based on some criteria and some random number. It will also eliminate all neighbors that received the same copy of the message from its forwarding list (originally containing all one-hop neighbors).
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Figure 2-7 shows the details of the 10 bytes that make up the power systems sensor packet group. Roomba spends a lot of time thinking about power, and that is reflected in this ROI
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When it comes to network security, there is no single solution, but there is one technology that comes close. That technology is cryptography, often referred to as simply crypto. Ironically, few companies use crypto in their security strategy because they don t really understand it or its potential. Cryptography encodes information in such a way that nobody can read it, except the person who holds the key. Just think about that for a second. If all of your information was encoded in this way, how much better would your security be With cryptography in place, if someone intercepted your communication or accessed your server, he or she would not be able to read any of the information contained there. If an attacker can t read the information, he or she has struck out. So if cryptography is that effective, why don t people use it all the time The answer isn t obvious because cryptography is a very complex and broad topic, one on which several books have been written. In this chapter I will cover some of the key concepts and principles that you need to know to understand how cryptography might or might not apply to your own need for secure communication.
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void drawRoombaStatus(float posx, float posy, float angle) { pushMatrix(); translate((int)posx,(int)posy); rotate(angle); smooth(); textFont(fontA, 10); textAlign(CENTER); fill(cBack); strokeWeight(2); stroke(100); ellipseMode(CENTER_RADIUS); ellipse(0,0, 50,50); fill(0); stroke(cTxt); strokeWeight(4); text( cliff , 0, -30); if( roombacomm.cliffLeft() ) stroke(cOn); else stroke(cOff); line(-35,-25, -40,-15); if( roombacomm.cliffRight() ) stroke(cOn); else stroke(cOff); line( 35,-25, 40,-15); if( roombacomm.cliffFrontLeft() ) stroke(cOn); else stroke(cOff); line(-30,-30, -20,-30); if( roombacomm.cliffFrontRight() ) stroke(cOn); else stroke(cOff); line( 30,-30, 20,-30); stroke(cTxt); strokeWeight(7); text( bump , 0,-17); if( roombacomm.bumpLeft() ) stroke(cOn); else stroke(cOff); line(-25,-20, -20,-20); if( roombacomm.bumpRight() ) stroke(cOn); else stroke(cOff);
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RAID Technology Redundant Array of Independent Disks (RAID) provides certain optional features that can improve performance for high-demand audio and video processing. RAID functionality is provided either as an add-in card or as an optional integrated motherboard feature. Only specifically marked motherboard packages are capable of RAID, and these packages usually carry a premium. The intrepid builder who places the utmost emphasis on pure performance should consider RAID performance benefits for inclusion in his or her build. RAID comes in a variety of levels and configurations, most of which are beyond the scope of this book. Only the applicable variations earn mention herein:
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