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In the static priority scheme that I have just described, the scheduler never actually examines the deadlines of any tasks. It simply uses a xed ordering specifying which tasks are generally more urgent than which others. However this is suboptimal, as illustrated by the following example: Task 1 runs for 50 ms every 100 ms, deadline 100 ms Task 2 runs for 60 ms every 140 ms, deadline 140 ms. This cannot be scheduled by any static priority scheme. To see this, consider what happens when both tasks are triggered at the same time. If task 1 has higher priority, the response time for task 2 is 160 ms, since task 1 runs twice before task 2 completes. If task 2 has higher priority, the response time for task 1 is 110 ms. This task set is illustrated in Figure 17.3. This task set can be scheduled by a dynamic priority scheme. In fact the Liu and Layland paper also proves that earliest deadline rst (EDF) scheduling is optimal. In this algorithm, whenever a scheduling decision is required the task with the deadline closest to expiry is selected. Note

An adaptive strategy for quality-based data reduction in wireless o sensor networks In Proceedings of the 3rd International Conference on Networked Sensing Systems (INSS 2006), Chicago, IL, June 2006, TRF, pp 29 36, ISBN 0-9743611-3-5 72 A Woo, T Tong, and D Culler Taming the underlying challenges of reliable multihop routing in sensor networks In Akyildiz et al [81], pp 14 27, ISBN 1-58113-707-9 73 E F Nakamura, F G Nakamura, C M S Figueiredo, and A A F Loureiro Using information fusion to assist data dissemination in wireless sensor networks Telecommunication Systems, 30(1 3):237 254, 2005, ISSN 1018-4864 74 C M S Figueiredo, E F Nakamura, and A A F Loureiro An event-detection estimation model for hybrid adaptive routing in wireless sensor networks In Proceedings of the 2007 IEEE International Conference on Communications (ICC 07), Glasgow, Scotland, May 2007, IEEE, New York IEEE CD-ROM 75 C-L Yang, S.

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that for a given set of tasks eligible for execution this policy will not always choose the same task to run the decision made depends on how close each task s deadline is, which depends on the past history of the system how the system got to the present state. The EDF algorithm is optimal in the sense that, provided the total CPU utilization of the tasks does not exceed 100%, then the task set can be scheduled by EDF. There are two main problems with EDF scheduling. The rst is that it is relatively complex to implement, and so introduces higher OS overheads than simple priority scheduling. The other problem is that it is unstable under overload. It is unpredictable which task will miss its deadline if the system is overloaded. This makes pure EDF scheduling unsuitable for systems containing both real time and non-real time tasks.

Bagchi, and W J Chappell Location tracking with directional antennas in wireless sensor networks In 2005 IEEE MTT-S International Microwave Symposium Digest, Long Beach, CA, June 2005, IEEE, New York doi: 101109/MWSYM2005151640 76 C Hongyang, D Ping, X Yongjun, and L Xiaowei A robust location algorithm with biased extended Kalman ltering of TDOA data for wireless sensor networks In Proceedings of the International Conference on Wireless Communications, Networking and Mobile Computing (WCNM 05), Vol 2, Wuhan, China, September 2005, IEEE, New York, pp 883 886 doi: 101109/WCNM20051544192 77 T Li, A Ekpenyong, and Y-F Huang Source localization and tracking using distributed asynchronous sensor IEEE Transactions on Signal Processing, 54(10):3991 4003, 2006 78 A Jain, E Y Chang, and Y-F Wang Adaptive stream resource management using Kalman lters.

In Proceedings of the 2004 ACM SIGMOD International Conference on Management of Data (SIGMOD 04), Paris, France, 2004 ACM, New York, pp 11 22, ISBN 1-58113859-8 doi: http://doiacmorg/10114510075681007573 79 INFOCOM, editor 20th Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM 2001), Anchorage, AK, April 2001, IEEE, New York 80 Mobicom, editor Proceedings of the 7th Annual ACM/IEEE International Conference on Mobile Computing and Networking (Mobicom 01), Rome, Italy, July 2001, ACM, New York, ISBN 1-58113-422-3 81 I F Akyildiz, D Estrin, D E Culler, and M B Srivastava, editors Proceedings of the 1st International Conference on Embedded Networked Sensor Systems (SenSys 03), Los Angeles, CA, USA, November 2003, ACM, New York, ISBN 1-58113-707-9..

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The results on scheduling algorithms that I referred to previously make certain assumptions about how the system operates. Chief among these are the assumptions that task switching takes zero time and that a higher priority task is never prevented from running by a lower priority task. If the latter situation does occur, it is termed priority inversion. There is also the implicit assumption that the execution time of each task is known, or at least that it is bounded.

direction of the destination Referring to Figure 82, we can see that node c, with angle csd, is smallest among all of the neighbors of s Thus, the goal is to minimize the change in direction from the source to the destination The DREAM [17] and LAR [18] projects, simultaneously proposed, use an idea similar to compass routing However, these two approaches are best suited in networks where nodes are mobile (Despite their application in mobile environments, we provide an overview for completeness) The node that holds a message m with destination d calculates an angular range where the message must be forwarded The angular range is calculated using (i) the circle centered at d with radius equal to the maximum movement of d since the last update and (ii) the tangents from the current location to the aforementioned circle.

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