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1 I F Akyildiz, W Su, Y Sankarasubramaniam, and E Cayirci Wireless sensor networks: A survey Computer Networks, 38:393 422, 2002 2 C-Y Chong and S P Kumar Sensor networks: Evolution, opportunities, and challenges Proceedings of the IEEE, 91(8):1247 1256, 2003 3 K Akkaya and M Younis A survey on routing protocols for wireless sensor networks Elsevier Journal of Ad Hoc Networks, 3(3):325 349, 2005 4 P Naik and K M Sivalingam A survey of MAC protocols for sensor networks In Wireless Sensor Networks, C S Raghavendra, K M Sivalingam, and T Znati, editors Kluwer Academic Publishers, Norwell, MA, 93 107 5 N Sadagopan, B Krishnamachari, and A Helmy Active query forwarding in sensor networks (ACQUIRE) Elsevier Journal of Ad Hoc Networks, 3(1):91 113, 2005 6 A Cerpa and D Estrin, ASCENT: Adaptive self-con guring sensor networks topologies IEEE Transactions on Mobile Computing, 3(3):272 285, July-August 2004 7 A.

Efrat, S Har-Peled, and J S B Mitchell Approximation algorithms for two optimal location problems in sensor networks In Proceedings of the 3rd International Conference on Broadband Communications, Networks and Systems (Broadnets 2005), pp 714 723, Boston, MA, October 2005 8 X Cheng, DZ Du, L Wang, and B Xu Relay sensor placement in wireless sensor networks ACM/Springer Journal of Wireless Networks, 14(3):347 355, June 2008 9 E L Lloyd and G Xue Relay node placement in wireless sensor networks IEEE Transactions on Computers, 56(1):134 138, January 2007 10 S Poduri, S Pattem, B Krishnamachari, and G S Sukhatme Sensor network con guration and the curse of dimensionality In Proceedings of the 3rd IEEE Workshop on Embedded Networked Sensors, Cambridge, MA, May 2006 11 A Bogdanov, E Maneva, and S Riesenfeld, Power-aware base station positioning for sensor networks.

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This is the most common form of preemptive scheduling, at least among real time operating systems. Each task is assigned a xed priority when it is created, and these priorities determine which tasks preempt which other tasks. The actual priority values used are derived using a very simple rule from the deadlines of the various tasks. The tasks are placed in increasing deadline order that is, the task with the shortest deadline has the highest priority and so on. This is known as deadline monotonic scheduling. This result was proved in the well-known paper by Liu and Layland2 for the case where Di = Ti and the more general result for Di < Ti was proved by Leung and Whitehead.3 For the case where Di = Ti the resulting priority assignment is called rate monotonic scheduling. For this case the Liu and Layland paper also derives a simple suf cient condition for a task set to be scheduled correctly by the rate monotonic algorithm; this is possible provided that

In Proceedings of the 23rd International Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM 2004), pp 575 585, Hong Kong, March 2004 12 J Pan, L Cai, Y T Hou, Y Shi, and S X Shen Optimal base-station locations in twotiered wireless sensor networks IEEE Transactions on Mobile Computing, 4(5):458 473, September/October 2005..

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50 C Jaikaeo, C Srisathapornphat, and C-C Shen Diagnosis of sensor networks In Proceedings of the 2001 IEEE International Conference on Communications (ICC 01), Vol 1, Helsinki, Finland, June 2001, IEEE, New York, pp 1627 1632 51 C-C Shen, C Srisathapornphat, and C Jaikaeo Sensor information networking architecture and applications IEEE Personal Communications Magazine, 8(4):52 59, 2001 52 C-f Hsin and M Liu A distributed monitoring mechanism for wireless sensor networks In Proceedings of the ACM Workshop on Wireless Security (WiSe 02), Atlanta, GA, September 2002, ACM, New York, pp 57 66 53 J Zhao, R Govindan, and D Estrin Computing aggregates for monitoring wireless sensor networks In Proceedings of the 1st IEEE International Workshop on Sensor Network Protocols and Applications (SNPA 2003), Anchorage, AK, May 2003, IEEE, New York, pp 139 148 54 J Zhao, R Govindan, and D Estrin.

So a task set satisfying the assumptions can be scheduled correctly using a rate monotonic xed priority assignment provided that the CPU utilization does not exceed 69%. The condition is suf cient but not necessary and is usually pessimistic. In reality, task periods tend to be multiples of each other and this increases maximum possible utilization. If every task period is a multiple of the next shorter period, 100% utilization is theoretically possible. Static priority based scheduling is by far the most common scheduling method used in real time systems. Even though it is not theoretically optimal, it is good enough for a large number of real world systems. It is also very simple to implement, which makes it suitable even for small, memory-limited systems. It can be implemented with fast, constant time algorithms, improving utilization and predictability when OS overheads

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