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23. H. Chan, M. Luk, and A. Perrig. Using cluster information for sensor network localization. In Proceedings of International Conference on Distributes Computing and Sensor Systems (DCOSS), 2005, pp. 109 125. 24. H. Chan and A. Perrig. ACE: An emergent algorithm for highly uniform cluster formation. In Proceedings of EWSN, 2004, pp. 154 171. 25. L. Doherty, K. S. J. Pister, and L. El-Ghaoui. Convex position estimation in sensor networks. In Proceedings of INFOCOM, April 2001, pp. 1655 1663. 26. L. Hu and D. Evans. Localization for mobile sensor networks. In ACM/IEEE International Conference on Mobile Computing and Networking (MOBICOM), September-October 2004, pp. 45 57. 27. R. Peng and M. L. Sichitiu. Probabilistic localization for wireless sensor networks. ACM Mobile Computing and Communications Review, 11(1):53 64, 2007. 28. R. Peng and M. L. Sichitiu. Angle of arrival localization for wireless sensor networks. In Proceedings of IEEE Conference on Sensor and Ad Hoc Communications and Networks), September 2006, pp. 374 382. 29. M. L. Sichitiu and V. Ramadurai. Localization of wireless sensor networks with a mobile beacon. In Proceedings of IEEE Conference on Mobile Ad Hoc and Sensor Systems MASS), October 2004, pp. 174 183.

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Vahdat and Becker [20] propose an epidemic routing protocol for intermittently connected networks When a message arrives at an intermediate node, the node oods the message to all its neighbors Hence, messages are quickly distributed through the connected portions of the network Epidemic routing relies on carriers of messages coming into contact with another node through node mobility When two nodes are within the communication range, they exchange pairwise messages that the other node has not seen yet The epidemic algorithm works as follows Assume that host A comes into contact with host B and initiates an anti-entropy session Step 1 A transmits it summary vector, SV a, to B SV a is a compact representation of all the messages being buffered at A Step 2.

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13.1 INTRODUCTION Wireless sensor networks (WSN) have been one of the main research foci in wireless networks area over the last decade [1]. With the evolution of the MEMS technology and the availability of low cost communication and computation hardware, WSNs have been transformed from conceptual paradigms to reality in this short period. Prototype and deployed WSNs currently serve as enablers of several applications such as environmental monitoring, urban safety, traf c monitoring, smart spaces, and surveillance of hostile and inaccessible areas. The majority of the early research efforts have focused on enabling technologies for WSNs, including development of communication protocols, localization methods, and simple application-dependent information processing. Major research efforts in WSN area have resulted in many deployed testbeds and other implementations. The driving motivation of delivering solutions that can be implemented in short time resulted in systems that can provide only best effort service. Minimization of energy consumption or achieving high ef ciency (in its versatile de nition) has been the objective of many communication protocols designed for WSNs. Their performance strictly depends on the con guration of the network and the load it carries. While the boosting effect of the existing solutions on the research community should be acknowledged, these solutions fall short of addressing requirements of all WSN applications and deployment scenarios. More speci cally, mission critical and real-time applications suffer from unpredictable performance levels when such communication protocols are used. Mission critical and real-time applications require performance guarantees from the system on which they are implemented. As an example, a real-time

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B performs a logical AND operation between the negation of its summary vector, SV b, (the negation of B s summary vector, representing the messages that it needs) and A That is, B determines the set difference between the messages buffered at A and the messages buffered locally at B It then transmits a vector requesting these messages from A Step 3 A transmits the requested messages to B This process is repeated transitively when B comes into contact with a new neighbor Given suf cient buffer space and time, these anti-entropy sessions guarantee eventual message delivery through such pairwise message exchange Their simulation results show that, in the special scenarios considered, epidemic routing is able to deliver nearly all transmitted messages, while existing ad hoc routing protocols fail to deliver any messages because of the limited node connectivity when the buffer capacity is suf ciently large.

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Another extreme is to let the source hold the message and deliver to the destination only when they are within the communication range This approach obviously has the minimal overhead, but the delay could be very long Grossglauser and Tse [21], propose a two-hop forwarding approach and have explored a theoretical framework where nodes with in nite buffer move independently around the network and every node gets close to any other node for some short time period per time slot Within this framework, a node s gives a message addressed to node t to another randomly chosen node one hop away in the network called a receiver When the receiver happens to be within the range of the destination node t, the receiver sends the message to the destination Hence a message will only make two hops, and no message will be transmitted more than twice.

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java code 128 reader

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java code 128 reader