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¥»¥Ã¥·¥ç¥ó 4C  ¥»¥ó¥µ¥Í¥Ã¥È¥ï¡¼¥¯À©¸æ£±(MBL)
Æü»þ: 2007ǯ7·î5Æü(ÌÚ) 8:30 - 10:10
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ºÂĹ: µÁµ× ÃÒ¼ù (µþÅÔÂç³Ø)

4C-1 (»þ´Ö: 8:30 - 8:55)
Âê̾Clustering and In-Network Processing Routing Algorithm based on Time Division Transmission: CIPRA
Ãø¼Ô*Eunhyoung Chu (¶å½£Âç³ØÂç³Ø±¡¥·¥¹¥Æ¥à¾ðÊó²Ê³ØÉÜÃÎǽ¥·¥¹¥Æ¥à³ØÀ칶), Ê÷ ¹±·û, ±«µÜ ¿¿¿Í (¶å½£Âç³ØÂç³Ø±¡¥·¥¹¥Æ¥à¾ðÊó²Ê³Ø¸¦µæ±¡ÃÎǽ¥·¥¹¥Æ¥à³ØÉôÌç)
Pagepp. 698 - 705
Keywordsensor-networks, data gathering, clustering, routing
AbstractRecent advances in computing technology have led to the development of a new computing device: the wireless, battery-powered, smart sensors. Sensors which are capable of sensing, computing and communicating may be deployed in ad hoc networks without infrastructure and centralized control. Self-organizing and self-configuring capability are requisite for the sensor networks. In addition, activities of sensors are severely constrained by limited resources such as battery power, memory and computing capability available, which require sensor networks to be energy-efficient. Communication may occur via intermediaries in a multi-hop fashion because of limited power. Moreover because adjacent sensor nodes obtain similar or identical data, using in-network aggregation in a multi-hop communication is useful to reduce the volume of transmission data. A clustering technique which gathers data from several representative sensor nodes by clustering sensors provides scalability for the sensor networks composed of hundreds or thousands of nodes. Clustering is essential for applications requiring efficient data aggregation. Another advantage of clustering technique is to reduce energy consumption of the network. We discuss an energy-efficient method based on Clustering and In-network Processing Routing Algorithm named CIPRA, which prolongs network lifetime by distributing energy consumption, for data gathering in sensor networks. Given a collection of sensors and a base station, CIPRA groups sensors into a cluster that has a single cluster-head transmitting data to the base station and normal-sensors (non-cluster-head) sending data to the cluster-head and organizes a connected data routing-tree path based on a Ring Topology composed of adjacent several rings, where sensors are permitted to aggregate incoming data packets. CIPRA reduces energy required to construct a spanning tree using a new hybrid flooding which employs Time Division Transmission (TDT) which lets sensors communicate with other sensors only during their assigned time slots and sensors take a role either transmitter or receiver. TDT helps to reduce a mount of energy consumption resulting from collisions, overhearing, and idle listening in CSMA MAC layer. Overhearing which is an energy consumption source because of broadcast medium decreases for sensor to have sensor receive messages only for their receiving time slot. In other words, because waiting to hear messages during their receive-time slot, sensors consume the same amount of energy for radio listening regardless of the distance from sensors to the cluster head. In addiction, CIPRA reduces the energy load of a cluster-head and the volume of transmission data by aggregating data at each member node within a cluster. In CIPRA after sensing data, each node sends the data to its neighbor node instead of its cluster-head. Neighbor nodes aggregate data to reduce amount of data and transfer the aggregated data to their neighbor nodes or their cluster-head. Using local communication among neighbor nodes lessens the communication distance. In-network processing at each member node distributes the energy load of cluster-heads to the member nodes. Experimental results show that our data gathering mechanism outperforms the direct scheme protocol and the LEACH protocol on the point of view of the network lifetime.

4C-2 (»þ´Ö: 8:55 - 9:20)
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Ãø¼Ô*¾¾²¬ ¹¬°ì, °¤Éô ¿­½Ó, ¼ÄµÜ µªÉ§, ļ»È²Ï¸¶ ²Ä³¤ (ÁϲÁÂç³Ø)
Pagepp. 706 - 713
Keyword¥»¥ó¥µ¥Í¥Ã¥È¥ï¡¼¥¯, TCPÀ©¸æ
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4C-3 (»þ´Ö: 9:20 - 9:45)
Âê̾Spatial-Correlation-Based Data Aggregation Scheme in Wireless Sensor Networks
Ãø¼Ô*Huifang Chen (Shizuoka University), Yoshitsugu Obashi, Tomohiro Kokogawa (NTT Corporation), Hiroshi Mineno, Tadanori Mizuno (Shizuoka University)
Pagepp. 714 - 721
Keywordwireless sensor networks, data aggregation, spatial correlation, estimation

4C-4 (»þ´Ö: 9:45 - 10:10)
Âê̾¥Ç¡¼¥¿¥»¥ó¥È¥ê¥Ã¥¯¥»¥ó¥µ¥Í¥Ã¥È¥ï¡¼¥¯¤Ë¤ª¤±¤ë°ÌÃÖ¾ðÊó¤òɬÍפȤ·¤Ê¤¤¥ë¡¼¥Æ¥£¥ó¥°Êý¼°¤ÎÄó°Æ
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Pagepp. 722 - 732
Keyword¥»¥ó¥µ¥Í¥Ã¥È¥ï¡¼¥¯, ¥ë¡¼¥Æ¥£¥ó¥°, GPSR, HVGF
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