Hongchao Zhou, Fei Liu, Xiaohong Guan

Slides:



Advertisements
Similar presentations
Approximations for Min Connected Sensor Cover Ding-Zhu Du University of Texas at Dallas.
Advertisements

Yang Yang, Miao Jin, Hongyi Wu Presenter: Buri Ban The Center for Advanced Computer Studies (CACS) University of Louisiana at Lafayette 3D Surface Localization.
SELF-ORGANIZING MEDIA ACCESS MECHANISM OF A WIRELESS SENSOR NETWORK AHM QUAMRUZZAMAN.
Maximum Battery Life Routing to Support Ubiquitous Mobile Computing in Wireless Ad Hoc Networks By C. K. Toh.
TDMA Scheduling in Wireless Sensor Networks
Sensor Network 教育部資通訊科技人才培育先導型計畫. 1.Introduction General Purpose  A wireless sensor network (WSN) is a wireless network using sensors to cooperatively.
POWER EFFICIENCY ROUTING ALGORITHMS OF WIRELESS SENSOR NETWORKS
Large wireless autonomic networks Sensor networks Philippe Jacquet.
Cooperative Multiple Input Multiple Output Communication in Wireless Sensor Network: An Error Correcting Code approach using LDPC Code Goutham Kumar Kandukuri.
Impact of Mobility and Heterogeneity on Coverage and Energy Consumption in Wireless Sensor Networks Xiao Wang, Xinbing Wang, Jun Zhao Department of Electronic.
Distributed Scheduling of a Network of Adjustable Range Sensors for Coverage Problems Akshaye Dhawan, Ursinus College Aung Aung and Sushil K. Prasad Georgia.
Practical Belief Propagation in Wireless Sensor Networks Bracha Hod Based on a joint work with: Danny Dolev, Tal Anker and Danny Bickson The Hebrew University.
Energy-Efficient Target Coverage in Wireless Sensor Networks Mihaela Cardei, My T. Thai, YingshuLi, WeiliWu Annual Joint Conference of the IEEE Computer.
On the Construction of Energy- Efficient Broadcast Tree with Hitch-hiking in Wireless Networks Source: 2004 International Performance Computing and Communications.
Maximum Network lifetime in Wireless Sensor Networks with Adjustable Sensing Ranges Mihaela Cardei, Jie Wu, Mingming Lu, and Mohammad O. Pervaiz Department.
1 Sensor Placement and Lifetime of Wireless Sensor Networks: Theory and Performance Analysis Ekta Jain and Qilian Liang, Department of Electrical Engineering,
1 Secure Cooperative MIMO Communications Under Active Compromised Nodes Liang Hong, McKenzie McNeal III, Wei Chen College of Engineering, Technology, and.
LPT for Data Aggregation in Wireless Sensor Networks Marc Lee and Vincent W.S. Wong Department of Electrical and Computer Engineering, University of British.
Secure Cell Relay Routing Protocol for Sensor Networks Xiaojiang Du, Fengiing Lin Department of Computer Science North Dakota State University 24th IEEE.
2008/2/191 Customizing a Geographical Routing Protocol for Wireless Sensor Networks Proceedings of the th International Conference on Information.
The Chinese Univ. of Hong Kong Dept. of Computer Science & Engineering POWER-SPEED A Power-Controlled Real-Time Data Transport Protocol for Wireless Sensor-Actuator.
Energy-Aware Scheduling with Quality of Surveillance Guarantee in Wireless Sensor Networks Jaehoon Jeong, Sarah Sharafkandi and David H.C. Du Dept. of.
Efficient Deployment Algorithms for Prolonging Network Lifetime and Ensuring Coverage in Wireless Sensor Networks Yong-hwan Kim Korea.
Trust- and Clustering-Based Authentication Service in Mobile Ad Hoc Networks Presented by Edith Ngai 28 October 2003.
Maximum Network Lifetime in Wireless Sensor Networks with Adjustable Sensing Ranges Cardei, M.; Jie Wu; Mingming Lu; Pervaiz, M.O.; Wireless And Mobile.
On Energy-Efficient Trap Coverage in Wireless Sensor Networks Junkun Li, Jiming Chen, Shibo He, Tian He, Yu Gu, Youxian Sun Zhejiang University, China.
Salah A. Aly,Moustafa Youssef, Hager S. Darwish,Mahmoud Zidan Distributed Flooding-based Storage Algorithms for Large-Scale Wireless Sensor Networks Communications,
P-Percent Coverage Schedule in Wireless Sensor Networks Shan Gao, Xiaoming Wang, Yingshu Li Georgia State University and Shaanxi Normal University IEEE.
WEAR: A Balanced, Fault-Tolerant, Energy-Aware Routing Protocol for Wireless Sensor Networks Kewei Sha, Junzhao Du, and Weisong Shi Wayne State University.
Using Polynomial Approximation as Compression and Aggregation Technique in Wireless Sensor Networks Bouabdellah KECHAR Oran University.
Efficient Energy Management Protocol for Target Tracking Sensor Networks X. Du, F. Lin Department of Computer Science North Dakota State University Fargo,
Mohamed Hefeeda 1 School of Computing Science Simon Fraser University, Canada Efficient k-Coverage Algorithms for Wireless Sensor Networks Mohamed Hefeeda.
A Dead-End Free Topology Maintenance Protocol for Geographic Forwarding in Wireless Sensor Networks IEEE Transactions on Computers, vol. 60, no. 11, November.
Multi-channel Wireless Sensor Network MAC protocol based on dynamic route.
Computer Network Lab. Integrated Coverage and Connectivity Configuration in Wireless Sensor Networks SenSys ’ 03 Xiaorui Wang, Guoliang Xing, Yuanfang.
Maximizing Lifetime per Unit Cost in Wireless Sensor Networks
Tufts Wireless Laboratory School Of Engineering Tufts University Paper Review “An Energy Efficient Multipath Routing Protocol for Wireless Sensor Networks”,
Ching-Ju Lin Institute of Networking and Multimedia NTU
An Energy-Efficient Geographic Routing with Location Errors in Wireless Sensor Networks Julien Champ and Clement Saad I-SPAN 2008, Sydney (The international.
Energy-Efficient Wake-Up Scheduling for Data Collection and Aggregation Yanwei Wu, Member, IEEE, Xiang-Yang Li, Senior Member, IEEE, YunHao Liu, Senior.
Localized Low-Power Topology Control Algorithms in IEEE based Sensor Networks Jian Ma *, Min Gao *, Qian Zhang +, L. M. Ni *, and Wenwu Zhu +
UNIT IV INFRASTRUCTURE ESTABLISHMENT. INTRODUCTION When a sensor network is first activated, various tasks must be performed to establish the necessary.
U of Minnesota DIWANS'061 Energy-Aware Scheduling with Quality of Surveillance Guarantee in Wireless Sensor Networks Jaehoon Jeong, Sarah Sharafkandi and.
Heterogeneous Wireless Access in Large Mesh Networks Haiping Liu, Xin Liu, Chen-Nee Chuah, Prasant Mohapatra University of California, Davis IEEE MASS.
Incremental Run-time Application Mapping for Heterogeneous Network on Chip 2012 IEEE 14th International Conference on High Performance Computing and Communications.
Data funneling : routing with aggregation and compression for wireless sensor networks Petrovic, D.; Shah, R.C.; Ramchandran, K.; Rabaey, J. ; SNPA 2003.
Event query processing based on data-centric storage in wireless sensor networks Longjian Guo, Yingshu Li, and Jianzhong Li IEEE GLOBECOM Technical Conference.
On Mobile Sink Node for Target Tracking in Wireless Sensor Networks Thanh Hai Trinh and Hee Yong Youn Pervasive Computing and Communications Workshops(PerComW'07)
Load-Balanced Clustering in Wireless Sensor Networks Gaurav Gupta and Mohamed Younis IEEE International Conference on Communications, (ICC 2003)
Energy-Aware Target Localization in Wireless Sensor Networks Yi Zou and Krishnendu Chakrabarty IEEE (PerCom’03) Speaker: Hsu-Jui Chang.
A Coverage-Preserving and Hole Tolerant Based Scheme for the Irregular Sensing Range in WSNs Azzedine Boukerche, Xin Fei PARADISE Research Lab Univeristy.
-1/16- Maximum Battery Life Routing to Support Ubiquitous Mobile Computing in Wireless Ad Hoc Networks C.-K. Toh, Georgia Institute of Technology IEEE.
Towards Optimal Sleep Scheduling in Sensor Networks for Rare-Event Detection Qing Cao, Tarek Abdelzaher, Tian He, John Stankovic Department of Computer.
Medium Access Control. MAC layer covers three functional areas: reliable data delivery access control security.
IEEE COMMUNICATIONS LETTERS, VOL. 9, NO. 9, SEPTEMBER 2005 Zhen Guo,
In the name of God.
Presented by Edith Ngai MPhil Term 3 Presentation
2010 IEEE Global Telecommunications Conference (GLOBECOM 2010)
Prof. Yu-Chee Tseng Department of Computer Science
Authors: Jiang Xie, Ian F. Akyildiz
Abstract In this paper, the k-coverage problem is formulated as a decision problem, whose goal is to determine whether every point in the service area.
Monitoring Churn in Wireless Networks
Presented by: Rohit Rangera
On the Critical Total Power for k-Connectivity in Wireless Networks
Introduction to Wireless Sensor Networks
SEP routing protocol in WSN
Authors: Ing-Ray Chen; Yating Wang Present by: Kaiqun Fu
Coverage and Connectivity in Sensor Networks
Speaker : Lee Heon-Jong
Hongchao Zhou, Xiaohong Guan, Chengjie Wu
Presentation transcript:

Critical Communication Radius for Sink Connectivity in Wireless Networks Hongchao Zhou, Fei Liu, Xiaohong Guan Tsinghua University / Xi’an Jiaotong University 8/6/2019

Outlines Introduction Asymptotic sink connectivity Critical communication radius for sink connectivity Effective communication radiuses for different link models 8/6/2019

Wireless Sensor Networks Small devices with capability of sensing, processing and wireless communication Distributed and autonomous wireless networks with self-organization and cooperation for information acquisition Wide variety of applications for infrastructure safety, environmental monitoring, manufacturing and production, logistics, health care, security surveillance, target detection/localization/tracking, etc 过去的情况是两个表校准, 现在发送数据包,无需记录当前时钟,只需要数据包记录自身寿命,进行数据融合的节点 根据每个数据包的寿命可计算出每个事件的发生时刻。 8/6/2019

Challenging problems and issues Limited node resources in terms of energy, bandwidth, processing capacity, storage, etc Energy consumption  {processing speed2-4, sensing radiusq=2-4, communication radiusq=2-4} Energy constrained communication protocol Special issues on connectivity, time synchronization, localization, sensing coverage, task allocation, data management, etc. 过去的情况是两个表校准, 现在发送数据包,无需记录当前时钟,只需要数据包记录自身寿命,进行数据融合的节点 根据每个数据包的寿命可计算出每个事件的发生时刻。 8/6/2019

Connectivity problem s G (n, s, r) : the network in consideration r s : disc radius A: disc area, r : communication radius, if , ij and ji n : the number of nodes d : , average number of neighbor nodes r Determine the minimal r to guarantee the connectivity of the network 8/6/2019

Existing result (P. Gupta and P. R. Kumar,1998) Critical radius for fully connected graph (no isolated node) The network is asymptotically ( ) fully connected with probability one if and only if with variable 8/6/2019

Issue Full connection may not be necessary for some applications To save energy and prolong lifetime, a very small fraction isolated nodes of in a wireless sensor network with thousands of nodes could be tolerated 8/6/2019

Introducing sink connectivity Assume the sink is a randomly selected node in the network Sink connectivity Cn is defined as the fraction of nodes in the network that are connected to the sink 8/6/2019

Goal Find the critical communication radius to guarantee , where is a constant close to 1 8/6/2019

Connected subnet Let be the number of nodes in the jth-largest connected subnet in sink 8/6/2019

Fully connected Cn=1 sink 8/6/2019

Partial connected Cn<1 The expectation of Cn sink 8/6/2019

Outlines Introduction Asymptotic sink connectivity Critical communication radius for sink connectivity Effective communication radiuses for different link models 8/6/2019

Asymptotic sink connectivity Based on the continuum percolation theory*, we can get the following two theorems * 8/6/2019

Comparison with the existing result Current result Goal as Requirement: Example: Gupta’s conclusion Goal as Critical radius: Example: 8/6/2019

Average neighbor number d Mapping: The connectivity is unchanged; is unchanged. Instead of r, we discuss the relationship between the connectivity and d for simplify. Using , we can get the corresponding communication radius. 8/6/2019

Connectivity versus average number of neighbors 8/6/2019

Outlines Introduction Asymptotic sink connectivity Critical communication radius for sink connectivity Effective communication radiuses for different link models 8/6/2019

a sink connected A network is a sink connected if with high probability. The minimal radius that makes the network a sink connected is the critical communication radius for a sink connected 8/6/2019

Required average neighbor number versus n Critical radius 8/6/2019

Observations If we tolerate a small percent of nodes being isolated, the critical communication radius will be considerable reduced. This could resulting in reducing communication energy consumption significantly since energy  {communication radiusq=2-4} 8/6/2019

Outlines Introduction Asymptotic sink connectivity Critical communication radius for sink connectivity Effective communication radiuses for different link models 8/6/2019

Link models Simple Boolean can communication with each other if and only if , where r is a constant. Random connection can send a message to with the probability Anisotropic can send a message to if and only if , see the figure. Random radius can communication with each other if and only if , where is a random variable. 8/6/2019

Effective Communication Radius as the effective communication radius where is connected with probability , is the effective communication area Numerous of simulation results show that If the effective communication radius > R, the sink connectivity of three other link models (or the combination of three other link models) is better than that of the simple Boolean model Note: Here the sink connectivity is the fraction of nodes that can receive the broadcasting messages from the sink. 8/6/2019

Average connectivity for different link models with the same 8/6/2019

Summary and conclusions Sink connectivity is proposed for wireless sensor networks If we tolerate a small fraction of nodes being isolated, we can reduce the communication radius, and thus the communication power consumption significantly. If the density of the nodes remain unchanged, the critical communication radius for sink connectivity would decrease opposite to the critical communication radius for full connectivity. Effective communication radius is introduced to describe the sink connectivity in more complicated link models. 8/6/2019

Thank you 8/6/2019