Online Data Gathering for Maximizing Network Lifetime in Sensor Networks IEEE transactions on Mobile Computing Weifa Liang, YuZhen Liu.

Slides:



Advertisements
Similar presentations
Multicast in Wireless Mesh Network Xuan (William) Zhang Xun Shi.
Advertisements

Maximum Battery Life Routing to Support Ubiquitous Mobile Computing in Wireless Ad Hoc Networks By C. K. Toh.
Minimum Energy Mobile Wireless Networks IEEE JSAC 2001/10/18.
Presented By- Sayandeep Mitra TH SEMESTER Sensor Networks(CS 704D) Assignment.
1 Routing Techniques in Wireless Sensor networks: A Survey.
A Query-Based Routing Tree in Sensor Networks In Chul Song Yohan Roh Dongjoon Hyun Myoung Ho Kim GSN 2006 (Geosensor Network) 1.
Network Correlated Data Gathering With Explicit Communication: NP- Completeness and Algorithms R˘azvan Cristescu, Member, IEEE, Baltasar Beferull-Lozano,
1 Minimum-energy broadcasting in multi-hop wireless networks using a single broadcast tree Department of Computer Science and Information Engineering National.
CS Dept, City Univ.1 Low Latency Broadcast in Multi-Rate Wireless Mesh Networks LUO Hongbo.
Beneficial Caching in Mobile Ad Hoc Networks Bin Tang, Samir Das, Himanshu Gupta Computer Science Department Stony Brook University.
Scheduling Algorithms for Wireless Ad-Hoc Sensor Networks Department of Electrical Engineering California Institute of Technology. [Cedric Florens, Robert.
On the Construction of Energy- Efficient Broadcast Tree with Hitch-hiking in Wireless Networks Source: 2004 International Performance Computing and Communications.
An Efficient Clustering-based Heuristic for Data Gathering and Aggregation in Sensor Networks Wireless Communications and Networking (WCNC 2003). IEEE,
A Hierarchical Energy-Efficient Framework for Data Aggregation in Wireless Sensor Networks IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 55, NO. 3, MAY.
1 TTS: A Two-Tiered Scheduling Algorithm for Effective Energy Conservation in Wireless Sensor Networks Nurcan Tezcan & Wenye Wang Department of Electrical.
WiOpt’04: Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks March 24-26, 2004, University of Cambridge, UK Session 2 : Energy Management.
[1][1][1][1] Lecture 2-3: Coping with NP-Hardness of Optimization Problems in Practice May 26 + June 1, Introduction to Algorithmic Wireless.
LPT for Data Aggregation in Wireless Sensor networks Marc Lee and Vincent W.S Wong Department of Electrical and Computer Engineering, University of British.
Extending Network Lifetime for Precision-Constrained Data Aggregation in Wireless Sensor Networks Xueyan Tang School of Computer Engineering Nanyang Technological.
Optimizing Lifetime for Continuous Data Aggregation With Precision Guarantees in Wireless Sensor Networks Xueyan Tang and Jianliang Xu IEEE/ACM TRANSACTIONS.
Mario Čagalj supervised by prof. Jean-Pierre Hubaux (EPFL-DSC-ICA) and prof. Christian Enz (EPFL-DE-LEG, CSEM) Wireless Sensor Networks:
Assignment 4. (Due on Dec 2. 2:30 p.m.) This time, Prof. Yao and I can explain the questions, but we will NOT tell you how to solve the problems. Question.
1 Algorithms for Bandwidth Efficient Multicast Routing in Multi-channel Multi-radio Wireless Mesh Networks Hoang Lan Nguyen and Uyen Trang Nguyen Presenter:
Yanyan Yang, Yunhuai Liu, and Lionel M. Ni Department of Computer Science and Engineering, Hong Kong University of Science and Technology IEEE MASS 2009.
1 Topology Control of Multihop Wireless Networks Using Transmit Power Adjustment Infocom /12/20.
Energy Saving In Sensor Network Using Specialized Nodes Shahab Salehi EE 695.
Special Topics on Algorithmic Aspects of Wireless Networking Donghyun (David) Kim Department of Mathematics and Computer Science North Carolina Central.
Steady and Fair Rate Allocation for Rechargeable Sensors in Perpetual Sensor Networks Zizhan Zheng Authors: Kai-Wei Fan, Zizhan Zheng and Prasun Sinha.
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2007 (TPDS 2007)
LPT for Data Aggregation in Wireless Sensor Networks Marc Lee and Vincent W.S. Wong Department of Electrical and Computer Engineering, University of British.
On the Construction of Data Aggregation Tree with Minimum Energy Cost in Wireless Sensor Networks: NP-Completeness and Approximation Algorithms National.
Mobility Limited Flip-Based Sensor Networks Deployment Reporter: Po-Chung Shih Computer Science and Information Engineering Department Fu-Jen Catholic.
Efficient Gathering of Correlated Data in Sensor Networks
The Minimal Communication Cost of Gathering Correlated Data over Sensor Networks EL 736 Final Project Bo Zhang.
Network Aware Resource Allocation in Distributed Clouds.
Minimal Hop Count Path Routing Algorithm for Mobile Sensor Networks Jae-Young Choi, Jun-Hui Lee, and Yeong-Jee Chung Dept. of Computer Engineering, College.
Mobile Relay Configuration in Data-Intensive Wireless Sensor Networks.
Wireless Sensor Networks COE 499 Energy Aware Routing
Prediction Assisted Single-copy Routing in Underwater Delay Tolerant Networks Zheng Guo, Bing Wang and Jun-Hong Cui Computer Science & Engineering Department,
Efficient Deployment Algorithms for Prolonging Network Lifetime and Ensuring Coverage in Wireless Sensor Networks Yong-hwan Kim Korea.
Maximum Network Lifetime in Wireless Sensor Networks with Adjustable Sensing Ranges Cardei, M.; Jie Wu; Mingming Lu; Pervaiz, M.O.; Wireless And Mobile.
Optimal Base Station Selection for Anycast Routing in Wireless Sensor Networks 指導教授 : 黃培壝 & 黃鈴玲 學生 : 李京釜.
Load-Balancing Routing in Multichannel Hybrid Wireless Networks With Single Network Interface So, J.; Vaidya, N. H.; Vehicular Technology, IEEE Transactions.
Optimization of Wavelength Assignment for QoS Multicast in WDM Networks Xiao-Hua Jia, Ding-Zhu Du, Xiao-Dong Hu, Man-Kei Lee, and Jun Gu, IEEE TRANSACTIONS.
Energy-Efficient Shortest Path Self-Stabilizing Multicast Protocol for Mobile Ad Hoc Networks Ganesh Sridharan
A Low-Latency and Energy-Efficient Algorithm for Convergecast in Wireless Sensor Networks Authors Sarma Upadhyayula, Valliappan Annamalai, Sandeep Gupta.
SIMPLE: Stable Increased Throughput Multi-hop Link Efficient Protocol For WBANs Qaisar Nadeem Department of Electrical Engineering Comsats Institute of.
Efficient Energy Management Protocol for Target Tracking Sensor Networks X. Du, F. Lin Department of Computer Science North Dakota State University Fargo,
NTU IM Page 1 of 35 Modelling Data-Centric Routing in Wireless Sensor Networks IEEE INFOCOM Author: Bhaskar Krishnamachari Deborah Estrin Stephen.
Copyright © 2011, Scalable and Energy-Efficient Broadcasting in Multi-hop Cluster-Based Wireless Sensor Networks Long Cheng ∗ †, Sajal K. Das†,
Maximization of System Lifetime for Data-Centric Wireless Sensor Networks 指導教授:林永松 博士 具資料集縮能力無線感測網路 系統生命週期之最大化 研究生:郭文政 國立臺灣大學資訊管理學研究所碩士論文審查 民國 95 年 7 月.
Maximizing Lifetime per Unit Cost in Wireless Sensor Networks
 Tree in Sensor Network Patrick Y.H. Cheung, and Nicholas F. Maxemchuk, Fellow, IEEE 3 rd New York Metro Area Networking Workshop (NYMAN 2003)
Collaborative Broadcasting and Compression in Cluster-based Wireless Sensor Networks Anh Tuan Hoang and Mehul Motani National University of Singapore Wireless.
Energy-Efficient Wake-Up Scheduling for Data Collection and Aggregation Yanwei Wu, Member, IEEE, Xiang-Yang Li, Senior Member, IEEE, YunHao Liu, Senior.
A Dynamic Query-tree Energy Balancing Protocol for Sensor Networks H. Yang, F. Ye, and B. Sikdar Department of Electrical, Computer and systems Engineering.
Distributed Data Gathering Scheduling in Multi-hop Wireless Sensor Networks for Improved Lifetime Subhasis Bhattacharjee and Nabanita Das International.
Self-stabilizing energy-efficient multicast for MANETs.
Energy-Efficient Randomized Switching for Maximizing Lifetime in Tree- Based Wireless Sensor Networks Sk Kajal Arefin Imon, Adnan Khan, Mario Di Francesco,
An Adaptive Zone-based Storage Architecture for Wireless Sensor Networks Thang Nam Le, Dong Xuan and *Wei Yu Department of Computer Science and Engineering,
Load Balanced Link Reversal Routing in Mobile Wireless Ad Hoc Networks Nabhendra Bisnik, Alhussein Abouzeid ECSE Department RPI Costas Busch CSCI Department.
Connected Point Coverage in Wireless Sensor Networks using Robust Spanning Trees IEEE ICDCSW, 2011 Pouya Ostovari Department of Computer and Information.
SERENA: SchEduling RoutEr Nodes Activity in wireless ad hoc and sensor networks Pascale Minet and Saoucene Mahfoudh INRIA, Rocquencourt Le Chesnay.
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)
Dynamic Proxy Tree-Based Data Dissemination Schemes for Wireless Sensor Networks Wensheng Zhang, Guohong Cao and Tom La Porta Department of Computer Science.
Construction of Optimal Data Aggregation Trees for Wireless Sensor Networks Deying Li, Jiannong Cao, Ming Liu, and Yuan Zheng Computer Communications and.
Introduction Wireless Ad-Hoc Network  Set of transceivers communicating by radio.
-1/16- Maximum Battery Life Routing to Support Ubiquitous Mobile Computing in Wireless Ad Hoc Networks C.-K. Toh, Georgia Institute of Technology IEEE.
Introduction Wireless Ad-Hoc Network
Minimizing Broadcast Latency and Redundancy in Ad Hoc Networks
Presentation transcript:

Online Data Gathering for Maximizing Network Lifetime in Sensor Networks IEEE transactions on Mobile Computing Weifa Liang, YuZhen Liu

2 Outline Introduction System model Generic Cost Model of Energy Consumption Problem Definition Algorithms For Online Data Gathering Performance Evaluation Conclusions

3 Introduction The main constraint of sensor nodes is their low battery energies, which limit the networks lifetime. The network lifetime of a wireless sensor network is defined as the time of the first node failure in the network. Energy efficiency in the design of routing protocols for sensor network is of paramount importance.

4 Introduction To prolong the network lifetime, there are many different energy optimization metrics have been proposed. – Minimize the total energy consumption – Maximize the lifetime of each node There are two typical forms of data gathering queries: – Periodic collection. – Event driven.

5 Introduction Consider an online data gathering problem in sensor networks: – There is a sequence of data gathering queries, which arrive one by one. – To respond each query as it arrives, the system build a routing tree for it. – Within the tree, the volume of the data transmitted by each internal node depends on not only the volume of sensed data by the node itself, but also the volume of data received from it children.

6 Introduction The objective is to maximize the network lifetime without any knowledge of future query arrivals and generation rates. The experimental results show that, among the proposed algorithms, one algorithm that takes into account both residual energy and the volume of data at each sensor outperform the others.

7 System Model Consider a wireless sensor network consisting of : – N stationary sensor nodes and a base station S distributed over a region. – Location of each sensor and base station are fixed and known a priori. – Every sensor equipped with an omni-directional antenna. We take into account the transmission energy consumption only and assume that the other energy consumption such as reception are negligible.

8 System Model The wireless sensor network can be modeled by a directed graph M = (N,A). – N is the set of nodes, A is the set of edges, – Edge in A if node v is within the transmission range of u. – For u, v with distance, the transmission energy at node u is modeled to be proportional to if a unit of message is transferred from u to v. – is a path-loss exponent parameter depending on the characteristics of communication medium.

9 Generic Cost Model of Energy Consumption – Symbol Definition Given a data gathering query, we aim to build a routing tree T rooted at the sink node. For each node v: – p(v) be the parent of v in T. – be the length of the sensed message by v – A relay node v in T, assume the lengths of messages that v received from t children are – be the amount of energy consumption of receiving and sensing a unit of message by a sensor.

10 Generic Cost Model of Energy Consumption v may or may not aggregate these messages and its own sensed message before transmitting them as a single message to its parent p(v). The length of message transmitted by v to its parent is a function f with parameters and

11 Generic Cost Model of Energy Consumption The definition of f may vary, depending on application domains. There are two frequently used f below: – The length of message transmitted by a relay node is independent of message lengths of its children and itself. – The length of the message transmitted by a relay node depends on the message lengths of its children and itself.

12 Generic Cost Model of Energy Consumption If v is a relay node with children the cost c(v) of v is thus – If v is a leaf node, f is a function of only the cost c(v) of v is thus –

13 Generic Cost Model of Energy Consumption-Three well-known model Case1: Assume that – Total transmission energy consumption is considered: – Minimum residual energy among the nodes is considered:

14 Generic Cost Model of Energy Consumption-Three well-known model Case2: Assume that – Total transmission energy consumption is considered: – Minimum residual energy among the nodes is considered: Case3 is the mixture of case1 and case2 that takes into account both optimization objectives simultaneously.

15 Problem Definition The online data gathering problems is to maximize the network lifetime without knowledge of future query arrivals and the generation rate. As a query arrives, the response by the system to query is to build a routing tree rooted at the sink and spanning the other nodes for it. All in all, the problem is to maximize the number of queries answered until the first node in the network fails.

16 Algorithms for Online Data Gathering -MNL The proposed algorithm is that, once a data gathering query arrives, a data gathering tree for the query is constructed using a greedy policy that maximize the minimum residual energy among the nodes. If node v is included into the tree if it leads to maximizing the minimum residual energy. The nodes are included into the tree one by one.

17 Algorithms for Online Data Gathering -MNL Symbol Definition T and : the tree and the set of nodes included in T. Initially, the set of nodes in T contains the sink node only. Each time the algorithms picks up a node v from such that is maximized. The algorithm continues until all nodes are checked

18 Algorithms for Online Data Gathering -MNL Let node be the considered node. 1. If there are l edges from v to the nodes in denoted by, where for all I, define 2. Otherwise( there is not any edge from v to the nodes in ), define

19 Algorithms for Online Data Gathering -MNL

20 Algorithms for Online Data Gathering -MNL

21 Algorithms for Online Data Gathering -MMRE Maximization of the minimum residual energy (MMRE) among the nodes in the network. An inverted spanning tree instead of a broadcast tree is constructed. Let T be tree tree and be the set of nodes in T. The sink node s in included in T and initially. Each time it picks up a node if v satisfies The Algorithm continues until

22 Algorithms for Online Data Gathering -SPT Minimization of the total transmission energy consumption of relaying the sensed message from a sensor to the sink node. An energy graph G(V,E) is derived from the sensor network. – V: the set of sensor nodes and sink node s – Directed edge in E from u to v if the residual energy at u is at least – The weight assigned to the edge is, which is the energy consumption of transmitting a init message between of two nodes A single-source shortest path tree rooted at the sink node is constructed. The minimum transmission energy consumption to send its k- units sensed message to the sink node is Thus, the total transmission energy consumption is

23 Algorithms for Online Data Gathering -BT An undirected, energy graph for the sensor network is defined. – V: set of sensor nodes – E: set of undirected links We aim to prolong the network lifetime by dealing with two opposite optimization. – Minimize the total energy consumption of all nodes – Minimize the total energy consumption by each node However, constructing a spanning tree that meets these two optimization objective is NP-hard

Balancing Minimum Spanning and Shortest Path Trees, Algorithms for Online Data Gathering -BT An approximate algorithm that balances these two optimization objectives is available, and a solution delivered by the proposed algorithm is within times of the optimum [15]. Despite algorithm BT taking the total energy consumption for realizing a data gathering query into consideration, it doesn’t take into account the residual energy at each individual node. This results in the nodes near the tree root running out of their batteries quickly.

Efficient Algorithms for Finding minimum spanning Trees in Undirected and Directed Graphs 25 Algorithms for Online Data Gathering -MDST Is similar to the one of algorithm BT, but different weight function is used. The weight assigned to a link is Where Beta is the energy utilization ratio at node u, between its consumed energy and its initial capacity.

26 Performance Evaluation

27 Performance Evaluation

28 Performance Evaluation

29 Conclusions The experimental results showed that algorithm MNL significantly outperforms the others algorithms including MDST, MMRE, SPT, and BT. An Algorithm take the total energy consumption for realizing a data gathering query into consideration, it would result in the nodes near the tree root running out of their batteries quickly after a number of data gathering queries are realized, since those nodes always relay messages for the other nodes.