Modeling of Wireless Sensor Networks for Localization and Mobile Targets Tracking 用於無線感測網路定位與移動目標物追蹤之模型

Modeling of Wireless Sensor Networks for Localization and Mobile Targets Tracking用於無線感測網路定位與移動目標物追蹤之模型 Prasan Kumar Sahoo Dept. of Information Management Vanung University 沙庫瑪萬能科技大學資訊管理學系 Present by C.T. Lee 2007 / 4 / 16, 30

Educational Background • 中央大學資訊工程博士候選人 • Ph.D. in Mathematicsfrom Utkal University, India with advisor from Department of Mathematics, Indian Institute of Technology (IIT), Kharagpur, India, April, 2002. • Master of Technology [M. Tech] in Computer Sciencefrom Indian Institute of Technology (IIT), Kharagpur, India. • Master of Science [M. Sc.] in Mathematicsfrom Utkal University, India.

Outline • Introduction • Boundary node Selection and TargetDetection Protocols • Analytical Model • Energy Consumption Analysis • Simulation Results • Experimental Setups • Implementation Strategies • Conclusion and Future Work

Introduction • Target detection and tracking can be classified into four different categories. • The first category is to find out the trajectory of the target. • The second category is to wake up the sensors by using predictive strategy in order to keep track with the target. • The third category is to use the predictive strategy to reduce the transmitted data between the sink and each sensor node. • The last category is to obtain more accurate information of the target.

Introduction • In this report • Authors propose the boundary node selection algorithms. • They also propose a target detection protocol to track the entry and exit of the single target. • Design of an extended linear feedback model taking binary exponential backoff mechanism of IEEE 802.15.4 CSMA/CA based wireless sensor network to analyze the energy consumption issues of the one hop sensors.

Boundary node Selection and TargetDetection Protocols • In this work, it is assumed that all sensors are randomly and densely deployed over the monitoring region. • The sensing range is variable, which may be larger or smaller than the communication range.

e.g. Cover nodes ≦ 6 Boundary node Selection and TargetDetection Protocols

A and D are BNs after initial phase B and C are BNs after second phase e.g. B and C communication range x 2  cover 2 BNs (A and D) Pruning phase is developed to reset the redundant BNs to Non-BNs Boundary node Selection and TargetDetection Protocols

The BN X, first detects the target at time Td, and it broadcasts the Detect X message to its neighbors. Besides, it checks and finds its recording table is empty, and then sends the Entering Time (Td, X) to the sink. After the target leaves the BN X's sensing range, it broadcasts the Leave X packet and checks its recording table again. Non-BN Y has already sent the Detect Y packet to the BN X. So, BN X finds a non-empty field in its recording table and therefore does not transmit the Leaving Time (Tn, X) to the sink. Thus, there is collaboration among nodes X, Y and Z to detect the entry or exit of a mobile target. Boundary node Selection and TargetDetection Protocols

Boundary node Selection and TargetDetection Protocols • Sam Phu Manh Tran and T. Andrew Yang,” OCO: Optimized Communication & Organization for Target Tracking in Wireless Sensor Networks,” International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing, IEEE, 2006.

Analytical Model • “Energy Efficiency Modeling and Analysis in Wireless Sensor Networks”, published in the Proc. of IEEE, AusWireless Conference, March, 2006, Sydney, Australia. • Either completed successfully or rejected owing to the retransmission limit, a backlogged device can immediately switch back to the thinking state.

Analytical Model • Authors consider a homogeneous WSNs that consists of Nnumber of nodes where nodes may be in the thinkingor backloggedstate, alternatively. • Let B0, B1,. . . ,BLrepresent those backlogged states. • Nodes in thinking state may generate new packets with probability g. • It remains in backlogged state if the medium sensed by it is busy due to the data transmission by other nodes of the network or due to collision of its packets with others. • L + 1 number of backlog states are considered, where L is the retry limit which is application oriented or set as default value as per IEEE 802.15.4 standard.

Analytical Model • Let, W0 be the initial size of the contention window. • The contention window of the r-th retransmission is defined as Wr= W0x 2r. • Backoff Time = INT(CW x Random()) x Slot Time

Analytical Model • Let, i0, i1,. . . ,iLare the number of backlogged nodes present within the backlogged statesB0, B1,. . . ,BLrespectively and Xtdenotes the total number of backlogged nodes present within all those backlogged states Br, . • So,

Analytical Model • The transition from state i to state j(i ≦ j) means that there are somethinking terminals entering to the backlogged state. Similarly, the transition from state i+1 to state irepresents that there is a successful packet transmission.

Analytical Model

Analytical Model Thinking state may generate new packets with probability g, • Authors denote Ras the state transition matrix for the last idle slot t + I. • Authors specify the transition probability matrix R= S + F, where the (i, k)-th element of S and F are defined as state i -- > state k and transmission successful state i -- > state k and transmission failed

Analytical Model • For any t [t + I + 2 (t+I+1); t + I + T], authors define the one-step transition probability matrix Q • If the transmission is successful, the busy period's length is Tslots and if it is unsuccessful, its length is Cslots. So the transmission matrix P, is expressed as

Analytical Model • where S, F, and Q are defined as follows 不可能的傳輸 In backlogged state 至少1個以上正在傳，減掉剛好 1個正在傳， thinking state不傳不成功的傳輸不可能的傳輸 In backlogged state 1個正成功傳，其他不傳（含thinking state ） In backlogged state至少1個以上與thinking state1個正在傳而是由thinking state 產生traffic傳輸成功 In backlogged state 沒傳不成功的傳輸 In thinking state有2個(含)以上正在傳不成功的傳輸不成功的傳輸 In busy period 還有其他node傳成功 In thinking state N-i個node 還有k-i個node 產生traffic

Analytical Model • where Jrepresents the fact that a successful transmission decreases the backlog by 1. So its (i, k)-th entry is defined as follows

Energy Consumption Analysis • Let, :be the expected successful probability of the r-th retransmission of transmission attempts, for . • : be the expected successful probability of the first transmission. • : be the total energy consumption of the successful transmission attempt with rnumber of retransmissions. • : be the total energy consumption of the failed transmission attempts with rnumber of retransmissions.

Energy Consumption Analysis • Then the expected energy consumption for any transmission attempts, due to L-retransmission attempts can be estimated as follows: 第1次傳輸即成功第1~L次間重傳成功重傳全部失敗

Energy Consumption Analysis • The expected successful probability of the r-th retransmission of the transmission attempts as follows: • where πiis the probability that the system status Xt equals to i. Ps(r, i) is the successful probability of the r-th retransmission of the transmission attempt while there are i nodes in the backlogged state.

Energy Consumption Analysis thinking state 1個正成功傳，其他不傳(含backlogged state) In backlogged state 1個正成功傳，其他不傳(含thinking state)(and r=0 ) In backlogged state 1個正成功傳，其他不傳(含thinking state)

Energy Consumption Analysis • Generalizing for any retry limit r, the total energy consumption is given by 「頻道空閒評估」（Clear Channel Assessment，CCA）用以偵測無線頻道是否處在忙碌（Busy）狀態或是閒置（Idle）狀態並回報MAC Layer來決定是否傳送資料

The energy consumption while the backoff counter is decreasing( ) The energy consumption while the backoff counter is halted ( ) due to the busy medium Energy Consumption Analysis

Energy Consumption Analysis

Simulation Results

Simulation Results • effective energy consumption means the energy consumption due to successful transmission attempts

Simulation Results

Experimental Setups

Implementation Strategies

Implementation Strategies • Implementation at the Mobile Mote

Implementation Strategies • Implementation at the static nodes (MICAz)

Implementation Strategies • Implementation at the SINK • Upon receiving the RSSI values from different static MICAz, The sink compares the RSSI values with corresponding ID of the MICAz.

Implementation Strategies • Implementation at the Database (Notebook) • In order to store the position of the mobile target, authors execute “XListen.exe” in the notebook with a SQL server. Once the “XListen.exe” is executed, the raw data is saved to DBTest.txt. Then authors use JAVA SDK to read those raw data and save it to the SQL database. This JAVA code estimates the position of the target if it is nearer or farther to any static MICAz.

Conclusion • Performance analysis show that the energy consumption of packet transmission in wireless sensor networks is increased with the • increment of contention window • traffic load • network population • An optimal contention window can be derived from the use of fixed contention window to achieve the best effective energy consumption.

Future Work • Multi-layer boundary nodes problem • Set cover problem • Maximizesubject to1. Energy constraint2. Coverage constraint • Tradeoff • Energy consumption vs. Successful delivery ratio

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References (Authors) [9] “Power Control Based Topology Construction for the Distributed Wireless Sensor Networks”, accepted for publication in Computer Communications (SCI), September, 2006. [10] “Energy Efficiency Modeling and Analysis in Wireless Sensor Networks”, published in the Proc. of IEEE, AusWireless Conference, March, 2006, Sydney, Australia. [11] “Boundary Node Selection and Target Detection in Wireless Sensor Network”, under review of IEEE, ICITA, China, 2007. [12] “A Routing Protocol for the Bluetooth Scatternet” published online in Wireless Personal Communications (SCI), September, 2006.

Modeling of Wireless Sensor Networks for Localization and Mobile Targets Tracking 用於無線感測網路定位與移動目標物追蹤之模型