Coverage and Scheduling in Wireless Sensor Networks Yong Hwan Kim Korea University of Technology and Education Laboratory of Intelligent Networks

Scheduling Basic Policy Sensor should be active or sleep? Scheduling (related to the coverage issue)  An interval: is active  Another interval: is active  So, the battery power can be saved 2/37

Scheduling Scheduling Type Centralized 1) All sensors send “their location information” to the centralized sink node. 2) The sink node performs “its scheduling algorithm” for the sensors 3) The sink node broadcasts “the scheduling information” to all sensor nodes 4) Each sensor becomes active or sleep according to the information Distributed  Each sensor self-determies its scheduling time  # of messages reduced 3/37

Energy-efficient target coverage in wireless sensor networks 4/37

Centralized Scheduling The contributions of this paper 1. Introduce a new model of maximizing the network lifetime of the target coverage problem  Define the MSC(maximum set covers) problem  Prove that MSC is NP-complete 2. Design two target coverage heuristics for solving the MSC  Linear programming  Greedy techniques 3. Analyze the performance of two approach through simulation 5/37

MSC (Maximum Set Covers)  Given a collection C of subsets of a finite set R, find a family of set covers with time weights in [0,1] such that to maximize and for each subset s in C, s appears in with a total weight of at most 1, where 1 is the life time of each sensor Centralized Scheduling 6/37 [Definition] Maximum Set Covers Problem

Centralized Scheduling MSC (Maximum Set Covers) For Example,  By MSC Scheduling Network Lifetime: 2.5 7/37 active time=0.5 active time=1 s2s2 s1s1 s4s4 s3s3 t3t3 t1t1 t2t2

Centralized Scheduling Integer Programming Formulation of the MSC Problem 8/37

Centralized Scheduling Integer Programming Formulation of the MSC Problem 9/37

Centralized Scheduling Integer Programming  Linear Programming 10/37

Centralized Scheduling Greedy algorithm of the MSC Problem 11/37 Critical target is covered by minimum number of sensors 1.Cover a larger number of uncover targets 2.Have more residual energy available

Centralized Scheduling MSC (Maximum Set Covers) Existing Algorithms  Linear Programming  Greedy (Complexity: ) i: # of set covers, m: # of targets, n: # of sensors 12/37

Power-Saving Scheduling for Multiple-Target Coverage in Wireless Sensor Networks 13/37

Centralized Scheduling The problem of previous work It has been assumed that sensors consume the same amount energy when transmitting the collected data, regardless of how many targets they observed. 14/37 (b)case 2 (a)case 1

Centralized Scheduling The contributions of this paper 1. Consider the transmitting energy according to the number of targets covered by the sensor 2. Removes the redundancy of overlapped targets 15/37

Centralized Scheduling Overlapped target examples 16/37

Centralized Scheduling Integer Programming Formulation of the MTC Problem The first constraint indicates the limited energy of sensors The second constraint guarantees that all the targets must be covered by at least one sensor in each joint set for 17/37

Centralized Scheduling RSSA(Responsible Sensor Selecting Algorithm) of the MTC Problem 18/37

Centralized Scheduling Condition of sensor selection 1. Sensor does not cover the critical target 2. Sensor monitors a smaller number of targets 19/37 s2s2 s1s1 s4s4 s3s3 t3t3 t1t1 t2t2 s1s1 s2s2 s3s3 s4s4 t3t3 t2t2 t1t1

Centralized Scheduling Simulation result Conventional scheme vs. Proposed scheme 20/37

Minimum Coverage Breach and Maximum Network Lifetime in WSN 21/37

Centralized Scheduling Additional consideration Limited bandwidth -> Coverage breach  If Bandwidth is less than the number of sensors in a sensor cover  Bandwidth : the total number of time slots/channels  Coverage breach : the state that targets are not covered  Example ( bandwidth = 2 ) Set 1 = {s 1, s 2, s 3 },|Set 1 | = 3 Set 2 = {s 4 }, |Set 2 | = 1 Coverage breach occurs in Set 1 22/37

Centralized Scheduling The contributions of this paper 1. Introduce coverage problems under bandwidth constraints  Define the MCBB(Minimum Coverage Breach under Bandwidth constraints) problem  Define the MNLB(Maximum Network Lifetime under Bandwidth constraints) problem  Prove that MCBB and MNLB are NP-hard 2. Design two target coverage heuristics for solving the problems  Linear programming  Greedy techniques 3. Analyze the performance of two approach through simulation 23/37

MCBB (Minimum Coverage Breach under Bandwidth constraints) Centralized Scheduling 24/37 [Definition] Problem MCBB

MNLB (Maximum Network Lifetime under Bandwidth constraints) Centralized Scheduling 25/37 [Definition] Problem MNLB

Centralized Scheduling Integer Programming Formulation of the MSC Problem 26/37 Energy constraint Coverage constraint Bandwidth constraint Lifetime constraint

Centralized Scheduling Integer Programming Formulation of the MSC Problem 27/37

Centralized Scheduling Integer Programming  Linear Programming 28/37

Centralized Scheduling GREEDY-MSC of the MCBB Problem 29/37

Centralized Scheduling Binary Search Algorithm for MNLB 30/37

Distributed Scheduling 31/37

Distributed Scheduling 1-Coverage Preserving Scheduling (1-CP) For Example The set of intersection points within ‘s area The set of sensors covering the target p T rnd =20 Ref 1 =2, Ref 2 =9, Ref 3 =11 Init Phase: 1) Each sensor exchange its location and Ref. value 2) Each sensor get its schedule (active) time 32/37

Distributed Scheduling 1-Coverage Preserving Scheduling (1-CP) /37

Appendix 34/37

Virtual GRID L is the set of all grid-point of a virtual grid on the unit square region 35/54

Virtual GRID 36/54

Virtual GRID 37/54