BICRITERIA OPTIMIZATION OF ENERGY EFFICIENT PLACEMENT AND ROUTING IN HETEROGENOUS WIRELESS SENSOR NETWORKS Mustafa Gökçe Baydoğan School of Computing, Informatics and Decision Systems Engineering Arizona State University (ASU) Tempe, AZ, USA Nur Evin Özdemirel, PhD Department of Industrial Engineering Middle East Technical University (METU) Ankara,Turkey 11/10/2010

MOTIVATION SOCIOECONOMIC Environmental monitoring Air, soil or water monitoring Habibat monitoring Seismic detection Military surveillance Battlefield monitoring Sniper localization Nuclear, biological or chemical attack detection Disaster area monitoring RESEARCH…

DESIGN ISSUES IN WSNs Deployment random vs deterministic; one-time vs iterative Mobility mobile vs immobile Heterogeneity homogeneous vs heterogeneus Communication modality radio vs light vs sound Infrastructure infrastructure vs ad hoc Network Topology single-hop vs star vs tree vs mesh Römer and Mattern, 2004, The Design Space of Wireless Sensor Networks, IEEE Wireless Communications, 11:6, 54-6

There are some events (targets) to be sensed in the monitoring area PROBLEM CHARACTERISTICS There are some events (targets) to be sensed in the monitoring area Locate sensors to possible locations so that events are sensed(detected) with a given probability Determine the rate of data flow between sensors and sink node (base station) Sink

LITERATURE

PROBLEM DEFINITION OBJECTIVES DECISIONS CONSTRAINTS Minimize total cost of sensors deployed Maximize lifetime of the network DECISIONS Location of heterogeneous sensors Data routing CONSTRAINTS Connectivity Node (sensor) and channel (link) capacity Coverage Battery power

PROBLEM DEFINITION CONNECTIVITY A sensor of type k located at location i can communicate with a sensor of type k located at location j if

PROBLEM DEFINITION COVERAGE Denoted as the detection probability of a target at point By a sensor of type k located at location Strength of the sensor signal decreases as distance increases† Detection probability of a target at point † Zou and Chakrabarty, 2005, A Distributed Coverage- and Connectivity- Centric Technique for Selecting Active Nodes in Wireless Sensor Networks, IEEE Transactions on Computers

PROBLEM DEFINITION ENERGY CONSUMPTION MODEL † Sources of energy consumption in a sensor Generating data Receiving data Transmitting data is a distance-independent constant term is a coefficient term associated with the distance dependent term is the distance between two locations is the path loss index † J. Tang, B. Hao, and A. Sen, 2006, Relay node placement in large scale wireless sensor networks, Computer Communications, 29:4, 490-501

PROBLEM FORMULATION total cost of sensors located lifetime of the network one sensor can be located at each location

PROBLEM FORMULATION data flow balance at a sensor all data is routed to sink node sensor capacity channel (link) capacity

PROBLEM FORMULATION coverage battery power data flow decision location decision

THE BICRITERIA PROBLEM DOMINATION dominates and if or

A BICRITERIA PROBLEM FINDING PARETO OPTIMAL SOLUTIONS Solve for to find lower bound on cost Solve for s.t. to find lower bound on lifetime Solve for to find upper bound on lifetime Solve for s.t. to find upper bound on cost For all integral values solve for

GENETIC ALGORITHM Nondominated sorting approach (Goldberg, 1989) Convergence to Pareto optimal front Diverse set of solution along Pareto optimal front

GENETIC ALGORITHM REPRESENTATION type of the sensor located on the corresponding location Disadvantages Flow allocation is not stored Lifetime cannot be determined Finding feasible solutions after mutation and crossover operators is very hard Advantages Problem reduces to LP with given sensor locations By solving LP, maximum lifetime and constraint violations can be determined

GENETIC ALGORITHM FITNESS Based on nondominated sorting idea considering three objectives Total sensor cost Network lifetime Overall constraint violation Connectivity Coverage Capacity violations (channel and sensor)

GENETIC ALGORITHM INITIAL POPULATION GENERATION Two phase approach Sensor location Location according to target coordinates Relay location Location according to sensor coordinates MUTATION Repair and improve Repair coverage constraints Improve cost and lifetime objectives Repair connectivity constraints Improve cost objective

GENETIC ALGORITHM

TEST PROBLEMS Small problems Problems with 24 possible locations 50 targets are dispersed across the monitoring area Each target has a random coverage threshold uniformly distributed between 0.7 and 1 The rate of data generated for each target is a random integer between 1 Kbps and 3 Kbps PS24 PS40 BS

COMPUTATIONAL RESULTS PERFORMANCE MEASURES Proximity Indicator (PI) For each solution found, find the Pareto optimal solution with closest normalized Tchebychev distance Reverse Proximity Indicator (RPI) For each Pareto optimal solution, find the solution with closest normalized Tchebychev distance Hypervolume Indicator (HI) Find the ratio of area bounded by nadir point that cannot be covered

COMPUTATIONAL RESULTS Smaller Problems

COMPUTATIONAL RESULTS

COMPUTATIONAL RESULTS

COMPUTATIONAL RESULTS TEST PROBLEMS We also introduce larger test problems Problems with 99 possible locations Problems with 111 possible locations

COMPUTATIONAL RESULTS Larger Problems

CONCLUSION COMMENTS and FURTHER RESEARCH GA provides reasonable solution quality with better solution times We can obtain better solutions than the exact approach has by representing the area with more grids (approximation of the continuous space) even with better solution times Future research Modification of ε-constraint approach Use of sensitivity analysis results (in progress) Incorporating decision maker’s preferences Different objectives such as minimization of total delay, total hop count or average path length Special network requirements such as K-coverage or K-connectivity

QUESTIONS AND COMMENTS? THANK YOU... QUESTIONS AND COMMENTS?

OUTLINE MOTIVATION PROBLEM DEFINITION GENETIC ALGORITHM COMPUTATIONAL RESULTS CONCLUSION

EXACT SOLUTION TEST PROBLEMS Small problems Problems with 24 possible locations Problems with 40 possible locations 50 targets are dispersed across the monitoring area Each target has a random coverage threshold uniformly distributed between 0.7 and 1 The rate of data generated for each target is a random integer between 1 Kbps and 3 Kbps PS24 PS40 BS

EXACT SOLUTION

GENETIC ALGORITHM Classical search and optimization methods Why evolutionary algorithms? Classical search and optimization methods find single solution in every iteration need repetitive use of a single objective optimization method assumptions like linearity, continuity Evolutionary Algorithms use a population of solutions in every generation no assumptions eliminate the need of parameters (like weight, ε or target vectors) find and maintain multiple good solutions Emphasize all nondominated solutions in a population equally Preserve a diverse set of multiple nondominated solutions Near optimal, uniformly disributed, well extended set of solutions for MO problems

COMPUTATIONAL RESULTS

COMPUTATIONAL RESULTS

CONCLUSION RPI and HI worsen as the problem size increases from 24 to 40 when capacity constraints are loose TC instances are harder to solve for the GA compared to LC instances, whereas they are easier for the ε-constraint approach. Performance measures for tight capacity are about twice as large as those for loose capacity. The problem size has less effect on the performance measures when the capacity constraints are tight. When the capacity constraints are loose, the GA solves problems of size 24 in one tenth of the ε-constraint CPU times. For problems of size 40, GA CPU time is about 100 times shorter than ε-constraint time. For the tight capacity case, GA CPU times are slightly longer than ε-constraint times with 24 possible locations, but they are 15 times shorter with 40 possible locations. For problems with 99 and 111 possible locations, the GA converges to a solution in about 160 minutes.