MULTI-OBJECTIVE EVOLUTIONARY ALGORITHMS FOR ENERGY-EFFICIENCY IN HETEROGENEOUS WIRELESS SENSOR NETWORKS

MULTI-OBJECTIVE EVOLUTIONARY ALGORITHMS FOR ENERGY-EFFICIENCY IN HETEROGENEOUS WIRELESS SENSOR NETWORKS José M. Lanza-Gutiérrez, Juan A. Gómez-Pulido, Miguel A. Vega- Rodríguez, Juan M. Sánchez University of Extremadura (Spain). Polytechnic school

Index 1. Introduction 2. Heterogeneous wireless sensor network 3. Problem resolution 4. Experimental results 5. Comparisons with other authors 6. Conclusions and future work 2

1. Introduction (I) 3 The use of wireless sensor networks (WSNs) has increased substantially in the last years: applications in both civil and military areas. An important aspect in the use of WSNs is the energy efficient. This kind of networks are powered by batteries: network lifetime depends on amount of information transmitted by sensors, as well as its scope, among others. Nowadays, WSNs are more complex due to inclusion of auxiliary elements (routers) in order to minimize communication between sensors,increasing both network speed and lifetime of sensors Heterogeneous WSN

1. Introduction (II) 4 In this work, we have solved the heterogeneous WSN design problem: how to place routers and sensors optimizing several objectives simultaneously. A good solution for heterogeneous WSN involves an increase of energy efficiency compared with its homogeneous equivalent. This is a NP-hard problem, so we need to use certain techniques to facilitate its resolution, like evolutionary algorithms. We have used two well-known MOEAs: NSGA-II and SPEA-2.

1. Introduction (III) Our work shows the following contributions: 1) The problem has been solved by means of evolutionary techniques. 2) We have optimized over three objectives that have not been considered jointly in any paper found: average number of hops, coverage and reliability. 3) The results obtained have been analyzed in depth using statistical procedures. 5 We study the deployment of a heterogeneous WSN as an alternative to traditional homogeneous WSN.

2. Heterogeneous wireless sensor network (I) A particular problem instance will be defined by several elements: (N Routers, M sensors and a sink node) A scenery (D x * D y ) A sensor obtains information about its environment with a sensitivity radius (R s ). 6

2. Heterogeneous wireless sensor network (II) A router allows us to establish network communications (links) and to collect information about sensors in its communication radius (R c ). Sink node collects information about all sensors in the network, it is the center node. In this work, a sensor only can communicate with routers, not with other sensors. 7

2. Heterogeneous wireless sensor network (III) The most important factors have been used to deploy the network. Those that define the router network quality: average number of hops (to minimize) and reliability (to maximize). The global coverage provided by sensors (to maximize). These objectives are simultaneously optimized using MOEAs. 8

2. Heterogeneous wireless sensor network (IV) Average number of hops (1): it is the minimum number of hops (routers that are necessary to cross) between each router and collector node, divided by the total number of routers. A hop is possible when the distance between two elements is less than communication radius. N number of routers, C collector node 9

2. Heterogeneous wireless sensor network (V) Sensor coverage (%) (2): it is the terrain percentage covered by sensor nodes. We use a boolean matrix of D x *D y points over scenery, so for each sensor, the points within its radius will be activated; finally, we have to count the activated points. R represents the boolean matrix and R x,y position (x,y) of this matrix. the 10

2. Heterogeneous wireless sensor network (VI) Reliability (%) (3): it allows us to define the network robustness. It is the number of possible paths between each router and collector node, divided by the maximum number of paths in a fully coupled topology. 11 TotalRoutes provides the number of paths between two routers (Dijsktra). We notice that when we use N+1 is because we have included the collector node (N is the number of routers).

3. Problem resolution (I) The design of a heterogeneous WSN is a NPhard problem. It is necessary to use non-conventional techniques to facilitate its resolution: Heuristics, EAs, We use MOEAs: the best results in literature. When we use this kind of techniques, there are some important aspects: encoding of individuals, crossover and mutation strategies, generation of initial population. 12

13 3. Problem resolution (II) EA is a search heuristic that mimics the process of natural evolution. This heuristic is routinely used to generate useful solutions to optimization and search problems. i Generation of initial population (solutions) F(x) Evaluation of individuals Se Selection of the best individuals Cr Crossover among individuals. Mu It alters values in a individual Re Worse individual die, population size is constant? Termination condition

3. Problem resolution (III) Encoding of individuals: Two parts, coordinates (x and y) of routers and sensors. Each part is divided in regions to split the available space in several portions, and to ensure a good distribution of elements. 14

3. Problem resolution (IV) Generation of initial population: We place routers and sensor randomly. Crossover: We only cross among elements from a same region. The objective is that each region can evolve separately. Mutation: We perform random changes over coordinates of elements. For each change, we evaluate the individual. If this change causes better fitness values will be accepted; in the negative case change will be discarded, back to previous coordinates. 15

3. Problem resolution (VI) Well-known MOEAs: (Selector algorithms) NSGA-II SPEA-2 Well-described in literature [18][19]. 16

4. Experimental results (I) The instance data used in this work represent a couple of scenarios of 100x100 and 150x150 meters, on which will be placed a set of routers and sensors with values of R c and R s, 25 and 15 respectively (in meters). Providing of other authors. For these instances, we use the less number of sensor nodes: scenario area divided by sensor area. 17

4. Experimental results (II) Instance A(m 2 ) N M N/M Inst1 100x100 4 16 4 Inst2 100x100 8 16 2 Inst3 150x150 4 32 8 Inst4 150x150 8 32 4 We solve these 4 instances by both algorithms NSGA-II and SPEA-2 and we obtain a solution set (Pareto front) for each of them. 18

4. Experimental results (III) To determine the goodness of solutions, we use hypervolume metric (it is based on physical area of this solution set). 19 If hypervolume is bigger, solution will be better. Certain to ideal values (maximum coverage and reliability (100%) and minimum number of hops (0)).

4. Experimental results (IV) By means of statistical techniques, we have detemine that SPEA-2 provides better results (hypervolumes) than NSGA-II for these instances. 20

5. Comparisons with other authors (I) we can found results from resolution of traditional WSN for energy efficiency, but we cannot compare our fitness values with theirs different conception. Some authors have been demonstrated that heterogeneous WSN provides better energy efficiency than its homogenous alternative, but their approaches are different from ours. 21

5. Conclusions and future work (II) In this work, we have tackled the deployment of a heterogeneous WSN optimizing some important factors: area covered by sensors, average number of hops and network reliability. We have used two well-known EAs, NSGA-II and SPEA-2, proving as SPEA-2 provides the best results. 22 Important: we have tackled how to obtain the best heterogeneous WSN, but we have not compare with its homogeneous conception.

5. Conclusions and future work (III) Future: more instances, new algorithms, parallelism And a new approach, first, we study the positioning of sensors maximizing coverage, and then we deployed the network of routers optimizing factors used in this work, including a new metric for energy efficiency: allowing as transform a real homogeneous WSN in a new more energy efficiency heterogeneous WSN. 23