GIS based topology for wireless sensor network modeling: Arc-Node topology approach

GIS based topology for wireless sensor network modeling: Arc-Node topology approach S.Amin Hosseini (Author) Zanjan Branch, Islamic Azad University, Zanjan,. Iran Email: s.a.hosseini86@gmail.com Behrooz Minaei Bidgoli University of. Science and Technology, Tehran, Iran Email: b_minaei@iustac.ir Mehdi Afzali Zanjan Branch, Islamic Azad University, Zanjan,. Iran Email: afzali@hacettepe.edu.tr Abstract in wireless sensor network, sensors usually deployed randomly over environment. Some important aspect of network including, coverage and hole detection are under research. Existing natural and artificial obstacles in environment may cause failure in algorithm that network using in real environment. Hence in this work, Arc- Node topology base on GIS is designed to modeling network. Our topology is based on spatial data structure which is important part of GIS. Keywords: wireless sensor network, GIS, Arc-Node topology, spatial mining. 1. INTRODUCTION Introducing of wireless sensor network and use this technology in electronic and transmission industry was useful because of tiny sensor and low energy consumption and multiple useful applications. This tiny sensor has ability to sensing environment and process of information due to create wide network as wireless sensor network. WSNs technology has very broad application respects, which can be used in military, industrial, and agriculture control and biomedical and etc. In majority of application, we have to release sensors in environment randomly, such as using airplanes because of wide region and numbers of sensors. Result of this spreading is unknown location of sensors in environment and region. In other hand protocols and algorithms over this type of network should be aware of situation of sensors and ability to self-deployment. Other unique feature of WSN is ability of cooperation and synchronization beside sensors. Every sensor has own processor. Instead of sending all information to base station, first sensor itself did some process with received data and send information to base station to use. One of the most important problems in sensor network is coverage which determines how well the sensors spread in region. Coverage problem is one of the factors of WSN 1 QoS (Quality of services) Coverage and in WSN is direct related to how modeling network. Node location, energy consumption, hole detection, and other aspect of network is based on network modeling.[2][3] Modeling sensor network is under study since WSN introduce but recently research focus on reality of models. In most of model that will be discussed in next section doesn't consider reality. For example existence of obstacle or lake in region can make mistake for model and 1 Wireless sensor network algorithm. Our model in this paper focuses on GIS feature and will use GIS topology to modeling network. This modeling could work with every GIS software and combination of GIS and sensor network feature can be creating useful model for sensor network. The rest of the paper is organized as follows. In section 2 the area of modeling sensor network over recent year is introduced. In section 3 spatial data is introduced. In section 4 we will compare spatial and aspatial mining. In section 5 we will explain how to presenting object in spatial. Base element of spatial mining explains in section 6. Spatial data structure and arc-node model introduce in section 7 and 8. In last section we will implement arc-node topology over wireless sensor network and mining location of sensor. 2. RELATED WORK Application of WSN in spatial domain includes region and environment monitoring using number of sensors. (Duckham et al. 2005, Werner-Allen et al. 2006). In some recent works GIS information use for defining best position of node location but most of the time putting sensor in exact time isn t possible. A geosensor network is define by Nittel et al.(2004) as a WSN that work on phenomena in geographic space. This type of network modeling is distributed model. One of the most important factors in WSN is decentralizing algorithm and make distributed monitoring. Because energy consumption is one of critical issue of WSN and making decision in own sensor will reduce energy consumption in compare of being one base station to handle and manage sensors. There is a lot of research in sensor network coverage in multiple ways, most of research is base of Voronoi diagram approach, here we have three of algorithm that use Voronoi approach to modeling sensor network for solve coverage problem. Voronoi itself, Vector base model and Minimax model are three of important model which are base of most of research. The Voronoi diagram [14] [15] is an important data structure in computational geometry. It represents the proximity information about a set of geometric nodes. The Voronoi diagram of a collection of nodes partitions the space into polygons. Every point in a given polygon is closer to the node in this polygon than to any other node. The algorithms rely on Voronoi diagram have several problem practically. One of the foremost assumptions of this work is that sensor can easily detect all or most of its Voronoi neighbors through local communication, but these assumption isn t correct in real world, because sensing range of sensor may not be enough to cover all neighbors. 315

Other issue about voronoi is significant sensing overlaps or voids among sensor may be ignored leading to poor network coverage. 3. SPATIAL DATA Advanced in database technology and data collection technique including text, remote sensing, geographical data, image, etc., have huge amount of data [5]. This huge data also need mechanism for handling. This growing data creates has necessity of knowledge discovery from data which data mining or knowledge discovery in database (KDD) are field to reach information. One of the most important data is geographical data which growing quickly over the world. The data that collect in geographical software also called spatial data. Spatial data is important part of GIS and spatial analyzing. In other hand spatial analyzing need spatial data with difference specification. Next we will describe some part of GIS system: Spatial data organization Defining map symbol and present them Making multiple element Difference in data structure In first part we describe two general spatial and aspatial data. Spatial data related to spatial object and location of object, so elements should be able to present over map. In second part we will describe symbol over map and determine ways to maintain object in database. In third part we study more detail about symbols. There is two multiple data model: raster and vector that will determine spatial object specification and at the end Arc-Node data model will be explain. 4. SPATIAL AND ASPATIAL MINING Data analyzing accomplish as spatial and aspatial way. In spatial analyzing we concentrate on data's location, prediction and description of phenomenon using location base data, while in aspatial analyzing for description of phenomenon, spatial data and details is not needed. Some variable has spatial meaning in compare of others. It depends on having spatial dependency; subject can be analyzing using spatial or non-spatial. For example capitation data are absolutely spatial base but can use it in non-spatial way too. 5. SPATIAL OBJECT: HOW TO PRESENT? In spatial-based analyzing we should present related symbols over map to make spatial relation between them. Location base data and feature processing has developed in GIS software. Best process need clear definition of spatial objects. Digital presentation of spatial objects present as point, line and polygon.[8][9] Point is zero dimensions that present geometrical location. Accordingly, point shape uses just for presenting location and other measurement is not meaningful. Although points has different size but area of point is not meaningful. Line is one dimension in geometrical and also present direction and size. 6. BASE ELEMENTS OF SPATIAL DATA Spatial mining has need six data element. Location: exact location of every spatial combination must be exist and be able to present in Cartesian coordinate. Attribute data: in this part very important feature about spatial combination is under study. For example if every point show the shaft in map, attribute information must be exist about height, quality, water level and ownership of shaft. Topology: Topology defines dependency among map's element. For example if there is polygon in map, maybe you need to know its polygon is inside other polygon or not. About linear shape maybe you need to know that two lines has been contact directly or using middle line or are completely separate. About points, distance of points from each other and distance from specific location is most interest in related work. Containment: determined that combination will be in polygon's scope or not. Adjacency: determined that polygons are neighborhood each other or not. Adjacency uses for study over spatial dependency between map's elements. Connectivity: determined that two line are connect or not. Connectivity issue using in transportation and routing and also use in finding minimum routing path. As we mention before, point, line and polygon is basic spatial object. Point is simplest spatial object that needed lowest level of information for analyzing. Minimum data for presenting point is location and attribute data. In spatial data, point represent as: Pi(x, y, z1, z2, z3 zn) (1) Where i is point identify number, x and y is coordinate of point and z1 to zn is attribute variable. Spatial Dependency among points represent with mathematic relation. Line and line are other important topological element is GIS. Thus, Line will present as: Lj(p1,p2,...,pn,z1,z2, zm,η1, η2, η3,.. ηq) (2) Where j is line identify number, p1 to pn determine direction of line using points order and z1 to zn are attribute variable. Variables η1 to η2 are extra information about line and. Extra information could use for line and explain how line connect each other. Other useful way to is Arc-node topology that will explain in this paper. Polygon combination is most complex than other spatial objects and define using set of line and direction. Polygons define over 2D environment, so every polygon has specific area and has unknown shape and size too. Exploring spatial dependency is very hard and complex without making specific structure for polygons. A necessary data element structure for polygon combination is like: Gk=(L1,, Ln,Z1,, Zm,S1,, Sr, φ1, φs φ1,, φt) (3) Where k is polygon identify number, L1,, Ln are combination of lines for drawing polygon lines, Z1, Zm are attribute variables, S1, Sr are neighbors polygon feature, φ1 φs is identify number of polygon that 316

polygon K include them, and φ1, φt is identify number of polygon that include polygon K. 7. DATA STRUCTURE Spatial data can be classified in two groups: Raster and Vector. In raster structure spatial attribute organized in coordinate system but in vector structure spatial data organized as set of vector. Data structure determined organization and processing multiple part of in GIS. In vector data structure every spatial combination present using set of vectors. In mathematic definition vector start with specific <x,y> coordinate and specific direction <0-360> and vector length. Every lines present using sequence of vector. sensing disk will be model's point. Arcs in every circle will be draw from every point to nearest point in each circle. For example, consider the simple Arc-node model for four sensors illustration in Figure 1. Arc-node topology maintains information in multiple files. For example we consider part of Urumie Lake and use some sensor for getting information about this lake. We put sensor randomly beside of lake that you could see sensors field with drawn circle as you can see in figure 3. 8. ARC-NODE DATA MODEL Arc-node topology is one of the basic structures that using for organization of different part of data in GIS based systems. This data model is efficient information system that works with vectors. In this model arc data are basic element and every arc has two start and end node which between two nodes is some vertex. Every node define as <x,y> coordinate and difference between vertex and node is how using of topology. Nodes use coordinate and topology, but vertex just has coordinated. Arc data has number of attribute: Arc identify number Start Node's ID End Node's ID Left Polygon's ID Right polygon's ID Start node coordinate End node coordinate Coordinate of all vertex Node's topology will be meaningful when presenting of crossing is proposed. In Arc-Node model every point uses as a point and scope of polygons present using set of arc and set of connected arc. In addition, topological relation between polygons represent as point. Topological relation is most important advantage of arc-node topology; For example supposed that we want check status of polygon's neighbor's. One simple way to do this purpose is checking arc that is beside of polygon. Arc information registered in multiple files. ATT and PAT are two important tables to region modeling. PAT table maintain information about polygons. First record of this table has whole region information and other regions information will maintain in next records. Information about arcs also maintain in ATT table. For every region and arc has considered identify number and in ATT table for every arc has maintain start node, end node, left region and right region. Connectivity, containment, adjacency are three important elements in Arc-model which using these model could find neighbors polygon easily. Fig 1: Simple Arc-Node model for four sensors Arc-node topology maintains information in multiple files. For example we consider part of Urumie Lake and use some sensor for getting information about this lake. We put sensor randomly beside of lake that you could see sensors field with drawn circle as you can see in figure 3. In next step topology should define arcs and nodes. Every points of contact sensing radius is model's node. As you can see in figure 4 nodes and arc has drawn over map. We maintain information of nodes, arcs and region (polygons) in separated files. Also you can see this information in figure 6, 7, 8. Fig 2: satellite image from part of Urumie Lake 9. ARC-NODE MODEL IN WSN As we mention before Arc-node model divided region using drawing arcs. For drawing arc we should define rule and formulate network. One can, we suppose that network's sensor is model's node and contact point of 317

8 9 10 11 P7 P8 P9 P10 71.2 35.3 268.6 253.9 89.5 44.6 398.6 345.4 Fig 3: spread sensors randomely over Lake Fig 4: first step of modeling: finding node location using sensing radius contact point Polygons information helps software to make better sensors decision about location and neighbor's sensor and environment. Next step is register arcs in ATT tables as below: TABLE 2: ATT TABLE FOR WIRELESS SENSOR NETWORK R Arc's ID Start Node End Node Right polygon Left polygon 1 1 N2 N1 P2-2 2 N13 N15 - P4 3 3 N15 N14 - P15 4 4 N12 N14 P3-5 5 N2 N1 P2 P1 6 6 N2 N3 P4 P9 7 7 N1 N10 P10 P3 8 8 N14 N11 P3 P5 9 9 N3 N4 P5 P9 10 10 N11 N9 P6 P5 11 11 N10 N7 P10 P6 12 12 N8 N7 P7 P6 13 13 N7 N6 P10 P7 14 14 N5 N6 P7 P9 15 15 N4 N5 P8 P9 16 16 N4 N9 P5 P8 17 17 N8 N5 P8 P9 10. STATE OF OUR WORK In this section our approach compare with similar work like Minimax and VEC and VOR. These solution are based on voronoi diagram which has problem on modeling network in obstacle consideration. Other different is on how deploying of sensors which these three method need complete connected network but in our work connected network isn t necessary. Fig 5: second step of modeling: draw arcs between nodes over network TABLE 3: COMPARITION OF PREVIOUS SHEMES WITH THE PROPOSED SHEME After drawing arc and making model's information file, we could set ATT and PAT tables for sensor network. We will use three information file that include node location, arc information and region (polygon) data. We register network polygons information in PAT table as below: Proposed solution Minimax Characteristic Deployment strategy Known other sensor location TABLE 1: PAT TABLE FORWIRELESS SENSOR NETWORK R 1 2 3 4 5 6 7 Polygon's ID 0 P1 P2 P3 P4 P5 P6 Area(km) 150 78.5 36.1 71.6 38.2 79.1 89.5 Surface(km 2 ) 1224 86.2 43.6 87.6 47.5 88.6 91.6 VOR VEC Our approach Distributed No (need just first hop) 318

environment without considering reality such as obstacle, lake, etc. Arc-node topology modeled network using arcs and environment feature and topological relation between them and registered information in two tables. In feature work should study more network factor like, hole detection, mobility, etc. under this type of network modeling. This model will be useable with other GIS software like ArcGIS and etc. 11. REFERENCES Fig 6: (a) arcs information file define using numbers. (b) Nodes location file define using numbers. (c) Region (polygon) information defines using numbers. [1] Chuan Zhu, A survey on coverage and issue in wireless sensor networks,journal of Network and Computer Application, 2012, 35, 619-632 [2] M.Amac Guvensan, On coverage issues in directional sensor networks: A survey, Ad Hoc Network, 2011, 9, 1238-1255 [3] M.Argany A GIS Based wireless sensor network coverage estimation and optimization: A voronoi Approach, Trans on Comput. Sci. XIV,2011, 6970, 151-172 [4] Kung-Ying, Hole detection and Boundary Recognition in WSN, IEEE 2009. [5] Linghe Kong, Surface Coverage in Sensor Network, IEEE TRANSACTION ON PARALLEL AND DISTRIBUTED SYSTEM, Vol X.No X 2012. [6] Subir Halder, Enhancement of wireless sensor network lifetime by deploying heterogeneous nodes, Journal of Network and Computer Applications,2013, (ARTICLE IN PRESS) [7] Megiddo, N.: Linear-time algorithms for linear programming in R3 and related problems.siam J. Computing 12, 759 776 (1983) [8] Sharifzadeh, M., Shahabi, C.: Supporting spatial aggregation in sensor network databases.in: Proc. 12th Annual ACM International Workshop on Geographic Information Systems,pp. 166 175 (2004) [9] Jie jia, Multi-objective optimization for coverage control in wireless sensor network with adjustable sensing radius, Computers and mathematic with application, 2009, 57, 1767-1775. [10] Peng-jun Wan, Coverage by randomly deployed wireless sensor networks, IEEE International Symposium on network Computing, 2005. [11] Yue Wang, Boundary Recognition in sensor networks by topological method, MobiCom '06 Proceedings of the 12th annual international conference on Mobile computing and networking,2006,122-133. [12] Nadeem Ahmed, The holes problem in wireless sensor networks: a survey, ACMSIGMOBILE Mobile Computing and Communications Review Homepage archive Volume 9 Issue 2, April 2005, Pages 4 18. [13] Fucai Yu, Anchor Node Based Virtual Modeling of Holes in Wireless Sensor Networks, Communications, 2008. ICC '08. IEEE International Conference on, 2008, 3120-3124. [14] Hwa Chun, Computational geometry based distributed coverage hole detection protocol for the wireless sensor networks, Journal of Network and Computer Application, 34, 2011, 1743-1756. 10. CONCLUSION AND FEATURE WORK In this paper we study new version of WSN modeling using GIS topology because previous work just try to modeling network with simulator over 2D and 3D 319