UC Berkeley International Conference on GIScience Short Paper Proceedings

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1 UC Berkeley International Conference on GIScience Short Paper Proceeding Title A novel method for probabilitic coverage etimation of enor network baed on 3D vector repreentation in complex urban environment Permalink Journal International Conference on GIScience Short Paper Proceeding, 1(1) Author Afghantoloee, Ali Karimipour, Farid Motafavi, Mir Abolfazl Publication Date DOI /B3114kv536qq Peer reviewed escholarhip.org Powered by the California Digital Library Univerity of California

2 Short Paper Proceeding A novel method for probabilitic coverage etimation of enor network baed on 3D vector repreentation in complex urban environment A. Afghantoloee 1, F. Karimipour 2, M. A. Motafavi 1 1 Center for Reearch in Geomatic, Department of Geomatic, Univerité Laval, Quebec, Canada ali.afghantoloee.1@ulaval.ca;mir-abolfazl.motafavi@cg.ulaval.ca 2 Dept. of Geomatic Engineering, Univerity of Tehran, Amir-abad Street, Tehran, Iran fkarimipr@ut.ac.ir Abtract Wirele Senor Network (WSN) are widely ued for monitoring and obervation of dynamic phenomena. A enor in WSN cover only a limited region, depending on it ening and communicating range, a well a the environment configuration. For efficient deployment of enor in a WSN the coverage etimation i a critical iue. Probabilitic method are among the mot accurate model propoed for enor coverage etimation. However, mot of thee method are baed on rater repreentation of the environment for coverage etimation which limit their quality. In thi paper, we propoe a probabilitic method for etimation of the coverage of a enor network baed on 3D vector repreentation of the environment. 1. Introduction Nowaday, WSN have found variou application in indutry, ecurity, agriculture, military and diater management. Efficient monitoring and management of dynamic phenomena in the real world neceitate it efficient and accurate coverage. The efficiency of the coverage of a enor network depend on optimal poition of each enor node within the network. An individual enor cover only a limited area, which depend on it ening capacity, range of communication a well a the environment complexity. The total area covered by a WSN i obtained from the union of the region covered by individual enor. Therefore, efficient deployment of enor in a WSN i a critical iue that affect the coverage a well a communication between enor. Several optimization method (i.e., global or local, determinitic or tochatic, etc.) have been propoed to detect and eliminate coverage hole and hence increae the coverage of enor network (Argany et al. 2015). One of the key iue of all deployment optimization algorithm i accurate etimation of the coverage of an individual enor. Sening model of individual enor which could be binary or probabilitic, omnidirectional or directional ha ignificant impact on the precie coverage etimation of a enor network uing divere optimization algorithm. Mot of the enor coverage etimation method ue a rater repreentation of the environment (Akbarzadeh et al. 2013) for optimization purpoe that limit their preciion and efficiency. Thi i becaue rater repreentation are contrained by their patial reolution, and their regular hape reult in redundant data for unoccupied area. Few vector-baed optimization algorithm are propoed in the literature, which are motly baed on 2D vector-baed repreentation of the environment and do not adequately conider the preence of manmade and natural obtacle in the ening area (Wang and Cao 2011). To overcome thee limitation, in thi paper we propoe a probabilitic enor coverage etimation method baed on precie 3D vector-bae repreentation of the environment and we preent ome reult of an ongoing reearch project that aim at better optimization of a enor network in a 3D complex urban area. 1

3 Short Paper Proceeding 2. Probabilitic ening model Akbarzadeh et al. (2013) preented an improvement to optimization model by propoing a probabilitic ening model for individual enor that conider not only the impact of ditance on ening capacity, but alo the impact of angle between the enor direction and the line connecting the enor to a given target (Figure 1). However, thi model i till limited in accurately optimizing and etimating the coverage of a enor network a it i baed on a rater repreentation of the environment. Figure 1: Probabilitic ening model of a enor with limited ditance and angle range (Akbarzadeh 2013) Advance in geopatial method and technologie provide precie and timely collection of 3D patial data allowing the creation of multi-reolution and multi-purpoe 3D vector model of the environment that can ignificantly improve the efficiency and accuracy of optimization and coverage etimation of a enor network in a 3D urban environment. 3. 3D vector-baed Probabilitic enor coverage etimation Conider a repreentation of the environment a a et of polygon that form building, terrain and obtacle, and a enor that ha a limited range in ditance and field of view. Then, in a 3D probabilitic enor coverage model, quality of detection of a polygon depend on it ditance to the enor a well a the angle between the polygon and the enor. In Figure 2, for example, polygon A i further from enor S compared to polygon B, thu polygon A i detected with a lower quality than polygon B. On the other hand, although polygon B and C are, in average, located at the ame ditance to the enor S, polygon C ha a lower detection quality than polygon B a it ha a more oblique direction repect to the enor S. Even, a the ditance and direction differ from point to point on an individual polygon, each point on a polygon may have a different detection quality. C S Figure 2: Detection quality of target baed on ditance and direction. To practically implement the propoed methodology, the polygon, or the fraction of polygon, that are viible by the enor are determined (Afghantoloee et al. 2014). Then, thee viible polygon are dicretized to a grid (Figure 3) and the detection quality of cell i etimated and finally categorized to certain clae. A B 2

4 Short Paper Proceeding C S Figure 3: Surface raterizing in 3D pace. The detection quality of a cell i determined uing the area of each cell multiplied by the probability of coverage repect to it ditance and direction from the enor. Thi i obtained uing the following equation (Akbarzadeh et al. 2013): C Area( q)* P( q p )* P( ( q, p )) (1) 1 q p d P( q p ) 1 exp q p 0 otherwie co( ( qp, )) 1 ( ) ( ( qp, )), P( q p ) 2 0 otherwie where Area(q) i the area of the pixel q; d and α are repectively the ditance and angle range of the enor; (q,p) i the pan angle of the enor relative to the pixel q; q-p i the ditance between the enor and pixel q; θ i pan angle of the enor; and α, β, and ω are the parameter for configuring the probability function which can be etimated from the obervation behavior of the enor. 4. Cae tudy In order to evaluate the propoed trategy, a directional enor with a 3 and 30 meter height and radiu, pan angle of 45 and direction of [1,1,-1] wa conidered in 3D environment. The parameter α, β, and ω are repectively conidered a 350, 10, and 3. All feature in the 3D vector model are contructed by polygon with 1 cm accuracy, which have a counterclockwie tructure. Uing perpective baed viibility etimation methodology and 3D raterizing of the polygon, the probabilitic coverage of all cell of the 3D model wa calculated. Figure 4 illutrate the probabilitic coverage conidering (a) ditance, (b) direction and (c) both together. Figure 5 how the reult for the ame cae in a 2.5 rater dimenion model with 20 cm reolution. Comparing the reult (Table 1) indicate that the propoed trategy allow a more precie probabilitic coverage etimation compared to a rater model. A B (2) (3) 4. Concluion Thi paper propoed a novel method baed on 3D urban vector model for etimation of the coverage of wirele enor network. Thi method ha more advantage compared to raterbaed DSM/DEM model a it conider the coverage of all facet of feature like building and wall, and even under the feature uch a bridge and balconie. The propoed method for a probabilitic ening etimation model led to a more realitic coverage etimation for individual enor in a enor network in a 3D complex environment. 3

5 Short Paper Proceeding (a) (b) (c) Figure 4: Probabilitic coverage etimation in a 3D vector model: (a) direction (b) ditance, and (c) both together. (a) (b) (c) Figure 5: Probabilitic coverage etimation in a rater model: (a) direction (b) ditance, and (c) both together. Table 1. The comparion coverage probabilitic etimation in Rater and Vector model. Coverage probability Rater_20cm (m 2 ) Vector_without wall (m 2 ) Vector_with wall (m 2 ) Ditance Direction Ditance & Direction Reference Afghantoloee A, Doodman S, Karimipour F and Motafavi MA, Coverage Etimation of Geoenor in 3d Vector Environment, GIReearch Akbarzadeh V., Gagne C., Parizeau M, Argany M and Motafavi MA, 2013, Probabilitic ening model for enor placement optimization baed on line-of-ight coverage. IEEE Tranaction on Intrumentation and Meaurement. 62: Argany M, Motafavi MA, Gagné C, Context-Aware Local Optimization of Senor Network Deployment. Journal of Senor and Actuator Network (JSAN). 4(3): Wang, Y., Cao, G., Movement-aited enor deployment, IEEE Infocom (INFOCOM 04), pp