Algorithm 1 Fuzzy spatial relation modeling by I5×5M in sensor networks
1:	Variables: SN= {s1,s2,...,sn}; SensorNetwork
2:	S-On, D-Ons; a sensor ontology, a domain ontologies
3:	I 5×5 M; Intersection matrix modeling the fuzzy spatial relation between A and B
4:	fA(si) → {(0,1)|(0,1,2)} ; Function used to detect A
5:	gB(si) → {(0,1)|(0,1,2)} ; Function used to detect B
6:	h(s)→ {0,1,2} ; Function used to detect the boundaries of A or B
7:	SpTr(SN); Built spanning tree over the sensor network
8:	Begin
9:	Setting the sensor network; establishing communication links among nodes
10:	Build the spanning tree (SpTr(SN)) over the SN topology
11:	Detection of the phenomenon A is computed over the SN: fA(SN)
12:	Detection of phenomenon B is computed over theSN: gB(SN)
13:	Decentralized spatial computingof A and B boundaries over the SN:h(si(AB))
14:	Compute spatial statefrom detected phenomena and boundaries: (si([x,y,z][x y z])
15:	Aggregatesensor spatial state
16:	From the last child node to sink node along SpTr(SN)
17:	Compile spatial states ofunequal value
18:	Compute I5×5M by alignment between spatial states values and I5×5M elements
19:	If...thenInference ofqualitative specifications about fuzzy spatial relationfrom I5×5M
20:	End.