Horizontal and Vertical Zone Based Location Techniques for Wireless Sensor Networks

International Journal of Wireless & Mobile Networks (IJWMN) Vol.2, No.4, November 2010
DOI: 10.5121/ijwmn.2010.2414 171
HORIZONTAL AND VERTICAL ZONE BASED LOCATION
TECHNIQUES FOR WIRELESS SENSOR NETWORKS
M. Bala Krishna1*
and M. N. Doja2
1*
University School of Information Technology, G G S Indraprastha University, Delhi, INDIA
Corresponding Author Email: mbalakrishna@yahoo.com
2
Department of Computer Engineering, Jamia Millia Islamia, Delhi, INDIA
Email: mndoja@gmail.com
ABSTRACT
Localization is an important feature in Wireless sensor networks (WSNs). Accuracy in node localization
with proper synchronization and required localization of sensor nodes, save node energy and enhance the
performance of communication network protocols. In this paper we propose distributed localization
algorithms and assume position known Cluster Head (CH) and position unknown three beacon nodes for
each cluster. Using trilateration technique beacon nodes are located. Additional beacon node is added to
confirm the location of beacon nodes and maintain location accuracy. These position localized beacon
nodes help to locate other sensor nodes. The proposed two distributed zone based localization algorithms
are (i) Horizontal Location Position System (H-LPS), where cluster is divided into Horizontal Zones
(HZs) and beacon nodes locate in horizontal direction and (ii) Vertical Location Position System
(V-LPS), where cluster is divided into Vertical Zones (VZs) and beacon nodes locate in vertical direction.
The main advantage of zone based localization is nodes belonging to a bounded zone (horizontal or
vertical) are localized and participate in WSN computing. If a bounded zone is eliminated during
localization, then nodes do not participate in localization and thus save WSN computing. We provide zone
based simulations for H-LPS and V-LPS in comparison with existing localization algorithms like Ad hoc
Positioning System (APS), Recursive Positioning Estimation (RPE) and Directed Positioning Estimation
(DPE). Performance evaluation of H-LPS and V-LPS illustrate that for zone based localization, H-LPS
and V-LPS perform better that existing localization techniques. Bounded zone performance is optimal for
H-LPS and V-LPS compared to existing localization techniques in WSN.
KEYWORDS
RSSI, Trilateration, Multilateration, Bounded-Box, Time of Arrival (TOA), DV-Hop (Distance Vector),
DV-Distance, Cluster Head (CH), Beacon Node (BN), Horizontal Zone (HZ) and Vertical Zone (VZ).
1. INTRODUCTION
WSN localization techniques [1] are used to locate sensor nodes with the support of reference
nodes and anchor nodes, whose location positions are known. Firstly, nodes are localized using
measurements like distance, time of arrival, time difference of arrival, angle of arrival, direction
of arrival and communication range [2]. Secondly, position computation techniques [3] like
trilateration, multilateration, triangulation, probabilistic approach and bounded box are used to
compute position of a node. Finally, centralized or distributed localization algorithms are used
to locate WSN nodes. GPS is used for node localization using global coordinate system. WSN
localization algorithms are classified [4] as (i) Centralized algorithms and (ii) Distributed
algorithms. In Centralized Localization Algorithms, nodes are localized with central located
base station with in a given radius. Distance estimation values between nodes are sent to the
central base station, where locations of each node are computed. CH maintains location
parameters of all nodes and corresponding network topology. Therefore, centralized localization
algorithms optimally compute node positions due to less dense structure and short range
distance from CH. The main drawback is traffic congestion caused by node density, and
location parameters exchange between nodes and CH that affect the performance for large scale

172
WSN. Hence, distributed localization techniques are used for large scale WSN. Primary
centralized localization algorithms in WSN are (i) Convex Position Estimation [5] where nodes
are localized with communication constraints between nodes. RSSI (Received Signal Strength
Indication) is computed with in a convex region and (ii) Multi-Dimensional Scaling (MDS) [6]
where quantitative data and node parameters with Euclidean geometrical parameters are used to
compute node distance [7]. Hierarchical MDS (HMDS) based localization algorithm for WSN is
given in [8]. In Distributed Localization algorithms, location of CH and reference nodes helps
to locate other nodes in an iterative manner. These algorithms are self-organizing and depend on
parameters like node energy, direction of localization and minimum distance from beacon
nodes. Network parameters that affect the performance of distributed localization are node
density and network topology. Node density is defined as number of nodes per square meter
which is directly proportional to localization process. Conversely, localization error in beacon
nodes gets propagated if there are more nodes per square meter. Therefore, solution would be to
re-compute location parameters of beacon nodes if average percentage of data loss is below a
threshold value. Communication parameters that affect the performance of localization
techniques are Radio Frequency (RF) channel. Parameters used in RF estimation are Time of
Arrival (TOA), RSSI [9], and Angle of Arrival (AOA). Distributed range-based localization
techniques in WSN are classified as direct range [10] and extended range [11] [12]. Distributed
position-based localization techniques in WSN is given as multi-hop localization, ad hoc
position localization, recursive position estimation, direction position estimation and mobile
beacon assisted localization. In this paper, we propose algorithms for distribute position-based
localization technique. This paper is organized as follows. Section 2 gives overview of related
work in distributed localization algorithms for WSN. Section 3 explains localization parameters
in our implementation. Section 4 explains in detail our proposed H-LPS algorithm and gives its
analysis. Section 5 explains our proposed V-LPS algorithm and gives its analysis. Section 6
gives simulation results, comparative analysis with existing localization techniques and
performance of H-LPS and V-LPS techniques. Section 7 concludes our discussion.
2. RELATED WORK
Distributed localization algorithms are mainly beacon-based, and calculate node positions from
known beacon positions. Beacon nodes are located using GPS system or a priori computed.
Various types of beacon-based localization algorithms used in WSN are bounded box, diffusion
and APIT. Bounding box [13] uses several beacon nodes, and nodes belonging to intersection of
bounded box are used to locate nodes. Diffusion uses centroid of beacon nodes with RSSI to
compute location of unknown nodes. Approximate Point In Triangle (APIT) [14] uses area
based techniques with more number of beacon nodes to locate a node. Nodes [9] are localized
with respect to signal strength and coverage area of beacon nodes. Beacon nodes exchange
information across other beacon nodes using Distance Vector (DV) and Distance Hop (DH)
techniques. APS [11] uses three beacon nodes and trilateration technique to compute node
positions. DV-Hop, DV-Distance and Euclidean geometrics are used to locate nodes. In
DV-Hop, beacon nodes exchange information with other beacon nodes using hop count and
time stamps. Node exchange information bi-directionally, computes average hop count and
calculates respective locations. In DV-Distance, beacon nodes exchange information with other
beacon nodes using distance and time stamps. DV-Distance uses RSSI to find average distance.
Beacon nodes exchange information bi-directionally, compute average distance and calculate
respective locations. In Euclidean technique, APS uses at least two nodes whose locations are
known. These two nodes send their location position and distance to unlocalized beacon node.
Using geometric parameters like angle and distance between beacon nodes, location position is
calculated. This localized beacon node and other reference node help to locate sensor nodes.
The main advantage of APS is less number of beacon nodes, which can lead to error position
computation. RPE [10] uses localized beacon nodes to identify unknown nodes. Atleast three

173
beacon nodes are used to identify unknown node. First, localized beacon nodes are identified.
Using RSSI, unknown nodes exchange information with known beacon nodes. Finally, using
trilateration unknown node positions are calculated and position parameters are broadcast to
neighboring nodes. These localized nodes function as beacon node to locate other sensor nodes.
This process is repeated recursively until all the nodes are located. The main advantage of RPE
is increase in node localization due to position known beacon nodes. Any error in localized
beacon nodes gets propagated and performance of RPE degrades. DPE [15] is variation of REP
with restrictions on localization path. DEP localization follows from single point recursion to
determined directed path with only two reference nodes. [16] Describes various types of
localization techniques. Challenges and current approaches in WSN localization are given [17].
3. LOCALIZATION PARAMETERS USED
3.1. Distance Computation
The distance estimation using Time of Arrival (ToA) is classified as:
• One-Way control exchange using DV and time stamps
• Two-Way control exchange using DV and time stamps
• Multipath-Way control exchange using DV and time stamps
In our proposed algorithm we use two-way control exchange to locate a node. For node distance
confirmation, we add a beacon node and use multipath-way control exchange to confirm node
position. We use TOA since minimum numbers of signals are used between nodes to compute
node distance. Distance between two nodes is proportional to signal propagation. If a signal S is
sent at time t1 and received at time t2, then distance between two nodes is calculated as:
*( 2 1)
d Signal_Propagation_Speed t t
= − .................................................. (1)
where signal propagation speed is given in Kbits/sec over single hop or multi hop. TDoA uses
combination of signals, S1 and S2 to estimate node distance.
3.2. Position Computation
Node position is computed using absolute, relative or global coordinates. Various methods used
in node position computation are trilateration, multilateration, triangulation, bounded box and
probabilistic approaches. In our proposed algorithm, we use trilateration and multilateration
techniques. In Trilateration, CH functions as reference node with known position (x, y).
Trilateration uses intersection of three beacon nodes to calculate distance from CH node. As
given in figure 1, CH and three beacon nodes form triangles and position of each BN is
calculated. Distances e1, e2 and e3 from CH to all BNs (BNA, BNB and BNC) are calculated as:
*( 2 1)
*( 2 1)
e1 Signal_Propagation_Speed t t
e2 Signal_Propagation_Speed t t
e3 = Signal_Propagation_Speed*(t2-t1)
= −
= − ............................................... (2)
Similarly distances d1, d2 and d3 across BNs are calculated. From geometric properties, the
triangle BNACHBNC with known position (x, y) and calculated distances e1, e3 and e3, position
(x1, y1) of BNA and position (x3, y3) of BNC are calculated. Similarly, from triangle
BNBCHBNC, the position (x2, y2) of BNB is calculated.

174
Figure 1. Computation of node position using CH, BN and correction BN
Multilateration - Combination of one or more trilateration sets form multilateration. Centroid is
computed from each trilateration set and node positions are calculated.
4. HORIZONTAL LOCATION POSITION SYSTEM (H-LPS)
Sensor cluster is divided into Horizontal Zones (HZs) as given in figure 2. Each row HZ has
number of column HZs. After locating first row HZ, the algorithm shifts to second row HZ
until all the HZs are covered. In H-LPS, we use four types nodes as (i) CH (ii) BN (iii) Settle
node (StN) and (iv) Sensor node (S). We place three beacon nodes horizontally whose location
position is not known. These beacon nodes are linked to form a fully connected network.
Beacon nodes use CH as reference node to locate their positions. Nodes exchange information
using DV-Hop and DV-Distance techniques to estimate node locations. In APS, if a node is
unable to receive packet from beacon node, new packet is re-transmitted and error corrections
(if any) are done. In our proposed H-LPS algorithm, we place a new localized beacon node to
reconfirm the location of beacon nodes. The new beacon node functions as correction factor for
localized beacon nodes. The new node is linked with other localized beacon nodes to form a
fully connected network. Figure 3 gives illustration of H-LPS. Let 1
CH HZ
∈ . Figure 3(a)
indicates how CH locates BNs. Each BN sends DV-Hop signals to other BNs and calculate node
distance and position. Additional localized beacon node, BNCr is added as correction factor to
reconfirm the location of beacon nodes (since their location position form the basic
coordinates). Localized beacon node now function as settled node and aid to locate other sensor
nodes. This process is repeated in figure 3(c) and 3 (d) until all nodes are localized in HZ2. This
process is repeated until HZ3, HZ4, ...., HZn are all localized with in a given cluster. Each
cluster has its set of beacon nodes and H-LPS is applied across HZs for all clusters. For some
WSN applications all the sensor nodes need not be localized. Let us say for some horizontal
zone HZk, localization is not necessary. Then we set the localization flag to FALSE for that
particular HZk and remaining nodes are localized. [18] Gives direction based distributed
location in WSN.

175
Figure 2. WSN Cluster divided into Horizontal Zones
Figure 3. Sequence of steps to locate nodes in HZs with help of BNs and correction B

176
Algorithm Horizontal-LPS
Parameters in H-LPS
// Place BN in horizontal positions
BeaconNodes BN : 1 to B; SettledNodes StN : 1 to St;
Positions : 1 to p; Nodes : 1 to n;
// Place Beacon Nodes in horizontal alignment , major change in x-coordinate and less change
In y-coordinate and calculate (x, y)
CalculateBeaconNodesPositions (BeaconNodes: 1 to B)
// Assumption : BN placed with major change in X-coordinate
Begin Function
For (i = 1 to B)
Position(BN[i]) = GetPosfromCH(BN[i], x, y)
For (j = 2 to B)
SendBeaconNodeLoc(BN[i], BN[j], x, y)
End For
End For
For ( i = 1 to B) // For all BNs find distance and hop count
For (j =2 to B)
Calculate(BN[i], BN[j], Distancei-j, HCi-j)
End For
End For
// Add extra beacon node BNk
For (i = 1 to B)
SendBeaconNodeLoc(BNk, BN[i], x, y)
CorrectionFactor(BN[k],BN[i])
End For
End Function
// Calculate horizontal node position for all sensor nodes
CalculateHNodePosition(Node: 1 to n)
// Horizontal Zones Row : 1 to HZR Horizontal Zones Column : 1 to HZC
Begin Function
For ( i = 1 to HZR; k = 1 to q) //Node j is adjacent to BNi
For (j = 1 toHZC)
For (m = 1 to p)
// Add extra beacon node BNk
LocateNode(BN[m], Node[j])
End For
CorrectionFactor(BNk, Node[j])
SetSettleNode(StN[k], Node[j]
End For
End For
End Function

177
Analysis
H-LPS location is done horizontally and CH helps to locate beacon nodes, which further aid to
locate sensor nodes. If nodes are to be localized in a particular horizontal zone block, bounded
HZ range [19] is defined between (x1, y1) and (x2, y2). Nodes that do not belong to bounded
HZ range are not localized and thus save WSN localization cost.
To split cluster C into n HZs during time interval t1 to t2 is given as:
1
1
( , )
t
n
t
HZ SplitCluster C nHZ
= ∫ .......................................................... (3)
Time to locate nodes in a HZ within a cluster C is given as:
( , )
HZ HZ
T LocateHZ C HZ CorrectionFactor
= + ........................................... (4)
Time to locate nodes in all HZs within a cluster C is given as:
[1 ]
1
( ( , ) )
n
HZ n HZ
HZ
T LocateHZ C HZ CorrectionFactor
−
=
= +
∑ .......................................... (5)
5. VERTICAL LOCATION POSITION SYSTEM (V-LPS)
In our proposed Vertical-LPS, unknown sensor nodes are located vertically. Sensor cluster is
divided into Vertical Zones (VZs). Each column VZ has number of row VZs as given in
figure 4. After locating first column VZ, the algorithm shifts to second column VZ until all the
VZs are covered. [20] Gives self-configuring localization systems. In V-LPS, similar to H-LPS
we place three beacon nodes vertically. These beacon nodes are linked to form a fully connected
network. Nodes exchange information using DV-Hop and DV-Distance techniques to estimate
their location. Similar to H-LPS algorithm, we place a new localized beacon node to reconfirm
the location of beacon nodes. New beacon node function as correction factor for localized
beacon nodes. The new node is linked with other localized beacon nodes to form a fully
connected network. This node exchange information with all the other localized beacon nodes
using DV-Hop and DV-Distance and confirms the location of nodes. In V-LPS, the localization
algorithms extend in vertical direction across various VZs until all the nodes are localized as
given in figure 5. Remaining functionality is similar to H-LPS. Algorithm of V-LPS is similar to
H-LPS. [21] Gives distributed weighted multidimensional scaling for node localization in sensor
networks.
Figure 4. WSN Cluster divided into Vertical Zones

178
Figure 5. Sequence of steps to locate nodes in VZs with help of BN and correction BN.
Analysis
V-LPS location is done vertically and CH helps to locate beacon nodes, which further aid to
locate sensor nodes. If nodes are to be located in a particular vertical zone, bounded VZ range is
defined between (x1, y1) and (x2, y2). Nodes that do not belong to bounded VZ range are not
localized and thus save WSN localization cost.
To split cluster C into n VZs during time interval t1 to t2 is given as:
1
1
( , )
t
n
t
VZ SplitCluster C nVZ
= ∫ ............................................................ (6)
Time to locate nodes in a VZ within a cluster C is given as:
( , )
VZ VZ
T LocateVZ C VZ CorrectionFactor
= + .............................................. (7)
Time to locate nodes in all VZs within a cluster C is given as:

179
[1 ]
1
( ( , ) )
n
VZ n VZ
VZ
T LocateVZ C VZ CorrectionFactor
−
=
= +
∑ ................................ (8)
Localization of wireless sensor networks with a mobile beacon is given in [22].
6. SIMULATIONS
Simulations are done using NS-2 and parameters are given in table 1. The simulation
comparisons are done for APE, RPE and DPE distributed localization techniques. Simulation
parameters considered for H-LPS and V-LPS algorithms are number of sensor nodes, percentage
zone localization error, simulation time and addition of beacon nodes.
Table 1. Simulation Parameters
We simulated the proposed algorithm H-LPS and V-LPS for localization in WSN with existing
localization techniques. Figure 6 gives comparison of percentage zone location position error
with varying number of sensor node for the proposed H-LPS, V-LPS and existing localization
techniques APS, RPE and DPS. As the number of sensor nodes increase, zone localization error
increases and beyond a threshold localization error becomes constant. For existing distributed
localization techniques, average localization error gets stabilized between 0.45% and 0.55%. For
H-LPS and V-LPS average localization error gets stabilized between 0.32% and 0.35%.
Percentage zone position error can further be minimized if the numbers of nodes are localized in
the direction of packet traversal. During simulations in bounded zone localization for H-LPS and
V-LPS, further reduction in percentage zone location position error was notified.
Figure 6. Sensor nodes and percentage zone Figure7. Number of zones (HZ/VZ) and zone
location position error dependence location position
This clearly indicates an improvement in H-LPS and V-LPS over existing localization
techniques. Figure 7 gives simulation results for localization zone dependence. Localization zone
Parameter Value Parameter Value
Simulation Area 1500m x 1500m Traffic Type CBR
Simulation Time 700s
Number of
Sources
9
Number of nodes 250 Packet size 512Bytes
Number of Beacon
Nodes
50 (Maximum) Arrival rate 20pkt/sec
Node Placement Random Node range 250m

180
dependence is less in existing protocols compared to our proposed algorithms. Due to zone
dependence, nodes within a given area are localized and remaining nodes are not localized.
Simulation results indicate localization zone dependence factor is 0.30 to 0.25 in existing
localization techniques and 0.80 to 0.85 in our proposed H-LPS and V-LPS techniques. This
saves WSN localization cost and improves the performance of localization techniques. Figure 8
gives results for simulation time and average percentage of zones localized (with nodes localized
in each zone). As the simulation time increases, more number of zones are localized and nodes in
each zone are identified by communication and network parameters. Percentage zone
localization is enhanced in H-LPS and V-LPS compared to APE, RPE and DPS. Figure 9 gives
simulation results for increase in beacon nodes. As the number of beacon nodes increase,
percentage localization error decreases for all localization algorithms (existing and proposed).
Clearly, simulation results indicate zone localization error is less in H-LPS and V-LPS
compared to existing localization techniques.
Figure 8. Simulation time and average percent Figure 9. Number of Beacon nodes and
zone location. percentage location position error
7. CONCLUSIONS
In this article we propose distributed localization algorithms, H-LPS and V-LPS with position
known Cluster Head (CH) and position unknown beacon nodes. Nodes are assumed to be static.
Using traditional trilateration technique, node position is estimated. We add extra position
known beacon node to reconfirm the localized beacon nodes. This eliminates percentage
localization error to 70%. The main advantage of zone based localization is, nodes belonging to
bounded zone are localized and nodes that do not belong to bounded zone are not localized
since they do not participate in WSN computing and thus save localization cost. Simulations
results indicate better performance for zone based localization in H-LPS and V-LPS compared
to existing localization techniques like Ad hoc Positioning System (APS), Recursive Positioning
Estimation (RPE) and Directed Positioning Estimation (DPE). Extension of this work can be
implementation of dynamic node structure for H-LPS and V-LPS.
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AUTHOR BIOGRAPHIES
M. Bala Krishna is assistant professor in University School of Information Technology,
Guru Gobind Singh Indraprastha University, Delhi, INDIA. His areas of interest include architecture and
protocols in wireless networks and mobile computing. He had worked in various research projects of
embedded systems design and wireless communication networks. He is working in the area of
wireless sensor and ad hoc networks, and embedded system applications. He is an active member of
various reputed technical organizations.
M. N. Doja is professor in Department of Computer Engineering, Jamia Millia Islamia, Delhi, INDIA.
His research areas include computer networks and mobile wireless networks, AI and 4G communications.
He has various publications in soft computing techniques. He is author of various books and has
publications in various national and international conferences and journals. He received awards from
many prestigious institutions. He is chair and co-chair of various technical organization committees at
national and international levels.

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