The 10th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI 2013)Oct. 30 – Nov. 2, 2013 at Ramada Plaza, Jeju, Korea

Link Quality Aware Route Selection inHeterogeneous Wireless Sensor Networks

Adnan Fida, Nor Tuah Jaidi and Trung Dung NgoUniversiti Brunei Darussalam

The More Than One Robotic Laboratory11h8401@ubd.edu.bn, norjaidi.tuah@ubd.edu.bn, ngo.trungdung@ubd.edu.bn

Abstract - This paper studies the problem of opti-mal route selection in heterogeneous wireless sensor net-works. The link quality aware route selection strat-egy is proposed by integrating realistic communicationmodel depicting the underlying dynamics of wirelesslinks. The Rician/Rayleigh fading environments consid-ers communication channels with both dominant Line ofSight (LOS) and non-dominant LOS (NLOS) where thedesired signal between two sensor nodes is representedby Rician fading channel while the interfering signalsare modelled by Rayleigh fading channel. Link qualityis portrayed by reception probability i.e. probability ofsuccessfully received packets over a communication link.Reception probability of Rician/Rayleigh fading channelis utilized as a metric for link quality aware route selec-tion. Rician/Rayleigh metric based performance is com-pared with conventional route selection metrics basedon simplified communication models. The simulationresults demonstrate that incorporating Rician/Rayleighmetric based link quality in route selection process pro-vides routes with optimal end-to-end throughput.

Keywords - Route selection, Rician fading, link quality,throughput

1. IntroductionWireless sensor networks (WSNs) are envisioned to

have a significant impact on wide range of applicationssuch as rescue operations [1], surveillance missions [2],and habitat monitoring [3]. Fundamental operations ofwireless sensor network require acquiring informationthrough sensing and transferring the collected informa-tion to peer nodes or base station. The sensor nodes re-quires reliable communication with other neighbouringnodes in an ad-hoc network to form multihop routing ofwireless sensor nodes acting as relay forwarding data toother nodes.

In the scope of this paper, it is assumed that sensornodes have collected environment exploited data throughtheir sensing capability and collected information mustbe transferred to peer nodes or base station through self-organized ad hoc network. WSNs borrow the existingrouting literature available for ad hoc networks. The vari-ation in signal strength due to fading is an overlooked factin conventional route selection strategies based on simpli-fied assumptions and unrealistic communication models.As the monitoring area becomes smaller, the effects ofthe channel impairments become different from the tradi-

tional ones such as shadowing and doppler effects. Thedominant effect in such scenarios is characterized by Ri-cian fading which appears when there is a line of sightbetween transmitting and receiving nodes. The receivedsignal is considered as a set of scattered components plusa dominant component, thus interference characteristicsin heterogeneous networks are significantly different thanin homogeneous networks. Different interference scenar-ios in heterogeneous networks are provided in [5] and itcan be inferred that it is highly probable to have a situ-ation where the desired signal experiences line-of-sightand interfering signals experience non-line-of-sight con-ditions.

In this paper, link quality aware route selection schemeis proposed to estimate the quality of communication us-ing realistic communication links in Rician/Rayleigh fad-ing environment. This scheme uses reception probabil-ity as a metric to search for high end-to-end throughputroutes. This integration of link quality and route selectionis necessary to realize the realistic capacities of wirelesssensor networks.

2. Related WorkThe conventional wireless route selection schemes in

multihop networks are based on simplified communica-tion models such as binary link model where nodes per-fectly communicate within a transmission radius [6]. TheHop Count (HC) metric is based on the concept of binarylink model and is widely used in ad hoc networks [7] [8]because of its simplicity in implementation. Hop Countmetric aims to minimize the number of hops on the routebetween source and destination nodes. Another popularapproach is to use Euclidean Distance (ED) as route se-lection metric utilizing the geographic position of nodes[9]. This metric relies on disk graph models where sig-nal strength decays according to distance [10] [11][12]neglecting the fact that link quality can change drasti-cally due to fading. The route selection metrics basedon simplistic communication models does not generateroutes with higher throughput as it neglects the sensitiv-ity of wireless links. [13] show that variations of wire-less links in realistic environment strongly influence theperformance of ad hoc wireless networks. Furthermore,[14] explained that simplistic assumptions of disk modelsyields unrealistic inclusions and limitations.

Recently, link quality incorporation with route selec-tion is considered to find the high quality routes. A linkquality estimation based routing protocol (LQER) is pre-

sented in [15], which uses a dynamic window concept torecord the link information and utilize it in routing de-cisions. The notion of QoS is incorporated into routingdecisions in Sequential Assignment Routing (SAR) de-veloped by [16]. [17] extended the work presented in [16]and proposed route selection scheme to meet the end-to-end delay requirements and provide maximum through-put. All the aforementioned work does not consider theaffects of fading on the link quality. In [18], we developeda framework for optimal route selection in wireless sen-sor networks considering Rayleigh fading channels dueto NLOS signal propagation. However, Rayleigh fadingmodels is not enough to portray many channels found inreal world. It is important to consider other models suchas Rician fading [19] applicable in scenarios when thewireless link between transmitter and receiver has a directcomponent in addition to diffused Rayleigh components.

3. Network ModelConsider ′N ′ numbers of sensor nodes equipped with

sensing, computing, and communication capabilities arespatially distributed to jointly monitor a specific area in agiven environment. The nodes sense and collect environ-mental information that should be shared with all othernodes in the network. We use the term ′router′ for anynode which is part of a route other than source and desti-nation. The objective is to search for optimal throughputroutes out of many available routes in realistic communi-cation environment, which can be done in two steps:• Find suitable metric to represent link quality in realisticcommunication environment taking into account fadingand interference affects.• Find optimal routes based on the selected metric.

3.1 Rician\Rayleigh Fading Link ModelWe assume that desired transmitter node’s signal is

subject to Rician fading link and each interfering signalSNR is debased by the respective Rayleigh fading link.This is a reasonable assumptions as interferers are faraway from each other, compared to wavelength of sig-nals. Given a transmission power of P , and a distance d,the nominal received power R with zero interference atreceiver in dB is:

R = P +Gt +Gr − PLo − 10log(d

do)−RFMP (1)

Here PLo is the free space path loss at reference dis-tance d0 and α is the path loss exponent [20]. The pathloss exponent is 2 for free space propagation and canbe as high as 4 in obstructed environments. For shortrange wireless sensor networks in office environment at2.4 GHz, the value for α is reported around 2.5 [21]. Gtand Gr are the antenna gains in transmitter and receiverrespectively. RFMP is the fading variable, which followsRician distribution in case signal is from desired trans-mitter and Rayleigh distribution for the case of interferer.The Signal-to-Interference-and-Noise-Ratio (SINR) de-

noted by ζ, is a discrete random process given by:

ζ =R

No + I(2)

R is the desired signal received power, No is the noisepower and I is the received interference power. Equation2 can also be written as:

ζ =ζ1

1 + ζ2(3)

where ζ1 = RN0

is the SNR for desired signal and ζ2 =IN0

is the interference SNR. ζ1 is affected by Rician fad-ing and ζ2 is affected by Rayleigh fading channel. Dueto the difficulty of predicting Rician fading, it is usuallymodelled as stochastic effect. The probability densityfunction (PDF) and cummulative density function (CDF)of ζ1 due to Rician fading is given as in [22]

fζ1(ζ) =(1 +K)−K

ζ1exp

((1− k)ζ

ζ1

)×Io

(√4K(1 +K)ζ

ζ1

), (4)

Fζ1(ζ) = 1−Qm

(√2K,

√2(1 +K)ζ

ζ1

)(5)

where ζ1 is the average of ζ1 and Io() is the zero ordermodified Bessel function of the first kind. Qm(, ) is theMarcum Q function [23]. K is called Rician factor andis defined as ratio between direct signal power and powerof indirect scattered signals. It is expressed in decibels as

K(dB) = 10 log10

(K2d

2σ2

)(6)

Furthermore, in case of interfering signal the PDF andCDF of ζ2 due to Rayleigh fading channel is given as:

fζ2(ζ) =1

ζ2e− ζ

ζ2 (7)

Fζ2(ζ) = 1− exp

(ζ

ζ2

)(8)

where ζ2 is the average of ζ2. In small monitoring areas,sensors are closely spaced and it is reasonable to assumethat such networks are interference limited only. We ap-proximate SINR as signal-to-interference ratio (SIR) de-noted by ζI and derived as in [24]:

ζ =ζ1

1 + ζ2=ζ1ζ2.ζ2

1 + ζ2

=ζ1ζ2.

(1− 1

ζ2+

(1

ζ2

)2

− ...

)∼=ζ1ζ2

= ζI ζ2 >> 1. (9)

3.2 Reception ProbabilityThe instantaneous SIR(ζI) between two nodes is uti-

lized to determine the quality of wireless communicationlink between two nodes and link quality typically requiresa minimum threshold level of SIR. The probability thatinstantaneous SIR (ζI) falls below a certain threshold ζtis called outage probability [25] i.e. po := P [ζI ≤ ζt].Subsequently, a packet is successfully received if SIR(ζI) between two nodes is above a certain threshold. Theprobability that (ζI) is above ζt is called reception prob-ability denoted by pr := P [ζI ≥ ζt]. The value of ζtdepends upon the modulation and coding scheme [26].The reception probability for ζI is given as:

pr := P [ζI ≥ ζt] = 1−∫ t

o

fζ(x)dx = 1− F (ζt) (10)

It is inferred from Eq.(10) that reception probabil-ity depends on the cumulative distribution function of ζevaluated at ζt. Hence, reception probability for Ricianand Rayleigh fading links can be determined by utilizingEq.(5) and Eq.(8) respectively. The reception probabilityfor a single interferer case in Rician\Rayleigh Scenario,where ζr is the SNR for desired signal subjected to Ricianfading and ζ2 is the SNR of interfering signal subjectedto Rayleigh fading is obtained as [24][27]:

pr = 1−[ζ2(K + 1)ζt

ζr + ζ2(K + 1)ζtexp

(−Kζr

ζr + ζ2(K + 1)ζt

)⌋(11)

We assume that there areM identical and independentRayleigh interferers. If ζr is the SNR of desired Riciansignal and (ζi : i = 1, 2, ...M) be SNRs of M Rayleighinterfering signals. Then SINR is approximated as

ζ =ζr∑M

i=1 ζi + 1≈ ζr∑M

i=1 ζi=ζrI

= ζI (12)

where ζI is the SIR and I =∑Mi=1 ζi is a hypoex-

ponential random variable [28]. Utilizing the PDF ofRayleigh fading in Eq.7, the PDF and CDF of I whenζi 6= ζj ∀ i 6= j is given respectively as

fi(ζ) =

M∑i=1

Ci,M1

ζiexp

(ζ

ζi

)=

M∑i=1

Ci,Mfζi (ζ) (13)

FI(ζ) =

∫ ζ

0

fs(t)dt =

M∑i=1

Ci,MFζi(ζ) (14)

Here,∑Mi=1 Ci,M =

∏j 6=i

ζj

ζj−ζi. The reception prob-

ability for single interferer given in Eq.(11) is utilized tofind the reception probability in case of multiple Rayleighinterferers. The expression for reception probability ofdesired Rician signal with M interfering Rayleigh sig-nals is given as

pr =

M∑i=1

Ci,M ×[1− ζi(K + 1)ζt

ζr + ζi(K + 1)ζtexp

(−Kζr

ζr + ζi(K + 1)ζt

)](15)

3.3 Link quality aware route selectionIn order to flow information from source to destina-

tion node, we need to determine a route using routingalgorithm. Link quality aware route selection schemeintegrates the routing decisions with knowledge of linkquality in Rician/Rayleigh scenarios. We show that end-to-end throughput of route depends on the throughput ofindividual independent links constituting the route. Therouting scheme utilizes the efficient shortest path algo-rithms such as Dijkstra algorithm [29] or Bellman-Fordalgorithm [30] for optimal route computation. These al-gorithms search for a route with maximum throughputbased on the given link metric.

In order to consider the effects of Rician and Rayleighfading channels, we use reception probability presentedin Eq.(15) as our route selection metric to select routeswith maximum reception probability. Fig.1 shows theflow diagram of link quality aware route selection scheme(LQAR). Whenever a route is required for data transmis-sion, all the nodes discover their one hop neighbors. TheSNR for desired signal experiencing Rician fading chan-nel and SNR of interferers subjected to Rayleigh fadingchannel are computed. These SNRs are used by receivingnodes to find the reception probability for its respectivelink using Eq.(15). We take negative logarithm of recep-tion probabilities for all the links to turn multiplication ofprobabilities into addition of non-negative weights. Di-jkstra algorithm utilizes these weights to find routes withminimum weight, representing routes with maximum re-ception probability.

Link Quality Aware Route Selection

Neighbour Discovery

Rician Fading Channel (For Desired Transmitter)

SNR

Rayleigh Fading Channel (For Interferers)

Reception Probability (RP)

Shortest Path Algorithm

SNR

Fig. 1 State Machine for LQAR.

4. Performance EvaluationWe illustrate the effectiveness of proposed link qual-

ity aware route selection scheme for wireless sensor net-works in Rician/Rayleigh fading scenarios. Throughputis a popular performance measure for Quality of Service(QoS) and conventional estimate for the amount of traf-fic delivered through the network [12]. We define nor-malized throughput as expected number of successfulpacket transmission for a given node per time slot [31].The end-to-end throughput is used as performance mea-sure of route selection that is the minimum of normal-ized throughput values of the individual links. We alsoquantify the benefits of considering link quality in rout-ing decisions by comparing the end-to-end throughput ofLQAR with Hop Count route (HCR) and Euclidean Dis-tance route (EDR) selection schemes.

4.1 Simulation SetupA wireless sensor network is represented as directed

graph G(N,L), where N is the set of sensor nodes and Lis the set of wireless links between the nodes, representedas edges of graph. Each edge has a weight equal to its re-spective reception probability. The proposed frameworkis based on following underlying assumptions: a) eachnode has a unique identification (ID), b) each node knowsthe relative distance to neighboring nodes. The values as-signed to fixed parameters in accordance with real worldlow power wireless networks [32] are shown in Table.1.

Table 1 Values for parameters used in simulation.

Parameter Description ValueN Total Nodes 200P Transmission Power 0 dBmNo Noise Variance -85 dBmζt SIR Threshold -10 dBmλ Wavelength 0.12 m

4.2 Results from illustrative scenariosWe check the correctness and validity of proposed

Link Quality Aware Route (LQAR) selection frameworkin case studies with different network scenarios.

Simulation 1. One-to-One: First we investigate thecase when a source node is required to share informa-tion with a specific far away destination node. The net-work scenario is shown in Fig.2, where randomly se-lected source node-1 needs to send data to destinationnode-189. The routes are selected using three route se-lection schemes i.e HCR, EDR and LQAR.

Fig. 2 Network of 200 nodes with routes selectedfrom source node-1(red) and destination node-189(green)using HCR(magneta), EDR(cyan) and LQAR(red).

Figure.3(a) shows the end-to-end throughput forroutes selected using different schemes. LQAR providesthe highest throughput in comparison to HCR and EDR.LQAR attains a throughput gain of approximately 20%as compared to HCR and 60% with respect to EDR inthis particular case. All the three route selection schemesfind routes with same number of hops i.e. 18 as shownin Fig.3(b). However, it is observed in Fig.2 that other

than few hops, LQAR chooses different route than EDRand HCR. LQAR scheme considers the stochastic natureof wireless links and selects links with higher receptionprobability, whereas EDR and HCR schemes select theroutes irrespective of condition and realization of wire-less links. Fig.3(c) depicts that EDR based routes travelsslightly less than HCR and LQAR routes as EDR schemeselects the route with the smallest physical distance be-tween source and destination.

Simulation 2. One-to-All: The proposed scheme isfurther validated by considering its effectiveness in a situ-ation where source node requires routes to all other nodesin network.

Fig. 4 Network of 200 nodes with routes selectedfrom source node-100(red) to all other destinations usingHCR, EDR and LQAR.

Node-100 is selected as source node in scenario shownin Fig.4 and routes to all other destinations are computedusing HCR, EDR, and LQAR. The statistical data of end-to-end throughput, hop count and euclidean distance for199 source-destination pairs is shown in Fig.5. We prefermedian instead of mean for the analysis of data becausedata distribution is quite skewed. The difference betweenedges of each box in Fig.5 stand for inter-quartile range(IQR). IQR is defined as difference between 25th and75th percentile of data and is used to assess the disper-sion of data. The performance of LQAR is again vali-dated as it provides median throughput gain of 34% and39% in comparison with HCR and EDR schemes respec-tively. The statistical data for number of hops utilizedin 199 source-destination pairs is approximately same asindicated in Fig.5(b). As expected, the routes based onEDR scheme travels the least euclidean distance.

Simulation 3. Dense Network: Here, we verifythe performance of LQAR in dense network scenario asshown in Fig.6, where nodes are closely spaced and in-terference is high.

Node-1 is randomly selected as source node and routesto all other destination nodes are computed using LQAR,HCR and EDR schemes. Fig.7(a) shows the statisticalcomparison of end-to-end throughput for 199 routes indense scenario. The median throughput gain achieved byLQAR is 30% and 13% more than HCR and EDR respec-tively. HCR scheme finds routes with the lowest num-

HCR EDR LQAR0

0.01

0.02

0.03

0.04

0.05

0.06

Thr

ough

put

(a)Median Throughput

HCR EDR LQAR0

2

4

6

8

10

12

14

16

18

Num

ber

of H

ops

(b)Median Number of Hops

HCR EDR LQAR0

10

20

30

40

50

60

70

Euc

lidea

n D

ista

nce

(m)

(c)Median Euclidean Distance

Fig. 3 Throughput, Number of hops, and Euclidean distance of routes for single source-destination pair using HCR,EDR and LQAR.

(a)Median Throughput (b)Median Number of Hops (c)Median Euclidean Distance

Fig. 5 Throughput, Number of hops, and Euclidean distance for 199 source-destination routes

(a)Median Throughput (b)Median Number of Hops (c)Median Euclidean Distance

Fig. 7 Throughput, Number of hops, and Euclidean distance for 199 source-destination routes

Fig. 6 Dense network of 200 nodes routes selectedfrom source node-1(red) to all other destinations usingHCR, EDR and LQAR.

ber of hops as shown in Fig.7(b). The proposed LQARscheme generates routes with higher number of hops thanconventional route selection schemes but manages to pro-vide optimized throughput by exploiting the informationregarding Rician and Rayleigh fading. In realistic sce-narios, minimizing the hop count does not necessarily in-creases the throughput of the route.

Intuitively, it seems that link quality is better at small

distances but in reality it can be vice versa. The receptionprobability metric used by LQAR scheme considers thedynamics of wireless link such as interference and fading.In dense network scenarios, LQAR based routes travelslonger distances than both HCR and EDR based schemesas shown in Fig.7(c).

5. ConclusionIn heterogeneous wireless sensor networks, it is im-

portant to attain high throughput for data transmissionbetween far away nodes connected through number ofrouter nodes. Therefore, understanding a heterogeneousfading environment is crucial for routing decisions insuch networks. We proposed link quality aware routeselection scheme to find high throughput routes in Ri-cian/Rayleigh fading scenario of heterogeneous sensornetwork. Link quality is estimated by reception probabil-ity and the performance of LQAR is compared with con-vention route selection schemes based on simplistic com-munication models. Simulation results demonstrated thatLQAR based routes provides higher end-to-end through-put in heterogeneous wireless sensor networks.

AcknowledgementThis research was supported in part by the University

Research Grant at the University of Brunei Darrusalam

(UBD/PNC2/2/RG/1(259)).

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