Impact of energy and link quality indicator with link quality estimators in wireless sensor networks

International Journal on Applications of Graph Theory in Wireless Ad hoc Networks and Sensor
Networks(GRAPH-HOC) Vol.6, No.4, December 2014
DOI:10.5121/jgraphoc.2014.6403 23
IMPACT OF ENERGY AND LINK QUALITY
INDICATOR WITH LINK QUALITY ESTIMATORS
IN WIRELESS SENSOR NETWORKS
Bouyahi Mohamed, Zrelli Amira, Rezig Houria and Ezzedine Tahar
SYSCOM Laboratory –NATIONAL ENGINEERING SCHOOL OF TUNIS- TUNISIA
ABSTRACT
The Link Quality Indicator (LQI) and Residual Energy have a fundamental impact on the network
performance in Wireless Sensor Networks (WSNs) and affects as well in the life time of nodes. This paper
will provide a comparative of Link Quality Estimator, the Link Quality Estimator with Link Quality
Indicator and Link Quality Estimator with Residual Energy. In this paper we develop a Collect Tree
Protocol (CTP) and compare the performance of LQI and Residual Energy, and show their effect on the
packet delivery ratio and throughput, covering the characteristics of low-power links, and their
performance to the best of our knowledge, we believe that our efforts would have implementations on
embedded application.
KEYWORDS
Link Quality Estimation (LQE), Collection Tree Protocol (CTP), Packet Receive Rate (PRR), the Window
Mean with Exponentially Weighted Moving Average (WMEWMA).
1. INTRODUCTION
In Wireless Sensor Network communication various problems appear with Self-organization
nodes. To ensure the best performance of network it is necessary to try to solve the
problems of unreliability of links. This paper has provided a comparative of Link Quality
Estimator, the Link Quality Estimator with Link Quality Indicator and the latter with Residual
Energy. It has been attracting a vast array of research works. The route can be built by selecting
the highest quality of link, the goal of this selecting is to maximize the lifetime of the sensor
network. Avoiding successive retransmission of packet in order to reduce the energy consumption
at each node to its task of routing in other words minimizing the route failed re-selection.
Consequently, the evaluation specifies the quality of link, minimize the errors and make the
interchangeability between nodes more effective to solve the problems imposed by the
unreliability of links [1] [3].
This article opens with related work carried out on link quality estimation which shall be
introduced in Section 2. Next section will present the most required parameter. Then, in section 4
an overview of the most Link Quality Estimator along with our implementations will be given.
While Section 5 includes an overview of the proposed method, Section 6 shall discuss links
performance. Last section concludes the paper.

24
2. RELATED WORK
Many protocols are proposed in the literature for wireless sensor networks based energy but very
few of them tackle real time applications.
There are some of WSN nodes available in market. The following paragraphs give the review of
some routing protocols, which are based on the link quality and the energy for the real application
and real world of WSN nodes.
1. RLQ: Resource Aware and Link Quality Based Routing Metric
The RLQ routing is based on the quality of the link and energy. The routing based on the link cost
and the energy cost which is the sum of normalized energy cost of transmission and reception
[12]. If the residual energy and link quality parameters are zero the routing based on the
minimum hop counts. If one of them is not zero, the routing based on one of them. For example
the minimum total energy path is the shortest path.
2. SHRP (Simple Hierarchical Routing Protocol)
The SHRP protocol is it the proactive protocol. Its route is selected based on the energy battery
and the link quality. The parent node selection is based on the information from the link layer.
The SHRP protocol eliminates the node which the value of RSSI and LQI is lower than the fixed
threshold or when the node is out of battery from the neighbor table.
The protocol chooses the route to the sink that has the maximum energy and the minimum hop
possible [7].
3. LQER (Link Quality Estimation based Routing Protocol)
The LQER protocol is a routing protocol which makes the path selection based on the historical
states of the link quality after the minimum hop field is established. A dynamic window concept
(m; k) is used to record the link historical information. The m is the number of successful
transmission of k preview transmission [2].
The LQER protocol is based on the minimum hop field establishment algorithm to find the
minimum hop neighbors and then each node will have the list of forwarding neighbors.
3. REQUIREMENTS AND OVERVIEW OF LINK QUALITY
ESTIMATION
In this section, we briefly review the literature related to link quality estimators in WSNs, as well
as their Overview evaluation. The most respected parameter in our test is the energy efficiency
and the stability.
Energy efficiency: since energy has an important concern in sensor networks, LQE is an
important factor to the management of low consumption and communication. The active
monitoring as well as the expenses of the beacon will be avoided.

25
Stability: it is necessary to tolerate the small variations to have a better LQE, the long-term
evaluation of quality of bond was carried out by the means of the filter (Exponentially Weighted
Moving Average) of EWMA with a great soft factor (α = 0.6). Also it can evaluate at an early
stage based on the last EWMA, a stable LQE leads to stable topology, if one regards the PRR as
LQE and one will frequently calculate the PRR we obtain a reactive LQE during the dynamic
capture of the bond, this reliability will be at the cost of stability because the PRR will consider a
certain transitory fluctuation of quality of bond which could be unaware of.
• Link Quality Metrics
Link quality metrics in wireless sensor networks can roughly be classified in two categories:
physical and logical indicators.
The former are provided by the radio hardware and are based on the signal strength of a received
packet, such as Received Signal Strength Indicator (RSSI), the Link Quality Indicator (LQI) and
Signal to Noise Ratio (SNR) [3]. The logical indicators estimate the link quality by keeping track
of message losses classified on three categories as follows. The first is PRR-based which includes
Packet Received Rate (PRR) and Window Mean with Exponentially Weighted Moving Average
(WMEWMA) the second is RNP-based which contains Required Number of Packet (RNP) [1],
Expected Transmission Count (ETX) and Four-Bit.
1. PRR-based
• Packet Receive Rate (PRR)
The PRR is approximated as the probability of successfully receiving a packet between two
neighbor nodes. If PRR is high that means the link quality is high and vice versa (1).
ܴܴܲ
=
ܰ‫ݐ݁݇ܿܽܲ ݀݁ݒܴ݅݁ܿ݁ ݂݋ ݎܾ݁݉ݑ‬
ܰ‫ݐ݁݇ܿܽܲ ݐ݊݁ܵ ݂݋ ݎܾ݁݉ݑ‬

• The Window Mean with Exponentially Weighted Moving Average (WMEWMA)
The WMEWMA is used to compute the last rate also this gives a good method to approximate the
reception rate of the link at the time [4]. To measure the quality of the path taken by the packet,
the product of the instantaneous reception rate was saved. The number of transmissions required
for sending a packet through along the mentioned path equals the sum of all the send attempts per
hop
ܹ‫ܣܯܹܧܯ‬ = ߙ ∗ ܹ‫ܣܯܹܧܯ‬ + ሺ1 − ߙሻ
∗ ܴܴܲ ሺ2ሻ
2. RNP-based
• Required Number of Packet (RNP)
The goal of RNP is to explain the distribution of the packet dropped of a link by estimating the
quality of link. Studies have shown that among links with the similar rates of the delivery, a link

26
will decrease dropped packets can deliver more packets of data with the same number of send
attempts along with consecutive losses during the same period.
The goal of RNP is to analyze all the transmission figures required in an automatic repeat request
(ARQ) where the underlying distribution of dropped packet is known [4].
ܴܰܲ =
ܰ‫ݏݐ݁݇ܿܽ݌ ݀݁ݐݐ݅݉ݏ݊ܽݎݐ݁ݎ ݀݊ܽ ݀݁ݐݐ݅݉ݏ݊ܽݎݐ ݂݋ ݎܾ݁݉ݑ‬
݊‫ݏݐ݁݇ܿܽ݌ ݀݁ݒ݅݁ܿ݁ݎ ݕ݈݈ݑ݂ݏݏ݁ܿܿݑݏ ݂݋ ݎܾ݁݉ݑ‬
− 1 ሺ3ሻ
• Expected Transmission Count (ETX)
The ETX reduces at least the number of data packet and estimates the number of transmission
required to send a unicast packet by measuring the packet reception ratio.
The ETX metric calculated as,
ଵ
௉ோோ೑∗௉ோோೝ
, where there are forwarded and reverse delivery which
rate for link.
When calculating PRR୤ andPRR୰, beacon packets are broadcasted by each node periodically.
Every beacon packet contains the reception rates of beacons received from each of its neighbors
[3]. From the beacon packets, the delivery rate from itself to its neighbors node can be read, and
the reverse delivery rate can be computed by counting the number of lost beacons from its
neighboring nodes (4).
‫ܺܶܧ‬
=
1
ܴܴܲ௙௢௥௪௔௥ௗ ∗ ܴܴܲ௕௔௖௞௚௥௢௨௡ௗ
ሺ4ሻ
• Four Bit Interface
The Four-Bit estimator uses the physical link and network layer information to build its
estimations [5]. This estimator can indicate if a link is or will be used for routing or not (5).
‫ݐܾ݅ݎݑ݋ܨ‬ = ߙ ∗ ‫ݐܾ݅ݎݑ݋ܨ‬ + ሺ1 − ߙሻ
∗ ‫ ܺܶܧ‬ሺ5ሻ
4. EXPERIMENTAL SETUP
Contiki is an open-source operating system for embedded systems and wireless sensor networks
which is being developed at Swedish Institute of Computer Science led by soft-ware engineer
Adam Dunkels. It provides both full IP (Internet Protocol) networking and low-power radio
communication mechanisms. Contiki is written in C [6].
Table I: Simulation Parameters
Parameter Value Unit
No of Node 12 Nodes
Dimension of Space 250*250 Meters
Simulation Time 30 Minute
Deployment uniform -
Update Interval 3 Seconds

27
The routing algorithm for enhanced CTP
if(Packet=DataPacket)
rtmetric<-ETX
sendAck(recievedPacket)
forwardPacket(parentNode)
end if
if(Packet=AckPacket)
for NeighborTable[i]
if(NeighborTable[i].nodeId= SenderAckNodeId)
UpdateNeighborTable[i].rtmetric<-rtmetric
end if
end for
for NeighborTable[i]
if(maxRtmetric<NeighborTable[i].rtmetric)
maxRtmetric<-NeighborTable[i].rtmetric
parentNode<-NeighborTable[i].nodeId
end if
end for
end if
5. OVERVIEW OF PROPOSED METHOD
We proposed two methods of routing based on Link Quality Estimator and Energy, Energy Link
Quality Routing (ELQR) and Quality Link Quality Routing (QLQR).
1. The Energy Consumption with Link Quality Estimator
Routing Decision
Figure 1: Architecture of ELQR
This method is based on residual energy of node and its estimators, each node makes the table
nodes for storing the link and energy of their neighbors. When the node arrives to send or forward
the packet it selects the maximum value of ELQR returned by neighbor table. When
acknowledgment has been received, it contains the value of ELQR.
2. The Link Quality Indicator with Link Quality Estimator
Link Quality Estimator
(ETX)
Energy
Estimator
ELQR
(rtmetric)

28
Routing Decision
Figure 2: Architecture of QLQR
This method is based on the quality of node and its estimators, each node makes the table nodes
for storing the link and energy of their neighbors. When the node arrives to send or forward the
packet it selects the maximum value of QLQR returned by neighbor table. When
acknowledgment has been received, it contains the value of QLQR.
6. SIMULATION RESULTS
1. Throughput
The first experiment considers 12 nodes distributed around one coordinator. The aim is to study
the throughput when different estimators are set up. The throughput is calculated from the ratio of
total packets received divided by total time of simulation multiplied by the nodes number (6).
ܵ =
‫ݐ݁݇ܿܽ݌_݀݁ݒ݅݁ܿ݁ݎ_݈ܽݐ݋ݐ‬
‫݁݉݅ݐ_݊݋݅ݐ݈ܽݑ݉݅ݏ‬ ∗ ݊‫ݏ݁݀݋݊_ݎܾ݁݉ݑ‬
ሺ6ሻ
In this section, we investigate the impact of energy residual and Quality Link Indicator on the
throughput. We compute the sum of Packet Receive Rate and the residual energy to make the
ELQR routing, then we compute the sum of the Packet Receive Rate and the Link Quality
Indicator to make the QLQR routing Figure 3.
We remark, in the upper curve, that the route decisions taken with ELQR and QLQR are better
than those which are taken with simple PRR, the possibility of route with energy residual and
quality indicator can make the route more flexible.
Figure 3: Throughput of PRR, ELQR and QLQR
Link Quality Estimator
(ETX)
Link Quality
Indicator
QLQR
(rtmetric)

29
Figure 4: Throughput of RNP, ELQR and QLQR
Figure 5: Throughput of WMEWMA, ELQR and QLQR
Figure 6: Throughput of Four Bit, ELQR and QLQR

30
In the normal case one uses only the estimate (PRR, WMEWMA, RNP, ETX and Four Bit). The
energy of the intermediate nodes consequently is weakened compared to the extreme nodes which
have the greatest probability of use. This causes a problem in our network. As a solution, adding a
parameter of energy or quality of link to the estimate would enable reaching all the extreme nodes
and consequently the flow of the network increases.
In previous Figures, we obtained that the WMEWMA is important than PRR, ETX, RNP and
Four Bit. WMEWMA is based on passive estimation and coefficient equals 0,6.
2. Packet Delivery Ratio
The second experiment evaluates the Packet Delivery Ratio which is defined by the ratio of
packets successfully received divided by packets sent multiplied by 100 (7).
ܲ݀
=
‫ݐ݁݇ܿܽ݌_݀݁ݒ݅݁ܿ݁ݎ_݈ܽݐ݋ݐ‬
‫ݐ݁݇ܿܽ݌_ݐ݊݁ݏ_݈ܽݐ݋ݐ‬
∗ 100 ሺ7ሻ
Figure 7 shows the results of PDR for each node when they use PRR, ELQR and QLQR as the
routing Method. Figure 7 establishes an interval from 50% to 60% to make the performance
evaluation clearer. On the basis of the results of figure 7, the total packet loss of QLQR is 58%,
ELQR is 55% and the PRR is the 20%. Thus, PRR increases the PDR by 45%.
Figure 7: PDR of PRR, ELQR and QLQR
Figure 8: PDR of RNP, ELQR and QLQR

31
Figure 9: PDR of WMEWMA, ELQR and QLQR
Figure 10: PDR of Four Bit, ELQR and QLQR
Like Throughput, the PDR increases in the event of use of the parameters of energy or quality of link
because the packets are distributed to the nodes according to the case and consequently the Packet Delivery
Ratio increases i.e. the loss of the packets decreases in a remarkable way.
The analysis showed that results with 12 nodes, i.e., those with a high density, had a PDR difference that
reached 60% when compared with CTP. Obtaining high PDR and low latency was due to the use of QLQR
or ELQR.
7. CONCLUSION
Energy is an important resource in WSN to enhance the lifetime of the network. The Link Quality
Indicator and Energy Residual of the nodes can increase the performance of network. The link
quality increases when the hop count decreases thus the node with high value of link quality will
be chosen as the forwarding candidate. In this work, the threshold for the link quality is changed
throughout the lifetime of the network to make it alive by selecting the longer routes with more
energy. The optimal path will be chosen when the route has more Residual Energy or through
Link Quality Indicator.

32
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