Novel energy aware routing protocol for multievent wireless sensor network

Kỹ thuật điều khiển & Điện tử

NOVEL ENERGY AWARE ROUTING PROTOCOL FOR
MULTIEVENT WIRELESS SENSOR NETWORK
Nguyen Thi Thu Hang*, Nguyen Chien Trinh, Nguyen Tien Ban
Abstract: Multievent wireless sensor networks (WSN) such as smart buildings,
intelligent environmental monitoring systems require different QoS (Quality of
Service) provision based on various event types. These networks contain large
numbers of sensor nodes but they have a very limited power and processing
capability, so efficient consumption is one of vital requirements for most WSNs.
Most of research papers in this area have dealt with one or two of QoS
requirements or with a limited number of event types and event sources. For this
reason, in this paper, we propose a novel solution combining an event driven
routing protocol, dynamic delivering scheme, and energy aware to support QoS
requirements for three event types in multiple event WSN. Simulation results show
that, the proposed solution significantly reduces packet loss rate for high reliability
requirement events and extends the network lifetime of multievent WSN. Moreover,
in case of high traffic load condition, sharing load over multiple paths would
decrease latency for the urgent events in the multiple events network.
Keywords: Energy aware routing, Dynamic routing, Delivering scheme, Multievent, Wireless sensor network.

1. INTRODUCTION
In some wireless sensor networks (WSN), there are different types of events based on
their important levels. Important events can be considered as abnormal situations.
Poisonous gas or liquid detection in chemical industry, fires in forest fire alarm systems
are such kinds of events [1, 2]. If the leakage occurs or wildfire happens, the monitor
system must know it immediately. Sometimes there may be several leaking points or
wildfires, so there are multiple events appear in the network. Then, it is more urgent to
locate all of them. It needs not only to locate the leaking points or hot spots but also to tell
the volumes of leak or the burn areas. Other environmental parameters, such as
temperature, pressure, humidity, light intensity, and so on, can also be monitored and

considered as normal events.
With WSNs for smart buildings, intelligent environmental monitoring, and industrial
process [1-7], multiple events with different levels of importance may happen in the
networks. Take an example of forest fire alarm system, forest fire risk usually occurs
during and after winters with little rain, after long periods of dry weather and during
summer heat waves, and especially if such conditions coincide with strong winds.
The forest fire risk indicates the probability of a forest fire occurring. For the forest fire
alarm system, there are five danger levels of forest fire: level 5 (very high): fires can start
at any time, the sensor data must be transmitted quickly to the base station; level 4 (high)
and level 3 (considerable): the sensor data should reach the sink with high reliability
because it could indicate a possibility of forest fire, level 2 (moderate) and level 1 (low or
none): the data is not too serious, so it can be transmitted without specific requirement of
low latency or high reliability [8]. Fire spots can appear in many different areas making
various events with different levels of QoS requirements such as latency and reliability.
For most WSNs, energy efficient consumption is one of crucial requirements because
sensors have limited power and processing capability [3]. The wireless sensor node can
only be equipped with a limited power source and in some application scenarios,
replacement of power resources might be impossible. Sensor node lifetime, therefore,

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shows a strong dependence on battery lifetime. So, many researchers have been focusing
on the design of power-aware protocols and algorithms for sensor networks [9-11].
To meet these requirements of QoS and energy efficiency, there are three essential
approaches as follows.

First, for providing different levels of reliability requirements, there have been many
techniques that many researchers are interested in, in which routing is one of the most
important techniques. There have been many research papers on single path routing and
multipath routing protocols [12-17]. Although the work of finding a single path is simple
with low computational complexity and minimum resource utilization [12], [13], it could
react slowly with the rapid change in the network topology (node or link failure) and can
not support reliability as required by limited capacity of a single path [c11]. So, many
multipath routing protocols have been researched and developed to overcome the
disadvantages of the single path routing protocols [15-17]. In the case of many event types
appear in the network which have different requirement of reliability, the dynamic routing
scheme which combines single path for normal event type and multipath for high
reliability requirement event type can be applied [14, 18].
Second, splitting traffic over multiple paths could support the bandwidth requirements
of different applications and reduce the probability of network congestion, then reduce
network latency [19].
Third, a lot of energy-efficient routing protocols have been proposed, they have been
categorized and described in [10], [20-24], all of the protocols aimed at energy efficient
consumption and expanding the network life time. Besides, the technique of transmitting
multiple copies of data packets over multiple paths in [14] will increases delivery
reliability but the energy consumption would be much more times, that is a trade-off
between energy and reliability. So, applying energy-aware with event driven routing
would even be more necessary to increase the energy efficiency in such multiple event
WSNs.
To the best of our knowledge, all of research papers in this area have dealt with one or
more requirements, and dealt with limited events and types of events. There has been one
research that raised the issue of challenges between a single-event wireless sensor network
and multi-event wireless sensor network [23], but in the probable situation of concurrent
events in the network, the research showed that it was unable to benefit effectively for data
transmission over multiple paths than over single path, it provided shorter life time. This
is the first work that uses energy aware dynamic routing and packet delivering schemes to

support the multi QoS requirements for multiple event type WSN.
In this paper, we proposes a combined solution for QoS provision, named EARPM
(Energy Aware Routing Protocol for Multievent Wireless Sensor Network) for multievent
wireless sensor network: to choose dynamic routing protocol and packet delivering
scheme in WSN based on residual energy of nodes and different event types. Our
contributions in this paper are as follows:
1. We propose a combination of dynamic routing scheme of single and multipath,
and different packet delivering schemes of copying or splitting packets based
on three event types to support the different reliability and latency
requirements in multievent WSN.
2. We also propose an energy-aware algorithm to dynamically discover energy
efficient paths for delivering event packets.

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3. We implement our proposed routing and delivering schemes in OMNeT++
simulation to evaluate the adaptation of the network to the multiple event
requirements and the efficiency of the energy aware scheme.
The paper is organized as follows: Section 2 discusses the related work. Section 3
describes our proposed solution. Section 4 introduces our theory analyses. The evaluation
of our protocol based on computer simulation is presented in Section 5. Finally the last
section is the summarization and our future research work.
2. RELATED WORK
Recently, there have been several research papers on multipath routing protocols and
energy aware routing protocols to achieve various performance benefits.

In ReInForM (Reliable Information Forwarding Using Multiple Paths [14]), the source
sends multiple copies of the same data through multiple paths to the sink. Each packet is
assigned a priority level based on the content of the information it contains. The source
computes the number of paths (or equivalently, the number of copies of the packet to be
sent) based on the importance of the information, local channel error and distance from the
sink. ReInForM does not distinguish between the actual source and an intermediate
forwarding node. Next hops are usually chosen among the nearest hops to the sink,
otherwise they would be chosen randomly. This helps in load balancing and avoids the
nodes on the “better” path to be quickly energy depletion. However, sending multiple
copies of all packets would waste energy and the routing protocol has not considered the
latency of the event. The research has considered only single event source scenario, not
multiple events.
A low-interference energy-efficient multipath routing protocol (LIEMRO) has been
designed for improving QoS in event-based WSN [23]. This protocol has discovered
multiple interference-minimized node disjoint paths between source node and sink node
and included a load balancing algorithm to distribute source node's traffic over multiple
paths based on the relative quality of each path. The simulation shows that in high traffic
load conditions, it can increase data reception rate, lengthen the network life time, and
significantly reduce end-to-end latency compared with single path routing approach. The
research has raised the issue of challenges between a single-event wireless sensor network
and multi-event wireless sensor network. LIEMRO tries to construct node-disjoint paths
for each detected event. Nevertheless, paths with shared nodes are probable when two or
more events occur in the network. Therefore, the research has also evaluated LIEMRO in
multiple event situations. The simulated results show that in this situation, LIEMRO is
unable to benefit effectively for data transmission over multiple paths than over single
path, it provides shorter life time.
In [18], a multipath routing protocol has been proposed in which the sink discovers
paths based on path weight factor by using link efficiency, energy ratio, and hop distance.
The sink selects the number of paths among the available paths based upon the criticalness
of an event, and if the event is non-critical, then single path with highest path weight factor

is selected, otherwise multiple paths are selected for the reliable communication. So this
research has just differentiated two types of events.
In [25] a distributed, scalable and localized multipath search protocol has been
introduced to discover multiple node-disjoint paths between the sink and source nodes. In
this research, a load balancing algorithm is used to distribute the traffic over the multiple
paths discovered, it allows the sink node to allocate traffic based on paths' cost, which
depends on the energy levels and the hop distances of nodes along each paths. The

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proposed scheme has been compared to the directed diffusion [26], directed transmission,
and the energy aware routing [9] protocols. Simulation results show that it has higher
node energy efficiency, low average delay. But the research uses limited number of sinksource scenario, one sink with two or four sources, two sinks with three sources, and has
not considered different packet types.
From the above analyses, it can be seen that all of these research works have just dealt
with only one or two event types which require QoS requirements of latency and/or
reliability, some work has considered the energy efficiency but has not investigated the
scenario of concurrent events, there has not been any research supported diversity QoS
requirements for multievent WSN.
Our proposal in this paper is to discovering energy-aware single and multiple paths,
and use dynamic load delivering scheme which adapt to the three types of events,
consequently it supports better performance for different event requirements of reliability,
latency and energy efficiency for multievent WSN.
3. PROPOSED SOLUTION
Based on the variety QoS requirements of multievent WSN and the benefits in getting

high reliability and low latency of multipath routing protocols, we propose our novel
energy aware dynamic routing protocol for multievent WSN.
Our routing protocol is a renovation work from GPSR single path routing protocol [27]
for event trigger routing WSN, so only greedy forwarding technique is applied when event
appears in the network. There are three dynamic changes have been done for the scheme.
First, based on the type of events, source node chooses single path for normal event type
(named A, which does not require high reliability and low latency), multiple paths for the
higher requirement event types (named B, which requires higher reliability, and C, which
requires lower latency because of its urgency). Second, the delivering schemes are
different from B and C: for B, data packets from source nodes should be copied and
forwarded over two paths simultaneously while for C, packets should be split and sent
over two paths. Third, to avoid quickly depleted energy node on the shortest path, nodes in
the network would choose the relay node(s) having residual energy more or equal to the
average residual energy of all live and sink-nearer neighbors.
We consider the average value of energy because time after time, the relay node will
turn over among alive neighbors due to their residual energy levels have decreased by the
time packets of an event passed by, so nodes will deplete their energy more slowly and
equally. Choosing the average value is better than choosing the highest residual energy
value because the highest residual energy neighbor node might have the longer distance to
the sink, so the energy consumption would be higher. Furthermore, that highest residual
energy neighbor node could be the good neighbor of other event node both in energy and
distance to the sink in the multiple event network, so it should be chosen as the relay node
of the other node.
Fig.1 shows a description of our dynamic routing schemes in multievent WSN. Source
nodes have to find the best neighbor(s) among the sink-nearer ones to deliver its sensed
data packets and relay nodes have to find only one best neighbor. There are five alive
neighboring nodes (1, 2, 3, 5, 9) and one dead node (12) of the source in which only four
nodes are alive sink-nearer (1, 2, 3, 9).
 For single path GPSR routing: there is one that is alive and nearest to the sink (node
3). So, source node would choose node 3 to be the best relay node on the routing

path to the sink (Fig 1.a).

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 For multipath routing: the four alive
nodes can be chosen in priority order of
3, 2, 1, and 9 if only shortest distance
evaluation is used (Fig 1.b).
 For EARPM: we consider three criteria
in order of priority: (1) neighbor’s
residual energy, (2) distance from
neighbor node to the sink, and (3)
distance from source node to neighbor
node. Then, at time, the residual energy
of node 2 is the highest and node 9 is
the second highest, the residual energy
of node 3 is equal to node 1, the
distances from neighbor nodes to sink
are in order 3, 2, 9, 1 as closer to the
sink, and the distances from source
node to its neighbors are in order of 1,
9, 2, 3 as nearer to the source. Then,
the priority order of paths is 2, 9, 3,
and 1. Source A would choose 2 as the
delay node while source B and C

would choose 2 and 9 as the delay
nodes (Fig 1.c).
A. Network Model
The WSN can be viewed as an undirected
graph G  V , E where V represents the set

d Source-BS

9

4

12

dmax

11

10

2

Source
A

5
6

a) Single path GPSR routing

d Source-BS

9
dmax

4

12

7

3

1

11

10

2

Source
B/C

SINK

8

13

5
6

b) Multipath routing

of vertices (sensor nodes and sink) and E
represents the set of edges. We assume there
are N S sensor nodes randomly place in an area
( S  S m 2 ) , there exists a link E  i, j  between

d Source-BS

node i and node j if the Euclidean distance
Euclidean  i, j  is not larger than the sensor
node’s radio transmission radius  d max  . There
is a single monitoring node (sink) at the center
of sensing area, it knows its position and all
nodes’ position. When sensor node detects an
event, it will send its data directly to the sink
if its distance to sink is less or equal to its
transmission range or indirectly over its
neighbors otherwise.
B. Energy Model
In our work, a simple radio model where
the radio dissipates Eelec energy per bit to run
the transmitter or receiver and  amp energy

7

3

1

SINK

8

13

B,C

dmax

4

12

1

Source
A/B/C

3
2

7

11

10

A,B,C

5
6

9

SINK

8

13

E >E >Eaverage >E3 =E 1
2

9

remained
energy

c) EARPM routing

Figure 1. A description of the
combining of energy aware single and
multiple path routing schemes.

per bit for the transmit amplifier. We also assume d 2 energy loss due to channel

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transmission [28]. So, the energy consumption to send a L -bit packet to next hop at a
distance of d is:
(1)
Ehop  2  Eelec  L   amp  L  d 2

L bit
packet

Transmit
Electronics

d

2
amp*L*d

Eelec*L

L bit
packet

Tx
Amplifier

Receive
Electronics
Eelec*L

Figure 2. Energy model – first order radio model.
C. Proposed Routing Scheme
Fig. 3 shows a brief description of our EARPM operation when node detects an event
or receives routing request from its neighbor node, then it has to select relay node(s) for
delivering sensed data packets afterward.
When sensor node detects an event, it will send routing requests to all of its alive
neighbors, then all alive neighbors will send their routing requests toward all of their alive
neighbors and so on. After that, the source and all other related nodes will receive reply
packets with the information of their neighbors’ residual energy to determine the node(s)
which is/are eligible to be selected as next hop relay. If a node’s residual energy Eresidual is
less than Edead then it can not send reply REQ message, if the residual energy is less than
Ethreshold then node can not send or forward data packets. Only source node has to decide
the number of paths for its sensed data based on the event type while all forwarding nodes
have to choose only one best relay node.
 If the distance to sink is equal or less than d max (the maximum transmission range of
sensor), then node directly sends data to the sink.
 If not, sensor node will have to find the best neighbors to deliver its data to the sink.
One or two best neighbors will be chosen based on three criteria: first, its/their
residual energy (the neighbor’s residual energy is equal or larger than the average
energy of all alive sink-nearer neighbors eResidualNeighbor[i]>=
tempEAvg); second, among the neighbors that satisfy the first criteria, one or two
neighbors that have shortest distance to the sink (as closer to the sink as possible)
would be chosen; third, if there are neighbors that satisfy the previous two criteria,
the order of best neighbors would depend on the distance from a neighbor to the
source node (as closer as possible).

In this section, we analyze packet latency and make the probabilistic formulation of
reliability for both single-path and multipath routing. The results show that load-sharing
on multipath would reduce the queuing time of packets in congested situation, then reduce
the packet latency in a simple way, and multi-path routing with redundant transmission is
effective in increasing the reliability.

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BEGIN

N
Node detect event
Node receive REQ
message (from other node)

Y

Eremain ≥ E dead

Node has enough energy to
reply back or not?

Y
Send back a REP message with
information of its residual energy

Node has enough energy to
deliver data packet or not?

Eremain ≥ E threshold
Y
d2SINK≤dmax

Y

Do not have to build
routing table, sink can
be reached directly

N
1. Send REQ messages to all of its alive
neighbors
2. Receive REP message(s) from the
neighbor(s)
3. Calculate the average residual energy of its
alive neighbor(s)
4. Maximum two best neighbors would be
selected as relaying node(s) based on alive
neighbors’ residual energy and distances

for (i=0;i{
if (eResidualNeighbor[i]>=eThreshold)
{
tempE=eResidualNeighbor[i];
tempETotal= tempETotal+tempE;

numOfneighborLive++;
}
}
tempEAvg= tempETotal/numOfneighborLive;

END

Figure 3. Description of relay selection operation.
4. THEORETICAL ANALYSES
A. Latency analysis
The total delay, denoted as d , experienced by a packet in a path of hop count h is the
sum of the delays at the intermediate nodes, d j (where j  1, 2,..., h ), and is given by
h

d  dj

(2)

j 1

Considering the propagation and processing delays as negligible, d j can be calculated
as follows
(3)
d j  dtrans  d MAC  d que

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where dtrans is the transmission delay, d MAC is the medium access delay and d que is the
queuing delay of a packet.
In this paper, we concentrate into the queuing delay of a packet. Queuing delay at any
node depends on the queue service time, number of packets in queue, and the packet
arrival pattern.
Fig. 4 shows the analysis of the queuing delay of packets. We compare the queuing
delay of packets over single and multiple paths using redundant transmission and loadsharing schemes. From source nodes, there are three event type packets that would enter
queues with the current queue length of Q* packets over a maximum capacity of Q
packets.
As we can see from Fig. 4, for event type A and B packets, only N packets would be
sent over one path, so the average queuing delay of packet type A and B is equal and can be
approximately calculated as the delay of the middle packet  N / 2  . For type C, it is less
and proportional to the inversion of M - the number of paths, which can be calculated as
d queA  d queB  (Q * 
d queC  (Q * 

N
)  d service
2

N
)  d service
2 M

(4)
(5)

Figure 4. Occupation of queue for the three event types.

If we denote the improvement of latency of C over A is limprovement , we can evaluate the
improvement at one queue as follows
limprovement

 d queB  d queC


d queB


N 


 NM 
 100%  
 100%
 2Q *N 




(6)

Let take Q*  x  N , then Eq.6 can be shortened as

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limprovement

1 

 1 M 
 
 100%
 2 x 1 



(7)

From Eq. 7, it is clear that splitting data packets over multiple paths would decrease the
load placed on each link, thus
reducing the packet processing
time, the larger the number of
multipath (larger M), the better
the advantage of latency of C
over A, the larger the queue
(larger x), the lesser the
advantage of latency of C over
A. Based on the fact that sensor
nodes have limited memory [3,
11], we can see that the sensor
queue capacity can not be so
large, so the value of limprovement
can have a great value.

Fig. 5 is a specific example
for the mathematical latency
comparison of multipath routing
using load sharing technique
Figure 5. Latency comparison of multipath routing
over single path routing with
using load sharing technique over single path routing
different numbers of paths and
with different numbers of paths and queue length.
queue length.
B. Reliability analysis
If the number of original packets sent by the source is N S , and the number of distinctive
packets received by the sink is N R , the reliability, denoted as R , is R  N r / N s . Here the
distinctive packet means that if sink receives multiplicative packets (the original data
packet and the copy one), it considers those as one received packet.
Reliability of Single-Path Routing
Consider a source and a sink which are h hops apart as in Fig. 6. Let the per hop
SINK
channel packet error rate (PER) at Source
PER
c
c
c
c
th
e1
e2
ej
eh
j hop in the path across the entire

c
network be a variable e j (where
(1-ec1)
(1-ec2)
(1-ecj)
(1-ech)
0  ecj  1 , and it is proportional to

the distance), then the per-hop
reliability at j th hop is (1  ecj ) .

Reliability
h hops

Figure 6. Single path scenario.

The reliability of a path is a
multiplicative metric. Thus, the probability that a packet is received
by the sink over a single path of h hops apart, p  h  , is
h

p  h    1  ecj 

(8)

j 1

Then single path packet error rate in this situation is

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h

(9)

PER single  1  p (h)  1   1  ecj 
j 1

Thus, in a multi hop sensor network, where channel errors could be very high and a
source could be far away from the sink, a naïve forwarding scheme will result in a high
PER, so single path routing is not proficient of attaining good reliability.
Reliability of Multipath Routing
ec1,2
ec1,j
Consider multiple paths from a
ec1,h1
ec1,1
source to as in Fig. 7. There are M
SINK
Source
th
c
c
i
c
c

paths and the hop count of the
e 2,j
e 2,h2
e 2,1
e 2,2
path is hi , the multipath packet
error rate in this situation is the
probability that all copy packets
would suffer error in all paths.
The reliability of sending a
packet by copying it and send over
multiple paths can be calculated as

ecM,hM

ecM,1
ecM,2

ecM,j

Figure 7. Multipath scenario.

hi
M
M
M 

PER multipath   PERi single   1  pi  hi     1   1  eic, j  
i 1
i 1

i 1 
j 1


(10)

where pi  hi  is
the probability of
success for the
i t h path defined in
Eq. 8 and eic, j is the
probability that a
packet is dropped
at the j th hop of the
i th path.
Then,
the
probability that at
least one copy of a
packet
is
successfully
received by the
sink over M paths,
p  M  , is

Figure 8. Packet error rate evaluation based on the number
of hops, paths for per-hop channel error rate of 1%.

hi

M 

p  M   1  PER multipath  1   1   1  eic, j  
i 1 
j 1


(11)

Packets may be lost due to channel error and queue overflow; in such cases, sending
multiple packets on multiple paths will improve the reliability or reduce PER.
Fig. 8 is a specific example for the mathematical PER evaluation of single-path and
multipath routing with a per hop channel error rates of 1%. As we can see, the higher the
number of paths the better the reliability, and the larger the number of hops, the lower the
reliability or the higher the PER.

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There are also special cases where multipath data transmission does not meet the
desired QoS requirements:
 Node has only one better neighbor towards the sink or sink is within the sensor
node's transmission range. The source node sends a packet over one path or sends it
directly to the sink without specifying the type of data, so sending copies or splitting
packets is not effective. If events appear randomly in the network, there would be a
large number of events is in this situation (in our simulation cases, there is

approximately 25% of events appearing around the sink and source nodes send data
directly to the sink).
 There are few forwarding nodes near the sink, causing the paths to converge at the
front of the sink, in the heavy traffic situation they would create congestion and
make longer delay and PER become worse.
 The latency improvement could not be good in two cases of traffic: the traffic is so
light that the queues are empty most of the time, and the traffic is too heavy and
pushes Q* to the maximum capacitor Q. A packet’s end-to-end delay is additive
and depends on the number of hops, queues as well as network traffic, so it is
difficult to estimate accurately and predictably by calculation (NP-complete
problem), then it is a reasonable way to deal it with a heuristic technique.
5. PERFORMANCE EVALUATION
A. Simulation Parameters
Table 1 shows main parameters used in our OMNeT++ simulation [29]. There are 3
types of events (A, B and C) occurring in the sensor network with equivalent ratio. We use
several traffic scenarios in our simulation. Each round (of 0.5 seconds), there are 2, 4, 5,
10, 20, and 25 nodes at random positions sending their data packets at random time, so the
total average traffics of network are 25,5; 51; 64; 128, 255 and 319kbit/s respectively. The
sink is in the center of the sensing field, and 100 sensor nodes are uniform randomly
placed. For the reason of simplicity and to differentiate only the three event types, we use
only single and two-path routing.
Table 1. Simulation Parameters for EARPM.
Parameter
Value
Network size
500m x 500m
Number of sensor nodes
100
Number of events per round
2, 4, 5, 10, 20, 25

Time interval (for one round)
0.5 sec
Number of packets/event (burstLength)
40
Sensor node’s radio transmission radius
120m
(dmax)
Routing Information packet size (from Sink
at the beginning to each node)
256 bits
Route Request, Route Response packet
size
24, 32 bits
Data and Data Acknowledgement packet
128, 8 bits
size
Link bit rate
30.720 bit/s
Processing time (for a packet at queue)
1 millisecond
Queue size (Data packet)
200
PER of one hop (%)
(10-rand(0,1))×10-2×(d/120)2/10

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Initial Energy of each node
15-rand(0,1)×10-2 J
Eelect
50nJ/bit
amp
100pJ/bit/m2
Energy Threshold
0.1J
The following performance parameters are assessed in the simulation:
 Network Lifetime: The lifetime of the multievent wireless sensor network is defined
as the period of time from network initialization to the time first node dies, namely
the minimum lifetime among all nodes.
 Number of Dead Nodes: It is the number of nodes which have residual energy less
than the threshold energy.
 Packet Error Rate: It is a ratio of loss packets to packets sent. For event type B, the
loss packets are the packets that could not reach the sink even over the first or the
second path and the packets sent are the original ones, not including the copy
packets.
 Latency: It is the total time taken to transmit the data from the event node to sink
node.
B. Result analyses
In this section, simulation results show that our routing protocol could extend the
network life time and adapt to the QoS requirements of multiple event types.
1. Lifetime extension and number of dead nodes
We compare EARPM against the GPSR single path routing scheme where only
distances from relay node to the sink and to the sending data node are considered. Fig. 9
shows that when using the residual energy criteria, the lifetimes in all 6 scenarios have
been extended (approximately over 70%). Such experimental results demonstrate that the

energy efficiency of our dynamic protocol is stable and has little impact by the increase of
the event density (in not too heavy traffic condition).

Figure 9. Lifetime comparison of EARPM and GPSR single path routing protocols
for multievent WSN.
The result in Fig. 10 shows that as larger the number of event nodes sending packets in
a round, as shorter the network lifetime. This is true because when more event packets are

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sent to the sink, the energy consumption increases and nodes die earlier. In our proposed
scheme, the first dead node appears more slowly than in the GPSR single path routing
scheme, that is because our scheme considers the average residual energy of nodes so it let
nodes in the network use energy more equally.

Figure 10. Number of dead nodes vs simulation time routing in multievent WSN.
2. Packet Error Rate
Fig. 11 shows the result in PER for 5 events/round simulation (other scenarios have the
same results). It can be seen that the PER of event B is significantly improved compared to
the A and C events. Furthermore, during the simulation time from the first node dead in
the distant scheme
(1380 seconds) to that
of
the
EARPM

scheme
(2400
seconds), PERs of all
three event types in
EARPM (denoted as
E-A, E-B, and E-C)
are less
than in
GPSR single path
routing
scheme
(denoted as G-A, GB, and G-C).
However, the PER
of all event type will
Figure 11. Analyses of data packet error rate of the three
become greater after
data types in multievent WSN.
the first node has dead
with both routing schemes. This is because dead nodes can not send or deliver any event
data packet because they lack of energy.

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3. Latency
Fig. 12 shows result in latency of for 5 events/round simulation. We just consider the

duration from the first event appearance to the time of 750 rounds (when network is in
congested situation and all nodes still alive, then there are chances for multiple paths to be
chosen). It can be seen that event C's packets have the smallest average latency, A is in the
middle, and B has the highest latency. The latency of C significantly improved over that of
A and B because the packets of event C could split on two paths so the number of C
packets on one path is reduced to half compared to A and B’s packets, that make the C’s
path less congested than the others. But, the improvement is good and distinct only with
low queue occupancy (at first 750 rounds), it will be decreased when the queue is almost
entirely occupied.
The latency of B
increases as the flow
packets of an event B
are twice that of events
A and C. Since B sends
the copy of the entire
packets to the second
line, B will cause much
congestion on both
paths of data and at
time B’s packets may
be merged into one path
before reaching the
sink.
Figure 12. Analyses of packet latency of the three data
According to the
types in multievent WSN.
simulation results, the
proposed
EARPM
scheme with the dynamic routing based on event types and residual energy consideration

brings contemporary benefits: smaller PER for event B, lower latency for event C and the
lifetime of network is extended.
6. CONCLUSION AND FUTURE WORK
The proposed QoS improvement solution for multievent wireless sensor networks
shows that in terms of energy limitation, it significantly improves the network life time
compared to the event driven shortest distant routing by considering the residual energy.
This is because node(s) with greater residual energy will have more chances to be the relay
node(s), then the energy consumption would be more balanced over the network and nodes
in the network burn energy more equally. By sending redundant data on multipath, the
protocol would greatly reduce packet error rates for high-reliability required events.
Furthermore, in congestion situation, splitting packets on multiple paths could lower the
packet latency for urgent events.
In the future, we will continue to improve the quality of communications for multievent sensor networks based on priority queues so that they can better prioritize events that
require high priority on latency and reliability in all network traffic conditions.
Acknowledgement: This work is partly supported by Motorola Solutions Foundation
under Motorola scholarship and research funding program for ICT education.

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REFERENCES

[1]. Y. Liu, X. Zhu, C. Ma, L. Zhang, “Multiple event detection in wireless sensor
networks using compressed sensing,” in Proc. ICT, Ayia Napa, Cyprus, May 8-11,
2011, pp. 27-32, DOI: 10.1109/CTS.2011.5898935
[2]. S. Abdullah, S. Bertalan, S. Masar, A. Coskun, I. Castle, " A wireless sensor network

for early forest fire detection and monitoring as a decision factor in the context of a
complex integrated emergency response system," in IEEE Workshop EESMS, Milan,
Italy, Jul. 24-25, 2017, DOI: 10.1109/ EESMS.2017.8052688
[3]. I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci, “A survey on sensor
networks,” IEEE Commun. Mag., Vol. 40, no. 8, pp. 102-114, 2002,
DOI: 10.1109/MCOM.2002.1024422
[4]. M. Z. Hasan, H. Al-Rizzo, F. Al-Turjman, “A Survey on Multipath Routing
Protocols
for
QoS
Assurances
in
Real
Time Wireless Multimedia Sensor Networks,” IEEE Commun. Surveys & Tutorials,
Vol. 19, no. 3, pp. 1424 – 1456, 2017, DOI:10.1109/COMST.2017.2661201
[5]. M. Li, C. Hua, C. Chen, X. Guan, “Application-driven virtual network embedding
for industrial wireless sensor networks,” IEEE ICC, Paris, France, May 21-25, 2017,
pp. 1-6.
[6]. L. Miller, K. Abas, K. Obraczka, “SCmesh: Solar-Powered Wireless Smart Camera
Mesh Network,” in Proc. ICCCN, Las Vegas, NV, USA, 2015, pp. 1-8,
DOI:10.1109/ICCCN.2015.7288462
[7]. Y. R. V. Prasad, R. Pachamuthu, “Neural Network Based Short Term Forecasting
Engine
to
Optimize Energy
and
Big
Data
Storage
Resources

of Wireless Sensor Networks,” in Proc. IEEE COMPSAC, Taichung, Taiwan, Jul. 15, 2015, pp. 511-516, DOI:10.1109/COMPSAC.2015.264
[8]. Natural Hazards Portal, “Danger levels forest fire,” Switzerland. [Online].
Available: />[9]. R. C. Shah, H. M. Rabaey, “Energy aware routing for low energy ad hoc sensor
networks,” in Proc. IEEE WCNC, Orlando, FL, USA, Mar. 17-21, 2002, pp. 350–
355, DOI: 10.1109/WCNC.2002.993520
[10]. M. M. Alam, M. A. Razzaque, M. Mamun-Or-Rashid, C. S. Hong, “Energy-aware
QoS provisioning for wireless sensor networks: Analysis and protocol,” IEEE J.
Commun. Networks, Vol.11, no. 4, pp. 390-405, 2009.
[11]. M. E. E. D. A. E. Kader, A. A. A. Youssif, A. Z. Ghalwash, “Energy Aware and
Adaptive Cross-Layer Scheme for Video Transmission Over Wireless Sensor
Networks,” IEEE Sensors Journal , Vol. 16, no. 1, pp. 7792 – 7802, 2016.
DOI:10.1109/JSEN.2016.2601258
[12]. D. Son, B. Krishnamachari, J. Heidemann, “Experimental Study of Concurrent
Transmision in Wireless Sensor Networks,” in Proc. SenSys ’06, Boulder, CO, USA,
31 Oct. 31–Nov. 3, 2006, pp. 237–250.
[13]. J. Kang, Y. Zhang, B. Nath, “End-to-End Channel Capacity Measurement for
Congestion Control in Sensor Networks,” in Proc. SANPA ’04, Boston, MA, USA,
Aug. 22, 2004.
[14]. B. Deb, S. Bhatnagar, B. Nath, “ReInForM: reliable information forwarding using
multiple paths in sensor networks,” in Proc. IEEE Int. Conf. LCN’03,
Bonn/Konigswinter,
Germany,
Germany,
2003,
pp.
406
–
415.
DOI: 10.1109/LCN.2003.1243166

66

N. T. T. Hang, N. C. Trinh, N. T. Ban, “Novel energy aware routing … sensor network.”


Nghiên cứu khoa học công nghệ

[15]. M. Radi, B. Dezfouli, K. A. Bakar, M. Lee, “Multipath routing in wireless sensor
networks: a survey and research challenges,” Sensors ISSN 1424-8220, Vol. 12, no.
1, pp. 650-685, 2012.
[16]. M. Masdari, M. Tanabi, “Multipath routing protocols in wireless sensor networks: a
survey and analysis,” IJFGCN, Vol. 6, no. 6, pp. 181-192, 2013.
[17]. A. A. Anasane, R. A. Satao, “A survey on various multipath routing protocols in
wireless sensor networks,” in Proc. ICCCV 2016, Mumbai, India, 2016, pp. 610–
615, DOI: 10.1016/j.procs.2016.03.077
[18]. A. V. Sutagundar, S. S. Manvi, “Location aware event driven multipath routing in
Wireless Sensor Networks: Agent based approach,” Egyptian Informatics Journal,
Vol. 14, no. 1, pp. 55–65, 2013.
[19]. J. R. Gallardo, A. Gonzalez, L. Villasenor-Gonzalez, J. Sanchez, “Multipath Routing
Using Generalized Load Sharing for Wireless Sensor Networks,” in Proc. 7th
IASTED Int. Conf. WOC2007, Montreal, QC, Canada, May 30–Jun. 1, 2007, pp.
374-379.
[20]. N. A. Pantazis, S. A. Nikolidakis, D. D. Vergados, “Energy-efficient routing
protocols in wireless sensor networks: a survey,” IEEE Commun. Surveys &
Tutorials, Vol. 15, no. 2, pp. 551-591, 2013.
[21]. W. Lou, “An efficient N-to-1 multipath routing protocol in wireless sensor
networks,” in Proc. IEEE Int. Conf. MASS, DC, USA, Nov. 7-10, 2005, pp . 665672.
[22]. S. Ehsan, B. Hamdaoui, “A Survey on Energy-Efficient Routing Techniques with
QoS Assurances for Wireless Multimedia Sensor Networks,” IEEE Commun.
Surveys & Tutorials, Vol. 14, no. 2, Second Quarter, pp. 265–278, 2012.

DOI:10.1109/SURV.2011.020211.00058
[23]. M. Radi, B. Dezfouli, S. A. Razak, K. A. Bakar, “LIEMRO: A Low-Interference
Energy-Efficient Multipath Routing Protocol for Improving QoS in Event-Based
Wireless Sensor Networks,” in Proc. SENSORCOMM, Venice, Italy, Jul. 18-25,
2010, pp. 551-557. DOI:10.1109/SENSORCOMM.2010.89
[24]. S. V. Kalore, P. Rewagad, “A review on efficient routing techniques in wireless
sensor networks,” in Proc. ICACEA, Ghaziabad, India, Mar. 19-20, 2015, pp. 803–
807, DOI:10.1109/ ICACEA.2015.7164813
[25]. Y. M. Lu, V. W. S. Wong, “An Energy-Efficient Multipath Routing Protocol for
Wireless Sensor Networks,” in Proc. IEEE VTC-2006, Montreal, QC, Canada, 25-28
Sep. 2006, pp. 2454–2459, DOI: 10.1109/VTCF.2006.505
[26]. C. Intanagonwiwat, R. Govindan, D. Estrin, J. Heidemann, F. Silva, "Directed
diffusion for wireless sensor networking," IEEE/ACM Trans. Networking, Vol. 11,
no. 1, pp. 2-16, Feb. 2003, DOI:10.1145/345910.345920
[27]. B. Karp, H. Kung, “Greedy Perimeter Stateless Routing”, in Proc. 6th Annual
ACM/IEEE Int. Conf. on Mobile Computing and Networking (Mobicom 2000),
Boston, Massachusetts, USA, Aug. 6-11, 2000, pp. 243-254, DOI:
10.1145/345910.345953
A. Chandrakasan, H. Balakrishnan, “Energy-efficient
[28]. W.R. Heinzelman,
communication protocol for wireless microsensor networks,” in Proc. HICSS, Maui,
HI, USA, Jan. 7, 2000. DOI:10.1109/HICSS.2000.926982
[29]. Omnet++ version 4.4 [Online]. Available: />
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TÓM TẮT
GIAO THỨC ĐỊNH TUYẾN NHẬN THỨC NĂNG LƯỢNG MỚI CHO MẠNG CẢM
BIẾN KHÔNG DÂY ĐA SỰ KIỆN
Mạng cảm biến không dây (WSN) đa sự kiện ứng dụng trong tòa nhà thông minh
hay hệ thống giám sát môi trường yêu cầu cung cấp chất lượng dịch vụ (QoS) khác
nhau dựa trên kiểu loại sự kiện khác nhau. Các mạng này chứa một số lượng lớn
nút cảm biến nhưng chúng có năng lượng và khả năng xử lý giới hạn, vì vậy tiêu thụ
hiệu quả năng lượng là một trong những yêu cầu thiết yếu. Hầu hết các bài báo
nghiên cứu trong lĩnh vực này mới đáp ứng được một hoặc hai yêu cầu về QoS hoặc
đáp ứng với số kiểu sự kiện và số nguồn sự kiện có hạn. Trong bài báo này, chúng
tôi đề xuất một giải pháp mới kết hợp giao thức định tuyến nhận thức năng lượng,
định hướng sự kiện mới với cơ chế truyền tin linh hoạt để hỗ trợ các yêu cầu QoS
cho ba loại sự kiện trong WSN đa sự kiện. Các kết quả mô phỏng cho thấy, giải
pháp đề xuất làm giảm đáng kể tỷ lệ mất gói đối với các sự kiện yêu cầu độ tin cậy
cao và kéo dài tuổi thọ mạng của WSN đa sự kiện. Hơn nữa, trong trường hợp có
điều kiện tải lưu lượng cao, kỹ thuật chia sẻ tải trên nhiều đường sẽ làm giảm độ trễ
cho các sự kiện khẩn cấp trong WSN đa sự kiện.
Từ khóa: Định tuyến nhận thức năng lượng, Định tuyến linh hoạt, Cơ chế truyền tin, Đa sự kiện, Mạng cảm
biến không dây.

Received date, 02nd April, 2018
Revised manuscript, 12th May, 2018
Published, 08th June, 2018
Author affiliations:
Posts and Telecommunications Institute of Technology (PTIT).
*Corresponding author:

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