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Research Article
Energy Optimized Routing Algorithm for Hybrid Wireless
Mesh Networks in Coal Mine
Haifeng Jiang, Renke Sun, and Shanshan Ma
School of Computer Science & Technology, China University of Mining and Technology, Xuzhou 221116, China
Correspondence should be addressed to Haifeng Jiang; jhfeng@cumt.edu.cn
Received May ; Accepted July
Academic Editor: Fuwen Yang
Copyright © Haifeng Jiang et al. is is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Mesh clients in hybrid wirelessmesh networks can participate in networking and routing. When the backbonetransmission network
is broken, the mesh client can route and forward the data, which will eliminate the absolute dependence on the backbone network of
traditional infrastructure wireless mesh networks in mine emergency rescue. However, the energy of mesh clients is limited. Based
on the comprehensive consideration of the eciency and balance of energy consumption of mesh clients for data transmission, a
new energy cost criterion is designed. Energy optimized and fault recovered routing algorithm is proposed on account of dierent
network states. At last, the simulation analysis on the performance of routing algorithm is conducted and compared with typical
routing algorithms. Simulation results show that the algorithm has eectively extended the network lifetime and achieved optimized
combination of energy eciency and energy balance. When mesh routers in the backbone network are failed, this algorithm can
rapidly rebuild the route and shows strong capacity of routing recovery.
1. Introduction
China is a major coal producer. Coal accounts for about
%indisposableenergyconsumptioninChinacurrently,
and it will play an important role in economic and social
developmentofChinaasakindofimportantstrategic
resource in a long period of time. Normal radio systems can
provide at best a very limited and ineectual communication
capability in conned spaces. As a result, the problem of
providing communications capability in hostile underground
environments such as tunnels in coal mine, both for exploita-
tion and emergency situations, has been an important issue
in recent decades, especially aer a series of unfortunate
underground disasters and accident [].
As coal mining and tunnel excavation, coal face and
tunnel face are constantly moving, with the harsh environ-
ment and complex conditions. So, the wired communication
networks could not reach these special areas and there
aresomeblindspotsexistinginthewiredcommunication
and monitoring systems in the underground mine []. At
the same time, rescue members need to transmit video,
voice, and environmental monitoring to the commanders on
the ground and the rescue sta underground through the
wireless emergency rescue communication system. e coal
mine needs wireless monitoring and wireless communica-
tions. Recently, wireless mesh networks have been considered
as an alternative solution.
As a promising wireless access network, wireless mesh
networks (WMNs) are multihop wireless networks which
consist of three types of nodes, mesh routers, mesh clients,
and gateway nodes, with self-healing and self-conguring
[–]. ey have the features of high transmission rate,
wide coverage, rapid deployment, exible networking, and
scalability [,], which can eectively solve some problems
that the wired network and traditional wireless networks
could not solve. e architecture of WMNs can be classied
into three main groups on the functionality of the nodes
[,].
Client WMNs. In this type of architecture, client nodes consti-
tute the actual network to perform routing and conguration
functionalities as well as providing end-user applications to
customers. Hence, a mesh router is not required for these
types of networks. Infrastructure/backbone WMNs are as
Hindawi Publishing Corporation
International Journal of Distributed Sensor Networks
Volume 2015, Article ID 237697, 11 pages
http://dx.doi.org/10.1155/2015/237697
International Journal of Distributed Sensor Networks
follows. WMNs include mesh routers forming an infrastruc-
ture for clients that connect to them. Client nodes in this type
do not have the routing function and could not communicate
with each other directly. It needs to connect to mesh routers to
achieve wireless broadband access. Infrastructure/backbone
WMNs are the most commonly used type. Hybrid WMNs are
as follows. is architecture is the combination of infrastruc-
ture and client WMNs. Mesh clients can access the network
through mesh routers as well as directly meshing with other
mesh clients, which will enhance the connectivity of the
network and expand the coverage. So, the hybrid architecture
will be the most applicable case.
e current WMNs in coal mine are infrastructure/
backbone WMNs. Adapting to the long and narrow under-
groundtunnel,thetopologyofthebackboneroutinglayer
is single-chain or strip (double-chain), with low connectivity
andpoorrobustness.efailureofmeshrouterscould
easily break the backbone transmission network, causing
that the corresponding clients could not communicate with
thegatewayandformingblindmonitoringspots.So,the
infrastructure/backbone WMNs in coal mine are dicult to
provide reliable communication support for special under-
ground region monitoring and emergency rescue.
Based on the features of networking and routing of mesh
clientsinhybridWMNs,thearchitectureofhybridWMNs
used for underground mine was proposed in this paper. We
use mesh clients to participate in networking to improve the
connectivity and reliability of WMNs, which will provide a
new method for constructing the mine broadband wireless
networks. But the client nodes have limited energy. So, in
this paper, comprehensively considering the eciency and
balance of energy consumption of mesh clients for data
transmission, a new energy cost criterion for mesh clients
is designed. Energy optimized routing algorithm for hybrid
wireless mesh networks (EOR-HWMN) in coal mine is
proposed on account of dierent network states, which will
eectively extend the network lifetime and rapidly rebuild the
routewhenmeshroutersinthebackbonenetworkarefailed.
e rest of this paper is organized as follows. In Section ,
we present related works. Section provides a brief review
of the network architecture of the hybrid wireless mesh net-
works for mine emergency rescue and describes the network
state. Section proposes the energy optimized routing algo-
rithm that could be used for hybrid WMNs in underground
mine. In Section ,weshowhowthealgorithmhasbeen
implemented. e simulation model and the comparative
performance evaluation of the proposed routing algorithm
are presented in Section .Finally,Sectionconcludes this
paper.
2. Related Works
estudyofWMNsusedincoalminemainlyfocuses
on the application of emergency rescue communications.
e research covers multihop transmission performance,
network planning and coverage, routing protocol, channel
allocation, and resource management, which can be divided
into two categories: single-chain and strip (double-chain), in
accordance with the topology of the backbone routing layer.
2.1. Single-Chain Topology. In [], an emergency commu-
nication system based on WMNs is proposed and the
deployment strategy of the gateway nodes is analyzed. But
achieving full coverage with WMNs in underground mine
is not necessary and realistic. In [], the emergency com-
munication system is constructed based on WMNs as [].
Simulation results show that the bandwidth of the WMNs
will decrease and the latency will increase aer multiple
hops. But this paper does not provide the solution. e
multihop performance of WMNs in underground mine is
studied in [,]. In [], the optimization strategies on
multihop performance of WMNs are given and analyzed.
e multihop transmission performance is tested on the
experimental platform, which is based on the nd WMNs
technology. In [], the multiradio structure of multihop
mesh backbone network is proposed based on .n, which
improvesthebasisbandwidthofbackbonenetworkand
decreases the bandwidth attenuation per hop. e design for
multiradio mesh node in this paper plays an important role
in the research of the resource management and network
capacity of WMNs in coal mine.
2.2. Strip Topology. In [], the structure of the coal mine
emergency rescue wireless communication system based on
WMNs is proposed with the strip topology. e theories
and key technologies are extracted according to the actual
requirements in underground mine and the characteristics
of spatial structure and radio transmissions, which would
provide theoretical support for the following research of
WMNs in coal mine. In [], link expected trac with
dierent routing schemes and distributions of bottleneck
collision domain with dierent radio interfaces are analyzed
in the application of emergency rescue WMNs with strip
topology in underground mine, and the nominal capacity
modelisproposed,whichcanprovidethetheoreticalbasisfor
stripWMNsincoalmine.Tomeettheunbalancedbandwidth
requirements of links in the strip WMNs, a modied and
classied channel allocation strategy is proposed to improve
the performance of the network, using distributed and static
channel allocation pattern in []. In [], according to the
application features of WMNs in underground mine, node
deployment and resource allocation schemes are proposed
based on the integration of the backbone routing layer,
gateway layer, and mesh client layer.
e above studies are based on infrastructure/backbone
WMNs and the problems of backbone routing layer with
strip topology are solved in a certain extent. However, the
WMNs with single-chain or strip topology in underground
mine have low connectivity and the failure of mesh routers in
backbone routing layer could easily lead to the disconnection
of backbone transmission network. Mesh clients in hybrid
WMNsareprovidedwiththefunctionsofdataforwarding
and routing, which will achieve rapid recovery from failure
when mesh routers are broken down. But these will consume
additional energy and deteriorate the network lifetime of
WMNs, because of the limited energy supply of mesh clients.
Since the radio transceiver typically consumes more energy
than any other hardware components onboard a mesh client,
International Journal of Distributed Sensor Networks
designing energy optimized routing algorithm is of great
importance to prolong network lifetime [].
In [], the energy ecient channel assignment and
routing problem is dened and two mixed-integer linear pro-
grams are proposed to optimally solve the problem. But these
algorithms have the central control architecture, which will
produce heavy communication overhead. In [], this paper
presents that power-aware routing plays an essential role
to prolong emergency service in an accident area network,
when preexisting communication infrastructure and power
resources have been destroyed. A power and node-type-
aware routing algorithm is proposed, which selects optimized
routes based on joint consideration of the nodes’ types and
power levels along the path.
Recently, there is a growing interest in the use of renew-
able energy sources to power wireless networks in order
to mitigate the detrimental eects of conventional energy
production or to enable deployment in o-grid locations [–
]. In [], an energy-ecient survivable routing protocol is
proposedbasedonsolarenergyandwindmixedforpower
supply in green WMNs. A new routing metric is dened,
which converts the remaining energy of the nodes into hop
penalty factor and achieves the combination of hops with
remaining energy. In [], according to the dynamics of
energy supply of renewable energy sources, adaptive resource
management and admission control schemes are proposed to
maximize the energy sustainability of the network. In [],
it formulates the problem of network-wide energy consump-
tion minimization under the network throughput constraint
as a mixed-integer nonlinear programming problem by
jointly optimizing routing, rate control, and power allocation.
But the formulated mathematical programming problem has
high computational complexity, which is hard to be realized
on the platform of WMNs. Although providing renewable
energy sources to WMNs is unavailable in underground
mine, the energy optimized strategies in these papers are
worth to be referenced for related research in underground
mine.
3. Hybrid Wireless Mesh Networks for
Mine Emergency Rescue
3.1. Network Architecture. WMNs deployed along the tunnel
in underground mine with single-chain topology based
onthebackbonearchitectureareshowninFigure.e
mesh clients in this network do not have the capability of
networking and routing and can only access the wireless
backbone transmission network through mesh routers. Any
mesh router’s failure will cause some mesh clients interrupt-
ing the communication with the gateway, and the security
monitoring system will lose some monitoring sites, which will
threaten the life of emergency rescue members.
Mesh clients in WMNs with hybrid network architecture
in underground mine could participate in networking based
on its routing function, which is shown in Figure .When
mesh routers are broken down, the mesh clients could estab-
lish new communication with the gateway through other
mesh clients, avoiding monitoring blind spot and enhancing
network reliability. However, the performance of mesh clients
Gateway
Mesh router
Mesh client
Breakdown Wireless backbone
transmission network
F : Infrastructure/backbone WMNs in underground mine.
Breakdown Wireless backbone
transmission network
Gateway
Mesh router
Mesh client
F : Hybrid WMNs in underground mine.
(e.g., CPU and storage space) is lower than mesh routers,
especially its limited energy supply. erefore, in the process
of routing, a joint optimization combining backbone routing
layer and mesh client layer should be employed depending on
the network states.
3.2. Network State. According to the relationship between
mesh clients with backbone routing layer and the status of
mesh routers, the network state can be divided into three
types: AP covering, network edge, and backbone-recovery.
3.2.1. AP Covering. If a mesh client is within the coverage of
ameshrouterwhichhasaroutingpathtothegateway,itis
the AP covering network state. e mesh client can access
the backbone transmission network directly through mesh
routers to communicate with the gateway, without the process
of route discovery, and reduce the energy consumption of
mesh clients for broadcasting RREQ messages.
3.2.2. Network Edge. If a mesh client is not under the
coverage of any mesh router, it could not access the backbone
transmission network directly. It needs to start the request-
reply route discover y mechanism and form a multihop ad hoc
network through adjacent mesh clients. en, it will access
the backbone transmission network via a mesh client which
International Journal of Distributed Sensor Networks
has already established a wireless connection to the mesh
router.
3.2.3. Backbone Recovery. If one or several mesh routers are
broken down, the backbone transmission network will be
interrupted and the mesh clients covered by the faulted mesh
routers cannot achieve communicating with the gateway. In
this status, the mesh clients can repair the interruption of
the backbone transmission network using its function of
networkingandrouting.emeshroutersandrelatedmesh
clients will start the request-reply route repairing mechanism
andrebuildtheroutetothegateway.
Based on the above description of dierent network
states, we can nd that the mesh clients participating in
routing or not depend on the network state. Designing the
energy optimized routing algorithm should comprehensively
consider dierent network states and achieve the optimized
combination of energy eciency and energy balance for
mesh clients.
4. Energy Optimized Routing Algorithm
4.1. Denitions and Notations. Wireless mesh networks stud-
iedinthispaperaremainlyusedintheapplicationofmine
emergency rescue communication. e network contains a
number of mesh routers, mesh clients, and a gateway node.
In order to simplify the system model, we use similar network
mode as in [], which is shown as follows:
(i) Nodes can dynamically adjust its transmitting power.
(ii) Nodes can read the power information from the
physical interface and pass up to its network layer.
(iii) Nodes use the whole antennas and have equal trans-
mission radius, which means that the radio channel is
bidirectional and symmetrical.
(iv) e energy of mesh routers and the gateway node is
unrestricted, but the power of mesh client is limited.
For the purpose of describing the routing algorithm more
clearly, we dene the mine hybrid wireless mesh networks
and neighbors.
Mine Hybrid Wireless Mesh Networks.eminehybrid
wireless mesh network can be expressed by an undirected
graph (,),inwhichdenotes the set of nodes, including
mesh clients, mesh routers, and gateway nodes, and denotes
the set of wireless links among nodes which can communicate
directly.
Consider =
𝐶∪
𝑅∪
𝐺,where𝐶represents the set
of mesh clients, 𝑅represents the set of mesh routers, and 𝐺
represents the set of gateway nodes.
Consider = {(,) | , ∈ 𝐶}∪{(,)|∈𝐶,∈
𝑅}∪
{(,) | ∈ 𝑅,∈
𝐺},(,) ≤ ,where(,)denotes the
distance between nodes and and denotes the maximum
communication distance among nodes.
Neighbor. e neighbor set of node is dened as () = { |
∈,(,) ≤}.
4.2. Energy Consumption Model. e energy consumption
of the mesh clients includes three components: sensing
energy, communication energy, and data processing energy.
Sensing and data processing require much less energy than
communication, so we only consider communication energy
consumption. We use the same energy consumption model as
is used in [] for wireless communicating hardware. In this
model, the transmitter dissipates energy to run the radio elec-
tronics and the power amplier, and the receiver consumes
energy to run the radio electronics, which is consistent with
the real situation better on energy consumption for wireless
communication module.
If the mesh client transmits an -bit packet over distance
, the radio expends
𝑇𝑥 (,)=
elec +amp𝛼,()
where elec denotes the energy/bit dissipated by the trans-
mitter electronics. amp denotes the energy consumed in
the transmission amplier and represents the path loss
exponent. e value of is for space channel model and
formultipathfadingchannelmodel.
When the mesh client receives an -bit packet, the energy
consumed is
𝑅𝑥 ()=
elec.()
4.3. Energy Optimization for Mesh Client. In some scenar-
ios of mine hybrid wireless mesh networks, for example,
network-edge state and backbone-recovery state, the mesh
clients will consume extra energy to receive, process, and
transmit relayed packets. But the mesh client is energy-
constrained, so it is important to design energy optimized
routing algorithm to optimize the energy consumption of
mesh clients.
4.3.1. Energy Cost. Toachievetheeectivenessoftheenergy
consumption of the mesh client, it is required that the
data transmission for the mesh client to the gateway node
consumes less energy. However, most energy-ecient routing
algorithms tend to route data via nodes on energy-ecient
paths and thereby drain their energy quickly. To achieve
energy balance of the mesh clients, the nodes with more
residual energy should be used to forward data, which will
oen lead to data relaying among many nodes, long routing
paths, large data transmission delay, and the waste of energy.
So, to prolong the lifetime of wireless mesh networks, the
routing algorithm must be designed to achieve both energy
eciency and energy balance together. It should not only
reduce the energy consumption for data transmission to
extend the lifetime of a single mesh client, but also balance
the energy consumption for the whole network. Based on
the above requirements, we have designed a new energy cost
function.
If mesh client transmits data to mesh client ,the
denition of energy cost is
EC𝑖𝑗 =𝑇𝑥 ,,
ce ()/ie (),()
International Journal of Distributed Sensor Networks
where 𝑇𝑥(,(,))denotes the energy consumption for data
transmission from node to ,ce(i)denotes the current
remaining energy of node ,ie()denotes the initial energy of
node ,andce()/ie()denotes the current remaining energy
level of node . e above part of energy cost function reects
the eciency of energy consumption for data transmission,
and the under part considers the energy balance. e less
residual energy the sending node has available, the greater the
value of the energy cost is, and the less probability this node
is selected as the transmitting node. Because the mesh router
has no energy limitation, if it transmits data, the energy cost
is dened as zero.
4.3.2. Path Cost. e basic idea of the routing algorithm
based on the energy cost is to build a path with the minimum
energy cost from data source node to the gateway node. e
total energy cost achieving data transmission, in which node
was selected as the relay node, is the sum of the energy cost
from node to and from node to (one of the neighbors
of node )itisshownas
EC𝑖,𝑗,𝑘 =EC𝑖𝑗 +EC𝑗𝑘.()
Based on the denition of the total energy cost, the mesh
client will not only consider the energy consumption for
data transmission from itself to the candidate node, but also
consider the energy consumption for the subsequent data
transmission, which will measure the energy eciency of the
candidatenodeselectedasthenexthopaccurately.
During the process of route discovery, the mesh client will
broadcast the route request (RREQ) messages. When RREQ
passes through each hop, the node will calculate the path
energy cost and the hop count according to ().emesh
router receives the RREQ messages from multiple paths and
calculates path cost according to (). e route reply (RREP)
message will be routed back along the reverse path to the
source node and the path with the least path cost will be
selected:
PC path ()=ECpath(𝑚)
max ECpath +(1−)path(𝑚)
max path ,
0<<1,
()
where ECpath(𝑚) represents the energy cost of path ,path(𝑚)
represents the hop count of path ,andadjusts the
proportion between the energy cost and the hop count.
4.4. Avoiding Strategy for Low-Energy Nodes. e above
energy optimization strategy can signicantly balance the
energy consumption of the mesh clients. If most of nodes in
a route have much residual energy, but only a few nodes have
little remaining energy, in this way the path cost is not very
high and this path may be chosen. However, when this route
is used to transmit data, the nodes with little residual energy
will deplete its energy, which will interrupt the processing of
the trac and decrease the network lifetime.
In order to solve this problem and further optimize the
energy consumption among nodes, we propose the avoiding
strategy for low-energy nodes. is strategy will delay the
low-energy nodes of which the residual energy is less than
a certain threshold for a period of time to broadcast RREQ
messages. So, this strategy can indirectly control the arrival
time of RREP messages. e arrival time of RREP message
with the route including low-energy nodes is much later
than other routes; therefore the source node can select other
routes avoiding low-energy nodes. Since the delay is only for
low-energy nodes, this strategy can avoid long delay during
routing discovery process.
When the intermediate node forwards RREQ messages,
it should examine its residual energy rstly. By comparing
theresidualenergywiththethreshold,thisstrategywill
determine when to broadcast RREQ messages aer a period
of time, which is dened as follows:
DT =
×
𝑁,ce ()
ie ()≥
1−ce ()
ie ()×
𝐿,ce ()
ie ()<,()
where DT denotesthe delay time to broadcast RREQ mes-
sages and Th denotes the threshold of residual energy in
nodes, which is set as %. 𝑁and 𝐿denote the delay
constant for normal node and low-energy node, respectively,
which is set as . seconds and . seconds. is a decimal
value obeying random distribution of [0,1].
5. Algorithm Implementation
5.1. Network Initialization. In the network initialization
phase, the mesh routers establish the path to the gateway
using proactive routing protocol. Hello messages are used to
establish, maintain, and update neighbor set. Nodes broad-
cast initialization messages with the preset transmission
power based on the neighbor distance, which include the
identication number and residual energy of nodes. If the
broadcasting node is the mesh router, the hello message
contains its hop count to the gateway in addition. Every node
receiving this setup message will determine its distance to the
transmitting node by the received signal strength and extract
the information from the message to establish its neighbor
set. e mesh clients join the mesh router with the least hop
count to the gateway from its neighbor set and record the hop
countoftherouter.
5.2. Route Discovery. e route discovery is initiated when-
everasourcenodeneedstocommunicatewiththegateway
and has no routing information in its routing table. In the
energy optimized routing algorithm proposed in this paper,
the route discovery and recovery mechanism is designed
basedonthenetworkstate.Whenasourcenode𝑠wants to
send data to the gateway node 𝑑and does not have a valid
route to it, 𝑠will rst check its local state.
If 𝑠is in the AP covering state, it can communicate with
𝑑by the backbone network through the mesh router, which
is used as the mesh access point. e route to the gateway
node is constructed among mesh routers by proactive routing
International Journal of Distributed Sensor Networks
protocol in advance, and the data will be transmitted along
thebackbonenetworktoward𝑑.
If 𝑠is in the network-edge state, it will start the route
discovery mechanism based on RREQ/RREP and make
routing decisions according to the path cost.
If 𝑠is in the backbone-recovery state, whether mesh
router or mesh client, it will start the request-reply route
discovery mechanism and rebuild the route according to the
path cost.
When a node receives the RREQ message, it will perform
thefollowingactions,showninFigure.
Firstly, it will check the network state of RREQ message.
If it is network-edge state, it indicates that the upstream mesh
client is at the edge of the wireless mesh network. So, the node
which received the RREQ message may be only mesh client.
is mesh client will check its own state. If it is in AP covering
state, it will perform action . Otherwise, if it is network-edge
state or backbone-recovery state, it will perform action .
If it is backbone-recovery state, it shows that the mesh
router connected to the transmitting node is broken down.
e node which received the RREQ message may be the mesh
client or the mesh router. If it is the mesh client, it will perform
action or action according to its own state, AP covering
state, or network-edge state, respectively. If it is the mesh
router, it will perform action .
Action 1. e receiving node automatically sets up the reverse
route to 𝑠. en it will generate the RREP message based on
its routing information to the gateway node and transmit the
RREP to 𝑠along the reverse route.
Action 2.enodeautomaticallysetsupthereverserouteto
𝑠. en it will rebroadcast the RREQ to its own neighbors
aer updating the hop count and the path energy cost.
Action 3. e mesh router compares its hop count with 𝑠.It
will drop the RREQ message, if its hop count is larger than
the hop count of 𝑠. Otherwise, it will compute the path cost
basedonthehopcountandenergycostintheRREQmessage
according to () and generate the RREP message including
the path cost and transmit it to 𝑠along the reverse path.
5.3. An Example. is section provides an example to clarify
the mechanism of the energy optimized routing algorithm.
In Figure , mesh client hasdatatobesenttothegateway.
But it has no route, so it will start the request-reply route
discovery mechanism and broadcast RREQ messages. When
RREQ passes through a hop, the node will accumulate the
energy cost and hop count. When RREQ has arrived at the
mesh router from dierent paths, which has the route to the
gateway, it will calculate the path cost. RREQ containing path
cost will be routed back along the reverse path to mesh client
, and the route with the least path cost will be selected.
Foreachalternativeroute,theenergyconsumptionfor
data transmission of each route can be obtained from accu-
mulating the energy consumption of wireless link along the
path (the third column of Table ), which is shown in the
second column of Table .Basedontheresidualenergylevel
of transmitting node (the second column of Table )and
T : Residual energy level and energy consumption for data
translation of mesh clients.
Mesh
client Residual energy level Energy consumption for
data transmission/J
a% a→b: ., a→c: .
a→d: .
bWithout energy restriction b→c: ., b→d: .
c% c→d: ., c→e: .
d% d→f: .
e% e→r: .
f% f→r: .
the energy consumption of wireless link, each node on the
path calculates the energy cost of each link, accumulating
the energy cost of each path (the fourth column of Table )
at router . e path cost (the h column of Table )is
calculatedbasedontheenergycostandhopcountofthe
path according to (),andtheroutewiththeleastpathcost
will be selected. Although the energy consumption for data
transmission of route is the same with route , the energy
cost of route is less than route , because the node on route
is mesh router, which has no energy limitation. Route has
the least energy consumption for data transmission along the
path, but the node has less residual energy (%), so the
path cost is more than route .
6. Simulation Results and
Performance Analysis
We have implemented the proposed EOR-HWMN algorithm
on the platform of QualNet Developer . to study a hybrid
wireless mesh network used in underground mine for the
communication in special underground region monitoring
and emergency rescue. In the simulation, the network con-
sists of mesh routers, mesh clients, and a gateway,
which are randomly distributed in a rectangular region of
× square meters abstracted as a tunnel in underground
mine. e maximum transmission range of nodes is
meters, and the simulation time is seconds. e initial
energy of mesh clients is J and the energy consumption
modelusedinthesimulationisthemodelin[]forwireless
communication hardware. e trac model is CBR and the
size of data packet for sending is bytes. Considering
thehybridWMNsinactualmineemergencyrescue,the
data collected by mesh clients is mainly sent through the
gateway to the commander center or outside network, so the
destination of data stream generated by mesh clients is set
as the gateway in the simulation. e duration of trac is
set as s and the transmission interval of data packet is s.
e application of CBR randomly starts and continues for a
xed duration, which makes the simulation close to reality of
underground mine and focuses on the analysis of the energy
consumption.
We compare the performance of EOR-HWMN algo-
rithm with power- and node-type-aware routing algorithm
International Journal of Distributed Sensor Networks
Receiving RREQ
Network-edge state?
Mesh client is in AP
covering?
Mesh cleint is in network-edge state or
backbone-recovery state?
Receiving node is
mesh client?
Receiving node is mesh
router?
Checking the state of RREQ
Ye s
No
Ye s
Ye s
No
Ye s
No
Ye s
Action 1
Action 2
Action 3
F : Flow chart of route discovery.
T : Route, energy consumption for data transmission, hop count, energy cost, and path cost.
Route Energy consumption for data transmission/J Hop count Energy cost Path cost
Route : a→d→f→r. . .
Route : a→b→d→f→r. . .
Route : a→b→c→d→f→r. . .
Route : a→c→d→f→r. . .
Route : a→c→e→r. . .
Mesh router
Mesh client
a
e
f
c
d
r
0.04
0.07
0.06
0.02
0.02
0.04
0.05
0.04
0.02
40%
80%50%
30%
50%
0.025
b
F : An example of EOR-HWMN.
(PNTARA) [] and ad-hoc on-demand distance vector rout-
ing (AODV) [] on energy eciency and energy balance,
such as average residual energy of mesh clients, unbalanced
degree of residual energy of mesh clients and the number of
nodes with energy depletion, and quality of service (QoS)
metrics including packet delivery ration and average end-to-
end delay.
6.1. Energy Eciency and Energy Balance
6.1.1. Average Residual Energy of Mesh Clients. In Figure ,
it shows that the average residual energy of mesh clients
decreasesslowlywiththeincreaseofthenumberofmesh
routers, when the rst mesh client exhausts its energy. e
average residual energy of mesh clients in AODV is higher
than that of PNTARA and EOR-HWMN. In AODV, the hop
count is used as criterion of route decision and the algorithm
does not consider the characteristics of energy supply of
nodes with dierent types, which will lead to heavy load, fast
energy consumption, and prematurely energy depletion of
mesh clients in the shortest path. EOR-HWMN has designed
a new energy cost criterion for mesh clients, which combines
the energy consumption for data transmission and residual
energy of mesh clients into path cost calculation. e less
energy consumption for data transmission and more residual
International Journal of Distributed Sensor Networks
Number of mesh routers
16 18 20 22 24 26 28 30 32 34 36
Average residual energy of mesh clients (J)
0
0.05
0.1
0.2
0.3
0.4
0.5
0.15
0.25
0.35
0.45
EOR-HWMN
PNTARA
AODV
F : Average residual energy of mesh clients.
energyofdatasendingnodewillleadtolessenergycost,
which has achieved the optimized combination of energy e-
ciency and energy balance. At the same time, EOR-HWMN
reduces the probability of the low-energy node relaying the
data through the avoiding strategy for low-energy nodes,
which will further balance the energy consumption of mesh
clients.
6.1.2. Unbalanced Degree of Residual Energy of Mesh Clients.
In this paper, we use the standard deviation of the residual
energy in all mesh clients to measure the unbalanced degree
of residual energy. e energy consumption of mesh clients
is not balanced and the energy of nodes is easily depleted
when the value of unbalanced degree of remaining energy of
nodes becomes large. In Figure ,thestandarddeviationof
the residual energy of mesh clients in EOR-HWMN is lower
than that of PNTARA and AODV, which means that EOR-
HWMN has better performance on energy balance thanks to
the new designed energy cost criterion and avoiding strategy
for low-energy nodes. At the beginning of the simulation,
the residual energy of mesh clients is the same and the
standard deviation of residual energy is zero. Along with
the data transmission, dierent mesh clients have dierent
energy consumption, and unbalanced degree of residual
energy of mesh clients increases. But the growth of EOR-
HWMN is slower than PNTRAR and AODV, and the value
of the standard deviation is always lower than PNTARA and
AODV, which shows signicant eects on energy balance.
In PNTARA, the residual energy of mesh clients is used to
calculate the path cost, which is divided into three levels by
setting the upper and lower threshold. But the residual energy
in the same level may have many dierences, which will aect
theresultofenergybalance.SincethetracofCBRrandomly
starts from to s and lasts seconds, the trac and the
Simulation time (s)
200 220 240 260 280 300 320 340 360 380 400
0.04
0.05
0.06
0.07
0.08
0.09
0.1
EOR-HWMN
PNTARA
AODV
Unbalanced degree of residual energy of
mesh clients
F : Unbalanced degree of residual energy of mesh clients.
Simulation time (s)
200 220 240 260 280 300 320 340 360 380 400
e number of mesh clients with energy depletion
0
1
2
3
4
5
6
7
8
EOR-HWMN
PNTARA
AODV
F : e number of mesh clients with energy depletion.
energy consumption of mesh clients decrease from s, and
the unbalanced degree of residual energy tends to be stable.
6.1.3. e Number of Mesh Clients with Energy Depletion.
From Figure ,wecanseethatthemeshclientwithenergy
exhaustion rst appears in AODV and the number of energy
depleted nodes rapidly increases over the simulation time,
because routing decision does not include the energy factor of
mesh client. e nodes on the shortest path have heavy data
forwarding task and the energy of mesh clients is consumed
rapidly. e mesh client runs out of energy at s in
PNTARA and at s in EOR-HWMN. e time in which
International Journal of Distributed Sensor Networks
the mesh client exhausted energy and the number of mesh
clients with energy depletion in EOR-HWMN are later or
less than in PNTARA and AODV, which means that EOR-
HWMN has consumed energy more balanced, prevented the
mesh clients exhausted energy prematurely, and achieved
theecientandbalancedenergyconsumptionamongmesh
clients. In WMNs used in the underground mine, if there are
nodes with energy exhausted, there will be lost monitoring
on some sites. If this node is used as intermediate node for
routing,itwouldrebuildtherouteandcausepacketloss
and more data transmission delay. erefore, EOR-HWMN
is more suitable for safety monitoring and emergency rescue
in underground mine, which requires higher reliability.
6.2. Quality of Service (QoS)
6.2.1. Packet Delivery Ratio. In Figure ,themeshclients
with energy depletion appear from s in AODV, and
the number of energy exhausted nodes increases over the
simulation time, so the packet delivery ratio (PDR) has
declined. EOR-HWMN and PNTARA have employed the
energy optimization strategy, delayed the time in which
nodes with energy exhaustion emerged, and decreased the
number of nodes running out of energy. But the hop count
of the route in EOR-HWMN and PNTARA is more than that
in AODV, which will increase the packet loss probability. So
the PDR in these three algorithms is basically the same in
the beginning period of the simulation. To verify the routing
recovery capacity of EOR-HWMN, we set arbitrary two mesh
routers’ failure at s in the simulation. From Figure ,
wecanseethatEOR-HWMNhasconductedtheroute
discovery and maintenance mechanism based on network
states when the mesh routers are broken down. Although
thevalueofPDRinEOR-HWMNhasslightlydecreased,it
has quickly recovered stability, which shows better routing
recovery capacity than PNTARA and AODV.
6.2.2. Average End-to-End Delay. In Figure ,theaverage
end-to-end delay in EOR-HWMN starts to increase aer
s. With the increase of the energy consumption of mesh
clients, EOR-HWMN needs to generate routes with more
hops to avoid low-energy nodes, which will increase the
average end-to-end delay. From s in the simulation, the
trac and the energy consumption of nodes decrease, so the
average end-to-end delay tends to stability. In AODV, the
route with the least hop count is selected to transmit data,
which causes the average end-to-end delay to be lower than
EOR-HWMN and AODV.
7. Conclusions
In WMNs with infrastructure/backbone architecture used
in underground mine, the breakdown of mesh routers
could damage the backbone transmission network, causing
corresponding clients out of contact with the gateway and
forming blind monitoring spots. Mesh clients in hybrid
WMNs can participate in networking and routing, which will
improve the connectivity and reliability of WMNs. Hybrid
Simulation time (s)
200 220 240 260 280 300 320 340 360 380 400
Packet delivery ratio (%)
60
65
70
75
80
85
90
95
EOR-HWMN
PNTARA
AODV
F : Packet delivery ratio.
Simulation time (s)
200 220 240 260 280 300 320 340 360 380 400
Average end-to-end delay (s)
0.17
0.175
0.18
0.185
0.19
0.195
0.2
0.205
0.21
0.215
EOR-HWMN
PNTARA
AODV
F : Average end-to-end delay.
WMNsaremoresuitabletoprovidecommunicationfor
safety monitoring and emergency rescue in underground
mine. Energy is one of the most critical resources for mesh
client in hybrid WMNs. e energy of mesh clients should be
consumed optimally.
In this paper, we have designed a new energy cost cri-
terion for mesh clients considering the energy consumption
for data transmission and residual energy of data sending
nodes, which has achieved the optimized combination of
energy eciency and energy balance. Energy optimized
routing algorithm for hybrid WMNs in underground mine
is proposed on account of dierent network states. e route
with the least path cost will be selected to transmit data.
International Journal of Distributed Sensor Networks
In order to further balance the energy consumption, we
have proposed the avoiding strategy for low-energy nodes to
decrease the probability of these nodes to relay data.
e designed algorithm EOR-HWMN demonstrates
its superiority to power- and node-type-aware routing
(PNTARA) and ad-hoc on-demand distance vector routing
(AODV) on energy eciency, energy balance, and quality of
servicemetrics.SimulationresultsshowthatEOR-HWMN
has balanced the energy consumption among mesh clients,
extended the network lifetime, and rapidly rebuilt the route
when mesh routers are failed.
ough the performance of EOR-HWMN has been
demonstrated by simulation study, the understanding of this
approachcanbedeepenedbybuildingtheoreticalfounda-
tions. Two theoretical works are being considered. One is
the building of the analytical models of the combination
of energy eciency and energy balance, and another is
the perfect weight () in the path cost obtained through
theoretical analysis. In the future, we plan to build a real
hybrid wireless mesh network in underground mine, through
deploying mesh clients, mesh routers, and the gateway in the
tunnel.EOR-HWMNwillbetestedontherealnetwork.
Conflict of Interests
e authors declare that there is no conict of interests
regarding the publication of this paper.
Acknowledgments
Financial support for this work provided by the Fundamental
Research Funds for the Central Universities (no. QNB)
and Natural Science Foundation of Jiangsu Province (no.
BK) are gratefully acknowledged.
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