Connectivity Node Set Generation Algorithm of Mine WSN
Based on the Maximum Distance
Xu Donghong*
School of Computer Science and Technology
China University of Mining and Technology
Xuzhou 221116, China
[email protected]
Wang Ke
School of Computer Science and Technology
China University of Mining and Technology
Xuzhou 221116, China
[email protected]
China. Thousands of miners died or got injured due to
unawareness of abnormal methane concentration, high
temperature or strain in coal mines. The accidents could be
avoided if a real-time monitor system can be implemented to
monitor multiple points at key locations in coal mines [12]. In
Chinese state-owned collieries, 56% of the mines have been
jeopardized by spontaneous combustion, and accounts for
90–94% of the total coal mine fires [13]. Author [14] aimed to
find the problem by automatically monitoring and interacting
with physical environments. Author [15] classified the
coverage problem from different angles, developing evaluation
metrics of coverage control algorithms, analyzing the
relationship between coverage and connectivity, comparing
typical simulation tools, and discussing research challenges and
existing problems. Instead of taking turns to replace the failed
node developed by author [16], Author [17] proposed another
distributed algorithm to recover the holes using the laws of
vectors to decide the magnitude and direction of mobile nodes .
Author [18] addressed a problem and determined current
coverage by non-deterministic deployment of static sensor
nodes and subsequently enhanced the coverage using mobile
sensors.
Abstract— Deployment strategy of sensors reflects cost and
monitoring capabilities of sensor networks. The strategy not only
determines the routine work of the sensor networks but also
affects energy consumption, survival time and quality of service
(QoS). In the coal mine, the network generally is long and narrow,
and network nodes are equipped with large electrical and
mechanical equipments. Those traits have serious impacts on
signal transmission of wireless sensor. Therefore, it is important
to conduct research on Connectivity Node Set (CNS) of wireless
sensor nodes (WSN) in coal mine. Wireless sensor backbone
network deployed in a coal mine laneway environment is
researched in the paper. We propose a Connectivity Node Set
Generation Algorithm of Mine WSN Based on Maximum
Distance. The algorithm can maintain stable network
communication through deployment of standby nodes on the WSN
even when the backbone network nodes malfunction. Meanwhile,
it can improve the robustness of WSN in coal mine environment.
IndexTerms—Sensing Arrange; Deployment; Coal Mine;
Optimization
I. INTRODUCTION
WSN nodes have different theoretical and physical
characteristics from individual nodes. Numerous models of
various complexities have been constructed based on
application requirement and working environment.
Nevertheless, most sensing device models share facets in
common [1][2]. The models assume that a random network is
deployed over an infinite area with nodes following Poisson
distribution, and the authors investigate the path coverage of
the network. They first study the path coverage over an infinite
straight line when each sensor node has a random sensing range.
Most research, for example [3, 4, 5], are related to coverage and
connectivity analysis either to maintain a network or to propose
a deployment strategy. Typically, sensors are randomly
deployed in an area, and no spare nodes are provided in the
network. If a coverage hole is observed due to node damage or
an active node count fails to meet a threshold, then the network
needs to be augmented with spare nodes. In paper [6] a
biogeography-based optimization is applied to dynamic
deployment of static and mobile sensor networks to achieve
better performance by increasing the network coverage area.
The initial deployment usually cannot generate an effective
coverage because of the randomness of the method. To tackle
this problem, various dynamic deployment algorithms have
been studied [7,8]. Artificial Bee Colony (ABC) algorithm [9],
which works well for WSNs consisted of only mobile sensors [9,
10, 11]. Frequent coal mine accidents are a very serious issue in
II. ANALYZING THE IMPACT OF THE MINE CONNECTIVITY
FACTOR
Recent deployment methods of wireless sensor network can
be classed into deterministic coverage and random coverage
according to network configuration and related application
properties. Deterministic coverage includes area, point, and
grid target converges. Random converge includes random
nodes converge and dynamic network converge. The related
application properties include energy saving, fence,
connectivity, and target location converge. Only after the
sensor nodes are deployed in the target area, optimization and
related other work can be done. To high construction costs of
underground WSNs, it is impossible to deploy many sensor
nodes. Therefore deterministic deployment is adopted.
Deployment of backbone nodes implies minimizing the number
of nodes under the condition of ensuring network connectivity.
2.1 Shielding position and impact on node deployment
Shielding refers to the phenomenon of occlusion of
communication signal due to the impaction of mine trucks,
building and laneway curve point in underground WSN. This
phenomenon limits the nodes communication area, reducing
real communication distance between nodes. This phenomenon
is more obvious in the deployment of fold point nodes or corner
point nodes in the laneway. As illustration in fig 2.1, the nodes
deployed in laneway L2 are shielded by L1, and thus the real
communication radius Rrc is shorter than Rc. The decrease of
real communication radius largely restricts the communication
This work was supported by“the Fundamental Research Funds for the
Central Universities”(2013QNB15).
Corresponding author* Xu Donghong, [email protected]
1
and deployment of sensors.
Fig2.1 Shielding phenomenon
L =
⎡ S ⎤
（3-2）
n=⎢
−1
⎢ α × Rc ⎥⎥
In formula 3-1 and 3-2, S: the length of laneway which is
required to deploy communication nodes. Rc:the
communication radius of sensor nodes. n: the number of nodes
deployed in the given laneway section. L: the distance between
the nodes in the real deployment. α: the environment factor
between sensor nodes.
Definition 3.2 Environment Factor (EF, α): Environment
Factor is a conversion coefficient between the real
communication radius and ideal communication radius of the
sensor node considering various factors that affect nodes
connectivity. EF between two nodes can be obtained based on
the relation between signal attenuation and propagation
distance considering temperature, wind velocity, pressure in the
complex mine environment. α can be obtained by equation 3-3.
Fig2.2 Signal attenuation phenomenon
As shown in the Fig 2.1, some laneway corners are signal
shielding areas. To prevent communication disorders in the
WSNs caused by signal shielding of the neighbor sensors nodes
deployed, the deployment of sensor nodes in those positions
must be increased to reduce the negative impact of shielding.
2.2 Signal attenuation phenomenon of underground
sensors and the impact on the deployment of nodes
Signal attenuation refers to communication signal intensity
becoming weak with increasing of time and distance. Therefore,
nodes at the end of communication radius may lose
communication capability, as depicted in Fig 2.2. The
communication capability becomes weak in the communication
area illustrated by the shadow in the figure 2.2. If the nodes are
deployed based on the area expressed by the full circle, there
will no communication signal between node A and node B. In
this figure, full communication radius Rrc is less than the ideal
communication radius Rc.
α
=
Pi − P0
d
1 0 lo g
ij
(3-3)
In equation 3-3, Pi is the acceptance power of node Si. d0 is
the distance between sensor anchor node and sensor reference
node. dij is the distance between node Si and Sj. dij,d0 can be
gauged and obtained, so α can be obtained between Si and Sj by
applying equation 3-3 and each pair of nodes can be calculated
as an environment factor. Under real condition, each pair of
nodes environment factor α may be completely different due to
the changing of temperature, humidity, acceptation power
measurement and others fluctuations factors. Firstly, we
consider the ideal situation that the distance between nodes
equals communication radius Rc.
In Fig 3.2, the length of the laneway AE which requires the
deployment node correctly equals the integral multiple of the
node communication radius Rc. In this situation, the distance
between each node is exactly equal to the communication
radius. In other words, the sensor nodes are deployed in the
points A、B、C、D、E. In this situation, the distance between
two nodes has been the maximum communication distance of
the sensor nodes, and thus this deployment schema is an
optimization method. However, few laneway arc length is
exactly an integer multiple of the radius of the communication
node deployed. Therefore, a more general case of the
deployment of sensor nodes is illustrated in Fig 3.3.
III. METHODS OF GENERATION CONNECTIVITY NODE SET IN
THE COAL MINE WSNS
Signals data of underground sensors must be transferred to
ground-level data collection centers by transferring connection
lines. The underground transferring lines are connected by
auxiliary shafts and ground, so the location of the auxiliary
shafts must be determined first to ensure the network
connectivity.
Definition 3.1 Connectivity Nodes Set (CNS): CNS stands for a
coordinate set in the mine underground laneways. CNS includes
shielding position coordinate, signal attenuation point
coordinate. If the sensor nodes are deployed according to the
CNS, the laneway backbone communication network can be
formed. Fig 3.1 is a topology structure graph of a part of mine
laneway. In this graph, node A is the position of auxiliary shaft
and C is the fold point of the laneway. We will deploy sensor
nodes in the laneway AC according to the maximum
communication distance rules between sensor nodes.
Fig 3.1 2D structure chart
（3-1）
S
S
⎡
⎤
⎢⎢ α × R c ⎥⎥
d
0
Fig 3.2 Nodes deployment under ideal situation
With an increasing distance, the generation of CNS should
obey the rule that maintains the network normal
communication, while minimizing the number of nodes. The
distance between nodes and the required number of nodes are
calculated by equations 3-1 and 3-2.
Fig 3.3 Sensor arrangement plan under practical situation
In figure 3.3, A: the point where the auxiliary shaft located. E、
F: the corner point of the laneway. To eliminate the shielding
effect on the communication, nodes on the points A、F、E must
2
be deployed. Assuming the distance between A and F is S and
the distance of E and F is S’, meanwhile, αRc <S<3αRc, S’<αRc,
so it is unnecessary to deploy nodes on the line EF while on line
AF it is necessary to deploy nodes. Based on equations 2-1 and
2-2, we can calculate that three nodes are necessary to deploy
equidistant in the AF section. Its location in Fig3.3 respectively
is B, C, D. The distance formula between nodes is:
S
⎡ S
⎤
⎢⎢ α × R c ⎥⎥
(3-4)
In mine real topology, each laneway can obtain the whole
connectivity nodes set by applying the above connectivity
nodes set generation method. According to those point sets,
sensor nodes are deployed in the mine and meanwhile the
backbone communication network is obtained.
Fig 4.1 Flow chart of M-CNSGA
4.3Robustness of R-MCPSGA
Applying M-CNSGA, connectivity point sets of wireless
sensor in the mine laneway can be obtained and the points in the
connectivity point sets establish wireless sensor backbone
communication network. The backbone communication
network can guarantee communications connectivity with
minimum redundancy. However, in real mine environment,
harsh underground environment can severely disrupt wireless
transmission signal. If data transmission is transferred only by
backbone nodes, the reliability of network cannot be
guaranteed, that is, if one node fails it will paralyze the entire
network. Therefore, it is necessary to implement redundant
deployment based on the deployment of network backbone
nodes. The method is robust mine connectivity point set
generation algorithm （R-MCPSGA）．Applying R-MCPSGA
can ensure the robustness of the sensor network. Therefore,
robust mine connectivity point set generation algorithm
（R-MCPSGA）is proposed and the working process of backup
nodes is introduced.
4.3.1 Backbone Network Robustness Optimization Method
To ensure long time maintenance and continuity of
information collection of the network routers, it is necessary to
deploy spare nodes of the backbone nodes. As depicted in Fig
4.2, the distance between A and B is R1 and between B and C is
R2. Rc is the communication radius of sensor nodes and Rs is the
sensing radius. Four rules of Robust-improved MAPGA are
proposed.
Rule 1: when R1=R2=Rs, at least one node should be deployed
with two spare nodes in the range of Rs in a given path.
Rule 2: when R1= Rc, R2<Rc, spare nodes should be deployed
to the node whose left distance is min{Rc-R2,Rs/2}. When R1<
Rc, R2=Rc, spare nodes should be deployed to the node whose
right distance is min{Rc-R1,Rs/2}.
Rule3:when R1< Rc, R2<Rc, spare nodes should be deployed to
node whose left and right distance are min{2Rc-R1-R2,Rs}.
Rule 4: in the turning point of laneway, based on the actual
situation of laneway, taking the node’s sensing radius as arc, a
pair of nodes is deployed at the intersection of the two sides of
laneway wall. Those four rules are specified divided into four
types.
IV. MINE BACKBONE NETWORK CONNECTIVITY NODE SET
GENERATION ALGORITHM (M-CNSGA)
4.1. Model of Mine Backbone Network Connectivity Node
Set Generation Algorithm (M-CNSGA)
The objective of Mine Connectivity Point Set Generation
Algorithm based on maximum communication distance is to
generate connectivity node set of sensor backbone network on
the basis of mine laneway topology graph. Assuming V for
connectivity set in mine laneway topology graph, E(i,j) for arc
connecting nodes i and j, the digital W(i,j) above the arc for the
weight of arcs. The graph can be expressed as G=(V,E,W)
Inputs：
Mine 2-D topology graph: G=(V,E,W)
Laneway point set：V={V1，V2，V3，…，Vn}
Laneway edge set：E={E1，E2，E3，…，En}
Weight set of each edge：W={W1，W2，W3，…，Wn}
Output：
Connectivity point set of nodes in the backbone network
V’={V1’,V2’,,V3’,…,Vn’}
Steps：
Step1: Input each edge E and its weight W, establish head node
Headnode and table node Node, establish adjacency list and
form the graph topology.
Step2: Locate the position of the auxiliary shaft, and judge each
laneway according to the given sequence.
Step3: Establish connectivity point set V’, calculus and travel
each laneway according to formula 3-1 and 3-2, add nodes to
meet the requirement of the point connectivity set V’.
Step4: Repeat step3, until there are no laneway’s points.
Step5: End of algorithm, return V’
The flow chart of M-CNSGA is illustrated in Fig 4.1
4.2 M-CNSGA Data Structure
Structure of node includes ID of node (ID), Laneway of node
located (Laneway), lx-coordination of node x (worldx),
y-coordination of y (worldy), the important factor (ac), location
of adjacent node, pointer to the next edge (nextaRc),
temperature, humidity, gas concentration, carbon monoxide,
wind speed, oxygen, mine press, dust, node type. The date
structure of sensor nodes is illustrated in table 4.1.
3
of each of the neighbor backbone nodes, and judge the location
of middle node and two sides of the node.
Step 5: Repeat Step 4 until no more new nodes location to add
Step 6: Terminate algorithm, return spare nodes deployment
set V
R-MCPGA algorithm flow char is illustrated as following：
V. R-MCPGA ALGORITHM AND EXPERIMENT SIMULATION
5.1 R-MCPGA Algorithm
In general, there are three states of sensor nodes which
include monitoring, standby, and communication. Backbone
nodes not only monitor the sensing area, but also are
responsible for communication and data transfer with
communication nodes of other backbone networks. The
working approach of the spare node is divided into two
categories based on the different locations of deployment.
(1) The spare nodes are deployed in proximity of backbone
nodes and keep network connectivity, but there is no strict
requirement to monitor nearby information. In routine work,
the spare nodes are in the standby state. Once there is signal to
enable the node, it can maintain communication.
(2) The spare nodes deployed in proximity of the monitoring
nodes. The spare nodes have special high requirement for
information monitoring. Those nodes not only serve as backup,
maintaining the route, but also have the responsibility to
continuously monitor the monitoring state. Meanwhile, those
nodes monitor the information in the sensing arrange and
transfer information to the backbone communication nodes.
Some spare nodes can monitor information to improve network
coverage and increase monitoring reliability. Through analysis
of the state of the spare nodes, the algorithm improves the
connectivity coverage and the robustness of the network.
The work flow of the backup route is depicted in Fig 5.1. We
assume other nodes transfer information to node A and C, while
node C cannot receive data from node D. In this situation, node
C sends a waking message to the spare node D’ which is the
spare node of node D. After node D’ is woken up, it sends
packet to node C and E to inform them that node D’ is the spare
node of node D and keep the communication network in normal
working state. While keeping the continuity communication,
node C sends an alarm message which includes the information
that node D does not work to the other nodes and sends
corresponding messages to remind the administrator update and
repairing node D.
Applying the spare node deployment algorithm to deploy the
spare node based on the backbone nodes deployment algorithm
and node data structure. The basis of the algorithm of the
connectivity point set is obtained as following.
(1)Input
Point set：V={V1,V2,V3,…,Vn}
Edge set：E={E1,E2,E3,…,En}
Weight set：W={W1,W2,W3,…,Wn}
Connectivity point set：V’={V1’,V2’,V3’,…,Vn’}
(2)Output：
Deployment nodes set of spare nodes: V={V1,V2,V3,…,Vn}
(3)Steps：
Step 1: Input the Node ID and establish head node list
(Headlist)
Step 2: Input edge E and its weight W, establish head node
(Headnode) and list node (Node) of each node, meanwhile,
establish adjacent list to form graph topology structure
Step 3: Start from the root node, connect each node by applying
depth first search, and record the two edges weight connected to
the node
Step 4: Establish spare nodes deployment point set V, thinking
Fig 5.1 The flow chart of backbone R-MCPGA
VI. SIMULATION
According to the data structure of table 4.1 and the
deployment of sensor nodes, we adapt two data set edges and
node and the date are given in table 6.1. In table 6.1, the
coordinate date sets represent a relative position and they are
not the actual geographical coordinates.
Table 6.1 Position Data Sets of Topological Nodes
4
Fig6.1 is the land way topology abstraction graph of all nodes.
This topology abstraction graph is being the input data of
simulation experience. Red starts are the Node nodes.
VII. CONCLUSION
In this paper, connectivity node set generation algorithm of
mine WSN based on the maximum distance is proposed on the
basis of mine topology structure. Sensors are deployed
according to the point set obtained by applying M-CNSGA, and
those sensors form the backbone network of mine monitoring
communication. To ensure the robustness of the network,
R-MCPGA algorithm is presented to locate the spare nodes set.
R-MCPGA can improve the robustness of communication
network. Even under an extreme harsh mine underground
condition, the algorithm can guarantee the reliability of
communication.
REFERENCES
Fig 6.1A Mine Laneway 2D Topology Abstraction Graph
[1]
In the experience, we assume the communication radius
Rc=0.1 and the environment factor α is defined a random digit
due to the influence of various factors. Those factors include
temperature, humidity and other factors. So the test takes a
random number to simulation. The simulation result is shown
in the fig 6.2.
[2]
[3]
[4]
[5]
[6]
[7]
[8]
[9]
[10]
Fig 6.2 A Backbone Node Deployment Diagram of a Laneway
The diagram is the part of the whole diagram due to too many
nodes. In the fig 6.3, the red stars are the laneway topology
nodes and the green stars are the backbone nodes. According to
the algorithm and the backbone nodes deployment, there will
be produce a backbone nodes set. Applying the algorithm,
when the input date is the backbone nodes so the output is spare
nodes, and we can find there are two spare nodes in the turning
point of the laneway.
[11]
[12]
[13]
[14]
[15]
[16]
[17]
[18]
Fig 6.3 A Spare Nodes Deployment of a Mine Laneway
5
Megerian S, Koushanfar F, Qu G, Veltri G, Potkonjak M. Exposure in
wireless sensor networks: theory and practical solutions. Wireless
Networks 2002,8:443–54.
Ram S, Majunath D, Iyer S, Yogeshwaran D. On the path coverage
properties of random sensor networks. IEEE Transactions on Mobile
Computing 2007,6(5): 494–506.
Tan
G,JarvisS,
Kermarrec,M.
Connectivity-guaranteed
and
obstacle-adaptive deployment schemes for mobile sensor networks. IEEE
Transactions on Mobile Computing 2009,8(6):836–48.
Wang D, Xie B, Agrawal D. Coverage and life time optimization of
wireless
sensor
networks
with
Gaussian
distribution.IEEETransactionsonMobileComputing2008,7(12):1444–58.
Wang B, Fu C, Lim H B. Layered diffusion-based coverage control in
wireless sensor networks. Computer Networks2009,53(May):1114–24.
Gaige Wang, Lihong Guo et ac. Dynamic Deployment of Wireless Sensor
Networks by Biogeography Based Optimization Algorithm . Journal of
Sensor and Actuator Networks,2012, June: 86-96.
Pilloni V,Atzori L. Deployment of distributed applications in wireless
sensor networks. Sensors2011, 11: 7395–7419.
Aitsaadi N, Achir N. Boussetta K. Pujolle, G. Artificial potential field
approach in WSNdeployment: Cost, QoM, connectivity, and lifetime
constraints. Comput. Netw. 2012, 55: 84–105.
Ozturk C, Karaboga D, Gorkemli B. Artificial bee colony algorithm for
dynamic deployment of wireless sensor networks. Turk. J. Elec. Eng.
Comp. Sci. 2012, 20: 255–232.
Zheng W, Shu J. An efficient relocation algorithm in mobile sensor
network based on improved artificial bee colony. Adv. Electron.
Commun. Web Appl. Commun. 2012, 148: 441–448.
Lavanya D, Udgata S. Swarm intelligence based localization in wireless
sensor networks. Lect. Notes Comput. Sci. 2011, 7080: 317–328.
C Zhuang, H Y Wang, C Q Fu and S M Zhuang, Data Integration
Technology-based Safety Supervisory System Information Transmission
Strategy, International Journal of Advancements in Computing
Technology, 2011,3(10): 23-29.
X C Li. Coal Mine Safety in China, China Coal Industry Press, Beijing,
China, (1998).
VURAL S,EKICI E. Analysis of hop-distance relationship in spatially
random sensor networks. In Proceedings of the 6th ACM International
Symposium
on
Mobile
Ad
Hoc
Networking
and
Computing,2005:204-209.
Chuan Zhu, Chunlin Zheng, et ac. A survey on coverage and connectivity
issues in wireless sensor networks,2012:620-631.
Tan G, Jarvis S, KermarrecA-M. Connectivity-guaranteed and
obstacle-adaptive deployment schemes for mobile sensor networks. IEEE
Transactions on Mobile Computing 2009;8(6):836–48.
Sahoo P, Tsai J-Z, Ke H-L. Vector method based coverage hole recovery
in wireless sensor networks. In:2010 second international conference on
communication systems and networks(COMSNETS);January,2010:1–9.
Ahmed N, Kanhere S, Jha S. A pragmatic approach to area coverage in
hybrid wireless sensor networks. Wireless. Communication. Mob.
Comput, 2011, 11: 23–45.