Download And we compared the energy remaining with cluster head

Data Collection using Multiple Mobile Collectors with Less Tour Length in Wireless
Sensor Networks
OMAR ABDULSALAM1, 2 , RAGHAV YADAV 3
1
Department of Computer Science & Information Technology, SHIATS-Allahabad
2
Republic of Iraq / Ministry of Higher Education & Scientific Research
Directory of Research & Development
E-mail: [email protected]
3
Department of Computer Science and Information Technology, SHIATS - Allahabad, India,
E-mail: raghavyash@gmail
ABSTRACT
In recent years, wireless sensor networks have emerged as a new information-gathering paradigm in a wide
range of applications, such as medical treatment, outer-space exploration, battlefield surveillance, emergency
response, etc. The data-gathering scheme is the most important factor that determines network lifetime because most
of the sensor acts as intermediate node so it consumes more energy. Energy consumption becomes a primary
concern in a Wireless Sensor Network. To pursue high energy saving at sensor nodes, a mobile collector should
traverse the transmission range of each sensor in the field such that each data packet can be directly transmitted to
the mobile collector without any relay. Since data packets are directly gathered without relays and collisions, the
lifetime of sensors is expected to be prolonged.
KEYWORDS: Wireless Sensor Networks (WSNs), Covering Salesman Problem (CSP), Data Gathering, MCollector (MC), Mobile Data Collector, Poling Point (PP)
I. Introduction
Owing to the rapid advances in wireless communications and micro electro mechanical systems
technologies, the micro sensor technologies have improved in terms of size, cost, sensitivity, and variety. However,
we note that the sensor nodes are still very limited in computational capacities, memory and power. Hence, the
routing algorithm of the network should be designed to be energy efficient allowing for the maximal lifetime of the
network, routing algorithms can be broadly divided into two categories are namely direct routing and indirect
routing using a cluster approach. In direct routing algorithms [1, 2], each sensor node directly transmits the acquired
data to the Base Station (BS). Conversely, indirect routing algorithms as shown in the fig. (1.1) involve a clustering
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algorithm that creates multiple clusters of sensor nodes. These clusters elect a Cluster Header (CH) node within a
cluster. Under this configuration, each sensor node transmits the acquired data to their CH node rather than the BS.
The CH collected the data and transmits it to the BS.In clustering technique, data transmission is more reliable. But
in this some unnecessary energy loss will occur in intermediate cluster head while no own data transmission.
Figure 1.1 Cluster Heads
II. Related work
[1], Authors proposed in this paper a new energy-efficient approach for clustering nodes in ad hoc sensor
networks. Based on Hybrid Energy-Efficient Distributed clustering, that periodically selects cluster heads according
to a hybrid of their residual energy and secondary parameter, such as nude proximity to its neighbors or node degree.
This approach can be applied to the design of several types of sensor network protocols that require energy
efficiency, scalability, prolonged network lifetime, and load balancing.
[2] Authors first presented how to place SNs by use of a minimal number to maximize the coverage area
when the communication radius of the SN is not less than the sensing radius, which results in the application of
regular topology to WSNs deployment. Mobile node rotation can extend WSN topology lifetime. It considers WSNs
that are mostly static with a small number of mobile relays not practically declared for Dynamic WSNs.
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[3] The authors dealt in this paper with mobile data gathering, which employs one or more mobile
collectors that are robots or vehicles equipped with powerful transceivers and batteries. An important issue is not
addressed in this paper, i.e. latency.
[4] Authors presented the design and analysis of novel protocols that can dynamically configure a network
to achieve guaranteed degrees of coverage and connectivity. This work differs from existing connectivity or
coverage maintenance protocols in several key ways. Capability of authors protocols to provide guaranteed coverage
and connectivity configurations through both geometric analysis and extensive simulations. It is not extending
solution to handle more sophisticated coverage models and connectivity configuration and develop adaptive
coverage reconfiguration for energy-efficient distributed detection and tracking techniques.
[5] In this paper authors have developed an embedded networked sensor architecture that merges sensing
and articulation with adaptive algorithms that are responsive to both variability in environmental phenomena
discovered by the mobile sensors and to discrete events discovered by static sensors. They also showed relationship
among sampling methods, event arrival rate, and sampling performance are presented. Sensing diversity does not
introduce which is used to enhance Fidelity Driven Sampling.
[6] In this paper authors have recently, mobility of sensor networks has been extensively studied radio tagged zebras
and whales were used as mobile nodes to collect sensing data in a wild environment. These animal-based nodes
randomly wander in the sensing field and exchange sensing data only when they move close to each other. Thus,
sensor nodes in such a network are not necessarily connected all the time. Moreover, the mobility of randomly
moving animals is hard to predict and control; thus, the maximum packet delay cannot be guaranteed. For sensor
networks deployed in an urban area, public transportation vehicles such as buses and trains, which always move
along fixed routes, can be mounted with transceivers to act as mobile base stations.
The rest of this paper is organized as follows: Section III contributions. Section IV gives simulation results. Finally,
Section VIII concludes this paper.
III. Contributions
We propose new data-gathering mechanisms for large scale sensor networks when single or multiple M-Collectors
(MC) are used. We used especial node which is equipped with more power resource and memory than other normal
nodes, these especial nodes called Poling Point (PP) as shown in the fig 3.1
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.
Figure 3.1: Poling Point
These nodes gather the data form other nodes, when these poling point near to reach minimum threshold memory it
send request to the base station asking for Mobile Collectors, an M-Collector is responsible for gathering data from
local sensors in the subarea. Mobile Collector that are represented by node number (52) travels through the felids
collecting data from all Poling Point and at the same time from the nodes that are located within the communication
range of MC as shown in the fig (3.2).
Figure 3.2: M-Collector collecting data from both Poling Point and Sensor Nodes
To reduce the delay; MC 52 was taken as an example. It is far away from the base station, but within the
communication range with MC 51 which have communication with the Base Station; MC 52 will forward its data to
MC 51 which will hand over the data to base station. So in this case the time needed from MC 52 to reach the
communication area is 10 minutes, but MC 51 is already at the communication area. So MC 52 will reduce 10
minutes delay by broadcasting its data to MC 51 as shown in the fig (3.3).
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Figure 3.3: Sensor Network with Poling Point
A. Steps of Algorithm
Step 1: Initial setup is to design the network as less hop count transmission.
Step 2: design the PP from senor devices. “Here we are setting PP can receive the data from number of nodes”
Step3: if sensor having the data, then sensor finding the PP, which is near to that sensor.
Step 4: When sensor node found PP point node then transfers data to PP
Step 5: if PP has more data then it informs to base station (BS).
Step 6: Base Station receives the number of control information from different PP’s.
Step 7: after collecting the control message, BS makes the shortest route to collect the data from PP’s.
Step 8: MC moves towards each PP’s and collects the info and returns back to BS
Step 9: continue all steps
B. Memory calculation
Let consider,
Total memory of polling points
= Mtp
Threshold memory of polling point
=Mthp
No of polling point
= Np
No of sensors in polling point
=Ns
Maximum data rate of each sensor
=DRsensor
Speed of Mobile collector
= Smc
Waiting time of mobile collector at each pp = Tw
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In this paper we are considering the threshold memory, which is defined as the memory which is used to
collect the data after informing to control station about mobile collector requirement. So that we have to calculate
minimum amount of threshold memory requirement. Threshold memory is depends on the mobile collector speed,
waiting time of mobile collector in each poling point (PP) and no. of sensors in that polling point.
The total data collection in pp is defined as
Mthp =(∑ni=0 DRsensor )((∑ni=1 di,j ) ∗ Smc + (Np ∗ Tw ))
C. MODULES
 Analyzing the data sink details
 Setting less hop count transmission

Problem in static forward node

Dynamic forward node
 Select sensor as polling point.

Static polling point.
 Find and collect data from polling point.
 Handover the data to BS
D. Analyzing the data sink details
Handover the data to data sink when data sink within the transmission coverage area of sensors. The sensors which
are located in the range of data sink it transforms all the information direct to the data sink with a single hops.
E. Setting Less Hop Count Transmission
Multi-hop routing, packets have to experience multiple relays before reaching the data sink. Minimizing energy
consumption on the forwarding path does not necessarily prolong network lifetime as some popular sensors on the
path. So to avoid the problem in multi-hop routing we are setting the less hop count transmission.

Static forward node
When the node forwarding the data continuously, then that node will loss more energy. It may causes node failure

Dynamic forward node
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If the forward node is dynamically changed with less hop count node then energy loss of node should be very less.
So, in the first path the hop count is 3 where as the Hop County for the second path is 2, therefore for data
transmission the preferable path is second path.
F. Select Sensor as Poling Point (PP)
A subset of sensors will be selected as the polling points, as shown in the fig (3.4) each aggregating the local data
from its affiliated sensors within a certain number of relay hops. These PPs will temporarily cache the data and
upload them to the Mobile Collector when it arrives. The PPs can simply be a subset of sensors in the network or
some other special devices, such as storage nodes with larger memory and more battery power
Figure 3.4: Sensor Network with Poling Point
From a group of sensors one sensor will be elected as a Polling Point (PP), which receives and send the information
to the sensors.
G. Find and Collect Data from PP’s
Since the Mobile Collector has the freedom to move to any location in the sensing field, it provides an opportunity
to plan an optimal tour for it.Our basic idea is to find a set of special nodes referred to as PPs in the network and
determine the tour of the Mobile Collector by visiting each PP in a specific sequence.When the Mobile Collector
arrives, it polls each PP to request data uploading. And then upload the data to Mc. The Polling Points collect the
information from all the sensors and that aggregated information is collected by the Mobile Collector.
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H. Handover the Data to BS
A Poling Point uploads data packets to the Mobile Collector in a single hop. The Mobile Collector starts its tour
from the static base station, which is located either inside or outside the sensing field, collects data packets at the
Poling Point PPs and then returns the data to the data sink, finally MC Handover the data to data sink, such as BS.
The Mobile Collectors move through all the polling points and collect the information and send it to Base Station.
All previous steps are illustrated in the fig (3.5).
Figure 3.5: Sequence Diagram
IV. Result and Discussion
We have simulated our proposed system with most popular simulation tool NS2. We got the two results
one is Nam Window and another one is X-graph. We created the network with 50 numbers of sensors and two
Mobile Collector and one Base Station, the blue colored nodes represent Poling Point, the red colored node represent
Base Station (BS) which is shown in the fig (4.1).
Figure 4.1: Implementation of Network
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There are number of nodes, we declared as polling points, and polling point will advertise about availability to
neighbor sensor, which is shown in fig.4.2.
Figure 4.2: Poling Point Advertisement
After receiving advertisement message, sensor can choose near polling point and it can send the data to PP, which is
shown in fig (4.3)
Figure 4.3: Data Collection at PP
After data collection at PP, the Mobile Collector can visit all activated PP to collect the data from PP, which is
controlled by the Base Station. Base station can inform the route to PP. fig.(4.4) shows PP scan message sharing.
Figure 4.4: PP Scans Message Sharing
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If there is more number of activated Polling Point then multiple Mobile Collector is need to be visit, the
Mobile Collector which is shown in fig.(4.5)
Figure 4.5: Mobile Collector Advertisement
To reduce the delay of data transmission, we proposed the technique which Mobile Collector can send the
data through another Mobile Collector to base station as shown in the fig (4.6).
Figure 4.6: Mobile Collector to Base Station Transmission through MC
If Mobile Collector is near to Base-Station then it can handover the data directly to base-station as Shown
in fig. (4.7).
Figure 4.7: Mobile Collector to Base Station Direct Transmission
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Our one type of our output x-graph, here we proposed the technique with multiple Mobile Collector to
maintain the tour length. There are the multiple Mobile Collector will start when the route length is touches the
threshold length.
Figure 4.8: Tour Length Variation with Respect to No. of PP
And we compared the energy remaining with cluster head selection process, we made the theory calculation.
Our model showing improvement compare than the cluster head selection process, the red color line
represent the Cluster Head technique , and the green color represent Mobile collector technique. As shown in
fig (4.9) the new technique consuming less energy than Cluster Head.
Figure 4.9: Energy Variation with Respect to Time Compared with Multi-hop Cluster Head Transmission
V. Conclusion
We proposed a mobile data-gathering scheme for large-scale sensor networks. We introduced a mobile data
collector, called an M-collector, which works like a mobile base station in the network. An M-collector starts the
data gathering tour periodically from the static data sink, traverses the entire sensor network, polls sensors and
gathers the data from sensors one by one, and finally returns and uploads data to the data sink. To reduce the delay
of data transmission, we proposed the technique which mobile collector can send the data through another mobile
collector to Base Station in such away, it can prolong the network life time significantly.
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Omar Abdul Salam He is received the B.Eng. degree in Computer
Engineering from the College of Electrical & Electronic Techniques Baghdad
- Iraq in year 2006 and his M. Tech in Computer Science & Engineering in
SHIATS-Allahabad- India in 2014 his research interests include Wireless
Sensor Networks, and Mobile Ad-hoc Network
Raghav Yadav He is received the Ph.D. degree from the Motilal Nehru
National Institute of Technology (MNNIT), Allahabad, India. He received
his B.E. degree in electronics engineering from Nagpur University and
M.Tech. degree in computer science and engineering from MNNIT,
Allahabad. Mr. Yadav is currently an assistant professor at Sam
Higginbottom Institute of Agriculture, Technology and Sciences (SHIATS),
Allahabad, India. He has authored more than 15 research papers in
national/international conferences and refereed journals. His research
interests are in the field of optical network survivability, ad-hoc networks,
and fault tolerance systems.
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