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Route Trajectory Identification Mechanism (RTIM) of MobiSink for Efficient Data Collection in WSNs

Kumar Swamy B. V.1*, Gowramma Y. P.2, Ananda Babu J.2 1 Robert Bosch Engineering and Business Solutions, Bangalore, India. 2 Kalpataru Institute of Technology, Karnataka, India. * Corresponding author. Tel.: +919844169562; email: [email protected] Manuscript submitted March 16, 2018; accepted June 8, 2018. doi: 10.17706/ijcee.2018.10.3.233-240

Abstract: WSNs are considered as the most promising technologies of this decade. The major objective of

sensor nodes is to gather information from distinct places where other mechanism cannot be applied.

However, the huge challenge is in collecting of data which is scattered across the network. Energy also plays

a significant role in deciding an optimized route to collect this data spread across the network. This

problem is analyzed and provided an efficient solution in the proposed algorithm. A new Route Trajectory

Identification Mechanism (RTIM) is introduced that will help in forming a trajectory for mobisink to

traverse through the network. An intelligent layer is defined which will find data collection points (DCP) in

advance in the network. Current research works focus on how to reduce the energy of sensor nodes to

increase the lifetime of the network. But, advance identification mobile sink's moving trajectory not only

optimizes the energy consumption, but also reduces the latency. The proposed algorithms and protocols are

validated through simulation experiments using Network Simulator (NS2).

Keywords: Energy aware secured, secure routing protocol, lifetime optimization, energy consumption, uniform energy.

1. Introduction

Wireless Sensor Networks (WSNs) consist of low cost sensor nodes which collect data from environment

and relay them to a sink where they are subsequently processed. WSNs are employed in wide range of

applications such as security surveillance, battlefield, intrusion detection, target tracking purposes etc.

Deployment of large set of sensor nodes specific to application makes them impossible to hand place where

battery replacement is usually cumbersome (especially in harsh environments like battlefields) [1]. Being

functional for a prolonged period is its fundamental objective. Usually in many-to-one multi-hop WSNs, the

sink’s one-hop vicinity nodes most often ‘funnel’ (forward) data on behalf of all other nodes. Clearly,

network in which data collection rate dominates data forwarding rate (in a traffic intense application, e.g.

Video-based target tracking), typically congestion builds up at the bottleneck nodes (nodes at the sink’s

one-hop neighborhood). Consequently, packet dropping incident and/or retransmission becomes more

frequent, leading to increasingly degraded network performance. Additionally, hot spot (sink’s one-hop

neighbor) nodes die earlier (they exhaust their energy as they forward higher volume of traffic compared to

other nodes) relative to other nodes in the network. Energy depletion to hot spot nodes causes network

partitioning, leading to compete isolation of the sink node resulting in entire network failure. Hence this hot

spot problem must be adequately dealt with, through measures that prevent the failure of the sink’s one

International Journal of Computer Electrical Engineering

233 Volume 10, Number 3, September 2018

hop neighbors by reducing load caused to these nodes.

Applying an efficient mobile sink strategy into the multi-hop routing protocols is an effective solution that

tends to prevent network partitioning in WSNs [2]. Instead of replacing these hot spot nodes, the key idea is

to move the sink periodically to various parts of the network with sufficient energy for data gathering.

During the sink’ trajectory, as the sink’s one hop neighbors keeps changing in time, the energy consumption

and the traffic load (for sink’s neighbor nodes) could be balanced all over the network.

This mechanism consequently increases the network lifetime [3]. Thus, optimization for energy

consumption is foreseen as an important problem, especially to prolong network lifetime in WSNs [4]. The

use of sink mobility in WSN is commonly recognized as one of the most effective means of load balancing,

ultimately leading to fewer failed sensor nodes and longer network lifetime. This also enhances reliability,

accuracy, flexibility, cost effectiveness and ease of deployment [5]. Recent research on data collection

reveals that gathering sensor data from error prone routes (long and multi hop routes) to a static sink,

leveraging sink mobility for data gathering is more promising for energy efficient data gathering [6]. The

main challenge of this technique is the difficulty is dynamic route discovery of the mobile sink to travel

across the network, such that the data collected from sensor nodes are within specific time.

2. Related Work

One of the major disadvantage in using mobile sink is the increased latency caused during data collection

due to the speed of the mobile sink typically ranging between 0.1 -2 m/s [7], [8]. Increase of latency leads to

more time consumption and performance of the network will be dropped drastically.

In a single hop approach, distance between the sink and the source is only one hop. The mobile sink visits

each sensor node and gathers its data, apparently, in such networks the energy consumption of sensor

nodes are minimized (as communication is done using only one hop).

However, the expense of high data delivery delay and possibility that mobile sink may visit only some

locations of the WSN is its drawback. Consequently, these types of approaches are more suitable for delay-

tolerant networks. Many other research works have been proposed on multi-hop data gathering approach

using mobile sink.

In Cluster based strategy [9], the network is divided into clusters. Each cluster is associated with a cluster

head, which is responsible for gathering data from sensor nodes, aggregating and transmitting them to the

mobile sink. In this type of approach, the sink consistently updates its current location to its nearest cluster

head, which is in turn communicated through control message to all other cluster heads in the network.

Additionally, cluster head should constantly be kept updated about the mobile sink’s current location

thereby creating considerable routing overhead.

In Adaptive Reversal Tree (ART) [10], tree is constructed to learn the route of mobile sink. Major

drawback in this approach lies in updating the whole network with the current position of the sink. The tree

reconstruction cost becomes higher when the affected area increases. Whereas in distributed tree-based

data dissemination (TEDD) approach [11], the new position of the sink was known only to the one-hop

neighbors, which leads to the less control packet overhead for effectively and efficiently managing the sink

mobility but finding on optimal data gathering tour in general becomes a hard problem. Due to constrained

access areas or obstacles in the deployed field, the problem of finding the sink movement path to optimize

the lifetime of the WSN is hard to solve and pose more complexity [12].

With direct communication between MobiSink and sensor node has gained more importance, but route

formation with all nearest possible points in the network increases the travel distance of MobiSink leads to

loss of energy [13]. Additionally, problem concerning mobile sink to avoid data loss due to sensor buffer

overflow causing high latency in existing approach was further evaluated and thus the proposed work was



designed to enhance effectiveness in mobile sink data gathering methodology with reduced latency and

prolonged network lifetime.

3. Proposed Work

This paper presents a Route Trajectory Identification Mechanism (RTIM) of MobiSink (MS) for Efficient

Data Collection in WSNs. Nodes within the network along with Mobisink together play the role of

identifying efficient data collection points (DCP) which help in identifying travel path of the MobiSink in

well advance.

Mean point finder (MPF) algorithm is used to locate the DCPs in the network. After identification, all the

DCPs will be updated to MobiSink via recursive data update approach.

3.1. Mean Point Finder

Source nodes along with MobiSink within the network will play this algorithm. The network will be

uniformly distributed throughout the network area. It is also considered that, network will have more

number of source nodes distributed randomly in the network. Random distribution is used to select the

source nodes in the network.

Assuming, there are N number of nodes in the network and S number of source nodes within the network.

Then, all nodes within a distance of 𝑑𝑐will form a group, where 𝑑𝑐 is a constant value called convergent

distance. Group formation is given using (1).

( ) 𝑑𝑐 (1)

Once the groups are formed, each group will find the mean position from the all points identified in the

group using (2).

(2)

3.2. Recursive Location Update

All data collection points will be found in mean point finder algorithm. Every source node group will have

one data collection point. In this approach, a node is selected as Informer Node (IN) for every source node

group. Selection of the node is based on their distance with respect to MobiSink node and is defined in (3).

(3)

Once all INs are identified in each source node group, they will update their DCP to the MobiSink node by

sending a message via shortest tree algorithm using (4).

(4)

where, R c u c , N x p .

3.3. Path Formation

Previous process will update all DCP to MobiSink node. MobiSink node will wait till a reset timer so that

all DCPs will be reached to MobiSink.

MobiSink will calculate distance of every point from its current location using (5).

𝑐 c 𝑑 𝑑 (5)



All 𝑐 distances are sorted in ascending order. Mobisink will then start traversing from the nearest

distance point till the farthest distance point.

3.4. Data Collection by MobiSink

MobiSink will frame the travelling route path (TRP) list after sorting the distance values obtained from

the DCPs. MobiSink with traverse through each of the points defined in the TRP. After reaching every TRP,

MobiSink will set a timer. It will wait in that position till the timer expires and will wait to receive data from

source nodes.

Source nodes will be notified once MobiSink reaches to the DCP. Upon receiving the notifier message from

MobiSink, source nodes will activate and start sending the data to the MobiSink.

4. System Architecture

Fig. 1. RTIM architecture.

Fig. 1 above shows the architecture design of the proposed algorithm. Nodes are distributed within the

network uniformly. Encircled area forms the source node groups at various locations. MobiSink is

considered to away from the network at beginning of the protocol. The number of source nodes will be

defined before, but the selection of the source nodes will be random.

5. RTIM Protocol

Proposed protocol is divided into two phases.

1. Route Identification Phase

2. Data Gathering Phase

Fig. 2 below shows the flow diagram of the proposed protocol.

Fig. 2. RTIM flow diagram.



5.1. Route Identification Phase

In Route Identification Phase, all the source nodes start broadcasting. Neighbor source nodes will be

notified with this and hence, all those nearby source nodes will form a group. Once the groups are formed,

all the source nodes within that group will find the DCP by calculating the mean position from the collected

location points of source nodes within the group set. An informer node will be identified within the group

which is the closest node to the MobiSink node from the group. This informer node will notify DCP to the

MobiSink node.

5.2. Data Gathering Phase

In data gathering phase, MobiSink node will collect DCP from all the source node groups in the network.

After finding all the DCP, MobiSink will form TRP and apply sort algorithm to find an optimized route.

By using this route information, MobiSink will start traversing in the network by moving each DCP in the

TRP.

After reaching every DCP, MobiSink will set a timer and will wait in the DCP to receive data. All source

nodes surrounding that DCP will be notified once MobiSink reaches the position. And, source nodes will

start sending data to the MobiSink node. Data transfer will be one hop where source node where data

originates will directly transfer data to the MobiSink node. Hence, data loss will be very minimal.

6. Performance Analysis

6.1. Energy Consumption

In our proposed approach, the main objective is to reduce the travelling distance of the MobiSink node.

Intelligence is incorporated into the network which will identify the possible travel path and will be

optimized by applying the proposed algorithm.

Energy consumption will be higher when node is travelling. Lesser the distance travelled will save the

energy of the node for long time. By considering it, the source nodes and MobiSink will take participation in

the route identification.

MobiSink node after reaching every DCP, will halt for a defined amount of time and will spend energy to

receive packets sent by source nodes. DCPs are further sorted which will ensure the travelling path covers

less amount of distance covered by MobiSink.

Fig. 3 shows the result of energy consumption by the network. Results obtained from the proposed

algorithm are compared with IMPR. It is to be observed that; proposed algorithm improves in the energy

consumption due to optimized path selection.

Fig. 3. Energy consumption.



As show in above results, convergent points calculated in IMPR will be more than the DCP in the

proposed RTIM protocol. Hence, MobiSink node will take more distance to traverse in the network. Hence,

the energy consumption will be higher.

6.2. Packet Delivery Ratio

Packet redundancy is not required in this protocol since the communication of data transfer is one hop

level. The data to be sent from source node to MobiSink is direct. Hence, the chances of packet loss are very

less. Only DCPs are updated through multi-hop and this mechanism takes place once in each iteration. This

will not create overhead to the actual data transfer. Fig. 4 shown below shows the results of packet delivery

ratio.

Fig. 4. Packet delivery ratio.

As shown in the above Fig. 4, packet delivery ratio decreases with increase in the number of source nodes

since more number of nodes will participate in data transfer. The results are compared with IMPR protocol;

where MobiSink node will travel all convergent point where mean point algorithm is not applied. This

would cause time synchronization problem between sender node and MobiSink and hence, data loss will be

higher. Whereas in RTIM, the DCPs are calculated through mean point, and hence, MobiSink will be

equidistant from all source nodes within the source node group and so, is reachable to all.

7. Conclusion

Though there are researches works that deal with mobile sink data gathering mechanism, most of these

works are limited to collection of data by reaching at each source points. For large scale wireless sensor

networks, challenge is to collect data in WSNs through optimized network performance. This paper

presents Route Trajectory Identification Mechanism (RTIM) of MobiSink for Efficient Data Collection

protocol suitable for large scale network also MobiSink initiates path formation algorithmic which creates

Travelling Route Path(TRP) list and optimizing TRP helps in creation of route in advance. During its

trajectory, MobiSink performs efficient data gathering using RTIM process. Data is gathered from sensor

nodes without incurring excessive delay thereby optimizing traffic flow thereby prolong the lifetime of the

network. RTIM protocol is scalable for large size networks. Multi-sink approaches could benefit the

advantages of both static and mobile sink approaches. Therefore, the hybrid multiple mobile sinks is an

open problem for future trends.



References

[1] Cordeiro, D. M. C., & Agrawal, D. P. (2011). Ad Hoc and Sensor Networks: Theory and Applications.

Singapore: World Scientific.

[2] Xing, G., Li, M., Wang, T., Jia, W., & Huang, J. (2012). Efficient rendezvous algorithms for mobility-

enabled wireless sensor networks. IEEE Transactions on Mobile Computing, 11(1), 47-60.

[3] Gao, S., Zhang, H., Song, T., & Wang, Y. (2011). Efficient data collection in wireless sensor networks with

path-constrained mobile sinks. IEEE Transactions on Mobile Computing, 10(4), 592-608.

[4] Chou, C., Ssu, K., Jiau, H., Wang, W., & Wang, C. (2010, Nov.). A dead-end free topology maintenance

protocol for geographic forwarding in wireless sensor networks. IEEE Trans. Computers, 60(11), 1610-

1621.

[5] Dantu, K., Rahimi, M., Shah, H., Babel, S., Dhariwal, A., & Sukhatme, G. (2005). Robomote: Enabling

mobility in sensor networks. Proceedings of the 4th International Symposium on Information Processing

in Sensor Networks (IPSN). (pp. 404-409). IEEE Press.

[6] Pon, R., Batalin, M., Gordon, J., Kansal, A., Liu, D., Rahimi, M., Shirachi, L., Yu, Y., Hansen, M., & Kaiser, W.

(2005). Networked info mechanical systems: a mobile embedded networked sensor platform.

Proceedings of the 4th International Symposium on Information Processing in Sensor Networks. (pp. 376-

381). IEEE Press

[7] Park, T., Kim, D., Jang, S., Yoo S. E., & Lee, Y. (2009, May). Energy efficient and seamless data collection

with mobile sinks in massive sensor networks. Proceedings of the IEEE International Symposium on

Parallel & Distributed Processing (IPDPS). (pp. 1-8).

[8] Sudarmani, R., & Kumar, K. R. S. (2012). Energy-efficient clustering algorithm for heterogeneous sensor

networks with mobile sink. European Journal of Scientific Research, 68(1), 60-71.

[9] Khan, N. M., Ali, I., Khalid, Z., Ahmed, G., Kavokin, A. A., & Ramer, R. (2008, May). Quasi centralized

clustering approach for an energy-efficient and vulnerability-aware routing in wireless sensor

networks. Proceedings of the ACM International Workshop on Heterogeneous Sensor & Actor Networks

(pp. 67-72).

[10] Ma M., & Yang, Y. (2008, Apr.). Data gathering in wireless sensor networks with mobile collectors.

Proceedings of the IEEE International Symposium on Parallel and Distributed Processing (IPDPS) (pp. 1-

9).

[11] Sharma, S., & Jena, S. K. (2014). Data dissemination protocol for mobile sink in wireless sensor

networks. Journal of Computational Engineering, 2014, 1-10.

[12] Wang, J., Yang, X., Zhang, Z., Li, B., & Kim, J. U. (2014). A survey about routing protocols with mobile sink

for wireless sensor network. International Journal of Future Generation Communication and Networking,

7(5), 221-228.

[13] Vijayalakshmi, K., &. Manickam, J. M. L. (2016). Mobisink- Intelligent mobility pattern based routing

protocol for efficient data gathering in large scale wireless sensor networks. Proceedings of the

International Conference on Control, Instrumentation, Communication and Computational Technologies,

ICCICCT.

Kumar Swamy B. V. received the B.E. and the M.Tech degree from Visvesvaraya

Technological University (V.T.U), Karnataka State, India in 2007 and 2009, respectively

and has got a Ph.D research scholar at V.T.U. He is currently a research scholar at the

Kalpataru Institute of Technology, Karnataka, India. His current research interests include

intelligent networking, big data analytics and deep learning. He has filed for 5 patents in

big data and application modernization areas.

Aut hor’s

formal photo



Gowramma Y. P. received a B.E. degree from A.I.T, Chikmagalore, India in 1995 and a

M.Tech degree from N.I.T.K, Surathkkal, India in 2000 and a Ph.D degree from Visvesvaraya

Technological University, Karnataka State, India in 2014. She is currently working as a

professor at the Kalpataru Institute of Technology, Karnataka, India. Her current research

interests include image processing, computer networks and system analysis.

Ananda Babu J. received the B.E. and the M.Tech degree from Visvesvaraya Technological

University, Karnataka State, India in 2007 and 2009, respectively and has got a Ph.D

research scholar at V.T.U. He is currently an assistant professor at the Kalpataru Institute

of Technology, Karnataka, India. His current research interests include computer

networks and wireless sensor networks.

Aut hor’s

formal photo



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