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
International Journal of Computer Electrical Engineering
234 Volume 10, Number 3, September 2018
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)
International Journal of Computer Electrical Engineering
235 Volume 10, Number 3, September 2018
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.
International Journal of Computer Electrical Engineering
236 Volume 10, Number 3, September 2018
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.
International Journal of Computer Electrical Engineering
237 Volume 10, Number 3, September 2018
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.
International Journal of Computer Electrical Engineering
238 Volume 10, Number 3, September 2018
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
International Journal of Computer Electrical Engineering
239 Volume 10, Number 3, September 2018
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
International Journal of Computer Electrical Engineering
240 Volume 10, Number 3, September 2018