11.comparative analysis of qo s aware routing protocols for

Innovative Systems Design and Engineering www.iiste.org
ISSN 2222-1727 (Paper) ISSN 2222-2871 (Online)
Vol 3, No 3, 2012

Comparative Analysis of QoS-Aware Routing Protocols for
Wireless Sensor Networks
V Naveen Kumar1* K Siva Sankar1 L.Srinivasa Rao1 Dr. I.Santhi Prabha2
1. GITAM Institute of Technology, GITAM University, Visakhapatnam, AP-530045, India.
2. Jawaharlal Nehru Technological University Kakinada, Kakinada, AP -533003, India.
*
E-mail of the corresponding author: vegenaveen@yahoo.co.in

Abstract
The main ability of wireless sensor networks (WSNs) is communicating and sensing between nodes, which
are deployed in a wide area with a large number of nodes. Wireless sensor networks are composed of a
large number of sensor nodes with limited energy resources. One critical issue in wireless sensor networks
is how to gather sensed information in an energy efficient way, since their energy is limited. The limiting
factors of the sensor nodes, such as their finite energy supplies and their moderate processing abilities, as
well as the unreliable wireless medium restrict the performance of wireless sensor networks While
contemporary best-effort routing approaches address unconstrained traffic, QoS routing is usually
performed through resource reservation in a connection-oriented communication in order to meet the QoS
requirements for each individual connection. This article surveys a sample of existing QoS-Aware Routing
Protocols for Wireless Sensor Networks and highlights their key features, including merits and limitations.
Keywords: Wireless sensor networks, Routing protocols, QoS-Aware Routing Protocols.
1. Introduction
Wireless sensor networks play a key role in sensing, collecting, and disseminating information about
environmental phenomena. Sensor networks hold the promise of revolutionizing sensing in a wide range of
application domains because of their reliability, accuracy, flexibility, cost-effectiveness, and ease of
deployment. (Sameer Tilak., Nael B. Abu-Ghazaleh., Wendi Heinzelman, 2002). One of the major design
goals of WSNs is reliable data communication under minimum energy depletion to extend the lifetime of
the network. This may be achieved via aggressive energy management techniques. Owing to their poor
energy conservation, traditional routing protocols are not suitable for WSN applications. It is highly
desirable to employ an energy-efficient route discovery and data relaying techniques to transfer data
between the sensor nodes and the base station (BS), the data aggregation point. For increasing network
lifetime it is essential to make the most efficient use of every node since each node of WSNs has limited
energy resources. The important key point in utilizing a network is to increase the network’s performance
by constructing an efficient routing protocol (Aries Kusdaryono., & Kyung Oh Lee, 2011).Routing of
sensor data has been one of the challenging areas in wireless sensor network research. It usually involves
multi-hop communications and has been studied as part of the network layer problems (C. Intanagonwiwat,
R. Govindan & D. Estrin, 2000; W. Heinzelman, J. Kulik, & H. Balakrishnan, 1999; Heinzelman, A.
Chandrakasan, & H. Balakrishnan, 2000; R. Shah & J. Rabaey, 2002). Moreover, there is a major energy
resource constraint for the sensor nodes. As a consequence, many new algorithms have been proposed for
the problem of routing data in WSNs.
QoS protocols have several applications including real time target tracking in battle environments,
emergent event triggering in monitoring applications etc. Energy-aware QoS routing will ensure guaranteed
bandwidth through the duration of connection as well as providing the use of most efficient path (Kemal
Akkaya & Mohamed Younis, 2003). This article surveys QoS-Aware Routing Protocols for WSNs. Section
2 classifies routing protocols. Section 3 discusses existing QoS-Aware Routing Protocols for WSNs.
Section 4 compares existing QoS-Aware Routing Protocols. Section 5 concludes the article.

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2. Routing Protocols for Wireless Sensor Networks
Data gathering and processing are important functions of sensor networks and all data from individual
sensor nodes need to be sent to the BS, where the end user can access the data. Several routing techniques
introduced for WSNs are based on special features like, data aggregation and processing, in network
processing, clustering, nodes’ role assignment, and position of node. Therefore, routing protocols for WSNs
can be classified into data-centric or flat-based, hierarchical or cluster-based and location-based, depending
on the network structure. Routing protocols can also be divided into multipath based, QoS-based, query-
based, and coherent-based, depending on how the protocol operates.
All nodes in a flat routing protocol are assigned equal roles or functionality and the nodes collaborate to
perform the sensing tasks. The BS sends queries to certain regions within the WSN and awaits data from
the sensors located in that region. SPIN ( W. Heinzelman, J. Kulik, & H. Balakrishnan,1999) and directed
diffusion (C. Intanagonwiwat, R. Govindan, & D. Estrin,2000) are examples of flat routing protocols.
Location or position information of sensor nodes is essential to calculate the distance between neighboring
nodes. If the location of sensor nodes are known then data transmission only occurs on that region to reduce
the number of transmissions. GAF (Y. Xu, J. Heidemann, & D. Estrin, 2001) and GEAR (Y. Yu, D. Estrin,
& R. Govindan, 2001) are examples of location-based energy-efficient routing protocols. In the cluster-
based routing approach the network is divided into clusters and nodes play multiple roles in the network.
Nodes in a cluster communicate with a cluster-head (CH) and the CH transmits the data to the global BS.
This reduces the transmission range of normal nodes to conserve energy.
The routing protocol that uses multipath instead of a single path to increase reliability is called multipath
routing protocol. Directed diffusion (C. Intanagonwiwat, R. Govindan, & D. Estrin, 2000) and proposal (R.
Shah & J. Rabaey, 2002.)are examples of multipath routing. QoS-aware routing protocols minimize the
end-to-end delay, energy consumption and bandwidth requirements of WSNs during data communication.
In query-based routing, the destination nodes initiate a query for data from a node through the network. The
nodes having the answer to the query send the data back to the parent nodes that initiated the query.
Examples of query-based routing protocols are directed diffusion (C. Intanagonwiwat, R. Govindan, & D.
Estrin,2000) and RUMOR (D. Braginsky & D. Estrin,2001) .Based on data processing techniques, routing
protocols for WSNs comprise coherent based and non-coherent-based. Proposal (D. Braginsky & D.
Estrin,2000) and directed diffusion (C. Intanagonwiwat, R. Govindan, & D. Estrin,2000) are examples of
coherent and non-coherent routing protocols (J. N. Al-Karaki & A. E. Kamal, 2004; Kemal Akkaya &
Mohamed Younis, 2003) .
3. QoS-Aware Routing Protocols
In traditional best-effort routing throughput and delay are the main concerns. There is no guarantee that a
certain performance in throughput or delay will be ensured throughout the connection. However, in some
cases where real-time or multimedia data are involved in communication, some performance guarantees in
certain metrics such as delay, bandwidth and delay jitter are needed. Such guarantees can be achieved by
employing special mechanisms known as QoS routing protocols. While contemporary best-effort routing
approaches address unconstrained traffic, QoS routing is usually performed through resource reservation in
a connection-oriented communication in order to meet the QoS requirements for each individual
connection.The key design goals of QoS-aware routing protocols are to ensure optimized QoS metrics such
as delay bound, energy efficiency, low bandwidth consumption while achieving energy efficiency in WSNs
applications. Few recent QoS-aware protocols are surveyed in the following sections.
3.1 QoS-Aware Routing
A cluster-based linear WSN topology (S. Tang & W. Li, 2006) was introduced to support QoS and optimal
energy allocation in both homogenous and heterogeneous WSNs. A multihop WSN is considered in which
each CH acts as the coordinator of a cluster. Each cluster is managed by a single server fixed rate (SSFR)
node with finite capacity and different data arrival rates. The CH communicates with its adjacent cluster’s
CH and hence data is transmitted from one cluster to another until it reaches the sink. The CH is modeled
using M/M/1/K queuing system in a first come first served (FCFS) manner. The CH monitors the data loss
probability (DLP) and controls data generation rate to ensure QoS requirements. DLP increases with

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increasing traffic load. Therefore, by continuously adjusting the data generating rate at individual clusters
the DLP is optimized. Optimal energy allocation algorithm makes all clusters have the same exhaustion
time and hence reduces the problem of early die in the cluster closest to the sink. The clusters with high-
density nodes decrease data generating rate per node to save energy. Transmission energy is optimized by
adjusting the radio range. The clusters near the sink relay data even shorter distances to reduce their energy
dissipation while clusters at the rear relay over longer distances to improve the relaying efficiency.
Numerical analysis shows that the DLP increases with increasing of data generating rate and drastically
increases at the clusters closer to the sink. The source-to-sink delay (SSD) also increases with data
generating rate but the increasing rate gradually slows as the data generating rate increases.
3.2 Energy and QoS-Aware Routing
The introduction of imaging sensors has posed additional challenges. Transmission of imaging data
requires both energy and QoS aware routing in order to ensure efficient usage of the sensors and effective
access to the gathered measurements. (K. Akkaya& M. Younis, 2005). The protocol finds a least-cost,
delay-constrained path for real-time data in terms of link cost that captures nodes’ energy reserve,
transmission energy, error rate and other communication parameters. Moreover, the throughput for non-
real-time data is maximized by adjusting the service rate for both real-time and non-real-time data at the
sensor nodes. Main aim of this protocol is to find a delay constrained path with minimum cost.
Communication cost, nodes’ energy stock, error rate, and other communication parameters determine the
link cost. M/M/1 queuing model is used for analysis and separate queues are used for real-time and non-
real-time data. Classifier checks the incoming packet and sends to the appropriate queue. Scheduler
determines the order of packets to be transmitted from the queues. End-to-end delay of a path is measured
by using queuing model. The bandwidth ratio (r) represents the amount of bandwidth to be allocated both
to the real-time and non-real-time traffic on a particular link. The queuing delay depends on this bandwidth
ratio r. Optimal selection of r can meet end-to-end QoS requirements. After calculating costs of each link
an extended version of Dijkstra algorithm (E. de Queirs Vieira Martins, M. M. B. Pascoal, & J. L. E. dos
Santos, 1998) is used to find the least-cost path which meets the end-to-end delay requirements. Single-r
and multi-r mechanisms are introduced to calculate end-to-end delay for a particular path and to find out an
optimal value for r. The effectiveness of the protocol can be validated through the evaluation of
performance parameters time to first node to die, average lifetime of a node, and average delay per packet
and network throughput.
3.3 Event-to-sink reliable transport protocol
Event-to-sink reliable transport (O. B. Akan & I. F. Akyildiz, 2005) is a transport protocol for WSN that
seeks to achieve reliable event detection with minimum energy expenditure and congestion resolution. The
reliability of event feature is measured by the number of received data packets which is closely related to
the amount of information acquired by the sink for the detection and extraction of event features. The event
is reliable if the observed event reliability is greater than the desired event reliability. The reporting
frequency rate f of a sensor node is the number of packets sent out per unit time by that node. The transport
problem in WSN is to configure the reporting frequency f, of source nodes so as to achieve the required
event detection reliability R at the sink with minimum resource utilization. The data generated by the
sensors are temporally correlated to tolerate individual packet losses to the extent where the distortion Di
observed when the event features are estimated at the sink does not exceed a certain distortion bound Dmax.
Reporting frequency f can control the amount of traffic injected to the sensor field while regulating the
number of temporally-correlated samples taken from the phenomenon. The desired event distortion is
achieved by selecting a small f.
Relationship between the observed reliability r and the reporting frequency f shows that the reliability
increases linearly with source reporting rate f, until a certain f = fmax, beyond which the reliability drops as
packets are dropped due to congestion. For f > fmax the behavior is not smooth and drop in reliability due to
network congestion is more significant with increasing n. Normalized reliability = r/R = 1 gives a suitable
point known as optimal operating region (OOR) to achieve protocol’s goal. Five characteristic regions are
identified: (NC, LR), (NC, HR), (C, HR), (C, LR), (OOR) considering congestion and reliability. Details of
these regions are given in (O. B. Akan & I. F. Akyildiz, 2005).

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The primary motive of ESRT is to achieve and maintain operation in the state OOR. So the aim is to
configure the reporting frequency f to achieve the desired event detection accuracy with minimum energy
expenditure. A congestion control mechanism serves the dual purpose of reliable detection and energy
conservation. An efficient transport protocol should keep track of the reliability observed at the sink and
accordingly configure the operating point. They developed methodology both for single event and multiple
events. The sink must accurately detect whether the flows generated by these multiple events pass through
any common nodes functioning as router.
4. Comparative Analysis of QoS-aware Routing Protocols
In QoS-Aware Routing, optimal energy allocation scheme increases network lifetime and ensures QoS
under heavy traffic conditions. This protocol saves energy of the network and supports QoS. But FCFS
queuing essentially relegates all congestion control to the sources and completely depends on traffic arrival
time. Inappropriate control of data generating rate increases energy consumption in QoS-Aware Routing.
Energy and QoS-Aware Routing protocols have enhanced throughput and average delays. By adjusting
service rate the throughput of non-real-time data is increased. Also it has good energy efficiency. But
inappropriate adjustment of packet service rate introduces delay per packet and wastes energy. Energy
consumption is comparatively high in multi-r mechanism.
In Event-to-sink reliable transport protocol, reliability and congestion control are achieved by changing the
reporting rate of nodes. Power consumption reduces with time and minimizes overall energy expenditure
by maintaining the operation in the OOR state. But regulating all sensors to have the same reporting rate
may not work well with heterogeneous sensors.
5. Conclusion
Owing to their versatility and efficient features, routing protocols play an important role in the operation of
energy-efficient WSNs. Therefore, they are promising research area. Based on application area and network
architecture, several routing protocols have been introduced that ensure energy-efficiency to prolong the
network life. In this article, we present a concise survey of QoS-aware routing protocols for Wireless
Sensor Networks. QoS-aware routing protocols are designed to minimize bandwidth consumption and
latency. Minimum-cost paths are selected for data communications to ensure integrity.
References
Sameer Tilak, Nael, B., Abu-Ghazaleh, Wendi Heinzelman. (2002), “Taxonomy of Wireless Micro-
Sensor Network Models”, ACM SIGMOBILE Mobile Computing and Communications
review, 6(2), New York, NY, USA.
Aries Kusdaryono, & Kyung Oh Lee, (2011), “A Clustering Protocol with Mode Selection for Wireless
Sensor Network”, Journal of Information Processing Systems, 7(1).
Intanagonwiwat, C., Govindan, R., & Estrin, D. (2000), "Directed diffusion: A scalable and robust
communication paradigm for sensor networks", Proceedings of the Sixth Annual
International Conference on Mobile Computing and Networking (MobiCOM '00), Boston,
Massachusetts.
Heinzelman, W., Kulik, J., & Balakrishnan, H. (1999), “Adaptive protocols for information dissemination
in wireless sensor networks”. Proceedings of the 5th Annual ACM/IEEE International Conference
on Mobile Computing and Networking.
Heinzelman, W., Chandrakasan, A., & Balakrishnan, H. (2000),"Energy-efficient communication protocol
for wireless sensor networks", Proceedings of the Hawaii International. Conference System Sciences,
Hawaii, 3005-3014.
Shah, R.., & Rabaey, J. (2002),"Energy Aware Routing for Low Energy Ad Hoc Sensor Networks",
Proceedings of the IEEE Wireless Communications and Networking Conference
(WCNC’02), Orlando, FL.
Kemal Akkaya & Mohamed Younis. (2003), “An Energy-Aware QoS Routing Protocol for Wireless
Sensor Networks” ,ICDCSW '03 Proceedings of the 23rd International Conference on Distributed

103

Vol 3, No 3, 2012

Computing Systems, IEEE Computer Society ,Washington, DC, USA.
Al-Karaki, J.N., & Kamal, A.E. (2004), “Routing techniques in wireless sensor networks: A survey”, IEEE
Wireless Communications.
Xu, Y., Heidemann, J., & Estrin, D., (2001), “Geography-informed energy conservation for ad hoc routing”,
Proceedings of 7th ACM/IEEE Mobicom’01, Rome, Italy, 70–84.
Yu,Y., Estrin,D., & Govindan,R.(2001) ,“Geographical and energy-aware routing: a recursive data
dissemination protocol for wireless sensor networks”, UCLA Computer Science, Tech. Rep.
TR-01-0023.
Braginsky,D., & Estrin,D.(2000), “Rumor routing algorithm for sensor networks”, Proceedings of
International conference on Distributed Computing Systems.
Sohrabi,K., & Pottie,J. (2000),“Protocols for self-organization of a wireless sensor network”, IEEE
Personal Comm., 7, 16–27.
Tang,S., & LiW.,(2006), “QoS supporting and optimal energy allocation for a cluster based wireless sensor
network”, Computer Communications, 29, 2569–2577.
Akkaya,K., & Younis,M.(2005), “Energy and QoS aware routing in wireless sensor networks”, Cluster
computing, 8, 179–188.
De Queirs Vieira Martins,E.,Pascoal,M.M.B., & Dos Santos,J.L.E.(1998), “The k shortest paths
problem” ,CISUC, Tech. Rep.
Akan, O.B., & Akyildiz, I.F. (2005), “Event-to-sink reliable transport in wireless sensor networks”,
IEEE/ACM transactions on networking, 13, 1003–1016.

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