TRAFFIC CONTENTION ASSESSMENT AND CONTROL FOR

IMPROVEMENT OF QoS IN HETEROGENEOUS

WIRELESS SENSOR NETWORKS

SUMMARY OF THESIS

SUBMITTED TO

PUNJAB TECHNICAL UNIVERSITY

JALANDHAR (INDIA)

IN FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF

DOCTOR OF PHILOSOPHY

IN

ELECTRONICS & COMMUNICATION ENGINEERING

By

Kamal Kumar Sharma

Registration Number 02.35.09

Dated 02.02.2009

Department of Electronics and Communication Engineering Rayat Institute of Engineering and Information Technology

Railmajara, Near Ropar, Punjab

2011

1

SUMMARY OF REPORT

Name of Candidate Kamal Kumar Sharma

Title of Thesis Traffic Contention Assessment and Control for improvement of

QoS in Heterogeneous Wireless Sensor Networks

Name of Institute Department of Electronics and Communication Engineering

Rayat Institute of Engineering and Information Technology

Railmajra, Distt. Nawanshahar, Near Ropar, Punjab

Registration number Registration Number: 02.35.09 Dated 02.02.09

Supervisors

Prof. (Dr.) Harbhajan Singh Prof. (Dr.) R. B. Patel Department of Electronics Comm Engg Department of Computer Engineering Rayat Institute of Engg & IT M.M Engineering College, Mullana Railmajra Punjab Ambala Haryana

Kamal Kumar Sharma.................................................

2

SUMMARY OF THESIS

Wireless Sensor Networks (WSNs) are key to gather the information, which comprise a new

class of networking technology in which collection of nodes organized into a cooperative

networks. In recent years, in order to access the network resources easily and efficiently, there

are several network protocols for users with portable devices. WSNs are considered as one of

the growing technologies. The sensor nodes are usually scattered and it is not necessary to

predetermine the position of the sensor nodes in the network. It can also be expressed that

WSNs are self-configuring network of tiny nodes connected by wireless links and

communicate with a sink node (base station). Moreover, WSNs have limited bandwidth and

limited battery power, due to the utilization of wireless channel.

WSNs are highly distributed self-organized system and depend upon a particular number of

scattered low cost small devices. These devices include some strong demerits in terms of

processing, memory, communications and energy capabilities and these are called as sensor

nodes. Sensor nodes collect measurements of interest over a given space and make them

available to external systems and networks at sink nodes. The power saving techniques is

commonly implemented to increase the independence of the individual nodes and this

technique makes the nodes to sleep most of the time. This can be balanced with low power

communications, which usually lead to multi hop data transmission from sensor nodes to sink

nodes and vice versa. In order to collect the data, WSNs uses an event-driven model and

depends upon the collective effort of the sensor nodes in the network. Greater accuracy, larger

coverage area and extraction of localized features are some of the advantages of the event-

driven model over the traditional sensing. It is important that the preferred events are reliably

transported to the sink for realizing these potential gains. Habitat monitoring, in-door

monitoring, target tracking and security surveillance are some of the applications where

WSNs can be used. WSNs have some problems to be overcome such as energy conservation,

congestion and contention control, reliability data dissemination, security and management of

WSNs itself. These problems often take part in one or more layers from application layer to

physical layer and it can be studied separately in each corresponding layer or collaboratively

cross each layer. For example, congestion control may involve only in transport layer but the

energy conservation may be related to physical layer, data link layer, network layer and higher

layers.

CHALLENGES IN WSNs

Bandwidth limitation: Bandwidth limitation is going to be a more pressing issue for WSNs.

Traffic in sensor networks can be burst with a mixture of real-time and non-real-time traffic.

Dedicating the available bandwidth solely to QoS traffic will not be acceptable.

3

Removal of redundancy: WSNs are characterized with high redundancy in the generated

data. For unconstrained traffic, elimination of redundant data messages is somewhat easy

since simple aggregation functions would suffice.

Energy and delay trade-off: Since the transmission power of radio is proportional to the

distance squared or even higher order in noisy environments or in the non-flat terrain, the use

of multi-hop routing is almost a standard in WSNs. Although the increase in the number of

hops dramatically reduces the energy consumed for data collection, the accumulative packet

delay magnifies.

Buffer size limitation: Sensor nodes are usually constrained in processing and storage

capabilities. Multi-hop routing relies on intermediate relaying nodes for storing incoming

packets for forwarding to the next hop. While a small buffer size can conceivably suffice,

buffering of multiple packets has some advantages in WSNs.

Support of multiple traffic types: Inclusion of heterogeneous set of sensors raises multiple

technical issues related to data routing. For instance, some applications might require a diverse

mixture of sensors for monitoring temperature, pressure and humidity of the surrounding

environment, detecting motion via acoustic signatures and capturing the image or video

tracking of moving objects.

Platform Heterogeneity: WSNs do not share the same level of resource constraints. Possibly

designed using different technologies and with different goals, they are different from each

other in many aspects such as computing/communication capabilities, functionality, and

number.

Dynamic Network Topology: Unlike WSNs where (sensor) nodes are typically stationary,

the actuators in WSNs may be mobile. In fact, node mobility is an intrinsic nature of many

applications such as, intelligent transportation, assisted living, urban warfare, planetary

exploration, and animal control. Dealing with the inherent dynamics of WSNs requires QoS

mechanisms to work in dynamic and even unpredictable environments. In this context, QoS

adaptation becomes necessary; that is, WSNs must be adaptive and flexible at runtime with

respect to changes in available resources.

Mixed Traffic: Diverse applications may need to share the same WSNs, inducing both

periodic and non-periodic data. This feature will become increasingly evident as the scale of

4

WSNs grows. Some sensors may be used to create the measurements of certain physical

variables in a periodic manner for the purpose of monitoring and/or control. Meanwhile, some

others may be deployed to detect critical events. This feature of WSNs necessitates the

support of service differentiation in QoS management.

Routing Protocols: Routing in sensor networks is also very challenging due to several

characteristics that distinguish them from contemporary communication and wireless ad hoc

networks. First of all, it is not possible to build a global addressing scheme for the deployment

of sheer number of sensor nodes. Therefore, classical IP-based protocols cannot be applied to

sensor networks. Second, contrary to typical communication networks; almost all applications

of sensor networks require the flow of sensed data from multiple regions (sources) to a

particular sink. Third, generated data traffic has significant redundancy in it since multiple

sensors may generate same data within the vicinity of a phenomenon. Such redundancy needs

to be exploited by the routing protocols to improve energy and bandwidth utilization. Fourth,

sensor nodes are tightly constrained in terms of transmission power, on-board energy,

processing capacity and storage thus requiring careful resource management. Almost all of the

routing protocols can be classified as data-centric, hierarchical or location based although,

there are few distinct ones based on network flow or QoS awareness.

WORK CARRIED OUT

In this thesis, a three-layered System (3 L-S) is developed to improve the quality of service

(QoS). For all these three layers, few protocols such as A Hop by Hop Congestion Control

Protocol (HHCC), A Reliable and Energy Efficient Transport Protocol (REETP) and

Changing Routing Congestion Control Protocol (CRCP) are designed to achieve the motive of

improvement of QoS of WSNs. The details of the 3 L-S systems are as follows.

THREE LAYER SYSTEM (3–LS) MODEL TO IMPROVE QoS IN WSNs

The Three Layer system (3–LS) is based on three protocols HHCC, REETP and CRCP as

shown in Figure 1. These protocols are developed one for each stage. All these three protocols

are improving the quality of network by modifying and introducing the contention and

congestion strategies, energy efficiency and routing techniques.

LAYER 1 HHCC

In 3–LS, the first layer is named as Hop-by-Hop Congestion Control (HHCC). This layer

receives data packets from any heterogeneous network and dynamically adjusts the

transmission rate of data packets by sensing the congestion degree. For this it calculates the

node ranks of each downstream node using the parameters, buffer overhead, Hop count and

5

MAC overhead. When the node rank crosses a threshold value T, the sensor node will set a

congestion bit in every packet it forwards.

Figure 1. 3-LS Model for WSNs

If the congestion bit is set, the downstream node calculates the rate adjustment feedback

based on the rank and propagates this value upstream towards the source nodes. The source

nodes will adjust their transmission rates dynamically based on this feedback. The output of

this layer may be fed to 2nd layer of this prototype if further processing is necessary,

otherwise here the output is highly efficient in terms of contention control only.

SIMULATION RESULTS AND DISCUSSION FOR 1ST LAYER

A comparison between the performances of proposed HHCC protocol with Dynamic

Contention Window based Congestion Control (DCWCC) [33] protocol is presented. Mainly

the performance according to the following metrics is evaluated:

• Aggregated Throughput: Aggregated throughput in terms of number of packets

received is measured.

• Average Energy Consumption: The average energy consumed by the nodes in

receiving and sending the packets is measured.

• Packet Delivery Ratio: It is the ratio of the fraction of packets received successfully

and the total number of packets sent. The performance results are presented graphically

below.

Simulation Results

The HHCC protocol’s performance is measured based on two conditions namely by varying

number of nodes and by varying the transmission rate. The details are as follows.

6

Varying number of Nodes

In order to test the scalability, in the first experiment, the performance of the protocols by

varying the number of nodes as 25, 50, 75 and 100 is measured.

Figure 2. Energy Consumption Vs. number of nodes

Figure 2 shows that the average energy consumed by the nodes in receiving and sending the

data. We can see from the Figure, the average energy consumption of the nodes increases as

the number of nodes increases from 15 to 100. Since HHCC make use hop-by-hop congestion

control, the overall energy consumption values are considerably less in HHCC when

compared to the DCWCC protocol. Figure 3 presents the packet delivery Ratio (PDR) values

for both the protocols. As the number of nodes increases, the number of hops involved in

routing the data towards the sink, also increases. So the PDR values are slightly decreased.

But we can see that the PDR for HHCC is more when compared to the DCWCC protocol, as

the sending rate is immediately controlled by the sources, when congestion occurs. Figure 4

shows the throughput obtained with for both the protocols when the number of nodes is

increased. For the same reason stated above, the throughput of HHCC is significantly more

than the DCWCC, as the number of nodes increases.

Figure 3. Packet Delivery Ratio Vs. number of Nodes

7

Figure 4. Throughput Vs. number of Nodes

Varying the Transmission Rate

To see the effect of increased sending rate, in the second experiment, we measure the

performance of the protocols by varying the transmission rate as 250,500,750 and 1000 Kb.

Figure 5. Energy Consumption Vs. Transmission Rate

Figure 5 shows that the average energy consumption of both the protocols, when the sending

rate of the sources increases. When the sending rate is increased, it results in more energy

consumption of the nodes. So the Figure shows that the energy consumption slightly increases

as the number of nodes increases. Since HHCC make use hop-by-hop congestion control, the

overall energy consumption values are considerably less in HHCC when compared to the

DCWCC protocol.

Figure 6 presents the packet delivery Ratio (PDR) values for both the protocols. Due to the

increased sending rate of the nodes, there is a slightly increase in the PDR values. We can see

that the PDR for HHCC increases, when compared to DCWCC protocol, as the sending rate is

immediately controlled by the sources, when congestion occurs.

8

Figure 6. Packet Delivery ratios Vs. Transmission Rate

Figure 7 shows the throughput obtained for HHCC protocol and DCWCC protocol. It shows

that the throughput of HHCC is significantly more than the DCWCC, because of the above

said reasons.

Figure 7. Throughput Vs. Transmission Rate

LAYER 2 REETP

Layer 2 of the 3–LS is named as Reliable Energy Efficient Transport Protocol (REETP). This

layer is taking input from the output of the 1st layer, if it is required. REETP has the objective

to improve the energy efficiency by improving the reliability of data in the channel. For this,

REETP is using the data encoding techniques. In this first of all, the sensors will be flooded in

the whole required sensing area. Using the E–node calculation algorithm efficient nodes will

be elected, which form a near optimal coverage with set with largest area and highest residual

energy level. These nodes are called as E-nodes and responsible for sending and receiving the

encoded packets and so transmission of the data.

In REETP transmission system, a data source first groups data packets into blocks of size n.

Then the source encodes these blocks of packets, and sends the encoded blocks into the

network. The data packets are forwarded from the source to the sink block by block, and each

block is forwarded to an E-node. In each E-node relay, the sender first estimates the number

of packets needed to send for the E-node to reconstruct the original packets. This number is

called as “MaxPacket”. Within the MaxPacket, the sender pushes the encoded packets to the

9

network fast. When the packet is reached, the sender slows down pack transmission, waiting

for a positive feedback from the E-node. After receiving encoded packets, the receiver tries to

reconstruct the original data packets. If the reconstruction is successful, it sends back a

positive feedback. Upon the reception of a feedback, the sender stops sending packets, while

the E-node encodes the original data packets again and relays them to the next E-node until

the sink is reached. The output of this layer has good quality of service, as the channel of the

network is contention free and highly reliable. This data stream is useful for the reception,

otherwise for more improvement the traffic may again fed to the third layer.

SIMUATION RESULTS AND DISCUSSION FOR 2ND LAYER

Performance of the REETP protocol is compared with A MAC-aware Energy Efficient

Reliable Transport Protocol (MAEERTP) [43] for WSNs. Mainly the performance according

to the following metrics is evaluated.

• Average Energy Consumption: The average energy consumed by the nodes in

receiving and sending the packets is measured.

• Packet Delivery Ratio: It is the ratio of the fraction of packets received successfully

and the total number of packets sent.

• Average Packet Loss: It is average number of packets lost at each receiver and the

sink.

Simulation Results

The REETP protocol’s performance is measured based on the two conditions namely by

varying number of sources and varying the transmission rate.

Varying Number of Sources

In the first experiment, in order to study the impact of increased the number of sources, vary

the number of sources as 2,4,6 and 8 and measure the performance of the protocols.

Figure 8. Numbers of Sources Vs. Packets Lost

Figure 8 shows the packet lost obtained with REETP protocol compared with MAEERTP

protocol. It shows that the packet lost is significantly less than the MAEERTP, as sources

increases.

10

Figure 9. Number of Sources Vs. Delivery Ratio

Figure 10. Number of Sources Vs. Energy

Figure 9 shows that the packet delivery Ratio (PDR) for REETP increases, when compared to

MAEERTP protocol.

Figure 10 shows that the average energy consumed by the nodes in receiving and sending the

data. Since REETP make use of energy efficient scheduling, the values are considerably less

in REETP when compared with MAEERTP protocol.

Varying the Transmission Rate

In the second experiment, in order to study the performance of increased traffic sending rate,

vary the transmission rate as 100,200,300,400 and 500Kb to measure the performance of the

protocols.

11

Figure 11. Rate Vs. Energy

Figure 11 shows that the average energy consumed by the nodes in receiving and sending the

data. Since REETP make use of energy efficient scheduling, the values are considerably less

in REETP when compared with MAEERTP protocol.

Figure 12. Rate Vs. Packet Lost

Figure 12 shows the packet lost obtained with proposed REETP protocol compared with

MAEERTP protocol. It shows that the packet lost is significantly less than the MAEERTP, as

rate increases.

From Figure 13, it is seen that the packet delivery Ratio (PDR) for REETP increases, when

compared to MAEERTP protocol.

Figure 13 Rate Vs. Delivery ratio

12

LAYER 3 CRCP

The third and last layer of the 3–LS is CRCP; on the name of the protocol used in this layer,

i.e., Changing Routing for congestion control protocol. In this protocol, once again a

congestion detection and control mechanism is applied but in this case instead of rate

adjustment the route of data will be changed using the given algorithm. For the detection of

congestion at this stage HHCC can be directly called. At this stage channel efficiency of the

network is improved by introducing storage strategies at each node or transceiver. If inevitably

congestion occurs due to contention of over fed data from the upstream node, it is adjusted in

the local buffer at each node up to a level. If the buffer capacity will be full, congestion bit will

be set in HHCC, which will change the route of one E-node to another E-node. The output

stream from this stage is the desired output of the proposed system model. This is giving high

quality data transmission channel for heterogeneous wireless sensor networks.

SIMULATION RESULTS AND DISCUSSIONS FOR 3RD LAYER

In the simulation, the channel capacity of mobile hosts is set to the same value 2 Mbps. The

distributed coordination function (DCF) of IEEE 802.11 as the MAC layer protocol is used.

Packet drop rate in the network

From Figure 14 it is observed that CRCP reduces data rates for all sensors in response to

congestion and altering routing tries to prevent congestion from being developed and therefore

avoids packet drops.

Figure 14. Packet drop rate

It sharply reduces the amount of traffic and removes the congestion more quickly than no

congestion control. CRCP + LDS try to prevent congestion from being developed and

therefore avoid packet drops.

13

Source rate in the network

Figure 15 compares the total source rates of the schemes with respect to time. During the

course of congestion control/avoidance, the total source rates are reduced.

Figure 15. Source rate

At around time 200 seconds, congestions are removed in all schemes (except for No

Congestion Control) and the total source rates stabilize. Congestion Avoidance achieves the

best source rate due to its capability of redirecting traffic towards other downstream paths that

are not congested.

Average routing distance of each packet

Figure 16 shows that CRCP has the smallest routing distance, which means nodes closer to the

base station, send much more than their fair share. The routing distance of CRCP + LDS is

much closer to that of Global Rate Control, which means distant nodes are penalized much

less.

Figure 16. Average routing distance

Routing delay

With Figure 17 it is seen that the average routing delay per packet after the congestions is

removed. The number of hops and per hop queuing delay mostly determines the end-to-end

14

routing delay. Backpressure fully utilizes the buffer space. Consequently the average routing

delay decreases when the average routing distance decreases.

Figure 17. Routing Delay

Simulation results show this protocol has better throughput, transmission reliability and

packet delivery ratio with reduced energy consumption

Advantages of 3-LS Model

Congestion in data networks plays a vital role for the improvement of Quality of Service

(QoS) of the whole system. This makes the network congestion a most challenging area. The

3–LS is an effort in the direction of getting high QoS network by synthesizing the incoming

traffic from its layers. This 3–LS is highly flexible as all the three layers can be used

independently as per the requirement of application of area and sensitivity of appliance. All

three layers are responsible to reduce congestion and improve reliability of data transmission

in wireless sensor networks. Also the effort of reduction of power consumption is important

because power is a big constraint of WSNs.

PUBLICATIONS FROM THESIS

1. Kamal Sharma, Harbhajan Singh, R B Patel, “Traffic Contention Assessment and Control for Improvement of QoS in Heterogeneous Wireless Sensor Networks – A Review” published in the International Journal of Information Retrieval of Serial Publication, vol. 3 (1-2), pages 1-8, New Delhi and proceedings of international conference on Trends and Advances in Computation and Engineering at Barkatullah University Institute of Technology, Bhopal M.P. INDIA, February, 2009, pages 235 – 242.

2. Kamal Kumar Sharma, Ram Bahadur Patel, Harbhajan Singh, “A Hop by Hop Congestion Control Protocol to Mitigate Traffic Contention in Wireless Sensor Networks” International Journal of Computer Theory and Engineering (IJCTE) of International Association of Computer Science and Information Technology Press (IACSIT), SINGAPORE, Vol.2, No.6, December 2010, pages 986 – 991.

3. Kamal Kumar Sharma, Ram Bahadur Patel, Harbhajan Singh, “A Reliable and Energy Efficient Transport Protocol for Wireless Sensor Networks” International Journal of Computer Networks & Communications (IJCNC) of Academy & Industry Research Collaboration Center, AUSTRALIA, Vol.2, No.5, September 2010, pages 92 -103.

15

REFERENCES 1. Mohammad Hossein, Donald Adjeroh, “Priority-based rate control for service differentiation and

congestion control in wireless multimedia sensor networks” Journal of Computer Networks, volume 53, issue 11, 28 July 2009, pages 1798-1811.

2. Guohua Zhanga, Yiyu Wua, Yonghe Liu, “Stability and sensitivity for congestion control in wireless mesh networks with time varying link capacities” Journal of Ad Hoc Networks, volume 5, issue 6, August 2007, pages 769-785.

3. V.C. Gungora, M.C. Vurana, O.B. Akanb, “On the cross-layer interactions between congestion and contention in wireless sensor and actor networks” Journal of Ad Hoc Networks, volume 5, issue 6, August 2007, pages 897-909.

4. Bjorn Scheuermann, Christian Locherta, Martin Mauvea, “Implicit hop-by-hop congestion control in wireless multihop networks” Journal of Ad Hoc Networks, volume 6, issue 2, April 2008, pages 260-286.

5. K. Sohrabi, J. Gao, V. Ailawadhi, G. Pottie, "Protocols for self organization of a Wireless Sensor Networks” IEEE journal of Personal Communications, volume 7, number 5, October 2000, pages 16-27.

6. Jennifer Yicka, Biswanath Mukherjee, Deepak Ghosal, “Wireless sensor network survey” Journal of Computer Networks, volume 52, issue 12, 22 August 2008, pages 2292-2330.

7. Iradj Ouveysia, Feng Shub, Wei Chenc, Gangxiang Shend ,Moshe Zukermane, “Topology and routing optimization for congestion minimization in optical wireless networks” Journal of Optical Switching and Networking, volume 7, issue 3, July 2010, pages 95-107.

8. Ben-Jye Changa, Shu-Yu Linb, Jun-Yu Jinb, “ LIAD: Adaptive bandwidth prediction based Logarithmic Increase Adaptive Decrease for TCP congestion control in heterogeneous wireless networks” Journal of Computer Networks, volume 53, issue 14, 18 September 2009, pages 2566-2585.

9. J. Kay, J. Frolik, “Quality of Service Analysis and Control for Wireless Sensor Networks” International journal of Mobile Ad Hoc and Sensor Systems, volume 9, october 2004, pages 103-112.

10. Shigang Chen, Na Yang, “Congestion Avoidance Based on Lightweight Buffer Management in Sensor Networks” IEEE Transactions on Parallel and Distributed Systems, volume 17, number 9, September 2006, pages 934-946.

11. Kang J., Zhang Y., Nath B., “TARA: Topology Aware Resource Adaptation to Alleviate Congestion in Sensor Networks” IEEE Transaction on Parallel and Distributed Systems, volume 18 (7), July 2007, pages 919–931.

12. Stann R, Heidemann J, “RMST: Reliable data transport in sensor networks” Proceedings of IEEE international Workshop on sensor network protocols and Applications, Alaska, May 2003, pages 102–112.

13. S.A. Munir, Y W Bin, Ren Biao, Ma Jian, “ Fuzzy logic based congestion estimation for QoS in wireless Sensor Networks” Proceedings of IEEE computer society international conference, Beijing, May 2007, pages 4399 -4344.

14. M. Aykut Yigitel, Ozlem Durmaz Incel, Cem Ersoy, “QoS-aware MAC protocols for wireless sensor networks: A survey” Journal of Computer Networks, volume 55, issue 8, June 2011, pages 1982-2004.

15. Ren-Shiou Liu, Kai-Wei Fana, Prasun Sinha, “Locally scheduled packet bursting for data collection in wireless sensor networks” Journal of Ad Hoc Networks, volume 7, issue 5, July 2009, pages 904-917.

16. Wan C.Y., Eisenman, S.B., Campbell, “CODA: Congestion Detection and Avoidance in Sensor Networks” Proceedings of 1st international conference on Embedded networked sensor systems, New York, California, November 2003, pages 266–279.

17. Ee C.T., Bajcsy R., “Congestion Control and Fairness for Many-to-One Routing in Sensor Networks” Proceedings of 1st international conference on Embedded networked sensor systems, Baltimore, New York, November 2004, pages 148–161.

18. K. Akkaya, M. Younis, “An Energy-aware QoS Routing Protocol for Wireless Sensor Networks” 23rd international Conference on Distributed Computing Systems, Hawai, 2007, pages 710-721.

19. Zhi Ang Eu, Hwee-Pink Tan, Winston K.G. Seah “Design and performance analysis of MAC schemes for Wireless Sensor Networks Powered by Ambient Energy Harvesting” Journal of Ad Hoc Networks, volume 9, issue 3, May 2011, pages 300-323.

20. Jaesub Kim, Kyu Ho Parka, “An energy-efficient, transport-controlled MAC protocol for wireless sensor networks” Journal of Computer Networks, volume 53, issue 11, 28 July 2009, pages 1879-1902.

21. R. Iyer, L. Kleinrock, “QoS Control for Sensor Networks” Proceedings of international conference on Computer communication, May 2003, pages 36-44.

16

22. Vivek Raghunathan, P.R. Kumara, “A counterexample in congestion control of wireless networks” Journal of Performance Evaluation, volume 64, issue 5, June 2007, pages 399-418.

23. C. Y. Wan, A. T. Campbell, L. Krishnamurthy, “Pump-slowly, Fetch-quickly (PSFQ): A Reliable Transport Protocol for Sensor Networks” IEEE Journal on Selected Areas in Communications, volume 23, number 4, April 2005. pages 302 -307.

24. Iradj Ouveysia, Feng Shub, Wei Chenc, Gangxiang Shend, Moshe Zukermane, “Topology and routing optimization for congestion minimization in optical wireless networks” Journal of Optical Switching and Networking, volume 7, issue 3, July 2010, pages 95-107.

25. Ramanuja Vedanthama, Raghupathy Sivakumara, Seung-Jong Parkb, “Sink-to-sensors congestion control” Journal of Ad Hoc Networks, volume 5, issue 4, May2007, pages 462-485.

26. Sudip Misra, P. Dias Thomas inousa, “A simple, least time, and energy-efficient routing protocol with one-level data aggregation for wireless sensor networks” Journal of Systems and Software, volume 83, issue 5, May 2010, pages 852-860.

27. Hongwei Zhanga, Anish Arorab, Young-ri Choic, Mohamed G. Goudac, “Sensor-Actuated Networks – SANETs” Journal of Computer Communications, volume 30, issue 13, 26 September 2007, pages 2560-2576.

28. Hyun Jung Choea, Preetam Ghoshb, Sajal K. Dasa, “QoS-aware data reporting control in cluster-based wireless sensor networks” Journal of Computer Communications, volume 33, issue 11, 1 July 2010, pages 1244-1254.

29. Tuan Lea, Wen Hub, Peter Corkeb, Sanjay Jhaa, “ERTP: Energy-efficient and Reliable Transport Protocol for data streaming in Wireless Sensor Networks” Journal of Computer Communications, volume 32, issues 7-10, 28 May 2009, pages 1154-1171.

30. Abinash Mahapatra, Kumar Anand and Dharma P. Agrawal, “QoS and energy aware routing for real-time traffic in wireless sensor networks” Journal of Computer Communications, volume 29, issue 4, 20 February 2006, pages 437-445.

31. Jalel Ben-Othman, Bashir Yahyaa, “Energy efficient and QoS based routing protocol for wireless sensor networks” Journal of Parallel and Distributed Computing, volume 70, issue 8, August 2010, pages 849-857.

32. Young-Duk Kim, Won-Seok Kang, Dong-Ha Lee, Jae Hwang Yu, “Distance Adaptive Contention Window Mechanism for Wireless Sensor Networks” Proceedings of 23rd International Technical Conference on Circuits/Systems, Computers and Communications, July 6-9, 2008, pages 1693-1696.

33. Md. Mamun-Or-Rashid, Choong Seon Hong, “Dynamic Contention Window based Congestion Control and Fair Event Detection in Wireless Sensor Network” Proceedings of 31st Korea information Processing Society (KIPS), Korea, May 2007, pages 1288-1290.

34. Costas Busch, Malik Magdon-Ismail, Fikret Sivrikaya, Bulent Yener, “Contention-Free MAC protocols for Wireless Sensor Networks” Proceedings of the 18th International Conference on Distributed Computing (DISC 2004), Amsterdam, The Netherlands, October 2004, pages 245-259.

35. Md. Mamun-Or-Rashid, Muhammad Mahbub Alam, Md. Abdur Razzaque, ChoongSeon Hong, “Reliable Event Detection and Congestion Avoidance in Wireless Sensor Networks” Journal of Computer science, Springer-Verlag, Berlin Heidelberg, 2007, pages 37- 43.

36. Bret Hull, Kyle Jamieson, Hari Balakrishnan, “Mitigating Congestion in Wireless Sensor Networks” Proceedings of the 2nd international conference on Embedded networked sensor systems- 2004, pages 745-751.

37. Ian F. Akyildiz, Mehmet C., Vuran, Ozgur B. Akan, “A Cross-Layer Protocol for Wireless Sensor Networks” 40th Annual Conference on Information Sciences and Systems- 2006, pages 42 – 47.

38. Wafa Ben, Jaballahand Nabil Tabbane, “Multi path Multi SPEED Contention Window Adapter” International Journal of Computer Science and Network Security, volume 9, number 2, February 2009, pages 201 -209.

39. Hongwei Zhang, Anish Arora, Young-ri Choi , Mohamed G. Gouda, “Reliable Bursty Convergecast in Wireless Sensor Networks” Proceedings of the 6th ACM international symposium on Mobile ad hoc networking and computing- 2005.

40. Ajit Warrier, Jeongki Min, Injong Rhee, “ZMAC: a Hybrid MAC for Wireless Sensor Networks” IEEE/ACM Transactions on Networking, June 2008.

41. Muhammad Mostafa Monowar, Md. Obaidur Rahman, Choong Seon Hong, “Multipath Congestion Control for Heterogeneous Traffic in Wireless Sensor Network” IEEE 10th International Conference on Advanced Communication Technology, Feb-2008, pages 5567 -5576.

42. Chonggang Wang, Kazem Sohraby, Victor Lawrence, Bo Li, YuemingHu, “Priority-based Congestion Control in Wireless Sensor Networks” Proceedings of the IEEE International Conference on Sensor Networks, Ubiquitous and Trustworthy, Computing-2006, pages 71 - 78

43. Sandip Dalvi, Anirudha Sahoo, Ashutosh Deo, “A MAC-aware Energy Efficient Reliable Transport Protocol for Wireless Sensor Networks” Proceedings of the IEEE conference on Wireless Communications & Networking Conference, pages 457 – 462.

17

44. Nurcan Tezcan, Wenye Wang, “ART: An Asymmetric and Reliable Transport Mechanism for Wireless Sensor Networks” International Journal of Sensor Networks, pages 69 – 75.

45. Yao-Nan Lien, “Hop-by-Hop TCP for Sensor Networks” International Journal of Computer Networks & Communications (IJCNC), June 2009, pages 203 -210.

46. Sunil Kumar, Zhenhua Feng, Fei Hu, Yang Xiao, “E2SRT: enhanced event-to-sink reliable transport for wireless sensor networks” Wireless Communications and Mobile Computing, 2008, pages 89 – 95.

47. Damayanti Datta, Sukhamay Kundu, “An Application-Specific Reliable Data Transfer Protocol in Wireless Sensor Networks” Journal of Networks, volume 8, pages 503 -509.

48. Xiaopeng Fan, Jiannong Cao, Weigang Wu, “Contention-aware data caching in wireless multi-hop ad hoc networks” Journal of Parallel and Distributed Computing, volume 71 , 4 April 2011, pages 603-614.

49. Ching-Wen Chen, Chuan-Chi Weng, Chang-Jung Ku, “Design of a low power and low latency MAC protocol with node grouping and transmission pipelining in wireless sensor networks” Journal of Computer communications, volume 31, issue 15, 25 September 2008, pages 3725-3738.

50. Kwan-Wu Chin, Dheeraj Klair, “E2MAC: An energy efficient MAC for RFID-enhanced wireless sensor networks” Journal of Pervasive and Mobile Computing, volume 7, issue 2, April 2011, pages 241-255.

51. Ilker Demirkol, Cem Ersoy, Fatih Alagoz, Hakan Delic, “The impact of a realistic packet traffic model on the performance of surveillance wireless sensor networks” Journal of Computer Networks, volume 53, issue 3, 27 February 2009, pages 382-399.

52. Marcel Busse, Thomas Haenselmann, Wolfgang Effelsberg, “Energy-efficient forwarding in wireless sensor networks” Journal of Pervasive and Mobile Computing, volume 4, issue 1, February 2008, pages 3-32.

53. Zhi Ang Eu, Hwee-Pink Tan, Winston K.G. Seah, “Design and performance analysis of MAC schemes for Wireless Sensor” Journal of Ad Hoc Networks, volume 9, issue 3, May 2011, pages 300-323.

54. Ilker Demirkol, Cem Ersoy, “Energy and delay optimized contention for wireless sensor networks” Journal of Computer Networks, volume 53, issue 12, 13 August 2009, pages 2106-2119.

55. Paulo Rogerio Pereira, Antonio Grilo, Francisco Rocha, Mario Serafim Nunes, Augusto Casaca, Claude Chaudet, Peter Almstrom, Mikael Johansson, “End-To-End Reliability in Wireless Sensor Networks: Survey and Research Challenges” Euro FGI Workshop on IP QoS and Traffic Control, 2007, pages 565-569.

56. Y. Challal, A. Ouadjaout, N. Lasla, M. Bagaa, A. Hadjidj, “Secure and efficient disjoint multipath construction for fault tolerant routing in wireless sensor networks” Journal of Network and Computer Applications, volume 34, issue 4, July 2011, pages 1380-1397.

57. Tai-Jung Chang, Kuochen Wangand, Yi-Ling Hsieh, “A color-theory based energy efficient routing algorithm for mobile wireless sensor networks” Journal of Computer Networks, volume 52, issue 3, 22 February 2008, pages 531-541.

58. Thomas A. Babbitt, Christopher Morrell, Boleslaw K. Szymanskiand Joel W. Branch, “Self-selecting reliable paths for wireless sensor network routing” Journal of Computer Communications, volume 31, issue 16, 25 October 2008, pages 3799-3809.

59. Hong-bing Cheng, Geng Yan Gand, Su-jun Hu “NHRPA: a novel hierarchical routing protocol algorithm for wireless sensor networks” The Journal of China Universities of Posts and Telecommunications, volume 15, issue 3, September 2008, pages 75-81.

60. Nidal Nasser, Yunfeng Chen “SEEM: Secure and energy-efficient multipath routing protocol for wireless sensor networks” Journal of Computer Communications, volume 30, issues 11-12, 10 September 2007, pages 2401-2412.

61. Emanuele Lattanzi, Edoardo Regini, Andrea Acquavivaand Alessandro Bogliolo, “Energetic sustainability of routing algorithms for energy-harvesting wireless sensor networks” Journal of Computer Communications volume 30, issues 14-15, 15 October 2007, pages 2976-2986.

62. Shun-Yu Chuang, Chien Chen, Chang-Jie Jiang, “Minimum-delay energy-efficient source to multi sink routing in wireless sensor networks” Journal of Signal Processing, volume 87, issue 12, December 2007, pages 2934-2948.

63. Hamid Shokrzadeh, A.T. Haghighatand, Abbas Nayebi “New routing framework base on rumor routing in wireless sensor networks” Journal of Computer Communications, volume 32, issue 1, 23 January 2009, pages 86-93.

64. Paul Vincent, Craven, Paul Oman “Modeling the NAJPTC network using NS-2” International Journal of Critical Infrastructure Protection, volume 1, December 2008, pages 29-36.

65. Sri ram Chellappan, Wenjun Gu, Xiaole Bai, Dong Xuan, Bin Ma, Kaizhong Zhang, “Deploying Wireless Sensor Networks under Limited Mobility Constraints” IEEE Transactions on Mobile Computing, volume 6, number 10, October 2007, pages 1142 -1157.

66. Ian F. Akyildiz, Tommaso Melodia, Kaushik R. Chowdhury, “A survey on wireless multimedia

18

sensor networks” Journal of Computer Networks (Elsevier), volume 51, 2007, pages 921–960. 67. Seema Bandyopadhyay, Qingjiang Tian, Edward J. Coyle, “Spatio-Temporal Sampling Rates and

Energy Efficiency in Wireless Sensor Networks” IEEE/ACM Transactions on Networking, volume 13, number 6, December 2005, pages 1339 –1352.

68. Huiyu Luo, Gregory J. Pottie, “Designing Routes for Source Coding With Explicit Side Information in Sensor Networks” IEEE/ACM Transactions on Networking, volume 15, number 6, December 2007, pages 1401 – 1413.

69. Mohamed Younis, Kemal Akkaya, “Strategies and techniques for node placement in wireless sensor networks: A survey” Journal of Ad Hoc Networks (Elsevier), volume 6, pages 621–655.

70. Ming-Fei Wang, Lin-Lin Ci, Ping Zhan, Yong-Jun Xu, “Design Issues Of Wireless Sensor Networks In ubiquitous Learning” Proceedings of the 6th International Conference on Machine Learning and Cybernetics, Hong Kong, 19-22 August 2007, pages 4149-4153.

71. CigremSengul, Inria-Saclay, Indranil Gupta and Matthew J. Miller, “Adaptive Probability-Based Broadcast Forwarding in Energy-Saving Sensor Networks” ACM Transactions on Sensor Networks, volume 4, number 2, article 6, March 2008, pages 6.1 -6.31.

72. Haobo Yu, Nader Salehi, “NS2 tutorial”, IEC'2000 ns workshop, San Diego, USA, June 2000. 73. Rui Wang and Guozhi Liu, Cuie Zheng, “A Clustering Algorithm based on Virtual Area Partition

for Heterogeneous Wireless Sensor Networks” Proceedings of the 2007 IEEE International Conference on Mechatronics and Automation, Harbin, China. August 2007, pages 372 -376.

74. Ritesh Madan, Shuguang Cui, Sanjay Lall, , Andrea J. Goldsmith, “Modeling and Optimization of Transmission Schemes in Energy-Constrained Wireless Sensor Networks” IEEE/ACM Transactions on Networking, volume 15, number 6, December 2007, pages 1359 -1372.

75. John Heideman, “IPAM tutorial: Network modeling and traffic analysis with ns-2”, presentation at the UCLA/Institute for Pure and Applied Mathematics, Los Angeles, USA, March 2002, pages 448-454.

76. H. Zhai, X. Chen, and Y. Fang, “How well can the IEEE 802.11 wireless LAN support quality of service?” IEEE Trans. Wireless Communication, volume 4, number 6, November 2005, pages 3084–3094.

77. Chonggang Wang, Kazem Sohraby, Bo Li, Weiwen Tang, “Issues of Transport Control Protocols for Wireless Sensor Networks” Proceedings of International Conference on Communications, Circuits and Systems (ICCCAS), 2005, pages 247-255.

78. Justin Jones, Mohammed Atiquzzaman, “Transport Protocols for Wireless Sensor Networks: State-of-the-Art and Future Directions”, International Journal of Distributed Sensor Networks, 2007.

79. Chonggang Wang, Kazem Sohraby, Bo Li, Mahmoud Daneshmand, Yueming Hu, “A Survey of Transport Protocols for Wireless Sensor Networks” IEEE Transactions on Networks, 2006, pages 1127 -1135.

80. Yogesh G. Iyer, Shashidhar Gandham, S. Venkatesan, “STCP: A Generic Transport Layer Protocol for Wireless Sensor Networks” Proceedings of IEEE International Conference on Computer Communications and Networks (ICCCN), pages 2280-2288.

81. Urs Hunkeler, Hong Linh Truong and Andy Stanford-Clark, “MQTT-S – A Publish/Subscribe Protocol For Wireless Sensor Networks” Proceedings of 2nd Workshop on Information Assurance for Middleware Communications, (IAMCOM), pages 441-449.

82. Yangfan Zhou, Michael R. Lyu, Jiangchuan Liu, Hui Wang, “PORT: A Price-Oriented Reliable Transport Protocol for Wireless Sensor Networks” Proceedings of the 16th IEEE International Symposium on Software Reliability Engineering, pages 262-270.

83. Chieh-Yih Wan, Andrew T. Campbell, Lakshman Krishnamurthy, “PSFQ: A Reliable Transport Protocol for Wireless Sensor Networks” Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications, pages 1661-1668.

84. Fred Stann , John Heidemann, “RMST: Reliable Data Transport in Sensor Networks” 1st IEEE International Workshop on Sensor Network Protocols and Applications (SNPA), pages 978-985.

85. Yogesh Sankara Subramaniam, Ozgur B. Akan, Ian F. Akyildiz, “ESRT: Event to Sink Reliable Transport in Wireless Sensor Networks” Proceedings of 4th ACM International symposium on Mobile ad hoc networking & computing, 2003, pages 1742-1749.

86. Michael Luby, “LT Codes” Proceedings of the 43rd Symposium on Foundations of Computer Science, pages 2428-2432.

87. S. Muhammad, Z. Furqan, R. Guha, “Wireless sensor network security: a secure sink node architecture” Proceedings of 24th IEEE International on Performance, Computing, and Communications 2005, 7-9 April 2005, pages 371 - 376

88. D. E. Burgner, L. A. Wahsheh, “Security of Wireless Sensor Networks” Proceedings of Eighth International Conference on Information Technology: New Generations (ITNG 2011), Las Vegas, 11-13 April 2011, pages 315 - 320

19

89. Yufei Wang, Weimin Lin, Tao Zhang, “Study on security of Wireless Sensor Networks in smart grid” Proceedings of International Conference on Power System Technology (POWERCON-2010), Hangzhou, Nanjing, China 24-28, October 2010, pages 1 – 7.

90. Soo-Hwan Choi, Byung-Kug Kim, Jinwoo Park “An implementation of wireless sensor network” IEEE Transactions on Consumer Electronics, volume 50, issue 1, Feb 2004, pages 236 – 244.

91. Wang Soo, Lee Kang, “SecSens - Security Architecture for Wireless Sensor Networks” Proceedings of third International Conference on Sensor Technologies and Applications Greece, June 18- 23.

92. Wei Wang, Sharif, H. Hsiao-Hwa Chen, “Rabbit-MAC: Lightweight Authenticated Encryption in Wireless Sensor Networks” Proceedings of International Conference on Information and Automation, (ICIA 2008), Changsha, 20-23 June 2008, pages 573 – 577.

93. Ruiying Du, Song Wen, Huijuan Tu, “A segment encryption and transmission scheme for wireless sensor network” Proceedings of International Conference on Wireless Communications, Networking and Mobile Computing, China, 23-26 September 2005, pages 1193 – 1198.

94. Honggang Wang, Hempel, M. Dongming Peng, “Index-Based Selective Audio Encryption for Wireless Multimedia Sensor Networks” IEEE Transactions on Multimedia, April 2010, volume 12, issue 3, pages 215 – 223.

95. Qian Wang, Kui Ren, Cong Wang, Wenjing Lou, “Efficient fine-grained data access control in wireless sensor networks”, IEEE Communications Conference (MILCOM 2009), Chicago, IL, 18-21 October 2009, pages 1–7.

96. Dirk Westhoff, Joao Girao, Mithun Acharya, “Concealed Data Aggregation for Reverse Multicast Traffic in Sensor Networks: Encryption, Key Distribution, and Routing Adaptation” IEEE transactions on mobile computing ,October 2006, volume 5, number 10, pages 1417-1431.

97. Shucheng Yu, Worcester Kui R “FDAC: Toward Fine-Grained Distributed Data Access Control in Wireless Sensor Networks” IEEE Transactions on Parallel and Distributed Computing, April 2011, volume 22, number 4, pages 673-686.

98. Shen Yang, Liao Ning, “Design of Wireless Sensor Network Node with Hyper-chaos Encryption Based on FPGA” Proceedings of International Workshop on Chaos Fractals Theories and Applications, China, November 06, 2008.

99. Steffen Peter, Dirk Westhoff, “A Survey on the Encryption of Converge cast Traffic with In-Network Processing” IEEE Transactions on Dependable and Secure computing, volume 7, number 1, January-March 2010, pages 20-34.

100. Arif Selcuk Uluagac, A. Beyah, Yingshu Li, John A. Copeland, “VEBEK: Virtual Energy-Based Encryption and Keying for Wireless Sensor Networks” IEEE Transaction on mobile computing, July 2010, volume 9, number 7, pages 994-1007.

101. Dai Zhi-Feng, Li Yuan-Xiang, He Guo-Liang, Tong Ya-La, Shen Xian-Jun, “Uncertain Data Management for Wireless Sensor Networks Using Rough Set Theory” Proceedings of International Conference on Wireless Communications, Networking and Mobile Computing, 22-24 September 2006, pages 1 – 5.

102. Network Simulator: www.isi.edu/ns.