Analysis of QoS Parameters of Sectoring-based Algorithm for Congestion Control in Wireless Sensor Networks

Analysis of QoS Parameters of Sectoring-based Algorithm for Congestion Control in Wireless Sensor Networks

Tejas Ambekar Department of computer Engineering,

JSPMs Jayawantrao Sawant College of Engineering, Pune

Aditya Mishra Department of computer Engineering,

JSPMs Jayawantrao Sawant College of Engineering, Pune,

Dipak Pawar Department of computer Engineering,

JSPMs Jayawantrao Sawant College of Engineering, Pune,

Dattatray Waghole

Department of computer Engineering, JSPMs Jayawantrao Sawant College of Engineering,

Pune,

Aachal Gawai

Department of computer Engineering, JSPMs Jayawantrao Sawant College of Engineering,

Pune,

AbstractIt is quite a challenging task to maintain and improve the QoS features in the Wireless Sensor Networks. The challenges like improving Quality of Service features of a network which has distributed topology, managing the nodes data transfer during a natural disaster, improving the life of the battery need to be achieved. In this paper we have analysed different routing protocols(AODV, DSDV, DSR) on nodes placement in a random scenario and in the proposed Sectoring Scheme. Different QoS features are also analysed by dividing the network field into different sectors and optimal Node Density. An energy-efficient sectoring algorithm is proposed which selects an optimal sector head for each sector in the network. The QoS features such as Packet Delivery Ratio and Delay is computed for Static and Dynamic nodes placement in the Sectoring Scheme and compared with the results of the node placement randomly. According to the simulation results, it has been concluded that the proposed Sectoring Scheme generates optimal QoS results.

KeywordsWSN- Wireless Sensor Network, Sectoring, QoS, Quality of Service..

1. INTRODUCTION

   In this growing field of Information Technology, the systems communicate with each other in a network field with the help of multiple sensor nodes. A wireless Sensor network is apprehended as a group of sensor nodes that are trained in sensing physical events in the surrounding area, sensed data is processed and eventually routed the typical or cumulative data to the base station. Wireless Sensor networks are relatively self-organized isolated systems, and they rely on large numbers of low cost tiny sprinkled devices having high restrictions in terms of memory, processing, energy capabilities and communications. Wireless Sensor networks are set of communication networks made up of various independent sensors that record various material or environmental parameters, such as pressure, sound, temperature, motion and vibration at various locations.

   wireless sensor network come up with a inexpensive, completely distributed computing and sensing solution for areas where traditional networks are unfeasible to use.

   The proposed work explores the issue of congestion in sensor networks. The issue of congestion is all-around and causes packet loss and packet delivery delay. In a wireless sensor network, a reliable protocol is one that permits data to be reliably transferred from source to destination with minimal packet loss. The majority of the protocols described merely offer reliable data transfer and congestion control. Several protocols, however, provide both functionalities of the transport protocol. To address these challenges, consideration should be given to protocols of transport layer that combine standardized data release with congestion control.

   In this paper [6], Node Energy consumption is the major and the biggest issue for life of a node in the network field. Energy in the network field can be used(consumed) by useful work and also by wasteful work. Nodes in network field works on battery and making nodes full of energy is one of the impossible task. Therefore survival of node depends on the remaining energy when it is in use in the network field. There are some challenges with energy consumption such as huge number of nodes in network field that results in impossible task of recharging every node and other challenge could be complexity of network, which result in replacing every dead node.

   In this paper[14] WSNs have a broadcasting nature and to avoid duplicity of the same data on the network they require tight control, else it might result in wastage of bandwidth and reduction in the network throughput. The deployment of a wireless sensor network in an uncontrolled manner might cause an unwanted behaviour like high packet drop. It might arise due to mac layer congestion, while attempting to send/ receive data packets on a shared channel with help of CSMA protocol between the sensor nodes. Multipath routing sends various copies of the similar data packets on different paths

   increasing data delivery ratio of the information between source and destination. This method also increases the throughput of a network, transmitting the data packets in pieces which are parallel along various paths with combining the complete data at destination. The transmission of data mostly relies on an ideal primary path and an alternate path is reserved to be utilized when nodes in that primary path fail. Using the sensor node routing table which consists of every neighbour node with the probability of transmission computed, the system identifies multiple route but utilizes just one of them for a particular transfer. This reduces the stress on a specific route and increases lifetime of the network.

   In sensors, real-time, computing indicates that their is presence of micro controller or micro processor which consume energy at high scale. These tools in sensors decides which part to work when and when to turn it on and when to turn it off. Energy consumption here is done in two ways energy used in switching and energy which is leaked. By the using software energy is consumed because of supply of voltage and capacitance. There are sometime when no computation is going on and still there a energy loss, this is nothing but energy leakage.

   In this paper[2] , CODA, or Detection of congestion and Sensor Networks congestion avoidance, is made up of three strategies for congestion control: congestion detection based on receiver, hop-by-hop open loop backpressure, and multisource regulation closed-loop . Congestion detection and avoidance(CODA) employs the current and previous channel loading condition, as well as the occupancy bufer level, to detect congestion. Without ARQ link layer , occupancy buffer cannot reflect correct congestion levels, as has been demonstrated.] To alleviate the control of congestion, CODA considers both data rate and node density. There is a greater likelihood of chronic congestion which is far from sink node and near the source node in networks with high node density and high data rate. Backpressure messages are sent from the congestion hotspot to the source nodes in this situation.

   In this paper[11] Delay in grid topology whole network field

   Step 2: Node which is nearest to Sink is assigned as Sector Head (SH).

   Step 3: Only activate the current sector, when an event is occurred and remaining sectors nodes are kept in sleep mode.

   Step 4: Nodes transfer packets to respective Sector Head where event is occured .

   Step 5: Sector Head, transfer the packets to sink.

   The proposed Sector-based algorithm can follow the below topology for packet transmission. Here the sink is at the extreme middle and the nodes are divided into three sectors where the nearest node to the sink is the Sector Head and is responsible for parsing all the data packets.

   Fig: Node Arrangement in the Sectoring Scheme

   able: Simulation Parameters Used

   Simulation Parameter Value

   Simulator NS2

   Scenario Area 1000*1000 m2

   Node Density 10, 30, 50, 90

   MAC Protocol IEEE 802.11

   Packet Size 50, 100, 150

   Number of Sink Node 1

   Position of Sink Node Center

   is divided into multiple organized shaped grids. Delay is also known as latency. Each grid contains one head called the

   Routing Protocol

   Transmission Rate

   AODV

   10 pkt/sec

   master node, which collects data and send it further. This data forwarding causes accumulation of data at some point in the network field.

   Now there can be two things, one that this could make data flow slower than usual flow speed due to congestion on the master nodes and this would lead to delay until it reaches destination node.

   Second, accumulation of data can also cause data loss as the master node might receive more data packets than it can handle and this would also be one of the case delay in wireless sensor network.

2. SIMULATION SCENARIO

   The proposed Sectoring scheme follows the algorithmic steps as:

   Step 1: Divide wireless sensor network in the n sectors.

   Network Traffic CBR

3. RESULT ANALYSIS

   Throughput Analysis:

   On comparing the throughput of the network of node placement in a random scenario to the node placement in the proposed sectoring scenario statically/dynamically, it can be concluded from the graph below that the Sectoring scheme gives better throughput

   Series1,

   Series1,

   Series1,

   Series1, Series1,

   THROUGHPUT

   RANDOM SCENARI O, 10.34

   STATIC SECTORS

   , 12.55

   DYNAMI C SECTORS

   , 12.52

   DELAY in ms

   	SCEN IO 751.4	AR SECTORS SECTORS
   		97

   RANDO M

   ,

   Series1, STATIC

   , 608.163

   DYNAMI

   C

   , 655.657

   SIMULATION SCENARIO

   Graph: Throughput Analysis

   Packet Delivery Ratio Analysis:

   On comparing the Packet Delivery Ratio of the nodes in the random scenario to the nodes in the proposed sectoring scenario statically/dynamically, it can be concluded from the graph below that the Sectoring scheme gives better PDR of 96% due to optimal path selection.

   Series1, Series1,

   SIMULATION SCENARIO

   Graph: Delay Analysis

4. CONCLUSION

   On comparing different routing protocols(AODV, DSDV, DSR) for data transmission in Wireless Sensor Networks, AODV gives better performance among the others by avoiding congestion and by providing better packet delivery.

   Delay Analysis:

   			S, 96.	03	SECT S, 96.	OR
   Series1, RANDOM						02
   SCENARI O, 89.16

   STATIC SECTOR

   SIMULATION SCENARIO

   Graph: PDR Analysis

   PDR in %

   DYNAMI C

   Hence, we have used AODV algorithm to perform our

   simulation scenario. On plotting the nodes in a simulating environment, we found that on increasing the Node Density the efficiency decreases and a major effect in the QoS parameters is observed. Therefore, a sectoring scenario with

   30 Nodes gives optimal results like increased Packet Delivery, Less Packet Loss, Less Delay, and an increased throughput.

   According to the simulations, it is observed that proposed sectoring technique performs better than random scenario in terms of QoS parameters. From the simulation results obtained it is concluded that the Sectoring technique serves better with 13% throughput, 96% PDR, 3% PLR and with 600ms Delay. Hence from the results it is deduced that the proposed sectoring algorithm gives better results on different QoS parameters, on comparing with a random node placement scenario.

   On comparing the Delay of the nodes in the random scenario to the nodes in the proposed sectoring scenario statically/dynamically, it can be concluded from the graph below that the Sectoring scheme generates less Delay of 600ms due to reduced congestion and optimal waiting time of the nodes to send the packets.

5. REFERENCES

[1] Sharmila Kharat, Congestion Control Techniques in Wireless Sensor Networks , International Journal of Engineering Research & Technology (IJERT) ISSN: 2278-0181 IJERTV4IS020655

www.ijert.org Vol. 4 Issue 02, February-2015 , 617 620 ,2015. [2] C.-Y. Wan, S.B. Eisenman, and A. T. Campbell, CODA:

Congestion detection and avoidance in sensor networks, in Proc. ACM SenSys, Nov.2003.

[3] Li Qiang Tao; Feng Qi Yu, ECODA: enhanced congestion detection and avoidance for multiple class of traffic in sensor networks in Consumer Electronics, Aug. 2010.

[4] Fengyuan Ren,Sajal Das and Chuang Lin,Traffic Aware Dynamic Routing to alleviate congestion in WSN,in IEEE transaction on parralel and distributed systems,vol 22,No 9,September 2011.

[5] Hull, Jamieson, Balakrishnan, 2004. Mitigating congestion in WSN. In: Proceedings of the 5th ACM Conference on Embedded Networked Sensor Systems (SenSys). and Babar Nawaz, Khalid Mahmood, Jahangir Khan, Congestion Control Techniques in WSNs, International Journal of Advanced Computer Science and Applications, April 2019

[6] Gagandeep Kaur and Navroop Kaur , Energy Consumption in Wireless Sensor Network, IJSRD – International Journal for Scientific Research & Development, Vol. 2, Issue 06, pg-227-229, 2014

[7] Mei Wu, Energy-Efficient Routing Protocol in Heterogeneous Wireless Sensor Network, A PhD thesis directed by Dr. MARTIN COLLIER at Dublin City University, 2012

[8] Debnath Bhattacharyya, Tai-hoon Kim and Subhajit Pal, A Comparative Study of Wireless Sensor Networks and Their Routing Protocols, Sensors, 2010.

[9] C. Schurghers, V. Raghunathan, S. Park, and M. Srivastava, Energy- Aware Wireless Microsensor Networks, IEEE Signal Proc Mag, pg- 4050, 2002

[10] Doina Bein and Bharat B. Madan, Reducing the Data Communication Delay in Wireless Sensor Networks, pg-361-368, 2016

[11] Omar Banimelhem, Samer Khasawneh, Grid-based multipath with congestion avoidance routing protocol in wireless sensor networks,pg- 1346-1361 ,Et Al. 2012

[12] C. Joo, J. G. Choi, and N. B. Shroff, Delay performance of scheduling with data aggregation in wireless sensor networks, in IEEE INFOCOM, 2010.

[13] Shweta K. Kanhere , Mahesh Goudar, Vijay M. Wadhai, End-To- End Delay Optimization in Wireless Sensor Network (WSN),International Journal of Smart Sensors and Ad Hoc Networks (IJSSAN) ISSN No. 2248-9738 Volume-1, Issue-3, 2012

[14] Antoine B. Bagula, Modelling and Implementation of QoS in Wireless Sensor Networks: A Multiconstrained Traffic Engineering Model, EURASIP Journal on Wireless Communications and Networking, Article number: 468737,2010

[15] Swati D. Kadu and Vivek S. Deshpande, Characterization of Throughput in Wireless Sensor Network for MAC and Routing protocol, International Conference on Cloud & Ubiquitous Computing & Emerging Technologies, pg-108-111,2013

[16] Yu-Kai Huang, Ai-chun Pang, Pi-cheng Hsiu and Weihua Zhuang, Distributed Throughput Optimization for Cluster -Tree Networks, IEEE Transactions on parallel and distributed systems , Vol 23 , No. 3 ,pp:13-520, 2012

[17] Mervat Mustafa raouf, Clustering in Wireless Sensor Networks (WSNs), JOURNAL OF BAGHDAD UNIVERSITY COLLEGE OF ECONOMIC SCIENCES- ISSUE-57- APRIL 2019,

[18] Shujuan Jin, Keqiu Li (2009), LBCS: A Load Balanced Clustering Scheme in Wireless Sensor Networks, Third International Conference on Multimedia and Ubiquitous Engineering, 978-07695- 3658-3/09,2009 IEEE DOI 10.1109/MUE.2009.47.

[19] Jbcesu Baghdad Iraq Baghdad College of Economic Sciences University,JOURNAL OF BAGHDAD UNIVERSITY COLLEGE OF ECONOMIC SCIENCES- ISSUE-57- APRIL 2019

[20] Alper Bereketli,Ozgur B. Akan (2009),EventtoSink Directed Clustering in Wireless Sensor Networks ,Next generation Wireless Communications Laboratory (NWCL) Department of Electrical and Electronics Engineering Middle East Technical University, Ankara

,Turkey, 06531 978-1- 4244-2948-6/09 IEEE

[21] Wategaonkar D.N. and Deshpande V.S., MARS: SECTORING SCHEME TO IMPROVE RELIABILITY IN WSN,International

Journal of Wireless Communication, ISSN: 2231-3559 & E-ISSN: 2231-3567, Volume 3, Issue 1, pp.-47-50, 2013

[22] Dhanashri Narayan Wategaonkar and S V Nagaraj, Analysis of QoS Parameters using Prediction Algorithms on CSO-based Energy- Efficient Reliable Sectoring Scheme in WSN, Journal of Physics:

Conference Series, Volume 1911, 2021

[23] Sadam M. Alkhalidi, Dong Wang and Zaid A. Al-Marhabi, Adopting Sector-Based Replacement (SBR) and Utilizing Air-R to Achieve R-WSN Sustainability, in Information, section: Information and Communications Technology, Volume 8, Issue 2, 2017

[24] Basilis Mamalis, Damianos Gavalas, Charalampos Konstantopoulos, and Grammati Pantziou, Clustering in Wireless Sensor Networks, Zhang/RFID and Sensor Networks AU7777_C012 Page Proof Page 324 2009-6-24.