Multi-metrics based Congestion Control protocol in Wireless Sensor Network

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1 e-issn Volume 2 Issue 5, May 2016 pp Scientific Journal Impact Factor : Multi-metrics based Congestion Control protocol in Wireless Sensor Network Sushma N. Rana 1, Dr. Pariza Kamboj 2 1,2 Computer Engineering Department, Sarvajanik College of Engineering and Technology, Abstract Congestion in network occurs when available resource better performance level when network demand reaches their boundary. Congestion in Wireless Sensor Network (WSN) affects packet loss, throughput, and energy consumption. Congestion in WSN, control by two way either increase or decrease transmission rate or increase resources such as node. WSN is enabling with large number of motes, use that nodes as resources to mitigate congestion. Here multi-metric based routing is discussed to alleviate congestion. We focus on Network lifetime of network increase by reducing energy consumption of network with help of congestion control. Buffer size play important role in congestion so here we analysis impact of different value of buffer size on network lifetime and energy consumption. Keywords Wirelss senor network, Congestion control, Queue size, Resource utilization based I. INTRODUCTION Wireless Sensor Network (WSN) has gained interest in recent year. WSN built using small, with limited computing and processing resources called motes. Wireless sensor network contains three basic steps: 1) Data sense by sensor nodes 2) Data are use communication technique to send data 3) data are collected by sink node. Data collected from all sections, sent to base station. Base station connects a sensor network to one or more end user. The network shown in fig. 1. consists eight nodes, numbered 1-6 which are connected by link having equal capacity. Here three source nodes are there, which send packet to destination D1, D2. If source transmitted packet with high rate between N1-N3, N2-N3, N3-N4 then incoming rate of node N4 became twice. And N3 send packet to N4 immediately so bottleneck formed so for that certain packet store in buffer available at node N4 and wait for forwarded to destination. But buffer size at node N4 is not infinite because of that after sometime buffer overflow occur so some packet come towards N4 may discard that affect throughput. The network situation when packets are discarded due to not available resources or insufficient bandwidth or buffer overflow occur is denoted as congestion. Fig.1: Congestion in network Congestion in WSN divided in two manners 1) congestion due to buffer overflow 2) congestion due to link collision. Buffer overflow type congestion occurs when buffer (Queue) at each intermediate node reaches to that limit and not able to store any packet further more so because of that packet loss occur. This type of congestion solves by two methods 1) Decrease/ increase transmission All rights Reserved 536

2 according to requirement 2) buffer size increase. The second way of occurring congestion is link collision or link failure. Congestion protocol in WSN classifies as Congestion Control (CC), Congestion Avoidance (CA), Congestion solves based on Reliable transport (CR). CC technique includes preventing congestion occurring it take reactive action to mitigate congestion. CA technique includes a technique that prevents to occurring congestion. CR contains algorithm that control congestion and also recover some or all part of lost information. II. RELATED WORK Multi- metrics based routing also called traffic aware routing used in wireless network and wireless sensor network. ET-AODV discussed in [7] is multiple metrics based routing use for ad-hoc network. This technique is use high energy path based on specific route selection functionality. ET- AODV use residual energy and traffic load during route selection. Reliable AODV discussed in [8] is used energy threshold to choose path for routing. EM-AODV [9] is composed of composite metric of signal strength, battery power, and bandwidth. MMRP and its two variants (MMRP-I, MMRP-A) [10] are based on AODV and consider weighted contribution of hop count, traffic load, and residual energy of nodes for route selection. ETL-AODV protocol discussed in [11] use Energy level, traffic load, link quality for RREQ packet request send. This technique is use for WSN network for smart metering infrastructure. TADR [12] use queue length and depth for choosing next node which is use for congestion control in WSN. III. FIRST-ORDER HEADINGS Multi-metrics based congestion control (MCC) technique for WSN solves congestion by choosing an alternate path on the basis of multiple metrics. MCC technique is based on routing. Congestion control in WSN solved by two ways: increase/decrease transmission rate or choose another path for packet transmission. Select another path to solve congestion contains two techniques: 1) based on topology structure 2) based on routing. MCC based on routing, if on next node congestion occur, it select alternate path based on multi-metric filed Proposed System Fig. 2: Flowchart for proposed system System architecture shown in fig. in that step 1 describes that if burst traffic occur eventually that sender node produce large amount of data. Various numbers of application is there for All rights Reserved 537

3 environmental monitoring, habitat monitoring, intrusion detection, controlling water and fuel tanks fullness in a thermal power plant which based on event where data is only generated when events occurs. Because of sudden data congestion occur in network that detected in step 2. If congestion occurs at next node than select another path to solve congestion describe in step 3. After change path for packet transmission, routing table updating taken place which mentioned in step 4. Step 5 contain finally packet transmission take place Multi- metric Parameter for Congestion Mitigation Here we take three parameter for next node selection those are buffer size of next node, residual energy of next node, depth of next node. Buffer size is calculated by dividing total number of packets at node to total buffer size. Residual energy of next node calculated by dividing residual energy of next node to total energy consumption. Depth of next node is total number of hop between sender node to sink. Multimertic field calculated by combining all three field discusses above denoted as M combine. M combine = Residaul energy of next node + buffer size of next node + depth of next node IV. SIMULATION To study the behavior of Energy Efficient Multimetrices based congestion control (EMCC) technique for WSN we have used network simulator tool (NS-2.35). Network simulator is based on object oriented simulator written in C++ and OTcl interpreter. Network simulator is a free and open source simulator tool in which we can add our own protocol and contribute to the code. Network simulator runs on Linux platform. It has a large and rich library of network and protocol objects. Different objects are written in C++ code in order to achieve efficiency in the simulation and faster execution time. The OTcl script, provided by the user at the simulation time is used to define and configure network topology and network elements and to schedule events. It covers a large part of applications (Web, FTP), protocols (transport and routing protocols), network types (Satellite links, wired and wireless LAN), and network elements (mobile nodes, wireless channel models). Network simulator allows to add and test new protocols and applications and also to modify existing ones. All the below simulation results are obtained in the network simulation tool. Network simulation parameters are given in Table 1. Here we take two scenario and analyses result of energy consumption, network lifetime. Also change buffer size at intermediate node and analysis of result. 4.1 Energy Consumption (EC) Energy consumption calculated by total energy required for transmitting, receiving, sensing. Here we take two secnirio (m1, m2, m3) = (0.33, 0.33, 0.33) and (m1, m2, m3) = (0.4, 0.4, 0.2) and compare result with exsiting technique TADR shown in Fig. form that we conclude when we give same weightage to residual energy and queue length so result are better result get. Now if we change queue length for 31 packet to 20, 25, 35, 40 for different number of All rights Reserved 538

4 Fig. 3: Energy consumption for different scenario Table 1. Simulation Parameter Simulation Parameter Simulation Value Simulation Area 1000m * 1000m Network architecture Flat network Number of nodes 1000, 500, 200, 100 No of sink, sink coordinate 1, (250 * 250) m Packet size 25 Bytes Buffer Size 31 packets Simulation time 400 seconds Least updating interval 1 seconds Most updating interval 10 seconds 4.2 Energy Consumption for Different Number of Queue Length Here energy consumption for 100, 200, 500, 1000 nodes for different number of queue length shown in Fig. 4(a), (b), (c), (d) respectively. Here when buffer size (queue length) increase than energy consumption decrease because buffer size increase, so capacity for storing packet increase because of that packet loss decrease and buffer size decrease than packet loss ratio increase. Fig. 4(a): Energy consumption for 100 nodes for different queue All rights Reserved 539

5 Fig. 4(b): Energy consumption for 200 nodes for different queue size Fig. 4(c): Energy consumption for 500 nodes for different queue size Fig. 4(d): Energy consumption for 1000 nodes for different queue All rights Reserved 540

6 4.3 Network Lifetime (NL) International Journal of Current Trends in Engineering & Research (IJCTER) Network lifetime is also known as network remaining energy. The network is not capable to transmit a single packet or network all node energy reached to its limit or no path available. Here we take two scenario (m1, m2, m3) = (0.33, 0.33, 0.33) and (m1, m2, m3) = (0.4, 0.4, 0.2) and compare result with existing technique TADR shown in Fig. form that we conclude when we give same weightage to residual energy and queue length so result are better result get. Now if we change queue length for 31 packets to 20, 25, 35, 40 for different number of nodes. It is directly related to energy consumption of network. In case of algorithm consume less energy than network lifetime would be more. Fig. 5: Network Lifetime for different scenario Network Lifetime for Different Number of Queue Length Here network lifetime for 100, 200, 500, 1000 nodes for different number of queue length shown Fig. 5.(a), (b), (c), (d) respectively. Here when buffer size (queue length) increase than energy consumption decrease so network lifetime increase and buffer size decrease than network lifetime decrease. 100 Nodes Scenario Fig. 5(a): Network lifetime for 100 nodes for different queue All rights Reserved 541

7 Fig. 5(b): Network lifetime for 200 nodes for different queue size V. CONCLUSION AND FUTURE SCOPE Here we discuss multi metrics based routing for congestion alleviation that is MCC and compare that technique with existing TADR. We also take different buffer size and analysis result of energy consumption and network lifetime form that we estimate if buffer size increase than energy consumption by node decease because packet loss decrease. Network lifetime of network is increase when buffer size increase. In future, we check this scenario for another parameter to estimate performance of network. Fig. 5(c): Network lifetime for 500 nodes for different queue All rights Reserved 542

8 Fig. 5(d): Network lifetime for 1000 nodes for different queue size REFERENCES [1] Jennifer Y., Biswanath M., and Dipek G., Wireless sensor network survey, Computer network(elsevier), Vol. 52, No. 12, pp , [2] Potdar V., Sharif A., and Chang E., "Wireless sensor networks: a survey," International conference on advanced information networking and applications workshops 2009, pp , ISBN , May [3] Priyanka R., Kamaldeep S., Hakima C., and Jean Marie Bonnin, "Wireless sensor networks: a survey on recent developments and potential synergies," Springer science and business media new york 2013, pp. 1-48, 9 Oct [4] Sergiou C., Antoniou P., Vassiliou V., "A comprehensive survey of congestion control protocols in wireless sensor networks," Communications surveys & tutorials, IEEE, Vol. 16, No. 4, pp , Fourthquarter [5] Kafi M.A., Djenouri D., Ben-Othman J., Badache N., "Congestion control protocols in wireless sensor networks: a survey," Communications surveys & tutorials, IEEE, Vol. 16, No. 3, pp , Third Quarter [6] Ghaffari A., Congestion control mechanisms in wireless sensor networks: a survey, Journal of network and computer applications, Vol. 52, pp , June 2015, ISSN [7] Kapil K., Shiva P., and Sumit s., An energy and traffic aware routing approach as an extension of AODV, International Journal of Computer Applications, vol. 27, no. 7, pp.6 10, [8] G. Kumar, P. Rani, and C. Kumar, Reliable-AODV, in Proceedings of the 2nd International Conference on Computer and Communication Technology (ICCCT 11), pp , September [9] N. Thanthry, S. R. Kaki, and R. Pendse, EM-AODV: Metric based enhancement to AODV routing protocol, in Proceedings of the IEEE 64th Vehicular Technology Conference (VTC 06 Fall), pp , September [10] L. Cao, K. Sharif, Y.Wang, and T. Dahlberg, Adaptive multiple metrics routing protocols for heterogeneousmultihop wireless networks, in Proceedings of the 5th IEEE Consumer Communications and Networking Conference (CCNC 08), pp ,January [11] Hasan Farooq and Low Tang Jung, Energy, Traffic Load, and Link Quality Aware Ad Hoc Routing Protocol for Wireless Sensor Network Based Smart Metering Infrastructure, International Journal of Distributed Sensor Networks, vol. 2013, Article ID , 13 pages, doi: /2013/ [12] Fengyuan R., Tao H., Das S., and Chuang L., "Traffic aware dynamic routing to alleviate congestion in wireless sensor networks," Parallel and distributed Systems, IEEE Transactions on, vol. 22, no. 9, pp , Sept. All rights Reserved 543