Congestion Control Based on QOS Improvement in Consensus in the Wireless Sensor Network

Congestion Control Based on QOS Improvement in Consensus in the Wireless Sensor Network

1Kavitha S, Research Scholar, R & D Centre,

Bharathiar University, Coimbatore,

2 Dr. R. Latha, HOD, MCA Department,

St.Peters University, Chennai.

Abstract -In the current world Wireless Sensor networks are used in many areas, shared by many applications, and very cost effective to collect various types of data. Since it is shared by many applications, congestion control is the main problem occurred in this type of sensor networks. Many algorithms are available to avoid this problem; among that weighted directed graph based congestion control algorithm is also the one. Due to the congestion, the network settled into a stable state, because of high traffic, giving little throughput. The increased time delay and packet loss caused by this congestion may deteriorate the quality of service (QoS). A congestion control algorithm based on QOS im- provement in Consensus analysis (CC-QoS-CA), using graph theory is proposed in this paper, considering the sink as a leader. Every node is selected based on the QoS values, apart from normal consensus. If there is an improvement in the QoS value of a node then it will be considered for routing process. If there is no improvement in quality of service of a node, then only consensus will be applied. Ns simulation results showed that the proposed system, main- tains a high throughput, a minimal delay time, and reduce the packet drop ratio.

Keywords: Congestion control, CC-CA, QoS improvement, CC-QoS-CA

1. INTRODUCTION

   For the past few years, there has been an extraordinary research interest in the area of autonomous networked system with emphasis on WSNs [1]. Wireless Sensor Networks are used in many applications like habitat monitoring [2], structural health monitoring [3, 4], image sensing [5], and physical game [6] greatly rely on conges- tion control techniques. Wireless Sensor Networks (WSNs) can be defined as a self-configured and infra- structure-less wireless networks to monitor physical or environmental conditions to considerately pass their data through the network to a main location or sink where the data can be observed and analyzed [7]. A Wireless sensor network consists of hundreds or thousands of sensor nodes. These sensors have the potential to communicate either among each other or directly to an external base- station (BS) [8] [9]. A larger number of sensors allows for sensing over larger geographical regions with greater ac- curacy [9]. Sensor nodes synchronize among themselves to produce high-quality information about the physical environment. Every sensor node bases its knowledge of

   its computing, and decisions on its mission, the informa- tion it currently has, communication, and energy re- sources. These scattered sensor nodes has the capability to collect and route data each to other sensors or back to an external base stations [10].The Sensor nodes which will deploy in the distant area, like the satellite in the out- er space, and the high mountain area, renewing is not practical. Thus, the most important aim for WSNs is on the low energy use within the independent, supportive nodes which may be controlled by means of a small memory and a low computing ability [11].

   A wireless sensor network consists of lots of autonom- ous sensors which are dispersed, to monitor physical or environmental conditions. Due to exchanges of the infor- mation and the physical parameters, in combination of the variable wireless sensor network conditions, may conse- quence in erratic behavior in the form of traffic load vari- ations and fluctuations in the link capacity. These are the possibility of increasing congestion in WSNs. When a network is congested, it reached to a stable state, in- creased time delay and packet drop ratio, affects the quality of service (QoS). Even though the traffic demand is more, but little throughput is available. Particularly, packet loss may trigger the time-out retransmission scheme of TCP. Retransmitted packet may deteriorate the congestion and stimulate more retransmission request. As a result, congestion in WSN increase the per packet ener- gy consumption and reduce delivery ratio.

   The limited and unpredictable nature of WSNs neces- sitates decentralized, robust, self-adaptive, and scalable mechanisms which are vital to the mission of dependable WSNs. Novel approaches should be simple to implement at individual node level with minimal exchange of infor- mation [12]. The consensus analysis of the complex net- work theory, such as fish swarming and bird flocking, is used in this paper. With the help of the graph theory, a congestion control algorithm based on QOS improvement in Consensus analysis (CC-QoS-CA) is proposed in this paper, considering the sink as a leader. Ns simulation results shows that our CC-QoS-CA could restrain the congestion over wireless sensor network and maintain a high throughput and a low delay time. It can also improve the QoS for the whole network.

   The paper is organized as follows. Section 2 presents the existing congestion model based on the graph theory for WSNs. In section 3, the theoretical results for conges- tion control based on QoS modified in the Consensus Analysis. Section 4 contains a performance evaluation of the proposed scheme. Conclusions are provided in Sec- tion 5.

2. EXISTING CONGESTION MODEL DESCRIPTION IN CC-CA

   The congestion control algorithm based on consensus analysis with graph theory is used in this paper. In CC- CA, sink is considered as a leader node and the rest of the nodes are follower nodes. The mass information is col- lected and processed by the Leader node. The two most popular congestion control schemes are Queue based and rate based. A backlog is inherently necessitated in Queue- based scheme [13], the rate based scheme is chosen in this paper, since it can provide early feedback for conges- tion [14].The base paper consider that G=(V, E, A) is a weighted directed graph with n + 1 nodes, where V de- notes the set of vertices vi= (i L={0,1,2,,n}),, E de- notes the set of edges eij=(i , j), i, j L of the graph G, and A = [aij] Rnxn for 1,2,..,n is a weighted adjacency matrix.

   This paper considers that the vertex indexed by 0 is assigned as the leader, which is the sink in WSN. The other vertices of the graph G indexed by 1,2,,n are re- ferred to as follower agents, which are autonomous between vi and vj, then considered that there is a path be- tween the two nodes; otherwise eij E. Define the weight matrix A for the graph G as follows:

   aij= 0.5 eij E, (1)

   0 eij E,

   Where xi R(I L) is denoted as the data bulk sent to node I, then the differential of x, denotes the data sent rate. If there is no data communication between node I and others, xi = 0.

   To study a leader-following problem, the connection weight between nodes I and the leader, denoted by bi, is shown as follows. The sink, as the last hop in the monitor area of WSN, is assigned with the largest weight:

   bi= 0.75 vi connected to the leader v0, (2)

   0 otherwise

   When the offered load exceeds the available capacity in the link, the packet will accumulate in the router buffer, which will induce the congestion. The congestion can be avoided, if the data bulk exchange of all nodes for one task converges to the same equilibrium point in the net- work. Then the congestion control problem can be attri- buted to the consensus problem of the complex network. Furthermore, in simulation, all the nodes are considered the same as each other, and they spilt the bandwidth fair- ly.

   Te leader (node 0) is the sink of WSN, through which all the information collected by the sensors is transmitted. So the sink is globally reachable in WSN topology. Theo- rem 5 proves that the CC-CA guarantees the convergence of the system error; in other words, the data sent rates for all nodes converge equally to the sinks.[11]. Though the congestion problem is inevitable, the CC-CA provides better performance comparing to the traditional conges- tion algorithm.

   III .PROPOSED SYSTEM

   The QoS improved CC-CA, is proposed in this paper where, the QoS values are considered for routing process. As a result, every node is selected based on the QoS val- ues, apart from normal consensus methodology. The pro- posed system proves that every node is selected based on the QoS values, apart from normal consensus methodolo- gy, causes increased packet received ratio with minimal delay, packet loss. If there is an improvement in the QoS value of a node then it will be considered for routing process. If there is no improvement in quality of service of a node, then only consensus will be applied. Node 0 is considered as a sink which can globally reachable in WSN topology. The CC-QoS-CA improves the quality of service by avoiding congestion.

   Figure 1 The flow chart of the QoS improved CC-CA.

   1. SIMULATIONS

      This section states the performance of CC-QoS-CA compared with the CC-CA and normal method

      .

      Packet Received Ratio

      Packet Loss Ratio

      Delay

      When compared CC-QoS-CA with normal and CC-CA, simulation results showed that our CC-QoS-CA is con- sume the network resources well, with improved packet received ratio and reduce packet loss ratio with minimal delay time.

   2. Conclusions

The congestion problem is inevitable due to many to one nature in WSN, which causes increased delays, pack- et drop and retransmissions. The proposed system im- proves the Quality of Service. Simulation results indicates that improved performance in packet received ratio, mi- nimal delay time and reduce packet loss ratio as com- pared to normal and CC-CA methods. This can be achieved by QoS value in the routing process, which gives better performance.

REFERENCES

1. I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, Wireless sensor networks: a survey, Computer Networks (Ams- terdam, Netherlands: 1999), vol. 38, no. 4, pp. 393-422, March.

2. R. Szewezky, A. Mainwaring, J. Polastre, J. Anderson, and D. Cul- ler, An analysis of a large scale habitat monitoring application, in Proceedings of the 2nd International Conference on Embedded Networked Sensor Systems (SenSys 04), pp. 214-226, November 2004.

3. K. Mechitov, W. Y. Kim, G. Agha, and T. Nagayama, High fre- quency distributed sensing for structure monitoring, in Proceed- ings of the 1st International Workshop on Networked Sensing Sys- tems (INSS 04), 2004.

4. J. Paek, K. Chintalapudi, R. Govindan, J. Caffrey, and S. Masri, A Wireless sensor network for structural health monitoring: per- formance and experience, in proceeding of the 2nd IEEE Work- shop on Embedded Networked Sensors ( EmNetS 05), pp.1-10, May 2005.

5. M. Rahimi, D. Estrin, R. Baer, H. Uyeno, and J. Warrior, Cyc- lops, image sensing and interpretation in wireless networks, in proceedings of 2nd International Conference on embedded Net- worked Sensor Systems (SenSys 04), p. 311, November 2004.

6. W. H. Tan, W.J. Li, Y.Z. Zheng, and X.C.Zhou, ePet: a Physical game based on wireless sensor networks, International Journal of Distributed Sensor Networks, Vol. 5, no. 1, p.68, 2009.

7. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, A Sur- vey on sensor networks, IEEE Communications Magazine, Vo- lume: 40 Issue: 8, pp.102-114, August 2002.

8. Perrig, R. Szewzyk, J.D. Tygar, V. Wen, and D.E. Culler, SPINS: security protocols for sensor networks. Wireless Networks Vo- lume: 8, pp. 521-234, 2000.

9. S. Hedetniemi and A. Liestman, \A survey of gossiping and broad- casting in communication networks, IEEE Networks, Vol. 18, No. 4, pp. 319-349, 1988.

[10]. Anquetil, N. and Lethbridge, T., Assessing the Relevance of Identifier Names in a Legacy Software System, in Proceedings of Annual IBM Centers for Advanced Studies Conference (CAS- CON98), December 1998, pp. 213-222.

1. Xinhao Yang, Juncheng Jia, Shukui Zhang, and Ze Li,Congestion Control Based on Consensus in the Wireless Sen- sor Network, International Journal of Distributed Sensor Net- works, Volume 2013, Pages. 7, August 2013.

2. P. Antoniou, A. Pitsillides, T. Blackwell, A. Engelbrecht, and L. Michael, Congestion control in wireless sensor networks based on bird flocking behavior, Computer Networks, Vol. 57, pp. 1167-1191, 2013.

3. T. Nakashima, Properties of the correlation between queue length and congestion window size under self-similar traffics. In- ternational Journal of Innovative Computing, Information and Control, Vol. 5, no. 11, pp. 4373-4381, 2009.

4. S. S. Kunniyur and R. Srikant, An adaptive Virtual Queue(AVQ) algorithm for Active Queue Management, IEEE/ACM Transac- tions on Networking, vol. 12, no. 2, pp. 286-299, 2004.