CONGESTION AVOIDANCE AND CONTROL IN WIRELESS SENSOR NETWORKS USING EPSILON CONSTRAINT BASED ADAPTIVE

Research Paper

Engineering

E-ISSN No : 2454-9916 | Volume : 3 | Issue : 5 | May 2017

CONGESTION AVOIDANCE AND CONTROL IN WIRELESS SENSOR NETWORKS USING EPSILON CONSTRAINT BASED ADAPTIVE CUCKOO SEARCH 1

Vaibhav Eknath Narawade | Uttam D. Kolekar 1 2

2

Research Scholar, Pacific Academy of Higher Education and Research University, Udaipur (Rajasthan)-313003.

Professor & Principal, A.P. Shah Inst. of Technology, Thane, Maharashtra-400615.

ABSTRACT Congestion control is one of the most important challenges in Wireless Sensor Networks (WSNs). The several scheme and techniques have been developed for improving performance in Wireless Network. Congestion has significant impact on network performance, Quality of Services (QoS) result in waste of energy of sensor nodes. To overcome this congestion issue, the paper proposed the optimized algorithm to detect and control the congestion in WSNs. The proposed approach formulated the fitness function with the aid of Epsilon parameter. The main idea is to detect the occurrence of congestion by incoming packets of sensor nodes. Once the congestion level is determined by virtual queue length it verifies congestion status. Occurrence of congestion fed into proposed optimized algorithm. The proposed approach is simple to implement as congestion occurs, it generate new solution by adjusting step size adaptively. With the aid of Epsilon parameter fitness function is formulated to find out optimal value. Finally proposed algorithm generates new fitness function to obtain the best solution where congestion free data transmitted in the network. The performance and result of the proposed algorithm is evaluated using the parameters such as delay, normalized packet loss, normalized queue sized.

The result shows that proposed algorithm attains minimum delay, minimum normalized packet loss and minimum queue sized to enhance the transmission efficiency.

KEYWORDS: WSN network, delay, normalized packet loss, normalized queue sized, congestion level, Epsilon constraint, Adaptive Cuckoo Search, Sending rate.

1. INTRODUCTION Wireless Sensor Network (WSN) is one of the emerging class of network with a variety of potential application like military surveillance, healthcare application and so on [1]. Sensor nodes equipped with sensing and computing capabilities. It read data and sends to the base station. WSN make possible to controlling and monitoring of physical environment from remote location. During this network suffers unavoidable situation which is termed as congestion. Congestion avoidance detects congestion and prevents is occurrence in the network whereas Congestion Control concentrates on enabling the network to recover from packet loss. Two types of congestion categories available in WSN i.e Location based and Packet based Congestion. [7,8,11,12]. Location based congestion classified in to source congestion, sink congestion and forwarded congestion. On the other hand, packet based congestion broadly classified into node level congestion and sink level congestion [18]. Due to packet loss in the congested network retransmission occurs at MAC layer. It leads to more consumption of bandwidth and power.

The problem of congestion is very complex in nature. Furthermore another important characteristic of network congestion is many to one nature of WSN traffic, traditional end-to-end strategies. In conflict congestion in the network, Congestion Control (CC) and avoidance becomes one of the challenging tasks when the traffic within the network is altered [2]. To assure the Quality of service (QoS) requirements [13] is a serious issue when network lifetime is enhanced.

QoS is get affected when more queuing delay and packet loss occurs causing buffer overflow, thus, researcher needs to designed transport protocol to avoid the congestion in the network [14]. Re-transmission of packages leads to wastage of energy because WSN has limited battery. Primarily, the congestion control protocol should developed to detect and control the congestion in the network significantly, and also apply such mitigation technique in WSNs [3].

In this paper we praposed the novel Epsilon Constraint-based Adaptive Cuckoo Search (EACSRO) algorithm for congestion avoidance in the network. Our contributions are as follows: Ÿ

In the proposed algorithm, the fitness function newly formulated. By the Epsilon value, the fitness is computed by the different parameter.

Ÿ

The adaptive cuckoo search algorithm exploits the proposed fitness function to determine the optimal share rate. Also, the step size is adjusted adaptively to generate the new solution in cuckoo search optimization algorithm.

Ÿ

A detailed simulation result of different parameters of delay, normalized packet loss and queue sized with their comparisons.

In the rest of this paper, we first formulate the problem description behind the congestion avoidance approach in Section 2. Related work is covered in Section 3. In Section 4 briefly explain the proposed methodology of Epsilon constraint-based Adaptive Cuckoo Search optimization algorithm. The experimental results and its performance validates in Section 5. Finally, this paper is concluded in section 6.

2. PROBLEM DESCRIPTION During the transmission between sensor nodes and sink node Congestion is the main problem in the wireless sensor network. Consider W be the number of sensor nodes and sink node in the network, where we can define the parental node and child node. Initially, the child nodes are defined by,

where, k represents the total number of child sensor nodes. Then, the parental node in the wireless sensor network is expressed as:

where, Ni exhibits the i number of parental nodes. Moreover, the data packet is the major concern to transmit the information from the source to destination node. Thus, the data packet DPj is denoted by,

The main challenge is to detect the congestion in the network using two analysis are sending rate of the child node and service rate of the parental node. If the sending rate is higher than service rate [6], then congestion occurs since the child node has to await in the queue to transmit the data.

3. RELATED WORK The most of prior work have focused on congestion avoidance and control algorithm. [20-23] is discussed. In [19], a probabilistic method for congestion prediction and rate control based on the back off selection was proposed. The channel state and channel free transmission were considered for the congestion control.

Liu et al.[4] proposed adaptive random early detection based congestion control.

Consider factor in this system is delay variation, leading to the poor network performance.

A back pressure routine and dynamic prioritization for congestion control technique was proposed in [5]. In this algorithm does not pre-calculate routes and next step is selected dynamically. Dynamic alternative path selection scheme was proposed in [6]. It is effective that control congestion while it keeps overhead to the minimum. But it's had high computation complexity and needs to have full knowledge of network wide per wavelength link-state. The congestion control and avoidance based on the bird flocking behaviour was proposed in [2].

It adopts a swarm intelligence paradigm inspired by the collective behaviour of bird flocks. In [3] Adaptive cuckoo search based rate adjustment for optimized congestion avoidance and control proposed. In this rate is optimized to improve the performance of the WSN system.

4. PROPOSED METHODOLOGY The main objective of the proposed architecture is to avoid and control the congestion in the WSN network using proposed optimization algorithm. In this consider the child node and parental nodes are to perform the optimization. Initially, the traffic from child node is carried out by the child priority and packet drop prob-

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E-ISSN No : 2454-9916 | Volume : 3 | Issue : 5 | May 2017

ability. Then, the congestion is detected by the service rate of the parental node. If congestion occurs, then congestion level is determined by virtual queue length.

In order to avoid the congestion in the network, the new Epsilon constraint-based Adaptive Cuckoo Search optimization algorithm is proposed. Firstly, in the proposed algorithm, the fitness function is mathematically designed by epsilon constraint parameter. Secondly, the step size is adjusted adaptively to generate the solution.

optimization algorithm for congestion avoidance in the network. Here, the performance is based on rate adjustments of share rate and sending rate analysis. The optimization algorithm is mainly used to control the congestion by [17] mitigating the average share rate of child nodes since it transmits the data packets to the parental node. In this proposed algorithm, the novel fitness function is designed with the aid of epsilon constraint parameter. The elucidation of proposed optimization algorithm is deliberated as follows.

Based on the epsilon value, the five QoS parameters are used to compute the fitness value.

i) Solution Encoding The solution encoding is the main concern while using optimization algorithm.

Based on this solution, the optimization finds out the most appropriate solution.

Here, the solution for congestion control is defined by share rate of child sensor nodes. Thus, the share rate of every child node is considered as the solution.

[10] Finally, the best solution (share rate) is determined adaptively to provide the better data transmission devoid of congestion.

The architecture of proposed algorithm shown in Figure 1.

ii) Fitness function The significant effect of optimization exhibits the fitness value of every child node by the service rate. In addition to designing the new formulation of the fitness function, multiple objectives are utilized. These objectives are defined as the QoS parameters to enhance the quality of sensor nodes. These objective parameters are data share rate, bandwidth, service rate, congestion in the network and virtual queue length. The core intent of proposed function is to control the congestion and also improves the sending rate between nodes. Then, the newly formulated fitness function is defined as:

where, f is the old fitness value comprises of a different number of QoS parameter, Q be the total number of objectives and Ni be the value of parameter evaluate by a threshold value xi , i.e., the epsilon value is fixed as 0.5. The Ni is expressed by,

Figure1. Architecture of Proposed optimization algorithm

where, f represents the fitness value comprises of multiple objectives, represented by,

A. Proposed System Model In WSN, sensor nodes classified into child node and parental node[9]. The child node sends the packet data to the parental node with respect to share rate. Then according to service rate, parental node send collected data to sink node. Thus the congestion model describes the evaluation of child priority and packet drop probability [15].

If the congestion occurs, the epsilon constraint is employed to fix the value is either to be zero or one. According to the condition of epsilon constraint, the value is assigned to every parameter. Finally, after the fitness is computed, every search agent (share rate) is fed as input to find the optimized solution.

B. Congestion detection in WSNs The status of congestion and congestion level are the two characteristics to detect the congestion in the network [18]. Depends on the size of the queue, the congestion is detected, which is then fed into the optimization algorithm to find out the appropriate data packets. Ÿ

Ÿ

Status of congestion: It is determined by two parameters assists by upper and lower threshold value ω1 and ω2, virtual queue length V . Then, the congestion is detected by,

Once the congestion status is detected in the network, the congestion level is computed. The congestion level is calculated using child node as well as overall node congestion level [15]. This level is estimated while the transmitted is performed by both the child nodes and parental nodes. The occurrence of congestion by the ratio of queue length and product of child priority with virtual queue length.

Similarly, the congestion level is evaluated for all sensor nodes in the network, which is formulated by,

The value nCLr is greater than the threshold value; it leads to incurring congestion in the network. In order to avoid the congestion, we propose a novel optimization algorithm.

C. Proposed ECASRO algorithm After the congestion is detected, it is then fed into the optimization algorithm. In this paper, we propose Epsilon Constraint-based Adaptive Cuckoo Search rate

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iii) Adaptive Cuckoo Search algorithm The Cuckoo search algorithm [18] is one of the optimization algorithms to acquire the best solution. The Adaptive Cuckoo Search (ACS) is the modified version of Cuckoo search algorithm. It is inspired by cuckoo behaviour of brood parasitism. The main characteristic in ACS algorithm is levy flight, which is used to attain the optimized solution. Then, the step size of the cuckoo search algorithm is adjusted adaptively, named as Adaptive Cuckoo Search algorithm.

5. RESULTS AND DISCUSSION The performance of the proposed optimize algorithm (EACSRO) is analysed using delay, packet loss and queue size. It is then compared with existing algorithms, such as Systematic Share rate reduction (SS), Cuckoo Search (CS), Optimal Rate Adjustment (ORA) scheme and Adaptive Cuckoo Search (ACS).

A. Performance analysis for delay The analysis curves based on the delay for the evaluation of the proposed system over the existing system are given in Figures 2.

For optimal performance in WSN delay for the transmission must be reduced.

Figure 2.a depicts the performance analysis for delay based on a different number of nodes and time variation. The figure 2.a shows the delay analysis for 100 nodes and 10 seconds time. When the time is six seconds, the existing SS method achieves 0.875, 0.281 and 0.151 is obtained for ORA and CS algorithm. Then, the existing ACS attains 0.078 delay value. Compared to the existing algorithms, the proposed EACSRO optimization algorithm acquires minimum delay value of 0.021.

Subsequently, the figure 2.b represents the delay analysis based on 200 numbers of sensor nodes and time is 20 seconds. Here, the delay performance is analysed by time is varied from 1 to 20 seconds. While using existing cuckoo search optimization algorithm, at 16th second, it attains 0.151 delay. Similarly, another existing algorithm is considered as ACS optimization. It achieves the delay value of 0.07, which is then moderately decreased. But, the proposed optimization algorithm acquires the delay value of 0.020 and gradually reduced when compared to algorithms like SS, CS, ORA and ACS optimization.

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E-ISSN No : 2454-9916 | Volume : 3 | Issue : 5 | May 2017 B. Performance analysis for Queue Size For optimal performance in WSN queue size should be minimum. The performance analysis curve for queue size shown in Fig. 4. In Fig.4 for the network with 100 nodes, the analysis curve is plotted between the queue size and the time instance. For the time instance 6, queue size attained by the EACSRO is 0.02 whereas queue size value of the system SS, ORA, CS taken for the experimental evaluation exceeds 0.1. At the same time instance 6, queue size attained by the SS 0.9, ORA is 0.29 and CS is 0.15. For successive increase in time instance, the queue size of the proposed system is lower as compared to the existing system.

(a) 100 nodes, Time t= 10 Secs.

Figure 2.(a). Performance analysis for Delay

100 nodes, Time t= 10 Secs.

Figure 4 Performance analysis for Packet Loss 6. CONCLUSION In this paper, we have proposed a novel approach for the congestion management in Wireless Sensor Network. The proposed Epsilon constraint-based Adaptive Cuckoo Search optimization algorithm detect and control the congestion in WSNs. During the transmission process congestion status was found on the basis of threshold value. If congestion occurs, the congestion level was determined by virtual queue length of data packets. Consequently, congestion based network was given as input to the proposed optimization algorithm.

(b) 200 nodes, Time t= 20 Secs.

Figure 2.(b). Performance analysis for Delay

In this algorithm, the proposed fitness function was developed using Epsilon constraint value. Then, the adaptive cuckoo search algorithm was employed to generate the solution in which the step size was adjusted adaptively. Thus, congestion free share rate was achieved to enhance the network performance.

B. Performance analysis for Packet Loss Due to congestion packet loss occurs in the system which leads to reduce the QoS of applications running on WSN platform. In WSN, Packet loss transmission must be reduced for better performance of the network. The analysis curve for packet loss is shown in Fig. 3

The performance of the proposed model is evaluated using parameters such as delay, packet loss and queue size. All results was compared with existing algorithms. Thus, to improve the QoS, the proposed optimization algorithm achieved minimum delay, minimum packet loss and minimum queue size to provide the better data transmission.

At every successive time instance increases in the packet loss because of more incoming data packets in the network. Even for increase instance the proposed algorithm provides minimum packet loss over the existing system. At 8th instance, the packet loss of the proposed system is 0.053 whereas the existing system has the packet loss value as 0.951,0.291 and 0.182 for the SS, ORA and CS system respectively.

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100 nodes, Time t= 10 Secs.

Figure 3 Performance analysis for Packet Loss

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