A Congestion Control Scheme Based on Fuzzy Logic ...

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Abstract—In wireless sensor networks, due to huge amount of packets convergent nature of upstream traffic and limited wireless bandwidth, network congestion ...
2012 9th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2012)

A Congestion Control Scheme Based on Fuzzy Logic for Wireless Sensor Networks Jutan Wei, Bing Fan,Yi Sun School of Electrical and Electronic Engineering North China Electric Power University Beijing, China Abstract—In wireless sensor networks, due to huge amount of packets convergent nature of upstream traffic and limited wireless bandwidth, network congestion happens easy, which is an urgent problem to be solved. The congestion control scheme is necessary to be carried out which can detect congestion precisely and regulate it fairly. To achieve this objective, a fuzzy logic based congestion control is proposed which takes advantage of current buffer occupancy and congestion index (CI) reflecting the congestion trend of each node as congestion level indications. In addition, it periodically calculates the congestion degree using fuzzy logic theory. At the same time each upstream traffic rate is adjusted according to the value of congestion degree. The scheme can quickly perceive the status and trends of the network load, and adjust quickly to avoid a lot of packet losses. Simulations are conducted for the proposal which shows that this implementation efficiently sorts out the traffic and minimizes the packet loss. Keywords-Wireless Sensor Network; congestion detection; fuzzy logic; rate control

I.

INTRODUCTION

Wireless sensor networks (WSNs) are networks of densely deployed and wirelessly interconnected devices that allow retrieving of monitoring data and are able to store, process, correlate and fuse it [1]. In wireless sensor networks, many-toone convergent nature [2] of upstream traffic and limited wireless bandwidth can easily result in congestion [3]. Congestion not only causes buffer overflow which may lead to higher packet losses and larger queuing delay , but also results in transmission collisions which in turn increase packet service time and waste energy. Therefore, it requires an efficient congestion control protocol to provide reliable data transmission and energy conservation in wireless sensor networks. In recent years, researchers have a lot of in-depth study of congestion control for wireless sensor networks. For example, Congestion detection and avoidance (CODA) [4] protocol detects congestion based on buffer occupancy as well as wireless channel load. Once congestion is detected, nodes signal their upstream neighbors via an open-loop hop-by-hop back pressure mechanism. Nodes that receive feedback signals can throttle their sending rates or drop packets based on the

local congestion policy. When the source rate is less than some fraction of the maximum theoretical throughput of the channel, the source regulates itself. In addition, When Channel load exceeds a specified threshold, however, a source is more likely to contribute to congestion and therefore closed-loop congestion control is triggered. The end-to-end rate adjustment is adopted to adjust rate, which results in slow response and relies highly on the round-trip time (RTT), which inevitably leads to packet loss similar to traditional transmission control protocol. References [5-7] also utilize buffer occupancy as congestion detection metric, which infers network congestion when the value of current buffer occupancy exceeds the set threshold. This detection method based on buffer occupancy is very easy to implement, and there is no additional overhead, but buffer occupancy as a separate metric is not a reliable indicator as is shown in CODA [4]. But the buffer occupancy of sensor node just indicates the current congestion, does not reflect the trends of congestion. While SenTCP [8] protocol and of PCCP [9] protocol use the ratio of packet inter-arrival time and packet service time as a measure of congestion. By incorporating information about packet inter-arrival time and the packet service time, congestion level at the node or at the link can be captured through a parameter. The joint participation of inter-arrival and service times provide helpful and rich congestion information to reflect the trend of buffer occupancy but not the actual congestion level. These issues show that a single indicator is difficult to reflect the network conditions accurately. Based on these considerations, this paper aims to address these problems by providing a congestion control scheme based on fuzzy logic theory using Fuzzy Inference System (FIS). This scheme considers the current buffer occupancy status and buffer changing trends. The latter metric reflects the congestion changing trends, referred to as congestion index (CI), which is defined as the ratio service time over interarrival time. Current buffer occupancy and congestion index (CI) of each node are taken as a fuzzy logic system input parameters in Fuzzy Inference System. And the value of congestion degree based on fuzzy logic (Fuzzy Congestion Degree, FCD) is acquired after fuzzy logic processing. Finally, rate adjustment can be made according to those decision values.

This work is supported by the National Science and Technology Major Project of China (NO.2010ZX03006-005-01).

978-1-4673-0024-7/10/$26.00 ©2012 IEEE

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FUZZY LOGIC BASED IMPROVEMENT PROGRAM

Fuzzy logic based management and control has been studied in the past for wireless networks and ad hoc networks. In reference [10], authors present a fuzzy congestion control approach for ad-hoc networks, in which a theoretical fuzzy logic based concept is used to control the congestion. The scheme is considerably different from this approach, as the article actually implements fuzzy logic with a fuzzy table derived from fuzzy sets and fuzzy variables, thus it is a more realistic implementation of fuzzy logic. To the best of our knowledge, Accurate and efficient congestion detection plays an important role in congestion control of wireless networks. Therefore, the research focuses on fuzzy logic theory for congestion detection within the wireless sensor network.

In order to illustrate the selection of membership function, choose the membership function (MF) of BO as an example to explain the selection of the membership function (MF). The range of BO is 0 to 1. The greater BO is, the more likely the node becomes congestion. When the value of BO exceeds 0.8, that would be considered that the probability of congestion in the node is 1. Considering the sub-file of fuzzy linguistic variables and the size of the rules table, In Fig. 1(a) the set of linguistic values for inputs MFs are {VS, S, M, H, VH} representing very small, small, medium, high and very high values respectively. Similarly, the membership function of CI can be obtained. Degree of membership

II.

A. Fuzzy logic approach for congestion detection The paper applies Mamdani fuzzy model based Congestion controller with a dual-input single-output system selecting the node buffer occupancy and congestion index to describe the status of the nodes, thus the network congestion degree is determined by Fuzzy Inference System (FIS). The FIS is composed of two inputs that are explained in following. Current buffer occupancy (BO) and congestion index(CI) are two input parameters in FIS. The first input considers the ratio of current buffer size and total buffer size, with the range of 0-1. The second one is defined as the ratio packet local service time ( Ts ) over mean packet inter-arrival time ( Ta ), as (1), with the range of 0-6. These are the two input parameters in fuzzy system above, which are fuzzified using the predefined input membership function shown in Fig. 1(a) and Fig. 1(b).

not found.

CI = min{T / T , 6} Error! Reference source s a (1)

(1)

In the paper, in the process of determining the congestion degree, the mean packet inter-arrival time ( Ta ) and packet local service time ( Ts ) are measured using EWMA (exponential weighted moving average) algorithm. Ta is updated periodically whenever there are N (=20 in the paper) new packets arriving as (2). Also, packet local service time is the average packet sending time in MAC layer. It is computed as follows under the assumption that packets have the fixed length. T is updated periodically whenever each time a packet is forwarded in a sensor node as (3).

(2)

Error! Reference source not found.

(3)

Where, 0< ωa , ωs