Adjust Fuzzy Model Parameters for Head Election in Wireless Sensor Network Protocols

Adjust Fuzzy Model Parameters for Head Election in Wireless Sensor Network Protocols

Walaa Abd el Aal Afifi (ISSR-Cairo University, Egypt) and Hesham Ahmed Hefny (ISSR-Cairo University, Egypt)
DOI: 10.4018/978-1-5225-0773-4.ch013
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Abstract

The clustering routing protocols attract many research papers that result from their well topology control, less demand resources, and less energy dissipation. The cluster routing protocols consist of single hop communication and multi hop communication. Single hop is applied between sensor node and its related cluster head. Multi hop is applied between cluster heads to base station. The previous two communication modes depend on the cluster head election. Appropriate cluster-head election can drastically reduce the energy consumption and enhance the lifetime of the network. The fuzzy models are used frequently for cluster head election. The fuzzy models can be built either expert's knowledge or numerical data. The authors propose fuzzy model using adaptive Takagi-Sugeno for wireless sensor network protocol (FATSN). The FATSN protocol is implemented by modified merging algorithm of fuzzy clustering with expected value (MCFEV). The FATSN protocol compares with the famous cluster routing protocol LEACH, EEUC, CHEF, and FCM protocols. The results show that FATSN protocol is efficiency
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Introduction

Wireless sensor networks are receiving a considerable degree of research interest due to their deployment in an increasing number and variety of applications. Wireless sensor networks are large scale networks that consist of hundreds or thousands of sensor nodes. The limited energy resource is the main constraint of the sensor node (Azzedin, 2009). Routing protocols are divided into cluster and multi hop routing protocols. Cluster routing protocols are more energy efficient than multi hop routing protocols. They are less demand resources and more scalable. Figure 1 shows cluster network topology. Sensor nodes are organized into 3 groups. Each group has a cluster head. Cluster heads aggregate data from other nodes in the same group, and deliver aggregating data to base station via single hop. The cluster head election has an important role in the increasing network life time, which is defined as the number of rounds until the first node dead (I.F Akyildizet al., 2002; Mohammad & Imad, 2005).

Figure 1.

Cluster network topology

The common cluster routing protocol is a low energy adaptive clustering hierarchy protocol (LEACH) (Hinzelmanetal., 2000). Cluster heads are elected randomly. Rotating the cluster heads’ roles applies at each round. Cluster heads connect to base station via single hop. The drawbacks of LEACH protocol are:

  • 1.

    Unbalancing energy dissipation.

  • 2.

    Cluster heads consumed a lot of energy in a transmitting data to base station.

  • 3.

    Cluster head election doesn’t take into account the energy level of sensor node.

In recent years, the multiple criteria are used to elect cluster heads such as energy, node degree, distance to base station, and centrality. (Chuen, 1990) Due to the malfunction sensor devices, the uncertainty degree of the collecting data may be presented. The fuzzy set theory can deal with the uncertainty data due to the partial membership values. The object belongs to multiple sets with membership degree . The fuzzy models are the famous application of fuzzy set theory. The fuzzy models are used more in the control applications and the decision making applications. The fuzzy models are built either by expert’s knowledge or from numerical data. The experts are not easy to find. Even if you find one, knowledge changes with time and is incomplete. There are multiple cluster routing protocols that use fuzzy models. They led to reduce energy dissipation, but these routing protocols are built from expert’s knowledge.

This chapter proposes fuzzy model using adaptive Takagi–Sugeno for wireless sensor network protocol (FATSN). The FATSN protocol is multi input and multi output Takagi–Sugeno models (MIMO TS) for cluster heads and relay nodes election. The protocol aims to extend network lifetime and identify the parameters of MIMO TS models from numerical data by using modified merging of fuzzy clustering algorithm with expected value (MCFEV).

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This section describes the related works that are related to the challenges of cluster routing protocols: the number of clusters and the cluster head election.

Key Terms in this Chapter

Routing Protocols: Determine the route between sensor nodes and base station. Routing protocols divided into single hop or direct communication and multi hop communication. Sensor node sends data directly to cluster head via single hop. Cluster heads send aggregated data packets to base station via multi hop communication.

TAKAGI–SUGENO Model: An example of fuzzy model. The consequent part is a crisp linear function of the input variables. The antecedent part partitions input space into fuzzy regions. The advantages of TS model are no need for defuzzification method and fuzzy antecedent participate in the calculation of the inferred output value.

Cluster Head: A senor node. It is responsible for collecting data from member nodes inside the cluster. It is also responsible for aggregating and delivering data to base station.

Fuzzy Model: The most common way to represent the human knowledge as natural language expressions of the type “IF–THEN” form. The IF part is called the antecedent and the THEN part is called the consequent. The antecedent part partitions input space into fuzzy regions. The consequent part partitions output space into fuzzy regions or represents linear function of input variables.

Wireless Sensor Networks: Large scale networks that consist of hundreds or thousands of sensor nodes. Sensor nodes have the ability to monitor physical or environmental conditions such as temperature, sound, pressure, etc. Sensor nodes work together to deliver data to base station.

Energy: The main constraint of the sensor node. Sensors’ batteries are not chargeable in harsh environment. The energy is more consuming in the transmission phase than the sensing phase. Energy is measured by joule unit.

Clustering Algorithms: The process of dividing objects into classes or clusters. The objects inside the cluster are more similar and dissimilar to other objects outside cluster.

Number of Clusters: A pre-request for the almost clustering algorithms. They are based on minimizing the objective function. The number of clusters is an indicator of the quality of the cluster results. The cluster results should be more separated and more compacted. From the energy dissipation point view, the number of clusters affect on the network life time. The large number of clusters increases energy consuming in the multi hop communication. The small number may reduce energy consuming but the high network density can have opposite effect on the network life time. So the determination of the number of clusters is demand for the cluster routing protocols.

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