Journal of Sensors

Volume 2015, Article ID 641235, 11 pages

http://dx.doi.org/10.1155/2015/641235

## A Cluster-Based Fuzzy Fusion Algorithm for Event Detection in Heterogeneous Wireless Sensor Networks

^{1}Key Laboratory of Communication and Information Systems, School of Electronic and Information Engineering, Beijing Municipal Commission of Education, Beijing Jiaotong University, Beijing 100044, China^{2}Department of Electronic Engineering, National Ilan University, Yilan 26047, Taiwan

Received 2 November 2014; Accepted 22 December 2014

Academic Editor: Qing-An Zeng

Copyright © 2015 ZiQi Hao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

As limited energy is one of the tough challenges in wireless sensor networks (WSN), energy saving becomes important in increasing the lifecycle of the network. Data fusion enables combining information from several sources thus to provide a unified scenario, which can significantly save sensor energy and enhance sensing data accuracy. In this paper, we propose a cluster-based data fusion algorithm for event detection. We use -means algorithm to form the nodes into clusters, which can significantly reduce the energy consumption of intracluster communication. Distances between cluster heads and event and energy of clusters are fuzzified, thus to use a fuzzy logic to select the clusters that will participate in data uploading and fusion. Fuzzy logic method is also used by cluster heads for local decision, and then the local decision results are sent to the base station. Decision-level fusion for final decision of event is performed by base station according to the uploaded local decisions and fusion support degree of clusters calculated by fuzzy logic method. The effectiveness of this algorithm is demonstrated by simulation results.

#### 1. Introduction

Recent advances of low-cost wireless sensor networks have brought about many applications such as military, environmental monitoring, and intelligent transportation system. Event detection has been widely studied as a typical application of WSN.

Heterogeneous wireless sensor networks (HWSN) are networks composed of different kinds of sensors, which are different in some aspects such as energy, computing power, and storage space. In HWSN such as a cluster-based network, cluster heads are more powerful than cluster members in all the resources like power, storage, communication, and processing data; this heterogeneity alleviates the overhead of cluster members for the fact that all the expensive computations can be performed by cluster headers [1]. Therefore, the load-balance and lifetime of network can be significantly improved.

Data fusion is a technology that enables combining information from several sources in order to form a unified picture, and it is widely used in various areas such as sensor networks, robotics, and video and image processing [2]. As an efficient method for collaborative decision making of multiple sensors, data fusion has many advantages in WSN. Using multiple sensors to detect the same event can largely eliminate data ambiguity which may be caused by only one sensor, thus to enhance data reliability and ability of fault tolerance effectively. Moreover, as WSN is energy limited network, the sensors are generally battery powered and once deployed it is hard to be recharged; therefore energy saving becomes an important factor for expanding the lifetime of WSN. Concerning the fact that energy consumption caused by communication is considerably larger than that of data processing, when data fusion is performed, sensor data are fused and only the result is forwarded; thus the number of messages is reduced, which can significantly avoid collisions and save communication energy.

Clustering is a frequently used physical architecture of data fusion; it groups sensor nodes into several clusters in order to achieve the network scalability objective [3]. Every cluster has a cluster head (CH) for executing data fusion and serving as a relay; as a consequence CH consumes more energy than ordinary sensor nodes; therefore a more powerful sensor is more likely to be selected as a cluster head.

Fuzzy reasoning is a theoretical reasoning scheme of data fusion; it introduces the novel notion of membership degree which enables dealing with imperfect data appropriately [4]. Generally, the sensing data of a single sensor may be vague and partial; thus it is difficult to obtain the final fusion decision result via precisely quantitative calculation of these imperfect data. Fuzzy logic uses the membership degree to fuzzify the partial data and then combines them with fuzzy rules thus to produce fuzzy output, which is an efficient solution to deal with the uncertainty of data.

Previous researches mainly focus on designing or improving the fusion or clustering algorithms independently; few works have combined the two technologies together to perform monitoring task. The authors of [5] have proposed a cluster based multisensor data fusion algorithm in WSN using fuzzy logic for event detection; the method adopted fuzzy logic approach to handle the uncertainty and vagueness present in the environment data in the local decision period of cluster head. However, the proposed fuzzy logic fusion method only considers data level fusion of intracluster, without designing the specific clustering algorithm and decision-level fusion method.

In this paper, we proposed a cluster-based fusion method for event detection. We consider a heterogeneous wireless sensor networks deployment environment, where two kinds of sensor nodes exist; one is ordinary sensors, which we assume to be same with nodes in [5], where each sensor node is equipped with diverse sensors (temperature, humidity light, and Carbon Monoxide); thus data of different attributes can be gained. The other kind of sensor is mobile nodes, which have the properties of mobility and high energy. Mobile nodes serve as CHs, which will move from random deployed initial position to the corresponding destinations calculated by -means algorithm. The proposed -means based clustering algorithm can achieve minimum energy consumption of intracluster communication. The function of CH is performing intracluster fusion and local decision, and it also serves as delay for sending the local decision results to the base station. In order to enhance the accuracy of event detection, we propose a* fusion support degree* for each cluster, which means the probability for a cluster to participate in the decision-level fusion for final decision of emergency event. The clusters of which the value of fusion support degree is less than a predefined threshold will be rejected from the data fusion, while the value serves as weight of local decisions when performing decision-level fusion. The fusion support degree is determined by distance between CH and event centre and remaining energy of CH and cluster members. Finally, the base station will perform decision-level fusion according to local decisions and* fusion support degrees* uploaded by CHs of clusters will join the data fusion to make final decision and make corresponding warning alarms.

The rest of paper is organized as follows: we present our related work in Section 2. Section 3 is some preliminaries before we introduce our cluster based fuzzy decision fusion algorithm (CFDF). Section 4 gives the CFDF overview and detailed descriptions. In Section 5, we evaluate the performance of the proposed algorithm. We conclude this paper in Section 6.

#### 2. Related Work

Numerous researches have been done in the field of data fusion and clustering. In this section, we give some review of the related work.

Khaleghi et al. [2] give a critical review of data fusion state-of-the-art methodologies; a new data centric taxonomy of data fusion methodologies was introduced and the challenging aspects and existing algorithms in each category are discussed. The date-related data fusion algorithms can be divided into dealing with data imperfection, data correlation, data inconsistency, and data disparateness. Generally, researches and algorithms about data fusion mainly focus on three levels [6]. The lowest level fusion is data-level fusion, of which the processing is to merge the observed raw data. Raw sensor data can be directly combined if the sensor data are commensurate. Techniques for raw data fusion typically involve classic detection and estimation methods such as averaging method. However, if the data are uncorrelated, the fusion should be performed at a feature or a decision level. Feature-level fusion can be performed after data-level fusion or directly performed by a single sensor. In feature-level fusion, features are extracted from multiple sensor observations and combined into a single feature vector for decision-level fusion. Decision-level fusion is the highest level fusion, of which the main idea is fusion of local decisions of each sensor; the local decision is a preliminary determination of an entity’s location, attributes, identity, and so on. The most commonly used decision-level fusion methods include voting techniques, Bayesian inference, and Dempster-Shafer’s method [6].

Gók et al. in [7] proposed a decision-level fusion algorithm based on fuzzy logic used for single target classification. The research adopts NN to make local decisions, which might cost a lot of time because it needs quite a few number of training samples to train classifiers. At each node, a fusion chance will be calculated according to the distance and energy of received information using a fuzzy method. The proposed decision-level fusion method achieves high efficiency on the occasion that the number of sensors is quite small. However, when a large number of sensor nodes are needed in a given sensing field, using every sensor to conduct local decision and uploading decision results will result in massive extra information processing and communication energy consumption. Grouping the sensors into several clusters could be a good way to solve the above problem, in that the majority of data processing and transmission could be done at CHs.

Clustering algorithms in the literature vary in their objectives; often the objective is related to application requirements [3]. For example, if the application is sensitive to data delay, the connectivity and length of data routing paths are usually considered as critical problems. Some other popular problems such as load balancing, fault-tolerance, minimal cluster count, and maximal network longevity are also concerned. In the field of distributed clustering algorithms, each sensor executes the algorithm independently and their states are based on the cluster membership decisions on their own states. Low Energy Adaptive Clustering Hierarchy (LEACH) [8] is the most popular distributed clustering algorithm; a dynamic adaptive probability-based CH selecting mechanism was proposed. Unlike the LEACH of which the selection of CHs is random, hybrid energy-efficient distributed clustering (HEED) [9] proposes a CH selecting method considering a hybrid of energy and communication cost. DWEHC in [10] proposed by Ding et al. can achieve more aggressive goals than those of HEED, the weight of CH is based on its residual energy and distance to its neighbors, and the largest weight node in a neighborhood may become a CH. Simulation results illustrated that DWEHC can do better in load-balance and has time complexity.

Manjunatha et al. in [5] suggested a data fusion method based on clustering for environment monitoring. In the proposed method, each sensor is equipped with diverse sensors, and the processing and fusion of these diverse sensor signals are carried out by CHs using proposed fuzzy rule based system. The reliability and accuracy of environmental detection are gained by multiple data fusion. However, this paper did not give a complete data fusion processing for it only considered fusion of intracluster and multiple sensing variables, but the decision-level fusion of intercluster was not mentioned.

#### 3. Preliminaries

In this section, we will give a brief introduction to some mathematical models used in our proposed cluster-based fusion model and also give some assumptions under the monitoring environment of this paper.

##### 3.1. A Brief Introduction to -Means Algorithm

-means clustering [11] is one of the simplest unsupervised learning algorithms that solve the clustering problem. The specific objective of -means is to classify a given data set into a certain number of clusters (assume clusters). The main idea is to define centroids randomly or in a cunning way beforehand and then assign each data point to the closest centroid to be a member of the current cluster; the first round is completed after all the data points are assigned. By now clusters are constructed and next the second round will be performed by recalculating new centroids as barycenters of the clusters resulting from the previous round, and then a new binding has to be done between the same data set points and the nearest new centroids. And then, do this loop until centroids do not move any more.

Finally, this algorithm aims at minimizing an objective function, in this case a squared error function. The objective functionwhere is a chosen distance measure between a data point and the cluster centre , is an indicator of the distance of the data points from their respective cluster centres. The specific steps of -means are described as follows [12]:(1)Place points into the space represented by the objects that are being clustered. These points represent initial group centroids.(2)Assign each object to the group that has the closest centroid.(3)When all objects have been assigned, recalculate the positions of the centroids.(4)Repeat Steps (2) and (3) until the centroids no longer move. This produces a separation of the objects into groups from which the metric to be minimized can be calculated.

##### 3.2. The Fuzzy Theory Introduction

The concept of fuzzy set was proposed by Zadeh in 1965 [4], and the introduction of fuzzy theory is to deal with problems involving knowledge expressed in vague or linguistic terms especially when the boundary of the set contained in the universe is ambiguous, vague, or fuzzy [5]. In general crisp set, the relation between an element and a given set is that the element is either belonging to the set or not. However, in a fuzzy set, the element is not definitely belonging to a set or not; instead some ambiguity or vagueness may exist; thus elements belong to a fuzzy set to a certain degree represented by a number in interval . The number is called membership degree; the larger the value of membership degree is, the greater the degree of the element belonging to the set is. When the values of all the elements are either 0 or 1, the fuzzy set will degenerate into a crisp set.

Considering a set whose element is represented by , the membership function is a function that associates with each in with a membership degree in . A fuzzy set in is a set of ordered pairs and is given as , , where represents the element in a fuzzy set .

Fuzzy logic inference is a process using the fuzzy input and fuzzy rules to obtain the fuzzy output, of which the substance is to map the input space into a given output space by fuzzy roles. The fuzzy roles normally use the IF/THEN statement, the format of which is IF is Then is , where , are the input linguistic variable and output linguistic variable, and , are the linguistic value of fuzzy set before reasoning and after reasoning, respectively. Mamdani fuzzy inference system proposed by Ebrabim Mamdani in 1975 is a widely used fuzzy inference method, which can implement the reasoning computation from input to output effectively through a serious of predefined fuzzy roles.

##### 3.3. Assumptions

In this paper, we consider a cluster-based data fusion algorithm for event detection; our research is based on the following assumptions:(1)To simplify the complexity of the problem, we assume the sensing field is a flat two-dimensional region; no barriers exist; thus the mobile nodes can smoothly move inside the sensing area. And also the nodes are uniformly deployed in the sensing filed and cannot be recharged after deployment.(2)In consideration of load-balance problem, we adopt a heterogeneous WSN composed of two kinds of sensor nodes, general static nodes and mobile nodes; the mobile nodes will act as CHs and the energy of which is preset higher than that of general nodes, because CHs will consume more energy on data transmission and processing.(3)Each sensor node can achieve its location by a GPS module or some other techniques such as the method proposed in [13]. The locations will be forwarded to BS only once and stored by BS for the subsequent clustering and fusion calculation.(4)Like LCA [14], a single-hop intracluster topology is established by our proposed -means based clustering algorithm, and TDMA is used for intracluster communication. Intercluster uses multiple-hop routing to arrive at the sink node.

#### 4. Cluster Based Fuzzy Fusion Algorithm (CBFFA)

In this section, we will introduce our proposed cluster-based fuzzy fusion algorithm (CBFFA). Considering the time complexity of clustering may decline the efficiency of event detection, we proposed a centralized none-event driven clustering method; namely, the clustering is performed before some event occurs. Algorithm 1 illustrated the pseudocode of CBFFA.