A Survey on Fuzzy Based Clustering Routing Protocols in Wireless Sensor Networks: A New Viewpoint

(1)

A Survey on Fuzzy Based Clustering Routing Protocols

in Wireless Sensor Networks: A New Viewpoint

Arefe Esalat Nejad

Young Researchers and Elite Club, Baft Branch, Islamic Azad University,

Baft, Iran

Email: amir9002001@yahoo.com

Marzieh Arbabi

Department of Electronic and Computer, Kashan Branch, Islamic Azad University,

Kashan, Iran

Morteza Romouzi

Department of Electronic and Computer, Kashan Branch, Islamic Azad University,

Kashan, Iran

Abstract – In recent times, wireless sensor networks (WSNs) have become progressively more attractive and have found their way into a wide variety of applications and systems because of their low cost, self-organizing behavior, and sensing ability in harsh environments. A WSN is a collection of nodes organized into a network. Routing is a vital technology in WSNs and can be roughly divided into two categories: flat routing and hierarchical routing. In a flat routing topology, all nodes have identical functionality and carry out the same task in the network. Nodes in a hierarchical topology do different tasks in WSNs and are usually arranged into clusters. We analyze a fuzzy clustering algorithm (FCA) which aims to prolong the lifetime of WSNs. This algorithm adjusts the cluster-head radius considering the residual energy and distance to the base station parameters of the sensor nodes. This helps to decrease the intra-cluster work of the sensor nodes, which are closer to the base station or have lower battery level. Fuzzy logic is utilized for handling the uncertainties in cluster-head radius estimation. We compare this algorithm with the low energy adaptive clustering hierarchy (LEACH) algorithm according to the parameters of first node dies half of the nodes alive and energy-efficiency metrics. Therefore, the FCA is a stable and energy-efficient clustering algorithm.

Keywords – Fuzzy Clustering, Wireless Sensor Network, Algorithm.

I. INTRODUCTION

A wireless sensor network (WSN) [3] consists of spatially distributed autonomous sensors to cooperatively monitor physical or environmental conditions, such as temperature, sound, vibration, pressure, motion or pollutants. The development of WSNs was motivated by military applications such as battlefield surveillance. They are now used in many industrial and civilian application areas, including industrial process monitoring and control, machine health monitoring, environment and habitat monitoring, healthcare applications, home automation, and traffic control [10].

In addition to one or more sensors, each node in a sensor network is typically equipped with a radio transceiver or other wireless communications device, a small micro-controller, and an energy source, usually a battery. A sensor node might vary in size from that of a shoebox down to the size of a grain of dust, although functioning "motes" [5] of genuine microscopic dimensions have yet to be created. The cost of sensor nodes is similarly variable, ranging from hundreds of dollars to a few pennies, depending on the size of the sensor network and the complexity required of individual sensor nodes. Size

and cost constraints on sensor nodes result in corresponding constraints on resources such as energy, memory, computational speed and bandwidth [2]. Figure 1 shows a typical WSN for more understanding.

Fig.1. A typical WSN architecture

Based on network structure, routing protocols in WSNs can be coarsely divided into two categories: flat routing and hierarchical routing. In a flat topology, all nodes perform the same tasks and have the same functionalities in the network. Data transmission is performed hop by hop usually using the form of flooding. The typical flat routings in WSNs include Flooding and Gossiping [16], Sensor Protocols for Information via Negotiation (SPIN) [9], Directed Diffusion (DD), Rumor [4], Greedy Perimeter Stateless Routing (GPSR) [7], Trajectory Based Forwarding (TBF) [1], Energy-Aware Routing (EAR) [14], Gradient-Based Routing (GBR) ‘[17], Sequential Assignment Routing (SAR) [6], etc. In small-scale networks, flat routing protocols are relatively effective.

(2)

Co Inter

solve this problem and to balance energ cluster-heads, a periodically rotati mechanism was proposed by Yu and Ch low-energy adaptive clustering hierarchy is a clustering algorithm that utilizes ran to balance energy consumption of cluste network.

A fuzzy clustering approach [4] to analyzed to maximize its lifetime [7]. T distributed competitive algorithm. It se head via energy-based competition am cluster-heads which are selected usin model. This approach mostly focuses on competition ranges to the tentative cluste to make wise decisions, it utilizes the re distance to the base station parameters of In addition to this, the clustering approac to handle uncertainties in competition [4]. This allows the algorithm to competition ranges to the tentative clu have higher residual energy levels, becau a larger region [1].

II. RELATED

WORK

- FUZZY

C

ALGORITHMS

There are several clustering algorith recent years [14]. Fuzzy logic is usefu time decisions without needing comp about the environment [9]. On th conventional control mechanisms genera and complete information about the en logic can also be utilized for making a different environmental parameters by according to predefined rules [17]. Some algorithms [6] employ fuzzy logic to ha in the WSNs. Basically; FCAs use blending different clustering parameters heads [4].They assign chances to tenta according to the defuzzified output of fu The tentative cluster-head becomes a clu the greatest chance in its vicinity. The and centralized fuzzy logic clustering app shows a sample WSN with a serie surrounded by gray circles. The red c sensor/node, and the surrounding green c detection range.

Analysis of Prominent Clustering Rou

in WSNs

In this section, we present a more co critical survey of prominent clustering for WSNs compared with previous work. We analyze 16 classical WSN c algorithms in detail based on the classific algorithm-stages, and highlight their ch advantages and disadvantages. Figure 3 of Clustering Methods in WSNs.

ternational Journal of Electronics Communication an Volume 5, Issue 4, ISSN (Online): 2249–071X,

ergy consumption of tating cluster-head Chang [16], namely hy (LEACH), which randomized rotation luster-heads over the ] to the WSNs is . This approach is a t selects the cluster-among the tentative sing a probabilistic on wisely assigning uster-heads. In order residual energy and of the sensor nodes. ach uses fuzzy logic tion range estimation to assign greater cluster-heads which cause they can serve

CLUSTERING

ithms for WSNs in ful for making real mplete information the other hand, erally need accurate environment. Fuzzy a decision based on by blending them me of the clustering handle uncertainties se fuzzy logic for ters to select cluster-ntative cluster-heads f fuzzy if-then rules. cluster-head if it has here are distributed approaches. Figure 2 ries of red circles circles represent a n circle is the sensor

Routing Protocols

comprehensive and ng routing protocols

rk.

N clustering routing ification of different characteristics with 3 shows Taxonomy

Fig.2. A sample of clu

Fig.3.Taxonomy of Cluster

III. LEACH CLUSTE

LEACH [6] is a distributed al decisions to elect cluster-head selected once and do not chang lifetime, then it is obvious tha die earlier than the ordinary n

and Computer Engineering 1X, ISSN (Print): 2278–4209

cluster based WSN.

tering Methods in WSNs

USTERING

PROTOCOL

(3)

Co includes randomized rotation of cluster-evenly distribute the energy dissipation LEACH also performs local data comp heads to decrease the amount of data th the base station. LEACH, cluster-head periodically to enable randomized rota heads. Every round consists of two phas phase and steady-state phase. In the set heads are selected and clusters are form state phase, data transfers to the base statio through the clustered network [6]. A node decides whether it is going to beco or not by generating a random number b this number is less than the predefined then the sensor node becomes a cluster-h the set of sensor nodes that have not bee the last where P is the desired percentag and r represents the current round num node n belongs to G using these para formulated as follows [8]:

If the sensor node n does not belong to is set to 0. Thus, n cannot become a clus 0, the probability of becoming a clus node is equal to P. However, this situatio following rounds. The cluster-heads of become cluster-heads during the follow This restriction prevents a particular n cluster-head frequently. However, this re drawback: it causes rapid decrease in cluster-heads. To handle this drawback, chance of the remaining sensor nodes to is also increased by adjusting the thresh remaining sensor nodes. This critica significant property of LEACH [8].

IV. APPLICATION OF

FCA

T

Fuzzy clustering algorithm is e (Algorithm 1). In every clustering round, generates a random number between 0 random number for a particular node is predefined threshold T which is the p desired tentative cluster-heads, then th becomes a tentative cluster-head. In FCA radius of each tentative cluster-head cha FCA uses residual energy [11] paramete the base station metric of the sensor competition radius. It is logical to dec area of a cluster-head while its re decreasing. If the competition radius do the residual energy decreases, the sensor battery rapidly. This approach takes th consideration and decreases the competitio tentative cluster-head as the sensor n decreases. Radius computation is accom predefined fuzzy if-then mapping rules uncertainty. These fuzzy if-then mappin in Table 1. The Mamdani method used

n over the network. mpression in cluster that is forwarded to ad selection is done rotation of cluster-hases, namely set-up set-up phase, cluster rmed. In the steady-tation are performed A particular sensor come a cluster-head r between 0 and 1. If ined threshold T (n), r-head. G represents een cluster-heads in tage of cluster-heads umber. If the sensor arameters, T (n) is

to G, then the T (n) luster head. At round luster-head for each ation changes in the of round 0 cannot llowing 1 / P rounds. node to become a is restriction brings a in the number of k, as r increases, the to be a cluster-head eshold T (n) for the itical balance is a

A

TO

WSN

explained below nd, each sensor node 0 and 1 [13]. If the is smaller than the e percentage of the that sensor node CA, the competition hanges dynamically. eter with distance to r node to calculate decrease the service residual energy is does not change as sor node runs out of s this situation into etition radius of each node battery level omplished by using les [12] to handle the ping rules are given ed by [9] is used as

fuzzy inference technique, b frequently used fuzzy inference

Algorithm 1: The propo

Table 1: Fuzzy if-then mappin radius calculatio

The second fuzzy input variab tentative cluster-head. The fuzzy energy input variable is illus medium and high are the linguis set. Low and high linguistic va membership function while m membership function [15].

V. RESULTS AND

Based on my evaluation and w with LEACH using WiseNet Si source Java based tool used to s topology with secured proto following features:

• Graphical interface for readin parameters (eg. power coverage of the network.) • Modeling types of attacks, p

sensors behavior and measu parameters mentioned above. In each round of the design, and clusters are formed. Next, e certain bits of data to its cluste

, because it is the most ce technique.

posed FCA for WSN.

pping rules for competition lation in FCA.

iable is residual energy of the zzy set that describes residual illustrated in Figure 3. Low, guistic variables of this fuzzy variables have a trapezoidal medium has a triangular

ND

DISCUSSION

d we compare proposed FCA t Simulator, which is an open to simulate the sensor network tocols. Wise Net has the ding the results of important consumption, reliability, , programming of malicious asuring the impacts on the

e.

(4)

Co Inter

aggregates the received data and forwa station with a particular routing pro transmits the aggregated data to the base cluster-heads transmit their data packets directly. The area of deployed wireless same for all designs and is 200 m x200 each ordinary sensor node transmits 4000 cluster-head. The cluster-head which from its cluster members aggregates the r a certain aggregation ratio. This aggrega 10% in our simulations

.In order to produce more reliable res is simulated for 50 times, and the averag taken. For each of the designs, we pro result table which represents the values (FND) and half node alive (HNA) metric algorithms simulated. After that, we pr chart which illustrates the values of metrics visually. We also generate distribution of the number of live sens distribution of the number of clusters pe using these simulation results, we c performance of the simulated algorithms.

Design

In this design, the base station is locate the WSN [5]. Each cluster-head forwar data to the base station directly without u The detailed configuration of this desi Table 2. The simulation of this design y shown in Table 3, which shows the rou FND and HNA for each simulated algorith

Table 2: Configuration parameters

Table 3: Design: Values of FND and HN each algorithm.

As seen in Table 3, the FCA perf LEACH. This algorithm is more efficien about 36.4%. LEACH’s performance is does not consider the residual energy le nodes during clustering. It uses a pure p for clustering, but this model itself is providing the best solution.

Figure 4 depicts the distribution of th sensor nodes with respect to the number algorithm. This figure clearly depicts th functional (dead) sensor nodes for approach begins after LEACH algorithm

ternational Journal of Electronics Communication an Volume 5, Issue 4, ISSN (Online): 2249–071X,

wards it to the base rotocol or directly ase station. LEACH ts to the base station ss sensor network is 0 m. In each round, 000 bits of data to its h receives the data e received data with gation ratio is set to results, every design rage of the results is provide a summary es of first node dead trics for each of the provide a summary of FND and HNA te charts for the ensor nodes and the per each round. By comment on the

s.

cated at the center of ards the aggregated t using a relay node. esign is depicted in n yielded the results rounds in which the orithm.

ters of Design.

d HNA metrics for

erforms better than ient than LEACH by is poor because it level of the sensor probabilistic model is not sufficient for f the number of live er of rounds for each the number of non-r fuzzy clustenon-ring m.

Fig.4. Design Distribution of aliv to the number of rounds

V. CONCL

As a result of these experime is a stable and energy-efficien WSNs. This algorithm is design stationary sensor nodes. As a clustering approach can be exte sensor nodes.

ACKNOWLEDG

The author thanks so much to IA University, for proof-editing.

REFERENCE

[1] Bagci H., 2010. "An energy algorithm for wireless sensor ne Systems (FUZZ) Conf, pp. 1-8. [2] Budiarto S. A. a. o., 2012.The w

accessed on 26th Aug. [3] Chen X., 2010."Research on hi

network architecture with m International Conference on Informatics (BMEI), vol. 7, Oct [4] Gupta D. R. I. and S. Sampa using fuzzy logic for wireless Conference on Communication vol. 2.

[5] Haining Q.; L. Shu and J. Network Lifetime Analysis Usi Systems," IEEE Transactions o pp. 416-427.

[6] Huruiala P. -C., A. Urzica, and routing protocol based on evo Sensor Networks," in Proc. 9th pp. 387-392.

[7] Ibriq J. and I. Mahgoub., 2004. sensor networks: issues an Symposium on Performanc Telecommunication Systems. [8] Jamal A. -K. N., 2004."Routin

Networks - A survey," IEEE W [9] Kim J. -M., S.-H. Park, Y

2008."CHEF: Cluster Head El logic in Wireless Sensor Netw Advanced Communication Tec 654-659.

[10] Kuhn T. M. F. and R. Watte deployed ad hoc and senso International conference on M New York, NY, USA, pp.

and Computer Engineering 1X, ISSN (Print): 2278–4209

alive sensor nodes according ds for each algorithm.

NCLUSION

ments, we conclude that FCA ient clustering algorithm for igned for the WSNs that have As a future work, the fuzzy xtended for handling mobile

EDGEMENT

to Dr. Amir Hossein Asgari, iting.

RENCES

gy aware fuzzy unequal clustering r networks," in Proc. IEEE Int Fuzzy

-8.

e wireless sensor network. [Online].

hierarchical mobile wireless sensor mobile sensor nodes," in 3rd on Biomedical Engineering and Oct.,pp. 2863-2867.

palli., 2005."Cluster-head election ss sensor networks," in 3rd Annual ion Networks and Services Research,

J. GAO., 2008."Wireless Sensor Using Interval Type-2 Fuzzy Logic s on Fuzzy Systems, vol. 16, no. 2,

nd L. Gheorghe., 2010."Hierarchical volutionary algorithms for Wireless th Roedunet Int. Conf. (RoEduNet),

04."Cluster-based routing in wireless and challenges (SPECTS)," in ance Evaluation of Computer .

ting Techniques In Wireless Sensor EEE Wireless Communications,.

Y.-J. Han, and T.-M. Chung., Election mechanism using Fuzzy etworks," in Proc. 10th Int. Conf. echnology ICACT 2008, vol. 1, pp.

(5)

Clustering Approach for Wireless Sensor Networks Using Energy Predication," IEEE Sensors Journal, vol. 12, no. 9, pp. 2891-2897.

[12] Meng Y. and L. for Kwok., 2012."A case study: Intelligent false alarm reduction using fuzzy if-then rules in network intrusion detection," in Proc. 9th Int Fuzzy Systems and Knowledge Discovery (FSKD) Conf, pp. 505-509.

[13] Moore T. and A. Mathew., 1997. "Improved fuzzy frequency hopping," in Proc. MILCOM 97, vol. 2, pp. 803-807.

[14] Turgut S. K. D. D., 2005."Optimizing Clustering Algorithm in Mobile Ad Hoc Networks Using Genetic Algorithmic Approach," in proc. The Global Telecommunications Conference (GLOBECOM), Taibel, pp. 62-66.

[15] Yazdanjouei H., H. Feizy, A. Khoei, and K. Hadidi., 2012. "Design of a fully programmable analog interval type-2 triangular/trapezoidal fuzzifier," in Proc. 19th Int Mixed Design of Integrated Circuits and Systems (MIXDES) Conf , pp. 243-248.

[16] Yu J. and P. Chong., 2005."A survey of clustering schemes for mobile ad hoc networks," IEEE Communications Surveys& Tutorials, vol. 7, pp. 32-48.