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An Enhanced LEACH Protocol using Fuzzy Logic for Wireless Sensor Networks

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An Enhanced LEACH Protocol using Fuzzy Logic for Wireless Sensor Networks Powered By Docstoc
					                                                              (IJCSIS) International Journal of Computer Science and Information Security,
                                                              Vol. 8, No. 7, October 2010




    An Enhanced LEACH Protocol using Fuzzy Logic
            for Wireless Sensor Networks
J.Rathi/Lecturer                                                                                      Dr.G.Rajendran/ Prof and Head
Dept.of.B.Sc(CT)                                                                                      Dept.of MATHS
K.S.Rangasamy college of technology                                                                   Kongu Engg. College
Tiruchengode-637215                                                                                   Perundurai, Erode-638 052,
Tamilnadu, India.                                                                                     Tamilnadu, India.


   Abstract--The Wireless Sensor Networks consists of a large                   effectively decrease energy consumption, and enable
number of small and cheap sensor nodes that have very restricted                efficient realization and routing protocols, data
energy, processing power and storage. They usually examine areas,
collect data and report to the base station (BS). Due to the
                                                                                aggregation, and security mechanisms.
achievement in low-power digital circuit and wireless                             A cluster [6] [17] is a collection of interconnected
communication, many applications of the WSN are developed and                   nodes with a dedicated node called clusterhead.
already been used in habitat monitoring, military object and object             Clusterheads are accountable for cluster management,
tracking. The disadvantage in this monitoring leads to clustering the           such as scheduling of the medium access, dissemination
networks. The hierarchal network structures which are created by
clustering technique are called clusters. Clusterhead is elected by its         of control messages, or data aggregation. Therefore, the
nearest networks. Clusterhead selection becomes a significant                   role of the clusterhead is critical for the appropriate
problem because of its dynamic environment. In this paper, the                  network operation. Failure of a clusterhead leads to
problem of suitable clusterhead selection in wireless sensor networks           expensive clusterhead re-election and re-clustering
is analyzed. Appropriate cluster-head node election can significantly
reduce the energy consumption and enhance the lifetime of the
                                                                                operations.
network. The fuzzy logic technique for clusterhead selection is                   In stagnant networks, the role of the clusterhead may
proposed in this paper based on three descriptors, namely, Energy,              be assigned to any node in the cluster in a self-organized
Concentration and Density. The experimental results shows the                   way. Often, this role is assigned in turn to the nodes in
substantial increase in the network lifetime depends on network                 order to ensure fairness, as a clusterhead consumes more
configuration as compared to probabilistically selecting the nodes as
cluster-heads using only local information.                                     energy than a regular sensor node. An essential criterion
                                                                                for the clusterhead selection is the remaining energy
   Keywords— Wireless Sensor Networks, Fuzzy Logic, sensor                      level of the node. However, for fault-tolerant clusterhead
networks, Cluster head                                                          selection in dynamic networks, some additional criteria
                                                                                for choosing a clusterhead are required. For example,
                        I.    INTRODUCTION                                      considering node mobility, if a clusterhead is close to the
                                                                                network partition border, it may disappear from the

W     IRELESS sensor networks (WSN) are composed
      of a compilation of devices that communicate with
                                                                                cluster earlier than a more centrally located node. On the
                                                                                other hand, a centrally located node should not be
each other over a wireless medium. Such a kind of                               selected as a clusterhead if its failure leads to cluster
sensor network forms spontaneously whenever devices                             partitioning.
are in transmission range. Joining and leaving of nodes                           The energy utilization can be minimized by allowing
occurs dynamically, particularly when they are like                             only a portion of the nodes, which called cluster heads,
mobile devices. Potential applications of wireless sensor                       to communicate with the base station. The data sent by
networks can be found in traffic scenarios, ubiquitous                          each node is then composed by cluster heads and
Internet access, collaborative work, and many more.                             compressed. After that the aggregated data is transmitted
Wireless sensor networks assemble and process                                   to the base station. Although clustering can reduce
environmental data. They consist of small devices                               energy consumption [8] [9], it has certain limitations.
communicating through radio. Normally, data                                     The main setback is that energy consumption is
processing in Wireless Sensor Networks occurs locally                           concentrated on the cluster heads [4]. In order to
and decentralized. The architecture of the model is                             overcome this demerit, the issue in cluster routing of
shown in Figure 1.                                                              how to distribute the energy consumption [10] must be
  In wireless sensor networks [5] [7], clustering is one                        resolved. The representative solution is LEACH (Low
of the mainly popular techniques for locality-preserving                        Energy Adaptive Clustering Hierarchy), which is a
network organization. Cluster-based architectures                               localized clustering method based on a probability

                                                                          189                            http://sites.google.com/site/ijcsis/
                                                                                                         ISSN 1947-5500
                                                  (IJCSIS) International Journal of Computer Science and Information Security,
                                                  Vol. 8, No. 7, October 2010



model. The main idea of LEACH procedure is that all                result. For a cluster, the nodes selected by the base
nodes are chosen to be the cluster heads periodically,             station are the nodes that have the higher chance to
and each period contains two stages:                               become the cluster heads using Fuzzy Logic based on
   •     Construction of clusters                                  their battery level, node density and distance.
   •     Data communication
   Cluster heads are selected according to the probability                           II.   RELATED WORKS
of optimal cluster heads determined by the networks.
After the selection of cluster heads, the clusters are                Handy et al., [1] proposed the Low Energy Adaptive
constructed and the cluster heads communicate data with            Clustering Hierarchy with Deterministic Cluster-Head
base station. Because LEACH is only depend on                      Selection. This paper focuses on reducing the power [11]
probability model, some cluster heads may be very close            [13] consumption of wireless microsensor networks.
to each other and can be located in the edge of the WSN.           Therefore, a communication protocol named LEACH
   These disorganized cluster heads could not maximize             (Low-Energy Adaptive Clustering Hierarchy) is
energy efficiency. To overcome the defects of LEACH                modified. The author extend LEACH’s stochastic
methodology, a cluster head election method using fuzzy            clusterhead selection algorithm by a deterministic
logic has been introduced. This method proved that the             component. Depending on the network configuration an
network lifetime can be efficiently prolonged by using             increase of network lifetime by about 30 % can be
fuzzy variables (concentration, energy and density). In            accomplished. Furthermore, a new approach is presented
this proposed method, a part of energy is spent to get the         to define lifetime of microsensor networks using three
data of the three variables especially concentration and           new metrics FND (First Node Dies), HNA (Half of the
density. The experimental show that the proposed                   Nodes Alive), and LND (Last Node Dies).
approach increases the network lifetime significantly                 W. Heinzelman et al., [2] presented an Energy-
when compared to LEACH approach.                                   efficient Communication Protocol for Wireless
                                                                   Microsensor Networks. In this paper, the author looks at
                                                                   communication protocols, which can have significant
                                                                   impact on the overall energy dissipation of these
                                                                   networks. Based on the findings that the conventional
                                                                   protocols of direct transmission, minimum-transmission-
                                                                   energy, multihop routing, and static clustering may not
                                                                   be optimal for sensor networks, the author propose
                                                                   LEACH (Low-Energy Adaptive Clustering Hierarchy), a
                                                                   clustering-based protocol that utilizes randomized
                                                                   rotation of local cluster base stations (cluster-heads) to
                                                                   evenly distribute the energy load among the sensors in
                                                                   the network. LEACH uses localized coordination to
                                                                   enable scalability and robustness for dynamic net-works,
                                                                   and incorporates data fusion into the routing protocol to
                                                                   reduce the amount of information that must be
                                                                   transmitted to the base station. Simulations show that
                                                                   LEACH can achieve as much as a factor of 8 reductions
                                                                   in energy dissipation compared with conventional
                                                                   routing protocols. In addition, LEACH is able to
                                                                   distribute energy dissipation evenly throughout the
                  Fig. 1: WSN Architecture                         sensors, doubling the useful system lifetime for the
                                                                   networks we simulated.
  In this paper, a method based on LEACH using Fuzzy                  Shen et al, [3] suggested the Sensor Information
Logic to cluster heads selection is proposed based on              Networking Architecture and applications; this paper
three variables - battery level of node, node density and          introduces a sensor information networking architecture,
distance from base station, and this method will be                called SINA that facilitates querying, monitoring, and
introduced based on the assumption that the WSN can                tasking of sensor networks. SINA serves the role of
get their coordinate. Although this method has the same            middleware that abstracts a network of sensor nodes as a
drawback as of Gupta’s method, it presents a better                collection of massively distributed objects. SINA's
                                                                   execution environment provides a set of configuration
                                                             190                             http://sites.google.com/site/ijcsis/
                                                                                             ISSN 1947-5500
                                                  (IJCSIS) International Journal of Computer Science and Information Security,
                                                  Vol. 8, No. 7, October 2010



and communication primitives that enable scalable and
energy-efficient organization of and interactions among               Where, λ is the path loss exponent and λ ≥ 2.
sensor objects. On top the execution environment is a                 The model of fuzzy logic control consists of a
programmable substrate that provides mechanisms to                 fuzzifier, fuzzy rules, fuzzy inference engine, and a
create associations and coordinate activities among                defuzzifier. The most commonly used fuzzy inference
sensor nodes. Users then access information within a               technique called Mamdani Method is used in the
sensor network using declarative queries, or perform               proposed approach due to its simplicity. The process is
tasks using programming script                                     performed in four steps:
                                                                      •     Fuzzification of the input variables energy,
                   III.   METHODOLOGY                              concentration and density - taking the crisp inputs from
                                                                   each of these and determining the degree to which these
   In this paper the cluster-heads are elected by the base         inputs belong to each of the appropriate fuzzy sets.
station in each round by calculating the chance each                  •     Rule evaluation - taking the fuzzified inputs,
node has to become the cluster-head by considering                 and applying them to the antecedents of the fuzzy rules.
three fuzzy descriptors:                                           It is then applied to the consequent membership function
   •     Node concentration                                        (Table 1).
   •     Energy level in each node                                    •     Aggregation of the rule outputs - the process of
   •     Node Density                                              unification of the outputs of all rules.
   In the proposed approach, the better cluster-heads are             •     Defuzzification - the input for the
produced by the central control algorithm in the base              defuzzification process is the aggregate output fuzzy set
station. This is because the global knowledge about the            chance and the output is a single crisp number.
network is contained in base station. In addition, base               During defuzzification, it finds the point where a
stations are many times more potent than the sensor                vertical line would slice the aggregate set chance into
nodes, having sufficient memory, power and storage. In             two equal masses. In practice, the COG (Center of
the proposed approach energy is spent to transmit the              Gravity) is calculated and estimated over a sample of
location information of all the nodes to the base station.         points on the aggregate output membership function,
Considering WSNs are meant to be deployed over a                   using the following formula:
geographical area with the main purpose of sensing and
gathering information, this paper assumes that nodes
have minimal mobility, thus sending the location                     Where, μA(x) is the membership function of set A.
information during the initial setup phase is sufficient.
   The cluster-head collects n number of k bit messages               Expert knowledge is represented based on the
from n nodes that joins it and compresses it to cn k bit           following three descriptors:
messages with c ≤ 1 as the compression coefficient. The               •     Node Energy - energy level available in each
operation of this fuzzy cluster-head election scheme is            node, designated by the fuzzy variable energy,
divided into two rounds each consisting of a setup and                •     Node Concentration - number of nodes present
steady state phase similar to LEACH. During the setup              in the vicinity, designated by the fuzzy variable
phase the cluster-heads are determined by using fuzzy              concentration,
[14] knowledge processing and then the cluster is                     •     Node Density – density of node in the cluster
organized. In the steady state phase the cluster-heads                The linguistic variables used to represent the node
collect the aggregated data and performs signal                    energy and node concentration, are divided into three
processing functions to compress the data into a single            levels: low, medium and high, respectively, and there are
signal. This composite signal is then sent to the base             three levels to represent the node density: sparse,
station.                                                           medium and dense respectively. The outcome to
   The radio model used here is with Eelec = 50 nJ/bit as          represent the node cluster-head election chance was
the energy dissipated by the radio to run the transmitter          divided into seven levels: very small, small, rather small,
or receiver circuitry and εamp = 100 pJ/bit/m2 as the              medium, rather large, large, and very large. The fuzzy
energy dissipation of the transmission amplifier.                  rule base currently includes rules like the following: if
   The energy expended during transmission and                     the energy is high and the concentration is high and the
reception for a k bit message to a distance d between              density is close then the node’s cluster-head election
transmitter and receiver node is given by:                         chance is very large.
                                                                      Thus, 33 = 27 rules are used for the fuzzy rule base. In
                                                                   this paper, the triangle membership functions are used to
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                                                                                             ISSN 1947-5500
                                                                                          (IJCSIS) International Journal of Computer Science and Information Security,
                                                                                          Vol. 8, No. 7, October 2010



                       represent the fuzzy sets medium and adequate and
                       trapezoid membership functions to represent low, high,
                                                                                                      1.0
                       close and far fuzzy sets. The membership functions
                       developed and their corresponding linguistic states are
                       represented in Table 1 and Figures 2 through 5.
                                                                                                      0.5 vsmall    small         rsmall      medium      rlarge    large          vlarge




                              1.0
                                                                                                      0.0


                              0.5         low                med                high                         0     10           30                   50        70             90      100

                                                                                                                                            Chance

                                                                                                                        Figure5. Fuzzy set for fuzzy variable chance
                              0.0
                                                                                                                                 TABLE1: FUZZY RULE BASE
                                     0                     50                       100
                                                         Energy                                             S.no    Energy           Concentration         Density          Chance
                                                                                                               1      low                 low               sparse           small
                                         Figure2. Fuzzy set for fuzzy variable energy
                                                                                                               2      low                 low              medium            small
                                                                                                               3      low                 low               dense           vsmall
                                                                                                               4      low                 med               sparse           small
                                                                                                               5      low                 med              medium            small
                                                                                                               6      low                 med               dense            small
                              1.0
                                                                                                               7      low                 high              sparse          rsmall
                                                                                                               8      low                 high             medium            small
                                          low                med                high                           9      low                 high              dense           vsmall
                              0.5
                                                                                                              10      med                 low               sparse          rlarge
                                                                                                              11      med                 low              medium             med
                                                                                                              12      med                 low               dense            small
                                                                                                              13      med                 med               sparse           large
                              0.0                                                                             14      med                 med              medium             med
                                     0       2           6     8   10         14    16                        15      med                 med               dense           rsmall
                                                      Concentration                                           16      med                 high              sparse           large
                                                                                                              17      med                 high             medium           rlarge
                                    Figure3. Fuzzy set for fuzzy variable concentration                       18      med                 high              dense           rsmall
                                                                                                              19      high                low                close          rlarge
                                  sparcity                    medium                     density              20      high                low                adeq             med
                          1
                                                                                                              21      high                low                 far           rsmall
                        0.8                                                                                   22      high                med                close           large
Degree of membership




                                                                                                              23      high                med                adeq           rlarge
                        0.6
                                                                                                              24      high                med                 far             med
                                                                                                              25      high                high               close          vlarge
                        0.4                                                                                   26      high                high               adeq           rlarge
                                                                                                              27      high                high                far             med
                        0.2
                                                                                                             Legend: med-medium, vsmall-very small, rsmall-
                                                                                                          rather small, vlarge-very large, rlarge-rather large.
                          0
                                                                                                             All the nodes are compared on the basis of chances
                                                                                                          and the node with the maximum chance is then elected
                              0              0.2             0.4       0.6          0.8         1         as the cluster-head. Each node in the cluster associates
                                                             Node-density                                 itself to the cluster-head and starts transmitting data. The
                                         Figure4. Fuzzy set for fuzzy variable density
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                                                                                                                                           ISSN 1947-5500
                                                   (IJCSIS) International Journal of Computer Science and Information Security,
                                                   Vol. 8, No. 7, October 2010



data transmission phase is similar to the LEACH steady-
state phase.

              IV. EXPERIMENTAL RESULTS

   The different experimental are conducted on the
proposed system and the results are discussed in this
section.
The reference network consists of 100 nodes randomly
distributed over an area of 400X400 meters. The base
station is located at 200, 450. In the first phase of the
simulation each node has a random energy between 0
and 100. The base station computes the concentration for                               (a) Energy Consumption
each node by calculating the number of other nodes
within the area of 20X20 meters by considering its
density as well. The values are then fuzzified and passed
to the fuzzy rule base for rule evaluation. After this,
defuzzification gives the cluster-head election chance. If
the chance is large or very large, then that node is chosen
as a cluster center. This techniques shows better finding
of cluster than the conventional methods such as
LEACH, etc,




                                                                                              (b) Nodes

                                                                                Fig. 7: (a) Energy Consumption (b) Nodes

                                                                      This results in a situation where the BS can receive at
                                                                    least 9000 more messages from the network before all
  Fig 6: Cluster formation of the simulated network                 energy is consumed. The energy consumed in the
using 4 clusters and a network size of 400x400 meters.              network is evenly distributed among the nodes in AROS.
                                                                    Clusters far away from the BS in the proposed system
  Figure 7(a) shows the energy consumption of the                   will survive until the end and continue to gather
proposed system compared with that of LEACH. The                    information.
increase rate of energy consumption of the proposed
system is much lower than the rate of LEACH. When                                        V.    CONCLUSION
LEACH has used all of its energy and demises, the
proposed approach still has 54% of its energy left.                   The new approach for cluster-head election for
  Figure 7(b) shows the nodes alive of the proposed                 Wireless Sensor Networks (WSN) is presented in this
system compared with the nodes alive of LEACH.                      paper. Cluster-heads were elected by the base station in
Besides, both the dead time of the first node and the               each round by calculating the chance each node has to
dead time of the last node of proposed system are later             become the cluster-head using three fuzzy descriptors:
than those of LEACH. Thus it is clear that, compared to             node energy, node concentration and node density. The
LEACH; the proposed has approximately 88% of its                    energy is the most important factor in designing the
nodes alive. So WSN can get longer life and enjoy                   protocol for WSN. The propose approach achieved
longer receiving of integral data by using the proposed             better reduction in the usage of energy for finding center
method.                                                             of cluster. The simulation result shows that the proposed
                                                                    approach has good energy consumption when compared
                                                                    to LEACH methodology. By the proposed method, the

                                                              193                             http://sites.google.com/site/ijcsis/
                                                                                              ISSN 1947-5500
                                                                      (IJCSIS) International Journal of Computer Science and Information Security,
                                                                      Vol. 8, No. 7, October 2010



better network life time is accomplished when compared
to LEACH.

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Description: Vol. 8 No. 7 October 2010 International Journal of Computer Science and Information Security