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Routing Protocols to Save and Balance Energy for Wireless Sensor Network using Fuzzy Set 1 MADHAVA C, 2SAROJADEVI H. Dept. of CSE, Nitte Meenakshi Institute of Technology, Bangalore Abstract Wireless sensor networks are used in different application like civil and military. Many routing protocols have been proposed to find suitable to transmit data. Most of energy aware routing protocols reduce energy consumption and often provide energy balancing. These protocols optimize or decrease the energy consumption of the network. Energy manager protocols will balance the energy consumption of network. The lifetime of WSN depends on the battery of sensor node. In this paper we propose fuzzy protocol to balance energy and use low energy to find the best route. Key words: Routing protocol, Wireless sensor network, energy aware, fuzzy set. 1. Introduction A wireless sensor network (WSN) is a wireless network consisting of spatially distributed autonomous devices using sensors to cooperatively monitor physical or environmental conditions, such as temperature, sound, vibration, pressure, motion or pollutants, at different locations. The development of wireless sensor networks was originally motivated by military applications such as battlefield surveillance. However, wireless sensor networks 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. WSN has gained its importance in recent years. It has become most important technology throughout the world. Wireless sensor network consists of many sensor nodes where each sensor node is small, of low cost and of limited power. The role of sensor node is to sense and gather information from the environment. They are also used for transmitting the sensed data to the user. Development of WSN was motivated by military application. Sensor nodes in WSN are tiny components that consist of sensor, processors, memory, and actuator. Major factor in sensor network is energy consumption. Since each sensor node has tiny batteries so it has limited processing power, so services will be limited. This is major issue in sensor network because every sensor node is used for routing and forwarding the data. The proposed fuzzy logic based routing protocol tries to balance and save energy in network. 2. Related works In WSN there are many routing protocols which are used to route the information among different sensor nodes. These protocols use route information in more energy efficient manner to increase the life time of sensor nodes. Low energy adaptive clustering hierarchy is a well known hierarchical protocol. The network in LEACH protocol is used to divide the network into different clusters and choose cluster head among them. Since LEACH is not used for large deployed network because data is transmitted directly to cluster head. Another cluster head election mechanism CHEF using fuzzy logic is proposed that uses fuzzy variable to find energy and local variable. Cluster heads are more evenly distributed over the network in CHEF than LEACH, so CHEF increases the network lifetime. Energy aware routing protocol based fuzzy logic is tuneable and soft algorithm. This cluster head algorithm is considered more powerful when compared to some other sensor node and has no energy limitation. The fuzzy cluster algorithm uses node’s residual energy to improve the lifetime of wireless sensor network which distributes clusters uniformly over network. S. M. Abolhasani et.al. [2] came up with “A Learning Automata Based Energy-aware Routing Protocol for Sensor Networks” which uses learning automata to find the paths in terms of balancing network traffic load. This method performs well in terms of balanced energy consumption of nodes and consequently, lengthening network lifetime. G. P. Hancke et.al. [3] proposed a “A Simple Energy Efficient Protocol for Wireless Sensor Networks” protocol to optimize network lifetime. SEER uses flat network structure along with event-driven reporting to reduce the number of message transmissions. Routing is based on the distance from the base station as well as remaining energy of battery of the nodes path towards the base station. SEER protocol minimizes the number of messages that are sent through the network and thus reduces the overall energy consumption. E. Ahvar et.al. [4], introduced a “Balanced EnergyAware Routing Protocol for Wireless Sensor Network”, which is an improvement of SEER routing protocol. BEAR routing protocol considers energy balancing and finding optimal distance. It finds a fair tradeoff between energy balancing and optimal distance by using learning automata concept. Gupta et.al. [5] , proposed a mechanism to find the cluster head called “Cluster head election mechanism using fuzzy logic in wireless sensor networks”. Cluster head node election method can reduce the energy consumption and enhance the lifetime of the network. A fuzzy logic approach to cluster-head election is proposed based on three descriptorsenergy, concentration and centrality. Depending upon network configuration, a substantial increase in network lifetime can be accomplished by selecting the nodes as cluster-heads using only the local information. 3. Implementation Basics of fuzzy set are from the inception of fuzzy set in 1965. It has advanced in different ways in many areas. Fuzzy set has its applications in artificial intelligence, computer science, medicine, logic, management science, operations research. Fuzzy set is a powerful tool for modeling uncertainty and for processing vague or subjective information in mathematical models. This mathematical model development has advances of very high standard and are still upcoming today. Since 1992 the theory of neural nets and evolutionary programming are known as computational intelligence. The relationship between these areas has naturally become close. The main reason for development have been diverse and its application to varied real problems. The notation of fuzzy system is that truth values or membership values that are indicated within range [0,1], with 0 and 1 representing absolute falseness and absolute truth respectively and in between values are used for representing the states that are within the range. 3.1 Fuzzy protocol routing There are many inherent characteristics factors of WSNs that have to be considered for design of efficient routing. Some factors may include node deployment, link heterogeneity, data reporting method, energy consumption, scalability, data aggregation, connectivity and quality of service. Fuzzy set protocol is a reactive protocol which uses lazy approach because the routes are discovered to destination only on demand. Bandwidth consumption in this protocol is less when compared to proactive protocol, but has large delay in determining route. Fuzzy set protocol is energy aware protocol. It has three major steps: 1) Neighbor discovery 2) Forwarding data 3) Energy update 1) Neighbor discovery The network has to be set up in the area where it has to be operated once this setup has been next step is to broadcast the message. The sink or base station floods the network with broadcast message. Each node after receiving the initial packet it makes an entry to neighbor table including neighbor id, energy level and hop count to it neighbor table. When base station sends anther packet it checks the neighbor table for node that transmitted the message. If not it adds an entry to neighbor table. The node then increases the hop count stored in the message and stores this hop count as its own. It then retransmits the broadcast message but change source address to its address. It also changes the energy level field to its energy level and then retransmits the broadcast message. Every node retransmits the broadcast message only once to all its neighbors in the network. When base station flooded the initial broadcast message every node in network knows its energy level and hop count of its neighbors. The sink node periodically keeps transmitting the message to network, so that nodes add new neighbors that joined the network to the neighbor table and remove the node that have failed to be active member of the network. Let α be obtained from the following formula: 2) Forwarding data Now, we define following decision maker equation: Routing process is started when the node observes the event. In node bases routing the decision is based on the hop count and remaining energy. The most important task is how to select the next hop. The node searches its neighbor table for all neighbors with small hop count than itself, if any such neighbor then it is selected as destination for the message. If two neighbors with same hop count is found then decision is made upon the remaining energy of the node. In the proposed protocol fuzzy protocol, that uses the fuzzy set technique to solve problem of next hop. It consists of two fuzzy set A and B. A is fuzzy set of all neighbor energy level. A={e1, e2,…..,en} A has a membership function, mA(ei) which can be defined as below, mA(ei ) ei , 1 i n n mA(e ) i i 1 n Then A ei mA(ei (2) Where α is energy threshold and Aα is used to remove the neighbors with unacceptable energy level. B is the fuzzy set of all neighbors hop counts with membership function mB(hi), and the decision maker equation is as below. mB(hi ) 1 hi MaxHop,1 i n (3) From this we find the neighbor with maximum amount of C is selected as next hop. Where, MaxHop ( x 2 ) ( y 2 ) / R mA (ei ) mB(hi ) c(i) 0 (4) ei , where 1 i n (5) ei 3) Energy update Nodes may be used by more than one neighbor for routing and therefore the energy value stored in the neighbor tables of both of the node’s neighbors’ will not be completely accurate. They update the energy level of sender node in their neighbor table by piggybacking technique. Nodes might be used by more than one neighbor for routing in this case the energy value stored in the neighbor table of both nodes neighbor will be completely accurate by overhearing technique. 4. Experimental results. (1) Where is a control parameter to limit energy factor in [0,1] interval, ei is energy level of (i)th neighbor. For simulation work we consider two features for the proposed protocol – 1) Fuzzy protocol with dynamic maxhop known as dynamic fuzzy 2)Fuzzy protocol with constant Maxhop known as static fuzzy. Computation is done using NS2 simulator. Simulation of the protocol started with a broadcast message. Simulations are performed to evaluate the network lifetime achieved by each protocol. Figure 3: Packet drop Figure 1: Packet delivery ratio 5. Conclusion The routing in this protocol is based on Fuzzy logic for energy saving and energy balancing in WSN. Here we use fuzzy set techniques in order to achieve energy balancing in wireless sensor network. We have simulated through ns2. We used two different methods static and dynamic methods to compute the energy balancing and energy saving. From the above results we can say that the dynamic methods has better performance than static method. This is based on the observation that the packet delivery ratio and packet drop are better for dynamic method, while the energy consumed is comparable to that of static method. Figure 2: Energy consumed REFERENCES [1] Ashwani Kumar, “A Survery on routing protocols for wireless sensor networks”, International Journal of Advances in Engineering Research(IJAER), Vol.No.I, Issue No.2, February 2011. [2]. S. M. Abolhasani, M. R, Meybod, M. Esnaashari, "LABER: A Learning Automata Based Energy-aware Routing Protocol for Sensor Networks", Proceedings of the Third Information and Knowledge Technology conference, Nov. 27-29, 2007. [3]. G.P. Hancke, C.J. 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