Review of Bio-inspired Algorithm in Wireless Sensor Network: ACO, ACO using RSSI and Ant Clustering

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1 Review of Bio-inspired Algorithm in Wireless Sensor Network: ACO, ACO using RSSI and Ant Clustering Niharika Sharma PG scholar DYPIT, Pimpri, Pune Prof. S. D. Chavan Associate Professor, DYPIT, Pimpri, Pune Abstract Biological inspired routing or bio-inspired routing is a new heuristic routing algorithm in wireless sensor network, which is inspired from biological activities of insects. ACO is ants inspired routing algorithm ACO, which has the ability to find shortest path and re-establish the new route in the case of route failure. In order to improve the network performance i.e. increase network lifetime and reduce transmission overhead, localization and clustering technique can be used in network. To locate the sensor node in network RSSI localization technique is used, which has an edge over other techniques. Another technique is clustering which groups the similar object in the network can also be used. For a large number of data objects clustering is very useful. In this paper we have studied about bio-inspired algorithm Ant Colony Optimization (ACO), ACO using localization technique (RSSI) and bioinspired clustering approach Ant clustering. sense the surrounding environment, Compute the sensed data, cooperate together and communicate wirelessly. The sensed data can be forwarded to the base station through single hop or multi hop for useful application. There are different types of sensors such as seismic, thermal, visual and infrared which sense the physical or environmental condition such as temperature, pressure, vibration, sound, humidity etc. [1, 2]. Figure 1 shows the basic architecture of Wireless Sensor Network. Keywords WSN, ACO, RSSI, ACO using RSSI, Ant Clustering, Clustering 1. Introduction A wireless sensor network can be defined as: A collection of tiny devices called sensor nodes with at least one base station connected to each other. These are distributed in a network area to perform an application specific task. Sensor nodes, Figure1. Basic architecture of Wireless Sensor Network 2. Routing protocol of WSN Routing is a very important function in WSN. We can classify the routing algorithms in many different ways [3]. 174

2 Out of them can be classified based on topology, position and bio-inspired routing as shown in figure 2. task and current routing protocols do not really scale well with these scenarios [4]. Bio-inspired routing protocols solve this problem. These protocols routing inspired by the biological insect such as ant, bee, particle swarm etc [5]. In below session algorithms are described in detail. 3. Biological Inspired Routing Figure 2. Routing Protocol of WSN Topology-based routing protocols use the information about the links that exist in the network to perform packet forwarding. They can be further divided into proactive, reactive and hybrid approaches. Proactive algorithms are a classical routing approach e.g. distance- vector routing (DSDV) or link-state routing (OLSR). The routing information about the available path is maintained for whole time if the path is not in used. The disadvantage of proactive approaches is to maintenance of path if they are not in use, this may occupy an available bandwidth if the topology of the network changes frequently. Reactive routing protocols maintain only the routes that are currently in use such as AODV and DSR. However, they still have some inherent limitations. First, since the routes are kept in records only when the system is active it is required to perform route detection before the actual data packets are exchanged between the nodes. Secondly, though the route detection is constrained to the routes in active, it may still generate a considerable amount of traffic in situations of change of network topology. Hybrid routing protocols is combine local proactive routing and global reactive routing in order to achieve a higher level of efficiency and scalability. However, even a combination of both strategies still needs to maintain at least those network paths that are currently in use, limiting the amount of topological changes that can be tolerated within a given amount of time [4]. Position based routing algorithms uses additional information, which eliminates some limitations of topology based routing. They require information about the physical position of participating nodes routing of packets in network with a large number of nodes or with high mobility is a very difficult The ideas to develop many routing algorithms in WSN are taken from the biological activities. To solve the routing problem in WSN bio-inspired algorithm classified into two main groups: swarm intelligence and evolutionary computing. The algorithms based on swarm intelligence are inspired by cooperative group behaviour of insects and animal which include algorithm inspired by Ant Colony Optimization, Particle Swarm Optimization, and Honey Bee Optimization. Evolutionary methods for WSN routing algorithm are based on Genetic Algorithms or inspired by the process and mechanism of biological evolution [6] Ant colony optimization Ant colony optimization is a bio-inspired metaheuristic algorithm introduced by M. Dorigo and his colleague in ACO algorithm is stochastic search procedure used to obtain good enough solution for combinatorial optimization problem. It is inspired by the foraging behaviour of real ant [7, 8]. The basic principle of ACO is the ability of ants to find the shortest path between food sources to anthill. There are three main functional structure of ACO Ant solution construct: This function performs solution construction process. According to the state transition rule artificial ants decide which adjacent state to move. It is an iterative solution. Pheromone update: this function perform update pheromone trail. After each iteration or complete solution have been built pheromone trail are updated. In additional it also includes pheromone trail evaporation. Daemons action: optional step for applying additional updates from global perspective i.e. the ant which construct the shortest path. 175

3 Inspirational source of ACO: Ant is real source in ACO algorithm. Ants live in colony, they are considered most socialized insect. Each ant non-verbally communicates with each other through chemical substance called pheromone, learn and cooperate. Ants have collective learning intelligence. The main idea of using the ant as an inspirational source is that they follow the selforganizing principles which allow the highly coordinated behaviour. Different kinds of ants algorithm can be inspired by different behaviour of ant e.g. foraging, division of labour, brood sorting, and cooperative transport [9]. The shortest path finding capability of Ant colonies are shown in figure 3. The ant process is as follows An ant wanders randomly around the colony for explores the area. Searching for food and deposit a chemical substance behind called pheromone. If it finds a food source, quantity and quality of food is evaluated. It returns back to the nest carries some of the food, leaving behind a pheromones trail. The quantity of pheromone deposited by ant is depends on the quantity and quality of the food. The pheromones trail which is laid to or fro to food source is useful to find out direction of the food source The closest ants will be attracted to these pheromones by smelling the pheromone and new ants follow the pheromone trail. Ant returning to the colony, and strengthened that route by adding more pheromone. Shortest route is traveled by more ants than the longest path to the food source if there are two or more routes. The amount of pheromones is increased by the no. of ant travelled by that route and therefore it will more attract to other. After a certain time interval pheromones are evaporated and longest route will be disappeared. Finally, all the ants have determined and chosen the shortest path. Figure 3. Shortest Path Finding Capability of Ant Colonies Artificial ant in network routing (real ant to artificial ant): ACO algorithm is applied to the network routing to find the shortest path between source node to destination node. To send the information packet, first a set of artificial ant or packet are travelled from source to destination. The ants select the node to be next travel based on the routing table. The probability that the ant select the next node to travel is called transition probability p k ij. Suppose ant k (1,2,3 m) to move from node i to j the probability pkij is...(1) Where τij(t): The level of pheromone edge, which join the node i and j, : Visibility of j when standing at i which is inverse of the distance i.e. =, d ij : Distance between node i and j, α and β: Parameter which control the pheromone trail intensity and heuristic information. Every moment of time t = (0, 1, 2, 3,..,n) represent 1 iterations in which all ants in the colony will perform one move. The following formula present the process (2) 176

4 Where ρ is evaporation coefficient of pheromone with value between 0<ρ<1,is residual coefficient of pheromone. τij(t) is increased pheromone of edge (i, j) by ant k may be calculated by vs, an ant uses the same path as it chose to reach vd, and it changes the artificial pheromone value associated to the used edge. More in detail, having chosen edge e i an ant changes the artificial pheromone value τij as follows: (3) τij(t) is the quantity of pheromone ant k deposit on the edge ij. If the ant reached the destination node update the pheromone deposited at the edges visited by them by an amount of (Q/L).(4) Where L is the length of path of ant k and Q is the parameter constant, some time value Ant Colony Optimization Model: In ACO algorithm shortest path can be selected by collectively learning process of ants. A discrete and simplified model of ant colony optimization algorithm is consists of graph G as shown in figure 4. Graph G = (V, E), where V consists of two nodes, namely source node v s (representing the nest of the ants), and destination node v d (representing the food source). Also E consists of two links, namely e 1 and e 2, between v s and v d.l 1 and l 2 is the length assign to the link respectively e 1 and e 2, where l 2 > l 1. In other words, e1 represents the short path between v s and v d, and e2 represents the long path. Real ants deposit pheromone on the paths on which they move. Thus, the chemical pheromone trails are modelled as follows. We introduce an artificial pheromone value (i for each of the two links e i, i = 1, 2. Such a value indicates the strength of the pheromone trail on the corresponding path. Finally, we introduce n a artificial ants. Each ant behaves as follows: Starting from v s (i.e., the nest), an ant chooses with probability between path e 1 and path e 2 for reaching the food source v d. (5) Obviously, if τ 1 >τ2, the probability of choosing e 1 is higher, and vice versa. For returning from vd to (6) Where as positive constant Q is a parameter of the model. In other words, the amount of artificial pheromone that is added depends on the length of the chosen path: the shorter the path, the higher the amount of added pheromone. The foraging of an ant colony is in this model iteratively simulated as follows: At each step (or iteration) all the ants are initially placed in node vs. Then, each ant moves from vs to vd as outlined above. In nature the deposited pheromone is subject to evaporation over time. We simulate this pheromone evaporation in the artificial model as follows: (7) The parameter ρ [0, 1] is a parameter that regulates the pheromone evaporation. Finally, all ants conduct their return trip and reinforce their chosen path. The shortest path finding capability of ant colonies results from a cooperation between the ants [10]. The main differences between the behaviour of the real ants and the behaviour of the artificial ants in our model are as follows: The artificial ants move in their environment in synchronous way i.e., at each iteration of the simulated system, each artificial ants moves from the nest to the food source and follows the same path while returning. In contrast to this, real ants move in their environment in an asynchronous way. Artificial ants only deposit artificial pheromone on their way back to the nest whereas real ants leave pheromone on the ground whenever they move. Real ants behaviour is based on an implicit evaluation of a solution (i.e., a path from the nest to the food source) so the shorter paths will be completed earlier than longer ones by them, and therefore they will receive pheromone reinforcement more quickly. While in case of artificial ants, during their return trip to the nest, some quality measure is used to determine the strength of the pheromone reinforcement. 177

5 Figure 4. ACO model Basic ACO Algorithm Frame work: The basic framework of ACO is as follows and also shown in figure 5. The pheromone values are initialized with a constant value at the start of the algorithm. Ant probabilistically constructs solution to the CO problem and then a local search is applied to the constructed solution. Finally pheromone trail is updated before the next iteration start [9]. Figure 5. ACO framework Presudo code for ACO: procedure ACOMetaheuristic ScheduleActivities ConstructAntsSolutions UpdatePheromones DaemonActions end-scheduleactivities end-procedure 3.2. Flowchart of ACO The flowchart of ACO algorithm is shown in figure 6 and the steps are as follows- Figure 6. Flowchart of ACO Step1: The choice of parameter has been obtained by trail i.e. n numbers of nodes, no. of ant, t maximum iteration, maximum distance for every ant tour, initial pheromone level Step2: First node can be selected based on probabilistic distribution. Step3: Local update rule process used to change the amount of pheromone to the visited path. Step4: No. of iteration is equal to until all the nodes dead Step5: The process is repeated for a predefined number of iterations N it. Step6: Shortest path contain maximum pheromone value and updated for global use. and the process is end if find the optimal path. 178

6 3.3. Advantages of ACO ACO adaptive in nature and allow to adaptation to changing environment for dynamic applications. It has an advantage of distributed computation and can be possible to prove that it is convergence. It gives positive feedback which leads to discovery of good solutions and can be used in dynamic applications and displays powerful robustness. In analyses of real dimension networks, to verify the possibility of developing a meta heuristic algorithm that allow network flows to be calculated more quickly than by using traditional algorithms. It allows dynamic rerouting through shortest path if one node is broken. Most other algorithms instead assume that the network is static. ACO algorithm retains memory of entire colony instead of previous generation The algorithm can be used to solve various Meta heuristic applications. The ACO performs better than other global optimization technique Limitation of ACO In standard ACO algorithms, the pheromone trail and the heuristic values depend on the scale of the problem. Theoretical analysis is difficult due to sequence of random decision and change of probability distribution by iteration. Its convergence is guaranteed but time to convergence is uncertain. Coding is complicated due to frequent change of global and local update 4. Location Based routing Localization of sensor nodes is useful in WSN to improve the network performance. To locate the sensor node adding a GPS to all nodes in the wireless sensor network but it is not practically possible because sensor nodes are tiny device adding another equipment i.e. GPS, which bulk the node, increase cost, high power consumption and addition GPS fails in indoor, underground or dense forest. Another easy method to locate the sensor nodes are range based localization techniques such as Time of arrival (ToA), Angle of arrival (AoA), receive signal strength indicator (RRSI) etc. and range free localization technique such as centroid, DV-Hop, MDS-MAP and so on [4]. In this section we have describe RSSI localization technique and ACO using RSSI algorithm RSSI (Receive Signal Strength Indicator) RSSI is a signal indicator that indicates the strength of the incoming signal in a receiver. It is a range based localization method RSSI is a method to find distance from attenuation of propagation path. If the transmission power is known, the total attenuation of signal propagation through the path can be calculated by subtracting the received power from transmitted power. Signal strength on the receiver decreases as distance from the transmitter increases. Theoretically the signal strength is inversely proportional to the squared distance between two sensor nodes Calculate the distance using the RSSI (Received Signal Strength Indication): Theoretically, to determine the distance between a transmitter and a receiver we can use the RSSI. RSSI is a generic radio receiver technology metric. RSSI propagation models in wireless sensor networks include free-space model, ground bidirectional reflectance model, and the received power can be calculated by Where P r : Receiving power P t : Transmitting power G t : Gain of a transmitting antenna G r : Gain of a receiving antenna L : wavelenght d: distance between the antenna λ: system loss factor (8) Or the RSSI value is calculated with the help of two ray ground model 179

7 the RSSI value each node can determine the distance between the nodes. P r : Receiving power P t : Transmitting power G t : Gain of a transmitting antenna G r : Gain of a receiving antenna h t :Transmittig antenna height h r : Receiving antenna height L : wavelenght d: distance between the antenna.(9) Discard the node Start route request Receive route reply with RSSI & calculate distance Determine the next node w.r.t. destination The distance using RSSI can be calculated using the FRIIS transmission formula. Then, to calculate the distance between two nodes that s equipped by transmitting antennas, the formula is: Yes If the low distance node act as source node No...(10) Advantages of RSSI It can be implemented on an existing wireless system with little or some time no hardware changes. Coordination or synchronization between the initiator and the responder for distance measurement is not required. Lower implementation cost. Lower implementation complexity Suitable for indoor and outdoor network Disadvantages of RSSI There are specific problem in implementing location awareness with RSSI method. Because of large variation of signal strength due to interference, multipath, and path blocking on the radio channel. Broadcast the updated routing table Start data transmission to the destination next node Receive the data transmitted at next node Destination node End transmission Yes No 4.2. ACO using RSSI Location based Ant colony Algorithm, which uses Received Signal Strength Indicator (RSSI) to calculate the distance to consider as a routing metric. Location based ant colony algorithm is a new ant colony optimization routing algorithm which improve efficiency of network to uses location information determined using RSSI. From Figure 7: Flowchart of Location based ANT location based ant colony algorithm is a reactive routing algorithm, thus a route is searched for only when there is a collection of data packets that are to be sent from a source node(s), to a destination node,(d). Figure 7 illustrate the flowchart of location based ACO algorithm (ACO using RSSI). Sending the data packets will start after a route from S to D is established. Before that, only 180

8 forward and backward ants are being exchanged. Initially Route Request (RREQ) broadcasts to all the nodes, when each node receives Route Reply (RREP) messages node will extract RSSI value from it and determine the location of nearby nodes. From the RSSI value each node can determine the distance between the nodes, and then use this location information to find the next node to reach the destination.this routing table will be updated with determined distance between the node using RSSI [11, 12] Advantages of ACO using RSSI Location information in ant colony algorithm having a short route establishment time, it reduces the number of generated overheads. It has a higher delivery rate with a shorter average packet delay. It also has a stable behavior. It is more suitable for Ad-hoc network with irregular transmission ranges. Improve efficiency of network. Figure 8. Data transmission in cluster 5.1. Classification of clustering algorithm There is different clustering algorithm based on different criteria. Figure 9 illustrate the classification of clustering algorithm 5. Clustering Clustering is the process of organizing object into group whose members are similar in same way. Cluster may be defined as A cluster is therefore a collection of object which is similar between them and are dissimilar to the objects belonging to other cluster. Clustering Criterion can be followed as Distance criterion Two or more object belongs to the same cluster, if they are close according to a given distance. This is called distance based clustering. Conceptual criterion Two or more object belongs to the same cluster if this one defines a common concept to all objects. Figure 9. Classification of clustering algorithm 181

9 5.2. Advantages of clustering Useful energy consumption (increase lifetime). Reduce communication overhead for both single hop and multi hop. Scalability: The ability of the algorithm to perform well with a large number of data object. Clustering provides the spatial reuse of resources to increase system capacity. For example, if the clusters are not neighbors, they can use the same frequency for wireless communication. Routing information of a cluster is shared with only other cluster-heads or cluster gateways. This restriction reduces the number of transmissions performed for distributing routing information. Interpretability and usability: The clustering results should be interpretable, comprehensible and usable Biologically Inspired Clustering Approach- ANT clustering Ant Clustering Algorithm was introduced in 1991 by Deneubourg, and algorithm upgraded by Lumer the in It comprises of distributed process which make use of positive feedback. Ant colonies provide a means for solving the clustering problem by formulating some powerful nature inspired heuristics where ant work collaboratively in the task of grouping dead bodies so that nest can be kept clean. Ant based clustering was inspired by the clustering activities observed in real ant colonies [13]. Ant based clustering works in following way: ants are modelled by simple agents that randomly move in their environment which is composed of square grid with periodic boundary conditions. The agent picks up, transport and drop, data item that are scattered within this environment. By introducing a bias for the picking and dropping operation, such that data items that are isolated or surrounded by dissimilar ones are likely to be dropped in the vicinity of similar ones, a clustering or sorting of these item is obtained [14]. The probability of picking an item is given by: The probability of dropping an item is given by: (12) Where f(i) is an estimate of the fraction of patterns located in the neighbourhood that are similar to an ant s current pattern, and kp and kd are real constants. In the work of Deneubourg et al. (1991), the authors used kp = 0.1 and kd = 0.3. Lumer and Faieta introduced a more general definition of f(i) that permits the algorithm s application to numerical data. An agent deciding whether to manipulate an item I now considers the average similarity of i to all items j in its local neighbourhood: (13) Here, d (i, j)[0, 1] is a dissimilarity function defined between points in data space, α [0, 1] is a datadependent scaling parameter, and σ2 is the size of the local neighbourhood (typically, σ2 {9, 25}). The agent is located in the centre of this neighbourhood; its radius of perception in each direction is therefore σ 1/2 [14]. Ant based clustering provides an advantage of automatic estimation of the number of cluster in the data set, easy implementation in terms of exchange of information of respective position the distance from Sink and the remained energy the whole network, on the basis of this we can calculate the possibility of each adjacent cluster heads being selected as the next hop to form the routing of adjacent clusters. Cluster heads are determined from the remained energy of node and the distance between nodes. When compared with classical clustering method ant based clustering provides higher quality cluster by avoiding overlap of cluster and even does not require to known in advance the number of cluster. The algorithm works on periodical cyclical round where each round is divided into clustering-forming stage and clustering-stability stage. The cluster is divided and then waits for data transfer at the new round beginning [15]. (11) 182

10 7. References [1] Sanjeev kumar Gupta, Poonam Sinha, Overview of Wireless Sensor Network: A Survey, International journal of advanced research in computer and communication engineering, Vol. 3, January [2] I.F Akyldiz, W. Su, Sankarasubramaniam, E. Cayirci, Wireless Sensor networks: a survey, Elsevier, [3] Adamu Murtala Zungeru, Li-Minn Ang, Kah Phool Seng, classical and swarm intelligence based routing protocol for wireless sensor networks: Asurvey and comparison, Elsevier, [4] Aditya H. Iche, Prof M. R. Dhage, Location based Routing Protocols: A Survey, Internation journal of computer application, January [5] Binitha S, S. Siva Sathya, A Survey of Bio inspired Optimization Algorithms, Internation journal of soft computing and Engineering, Vol. 2, May [6] M. Janga Reddy, D. Nagesh kumar, Computational algorithm inspired by biological process and evolution, current science, Vol. 103, August [7] Sonika, Pardeep kumar Mittal, Ant Colony Optimization: An Overview, International Journal of computer Application, [8] N. Sakthipriya and T. Kalaipriyan, Variants of Ant Colony Optimization- A State of an Art, Indian Journal of Science and Technology, Vol. 8(31), Nov Figure 10. Flowchart Ant clustering Algorithm 6. Conclusion In WSN due to limited battery life of sensor nodes, designing of routing algorithm plays an important role not only deciding and minimizing the routes length but also helps in maximization of the network life. In this paper we described Ant colony optimization algorithm as well as how localization technique and clustering approach is used in ACO. An Ant Colony Optimization Algorithm works on localization technique, which uses Received Signal strength to calculate the distance of nodes. The use of location information in route establishment from a source to a destination reduces the time. Ant colonies provide a means to formulate some powerful nature inspired heuristics approach in solving the clustering problem which reduces energy consumption and provide stability in wireless sensor networks. [9] Marco Dorigo, ChristianBlum, Ant colony Optimization theory: a survey, Elsevier, [10] O. Deepa, Dr. A. Senthilkumar, Swarm intelligence from natural to artificial systems: Ant colony Optimization, International journal on application of graph theory in wireless Ad hoc networks and sensor, Vol. 8, March [11] Vallikannu Alagappan, Jubin Sebastian E, A location Based ACO Routing Algorithm For Mobile Ad Hoc Networks Using RSSI, IEEE, [12] Vallikannu R., A. George, S. K. Srivatsa, Autonomous Localization based energy saving mechanism in indoor MANETs using ACO, Elsevier, [13] O. A. Mohamed Jafar, R. Sivakumar, Ant based clustering Algorithms: A Brief Survey, International journal of computer theory and engineering, Vol. 2, [14] Wu bin, Shi Zhongzhi, A clustering Algorithm based on Swarm intelligence, IEEE,

11 [15] Urzula Boryczka, Ant clustering Algorithm, Intelligent information system, [16] Hossein Jadidoleslamy, An introduction to various basic concepts of clustering techniques on wireless sensor networks, International journal of Mobile Network Communications & Telematics (IJMNCT), Vol. 3, February