Routing Problem: MANET And Ant Colony Algorithm

Transcription

1 Routing Problem: MANET And Ant Colony Algorithm Dr Madhumita Dash 1, Mrs Madhusmita Balabantaray 2 Department Of Electronics & Telecommunication, OEC, Bhubaneswar,India 1 Professor, 2 Asst. Professor madhumitadash44@yahoo.com, madhusmita.balabantarayyahoo.com Abstract: Mobile ad hoc network (MANET) is a collection of wireless mobile nodes dynamically forming a temporary network without the use of any centralized administration and the biggest challenge in MANETs is to find a path between communicating nodes.this paper shows the issues related to the adoption of MANET technology in real world situations drawing from the results from simulations. Biologically inspired techniques are employed to solve the problems of MANET routing. This paper study the Ant Colony based routing algorithms carried out by taking into consideration two of the most popular algorithms Ant based algorithms, Ant Hoc Net and the Ant Routing Algorithm (ARA) and also realizes the application of MANETs in real world applications by solving the problem of routing. The Ant Algorithm mimics the behavior of ants in nature while they are searching for food. Key Words: MANET, Ant Colony, Ant Hoc Net and the Ant Routing Algorithm (ARA) I. INTRODUCTION Mobile ad-hoc networks (MANETs) are a collection of mobile nodes communicating wirelessly without a centralized infrastructure and Selfcreation, selfconfiguration and self-administration are the most important features of these networks [1]. The biggest challenge in MANETs is to find a path between communicating nodes, that is, the MANET routing problem. MANETs are composed of power limited devices with a limited transmission range, so in most cases they will not be able to communicate directly with the destination device. Thus, communication must be relayed through intermediate devices resulting in multiplehops to the destination. MANETs may also be composed of different types of devices, which have different transmission ranges, this heterogeneous situation results in various problems, for example, unidirectional links [2]. As Mobile Ad hoc Networks (MANET) is a communication network without any fixed infrastructure that communicate with one another via wireless links, the routes between a source and a destination are often multi hop. This kind of network is very flexible and suitable for applications such as temporary information sharing in conferences, military actions and disaster rescues. As compared with the traditional networks, MANETs suffer from the resource constraints in energy, computational capacities and bandwidth[4]. The motion of nodes in MANETs results in nodes frequently going out of the transmission range of other nodes, thus interfering with MANET routing. Also, since the MANET is a decentralized network, information about the state of the network is not known to any one node. Thus to support the routing function, nodes frequently exchange data to become aware of the state of the network. To simplify the description of a MANET, the MANET model is usually illustrated as shown in Figure 1. Nodes i, j, and k are mobile nodes in the network. The dashed circles shown in the figure imply the radio coverage areas of nodes. In wireless networks, node i can hear node j if i is within the radio range of j. Node i is a neighbor of node j if node j can also hear node i. This is called a bidirectional connection. Two nodes are disconnected if one node is not in the radio range of the other. For example, nodes j and k are disconnected in the figure. Figure 1. A simple network model for a mobile ad hoc network. II. MANET ROUTING Page 954

2 The objective of routing in MANETs is to find a path between the source and destination over which packets can be forwarded. A MANET routing algorithm should not only find the shortest path between the source and destination, but it should also be adaptive, in terms of the changing state of the nodes, the changing load conditions of the network and the changing state of the environment. Every MANET routing algorithm has three essential components: A route discovery mechanism, a route error correction mechanism, and a route maintenance mechanism. The route discovery mechanism finds initial routes between the source and destination nodes, the route maintenance mechanism maintains the routes discovered during the transmission of packets and the route error correction mechanism rebuilds routes when they fail. MANET routing algorithms can be classified into three categories as proactive, reactive or hybrid [5]. Proactive algorithms try to maintain up-to-date routes between all pairs of nodes in the network at all times. The advantage is that routing information is always readily available when data need to be sent, while the main disadvantage is that the algorithm needs to keep track of all topology changes, which can become difficult when there are a lot of nodes or when they are very mobile. Examples of proactive algorithms are Destination-Sequence Distance-Vector routing (DSDV) and Optimized Link State Routing (OLSR) [9]. Reactive algorithms only maintain routing information that is strictly necessary: they set up routes on demand when a new communication session is started, or when a running communication session falls without route. This approach is generally more efficient, but can lead to higher delays as routing information is often not immediately available when needed. Examples of reactive routing algorithms include Dynamic Source Routing (DSR) [12] and Ad-hoc On-demand Distance-Vector routing (AODV) [13]. Finally, hybrid algorithms use both proactive and reactive elements, trying to Hybrid Adaptive Routing Protocol (SHARP) [19]. As stated above, the traditional routing protocols face many problems due to the dynamic behavior and resource constraints in MANETs. To overcome this limitation, a routing protocol is required to have a self-organizing or an autonomous feature. An approach to achieve such feature is to use a biologically-inspired mechanism. III. BIOLOGICALLY INSPIRED ALGORITHMS It has been observed that, when the natural patterns are applied to complex engineering problems, they provide good solutions. Many biological systems possess the ability to maintain their stable condition themselves regardless of the external influences or dynamic conditions. Ant colonies are complex biological systems that respond to changing conditions in nature by solving dynamic problems. Their ability of decentralized decision-making and their self-organized trail systems, have inspired computer scientists since 1990s, and consequently initiated a class of heuristic search algorithms, known as ant colony optimization (ACO) algorithms. These have proven to be very effective in solving combinatorial optimization problems, especially in the field of telecommunication. ACO is based on the ant foraging behavior, utilizing pheromone deposition as a means of evaluation for the travelled route. IV. AD HOC NETWORKING WITH SWARM INTELLIGENCE (ANSI) [7]: Rajagopalan and Shen propose a hybrid routing protocol utilizing swarm intelligence (SI) to select good routes in a network. SI allows self-organizing systems and helps maintain state information about the network. ANSI employs a highly flexible cost function which uses information collected from local ant activity. The protocol takes advantage of the basic principles of ant based routing algorithms which allows the maintenance of multiple routes to a destination. In ANSI, the nodes using proactive routing perform stochastic routing to select the best path, while those performing reactive routing use the extra routes in the event of route failure. The pheromone trail concept allows selecting the routes to the destination from every node. Nodes using reactive routing first broadcast a forward reactive ant towards the destination. Once the destination receives the forward ant, it replies with a backward reactive ant which updates routing tables for all nodes in the path. If route failure occurs at an intermediate node, that node buffers the packets which could not be routed and sends a forward ant initiating route discovery. Nodes which are part of a less dynamic, infrastructure network maintains routes proactively Page 955

3 by periodic routing updates using proactive ants. Proactive ants are not returned as the reactive ants, rather they help reinforce the path taken by the ant. Local route management is achieved by reinforcement due to movement of data packets and an explicit neighbor discovery mechanism (see Fig. 2). Figure 2. Local reinforcement in ANSI. (a) Reinforcement by data packets. Node i, upon receiving a data packet from S via node j, reinforces the pathto node j via j and the source S via j. (b) Reinforcement in neighbor discovery mechanisms. Upon receiving a HELLO beacon from j all nodes I reinforce trails via j V.ANT COLONY OPTIMIZATION (ACO) ALGORITHM ACO algorithms have been inspired by the behavior of a real ant colony. The algorithm can find the optimum solution by generating artificial ants. Just as real ants search for food in their environment, the artificial ants search the solution space. The probabilistic movement of ants in the system allows the ants to explore new paths and to re-explore the paths visited earlier. The strength of the pheromone deposit directs the artificial ants toward the best paths and pheromone evaporation allows the system to forget old information and avoid quick convergence to suboptimal solutions. The probabilistic selection of paths allows the artificial ants to search for a large number of solutions. ACO has been applied successfully to discrete optimization problems such as the traveling salesman problem and routing [8, 9]. A number of proofs for the convergence to the optimum path of the ACO can be found in [10,11]. Basic and Background Ants are creatures of limited intelligence, yet in nature they manage amazing feats such as building nests and finding food. They do this through an organized collaborative behavior that exploits the intelligence of the swarm of individuals in the ant colony. The French entomologist, Pierre Paul Grass e, investigated the social behavior of insects and discovered that ants are capable to react to what he referred to as significant stimuli, which are signals that activate a genetically encoded reaction. He observed that the effects of these reactions can act as new significant stimuli for both the insect that produced them and for the other insects in the colony. Grass e coined the term Stigmergy to describe this particular type of indirect communication in which the workers are stimulated by the performance they have achieved. Ants start from their nest and find different paths to the food and the local information available to the ant is the path that it took to the destination. Ants thus communicate with each other by depositing traces of pheromone as they walk along their path. Subsequent ants that arrive in search of food, base their decisions of which path to take on the pheromone traces left in that locality by the previous ants. This form of communication is indirect, i.e., one ant releases the pheromone information into the environment, and another ant senses that pheromone information from the environment just as Grass e had defined. Shortest paths by Ants colonies Many of the species of ants have a trail-following behavior when foraging [15]. While moving, individual ants deposit on the ground a volatile chemical substance called pheromone, forming in this way pheromone trails. Figure 3. All ants take the shortest path after an initial searching time Ants can smell pheromone and while choosing their way, they choose, in general, the paths marked by stronger pheromone concentrations. Also, they can be used by other ants to find the location of the food sources discovered by their nest mates, shown in Figure 3. The binary bridge experiment with same branch length : Page 956

4 The binary bridge experiment [15] is shown in Figure 4. The nest of a colony of ants and a food source has been separated by a diamond-shaped double bridge in which each branch has the same length. Ants are then left free to move between the nest and the food source. The percentage of ants which choose one or the other of the two branches is observed over time. As a result it has been observed that after few minutes ants tend to converge on a same path. Figure 4: Binary bridge experiment The binary bridge experiment with different branch length If the branches of the bridges are of different length, then due to the pheromone the majority of the ants in the colony choose the shortest path between the two available paths, as it is shown in fig 5 [17]. present in the corresponding graph representation which can be done easily by the ant algorithms. VI. AD-HOC ON-DEMAND DISTANCE VECTOR ROUTING (AODV) Ad-Hoc On-Demand Distance Vector Routing (AODV) [14] is one of the pioneering routing algorithms for MANETs and has become very popular in literature with many newer algorithms comparing their efficacy with respect to AODV. AODV is a purely reactive routing algorithm and establishes a route to the destination only on demand. AODV also avoids the counting-to-infinity problem of other distance-vector protocols by using sequence numbers on route updates. In AODV, there is no network communication until and unless a connection is needed to send a packet from a source node to a destination node. The advantage of AODV is in its reactive nature, that it incurs no network overhead due to transmission of control packets on links that are not being used. AODV is also a very simple algorithm, and does is not high time or memory complexity. However AODV requires more time to establish a connection, and the initial communication to establish a route is heavier than some other approaches. Proposed modifications to ARA Figure5: Experiment with a binary bridge with different branches length. Solving Network Routing Using ACO Mobile ad hoc network routing is a difficult problem because network characteristics such as traffic load and network topology may vary stochastically and in a time varying nature. The distributed nature of network routing is well matched by the multi agent nature of ACO algorithms.[19] The given network can be represented as a construction graph where the vertices correspond to set of routers and the links correspond to the connectivity among routers in that network. Now network route finding problem is just finding a set of minimum cost path between nodes ARA and AODV are compared by the author in [18] and ARA is found better than AODV. Since ARA is a reactive protocol, that is why it is used in such situations where mobility of nodes are higher. The modifications to the algorithm by which the potential of ARA will increase in high mobility scenarios. Pheromone updates play a critical role in the performance of the ant algorithm. In ARA algorithm, initial pheromone value is computed by number hops during the route discovery. This method may not be suitable when nodes are mobile. Pheromone equations are classified in different categories. Two of them are the Classic pheromone filter, where route quality is not taken into consideration, for example the original ARA pheromone equation, and the Gamma pheromone filter, which takes time and route quality into consideration. Since pheromone updates are responsible for the route maintenance of the ARA algorithm, they play a critical role in the performance of the ant algorithm. The authors of [20] study and classify the various pheromone update functions used in ant algorithms. Page 957

5 The original ARA algorithm uses a function of only the number hops to compute the initial pheromone value during the route discovery, that is, it uses a classic pheromone filter. This metric might be suitable for static routing, but it is not robust for highly mobile nodes. The simulation parameters for the testing of ARA are shown in Fig. 6.1, 6.2, 6.3, 6.4,the performance measured in terms of Packet Delivery Ratio, Throughput, End-to-End delay and Jitter for various values of pause time respectively. The observations show the efficacy of the modifications to the ARA algorithm. For the delivery ratio, throughput, and jitter metrics, the modified ARA algorithm performs better then the ARA and the AODV algorithm for value of all the measured values of pause time. For the delay, the performance of the ARA algorithm, while remaining better than the original ARA algorithm, is not as good as AODV. Figure6.1. Delivery Ratio vs. Pause time Figure6.2. Throughput vs. Pause time Figure6.2.End-to-End delay vs. Pause time Page 958

6 Figure6.3. Delay vs. Pause time Figure6.4.Jitter vs. Pause time VII.CONCLUSION In this paper, we have proposed an ant based routing protocol for mobile ad hoc networks. ACO based algorithms have specialized to provide adaptive and efficient solutions to network routing. Ant Colony algorithms are very adaptive to the changing environments. However, their performance must be improved further and they must be able to solve the problems of heterogeneous networks. REFERENCE [1] S. Chakrabarti, A. Mishra, QoS issues in ad hoc wireless networks, IEEE Communication Magazine 39 (2001) [2] Al Huda Amri and et. al. Scalability of manet routing protocols for heterogeneous and homogenous networks. Computers and Electrical Engineering, [3] S.A. Hosseini, R. Budiarto, T. Wan, Survey and new approach in service discovery and advertisement for mobile ad hoc networks, IJCSNS 7 (2) (2007) [4] Chlamtac, M. Conti, and J. Liu, Mobile ad hoc networking: imperatives and challenges, Ad Hoc Networks, No. 1, [5] Z.Liu, M. Z. Kwiatkowska and C. Constantinou, A biologically inspired QoS routing algorithm for ad hoc network, International Conference on Advanced Information Networking and Applications, AINA : , [6] Dr. Shuchita Upadhayaya and Charu Gandhi, quality of service routing in mobile ad hoc networks Using location and energy parameters, International Journal of Wireless & Mobile Networks (IJWMN), Vol 1, No 2, November [7] S. Rajagopalan, C.-C. Shen, ANSI: A swarm intelligence-based unicast routing protocol for hybrid ad hoc networks, Journal of Systems Architecture 52 (8-9) (2006) [8] A. Sabari, K Duraiswamy, ``Ant Based Adaptive Multicast Routing Protocol (AAMRP) for Mobile Ad Hoc Networks'' International Journal of Computer Science and Information Security Vol 6, No 2, [9] FJ Arbona Bernat, ``Simulation of Ant Routing Protocol for Ad-hoc networks in NS-2'', Faculty of Electrical Engineering, Mathematics and Computer Science Network Architectures and Services Group. [10] P.Deepalakshmi1, Dr.S.Radhakrishnan2, Ant Colony Based QoS Routing Algorithm For Mobile Ad Hoc Networks, in proc International Journal of Recent Trends in Engineering, Vol. 1, No. 1, May [11] Chunxue Wu, Fengna Zhang, Hongming Yang, A Novel QoS Multipath Path Routing in MANET, International Journal of Digital Content Technology and its Applications Volume 4, Number 3, June [12] DSR: The Dynamic Source Routing protocol for multi-hop wireless ad hoc networks, chapter 5, pages Addison- Wesley, [13] C.E. Perkins and P. Bhagwat. Highly dynamic destination- sequenced distance vector (dsdv) for mobile computers proc. of the sigcomm 1994 conference on communications architectures, protocols and applications. pages , Aug Page 959

7 [14] C. Perkins. Ad hoc on-demand distance vector routing. Internet-Draft, draft-ietf-manet-aodv-00.txt, November [15] B. H olldobler and E.O.Wilson. The Ants. Springer- Verlag, Berlin, Germany, [16] J.-L. Deneubourg, S. Aron, S. Goss, and J.-M. Pasteels. The self-organizing exploratory pattern of the argentine ant. Journal of Insect Behavior, 3: , [17] S. Goss, S. Aron, J. L. Deneubourg, and J. M. Pasteels. Self- organized shortcuts in the Argentine ant. Naturwissenschaften, 76: , [18] M. Gunes, M. Kahmer,, and I. Bouazizi. Antroutingalgorithm( ara) for mobile multi-hop ad-hoc networks -new features and results. In Proceedings of the 2 nd Mediterranean Workshop on Ad-Hoc Networks (Med -Hoc-Net 2003), Mahdia, Tunisia, 25-27, June [19] P.Deepalakshmi1, Dr.S.Radhakrishnan2, Ant Colony Based QoS Routing Algorithm For Mobile Ad Hoc Networks, in proc International Journal of Recent Trends in Engineering, Vol. 1, No. 1, May [20] M. Roth and S. Wicker. Asymptotic pheromone behavior in swarm intelligent manets. In Proceedings of the Conference on Mobile and Wireless Communication Networks (MWCN 2004), pages , Page 960