Simple Ant Routing Algorithm

Simple Ant Routing Algorithm Fernando Correia Inesc-ID and Instituto Superior Técnico Lisboa,Portugal Email: fernando.correia@tagus.ist.utl.pt Teresa Vazão Inesc-ID and Instituto Superior Técnico Lisboa, Portugal Email: teresa.vazao@tagus.ist.utl.pt Abstract A Mobile Ad-hoc Network has limited and scarce resources and thus routing protocols in such environments must be kept as simple as possible. This paper presents a MANET routing protocol, inspired in insect societies biological models, the Simple Ant Routing Algorithm (SARA), which provides a simple and efficient routing solution. SARA uses a controlled neighbour broadcast route discovery procedure, aimed at reducing the routing overhead of existing solutions. In this controlled neighbour broadcast strategy, every node collects routing information received from its neighbours and updates its own routing information accordingly, but only one of them is responsible for forwarding this information. Simulation results have shown that, besides reducing the overhead incurred by the routing protocol, SARA also provides a solution to detect early congestion link situations and tries to re-route the traffic through alternative routes (if available). I. INTRODUCTION At the earliest stage of Mobile Ad-hoc Network (MANET), most of the applications were military. However, rapid advances in this research area led to the development of new technologies and to the use of such networks in other applicational domains, including sensor, personal or home networking. In spite of this growing interest in MANET, these networks share many problems with traditional wireless communications, such as the limited bandwidth of the shared wireless channel and the highly variable quality of the transmission. In addition, their mobility, multi-hop nature and the lack of a fixed infrastructure pose problems as the nodes can move freely and the network topology may change very often. To support this new communication paradigm, robust, reliable and efficient algorithms are needed to overcome such problems and to allow the network to offer a good, or at least an acceptable, level of service. Routing in such kind of networks is a major research issue and many proposals have appeared within its scope. Some of them resulted from the adaptation of classical routing protocols, mostly designed to route information in wired networks that do not suffer from typical wireless network problems, such as resource constraint or frequent and unpredictable topological changes [1], [2]. The challenges in MANET are thereof much bigger and new designs are necessary to guarantee even the most basic connectivity service. Taking a quite different approach, insect societies have become a source of inspiration for MANET routing as they adopt robust and effective solutions to find food in environments that change very often, which has proved to be a keyaspect of their biological success. Usually, routing algorithms based on these biological models are simpler than the ones based on the traditional solutions. Nevertheless, they still introduce significant overhead, as their efforts are focused on maintaining multiple paths per destination, which requires a significant amount of control messages. Moreover, in highly dynamic environments, a burden of information is needed to maintain those paths and it may not be possible to guarantee their accuracy. We believe that keeping the routing as simple as possible is the best solution for MANET, due to its scarce and highly variable resources. This paper proposes a routing protocol, inspired in insect societies behaviour, the Simple Ant Routing Algorithm (SARA), which offers a very simple routing solution for MANET that provides very reduced overhead and a solution to detect early congestion link situations. The remainder of the paper is organised as follows. Section II presents the related work which contains further motivation to our proposal. Section III describes the SARA architecture, comprising the route discovery, maintenance and repair mechanisms. This architecture was evaluated through simulation and some relevant results are shown in section IV. Finally, section V presents the conclusions reached and the future work planned. II. SCIENTIFIC BACKGROUND A. MANET s Routing Algorithms Although many different MANET s routing proposals have been presented, so far there is a generalised consensus on a classification framework that distinguishes the algorithms according to the strategy they use to calculate the routes. Table-driven algorithms are purely pro-active as they calculate and maintain routes to all possible destinations. Keeping track of topology variations is difficult to achieve in a MANET and requires the exchange of a lot of control information. This kind of strategy is used in algorithms that are based on the classical wired routing approach, such as the Dynamic Destination Sequenced Distance Vector Routing (DSDV) [3] and the Optimise Link State Routing (OLSR) [4]. Demand-driven algorithms, such as the Ad-hoc on demand distance vector routing (AODV) [5], only gather routing information and calculate the routes when a new data session starts, implying the use of a reactive strategy. A more scalable

solution is achieved as the amount of control information exchanged is reduced. Nevertheless, the network is not able to have a fast reaction to topological failure events and service disruption may happen in such a case. Well-known algorithms, such as the Dynamic Source Routing (DSR) [6], the Adhoc On-demand Distance Vector routing (AODV) [5] and the Temporally Ordered Routing Algorithm (TORA) [7], belong to this category. In the Hybrid strategy, the network is divided into clusters and different routing protocols may be used for inter and intracluster routing: the goal is to find out an optimal solution by combining both types of strategies. Its performance deeply depends on the organisation of the cluster and the variability of the network topology. Small or highly dynamic networks may not be able to take advantage of hybrid strategies due to the overhead associated to cluster creation and maintenance. Examples of this kind of routing are the Zone Routing Protocol (ZRP) [8] and the Hybrid Ad Hoc Routing Protocol (HARP) [9]. Demand-driven algorithms are more scalable [10] due to the reduced overhead. The routing algorithm should present a fast route convergence feature and the network performance may increase, as less time is required to find and maintain a stable route. Nevertheless, the working conditions can always be affected if the algorithms are not prepared for broken link situations. B. Swarm Intelligence-based Routing Algorithms Several routing algorithms have been proposed that modulate the behaviour of real ants searching for food [11], [12]. As it can be easily observed, real ants can converge on moving to the shortest path that connects their nest to a source of food. This behaviour is caused by a chemical substance, the pheromone: while moving, the ants deposit the pheromones and tend to follow the paths with the highest intensity of pheromones. The paths that attract more ants will experience an increasing level of pheromones, until the majority of the ants converge on the shortest path. This indirect communication process used by the ants, which modify the environment and react to these modifications, is known as stigmergy [13]. By simulating the ants behaviour in routing protocols, route agents and data packets can act as ants leaving a pheromone trail as they pass through the path between the source and the destination. The path is marked without more control packets being introduced into the network. The result is lower overhead. Different routing protocols modulate this behaviour by using a set of routing agents that cooperate among each other. Three different phases are usually considered: Route Discovery, Route Maintenance and Route Recovery. The Route Discovery is accomplished by using two types of control packets that execute the following procedure: a Forward Ant Packet (FANT) is sent from the source to the destination to discover a path and to establish the pheromone track back to the source; a Backward Ant Packet (BANT) is sent from the destination to the source to confirm the existence of a path between the two nodes and to establish the pheromone track back to the destination. In the basic Ant Routing Algorithm (ARA) [14], the control packets are broadcasted in the network, leading to the discovery of all paths between any pair of source-destination nodes. Nevertheless, during the Route Discovery phase, the network is flooded with control traffic and thus collisions will frequently happen and routing convergence may be hardly achieved. During a communication process, the associated routing entries need to be kept up-to-date. In ARA, this task is accomplished exclusively by data packets, which refresh both the direct and the reverse path entries. The pheromone value is periodically decreased using an exponential function, in order to remove unused paths. No overhead is needed to perform state maintenance in ARA. Nevertheless, the solution adopted is not realistic as traffic network is usually asymmetric, leading to a wrong perspective of the paths situation. For instance, an HTTP session, where most of the traffic comes from the server, will cause a higher pheromone level in the direct path (clientto-server), which is actually the least loaded one. AntHocNet routing [15] solves this problem by pro-actively maintaining the information of the active paths. Control packets (FANT and BANT) are used in this process, but a unicast procedure is used whenever the path information is still valid. In spite of the advantages of having additional mechanisms that are able to deal with traffic asymmetries, the incurred overhead and additional complexity make the AntHocNet state maintenance solution inadequate to deal with the reduced wireless resources. A Route Recovery procedure is executed when a topological failure is detected. In ARA, when a node detects a broken link, it uses an alternative path if it is available, or starts a re-routing procedure when this is not the case. As a missing ACK is used to detect this event, frequent and unnecessary routing recovery procedures may be triggered, leading to unstable data paths. AntHocNet solves this issue by using Hello packets to detect and maintain neighbourhood information. The route recovery procedure is triggered only when a certain number of missing Hello packets is detected. A complex re-routing procedure is used, where the node that detects the broken link signals this event to all its neighbours, which starts a recovery procedure which is similar to the one described above. III. SARA ARCHITECTURE SARA is a swarm intelligence routing algorithm, based on the ACO framework, which provides reduced overhead and procedures to identify possible link congestion situations. SARA uses the on-demand routing strategy and is able to discover and maintain multiple paths per destination. The algorithm quality converges to the shortest discovered paths, which will be selected to forward data packets. SARA gets some ideas from Opportunistic Routing to be applied on heuristic used to calculate the link probabilities. Accordingly with the state and routing capability for a particular link, SARA chooses one to forward packets to next hop

towards destination. A. Route Discovery Let us consider a network (see fig. 1) represented as a direct weighted graph G = (V, E), where V denotes the set of vertices and E denotes the set of edges with weight function w : E R. Also considering a source node s V, a destination node d V and a generic node u V. Adj[u] is the list of adjacencies of node u containing all the vertices to j G on node u neighbourhood. The Route Discovery procedure deals with the process of defining a path from s to d, when node s starts a new data session towards node d. To discover the path, FANT packets are sent from the source s to the destination d, regularly, using a controlled neighbour broadcast process, and BANT are unicasted from the destination d to the source s to mark the path. As soon as a BANT arrives at the source node, a trail of pheromones is left in the network and a path between s and d is ready to be used. During this process, the pheromone trail usually indicates the short discovered path 1, δ(s, d). In SARA, the FANTs are transmitted using a new broadcast scheme, the controlled neighbour broadcast, where every neighbour node J of a given node u receives a broadcasted FANT, but only the selected one (node J 0 ) will be able to re-broadcast it (see fig. 1). To implement this controlled neighbour broadcast scheme, every node must maintain the list of its own adjacencies upto-date. Thus, each node J periodically broadcasts very small packets, known as HELLO packets, which, when received by an adjacent node u, means that node u knows that node J is one of its neighbours, e.g. J Adj[u]. When a node u receives a FANT, it updates its own routing table with information of the path from itself to the source s (reverse path), using the relevant information contained in the FANT. Each routing entry carries the information of the destination node (d), the next hop (next) and the number of hops (n hops) towards the destination. As each FANT carries the information of the last node responsible for the broadcast process and the number of hops it travels, the receiving node u is able to update the reverse shortest discovered path δ(u, s), by comparing the stored information with the newly received one. Should a smaller number of hops found by the FANT, the next hop towards the source must be replaced. If the number of hops is the same as the smallest number found at that time, the routing table can add another entry. If a node has more than one entry in its routing table towards the source or the destination, it means that multiple paths that can be used, have been found. To minimise the occurrence of congestion, control packets should be as small as possible. Thus, every FANT is composed of the following set of fields: 1 The shortest discovered path represents the shortest path that is found during the route discovery phase, which may be different from the network s shortest path. the FANT identifier, N, which is generated by the source node and is unique for each pair of communicating nodes (s, d); the relevant path information, comprising the identification of the source node s, the destination node d and the previous node prev (for instance u); the identification of the node responsible for rebroadcasting the FANT (e.g. a specific node J Adj[u], named J 0 ); the number of hops, n hops, crossed since the FANT leaves the source node s until it reaches the current node (e.g. for J 0 this value is represented by n hops(s, J 0 )). F ANT (N, s, d,,, ), indicates the table of FANTs on node u and contains information about its neighbour nodes which have already processed FANT with identification N. After the node u terminates the update process, it selects which one of its neighbours, J, will be responsible for rebroadcasting the FANT. The selected node J 0 is probabilistically given by eq. 1. This selection deeply depends on the link weight, w (u,ji,d), of the adjacent nodes, J Ad[u]. p (u,j,d) = w (u,j,d) i Adj[u] w u,i,d A broader selection scheme is needed to allow a wide range of paths to be discovered. Thus, the previously selected nodes will be weighted according to the pheromone level and to the number of hops needed to reach the destination; and the nodes which have not been selected yet will have a unitary weight to increase their probability of selection. During the route discovery procedure, when a FANT is transmitted, all j nodes in the neighbourhood receive that FANT and all the nodes know where that FANT came from and which nodes have already processed that agent. This information is stored in a table. When a node u uses eq. 1 to calculate p (u,j,d) and every node that has already been visited by the FANT (in accordance with the information store locally), the link weight for node J will be 0 (see eq. 2). This way, it is possible to avoid loops and ensure that FANT will follow a unique path from the source to the destination. If a node u has no available way out to transfer the FANT to another node, when all its neighbours have processed the FANT, it sends an error message to the previous node reporting it can not forward the FANT. The previous node then updates the FANT information table and uses the 1 to recalculate the link probability. This procedure is repeated until the FANT reaches the destination (the network has an available path) or the error message arrives in the source node (there is not a valid path to the destination). Considering a pheromone level ϕ (u,ji,d); the convergence parameters used to reach the shortest discovered path δ(s, u) faster, F and e n hops (s,u) ; the link weight w (u,j,d) E is given by eq. 2. (1)

w (u,ji,d) = 0 if J i F ANT (N, s, d,,, ) 1 if ϕ (u,ji,d) = 0 ϕ F (u,j i,d) e nhops (s,u) if ϕ (u,ji,d) 0 (2) Through the F parameter, the heuristics used to calculate the link weight become more or less greedy. Greedy heuristics allow the traffic to converge faster to one route, but reduces the probability of exploring alternative routes. The value of F must be a commitment between the kind of traffic and the resources available in the network. When a FANT arrives at the destination node d, this node sends a BANT to the source node s that is used to mark the path, using unicast communication. When a node u receives a BANT, it uses this packet to update the direct path from itself to the destination node d. The BANT is then unicasted to the next neighbour node, which is the one that leads to source node s through the shortest discovered path (see fig 1). Fig. 1. FANT transmission using controlled neighbour broadcast, BANT transmission using controlled neighbour broadcast When a BANT is received by a node, it updates the reverse shortest path. Should a shorter path be discovered, the oldest routing information will be replaced by the new one (see fig. 1). When the first BANT arrives at the source node, it means that the network has established a route between s and d and may start transmitting data. B. Route Maintenance The Route Maintenance procedure deals with the process of maintaining the paths active. Usually, data traffic is used to refresh these paths and to update the pheromone values of the links. However, as the pheromones decay with time, the least used links may reach a very small weight and, if multiple paths are required, an on-demand Route Maintenance procedure must be triggered. The node which is responsible for triggering such procedure is the source node s, which transmits a Refresh FANT (RF FANT) packet to the destination d, whenever it detects a link weight smaller than a given threshold value, lower lim. A node which receives the RF FANT replies with a Refresh BANT (RF BANT) packet whenever it knows a path to the destination node (it has a valid routing entry associated with that node); otherwise, it replies with an RF ERROR indication, meaning that it does not know how to reach the destination. When a node receives an RF BANT, it refreshes the routing entry associated with the destination node; otherwise, if an ERROR or a time-out are received, the corresponding entry is removed from the routing table. The pheromone level decays according to eq. 3, where ϕ (u,j,d) is the new pheromone value and ϕ (u,j,d) is the old value. γ represents the decreased quantity of the pheromone level and depends on the existence or absence of traffic passing through the link. The behaviour of the pheromone level presents an exponential arc. C. Route Recovery ϕ (u,j,d) = ϕ (u,j,d) γ { γ 1 with traffic = γ 2 without traffic During the transmission of data connectivity may be broken, due to a wide variety of reasons. To assure the transmission this event must be detected as soon as possible and the broken route must be repaired very fast, in order to avoid service disruption. A link is considered broken when a node misses a certain number of ACK from CTS/RTS. Whenever a broken link is detected by a node, this node should search its own routing table and should try to find alternative paths to the destination node. If such paths are available, data traffic may be sent again, using the new route. Otherwise, the node tries a local recovery procedure, which is executed within the node neighbourhood by sending a Route Recovery FANT (RR FANT) that attempts to repair the route within this neighbourhood. When a node receives an RR FANT and has a valid routing entry to the destination node, it sends a Route Recovery BANT (RR BANT) through the same path; otherwise, it re-broadcasts the RR FANT packet. The Route Recovery procedure ends when the node that triggers it receives an RR BANT packet. If no RR BANT packet is received at the node which started the repair procedure, it means that the local recovery procedure was not successful and this node sends a Route Recover ERROR (RR ERROR) to the source node. Upon receiving it, the source node starts a new Route Discovery procedure. D. Congestion links - early detection procedure SARA uses the pheromone to label a route from the source to the destination. The pheromone level is maintained by the traffic which travels on that path. The pheromone level can be an indicator which represents the network state. When the network has enough resources to accommodate new data sessions or to allow the increase of the data rate for the existing ones, the pheromone level also increases. However, when the available resources become scarce, the data traffic may not be enough to maintain the pheromone level. (3)

Looking the way the pheromone level changes, it is possible to make a decision about what to do: use another route to balance the load through the network (if multiple routes are available) or start a new route discovery procedure and try to find another (less congested) path. This way, biological algorithms like SARA have tools to adapt themself to network variations without applying undue overhead and have the benefit of reducing the packet loss rate and the delay between the source and the destination. B. Performance evaluation on a MANET The test scenario simulates an area of 1000m x 1000m, illustrated in fig. 2. Each node was configured with mobility parameters. In each simulation, all the nodes present the same speed with a random direction function. The node speed changes from 0 ms -1 (to obtain some reference values) until 10 ms -1. IV. SIMULATION STUDIES The proposed method was compared with AODV and ARA via simulation, performed using the Network Simulator 2.29 version, enhanced to support both the ARA and the SARA routing protocols. A. Simulation set-up The SARA set-up procedure took place by tuning some working parameters which allow the algorithm to improve its performance. The parameters to be set-up are: the convergence greedy factor F (eq. 2) and the pheromone lifespan. The F factor allows for the verification of the SARA convergence capability to use the shortest path found. The pheromone lifespan gives SARA a certain level of autonomy to allow forself configuration in accordance with the load at the network. The F value is a parameter which allows for SARA to converge faster to one route when in the presence of a multipath to the destination. The heuristics used to calculate the probability of choosing a certain link are greedy heuristics. The higher the F, greedier the heuristics. When the value of F increases, the probability that SARA will choose a path where the passage of packets has already been noted, will also increase. However, it is possible that this path is not the the best path on the network. So, it is necessary to identify what the ideal value of greedy is to optimise the algorithm s performance. To find an optimal value for the time to consider the pheromone is active, a test simulation scenario with different values was used. The pheromone lifespan must have a weight value which can adjust to the network traffic variations, reducing broken link and congestion situations. It must also be able to perform a self configuration when the network resources change. If the lifespan is small, the presence of a higher rate packet session is necessary to maintain the pheromone level. If it is not possible, the algorithm will frequently lose information about how to reach the destination node and will start a new route discovery procedure. As the pheromone s lifespan increases, the route stability also increases. However, for higher values, because SARA uses greedy heuristics, when in the presence of a congestion link, even if SARA intends to start a route discovery procedure to find a new route, that might not be possible because the old path still has a strong pheromone value leading to the congested path. The pheromone lifespan should be between 1s and 5s. Fig. 2. Test scenario In each test, the user session was modelled by a file transfer, accomplished through the use of the File Transfer Protocol (FTP) over the Transport Connection Protocol (TCP), using data packets of 1000 bytes. The network load was gerenated by four user sessions, for 60 seconds (simulation time). Each session starts 5 seconds after the previous one, the test session being the last one to be established. The source and the destination nodes are randomly selected, being kept the same for all the three routing protocols under evaluation. The SARA configuration values are presented in tab. I. Name Value F 5 Pheromone life time 1s TABLE I SARA CONFIGURATION VALUES In the test scenario, the pair of nodes in communication will become closer in an initial phase and will, later, become further from each other. Each phase is related to the network node s velocity. In this scenario, due to the simulation time chosen (60 seconds), for a velocity of 6ms -1, the nodes in communication will present an approach behaviour. With velocities higher than 7ms -1, it is possible to notice the two phases behaviour. Thus, when velocity increases, the three algorithms show a better performance due to node proximity because the path becomes shorter and the time the packets spend on network travelling from the source to the destination is also lower. C. Simulation results The simulation studies that were carried out are aimed at evaluating and comparing the performance achieved by SARA and by the other routing protocols under analysis, during the Route Discovery process.

The evaluation comprises three metrics: Routing protocol overhead, which is determined by the ratio between the amount of information needed to carry control traffic over the amount of information need to carry data traffic. Data delivered at the destination, which evaluates the quality of the routes and the capacity of the routing algorithm to maintain the data flow associated with a session active. The study of SARA s performance is illustrated in figs. 3 and fig. 4 considering the network mobility. At lower velocity, the network topology presents small changes. This way, the congestion situations tend to be maintained through the whole simulation time. Therefor, the number of route repair and route discovery procedures will increase. This fact is shown in fig. 3 with higher overhead at low velocity. When the network node s mobility increases, the nodes in communication get closer and the communication lines associated with each session spread through the simulated scenario. This event makes the reduction of the number of collisions and the improvement of the algorithms performance possible. SARA was written to present optimised overhead values. During the route discovery procedure, the controlled neighbour broadcast allows for the reduction of control traffic. The amount of overhead generated by SARA tends to be the same with slight variations as a consequence of node mobility. Control/Data Traffic [%] 100 90 80 70 60 50 40 30 20 10 0 Algorithm behaviour - OVERHEAD 0 1 2 3 4 5 6 7 8 9 10 Fig. 3. Network node velocity (m/s) SARA ARA AODV Simulation scenario - Overhead The volume of data received at the destination in a certain time period is related to the time spent on route discovery and on route repair, as well as to the delay in transporting the packets through the network (link transmission time and node process time). Thus, the algorithm s performance can be measured by the amount of information (data packets) delivered at the destination according to the period of time. In accordance with fig. 4, SARA can route a larger volume of data packets. This value is associated with the amount of control traffic generated and the capability to adapt itself to network changes. V. CONCLUSIONS MANET has limited and scarce resources and thus routing protocols in such environments must be kept as simple as possible. The routing protocol proposed in this paper, SARA, inspired in insect societies behaviour, uses a controlled neighbour broadcast scheme as a way of achieving faster convergence Data received [in MB] 3 2.5 2 1.5 1 0.5 0 Fig. 4. Algorithm behaviour - Data received 0 1 2 3 4 5 6 7 8 9 10 Network node velocity (m/s) SARA ARA AODV Simulation scenario - Data traffic delivered with very reduced overhead. 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