Bio-inspired Adaptive Routing in Self-Organized Networks: A Survey

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1 2013 8th International Conference on Communications and Networking in China (CHINACOM) Bio-inspired Adaptive Routing in Self-Organized Networks: A Survey Xu Zhang, Yanling Zhang, Yang Li, Zhongshan Zhang* and Keping Long Institute of Advanced Network Technology and New Services (ANTS) and Beijing Engineering and Technology Center for Convergence Networks and Ubiquitous Services University of Science and Technology Beijing (USTB), Beijing, China zhangxuustb@yeah.net; zhangyanling221@163.com; detective121@sina.com; {zhangzs, longkeping}@ustb.edu.cn Abstract Since the study of collective behavior of social species can help humans manage complex systems, bio-inspired algorithms will consequently improve the routing performance in self-organized networks. A plethora of studies on adaptive routing in self-organized networks has already been carried out. In this paper, adaptive routing as one of important self-organization issues is surveyed. Several well-known bio-inspired adaptive routing algorithms, such as Ant Colony Optimization (ACO), AntNet, AntHocNet, BeeHive, Multiple Ant Colony Optimization (MACO), Multiple Ant-Bee Colony (MABC), as well as their merits and demerits are analyzed in detail. The remaining challenges to face in adaptive routing in the future are also discussed in this paper. I. INTRODUCTION Self-organization, which is expected to bring us remarkable benefits in terms of network survival improvement, costs reduction in network configuration/management/operation/optimization, etc, is not uncommon in artificial communications systems [1] [3]. Since the study of collective behavior of social species(such as honey bees, army ants, bird flocks) can help humans manage complex systems, bio-inspired algorithms will consequently give us some illumination on designing/optimizing artificial SON systems. Enormous bio-inspired algorithms about adaptive routing have been proposed. Among them, the most famous algorithm is the Ant Colony Optimization (ACO) algorithm [4], which has already been adopted as problem solution in various areas such as telecommunication networks routing optimization and hard combinatorial optimization problems [5] [7]. ACO algorithm has also been applied to solve the routing algorithms in other SON systems such as mobile ad hoc networks [8] and WSN [9] with a better scalability or the other performance metrics. In this paper, bio-inspired adaptive routing as one of important issues related to network operation and optimization will be investigated. Several bio-inspired adaptive routing algorithms, such as ACO, AntNet, AntHocNet, BeeHive, MACO, MABC, are analyzed in detail. The remainder of this paper is organized as follows. The current progress in adaptive routing is introduced in Section II. Bio-inspired adaptive routing, including ACO, AntNet, AntHocNet, BeeHive, MACO, MABC, is analyzed in Section III. The remaining challenges faced in adaptive routing are analyzed in Section IV. Finally, conclusions are drawn in Section V. II. CURRENT PROGRESS IN ADAPTIVE ROUTING Adaptive routing, regarded as a critical functionality of wireless self-organized networks, has already been studied considerably in the past decades. Applying multi-hop routing to the wireless ad hoc network poses two challenges either due to its constantly changing topology or because its routing database is insufficient to support a trusted data storage [10]. Several well-known routing algorithms which have been proposed to address those challenges are traditionally classified as either proactive or reactive. In the former (e.g., Destination- Sequenced Distance-Vector routing (DSDV) [11]), nodes try to maintain routes to all other nodes at all times, but in the latter (e.g., Ad-hoc On demand Distance Vector routing (AODV) [12] or Dynamic Source Routing (DSR) [13]), nodes only gather routing information on demand: they construct a path only when they have data for a certain destination, and they will search a new path only when the path becomes infeasible. Therefore, the routing load in the reactive algorithms is reduced greatly. Furthermore, hybrid algorithms such as Zone Routing Protocol (ZRP) [14] combine the advantages of both the proactive and reactive approaches. Besides the single-path algorithms aforementioned, some multipath routing algorithms (e.g., [15]) have also been proposed to offer an alternative in terms of link failure robustness and load balancing capability. However, one challenge may arise in developing routing protocols for dynamic networks: transmitting large routing table or flooding multiple copies of link-state-packets (LSPs) in Open Shortest Path First (OSPF) in short or regular intervals may incur large routing overhead, while longer intervals may result in slower responses to changes in network topology. As compared to that, ACO is more appropriate for routing in dynamic network, because ants are relatively small (in general, the size of ants is relatively small and in the order of 6 bytes [16]) and they can be piggybacked in data packets so as to enable a more frequent ants transmission. ACO has already been proven to be effective in improving the quality of service (QoS) in dynamic network environments. In ACO algorithms, the problem-solving paradigm can reach its convergence within a short period of time without IEEE

2 transmitting a lot of routing entries in comparison to the Distance Vector/Link State Routing. Besides, a number of desirable properties, such as highly adaptive to environment changes, work in a distributed way with a high robustness, and have a capability of providing automatic load balancing, etc, are exhibited in ACO algorithms. The concentration of pheromone in ACO is used as an indicator to replace the distance or number of hops to indicate the route s preference. Since the exchange of pheromone is implicit without directly transmitting routing entries, routing overhead can thus be considerably reduced. Moreover, ant s pheromone laying can also reflect network configuration or topology changes, and this capability makes ACO more adaptive and decentralized. III. BIO-INSPIRED ALGORITHMS In this section, Adaptive routing will be investigated, with variant Bio-inspired routing protocols, including ACO, AntNet, AntHocNet, BeeHive, MACO, MABC, being surveyed in detail. A. ACO Principle Following the principle of ACO algorithm which is generalized from the ant s foraging process, the ACO algorithm can be mathematically described [4], [17]. By using i and j to represent the two end nodes of link (i, j), let s define p ij as the transition probability for ant k to move from i to j, as given by [τ ij(t)] α [η ij(t)] β if j J [τ il (t)] p ij = α [η il (t)] β i k l J, (1) i k 0 otherwise where Ji k represents the list of not-yet-visited nodes, and ant k can avoid visiting node i more than once by exploiting Ji k; η ij (t) is the visibility of node j when standing at node i at time t (stands for the inverse of the distance between i and j); τ ij (t) represents the pheromone level of link (i, j), and a higher pheromone level implies a higher transition probability from i to j; α and β are parameters used to control the relative weight of τ ij (t) and η ij (t), respectively. The pheromone decay and update rule is defined as τ ij (t) (1 ρ) τ ij (t) + k where 0 ρ < 1 represents a decay coefficient, τ k ij(t) = { Q L k (t) if (i, j) T k (t) 0 otherwise τ k ij(t), (2) stands for the quantity of pheromone of link (i, j) labelled by ant k, with T k (t) representing the tour done by ant k at time t and Q being a parameter with very weak influences on the final pheromone density. In applying ACO in network routing, an artificial ant is realized as a program consisting of procedures that simulate the laying and sensing of pheromone, and data structures that record trip times and nodes it passes. The laying of pheromone is simulated by recording in a counter the number of ants that (3) pass a node, and the decision of an ant is influent by a function of pheromone concentration. By using a society of ants, an ACO provides a framework for solving variant optimization problems. The main idea in those ACO-based algorithms is to continuously sample possible paths with ant-like agents, and to indicate the quality of paths by means of artificial pheromone variables. By analogy, routing tables correspond to pheromone tables that can be manipulated by a colony of ants distributed over a network. However, the strategy that all ants prefer to choose the optimal path may lead to Stagnation: i) congestion is prone to happen in optimal path, and ii) the probability of ants selecting other paths is dramatically reduced (i.e., load balancing degrades). Based on ACO algorithm, several famous algorithms such as AntNet [8] and AntHocNet [18], have been developed to mitigate stagnation in packet-switched networks. Furthermore, some advanced ACO algorithms that employ multiple colonies of agents have also been proposed to further mitigate the stagnation and improve the network load balancing. B. AntNet Routing techniques in communications networks inspired by the foraging behavior of ants have been addressed by AntNet approach [8]. Routing in AntNet is achieved by launching forward ants at regular intervals from a source node to a destination node to discover a feasible low-cost path and by backward ants that travels from destination to source to update pheromone tables at each intermediate node. The routing tables of all networked nodes are the most important parameters to be maintained by the agents. Without loss of generality, the routing table T k is used to define the probabilistic routing policy currently adopted for node k. A forward ant selects the next hop node N i by taking into consideration of both the probability of choosing N i and a heuristic correlation factor l ni which is based on the queue length at N i. The probabilistic value P nd, which expresses the quality (desirability) of choosing neighbor node n as a next hop towards destination d, will be stored in T k. The outgoing probabilities are thus constrained according to P nd = 1, (4) n N k where N k represents the set of neighbor nodes of k, and d [1, N] (N is the total number of nodes in the network). The specific capabilities of AntNet can be mentioned explicitly as follows. First, AntNet maintains a probability P d instead of a fixed period for launching an agent towards node d at node s according to P d = N f sd f sd d =1, (5) where f sd is a measure of the data flow from s to d. In this way, the ants adapt their exploration activity to the varying data-traffic characteristics, and therefore AntNet incorporates 506

3 algorithm-inherent mechanisms to counteract network congestion. Secondly, AntNet relies on a reinforcement strategy for maintaining the routing table T k, with the returning agent updates the routing table entry for destination d (i.e. the probability P fd ) according to P fd P fd + r(1 P fd ), (6) where the factor r is a dimensionless value used by the current node for positive reinforcement with r (0, 1). A backward ant which travels from the destination to the source via the same path will update the pheromone table at each intermediate node and estimate trip time from source to the intermediate node. Finally, the probabilities P nd for destination d of the other neighboring nodes n implicitly receive a negative reinforcement P nd P nd rp nd, n N k, n f (7) so as to satisfy the constraint in (4). Since the goodness of a path is reinforced according to the trip times of forward ants, the selection probabilities updated by backward ants can influence ants travelling in the forward rather than backward direction. Therefore, AntNet is proven to be not restricted to routing application in networks with symmetric costs only [19]. A number of properties such as highly adaptive to network changes, use active path sampling, are robust to agent failures, provide multipath routing, and take care of data load spreading, are exhibited in AntNet, and those properties make AntNet be desirable in SON systems such as ad hoc networks. C. AntHocNet Since AntNet may have a significant overhead, which may be induced by its repeated path sampling, AntHocNet [18] as an improvement of AntNet has been proposed to relieve this overhead. Both reactive and proactive components are included in AntHocNet: In a path setup phase, AntHocNet represents a reactive routing approach by setting up paths when they are needed at the start of a session, with paths being represented as pheromone trails to indicate their respective quality. Then data packets are stochastically routed as datagrams over different paths using the pheromone information, which leads to automatic load balancing. When a path is clearly worse than others, it will be avoided so as to relieve its congestion. The other paths will thus get more traffic, leading to a higher congestion and an increased end-to-end delay. By continuously adapting the data traffic, the nodes try to spread data load evenly over the network, and load balancing is eventually obtained. While a data session is active (i.e., during the course of the communication session), paths are proactively probed, maintained and improved. The source node will send out proactive forward ants, which follow the same way as the data but have a small probability of being broadcasted at each node. Two purposes are served by the ants: i) If a forward ant reaches the destination without a single broadcast, it simply samples an existing path and gathers up-to-date quality estimates, and updates the pheromone values along the path from source to destination. The same operation is performed by a backward ant from the destination back to the source. ii) If the ant got broadcasted, it will arrive in all the neighbors of the broadcasting node. Moreover, it will be broadcasted again if in this neighbor it does not find pheromone pointing towards the destination. The proactive ant will be deleted if it does not find routing information within two hops. Nodes can detect link failures when unicast transmissions fail or when expected Hello Messages (A messages used to enable nodes to know about their immediate neighbors and have pheromone information in their routing table) were not received. Once link failure happens, a route repair ant that travels to the involved destination will be broadcasted: it follows available routing information when it can, and is broadcasted otherwise. The node waits for a certain time and concludes that it was impossible to find an alternative path to the destination, if no backward repair ant is received. The node needs only to send a notification to its neighbors in three cases: i) the node still has other entries for the destination(s) involved in a link failure, or ii) the link failure was due to an ant packet, or iii) a failed path is repaired. Several information, such as a list of the destinations the node lost a path to, and the new best estimated end-to-end delay and number of hops to this destination, will be contained in the notification, which can be used by all the neighbors to update their pheromone table. The neighbors will broadcast the notification further if they lost their best or their only path to a destination, until all nodes along the different paths are notified of the new situation. A number of studies have been carried out to investigate the network behavior of AntHocNet. For example, the performance comparison of AntHocNet and AODV is performed for various speed values for Random Way Point (RWP) mobility in an ad hoc network, and shows that AntHocNet outperforms AODV in terms of both the delivery ratio and the average packet delay. D. BeeHive Many features, such as efficient allocation of foraging force to multiple food sources without central control, etc, have been observed in honey bee colonies and are desirable in designing and optimizing artificial SON systems [20]. Inspired by this observation, BeeHive [21] which was developed by Farooq et al., has been applied in telecommunications networks with load balancing being emphasized. The following principles are followed by BeeHive algorithm: Each node is regarded as a hive that consists of several bee agents, which are periodically launched by the node to explore the network environment and routing information. Agents provide some parameters, such as the information on the propagation delay and queuing delay of the paths they explored, to their visited nodes. 507

4 A threshold is set up for the number of hops that an agent is allowed to take, and an agent may provide its path information only if the quality of the path is above this threshold. The BeeHive algorithm not only reduces the overhead of collecting the routing information, but also helps in maintaining smaller local routing tables. All the agents launched by the same node but exploring different paths will exchange their information via the routing table, which helps in evaluating the overall quality of paths that the launching node involves in. In this algorithm, the quality of a node is formulated as a function of proportional quality of only those neighbors that possibly lie in the path toward the destination. Once data packets arrive at a node, the routing information about the quality of that node s different neighbors for reaching their destinations is accessed, and the packets will select the next neighbor toward the destination in a stochastic manner depending on its quality. Following this rule, not all packets follow the best paths, which helps in improving the load balancing in the future complex networks. E. MACO Different from the single-colony ACO algorithms aforementioned, Multiple Ant Colony Optimization (MACO) technique, which employs more than one colony of ants to search the optimal paths, can be used to address the stagnation problems observed in conventional ACO and improve the load balancing significantly [22], [23]. The process of paths creation in MACO is illustrated in Fig. 1 with each type of pheromone represented by a different color, following the steps [22]: Suppose there are 4 ants, i.e., r1, r2, b1 and b2, where (r1, r2) are red colony ants and (b1, b2) represent blue colony ants. Three routes (R1, R2, R3) lead to a food source N2, with R1 > R3 > R2 being satisfied. Initially, all ants are in the nest N1 and have to select a route from among {R1, R2, R3} to reach N2 (see Fig. 1(a)). At N1, all ants randomly select their routes from {R1, R2, R3}. Suppose that b1, b2, r1, r2 choose R2, R1, R3, R1 respectively. b1 and b2 each deposits one unit of blue color pheromone τ b along R2 and R1 respectively, and r1 and r2 each deposits one unit of red color pheromone τ r along R3 and R1 respectively (see Fig. 1(b)). Since R1 > R3 > R2, b1 reached N2 before the other ants (see Fig. 1(c)). And b1 discovers that the pheromone densities in the three paths are τb R2 = 1 and τb R1 = τb R3 = 0 (only R2 has one unit of blue pheromone, see Fig. 1(d)). Therefore, b1 will choose R2 as return its path, and thus the pheromone density of R2, i.e., τb R2, will be increased to 2 (see Fig. 1(e)). Subsequently, r1 reached N2 before b2 and r2 since R1 > R3. To return from N2 to N1, r1 discovers that τr R3 = 1 and τr R1 = τr R2 = 0 (note that r1 cannot sense the blue pheromone). Thus r1 selects R3 again as its return path (see Fig. 1(f))and τr R3 will be increased to 2. Fig. 1. The steps of MACO algorithms. When b2 and r2 finally reached N2, they selected their return paths by sensing pheromone densities τ b and τ r, respectively. Since b2 discovered that τb R2 > τb R1 and τb R2 > τb R3, it will select R2 as its return path. Similarly, r2 discovered that τr R3 > τr R1 and τr R3 > τr R2, it will select R3 (see Fig. 1(g)). When all ants have returned to N1, R2 and R3 has the heaviest concentration of τ b and τ r, respectively (see Fig. 1(h)). Then the following ants from the blue colony will prefer to select R2, while ants from the red colony will tend to select R3, if they travel to N2. Since MACO enhances the adaptiveness of optimal path creation and increases the probabilities of allowing new or better paths to be exploited in a dynamic network, the load balance in packet-switched networks can be improved considerably by maintaining different pheromone tables. F. MABC By combining MACO with bee colony, Multiple Ant-Bee Colony (MABC) Optimization, which uses different colonies of ants and bees and appoints these colony members as intelligent agents to monitor the network and update the routing information, is proposed to improve the load balancing and avoid congestion in the most optimal path [24]. The main idea of MABC is: at the beginning, the algorithm uses forward ants to find a suitable solution form one node to another, and then bees are used to update the routing tables based on ants collected data. The main purpose of MABC algorithm is to improve the convergence time in MACO algorithm using more accurate pheromone laying strategy. In order to achieving this objective, more information must be 508

5 stored in the routing tables. An L N routing table is reserved in each node, with L representing all the outgoing links and N standing for the number of nodes minus one (the node itself). In each table entry, P ij is used to denote the chance of node i to be selected as the next node on the way to node j, and M ij = [ Tij 1, T ij 2, ] in which Tij k represents the k-th dancer bee s recorded trip time. The detailed routing procedure is explained as follows. A forward ant starts travelling through the network, and it will be directed toward the destination whenever it reaches a non-destination node. Note that the forward ants have the same priority as data packets. When a forward ant reaches the destination, a backward bee will be created based on the information provided by this forward ant. After that, the forward ant will be killed and the backward bee will continue its journey. The backward bee will traverse the forward ant s travelled route in reversed direction and update the pheromone table on its way. The backward bee is finally killed once it reaches the start node, which had initiated the forward ant. Note that the backward bee has a higher priority than the data packets in order to be able to apply the emergency changes as rapidly as possible. Three different kinds of bees are used in MABC, including: i) Dancer bee: whenever this bee reaches a node, it will update the node s pheromone table and set down its traverse time in related field. The pheromone update strategy in this table is identical to that of the backward ants in ACO. ii) Follower bee: These bees should collect information based on dancer bee s dancing parameters. Assume that the follower bee has come from node j to i on its way to destination d by using equation T jd = βt flw + (1 β)tjd dnc, where 0 β 1 is an T flw jd jd impact factor that determines the two first factor s effect, represents the Follower bee s trip time, and Tjd dnc denotes the selected trip time among dancer bees based on M ij. Having a follower bee starting from node d, travelled from j to i, the k-th trip time in M ij will be selected with probability P k = 1 T k jd 1 k Tjd k. The shorter the trip time is, the higher its probability to be chosen. iii) Introvert bee: These bees act exactly the same way as backward ants in ACO algorithm and completely ignore other bees activities. By using several ant-bee colonies with different types of pheromone, MABC has endeavored to distribute the network traffic to several optimal local paths by creating several pheromone tables in each node and routing data packets according to these tables. It has been proven that the MABC outperforms MACO in terms of fault-tolerance [24]. G. Comparison among Variant Routing Algorithms The comparison among variant bio-inspired routing algorithms is given by Table I. Evidently, MABC outperforms the other algorithms in terms of both load balancing and TABLE I COMPARISON AMONG ACO-BASED ROUTING ALGORITHMS Algorithm Demerits Merits Significant overload Robust multipath routing; AntNet due to repeated Automatic load balancing; path sampling Adaptivity A high average delay Overload due to repeat AntHocNet is observed in the path sampling is avoided; simple scenarios Better delivery ratio Initial path selection Congestion in the optimal MACO may impact its path is relieved; convergence High load balancing Routing information Load balancing enabled; BeeHive exchange imposes an Quality paths additional traffic burden being created Delay caused by using A better Fault-tolerance MABC multiple colonies at the capability than MACO; beginning of failure High load balancing fault-tolerance capabilities. This fault-tolerance capability may become more critical in the future CRNs, where an ever worsening situation of spectrum scarceness will be met and the dynamic of network may become worsen. All the facts aforementioned increase the complexity of the routing scheme greatly, especially in the presence of network failure or spectrum handoff [25], [26]. Furthermore, new challenges may also arise in designing cognitive routing algorithms in the future complex network environments such as heterogeneous networks [27] and cognitive wireless mesh networks [28], in which the ACO algorithms with multiple colonies will obviously exhibit their advantages over the single-colony algorithms in terms of both adaptiveness and robustness. IV. REMAINING CHALLENGES Although bio-inspired adaptive routing algorithms have already attracted more and more concerns and outperform the traditional algorithms, there are still several major challenges in addressing the technical issues aforementioned, such as: 1) Stagnation mitigation in static networks will become challenging issue: As indicated by [29], the shortest path created by ACO is only statistical, and if by chance, many of the ants initially choose a non-optimal path, other ants are more likely to select it, leading to further reinforcement of the pheromone concentration along this non-optimal path. This is especially undesirable for static networks in which ants may always choose a non-optimal path. Even if an optimal path is created, congestion may happen in the optimal path to degrade both the adaptiveness and load balancing in a dynamic network. 2) The randomness in the initial path selection may impact the convergence of the MACO seriously: Although MACO improves the load balancing capability of a dynamic network considerably by employing more than one colony of ants to search the optimal paths, the final convergence state of MACO heavily depends on the initial path selection of each type of ants. Moreover, 509

6 the impact of the randomness in the initial path selection on the convergence of the MACO may become serious if the types (and number) of ants in MACO are too few. 3) Proposing more efficient multi-colony routing algorithms for heterogeneous CRNs will be another challenging task.: In consideration of the worsening spectrum environment and an ever increasing complexity in network, developing a robust routing algorithm with high capabilities of fault-tolerance, adaptiveness, load balancing and convergence will become more critical to optimize the entire network. Moreover, further exploiting the intelligences of variant natural colonies (e.g., that beyond ants and honey bees) and utilizing them to optimizing the routing protocols for large-scale SON systems would also be a promising topic in the future. V. CONCLUSIONS Adaptive routing in self-organized networks were investigated in this paper to pave the way towards operating and optimizing the artificial SON systems. Several bio-inspired adaptive routing algorithms, including ACO, AntNet, AntHocNet, BeeHive, MACO, MABC, being investigated by borrowing ideas from social species. Be aware of the ever increasing complexity, heterogeneity and dynamics of communications networks, proposing more optimization adaptive routing algorithms that are universally applicable to variant heterogeneous networks will still be a challenging task in the future. ACKNOWLEDGEMENT This work was supported by the National Natural Science Foundation of China (No ), Beijing Science and Technology Program (Z ), the Program for New Century Excellent Talents in University (NECT ), the Foundation of Beijing Engineering and Technology Center for Convergence Networks and Ubiquitous Services, and National Key Projects (2012ZX and 2012ZX ). The corresponding Author is Dr. Zhongshan Zhang. REFERENCES [1] Z. Zhang, K. Long, and J. 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