The Multi-agent System for Dynamic Network ... - Semantic Scholar

The Multi-agent System for Dynamic Network Routing Ryokichi Onishi

Saneyasu Yamaguchi Hiroaki Morino Hitoshi Aida The University of Tokyo Saito-Aida Lab, Department of Electronic Engineering 7-3-1 Hongo, Bunkyo-ku, Tokyo 1138656, JAPAN {ryo, sane, morino, aida, saito}@sail.t.u-tokyo.ac.jp

Abstract The single-hop communication method of the current wireless network cannot meet new demands in the domain such as ITS 1 and Home LAN. Even though the ad-hoc network architecture is expected to solve this problem, but the nature of a dynamic topology makes this routing hard to be realized. This paper introduces a new ad-hoc routing algorithm, which is inspired by [12]. In their system[12], some control agents explore the network and update routing tables on their own knowledge. Using these routing tables, other agents deliver messages. They considered the feasibility of the agent-based routing system, but did not refer to an efficient algorithm. In this paper, we consider that algorithm without increasing network load. We propose multiplying each entry in the routing table to store much more information from agents and evaluating them to make better use of information, which succeeded in raising the network connectivity by about 40% by simulation.

Tadao Saito

think a multi-hop ad-hoc wireless network (MANET) is one of the most important architecture to solve these problems, whose routing protocol can be modeled as the highly distributed architecture. In this paper, we define the MANET as a network architecture where each mobile host is treated as a router and makes a peer-to-peer communication without any basestations.

1.1. Home LAN The ubiquitous computing [19] is one of the hottest topics in computer networks. This concept means that ubiquitous devices, such as TV set, refrigerator and smoke detector, form network connections and work together. These networks are likely to be a kind of the MANET including many devices linked through radio waves as shown in Figure 1. It seems hard to realize this architecture with a single-hop communication, because of many obstacles in the house and short range of wave to make good use of bandwidth resource. From preliminary studying, We believe the MANET has high possibility to solve this problem.

1. Introduction The advance of hardware technology lets people enjoy taking out smaller, cheaper, and better performance devices, and the advance of radio frequency communication technology lets them enjoy networking anytime and anywhere. Due to this, people want any device to form a network connection through the alternative ways to access information distributed there. As a result, I think, the network tends to be dynamic, dense, heterogeneous, and overloaded in the future. In the centralized management fashion, some special management nodes such as cell towers tend to be bottlenecks for system administration, cost performance, scalability, and diversity of service. Moreover, the singlehop communication method cannot support new demands in such network, especially Home LAN and ITS. Therefore, I 1 Intelligent

Transport Systems

source

destination

Figure 1. The MANET in the house

1.2. Intelligent Transport Systems (ITS) Since the wireless communications, especially beacons, play an important role in ITS domain; thus, more convenient

and flexible transport systems are considered. For example, a toll free system on the highway, more flexible navigation system, easier management of cars on the job, say taxi cabs, and etc. The beacon is superior to the cellular in communication cost, to radio wave broadcast in interactivity, to the LCX2 in initial cost, but inferior to the other wireless media in continuity. For example, if a car faces with a traffic jam, it can get few chances to communicate with information sources. Though using the beacon together with the other media can solve this problem, but we think of a solution with the MANET among cars as shown in Figure 2. In addition, we refer to the high density of nodes which makes this solution more reasonable.

2. Mobile agents can identify and use limited resources prepared for them where they stand. After mobile agents left, all resources occupied by them are completely released. These resources are extremely prepared and limited for security reasons. 3. Mobile agents can always stop processing and preserve their states, when they need to move. On arrival of their destinations, they resume their works. They must contain code which can be interpreted by any hosting nodes. We, then, consider advantages and disadvantages of using mobile agents substitute for control packets as follows, taking [9][4][12] into consideration. Advantage 1 It is easy to install new routing system. All you have to do is to collect old routing agents and release new agents over the network. This easiness enables routing algorithms to quickly adapt themselves to variable circumstances.

Figure 2. The MANET on the road.

2. The routing architecture for the MANET environment Though computer networks will become more fruitful in the MANET environment, but the nature of a dynamic topology makes this multi-hop routing hard to be realized. In this section, we discuss the routing for the MANET environment. First we introduce agent-based researches, especially [12] which inspired our model. Then we consider five well-known related works, which take non-agent approaches, from an aspect of characteristics. And we finally discuss the potential of the agent-based approach in comparison with non-agent works.

2.1. Cooperating mobile agents for a dynamic routing There are many researches on cooperating mobile agents for network routing such as [1][5][18][4][12]. We simply define mobile agents as follows in this paper, taking [6][10][12] into consideration. 1. Mobile agents are code-containing data that can be transmitted among nodes. These size must be as minimal as possible to make good use of bandwidth resource. 2 Leaky

Coaxial Cable

Advantage 2 Various routing services are locally embedded in agents. Moreover, heterogeneous networks where agents are supported can be linked together. Advantage 3 Agents release all resources they occupied after they left. Disadvantage 1 It is easy for malicious hosts to attack agents. Nevertheless, it is possible to deploy a mobile-agent system that adequately protects a machine against malicious agent by restricting resources which agents can access. Disadvantage 2 Agents themselves are often written in relatively slow interpreted languages and slightly gain weight for containing code. However, if the entire system saves more bandwidth resource for control and reacts more quickly, these overheads of agents might be acceptable. These researches on the agent-based routing algorithm are grouped into two types. One[1][5][18] is for a dynamic traffic in the wired network and the other[4][12] is for a dynamic topology in the wireless network. Most of them rely on social insect metaphor such as ants, which leads the shortest path finding algorithm. When ants walk from the nest to the food, they deposit pheromone trails. The shorter the path is, the stronger the pheromone remains, because ants can go and return faster, that is, more frequently through the shorter path. The detail principal is shown in [2]. These works apply ants depositing pheromone trails as ant-like agents increasing the probability that nodes where they stand lie on the shortest paths. In [12], they surveyed

how feasible such routing adapts to a dynamic topology. Their model is simply explained as follows. As shown in Figure 3, their model consists of mobile nodes with wireless links, routing agents and messenger agents. Routing agents explore the network updating routing tables on their own knowledge (so we call them ‘explorer agents’ or simply ‘EA’). Messenger agents (‘MA’) read routing tables to select next node for their destination. The routing algorithm is relatively simple. In each routing table, a messenger agent looks up a neighbor node where the latest explorer agent from its destination comes through, and goes there. The messenger agent repeats this algorithm until it reaches its destination node, which causes message transmission. Miner considered the feasibility of agent-based routing system, but didn’t refer to an efficient algorithm. the latest EA from Node Q

D

Q

A

P

MA

C

B

Figure 3. A messenger agent wanting to go from ‘P’ node to ‘Q’ node selects ‘A’ node that the latest explorer agent came from ‘Q’ node through.

[3][11][17]. 1. Routing Time Proactive routing (DSDV and CBRP) This is also called ‘global, precomputed routing’ or ‘table-driven routing’. Nodes exchange their route information periodically to make all routes always available. This method has the advantage of less delay on making a communication and higher stability because of the constant amount of control packets. Reactive routing (DSR, AODV and TORA) This is also called ‘On-demand routing’. Nodes search routes to their destinations on demand. This method has the advantage of saving the amount of control packets and higher scalability. 2. Routing Direction Retrospective updating (DSDV and TORA) This is also called ‘Backward learning’. Routes are made in the opposite direction of searching on the assumption that every link is bidirectional. This method has the advantage of saving the amount of control packets. Incremental updating (DSR, AODV and CBRP) Route are made in the same direction of searching, and control packets often flood into the network. On making routes, AODV uses the assumption that every link is bi-directional. Except for AODV, this method has the advantage of ability to make asymmetrical links. 3. Information Placement

2.2. Related works and their characteristics The Multi-hop Ad-hoc Wireless Network (MANET) working group in the Internet Engineering Task Force (IETF) has considered a lot of state-of-art works on the routing algorithm for the MANET environment. We think these MANET routing algorithms can be roughly categorized according to next five characteristics - routing time, routing direction, information placement, route structure and alternative route. We categorize five well-known routing algorithms - DSDV3 [15], DSR4 [8], AODV5 [16], TORA6 [14] and CBRP7 [7] as follows, with advantages of each characteristic choice explained. We owe these explanations to 3 Destination-Sequenced

Distance-Vector Source Routing 5 Ad-hoc On-demand Distance Vector 6 Temporally-Ordered Routing Algorithms 7 Cluster Based Routing Protocol 4 Dynamic

Source-based (DSR) Source nodes have all information on the route to destination. Hop-by-hop (DSDV, AODV, TORA and CBRP) Every node knows which next nodes to relay message packets to. Repeating this relay algorithm until packets reach destination node causes message transmission. This method has the advantage of saving size of message packets, higher scalability and higher robustness because of distribution of routing information 4. Route Structure Flat routing (DSDV, DSR, AODV and TORA) There is no special management node on every route, that is, every node distributed over the network plays the same role in routing. This method has the advantage of easier maintenance of routing management.

Hierarchical routing (CBRP) Nodes are classified into three roles - cluster head, gateway node and normal node. Nodes are directly linked together through bi-directional links and make a group called ‘cluster’, and decide one ‘cluster head’ node who manages the cluster state. Nodes which belong to more than one cluster are called ‘gateway node’ and link these clusters. This method has the advantage of higher scalability.

3. Our proposed model We aim high and reasonable connectivity and short path oriented routing system based on [12] without any additional control packets. Our research consists of two steps. The first step, we think of multiplying each entry in the routing table to collect much more information from control agents. The second step, we think of adding entries of information on freshness and distance to routing table in order to evaluate collected information suitably and happily.

5. Alternative route Single-path (TORA) Message packets are transmitted through a single route. This message has the advantage of saving size of routing tables. Multi-path (DSDV, DSR, AODV and CBRP) Message packets can be transmitted through various routes. This method has the advantage of higher robustness and flexibility. Our proposed routing method is categorized into (1) proactive routing, (2) retrospective updating, (3) hop-byhop, (4) flat routing and (5) multi-path. We consider character (1)(2) as low and stable flow control packets, character (3)(4)(5) as flexible and robust routing system against a dynamic topology. We think the proactive routing method is important characteristics because of suitableness for realtime, interactive and QoS8 -guaranteed applications. One of the proactive and non-agent routing algorithms is DSDV. This method tries to grasp a whole network state information on traffic and topology. However, in [5] it was proved that these kinds of methods, say OSPF, behave more wasteful and less flexible than the method based on ant metaphor, that is, statistics or probability in a case of a dynamic traffic network. We think it is also true in a case of a dynamic topology network. Our model is interested in not a whole network map but a probable route. Another kind of proactive and non-agent routing algorithm is CBRP. This method has two difficulties in implementation, caused by its hierarchical routing characteristics. First, it is difficult to assign a unique ID to each cluster which is easy to form and vanish. Second, it is difficult to decide cluster head, especially on merging and splitting clusters. We think these problems are due to its own complicated structure whose centralized management fasion is imported from current wired network into a dynamic wireless network. Our model is the completely distributed peerto-peer architecture. 8 Quality

of Service

3.1. Multiplying each entry in the routing table Figure 5(a) shows the example of [12] routing table. We propose a basic algorithm that multiplies each entry in routing table to store much more information from explorer agents as shown in Figure 5(b). In this routing table, a messenger agent looks up a neighbor node where the greater number of explorer agents through its destination comes, and goes there. We expect routing system would be improved. Figure 4 and Figure 5 show a simple example. Figure 4 should be compared with Figure 3. Figure 5 shows a routing table of ‘P’ node. At a time that, ‘P’ node wants to send a message to ‘Q’ node. The table in Figure 5(b) shows three explorer agents came from ‘Q’ node to ‘P’ node through ‘A’ node, and one through ‘B’ node. According to the algorithm above, a messenger agent goes to ‘A’ node. The messenger agent repeats this algorithm until it reaches its destination node, causing a message transmission. three EAs from Node Q

D

A

P C

Q

MA

B one EA from Node Q

Figure 4. A messenger agent wanting to go from ‘P’ node to ‘Q’ node selects ‘A’ node that more explorer agents come from ‘Q’ node through.

new destination next

old

destination next next next next

N A B O null P Q A C R (a) a single entry

N O P Q R

A D A A B C C D null null null null A B A A C C C C (b) multiple entries

destination next next next next

N O P Q R

destination

Figure 5. An example of ‘P’ node routing table, each entry is multiplied up to 4 entries.

N O P Q

3.2. Adding entries of information on freshness and distance to routing tables We also think if routing tables just engage gathering much more information, these information may behave harmful toward routing efficiency according to circumstances. We suggest that it solves this problem and improves routing efficiency more without any additional control packets to properly evaluate each table entry with two attached entries of information on freshness and distance. Please note that we call each attached entry for evaluation as ‘evaluation entry’. Information on freshness means how long time has passed since a routing table has got the information from explorer agent. Information on distance means how many hops the explorer agent takes to give routing table the information. Figure 6 and Figure 7 are a simple example, which should be compared with Figure 3 and Figure 4. three EAs from Node Q

D

A

P C

Q

MA

B

R

A B null A C

D C null B C

A C null A C

next time hops

28 22

A B

3 9

null null null

28 30

A C

13 2

A D null A C

Adding entries of arrival time and the number of hops.

next time hops

26 21

D C

next time hops

25

6 3

15

null null null

26 25

B C

A C

Figure 6. A messenger agent wanting to go from ‘P’ node to ‘Q’ node selects ‘B’ node that has the highest value of the information on ‘Q’ node.

2

null null null

2

20

2

20

A C

11 3

17 12

A D

4 3

null null null

16 10

A C

2 2

Figure 7. Routing tables have the entries of information on freshness and distance.

3.3. The way of evaluation We evaluate the degree of freshness and distance with a reliability of links. Each node measures the number of links at the beginning of each time step (L) and the number of its broken links during each time step (Lb ). Then p = Lb /L means the rate of links broken per a time step and 1 − p means the reliability of links. Our evaluation uses these values to all links for a rough estimation. We show how a source node (‘S’) evaluates the value of information on a destination node (‘D’) as shown in Figure 8. An explorer agent took h hops to bring the information and t time steps have passed since then. Value p is the rate of links broken per a time step. Value hEA is the rate of explorer agents moving per a time step. Nodes labeled ‘R#’ are relay nodes. Value RAB is the reliability of the link between ‘A’ node and ‘B’ node. First, we evaluate the reliability of link between ‘S’ and ‘R1’ as Formula (1) because t time steps have passed since the agent came to ‘S’. RSR1 = (1 − p)t

one EA from Node Q

3

next time hops

(1)

Second, we evaluate the reliability of link between ‘R1’ and ‘R2’ as Formula (2) because t + (1/hEA ) time steps have passed since the agent went through the link, which is the sum of t time steps at ‘S’ and 1/hEA time steps on coming from ‘R1’ to ‘S’. t+ h 1

RR1 R2 = (1 − p)

EA

(2)

In the same way, we evaluate the reliability of link between

4. Simulation and result

explorer agent (1- p)

S

t

t+ 1 hEA

(1- p)

R1

t+

(1- p)

R2

2 hEA

t + i -1 hEA

(1- p)

R3

Rh-1

source node

D

destination node h i -1 ht + Σ h i =1 EA

The total reliability

(1- p)

Figure 8. Total reliability evaluation.

‘Ri’ and ‘Ri+1’ as Formula (3). t+ h i

RRi Ri+1 = (1 − p)

EA

We did a simple simulation to examine the effect of two-step routing improvements, which consisted of mobile nodes, gateway nodes and explorer agents. Gateway nodes remained stationary, wired to a larger network such as LAN, Internet and information sources for mobile nodes. Each explorer agent ramdomly decided next movement avoiding back-tracking of its history. Under the condition of Table 1, we counted the number of mobile nodes that had a valid route to at least one gateway to measure connectivity as same as [12]. Furthermore we also counted the average route length from all mobile nodes to the connected gateway. Circular routes and broken routes on the way were invalid routes and counted as 0 route length. Each mobile node went along a constant vector that was initialized randomly at the beginning of the simulation and reflected at the edge.

(3)

Finally, we evaluate the total reliability of links between ‘S’ and ‘D’ as Formula (4).
h i−1 h(h−1) ht+ ht+ 2h t=1 hEA = (1 − p) EA RSD = (1 − p) (4) In this way, we know ‘R1’ node has the value RSD for sending messages from ‘S’ node to ‘D’ node. ‘R1’ node may have the other values or the other neighbor nodes of ‘S’ node also have values. A source node just compares the sum of every neighbor node’s value and decides the neighbor of the biggest sum as relay node to the destination. We notice that the sum of values is not a probability but they are compared with each other under the law of probability.

3.4. Example We demonstrate how to evaluate information and decide next node with Figure 6 and Figure 7. As shown in the routing table in Figure 7, three explorer agents came from ‘Q’ node to ‘P’ node through ‘A’ node having taken 2 hops at 16 time steps, 11 at 20 and 13 at 28. Moreover, one explorer agent came through ‘B’ node having taken 2 at 26. Now 30 time steps have passed. The rate of links broken is 0.1 links per a time step and a node. According to Formula (4), a reliability of each neighbor to ‘Q’ node is calculated as follows. The reliability of ‘A’ node = 0.047(2 at 16) + 0.000(11 at 20) + 0.000(13 at 28) = 0.047 The reliability of ‘B’ node = 0.387(2 at 26) As a result, the reliability of ‘B’ node is higher than that of ‘A’ and messages are sent from ‘P’ node to ‘Q’ node via ‘B’ node. (Figure 6)

Table 1. Important parameters of this simulation The network size 400 × 400[m2] The discrete time step 1[sec/timestep] The number of mobile nodes 100[units] The speed of mobile nodes 3.6[km/hr] The number of gateway nodes 4[units] The range of radio wave 60[m] The number of EAs 100[units] The frequency of EAs moving 1[hop/sec] The history size of EA 10[histories/unit] The average number of links 6.4[links] The speed of links breaking 0.078[links] The cost for routing control 8[bits/sec · node]

We calculated connectivity and route length 50 times in the case of 1 entry per a destination as [12], 60 entries per a destination as the first step improvement, and 20 entries per a destination with 40 evaluation entries (see the section 3.2 in this paper) as the second step improvement (We just call it as ‘20 entries with evaluation’). The results are shown in Figure 9 and Figure 10. Table 2 shows each average and standard deviation (StDev) after 50 time steps as a conclusion of the result. From these results, we find four things. First, making a comparison between 20 entries with evaluation and 1 entry as Miner’s model, the average of network connectivity at 20 entries with evaluation was approximately 140% as much as that at one entry, and that of route length approximately 95%. We think the result on connectivity shows the great improvement of routing system. But the result on route length shows no so much improvement. This is be-

Connectivity 100% 90% 80% 70% 60% 50% 40%

ideal 20 entries with evaluation 60 entries 1 entry as Miner's model

30% 20% 10% 0

10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200

Time steps [sec]

Figure 9. Average Connectivity of 50 runs over time

Route length [hops] 60 entries 1 entry as the Miner's model 20 entries with evaluation ideal

4.5 4 3.5 3 2.5 2 1.5 1

0

10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200

Time steps [sec]

Figure 10. Average route length of all nodes of 50 runs over time

Table 2. Important values of the result model \ connectivity Average StDev 1 entry 66.17% 0.09173 60 entries 82.90% 0.06569 20 entries with evaluation 93.46% 0.04652 ideal 97.88% 0.01907 model \ route length Average StDev 1 entry 2.848 0.4104 60 entries 3.204 0.5133 20 entries with evaluation 2.735 0.2968 ideal 2.075 0.1817

cause some circular routes counted as no length were eliminated and then counted as relatively longer routes. Second, making a comparison between 20 entries with evaluation and 60 entries, the average of network connectivity at 20 entries with evaluation was approximately 110% as much as that at 60 entries, and that of route length approximately 85%. These results prove that our evaluation method is effective and precise. Furthermore, it can improve not only the network connectivity but also the route length. Third, network connectivity and route length at 60 entries were becoming worse over time. This proves that if routing system just engages gathering much more information, these information behaves harmful toward routing efficiency according to circumstances. Those at 20 entries with evaluation oriented toward a certain state against a dynamic topology over time. Explorer agents repaired the routing broken by the movement of mobile nodes in succession. We confirm the stability of our system. Fourth, The standard deviation of network connectivity at 20 entries with evaluation was approximately half as much as that at one entry as Miner’s model, and that of route length approximately 70%. We find our evaluation method makes the connectivity and the route length less scattering. If we gave appropriately the number of explorer agents, history size, and the number of table entries, the system would indicate much better condition. That is a future work. In addition, we pay attention to the amount of transferred data for routing management. The communication amount for routing control in this simulation is only 8k[bits/sec], which is calculated by 8[bits/nodeI D](the node ID size) ×10[nodeIDs/agent](the agent histry size) ×100[agents/sec](the frequency of agent movements), if the code size of each agent is ignored. In the case of DSDV, the communication amount is at least 500k[bits/sec], which is calculated by 8[bits/datum](the size of a time datum) ×100[data/node](the size of a distance vector packet) ×6.4[nodes/node](the average number of links) ×100[nodes/sec](the frequency of packet transferred). It is true that the above equation seems not to be an appropriate formulation for a scientific paper. However, we expect that our method aiming not a perfect answer but a statistical answer could be a reasonable answer for a dynamic topology network.

5. Conclusion and future works The single-hop communication method of current wireless network cannot meet new demands in the domain such as ITS and Home LAN. Though the ad-hoc network architecture is expected to solve this problem, the nature of a dynamic topology makes this routing hard to be realized. This

paper introduces a new ad-hoc routing algorithm, which is inspired by [12]. In their system, some control agents explore the network and update routing tables on their own knowledge. Using these routing tables, other agents deliver messages. They considered the feasibility of agent-based routing system, but did not refer to an efficient algorithm. In this paper, we think of it without increasing network load. We propose multiplying each entry in the routing table to store much more information from agents and evaluating them to make better use of information, which succeeded in raising the network connectivity by about 40% by simulation. As future works, we plan to examine this algorithm from various aspects and compare it with some related works under the condition of the same flow of control packets. Our model in this paper doesn’t make good use of agents’ advantages yet. Onishi, the first author of this paper, will introduce more powerful multi-agent model, where the agent algorithm is dynamically changed according to network density in his master’s thesis[13]. Then, we will model the situation of simulator specialized in house, ITS, and etc. For example, mobile nodes in house may have battery and heterogeneously avoid relaying. Moreover, we will implement that simple prototype with the Bluetooth technology. Our project including this paper aims the robust and flexible routing system against dynamic topology with three characteristics - (1) the distributed management with cooperating mobile agents, (2) the routing algorithm oriented toward the shortest-path, and (3) the low and stable flow of control packets.

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