MPRP: A Novel Mobility Prediction Algorithm for Improving Routing Protocols of Mobile Ad Hoc Networks Mahmood Hasanlou
Hossein Mohammadi
Ali Movaghar
Department of Computer Engineering, Sharif University of Technology, Tehran, Iran
Router Laboratories, School of Electricall & Computer Engineering, University of Tehran, Tehran, Iran
Department of Computer Engineering, Sharif University of Technology, Tehran, Iran
[email protected]
[email protected]
ABSTRACT A mobile ad hoc network (MANET) is a network without any predefined infrastructure in which every node not only enjoys the services from the network but serves the network as a relaying router. Because of nodes mobility, these kinds of networks are highly dynamic. Therefore, link failure is so frequent. This paper provides a novel Mobility Prediction algorithm for improving Routing Protocols (MPRP). Routing protocols by using of the mobility prediction algorithm, gain better performance. MPRP tries to maintain routes resulted from a route discovery phase by capturing the nodes which have became useless due to mobility and replacing them with newly founded nodes. Simulation results show that MPRP can reduce average number of hop counts, average number of broken links and provide longer network lifetime. Index Terms- Mobile Ad Hoc Network, Routing protocol, Mobility prediction.
1. INTRODUCTION Wireless Mobile ad hoc networks consist of several mobile nodes which connect each other without any fixed network infrastructure. These types of networks recently have became popular because of their ability for supporting user mobility and support of particular cases which cannot be satisfied by any other type of networking (e.g.: search and rescue networking). Ad hoc Routing protocols can be classified into proactive and reactive categories. Proactive protocols like DSDV [1] and OLSR [2] are often based on tables, called routing table which maintains all possible routes between nodes and updated frequently by sending beacon packets. These protocols have remarkable overhead and waste resources of the network such as bandwidth and battery power of nodes. On the other hand, proactive protocols like DSR [3] and AODV [4], which also known as on-demand protocols, find a path between source and destination nodes only when one is required in order to reduce communication overhead. In these protocols when a node has data for sending to other, it initiates a route request packet (RREQ) and broadcasts it to all of its neighbors. When an intermediate
[email protected]
node receives a RREQ packet it checks its routing table; if it knows any route to the destination, it initiates a RREP to the sender. Otherwise, if this is the first time that node receives this RREQ then it also broadcasts this packet to its neighbors. This process continues until the destination node or an intermediate node which knows a path to the destination node receives this packet and initiates a route reply packet. When a node receives a RREP packet, look at its table and find the node(s) which previously receives a RREQ packet from them for this destination and forwards this packet to them. When source node receives first RREP packet, sends its data to a node which receives this RREP packet from it, by this assumption that this node is the nearest node to destination node. In the absence of nodes mobility almost of these protocols have good performance but in the case of high mobility, link breakage is possible in through data transferring. So we should maintain a solution in the routing protocols to address this problem. This problem has two main solutions; first, simply we do not care and upon link breakage we find another path. This solution significantly harms performance of routing protocols and increase network overhead, so is not very useful. Second approach is adding a prediction mechanism to the routing protocol in which nodes can predict link breakage time and find an alternative path before it occurs. Prediction mechanisms reduces link breakage rate, so routing protocol performance is increased. Performance of a routing protocol is evaluated by measures such as packet delivery ratio, control packet overhead and so on. In this paper we propose a novel on-demand ad hoc routing protocol called MPRP which has better performance than AODV using a novel mobility prediction. As we know AODV is the best routing protocol in medium size MANETs. MPRP can predict link breakage of a route and find an alternate link for it before link breakage occurs based on accurate information about nodes positions. It also can recognize those nodes which become so close to each other in the route and remove one of them to save power. This mechanism can reduces link breakage ratio
and number of hop counts needed in a route which are crucial in routing protocols since they can increase packet delivery ratio and reduce end-to-end delay and consequently increase the life time of the whole network. The rest of the paper is organized as follows: Section 2 reviews the related works briefly. In Section 3 we describe the proposed MPRP routing protocol, while simulation results reported in section 4. Finally, section 5 concludes the paper.
2. Related Works In recent years many works tried to reduce impact of mobility on routing protocols performance. As we know routing process include two main phases: 1) route discovery (in reactive protocols) or constructing routing table (in proactive protocols); and 2) route maintenance during data transmission. Most of methods try to find stable routes in discovery phase to postpone path failure as long as possible and predict link breakage and do local recovery in maintenance phase to avoid network wide flooding of route request packets. These methods can be categorized into three broad categories based on means that they use to do prediction: 1) signal strength based methods; 2) beacon packet based methods; and 3) GPS (Global Positioning System) based methods. Following we describe characterizes of each category and give instance method of that category. Signal strength based methods, try to estimate distance between two adjacent nodes with strength of received signal and find an alternate link when the current one will go to break. For example in [8] authors try to improve TCP performance over MANETs. They mentioned that node mobility and link layer congestion are the two main reasons for packet losses in MANET environments. In order to address packet-loss problem and improve TCP performance, it is important to estimate whether a link failure caused by mobility or by congestion. The authors propose a simple way to distinguish between these causes. In their method each node keeps a record of the received signal strengths of neighboring nodes. When a node receives a packet from a neighbor, it measures the received signal strength. The node then observes how Pr changes over time. These signal strength measurements would provide an indication of whether a neighbor is still within range. Also some others [7, 9, 10] used signal strength in order to improve routing protocols performance. The most important problem of these methods is their capability of recognizing of received signal strength. As we know signal strength measurements are taken in physical layer. This capability needs a more complicated antenna which most of mobile nodes (like PDAs, laptops and etc.) have not this type of antenna. So perhaps these types of methods are not very practical in real world scenarios.
In beacon packet based methods, neighbor nodes identify each other by means of broadcasting a packet periodically to their one hop neighbors. When a node after a threshold time dose not receives any packet from its neighbor then it decides that the link between them breaks. For example in [11] each node broadcast a hello message periodically and also maintains a life time record for each of its neighbors. This record includes the number of hello packets that this node received from that neighbor. When a route discovery process initiated, this method try to establish path on the more stable nodes. In GPS based methods, the location information obtained from GPS is used to make decisions about routing. In [6] a prediction mechanism for link expiration time (LET) between any two ad hoc nodes proposed to enhance various unicast and multicast protocols and latter used in some papers [12]. In this mechanism by piggybacking GPS position information on data packets link expiration time estimated between any two nodes. By predicting the LETs of all links of a route the Route Expiration Time (RET) is given as the least of LET values. This method find a more stable route by selecting a route with maximum RET for data transmission. Also this method enables to initiate route recovery before a link breakage, because it knows RET value of each route. In [13] authors used GPS information to determine the duration of time between two connected mobile nodes and discover a request region between the source and the destination nodes for route discovery. They select the routing path with the longest duration of time for transmission to increase route reliability. In this method when a node want sends/forwards a message to the destination node, inserts its own information as measured by GPS into packet header and broadcast the packet to neighboring nodes. So a node can get the information of the last and the next nodes and uses it to do partial route reconstruction when a it finds that a path will break. As we mentioned above in this mechanism all packets forwarding do by broadcasting and we know that when a packet broadcasted all neighbors receive this packet and process it. So this mechanism uses scare resource like battery power, CPU and etc. of MNs inefficiently. Also many works like [14, 15] used GPS information to route discovery, route maintenance and route recovery.
3. MPRP Routing Protocol 3.1 Motivation and Key Ideas In our proposed mechanism a node that sends/forwards data packets, copies its coordination into packet header. When a node receives a data packet, it computes its distance from the previous node. A node by receiving two consecutive data packets compares the new distance with previous one. If the node concludes that the link between them will break soon, it asks from the previous node to find
remove node 2 from the route without with no special concern about packet delivery. This can save battery power of node 2 for future using and reduce number of hop counts of current route.
Figure 1: Node 1 can send its packets directly to node 3
another path to the next hop. If the node concludes that its presence in the path is not necessary because of their distance is smaller than a minimum threshold, then it asks from its previous node to sends/forwards the packets directly to the next hop. Except these two mentioned cases, the node just copies its coordination and forwards the packets. Our work is different from all previous work in the following ways: 1) our algorithm is very simple and not need a complicated antenna, periodically broadcasting hello packet, or broadcast packet to neighbors instead of sending it to a specific node, 2) our work additionally of link breakage prediction and replace it with a newer one like others, can capture needless links. Needless links are those links that two end of link, say 1, 2, (fig. 1) are so close to each other as next nod of 2, node 3 can hear 1. 3) finally our work has much accurate prediction because distance of each two neighbor nodes recomputed for each packet instead of estimating route expiration time in the route discovery phase based on worst case conditions. So our method uses a founded route as long as possible.
3.2 Protocol Specification We propose MPRP routing protocol which involves two major phases: route discovery and route maintenance. The major advantage of MPRP protocol is inherited from its prediction algorithm which forecasts the next topology of the network that is useful in the maintenance phase. Route discovery in MPRP is the same as other on-demand routing protocols especially AODV. After route discovery phase and when a route established between two nodes, source node starts packet sending to the destination. In this phase occurring of two bad states are possible for any two neighbor nodes in the route. First, two node is go away from each other such that the link between them breaks and route is expired. Later, two nodes become so close to each other, such that the link between them becomes useless. As shown in figure 1, in this case source node (node 1) can directly sends its packets to destination node (node 3) and intermediate node (node 2) is no longer needed. So we can
In our scheme we add two additional fields namely x, y, to IP header in order to recognizing and solve these two bad states. Also we add two distance variable min-, maxdistance which defines minimum and maximum allowable distance of two neighbor nodes in a route of AODV. For example, by assuming radio range of 200m, we set mindistance to 30m and max-distance to 170m. Finally, we add a table, called prediction table to AODV protocol in which a node stores distances of previous nodes in routes that this node is an intermediate node of them. A node copies its x and y coordination to X and Y fields of IP header while it sends/forwards data packets to the next node in the route. When an intermediate node receives a data packet; retrieves previous node address, X and Y fields of IP header and looks in its prediction table for this node, if it does not exist in the table, it simply adds this node and its Euclidean distance to the prediction table. Otherwise, two nodes have three possible configurations: •
D < min-distance
•
D > max-distance
•
min-distance < D < max-distance
Where D is the Euclidean distance of two nodes and can be obtain from equation 1. Equation 1 In the last case, node does not need any extra action but in others, current node initialize route expire (REXP) packet which contain address of the next node in the route and a field which shows the type of message and send it to the previous node. Type of message field in an REXP packet set to ‘remove-me’ in first case and find-another-node in the second case. When a node receives an REXP packet, it checks the type of the message. In case of a ‘remove-me’ packet, receiver looks in its prediction and routing tables and replaces all occurrences of the sender node with the one mentioned in the received packet. If the received packet is a ‘findanother-node’, then the receiver initialize a RREQ packet to find a path from it to the one mentioned in the received packet and broadcast it to all neighbors. Of course nodes that have this node address in their prediction tables and distance between them and the specified node is not between min and max allowable distance, does not reply and just forward this RREQ packet. Employing the mechanism stated in fig. 2, we gain two major results: Removing some nodes which are inessential
Figure 3: number of packet routed for another node
Figure 5: average number of hop counts
in packet forwarding from the route that has two consequences. First, this can reduce total hop count of the route, resulting in faster packet delivery and consequently lower end-to-end delay. Secondly, this can save power of the removed nodes for future usage and increase overall lifetime of the network. Finding an alternative path before the current one is broken, that have two major consequences. First, It reduces link breakage ratio, so packet delivery ratio is increased. Secondly, this can increase reliability of the network which is an important factor in ad hoc scenarios.
4. Evaluation and Simulation 4.1 Simulation Framework We use Global Information systems Simulation Library (GloMoSim) [5] which is widely used in wireless communication research to simulate MPRP. Simulation area is the size of 2000m×2000m and number of existing nodes is 30. The radio coverage is within 200m radius and all links are assumed to be bi-directional. Each pair of nodes can communicate directly if the two nodes are within the radio coverage. The node mobility model used here is the Random Way Point Mobility model. The simulation scenario is performed three times with mindistance 20m, 25m, 30m and max-distance 180m, 175m, 170m respectively. We found out that the case with min-
Figure 4: average number of broken links
Figure 6: packet delivery ratio vs. node maximum velocity
distance 25m and max-distance 175m is better than the others. We evaluate average number of packets routed for other nodes, packet delivery ratio, average hop count and number of broken links as performance parameters.
4.2 Simulation Results It can be seen from fig. 3 the number of packets routed for other nodes in MPRP comparably lower than AODV at different speeds. The decrease of this factor is mainly introduced with prediction algorithm. Because of routing done with regard to distance of two neighbor nodes in a route; an intermediate node considered needless and removed from route, if its Euclidean distance of its previous node in route be less than min-distance, hence total number of packets routed for other nodes reduce. As it can be seen form fig. 4 average number of broken links per route in MPRP is less than original AODV. This difference increase when node maximum velocity increased. Also this difference introduce with prediction algorithm. When nodes can move around with high speed, the probability of link breakage increased. In this case our algorithm predicts link breakage and finds an alternate link for it before of link breakage occurs. From fig. 5, average number of hop counts increase with increase of node mobility velocity. But as we can see,
always average number of hops in MPRP is less than AODV. Packet delivery ratio is the ratio of received packets vs. transmitted packets. Fig. 6 demonstrates the packet delivery ration vs. nodes mobility velocity. As the mobility velocity increase, packet delivery ratio in AODV and MPRP all decrease. But decreased value is not dramatically. In some cases AODV is slightly inferior to MPRP, because the average link breakage in AODV is more than MPRP.
5. CONCLUSION This paper presents an on-demand routing protocol named MPRP which it takes advantages of a novel mobility prediction algorithm. Like any other on demand routing protocol, MPRP consists of two phase: route discovery and transmit data. Route discovery phase of MPRP is the same as the AODV, but in the second phase MPRP based on nodes mobility maintain routes which found previously in route discovery phase. MPRP find those links which will break before than they occur and replace them with new ones and remove those nodes form route which become so close to each other as source node can directly sends its packets to destination and intermediate node is no longer needed. We introduce GloMoSim to simulate algorithm and protocol. As a result, the MPRP could reduce the average number of packet routed for other nodes by a node, the average number of broken links, and the average number of hop counts significantly. Also the MPRP perform slightly better than the original AODV about packet delivery. By reducing the average number of hop counts, the number of nodes which participate in packet forwarding is reduced, so their power saved for future usages. Also by reducing the average number of packet routed for other nodes, a node consumes less power. By these two advantages we can conclude the overall network lifetime is increase which is a very important factor in MANETs that their nodes use battery power. REFERENCES [1] Chiang, C.C., Wu, H.K., Liu, W. and Gerla, M.
Routing in Clustered Multihop, Mobile Wireless Networks with Fading Channel. IEEE Singapore International Conference on Networks, SICON'97 [2] Jacquet, P., Muhlethaler, P., Qayyum, A., Laouiti, A.,
Viennot, L., Clausen, T. Optimized Link State Routing Protocol (OLSR). RFC 3626. [3] Johnson, D. B. and Maltz, D. A. Dynamic Source
Routing in Ad Hoc Wireless Networks. Mobile Computing, 1996, Kluwer Academic Publishers, Boston, USA, pp. 153-181. [4] Perkins, C.，Royer, E. and Das, S. “Ad hoc On-
Demand Distance Vector (AODV) Routing”， Mobile
Ad Hoc Networking Working Group,IETF Internet Draft ， Jun. ， 2002. [5] The
network simulatorhttp://pcl.cs.ucla.edu/projects/glomosim/
GloMoSim,
[6] W. Su and M. Gerla, “IPv6 Flow Handoff in Ad-Hoc
Wireless Networks Using Mobility Prediction”, Proceedings of IEEE GLOBECOM ’99, 1999. [7] Amit P. Jardosh, Elizabeth M. Belding-Royer, Kevin
C. Almeroth, and Subhash Suri, “Real-World Environment Models for Mobile Network Evaluation”, IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, 2005 [8] Fabius
Klemm, Zhenqiang Ye, Srikanth V. Krishnamurthy, Satish K. Tripathi, “Improving TCP performance in ad hoc networks using signal strength based link management” , Elsevier, Ad Hoc Networks, 2005.
[9] Ruay-Shiung Chang; Shing-Jiuan Leu; Long-lived
path routing with received signal strength for ad hoc networks, 1st International Symposium on Wireless Pervasive Computing, 2006. [10] San-Yuan Wang; Jia-Yu Liu; Chun-Chien Huang;
Mao-Yuan Kao; Yi-Ho Li; “Signal strength-based routing protocol for mobile ad hoc networks”, Proceedings of the 19th International Conference on Advanced Information Networking and Applications (AINA’05), 2005. [11] Chun-Yen Hsu, Jean-Lien C. Wu, Shun-Te Wang,
“Finding Stable Routes in Mobile Ad Hoc Networks”, 18th International Conference on Advanced Information Networking and Applications (AINA'04) [12] Sathyaraj, B.H.; Doss, R.C.; “Route maintenance using
mobility prediction for mobile ad hoc networks”, IEEE International Conference on Mobile Adhoc and Sensor Systems Conference, (MHWMN05), 2005. [13] Nen-Chung Wang and Shou-Wen Chang, “A reliable
on-demand routing protocol for mobile ad hoc networks with mobility prediction”, Elsevier, Computer Communications, 2005. [14] Inayat, R.; Gardezi, U.H.; Shahid, A.R.; “Adaptive
mesh based routing for efficient multicasting in highly mobile ad hoc networks”, International Conference on Emerging Technologies, ICET '06. 2006. [15] Hussain, S. A.; Garcia, E.; Idrees, M.; “Throughput
Enhancement in AODV Routing Using Mobility Awareness”, 9th International Multitopic Conference, IEEE INMIC 2005.
