CHAPTER 5 MULTICAST GEOGRAPHY BASED ROUTING IN AD HOC NETWORKS

Transcription

1 89 CHAPTER 5 MULTICAST GEOGRAPHY BASED ROUTING IN AD HOC NETWORKS 5.1 INTRODUCTION Efficient routing in MANET is a tough task due to their highly dynamic network topology, bandwidth controlled links and resource controlled nodes. Routing protocols, developed during advanced stages, depend on the topology information, which used the knowledge about the links between the nodes to begin and maintain end-to-end paths for routing. Topology-based routing has important overhead in maintaining up-to-date paths between source and destination, when the topology changes regularly. Moreover, it has scalability issues, as the overhead increases according to the number of nodes in the network. Routing based geographic information has gained significant attention, with the availability of logically priced and low-power GPS receivers or further localization systems. In geographic routing, a node uses the position of the sender, its adjacent nodes and the destination node, to forward the packet toward the destination at each hop. The simplest variety of geographic routing is, too greedy forwarding the message to the node, which minimizes a local forwarding metric. An easy example of such a metric is the space to the destination. In this case, the message is forwarded to the neighbor node which reduces the space to the destination.

2 90 In the greedy routing algorithm, each node in the route forwards packets to the neighbor which is the closest to the destination among its neighbors. Only the neighbors that are closer to the destination than the current node are considered. In this case, greedy routing has to stop and an alternative routing node called invalid handling has to be used to avoid the invalid regions. Invalid handling based on routing in planar geometric graphs attracted a main amount of research attempt, due to its stateless property and assured delivery in planar graph models. The planar graph algorithms use graph traversal rules (right hand rule and face changes rules) to find a path from the source to the destination on planar graphs next to the margins of the void regions. Existing geographic greedy routing and void handling methods do not want the concern and protection of end-to-end paths. This work presented an ECGMRR, which presented the method for energy efficient geographic routing for wireless ad hoc network, that discovers network redundancy and measures network mobility, leading to energy conservation. 5.2 GEO ROUTING SCHEME FOR SCALABLE AD HOC NETWORK In large networks or in networks where nodes travel at high speeds, the routing order for source destination pairs is likely to become very fast. This occurs in big networks, because longer paths have a shorter time to failure (a path fails when any link on it breaks). In both of these environments, caches have reduced lifetime which in turn implies that finding or keeping correct routing order can be exclusive. In this approach, for developing a scalable routing protocol is to regularly maintain estimated location information about nodes in the network and only to find accurate routes to specific nodes when there are packets to be sent to them.

3 91 Particularly, when a node needs to send a packet to some destination, it first decides the destination's fairly accurate position and then uses a simple geographic routing protocol. This approach allows reducing the cost of maintaining routing information and at the same time, providing the capability to find relatively cheap when needed. Thus, future method is based on a mixture of approximate geographical routing and an easy static mapping procedure to maintain the approximate location of the nodes. Source S Geographic routing Efficient path Encounter Voids Destination D1 Local minimum problem Can t apply greedy forwarding Destination D2 Figure 5.1 Geographic Routing Geographic routing attains an efficient path from source S to destination D1 (Figure 5.1). However, nodes are not always homogeneously populated in ad hoc networks. Networks may contain areas without any nodes; these areas are called voids or communication holes. When geographic routing encounters a void, it cannot apply greedy forwarding because there are no neighbor nodes that are closer to the destination than the current node. The straight line connecting source S and another destination D2 crossing a void, and greedy forwarding cannot further move at node x. This problem of not being able to apply greedy forwarding is called the local minimum

4 92 problem. Several schemes have been proposed to solve this problem. The routing performed to solve the local minimum problem is called fallback or recovery routing. The geographic routing is performed on an overlay network of topological multi-hop clusters. The CH constitute the vertices of the overlay graph. The CH collects and keeps information about their neighboring clusters and establishes links towards the neighboring CH. These links constitute the edges of the overlay graphs. Since the cluster hop counts are limited, Geo remains a localized routing algorithm. Using topology-based multi-hop clustering to create a robust overlay graph for geometric routing is a novel idea. Terminodes need a global map of the anchor nodes or path discovery protocols to obtain the anchored path. Contrary to these works that create geographic clusters or geographic cells, this work is based on the idea of topological clustering. Terminode routing combines hierarchical and geographic routing. The hierarchy created in Terminodes is by using anchor nodes, whereas in Geo, the hierarchy is created by topological clustering. Topological cluster based geographic routing has three different processes, namely 1) Multi-hop clustering and CH selection, 2) Overlay network formation and 3) Geo routing. Geo uses any multi-hop clustering algorithm, e.g, k-hop connectivity ID algorithm, for clustering and CH selection. After the clustering process, virtual nodes are created at the center of gravity of the clusters. These nodes are chosen as the vertices of the overlay graph, as they are more location fault tolerant, than the real CHs. In principle, a node close to the location of virtual cluster head (VCH) or the real cluster head (RCH)

5 93 itself serves the functions of the VCH. Now, virtual links are created between neighboring VCHs. After the overlay graph formation, the Geo routing process is executed. Geo routing uses topological information of the cluster member nodes for routing within the clusters. During the clustering process, each CH collects topology information about its member nodes. The cluster member nodes keep information about the path to reach their CH. This information is used in the topology-based intra-cluster routing. For routing beyond the source node cluster, Geo uses geographic routing. In geographic routing, the typical GFG type protocol is used to route packet along the edges of the overlay graph. In the proposed framework, greedy-forwarding method performs various optimization techniques to choose the next-hop node neighboring to the target node. Face routing is one of the essential methods for routing packets using compass routing on geometric networks. The intention is to choose the best path along the faces overlapped by the line fragments between a source and a destination. To keep away from loops using face routing, a planar graph of the original network is necessary. A planar graph is a sub- graph without crossing edges, which signifies the similar connectivity as the original network. Hence, face routing ensures to attain the destination, as long as the network topology is a planar graph. Existing geographic greedy routing and void handling methods, do not want the concern and protection of end-to-end paths. The planar graph algorithms use graph traversal rules to find a path from the source to the destination on planar graphs next to the margins of the void regions. In the greedy-forwarding mode of the protocol, each CH forwards the packets to its neighboring CH based on any forwarding criterion that

6 94 guarantees loop-free paths; e.g., the most-advance based forwarding criterion. When the packets get stuck at voids during greedy-forwarding, they use the planar graph traversal, based on face routing for void handling. This helps the packets to circumvent the void region. Once they circumvent the void region, the routing is switched back to the greedy-forwarding mode. When the packets reach the CH of the destination node, the local topology routing protocol is used to forward the packets to the recipient node. 5.3 ENERGY CONSERVED AD HOC NETWORK GEO MULTICAST SCHEME The proposed ECGMRR presented the scheme for energy efficient geographic routing for wireless ad hoc network which identifies network redundancy and measures network mobility more accurately, so that more energy can be conserved. The scheme includes the process of minimizing network redundancy by cluster formation, adapting to node mobility and energy consumption pattern for multicast service provisioning Cluster Formation to Minimize Network Redundancy ECGMRR organizes nodes into overlapping clusters that are interconnected to each other as shown in Figure 5.1. A cluster is defined as a subset of nodes that are mutually reachable in at most 2 hops. A cluster can be viewed as a circle around the CH with the radius equal to the radio transmission range of the CH. Each cluster is identified by one CH, a node that can reach all nodes in the cluster in 1 hop. A gateway is a node that is a member of more than one cluster. The gateway nodes connect all clusters together to ensure overall network connectivity. A node is ordinary if it is neither a CH nor a gateway node and is thus redundant.

7 95 Figure 5.2 Cluster Formation for Geographic Multicast Routing Figure 5.2 shows the cluster formation for geographic multicast routing in which all nodes have the same estimated network operational lifetime. Nodes 1 and 10 can directly decide they are the CHs because they have the lowest ID of all of their neighbors. Node 7 becomes a CH after nodes 2 and 3 choose node 1 as their CH. Nodes 2 and 3 are primary gateway nodes because they are neighbors of two CHs: nodes 1 and 7. Note that one of nodes 2 and 3 is redundant. Nodes 9 and 11 are secondary gateway nodes between clusters 7 and 10. In order to elect CHs and gateway nodes, each node periodically broadcasts a discovery message that contains its node ID, its cluster ID, and its estimated lifetime. A node s estimated lifetime can be conservatively set by assuming that the node will constantly consume energy at a maximum rate until it runs out of energy. While forming clusters, Energy conserved Geo-Routing Protocol (EGRP) first elects CHs, then elects gateways to connect clusters. A node selects itself as a CH if it has the longest lifetime of all its neighbor nodes, breaking ties by node ID. Each node can independently make this decision, based on exchanges discovery messages. Each node sets its cluster ID to be the node ID of its CH. Among the gateway nodes, those nodes that can hear multiple CHs are primary gateway nodes and

8 96 those that can hear a combination of CHs and primary gateway nodes are secondary gateway nodes. When multiple gateway nodes exist between two adjacent clusters, EGRP suppresses some of them in order to conserve energy since these gateway nodes are redundant. Gateway selection is determined by several rules. First, primary gateway nodes have higher priority than secondary gateway nodes, since at least two secondary gateway nodes, instead of just one primary gateway node, are needed to connect adjacent clusters. Second, gateway nodes with more CH neighbors have higher priority, since this will require fewer nodes to be kept awake. Third, gateway nodes with longer lifetimes, have higher priority in order to balance node energy. Note that the gateway selection algorithm does not guarantee that only one or one pair of gateway nodes exists between adjacent clusters. In order to support gateway selection, EGRP extends the basic discovery message to include the IDs of the clusters that a gateway node can connect Adapting to Node Mobility and Energy Conservation Levels With only a subset of the nodes active, it is possible that network mobility could cause a loss of connectivity. If a CH moves then it might no longer be able to serve as a CH. ECGMRR uses mobility prediction in order to maintain network connectivity. By estimating how soon a CH will leave its current cluster and inform all nodes in the cluster of that time, the clustered nodes can power themselves on before the CH leaves its cluster. This time is estimated as R/s where s is the CH s current speed and R is its radio transmission range. Suppose if the R/s estimate is too large, the connectivity between the moving CH and some nodes might be lost before this time. However, if this estimate is too small, ECGMRR will not be able to conserve any energy.

9 97 In EGRP implementation, set the estimate as R/4s to balance energy conservation and connectivity. To extend the basic discovery message to include the predicted cluster-leaving time. All nodes in a cluster should wake up to reconfigure clusters before the shorter of Ts and the cluster-leaving time of its current CH. The cluster-leaving time estimate is used analogously in the gateway node selection process. For the speed of mobile nodes in the network, each mobile node moves at a different speed and the maximum node speed is considered as another critical factor deciding the level of inaccuracy. The mobility pattern of mobile nodes are considered. If the node movement exhibits a different pattern, the effect of node mobility on the geographic routing protocol will be different. To estimate the effect of inaccurate location information caused by node mobility on the geographic routing protocol, conducted simulations with NS2 varying the beacon interval and the maximum speed of mobile nodes for each mobility model. Geographic routing is selected for the simulation, because it uses greedy-forwarding with face routing that are generally accepted schemes for geographic routing in sensor networks. The experiments were carried out in NS2 with each experiment running for 900 seconds. The results given here are averaged over six runs, each using a different random speed. Fifty nodes are placed randomly in the 1500m X 300m field and the combination of beacon intervals of 0.25, 0.5, 1.0, 1.5, 3.0, 6.0 sec and maximum node speed of 10, 20, 30, 40, 50 m/sec are simulated. The radio range of each node was assumed to be 250 meters. The nodes move according to the random waypoint model. Constant Bit Rate (CBR) traffic sources with each source generating four packets per second is used. The size of data packets was 64 bytes and selected 15 random sourcedestination pairs as the traffic flows.

10 PARAMETRIC MEASURE ON GEOGRAPHIC MULTICASTING For evaluating the performance of ECGMRR, the metrics chosen are packet delivery ratio, energy, disatance, throughput and data loss Packet Delivery Ratio It is defined as the ratio between the total number of packets that have reached the destination node and the total number of packets created at the source node. The location information of the nodes builds the packets route, loop free which results in a high packet delivery ratio. On rising the mobility or speed of the nodes, the delivery ratio reduces since most of the nodes move away from each other. Raising the number of nodes increases the delivery ratio due to strongly attached cluster configuration Energy The multicast leader election is depending on the traditional termination-detection algorithm for diffusing computations and how this algorithm is able to be modified to a mobile node energy setting. When assuming MANET with n nodes, regard as the existence of a method that permits each node to be conscious of its own location and residual energy. These organize and energy values are replaced among nodes so that each node obtains the information about the other nodes in the network for routing purposes. To reduce network overhead, each node broadcasts a message about its ID and position, to any other nodes from time to time over a long period, T. On the other hand, every node transmits a message regarding its ID, location and residual energy value to its neighbor nodes occasionally over a short period, t. In this protocol, only the neighbors gain residual energy information about each other.

11 Distance The longer the distance between two nodes, the lesser the update frequency of information is reliable with the simulation. Each node maintains a location and energy table, which includes the above information and its time of informing. There is no need for each node in the EGR to be aware of all of the other nodes' information and can adopt any existing location service methods in the simulation. Consequently, the resource consumption is similar for all protocols Throughput This metric gives the number of bits which are successfully delivered to related destinations in unit time in the network. When mobility increases, throughput also increases first and when it reaches the mobility of 15, it starts reducing in both algorithms. When compared to zone-id based algorithm, throughput is high in the rank based algorithm. In zone-id based algorithm throughput is low which infers in the usage of the rank based algorithm for most of the multicasting applications in MANET Data Loss When compared to the rank based algorithm, data loss is higher in the zone-id based algorithm but rank based algorithm performs a little better in data loss, with the implication of better usage of the rank based algorithm in MANET multicast scenarios. Data loss is an error condition in which information is damaged by failures or ignores in storage, transmission, or processing. Systems realize backup and disaster recovery equipment and processes to avoid data loss or restore lost data.

12 100 Data loss is well-known from data unavailability, such as may happen from a network outage. Data loss is also separate from data fall, although the term data loss has been sometimes used in those incidents. Data loss incidents can, also be data spill incidents, in case media containing responsive information are lost and afterward acquired by another party. Conversely, data spills are possible without the data being lost in the originating side Forwarding Data Packet The following describes the model for this protocol: Update the latest location information of destination node D to its location server at t0. At t1 (t1 t0<t) a source node S wants to transmit a data packet P to D, and it acquires the location of D from D's location server (in this simulation, S gets the location from its own). Then S adds the location of D and itself as well as time difference t1 and t0 to P's header. The scheme for predicting the destination node's expected zone. The center of the zone is the coordinate of D at t0, and the radius of the zone is the upper boundary of the predicted distance of D's movement. The destination of a data packet should be an area. However, an attempt to make some optimization. If S is quite far away from D, the angle µ will be too small for S to find the next hop. Consequently, modify the former tangents to the outer tangent lines between the two circles. One circle is centered on S whose radius is the transmission distance of S. The other is the scope of the D's expected zone. 5.5 SIMULATION OF GEOGRAPHIC MULTICAST IN MANET Implemented the proposed ECGMRR in the NS2.27 simulator and used AODV to route packets. All the model on the same simulated scenarios

13 101 to compare the effects of variations in node movement, traffic patterns and energy models on the performance of the protocols, as measured by energy use and data delivery quality. In geographical-based routing protocols is that every node knows the position of itself, the destination and all its neighbors. Stored in the routing table at each node are entries (pi, Si), where pi is the geographic position of some node and Si is one of its neighbors, which means the Si is closer to pi than the node itself. When source S wants to send data packets to destination D, S puts the geographical information of itself and D in the data packet, searches its routing table to find the nearest neighbor to D and forwards the packet to that neighbor. When another node A receives it, it will forward it to the neighbor to nearest D, as S does. However, if node A finds itself closer to D than any neighbors, but it has no entry for D in the routing table, it initiates a route discovery procedure (e.g., Depth First Searching). The entire path from A to D will be recorded in the route request packet when it arrives at D, so D can answer with an ACKnowledgement (ACK) to update the routing table at the nodes along the path from D to A. When A receives the ACK, the data packet flow can continue Traffic, Mobility and Radio Models Nodes in the simulation, move according to the RWM. Nodes pause and then move to a randomly chosen location at a fixed speed. Consider seven pause times: 0, 30, 60, 120, 300, 600 and 900 seconds and for each generate 10 sets of initial placements and random way-points. Nodes move at two different speeds, from uniform distributions between 0 and 20m/s and 0 and 1m/s. Nodes move in a 1500m by 300m area. In most scenarios 50 transit nodes are used that route data and 10 traffic nodes that act as sources and sinks. Then, vary node density, use 100 and 200 nodes, while keeping the area constantly. Traffic is generated by CBR sources spreading the traffic randomly among the 10 traffic nodes. The packet sizes are 512 and 1024

14 102 bytes and the packet rate is set to four different values: 1 pkt/s, 10 pkts/s, 20 pkts/s and 200 pkts/s. Note that when packet size is 1024 bytes and packet rate is 200 pkts/s, the traffic reaches the maximum link bandwidth of 2Mb/s. This model, a radio with a nominal range of 250 meters both with the two-ray-ground propagation model and a non-deterministic shadowing model Energy Model Energy consumption model contributed an extended version of DSR and a validated MAC layer with the simulation package. The proposed AODV implementation is an improved version of the AODV. Integration of ad hoc routing reproduces the similar standard results which have been verified and found that the simulation results of unmodified ad hoc protocols are consistent with other published results. The simulation used 1Mb/s Wave LAN (pre ) wireless LAN. They measured energy of 1.6W for transmitting, 1.2W for receiving, and 1.0W for listening. To this add an energy of 0.025W when sleeping. Since it is impossible to evaluate the behavior of the network, the traffic nodes run out of energy before the transit nodes, traffic node s infinite energy is given. Traffic nodes follow the same mobility model as transit nodes, but they do not run Geographical Adaptive Fidelity (GAF) or forward traffic. Because of traffic nodes speciality, do not count them when reporting the number of nodes in the simulation. Each transit node is given enough energy so that it can listen for about 450 seconds. In this home agent based EGRP simulations, a model intermediate node is consuming 0.033W, the amount of power necessary for reporting location every 8 seconds, since the home agent based node does not require constant position information. Do not turn off intermediate location based node, when turn off the radio in order to avoid modeling satellite acquisition time and because the energy function is quite small (about equal to radio sleep mode).

15 103 The simulation for home agent and grid based EGRP are conducted for comparison in two phases. In the first phase, it is simulated 50 nodes for 600s. The main goal in this phase is to show that the schemes do not reduce the quality of routing, but do in fact conserve energy and extend network operational lifetime. In the second phase, do the same comparison for 2400s while varying the number of nodes in order to see how long network operational lifetime is extended for different node deployment densities. In each phase consider 340 simulations and all being combinations of 2 protocols, 7 movement patterns, 10 placement initials, 3 traffic loads, and 2 movement speeds. The obtained results and the difference when running ECGMRR are presented in the next section. 5.6 RESULT AND DISCUSSION ON ENERGY METRICS ON GEO MULTICASTING In order to quantify energy consumption, the Mean Energy Consumption per Node (MECN) is defined as follows: At the start of the simulation, n nodes have a total initial energy Eo. After time t, the remaining total energy of m nodes is E t. The MECN equals (E0-Et) / n*t. The results show that EGRP uses almost half the energy of existing methods except in the scenario where nodes move at high speed (20m/s) constantly (zero pause time). EGRP saved 20-30% more energy and grid based ECGMRR saved 65-75% more energy than other methods. When nodes move at high speed, grid ECGMRR adjusts it by turning off nodes for shorter times, leading to more frequent cluster formations. Such overhead causes grid ECGMRR to use more energy than home agent based ECGMRR in this scenario, though still about 30% less. Varying the traffic load does not affect the energy conservation results.

16 Network Lifetime The energy savings extend network s operational lifetime. The fraction of the network with remaining energy over time when nodes move at 20m/s (When nodes move at low speed, 1m/s, the grid ECGMRR plot is close to that of the 600s pause time EGRP curve, regardless of actual pause time). For both grid ECGMRR and EGRP, plot a zero pause time, representing constant node movement and a 600s pause time, representing almost no node movement. All nodes running plain AODV run out of energy at the same time, around 430s. Since AODV does nothing to conserve energy, this result reflects the energy of continuous listening. It can see that grid ECGMRR balances energy use more evenly among nodes than ECGMRR. For example, at time 600s at least 80% of EGRP nodes are still alive while at most 40% of Adaptive Location Aided Flooding (ALAF) nodes are alive except the scenario with a pause time of 0. ECGMRR is more effective at balancing energy because of its connectivity measurement based approach. It has developed a simulation environment in NS2 to evaluate the performance of grid ECGMRR and compared it with an ECGMRR algorithm for energy conservation. It is found that grid ECGMRR achieves higher energy savings when compared with ECGMRR. It also found that the nodes with a higher degree (i.e., Nodes with more one-hop neighbors) disseminate more data per unit energy. Thus, dense ad hoc networks are likely to benefit more from using the grid ECGMRR for data dissemination in terms of energy savings. Initially a set of 50 nodes is deployed in a 200mx200m ad hoc field using the location information given in the scenario file. The ad hoc field is divided into four uniform sized grids and each node associates itself with a grid. The nodes are assigned the grid ids 0, 1, 2 and 3 depending on their association. The nodes are further classified as either gateway nodes or internal nodes. Simulation has been done for the propagation time and data

17 105 dissemination by dividing the deployed area as 4 grids and 8 grids and the results are depicted in the following Figures 5.3 and 5.4. Data Dissem ination Propagation Time (sec) Without Grid 4 Grid Figure 5.3 Propogation Time Vs Data Dissemination for 4-grid 120 Data Dissemination Propagation Time (sec) Without Grid 8 Grid Figure 5.4 Propogation Time Vs Data Dissemination for 8-grid

18 106 Simulation has also been done for the propagation time and Energy consumption by dividing the deployed area as 4 grids and 8 grids and the results are depicted in Figures 5.5 and 5.6. Energy Consum ption(joules) Propagation Time(sec) Without Grid 4 Grid Figure 5.5 Propogation Time Vs Energy Consumption for 4-grid Energy Consum ption(joules) Propagation Time(sec) Without Grid 8 Grid Figure 5.6 Propogation Time Vs Energy Consumption for 8-grid

19 107 ECGMRR also shows a different trend from the grid based ECGMRR with regard to mobility. With Cluster-based Energy Conservation (CEC), more nodes survive under low mobility (600s pause time) and more nodes survive under high mobility (zero pause time). With grid EGRP, high mobility causes more frequent cluster formations and more overhead. With home agent EGRP, high mobility helps balance energy use, because changes in node location cause active node re-election within grids. In addition, ECGMRR is more efficient in predicting mobility due to its access to global location information. With the result, clearly observed that the time at which only 20% of the nodes remain alive against varying degrees of mobility. From this, it can be seen that grid ECGMRR extends the network operational lifetime almost two times longer than home agent ECGMRR, network operational lifetime increases with the pause time. As explained above, this is the effect of the adjustments of ECGMRR for high mobility. 5.7 SUMMARY The ECGMRR presented, is an energy-efficient geographic multicast routing, which utilized grid based geographic dissemination in wireless ad hoc networks. This mechanism is capable of measuring network mobility and network redundancy more accurately so that more energy can be conserved by minimizing subspace and initiating switch off the redundant node radio signal. ECGMRR determines redundant nodes and controls node duty cycle to extend network operational lifetime while maintaining network connectivity, independent of the involvement of ad hoc routing protocols. The rank based leader election introduced in multicast showed it to be self-stabilizing. The simulation process is carried for rank based multicast leader election which provides useful insights in group communication in case of varying topologies of the ad hoc network. ECGMRR substantially conserves energy (consumes 15% less energy than an unmodified ad hoc

20 108 routing protocol), allowing network operational lifetime to increase in proportion to node density. ECGMRR eliminates the dependency on location information node and its assumption about radio range. Grid ECGMRR measures local connectivity with low overhead and is thus able to dynamically adapt to a changing network. In addition to an analysis and simulation results, it has presented measurements from an experimental simulation. These results show that the proposed ECGMRR is effective with varying node mobility rate and maintaining the node energy levels for efficient multicast service provisioning.