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Location-Aided Routing LAR in Mobile Ad Hoc Network by Young-bae ko Nitin H. Validya presented by Mark Miyashita

Organization. IntroductionRelated WorkLocation-Aided Routing (LAR) protocolRoute Discovery using FloodingLocation informationExpected Zone and Request ZoneLAR Scheme 1LAR Scheme 2Error in location estimateSimulation Model and ResultsVariations and Optimizations. Introduction. Mobile Ad hoc

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Location-Aided Routing LAR in Mobile Ad Hoc Network by Young-bae ko Nitin H. Validya presented by Mark Miyashita

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    1. “Location-Aided Routing (LAR) in Mobile Ad Hoc Network” by Young-bae ko Nitin H. Validya presented by Mark Miyashita

    2. Organization Introduction Related Work Location-Aided Routing (LAR) protocol Route Discovery using Flooding Location information Expected Zone and Request Zone LAR Scheme 1 LAR Scheme 2 Error in location estimate Simulation Model and Results Variations and Optimizations

    3. Introduction Mobile Ad hoc Network(MANET) Node mobility which is the cause of frequent and unpredictable topology changes leads to difficult task of route maintenance in MANET Many protocols have been proposed for MANET to achieve efficient routing This paper suggest an approach to decrease overhead of route discovery by utilizing location information (GPS or other method to obtain location information) Two LAR protocols for route discovery presented in this paper uses location information(may be out of date) to limit search space which results in fewer route discovery messages

    4. Related Work Many protocols have been proposed for MANNET such as DSR, AODV, TORA, ZRP Existing MANET routing algorithm mentioned do not utilize physical location of a destination node Similar idea (utilizing location information) have been applied and developed called “selective paging for cellular PCS (personal communication service) networks In selective paging, the system pages a selected subset of cells close to the last known location of mobile host which decrease location tracking cost This paper propose and evaluate an analogous approach for routing in MANET

    5. Route Discovery Using Flooding This paper discuss the basic flooding algorithm and location-aided route discovery based on limited “flooding” Basic Flooding Algorithm A source node S needs to find a route to destination node D, node S broadcasts a route request to all its neighbors Intermediate node X receives a route request and compares the destination with its own identity If it does not match, then node X broadcast the request to its neighbors(sequence numbers used to detect duplicate and eliminate/avoid redundant transmissions) Node D responds by route reply messages to sender which traverse the path in reverse of the path received by D (route request packet contains path of all nodes traversed starting S)

    6. Route Discovery Using Flooding Basic Flooding Algorithm Timeout scheme is also used to re-initiate route request with new sequence number due to transmission error or node D is unreachable from S

    7. Route Discovery Using Flooding In this paper, implementation assumes that node S can know that route is broken only if it attempts to use the route by sending data packet and receiving route error messages – it initiates route discovery for D Note that route request may reach every node in the network that is reachable from S (potentially all nodes in the MANET) This paper claims that by using location information reduces the number of nodes to whom route request is propagated (limit the scope of route request propagation)

    8. Location Information Location information can be obtained by the use of Global Positioning System (GPS) With use of GPS, mobile host can know its physical location – note that GPS includes some degree of error compared to the real coordinates and GPS-calculated NAVSTAR GPS has positional accuracy of 50-100 meters Differential GPS has positional accuracy of few meters This paper assumes that each node knows its current location precisely – possibility of error in location are discussed separately in the performance evaluation Also assume that the mobile nodes are moving in a two-dimensional plane

    9. Expected Zone The Expected Zone is the region where source node S thinks that the destination node D may contained at some time t – only an estimate made by S Assume that node S knows that the node D was at location L at time t0 and current time is t1 From the viewpoint of S, expected zone of node D is the region that node S expects to contain node D at time t1 based on the knowledge that node D was at location L at time t0 If S knows that D travels with average speed v, then S assumes that the expected zone is the circular region of radius v(t1- t0) centered at location L Note that if actual speed is faster than the average, then the destination may be outside the expected zone at t1

    10. Expected Zone Without the previous knowledge of the location of D, S will assume that the entire region is the expected zone and implementation uses the basic flooding algorithm The size of expected zone can be reduced if node has more information about the mobility of a destination D

    11. Request Zone Node S defines (implicitly or explicitly) a request zone for the route request Node forwards a route request only if it belongs to the request zone (it does not forward a route request to its neighbor if outside of the request zone) Two LAR scheme differ in determining the membership of request zone The request zone includes expected zone in addition to (possibly) other surrounding zone around the request zone If a route is not discovered within the timeout period, S initiates a new route discovery with expanded request zone – all paths from S to D include nodes that are outside the request zone Note that the probability of finding path can increase as size of request zone increases (route discovery overhead also increases with the size of the request zone

    12. Request Zone

    13. LAR Scheme 1 The request zone is rectangular in shape Assume S knows that the node D was at location (Xd,Yd) at time t0 Assume S knows the average speed v with which D can move From above two, S defines the expected zone at time t1 with radius R = v(t1- t0) centered at location (Xd,Yd) The request zone is the smallest rectangle that includes current location S and the expected zone such that the sides of the rectangle are parallel to the X and Y axes Node D sends route reply message with its current location and time (may include average speed but simulation assumes all nodes knows each other’s average speed)

    14. LAR Scheme 1

    15. LAR Scheme 1

    16. LAR Scheme 1 Size of the request Zone is proportional to (i)average speed of movement v and (ii)elapsed time since recorded last location of the destination Recall that R = v(t1- t0) is used to determine the size of request zone In general, a smaller request zone may be formed at speed that are neither too small nor too large For instance, at low speed, factor (i) is small but route discovery occur after long intervals making (ii) larger (t1- t0 is large)

    17. LAR Scheme 2 Node S includes two pieces of information with its route request Assume that S knows the location (Xd,Yd) of D at some time t0 which route discovery is initiated by S at t1 where t1 ? to S calculates its distance from location (Xd,Yd) denoted DISTs and included with the route request The coordinate (Xd,Yd) are also included with the route request When node I receives the route request from S, node I calculates its distance from (Xd,Yd) denoted DISTi and: For some parameter ?, if DISTs + ? ? DISTi, then I forwards request to its neighbors – this request includes (Xd,Yd) and DISTi replacing original DISTs and (Xd,Yd) from S Else DISTs + ? ? DISTi, node I discards the route request Each intermediate nodes repeat the process above

    18. Comparison of the two LAR Schemes

    19. Comparison of the two LAR Schemes

    20. Error in Location Estimate Both LAR schemes assume that each node knows its own location accurately. However, just like GPS, there may be some error in the estimated location Let e (location error) denote maximum error in the coordinates estimated by a node If a node N believes that it is at location (Xn,Yn), then the actual location of node N may be anywhere in the circle of radius e centered at (Xn,Yn) If LAR Scheme 1 is modified to take e into account, then the expected zone is a circle of radius e + v (Xn,Yn) which makes request zone larger since it includes larger expected zone No modification is made to the LAR Scheme 2

    21. Performance Evaluation The simulation is performed using modified version of MaRS (Maryland Routing Simulator) MaRS is discrete-event driven simulator providing a flexible platform for the evaluation and comparisons of network routing algorithm Simulations were performed on flooding, LAR scheme 1, and LAR scheme 2 Simulations are conduct by varying the number of nodes, transmission range of each node, and moving speed

    22. Simulation Model

    23. Simulation Model

    24. Simulation Model As the speed of mobile nodes is increased, the number of routing packets begins to increase for all protocols (routing overhead increases as frequency of route breaking increases) As the speed of mobile nodes is increased, the number of routing packets begins to increase for all protocols (routing overhead increases as frequency of route breaking increases)

    25. Simulation Model In general, the routing overhead decreases with increase in transmission range (larger transmission range, links break less frequently) LAR Scheme 1 performs worse when smaller transmission range decreases the number of neighbors and the probability of a route discovery within timeout period using initial request zone – results in flooding In general, the routing overhead decreases with increase in transmission range (larger transmission range, links break less frequently) LAR Scheme 1 performs worse when smaller transmission range decreases the number of neighbors and the probability of a route discovery within timeout period using initial request zone – results in flooding

    26. Simulation Model

    27. Simulation Model

    28. Simulation Model

    29. Simulation Model

    30. Optimization

    31. Optimization

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