Mobility-enhanced Positioning in Ad hoc Network

1. Mobility-enhanced Positioning in Ad hoc Network April 2013 Soojin-Shin soojin1116@kut.ac.kr http://link.koreatech.ac.kr

2. Abstract • This paper discusses and investigates the effects of mobility on positioning of wireless ad hoc networks. • We present a Mobility-enhanced Ad hoc Positioning (MAP) scheme. • Simulation shows that using mobility does improve the performance of such “hop count”-based positioning schemes.

3. Introduction • There has been an increase in the availability and use of small, handheld(or body-carried) equipment. • Because of the portability of the nodes, mobility becomes an issue to be considered in the design of these communication protocols. • Using a GPS-enabled positioning scheme may not be feasible due to cost, processing power and locality constraints. • This paper describes a positioning scheme to be used in mobile ad hoc networks – the Mobility-enhanced Ad hoc Positioning (MAP) scheme. • MAP seeks to predict the position of nodes in a distributed manner, using the hop counts. • The main contribution of this paper is the exploration of the effects of mobility on “hop count”-based positioning methods

4. The MAP Scheme • We will first describe the “hop count”-based positioning method briefly, followed by a description of the mobility-enhanced part of the scheme. • In order for MAP to work, the following assumptions are made about the conditions of the ad hoc network: • The signal coverage of each node is approximately similar. • There exist a multi-hop network communication protocol and media access control (MAC) within the network.

5. The MAP SchemeA. “Hop Count”-based Positioning • Hop count information propagates from each reference node to the other nodes throughout the network. • A node that is the immediate neighbor of a reference node (i.e. 1 hop count away) will broadcast this information to its immediate neighbors. • These neighbors will know that they are 2 hop counts away from the reference node and proceed to forward this new information. • Any new information received will be forwarded only if it a “better” one, (i.e. when the hop count value is lower than what the node already has. ) • Eventually, this information will propagate throughout the whole network and every node will have information about the hop counts to each reference node. • Triangulationwill be performed next to locate the position of the nodes.

6. The MAP SchemeA. “Hop Count”-based Positioning The average hop distance discovery scheme • To effectively perform triangulation, we need a way to translatehop counts into actual distances. • the distance of one hop can vary according to the density of the nodes in a network as well as the uniformity of the node distribution. • In our scheme, we make use of the averaging effect of nodes distributed in the network to determine an average hop count distance (Davg) • Using this value, we can compute the distance each node is from the reference nodes by simply multiplying the hop counts with it.

7. The MAP SchemeA. “Hop Count”-based Positioning • Average hop distance discovery (Davg) • After the initial startup phase, Ri will contain a set of hop count values from all other reference nodes (Rj),hij where i ≠ j • Since the distance to all other reference nodes, dij is known, Rican compute a total distance value Di and total hop count value Hi , where i ≠ j and N is the total number of reference nodes

8. The MAP SchemeA. “Hop Count”-based Positioning • Average hop distance discovery (Davg) • Di, Hi, and (xi , yi) are propagation throughout all the nodes in the network by controlled flooding. Every node only rebroadcasts the information if it has received it for the first time. • Upon receiving information from all the reference nodes, each node computes the average hop distance

9. The MAP SchemeA. “Hop Count”-based Positioning • Average hop distance discovery (Davg) • It should be noted that in a perfectly continuous network.(i.e. where each node is connected directly or indirectly to all other nodes.) • If the network is not perfectly continuous, some nodes will not receive the total distance and hop value from certain reference nodes. • We assume that the network is continuous and therefore Davg is constant. With this value, each node can proceed to determine its position using a triangulation algorithm.

10. The MAP SchemeA. “Hop Count”-based Positioning • Triangulation • A node, SK can compute the estimated distance from each reference node using • With the estimated distance, we use a simple triangulation technique to locate the position of SK. This method is widely used and is described in [8]. Due to space constraints, we will not include the algorithm here.

11. The MAP SchemeB. Mobility-enhanced Positioning • The “hop count”-based positioning provides a simple and computationally feasible way of locating a node. However, the accuracy of the positioning may not be satisfactory. • In some ad hoc networks, some or all the nodes have the ability to move around the network region. Under such circumstances, we explore the possibility of using mobility to further enhance the positioning scheme.

12. The MAP SchemeB. Mobility-enhanced Positioning • In MAP, when a mobile node, SK moves and leaves its neighborhood, it removes the hop count information since this information is no longer valid. • However, its original neighbors need not change their hop count information, even though SK has moved, the distance represented by this hop count is still valid, as shown in Fig. 1. • When the mobile node arrives and stops at a new location, it sends out a hop count request to its new neighbors. • Assuming the neighbors have valid hop count information, they will reply SK with the relevant values. SK will compare and form the best (smallest) hop count information to all the reference nodes. • With this information, it determines its position using triangulation.

13. The MAP SchemeB. Mobility-enhanced Positioning

14. The MAP SchemeB. Mobility-enhanced Positioning • SK can help to improve the positioning of its neighboring nodes. • After S receives and computes the smallest hop count information, it broadcasts this information to its immediate neighbors. • This allows some neighbors to receive a better hop count than what they currently have. • This will happen in one of the following cases:

15. The MAP SchemeB. Mobility-enhanced Positioning • Situation 1 • Fig. 2(a) shows an example of a discontinuous region. • Situation 2 • Fig. 3(a) shows an example of a indirect-path region.

16. Simulation • Simulation Model • The simulator consists of a single event list managed by a scheduler function. • Each node created contains information regarding its actual position (x, y), its estimated position (xˆ, yˆ) and the number of hop counts away from each reference node (h1, h2, ..., hN) • As our simulation is done in 2-dimensional space, we assume a circular radio propagation model. • Depending on the radio range R, we create a circular region around a node of radius R, where nodes found within the region will be able to “hear” any broadcast from this node. • Based on this model, and making use of the actual positions of the nodes created, we are able to maintain a neighborhood set for each node.

17. Simulation • MAP Algorithm • We evaluate the scheme by running each simulation scenario over 100 trials. • From the trials, we collect information regarding the actual position (x, y), and predicted position(xˆ, yˆ). • Two sets of actual and predicted positions are collectedeach time • Based on the results, we compute the absolute error δ between the actual position and the predicted position, expressed in percentage of node range, R.

18. Simulation • Simulation Results • Effect of Mobility • Fig. 4 shows the cumulative error distribution over 100 trials. • As can be seen, the performanceimproves when mobility is used to enhance the positioning. • More nodes are able to predict their position to a lower error than when there is no mobility.

19. Simulation • Simulation Results • Effect of MobilityFactor • Fig. 5 shows the cumulative error distribution for mobility factor ranging for 0.01 (1%) to 0.8 (80%) • In fact, we found that in our simulation, changes in the mobility factor have little effect on the positioning accuracy. • The reason for this is that over the entire simulation run, the mobile nodes are able to move to enough positions to “bridge” the indirect-path region, regardless of the proportion of mobile nodes..

20. Simulation • Simulation Results • Effect of Range Error • Fig. 6 shows the cumulative error distribution before mobility starts. • Fig. 7 shows the distribution at the end of the simulation. • In both cases, we see that performance degrades in the presence of increase range errors. • The reason for this is that with the irregularity in the signal coverage • From the results, we recognize a need to maintain a fairly stable signal range for the mobile nodes, in order to improve positioning accuracy. • This is a challenging task, as many environmental factors can change signal range values.

21. Conclusion This paper seeks to investigate the effects of mobility on the performance of positioning schemes that use hop counts to perform triangulation. We present the MAP scheme, a “hop count”-based positioning that leverages on the mobility of nodes. Through simulations, we find that mobility-enhancement improves the performance of such positioning schemes. We also recognize that for such a scheme to work with acceptable accuracy, the challenge is to maintain stable and uniform radio signal coverage. This is especially difficult in an indoor environment.