Wireless Networking Trends Architectures, Protocols & optimizations for future networking scenarios

Transcription

1 Wireless Networking Trends Architectures, Protocols & optimizations for future networking scenarios H. Fathi, J. Figueiras, F. Fitzek, T. Madsen, R. Olsen, P. Popovski, HP Schwefel Session 1 Network Evolution & Mobility Support (HPS) Session 2 Ad-hoc networking (TKM/FF) Session 3 Enabling technologies for ad-hoc NWs (TKM/FF) Session 4 Wireless Sensor Networks (PP) Session 5 Performance aspects & optimizations (HF/TKM) Session 6 Context-sensitive Networking (RLO/JF) Page 1

2 Context-Sensitive Networking: Location Information Page 2

3 Content 1. Motivation The use of Location Information 2. Obtaining Location Information Location techniques An example of triangulation in Bluetooth networks 3. Common problems and questions Common problems in obtaining Location Information Influencing factors on location information accuracy 4. Common solution: Filtering The basics of a filter Filters Implementations 5. Network Optimization based on Location Information Bluetooth Basics An example of topology formation in Bluetooth networks Page 3

4 Motivation The use of location Information Government laws persuading wireless companies to provide Location Information in their networks. Ex: The FCC E911 service. Applications that use navigation or tracking of people, robots or objects. Tracking people in football fields for game planning Tracking animals in stables for health studies Tracking robots in arenas to compare with human movements Context-sensitive networking: Location Information has been used to optimize the wireless networks in several aspects such as topology formation and routing. Page 4

5 Content 1. Motivation The use of Location Information 2. Obtaining Location Information Location techniques An example of triangulation in Bluetooth networks 3. Common problems and questions Common problems in obtaining Location Information Influencing factors on location information accuracy 4. Common solution: Filtering The basics of a filter Filters Implementations 5. Network Optimization based on Location Information Bluetooth Basics An example of topology formation in Bluetooth networks Page 5

6 Obtaining Location Information Location Techniques Triangulation Makes use of the properties of the triangles Proximity Uses the contact, the proximity or the area of coverage Environment Analysis Analyses the scenario where the device is included Page 6

7 Obtaining Location Information Location Techniques Lateration Makes use of at least 3 Base Stations in a 2D scenario Measurements of Received Signal Strength (RSS) or (Time of Flight) ToF Propagation problems such as shadowing and multipath Page 7

8 Obtaining Location Information Location Techniques Angulation 2 Base Stations in a 2D scenario Accuracy dependent on how narrow is the angle Propagation problems such as shadowing and multipath. Needed method to measure angles in the Base Stations. Page 8

9 Obtaining Location Information Location Techniques Physical Contact Very accurate since the Base Station location in known Mandatory existence of physical contact No calculations needed Page 9

10 Obtaining Location Information Location Techniques Cell Identification Low accuracy dependent on the AP range No calculations needed Page 10

11 Obtaining Location Information Location Techniques Database Correlation Good accuracy under indoor scenarios Accuracy dependent on the amount of information stored in the database Update of the database may be a problem Page 11

12 Obtaining Location Information Location Techniques Video Analysis The scenario where the device is included must be know. A big amount of processing time The size of the video camera may be a disavantage Page 12

13 Obtaining Location Information Measured Power value P A, P B, P C to each AP α = α 1 * α 2 * α 3 (triangulation) AP A An example of triangulation on Bluetooth networks d 3 d 1 AP C Normalize all the area d 2 AP B db m AP A and B db m AP C P 3 P 1 P 2 d 1 d 2 10log(d) PDF PDF PDF d 3 10log(d) α 1 α 3 α 2 P 1 P A db m db m db m P 2 P B P 3 P C Page 13

14 Content 1. Motivation The use of Location Information 2. Obtaining Location Information Location techniques An example of triangulation in Bluetooth networks 3. Common problems and questions Common problems in obtaining Location Information Influencing factors on location information accuracy 4. Common solution: Filtering The basics of a filter Filters Implementations 5. Network Optimization based on Location Information Bluetooth Basics An example of topology formation in Bluetooth networks Page 14

15 Common problems and questions Common problems in obtaining Location Information Channel Characterization Multipath NLoS (Non Line of Sight) Moving Objects Packet collision Timing Aspects Network/Measurements delays in moving devices scenarios Hardware/Technology/Protocols Limitations Page 15

16 Common problems and questions Influencing factors on Location Information accuracy Power db m ) Maximum proximity from the most probable areas to find the devices 2dB 2dB m 7m Distance(m) 2) Maximize triangulation conditions 3) Consider auto-calibration issues Page 16

17 Content 1. Motivation The use of Location Information 2. Obtaining Location Information Location techniques An example of triangulation in Bluetooth networks 3. Common problems and questions Common problems in obtaining Location Information Influencing factors on location information accuracy 4. Common solution: Filtering The basics of a filter Filters Implementations 5. Network Optimization based on Location Information Bluetooth Basics An example of topology formation in Bluetooth networks Page 17

18 Common Solution: Filtering The basics of a filter These techniques are used widely within several contexts, such as Signals & Systems, Image Processing or Mobile Tracking. Bayesian filtering techniques are techniques that estimate the state of a dynamic system based on noisy sensors measurements. To the specific case of location information estimation in scenarios with static or moving devices, the Bayesian filtering techniques are being used in order to optimize accuracy. X 0 X 1 X 2 X 3 X 4 X 5 Prediction X 1 X 2 X 3 X 4 X 5 Correction P 1 P 2 P 3 P 4 P 5 Measurement Time scale Page 18

19 Common Solution: Filtering The basics of a filter The Bayes filter is an abstract concept that describes a probabilistic framework for recursive state estimation. Start Up Parameters Time Update ( predict ) Measurement Update ( correct ) The main idea consists in estimate the distribution of the uncertainty, called belief, over the possible state space given the sensor measurements obtained until the time of the estimation. Page 19

20 Common Solution: Filtering The basics of a filter Prediction At each time, the current state is updated based on previous states. describes the system dynamics (motion model). The belief for state space at time t 0 is usually defined as an uniform distribution in case no a-priori knowledge exists. Correction represents the perceptual model (depends on the technology). normalization factor Page 20

21 Common Solution: Filtering Filters implementations The Kalman Filter The most widely used Approximate beliefs by Gaussians, making use or their first (mean) and second moment (covariance matrix) The observation model and system dynamics are linear functions of the state Example: Object moving with quasi-constant speed v. Measured RTT (Round Trip Time). system dynamics (motion model): x t =v t+x t-1 observation model : x t =ct/2 Where c the light speed T the RRT x t the state in time t Page 21 Time t Time t+1 T/2 v T/2

22 Common Solution: Filtering Filters implementations The Extended Kalman Filter The observation model and system dynamics do not need anymore to be a linear functions of the state The Particles Filter Represents beliefs by sets of samples or particles. Each samples is composed by a state and a non-negative weight called importance factors. Beliefs do not need to be Gaussian Page 22

23 Content 1. Motivation The use of Location Information 2. Obtaining Location Information Location techniques An example of triangulation in Bluetooth networks 3. Common problems and questions Common problems in obtaining Location Information Influencing factors on location information accuracy 4. Common solution: Filtering The basics of a filter Filters Implementations 5. Network Optimization based on Location Information Bluetooth Basics An example of topology formation in Bluetooth networks Page 23

24 Network optimization Bluetooth Basics Bluetooth is a short range wireless technology. The type of network is designated as Scatternet. A Scatternet is composed by several piconets, where each piconet may be composed up to 8 devices In a piconet one of the devices plays the roll of master, controlling all the communication in the network, while the other devices play the roll of a slave. The nodes that belong to more than one piconet are called bridges are responsible to establish connection among the several piconets. The range of a piconet may be controlled by a power control system included in the Bluetooth specifications V1.2. This reduces the power consumption and may reduce the collision rate among piconets. Page 24

25 Network optimization An example of topology formation on Bluetooth networks Without Location Information With Location Information Master arbitrarily connected to slaves when there is more than 7 devices in its range Master connected to closest slaves when there is more than 7 devices in its range Source: Master Thesis of Satya Krishna Venkata Page 25

26 Network optimization An example of topology formation on Bluetooth networks Average Farthest Slave Distance (AFSD) is optimized with location information. For small number of Devices, the AFSD does not depend on having or not location information As larger the number of Devices, better the AFSD value with location information Average Farthest Slave Distance (meters) Room 20m*20m 7 slaves/piconet Page 26 Total number of nodes Source: Master Thesis of Satya Krishna Venkata

27 Network optimization An example of topology formation on Bluetooth networks Number of Piconets formed Total number of nodes The number of piconets with location information (non-red colors) is most of the cases higher than if no information (red line) was use. However the number of collisions may be decreased, depending on the algorithm. Source: Master Thesis of Satya Krishna Venkata Collision Rate with transmission in every slot Page 27 Total number of nodes

28 Summary Location Information can be used as a context-sensitive networking factor Filtering techniques can give a big help on correcting several of the common problems in obtaining Location Information Location Information gives spatial organization in the network. Topology formation in Bluetooth networks using Location Information increases the number of piconets in the network, but may reduce the collisions rate. Page 28

29 References [1]. Location Information. On going technical report for WANDA project. [2], Hans-Peter Schwefel, and Istvan Kovacs. Accuracy and timing aspects of location information based on signal-strength measurements in bluetooth. In Proceedings of the 16th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC. IEEE, September [3] Satya Krishna Mohan. Optimization of Topology Formation using Location Information in WPAN. Master s project, Aalborg University, Mobile Communications Department, Spring 2005 [4] Guillaume Monghal, Yann Malidor,, Hans-Peter Schwefel, István Z. Kovács. Enhanced triangulation method for positioning of moving devices. In Proceedings of the 8th International Symposium on Wireless Personal Multimedia Communications, WMPC. September [5] Hans-Peter Schwefel, Tatiana K. Madsen. Lecture Notes in Wireless Networks III: Advanced IP-based Networking Concepts. Page 29

30 Thank you! Questions? Page 30