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1 Perpendicular Intersection: Locating Wireless Sensors with Mobile Beacon Zhongwen Guo, Ying Guo, Feng Hong, Xiaohui Yang, Yuan He, Yuan Feng, Yunhao Liu Ocean University of China Hong Kong University of Science and Technology

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2 Outline Introduction Observations on RSSI Our Scheme: PI (Perpendicular Intersection) Design of Optimal Trajectory Experiments Conclusion and Future Work

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3 Introduction Locating sensor nodes is a crucial issue in WSN applications. OceanSense https://www.cse.ust.hk/~liu/Ocean

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4 Existing Approaches (1) Range-Based Approaches TOA, TDOA, AOA Require additional hardware support Expensive in manufactory cost and energy consumption RSSI-based (Received Signal Strength Index) Easy to implement; Based on the log-normal shadowing model Rely on absolute RSSI values (unstable & irregular) Inaccurate due to channel noise, interference, attenuation, reflection, and environmental dynamics Environment-dependent !

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5 Existing Approaches (2) Range-Based Approaches Mobile-assisted Avoid cumulative errors of coordinate calculations and unnecessary communication overhead Still rely on absolute RSSI values Range-Free Approaches Rely on connectivity measurements (e.g. hop-count) Accuracy and precision affected by node density and network conditions

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6 More about OceanSense Sparsely deployed restricted floating sensors Unstable wireless communications Varying network connectivity The existing localization approaches cannot well support such a practically complex scenario.

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7 Design Goals A localization approach which is easy to implement in practice which has better accuracy, especially under dynamic and complex environments. which is time and energy efficient in locating a network of wireless sensors.

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8 Observations on RSSI The closer a node is to the signal sender, the larger RSSI value it perceives. Outdoor observation

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9 Observations on RSSI The closer a node is to the signal sender, the larger RSSI value it perceives. Indoor observation

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10 PI (Perpendicular Intersection) N P1(x1,y1)P1(x1,y1) H(x, y) (x, y) start stop start stop M Virtual Triangle (VT) P2(x2,y2)P2(x2,y2) P3(x3,y3)P3(x3,y3) No longer absolute RSSI values!

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11 Theoretical Estimation Error Given the velocity of the mobile beacon as V and the broadcast frequency as F, the distance between two beacon points is V/F. or

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12 Optimal Mobile Trajectory (1) The optimal mobile trajectory to locate a network of sensor nodes satisfies the following requirements: All the sensor nodes can be located. The optimal trajectory consists of multiple joint VTs, which cover the entire deployment area. It is the shortest trajectory. The mobile beacon traverses the entire area in the shortest time and consumes the minimum energy cost. The localization latency of a sensor node is minimized. The node should be located as soon as the mobile beacon traverses along the two sides of the VT.

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13 Optimal Mobile Trajectory (2) The node is in the VT which has largest sum of RSSI values. Side length of a VT = R (transmission range)

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14 Optimal Mobile Trajectory (3) Trajectory length: Localization latency:

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15 Analysis on Overhead Communication Cost Zero communication cost among the sensor nodes to be located. The number of beacon messages received by a sensor node when the mobile beacon traverses one VT side is FR/V. A sensor node receives the beacon messages from at most 6 sides. The upper bound of communication cost of a sensor node is 6FR/V. Computation Overhead The computation overhead on a sensor node is O(FR/V). Storage Overhead PI only needs to store at most 14 vertices with their corresponding RSSI values, which cost 70 bytes.

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16 Experiments (1) A prototype system with 100 TelosB motes Various environments: library hall, laboratory, racket court, parking lots, and the sea. The mobile beacon is also a TelosB mote. A base station is deployed to collect the localization results. PI is compared with two existing approaches TRL [1] : a range-based approach using trilateration. (3 vertices) BI [2] : a mobile-assisted localization approach that exploits Bayesian inference to improve the estimation accuracy. (3 vertices + 3 random) [1] J. Hightower and G. Borriello. Location systems for ubiquitous computing. IEEE Computer, 34(8):57 – 66, August [2] M. Sichitiu and V. Ramadurai. Localization of wireless sensor networks with a mobile beacon. Proceedings of IEEE MASS, 2004.

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17 Experiments (2) Hall Experiment In a hall of our library, which is about 450m 2. The side length of a VT R=15m. The moving velocity V=0.1m/s The broadcast frequency F=1time/sec.

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18 Experiments (3) Hall Experiment – Group 1 14 sensor nodes are deployed randomly. RSSI values perceived by node N 6 Ave. Estimation error: PI m BI m TRL m

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19 Experiments (4) Hall Experiment – Group 2 35 sensor nodes are deployed randomly. Ave. Estimation error: PI m BI m TRL m

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20 Experiments (5) Laboratory Experiment In the laboratory of computer software center, a room of 324m 2 with 120 computers and desks inside. People sit, stand, or walk in the room. R=9m. V=0.1m/s. F=1time/sec.

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21 Experiments (6) Laboratory Experiment – Group 1 12 sensor nodes are deployed. Ave. Estimation error: PI m BI m TRL m

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22 Experiments (7) Laboratory Experiment – Group sensor nodes are deployed. Ave. Estimation error: PI m BI m TRL m

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23 Experiments (8) Racket Court Experiments R=15m. V=0.1m/s. F=1time/sec. Ave. Estimation error: PI m BI m TRL m

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24 Experiments (9) Parking Lots Experiments R=15m. V=0.1m/s. F=1time/sec. Ave. Estimation error: PI m BI m TRL m

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25 Experiments (10) Offshore Experiment V=1.5m/s. F=1time/sec. Ave. Estimation error: PI m BI m TRL m

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26 Experiments (11) Impact of Different Factors Evaluated with the hall experiments. V is set at 0.05m/s, 0.1m/s, 0.2m/s and 0.4m/s, F is set at 0.5, 1, 2, and 4 times per second, respectively. The intersection point of the two curves in the right figure represents a good setting in practice, which sets appropriate trade-off between the localization accuracy and the communication cost.

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27 Conclusion and Future Work Conclusion We propose a mobile-assisted localization approach: Perpendicular Intersection (PI). Easy to implement Accurate in dynamic and complex environments Time and energy efficient We examine the performance of PI by implementing a prototype system. Further Work Improve the prototype system, introducing an automatic mobile beacon. Large-scale field tests on the OceanSense platform. Extend PI in the underwater acoustic sensor networks.

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28 Thanks !

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