An Empirical Characterization of Radio Signal Strength Variability in 3-D IEEE Networks Using Monopole Antennas Dimitrios Lymberopoulos, Quentin Lindsey and Andreas Savvides Embedded Networks and Applications Laboratory (ENALAB) Yale University

Can RSSI provide reliable distance estimation?  More than measurements were acquired  40 wireless sensor nodes were used  Acquired data along with ground truth data are available at:  Quantify variability in typical office environments  3-D deployments  Low power radios  What other type of information can RSSI provide? EWSN 2006 February 15 th Dimitrios Lymberopoulos

Background  MAP based approaches (RADAR, Bahl et. al)  Create a database of RSSI fingerprints:  Find the fingerprint with the minimum distance to the recorded RSSI array  15ft error using wireless radios  RSSI distance prediction (Ecolocation, Yedavalli et. al)  Use ordering or triangulation to refine the initial estimates  10ft error in a small indoor experiment with CC1000 wireless radios  Probabilistic approaches (Madigan et. al)  Every node computes a belief about its location  A probabilistic signal propagation model is assumed  20ft error using wireless radios EWSN 2006 February 15 th Dimitrios Lymberopoulos

Infrastructure  XYZ sensor node designed at Yale (  CC2420 wireless radio from Chipcon  2.4 GHz IEEE /Zigbee-ready RF transceiver  DSSS modem with 9 dB spreading gain  Effective data rate: 250 Kbps  8 discrete power levels: 0, -1, -3, -5, -7, -10, -15 and -25 dBm  Power consumption: 29mW – 52mW  Monopole antenna with length equal to 1.1inch. EWSN 2006 February 15 th Dimitrios Lymberopoulos

Received Signal Strength Indicator (RSSI) P = RSSI + RSSI OFFSET [dBm]  The power P at the input RF pins can be obtained directly from RSSI:  RSSI is an 8-bit value computed by the radio over 8 symbols (128μs)  RSSI OFFSET is determined experimentally based on the front-end gain. It is equal to -45dbm for the CC2420 radio  Sources of RSSI Variability  Intrinsic  Radio transmitter and receiver calibration  Extrinsic  Antenna orientation  Multipath, Fading, Shadowing EWSN 2006 February 15 th Dimitrios Lymberopoulos

Path Loss Prediction Model  Log-normal shadowing signal propagation model: RSSI(d) = P T – PL(d 0 ) – 10ηlog 10 (d/d0) + X σ  RSSI(d) is the RSSI value recorded at distance d  P T is the transmission power  PL(d 0 ) is the path loss for a reference distance d 0  η is the path loss exponent  X σ is a gaussian random variable with zero mean and σ 2 variance Model verification using data from a basketball court EWSN 2006 February 15 th Dimitrios Lymberopoulos

Radio Calibration Receiver 1.31ft Transmitter  For each location and orientation 20 packets were -15dBm EWSN 2006 February 15 th Dimitrios Lymberopoulos

Transmitter Radio Calibration Receiver 1.31ft  For each location and orientation 20 packets were -15dBm EWSN 2006 February 15 th Dimitrios Lymberopoulos

Radio Calibration Receiver 1.31ft Transmitter  For each location and orientation 20 packets were -15dBm EWSN 2006 February 15 th Dimitrios Lymberopoulos

Radio Calibration Receiver 1.31ft Transmitter  For each location and orientation 20 packets were -15dBm EWSN 2006 February 15 th Dimitrios Lymberopoulos

Radio Calibration  Experiment in an empty room  TX calibration: 9 different transmitters  RX calibration: 6 different receivers TX Standard Deviation: 2.24dBmRX Standard Deviation: 1.86dBm EWSN 2006 February 15 th Dimitrios Lymberopoulos

Antenna Characterization Side View 8ft 6.5ft 3.5ft 1.25ft Top View 2ft : measurement point EWSN 2006 February 15 th Dimitrios Lymberopoulos  Experiment took place in a basketball court  Minimize multipath effect  At each measurement point dBm were received

Antenna Characterization  Optimal antenna length-1.1inch  Random RSSI values due to multipath  Large communication range  Suboptimal antenna with 2.9inch length EWSN 2006 February 15 th Dimitrios Lymberopoulos

Antenna Characterization  Similar distances (<1ft difference) can produce very different RSSI values (even up to 11dBm)  Very different distances ( even >18ft) can produce the same RSSI values EWSN 2006 February 15 th Dimitrios Lymberopoulos 1.25ft3.5ft6.5ft

Antenna Characterization Best antenna orientationWorst antenna orientation EWSN 2006 February 15 th Dimitrios Lymberopoulos  Antenna orientation effect  For a given height of the receiver very different RSSI values are recorded for different antenna orientations

Radiation Pattern Side ViewTop View Communication range Symmetric Region Antenna orientation independent regions Communication range EWSN 2006 February 15 th Dimitrios Lymberopoulos

Antenna Effects in Indoor Environments  The basketball court experiment was performed inside our lab  We focused on the best antenna orientation EWSN 2006 February 15 th Dimitrios Lymberopoulos

Large Scale Indoors Experiment  40 nodes were placed on the testbed (15ft (W) x 20ft(L) x 10ft(H)) installed in ENALAB  Each node transmitted 10 packets at each one of the 8 power levels. The recorded RSSI values were transmitted to a base station for logging. Placement and Connectivity EWSN 2006 February 15 th Dimitrios Lymberopoulos

Large Scale Indoors Experiment  RSSI does not change linearly with the log of the distance  Multipath  3-D antenna orientation EWSN 2006 February 15 th Dimitrios Lymberopoulos Maximum (0dBm)Medium (-5dBm)Low (-15dbm)

Link Asymmetry  Asymmetric link between nodes A and B  RSSI(A) ≠ RSSI(B) One way linksAsymmetric links EWSN 2006 February 15 th Dimitrios Lymberopoulos

What else can we do? More than 30% of the links are affected by human presence or motion  Detection of:  Human presence  Human motion EWSN 2006 February 15 th Dimitrios Lymberopoulos

Conclusions  3-D space is very different that 2-D space  Antenna orientation effects are dominant in 3-D deployments  3-D deployments are a more realistic for evaluating RSSI localization methods  RSSI distance prediction in 3-D deployments is almost impossible  Ordering of the RSSI values is not helpful  Even if antenna orientation is known!  Probabilistic approaches  A probabilistic model of RSSI exists for the symmetric region of the antenna  Generalizing this model to 3-D deployments is extremely difficult if not impossible.  Radio calibration has minimal effect on localization EWSN 2006 February 15 th Dimitrios Lymberopoulos

Useful Lessons Learned EWSN 2006 February 15 th Dimitrios Lymberopoulos AKW #000 ENALAB Becton Center To Davies Auditorium Professor’s Kuc Lab Loading Dock MTC LAB Ed Jackson IT support Machinery Room Outdoor space CorridorLabOfficesOther XYZ Hardware Abstraction Module Communication Memory Manager Static SOS Kernel Dynamic Loadable Binary Modules Dynamic Loadable Binary Modules  Matlab interface to the network to:  Wire up multiple services to create user specific services  Log data from the network  Push data to the network

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