1.1 The growth of connected wearable devices worldwide . . . 3 1.2 A BAN demonstrating wireless on-body and in-body links with on-
body and implanted sensors/hub . . . 5 1.3 A BBN demonstrating body-to-body communications between on-
body hubs and sensors . . . 6 1.4 BBN providing outdoor medical service . . . 7 1.5 Thesis Flowchart . . . 12 2.1 Multi-tier architecture for body-centric communications. . . 18 2.2 (a) Cluster-based and (b) Distributed architecture of coexisting BANs. 20 2.3 Some of the available bands for BAN based on RF technology [1]. . 21 3.1 An illustration of a sleeping person wearing transceivers and receivers 44 3.2 Three-branch cooperative combining: one of the branches is a direct
link (hsd) and the other two are cooperative relay links (with two hops). . . 45 3.3 Outage probability as a function of receive sensitivity withTxpower
of 0 dBm, for direct link (DL), selection combining (SC), and switch- and-examine combining (SwC), with on-body and off-body transceivers. 48 3.4 Percentage of continuous outage duration (at x-axis), below a given
Rx sensitivity with Tx power of 0 dBm, from agglomerate data for on-body transceiver; with direct link (DL), switch-and-examine com- bining (SwC), and selection combining (SC) . . . 50
3.5 Percentage of continuous outage duration (at x-axis), below a given
Rx sensitivity with Tx power of 0 dBm, from agglomerate data for off-body transceiver; with direct link (DL), switch-and-examine com- bining (SwC), and selection combining (SC) . . . 50 3.6 Two tiered architecture with 4 coexisting BANs (intra-BAN com-
munications at the lower tier and inter-BAN communications at the upper tier); Hub on the left-hip and two sensors/relays on the left- wrist and right-upper-arm, respectively. . . 53 3.7 Cross-layer optimisation between Physical and Network layers. . . . 54 3.8 The radio-frequency testbed with major components highlighted.
Battery (disconnected) is on reverse side . . . 56 3.9 Shortest path routing (SPR), with and without hop restriction. The
path taken without hop restriction can be a longer path with the lowest cost. . . 60 3.10 Cooperative multi-path routing (CMR) with 3-branch selection com-
bining in each route hop. . . 62 3.11 Outage probability of the averaged gains (over the network with 10
BANs) found from ORPL (routing metric: EDC, k = 4), LOADng (routing metric: hop count), SPR and CMR (routing metric: ETX, hop count); transmit power 0 dBm. Black dotted curves represent the theoretical cdf (cumulative distribution function) of the corre- sponding outage probability and are well aligned. . . 67 3.12 Outage probability of the averaged gains (over the network with
10 BANs) found from SPR and CMR (routing metric: ETX, hop count); transmit power 10 dBm (at hubs) and 5 dBm (at relays). Black dotted curves represent the theoretical cdf (cumulative dis- tribution function) of the corresponding outage probability and are well aligned. . . 68 3.13 Average throughput (packets/s) for SPR, CMR, ORPL, and LOADng;
at−100 dBm receive sensitivity with transmit power 0 dBm. . . 69 3.14 Average end-to-end delay at continuous times for SPR, CMR, ORPL,
and LOADng; at−100 dBm receive sensitivity with transmit power 0 dBm. . . 71
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3.15 Average and maximum end-to-end delay over the whole period for SPR, CMR, ORPL, and LOADng with the network consisting of 10 BANs; with transmit power 0 dBm at −100 dBm receive sensitivity. 72 3.16 Average energy consumption (per packet delivery) at continuous
times for SPR, CMR, ORPL, and LOADng; at −100 dBm receive sensitivity with transmit power 0 dBm. . . 73 3.17 Average and maximum energy consumption (per packet delivery)
over the whole period for SPR, CMR, ORPL, and LOADng with the network consisting of 10 BANs; with transmit power 0 dBm at −100 dBm receive sensitivity. . . 74 3.18 Percentage of hop count of routes with different protocols (SPR,
ORPL, LOADng), at −100 dBm receiver sensitivity. . . 77 3.19 The empirical probability density of combined channel gain data
from SPR with a gamma distribution fit, where κ = 9.58 and θ = 0.00000334 are the shape and scale parameter, respectively. . . 78 3.20 The empirical probability density of combined channel gain data
from CMR with a Rician distribution fit, where ν = 0.0000626 and
σ = 0.0000185 are the two shape parameters. . . 78 4.1 Two-tiered architecture of 4 coordinated BANs . . . 86 4.2 Cross-layer optimisation across physical-MAC-network layers . . . . 86 4.3 Average outage probability with respect to SINR threshold for SPR
and CMR, with different duty cycles (dc) per node for the 4 coor- dinated BANs. Receiver sensitivity −100 dBm, transmit power 0 dBm; black dotted curves represent the theoretical cdf of SINR with corresponding duty cycles . . . 92 4.4 Average packet delivery ratio (PDR) in terms of different receive
sensitivities for SPR and CMR, with different duty cycles (dc) per node for the 4 coordinated BANs, at 0 dBm transmit power . . . . 94 4.5 Average spectral efficiency with respect to −95 dBm receive sensi-
tivity for SPR and CMR, with different duty cycles (dc) of 4, 5 and 6 co-ordinated BANs (coBANs) . . . 95
4.6 Average spectral efficiency with respect to −88 dBm receive sensi- tivity for SPR and CMR, with different duty cycles (dc) of 4, 5 and 6 co-ordinated BANs (coBANs) . . . 96 4.7 Percentage of continuous back-off duration of CSMA/CA links with
an adaptive carrier sense threshold (CSth) and different static carrier sense thresholds (CSth), at transmit power 0 dBm, with 10 coexist- ing BANs. . . 101 4.8 Average throughput (successful packets/s) vs. packet arrival rate
over 10 coexisting BANs for SPR and CMR (associated with CSMA/CA), with adaptive/static carrier sense thresholds (CSth) and TDMA with 8.3% duty cycle (dc). Transmit power 0 dBm and receiver sensitivity −90 dBm . . . 102 4.9 Average outage probability with respect to SINR thresholds for SPR
and CMR (associated with CSMA/CA), with different routing met- rics (e.g., only ETX, ETX + max. 2 hops) for 10 coexisting de- centralised BANs; Subscript a and s refers to adaptive and static carrier sensing, respectively. Receiver sensitivity −100 dBm, trans- mit power 0 dBm . . . 103 4.10 Average packet delivery ratio with respect to different receive sen-
sitivities for SPR and CMR (associated with adaptive CSMA/CA and 8.3% duty cycle TDMA), with different routing metrics (e.g., only ETX, ETX + max. 2 hops) for 10 coexisting BANs; Transmit power 0 dBm . . . 104 4.11 Average spectral efficiency for 10 coexisting BANs with respect to
different receive sensitivities for SPR and CMR (associated with adaptive CSMA/CA and 8.3% duty cycle TDMA), with different routing metrics (e.g., only ETX, ETX + max. 2 hops), at transmit power 0 dBm . . . 105 5.1 (a) Different on-body sensor locations and example of body-centric
links (on-body, body-to-body) with two co-located BANs; (b) The radio-frequency testbed with major components highlighted. Bat- tery (disconnected) is on reverse side . . . 114
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5.2 (m−1) pairwise comparison (Pc) across two consecutive intervals (`) 116 5.3 Average probability of stationarity with (a) ANOVA, (b) B–F, and
(c) K–S hypothesis tests for different body-to-body links of dataset 1, i.e., R. Hip to L. Hip (RH–LH), R. Hip to R. upper Arm (RH– RA), R. hip to R. hip (RH–RH), R. Hip to L. Wrist (RH–LW). The results for each link are averaged over (3×7) links except RH–RH link (averaged over (3×7) links). The minimum required window length to precisely investigate WSS is 1 s (indicated by black dotted vertical line). . . 120 5.4 Average probability of stationarity with (a) ANOVA, (b) B–F, and
(c) K–S hypothesis tests for different body-to-body links of dataset 2, i.e., L. Hip to L. Wrist (LH–LW), L. Hip to R. upper Arm (LH– RA), L. hip to L. hip (LH–LH). The results for each link are averaged over (8×7) links from 8 subjects. The minimum required window length to precisely investigate WSS is 2.4 s (indicated by black dot- ted vertical line). . . 121 5.5 Average probability of stationarity with (a) ANOVA, (b) B–F, and
(c) K–S hypothesis tests for different body-to-body links of dataset 3, i.e., L. hip to L. hip (LH–LH), L. Hip to R. upper Arm (LH–RA), L. Hip to L. Wrist (LH–LW). The results for each link are averaged over (10×9) links from 10 subjects. The minimum required window length to precisely investigate WSS is 3 s (indicated by black dotted vertical line). . . 122 5.6 Average probability of stationarity with different hypothesis tests,
i.e., ANOVA, B–F, K–S for body-to-body links of dataset 4, i.e., Right-Hip-to-Right-Hip (RH–RH). The results for each link are av- eraged over 160 links from 20 subjects. The minimum required win- dow length to precisely investigate WSS is 6 s (indicated by black dotted vertical line). . . 123
5.7 (a) K–S and (b) B–F hypothesis test results for the probability of stationarity (with c` = 0.95) across different body-to-body links, i.e., left-hip-to-left-hip (LH-LH), left-hip-to-right-upper-arm (LH- RA), left-hip-to-left-wrist (LH-LW). Subscript ‘b’ and ‘w’ imply the best and worst case, respectively. . . 124 5.8 Power fit and exponential fit to averaged auto-correlation decay of
different body-to-body channels, i.e., LH–LH, LH–RA, LH–LW from Dataset 3 (10 co-located BANs); SSE implies to the sum squared error of the fits. A SSE value closer to 0 indicates that the model has a smaller random error component, and that the fit will be more useful for prediction. . . 130 5.9 Power fit and exponential fit to averaged auto-correlation decay of
different on-body channels, i.e., LH–RA, LH–LW from Dataset 3 (10 co-located BANs); SSE is the sum squared error of the fits . . . 132 5.10 Hurst regression from averaged R/S values for different B2B and
on-body links, i.e., LH–LH, LH–RA, LH–LW from Dataset 3 (10 co- located BANs). Hurst exponent (hE) is calculated from the slopes of the red lines. The higher value (around 0.9) ofhE is indicative of having long-range dependence. . . 134 5.11 Statistical model fits to probability distribution of measured aver-
aged channel gains (amplitudes) for B2B links with different sensor- location pairs — i.e., (a) LH–LH, (b) LH–RA, (c) LH–LW — of dataset 3. . . 139 6.1 Pareto optimality between two objectives in a multi-objective function.151 6.2 MDP transition probabilities from one state to another state with a
given action a A. . . 152 6.3 (a) Different on-body sensor locations and example of body-to-body
links with two co-located BANs; (b) The radio-frequency testbed with major components highlighted. Battery (disconnected) is on reverse side . . . 154
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6.4 Average throughput from 168 tested channels over the whole period for the Pareto optimum (f∗) of the MOMDP and individual actions (a A). . . 164 6.5 Average PDR from 168 tested channels over the whole period for the
Pareto optimum (f∗) of the MOMDP and individual actions (a A). 164 6.6 Average throughput for different B2B channels (i.e., (a) LH–LH,
(b) LH–RA, (c) LH–LW) estimated over 56 channels for each sensor location pair, for the Pareto optimum (f∗) and for individual actions (a A). . . 165 6.7 Fraction out of the total measured time for continuous latency≥x s
(x axis values) for the Pareto optimum (f∗) and individual actions (a A) over the 168 tested channels. . . 166 6.8 Fraction out of the total measured time for continuous latency ≥
x s (x axis values) for different B2B channels, (a) LH–LH, (b) LH– RA, (c) LH–LW, for the Pareto optimum (f∗) and individual actions (a A) . . . 167 6.9 Active fraction (%) from all (168) tested channels over the whole
period for the Pareto optimum (f∗) and individual actions (a
A). . 168 6.10 Active fraction (%) for different B2B channels (i.e., (a) LH–LH, (b)
LH–RA, (c) LH–LW) over the whole period for the Pareto optimum (f∗) and individual actions (a
A) over 56 channels (for each sensor location pair). . . 169 6.11 (a) Average outcome of the MOMDP with respect to all three objec-
tives; (b) Spatial distribution of the outcome of the MOMDP with respect to all three objectives. The x-axis shows the percentage of continuous latency >125 ms over the total operating period. . . 171 6.12 (a) Linear regression fit and (b) sum squared error (SSE) of the