Loss Aware Sample Packetization Strategy for Improvement of … · Loss Aware Sample Packetization Strategy for Improvement of ... security and integration with web based ... IEEE

Loss Aware Sample Packetization Strategy for

Improvement of Body Sensor Data Analysis

Ming Li1 Yu Cao

2 and B Prabhakaran

3

1Department of Computer Science California State University Fresno CA 93619 USA

2Department of Computer Science University of Massachusetts Lowell MA 02148 USA

3Department of Computer Science The University of Texas at Dallas Richardson TX 75080 USA

Email minglicsufresnoedu ycaocsumledu prabautdallasedu

Abstractmdash With limited bandwidth and high channel loss in

Wireless Body Area Networks (WBANs) it remains a

challenging issue to transmit time series body sensor data for

satisfactory data analytics In this paper we investigate the

interesting problem of sample packetization ie assembling

multiple samples in each packet before transmission over the

wireless link We first illustrate the ineffectiveness of the

default sequential packetization under channel loss Then we

propose Loss Aware Sample Packetization (LASP) a heuristic

loss aware sample packetization strategy to improve the data

quality for improvement of the sensor data analysis The core

idea of LASP is to minimize contiguous sample loss during the

data transmission thereby significantly increasing the accuracy

of data recovery at the aggregator side Extensive simulations

are conducted to evaluate the effectiveness of the proposed

approach on data recovery quality as well as data analysis

accuracy Results show that LASP yields a nicer sample loss

pattern therefore significantly improve the data recovery

quality Index TermsmdashData analysis sample packetization time series

data body area networks

I INTRODUCTION

Recently there has been increasing investigation on a

new type of network architecture generally known as

Body Sensor Networks (BSNs [1]) or Wireless Body

Area Networks (WBANs [2]) attributed to the exciting

advances in design of lightweight small size ultra-low

power and intelligent monitoring wearable sensors

Compared with existing technologies such as WLANs

BSNs enable the wireless communications inside or

around the vicinity of human body therefore further

extending the desirable concept of pervasive wireless

computing into a completely new level Key applications

that may benefit from this new technique include remote

health monitoring sports training and entertainments

Extensive research [3]-[12] has been conducted on data

analytics of body sensor data However due to the

limited capacity of body sensor networks and the channel

fading packet loss occurs frequently during data

transmission which in turn imposes significant impact on

Manuscript received March 1 2015 revised November 18 2015

This work was supported by the US National Science Foundation

under Grant No 1229213 Corresponding author email minglicsufresnoedu

doi1012720jcm1011851-858

the accuracy of data analysis Existing approaches to

address network loss include ensuring Quality of Service

(QoS) support for specific data streams [13]-[15] as well

as increasing network throughput [16]-[18] These

strategies although effective in general have not fully

considered the impact of packet loss on analysis accuracy

of BSN sensor data

Basically the accuracy of data analysis under the

scenario of wirelessly transmitted sensor data depends on

two factors 1) network delivery ratio ie how many

samples are successfully delivered through the network to

the receiver which is in turn determined by network

capacity channel stability and traffic load 2) data

recovery ratio ie among all lost samples how many of

them can be recovered to a level that approximates the

original samples While network delivery ratio can be

largely improved by QoS strategies data recovery has to

be addressed by closely looking at data characteristics

and specific analytics algorithms

In this paper we investigated the critical issue of

improving data recovery ratio to ensure the success of

data analysis First we analyzed the factors that affect

data recovery under BSN channel and determined the

appropriate system model and assumptions Then we

focused on the largely overlooked difference between

sample loss and packet loss and identified the problem of

sample packetization ie how to group samples in

different packets We found that packetization strategies

have significant impact on data recovery Given the

sample packet loss ratio it is possible to improve data

recoverability by avoiding certain ldquoworst caserdquo sample

grouping patterns For example the natural packetization

or the default sequential packetization which group

contiguous packets together has the high likelihood of

losing high importance samples Further we formulated

the problem of improving data recovery as finding a

pattern of sample packetization such that the number of

continuous sample pairs between any two packets is

minimum This way lost samples can be largely

recovered with higher accuracy through estimation from

received neighboring samples Meanwhile we introduced

the concept of critical samples and defined it as samples

that cannot be recovered with satisfactory accuracy even

if immediate neighboring samples are received

Considering the challenge of time complexity of the

algorithm as well as the reassembly overhead of received

Journal of Communications Vol 10 No 11 November 2015

851copy2015 Journal of Communications

out of order samples we propose LASP (Loss Aware

Sample Packetization) an efficient heuristic approach

where a fixed pattern is followed for each sample group

of a specific size The fixed pattern has the advantage of

low number contiguous sample pairs and can be easily

applied at the receiver side for reassembling of samples

Then an additional reliability enhancement with

approach similar to selective repeat [19] for critical

samples was proposed for further improvement

II RELATED WORKS

Quality of Service (QoS) scheduling for streaming

body sensor data has been well investigated Zhou and Lu

[13] proposed BodyQoS a virtual MAC for quality of

service scheduling in BSNs The approach basically

measures the effective bandwidth and adaptively

allocates remaining resources to meet the QoS

requirements of applications A desirable feature of

BodyQoS is that it does not require the modification of

the underlying MAC layer implementation Fulford-Jones

and Malan [20] proposed CodeBlue an ad hoc

infrastructure for emergent medical care In this project

several types of body sensors (eg pulse oximeter

ECGEKG sensor) are individually connected to Zigbee

enabled radio transmitters which communicate with

access points directly Due to the ad hoc architecture and

the capability of self-organizing CodeBlue yields

scalability for network expanding and flexibility to

connect various wireless devices Jiang and Cao [21]

proposed CareNet an integrated wireless environment

used for remote health care systems CareNet offers

features such as high reliability and performance

scalability security and integration with web based portal

systems High reliability is achieved by using two-tier

architecture Younis and Akkaya [22] proposed

Distributed Queuing Body Area Network a MAC

protocol aiming at providing better QoS It uses a cross-

layer fuzzy rule based scheduling algorithm to optimize

MAC layer performance in terms of QoS and energy

efficiency

There have been considerable standardization efforts

during recent years Among many potential technologies

Zigbee and Bluetooth are most widely deployed ZigBee

is a very low power collision avoidance protocol

optimized for lower power sensors It has developed a

health care specific protocol and are compliant with all

IEEE 11073 devices as well as most other IEEE 802154

[23] wireless devices Bluetooth supports high-bandwidth

and many several existing devices and has a health care

compliant version defined but has very high power

requirements and uptime for the radios Bluetooth Low

Energy [24] is a new proposed system from Bluetooth

which will have lower energy requirements and still be

interoperable with Bluetooth Classic but details are still

forthcoming at this time Being aware of the unique

requirement of supporting a wide range of applications by

body area networks an IEEE 802156 [25] task force has

Channel is unreliable and may drop data

With multiple body sensors communicating with the

same aggregator channel may reach saturation status

and therefore dropping packets in the queue

Reliable transmission of all samples incurs high

protocol overhead for relatively small body sensor

samples

A Data Recovery and Critical Samples

With quite strict requirement on the completeness of

sensor samples by data analysis algorithms missing

samples at data aggregator should be recovered at the

maximally possible level Itrsquos possible to use selective

repeat [19] to send back to specific sensors and request

retransmission of all lost samples However this

approach has the disadvantage of higher overhead and is

not efficient for real-time analysis Instead it is desirable

to recover samples using received ones and try to achieve

satisfactory approximation Usually lost samples can be

recovered by estimation (eg linear interpolation) at the



been working on finalizing a Wireless Body Area

Network (WBAN) standard The new standard aims at

providing flexible and configurable energy efficient

MAC operations

Plenty of research has been conducted using body

sensors to monitor Activity of Daily Living (ADL) Two

survey papers [3] [4] give details on the state-of-art-of

research in the area of sensor network with inertial

sensors and their applications in healthcare and wellbeing

Due to the growing interest on body sensor-based ADL

recognition several sensor-based human activity datasets

[10] [11] were introduced in the last few years A most

recent paper published in 2014 ACM Computing Survey

[12] discuss the key research challenges that human

activity recognition shares with general pattern

recognition and identify those challenges that are specific

to human activity recognition This paper also describes

the concept of an Activity Recognition Chain (ARC) as a

general-purpose framework for designing and evaluating

activity recognition systems Most of the techniques in

the current state of the art are focusing on the data

analysis and pattern recognition using the raw sensor data

Very little research in data analytics area considers the

issues of developing new networking strategy to improve

the accuracy and speed of sensor data analytics

III SYSTEM MODEL AND ASSUMPTIONS

In a typical medical application multiple sensors such

as ECG EKG EMG EEG motion sensors and blood

pressure sensors send multimodal time series data to

nearby Data Aggregators (DAs) which can be a cell

phone watch headset PDA laptop or robot based on

the application needs Then through BluetoothWiFi

these data can be delivered remotely to physician side for

real time diagnosis or to medical database for record

keeping or to request for emergency For such systems

the following assumptions are made

data aggregator In addition for most sample recovery

methods estimation accuracy of a missing sample is

significantly higher when the immediate previous and

next samples are received

For formal representation we define data recovery

ratio as the percentage of lost samples that can be

estimated using received samples with satisfactory

approximation from the data analysis requirement In

addition we define critical samples as samples that

cannot be recovered with satisfactory accuracy even if

immediate neighboring samples are received Obviously

it is ineffective to recover critical samples with received

ones for data analysis Instead selective repeat can be

adopted

B Sample Loss vs Packet Loss

Existing efforts on QoS support help address packet

loss However from data analysis aspect it is sufficient

as long as enough samples are received or recovered

Therefore understanding the relationship between packet

loss and data recovery ratio is critical For this purpose it

is important to consider the largely overlooked difference

between packet loss and sample loss Given specific

packet loss ratio it is highly desirable that sample loss

pattern yields optimal data recovery ratio

Since samples are usually assembled in packets it is

common that multiple samples are lost at the same time

In FIFO queuing based transport protocols such as UDP

samples are packetized through sequential order by

default ie samples i i+1 i+2 hellip i+L

the sample packet with L being the packet size However

if a packet is dropped then all the L continuous samples

are lost In general recovering L continuous samples

incurs significant error especially when L is not small

As illustrated in Fig 1 it will be better off to distribute

adjacent samples in different packets such that if one

packet is lost each missing sample can still be estimated

by received adjacent samples with higher accuracy It

should be noted that how samples are assembled can be

largely manipulated without affecting network operation

correctness and efficiency We name this process sample

packetization [26] The issue is therefore how to identify

an appropriate pattern for sample packetization such that

the effect of loss on data analysis is minimized

Fig 1 Comparison of two basic packetization approaches

IV LOSS RESILIENT SAMPLE PACKETIZATION (LASP)

Understanding the potential of sample packetization on

improving data recovery ratio we can formulate the

problem statement as follows given a set of N collected

samples channel loss condition and a specific estimation

technique how to packetizesamples such that the overall

chance of missing continuous samples is minimized

Obviously with minimum continuous sample loss the

data sample recovery accuracy can be significantly

improved Then the solution for the aforementioned

problem is to find a pattern of sample packetization such

that the number of continuous sample pairs between any

two packets is minimal

A Possible Solutions and Challenges

To better understand the idea letrsquos take 16 samples

with index from 0 to 15 Fig 2 shows several possible

packetization patterns with corresponding numbers of

continuous sample pairs For simplicity it is assumed that

four samples are assembled in each packet naming P0 P1

P2 and P3 N1 represents the number of continuous

samples in the same packet N2 and N3 represent the

average and maximum numbers of contiguous sample

pairs between any two packets It is clear that approach (a)

is worst since it incurs N1 of 3 which indicates difficulty

of recovering samples even if one packet is lost Approach

(c) works slightly better than (b) since its N2 and N3 are

smaller indicating better performance when multiple

packet loss occurs

Basically given N samples finding the optimal

packetization pattern is computationally expense

especially when N is not small For low energy consuming

body sensors it is not acceptable to enumerate all

possibility for comparison Approach (c) in Fig 2 is

slightly better than (b) but finding such pattern at real-

time incurs overhead On the other hand one may think

that random pattern works well and may be able to

generate small number of continuous sample pairs

However even if it works well the reassembly of samples

according to their original order is quite difficult In order

to restore the order significant effort must be made to

deliver the pattern therefore incurring high network traffic

and leading to a waste of network bandwidth Therefore

we decide to take a heuristic solution based on approach

(b) described in the next subsection

Fig 2 Comparison of packetization approaches of 16 samples

B Proposed Approach

We proposed to design a heuristic packetization pattern

based on the approach (b) in Fig 2 Fig 3 illustrates the

For a total of 8 samples two packetization approaches are

considered

Sequential Packet 1 (0 1 2 3) Packet 2 (4 5 6 7) Alternate Packet 1 (0 2 4 6) Packet 2 (1 3 5 7)

Assuming the first packet gets lost the ldquoalternaterdquo approach yields better data analysis performance since it is easier

to recover recover samples 2 4 6 with good accuracy However

ldquosequentialrdquo approach will have difficulty since the lost samples

are too far away from received ones



-1 are put into

procedure of the proposed approach which is described

as follows

Step 1 for each collected sample group packetization

is performed using alternate pattern Assuming N samples

and M packets for each sample i (0ltiltN) the ID of

corresponding packet that it should be inserted into is

iM

Step 2 for the same group all critical samples are

identified A sample i is considered critical if |estimatedi-

originali| gt α originali Here the estimated value depends

on the specific estimation method and the values of the

neighboring samples α is a constant and is fixed as 025

in this paper

Step 3 all packets are sent to the aggregator At the

same time a special packet containing the IDs of all

critical samples is also sent

Step 4 upon the receipt of packets data aggregator re-

assembles received samples based on the same pattern

Therefore the kth sample in jth packet has the sample ID

of j+kN

Step 5 aggregator identifies lost critical samples by

comparing the IDs of the lost samples and the IDs of all

critical samples

Step 6 aggregator requests the sensor to retransmit

critical samples and ensure its successful delivery

Step 7 aggregator performs data recovery for other

lost samples before performing analysis


It should be noted that the proposed strategy is an

application layer approach and therefore is different from

the physical layer network coding [27] which aims at

coordination of multiple node transmission in order to

achieve higher network throughput especially in wireless

networks However the system performance will

potentially benefit from physical layer network coding if

implemented

C Data Quality Model

In this section we present a variance based data

quality model for the analysis of data recovery ratio We

define estimation variance (EV) as the relative difference

between the estimated sample values and original values

which can calculate it as

(1)

where di is the original value of the sample i and esti is the

estimated value of sample i from received samples

Further the total estimation variance is calculated as

(2)

(3)

where K is the total number of estimated samples in any

evaluation group Clearly smaller EV and AEV indicates

better data recovery which is critical for the success of

data analysis

Now letrsquos assume that a group of M independent

packets are transmitted For each packet j its total

estimation variance of all samples in the packet EV(j)

depends on the specific characteristics of the data samples

in the packet Thus for a given packet loss ratio of l the

expected total estimation variance is calculated as

If EV(j) is the same value (EV) for all packets which is

not usual in real scenarios due to the statistic dropping of

packets we then have that

Clearly the total EV depends on total number of

packets loss ratio and data characteristics Further

average expected estimation variance is now

(6)

That means if packets are independently recovered

then loss ratio does not affect the level of data recovery

However when two or more adjacent packets are lost it is

difficult to have independent data recovery therefore

higher loss ration does decrease the data recovery ratio

TABLE I EV VS DATA CHARACTERISTICS

Strategy Data Characteristics

sqrt linear square cubic quartic

Sequential 0075 0 039 090 140

Alternate 0003 0 002 006 012

To illustrate the effect of the packetization on data with

different characteristics we take a data segment that

follows different patterns and calculate the total EV for

the segment with two different packetization schemes

Each data segment contains 8 samples which are

packetized into two different packets with either the

ldquosequentialrdquo or ldquoalternaterdquo approach under the condition

that the second packet is lost Table I shows the result It

can be seen that as the curve deviates more from the linear

pattern higher variance is yielded for both ldquosequentialrdquo

and ldquoalternaterdquo approach However ldquoalternaterdquo approach

shows a much slower increase compared with ldquosequentialrdquo

V PERFORMANCE EVALUATION

We performed extensive simulations to study the

performance of the proposed packetization strategy For

simplicity we label the proposed strategy as LASP and

sequential packetization as ldquoExistingrdquo The following four

strategies were compared

Existing samples are packetized in order of creation

ie sequential

Collect

Samples

Perform

PacketizationSample

Assembling

Identify Critical

Samples

Request Lost

Critical Samples

Resend Critical

Samples

Data

Recovery

Sensor Side Aggregator

iiii ddestEV

i iEVEV

KEVAEV i i

M

m

m

j

mMm jEVllm

MEV

0 1

)()1(

EVlMmllm

MEVEV

M

m

mMm

0

)1(

EVlMEVlMAEV



(4)

(5)

Finally the Average Estimation Variance (AEV) is

calculated as

Existing+cs existing approach with lost critical

samples retransmitted

LASP proposed approach described in section 42

LASP-cs proposed approach but lost critical samples

are not retransmitted

We use the Body Sensor Network (BSN) Development

Kit v3 [28] developed by Prof Guang-Zhong Yang of

Imperial College to collect the accelerometer data from

users The BSN development kit v3 includes a USB

programming board a sensor board a battery board a

prototype board and a pair of the new BSN v3 nodes The

sensor board has a 3D accelerometer and a temperature

sensor In our experiments we recruit three subjects and

attach the sensor board to the arm of the subject Each

subject is asked to perform three types of Activity of Daily

Living (ADL) jogging sitting and walking The duration

of each activity is around 3 minutes The 3D

accelerometer data was collected when the subjects

performed the ADL activity We chose to use the

sampling rate as 10 Hz We also implemented linear

interpolation to calculate a sample directly between any

two neighboring ones the interpolation effectively yields

a 33 Hz sampling rate with the smoothness of the 16 Hz

rate Using the 10 Hz sampling rate with linear

interpolation yielded a rather smooth graph without a lot

of noise and yet it was fast enough that the peaks and

troughs of ADL were all present

Our evaluation consists of two parts In part 1 received

samples with different approaches are compared in term

of several key metrics In part 2 recovered data are

analyzed by various representative analysis algorithms for

comparison of the accuracy

Fig 4 Comparison of Average Estimation Variance (AEV)

A Sample Quality Analysis

For each received samples we evaluate how

satisfactory the data is recovered by comparing the

estimated values with original ones Fig 4 shows the

comparison of average estimation variance of lost samples

for x values of jogging data It can be seen that LASP has

only about 6-9 normalized variance on average while

existing approach yields an average of 15-17 Also

retransmission of critical samples does not help much for

existing approach mainly due to the high estimation

inaccuracy when a contiguous sample train gets lost Fig 5

compares number of lost samples with normalized

variance higher than 25 Itrsquos clear that LASP is much

better than existing approach even if critical samples are

not retransmitted This is due to the fact that LASP creates

a much nicer sample loss pattern as described earlier and

therefore minimizes the variance for lost samples

Fig 5 Number of samples with low recovery ratio

Fig 6 Comparison of data recovery

To provide a visual representation of how well LASP

improves data recovery 100 samples are extracted from a

sequence of 1043 samples for jogging Itrsquos clear that

LASP while having some variance creates a good

approximation of the original values However existing

approach easily mis- estimate many samples

B Data Analysis

In order to determine whether the proposed

packetization strategy could contribute to improved

accuracy of data analysis we design our experiments as

follow First we employ the sliding window techniques

with dynamic window length For each sliding window

we extract high level features (eg average absolute

difference time between peaks and etc) instead of using

the raw sensor value Second we choose two widely used

data mining and machine learning classification

algorithms Naive Bayes Classifier and Support Vector

Machines (SVM) Classifier Next we train two models

using the aforementioned classifiers We define these two

models as ldquoNBCrdquo (which is trained using Naive Bayes

Classifier) and ldquoSVMrdquo (which is trained using Support

Vector Machines) Once the two models were trained we

will apply each model to the data sets generated by the

four strategies (introduced in the beginning of Section 5)

For each testing six accuracy measurements are used

True Positive Rate (TP Rate) False Positive Rate (FPR)

Precision Recall F-Measurement and Area Under an

ROC Curve (ROC Area)

We will first introduce our feature extraction

techniques for generating semantic features from sliding

windows In our previous work [29] we mainly employ

raw time-series data In the experiments we extend our

previous work by employing six types of features which

are listed as follows The first and second type of feature

is Average and Standard Deviation respectively The

third type of feature is called Average Resultant

Acceleration [30] [31] which is defined as the average

of the square roots of the summation of the sensor





readings within each sliding window The fourth type of

feature is named as Average Absolute Difference [30]

[31] which is defined as the average absolute difference

between the value of each of the raw sensor reading in the

sliding window and the mean value over of all the sensor

reading The fifth category of features is called Time

Between Peaks [30] which is defined as time differences

(calculated in milliseconds) between peaks in the

sinusoidal waveform from the raw sensor readings within

the sliding window

1) Comparison with various models

Table II shows the detailed results using the NBC

model As a representative scenario the results for a loss

rate of 20 are studied As we can see from this table the

proposed enhancement in this paper does contribute to the

improvement of the accuracy For example if we compare

the ldquoF-Measurementrdquo (row six of Table II) values without

lost critical samples retransmitted and with lost critical

samples retransmitted (+cs) we will find that the values

have been improved more than 5

TABLE II ACCURACY COMPARISON AMONG DIFFERENT STRATEGIES

USING NBC MODEL

Strategy Name

Existing Existing+cs LASP-cs LASP

TP Rate 0791 0803 0824 0837

FP Rate 0115 0112 0090 0089

Precision 0809 0814 0831 0847

Recall 0793 0904 0815 0817

F-Measure 0785 0801 0821 0824

ROC Area 0924 0927 0946 0949

TABLE III ACCURACY COMPARISON AMONG DIFFERENT STRATEGIES

USING SVM MODEL

Strategy Name


TP Rate 0633 0645 0693 0699

FP Rate 0174 0169 0147 0145

Precision 0836 0839 0849 0851

Recall 0635 0645 0692 0699

F-Measure 0631 0630 0689 0695

ROC Area 0728 0737 0783 0782

We can draw similar conclusions from Table III if we

employ the second classification model (SVM

classification model) Again we only show the results

when the loss rate is 20 and the results under other loss

rate are similar As shown in Table III the average

performance improvements are around 10 We also

performs two sample t tests Results show that the p-value

was less than 5 which means the performance

improvements are statistically significant

2) Accuracy under various loss rates

We then study whether or not the proposed LASP

strategy is robust under different loss rates To do so we

evaluated the classification accuracy under different loss

rate within LASP Specifically we evaluated the accuracy

(precision and recall) under three loss rates 10 20

and 30 using the first classification model (NBC

model) Fig 7 shows the precision and recall of our NBC

classification algorithms As shown in this figure while

the classification performance does suffering when the

loss rate increases the reduction of classification

performance is minor For example when the loss rate is

increased for 20 (from 10 to 30) the precision and

recall are reduced by less than 3 These results are

strong indication that our proposed LASP strategy are

very robust for sample loss

Fig 7 Accuracy of LASP under different loss rate with NBC

classification model

3) Comparison with various sample lengths

Finally we conducted a third experimentin order to

determine whether the proposed approach can reduce the

detection time For example given two groups of time

series T1 and T2 we apply the same classification model

to both of them If the classification accuracy for T1 is

better than the result for T2 and the difference between the

accuracy is statistically significant we can claim that

strategy for T1 is more effective (in terms of data analytics)

than the strategy for T2 In our experiments we choose

three lengths 15 seconds (L1) 30 seconds (L2) and 45

seconds (L3) For each length we generate two data

streams the first data stream is generated by sequential

packetization (ldquoExistingrdquo) and the second data stream is

generated by the proposed strategy (LASP) As shown in

Table IV each column represents the results of data

stream with different length For example the second row

(ldquoExisting-L1rdquo) indicates the data stream whose length is

15 seconds (L1) using the sequential packetization

(ldquoExistingrdquo) strategy Each row represents different

accuracy measurements which are introduced in the

beginning of section VB From Table IV we can see that

given the same length of the data stream the accuracy

measurement for data stream from LASP is substantially

higher than the accuracy measurement for data stream

from ldquoExistingrdquo strategy This verifies that if we employ

the proposed approach we could achieve faster detection

speed In another world LASP has a good potential in

applications where real-time classification is desired

TABLE IV ACCURACY COMPARISON AMONG DIFFERENT LENGTHS

GENERATED BY DIFFERENT STRATEGIES

Strategy Existing LASP

L1 L2 L3 L1 L2 L3

TP Rate 0582 0609 0611 0655 0677 0681

FP Rate 0228 0206 0201 0188 0164 0167

Precision 0789 0811 0815 0804 0828 0827

Recall 0570 06 0611 0645 0679 0688

F-Measure 0558 0581 0587 0645 0673 0677

ROC Area 0679 0699 0701 0729 0755 0759

VI SUMMARY

Existing strategies for transmission of time series body

sensor data assumes sequential sample packetization



which leads to difficulty of data recovery in the high loss

ratio WBANs In this paper we investigated the issue of

sample packetization pattern and proposed a heuristic

approach to minimize the effect of channel loss on success

of data analysis The proposed approach is very efficient

for sensor data processing incurs no extra overhead and

does not require modification of the transport protocol

In the future we will implement the packetization

scheme in real sensor network platform such as TinyOS

[32] and experiment with more biomedical sensor data to

validate its effectiveness

ACKNOWLEDGMENT

The authors wish to thank ICNC 2015 reviewers for

their valuable comments that significantly improved the

quality of the paper This work was partially supported by

the US National Science Foundation under Grant No

1229213

REFERENCES

[1] G Z Yang Body Sensor Networks New York USA Springer

Science+Business Media LLC 2006

[2] C Cordeiro and M Patel ldquoBody area networking standardization

present and future directionsrdquo presented at the Proceedings of the

ICST 2nd International Conference on Body Area Networks

Florence Italy 2007

[3] J Ko C Lu M B Srivastava J A Stankovic A Terzis and M

Welsh ldquoWireless sensor networks for healthcarerdquo Proceedings of

the IEEE vol 98 pp 1947-1960 2010

[4] O Amft H Junker P Lukowicz G Troster and C Schuster

ldquoSensing muscle activities with body-worn sensorsrdquo in Proc

International Workshop on Wearable and Implantable Body

Sensor Networks 2006

[5] B French A Smailagic D Siewiorek V Ambur and D

Tyamagundlu ldquoClassifying wheelchair propulsion patterns with a

wrist mounted accelerometerrdquo presented at the Proceedings of the

ICST 3rd International Conference on Body Area Networks

Tempe Arizona 2008

[6] L Jones N Deo and B Lockyer ldquoWireless physiological sensor

system for ambulatory userdquo in Proc International Workshop on

Wearable and Implantable Body Sensor Networks 2006 pp 4

pp-149

[7] K V Laerhoven H W Gellersen and Y G Malliaris ldquoLong

term activity monitoring with a wearable sensor noderdquo in Proc



[8] U Maurer A Smailagic D P Siewiorek and M Deisher

ldquoActivity recognition and monitoring using multiple sensors on

different body positionsrdquo in Proc International Workshop on

Wearable and Implantable Body Sensor Networks 2006

[9] M Quwaider and S Biswas ldquoBody posture identification using

hidden Markov model with a wearable sensor networkrdquo presented

at the Proceedings of the ICST 3rd International Conference on

Body Area Networks Tempe Arizona 2008

[10] R Chavarriaga H Sagha A Calatroni S T Digumarti G Tr et

al ldquoThe opportunity challenge A benchmark database for on-

body sensor-based activity recognitionrdquo Pattern Recogn Lett vol

34 pp 2033-2042 2013

[11] M Zhang and A A Sawchuk ldquoUSC-HAD A daily activity

dataset for ubiquitous activity recognition using wearable sensorsrdquo

presented at the Proceedings of the 2012 ACM Conference on

Ubiquitous Computing Pittsburgh Pennsylvania 2012

[12] A Bulling U Blanke and B Schiele ldquoA tutorial on human

activity recognition using body-worn inertial sensorsrdquo ACM

Comput Surv vol 46 pp 1-33 2014

[13] G Zhou J Lu C Wan M Yarvis and J Stankovic ldquoBodyqos

Adaptive and radio-agnostic qos for body sensor networksrdquo in

Proc IEEE INFOCOM Phoenix AZ 2008 pp 565-573

[14] G Zhou C Y Wan M D Yarvis and J A Stankovic

ldquoAggregator-centric QoS for body sensor networksrdquo presented at

the Proceedings of the 6th International Conference on

Information Processing in Sensor Networks Cambridge

Massachusetts USA 2007

[15] B Otal L Alonso and C Verikoukis ldquoNovel QoS scheduling

and energy-saving MAC protocol for body sensor networks

optimizationrdquo presented at the Proceedings of the ICST 3rd

International Conference on Body Area Networks Tempe

Arizona 2008

[16] J J Garcia and T Falck ldquoQuality of service for IEEE 802154-

based wireless body sensor networksrdquo in Proc 3rd International

Conference on Pervasive Computing Technologies for Healthcare

2009 pp 1-6

[17] F Gengfa and E Dutkiewicz ldquoBodyMAC Energy efficient

TDMA-based MAC protocol for wireless body area networksrdquo in

Proc 9th International Symposium on Communications and

Information Technology 2009 pp 1455-1459

[18] N Read M Li Y Cao S H Liu T Wilson and B Prabhakaran

ldquoLoss resilient strategy in body sensor networksrdquo presented at the

Proc of ACMIEEE International Conference on Body Area

Networks (BodyNets) Beijing China 2011

[19] E Weldon Jr ldquoAn improved Selective-Repeat ARQ strategyrdquo

IEEE Transactions on Communications vol 30 pp 480-486

1982

[20] T Fulford-Jones D Malan M Welsh and S Moulton

ldquoCodeBlue An ad hoc sensor network infrastructure for

emergency medical carerdquo in Proc International Workshop on

Body Sensor Networks 2004

[21] S Jiang Y Cao S Iyengar P Kuryloski R Jafari Y Xue et al

ldquoCareNet An integrated wireless sensor networking environment

for remote healthcarerdquo presented at the Proceedings of the ICST

3rd International Conference on Body Area Networks Tempe

Arizona 2008

[22] M Younis K Akkaya M Eltoweissy and A Wadaa ldquoOn

handling QoS traffic in wireless sensor networksrdquo in Proc 37th

Annual Hawaii International Conference on System Sciences Big

Island HI USA 2004

[23] P Baronti P Pillai V W C Chook S Chessa A Gotta and Y

F Hu ldquoWireless sensor networks A survey on the state of the art

and the 80215 4 and ZigBee standardsrdquo Computer

Communications vol 30 pp 1655-1695 2007

[24] Bluetooth Low Energy Core Specification Version 40 [Online]

AvailablehttpwwwbluetoothcomEnglishTechnologyWorks

PagesBluetooth_low_energy_technologyaspx

[25] K K Sup M A Ameen K Daehan L Cheolhyo and L

Hyungsoo ldquoA study on proposed IEEE 80215 WBAN MAC

protocolsrdquo in Proc 9th International Symposium on

Communications and Information Technology 2009 pp 834-840

[26] M van der Schaar and D S Turaga ldquoCross-Layer packetization

and retransmission strategies for delay-sensitive wireless

multimedia transmissionrdquo IEEE Transactions on Multimedia vol

9 pp 185-197 2007

[27] S Zhang S C Liew and P P Lam ldquoHot topic Physical-layer

network codingrdquo presented at the Proceedings of the 12th annual

International Conference on Mobile Computing and Networking

Los Angeles CA USA 2006

[28] J Ellul B Lo and G Z Yang ldquoThe BSNOS platform A body

sensor networks targeted operating system and toolsetrdquo in Proc



Fifth International Conference on Sensor Technologies and

Applications 2011 pp 381-386

[29] M Li Y Cao and B Prabhakaran ldquoLoss aware sample

packetization strategy for improvement of body sensor data

analysisrdquo in Proc International Conference on Computing

Networking and Communications Anaheim California 2015

[30] L Bao and S S Intille ldquoActivity recognition from user-annotated

acceleration datardquo in Pervasive Computing Springer 2004 pp 1-

17

[31] J R Kwapisz G M Weiss and S A Moore ldquoActivity

recognition using cell phone accelerometersrdquo ACM SigKDD

Explorations Newsletter vol 12 pp 74-82 2011

[32] TinyOS [Online] Available wwwtinyosnet

Ming Li is currently an Associate Professor

and Chair in the Department of Computer

Science California State University Fresno

Prior to that he was an Assistant Professor

from August 2006 to 2012 He received his

MS and PhD degrees in Computer Science

from The University of Texas at Dallas in

2001 and 2006 respectively His research

interests include QoS strategies for IEEE

80211 wireless LANs mobile ad-hoc networks and heterogeneous

wired and wireless networks multimedia streaming over wireless

networks body area networks and robot swarm communications

Yu Cao has been an Assistant Professor at the

Department of Computer Science The

University of Massachusetts Lowell since Aug

2013 From 2010 to 2013 he was a faculty at

The University of Tennessee at Chattanooga

(UTC) From 2007 to 2010 he was a faculty at

California State University Fresno Prior to

that he was a Visiting Fellow of Biomedical

Engineering at Mayo Clinic Rochester

Minnesota He received his MS and PhD degrees in Computer

Science from Iowa State University in 2005 and 2007 respectively He

received the BEng degree from Harbin Engineering University in 1997

the MEng degree from Huazhong University of Science and

Technology in 2000 all in Computer Science His research interests

span a variety of aspects of knowledge discover from complex data

which include the area of biomedical informatics and intelligent system

B Prabhakaran is a Professor of Computer

Science in the University of Texas at Dallas

Prof B Prabhakaran works in the area of

multimedia systems He has published several

research papers in prestigious conferences and

journals in this area Dr Prabhakaran received

the prestigious NSF CAREER Award FY

2003 for his proposal on Animation Databases

Dr Prabhakaran is General Co-Chair of ACM

Multimedia 2011 He is also Technical Program Co-Chair of IEEE

WoWMoM 2012 (World of Wireless Mobile and Multimedia

Networks) He served as the TPC Co-Chair of IEEE ISM 2010

(International Symposium on Multimedia) Dr Prabhakaran is a

Member of the Executive Council of the ACM Special Interest Group

on Multimedia (SIGMM) and is the Co-Chair of IEEE Technical

Committe on Multimedia Computing (TCMC) Special Interest Group

on Video Analytics (SIGVA) Dr Prabhakaran is the Editor-in-Chief of

the ACM SIGMM (Special Interest Group on Multimedia) web

magazine He is Member of the Editorial board of Multimedia Systems

Journal (Springer) and Multimedia Tools and Applications journal

(Springer) He has served as guest-editor (special issue on Multimedia

Authoring and Presentation) for ACM Multimedia Systems journal

out of order samples we propose LASP (Loss Aware

Sample Packetization) an efficient heuristic approach

where a fixed pattern is followed for each sample group

of a specific size The fixed pattern has the advantage of

low number contiguous sample pairs and can be easily

applied at the receiver side for reassembling of samples

Then an additional reliability enhancement with

approach similar to selective repeat [19] for critical

samples was proposed for further improvement

II RELATED WORKS

Quality of Service (QoS) scheduling for streaming

body sensor data has been well investigated Zhou and Lu

[13] proposed BodyQoS a virtual MAC for quality of

service scheduling in BSNs The approach basically

measures the effective bandwidth and adaptively

allocates remaining resources to meet the QoS

requirements of applications A desirable feature of

BodyQoS is that it does not require the modification of

the underlying MAC layer implementation Fulford-Jones

and Malan [20] proposed CodeBlue an ad hoc

infrastructure for emergent medical care In this project

several types of body sensors (eg pulse oximeter

ECGEKG sensor) are individually connected to Zigbee

enabled radio transmitters which communicate with

access points directly Due to the ad hoc architecture and

the capability of self-organizing CodeBlue yields

scalability for network expanding and flexibility to

connect various wireless devices Jiang and Cao [21]

proposed CareNet an integrated wireless environment

used for remote health care systems CareNet offers

features such as high reliability and performance

scalability security and integration with web based portal

systems High reliability is achieved by using two-tier

architecture Younis and Akkaya [22] proposed

Distributed Queuing Body Area Network a MAC

protocol aiming at providing better QoS It uses a cross-

layer fuzzy rule based scheduling algorithm to optimize

MAC layer performance in terms of QoS and energy

efficiency

There have been considerable standardization efforts

during recent years Among many potential technologies

Zigbee and Bluetooth are most widely deployed ZigBee

is a very low power collision avoidance protocol

optimized for lower power sensors It has developed a

health care specific protocol and are compliant with all

IEEE 11073 devices as well as most other IEEE 802154

[23] wireless devices Bluetooth supports high-bandwidth

and many several existing devices and has a health care

compliant version defined but has very high power

requirements and uptime for the radios Bluetooth Low

Energy [24] is a new proposed system from Bluetooth

which will have lower energy requirements and still be

interoperable with Bluetooth Classic but details are still

forthcoming at this time Being aware of the unique

requirement of supporting a wide range of applications by

body area networks an IEEE 802156 [25] task force has

Channel is unreliable and may drop data

With multiple body sensors communicating with the

same aggregator channel may reach saturation status

and therefore dropping packets in the queue

Reliable transmission of all samples incurs high

protocol overhead for relatively small body sensor

samples

A Data Recovery and Critical Samples

With quite strict requirement on the completeness of

sensor samples by data analysis algorithms missing

samples at data aggregator should be recovered at the

maximally possible level Itrsquos possible to use selective

repeat [19] to send back to specific sensors and request

retransmission of all lost samples However this

approach has the disadvantage of higher overhead and is

not efficient for real-time analysis Instead it is desirable

to recover samples using received ones and try to achieve

satisfactory approximation Usually lost samples can be

recovered by estimation (eg linear interpolation) at the



been working on finalizing a Wireless Body Area

Network (WBAN) standard The new standard aims at

providing flexible and configurable energy efficient

MAC operations

Plenty of research has been conducted using body

sensors to monitor Activity of Daily Living (ADL) Two

survey papers [3] [4] give details on the state-of-art-of

research in the area of sensor network with inertial

sensors and their applications in healthcare and wellbeing

Due to the growing interest on body sensor-based ADL

recognition several sensor-based human activity datasets

[10] [11] were introduced in the last few years A most

recent paper published in 2014 ACM Computing Survey

[12] discuss the key research challenges that human

activity recognition shares with general pattern

recognition and identify those challenges that are specific

to human activity recognition This paper also describes

the concept of an Activity Recognition Chain (ARC) as a

general-purpose framework for designing and evaluating

activity recognition systems Most of the techniques in

the current state of the art are focusing on the data

analysis and pattern recognition using the raw sensor data

Very little research in data analytics area considers the

issues of developing new networking strategy to improve

the accuracy and speed of sensor data analytics

III SYSTEM MODEL AND ASSUMPTIONS

In a typical medical application multiple sensors such

as ECG EKG EMG EEG motion sensors and blood

pressure sensors send multimodal time series data to

nearby Data Aggregators (DAs) which can be a cell

phone watch headset PDA laptop or robot based on

the application needs Then through BluetoothWiFi

these data can be delivered remotely to physician side for

real time diagnosis or to medical database for record

keeping or to request for emergency For such systems

the following assumptions are made














adopted


























































packet loss occurs


















B Proposed Approach




considered








-1 are put into


as follows





iM






in this paper







of j+kN



critical samples













implemented







(1)




(2)

(3)




data analysis













(6)









Sequential 0075 0 039 090 140

Alternate 0003 0 002 006 012




















ie sequential

Collect

Samples

Perform

PacketizationSample

Assembling

Identify Critical

Samples

Request Lost

Critical Samples

Resend Critical

Samples

Data

Recovery


iiii ddestEV

i iEVEV

KEVAEV i i

M

m

m

j

mMm jEVllm

MEV

0 1

)()1(

EVlMmllm

MEVEV

M

m

mMm

0

)1(

EVlMEVlMAEV



(4)

(5)


calculated as



























































B Data Analysis













































the sliding window












USING NBC MODEL

Strategy Name


TP Rate 0791 0803 0824 0837

FP Rate 0115 0112 0090 0089

Precision 0809 0814 0831 0847

Recall 0793 0904 0815 0817

F-Measure 0785 0801 0821 0824

ROC Area 0924 0927 0946 0949


USING SVM MODEL

Strategy Name


TP Rate 0633 0645 0693 0699

FP Rate 0174 0169 0147 0145

Precision 0836 0839 0849 0851

Recall 0635 0645 0692 0699

F-Measure 0631 0630 0689 0695

ROC Area 0728 0737 0783 0782




























































L1 L2 L3 L1 L2 L3

TP Rate 0582 0609 0611 0655 0677 0681

FP Rate 0228 0206 0201 0188 0164 0167

Precision 0789 0811 0815 0804 0828 0827

Recall 0570 06 0611 0645 0679 0688

F-Measure 0558 0581 0587 0645 0673 0677

ROC Area 0679 0699 0701 0729 0755 0759

VI SUMMARY
















ACKNOWLEDGMENT





1229213

REFERENCES






Florence Italy 2007



the IEEE vol 98 pp 1947-1960 2010









Tempe Arizona 2008




pp-149
















34 pp 2033-2042 2013




















Arizona 2008




2009 pp 1-6











1982









Arizona 2008




Island HI USA 2004















9 pp 185-197 2007

















17




































































adopted


























































packet loss occurs


















B Proposed Approach




considered








-1 are put into


as follows





iM






in this paper







of j+kN



critical samples













implemented







(1)




(2)

(3)




data analysis













(6)









Sequential 0075 0 039 090 140

Alternate 0003 0 002 006 012




















ie sequential

Collect

Samples

Perform

PacketizationSample

Assembling

Identify Critical

Samples

Request Lost

Critical Samples

Resend Critical

Samples

Data

Recovery


iiii ddestEV

i iEVEV

KEVAEV i i

M

m

m

j

mMm jEVllm

MEV

0 1

)()1(

EVlMmllm

MEVEV

M

m

mMm

0

)1(

EVlMEVlMAEV



(4)

(5)


calculated as



























































B Data Analysis













































the sliding window












USING NBC MODEL

Strategy Name


TP Rate 0791 0803 0824 0837

FP Rate 0115 0112 0090 0089

Precision 0809 0814 0831 0847

Recall 0793 0904 0815 0817

F-Measure 0785 0801 0821 0824

ROC Area 0924 0927 0946 0949


USING SVM MODEL

Strategy Name


TP Rate 0633 0645 0693 0699

FP Rate 0174 0169 0147 0145

Precision 0836 0839 0849 0851

Recall 0635 0645 0692 0699

F-Measure 0631 0630 0689 0695

ROC Area 0728 0737 0783 0782




























































L1 L2 L3 L1 L2 L3

TP Rate 0582 0609 0611 0655 0677 0681

FP Rate 0228 0206 0201 0188 0164 0167

Precision 0789 0811 0815 0804 0828 0827

Recall 0570 06 0611 0645 0679 0688

F-Measure 0558 0581 0587 0645 0673 0677

ROC Area 0679 0699 0701 0729 0755 0759

VI SUMMARY
















ACKNOWLEDGMENT





1229213

REFERENCES






Florence Italy 2007



the IEEE vol 98 pp 1947-1960 2010









Tempe Arizona 2008




pp-149
















34 pp 2033-2042 2013




















Arizona 2008




2009 pp 1-6











1982









Arizona 2008




Island HI USA 2004















9 pp 185-197 2007

















17
























































as follows





iM






in this paper







of j+kN



critical samples













implemented







(1)




(2)

(3)




data analysis













(6)









Sequential 0075 0 039 090 140

Alternate 0003 0 002 006 012




















ie sequential

Collect

Samples

Perform

PacketizationSample

Assembling

Identify Critical

Samples

Request Lost

Critical Samples

Resend Critical

Samples

Data

Recovery


iiii ddestEV

i iEVEV

KEVAEV i i

M

m

m

j

mMm jEVllm

MEV

0 1

)()1(

EVlMmllm

MEVEV

M

m

mMm

0

)1(

EVlMEVlMAEV



(4)

(5)


calculated as



























































B Data Analysis













































the sliding window












USING NBC MODEL

Strategy Name


TP Rate 0791 0803 0824 0837

FP Rate 0115 0112 0090 0089

Precision 0809 0814 0831 0847

Recall 0793 0904 0815 0817

F-Measure 0785 0801 0821 0824

ROC Area 0924 0927 0946 0949


USING SVM MODEL

Strategy Name


TP Rate 0633 0645 0693 0699

FP Rate 0174 0169 0147 0145

Precision 0836 0839 0849 0851

Recall 0635 0645 0692 0699

F-Measure 0631 0630 0689 0695

ROC Area 0728 0737 0783 0782




























































L1 L2 L3 L1 L2 L3

TP Rate 0582 0609 0611 0655 0677 0681

FP Rate 0228 0206 0201 0188 0164 0167

Precision 0789 0811 0815 0804 0828 0827

Recall 0570 06 0611 0645 0679 0688

F-Measure 0558 0581 0587 0645 0673 0677

ROC Area 0679 0699 0701 0729 0755 0759

VI SUMMARY
















ACKNOWLEDGMENT





1229213

REFERENCES






Florence Italy 2007



the IEEE vol 98 pp 1947-1960 2010









Tempe Arizona 2008




pp-149
















34 pp 2033-2042 2013




















Arizona 2008




2009 pp 1-6











1982









Arizona 2008




Island HI USA 2004















9 pp 185-197 2007

















17

















































































































B Data Analysis













































the sliding window












USING NBC MODEL

Strategy Name


TP Rate 0791 0803 0824 0837

FP Rate 0115 0112 0090 0089

Precision 0809 0814 0831 0847

Recall 0793 0904 0815 0817

F-Measure 0785 0801 0821 0824

ROC Area 0924 0927 0946 0949


USING SVM MODEL

Strategy Name


TP Rate 0633 0645 0693 0699

FP Rate 0174 0169 0147 0145

Precision 0836 0839 0849 0851

Recall 0635 0645 0692 0699

F-Measure 0631 0630 0689 0695

ROC Area 0728 0737 0783 0782




























































L1 L2 L3 L1 L2 L3

TP Rate 0582 0609 0611 0655 0677 0681

FP Rate 0228 0206 0201 0188 0164 0167

Precision 0789 0811 0815 0804 0828 0827

Recall 0570 06 0611 0645 0679 0688

F-Measure 0558 0581 0587 0645 0673 0677

ROC Area 0679 0699 0701 0729 0755 0759

VI SUMMARY
















ACKNOWLEDGMENT





1229213

REFERENCES






Florence Italy 2007



the IEEE vol 98 pp 1947-1960 2010









Tempe Arizona 2008




pp-149
















34 pp 2033-2042 2013




















Arizona 2008




2009 pp 1-6











1982









Arizona 2008




Island HI USA 2004















9 pp 185-197 2007

















17


































































the sliding window












USING NBC MODEL

Strategy Name


TP Rate 0791 0803 0824 0837

FP Rate 0115 0112 0090 0089

Precision 0809 0814 0831 0847

Recall 0793 0904 0815 0817

F-Measure 0785 0801 0821 0824

ROC Area 0924 0927 0946 0949


USING SVM MODEL

Strategy Name


TP Rate 0633 0645 0693 0699

FP Rate 0174 0169 0147 0145

Precision 0836 0839 0849 0851

Recall 0635 0645 0692 0699

F-Measure 0631 0630 0689 0695

ROC Area 0728 0737 0783 0782




























































L1 L2 L3 L1 L2 L3

TP Rate 0582 0609 0611 0655 0677 0681

FP Rate 0228 0206 0201 0188 0164 0167

Precision 0789 0811 0815 0804 0828 0827

Recall 0570 06 0611 0645 0679 0688

F-Measure 0558 0581 0587 0645 0673 0677

ROC Area 0679 0699 0701 0729 0755 0759

VI SUMMARY
















ACKNOWLEDGMENT





1229213

REFERENCES






Florence Italy 2007



the IEEE vol 98 pp 1947-1960 2010









Tempe Arizona 2008




pp-149
















34 pp 2033-2042 2013




















Arizona 2008




2009 pp 1-6











1982









Arizona 2008




Island HI USA 2004















9 pp 185-197 2007

















17




































































ACKNOWLEDGMENT





1229213

REFERENCES






Florence Italy 2007



the IEEE vol 98 pp 1947-1960 2010









Tempe Arizona 2008




pp-149
















34 pp 2033-2042 2013




















Arizona 2008




2009 pp 1-6











1982









Arizona 2008




Island HI USA 2004















9 pp 185-197 2007

















17

































































17























































Documents

Loss Aware Sample Packetization Strategy for Improvement of … · Loss Aware Sample Packetization Strategy for Improvement of ... security and integration with web based ... IEEE