2014 International Conference on Reliability, Optimization and Information Technology -

ICROIT 2014, India, Feb 6-8 2014

Quality of Service Provisioning Transport Layer Protocol for WBAN system

Madhumita Kathuria Research Scholar

Department of Computer Science, YMCA University of Science & Technology, Faridabad, Haryana, India

madh um ita. fet@mriu.edu. in

Abstract: Wireless Body Area Network (WBAN) applications are looking forward for better and effective environment because of its heterogeneous and wearable nature. Recent applications of WBAN need to be support both real time traffic (sensitive to end­to-end packet delay) and non-real time traffic (sensitive to packet loss), which further origin the problem of diverse QoS requirements. So the first step of this paper is to study QoS issues related to each layer and extort why transport layer is more devoted to QoS issue. Then we inspect the limitations of current

existing transport protocols for WBAN system. Considering these

limitations we have designed a protocol, which tries to handle QoS

in an efficient way. As we know transport layer deals QoS

significantly, proposed protocol was structured to overcome some QoS problems related to this layer like packet handling, reliable packet transmission with loss recovery and congestion control. The intention of proposed schema is to provide end-to-end

bidirectional (both upstream and downstream) and bi-functional

(both packet based and event based) reliability module. This is also taking care of each kind of packets loss. Its intelligent packet handling method provides priority fairness to overcome starvation problem. This proposed work is making an effort to reduce retransmission of duplicate packets and relevant to control congestion.

Keywords: WBAN, Quality of Service (QoS), Reliability, Congestion, Packet handler, Classifier, Scheduler.

I. INTRODUCTION

Now-a-day's demand of wearable wireless devices in each and every application has grown. The consumers of each environment are demanding for advance technology in wireless world with low cost, pervasive and real time data communication. These environments need information about

their surroundings as well as about their internal working. A WBAN is a human centric network used to serve a variety of

applications including healthcare, personal entertainment,

advanced sports training, live events, aviation, special forces

(i.e. military, air force, fIre fIghters, bomb diffusers, astronaut monitoring etc.), disasters and consumer electronics devices. So, it is essential for WABN to provide Quality of Service with timely delivery of real time data as well as reliable delivery of

non-real time data without any loss. QoS requirements are more

complex due to lack of appropriate attention, dynamic

topology, time varying wireless channel, limited battery,

power, and bandwidth. Heterogeneous nodes generate different

traffic flow with variable rate, delay and loss tolerances. For example, some

978-1-4 799-2995-5/14/$31.00©20 14 IEEE

Sapna Gambhir Associate Professor,

Department of Computer Science, YMCA University of Science and Technology, Faridabad, Haryana, India

sapnagambhir@gmail.com

low data rate nodes (i.e. heartbeat, ECG sensors) may generate very time critical data packets, which must be delivered at the sink with guaranteed reliability and some high data rate nodes (i.e. streaming of ECG signals) may allow a certain percentage of packet losses but negligible delay. WBAN system shown in fIgure-l is a consistent system that assures modest, safest, reliable congestion free transmission of information on internet. To make WBAN system persistent and realistic, QoS issues should be solved properly.

This paper has been classifIed as follows: Section 1 signifies a brief introduction to the subject matter. Section 2 states about exploitation QoS issues. It also gives idea how transport layer related to QoS. Section 3 spotlights the matter and problems exist in current WBAN protocols. Section 4 presents brief

knowledge and motto about the proposed work; it gives a detail

plan of our motivation. Section 5 provides the conclusion part along with the future work in the direction of solving some

open issues of WBAN.

Base

station

Ni=Node, Sink=Controller node, Ui=User.

Fig. 1 WBAN system

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II. QUALITY OF SERVICES REQUIREMENTS FOR WBAN

With the growth of technology we face huge demand for elegant, smart and more capable devices tied with much faster wireless network to tackle and to support human necessities in terms of information, communication and enjoyment. Different WBAN applications require different level of QoS. Real time QoS is essential to maintain and provide better service for the users in a limited time. We have analysed each and every layer

of a WBAN system and tried to explain their QoS issues,

metrics and requirements in a tabular form as given in table1.

As we know, transport layer service is the strength of a network and its primary function is to enhance Quality of Service, so we have emphasized to design a protocol considering this layer. As

given in table 1, the QoS at transport layer is determined by several parameters such as reliability, delay, jitter (delay variance), congestion and others.

Table I: Layered wise QoS issues, metrics and requirements for a WBAN system.

Layer QoSlssues QoSMetric QoS Requirements

Application • System lifetime, Request and • Coverage area • Timer for system lifetime, request response time • Fair resource allocation and response time

• Data integrity • Service time calculation • Data novelty and reliability • Data freshness • Sensor querying. • Data extraction and resolution. • Data discovery • Fault detection • Data security • Application software fault • Sensor management

• Task assignment • Data advertisement • High quality consistent service by

ensuring fault-free information flow to lower layers.

Transport • Reliability • Out of sequence packet • Reliable transmission with • High Latency detection minimum latency and energy • Duplicate packets • Delay and jitter • Efficient classification of • Packet Loss or drop • Classification policy heterogeneous traffic • Packet corruption • Buffer status • Active management of buffer • Congestion • Scheduling strategy • Dynamic and Fair scheduling • Node or link level • Loss ratio • Reduce duplicate packet and

faults • Duplicate Packet packet loss • Congestion faults • Transmission ratio • Rate control

• Error packet ratio • Fault prevention and recovery • Fault diagnosis • Congestion control • Congestion detection • Energy consumption

Network • Path latency • Path latency rate • Minimize path latency and route • Route destruction • Route congestion maintenance cost • Congestion probability • Low routing control overhead • Mobility

• Routing robustness • Data aggregation and • Energy efficiency

• Fault discovery differentiation transient link failure • Congestion control

• Routing faults (encountered • Network mobility on established communication • Minimum energy consumption path) • Route fault tolerance

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Layer QoSlssues QoSMetric QoS Requirements

MAC • Throughput • Reliability proportion • Link level reliability • Transmission delay • Throughput ratio • Error control and Loss • Erroneous delivery of packets • Data transmission rate recovery • Collision • Collision Probability • Channel coding • Reliability • Interferences claim • Power scaling • Faults in link • In-order delivery • Time slot wise • Power • Bandwidth utilization scheduling

• Link fault detection • Minimize collision and (radio interference, data interference rate distortion between • Data suppression and the nodes) aggregation

• Link fault tolerance

Physical • Dynamic topology • Capacity of mediums • Maintenance of • Physical device, medium and • Inspection of standards dynamic topology

standards • Data rates • Data rate and priority of • Fixed bandwidth • Signal to noise ratio heterogeneous traffic. • Limited transmission range • Interference • Attenuation • Transmitter faults • Receiver faults • Noise due to fault in signal

III. RELATED WORK The impact of existing transport layer protocols for WBAN is given below. Pump Slowly Fetch Quickly (PSFQ) [12] is based on slowly injecting packets in "pump operation" , which provides timely controlled data forwarding. It provides a hop­by-hop error recovery in case of packet losses using" fetch operation", which use cache at intermediate nodes to store out­of-order sequence packet. To minimize signal overhead, NACK is used. In PSFQ congestion will occur as the node numbers increase. It cannot detect the loss of full message or loss of a single packet, Pump slow mechanism may result large delay, hop-by-hop recovery with cache buffer need more energy and inappropriate handling of time to live values. Reliable multi segment transport (RMST) [14] involves Direct Diffusion with two transmission modes: In non-caching mode, only sources and sinks maintain a cache, and only sink set timers to detect loss. In caching mode, each sensor node has a cache memory. Here receivers are responsible for detecting losses and trigger the recovery of the missing segments through NACKs. In RMST data blocks are reconstructed at each hop, so it requires significant memory resources at individual nodes and the data transmit rate is manually set by a System Administrator. Event­to-Sink Reliable Transport (ESRT) [13] provides upstream event reliability, congestion control, avoiding the dropping of packets and minimum energy consumption. The base station decides that the event is reliably detected or not. ESRT periodically computes a reliability figure (r) . representing the rate of packets received successfully in a given time interval. ESRT still have the limitations, such as power and processing if

• Fair channel allocation • Fair load distribution • Fault tolerance

the source node produce data too slowly than the required reliability is not achieved, but if data produced by source node is very fast, this may lead to loss of packets and network congestion. It does not retransmit lost packets. Sensor Transmission Control Protocol (STCP) [11] provides upstream reliability with fixed window size and congestion control. The receiver uses a timeout mechanism to fmd out a packet loss and recovers the lost packets by sending the NACK. For unpredictable event-driven packets, ACK is used. In STCP, when network size increases, latency increases due to congestion and channel contention and it transmits more numbers of unnecessary NACKs. Rate-Controlled Reliable Transport Protocol (RCRT) [6] gives reliability and congestion control schema. It provides (1) multipoint-to-point reliability (2) Sustain network efficiency by avoiding congestion collapse. (3) Flexibility to choose capacity allocation policies, which determines how the overall network capacity is divided up among the differentsources. (4) Robustness to routing dynamics and to nodes entering and leaving the system. Improved RCRT uses timer as congestion indicator and NACK for hop-by-hop loss recovery. RCRT fails to manage convergence time with highly varying RTTs. CODA [15] detects congestion by comparing buffer occupancy and channel weight. When a node notices congestion, it will notify its upstream neighbor nodes to decrease rate using AIMD and open-loop hop-by-hop backpressure. CODA regulate multi­source rate through closed-loop end-to-end approach. When a sensor nodes flow flooded the throughput, it will set policy bit in event packet. If the event packet received by sink has policy bit,

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sources. (4) Robustness to routing dynamics and to nodes entering and leaving the system. Improved RCRT uses timer as

congestion indicator and NACK for hop-by-hop loss recovery. RCRT fails to manage convergence time with highly varying

RTTs. CODA [15] detects congestion by comparing buffer occupancy and channel weight. When a node notices congestion, it will notify its upstream neighbor nodes to decrease rate using AIMD and open-loop hop-by-hop backpressure. CODA regulate multi-source rate through closed­loop end-to-end approach. When a sensor nodes flow flooded the throughput, it will set policy bit in event packet. If the event packet received by sink has policy bit, sink will send ACK packet to sensors to inform them to decrease their rate. CODA does not provides reliability and its response time of closed­loop multi-source is increased with congestion.

IV. PROPOSED WORK

Modular architecture of proposed schema: QoS is the capability to provide better services to different applications. To retain this at transport layer of WBAN, end-to-end reliability and congestion free communication, packet handling, resource utilization (bandwidth, buffer and power) and fault tolerance

networking are desirable. Hence we catalog our work according

to figure 2.

QoS

Module

- . , .............. /

................................ /

1.

......... "...,.

. ...................... ",....

. ...... ........ . ........ . .. -... -... -... -.... -... -�

Fig. 2 The modular architecture of QoS parameters

J) Packet handling module: Packet handler is responsible for handle traffic flow at different levels so that both real time traffic like IP Telephony, web browsing, audio on demand, video on demand and file sharing services and non-real time traffic get benefit in terms of QoS in the same network. Packet classification, buffering or queuing, scheduling and dropping have a direct impact on QoS characteristics. The desired QoS for multiple classes of traffic is employed with the help of priority queues.

a) Packet classifier module: Packet classification is the most tough and vital challenge for controller or sink node. It requires comparing each incoming packet against a policy based database. Packets are classified into appropriate class by examining specific fields such as < Source address Destination_address Source �ort, Destination �0I1, Packet

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type, Packet size, Alert level> in the packet header. This module use the ID3 decision tree algorithm, which checks the policy based database considering Packet type, Packet size, Alert level as main attribute and priority assignment as action. In a decision tree each branch node represents an option between a number of alternatives, and each leaf node represents a decision. The search starts with a root node and proceeds towards the leaf node to take an action. From root node, recursively splitting of each node take place. The final result of a decision tree with its branch represents a possible scenario of decision and its outcome. The basic idea of ID3 algorithm is to construct the decision tree by employing a top-down, greedy search through the given sets of rule or policy to test each attribute at every node of tree. A decision tree for proposed classifier module is given in figure

3.Based on this information from database; packet classifier categorizes packets into different flows and assigns them

different priorities.

Fig. 3 The decision tree based packet classification module .

b) Packet buffering module: Now these prioritized packets are passed to the next phase to check whether enough network resources in terms of buffer and bandwidth are available or not. If required amount of space is available for non-real time packet, then it gets stored into buffer and if required amount of bandwidth is available for real time packet then it gets forwarded for servicing, else it is kept in a wait queue or discarded accordingly.

c) Scheduler module: This module uses strict priority policy assigned by classifier. It always serves the alert queue first. If there is no packet waiting in this queue, it will serve the waiting queue. But this concept starves low priority traffics. We have proposed an earliest dead line based priority packet scheduling algorithm, which reduce the low priority packet starvation rate efficiently. It also assists real time traffic to transmit before non­real time traffic with expected available bandwidth. It basically computes the deadline (or waiting time) of each low priority packet and give them a chance to get service before its life time was elapsed. The deadline for eachtcket i is calculated from the equation (1). I?eadline = Calculated wait time in it queue / Expected wait tune

Di = (Ci-Ai)/Ti. <Th . . . . . . . . . . . . 1)

=Th >Th

Where Di= Deadline for packet i, Ci= Current time, Th= threshold value=l,

Ai= Arrival time of packet i into queue, Ti

Expected wait time.

Packet handler module

Packet handler module Received Packets

" Classifier module -

Check packet header field

I

T r--.

I I � Real time packet I I Non-real time packet I (F irst level Priority= I)

1 �icality�

High 1 Medium Low

Cri tical data On demand data Periodic data Second level Priority=O Second level Second level

Priority=2 Priority=3

Packet buffering module .. Alert queue I. Admits Packets having priority=O or I II Wait queue

I. Admits stay behind packets I I

� Scheduler module

Alert queue empty?

No Yes

Yes

Bandwidth Wait queue Available empty"

?

No

No Yes >Th Deadline

? � Th I. I. I. Find <Th Save Forward and t ... 111 them forward I. Find and 1. Find and alert high

forward high forward low queue priority

priority priority earliest packets packets deadline packets

.. Packet drop I Drop the deadline exceeded packets m wait I module

----- I Find and drop low priority packets from wait I I Drops dupl ieate packets I I

RelIabilIty module

Re-ordering modul e

Match received packet sequence no.

wi th expected sequence no,

= >

No loss Packet loss

1. Admit I. Send SLDNACK control the packet packets to sender into buffer 2. Save the out of sequence queue packets into buffer queue

t-Loss �NA5:> recovery module

I No I Yes

I Transmit I I Retransmits I lo�t n�r.ket�

<

l Duplicate handler module ..............,� C ongestion controller Module

Con estion de tection module

Check Check

buffer channel occuuancv load

Check I I Check loss rate latency

":II. I Calculate eon�estion levd (CLl I

< Tmin � >Tmax

rr �--

Tmin<CL<Tma

" Congestion notification (CN) module

II. CN bit activated by sink 2. Advertize implicitly or explicitly

• Congestion avoidance module

I I. Adjust flow rate 2. Redirect trallie

I. Selective Retransmission

2. Reduce duplicate packet transmission 3. Active buffer management (ABM)

I

Fig. 4 Workflow diagram of proposed schema.

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d) Packet dropping module: We have designed an intelligent packet dropping algorithm, where the minimum priority packets and least frequent used packets are discarded at the time of congestion by emulating buffer occupancy and by checking bandwidth capacity. To reduce retransmission ratio, duplicate packets are dropped and a notification regarding this is advertised to each sender.

ii) Reliability module: It specifies whether or not data will

deliver reliably. Link quality degradation, congestion, node

mobility, fault in link or node etc. are the main causes of packet

loss, corruption or delay. Our proposed protocol provides bidirectional reliability both in upstream and downstream direction and will deal with both packet level reliability (where all packets will successfully receive at sink) and event level reliability (if more than one sensor in a field captures same data

and reports to the sink, then at least one packet and not

duplicate packets should be received at sink). Our reliability module has been classified into three categories to handle out­

of-sequence, loss and duplicate packets: a) Re-ordering

module: It will take care of successful delivery of in-order

packet by using min_max fair based double ended priority

queue (MMFDEPQ) based packet servicing and on-time

delivery of time critical emergency packets.

b) Loss recovery module: Our proposed protocol is also able to detect lost packets from selective loss and duplicate packet sequence number based negative acknowledge (SLDNACK)

based feedback packet. It performs end-to-end loss recovery, where the end nodes are responsible for loss and drop

detection, notification and retransmission.

c) Duplicity handling module: Duplicate packets can be identified from the duplicity notification field of SLDNACK packet, which notifies all packets which have not been received yet as well as about duplicate packets.

iii) Congestion control module: Congestion in WBAN usually

occurs when the sending rate is more than service rate. Buffer occupancy and bandwidth status are key indicators of congestion. Congestion collapse is the situation in which the congestion becomes so heavy that it causes packet drops, unnecessary packet retransmissions, decrease throughput and exhaust energy. Proposed congestion module has three phases:

a) Congestion detection phase: This phase recognizes the level of congestion by measuring the following metric.

i) Buffer occupancy: When buffer tenancy reaches maximum threshold.

2) Packet rate: incoming packet rate exceeds the packet forwarding rate. Packet service time exceeds the packet inter-arrival time and it leads to long queue delays.

3)

b)

Channel status and bandwidth occupancy: The channel condition and available bandwidth give an idea about congestion probability.

Congestion notification phase: To intimate senders about congestion probability, sink implicitly embed the congestion notification bit in the header of each packet and send to sender.

c) Congestion avoidance phase: Once the congestion notification is received by sender nodes. S e n d e r n 0 d e adjusts their flow rate in Multiplicative Increased and Multiplicative Decreased manner by considering their priority. The work flow diagram of our proposed protocol revealed in figure 4 tries to accomplish QoS issues. We will analyze our performance using NS-2 simulator by testing the following metrics: i) reliability test; ii) packet delivery ratio; iii) packet sequencing test; iv) loss rate; v) drop rate; vi) probability of congestion; vii) transmission and retransmission ratio; viii) delay ratio; ix) jitter ratio; and ix) throughput.

V. CONCLUSION AND FUTURE SCOPE The proposed protocol for WBAN will provide reliable, congestion free system with dead line based prioritized data handling to overcome all kinds of QoS issues. Decision tree based packet classification is very effective since it reduces space and time. Packet drop and re-sequencing with MMFDEPQ is used to give a better and low complex alternate for continuous and event triggered monitoring applications. Future work can be done by scheming protocols which reduce resource complexity and overcome fault and security issues.

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