Optimal Performance of Wireless Body Area Networks in varying …tjfeonline.com/admin/archive/427.06.20171498569984.pdf · 2017-06-27 · Islamabad†††NAYATEL Pvt Ltd [email protected]

Rev. Téc. Ing. Univ. Zulia. Vol. 40, Nº 1, 26 - 37, 2017

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Optimal Performance of Wireless Body Area Networks in varying Postural

Orientations

Mustafa Shakir†, Obaid Ur Rehman†, Aamir Ullah Khan††, Muhammad Abdullah Abbasi†††,

and Mahmood A. Khan

†COMSATS Institute of Information Technology Islamabad,††National University of Computer and Emerging Sciences,

Islamabad†††NAYATEL Pvt Ltd [email protected]

Abstract Recent advancements in wireless communication and electric manufacture has made it possible to use different types of sensors in Wireless Body Area Networks (WBANs) for real time applications such as patient diagnostics, military, sports training . A prototype WBAN has been constructed for characterizing and capturing onbody topology disconnections due to ultrashort radio range, unpredictable RF attenuations due to clothing and human postural mobility. This research paper investigates the optimal positioning of sink (On body WBAN data coordinator) node in WBAN in the presence of numerous postural orientations. We have developed the complete communication framework of a WBAN in OMNET++ by considering postural disconnections. We also evaluate the performance of each posture by considering the performance metrics such as energy efficiency, End to End delay, Number of Back offs (NBs) and Packet Delivery Ratio (PDR). Simulation results demonstrate that the postural locality of sink node plays a vital role in network performance, thus representing an interesting framework for the WBAN planning problem.

Key words: Wireless Body Area Network, Packet Delivery Ratio, Sink Location, End to end Delay, Energy efficiency

1. INTRODUCTION A number of wireless sensors placed strategically on specific body parts for the purpose of collecting physiological data

forms a WBAN. Wireless sensors can either be implanted or placed over the human body. Data transferred in WBAN is

carried out via point to point or point to multipoint communication. Advancements in technology specifically

nanotechnology has made the field of WBAN very attractive for researchers and developers. Wireless Sensor Networks

(WSNs) includes various applications in the fields of military, sports and medical [1]. In WBAN, nodes are deployed on a

patient’s body in order to measure his physiological data. Patients in critical condition need to be monitored continuously

so that they can be provided with timely and effective treatment. For such purposes a health care service can be used which

can provide the monitoring of patients, those are not confined at a single place [2].

In a WBAN network, sensor nodes are of vital importance. These nodes collect all the data and perform all functions to

make a WBAN active and functional. Mobility is an important aspect of a WBAN. Postural body movements can give rise

to WBAN topological disconnections. These disconnections are due to the RF attenuation caused by the blockage of signal either by clothing material or body segments or due to mobility. Thus, in order to keep a WBAN in operation, we need to

synchronize the mobility of nodes such that there is always a high probability of disconnections between nodes on the

body.

In WBAN due to postural mobility the disconnection between sensor nodes are quite common and sometimes of a greater

duration. This affects the efficiency of entire network. In this environment, we want to minimize this delay and create such

a network which adapts itself in the presence of frequent and long lasting delays. Such a network is referred as Delay

tolerant Network (DTN). The common characteristics of DTN’s include intermittent connectivity between nodes,

ambiguous mobility patterns, lack of continuous end-to-end connection between nodes, high delays and high error rates

[3]. Moreover, nodes used in such scenarios are very limited in resources e.g. battery life, buffer size etc. DTN protocols

and architectures can be used in scenarios in which there is no certainty about the connection between the nodes.

The present work which has been done in the field of WBANs is divided into two types. The first being the replication

based and the second being the single copy. The replication approach defines the ways in which several copies of the

packet can be transferred via several carrier nodes so that the chances to deliver these packets to their desired destinations

can be increased. On the other hand the single copy mechanism utilizes the information about topology dynamics and

connectivity in order to make forwarding decisions with low latency and with minimum amount of replication overhead.

For these types of mechanisms to work, it is necessary that nodes must have temporal and spatial locality in their mobility

[4].

In WBAN scenarios, there are different parameters which affect the characteristics of the planned networks.

Several routing schemes have been developed for WBAN’s with short transmission range [5, 6, 7, 8]. For the case of single


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and multi-copy DTN routing, the End-to-End delay (E to E delay) performance is evaluated using the random walk

mobility model without any information of a specific node location. In order to model these different routing protocols, a

working WBAN prototype system has been developed. Certain mechanisms have been developed for capturing locations of

on-body movements which are due to the human postural mobility and are applied for packet routing [9, 10].

Prolonging the lifetime of the WBAN strongly depends on controlling the energy consumption of sensor nodes. To achieve

energy efficiency, low duty cycle MAC protocols are used, but for medical applications, where data have time-limited relevance, these protocols increase latency which is highly undesirable and leads to system instability.

Hence, low delay MAC protocols are discussed in [11, 12, 13, and 14].

In order to increase the network life time, authors in [15] proposed the optimal design of WBAN by considering the joint

data routing and relay positioning problems. They also optimized the number and location of relays to be deployed toward

the sink. Hence, minimized the both energy consumption and network installation cost.

Several mobility models which are available in OMNET++ that can be used to demonstrate the postural mobility in which

nodes are deployed on the human body which have to continuously measure the physiological data of the patient [16, 17].

The importance of mobility can be judged from the fact that even in stationary postures such as sitting and standing, where

movement of the patient is limited mobility plays a significant role. For instance when a person is sitting, he can be doing

some work while sitting. They might be moving their arms and head and therefore mobility comes into play. Similar cases

can arise for standing posture. Individual movement of any sensor node within the WBAN depends on the selected posture. In this paper, we provide the complete communication framework of a WBAN in OMNET++ by considering all possible

postural orientations. We also evaluate the performance of each posture by considering the performance metrics such as

energy efficiency, E to E delay, NBs and PDR. This work also provides the optimal positioning of sink node in the presence

of postural disconnections.

The rest of the paper is organized as follows. Section 2 presents an overview of characterization of network topology for

WBAN. In section 3, we describe the network model. System model with expressions of energy efficiency are discussed in

section 4 of this paper. Metrics on which the performance of WBAN can be judged is defined in section 5. Section 6,

gives a detailed discussion of simulation results and finally Section 7 concludes the paper.

2. CHARACTERIZATION OF ON BODY NETWORK TOPOLOGY

To evaluate different sink positions for WBAN, we have implemented a WBAN prototype in OMNET++. This section

describes the WBAN prototype and its applications for topology characterization along with variations in postural

positions.

2.1 WBAN prototype and Variations in Network Topology

We construct a WBAN prototype in OMNET++ by deploying twelve sensor nodes on a human body as shown in Figure 1.

All simulations in this paper correspond to different positions of sink node, in which data from all other nodes are sent to

the respective sink node. This node collects raw data and sends processed results or events to an out-of-body server using a

wireless link. The topology changes are dynamic and are based on the human postural movements. In this paper we have

developed five human postures (running, walking, standing, sitting and lying down). A human subject fitted with these

nodes is made to follow a set of pre-determined sequence. Each posture lasts for 30 seconds. The coordinator specifies the

respective position of each node, and other nodes have no information about location of each other. Coordinator controls

the mobility of nodes and these nodes send their data to the respective sink node which is one of the twelve nodes deployed on the human body. Certain links are connected during specific posture as there are variations in the topology for that

posture. Moreover, within a posture a link can have intermittent disconnections. The reasons for this include body

movements, RF attenuation through the clothing material and body segments; it also depends on the node-pair orientation

forming a specific link.

For the running posture, as we know that some parts of our body such as arms and legs go through a lot of work. Similarly

the nodes implanted on these parts also have to efficiently coordinate data with sink and send to the body server. This

posture is depicted in Figure 1 (a). Like running, walking is another mobile posture that we have developed. The posture is

shown in Figure 1 (b). Nodes on the arms and feet are involved much more in mobility as compared to the rest of nodes. So

in order to accurately monitor the physical condition, these nodes have to perform their functions more accurately and

efficiently. It is shown in Figure 1 (c) that nodes 2,3,5 and 6 are placed on the arms in the standing posture. Although

standing posture is a stationary posture but still different positions in the standing postures can be described e.g. arms in

straight position, arms crossed on the chest, left faced and right faced with respect to the viewer. In depiction of these various positions, the involvement of nodes depends upon the postural locality of the nodes e.g. the nodes on arms will

contribute more to the mobility depicting various standing actions.

After dealing with the mobile postures, we come across the stable postures such as sitting and lying. For the case of sitting


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posture, the patient is not mobile and nodes do not have to encounter frequent topological changes. This posture is shown

in Figure 1 (d). One of the stable postures is lying. For such postures the node movement is very limited. This posture is

depicted in Figure 1 (e).

Figure 1 .Posture Orientation of WBAN in OMNET++

2.2 Posture Selection Strategy

In order to simulate all five postures, a posture selection strategy is developed which is shown in Figure 2. There are two

types of postures in our scenario.

Mobile postures (Running, Walking)

Stationary postures (Standing, Sitting, Lying-Down)

In both types the strategy adopted is different. First of all posture selector determines whether it is mobile or stationary

posture. In case of a mobile posture, the coordinator specifies the target location. Coordinator also determines the speed of

the moving nodes. Then it is checks whether destination is reached or not. If destination is not reached then nodes keep on

moving towards target position until it reaches or simulation timer expires. If destination is reached then it checks whether

all postures are executed or not. If not then control is passed to posture selector otherwise the simulation ends.

In case of a stationary posture, duration of the simulation is determined. After that the selected posture is simulated for the determined time period. Then it checks whether simulation time is complete or not. If not then simulation keeps going. If

yes then it checks whether all postures are executed or not. If not then control is passed to posture selector otherwise the

simulation ends.

3. NETWORK MODEL

We assume the network consisting of sensor nodes and static sink nodes. All sensor nodes have limited amount of energy

depends on the capacity of battery. Sensor nodes are mobile due to postural movement of body. However, there is no

energy dissipation due to postural mobility. Each sensor node periodically generates small amount of data and this data is

sent to the sink node in single hop manner. We also assume that successful delivery of data message to the sink node needs

to be acknowledged. Hence, there is requirement of backward communication from the sink to the sensor nodes. Therefore,

overall communication is bidirectional.

We consider a WBAN scenario where biosensors for data collection are directly connected to sink node. A common

approach to the network design problem is to consider feasible location of sinks. On the other hand, the sensor positions

(e.g. arms, legs) are usually predetermined according to the medical applications for which they are deployed.


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START

IS STABLE OR MOBILE

POSTURE?

POSTURE SELECTOR

IF STABLEIF MOBILESELECT

DESTINATION

SELECT DURATION

T sec

SIMULATE SELECTED POSTURE

DURATION COMPLETE

NO

SELECT SPEED

MOVE TOWARDS

DESTINATION

REACHED DESTINATION

NO

ALL POSTURES EXECUTED

YES NO

END

YES

YES

Figure 2. Posture Selection Strategy

Let “S” denote the set of sensors and “N” is the set of sinks. Each sensor establishes a wireless link with sink located

within its communication range “r”. The weight (w) associated with installing a sink is given by equation (1).

𝑤 = [ 𝑋𝑌

𝑟 - 1] (1)

Where, “XY” is the Euclidean distance between sensor node “S” and sink and” r” is the radio range of each node.

According to the sensors and sinks locations, the following connectivity parameter (𝑎𝑆𝑁) can be calculated according to equation (2).

𝑎𝑆𝑁 =

otherwise 0

sink with connection

establish s"" nodesensor if 1

(2)

To emphasize the significance of postural location of sink node, we have selected five different nodes (0, 1, 4, 5, and 9) as a

sink node; these are located on the different locations on human body. Obviously, each sink node has different weight

because it has diverse distances from each sensor node. A sink node that experiences minimum distance from each node

has highest weight according to equation 1.

4. ENERGY MODEL

We adopt the radio model used in [19] and [20], the path loss coefficient on the wireless link between node “S” and sensor

sink “N” is denoted by “ 𝑍SN ” and is equal to 3.38 along the human body. For calculation of energy consumption in wireless nodes like sensors and sink, we assume that sensing and processing energies are negligible with respect to

communication energy. The total energy consumption is represented by transmission and reception energy of all wireless

nodes. The amount of energy that is dissipated by radio to run the circuitry for transmitter and receiver is “𝐸𝑇𝑋𝑒𝑙𝑒𝑐 ”and

“𝐸𝑅𝑒𝑙𝑒𝑐 ” respectively. 𝐸amp , represents the energy for the transmitter amplifier and 𝐷NS is distance between nodes and

sink. The transmitter energy can therefore be computed as in equation. 3.

𝐸𝑇𝑋 = 𝑤[𝐸𝑇𝑋𝑒𝑙𝑒𝑐 + 𝐸amp 𝐷SN𝑍SN ] (3)

While, reception energy is 𝑤𝐸𝑅𝑒𝑙𝑒𝑐 . Where,”𝑤“is the total number of transmitted / received bits. Therefore, the total

energy consumption is given in equation. 4.


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𝐸𝑇𝑜𝑡 = 𝑤 [𝐸𝑇𝑋𝑒𝑙𝑒𝑐 + 𝐸amp 𝐷SN𝑍SN ] + 𝐸𝑅𝑒𝑙𝑒𝑐

𝑛

𝑘=1(4)

This network depends on direct communication between sensors and sink. Therefore, the transmitters are more than “1”

and the sink is only receiver.

5. PERFORMANCE PARAMETERS The primary performance index is the end-to-end Delay (E-to-E Delay), which is modeled in this paper and is attempted to

be explicitly minimized by the postural selection of sink node. The E-to-E Delay depends upon the distance between the

sink node and all other nodes along with the distance of the nodes from the coordinator. The Coordinator specifies the

respective positions of the nodes i.e. handle mobility. Three secondary metrics, namely, Energy Consumption, Packet

Delivery Ratio (PDR) and Number of Back-offs (NB) are also recorded for a more complete understanding. The index

NB describes the ease of access to medium whenever required i.e. smaller NB, better choice for destination.

End-to-End Delay: It is an important parameter in both cases i.e. WSNs and WBAN. In general there are three types of

delays in a WBAN environment. The first type is the propagation delay. As source and destination is located at a distant from

one another, it always takes some time for a packet to travel from transmitter to receiver. It is computed by taking the ratio of

distance between source and destination to the speed. The second type is processing delay. Every node takes some time

before transferring the data to its destination. This time called “Processing time” can be because of the absence of a direct connection between source and destination or it can be due to unavailability of medium in a shared environment. The third

type of delay is transmission delay. It depends upon the number of bits in a packet and data rate of the link. It is computed by

taking the ratio of packet bit length to the transmission rate. E-to-E delay is the sum of these 3 types of sub-delays.

𝐸 𝑡𝑜 𝐸 𝑑𝑒𝑙𝑎𝑦= 𝑃d + 𝑄d + 𝑇d (5)

Where, 𝑃d is processing delay, 𝑇d is transmission delay and 𝑄d is queuing delay for buffering packet.

Packet Delivery Ratio: It is the ratio between numbers of packets received at destination to number of packets sent to

destination. Difference between number of packets sent and received gives us packet lost count.

Energy Efficiency: Similarly battery life (battery consumption) is another parameter on which efficiency of a network

depends. Such a network with less energy consumption is more efficient as compared to a network which utilizes more

energy. An energy efficient network means a greater life time for a network which is very important in WBAN

environment.

6. SIMULATION RESULTS

We have implemented all the human body postures in OMNET++ environment and performed series of simulations. In

order to avoid collision, we used CSMA MAC protocol, so only one sensor node accesses the medium at a time. In our

scenario, a sink node transmits “KEEP-ALIVE” messages after a certain period time so that the nodes in the network

remain alive and active. We performed simulations for each of the five postures (running, walking, and standing, sitting and

lying down) for 30 seconds. We have also run our simulation to evaluate the performance of overall network i.e. all

postures 50 seconds (each posture for 10 seconds). Network parameters used for our simulations are listed in Table 1. We

well thought-out five different locations of sink nodes to attain the best location of sink node. In our case, we considered

node 0, 1, 4, 5 and 9 as sink nodes and performed simulations to take one node at a time as a sink node.

6.1 End to End delay We have performed simulations for five different nodes as a sink node depends on the weight. Each sink node has different

weight and ID. It is also depends on the distance between the sink and sensor node and the connectivity parameter.

Connectivity parameter highly relies on the postural movement of the body. In running posture probability of link

disconnection is higher than the relatively stationary postures such as standing, sitting and lying down postures. In

stationary postures body movement and posturemovement is much lesser than the mobile postures. In WBAN E to E delay

experiences all of these factors. Hence, the location of sink node is significant in these sorts of networks, where, the links

suffer from intermediating disconnections. It can be seen E to E delay comparison of overall network in Figure 3 (a),

node.4 experiences less end to end delay as compared to other nodes as a sink node.


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Table 1.Network Parameters

PARAMETER NAME VALUE

PMax 110.11mW

Carrier Frequency 2.412x 109Hz

Path Loss Exponent 3.38

Number of nodes 12

Time Tx to Rx 0.00012s

Time Tx to sleep 0.000032s

Queue Length 5

Header Length 24bit

Maximum retransmissions 14

Bit rate 15360bps

Transmission power 110.11mW

Transmission current 5000

6.1 End to End delay

We have performed simulations for five different nodes as a sink node depends on the weight. Each sink node has different

weight and ID. It is also depends on the distance between the sink and sensor node and the connectivity parameter.

Connectivity parameter highly relies on the postural movement of the body. In running posture probability of link

disconnection is higher than the relatively stationary postures such as standing, sitting and lying down postures. In

stationary postures body movement and posturemovement is much lesser than the mobile postures. In WBAN E to E delay

experiences all of these factors. Hence, the location of sink node is significant in these sorts of networks, where, the links suffer from intermediating disconnections. It can be seen E to E delay comparison of overall network in Figure 3 (a),

node.4 experiences less end to end delay as compared to other nodes as a sink node.

Figure 3b. E to E delay comparison of running Posture

0 5 10 15 20 25 30 351.5

2

2.5

3

3.5

4

4.5

Posture Duration (s)

End

-to-E

nd D

elay

(10e

-7 s)

End-to-End Delay Comparison

Node 0

Node 1

Node 4

Node 5

Node 9


32

Figure 3c. E to E delay comparison of walking posture

Figure 3d. E to E delay comparison of standing posture

Figure. 3e. E to E delay comparison of sitting postures

0 5 10 15 20 25 30 351.5

2

2.5

3

3.5

4

4.5


End-

to-E

nd D

elay

(10e

-7 s)


Node 0

Node 1

Node 4

Node 5

Node 9

0 5 10 15 20 25 30 351.5

2

2.5

3

3.5

4


End-

to-E

nd D

elay

(10e

-7 s)


Node 0

Node 1

Node 4

Node 5

Node 9

0 5 10 15 20 25 30 351

1.5

2

2.5

3

3.5

4


End-

to-E

nd D

elay

(10e

-7 s)


Node 0

Node 1

Node 4

Node 5

Node 9


33

Figure 3f. E to E delay comparison of lying posture

6.2 Energy Consumption:

Energy is an important parameter in WBAN’s. Therefore, we tend to find such a location of sink node which is more energy

efficient. Energy depends upon the number of transmissions by each node along with the distance of the destination from the

respective nodes. We performed simulation for five different nodes as sink positioned on different locations on human body these are node (0, 1, 4, 5, and 9). The energy consumption is directly related to postural mobility i.e. greater the probability

of disconnection between sensor node and sink requires more number of retransmission, hence, greater will be the energy

consumption. Similarly, energy consumption depends upon the distance of sink node from all other nodes in the network. It

is clear from the Figure 4 (a) that energy consumption of nodes (4,5) are smaller as compared to the energy consumption of

nodes (0, 1, and 9).

As Node 5 is more energy efficient as compared to other nodes, node 5 proves to be the best choice for sink node as far as

energy is concerned.

Energy consumption analyses of all postures are shown in Figure 4 (b-f), that node. 4 is best choice for sink node, as it

consumes less amount of energy as compared to other sink nodes.

Figure 4a.Energy consumption of overall network

0 5 10 15 20 25 30 35

1.5

2

2.5

3

3.5

4


End-

toEn

d D

elay

(10e

-7 s)


Node 0

Node 1

Node 4

Node 5

Node 9

0 5 10 15 20 25 30 35 40 45 501.75

1.8

1.85

1.9

1.95

2

2.05x 10

4


Ca

pa

city

(m

Wh

)

Energy Consumption Comparison

Node 0

Node 1

Node 4

Node 5

Node 9


34

Figure 4b.Energy consumption of running posture

Figure 4c. Energy consumption of walking posture

Figure 4d. Energy consumption of standing posture

0 5 10 15 20 25 301.86

1.88

1.9

1.92

1.94

1.96

1.98

2

2.02x 10

4


Ca

pa

city

(m

Wh

)Energy Consumption Comparison

Node 0

Node 1

Node 4

Node 5

Node 9

0 5 10 15 20 25 301.86

1.88

1.9

1.92

1.94

1.96

1.98

2

2.02x 10

4


Ca

pa

city

(m

Wh

)


Node 0

Node 1

Node 4

Node 5

Node 9


35

Figure 4e. Energy consumption of sitting posture

Figure. 4f.Energy consumption of lying down postures

6.3 Packet Delivery Ratio (PDR)

Due to postural mobility some nodes which appear to have a connection with sink node at time “t0” might not have a

connection with sink at time “t1. Hence, postural mobility of sink and transmitting node plays an important role in packet

drops. Greater the postural mobility between a sink and a transmitting node, greater is the probability that they are out of

communication range. Similarly, greater the number of nodes trying to send packets to the sink, greater is the probability of

packets drop. Thus, the postural locality of sink node plays an important part in increasing reliability i.e. PDR of network.

PDR of overall network is portraying in Figure 5. It is observed that node 4 as a sink achieved highest PDR. Static postures

as shown in Figure 5 achieve high DPR due to their connection stability. On the other hand, mobile postures as observed in

Figure 5 are lacking in terms of PDR.

7.CONCLUSIONS AND FUTURE WORK

Postural locality of sink node plays a vital role in improving the efficiency of a network. However, it is not a straight forward

decision and different factors need to be considered in making this decision. As it is evident from the results, that the best

position of sink node is waist i.e. node 4. As node located at waist has very little involvement in postural mobility along with

the fact that the distance of all other nodes from waist is almost the same i.e. its weight is higher than other nodes. Therefore,

waist position is the best position for sink node yielding minimum E to E delay and highest PDR along with better energy

consumption and Back-offs as compared to other nodes. Similarly the sink node position also depends upon the application

at hand. Such applications which require minimum energy consumption can consider node 5 i.e. chest node as sink node but

such a decision would have its demerits such as increased end-to-end delay and low reliability. In this paper, we considered

direct communication between sensors and sink node for finding the best sink location. In future, our work will be focused

on impact of relay positioning in WBAN for multihop communication.

0 5 10 15 20 25 301.85

1.9

1.95

2

2.05x 10

4


Ca

pa

city

(m

Wh

)Energy Consumption Comparison

Node 0

Node 1

Node 4

Node 5

Node 9

0 5 10 15 20 25 301.86

1.88

1.9

1.92

1.94

1.96

1.98

2

2.02x 10

4


Cap

acit

y (m

Wh

)


Node 0

Node 1

Node 4

Node 5

Node 9


36

Figure 5. Packet Delivery Ratio

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[20] Heinzelman, Wendi Rabiner, AnanthaChandrakasan, and HariBalakrishnan. "Energy-efficient communication protocol for wireless

microsensor networks."System sciences, 2000.Proceedings of the 33rd annual Hawaii international conference on.IEEE, 2000.

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Dr. Mustafa Shakir got his PhD in Information and Communication Engineering from Beijing University of Posts and Telecommunications,

China. He is an Assistant Professor at Department of Electrical Engineering, COMSATS Institute of Information Technology, I slamabad. He has

worked for Pakistan Telecommunications Company Limited and conducted sessions and trainings on Broadband Access, Next Generation

Networks, IPTV, Data Communications and Understanding Telecom Networks and Services. His areas of interest include wireless networks and

communication systems, ad hoc networks, sensor networks and next generation networks.

Obaid Ur Rehman received the BSC. Electrical Engineering from Riphah International University Islamabad on the basis of outstanding talent

scholarship from government of Punjab in 2010 and achieved MS in Electrical Engineering from CIIT, Islamabad in 2013. He joined CIIT

Electrical Engineering Department as Research Associate and now he is serving as a Lecturer in CIIT, Islamabad. His research interests include

Energy Efficient Routing Protocols, Error Correction Coding and Cooperative Communication in Wireless Sensor Networks (WSNs).

Aamir Ullah Khan got his degree inElectrical (Telecommunication) Engineering from COMSATS Institute of Information Technology

Islamabad. He is currently working as Lab Engineer at Electrical Engineering Department in National University of Computer and Emerging

Sciences Islamabad. He has worked as Associate VoIP Engineer in Telecom Services and Consultants Pvt Ltd. He is also a certified instructor of

Huawei Technology Ltd, a leading ICT vendor in Pakistan, and has conducted trainings of HCNA (R&S) and HCNA (Storage) for students and

ICT professionals. His areas of interest include wireless networks and mobile computing, privacy security and data integrity in IoT, cross-layer

optimization and network design and next generation networks.

Muhammad Abdullah Abbasi has done Bachelors of Electrical (Telecommunication) Engineering from COMSATS institute of Information

technology Islamabad Campus. He is currently an Operations Engineer At NAYAtelPvt Ltd serving in the Next Generation Networks (NGN)

Department. His Areas of interest include Telecom networks, Next Generation Networks and Wireless Body Area Networks.

Dr. Mahmood Ashraf Khan has done has Ph.D. from Aston University Birmingham UK. Before going for Ph.D. has completed his B.Sc

(Electrical Engineering) from UET Peshawar. Dr. Khan is presently working as Director, Centre for Advanced Studies in Telecommunication

(CAST), CIIT, Islamabad. His focus is industrial research and carrying out different projects in collaboration with IT and Telecom industry.

Before joining COMSATS Institute of IT he worked in Institute of Communication Technologies (ICT), PTCL as Principal/General Manager. He

worked in PTCL on different position looking after academic, technical sand other managerial duties.His research interests are in the field of

Broadband Access Networks, NGN, Sensor Networks, Smart Grid and Telecom Networks. He has also secured research funding from different

research organizations. He has several research Publications in his account.

Documents

Optimal Performance of Wireless Body Area Networks in varying …tjfeonline.com/admin/archive/427.06.20171498569984.pdf · 2017-06-27 · Islamabad†††NAYATEL Pvt Ltd [email protected]