A REAL TIME PRIORITY BASED SCHEDULER FOR LOW RATE WIRELESS SENSOR NETWORKS

International Journal of Computer Networks & Communications (IJCNC) Vol.9, No.3, May 2017
DOI: 10.5121/ijcnc.2017.9306 87
A REAL TIME PRIORITY BASED SCHEDULER FOR
LOW RATE WIRELESS SENSOR NETWORKS
Sambhaji Sarode, Jagdish Bakal
Department of Computer Science & Engineering,
GHRCOE, RTM University, Nagpur, Maharashtra, India
{sambhajisarode, bakaljw}@gmail.com
ABSTRACT
This paper presents a priority approach for censorious real-time traffic which flows particularly for low
data rate wireless sensor and actor network (LR-WSAN). In the recent years, the demand for low rate
wireless data transmission has been increased drastically in small scale industrial and non-industrial
applications. The different traffic flows are increased by incorporating a variety of distinct sensing devices.
In particular, injection of different traffic into network makes the communication system unstable and
unreliable because of unnecessarily resource utilization, wasting energy for surplus delivery management
and violation of time constraints. This paper aims to presents the new priority-based algorithm at actuator
node for appropriate classification and categorization of data packets for delay sensitive and delay-
tolerant applications. A novel real-time priority-based scheduler (RTPS) is proposed to handle
heterogeneous data flows simultaneously according to their transmission type. The priority-based data
delivery is an essential research topic for low rate multi-event IEEE 802.15.4 sensor networks. A priority
metric is designed to dynamically control various types of traffic based flows with considerations of packet
type, delay, and buffer processing rate. TestBed results describe significant improvements in data reporting
mechanism for delay sensitive and delay-tolerant applications over various topologies. The high priority
transient traffic suffers less delay and presents effective packet delivery ratio compared with traditional
approaches.
KEYWORDS
Transport protocol, Packet Scheduling, Reliability, Wireless sensor networks, WSN TestBed, Priority-based
data reporting, IEEE 802.15.4 sensor networks, queue management, congestion control.
1.INTRODUCTION
This priority-based data reporting is an important task in industrial automation processes.
Handling the multiple tasks [1]-[6] simultaneously increases the complexity of data transmission
mechanism. A distributed coupon collection algorithm [4] is proposed to address the problem of
delay using probabilistic model. It achieves the higher throughput. A channel access mechanism
is designed based on the priority level in [5]. Nowadays [7]-[9] small scale industries have also
started the automation process for improving the productivity and capability of managing the jobs
remotely using small size short range devices. The periodic scheduling is useful in engineering
applications to control and save an energy utilization of heterogeneous network [7] otherwise
unnecessary waiting time hampers the network resources which shortens the system life. Thus,
developing a priority-based data gathering mechanism is essential to handle large heterogeneous
data simultaneously. This particular need has gained the attention of many active researchers in
wireless sensor networks (WSNs) to develop an application specific packet scheduler over the
MAC protocol. To evaluate the proposed protocol, a discrete event Markov chain model is
developed. For example, Hypoxemia: a pulse oximeter, where reading ranges from 95 to 100

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percent. The value below 90 percent indicates low oxygen or need of external oxygen supply. It
alerts the problem of breathing or circulation. In such applications, delivering the essential data
[10], [11] timely to the doctor without a single packet loss is the crucial job of arterial blood gas
sensor device. Nowadays sensor network is becoming the backbone of almost all types of
application such as emergency, time constraint industrial controlling processes, and surveillance
or observing events.
The current research not only focuses on the innovation in optimization algorithms but also
motivates to develop the lightweight attribute aware schemes for specific events with hardware
considerations. Sometimes researcher has to choose a little computing and longer life embedded
devices purposefully for lightweight data delivery considering application requirements. RF
application framework and routing protocols are designed by RF alliance whereas IEEE 802.15.4
community defines PHY & MAC. The priority-based approach is presented over a normal real-
time data transmission flows. Instead of treating distinct data flows equally at all the time, there is
the need to design a flow based scheduling mechanism for delivering the sensor readings. Thus
RTPS protocol is introduced to categorize the different traffic flows during in-network processing
of a sensor network. Furthermore, it proposes the new priority metric for updating the priority
type as it travels towards the sink; buffer processing rate is taken into consideration for measuring
the congestion level of the buffer and additive-increase multiplicative-decrease (AIMD) method
is used for renewing packet air rates. RTPS shows the less power consumption, optimal reporting
rate, and high throughput, and less network latency as compared with First-Come-First-Serve
method (FCFS) and Earliest Deadline First (EDF) method.
The strategies of the proposed work are briefed as follows.
• RTPS protocol presents the priority-based real-time data delivery approach using queuing
operations efficiently. This protocol mainly delivers the higher traffic first over the
regular traffic.
• A new priority weight metric is designed to allocate the priority dynamically at hop node
in runtime which reduces the delay of long distance data packets.
• Auto-retransmission mechanism is modified and made it a flow based scheme to retry the
lost packets in time to meet the deadline of time constraint applications.
• The mathematical model of RTPS protocol is implemented over nRF24L01 RF module
(Nordic) with Arduino Nano-Atmega328 compatible) Testbed platform.
The flow of the paper goes as follows: The current research work overview and research gap
identification are briefly discussed in part II. RTPS algorithm is introduced in part III for
delivering higher priority information earlier than usual continuous information flows. Section
IV, presents the real-time Testbed setup background, design consideration, and result in analysis
and discussions. Finally, part V concludes and presents research directions.
2.RELATED WORK
A prime objective is to deliver essential information over underlying data delivery transmission
protocols in the world of networking. However, considering the deadline and sensitivity of the
information opens the dimensions for redesigning or inventing a new data transmission
approaches for specific requirement of applications. The current reference work focus on the
below aspects:

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• Preemptive and non-preemptive priority-based data reporting mechanisms
• Prioritized data packet scheduling over the MAC protocols.
• Priority-based scheduling using FCFS or EDF approach by managing single queues.
• Dynamic distributed priority updating mechanism over mesh hops during in-network
processing. The transient and local traffic contributions are handled by considering their
deadlines.
The transmission as mentioned earlier core operations of communication protocols plays an
important role when traffic is significant in dense network wherein applications are less delay
tolerant. In such situation utilizing the bandwidth effectively becomes the necessity. Therefore,
after going through a literature review, it has been observed that work focus has been
considerable gained an attention towards MAC layer core operations instead of managing
ultimately at the transport or the application layer. The purpose is to decrease the waiting period
of delay sensitive applications by avoiding unnecessary channel hearing. Moreover, the slot based
TDMA medium access protocol plays a vital role in addressing this issue. Thus, the use of
effective data scheduling algorithms with slot based TDMA approach prevents the signal
interference and overheads for actual data transmission. In wireless sensor network, saving
energy is an important challenge. Making communication reliable with less energy utilization is
the need of industrial automation processes. In real time data flows, a delay is not significantly
addressed yet. Industrial applications are having stringent timing requirements with different
event data requirements simultaneously; therefore, developing a flow-aware data gathering
mechanism is the necessity. This literature review mainly focuses on the existing MAC based
data transmission mechanisms for LR-WSNs. This paper primarily focuses on real time and
priority based data transfer. A cross-layer optimization or configuration approach (called ShedEx-
GA) is introduced in [9]. A novel approach considers an application specific network constraints,
namely, event reliability, data latency, reporting rate, service priority based differentiation. It does
the micromanagement of cross-layer procedures like routing and channel specific controls. It
manages to optimize the scheduling by minimizing the layering constraints.
However, two main approaches in Wireless HART networks are discussed in [10]. The local
search algorithm is developed for optimal priority flow assignment for worst case analysis for
given test schedulability. And the worst cases are investigated wherein the local search works
optimally. It describes the optimal rate mechanism for managing the network dynamically. S-
MAC [12] uses periodic duty cycle for synchronizing sensor nodes together. It makes use of
RTC/CTS to avoid the overburden of unwanted data transmission and helps to reduce the
congestion. It saves the energy by preventing unnecessary data transmission overheads. D-MAC
[13] works over the one way upstream based tree topology. It is an extended version of S-MAC to
improve the congestion management in a high traffic situation. Hence, it reduces the latency due
to the effective duty cycle. As compared with S-MAC, it increases the life of the network. T-
MAC [14] is equipped with effective flexible duty cycle mechanism. It is useful to reduce the
listening period of the waiting traffic in the network. However, fixed duty cycle has worst side
effects over an overall system like high latency and low throughput. Moreover, it uses immediate
switching mechanism from listening state to sleeping state when no event occurs, or data is absent
at a particular moment. Thus, results prove the less listening time against the fixed scheduling
mechanism.
P-MAC [15] presents switching schedule for minimizing the energy consumption using preamble
sampling approach. When no event reported then it immediately switches from hearing state into
the sleeping state for the particular period, though it requires long preamble that wastes the
network bandwidth. P-MAC follows the pattern schedule which is determined by following the
duty cycle pattern of adjacent nodes. This protocol keeps itself into a sleep state for a longer
period to preserve resources. Analysis illustrates less power utilization, however switching period

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needs to also taken into consideration. RAP protocol uses velocity operation to deliver high
priority data over longer distance whereas Earliest Deadline First algorithm uses short deadline
first approach to transport data to the desired destination. However, it does not give the guarantee
of data classification and priority packet first. In PQMAC [16] classification of data is discussed.
It does not ensure the data priority in non-uniform events. RF-MAC [17] presents the novel
approach of charging nodes remotely through energy harvesting schemes. The energy transferring
module is developed over the Mica2 hardware. In protocol design consideration, wherein nodes
report the power level below a threshold; a high priority is given to energy delivery packets over
the data delivery packets. It ultimately functions over the link layer. However, congestion
management is not addressed. In [18] PriorityMAC protocol is introduced for priority based data
transmission especially for critical traffic and periodic traffic in IWAN. It provides the service
differentiation for higher priority traffic and reduces the latency and is implemented over the
TelosB motes Testbed system. The traffic is categorized into four different classes, namely, TC1,
TC2, TC3, and TC4 according to their priority levels, tolerance level, and access methods. At
lower traffic levels, TDMA medium access approach is used whereas in TC2 class backoff-
indication approach is applied. It suffers from unfairness issue.
The CSMA/SF [19] presents the novel approach of priority-based traffic data packet first. It
implements the length detection and anti-starvation mechanism by modifying the existing
CSMA/CA protocol. The priority approach with multi-hop for linear wireless sensor networks
(LWSNs) [20] demonstrates the collision-free data transmission. It works in two modes; namely,
over straight line topology where only leaf node generates the traffic and delivers it over multiple
hops; in the second mode, leaf and hop nodes generate the traffic over straight line topology. The
nodes closer to sink nodes are given a high priority but for a shorter time. The different priority
levels are assigned for contributing nodes depending upon hop distance away from sink node. An
event-driven approach SIFT [21] MAC-based approach is presented. This strategy is applied to
set of sensor nodes where the event occurs, and probability model is designed to deliver data with
less collision, but unfortunately, early stage nodes follow bigger estimates for large node
populations and hence small possibility for transmission. In real time TestBed emulation over 11
nodes and simulation support, transmission control protocol for media access present the adaptive
rate control (ARC) mechanism for balancing power utilization and fairness of a sensor network.
In [22], DBR MAC approach presents the high priority to key nodes. Sources whose
contributions are greater called key nodes. Backoff time is used to reduce the congestion at key
nodes, and more priority is given while accessing the channel. It uses cross-layer approach and
delivers data via least hop distance route to floating base station precisely using a pressure gauge.
In [23], a priority based data transmission protocol is designed for the multi-event sensor
network. It is based on multiple buffers and scheduler is developed to handle the traffic flows. A
FCFS mechanism is discussed for scheduling different traffic flows using packet scheduler in the
multi-event sensor networks whereas in [24] EDF algorithm is applied for urban traffic
management with considering the short deadline vehicle at various cross junctions of the road. In
[25]-[27], a priority approach is designed for multi-event wireless sensor networks. It achieves
the priority-based reliability based on the priority index of a packet and distance to reach the base
station.
3.RTPS PROTOCOL DESCRIPTION
The various applications require chain-based topology setups like roadside infrastructure
monitoring, oil gas pipeline, water tanks, and crop spaced over the straight line in the agriculture
field. The objectives of RTPS are summarized as follows.
• To develop priority aware data reporting scheduler using data link layer protocol.
• To configure chain-based topology for protocol execution.

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The sensing device in the sensor field is called as reduced function device (RFD), and router/hop
or a base station/ RF coordinator is defined as full function device (FFD). The RFD nodes are
designed to sense, transmit, and receive and are less resource equipped nodes compared with FFD
nodes. However, FFD nodes have more resource, and therefore they have additional processing
overheads. The core network management operations consist of RTS/CTS mechanism for
communication establishment before actual data transmission begins. It avoids hidden node
problem during in-network processing. The reference topologies are described in figure 1. A few
questions are considered to be answered through this research work as follows:
1) How would sensor select the neighboring node for data delivery?
2) What kind of channel access mechanism is appropriate for priority based data
transmission?
3) How is hop updating the priority level of different traffic?
4) How will flexible retry mechanism help for preserving energy and reducing a delay of
priority flow?
Figure 1. Multi-hop topological view captured during experimentation using sensor cloud solution to
analyze the node and their sensor connectivity
A network consists of RF end devices (ED), RF router device (RD), RF Coordinator, and sensing
area. Each ED performs information sensing and transmission to its upstream RD. A routing
device performs data transmission based on the level of priority whereas sometimes it also acts as
a relay node for delivering only received data. The RF Coordinator computes the reliability of
each flow separately and generates positive or negative acknowledgment based on packet
sequence number. The beaconless shock-burst data link protocol is used to implement the priority
queue. The queuing operations are designed from priority event perspective. The first preference
is given to delay sensitive activities at each RD device. The aim is to serve them in time by
considering their sensitivity level. The proposed scheduler is tested over multi-hop topology with
a finite number of nodes. While setting up the topology, nodes are placed randomly far from each
other. Our proposed work differentiates concerning priority approach of data transmission
compared with existing work. It provides 1) heterogeneous traffic based priority assignment, 2)
priority renovation, and 3) flexible flow based auto retransmission.
The operations of priority queue are defined with considering its level dynamically. A threshold
level ( ) is set to ensure the incoming packet flow is under control. Based on the queue status at
various RD devices, the reporting rate is updated. This priority queuing approach helps to prevent
the congestion in time and saves the energy of the network. While scanning the packets, the
scheduler checks the priority weight of each incoming data packets. A packet whose weight is

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higher selected first for transmission sequentially. When any particular packet is selected; its
priority weight is updated before it delivers to next RD node. The priority weight considers the
hop count i.e. distance to reach to the RF coordinator and propagation time i.e. from ED device to
current device. This approach helps to serve the more sensitive data packets first over the regular
monitoring traffic. In real time environment, taking a decision in right time is a non-trivial task
for critical events. Therefore, this metric has been designed to address the long distance priority
traffic first which get hampered with newly sensed traffic at RD devices. Hence, the priority
metric is designed considering the distance in terms of hop count away from the base station and
propagation time (i.e. delay) taken from origin to current node. The priority metric is expressed in
Equation 1.
The real values of hop count and delay bring the dynamism into the selection of each packet from
the priority queue. Equation 1 computes the new priority weight value to each outgoing packet at
router node. The purpose is to serve the long distance with sensitive data packet first over the
regular traffic. The packet processing rate of queue ( ) is defined as expressed in Equation
(2). It indicates the saturation state or unsaturated state of the network. It helps to find the system
performance concerning root causes of lost packets and successfully delivered packets.
The difference between the current level and threshold limit is shown in equation 3. is an
indicator of the queue. It is close to means; it shows the probability of the optimal state.
The end to end time includes node processing time and traveling time in-between nodes. It gives
the overall time during in-network processing data packets i.e. from source to the sink node.
The additive increase and multiplicative decrease mechanism (AIMD) have been used for making
the data reporting traffic aware during in-network processing. If hop node detects that the queue
level is being exceeded or packet loss is reported due to queue overflow event; then hop node
immediately generates the control packet to sink. Sink takes a decision of change in reporting
rate. An additive increase method is mentioned in Equation 5 which increases the air data rate
when packet level in the buffer is detected low. When the performance is detected low due to low
data rate, additive increase method (AI) increases the reporting rate to achieve higher system
throughput.
The multiplicative decrease method (MD) is expressed in equation 6 which exponentially
decreases the air data rate to control the buffer overflow. Whenever congestion is detected, the

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multiplicative method exponentially reduces the reporting rate to make the network congestion
free.
A priority node for getting chance during scheduling process is
Where P indicates probability of packet delivery of high precedence packet and N indicates the
number contributing nodes.
Table 1. Summary of mathematical notations
Term Definition Term Definition
Ttype Traffic flow type tuning parameter (0.03)
packet level of a queue t queue threshold level
Pwt Priority weight incoming packet rate
hc hop count outgoing packet rate
d Delay (seconds) m Number of hops
n Number of links Processing time at hop
, β Tuning parameters (1.1, 0.9) Propagation time between nodes
The RTPS algorithm describes the traffic scheduling mechanism for simultaneously occurring
events in the supervised sensing area. The single queue is used for storing the packets. A
threshold value is set to queue to prevent the packet loss in an overflow condition. The 2/3 packet
level is the threshold value of the priority queue. The backpressure message is used to inform the
packet level status in the queue. This approach helps well in advance to avoid unnecessary packet
transmission and energy consumption. A data packet structure is restructured for different transfer
flows in WSNs. For classification of data packets, additional filed are added into the basic data
packet structure as shown in figure 2.
PP-1
byte
Address
Field 5
bytes
Packet
control
field
Payload (32 bytes) CRC
1-2
bytetimestamp
Priority
bit
Hop
count
Data
Figure 2. Data Packet Structure of RTPS for priority-based data packet scheduling
A priority bit, hop count, and timestamp attributes play important role in data classification and
scheduling phase of the RTPS data communication protocol. To check the packet error, a CRC
filed is also taken into consideration during the experimentation.

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4. RTPS SETUP & EVALUATION
We designed the TestBed using the NORDIC 2.4GHz nRF24L01 RF module [28] and ATmega
328 Arduino Nano [29] for evaluating the proposed work. It uses ISM band and operates over 2.4
– 2.4835GHz. It supports maximum 2Mbps air data rate. A standard configuration of IEEE
802.15.4 MAC is considered for experimental setup. However, we set 250kbps air data rate for
the proposed protocol evaluation. The module has implicit core operations like automatic packet
handling, auto-acknowledgement, selective auto retransmission and 6 logical data pipes are used
and also, priority weight information is added into it.
The data pipe is enabled using EN_RXADDR register. Out of six data pipes, one data pipe
becomes active and receives the packet. This feature is called as multiceiver. Figure 3 shows the
actual end device used for experimentation. The same node hardware is used for hop node and RF
coordinator. All have used shielded battery backup during emulation.The elapsed time is 250 .
It was chosen due to ultra lower power consumption feature. This module has 11.3mA transmit
power, with 0dBm, 12.3mA receiver power, and packet pace is 2Mbps, and 900nA in power
down, 22µA at standby mode, and operates over the 1.9 to 3.6V power supply range. It takes
maximum 1.5ms to wake from power down mode and 130us start-up time from standby mode. A
delay bound is set to 0.2 seconds. It uses GFSK modulation technique. We have used 16MHz
crystal frequency to manage the voltage level. The automatic packet handling includes flexible
payload (25 bytes), dynamic data accumulation, dynamically data confirmation, and automatic
packet splitting. Figure 3 shows the operational flow of the routing node. It is a heart of the RTPS
protocol. Different functions like classification, sorting, priority revision, and scheduling are
performed by the hop node.

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Figure 3. Block diagram of Operational flow Routing Node for scheduling high priority data packets
A RTPS uses FCFS mechanism and EDF approach [24]. It is modified according to the proposed
priority approach. To authenticate results both scheduling approaches are put into service and
compared with each other for delivering the real-time data packets. The packet structure is
defined in figure 1. Due to variable payload feature, we have utilized 4 bytes payload for storing
the temperature, humidity, accelerator, and heart beat counting. The packet size is 32 bytes in
total. The network is setup with 8 RF battery powered source nodes with one RF controller
equipped with main supply. Each source node except leaf source node(s) senses its data and
transmits the same if it is received from its downstream source node called as a repeater node.
The real experimental view is depicted in figure 4.
Figure 4. Experimental field view captured when nodes were equipped with 3 hop topology with
heterogeneous sensing devices
They are generated by each RF end device separately. We have provided one sensor to each
device distinctly to produce different traffic so that RF routing device will be able to get enough
traffic at a time for classification dynamically. The priority bit information and hop counts are
used at each RF routing device for revising the priority weight of each outgoing packet.
Data Classification Packet Queuing
Receiving Data
Packets
Sorting & Priority
Revision
Scheduling High
data packets
Transmitting
Packets

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4.1 Real Time Analysis using Sensor Cloud Solution
It is designed to monitor the status of each sensor device through this cloud solution in real time.
Each active device could be controlled using various analytical tools which show the current
reading through graphical representation, history of each sensor, battery level, the number of
packets received, the path used, packet indication using beep, and also various filtering equation
would be used to get the desired outcome. This solution saves the time of analysis and also gets to
know the status of each device. It is easy to address the failure in time by monitoring the sensor
activities. The end device could be equipped with many sensors because it assigns the unique
identity to each sensor attached to a particular node. Therefore, we have generated multiple event
data at each sensor node and which help us to create significant traffic to test priority queue
efficiently. It is observed that queuing operations work efficiently for handling the different flows
simultaneously. The data packets are monitored using the cloud solution, as shown in figure 5.
The battery levels of an active node are monitored using group sensors as depicted in figure 6.
Figure 5. Screenshot of cloud interface for monitoring data and checking live sensing devices
Figure 6. Screenshot of real-time energy consumption view of the source nodes

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The experiments are performed by varying the network topology, as described in secenario-1 and
2. Scenario-1: in this section, we configured the system with maximum three hops and minimum
two hops recorded. The performance of the proposed work is tested over different
experimentation time. An average packet delivery ratio and throughput by varying the simulation
time and packet interval time are rendered in figure 7 and 8. An efficient queue management
improves the packet delivery ratio, automatic retransmission for missed packets, and automatic
path finding operation in case of link failures.9596979899
Time(seconds)
AveragePDR(%)
3 6 9 12 15 18
RTPS-EDF RTPS-FCFS
Figure 7. (scenario-1) shows the analysis of packet delivery ratio over a different period
The result indicates that many packets are received by the RF coordinator using RTPS approach
compared with FCFS mechanism are more. The PDR is improved on an average 99%, over
average 96% against FCFS. It is achieved due to packet prioritization to sensitive sensor over
regular sensors and effective queue management. Due to this approach, it is noted that the
throughput is also significantly increased and the average throughput variation is observed around
6% higher than FCFS mechanism. The reporting rate is updated based on the state of a queue and
has observed improvements with fewer packet drops. However, this protocol is tested over 8
nodes with 6 sensing devices. The observed PDR difference is small, but graph progress shows
that when number source nodes increase with sensing devices, the difference is also
proportionally increased.
500060007000
Time(seconds)
Avg.Throughput(bps)
3 6 9 12 15 18
RTPS-EDF RTPS-FCFS
Figure 8. (scenario-1) shows the analysis of network throughput over a different time

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The average power usage and delay in diverse experimentation period is depicted in figure 9 and
10. The graph clearly describes that the RTPS consumes 12% less energy than the FCFS
approach. This power consumption is because long distance packets are allowed first over the
network. According to scenario-1, the delay is noted around 11% less as compared with FCFS.
Figure 9. (scenario-1) shows the analysis of energy consumption over a different period
Figure 10. (scenario-1) shows the analysis of delay over a different period
Scenario-2: The performance of the presented work discussed over maximum recorded value of 5
hops and a minimum value of 2 hops. And with the variable inter-departure period, the same
protocol setup is executed and observations regarding PDR, energy, delay, and throughput is
discussed.
Thus it proves that the dynamism in priority weight metric helps to process the short deadline
packets faster over newly sensed little weight packets. According to scenario-2, the average PDR
ratio is noted approximately 99% over whereas the throughput is observed around 7% higher than
FCFC method, shown in figure 11 and 12.

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Figure 11. (scenario-2) shows the analysis of packet delivery ratio over a different period
Figure 12. (scenario-2) shows the analysis of network throughput over a different time period
Figure 13. (scenario-2) shows the analysis of energy consumption over a different time period
Furthermore, this is achieved due to less retransmission of long distance packets. The RTPS
schedules the short deadline packets first and prevents the packet loss because of deadline expire.
Though the processing overheads are incorporated into repeater node; but it does not degrade the
performance because processing time is minimal than retransmission time overheads. Thus

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reduces the propagation time on an average of each travelled packet in the network. The average
1.42% energy is spent and 11% less as compared with the FCFS mechanism and 6% delay is
observed less compared with the FCFS method, as shown in figure 13 and 14.
Figure 14. (scenario-2) shows the analysis of delay over a different time
It proves that the priority approach works better with EDF [20] algorithm for delivering the delay
sensitive data packets. It reduces the chances of packet expiry due to deadline termination. The
priority metric is designed with delay consideration, thus it not only increases the packet delivery
ratio but also saves the overall life of the network. As a result, it shows the better system
throughput against the traditional FCFS method. Therefore, RTPS can be used in time bounded
applications such as heart beat counting, earthquake detection, fire alert systems, and much more.
8. CONCLUDING REMARKS
In this work, we discuss the priority –based real-time transmission protocol, addressing the
problems of flexible prioritization and variable rate differentiation. The priority is altered at
various levels to reduce the packet loss of short deadline packets and to satisfy deadlines of
sensitive packet first. In second part of the paper, RTPS performs the updating in the service rate
differentiation. However, indirectly it contributes to reducing the delay of high priority traffic.
Variable rate algorithm achieves target reliability and prevents the traffic jam in the successive
interval of the network which results in less power utilization. Our proposed scheme shows 99%
PDR and 12% less power usage as compared against FCFS method. In future, we plan to
investigate the optimal operating frequency for higher priority classes and lower priority classes
to function effectively. Besides, we plan to develop the beacon enabled priority-based MAC
protocol for critical data delivery using interference free model.
ACKNOWLEDGEMENTS
The paper authors would like to take an opportunity to thank all reviewers for their valuable time
for model discussion and constructive comments. We also would like to extend our thanks to my
sensor open source forum community for providing help time to time.

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Authors
Sambhaji Sarode received BE and ME in information Technology from the SPPU
University Pune in 2005 and 2010, respectively. Currently, he is working as an assistant
professor in the Department of Computer Engineering, MIT College of Engineering Pune
since Jan 2006. He is having 11+ years of experience. He is currently working towards a
Ph.D. degree in Computer Science and Engineering; RTM Nagpur University under the
supervision of Prof. Dr. J. W. Bakal. His research interest includes reliability approaches
in wireless sensor networks, Cognitive security, and the Internet of Things. He has
completed two research funded projects. He has delivered many expert sessions in STTP
programs and workshops to name a few “Use of Modeling in Network” in TEQIP-II
STTP development program, “IoT: Sensor Network” in TEQIP-II STTP development
program, etc. He is the author of three books and published many research articles in
journals and conferences.
Jagdish Bakal received M. Tech. (EDT), from Dr. Babasaheb Ambedkar Marathwada
University, Aurangabad. Later, He completed his Ph.D. in the field of Computer
Engineering from Bharati Vidyapeeth University, Pune. He is presently working as
Principal at the S.S. Jondhale College of Engineering, Dombivali (East) Thane, India. In the
University of Mumbai, he was on honorary assignment as a chairman, board of studies in
Information Technology and Computer Engineering. He is also associated as chairman or
member of Govt. committees, University faculty interview committees, for interviews, LIC
or various approval works of institutes. He has more than 27 years of academics experience

103
including HOD, Director in previous Engineering Colleges in India. His research interests
are Telecomm Networking, Mobile Computing, Information Security, Sensor Networks
and Soft Computing. He has publications in journals, conference proceedings in his
credit. During his academic tenure, he has attended, organized and conducted training
programs in Computer, Electronics & Telecomm branches. He is a Professional member
of IEEE. He is also a life member of professional societies such as IETE, ISTE INDIA,
CSI INDIA. He has prominently contributed in the governing council of IETE, New
Delhi INDIA.

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