AN EFFICIENT BANDWIDTH OPTIMIZATION AND MINIMIZING ENERGY CONSUMPTION UTILIZING EFFICIENT RELIABILITY AND INTERVAL DISCREPANT ROUTING (ERIDR) ALGORITHM

International Journal of Computer Networks & Communications (IJCNC) Vol.10, No.4, July 2018
DOI: 10.5121/ijcnc.2018.10407 91
AN EFFICIENT BANDWIDTH OPTIMIZATION AND
MINIMIZING ENERGY CONSUMPTION UTILIZING
EFFICIENT RELIABILITY AND INTERVAL
DISCREPANT ROUTING (ERIDR) ALGORITHM
Sivashanmugam.N1
and Jothi Venkateshwaran.C2
1
Research Scholar, PG & Research Department of Computer Science, Presidency
College, Madras University, Chennai-60005, Tamilnadu, India
2
Professor & Head, Dept. of Computer Science and Engineering, St. Peter's Institute of
Higher Education and Research, Chennai, Tamilnadu, India
ABSTRACT
The bandwidth utilization plays a vital role in a Wireless Sensor Network (WSN) that transmits data
packets from source peer to perspective destination peer without any packet loss and time delay. In a
conventional system, two main features cannot be satisfied concurrently such as low delay and high data
reliability and then the peer was transferred fewer data packets and it optimized with regular bandwidth
rate. Moreover, the convention of bandwidth in network routers influences the quality of service (QoS). To
overcome the above issues, an Efficient Reliability and Interval Discrepant Routing (ERIDR) algorithm is
proposed to optimize bandwidth utilization on the router network with the help of bandwidth optimizer. The
bandwidth optimizer allocates required bandwidth for data transmission to each peer simultaneously to
ensure the bandwidth efficiency. The proposed design is to optimize bandwidth utilization of every peer and
increase data processing via higher bandwidth rate that reduces time delay and minimizes energy
consumption. The proposed method establishes a high bandwidth rate router to transmit data concurrently
from source peer to destination peer (peer-to-peer) without any packet loss by initializing host IP address
for every peer. Based on Experimental evaluations, proposed methodology reduces 3.32 AD (Average
Delay), 0.05 ET (Execution Time), 5.44 EC (Energy Consumption) and 0.28 BU (Bandwidth Utilization)
compared than existing methodologies.
KEYWORDS
Bandwidth Optimization, Energy Consumption (EC), Efficient Reliability and Interval Discrepant Routing
(ERIDR) Algorithm, quality of service (QoS), Average Delay (AD), Execution Time (ET), and Bandwidth
Utilization (BU).
1. INTRODUCTION
Wireless networking communications evolved in our environment widely through various
technologies for data transmission from one place to another with bandwidth capacity through
channel mediums. The bandwidth utilization is a key concept that transmits data in bits/second,
the rate of bandwidth speed differs (kbps or Mbps) according to the size of data packets and to
control the data transceiver channels for quality throughout. Internet protocols are the emerging
routing network for transmission and receiving of data packets with the help of sliceable optical
transponder (SOT) carries multiple data flows concurrently. Data packet contains control
information and payload of the user during transmission. Cognitive radio networks found an
optimal route that increased bandwidth utilization on the selected path for following paired
network devices and throughput performance was a highly efficient range of (20 to 40 Mbps)
strength in the selected optimal path. In the channel, bandwidth efficiency is necessary to

92
communicate router peers who carry the volume of data packets simultaneously without any
modification via congested traffic and transmit faster for further re-transmission if any packet
loss.
The fundamental issues in the existing system are a delay, energy consumption, data reliability
and bandwidth utilization. Information Communication and Technology (ICT) industries
consuming more energy that outlets carbon dioxide emissions, a large amount of bandwidth
utilization occur on the network. The high-performance computers (HPC) consumes Mega-Watts
of electricity every day for processing data packets from one peer to another where multiple
cooling systems are required to cool down HPC. Un-usable bandwidth wasted in the network
itself where it is not shared or used among another peer which carries a huge amount of load and
consume too much of energy as well as bandwidth. The complexity is data congestion in network
traffic due to low bandwidth rate for a large amount of data packet transmission.
The proposed system implements an Efficient Reliability and Interval Discrepant Routing
(ERIDR) algorithm optimizes un-usable bandwidth on the network and minimizes the bandwidth
capacity according to the size of data packets transmitted. Modules consist of Source Peer (SP),
Network Router (NR), Destination Peer (DP), and Bandwidth Optimizer (BO). Source peer
selects the file from the perspective directories, initializing each peer to further transmission and
proving destination address for packet delivery. Network Router (NR) transmitting data through
peer for minimal bandwidth utilization, less energy consumption and processing quicker for
reducing time delay and increasing execution time. Destination Peer (DP) receives the data
packets from routers efficiently. Bandwidth Optimizer is initialized to monitor transmission and
calculates the amount of bandwidth utilized by each peer; the un-usable bandwidth is later shared
among another peer, or bandwidth rate for the following peer is reduced. The contribution of this
paper follows:
• To minimize bandwidth utilization during the data transmission of the packets through
peers.
• To avoid un-usable bandwidth on the routing network.
• To transmit the data packet with minimal time delay and less energy consumption.
• To monitor a routing process of the data packets in a faster execution standard.
• To reduce the Average Delay, Execution Time, Energy Consumption and Bandwidth
Utilization compare than existing approaches.
The rest of the paper contribution is followed by section 2 illustrates the related work which
expresses the closest technology of the proposed methodology. Section 3 introduces the proposed
methodology, design, implementations steps with proposed algorithms. Section 4 discusses the
implemented result and comparison analysis. Section 5 concludes the overall work with future
outcomes.
2. RELATED WORK
Guo et al. [1] discussed an extended SDN controller, and it enhanced the light-path bandwidth
consumption. It combined numerous fine-granularity streams into a light-path, through an
objective of optimizing bandwidth utilization while guaranteed broadcast performances.
However, an extended SDN controller does not offer bandwidth resource management. Zhou et
al. [2] described an iterative sub-cluster partition algorithm utilized to share data among mobile
devices in a coverage range by utilizing base stations and device-to-device communication
clusters. D2D communication clusters performed retransmission process simultaneously if any
packet loss occurs on the cluster. However, retransmission time is high. Zhu et al. [3] developed
the integration of RMSA (routing, modulation, and spectrum assignment) and Hybrid Single-
/Multi-Path Routing (HSMR) algorithm and it analyzed the path carries data from one peer to
another for data transmission. The disadvantages of the designed algorithm utilized multiple hops

93
which transfer in the same path. Han et al. [4] designed a Green-energy Aware Latency-Aware
(GALA) algorithm which reduced power consumptions on Macro base-stations (MBS) and Pico
base-stations (PBS) coverage areas. The MBS & PBS technologies are converted to green which
controls power consumption on the grid. In the algorithm, energy was not stable and reliable due
to traffic congestion.
Barshan et al. [5] introduced an efficient architecture with the integration of Customer premises
equipment (CPE) and Network Function Virtualization (NFV). It utilized to transfer data packets
to the external network which increased the flexibility of data transmission, energy efficiency
framework and it offered clients better commodity services. It requires changing the functions of
aspects according to the users when the architecture was deployed. Guo et al. [6] suggested a
scheme Fair Network Bandwidth Allocation (FALLOC) and it offered VM services to clients in
effective base bandwidth utilized a distributed algorithm which managed un-usable bandwidth on
the network. It shared with other VM that needs high bandwidth rate with the help of falloc.
However, the falloc scheme does not accomplish the conventional TCP (Transmission Control
Protocol). Barakat et al. [7] described an off-path cache method, and it solved content cache over
the network. It increased the performance in egress, and it was a global hit ratio on multiple
routers. The method reduced bandwidth usage and improvised the capacity of cache since the
method changes router allocation for every deployment process. Wang et al. [8] designed an
energy-aware routing algorithm which reduced power consumption in the software-defined
network. In network topology, the six-node topology initialized with 400 nodes to process
requests more than capable of nodes initialized and it reduced processing time. The disadvantage
of the routing algorithm will not save energy and data consistency at the similar time. Bradai et
al. [9] illustrated a cellular software-defined network (CSDN), and it improved the performance
of data reliability, bandwidth availability. CSDN controller managed server authentication,
accounting, security and third-party application from LTE Network. However, the CSDN
architecture was inadequate for mobile networks.
Chai et al. [10] investigated RRM (Radio Resource Management) algorithm over congestion
traffic network utilized dynamic QoS-based bandwidth allocation framework which monitored
the existing and new connection automatically. It assigned bandwidth based on their service and
increased the capacity of transmission. Reddy et al. [11] developed an Ad-hoc On Demand
Vector (AODV) routing protocol, and it identified complex migration during data transmission
and associates waiting for a queue of packets. It computed available bandwidth which increased
the performance of the system. Bartelt et al. [12] evaluated the Macro-base station and Pico-base
station power consumption, migration cost, congestion networks where interference of coverage
range was solved by providing Joint and Optimization algorithm for managing bandwidth
utilization among those stations in a particular range. Felemban et al. [13] implemented
MMSPEED routing protocol for data reliability and reduced time consumption during data
transmission from the service provider to destination peer without any packet loss; the proposed
algorithm ensures QoS. Wang et al. [14] expressed multi-hop nodes around source node which
reduced the energy consumption of router nodes as well as time taken for reaching the destination
(Sink) through Pair-Wise Directional Geographical Routing algorithm that monitored
surrounding nodes power consumption and data transmission rate concurrently. In [15] developed
an efficient cluster-based multipath routing method which every cluster includes two cluster
heads, one to gather cluster information and the other to broadcast information.

94
3. SYSTEM METHODOLOGY
In the section explained the system architecture with proposed techniques and algorithm details.
The proposed system architecture is explained in Figure 1. The main objective of the proposed
system improves the data reliability and delay distinguished services (RDDS) simultaneously,
and a bandwidth optimizer allocates required bandwidth for data transmission of every peer
concurrently to ensure bandwidth efficiency. An Efficient Reliability and Interval Discrepant
Routing (ERIDR) algorithm is proposed to reduce the amount of un-usable bandwidth of each
peer. It establishes high bandwidth rate router to transmit data concurrently from source to
destination (peer-to-peer) without any packet loss by initializing host IP address for every peer. It
enhances the throughput and minimizes the energy, delay and bandwidth utilization.
Figure.1 Architecture Diagram of ERIDR Algorithm
3.1. Source Peer
The source peer selects the content file, initialize the routers and after that transmit to the
particular destination peer. The source peer will transfer the selected content file to the router, and
then the router selects the minimum distance path and transmits to the particular destination peer.
3.2. Router
The router controls the various networks to offer data storage service. In network n-number of
peers are presented (p1, p2, p3, p4, p5…). In a router source, the peer can provide analysis peer
details and attacked peers. The source peer will transmit their content file to router and router will
select minimum distance path and transmits to the particular destination peer. If a few attackers
are investigated in a peer after that, the router will connect to another peer and transmit particular
client.
3.3. Obtrusion Discovery Framework (ODF) Controller
The ODF controller contains the two stages. If reliability or malicious content occurs in router
after that ODF controller is enabled. In the first stage, the DNS packets, clear stream, fine-grained
ODF user discovery and traffic filter are present. The main objective is detecting all peers within
the observed network that connect in ODF communications. To investigate raw traffic gathered at
the edge of the observed network and apply a pre-filtering stage to reject network streams that are
unlikely to be produced by ODF applications. Then investigate the remaining traffic and extract

95
some statistical aspects of recognizing streams produced by ODF users. In the second stage,
Coarse-grained ODF reliability or malicious content discovery, Fine-grained ODF client
discovery and Reliability or Malicious content are present. In the system investigates the traffic
produced by the ODF users and categorizes them into each legitimate ODF users or ODF
reliability or malicious content.
3.4. Destination Peer
The destination peer can receive the content file from the router. The source peer will transmit the
content file to the router, and then the router will transfer to the particular destination peer. The
destination peer receives the content file without modifying the file contents. The clients may
receive particular content files within the network only.
3.5. Bandwidth optimizer
In the module, bandwidth optimizer will manage bandwidth rate among other peers. If peer
carries fewer amounts of data packets, and the peer bandwidth is shared with another peer who
carries a huge amount of data packets.
2.6. Efficient Reliability and Interval Discrepant Routing (ERIDR) Algorithm
An Efficient Reliability and Interval Discrepant Routing (ERIDR) algorithm is reducing the
amount of un-usable bandwidth of each peer. Four features are affecting the peer-to-peer delay
such as transfer interval, the competition of the radio channel, queuing interval, and path length.
Transfer interval is limited and it associated with bandwidth. Competition of radio channel is
particularly based on the contention of MAC; a packet has to compete for the channel access and
wait for content transfer until the channel is idle. Queuing delay is a huge queue seriously takes
the delay of transfer of the packets. Generally, the path length has additional hops for packet
transmission; the huge possible delay will suffer. The physical constraint establishes the transfer
interval, and the MAC influences the competition of radio channel. The ERIDR algorithm aims to
minimize the transfer interval, packet loss, bandwidth utilization, energy and improves the
throughput. Consider the network with different high reliability or delay content files. Let c be the
identifier of various applications. The main process of ERIDR algorithm at peer works as
follows. First, if the queue at peer is not empty, then bandwidth is computed for the packet at the
head of the queue. Calculate the bandwidth and select the next hop. The peer sends the content
file to the destination peer. ERIDR build up a potential field along with the bandwidth and queue
length data to investigate the under-utilized paths. The packets with huge reliability requirements
will be forwarded to the next hop with lesser queue length. Every application allotted a packet
size, and it describes the degree sensitivity to the delay.
ERIDR describes a Maximum Update Interval (MUI) and a Least Update Interval (LUI) among
two successive modify data. MUI is always larger than LUI. The modified data should be transfer
among an LUI and MUI at least once. If no data file is received from a neighbour through two
MUIs intervals, in the neighbour will be measured dead, and ERIDR will recompute the
bandwidth and other related values. A modify data will be transfer. If MUI timer expires the time
will be elapsed because transferring the last modified data exceeds the MUI; a new modify data
will be transferred immediately no matter whether the bandwidth or queue length has changed.
The queue length variation exceeds a certain threshold. If the queue length of a peer has varied
compared with last successful modified data and the elapsed time exceeds the LUI because the
last modify data. If the bandwidth size of a peer has modified and the elapsed time exceeds the
LUI because the last successful modify data. The pseudo code of proposed algorithm is explained
below.

96
Input: Text Types of Input Data
Output: Display the Average Delay (AD), Execution Time (ET), Energy Consumption (EC) and
Bandwidth Utilization (BU).
Procedure:
Start
Arrange all the peers (source peer, router, and destination peer)
Browse any data file to send destination peer;
Select destination peer;
Apply ERIDR algorithm;
Initialize peer
If compute optimization of bandwidth
Measure un-usage bandwidth during data transmission
Compute optimization of bandwidth
Else
Bandwidth waste
End if
Identify the shortest path for data transmission process
If send data
Transmit data to destination peer;
Calculate & visualize AD, EC, ET, and BU;
Else
Failed to transmit the data
Fail to compute AD, EC, ET, and BU;
End
4. RESULT AND DISCUSSION
4.1. Implementation Setup
The proposed techniques are deployed with Intel i6 Core processor, with 16 GB RAM, 60 GB
Memory with Windows7 Ultimate operating systems. The algorithm is implemented in Java
programming environment by using NetBeans 8.0.2, MYSQL database 5.5, Java Remote Access
Library with 50 peers in a centralized server environment. The proposed algorithm is computed
with following constraints namely Average Delay, Execution Time, Throughput and Bandwidth
Utilizations performance evaluation metrics.
4.2. Performance Matrix
The proposed methodology is computed on different kinds of constraints to calculate the
efficiency of techniques. The proposed technique is highly dedicated to providing efficient
content data file transmission with minimal resource optimization on similar QoS guarantee
services.

97
4.2.1. Average Delay (AD)
The average delay is described as the time difference between the current data packets received
time, and the previous data packets received time. It exhibits data transmission delay between
source peer and destination peers. The average delay is computed in Equation (1).
(1)
Where Pkt is packets and Recvd is Received packet.
4.2.2. Throughput
Throughput is the average ratio of successful data delivered to the destination peer. The
throughput specifies efficiency of the proposed method. The throughput is calculated in Equation
(2).
(2)
4.2.3. Energy Consumption
The amount of energy remaining in a node at the current instance of time is called as residual
energy. The difference between the initial energy and the residual energy over the simulation time
provides the rate at which energy is consumed by the network operations. The energy
consumption is computed in Equation (3) and (4).
(3)
(4)
Where Rx is Residual Energy and Tx is Initial Energy.
4.2.4. Bandwidth Utilization
The bandwidth utilization is computed on each peer require file transmission size & system
efficiency. The bandwidth ensures the how techniques effective to avoid traffic and congestion
issues with minimal physical resource utilization. The Bandwidth Utilization is estimated in
equation (5).
(5)
Where, BWAssigned is bandwidth allocation of a peer in-network for data transmissions. Npr
indicates the total number of peer request for efficient data transmissions in networks.

98
4.2.5. Execution Time
The execution is defined as the total time to transmit the data source peer to destination peer. The
execution time is computed in equation (6).
Execution Time = FS X CPFS X T (6)
Where CPFS- cycles per file size, FS- File Size and T is time.
Tables (eg, Table 1) explain the Average Delay (AD), Energy Consumption (EC), Execution
Time (ET) and Bandwidth Utilizations (BU) for respective input parameters with existing
methods. Tables (eg, Table 1) displays the average value on all respective evaluation matrix &
input parameters with Adhoc On-Demand Distance Vector (AODV) [16], Joint Routing,
Dynamic Source Routing Protocol (DSR) [16] and Dynamic Manet On-demand Routing Protocol
(DYMO) [16] existing methods. Along with Tables (eg, Table 1), it noticed that Efficient
Reliability and Interval Discrepant Routing (ERIDR) algorithm performs well on all evaluation
matrix and Input parameters compare than existing methods.
Table 1. Average Delay, Energy Consumption, Execution Time, Bandwidth Utilization for Dynamic
Routing.
Figures (eg, Figure 2) demonstrate the average delay in seconds, and the proposed algorithm
ERIDR is comparing existing algorithms such as AODV, DSR, and DYMO. The processing of
data packets from the source peer to destination peer utilizing ERIDR algorithm produced low
average delay when it compared to existing methodologies. The DSR method provides the high
average delay for transmitting data packets from the source and destination. DYMO algorithm is
the closest existing algorithm compare than the proposed algorithm.
7.58
24.3
4.68
1.36
0
5
10
15
20
25
30
AODV DSR DYMO ERIDR
Seconds
Algorithms
Average Delay
Fig. 2 Average Delay of Dynamic Routing

99
Figures (eg, Figure 3) demonstrate the throughput in kbps, and the proposed algorithm ERIDR is
comparing existing algorithms such as AODV, DSR, and DYMO. The processing of data packets
from the source peer to destination peer utilizing ERIDR algorithm produces less execution time
when it compared to existing methodologies. The AODV algorithm provides high execution
time for transmitting data packets from the source and destination. DYMO algorithm is the
nearest existing algorithm to compare than proposed algorithm.
0
0.05
0.1
0.15
0.2 0.17 0.16 0.15
0.1
seconds
Algorithms
Execution Time
Fig. 3 Execution Time of Dynamic Routing
Figures (eg, Figure 4) demonstrate the energy consumption in joules, and the proposed algorithm
ERIDR is comparing existing algorithms such as AODV, DSR, and DYMO. The processing of
data packets from the source peer to destination peer utilizing ERIDR algorithm consumed low
energy when it compared to existing methodologies. The AODV algorithm is consuming more
energy for transmitting data packets from the source and destination peer. DYMO algorithm is
the nearest existing algorithm to compare than proposed algorithm.
0
10
20
30
40
50
60
58.55
53.18 52.89
47.45
Joules
Algorithms
Energy Consumption
Fig. 4 Energy consumption of Dynamic Routing
Figures (eg, Figure 5) demonstrate the encryption time in milliseconds, and the proposed
algorithm ERIDR is comparing existing algorithms such as AODV, DSR, and DYMO. The
processing of data packets from the source peer to destination peer utilizing ERIDR algorithm
consumes less bandwidth when it compared to existing methodologies. The AODV algorithm is
utilizing more bandwidth for transmitting data packets from the source and destination peer.
DYMO algorithm is the nearest existing algorithm to compare than proposed algorithm.

100
0
1
2
3
4
AODV DSR DYMO ERIDR
3.78
2.65
1.63
1.35
Mbps
Algorithms
Bandwidth
Utilization
Fig. 5 Bandwidth Utilization of Dynamic Routing
Based on Figures (eg, Figure 2,3,4,& 5), it is observed that, the proposed ERIDR technique
computes average delay, execution time, energy consumption and bandwidth utilization for
finding the efficiency. The proposed ERIDR is evaluated with AODV, DSR and DYMO existing
techniques on behalf of average delay, execution time, energy consumption and bandwidth
utilization. The AODV is utilized to determine multiple paths between the source peer and the
destination peer in every route determines. However, it is the high overhead of data transmission
and failed to maintain complete path details a t every peer. The proposed ERIDR approach is less
overhead and effectively maintains complete path details. The DSR is an efficient source routing
the method, when the total number of peers is less it transmits data to source peer to destination
peer effectively. However, the number of peers is increased; the data transmission is failed due to
more traffic in the network. The proposed ERIDR method is utilized to transmit data effectively
for high number of peers. The proposed technique closest competitor is DYMO on overall
parameters. DYMO techniques improved route maintenance and route discovery. It improved the
performance of various commercials application for data transmissions. However, DYMO
technique failed to support asymmetric links. Proposed method improves the bandwidth
utilization of un-usage bandwidth. Proposed ERIDR reduces 3.32 AD (Average Delay), 0.05 ET
(Execution Time), 5.44 EC (Energy Consumption) and 0.28 BU (Bandwidth Utilization). Finally,
the paper claims the proposed ERIDR algorithm performs best on every evaluation matrix &
respective input parameters.
5. CONCLUSIONS
The An Efficient Reliability and Interval Discrepant Routing (ERIDR) algorithm utilized to
minimize some quality of service metrics such as bandwidth utilization and energy consumption.
The main concept of a proposed system to optimize bandwidth utilization on router network with
the help of the bandwidth optimizer that allocates required bandwidth for data transmission of
each node simultaneously to ensure bandwidth efficiency. It optimizes bandwidth utilization of
every node and increases data processing via higher bandwidth rate that reduces time delay and
minimizes energy consumption. It establishes high bandwidth rate router to transmit data
concurrently from source to destination (peer-to-peer) without any packet loss by initializing host
IP address for every node. Proposed ERIDR reduces 3.32 AD (Average Delay), 0.05 ET
(Execution Time), 5.44 EC (Energy Consumption) and 0.28 BU (Bandwidth Utilization). Finally,
the paper claims the proposed ERIDR methodology performs best on every evaluation matrix &
respective input parameters.
ACKNOWLEDGMENTS
The authors would like to thank everyone, just everyone!

101
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AN EFFICIENT BANDWIDTH OPTIMIZATION AND MINIMIZING ENERGY CONSUMPTION UTILIZING EFFICIENT RELIABILITY AND INTERVAL DISCREPANT ROUTING (ERIDR) ALGORITHM

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