AN OPTIMUM ENERGY CONSUMPTION HYBRID ALGORITHM FOR XLN STRATEGIC DESIGN IN WSN’S

International Journal of Computer Networks & Communications (IJCNC) Vol.11, No.4, July 2019
DOI: 10.5121/ijcnc.2019.11404 61
AN OPTIMUM ENERGY CONSUMPTION HYBRID
ALGORITHM FOR XLN STRATEGIC DESIGN IN
WSN’S
Md. Khaja Mohiddin1
and V. B. S. Srilatha Indira Dutt2
1
Research Scholar, Department of ECE, GITAM (Deemed to be University),
Visakhapatnam, Andhra Pradesh, India
2
Associate Professor, Department of ECE, GITAM (Deemed to be University),
Visakhapatnam, Andhra Pradesh, India
ABSTRACT
In this paper, X-Layer protocol is originated which executes mobility error prediction (MEP) algorithm to
calculate the remaining energy level of each node. This X-Layer protocol structure employs the mobility
aware protocol that senses the mobility concerned to each node with the utilization of Ad-hoc On-Demand
Distance Vector (AODV), which shares the information or data specific to the distance among individual
nodes. With the help of this theory, the neighbour list will be updated only to those nodes which are mobile
resulting in less energy consumption when compared to all (static/mobile) other nodes in the network.
Apart from the MEP algorithm, clustering head (CH) election algorithm has also been specified to identify
the relevant clusters whether they exists within the network region or not. Also clustering multi-hop routing
(CMHR) algorithm was implemented in which the node can identify the cluster to which it belongs
depending upon the distance from each cluster surrounding the node. Finally comprising the AODV
routing protocol with the Two-Ray Ground method, we implement X-Layer protocol structure by
considering MAC protocol in accordance to IEEE 802.15.4 to obtain the best results in energy
consumption and also by reducing the energy wastage with respect to each node. The effective results had
been illustrated through Network Simulator-II platform.
KEYWORDS
IEEE 802.15.4, AODV Protocol, Two Ray Ground Propagation Model, Mobility Error Prediction
(MEP)Algorithm, Clustering Multi-Hop Routing (CMHR) Algorithm, Energy Consumption, End-to-End
Delay; Throughput
1. INTRODUCTION
IEEE 802.15.4 standard has its requirements in Medium Access Layer as well as in Physical
Layer. It upholds the network topologies based on mesh, cluster, tree & star. In a star topology,
the nodes cannot communicate directly without passing through the data collector node through
peer-to-peer topology concept, the node itself communicates with irrespective of passing through
the sink node which is implemented to multiple network strategies. This standard operates on two
different nodes: One is Beacon mode that is Slotted CSMA/CA and the other is Non-Beacon
Mode which is Non-Slotted CSMA/CA. Mainly they are dual things that are necessarily to be
determined. Out of which, the primary is how to stabilize the energy of the nodes & the second is
how to acquire the energy efficiency within the LR-WANs. Universally, all the protocols had to
oversee the energy related issues as mentioned below:

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1. Idle Listening: This state resembles that no transmission is done throughout the simulation
process.
2. Over Hearing: This state resembles that transceiver is actively listening to the message
without worthwhile.
3. Collisions: This state occurs when multiple transmissions are done in such a way that the
acknowledgment of any one of the message fails.
4. Over Emitting: This state resembles the packets that are transmitted when the destination
node is not prepared to accept them.
5. Signaling Overhead: This state resembles to the energy based on acknowledgments &
synchronization of the packets.
The recent advancements in the field with concern to the above aspects have observed a rapid
growth in MEMS, low power consumption, huge digital integrated circuits, small scale energy
industries, and radio technologies with less power, cost effective and multi-functionality WSNs,
which can analyze the changes, occurred in the environment [30]. These devices are integrated
with a mini size battery, a mini microprocessor, an aerial, and an array of transducers which
converts one form of energy to another, which is used to gain the information that responds to the
changes occurred, in the environment of the sensor node [7]. The necessity of these type of device
in the WSNs has motivated the emerging research in the recent decades relevant to the potential
of association among sensors in data collection and operation, which leads to the era of WSNs
[22-23].
The utmost originating prototype IEEE 802.15.4 which has been employed along with the X-
Layer model is a standard arising intelligence medium access control protocol which is widely
opted for Low-Rate WAN's also it is convenient for mobile sensor networks localization [1]. It is
accomplished as a medium of intercommunication linking (MAC) & (PHY) layer [37]. MAC &
PHY routing in a zig-zag style along with which it is a structure for secured & data assembling of
efficient energy. AODV performs better as compared to DSR (Dynamic Source Routing) protocol
with respect to few parameters like high packet delivery ratio, high system throughput, minimum
energy consumption as it (AODV) is faster at efficient data circulation [18]. Two-Ray Ground
method is taken into consideration in this paper as a propagation model in contrast with the
shadowing method as it is having genuine performance in some of the parameters like energy
consumption, end-to-end delay & system throughput [19-20].
Table 1 Different Approaches & Methods of IEEE 802.15.4 based MAC Layer
Types Definition
Parameter
Tuning
Method
Tuning of super-frame parameters to enhance the performance without
rectifying the level & specification of IEEE 802.15.4. It is entirely dependent
upon the application & its performance is based upon its parameter’s value.
Cross-Layer
Method
Provides key solutions depending upon the influence of various layers within
the protocol, but leads in enhancement in latency.
802.11
Method
Relocates the key solutions that had been projected by it to 802.15.4
environment. It has a capability of re-utilizing the experimented technology
where power consumption has no priority in this method.
Priority
Method
Improves the Quality of Service assistance so that all the nodes along with its
traffic is been given the priority where power consumption has no importance.
Duty Cycle
Method
Manages the active frames to obtain maximum power preservation with
minimum manipulations.

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Back-Off
Method
Provides dynamic assistance to various topologies which requires no hardware
up-gradation & may lead to maximum manipulation of the standard.
QoS Method Better assistance for applications based on time-sensitivity.
Hidden
Term
Resolution
Drastic collisions are being minimized with less packet re-transmissions.
2. RELATED WORK
According to the existing scenario of WSNs, it is exclusively mandatory to enhance the network
specification performance in which XLN has a major role [3]. Adjustment of the transmission
power certainly outperforms the deduction of the requirement of energy consumption [5]. Apart
from the above solution, there is the possibility of another approach where the parameter ‘ED’ has
being estimated to overcome the drawback of control overhead, where E represents the energy
and D represents for the degree. In this paper, the results are compared with EQSR, X-layer as
well as Enhanced Model in which the enhanced model is actually the proposed and performs
better in terms of efficiency evaluation [6]. EQSR mainly focuses to identify the best linking
route. The crucial drawback of the X-layer method used in this paper is that it sustains from
broadcast overhead, if the node has maximum mobility which results in huge consumption of
energy during the establishment of the link discovery process. In, all the 3 methods were being
compared and it was observed that energy consumption per packet is less in the proposed(ED)
model along with the increase in network lifetime. Also, the channel occupation as well as energy
consumption is minimized which also results in minimization of the in the delay parameter [3-4].
In this paper, the authors implemented a mobility based clustering algorithm for WSN with few
mobile nodes. According to this new algorithm, a sensor node elects itself as a cluster-head based
on its remaining energy level available with it and mobility parameter. Whereas, a non-cluster
head, focuses on its link stability with CH during clustering within the estimated duration of time.
The Individual time slot for data transmission is assigned to the non-cluster head node in
increasing order in a TDMA scheduled within the given time. During the static-state, a sensor
node sends its sensed data in TDMA pattern as slots and transmits the REQ message to join under
a new cluster also it avoids the loss packets to stable its connection with its cluster head [21, 26].
There is an innovative approach for routing as well as for clustering with respect to the grid view
in WSN. Knowing the area of the deployment & transmission range, the grid size is being
estimated and thereafter the CH is selected depending upon the nearest available mid-point of the
grid. Here the proposed algorithm in this paper outperforms better when compared to the existing
methods like GCMRA, LPGCRA. In this GCCR which represents Grid Based Clustering &
Combinational Routing is established where CH is assigned knowing the residual energy and is
more eligible for hybrid WSNs [5].
As the mobile nodes continuously change the network coordinator at regular intervals, it has been
observed that in active condition of the beacon, IEEE 802.15.4 can’t sustain the inter-connectivity
for that particular nodes which may lead to a major issue in the association of the node's with the
network coordinators [36]. Due to the various nodes with different mobility’s within the network,
it may lead to improper performance & malfunctioning in the synchronization with the network
coordinators. When the respective network node exits out of the concerned cluster range, then it
has to directly associate in looking out the new coordinator/cluster without providing the
loneliness notification so that the energy can be saved to a certain extent [6-7].

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As far as the on-demand routing protocols is concerned in wireless sensor networks, they are
majorly three types namely DSR, DSDV & AODV. After analyzing the simulation results of
various specifications such as packet loss, energy consumption & average delay, the AODV’s
energy consumption is around 18.07% less when compared to DSDV [38-39].
Wireless Sensor Networks has its own importance when IEEE 802.15.4/Zigbee comes into
existence in-terms of development or enhancement where a cross layer model has been addressed
to choose such a network coordinator which depreciates the energy consumption requirement of
the nodes, reduces repetition transmission of the same packet and ease of association of the node
with other network coordinators [40].
To achieve the congestion control, MAC & efficient routing mechanism, cross layer protocol has
been implemented where it consists of such a features which makes the comparison of
functionalities easier with the help of thresholds. It is first protocol which has layer-to-layer
communication [13]. It is completely based on the architecture of layered protocol in terms of
providing good performance & critical implementation [14].
The XLP protocol with respect to the Code Division Multiple Access Ad-hoc networks is
concerned, the energy optimization can be done more efficiently if the route request messages
could be stored during the process of route establishment when packets have been transmitted
more in number due to which the number of hops also increased [35]. Corresponding to this issue,
a modified XLP protocol is been combined with AODV protocol to provide more ease in
extracting the shortest path towards the destinations [32-33].
3. FEATURES OF IEEE 802.15.4
Multiple sensors occupies a small vacancy which states a word called “wireless”, thereafter
resulting in low-power which in-turn implies finite distance. With respect to this, all the nodes
should be in a self-organized manner for which low cost approaches can be implemented to
achieve the concerned target. Wireless approaches have following benefits like: connectors are
not required, secure & reliable connectivity, increasing inflexibility of sharing resources, mobility
& effortless installation [8]. Each & every node has its own bounded range to which the message
has to be transferred. But if at the range is more than the desirable range then it takes the help of
the other nodes so that the message can be reached to the destination safely, this mode of
communication is known as “Multi-hop Communication”. In this, the network itself changes the
topology in the wireless environment [15]. 802.11 relates to the Wireless LAN concept where it is
used in a centralized way in the form of Embedded Sensors within a block (or) building (or)
office etc which costs very huge for its implementation. Whereas 802.15.1 commonly known as
Bluetooth can be used as Wireless PAN which is the combination of both Video as well as
WLAN as shown in Figure (1) where the cables are being replaced by a protocol which costs
moderate. Thereafter, 802.15.4 commonly referred to Zigbee used as a sensor as well as actuator
devices for the industrial & commercial use with cost effective property & operates for real-time
applications [25, 31].

Figure
It operates with the data rates of 20 Kb/sec, 40 Kb/sec & 250 Kb/sec with dynamic device
addressing. It also has the special characteristics like less power consumption, fully handshake
protocols for transmission etc
respect to Figure (2):
1. 2.4 GHz ISM-Bandwidth “16” Channels
2. 915 MHz ISM-Bandwidth “10” Channels
3. 868 MHz with “01” Channel
IEEE 802.15.4 has two device type classes namely:
1. Full-Function Device
2. Reduced-Function Device
Figure 1 Wireless Networking Protocol Stack Model
etc [10]. It has the frequency bands of operation as follows with
Bandwidth “16” Channels
Bandwidth “10” Channels
868 MHz with “01” Channel
Figure 2 Protocol Architecture
802.15.4 has two device type classes namely:
Function Device
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. It has the frequency bands of operation as follows with

The FFD is capable to access in any type of topology along with the monitoring of Personal Area
Network Coordinator Capabilities to interact with any other devices too. Whereas RFD is limited
to star topology & does not
implementation to interact with only one network coordinator. FFD (or) RFD cont
802.15.4 in which it manages the interface of the both MAC sub
wireless medium [29]. Frame Composition of IEEE 802.15.4 MAC
Beacon, MAC Command & Acknowledgement Frames whereas the Su
IEEE 802.15.4 MAC also contains few types as follow:
1. Network Beacon: This beacon is being transmitted by the Personal Area Network
Coordinator which contains its information, frame structure along with the notifications of the
awaiting node information.
2. Beacon Extension Period
3. Contention Period: This can be monitor
Access-Collision Avoidance.
4. Guaranteed Time Slot: This s
bandwidth.
IEEE 802.15.4 MAC has few types of traffic relevant features namely: Periodic Data (sensor
applications based on application defined), Intermittent Data (light switch based on external
stimulus defined) & Repetitive Low Latency Data (based on time slot allocation applications as
elaborated in Figure (3).
IEEE 802.15.4 MAC has various applications as follows:
1. Residential Heartbeat System
2. Residential Home Awareness System by utilizing the se
etc.
3. Wireless Lighting Control
4. Energy Conservation for industrial use b
sensing applications
5. Energy Sensing Applications
6. LRWPAN Field Test Site
7. Energy Savings based on
8. Securing Energy on Infrastructure Capabilities
9. Prediction Based System
Figure 3 Packet Structure
Coordinator Capabilities to interact with any other devices too. Whereas RFD is limited
to star topology & does not has the capability to become a network coordinator with easy
implementation to interact with only one network coordinator. FFD (or) RFD cont
802.15.4 in which it manages the interface of the both MAC sub-layer as well as PHY layer to the
. Frame Composition of IEEE 802.15.4 MAC is of
Beacon, MAC Command & Acknowledgement Frames whereas the Super Frame Composition of
IEEE 802.15.4 MAC also contains few types as follow:
: This beacon is being transmitted by the Personal Area Network
awaiting node information.
Beacon Extension Period: This period is the space reserved for the awaiting notifications.
: This can be monitored by any node using Carrier Sense Multiple
Collision Avoidance.
: This slot is reserved for such nodes which require mandatory
ulus defined) & Repetitive Low Latency Data (based on time slot allocation applications as
IEEE 802.15.4 MAC has various applications as follows:
Residential Heartbeat System
Residential Home Awareness System by utilizing the sensors like temperature, water, power
Wireless Lighting Control
Energy Conservation for industrial use based on motor/system efficiency
Energy Sensing Applications
LRWPAN Field Test Site
Energy Savings based on closed loop
Securing Energy on Infrastructure Capabilities
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Coordinator Capabilities to interact with any other devices too. Whereas RFD is limited
capability to become a network coordinator with easy
implementation to interact with only one network coordinator. FFD (or) RFD contains the IEEE
layer as well as PHY layer to the
4 types like Data,
per Frame Composition of
: This beacon is being transmitted by the Personal Area Network
: This period is the space reserved for the awaiting notifications.
by any node using Carrier Sense Multiple
lot is reserved for such nodes which require mandatory
ulus defined) & Repetitive Low Latency Data (based on time slot allocation applications as
nsors like temperature, water, power
ased on motor/system efficiency along with safe

67
4. METHODOLOGIES USED IN THE PROPOSED SYSTEM MODEL
4.1. Mobility Error Prediction (MEP) Method
In this MEP method, the mobility of each and every node is being estimated depending on its
deviation from it's initial location. It is not necessary that every node should be mobile, some of
the nodes may be mobile and others may be static [34]. So, to identify the node's status, the
mobile aware protocol is being executed in the form of calculating it's deviation in X-Axis & Y-
Axis from it’s actual position. From existing, we change the location discovery process by the
adaptive beaconing model which integrates beaconing only after moving from current to new
location with distance threshold (For Example: >5m). Also, this process change the energy
consumptions depending on distance they transmit, so we change the energy formula by
following methods:-
= { ÷ + + + }
(1)
Where,
"# = Energy consumed during transmission of all packets as per distance
# = Energy consumed during reception of all packets as per distance
$ % = Energy consumed during idle node waiting for request time
&% ' = Energy consumed during sleep mode (after long idle mode, the node
goes to sleep mode)
4.2. Cluster Head Selection
Cluster Head (CH) Selection should be done at regular intervals so as to identify the remaining
clusters existing within the same region or not. Apart from the above, all nodes also need to select
their corresponding cluster by sending the signals in form of beacons.
4.3. Clustering Multi-Hop Routing Method
If at all the cluster head (CH) is at a certain distance from the base station, then it needs to select
multiple clusters to occupy the location as well as position nearby the base station as explained in
Figure (4). The simulation parameters along with the considerations of various protocols and
models are being mentioned in Table 2.
Figure 4 Clustering Multi-Hop Routing

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Table 2 Simulation Parameters & its Specifications used for analysis.
Simulation Parameters Specifications
No. of Sensor Nodes 10-100
Initial Energy 100 Joules
Network Size 250 x 250
Sink Location Centre of the Region
Mobility 0 m/s to 5 m/s
Transmission Range 35 meters
Simulation Time 100 seconds
Radio Propagation Two-Ray Ground Model
Transport Protocol User Datagram Protocol
Application Protocol Constant Bit Rate (QoS)
MAC Protocol IEEE 802.15.4
Packet Size 256 Bytes
Queue Length 100 Packets
Routing Protocol
Ad-hoc On Demand Distance Vector
Mobility Aware Secure Routing Protocol
5. PROPOSED SYSTEM MODEL
If the movement of the nodes is confined to a few nodes likewise sink nodes, and then the stable
nodes can be mitigated in terms of routing link originated towards the node till it reaches the
destination. The base station nodes can move around through stable nodes & accumulate the
information sensed by the source nodes via beacon. The collector nodes which are mobile may
also improve the network link connectivity through reducing the bottleneck problem that may
arise during the network traffic overflow. In the existing methodology, it is being observed that
the location discovery process is implemented in the application layer of the corresponding
architecture through beaconing process to track the position of the node at regular intervals. This
may also result in improvement of the consumption of energy in terms of packet delivery ratio.
5.1. Design Assumptions:-
1. The system model is analogous.
2. All the source nodes were mobile so as to overcome the drawback of non-beaconing.
3. A stable data collector node is being deployed at the center of the cluster.
4. The deployment scenario is flat.
5. Vicinity range of each other what is within the Line of Sight for successful transmission.
In this paper, it is has proposed that the XLN (cross-layer network operation) model [2] whose
initialization process begins with the broadcasting of the NB discovery request to originate the
NB information assembling and store it in the NB-List. As soon as the above mentioned process
is initiated, the position, location & detailed information of the node are shared either by GPS
device or by other means of communication so that it can gather and estimate the location of the
individual nodes. Then the correspondent node starts sending the route-request packets to
construct the route link towards the destination node so that it is entirely feasible to prefer NB list
from the N-layer within the D/L layer.

69
In Figure (5), the flow chart of the proposed system has been discussed originating from the
initial deployment process where the nodes take their initial positions, thereafter the positions are
being updated via adaptive beaconing process through which the neighbor discovery process can
be initialized for updation of the neighbor list. After this, the route discovery process will be
initiated by sending the Route Request Packets (RREQ), if it receives the acknowledgment then it
calculates the energy as well as link metrics based on which an optimal path is being chosen.
After the completion of this whole process, it initiates the data communication, but if in between
the link changes due to the mobility then once again the updation of the neighbor list is required.
Figure 5 Proposed System Flow Chart
5.2. Comparison of Proposed Methodology with the Existing System:-
As per the existing system is concerned, the cross layer approach has been implemented to
compensate the drawbacks occurred in the energy consumption [1]. Apart from this, the WSN
also suffers from huge link failures [12]. To overcome the above problems in the existing system,
an innovative approach is been proposed in this paper combining 3 different algorithms to
minimize the issues occurring in the existing model. First algorithm identifies which are mobile
and also considers the static nodes for future communication which minimizes the energy
consumption. Second elects a proper cluster head so as the node can stay within the same cluster
for longer duration to avoid link failures. And the third algorithm implements a multi-hop
clustering along with the above two algorithms. By contrasting all the 3 algorithms, we can
observe maximum changes in the energy consumption with less packet loss and deserved system
throughput.
6. ALGORITHM IMPLEMENTED
6.1. Algorithm 1: Forwarding Node Mobility Error Prediction for Next Possible
Relay
Input:
= Nodes (1, 2, 3……i)
# = X-Coordinate with respect to the ()
node

70
= Y-Coordinate with respect to the ()
node
* = Time with respect to the ()
node
Output:
Mobile Deviation
Step 1 : Mean Distance in X-Axis by ()
node
Step 2 : Initiate Neighbour
+ ,- =
x/t/ + x1t1 + x2t2 … … … … … . x5t5
k
(2)
Here it calculates the probability mean distance moved by the node in time 1, 2, 3.....k in X-Axis
Step 3 : Mean Distance in Y-Axis by ()
node
Step 4 : Initiate Neighbor Discovery Process & Share the Location
+ ,7 =
y/t/ + y1t1 + y2t2 … … … … … . y5t5
k
(3)
Here it calculates the probability mean distance moved by the node in time 1, 2, 3.....k in Y-Axis
Step 5 : Distance Variations
9 = x: + P<μ=> 1
+ y: + P ?μ@
A 1
(4)
Step 6 : When the ’D’ is varied for maximum 5m distance, then Source disconnects the
link between the neighboring nodes
Step 7 : Receiving Node Error Reporting
Step 8 : Calculate remaining energy level by general energy parameters
BC = EE=F × NE= + EI=F × NE= + P: × T: + PK × TK
(5)
Where,
EE=F = Energy Consumed during Reception Mode
EI=F = Energy Consumed during Transmission Mode
NE= = No. of Packets Delivered (Received) by the Node
NE= = No. of Packets Sent (Transmitted) by the Node
P: = Power Consumption during Idle Mode
PK = Power Consumption during Sleep Mode
T: = Node Spend Time during Idle Mode
TK = Node Spend Time during Sleep Mode
BL = Remaining Energy Level = Initial Energy − EZ
(6)
Step 9 : Returning to the Next Possible Relay Based on Remaining Energy Level.
In the section (6.1), the algorithm elaborates that the distance of the node has been calculated as
per the mobility parameters in X-Axis & Y-Axis [9]. The mobile nodes start moving from one
place to another, if the distance variation from its previous location in more than 5m (For
Example) then the nodes starts sharing it’s information via adaptive beaconing process through
which the base station can also calculate the remaining energy level of each node and can
estimate the what range up to which it can utilized.

71
6.2. Algorithm 2: CH Election Procedure
Cluster Head Election Procedure
Step 1 : Each node calculates it waiting time, [
]
based on its residual energy
^_
`
= a1 −
E:
Ecd=
e T1. VE
(7)
Where B_ is Residual energy of iIg
node & Bhi- is the extreme initial energy of the relevant
nodes in the network, jL is a random real parameter value, which is invariably disseminated in the
interval [0.9, 1].
Step 2 : IF `k (expires)
Initiate CH election
IF (l_ not receives any ADVop)
IF (^_
`
expires)
End ADVop
End
Else (After receiving the ADVop)
l_ Receives the advertisement from lq & maintains the node ID as well as power level
Calculate Dist (l_,lq)
Set l_ non-cluster head node
End
Step 3 : Calculate Overlying Radius (l_,lq)
rs_ = [1 − α dcd= − D: ÷ dcd= − dc:F ]Rcd=
(8)
Where whi-, wh_x are maximum as well as minimum distances from the corresponding base
station.
9_ Represents the distance from the base station to the relevant node, ‘α’ is the random value [0;
1], rhi- is the maximum value of the tolerable competition radius.
Step 4 : Calculating rs_, the single maximum energy node will choose itself as a CH
node.
Cluster Formation
Step 5 : Calculate the Dist yz, yz ÷ yz| Where the CH advertisement is received.
Step 6 : Send the join cluster message to member nodes (ID, Residual energy)
Step 7 : CH formulate the node-schedule (NS) list including &yz } for its members
of the cluster.
Step 8 : After receiving the &yz } Member nodes get an idea about the time of transfer
of data to the CH node.
In the section (6.2), the cluster needs to search & verify that how many nodes exist within the
same region for which ^_
`
is being estimated to represent the time taken for the nodes spending
in the same region. Further it is required to calculate the residual energy between the error value
0.9 and true value 1. Clustering has to done as it saves the energy in wireless sensor network

72
environment & also it needs to be placed at the centre of the location [11]. Cluster Head Selection
is entirely dependent on its energy level whereas the spending time of the node in the same
location is based on its position [5].
6.3. Algorithm 3: Clustering Multi-Hop Routing
Flowchart Representation
Step 1 : Estimate the total number of cluster heads based on region (or) no. of nodes
Step 2 : Calculate the grid size of mobile nodes in corresponding region
Step 3 : For all node placed in region calculate min and max of (x, y)
Step 4 : For i = 1 represents no. of nodes in region
If min x ≤ node. x ≤ max x and
min y ≤ node. y ≤ max y , then
CH i ≪ node. ID
D: j ≪ Distance between midpoint and node location
End if
End for
Step 5: Calculate distance of the elected CH with its neighbors
9 „…, x = †{CH: x + n: x }1 + {CH: y + n: y }1‡
ˆ
:‰Š
(9)
Step 6: Choose the CH which has minimum distance to reach their neighbor
CH ← min[D CH, n ]
(10)
In this section (6.3), the total number of the clusters are been identified for the alignment of the
CH within the respective regions [27]. The CH representation may be preferred either by single-
hop routing or by multi-hop routing. After which, the grid dimensions are being estimated so as to
place all the nodes within the region of X & Y. To calculate the distance of the each node from its
cluster, which is to be elected is done by estimating the distance of each cluster with its neighbors
by taking the consideration of the node ID as well as its minimum & maximum value of the
dimensions in X & Y-Axis by evaluating the equation mentioned the section (6.3)[5].
7. RESULTS AND DISCUSSION
To estimate the performance evaluation characteristics of the XLN (Cross Layer Network) model,
extensive simulation results and its evaluations have been performed using NS-2 platform.

73
Figure 6 Energy Consumed Per Offered Load in Bits
Figure 7 Average Energy Consumed Per Number of Nodes
The proposed approach mainly focuses on the mobility awareness protocol which has been
suggested as well as preferred with the help of mobility error prediction model so that the base
station can be aware of mobility of the nodes through its adaptive beaconing signal & also assists
to know the remaining energy levels of the corresponding node which can be used for further
transmissions where the node can be selected with respect to the distance it used for
communication. Taking into consideration, the concept of Cluster Head Selection via GCCR
algorithm [5], the algorithm 2 has been proposed and thereafter the performance evaluation of this

74
proposed model has been compared with EQSR model as well as ED (Enhanced) of the cross
layer model and outperforms better results [4].
Figure 8 End-to-End Delay Per Offered Load in Bits
Figure 9 End-to-End Delay Per Number of Nodes
Energy Consumption Evaluation: The energy consumed is expressed in micro joules with
respect to the offered load (bits) representing the data that has to be transmitted in parallel which
is expressed in Figure (6). Whenever the offered load is amplified, energy consumption has also
been increased. The energy consumed is also expressed in micro joules with respect to the

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number of nodes which is shown is Figure (7). As the no. of nodes has been improved, the
average energy consumption gets reduced as they will availability of lot of mobile nodes with
different energy levels to choose the node with shortest path with high energy levels.
Figure 10 System Throughput Per Offered Load in Bits
Figure 11 System Throughput Per Simulation Time
End-to-End Delay: This attains higher-levels in the modified approach when it is compared to
offered load, as the offered load is increased then definitely it has an impact on the delay also, as

76
shown in Figure (8), Whereas when it is compared with the no. of nodes, the end-to-end delay
gets low as displayed in Figure (9).
System Throughput: This is expressed in kbps and is extremely prominent in the proposed
approach when the offered load is increased which is shown in Figure (10), and also the
throughput get increased even after comparing it with the simulation time shown in Figure (11).
The throughput has efficient data rate transmission which is positively good when compared with
the remaining conventional models.
8. CONCLUSION AND FUTURE SCOPE
A mobility error prediction & clustering multi-hop routing (CMHR) protocol have been
implemented in this paper. The XLN approach was adopted as the best layer approach which is a
combination of,
1. MAC/Data link layer whose purpose is to share the information of the energy levels, link
capacity & received signal strength values which also acts as a service provider [16].
2. Network layer whose purpose is to establish the connection between the communication
protocol like TCP (or) UDP [17].
Apart from the above two layers, transport layer also exists whose purpose is to control the
communication by FTP (or) STTP (or) CBR [28]. This concept is having its feasibility as well as
suitability for indoor applications [24]. By implementing the mobility error prediction model, the
evaluation of remaining levels has been done. Also with the help of CH election & clustering
multi-hop routing protocol, it was very feasible to choose the desired cluster head within the
region to which it actually belongs [5, 18]. AODV as well as Two-Ray Ground Model has been
preferred to provide the best results during the performance evaluation. Concluding that, it has
been surveyed that the proposed model of the hybrid algorithm which is a combination of MEP as
well as CHMHR outperforms better outputs when compared to the existing/conventional (EQSR,
ED) models [4, 29] with respect to the variables such as energy consumption, end-to-end delay &
throughput.
In the future, this method of approach can also be extended in terms of estimating other
parameters like PDR, the number of live nodes, routing load with minimal usage of the energy
consumption etc.
9. ABBREVIATIONS
The following abbreviations were used in this manuscript:
XLN Cross Layer Network
MEP Mobility Error Prediction
CMHR Cluster Multi-Hop Routing
MAC Medium Access Control
PHY Physical
N-layer Network Layer
D/L layer Data Link Layer
AODV Adhoc On-Demand Distance Vector
DSR Dynamic Source Routing

77
CSMA/CA Carrier Sense Multiple Access/Collision Avoidance
LR-WAN’s Low Rate Wide Area Networks
DSDV Destination-Sequenced Distance-Vector Routing
LLC Logic Link Control
FFD Full-Function Device
RFD Reduced-Function Device
EQSR Energy Efficiency & QoS Aware Multipath Routing
GCCR Grid Based Clustering & Combinational Routing
PDR Packet-to-Delivery Ratio
REFERENCES
[1] Marwan Al-Jemeli and Fawnizu A. Hussin, (2015) “An Energy Efficient Cross-Layer Network
Operation Model for IEEE 802.15.4-Based Mobile Wireless Sensor Networks”, IEEE Sensor Journal,
Vol. 10, No. 2, pp.684-692.
[2] Md. Khaja Mohiddin and V.B.S. Srilatha Indira Dutt, (2017) “Minimization of Energy Consumption
Using X-Layer Network Transformation Model for IEEE 802.15.4-Based MWSNs”, Proceedings of
the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications,
Advances in Intelligent Systems and Computing, Vol. 515, pp741-751.
[3] Md. Khaja Mohiddin and V.B.S. Srilatha Indira Dutt, (2017) “An Efficient Energy Optimization XLN
Operation Model for IEEE 802.15.4-Based Mobile WSNs”, International Journal of Control Theory
and Applications, Vol. 10, No. 9, pp255-264.
[4] Sudhakar Pandey and Deepika Agrawal, (2017) “An ED-Based Enhanced Energy Efficient Cross
Layer Model for Mobile Wireless Sensor Network”, National Academic Science Letter, Vol. 40, No.
6, pp421-427.
[5] K. Lalitha, R. Thangarajan, Siba K. Udgata, C. Poongodi and Ambika Prasad Sahu, (2017) “GCCR:
An Efficient Grid Based Clustering and Combinational Routing in Wireless Sensor Networks”,
Wireless Personal Communication, Vol. 97, pp1075-1095.
[6] Kartinah Zen, Daryoush Habibi, Alexander Rassau and Iftekhar Ahmad, (2008) “Performance
Evaluation of IEEE 802.15.4 for Mobile Sensor Networks”, 5th IFIP International Conference on
Wireless and Optical Communications Networks, pp1-5.
[7] Xinbing Wang, Xiaojun Lin, Qingsi Wang and Wentao Luan, (2013) “Mobility Increases the
Connectivity of Wireless Networks”, IEEE/ACM transactions on networking, Vol. 21, No. 2.
[8] SamerA. B.Awwad, CheeKyun Ng, NorK.Noordin and Mohd. Fadlee A. Rasid, (2010) “Cluster
Based Routing Protocol for Mobile Nodes in Wireless Sensor Network”, Wireless Personal
Communications, Vol. 61, pp251-281.
[9] Tao Yang, Tetsuya Oda, Leonard Barolli, Jiro Iwashige, Arjan Durresi and Fatos Xhafa, (2012)
“Investigation of Packet Loss in Mobile WSNs for AODV Protocol and Different Radio Models”,
26th IEEE International Conference on Advanced Information Networking and Applications, pp709-
715.
[10] Tommaso Melodia, Dario Pompili and Ian F. Akyildiz, (2010) “Handling Mobility in Wireless Sensor
and Actor Networks”, IEEE Transactions on Mobile Computing, Vol. 9, No. 2, pp160-173.

78
[11] Woo-Yong Lee, Kyeong Hur and Doo-Seop Eom, (2008) “Navigation of Mobile Node in Wireless
Sensor Networks without Localization”, Proceedings of IEEE International Conference on Multi-
sensor Fusion and Integration for Intelligent Systems, pp1-7.
[12] Shibo He, Jiming Chen, David K.Y. Yau and Youxian Sun, (2012) “Cross-Layer Optimization of
Correlated Data Gathering in Wireless Sensor Networks”, IEEE Transactions on Mobile Computing,
Vol. 11, No. 11, pp1678-1691.
[13] Mehmet C. Vuran and Ian F. Akyildiz, (2010) “XLP: A Cross-Layer Protocol for Efficient
Communication in Wireless Sensor Networks”, IEEE Transactions on Mobile Computing, Vol. 9, No.
11, pp1578-1591.
[14] Jianping Wang, Deying Li, Guoliang Xing and Hongwei Du, (2010) “Cross- Layer Sleep Scheduling
Design in Service-Oriented Wireless Sensor Networks”, IEEE Transactions on Mobile Computing,
Vol. 9, No. 11, pp1622-1633.
[15] Emad Felemban, Serdar Vural, Robert Murawski, Eylem Ekici, Kangwoo Lee, Youngbag Moon and
Sangjoon Park, (2010) “SAMAC: A Cross-Layer Communication Protocol for Sensor Networks with
Sectored Antennas”, IEEE Transactions on Mobile Computing, Vol. 9, No. 8, pp1072-1088.
[16] Pangun Park, Carlo Fischione, Alvise Bonivento, Karl Henrik Johansson and Alberto L. Sangiovanni-
Vincentelli, (2011) “Breath: An Adaptive Protocol for Industrial Control Applications Using Wireless
Sensor Networks”, IEEE Transactions on Mobile Computing, Vol. 10, No. 6, pp821-838.
[17] Liqi Shi and Abraham O. Fapojuwo, (2010) “TDMA Scheduling with Optimized Energy Efficiency
and Minimum Delay in Clustered Wireless Sensor Networks”, IEEE Transactions on Mobile
Computing, Vol. 9, No. 7, pp927-940.
[18] Hsueh-Wen Tseng, Shan-Chi Yang, Ping-Cheng Yeh and Ai-Chun Pang, (2011) “A Cross-Layer
Scheme for Solving Hidden Device Problem in IEEE 802.15.4 Wireless Sensor Networks”, IEEE
Sensor Journal, Vol. 11, No. 2, pp493-504.
[19] F. Yu, S. Park, E. Lee and S.-H. Kim, (2009) “Elastic routing: a novel geographic routing for mobile
sinks in wireless sensor networks”, IET Communications, Vol.4, No.6. pp716-727.
[20] Yuan-Yao Shih, Wei-Ho Chung, Pi-Cheng Hsiu and Ai-Chun Pang, (2013) “A Mobility-Aware Node
Deployment and Tree Construction Framework for ZigBee Wireless Networks”, IEEE Transactions
on Vehicular Technology, Vol. 62, No. 6, pp2763-2779.
[21] S. Deng, J. Li and L. Shen, (2011) “Mobility-Based Clustering Protocol for Wireless Sensor
Networks with Mobile Nodes”, IET Wireless Sensor Systems, Vol. 1, No. 1, pp39-47.
[22] L. Karim and N. Nasser, (2012) “Reliable location-aware routing protocol for mobile wireless sensor
network”, IET Communications, Vol. 6, No. 14, pp2149-2158.
[23] Bashir Yahya and Jalel Ben-Othman, (2009) “An Energy Efficient and QoS Aware Multipath Routing
Protocol for Wireless Sensor Networks”, IEEE 34th Conference on Local Computer Networks.
[24] Debnath Bhattacharyya, Tai-hoon Kim and Subhajit Pal, (2010) “A Comparative Study of Wireless
Sensor Networks and their Routing Protocols”, Sensors, pp10506-10523.
[25] Subhajit Pal, Debnath Bhattacharyya, Geetam S. Tomar and Taihoon Kim, (2010) “Wireless Sensor
Networks and its Routing Protocols: A Comparative Study”, International Conference on
Computational Intelligence and Communication Networks, pp314-319.
[26] Dilip D. Chaudhary and Dr. L. M. Waghmare, (2013) “Energy Efficiency and Latency Improving
Protocol for Wireless Sensor Networks”, International Conference on Advances in Computing,
Communications and Informatics (ICACCI), pp1303-1308.

79
[27] Giuseppe Anastasi, Marco Conti and Mario Di Francesco, (2011) “A Comprehensive Analysis of the
MAC Unreliability Problem in IEEE 802.15.4 Wireless Sensor Networks”, IEEE Transactions on
Industrial Informatics, Vol. 7, No. 1.
[28] Martin Enzinger, (2012) “Energy-Efficient Communication in Wireless Sensor Networks”, Network
Architectures and Services, pp25-31.
[29] Jun Yu and Xueying Zhang, (2015) “A Cross-Layer Wireless Sensor Network Energy-Efficient
Communication Protocol for Real-Time Monitoring of the Long-Distance Electric Transmission
Lines”, Journal of Sensors, pp1-13.
[30] Mounib Khanafer, Mouhcine Guennoun, and Hussein T. Mouftah, (2011) “A Survey of Beacon-
Enabled IEEE 802.15.4 MAC Protocols in Wireless Sensor Networks”, IEEE Communications
Surveys & Tutorials, Vol. 16, No. 2, pp856-876.
[31] Asma Mesmoudi, Mohammed Feham and Nabila Labraoui, (2013) “Wireless Sensor Networks
Localization Algorithms: A Comprehensive Survey”, International Journal of Computer Networks &
Communications (IJCNC), Vol. 5, No. 6, pp45-64.
[32] Haithem Ben Chikha, Amira Makhlouf and Wiem Ghazel, (2000) “Performance Analysis of AODV
and DSR Routing Protocols for IEEE 802.15.4/Zig-Bee”.
[33] Wu Youjun and Nie Jingnan, (2000) “Performance Evaluation for Cross-Layer AODV Protocol in
CDMA-Based Ad-Hoc Networks”, Funded by National Natural Science Foundation of China
(60503049) and by the open research fund of National Mobile Communications Research Laboratory,
Southeast University (N200602).
[34] Mario Di Francesco, Giuseppe Anastasi, Marco Conti, Sajal K. Das, and Vincenzo Neri, (2011)
“Reliability and Energy-Efficiency in IEEE 802.15.4/ZigBee Sensor Networks: An Adaptive and
Cross-Layer Approach”, IEEE Journal on Selected Areas in Communications, Vol. 29, No. 8,
pp1508-1524.
[35] Kiran Maraiya, Kamal Kant and Nitin Gupta, (2011) “Application based Study on Wireless Sensor
Network”, International Journal of Computer Applications, Vol. 21, No. 8, pp9-15.
[36] Marco Cattani, S¸ tefan Guna and Gian Pietro Picco, (2011) “Group Monitoring in Mobile Wireless
Sensor Networks”, IEEE.
[37] Chih-Min Chao and Hsin-Hsiang Wang, (2004) “The Bottleneck Problem in Large Scale IEEE
802.15.4/ZigBee Networks”, International Journal of Signal and Imaging Systems Engineering, pp1-
17.
[38] Norrozila Sulaiman, Ghaidaa M. Abdulsahib, Osamah I. Khalaf and Muamer N. Mohammed, (2014)
“Effect of Using Different Propagation’s on Performance of OLSR and DSDV Routing Protocols”,
5th International Conference on Intelligent Systems, Modeling and Simulation, pp540-545.
[39] Divya Upadhyay, P. Banerjee and A.L.N Rao, (2014) “Critical Performance Comparison of On-
Demand Routing Protocols for Optimal Application in Wireless Sensor Network”, 5th International
Conference- Confluence The Next Generation Information Technology Summit, pp462-466.
[40] Francesca Cuomo, Anna Abbagnale and Emanuele Cipollone, (2011) “Crosslayer network formation
for energy-efficient IEEE 802.15.4/ZigBee Wireless Sensor Networks”, Ad Hoc Networks (Elsevier),
pp1-15.

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AUTHORS
Md. Khaja Mohiddin received his B. Tech Degree in Electronics and
Communication Engineering from Al-Ameer College of Engineering & Information
Technology affiliated to JNTU, Andhra Pradesh, India, in 2009, M.Tech. Degree in
Digital Electronics & Communication Systems from Chaitanya Engineering College
affiliated to JNTU, Andhra Pradesh, India, in 2012. He is currently pursuing the
Ph.D. in Wireless Sensor Network from GITAM University, Visakhapatnam, Andhra
Pradesh, India. He is currently working as an Assistant Professor in the Department
of Electronics and Telecommunication Engineering, Bhilai Institute of Technology,
Raipur, (C.G.).
V. B. S. Srilatha Indira Dutt received her B. Tech. Degree in Electronics and
Communication Engineering from Nagarjuna University, Andhra Pradesh, India, in
1994, M. Tech. Degree in Radar and Microwave Engineering from Andhra
University, Andhra Pradesh, India, in 2007. She was awarded with Ph.D. degree in
Global Positioning System from the Andhra University, Andhra Pradesh, India, in
2011. She is currently working as an Associate Professor in the Department of
Electronics and Communication Engineering, GITAM Institute of Technology,
GITAM University (GITAM), Visakhapatnam, Andhra Pradesh, India. Her research interests include
Global Positioning System, Satellite Signal processing and Mobile Communications. She published more
than 40 research papers in referred international journals, and international and national conferences.

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