Efficient addressing schemes for internet of things

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 12, No. 4, August 2022, pp. 4415~4429
ISSN: 2088-8708, DOI: 10.11591/ijece.v12i4.pp4415-4429  4415
Journal homepage: http://ijece.iaescore.com
Efficient addressing schemes for internet of things
Venkatesh Thamarai Kannan, Rekha Chakravarthi
Department of Electronics and Communication Engineering, Sathyabama Institute of Science and Technology, Chennai, India
Article Info ABSTRACT
Article history:
Received Nov 9, 2020
Revised Mar 30, 2022
Accepted Apr 12, 2022
The internet of things (IoT) defines the connectivity of physical devices to
provide the machine to machine communication. This communication is
achieved through various wireless standards for sensor node connectivity.
The IoT calls from the formation of various wireless sensor nodes (WSNs)
in a network. The existing neighborhood discovery method had the
disadvantage of time complexity to calculate the cluster distance. Our
proposed method rectifies this issue and gives accurate execution time. This
paper proposed mobility management system based on proxy mobile IPv6 as
distributed PMIPv6 with constrained application protocol (CoAP-DPMIP)
and PMIPv6 with constrained application protocol (CoAP-PMIP). It also
provides the optimized transmission path to reduce the delay handover in
IoT network. The PMIPv6 described the IPv6 address of mobile sensor
device for efficient mobility management. The network architecture explains
three protocol layers of open systems interconnection model (OSI model).
The OSI layers are data link layer, network layer and transport layer. We
have proposed the distance estimation algorithm for efficient data frames
transmission. This paper mainly focuses the secure data transmission with
minimum loss of error. The evaluation result proved that proposed technique
performance with delay, energy, throughput and packet delivery ratio (PDR).
Also, it measures the computational time very effectively.
Keywords:
Internet of things
Internet protocol version 6
addressing
OSI protocol
Wireless sensor-network
This is an open access article under the CC BY-SA license.
Corresponding Author:
Venkatesh Thamarai Kannan
Department of Electrical and Computer Engineering, Sathyabama Institute of Science and Technology
Chennai, India
Email: venkateshphd15@gmail.com
1. INTRODUCTION
Wireless sensor network (WSN) plays as the key entity for accessing internet of things (IoT). The
more devices within IoT network, and their access to internet, have faced complexities in integration and
establishing connection between those devices [1]. WSN comprised of the spatial distributed autonomous
device by utilizing the sensors to the environmental conditions. Wireless sensor network has limited
properties in computation power, memory, energy, and bandwidth. It is the small physical size that can be
embedded in the physical environment. Mainly, the sensing devices can communicate the data wirelessly.
From the wireless network system that incorporates the gateway which affords the wireless connectivity. The
WSN generally contains ten to thousands of nodes, which communicate by wireless channels for the sharing
of information. This sensor network is the network of multiple tiny disposable minimum power of devices
are called nodes. From WSN is expected to integrate into the internet of things (IoT), also the internet
performs dynamically whether joining the sensor nodes and utilize to work together and complete their tasks.
IoT is the physical object network or things, which can interact with each other to share the information. But,
the WSN becomes a part of the internet while analyzing and investigating the difficulties involved by this
integration. Choi and Koh [2] studied and proposed the dual mobility management system depends on the

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Int J Elec & Comp Eng, Vol. 12, No. 4, August 2022: 4415-4429
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proxy mobile IPv6 (PMIPv6) as distributed PMIPv6 with constrained application protocol (CoAP-DPMIP)
and PMIPv6 with constrained application protocol (CoAP-PMIP). The CoAP-DPMIP plays the role of local
mobility anchor (LMA) which was disseminated to every mobile access gateways (MAG) [2]. Therefore, this
system could be providing an enhanced transmission path and reduced the delay handover. The LMA, and
CoAP-PMIP were utilized to deliver the network based mobility supports sensor devices. Every device is
registered with IPv6 address, LMA. The data transmission was done by using LMA. The proposed schemes
of PMIPv6 based CoAP was used for effective mobility management and described the address of IPv6 of
mobile sensor device which are followed as hashed unconventional prefoldin RPB5 interactor (URI) of the
sensor device and the prefix of MAG. Another related study [3] discussed the internet protocol version6 with
minimum power of wireless and personal area network (PAN) for the IoT. It analyzed and developed for the
resource constrained of internet protocol (IP) based on the systems. The number of devices was increased and
the number of traffic would develop together by data. IoT would have multiple numbers of smaller devices
with sensitive and they are profitable to put the essential constraint in the total system of bandwidth. The
internet protocol (IP) are the communication protocols on transmission control protocol/internet protocol
(TCP/IP) suite and that the normal usage of different function likes packet structure, routing, and delivering
packets. However, IPv6 had the latest version of the IP melodramatically had to increase the number of
addresses. The proposed method of link-layer was connected and sorts of different links. The feature key was
required through the link layer and to support the IP as length indication, addressing, error checking, framing
and features of broadcasting and uni-casting. The features of IPv6 low power wireless personal area network
(6LoWPAN) are followed to support the 16-bit or 64-bit addressing was provided. The proposed method was
increased security support from the wireless network. Therefore, this security methodologies had easy to
spoof the nodes or packets and easy to eavesdrop.
Lastly, IP are utilized to modify, fine-tune, and change to enable the era of IoT. Also, the proposed
method had heterogeneous features like targeting at minimum power protocol and user datagram protocol
(UDP) headers, neighbor discovery and network auto-configuration, supporting the concept of fragmentation.
The IoT connection framework is evaluated for their performance through assessing all the factors including
mobility-energy, usage of network, execution-time, node energy, network delay and the delay by IoT-node
[4]. There are some studies implemented the IoT mobility management challenges on cluster binding and
updating the method based on PMIPv6 [5]. It made the better utilization of the group character on IoT and
specifically extended the PMIPv6. It introduced the group identifier to maintain the group binding and
reduced the signaling cost and also described the full operation flow in detail. The PMIPv6 is based on group
binding update methodology had designed to optimization of binding operation and updated for the
operations on mobility sessions to identify the group.
In similar to that study, analysis [6] contributed the generic techniques on mapping the pub-sub
middleware to the IPv6 multicast. To decrease the network overhead, and then the subsequent of the
multicast method was embedded in the concrete models as low flexible but it consists of lightweight of the
implicit multicast model. Also, versatile are the costlier explicit multicast model. The estimations are
displayed the proposed framework to improve the scalability. It reduced all over network traffic and achieved
the best energy efficacy. Here, the bandwidth consumption had the multicast sensor which was reduced by up
to 54% for the 10 subscribers and 66% for the next 20 subscribes. These obtained the benefits by the limited
memory in footprint and corresponded to the extra usage of total flash memory as 4.7% also, the total random
access memory (RAM) as 1.3%. Lastly, it provided the techniques based on the bath beneficial and
feasibility of IoT things. In future, the encoding scheme and derivation function for the investigation of
implicit model. That left the enough bits and the function or unused bits being utilized to support the
dissimilar quality of services (QoS) levels. The concepts and features, evolved [7] IoT which had the
technology would change daily life. It was easier to utilize and develop the needs of its users. However,
discussed the simple definition for the IoT, and it gave the detail view of WSN based on the IoT. Besides, the
6LoWPAN were introduced the solutions for sensor networks by the least power to connect with the
network. IoT was gathered the various domains and innovative ideas. The use case of management of
constrained network and devices (COMAN) were characterized by the network management to monitor the
network parameters, network status, detecting faults, maintain normal operation of a network. Such
applications defined for the constrained network management as medical applications, automated metering
infrastructure (AMI), environmental monitoring, and infrastructure monitoring. Here, the management
protocol was used to connect the huge number of related devices by IoT and it provided better quality to the
consumer then reactive the network face to change. Then, we viewed the complexity of the network from the
Internet of Things that increased day today and took into account the devices that are connected to the
network. Their management becomes a significant need for two-management protocol they are, sensory
neuron membrane protein (SNMP) and last normal membrane protein (LNMP). This would allow the easy
way of deployment and more comfortable usage for the end-user by the best quality service. The following

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Efficient addressing schemes for internet of things (Venkatesh Thamarai Kannan)
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difficulties are identified in the existing methodologies: i) the selection of cluster is very difficult in that time
of new data enclosed and ii) the distance measurement of cluster error data cannot give the execution time
accurately. The small data can easily cluster and identify the error but the huge amount of data has such
difficulties to cluster and detect the error to overwhelm the issues this paper proposed as: i) to cluster the big
data by applying such novelties, ii) to reduce the cluster error from the selection of data by using a distance
estimation algorithm, and iii) to measure the computational time as effectively.
The mobility analysis and network assessment [8] surveyed the advanced relate by the mobility and
routing in the 6LoWPAN networks. Currently, the scientific and industrial community starts the multiple
misconceptions around the usage of internet protocol in all the proposed nodes. The proposed techniques
might be a convergence solution to link the dissimilar link layer protocols and physical layer protocols were
enabled to connect the proposed devices to the network. The wireless mesh had support to consider the
best-fit routing protocol for the LoWPAN. Because of the capability to self-configured and self-organized.
Router-over and mesh-under techniques could be utilized to support the mesh routing on the proposed
networks. From this, it specified both advantages and disadvantages. The normal routing protocols utilized
on the route over techniques were adapted from the mobile ad hoc network (MANET) protocols. The design
of the method that addresses the mobility requirements and simultaneous routing is crucial to attaining the
IoT vision. The existing IoT mobility management system [9] offered the advanced protocol of glowbal IP
for the extension of devices that were previously deployed and inherit technologies to IPv6. It permits a user
to generate the network layer homogeneity and global communications over IPv6. Also, the glowbal IP
resolves the deficiency of optimization from 6LoWPAN with esteem to the global communications that
required from 26 to 41 bytes for the proposed of 6LoWPAN header. While the access address identifier
(AAID) was required an only 5-byte header and the identification of the session was carried out by the
method of AAID. Then the investigation had shown how to achieve the network-based data systems via
recent domain name system (DNS) positioning, with the multicast DNS and DNS service directory. This
allowed a system to define the data in a network. The devices such as model, family, web services, and
features were offered to interrelate with it. Lastly, this had estimated in a multiprotocol card, and this system
signifies the least delay and mapping tasks to define the global IP as an appropriate technology to be
positioned. Transmit packet coding (TPC) is presented in vehicular ad-hoc network (VANET) framework to
check out efficient multi-casting and reliability. The network-devices is utilized for minimizing the
transmitted packets count [10].
In the scenario of cluster based WSN, the cluster heads would collect and fuse those data-packets
obtained from sensor-nodes. The packets from fused data-packets were forwarded to SN-Sink node. This
process aids sensor-network to do energy balance, and prolongs their life-time effectively. But still these
sensor-nodes in WSN may subject to certain intrusions or disconnection of sensor signals in-between. The
time taken to establish the transmission path for these data in IoT network may be longer corresponding to
their location. There occurred considerable delay in handing over the data in IoT-networks. In cluster
distance calculation in IoT sensor nodes, it takes prolonged time to measure this sensor nodes detection for
establishing the clear transmission path for delay handover within IoT network. By using existing methods
neighborhood discovery method in mobility management system in IoT sensor networks, this time
complexity could not be solved. Hence it is necessary to overcome this issue and ought to aid in execution
time calculation accurately. The study implemented mobility management system on the basis on Proxy
mobile IPv6 as CoAP-DPMIP and CoAP-PMIP. This framework generated the optimized path for data
transmission to minimize the delay handover within IoT network. The PMIPv6 elaborated IPv6 address of
this mobile sensor-device for effective mobility-management. The architecture of network enumerated open
systems interconnection (OSI) protocol layers. These OSI layers are data-link layer, network layer and
transport layer. This mobility management operation is explored through distance estimation algorithm for
transmission of data frames effectively. As a whole, the study focusses on secure transmission of data with
minimum error loss in data. The outcomes of this evaluation evidenced that this proposed-framework showed
better results with respect to throughput, packet delivery ratio (PDR) and delay energy. The computational
time is also determined through this evaluation effectively. The results of this evaluation proved that our
proposed framework in distance estimation algorithm attained lower time-consumption. The analysis in their
performance that framework exhibited better throughput rate, packet delivery ratio, and energy value and
lesser delay time.
The major contributions of the paper are stated: i) this proposed-framework in this study of IPv6
based hierarchical machine to machine (M2M) network using CoAP-DPMIP and CoAP-PMIP is capable to
provide extensive-spacing in reaching out unique-address globally, ii) this mobility-management system
acquired IP address automatically and in cluster distance calculation, without the interventions of human
demands for dynamic host-configuration protocol (DHCP) servers. The remaining portion of this paper is
schematized: section 2 demonstrates the conventional mechanism associated with the several clustering
techniques in big data analytics by such datasets. Section 3 provides the proposed distance estimation

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algorithm for measuring the distance as effectively and accurately. Section 4 exhibits the performance study
of the projected mechanism and in conclusion, section 5 concludes the proposed work.
2. RELATED WORKS
This section describes the literature review of the clustering efficiency in big data algorithms. For
the demands in IoT framework, a study [11] recommended the techniques for the analysis and extraction of
networking evidence was utilized in the proposed method of 6LoWPAN. In this work, they developed the
series of complementary devices that were included as facilitates and automates analysis. Thus, it allowed the
investigation to focus on the task, which required the domain expertise. However, these devices are fast,
reliable and produced fewer false positives. Also, it demonstrated to reconstruct the network layers of two
and tree topologies. It correlated the information to extract the subset of nodes which formed the network.
Moreover, it aimed to study the requirements that are essential to satisfy before reconstruction of the
complete topology. In this proposed work, hundreds of devices were deployed largely and also had potential
to attain better results on the selected nodes. In combination, the traffic is analyzed by techniques that used
multiple-image correlation for congestion analysis to detect the particular attack patterns. In upcoming work,
it is aimed to extend the device that supports the analysis of RAM dumps took from devices. It also,
supported the anorectal malformation (ARM) and MSP430 cortex-based devices.
This IoT sensor framework enabled efficient connection of application devices and in their tasks
coordination. These applications are employed in various fields including automation process in industries,
smart home application and in health-care implementation. For these purposes, various protocols is applied in
providing efficient device or mobile communication for resource-limited IoT devices [12].
Similarly in data transmission analysis [13] implemented in the semantic and unified data model by
the ontology in accessing and data storage. Novel usage of IoT technology application in the real time mobile
environment provided the results as: i) in data, intensive provided platform used to access the large data
scales, ii) then the methodological were demonstrated the different IoT data could access ubiquitously,
and iii) the usage of the proposed model of ubiquitous data accessing method in IoT (UDA-IoT) has
encapsulated the unified format of resources.
Hence in overcoming these type of issues one of the study [14] implemented the novel framework
depends on the RFC-5949 that is known as fast handovers for PMIPv6. Though, the mobility system for the
sensor nodes depends on the mobility of network PMIPv6 was efficient. But, it reduced the packet loss by the
help of the proposed method and the data packets were buffered at the nMAG which were delivered to the
mobile node (MN)/mobile router (MR)/mobile host (MH). After the handoff (HO) process completion, with
minimum loss or without loss, the HO signaling message was exhibited both of the proposed PMIPv6 and
sensor fast proxy mobile IPv6 (SFPMIPv6) mobility protocol. These are very much clear in which the
proposed SFPMIPv6 minimized the termination or connection interruption. Also, the packets were buffered
at the new mobile access gateway (nMAG) or previous mobility access gateway (pMAG), so there is less
packet loss. Finally, the proposed method of SFPMIPV6 would improve the performance as reducing the
packet loss to the larger extension. So, the proposed protocol reduced the signal costing and mobility cost
when compared with PMIPv6 and MIPv6.
One of another implementation that aids in providing efficient and secure smart-home gateway is
achieved through Raspberry Pi-hardware in IoT framework [15]. Another study presented the concept of
identifier and also, proposed the effective and novel (CCHA) context aware of clustering in hierarchical
Addressing for nomadic things in IoT and clustering of ubiquitous things to accomplish the robustness, and
scalability. In the proposed method of CCHA, the context was integrated by hierarchical and clustering
addressing for the identifier format. The experimental results are shown how the proposed method was
beneficial to create dissimilar namespace and outcomes in terms of energy, throughput, and end-to-end delay
into the better performance of the network. Then the comparison of proposed method, with the existing
method as for better results. So, the proposed techniques of CCHA are less prone to fail with the possibility
of make the right choice as CCHA in IoT and nomadic networks. In future research, it would be extended to
addressing scheme and identifier format to ensure security. Likewise, the monitoring system for home
security purposes, is explored through Raspberry Pi-system. Various sensors are interconnected in the model.
Telegram media is utilized to transfer notification from these sensors to tool-users [16].
Various techniques evolved for implementing the electrocardiogram (ECG) wireless wearable
monitoring system in the IoT platform to integrate the different applications and nodes [17]. It provided the
battery life time, and maximum quality of ECG signal. The system was allowed the monitoring multi patients
on the reasonably as indoor area. The ECG sensor had exhibited the low record of energy per effective
number of quantized levels (EEQNL) of solution and merit with available integrates and discrete frontends.
Certainly, the proposed ECG based sensor had the high performance of analog to digital converter (ADC),

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and microprocessor radio combo. It provided the best performance in name of noise and power consumption
than the multiple proposed systems. The other proposed remarkable system are low marginal to the sensor. It
enabled a single low-cost gateway to achieve multiple sensors. The disadvantage of the proposed techniques
is related to the sensors, correlation, and transducer techniques to which improves strength and reliability.
The protocol analysis [18] elucidated the challenges of relating the protocol of TCP and IP to the IoT
networks. Which ascend from the transport layer and network layer. Also, we discussed how the application
layer protocols such as CoAP delivered the own solutions to desire the functionalities of lower layer failed to
support. The disparity was made on the extra evident that compared to the recent IoT stack. To desire the
architecture from the application of view point, first developed the TCP/IP protocol stack via wired
connectivity. The protocol stack was evolved using the IP specification and fundamental assumption to
design the architecture. The proposed techniques of architecture changed to move the representational state
transfer (REST) linked components and into the eventually arrived with the core network layer. It had the
more efficacies to the application layer solutions. A novel IoT stack would use in the information-centric
network (ICN) that was implemented and designed the required functionalities for more efficacy of the
network. The lightweight mutual-authentication protocol is applied on the basis of CoAP-constrained
application-protocol , employed for IoT-devices for secure level of data-transmission [19].
For this secure data transmission [20] developed an algorithm on ECG signal of classification,
implementation, and analysis of embedded platform in IoT. This proposed algorithm was suitable for the
proposal of an ECG diagnosis device that had the maximum amount of time monitoring on the patient. The
usage of discrete wavelet transform (DWT) for the analysis of ECG and also used the classifier of support
vector machine (SVM). The application of implementation was read by the ECG signal and analysis flow as
heartbeat detection, filtering, heartbeat segmentation, feature extraction, and classification was performed.
This analyzed the overall framework of the ECG signal to select the optimal, computation demand, and
configurations from the design space exploration (DSE). This DSE was performed all combination of DWT
coefficients. It reduced the exploration time but calculated at once from the database of heartbeat detection.
The main goal of the classifier is to produce the maximized computational cost and maximized accuracy.
This implementation was achieved by the embedded with the IoT platform. It is convert the code and
designed as the chip like low power consumption, and small core products. Therefore, the better classifier
had achieved the accuracy as 98.9% and then the execution time and total average time for the optimal
configurations best of the output were detected. In the real-time process, the feature vector size on 18 and
support vectors of 2493.
Similarly another study [21] presented to demonstrate the cloud and IoT infrastructure could simply
be organized to utilize the one lab federation of test beds. The IoT context was developed by the novel
hardware device’s novel communication protocols and the novel method was used to analyze and collect the
data. The challenges of IoT to address with a federation of test beds which offered to access the virtual
machines, FIT cloud, virtual wall, wired servers, and cloud with Planet Lab. From the federation of a test,
beds were provided to the FIT wireless, W-I Lab. Wireless test beds. Next, the FIT IoT Lab was very large
with physical devices to deploy the FIT equipex and then the novel IoT technologies rely on the dissimilar
hardware devices when monitor by the energy consumption of the same time. One lab federation of the test
beds provided to the users on a single point to access the web portal. These experimentations could automate
by utilizing the scripts and to provide the different setup as the service. This extension leads to enable the
results and to create the applications on the top of service. The multiple applications could be detected that
the robots or humans present in the room mean proposed nodes of FIT IoT Lab were deployed. It would be
used for the light-sensing and temperature in the context of smart building applications.
This type of sensor network communication [22] introduced the standard and background to
supports the IPv6 based hierarchical machine-to-machine communications. The challenges were investigated
to the enormous IPv6 access, based on the hierarchical machine to a machine communication network by the
different IoT applications. The main benefits of the proposed approach of IPv6 based hierarchical M2M
network are: i) able to afford the extensive spacing of globally reached a unique address, ii) automatically
acquired the IP address without the human needs for the DHCP servers, and iii) it is not required by the low
energy consumption and low cost on the M2M devices. In the end, the systematic distributed access control
framework was used for improved the network, achieved fairness, and deal with the dynamic network. Also,
the potential algorithms and optimal control was developed for the proposed control framework. The results
of the evaluation were exhibited the essential performance in terms of application differentiation and utility
maximization.
The IoT devices infrastructure [23] explicated the present novel techniques of frequently changed
IPv6 address to add the extra layer on the security purpose in the IoT. A protocol was utilized in the wireless
sensors that founded as smart meters and home automation systems. The proposed techniques of 6LoWPAN
were allowed the IoT to extend of WSN. The field of the proposed system was included the security against
fragmentation attacks and the encryption congestion on the IPsec. To launch the malicious fragment, that is

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increased the availability of moving target IPv6 defense (MT6D) tunnels congestion by integrity and
confidentiality.
The network framework [24] For healthcare system environments this paper expounded a moderate
IoT depends on the 6LoWPAN. To recover the effectiveness, costs, and overall quality in healthcare,
proposed IoT based architecture was used. To start from the gathered bio-signals that are integrated with the
medical sensor nodes of 6LoWPANby analog based front-end devices. Lastly, the contextual data and
present health were saved in a cloud server for the end-users. In the network, it also included the tunneling
gateway based on packets routing from the sensor nodes to the. Thus, it also examined the power
consumption and architecture for dissimilar scenarios. Therefore, verify the proposed architecture was
suitable for the applications by the streaming data in health protection environments. In the coming work, the
additional layer would be added to the proposed technique stack to improve security and efficacy. Also, the
data compression and data filtering algorithms would apply on the node level to protect the battery power and
network bandwidth.
The mapping phenomena [25] expounded a mapping among the every technology of native
addressing and IPv6 address to follow the group of rules. Exactly, this presented a technology on IPv6
addressing proxy in that draws every device to the dissimilar sub networks. It creates the prefix addresses of
IPv6 and afforded by the ISP for every user, building, and home. The IPv6 address provides a general
addressing scheme depends on IPv6 to overall devices, with regards to device technology. Thus, this deals
with a homogeneous and scalable solution to interrelate by devices that do not support the IPv6 addressing.
The IPv6 address proxy had developed in multi-protocol card and calculated successfully in their
interoperability, scalability, and performance by the protocol was created over IPv6. The benefits of mapping
legacy-addressing spaces to IPv6 as, mapping to IPv6 permits the hiding of the particular aspects of the
technologies that were utilized with the sensor, to offer a structure, which works on independent of the
technology. The features of IoT, their concepts, protection mechanism and IoT applications with authorized
microbial electrolysis cells (MEC) drives globally [26]. Likewise, IoT analysis [27] illustrated an innovative
internet stack by the group of adaptation layers starting from the non-internet protocol to the IPv6 depends on
the network layer. To work the different access for the services and applications.
3. PROPOSED WORK
This section illuminates the proposed distance estimation algorithm for measuring the distance as
effectively and accurately. There are various sensor nodes are presented in network, and some of the nodes
have an identifier such us media access control (MAC) and some of the nodes do not have the identifier. In
network, to achieve the perfect data transmission, every node in network should have a unique identifier. So,
the distributed address allocation schemes are ensured with continuous running of self-adaptive network. An
addressing scheme is used for creating communication between the network devices. Using the addressing
scheme, information is forwarded from one location to another. Figure 1 defines the representation of
network architecture with various protocols.
Figure 1. Network architecture
3.1. Data link layer (layer 2)
In OSI layer, the data link layer transmits the message or data frames from the network layer. It
sends the data frames between the two devices without any traffic and error using the physical layer, so it is
called an error-free delivery layer. It also includes error detection information as data packaging from the

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network layer. In receiver section the data link layer received the input data frame and it sends it to the next
network layer. Based on received data frames, this layer generates the error based information. After
receiving the overall data frames, these frames are compared to its error detection value with input of data
frames. If the frames are matched then they received correctly. The error control protocol is used to provide
the acknowledgment to the sender that is positive or negative. The positive acknowledgment defines the
successful frame received without any errors. And negative acknowledgment defines the error in data frames.
The data link layer is described with MAC. MAC uses the source machine for sending the Ethernet
frame to the device which is going to involve the ‘routing’ process of the IP data frame. A 6-byte with 48 bit
field called as MAC address and it is also represented by a 6-field of hexadecimal number that is 89-A1-32-
2B-C3-85. Two bytes long in each field. Each incoming and out coming data frame of the networking device
has the connection with link interface, and each interface had a unique MAC address. Generally, there are no
two interfaces shares the same MAC addresses. A header of the data link-layer contains both a source
interface and a destination interface of MAC addresses.
3.2. Network layer (layer 3) addressing
The data link layer works as the service provider for the network layer and it provides end-to-end
communication. This layer transfers the network packets from source IP to destination IP through various
networks. A 4-byte with 32-bit field called IP address and that is represented by a 4-field dot-separated
number that is represented by 192.2.32.84. Each field have one byte long in address. Each data in a network
must have an IP address and it should to be identified the data frame communication. A network layer header
contains both source node IP addresses and destination node IP addresses. The length of the network packet
must be included with network layer header.
3.3. Transport layer (layer 4)
The transport layer also develops the end to end communication and the services are provided by a
transport protocol. In the header, each data frame contains the source and destination port address. In this
layer, A 2-byte with 16-bit field called as port number, which is represented by 4,892. In the communication
the port number identifies the two end hosts. Any host can run on various network applications in same time
and every application requires to identifying by another host communication to a targeted application.
The destination port number defines the destination server based on their requested service. It
mainly depends on the delivery. The source port number is randomly generated by the sending device for
identifying the conversation between the devices. Therefore the identifiers are used to uniquely identify an
object. For a representation of object, the identifiers must be uniqueness.
In the proposed algorithm, first, initiate the neighbor discovery process. If a source sends the data,
then set the clock time. Second, initiate the route discovery procedure. Select the intermediate nodes based on
the clustering distance. Then source selects a route (RTS) to destination from its cache. If the route does not
have the destination, then again starts the route discovery procedure. If the route contains the nodes belongs
to the primary suspect list, the cluster near nodes are switched ON in the listening node. Then calculate
particle swarm optimization (PSO) metrics for each node and each path to make the routing table. At the end,
it sends the data packet with a secure key.
3.4. Internet protocol version 6 addressing (IPv6)
The IPv6 protocol can support the large number of connected devices that is expected to be part of
the IoT. It potentially supports 2^128 of unique IP addresses. It can provide unique addresses to each node.
The intelligent resolution system used in IoT-A and it allows avoiding subtleties such as meaningful
real-world information’s within the IP address. In IPv6, the prefix form contains identical for all the hosts in
network forms. The suffix part, uniquely identified the tag itself, this identification should be based on its
built-in identification number.
This paper proposed DNS name auto configuration (DNSNA) method that auto-configures the DNS
names of IoT devices for IoT device sand manages the DNS naming. In the IoT network, the users easily
analyze the IoT devices of DNS names based on DNSNA for remote controlling and monitoring. For the
resolution of IoT devices and DNS name generation, proposed DNS name auto configuration method, such
as home network and road network. For IoT home devices it provides an efficient DNS name services. Since
auto configured DNS names contains the device model of the categories, home residents can easily identifies
the DNS names devices.
The node addressing scheme had high efficient if it reduces the overhead header that is identified
with every data packet. This addressing scheme works better if the absence of collisions at allocation time
and it is also works with the collision-free network. To minimize the overhead associated allocation scheme
address itself and it should be distributed and not centralized. In network size increasing, the addressing
scheme should scale well.

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Our proposed model enhanced with IPv6 Addressing scheme in terms of hierarchical architecture to
identify the device quickly and respond to the device for further actions. And also it can be enhanced by
providing privacy for users by hiding their address by converting it into hash values. Identification or
addressing schemes: i) Clock skew addressing (CLOSA) and ii) N-dimensional addressing.
Algorithm-1: Distance estimation algorithm
Protocol Functional Steps:
Step 1: Begin REQ to the network, initiate the process of Neighbor Discovery;
Step 2: if (the Source sends the data) then
Step 3: ts = set clk time (node (Source)) where CLK-Clock
Step 4: Initiate the Route Discovery Procedure
Step 5: Select the intermediate nodes based on the Clustering Distance
(Int_Nodei,Int_Nodej) < 𝑀𝑖𝑛𝑠 (𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒);
Calculate Distance based on RERR msg.,
Distance (n1,n2) = √(X_Pos2 − X_Pos1)2+(Y_Pos2 − Y_Pos1)2
Where n1= (X_Pos1,Y_Pos1) and n2=(X_Pos2,Y_Pos2)
Step 6: From the cache source selects a route (RTS) to Destination;
Step 7: if (absence of route to Destination in its cache)
Go to Step 4;
Step 8: if (route defines the nodes belongs to the primary suspect list)
{
Cluster Head nodes near to the indicated nodes are switched ON in the promiscuous listening node;
}
Step 9: if (Current source node clock time ts)
mod check time == 0)
{
Send Routing Table;
Calculate PSO metrics of each node;
Calculate overall PSO metrics of each path;
Make entry in the routing table;
Transmit the data packet with Secure key;
Go to Step 6;
}
Step 10: if (receives suspect list || malicious list)
{
If (receives suspect list)
{
Update the primary suspect list
}
If (receives malicious information)
{
Update malicious list and primary suspect list;
}
If (end of data)
{
Do-nothing;
}
Else
{
Go to Step 6;
}
If (receives RERR)
{
Go to Step 6;
}
}
Step 11: End if;
3.5. Clock skew addressing (CLOSA)
The clock skew addressing (CLOSA) is a new addressing scheme and it assigns unique IPv6 address
to every node in the IoT network. The CLOSA establishes the uniqueness for each node of skew extracting
and it converts the IPv6 address. It eliminates the requirements of duplicate address detection (DAD) and
does not requires the allocation tables.
Figure 2 defines the tree based addressing and stochastic addressing in IoT. Each node has the
transmission path from source to destination. It finds the minimum path for transmission and the data packets
are transferred. The tree based addressing contains the certain network topologies and it defined top of the
tree depth and run out of addresses in branches. The stochastic addressing scheme assigns the network
addresses randomly and avoiding topology constrains on network deployments. This addressing scheme
assigns the short addresses randomly to new devices. When the new device is generates, it gets the random
address from its parent.

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3.6. N-dimensional addressing
It utilizes the available address space without losing of address. To reduce the loss we have
proposed the n-dimensional addressing. For assigning the router nodes, this method involves a space
partitioning approach. It mainly solves the memory requirements problem and address wastage problem. For
example, the 16-bit address space contains two subspaces. The first 8 bits are assigned for x-axis and second
unassigned bits for the y-axis. It is called a single address space (x, y). The x-axis defines the router node of
the tree-based routing network and the y-axis defines the corresponding value to the sensor node, which is
connected to the router in the x-axis. Figure 3 defines the representation of N-dimensional addressing with
various nodes.
Figure 2. Tree-based addressing/stochastic addressing
Figure 3. Representation of N-dimensional addressing
4. PERFORMANCE ANALYSIS
This section exhibits the performance study of the projected mechanism. The packet delivery ratio
and end-to-end delay, throughput, and energy metrics are also assessed for a varying number of nodes
deliberated are exhibited following the simulation time with the assistance of the NS-2 simulator shown in
Table 1. The Figure 4 defines the output of the nam file. The source determines the shortest path of the
neighborhood and sends the data frame. The evaluation result proves the data transmission with the minimum
rate of error, delay, energy, throughput and packet delivery ratio (PDR).

 ISSN: 2088-8708
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Table 1. Simulation parameters
Simulation Parameter Value
Version-NS2 ns-allinone-2.35
Protocol AFFOADV
Area OG Coverage 1000X1000 m
Simulation Time 200
Type of Antenna Omni Antenna
Energy Mode Energy Mode(true)
Initial energy 10000mjoules
No of Nodes 150
Length of Queue 64
Data Rate Variable
Type of Interface Wireless Physical Interface
Radio Range for Node ~250 m Figure 4. Representation of nam file
4.1. Packet delivery ratio (PDR)
The proportion acquired between overall numbers of data packets and it is efficaciously
disseminated to a specific destination following the overall number of packets and transmitted from the
source node accounts for PDR. The Figure 5 represents the numerical variations in PDR values are obtained
for a particularly simulated environment with various simulation times. The PDR value increased based on
simulation time. At the end the ratio of packet delivery attained the value of simulation time.
Figure 5. Representation of packet delivery ratio (PDR)
4.2. Throughput
For network, the throughput is obtained as the proportion of the overall number of packets
disseminated to the destination within a confined number of time. Figure 6 defines the throughput
representation for varying amount of simulation time. The throughput has the same value from 2.0000 to
8.0000 simulation time. Then, the value of throughput increased and decreased based on simulation time.
𝑇ℎ𝑟𝑜𝑢𝑔ℎ𝑝𝑢𝑡 =
𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑑𝑎𝑡𝑎 𝑝𝑎𝑐𝑘𝑒𝑡𝑠 𝑠𝑒𝑛𝑡 (𝑏𝑖𝑡𝑠)
𝑡𝑖𝑚𝑒 (𝑠𝑒𝑐𝑠)

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Figure 6. Representation of throughput
4.3. Delay analysis
End-to-end delay is contained as an impact of overall time obligated for commencing cluster
realization and trailing information dissemination. The Figure 7 represents the variations of end-to-end delay
concerning the simulation period. The delay analysis increased in the simulation time of 1.40000. Energy:
Each sensor node has the initial energy 𝐸0 and n number of targets. Every cluster has same energy
consumption. The Figure 8 represents the energy consumption with various simulation periods. The energy
will increase if the simulation time has minimum value. The maximum energy value is 49.5237.
Figure 7. Representation of delay

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Figure 8. Representation of energy
4.4. Comparison analysis
The performance of the framework is assessed through this comparison analysis section. The
performance of this framework is analyzed by using various parameters such as hand over delay factor, data
packet loss value, throughput value, end to end delay value and the energy consumption rate. In this Figure 9,
the handover delay time of data packets is determined for existing methods using PMIPv6 and PB-PMIPv6.
This handover delay time is calculated for proposed distance estimation algorithm framework. It is depicted
from the results that proposed-framework exhibited lesser handover delay time of data-packets within IoT
network. It seems that this proposed-framework possess lesser delay time showing the efficiency of
framework, reducing the time complexity. Figure 10 enumerated the comparison analysis of data packet loss
values of all existing protocol models with that of proposed-framework. The data packet loss seems to have
lesser value for CoAP-DPMIP and CoAP-PMIP mobility management system in comparison to other
existing techniques. The data-packet loss ratio value is higher for other systems using PMIPv6 and
PB-PMIPv6 protocols. From this data evidence, it is clearly showed the efficiency of proposed-framework,
exhibiting less data loss while in data transmission in IoT sensor data transmission.
Figure 9. Handover delay time comparison analysis
of proposed framework with existing methods
Figure 10. Data packet loss comparison analysis of
proposed framework with existing methods

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Figure 11 represents the comparison results of throughput value between existing methods and
proposed-framework. From the figure it enumerated that proposed-framework exhibited higher throughput
value in comparison with other existing systems using PMIPv6 and PB-PMIPv6. This has clearly depicted
about the framework efficiency in terms of throughout parameter.
Figure 12 described about the comparison results of end to end delay time of proposed-framework
with existing mobility management system using PMIPv6 and PB-PMIPv6. From this above analysis, the
end to end data delivery delay time of existing methods is made comparison with proposed-framework. From
the outcomes of the results, it is depicted that this end to end data packets delivery delay time is lesser in
compared to other mobility management-system using PMIPv6 and PB-PMIPv6. This has clearly described
about the efficiency of proposed-framework to transmit the data packets rapidly with reduced data loss.
Figure 11. Throughput comparison analysis of
proposed framework with existing methods
Figure 12. Performance analysis of proposed
framework with existing methods in delay time
parameter
Figure 13 explains about comparison results of energy consumption value of existing methods and
proposed-framework. The energy consumption value of proposed-framework is lesser in comparison with
other existing mobility management system using PMIPv6 and PB-PMIPv6. The proposed-framework
utilizes lesser energy consumption in comparison with other existing cluster distance measurement
algorithms for mobility management system using PMIPv6 and PB-PMIPv6. This shows the higher
efficiency capacity of proposed model using CoAP-DPMIP and CoAP-PMIP.
Figure 13. Performance analysis of proposed framework with existing methods in energy consumption
parameter

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5. CONCLUSION
IoT had the large number of devices and it increases the traffic together with data. To overcome this
problem, this paper explained unique identifiers with the various addressing schemes for communication
devices, and then it converts into IPv6 unique address. It also reduces the wastage problem. Our proposed
scheme does not require duplicate address detection with allocation tables and no additional memory storage
for routing. It gives the effective distance measurement of the cluster. In the evaluation result, we proved our
algorithm achieved with minimum time consumption. In performance analysis, we have proved energy,
delay, throughput and packet delivery ratio (PDR). The future work will be applying the address schemes in
the ZigBee sensor network and should compute this algorithm with the ZigBee sensor network.
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BIOGRAPHIES OF AUTHORS
Venkatesh Thamarai Kannan has received the Master’s degree in Digital
Communication and Networking from Kalasalingam University, Srivilliputhur, India in 2011.
Currently, he is a Ph.D. student in the Department of Electronics and Communication
Engineering, Sathyabama Institute of Science and Technology, Chennai, India and he is
working as an Assistant Professor (Sr.G) in KPR Institute of Engineering and Technology,
Coimbatore, Tamilnadu, India. His research interests fall in the area of Internet of things,
networking and Wireless sensor networks. He has published more than 10 papers in National
and International Journals/Conferences. He is a Life time member of IE(I) and ISTE. He can
be contacted at email: venkateshphd15@gmail.com.
Rekha Chakravarthi has graduated from Madras University in 2001 with
Bachelor’s Degree in Electronics and Communication Engineering. She has received her M.E
degree in Applied Electronics Engineering from Sathyabama Institute of Science and
Technology, Chennai in 2004. She has more than 17 years of teaching experience. Presently
she is working as an Associate Professor in the department of Electronics and
Telecommunication Engineering, Sathyabama Institute of Science and Technology, Chennai.
Her areas of interest include Wireless Sensor Networks, Mobile networks and High
Performance Networks. She has published more than 30 papers in National and International
Journals/Conferences. She can be contacted at email: rekha_2705@yahoo.co.in.

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