NEW TECHNOLOGY FOR MACHINE TO MACHINE COMMUNICATION IN SOFTNET TOWARDS 5G

International Journal of Wireless & Mobile Networks (IJWMN) Vol. 9, No. 2, April 2017
DOI: 10.5121/ijwmn.2017.9201 1
NEW TECHNOLOGY FOR MACHINE TO MACHINE
COMMUNICATION IN SOFTNET TOWARDS 5G
Rakshith K and Mahesh Rao
Research Scholar, Dept.of Electronics, and Communication Maharaja Institute of
Technology, Mysore Mandya, India
Professor, Dept.of Electronics, and Communication,Maharaja Institute of
Technology,Mysore Mandya, India.
Abstract
Machine to Machine communication or M2M, refers to a model of communication where devices
communicate directly with each other using the available wired or wireless channels. M2M is a new
concept proposed under 3GPP(3rd
Generation Partnership Project); several research are working on
providing solutions for M2M communication for the 5G networks. Challenges associated with M2M
communication are the lack of standards, security, poor infrastructure, interoperability and diverse
architecture. In this paper, we propose a new mechanism called TM2M5G (The Machine to Machine for
5G) based on SOFTNET platform which results in support of 5G heterogeneous network. In this paper, we
propose the architecture for M2M communication based on SOFTNET and provide new features support
like security algorithms for data transmission among devices and scheduling algorithm for seamless
transmission of data packets over the network. Finallysimulation results ofthis algorithm based on a system
level simulator, considering two different approaches for analyzing the parameters such as delay,
throughput and bandwidth are presented.
Keywords
5G,heterogeneous network,Machine-to-Machine Communication, and Softnet.
1.INTRODUCTION
Fifth Generation (5G) cellular network will enable people to globally use Machine to Machine
Communication (M2M) technology for a better user experience. The M2M communication will
access devices without the need of cellular infrastructure such as Access Points (AP’s) or the
evolved Base Stations (eNB’s). The M2M communication could use the technologies of
Bluetooth, WI-Fi, Wi-Fi direct, NFC(Near Field Communication) and Proximity services as
needed. Compared to the number of connected devices in the current generation 5G would
support 10-100 times more number of connected devices with longer battery life along with
greatly reduced latency. In future 5G cellular networks will involve key technologies such as
spatial modulation, millimeter wave and Visible Light Communication [1].
The major issue with 5G cellular network is the need for super fast downloads to small data
requirements of Internet of Things (Iot) devices and various wide ranges of variations of data
rates. In essence 5G is not just a mobile communication technology, it is ubiquitous access to
high and low data rate services. M2M communication in 5G can also incorporate the millimeter
wave technology to sustain large communication ranges which can be accomplished with high
gain directional antennas with reduced propagation losses [2].
Since M2M communication involves an increased number of user devices compared to available
cellular users, resource allocation for this type of services play a major role in handling the large

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number of devices. To have a resource allocation scheme for busty data traffic like M2M
communication, several cross layer design and stochastic optimization techniques, considering
channel state information (CSI) and the queue state information (QSI) should be measured.
Although cross layer performance evaluation and resource optimization in available cellular
network is well researched subject, the effect of these issues on M2M communication is of great
interest [3]
Heterogeneous network is a network which provides service through a wireless Local Area
Network (LAN) that connects and communicates with different operating system. Figure 1
diagrammatically represents a cluster of network like 2G,
3G, 4G, Wi-Fi and Wi-Max forming a heterogeneous network. For example deploying Wi-Fi
access point in an environment improves the coverage of a cellular network like inside a building
or providing last mile connectivity for areas where cellular services reach is limited. This creates
opportunities for M2M Communication, by connecting devices in locations that are traditionally
difficult to reach by cellular network. M2M access points can be deployed for carrying large and
small data services among users.
Due to the explosive growth of user devices to access the high and low data rate multimedia
traffic has resulted in the dramatic increase in the energy consumption due to wireless devices in
recent times. . The large energy consumption has resulted in high operational expenditures and
large amount of green house gas emission. As a result many recent studies have focused on
energy -efficient design for the current and the future communication technologies like
LTE(Long Term Evolution), LTE-Advanced and 5G communication, and 3GPP also integrated
green communication research as an important part of LTE standard documentation. Authors [2]
propose an efficient near-optimal algorithm to perform QoS-aware energy-efficient resource
allocation which guarantee Quality of Service (QoS) particFularly at high Signal to Noise Ratio
(SNR) and low network load.
Figure 1: Heterogeneous Network
In [3], authors propose a novel approach for hybrid random access and data transmission protocol
for M2M communication to reduce the excessive signaling overhead. In this hybrid process the
M2M UE’s (User Equipment) would transmit the data on Physical Random Access Channel
(PRACH) before a connection is established with the base station to remove the inefficiency. In
the conventional method M2M UE’s would transmit the data only after the connection is
established with the Base Station. The protocol operation is explained in six steps such as(i)
PRACH scheduling, (ii) Access barring, (iii) Preamble transmission, (iv) Data channel
scheduling, (v) Uplink data transmission and (vi) Acknowledgement. A joint adaptive resource

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allocation with access barring scheme has been proposed to maximize the M2M throughput and
to resolve the congestion control problem in the random access procedure.
Latency is the time delay between the cause and the effect of some physical change in a
network.The 3GPP LTE standards aim in achieving high packet data rates and low latency for
M2M communication. Jason Brown and Jamil Y Khan [4] propose one way algorithm for
reducing latency in the prediction of resource allocation, the challenge is to identify the nearest
node for transmitting the packet through it and reaching the destination node without any loss.
Two way algorithms along with both forward and reverse direction were proposed but are
significantly more difficult to model via theoretical analysis.
M2M communication is mainly focused on proximity based services; the issues related to
proximity based services are: (i) Device Discovery and (ii) Resource Allocation. To realize the
proximity based service in the system, it is necessary to design a Device Discovery Scheme by
which UE can discover another UE in its proximity. Authors [5] propose a discovery scheme
based on random access procedure in LTE- Advanced network. The proposed scheme starts with
discovering the UE and then allocating RB (Resource Block) for communication. Algorithms
proposed here were designed to reduce the underutilization of resource blocks.
The discovery occurs in a centralized manner, where as the resource block allocation is through
random access procedure. In the random access procedure, each UE transmits a preamble via a
physical random access channel to BS. Upon receiving the preambles, the BS allocates the RB’s
both in uplink and downlink request. This entire algorithm takes place using the four following
steps such as (i) Preamble Transmission, (ii) Random Access Response (RAR), (iii) Connection
Setup Request Message and (iv) Connection Setup Response Message. Previous algorithms
maintained a fixed number of RB for a particular network which was not utilized completely by
the users. Entire process is split into three phases such as PRACH access phase, Receiver- user
equipment reporting phase and transmitter UE phase.
Researchers Jean-Pierre Charles, Anders Furusk¨aret.al.proposed a simple operator specific way
that chose aimed at meeting future traffic and data rate demands. The operators have opportunity
to select between a variety of network evolution approaches. This method includes both
conventional dimensioning parameters like targeted user experience, average traffic demand, user
density, cell sizes, and three dimensional spatial user parameters like traffic distribution, site
specific propagation and deployment strategies. This method has limited complexity for
addressing the need for practical model [7].
Although the literature survey focuses on various issues related to M2M communication, there
have been no efforts made to implement M2M communication model on a simulation platform
like SOFTNET to understand the various nuances and the solutions to those issues. This paper
contributes to the framework for M2M communication as follows:
(i) A novel technology for Implementing M2M communication on SOFTNET platform by
modeling the architecture using both M2M model and SDN (Software Defined Network) model.
(ii) M2M UE’s are provided with security feature along the uplink direction on user side. The
security algorithm proposed is HMAC-SHA(Hash based Message Authentication Code) 384
cryptographic algorithm.
(iii)For the large number of M2M devices communicating on the network the scheduling of these
data packets are using MidFuzzyRR (Radio Resource) scheduling algorithm.
(iv)Efficient transmission and routing of data packets among the network node elements by Tree
Based Intelligent Routing Algorithm.

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(v)Handover mechanism based on Fuzzy logic system is designed for handling mobility of
devices between the base stations.
The remainder of this paper is organized as follows. Section II briefly describes the related work
on SDN and issues related to M2M communication, whereas Section III defined the problem
definition and followed by the proposed work in Section IV. Finally, the simulation results,
conclusion, and future work are described in Section V.
2.RELATED WORK
Software Defined Network (SDN) and Open Flow represent the programmable networks which
could be the commonly deployed networks of the future. SDN is an emerging network
architecture, which performs the subdivision of control plane and data plane which allows greater
performance in terms of routing and forwarding. Authors G. Araniti et.al.,[6] presented the
performance analysis of Software Defined Networking (SDN) and Open Flow in a wireless
networks. The aim of their research was to evaluate potential advantages introduced by SDN
architecture based on Quality of Service (QoS) metrics such as packet loss, throughput, end-to-
end delay and packet delivery ratio.
Various applications can be deployed based on the concept of M2M communication, one such
application is mobile relay technique supported for communication in LTE-Advanced networks
for high speed rail networks. However, due to increased train speed mobile relays suffer from
frequent handover from the serving BSs. This process incurs network loads and degrades network
performance. To overcome this problem a handover scheme is devised compatible with LTE-A
layer 2/3 protocols. The scheme contains enhanced measurement procedure and a group in-
network handover procedure. They use a special Control Mobile Relay (cMR) node and several
general mobile relays in train for packet transmission [8].
Currently, due to emerging online streaming videos and websites the cellular users tend to watch
videos online than offline mode. This results in latency and reduced quality of service in mobile
networks. Ching-Feng Lai et.al studied a buffer-aware HTTP live streaming approach, which
evaluates the weights of media segments to decide the transmitting priorities based on the current
playing time and adjust the appropriatetransmission path through the decision making network
controller according to the utilization and stability of the routers and switches in SDN [9].
Mobile social networking and IoT,have brought challenges in flexibility, efficiency, and
scalability to the current LTE network. Inspired by thinking of the fundamental mechanisms in
LTE as reasons causing those problems, SoftNet, a software defined decentralized mobile
network architecture toward 5G, is proposed in this article following principles proposed for
designing an efficient and scalable network. The analysis of the working mechanisms of SoftNet,
including its dynamically defined architecture, decentralized mobility management, distributed
data forwarding, and multi-RATs (Radio Access Technology) coordination, show that SoftNet
has improved system capacity and performance. Simulations are conducted to demonstrate that
signaling cost in SoftNet, as an important performance metric, can be decreased significantly
compared with LTE networks [10].
Research such as Massimo Condoluci et.al introduced a novel Tird Generation Partnership
Project (3GPP) complaint architecture which absorbs the Machine Type Communication (MTC)
System traffic via homeevolved NodeBs. This allowsto significantly reduce congestion, core
networks and overload on radio access. These quantify the performance of a network which
handles ultra-dense MTC over LTE-A networks [11].

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The Fifth generation mobile communication will be the next major phase of mobile technology,
which is anticipated by several researches throughout the world. Bjo¨rnSkubicet.al [12] proposed
an optical approach to pave the way for 5G and the Network Security. Their focus is not limited
to higher peak data rates, but on higher number of simultaneously connected device (M2M, D2D
and MTC) higher spectral efficiency, lowers battery consumption and lower latency. Author
suggested a key defining factor for 5G transport and outlines a concept for programmable
transport based on WDM (Wavelength Division Multiplexing) and exploits the emerging optical
devices which are enabled by integrated photonics. This optical transport continues to hold its
place in metro and core segments. The integrated photonics have low-cost, low footprint and
reduced power consumption. An Increasedlevels of flexibility that was done by low cost optimal
components which provide more degree of freedom in transport layer which facilitates higher
optimization from antenna to core.
In the present cellular network 50% of the traffic generated is through video, and this evidence
suggests that demand for videos will increase at a much faster pace. The challenge for the
network operators is to offer higher data rates to keep up with the demand for high quality video.
Researchers such as, AntoniosArgyriou et.al [13] investigated a video communication in
Heterogeneous Cellular Networks (HCN) when Time Domain Resource Partitioning (TDRP) is
employed. The main aim of this process is maximizing the experience of video quality for end
users. This is done with help of jointly optimizing of the TDRP for HCN, video quality is
transmitted that is specifically selected by user and rate allocated for each user. This includes
system-level parameters like fractioning of users that receive video and it supports DASH-based
video streaming.
5G technology has no limitations to have new technologies to incorporate within, Author Min
Chen et.al [14] has thought about emotion aware mobile cloud computing for 5G. He has
proposed a frame work called EMC (Emotion-Aware Cloud Computing) that offers emotional
aware services for better user experience. The Quality of Experience (QoE) is achieved by
modifying the Mobile Cloud Computing (MCC). MCC architecture is modified to achieve the
required QoE in emotion aware applications. The proposed framework would be helpful in
providing personalized, human centric, intelligent emotion-aware services in 5G.
Authors [15], presented an infrastructure which allows mobile users to securely share and search
for real-time video data. This proposed infrastructure involves combination of 5G technologyand
cloud platform for achieving their goals. The goal of this framework allows real time video
sharing between mobile users and friends and also to their family members using cloud service.
Here other user has no permission to get informationabout the videoi.e.users from outside cannot
get permission for accessing the files and they even cannot get any information about the video
files. Here searching is provided additionally and securely with users own video data.
Users in the 5G network will likely be able to use 3GPP standards, IEEE 802.11 and other
technologies simultaneously so as to maximize the user quality of experience .Researchers such
as Olga Galinina, Alexander Pyattaev et.al [16] have analyzed the uplink performance gain of
multi radio access technology solutions versus the legacy approach. The authors predict that an
unprecedented paradigm shift in user experience and network design of Cellular to Wi-Fi
offloading will be required for integrating technologies.
Pablo Ameigeiraset.al[17] proposed an SDN based architecture for the 5G packet core. Authors
focus on designing access cloud with the goal of providing low latency and scalable Ethernet-like
support to terminal and MTC (Machine Type Communication) devices including mobility
management. The authors also proposes architectural enhancement on issues like network

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scalability and mobility, edge network elements (Access Points) and finally SDN enhancements
for broadcast and network management issues.
3.PROBLEM DEFINITION
The evolution of mobile technologies has resulted into new network services such mobile social
networking, mobile device–to-device communication, M2M communication, inter vehicular
communication and so on. These new services create diverse requirements for mobile networks,
like accessing the cloud servers with latency less then10ms,high speed data transmission of
5Gbps and several other factors. Such requirements cannot be satisfied by the LTE networks and
here there is a need for the further development and redesigning of the centralized core network.
Many organizations have proposed better and new approaches for designing the core network like
Open RAN, OpenRadio, MobileFlow , and Soft cell .
SofNet is a Software defined Network which is a decentralized network architecture for 5G
mobile networks. Today’s cellular network involves large number of network protocols and
network elements to perform mobility management, centralized routing mechanisms which
involves nodes such as Mobility Management Entity(MME), Packet Data Gateway(PGW),
Serving Gateway (SGW) the evolved gateway. All the functionalities of the above mentioned
LTE network can be implemented on a SoftNet platform by employing SDN and NFV (Network
Virtual Functionality) technologies.
SoftNet has excellent scalability and flexibility to accommodate different communication
scenarios .Secondly, signaling overhead in the core network is reduced dramatically due to
decentralized mobility management. Finally, both system capacity and performance are improved
by supporting decentralized mobility management, distributed data forwarding and multi RATs
(Radio Access Technologies) coordination. Several factors influence the drawback of SoftNet
which are low decentralized mobility, lower coverage and QoS to maintain the minimum delay
between nodes. In this work we propose to maintain a minimum quality of service by calculating
the delay between nodes and allocating the required bandwidth to maintain the QoS of the
system.
The simulation considers large number of nodes deployed in the defined environment, where each
node is represented by (vi) . Initially the transmission power of each node is equal and it changes
dynamically while transmitting the packets. The model with nodes and connecting links are
defined by the graph G(V,E). Where V represents the finite number of nodes and E represents the
finite number of links in the environment. The total number of nodes V ={v1, v2… vn} and
E={(i, j) |vi, vj∈ V}, (i,j) ∈ E that denotes both vi and vjare within the coverage area of each
other.
Each node in the environment has to be allocated with a minimum transmitting bandwidth (bi, j),
where the distance between each nodes is represented by (li, j) having a transmitting delay of (di,
j). Hence the data transmission delays between the nodes viand vjis denoted as (di, j) delay which
also include queuing delay and propagation delay between nodes. Euclidean distance between
nodesis denoted as (li, j) and also bandwidth link between nodesis represented as (bi, j).
The values (di, j), (li, j) and (bi, j) are all real positive numbers. In a transmission scenario we
consider source as (s ∈ V) and destinations as (D ⊆ V – {s}),let d =|D| be the number of
destination nodes in a network. M represents a destination group and {s} U D is the multicast
group. A multicast tree is described as T(s, D) ⊆ M, this tree has root from single source to all the
destinations in D. Here delay in the data transmitted path in tree (T) from source (s) to destination

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(vt∈ D) is denoted as delay(pT(s,vt)). Let (pT(s,vt)) is unique path from s to (vt∈ D). The delay for
the destination node is represented by
Delay (pT(s,vt)) = Σ(i,j)∈ pT(s,vt) di,j
Minimum bandwidth of a path from source s to destination (vt∈ D) is denoted as bandwidth
(pT(s,vt)).
Bandwidth (pT(s,vt)) = min{bi,j, (i,j) ∈ pT(s,vt)}
Let Δdbe the delay constraint and Bdbe the bandwidth constraint of the destination node (vt∈ D).
Then, the delay bandwidth constraint minimum Steiner tree problem is to find minimum cost
multicast tree T* (s, D) is,
Delay (pT*(s,vt)) ≤ Δd, ∀ vt∈ D
Bandwidth (pT*(s,vt)) ≥ Bd, ∀ vt∈ D
Once T* (s, D) is identified, during data packets transmission in tree T* will adjust the
transmission power level.
Figure 2: Architecture for TM2M5G Mechanism
4.PROPOSED WORK
4.1 OVERVIEW
In this work we propose a novel mechanism called TM2M5G instead of enhancing the existing
SoftNet Architecture. This mechanism will overcome the problems and challenges existed in
SoftNet Architecture. The TM2M5G is capable of accessing different wireless technologies such
as 2G,3G, LTE,Wi-Fi and Wi-Max. The proposed mechanism introduces the concept of SofNet
for Machine to Machine Communication with additional improvements to the existing SoftNet
Architecture which provides better performance in terms of signaling cost, throughput and
bandwidth requirement of our network architecture.
Additional architectural enhancement in our proposed mechanism includes L7 switch for
scheduling the data packets inside the network, Open flow controller for routing the packets, we

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also provide a security feature for the user equipments in uplink direction. We employ SDN
(Software Defined Network) technology as a base to construct the SoftNetarchitecture which
enhances the scalability and flexibility to accommodate the communication services. Owing to
availability of various heterogeneous wireless networks, mobile users can experience a better
service for effective communication using several preferences. We also provide handover
mechanism for the users to have mobility from one network to another for which fuzzy logic is
used for handling multivariable problem where a joint correlation analysis for several input is
required.
Figure 2 describes our proposed mechanism, which consists of SDN (Software Defined Network)
core network, CDN (Content Delivery Network) server, Open Flow controller and L7 switch. The
architecture diagrammatically explains the concepts of each device. Here L7 networking switch is
used for scheduling the large amount of incoming requests from user and to distribute the packets
based on requirements. Open flow controller switch is used for routing process. Other
components such as CDN server and SDN core network are most essential components used for
effective handover mechanism. The SDN core network has a main role that controls the entire
network which also composes of policy control, communication control and network
management.
Tree based Intelligence Routing (TIR) protocolis usedfor selecting the best path in the network.
The CDN server acts as decentralized control to maintain multiple radio access technologies for
coordination.The The Key to CDN server is to deliver contents at higher user avalibality and
better performance for the end user.Mobile terminals do not the support of the core network to
access to Internet it uses the CDN server.Fuzzy logic based Handover mechanism is used to
coordinate between multiple radio access technologies.
4.2 ROUTING
Our Proposed mechanism uses TIR protocol. This routing generally works for selecting the best
path in a network for transmitting data to reach the destination nodes at a specific time without
delay. In thismechanism routing is done based on open flow controller for determining the best
routing path. The rules for finding best path are placed in open flow controller. Forwarding the
packets to destination and dynamically changing the traffic parth is the responsblity of Open flow
controller,Quality of Service of the traffic path and scheduling has to be taken care. For routing
process, the initial tree is constructed called Steiner tree [18]. This tree is constructed based on
total number of hops needed to transmit the packet to all destinations which must be as small as
possible. After the complete construction of tree, we need to find the neighbors i.e. next hop
nodes. If neighbor nodes are present then, greedy multicast forwarding is used. If the current node
has more than one next hop, then the packet is split. The purpose of packet splitting is for
reaching the destination node in several directions. The set of next hop is identified by,
𝑤 =
𝜆|𝑊|
| |
+
− 𝜆 ∑ 𝑖
∈𝑤
,
∈𝐷
∑ ,
∈𝐷
Here, s is the forwarding node.
N is the set of all neighboring nodes.
W is the set of all subset of N.
D is the set of all destination nodes.
d is the destination d1,d2 or dn.
l(x,y) is the function measure distance between nodes.

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The equation contains two parts, first part providesnumber of neighbors that packets are
transmitted and second part specifies the remaining distance to destinations. The best route for
delivery is based on delivery probability [19], which is defined as that delivering packet at the
good level of energy consumption and time interval. The delivery probability is estimated
efficiently based on Kalman filter for the selection of the route. Here we include two attributes
they are changing in degree and future co-location.Kalman filter is used as a prediction filter for
nodes identification that are in mobility , highest mobility nodes has to be identified as they have
highest probability to reach maximum destination nodes.
Let HS be represented as host source and DS as destination node.. The change of connectivity of
a host HS is
𝒕 =
|𝒏 𝒕 − 𝒏 𝒕 | − |𝒏 𝒕 − 𝒏 𝒕 |
|𝒏 𝒕 − 𝒏 𝒕 |
Where,
n(t) is host neighbor set at time t.
The above formula yields the number of hosts which became neighbor in time interval [t-T,t].
𝑜 ℎ,𝑖 = {
𝑖 ℎ 𝐻 𝑖 _ 𝑎 𝑤𝑖 ℎ ℎ 𝑖
ℎ 𝑤𝑖
The above formula denotes the calculation of co-location HS with host I .Figure 3 describes the
pseudo code for routing processand Figure 4 describes the flowchart of the routing process.
Pseudo code: Tree based routing Intelligent.
=====================================
Step 1: Construct T
Step 2: FindNhop
Step 3: FindDProb
Step 4: CalculateDcon
Step 5: CalcualteCLoc
Step 6: Select Pbest
Step 7: Transmit M
Figure 3: Pseudo Code for Tree Based Intelligent Routing.
Figure 3 describes the TIR routing process which initially constructs the Steiner tree (T) and it
checks for next hop (Nhop) for the transmission. Here by using Kalman filter we determine the
delivery probability (DProb) followed by its degree of connectivity (DCon) and the Co-location
(CLoc) of the route. Then finally we determine best path (Pbest) for packet (M) transmission.

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Figure 4: Flow Chart for routing Process
4.3 HANDOVER
In cellular communication and telecommunication process, the user devices are in mobility
always, for these devices the support from the network must always be served. Handover is
defined as a process of transferring the call or data session from one cell site to another without
any session discussion. In this work we handle two types of handover they are,
• Horizontal Handover
• Vertical Handover
Horizontal handover refers to a type where the UE’s (User Equipment) moves in the same cell i.e.
between the Base Station or GSM to GSM handover. Vertical handover refers to a network node
(UE) that changes the type of connectivity from one network to another network, i.e., UMTS and
WLAN.
Trigger in vertical handover decision is based on several events such as
• Request from new service.
• Mobile Node in proximity to wireless network.
• Low Coverage in wireless network.
• When any requirement of resource blocks.
• Insufficient resources in some network.
We perform handover mechanism based on Fuzzy logic which is initiated by mobile nodes. When
mobile nodes move around a network like LTE, WLAN or Wi-Max the node discovers a new
wireless link or may have low coverage of the wireless network. At this situation, the fuzzy based
logic is useful for selecting a network for the mobile node.Fuzzy algorithms are implemented in
mobile nodes; handover decision engine provides rules for decision making. The result of the
fuzzy logic system changes is time dependent.
Figure 5,representsthe architecture of Fuzzy Logic based Controller,Where in the input and
output parameters are considered to determine the handover decison. Fuzzy logic is used for
dealing with the uncertainty cases when input arises from roughly estimated values. It provides an
interference process that interprets and executes commands.

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The Fuzzy Controller consists of three blocks. First block being Fuzzifier, second Interference
engine, and the last Defuzzifier. In Fuzzifier, the input values are transformed to linguistic values
based on membership function. It’s a linguistic system for defined rules, these rules are described
in the form of IF-THEN conditions and it also use Boolean operations such as AND, OR and
NOT.
Figure: 5 Fuzzy Logic based Handover Decision
Here the interference engine which relates the inputs and outputs using “IF…..THEN” rules. In
the end, Defuzzifier generates crisp values from the output values which, concludes the handover
decision.
In our TM2M5G mechanism, we use three different parameters which are the distance of mobile
nodes (MN), Speed of the mobile node and reachability of mobile node. We consider
threedifferent typesof networks they are LTE, Wi-Max and Wi-Max networks . Initially, the
process starts at the first blockfuzzifier where the input parameter gets transformed to fuzzy sets
of values. Next in the interference engine, the fuzzy sets are fed by IF….THEN rules which are
applied for getting fuzzy decision sets.
The output fuzzy decision sets are finalized to the single fuzzy set, and it is passed to Defuzzifier.
In Defuzzifier, the final decision for the handover is taken.
Let us look at three input parameters and their effect:
• Reachability (RA)
• Distance (D)
• Speed (S)
Table 1 represents the above parameter values and their corresponding handover decision values.
The output parameters are represents as “low” and “high” and they are denoted as „L‟ and „H‟.
Fuzzy Rules are specified as follows:

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Begin If (RA=D=S=H)
{
Select Wi-Max
}
Else if (D=S=L || S=RA=L || D=RA=L)
{
Select Wi-Fi
}
Else if (D=S=H || S=RA=H || D=RA=H)
{
Select Wi-max
}
Else
LTE
End
The handover decision value ranges between 0 to 1. Here the handover is considered when the
decision is higher. When the decision value is lower, the mobile node stays in the network. The
output of interference engine is defined by state levels , These state levels are given into
Defuzzifier which converts them to crisp values.
The handover decision takes place as follows:
i. Mobile nodes move to LTE network if the value of the state is less than 0.25.
ii. Mobile nodes moves to Wi-Fi if the value of the state is between 0.25-0.50
iii. If the output value is between 0.50-1.0 then the mobile nodes takes handover decision
and moves to Wi-Max.
Based on above three points the handover decision is made. These values are considered for
selection of handover in our proposed mechanism.
Table 1: Handover Decision Based On Fuzzy Rule
THEN
REACH
ABILITY
SPEED DISTANCE
HAND-
OFF
DECISION
VALUES
L L L Very L
H L L L
L H L L
H H L H
L L H L
H L H H
L H H H
H H H Very H
4.4 LOAD BALANCING
Load balancing is defined as the ability to balance traffic across wireless nodes using routing
protocols. When there are more number of incoming users accessing the same network, the load
on the network increases rapidly .To reduce the load on the network and balance the load we have

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implemented the L7 (Layer 7) switch. The queues of pending requests in Layer7 switch is used
for predicting the load on servers. L7 switch being a multilayer switch consists of in-built load
balancing programs and accepts incoming packets from various users.
The users request increases the traffic load in the network, which has be balanced between the
network and is the main goal of load balancing .A new load balancing algorithm is developed to
solve the traffic issues at different cell of Machine to Machine Cluster.The numbers of incoming
tasks are to be scheduled, which is done by a novel Mid-Fuzzy Round Robin Scheduling
algorithm in our proposed mechanism.
In this Mid-Fuzzy Round Robin scheduling, the fuzzy rules are noted based on handover
mechanism which gives the output as handover decision value ranges i.e. network ranges. The
number of requests occurs and we calculate the values of requests. Then we calculate the average
of requests values for choosing the middle value. The middle value is compared to other values
and then request for higher prioritized nearer value to the middle value is obtained.
============================================
Pseudo code: Mid-Fuzzy Round Robin Scheduling
=============================================
Input:
Output: Scheduled data
Begin
Step 1: Tq= 𝑖 𝑖=1
Step 2:
Step 3: MV = 𝑖 𝑖=1
Step 4: while (MV nearer to Ui)
If (Bi <= Tq)
Process Ui
Remove Uifrom RQ
Else
End if
End while
End
============================================
Figure: 6 Mid-Fuzzy Round Robin Scheduling Algorithm
Figure 6 describes Mid-Fuzzy Round Robin algorithm where RQ is the Ready queue, PQ is the
process queue, U1, U2, U3… Unis the number of user requests and B1, B2, B3… Bnis the burst
time according to the user requests. Tqis the time quantum which is calculated by average of the
burst time. MV is defined as middle value, which is calculated by the average value of user
requests. The values of user requests are calculated based on fuzzy rules of handover mechanism.
Initially the Uiare allowed to ready queue and we calculate middle values for user requests. After
middle value calculation, the value is compared to all other user request values. The Uiis selected
with higher priority when its value is nearer to middle value and it is moved to process queue.
When the Tqexpires for Ui, while it is processing then it is moved to last of the ready queue.
4.5 SECURITY
Security is defined as the set of policies which is adopted to protect and monitor unauthorized
access, modification or misuse of data during transmission in the network. Currently in
heterogeneous network, as the number of device users has increased threat perception and

14
security needs also increased. In our proposed mechanism, HMAC-SHA384 security algorithm is
proposed for providing security. This includes the combination of a keyed-hash message
authentication code (HMAC) and SHA384 cryptosystem.
Initially the message M is hashed, and they are padded with its length (l) which is also 1024 bits
long. Then they are parsed into 1024-bit message blocks M1, M2 … MN. Calculate the hash
values based on [20],
H (0) = H (i-1) + CM (i) (H (i-1))
Here C denotes the SHA compression function which gives a secret key “K” and + denotes word-
wise modulation 264addition. H (N) is hash of M. After computing the hash function and
expanding, finally, 384-bit hash is obtained. After this perform the padding operations for the
values. Finally the operation of HMAC is described as,
𝐻 = ℎ 𝐾 ⨁ 𝑎 ||ℎ 𝐾⨁ 𝑖 𝑎 ||
============================================
Pseudo code: HMAC-SHA 384 Algorithm
Input:
Message M, bits b=1024, C1=00110110, C2=01011100
Step 1: Message M
Step 2: Compute K using SHA384
Step 3: Compute Hash () for K
Step 4: if length (K) < b
Pad 0 to K
Step 8: [K+ opad with [C2] by b/8
Step 9
Figure 7: HMAC-SHA384 Algorithm
Finally Message Authentication Code is generated for the message. Figure 7 represents the
pseudo code for HMAC-SHA384. Figure 8 describes flow chart of HMAC-SHA384.Here
message m is encrypted with key K using SHA384 compression function. Then hash value is
calculated for K based on SHA-384. If length of the key (K) is less than b values then it is padded
with 0 then assigned to K+. Then K+ is ipad with constant value C1 for repeatedly by 128 times
and it is said to be S. we need to append the original message M and S value to form deformed
value as Z. Again we compute Hash function for Z to get Z1 and followed by pad with constant
value C2 repeatedly by 128 times and it is said to be S0. Attach Z1 and S0 to get Z2. Finally by
computing hash function for Z2 we get HMAC.

15
Figure 8: Flow chart of HMAC-SHA384 Algorithm
4.6 PERFORMANCE EVALUATION
This section evaluates the proposed mechanism by considering various performance parameters.
The parameters considered are number of hops, coverage and handover. In our proposed system,
we added additional features to SoftNetarchitecture which enhances the communication in 5G
network and provides security on user side.
 Number of Hops:
Hop is defined as the packet transferring from source to the destination. The number of hop
changes for each transmission which is based on path selected. Normally, single hop is said to be
the direct transmission between source and destination. In our mechanism, Multi-hop
transmission is supported.
 Coverage:
The coverage is defined as the geographical area of the radio station where it can communicate
with the higher performance efficiency. Also this area is specified as service area. The simulation
consists of 15 eNB’s spread over the region with distance between each station of 500m.
 Handover:
SoftNet has an s1 handover and it is performed between source eNB and target eNB through s1
interface. Inter-access server handover takes place that results in more signaling cost at single hop
itself.
Table 2 compares the existing system called SoftNet [10] and our TM2M5G mechanism. The
problems inSoftNetare compared with our mechanism.The parameters considered for simulation
are signaling cost, bandwidth, delay, handover and throughput. Effects on these are discussed.

16
Characteristics SoftNet TM2M5G
Security No Security is
provided
Security
based on
HMAC SHA
384
Delay Router
increases the
delay
Open flow
controller
reduces the
delay
Signaling Cost Reduced
because of
single hop
transmission
Decreased by
30%
Hops Single Multiple
Scheduling No process is
enhanced
Uses Mid
Fuzzy RR for
overcoming
packet loss
Signaling
Overload
Reduced due to
low
decentralized
mobility
Reduced
compared to
SoftNet
Table 2: Comparison of Softnet Framework and Our Tm2m5g Mechanism
4.6.1SIMULATION SETUP
We implement our proposed work using OMNET++ simulation framework, the simulator is
supported by GUI (Graphical User Interface). This simulation tool helps us to support all the
newly proposed protocols, mechanism and algorithms.
Simulation parameters of the proposed work are as described in Table 3.
PARAMETERS VALUES
Simulation Area 2000*2000 m2
Duplex mode True
Mobility Speed 10-100 Mbps
Connected Address Wireless Core Network
Bit Rate 2-60Mbps
Carrier Frequency 2.4GHz
Transmission Rate 100 Mbps
Queue Type Drop Tail queue
Management SSID HOME
Table 3: Simulation Parameters
Table 3 describes values of parameters that has been taken for simulation. For conducting our
simulation, we consider oneeNode, one server and 40 Mobile Nodes. In our proposed mechanism
our mobile nodes are in mobility i.e. moving one cell to another cell. Our area of simulation is
about 2000m *2000m. The transmission rate is 100 mbps for information processing. Due to
mobility of nodes, the mobility speed is taken as 10 mbps - 100 mbps. Here drop tail queue is

17
used for queuing type which is a simple queue management algorithm. The number of bits
processed per unit time is 2-60 mbps. Center frequency is set as 2.4 GHz which is termed as
carrier frequency.
4.6.2PERFORMANCE METRIC:
We consider some metrics which are to be conducted for experiments on OMNeT++ simulation
framework which existing system and proposed system. The parameters are,
 Signaling Cost:
It is defined as the summation of packet delivery cost and cost of location update. The location
update cost is calculated by
𝐿𝑈 =
𝐸[ ] 𝐻 + ℎ
𝐸[ ]
and packet delivery cost is obtained by
𝑃𝐷 = ŋ𝜆𝑎 + ( ℎ− 𝑓 + )𝛿𝐷
So,
𝑡𝑜𝑡 = 𝐿𝑈 + 𝑃𝐷
 Delay :
It is defined as the amount of time taken for multicasting the data from source node to multiple
destination nodes. This is calculated by
Delay =
∑ i i − i
∑ N i
 Throughput:
There are two throughput such as Uplink and Downlink throughput. Here the Uplink throughput
is calculated by the average of successfully transmitted data from Mobile node to the eNB. While
the Downlink throughput is calculated by the amount of data transmitted from eNB to the Mobile
node.
 Bandwidth:
It is defined as total amount of information that can be transferred to the network on every
second. This represents the transmission capacity of the network. The bandwidth is calculated as
Time of sending a file =
iz i i
i
4.6.3 COMAPRITIVE ANAYSIS :
Our TM2M5G mechanism is compared with previous framework with different scenarios such as
1) TM2M5G Mechanism with Handover and
2) TM2M5G Mechanism without Handover.
By concatenating variance of parameters like signaling cost, overall handovers, uplink
throughput, downlink throughput, and QoS parameters such as delay and bandwidthare presented.

18
4.6.3.1TM2M5G Mechanism with Handover:
In our proposed TM2M5G mechanism, let us consider three main parameters for fuzzy logic
system for handover decision as already mentioned in the fuzzy logic section. The three main
input parameters for our fuzzy logic are (i) Reachability (RA) ,(ii)Distance (d),(iii) Speed. Where
‘d’ is the distance between the mobile nodes, ‘s’ is speed of each mobile nodes and ‘RA’is
reachability of mobile nodes. For handover we consider three different networks used of the
mechanism they are LTE, Wi-Fi and Wi-Max.
This provides good results when compared with existing solutions. Figure 9 represents the
performance results of signaling cost in proposed mechanism on a time interval (seconds). In the
previous framework [10], signaling cost is a critical issue which gives lower performance while
our TM2M5G mechanism gives higher improvement in it.
Figure: 9 Performance of Signaling Cost
Our proposed method TM2M5G is flexible and scalable system that dynamically enable/disable
related virtual network functions and employs new mechanism to improve the system capacity.
By employing the decentralized management in the system a decreased signaling cost compared
to Softnet is achieved in TM2M5G.
Figure 10 describes the uplink throughput comparison between our system (TM2M5G) with that
of the existing system [21]. Higher uplink throughput is good. This uplink throughput is
compared with load as increased number of mobile UEs in the network.
Both uplink throughput and downlink throughput have been achieved better then LTE network.
The typical throughput is what user experience when they are near the base stations. The typical
throughput is hard to measure and depends on the protocols and transmission used , slower
schemes of transmission are used at longer distance from access point due to redundancy.
0
500
1000
1500
2000
40 80 120 160 200
Signaling
cost
Time (S)
SoftNet
TM2M5
G

19
Figure: 10 Performance of Uplink Throughput
Figure 11 represents the Downlink performance which compared with the existing system [21]. It
shows the plots between downlink throughput and load for both existing and proposed system.
Our TM2M5G mechanism shows the improvement in downlink throughput also
Figure: 11 Performance of Downlink Throughput
Figure 12 shows the ranges of network bandwidth during handover from one network to another
network. When a mobile node moves in their network area, that results in variation of bandwidth.
Here the handover takes place from LTE to Wi-Fi, bandwidth varies initially and it is maintained
at the constant level. While in handover from Wi-Fi to Wi-Max, there is a slight change on
bandwidth with respect to time.
0
200
400
600
800
1000
1200
20 40 60 80 100
Uplink
Throughput
(Mbps)
Load
4G LTE
TM2M5G
4G LTE
TM2M5G
0
200
400
600
800
1000
1200
20 40 60 80
Downlink
Throughput
(Mbps)
Load
4G LTE
TM2M5G

20
Figure: 12 Performance of Bandwidth with respect to Time during Handover
Figure 13, shows the last packet transmission during handover of user. Delay is maintained at
constant range i.e. no increase in delay with respect to time, during handover. In our TM2M5G
mechanism, the handovers such as LTE to Wi-Fi and Wi-Fi to Wi-Max delay varies but stays
constantly.
Figure: 13 Performance of Delay with respect to Time during Handover
4.6.4 TM2M5G Mechanism without handover:
In this scenario, our TM2M5G mechanism provides better results. Figure 14 is a plot of delay for
the different networks such as LTE, Wi-Fi, Wi-Max with respect to time. Our TM2M5G
Mechanism provides good results in terms of delay on without handover scenarios.
0
200
400
600
800
1000
1200
1400
1600
1 3 5 9 11 13 15 17 19 21 23 25
Bandwidth
(Kb/s)
Time (s)
LTE to
Wi-Fi
Wi-Fi-to
WiMax
0
5
10
15
20
25
30
35
40
3 6 9 12 15 18 21 23 25 27
Throughput
(Kb/s)
Time (S)
Wi-Fi-to WiMax
LTE to Wi-Fi

21
Figure 14: Performance of Delay in LTE, Wi-Fi and WiMax
Figure 15 is a plot of throughput which shows the maximum throughput is achieved by the
user in different networks. Wi-Fi saturation of 30Mbps is achieved due to the practical limitations
of Wi-Fi where the maximum data rate achievable is 54Mbps.
Figure 15: Performance of Throughput inLTE, Wi-Fi, WiMax
5. CONCLUSION
Our TM2M5G mechanism consists of three types of network they are LTE, Wi-Fi, and WiMax.
This involves Machine-to-Machine communication and it is collectively said to be heterogeneous
network. This mechanism mainly focuses on security process on the user side called HMAC-
SHA384 and load balancing algorithm called Mid-Fuzzy RR forcontrolling the access requests.
We use fuzzy rule for handover mechanism when a mobile node moves it from one place to
another place. The loads are reduced with use of L7 switches. We consider parameters like delay,
signal overhead, throughput and bandwidth. Based on these parameters, our simulation result
shows better performance compared to existing Softnet. An important challenge of heterogeneous
network was providing reliable network communication and connection for large number of
devices and machines. Here we analyzed overall handover process which takes place in network
-0.002
0
0.002
0.004
0.006
0.008
0.01
0.012
0.014
0 50 100 150 200 250 300 350 400
Delay
(Kbps)
Time (S)
LTE Wi-Fi WiMax
0
10000000
20000000
30000000
40000000
50000000
60000000
70000000
80000000
90000000
100000000
100 150 200 250 300 350 400
b/s
Time (S)
LTE
Wi-Fi
WiMax

22
and proposedsolutions to reduce handover in network. Our TM2M5G Mechanism proves the
improvement in QoS parameters and machine-to-machine communication is more efficient than
previous system. The entire throughput is increased in the network which shows the improvement
of network performance.
For the further enhancement, we have planned to test with multimedia transmission and
evaluate the performance. Further the system will be secured from external threats.
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