Fast and Secure Intelligence Re-authentication Mechanism …serialsjournals.com/serialjournalmanager/pdf/1474360265.pdf · Fast and Secure Intelligence Re-authentication Mechanism

Fast and Secure IntelligenceRe-authentication Mechanism forNext Generation SubscribersK.S. Balamurugan* and B. Chidhambararajan**

ABSTRACT

Now a day, mobile subscribers expecting high speed, high secure communication at anywhere, anytime with lowcost. Heterogeneous network provide seamless connection and user preference networks to utilize the benefits ofall wireless network to achieve desired quality of service. Heterogeneous network has number of practical challengeson the efficient handover organization and optimization. From academia and industry in deploying more usefulsolutions based on artificial intelligence (AI) techniques, e.g., machine learning, games theory, bio-inspired algorithms,fuzzy neural network, and so on, because AI techniques can logically handle the complexity of any difficult systems.In this paper, we proposed fast and secure intelligence Re-authentication for next generation subscriber usingartificial immunes system. The proposed Method provides desired Quality of Service and Consolidate billing.Computed artificial immunes system optimization using MATLAB code and Simulation was implemented usingthe test bed setup evaluated with OPNET simulation engine. Statistical analysis and Simulation results have shownthat Real time multimedia serviced user was able to seamlessly connect to networks with low latency and betterQoS. Compared with existing Ant’s Colony based vertical handover methods, our proposed mechanism shownbetter results in reducing the Handover Failure Probability, Unnecessary handover Probability, Cost Ratio, PowerConsumption and re-authentication delays in the way of choosing best suitable optimized target networks/Accesspoint/Channel on a given network scenarios.

Keywords: Mobile Network, Quality of Service, Optimum Network Selection.

1. INTRODUCTION

Wireless communication is drastically growing technology and it is one of the emerging fields used byentire world every day. It is predicted that the mobile traffic is increased more than 1000 times in every 10years (2000–2010–2020). Similarly various types of devices interconnected through internet is also increasedmore than 5000 billions [1]. Due to more number of devices, communication and sharing services it is agreat demand to improve the network performances such as low-delay, high-throughput, less-energyconsumption and high security. Mobile networks like WLAN (e.g. Wi-Fi), WMAN (e.g. WIMAX) andWWAN (e.g. Cellular Network) such as 2G (GPRS/EDGE), 3G (UMTS) and 4G (LTE-A) has individualityin data rate, Security level, Coverage area, Cost, Bandwidth, Power level and Signal strength. Now a day,subscriber want to utilize the various network benefits depends on user preference. Heterogeneous mobilenetwork allow the user to move from one network to others. 5G wireless networks has interworking andintegrating different wireless technology such as UMTS (Universal mobile telecommunication system),WLAN (Wireless local area network), WIMAX (worldwide interoperability for Microwave access), LTE(Long term Evaluation) and LTE-A.

Table 1. illustrate the characteristics of different networks. WLAN provide high data rate, low cost andflexible for real time multimedia application /video download users but it’s not suitable for mobility. WIMAX

1 Assistant Professor, ECE, Pandian Saraswathi Yadava Engineering College, Madurai, Tamil Nadu, India, Email: [email protected] Professor & Principal, ECE, SRM Valliammai Engineering College, Chennai, Tamil Nadu, India.

I J C T A, 9(9), 2016, pp. 4175-4189© International Science Press

4176 K.S. Balamurugan and B. Chidhambararajan

provide moderately data rate, low cost as well as sufficient for mobility. Cellular network available inanywhere so good choice for mobility but it’s very high cost and low data rate. In this way, every networkshas own characteristics. Interworking integrating different types of this network provide consolidate billingand seamless connection. Heterogeneous network bring in authentication problem when subscriber changefrom one radio link to others. Now a day, mobile nodes automatically choose the suitable target networkdepend on user preference; to achieve desired Quality of service. Network selection depends on variousparameters Vs network or terminal/ user. Figure1.Shown that the network selection parameters.

Mobile node must execute the re-authentication mechanism When change the radio link.Authentication vectors derived from HSS (Home subscriber server). 3GPP developed re-authenticationmechanism like EAP-AKA, UMTS-AKA, ERP-AKA and EAP-AKA’. But it introduces high re-authentication delay. So reduce re-authentication is challenging issue to seamless connection and withoutbuffer; download video on mobility. Choosing optimized target network significantly reduce the re-authentication delay and provide high Quality of Service. Many research works described about optimizedtarget network/ Channel selection. Aleksandra Checko et al. (2016) presented a detailed study aboutCloud Radio Access Network (C-RAN) is a mobile network architecture. C-RAN can solve so manyissues and provides many solutions for satisfying the end user’s needs. C-RAN interconnects all the basestations in a single pool to make it as a centralized communication base. The communication improvesthe performance regarding multiplexing method. Daniel Granlund et al. ( 2015), proposed anAuthentication, Authorization and Accounting (AAA) method RADIUS-AAA protocol where it can

Table 1Network Vs its Characteristics

Coverage CostMin/Max

Network/Characteristics Data Rate Security Cell Mobility Bandwidth Power

0<W<1 Radius Km/sec Data voice (Mbps)

IEEE Std WLAN 0.6 Weak 100/300m 3-10 0.1 /0.4 0.8/3.0 1/4 0.1

WIMAX 0.5 Low 2/5km 10-40 0.3/0.5 1.0/4.0 2/6 0.5

Cellular Network 2G 0.1 Medium 3/10km >100 0.7/2.5 0.5/2.0 0.1/0.384 1.0

3G 0.2 >Medium 0.8/3.0 0.5/2.0 0.1/0.5 0.9

4G 0.3 High 0.9/4.0 0.4/1.0 0.1/0.5 0.8

5G 0.4 V. High 1.0/5.0 0.2/1.0 0.1/.5 0.8

Figure 1: Target Network selection parameters.

Fast and Secure Intelligence Re-authentication Mechanism for Next Generation Subscribers 4177

enable the necessary AAA functionality in heterogeneous networks. AAA provides a high securedaccessibility global wise. NishaPanwar et al. (2015),discussed 5G networks and the required architecturesand technologies to be developed to meet the state-of-the-art. Some mobile devices, the amount of dataand data transfer rate are growing speedily, and it makes the research people to re-think about the presentgeneration of cellular mobile communication. Pedro Neves et al. (2016) discussed the key challenges of5th generation system because of heterogeneous networks are more complex. Using cutting edgetechnologies like Software Defined Networks (SDN), virtualization and so on. Some of the architecturesderived from SELFNET are ETSI NFV, Dominique Roche et al. (2015) and Open Networking Foundation(ONF) Dan Pitt et al. (2015), NFV also belongs to STSI group having standard virtualization method fornetwork foundations. ONF is a self-organizing approach derived from SDN where it uses software-defined technologies to improve the communication efficiency. Aniruddha Singh et al. (2014), discussedabout the issues and challenges of channel selection for choosing a QoS based channel. Suliman et al.(2013), presented a simple heuristic method including single swarm mutation to reduce the usage ofavailable channels and it allocate channels to satisfy the demands requested from each cell in a particularnetwork. From the above literature review it is decided that handoff quality can be increased byinvestigating the properties of channel. The aim of this paper is to address these challenges andrequirements of re-authentication mechanism for future seamless wireless communication. Novel approachproposed in this paper aims to support i) higher transition rate (100 times more) than today ii) lowerdelay ( few millisecond level) iii) more device connectivity(500 billion device always ON) iv) 1000times traffic density; v) upto 500km/h fast mobility of User Equipment (UE). At the same, it is attractiveto have 99.999% coverage, while energy consumption and cost for the infrastructure should not increase.The Proposed intelligent protocol is deployed in user node and base station which gives better results asit eliminates involvement of too many network elements causing severe processing overhead and multilevelcommunications compared to other known works. Moreover, the simulation-based investigation hasshown that the use of Artificial immunes systems has the potential to perform seamless handover tonetworks with zero latency and improved quality of service for user accessing multi-media services. Theremainder of this paper is organized as follows: in section 2 Analysis the Challenges of Handoveroptimization in Heterogeneous network, section 3 Artificial immune systems based optimized networkselection mechanism was described, section 4 Simulation result of proposed method Compare withAnt’s Colony based Vertical Handover Mechanism. Finally, section 5 concludes the paper.

2. CHALLENGES OF HANDOVER OPTIMIZATION IN HETEROGENEOUS NETWORK

In 2020, around 500 billion device may be ‘Always ON’. 5G mobile equipment used for communication,medical/health, banking, astronomy, education, private and public portal, defense, transportation and searchengine in future. Everyday new users enter in mobile communication network to enjoy the voice call, datausage, video conference, real time gamming and various multi-media services at anywhere, any time.Mobile network like 2G (GPRS/EDGE), 3G (WIMAX, LTE) and 4G (LTE-A) has own characteristics likedata rate, security, coverage area and Signal strength. Now a day, subscriber want to utilize the variousnetwork benefits depends on user preference. Heterogeneous mobile network allow the user to move fromone network to others to utilize the benefits of different networks. Figure2 shown that HeterogeneousNetworks for next generation subscriber travel from one network to various network. Here eight indicatorsmentioned different scenarios.

Scenario 1: Mobile node choose either WLAN/WIMAX/3G

Scenario 2: Mobile node Choose 2G or 3G or 4G.

Scenario 3: Choose WiMax_01 or WiMax_02.

Scenario 4: One ISP to another ISP.


Commonly, Local base stations are represented in AP (Access point) for WiMax, BS for WLAN, eNodefor LTE. Here we use Channel as either of AP/Base Station/Network. Our aim is to select the user preferenceChannel during handover. The research people were frequently used the keywords as adaptive, learning,cognitive and intelligent. These keywords are practically applied in BuNGee [Beyond Next GenerationMobile Broadband] which motivated to increase the overall capacity of the mobile network infrastructureincluding density [11]. The expectation of the BuNGee is also to improve the infrastructure capacity in anorder of magnitude (10x) to an ambitious goal of 1Gbps in 1km x 1km area anywhere in the cell [12]. Dueto increase the long term connectivity the basic requirement is seamless mobility support for roamingusers. All the connectivity based services and supports can be provided by improving the quality of QoSparameters whereas the parameters are optimized by various optimization functions [13, 14, and 15].

(Mohamed Lahby, 2013) Proposed novel ranking algorithm, which combines multi attribute decisionmaking (MADM) and Mahalanobis distance. Firstly, a classification method is applied to build a classeswhich having the homogeneous criteria. Afterwards, the Fuzzy AHP, MADM method is applied to determineweights of inter-classes and intra classes. Finally, Mahalanobis distance is used to rank the alternatives.(L. Nithyanandan1, 2013) discussed that Network selection is a challenging task in heterogeneous networksand will influence the performance metrics of importance for both service provider and subscriber .theproposed work analyses user centric network selection based on QoE (Quality of Experience) which includeboth technical and economical aspects of the user. WLAN-WiMAX-UMTS networks are integrated andthe network selection for the integrated network is performed using game theory based network selectionalgorithm.

Loveneet Kaur Johal 2016 proposed algorithm uses hybrid of Fuzzy logics and AHP to assign weightsto the parameters and since the algorithm is utility based, the network are ranked using simple weightedsum of the parameters. The proposed algorithm selects the network which satisfies all the network selection

Figure 2: Heterogeneous Networks


criteria. Higher the level of user satisfaction served by the network, more it is suitable for handover inheterogeneous environment. It provides higher level of user satisfaction and well suited for random andimprecise wireless environment since it makes use of fuzzy logics instead of crisp values. K.S.Balamurugan,(2016) proposed SON based Simple Light-weight Seamless Handover Rapid Re-authenticationProtocol (3SH_RRP) is proposed in this paper. figure 1 shown user preference target network selection.This protocol provides fast seamless handover using Cloud RAN architecture based deployment. It providesa rapid re-authentication within Cloud RAN with the help of virtual homing (VH) concept in SON. 3SH_RRPis implemented in SON which gives better results as it eliminates involvement of too many network elementscausing severe processing overhead and multilevel communications. This protocol uses log likelihoodweighted factor (Bandwidth, Security level, Power Level, and Received Signal Strength Indicator) functionto maximize the probability of successfully selecting the most appropriate BBU well in advance to performsuper-fast or rapid re_authentication during inter_RAT HO. Figure3 shown that network selection parametersto take handover decision for choose best target network.

Table 2. point out the four level of network parameters threshold as NULL, LOW, MEDIUM and HIGH.

3. ARTIFICIAL IMMUNE SYSTEMS BASED OPTIMIZED NETWORK SELECTION

Artificial Immune System (AIS) algorithm is basically a biological evolutionary algorithm. It is a natural selectionand gene representation algorithm. AIS is used in various kinds of comparison and searching problems from

Table 2Network Parameters threshold level

Inputs/ Level 1 2 3 4

Service Voice call Internet Surfing Video Real Time application

Mobility <3 <10 <80 <300

Signal Strength Null Low Medium High

Battery level Null Low Medium High

Security Null Low Medium High

Figure 3: Network Selection Mechanism

Par

amet

ers


medium to large in size. In this paper using AIS, a hybrid channel selection and assignment algorithm is applied.AIS has a large size populations which represents solution of the problem, whereas each solution is representedas a chromosome. Collection of chromosome forms a population. In human body a chromosome consists ofseveral genes and it is represented in binary format. Each bit in the chromosome denotes a gene. Chromosomesare also called as individuals or strings. From the population AIS selects a best possible solution on the basis ofa Fitness Function (FF) value (also called as threshold value) which is defined by the user according to theproblem solution required. The FF is unique for each optimization problem. The fitness of the entire chromosomein the population is measured and the best one is selected. AIS ensure a fast convergence to the near-optimalsolution. Any problem which is represented as an optimization problem can be solved using AIS. This process isrepeated in an iterative manner until meet the termination condition reached or the iteration reached. In thispaper, AIS is utilized for selecting the best channel suit for present cellular communication in the WCN. Theattributes of the channels are taken as a chromosome and investigated by comparing with the FFV.

Many optimization techniques are used to predict the best optimized Channel. But artificial immunesystem only can support to compare N numbers of variable with multiple constrain hierarchy level of threshold.To understand in better manner the AIS algorithm is given in the form of algorithm and pseudo code below.

3.1. AIS Algorithm

1. Assign a random population P

2. For each population, compute OFV as the optimum channel

3. In order to provide more solutions, compute the affinity value where it can be calculated by1/OFV for P

4. To determine the number of new solutions, compute the rate of cloning

1* / ROC P Total affinity value

OFV

5. Clone generation for the problem, according to the ROC.

6. Check and maintain the size of population is S after successful cloning.

7. Do inverse and pairwise mutation on S, arrange all P in ascending order and eliminate R% ofhighest OFV based clones.

8. Replace R% of the highest OFV based solutions by new random population generation.

9. Repeat (2) until obtaining a best OFV.

Optimization Technique• Particle swarm optimization• Ant colony optimization• Artificial bee colony algorithm• Artificial Swarm Intelligence• Artificial immune systems• Bat algorithm• Gravitational search algorithm• River formation dynamics• River formation dynamics

�

Statistical Model• Fuzzy logic• TOPSIS• SAW• Game theory• Log-likelihood Ratio• Genetic Algorithm• Multi variable ANOVA

Artificial intelligent algorithms

� �


Where, dfn is the decision function, the decision is made according to the bandwidth ban

n, power

consumption En and cost Cost

n. w

a, w

b and w

c are the weight factors determines the weight of the parameters

such as �wi = 1

arg min ,choptCh f ch

Where, fch is the channel optimization function for WCNn, and it can be calculated as:

;; ; ;ch ch ch ns j

is j

f E s i f s j w N Q s j

N(Qn s; j) is the normalized Quality of Service parameters, Qn s; j represents the best quality of channelcarry out service s in cell, on network n. fch s; j(w

s;j) represents the weighting function for service s and

Ech s; i represents the elimination factor of service s. The best channel is selected according to the availablebandwidth, RSS, power consumption, distance and roaming access quality. The optimization function iswritten as:

OFV = avgmin (fch)

OFV(ch, q) = wq � Quality(ch, q) + w

d � distance(ch) + w

pc

� pc(ch) + wc � capacity(ch) + w

co � cost(ch, q)

for all ch � CH = {ch1, ch

2, ..., ch

n}n � Z

and q � Q(ch) = {q1, q

2, ..., q

m}m � Z

Where ch the set of channels is perceives and Q(ch) is the set of quality of levels at which the channelch can be selected for the channel allocation and service s under consideration. Each q

i represents various

QoS parameters of a channel like bandwidth, RSS and roaming access etc.

The entire quality of the channel ch is determined by the parameters assigned values Q(ch). Theoptimization of ch is decided from the quality levels of Q(ch) and it can be written as:

max , ch CH q Q ch OF ch q

From the above channel optimization methods AIS creates a chromosome by choosing some importantparameters. The chromosome created in this paper is:

S = {P, Th, Co, BW.S. RSS.D.M, V, U, A}

P : Power Consumption level

Th : Throughput

Co : Cost

BW : Bandwidth level

S : Security

RSS : Received Signal Strength

D : Distance away from access point

M : Mobility support level

V : velocity of mobile node

U : User profile

A : Availability of Channel


Each entity in the S is assigned by two values as “1” and “0”. If the entity satisfy the QoS level to thedemand of the user then it is assigned as “1” else it is “0”. It also dynamically changes the MSB-LSBpriority level depends on application and user preference to choose optimized channel selection.

3.2. AIS – Numerical Illustration

The main objective of this paper is to fulfill the demand of the user regarding a network selection withmaximum capacity, throughput, and minimum energy consumption, distance, cost within minimum time[MAX(QoSNet)]. In order to fetch a QoS

Net, for all P compute the affinity value from OFV using:

Affinity value = 1/OFV

3.3. Clonal Selection and Expansion

A single clone represents a solution. The affinity value is inversely proportional to the objective functionvalue. From the affinity value the best case clones can be chosen for finding optimum value called as Rateof Cloning (ROC), it can be calculated using:

* [ ]

affinity value Population SizeRate of cloning ROC

Total of affinity value of the solution

One clone is the original copy of one string. In order to improve the optimization accuracy it is necessaryto generate more solutions. So with respect to ROC value new clones are generated. If the ROC value is1.4, then the new number of clones is 2. This process increase the temporary population of the clones andit is called as Clonal Expansion.

3.4. Mutation

To create new clones there are two different kinds of mutation is applied such as Inverse Mutation and Pairwise Mutation. By applying mutation new clones of the population can be generated and it is shown in thefollowing Figure-4 and in Figure-5.

Original String 1 1 1 0 1 0 1 0 0

Changed String 1 1 0 1 0 1 1 0 0

Figure 4: Inverse Mutation

Figure-3 shows inverse mutation on a string S. After inverse mutation the OFV is calculated for themutated string and compare with the OFV of the original string. If the OFV of mutated string is maximumthan original string then the original string is replaced by the mutated string, else retain the original stringand proceed with pairwise mutation.

Original String 1 1 1 0 1 0 1 0 0

Changed String 1 1 0 1 0 1 1 0 1

Figure 5: Pairwise Mutation

Figure-4 shows pairwise mutation on a string S. After inverse mutation the OFV is calculated for themutated string and compare with the OFV of the original string. If the OFV of mutated string is maximumthan original string then the original string is replaced by the mutated string, else retain the original string.


After mutation process completion, it is clear that the population size is increased than the initializationsize P as 50. In order to maintain the population size as 50, the entire population is arranged in ascendingorder according to the OFV value. From that the top 50 populations are only taken for further process andthe remaining populations (more than 50) are deleted from the list.

3.5. Robust Replacement Process

AIS have a unique feature that eliminating worst-case scenario to avoid/reduce the computational complexity.To do this, R% of worst-case populations are removed from the available population P, then new populationsare generated randomly and newly for the same R% which maintains P. This process is repeated iterativelyuntil reach the objective or until meet the termination constraints. It is assumed that a best channel can bechosen when S is as:

S = {1, 1, 1, 1, 1, 1, 1, 1, 1}

But it is rare to obtain all the Quality of service factor meets the user requirement at the same time.

3.6. Simulation Results and Discussion

In this paper MATLAB software is taken for experiment our proposed AIS approach and evaluates the performance.Here, there are various number of cells with various number of channels is deployed. Each time the channelselection and channel allocation is applied by executing the AIS code. The AIS algorithm is implemented inMATLAB software (m code) and experimented. While evaluating the obtained values of the parameters arecompared with the values given in Table-3. If it matches mean the channel meets the user demand and it can beallocated to the appropriate user. Our proposed system consider the Availability of channel parameters to avoidthe handover failure. Our simulation result shown that NULL failure in HO in any scenarios.

Figure 6. Shown that OPNET with Simu-LTE network simulation setup. Here we implemented twoWLAN (WiFi) and one LTE Cellular network. Our AIS based optimized algorithm loaded in mobile node,WLAN access points, LTE and server. Consider our proposed algorithm automatically deployed whenregister the mobile node in networks/HSS. Also consider full authentication process was done, user profileupdate automatically, mobile node is software define radio and network is self-organizing network anddifferent service provider are mutually interconnected. Mobile node become intelligent node to choose thebest channel depends on availability of service, cost, applications any analyze many parameters with helpof our proposed algorithm.

Table 3Simulation Parameter Evaluation Values

Network Selection Variable Weight Factors Lower UpperParameters (0<P<1) Bound Bound

Power P1 0.122 0.1 0.162

Throughput ( Mbps) P2 0.234 54 540

Cost (per KB) P3 0.411 1 4

Bandwidth(Mbps) P4 0.321 11 15

Security P5 0.0431 <5 <10

RSS P6 0.1243 25 100

Distance (m) P7 0.054 50 150

Mobility P8 0.012 1 300

User Profile P9 0.5 1 4

Availability of Channel P10 0.3 5 50


In order to examine the performance of the channels there are ten numbers of parameters are used here.Channels are selected initially by the availability and it should be little closer to the user one need forchannel connection. This functionality is verified by investigating the total number channels, mode of thechannels and the distance from the user. The obtained results from the experiment are shown, where itillustrates the dynamic behavior of the channels and number of channels can be selected. Here, the lessdistance based channels are selected and their availability is examined. A channel may in any one of themode such as: Sleep, Listen, Service and Idle, where in our experiment it looks for the channels which arein sleep of in idle mode. In case of scalability the number of users gets increased and the availability of thechannel becomes poor. Second parameter which leads to select a best channel is the amount of energyconsumption taken by the channel and the communication among the channel and the user. The amount ofenergy consumed by the channels and users are different, since the channels are well configured and little

Figure 6: OPNET with Simu-LTE network simulation setup

Figure 7: Energy Consumption


more memory than the users. Because a channel can transmit and receive the information from a userlocated far away and its RSS is good also it is capable for more communication at the same time. Energyconsumed by the channel and the user are more or less equal and it depends on the distance and the datarate. According to the changes in the data rate, distance and size of the data energy is consumed. The energyconsumed by the channel and the user is examined experimentally and the result is shown in Figure-7.

Figure-8 and 9 shows that, with our AIS approach it can find a solution with channel selection andallocation failure probability less than the specified value (0.005) and unnecessary channel comparisonfor selection-allocation probability less than the specified value (0.005) independently of the mobileuser mobility. The parameters of the channels are examined are channel selection and unnecessary channelcomparison probability to the total number of channel availability, bandwidth, service, cost and time ofservice. In this simulation AIS is compared with Ant Colony Optimization – (ACO) method to RSSbased.

Figure 8: Unnecessary Handover Probability

Figure 9: Failure of HO Probability


From Figure-10 to 13, the proposed AIS approach and ACO approach are compared in terms of energyconsumption ration, security ratio, cost ratio and bandwidth ratio obtained while mobility for various numberchannels and users. It is aimed to achieve much better performance than ACO, in terms of QoS parameters.The reason for comparing with ACO is, ACO is already compared with RSS and HNE based methods and

Figure 10: Power Consumption Ratio Comparison

Figure 11: Security Ratio Comparison

Figure 12: Cost Ratio Comparison


proved it as a better approach. From the Figure-10 to 13, it is clear that it cannot reduce the number offailures and unnecessary channel selection and allocation up to 95% regardless of the mobility of the users.Even though, we got better QoS in terms of bandwidth, security, power consumption and cost of service.For better explanations on the performance comparison, bandwidth cost of service, security and powerconsumption of the ACO method and AIS are compared for different number of users and different numberof channels. From the figures, it is clear and proved that AIS approach obtained a better performance thanACO. Also the following Table-4 gives the summarized values of bandwidth, cost of service, security and

Figure 13: Bandwidth Ratio Comparison

Table 4Performance Comparison among RSS, ACO and AIS

No. of size Cost Baadwidth Security Power consumption

MTs RSS ACO AIS RSS ACO AIS RSS ACO AIS RSS ACO AIS

100 10 4 3 2.9 830 930 942 6.5 8 8.3 0.17 0.05 0.048

100 20 3.7 2.7 2.68 840 940 953 6.7 8.3 8.6 0.12 0.03 0.026

100 50 3.4 2.5 2.43 860 960 967 7.2 8.4 8.7 0.075 0.025 0.022

100 100 3.1 2.4 2.3 880 970 972 7.4 8.5 8.7 0.055 0.02 0.019

50 100 3.1 2.6 2.58 880 955 964 7.4 8.4 8.6 0.055 0.025 0.021

20 100 3.1 2.7 2.65 880 930 943 7.4 8 8.2 0.55 0.035 0.031

10 100 3.1 2.9 2.87 880 915 921 7.4 7.8 8.1 0.55 0.043 0.041

50 10 4 3.2 3.1 830 920 929 6.5 7.7 7.9 0.17 0.07 0.68

50 20 3.8 3 2.88 840 930 936 6.8 8 8.3 0.12 0.042 0.037

50 50 3.4 2.7 2.61 870 950 961 7.2 8.2 8.5 0.075 0.032 0.03

20 20 3.4 3 2.76 860 920 934 7.1 7.9 8.3 0.075 0.042 0.037

20 10 3.8 3.2 3.14 840 910 918 6.8 7.7 8.2 0.12 0.06 0.056

20 10 4.1 3.5 3.39 830 900 911 6.6 7.5 7.9 0.18 0.09 0.081

10 10 4.1 3.6 3.48 825 880 895 6.6 7.3 7.8 0.18 0.11 0.08

10 20 3.8 3.4 3.36 840 890 898 6.8 7.5 7.8 0.12 0.08 0.04

10 50 3.4 3.1 2.78 865 905 912 7.2 7.7 8.1 0.078 0.052 0.034


power consumption under different solutions. It is clear from the table and the figure, when increasing thenumber of users we can obtain a better performance.

4. CONCLUSION

The main objective of this paper is to select the best suitable Access point/Channel in HeterogeneousNetworks. Due to more number of users, available of various characteristics of network, different technologiesand Real time Services it is necessary to provide an optimum channel selection for the users in order toprovide seamless connection and user preference QoS. This paper utilizes Artificial Immune System (AIS)approach for optimizing the channel to be selected for next generation subscribers. The Networks/APs/Channels parameters are examined and evaluated by comparing the objective function value andautomatically channels are elected. From the experimental results it is proved that AIS based Fast andsecure intelligence re-authentication mechanism is suitable for next generation subscriber utilizes the benefitsof different network in any scenarios. The scope for our algorithm getting implemented in any futurenetworks like IEEE 802.22 and SON (Self Organizing Network) like C-RAN etc. Future works also can becarried out on reducing the computational overhead in access point and user device.

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