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A New Model of Genetic Zone Routing Protocol (GZRP) : The Process of Load Balancing and Offloading on The UMTS - IEEE 802.11g Hybrid Networks Setiyo Budiyanto Department of Electrical Engineering, Universitas Mercu Buana, Jakarta, Indonesia Email : [email protected] Abstract - The stages of the process of Genetic Algorithm (GA), are: Encoding Genotype and Chromosome; Set Initialization Population; Evaluation Fitness Function; and Selection Process as well as in the later stages Cross Over Process and Mutation. Outputs from the tests performed in this study can be obtained by comparing the Genes of the "Child" (condition data traffic on the UMTS Hybrid - 802.11g network after the GA) against Gen "Holding" (traffic data before the GA process). The research was conducted by calculating the environmental factors, namely: The scheme Two - Ray Model Propagation and Overlapping Channel Interference Factor, the Doppler Effect be ignored because the User Equipment (UE) is considered not to shift significant arenas on the IEEE 802.11g networks. The results of the research is as follows: In the process of cross over, there is a significant change in the bandwidth, data traffic capacity and Power parameter changes by 9 MHz, 36 MB, and 40 dBm. In the process of mutation, there is a significant change in the bandwidth, data traffic capacity, and Power parameter by 17 MHz, 32 MB, and 20 dBm. Keywords : Genetic Algorithm, UMTS - IEEE 802.11g Hybrid Networks, Two - Ray Model Propagation, Overlapping Channel Interference Factor 2 Introduction The utilization of data traffic on the Universal Mobile Telecommunications System (UMTS) has been increased, along with the supporting technology development [1]. It resulted in the idea to stream the mobile data traffic to other wireless networks such as Wireless Fidelity, in this study used tissue type IEEE802.11g[2-3]. Mobile Advanced Delivery Network (MADNETs) is the network is able to process the transfer of data from several different networks (UMTS and WIFI)[4-5]. The process of moving data traffic from UMTS network to a WiFi network and also the reverse process (the transfer of data traffic from the WiFi network to a UMTS network) is namely the process of UMTS - WiFi Offload. Vertical Hand Over (VHO) algorithm Used in order to perform the offloading process [67]. Until nowadays, there has not been a study that addresses the special routing protocols that can divert traffic to other access points that are in a cluster. Meanwhile, on the other hand technology Mobile Ad-Hoc Network (MANET) has some concept of routing protocols : Reactive, Proactive, and Hybrid [8]. The activities carried out at the investigation are: Development of Genetic Zone Routing Protocol (GZRP) that has been developed in previous studies [9-11]. Namely by elaborating the Genetic Algorithm begin from population initialization process with the best individuals search by cross over methods as well as to the process of genetic mutation. 1 Related Research The development of technology resulted in increased usage of data in the mobile network. On the other hand, the technology that existed at present incapable of becoming the solution of the problem of increased traffic on these data, it brought the idea to drain the mobile traffic to other wireless networks such as WiFi [12-13]. A MADNET technology architecture that is capable of forwarding traffic to mobile wireless networks such as WiFi accordance research conducted in [4]. On the process of offloading traffic from UMTS network to a WiFi network, contained VHO algorithm is : capable of performing the handover process from the UMTS network to a WiFi network [6]. In UMTS - WiFi Offload technology there has been no special routing protocols that can divert traffic to other access points that are in a cluster. On the other hand, wireless technologies such as Mobile Ad-Hoc Network (MANET) has some concept of routing protocols : Reactive (on-demand), Proactive, and Hybrid [8]. In [10-13], a concept known as load balancing using Genetic Zone Routing Protocol (GZRP) on the wireless network MANET, which is a load balancing with bringing the total packets received to an alternative route so as to reduce the traffic load on a standalone service. According to [9], GZRP is the development of hybrid routing protocols in MANET 1 namely Zone Routing Protocol (ZRP) which is coupled with Genetic Algorithm (GA) [14]. ZRP is used to reduce the burden of proactive routing protocol control and reduce the latency caused by the discovery in a reactive routing protocol. In [15] described a WiFi network interworking and UMTS networks, one of which is the handover process that goes from the cellular network to the WLAN. In [6] described a concept handover algorithms set up with both the process of handover from UMTS to WiFi known as algorithms Vertical Handover (VHO). In [6], Vertical Handover (VHO) focused on Goodput and RSSI, known as Hybrid-RSSI algorithm. One thing that is not easy to get an accurate estimate of goodput in the real environment. The second proposal is a hybrid-RSSI estimation algorithms utilizing VHO goodput to ensure the quality of services provided to users. 3 Proposed Algorithm Offloading process occurred conditions at the time a UMTS RSSI value less than the RSS threshold (carried out process of moving the traffic from a UMTS networks to WiFi). The parameters used in this study were related to load balancing using GZRP (in the WiFi network) is the bandwidth, capacity and power. These three parameters will be linked with other methods of dealing with the propagation and interference, while the effect doopler ignored for User Equipment (UE) is considered not to shift a significant place. Propagation models used is the: Two-ray propagation model models, while the interference parameters using Overlapping Channel Interference Factor. The algorithms developed in the study can be seen in Figure 1.: In [10-11] [16-17], has carried out research related to the use of protocols GZRP combined with VHO occupancy in order to achieve efficiency levels of data traffic, either on the network or UMTS and WiFi networks; as well as the simultaneous performance of both networks. The discussion in detail to the discussion of performance improvement GZRP protocol can be used as a very interesting discussion in order to seek more comprehensive solutions related to problems that occur in the process of load balancing using GZRP protocol.. The Hybrid protocol used is the Genetic Zone Routing Protocol (GZRP) [10-11], is able to improve the efficiency of the performance of a network. It can also reduce the load on the track by balancing the distribution of packet delivery over the course of the alternatives available. Utilization concept GZRP especially in the process of traffic load balancing between nodes. This study aims to investigate intelligent systems load balancing algorithms and WiFi offload in the wireless communication traffic bottlenecks can be overcome; in addition, also equal distribution of traffic and to develop methods of genetic algorithm combined routing protocol ZRP (Zone Routing Protocol) in the process of offloading in the load balancing and IEEE 802.11g WiFi network. In this case the load balancing process in the Hybrid UMTS networks - IEEE 802.11g. Load balancing happened in the WiFi network is a follow up of the process of offloading data traffic from UMTS network to a WiFi network. Offloading process occurs when the condition value is smaller than the UMTS RSS RSS threshold (do the process of moving traffic from UMTS networks to WiFi). START Inactive no no RSS > Threshold no RSS > Threshold yes yes WiFi Active UMTS Active yes RSS > Threshold yes Link Going Down Event Mx = 0 yes yes Mx = 0 no no RSS > Threshold no Data Transmit no no no Data Transmit Capacity Fall Or Status Down yes T = 50s GZRP T = 50s yes END yes Figure.1. The Proposed Algorithm Figure 1. Schematic of a working system algorithm developed in the the research, it can be seen Wi-Fi offload process that can be described as follows: ๏ท User Equipment (UE) is automatically connected to the UMTS network, if RSS> RSS threshold, then it would use the UMTS network; If the RSS <RSS threshold then alert will be given a link going down and will carried out Initialization network 2 ๏ท ๏ท conditions and checking RSS in the nearest WiFi node. If the WiFi network also did not have RSS> RSS threshold, then the demand for packet was suspended for a specified timeout. Subsequently, the EU network be deactivated. At the time of the network used, has one of the parameter value 0 of the variable Mx, it will be vertical handover. The parameters that determine handover is defined in equation (1). ๐๐ฅ = ๐(๐๐ฅ โ ๐๐กโ ) . ๐(๐ ๐๐๐ฅ โ ๐ ๐๐๐กโ ) . (๐๐ฅ โ ๐๐กโ ) . ๐(๐ถ๐ฅ โ ๐ถ๐กโ ) Annotations : Pt = Power Transmitter r1 = Direct distance from Tx to Rx r2 = Non-direct distance from Tx to Rx ฮ(ฮฑ) = The reflection coefficient depends on the angle ฮฑ and polarization Where as, (1) K(two ray) = Gt Gr ht2 hr2 / L (3) Annotations : ๐๐ฅ = The bandwidth used by the network to connect (to a UMTS network or WiFi network) bth = The threshold of the bandwidth required by the EU = The value of the received signal strength by the EU = The RSS threshold value of the EU ๐ ๐๐ ๐ ๐๐๐กโ ๐๐ฅ Pth ๐ถ๐ฅ Cth = The energy possessed by the base station and the access point = The energy required by the EU = The capacity of the network service capabilities by the EU = The threshold of capacity is required to process UE connection to the network In a WiFi network, are applied the algorithm with the ability to determine when capacity nodes is in the process of transition or receive the data reaches the threshold or link node status is "down" applied GZRP routing protocol. The parameters of the test conducted The novelty of the system testing parameters Model Genetic Zone Routing Protocol (GZRP) in the IEEE 802.11g networks; It is the process of load balancing and offloading in the Hybrid a UMTS - 802.11g networks composed by some of the main discussion, namely: Two-Ray models Propagation, Overlapping Channel Interference Factor and processes on the network GZRP MADNETs. The following discussion related of testing parameters: Two-Ray Model Propagation Propagation be one of the testing parameters in order to consider factors existing environment. This parameter is used in the testing by considering previous studies [18-22]. In this test used models Two-Ray Propagation Model. The Power receive signals (Pr) on the propagation of two - ray models, defined in equation (2) : ๐ 1 1 4๐ ๐1 ๐2 ๐๐ = ๐๐ก ( ) [ ๐ (๐๐๐1) + ๐ค(๐ผ) ๐ (โ๐๐๐2) ] 2 (2) Gt and Gr = Gain Tx and Rx ht and hr = Antenna high Tx and Rx ฮป = Wave Length (ฮป= c/f) L >=1, loss factor at the systems (L=1) ๐ค(๐) = cos ๐โ ๐ผโ๐๐โ๐ ๐๐2 ๐ cos ๐+ ๐ผโ๐๐โ๐ ๐๐2 ๐ (4) Annotations : ฮธ = 90-ฮฑ a = 1/ฮต or 1 for polaritation vertical or horizontal r = The relative dielectric constant in the reflective surface Overlapping Channel Interference Factor Model overlapping channel interference is to determine factor overlapping channel interference; wij be the percentage increase in value relative in the interference generated by two access points i and j using overlapping channels [23-25]; The next channel is given to the AP must be chosen a thorough. Method aims overlapping channels to minimize channel interference from several different AP, it is the development of research assumes that some APs occupy the same channel. As we know that interference in the communication network greatly affects the value of in the AP power transmitter and power receiver in the UE. The value of the overlapping channel interference factor can be determined by taking into account the equation (5) as follows: 1 โ |๐น๐ โ ๐น๐ |๐ฅ ๐ถ ๐ค๐๐ โฅ 0 ๐ค๐๐ = { 0 ๐๐กโ๐๐๐ Annotations : ๐น๐ ๐น๐ (5) = channel assigned to the access point i = channel assigned to the access point j 3 ๐ถ = Overlapping channel factor 4 Simulation, Results and Discussion The study bounded during load balancing from a UMTS network to the IEEE 802.11g network. The Genetic Algorithm process begins; As for the stages of the process is carried out as follows: Encoding, Population Initialization, Determination of Value Fitness, Selection, Cross Over and Mutation. WiFi To initiate this process first define the problem, the scenario was made of the condition of User Equipment (UE) and Access Point (AP). AP assumed each of which there are 10 with the bandwidth, capacity and power as follows: UE AP 2 AP 3 AP 4 AP 5 AP 6 13 25 20 AP7 14 30 20 AP8 16 40 20 AP9 18 45 20 AP10 20 54 20 Details of the process, described further, as follows: Encoding in the process of Genetic Algorithm The encoding process in the genetic algorithm is the process to encode a gene in a chromosome. These genes can be represented in the form of binary bits, real number, a list of rules, permutation elements, elements of the program or other representation that can be operated in the genetic operations. In load balancing, to a WiFi network each node is encoded form of binary numbers are adjusted for the number of nodes. Forms for each individual chromosome can be seen in Table 2, as follows: Power 10 dBM Video via Youtube 5MB -GZRP- AP 1 AP6 Table 2. The form of chromosomes to each individual (in binary format) AP 7 AP 8 AP 9 AP 10 Figure 2. Assumptions for User Equipment (UE) Annotations : Bandwidth (Bth) = 5 MHz Capacity (Cth) = 5 MB Power (Eth) = 10 dBm The value of the parameters contained in each access point can be seen in Table 1, as follows: Table 1. Bandwidth, Capacity and Power in the AP Bandwidth (MHz) Capacity (MB) Power (dBm) AP1 5 6 20 AP2 6 8 20 AP3 8 10 20 AP4 9 15 20 AP5 11 20 20 Bandwidth (MHz) Capacity (MB) Power (dBm) AP1 000101 000110 010100 AP2 000110 001000 010100 AP 3 001000 001010 010100 AP4 001001 001111 010100 AP5 001011 010100 010100 AP6 001101 011001 010100 AP7 001110 011110 010100 AP8 010000 101000 010100 AP9 010010 101101 010100 AP10 010100 111000 010100 Based on the data presented in Table 1, it can be seen that the WiFi network (IEEE 802.11g) consisted of 10 individuals (access point) where each chromosome is defined in binary form. Defining chromosomes are based on three parameters such as bandwidth that put the group first column, column group capacity in the second and third 4 power in the column. Each chromosome is made up of six genes that total in a single individual genes to 18 genes. Based on the simulation parameters used the number of nodes that there are 10 nodes AP as shown in pseudo code as which can be seen in the section below: ๐ธ๐ฅ๐ = Power of Access Point ๐ธ๐กโ = Power threshold (contained in the UE) The following pseudocode for the formation a fitness function that has been presented at the equation (6): void fitness () {char *fitness = malloc (sizeof (*fitness) *Num_Elements) # AddParams set cluster_num_ # AddParams set nodes_num_ for (i=0; i <nn; i++) set node_(0) [$ns node] { if(bandwidth_remain > bandwicdthTh) set node_(1) [$ns node] {A=1} set node_(2) [$ns node] else {A=0} set node_(3) [$ns node] if(Capacity_remain > CapacityTh) set node_(4) [$ns node] {B=1} set node_(5) [$ns node] else {B=0} set node_(6) [$ns node] if(RSSI_remain > RSSIth) set node_(7) [$ns node] {C=2} set node_(8) [$ns node] else {C=0} set node_(9) [$ns node] X[i] = [A*(Bxi โ Bth) + B*(Cxi โ Cth) + 1 / (C*(Exi โ Eth))] Fitness Function in The Genetic Algorithm fitness = sum (X[i]) A function is a reference for the optimal value based on the objectives, namely: the selection of the optimal AP for the EU to consider the bandwidth, capacity and power. The fitness function used can be seen in equation (6) as follows: } ๐[๐] = [๐ด โ (๐ต๐ฅ๐ โ ๐ต๐กโ ) + ๐ต โ (๐ถ๐ฅ๐ โ ๐ถ๐กโ ) + ( 1 ๐ถโ(๐ธ๐ฅ๐ โ๐ธ๐กโ )] (6) Description of the parameters used in equation (6): X[i] = A = Fitness Function for the access point number i Coefficient of Bandwidth B = Coefficient of Capacity C = Coefficient of Power Consumption ๐ต๐ฅ๐ = Bandwidth contained in the access point ๐ต๐กโ = Bandwidth Threshold (contained in the UE) ๐ถ๐ฅ๐ = Capacity of Access Point ๐ถ๐กโ = Capacity Threshold (contained in the UE) Results and Analysis of The Research In this section will be discussed related to the results and analysis of outcomes which have been obtained. In this research, discusses the process of genetic algorithms starting from the stage of the gene encoding the individual constituent (access point) and ending with the mutation of an existing gene. The following discussion details about the results of research and analysis of the results of this study: Process of Cross Over at the MADNETs Network by GZRP The discussion done with the assistance of software NS2.35 and Matlab; for further validation of the data based on by parameter adjustment (6). Be discussed in more details regarding the parameters of bandwidth, capacity and power before and after through GZRP (by comparing the value of both of them using the parameter propagation and interference). Parameter Bandwidth in the Crossover Process The first parameter is used as a media data validation at the the cross over is bandwidth. Values bandwidth before experiencing GZRP process is the result of the bandwidth 5 that has been presented in Table 1 while the value of the bandwidth after processing GZRP is the value of the match by fitness on the bandwidth which has been interbred by another access point. Outcomes bandwidth obtained before and after the process of GZRP, as shown in Figure 3: Based on the figure 4., can be seen the results of the cross over process in the capacity of traffic data parameters contained in the access point. It was concluded that the access point 5 has a value change is the greatest capacity of traffic data is 36 MB. Parameter Power in the Crossover Process The value of power before experienced the process GZRP is the result of a power that has been presented in Table 1., while the value of power after processing GZRP is the value of the match with fitness on the part of the capacity that has undergone cross over to the other access point. Outcomes of power earned before and after the process of GZRP, as shown in Figure 5: Figure 3. Bandwidth on the conditions before and after the process GZRP Based on the figure 3., can be seen the results of the cross over process in the bandwidth parameters contained in the access point. It was concluded that the access point 5 has a value change is the greatest bandwidth is 9 MHz. Parameter Capacity Data Traffic in the Crossover Process In the cross over process, the data traffic capacity is used as a parameter in terms of measurements and data validation. The value of capacity in before the GZRP process is the result of the capacity that has been presented in table 1, while the value of capacity after processing GZRP is the value of the match with fitness on the part of the capacity that has undergone cross over to the other access point. Outcomes of capacity earned before and after the process of GZRP, as shown in Figure 4. Figure 4. Capacity Data Traffic on the conditions before and after the process GZRP Figure 5. Power Bandwidth on the conditions before and after the process GZRP using "two ray model" and interference "dynamic overlapping channels" Based on the figure 5., can be seen the results of the cross over process in the power parameters contained in the access point. It was concluded that the access point 5 has a value change is the greatest power is 40 dBm. Process of Mutation at the MADNETs Network by GZRP Mutations in the the GA will influence the gene at the an individual, which generates the best individual or otherwise decreased of quality. In the process of testing mutations in MADNETs network using GZRP, which used the same parameters as in the process of Cross Over : Bandwidth, Capacity Data Traffic and Power. Parameter Bandwidth in the Mutation Process The first parameter is used as a tool for validation data on the mutation process is bandwidth. The value of bandwidth before experiencing the GZRP process is the result of the bandwidth that has been presented in table 1.; The value of Bandwidth after processing GZRP is the value of the matches with fitness on the bandwidth that has develop of mutation. Outcomes of bandwidth earned before and after the process GZRP, can be seen in Figure 6: 6 Parameter Power in the Mutation Process The value of power before experienced the process GZRP is the result of a power that has been presented in Table 1., while the value of power after processing GZRP is the value of the match with fitness on the part of the capacity that has undergone mutation to the other access point. Outcomes of power earned before and after the process of GZRP, as shown in Figure 8: Figure 6. Bandwidth on the conditions before and after the process GZRP Based on the figure 6., can be seen the results of the cross over process in the bandwidth parameters contained in the access point. It was concluded that the access point 8 has a value change is the greatest bandwidth is 17 MHz. Parameter Capacity Data Traffic in the Mutation Process In the mutation process, the data traffic capacity is used as a parameter in terms of measurements and data validation. The value of capacity in before the GZRP process is the result of the capacity that has been presented in table 1, while the value of capacity after processing GZRP is the value of the match with fitness on the part of the capacity that has undergone mutation to the other access point. Outcomes of capacity earned before and after the process of GZRP, as shown in Figure 7. Figure 8. Power Bandwidth on the conditions before and after the process GZRP using "two ray model" and interference "dynamic overlapping channels" Based on the figure 8., can be seen the results of the cross over process in the power parameters contained in the access point. It was concluded that the access point 5 has a value change is the greatest power is 20 dBm. 5 Conclusion In this section, there will be a discussion and analysis of results obtained from testing the model renewal GZRP on IEEE 802.11g network, the load balancing the process and offloading on Hybrid UMTS networks - IEEE 802.11g. The process of discussion and analysis of the test results of the research that has been done is to determine the performance of the performance of the system that has been created based on the methodology proposed in this study. 1. 2. Figure 7. Capacity Data Traffic on the conditions before and after the process GZRP Based on the figure 7., can be seen the results of the mutation process with a focus on parameter capacity data traffic at the the access point. It was concluded that the access point 5 has increased value changes most capacity is equal to 20 MB, while AP 8 has a change of impairment greatest capacity that is equal to 32 MB. 3. The process of GZRP on the MADNETs network; The steps undertaken in this process starting from the Encoding, Population Initialization, Determination of Value Fitness, Selection, Cross Over and mutation. The discussion on the results of research that has been done by calculating the propagation factor "two ray model" and interference "dynamic channel assignment". In the the process crossovers; Access point 5 have the value changes bandwidth, capacity and power of the greatest in the amount of 9 MHz, 36 MB and 40 dBm. Retrieved a conclusion based on these conditions, namely: User Equipment will be connected to the Access Point 5 at the time of the 7 4. selection of the access point with the network UMTS - WiFi (IEEE 802.11g). In the process of mutation; Access point 5 have the value changes bandwidth, capacity and power of the greatest in the amount of 17 MHz, 20 MB and 20 dBm MHz. Retrieved a conclusion based on these conditions, namely: User Equipment will be connected to the Access Point 5 at the time of the selection of the access point with the network UMTS - WiFi (IEEE 802.11g). [9] P. Sateesh Kumar and S. Ramachandram, โGenetic Zone Routing Protocolโ, Int. Jrl. of Theoretical and appl. Infn. Tech., 4(9), pp 789- 794, Sept. 2008 [10] Budiyanto, S., Asvial, M., danGunawan, D.,โ Implementation of Genetic Zone Routing Protocol (GZRP) in UMTS-WiFi Offloadโ, Institute of Electical and Electonics Engineers (IEEE) Tencon 2013โ, pp. 72 โ 77, Xiโan โ China 22 โ 25 Oktober 2013 [11] Budiyanto, S., Asvial, M., dan Gunawan, D.,โ Performance Analysis of Genetic Zone Routing Protocol Combined with Vertical Handover Algorithm in UMTS โ WiFi Offloadโ, Journal of ICT Research and Applications Vol. 8 no.1 pp. 49 โ 63, Mei 2014 6 References [1] Cisco , โCisco Visual Networking Index: Global Mobile Data Trafik Forecast Update, 2012โ2017โ, White Paper, USA, Feb. 2013 [2] B. 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