- Email: [email protected]
Signal to Noise Ratio Based Wi-Fi Offloading Decision Algorithm in Vehicular Networks
A Available onlin ne at www.scie encedirect.com m
Scie enceDir rect Available online at www.sciencedirect.com Procedia Compputer Science 00 (2018) ( 000–000
ScienceDirect
www.elsevier..com/locate/proceedia
Procedia Computer Science 125 (2018) 910–916
6th Internaational Confference on Smart S Compputing and Communica C ations, ICSC CC 2017, 7--8 Decem mber 2017, Kurukshetra K a, India
Siignal to Noise Ratio R Bassed Wi-F Fi Offloaading Deecision A Algorith hm in Vehicula V ar Netwo orks a, Krishan Kumar K * , Ranjan R Kum mar Guptaa a
Nationaal Institute of Tecchnology, Hamirp pur, India
Absttract Now wadays, the rapid increase in mobile m data dem mand is faced byy the existing mobile m networks. It becomes cclear that the ex xisting celluular infrastructuure, along with its next generattion upgrades is i unable to meet the requirem ments of the useers to provide th hem a requiired level of thhroughput for thheir services. This T paper provvides an overvieew of the diffeerent standards and requirements in Wi-F Fi offloading syystem. In Het-N Nets, multi Raddio Access Techhnology may bee operating joinntly with the ceellular network. This schem me is aimed at evaluating the performance of incorporating Wi-Fi Access Points (APs) along with the ccellular network k. The decission is based onn the selection of best Wi-Fi AP A for offloadinng the data, forr which the Signnal to Noise Raatio value at thee user locattion is more thaan the thresholdd value in vehiccular environmeent. At the end, the numerical results evaluatee the performan nce of preseented scheme which w justify its application in vehicular v netwoorks. © 20018 The Authorrs. Published byy Elsevier B.V. Peer--review under responsibility r of the scientific committee of thhe 6th International Conferencce on Smart Com mputing and Com mmunications. Keyw words:Cognitive radio; r Wi-Fi; Acccess Points; SNR;; Offloading; Vehhicular networks.
1. In ntroduction Due to the rapid increase in thhe expansion of informatioon and commu unication techhnologies[1], it is a challen nging w comm munication ab bilities for thhe growth off future Intellligent task to fit-up thee moving vehhicles with wireless oticed in thiss area. Vehiccular networks are Trannsportation Syystems (ITS).. A consideraable progresss has been no efficcient and proovide road saafety to the vehicular ussers by using g wireless coommunicationn and inform mation technnologies withh the transportation system. The applicatiions originated d in vehicularr networks cann be categorizzed in two ways: safety applications, a w which includees collision avvoidance, remo ote vehicle diaagnosis and saafety warningss [2] __ ____________ ___ C Corresponding a author. Tel.: +91-9318811231 E-maail address:[email protected]
B 1877--0509© 2018 Thee Authors. Publisshed by Elsevier B.V.
Peer--review under responsibility r of the scientific committee of thhe 6th International Conferencce on Smart Com mputing and Com mmunications.
1877-0509 © 2018 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the 6th International Conference on Smart Computing and Communications 10.1016/j.procs.2017.12.116
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Krishan Kumar et al. / Procedia Science (2018) 910–916 "Krishan Kumar" / Procedia ComputerComputer Science 00 (2018)125 000–000
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Base Station
RSU
RSU
RSU
V2R communication V2V Communication V2I Communication
Fig. 1. System Model Showing Vehicular Offloading Networks
and entertainment application, which includes file downloading, E-mail forwarding, internet surfing and audio or video downloading [3]. To facilitate these types of applications, the bandwidth of 75 MHz in the 5.9 GHz band has been provided by United States Federal Communication Commission (UNFCC) for Dedicated Short Range Communications (DSRC). Apart from this, in Europe, the Car-to-Car Communication Consortium(C2C-CC) has been incorporated by the vehicle manufacturers, suppliers and research institutes aiming at the utilization of vehicle to vehicle communication for enhancement of efficiency and safety. From the literature survey, it is observed that vehicular networks have unique features in comparison to mobile ad hoc networks, which reflects different challenges on networking[4,5]. Vehicular networks are potentially large-scale mobile networks, which may cover the whole road network with a large number of vehicles and Road Side Units (RSUs). High mobility of users is also one of the features of vehicular networks because the users are extremely dynamic. The vehicle speed is very high on highways, which could be more than 140 km/h, while it could be more than 50 km/h, when the users are moving in the middle of the city and there may be very large number of vehicles during rush hour. The feature of vehicular networks is that it is partitioned network because in sparse scenarios, a large inter-vehicle distance may be there and it is because of the high mobility of vehicles. Therefore, the network is partitioned usually, which consists of isolated clusters of nodes. The other feature of vehicular networks is its network topology and connectivity. Vehicular networks are very dynamic since vehicles are moving from one position to other constantly. Hence, there is a constant change in the network topology and wireless links between the vehicles connect and disconnect continuously[6,7]. In addition to this, outdoor wireless channels also affect wireless link between vehicles. The other feature of vehicular networks is its varied application because vehicular networks have a large variety of applications having different QoS requirements. The features of vehicular networks discussed above causes difficulties in network protocol design, its implementation and performance evaluation. Vehicular networks as shown in Fig. 1 are categorized into three types of communication instances, which are Vehicle-to-Roadside (V2R) communications[8], Vehicle-to-Vehicle (V2V) communications[9] and Vehicle-toInfrastructure (V2I) communications [10]. In V2V communication, vehicles equipped with the On-Board Units (OBUs), can exchange their information with one another in ad hoc manner without the requirement of any existed infrastructure. By circulating the information such as vehicle location, speed and emergency warning messages to the neighboring vehicles using V2V communication, various applications are supported by vehicular networks,
Krishan Kumar et al. / Procedia Computer Science 125 (2018) 910–916 "Krishan Kumar" / Procedia Computer Science 00 (2018) 000–000
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which can be public safety applications, vehicular traffic coordination, road traffic management [11] and some entertainment applications, e.g., online gaming and file sharing etc [2]. The paper shows that presented offloading model with algorithm to make effective for vehicular networks.The paper makes following key research contributions • Due to the movement of vehicles, users will get short connection time withWi-Fi AP and will be able to deliver a small amount of data in each drive-thru. • If the data services are such that users can tolerate some delay for itsfulfilment, the offloading performance may be increased, as the vehicular userscan meet multiple APs on their way. 2. Offloading MathematicalModelling This section provides the path loss, noise and other environmental factors whichaffect the radio wave propagation. 2.2.1 Path LossEstimation:The two main factors which affect the radio wave propagation are path loss andshadowing. The effect of shadowing is neglected for simplicity reason. Free SpacePath Loss (FSPL) is mainly related with the distance between the transmitterand receiver and the frequency of radio wave [12]. ��� �
���� � � or
�
� (1)
���� �
� (2) ���� � � � where, λ is the radio signal wavelength in meters, f is the signal frequency in Hertz, d is the reference distance calculated from the transmitter end in meters,c is the speed of light in vacuum. Here we take the value of FSPL in decibels. So FSPL in decibels isgiven by following equations. when d is in meters and f is in megahertz [12], (3) �������� � �0 ����� � � �0 ����� � − ��.�� when“d is in meters and f is in kilohertz, (4) �������� � �0 ����� � � �0 ����� � − 8�.�� when d is in kilometers and f is in megahertz, (5) �������� � �0 ����� � � �0 ����� � � 3�.�� when d is in kilometers and f is in gigahertz, (6) �������� � �0 ����� � � �0 ����� � � ��.�� As”we have considered the area in which we want to model the offloading decisionalgorithm as in meters ×meters and the operating frequency is in megahertz,we will calculate the FSPL from equation 3.
2.2.2 Received Power Estimation:For SNR calculation at each user location from each AP, we first calculate thesignal strength at each user location, which is similar to received power calculationat a particular distance from APs. The received power in wireless communicationenvironment is calculated by Log distance path loss model. Power received Pr ata distance D from a Wi-Fi AP is given by the equation 7 [12] � �� ����� � �� ����� − �������� − 10Ƞ ��� − �(7) � where, Pt is the transmitted power from Wi-Fi AP in dBm, FSPL is thefreespace path loss in dB at a reference distance d from the transmitting antenna, which is calculated by the equation 3, D is the distance between the WiFiAP and the user, Ƞ is the path loss exponent andȠ is the zero mean Gaussianrandom variable.In our received power calculation, we have neglected the effectof Gaussian random variable.Since we are calculating Pr in urban area, we will take the valueof Ƞ as 3. The Noise power Pn in the receiver section is generated from the amplifier, whichis also known as thermal noise. This is the unwanted energy generated fromman-made and natural sources. It is calculated from the given equation 8 [12] �� � ���(8) where“K is the Boltzmann’s constant, which is numerically equal to 1.38 × 10��� , T is the temperature in Kelvin and B is the operational bandwidth in Hertz.The Signal to Noise Ratio (SNR) is in terms of the bandwidth and channel capacity of a communication channelare related by the Shannon-Hartley law as � � � � ���� �1 � �(9) ��
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Krishan Kumar et al. / Procedia Computer Science 125 (2018) 910–916 "Krishan Kumar" / Procedia Computer Science 00 (2018) 000–000
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where, C is the channel capacity, B is the bandwidth and Pn is the noisepower. Equation 9 is used to calculate the minimum value of SNR which isthe SNRthreshold required for comparison with the received value of SNRWi-Fi at each user location, where SNRWi-Fi is the SNR received from Wi-Fi AP [12]. 3. SNR Based Wi-Fi Offloading Decision Algorithm
Fig. 2: Offloading Decision at Different Time Instants
In the literature, several offloading decision algorithm are given to take decisionfor offloading the data to a Wi-Fi network originally targeted to cellular networkto reduce the loading of cellular network. The presented offloading decisionalgorithm is as follows: 1. Set numerical value of SNRthreshold, 2. Calculate Pr from each AP at each user location, 3. Calculate Pn, 4. Find the value of SNRWi-Fi from each AP at each user location, 5. Choose best Wi-Fi AP on the basis of SNR, 6.If SNRWi-Fi≥ SNRthreshold, user is connected to best Wi-Fi AP, 7.If SNRWi-Fi< SNRthreshold, user is connected to cellular network, 8. Repeat from step 2 for specified time interval of one second.. The target Offloading Scenario is as follows: The presented system consists of a region in urban area in which a BS and Wi-FiAPs are deployed at fixed locations. The users are distributed randomly in thisregion. The users are assumed to be moving in +X direction with a velocity inthis region. This region consists of single cellular BS at the centre of the regionand five Wi-Fi APs at the fixed location. It is assumed that coverage from cellularBS is present at each location in this region and all the moving users are able to get the cellular coverage from the BS. Each Wi-Fi AP has same coverage of area.No two Wi-Fi APs coverage area have overlapping region. All APs are assumedto transmit same power in all direction. But due to the path loss in environment,different signal strength is achieved at users. So the presented offloading decisionis based on received SNR at vehicular user location. As the users are
Krishan Kumar et al. / Procedia Computer Science 125 (2018) 910–916 "Krishan Kumar" / Procedia Computer Science 00 (2018) 000–000
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moving fromone point to other, their location is not fixed at a point at a given time of instant,users get access from different Wi-Fi APs and the value of SNR at user locationchanges continuously. We set a threshold value of SNR termed as SNRthreshold,to select the particular Wi-Fi AP for offloading the data. This algorithm aimsat analyzing the performance of presented offloading model in terms of averagenumber of connected users to all Wi-Fi APs, average number of connected usersto each AP individually and blocking ratio in vehicular environments.Now it is elaborated that how SNR value at each user location is calculated.For this purpose, the received power from each AP is required at each user location.The received power calculation is based on path loss and other environmentaleffects in wireless communication environment. 4. Results and Discussion Firstly, it will be show that how the vehicular users are able to connect to thebest Wi-Fi AP based on the available SNR received from Wi-Fi APs. Here,300 numbers of users have been that are present in an urban area. Five WiFiAPs are deployed at fixed location and a cellular BS is present at the centre ofthe urban region. The users get the coverage from the nearest Wi-Fi APs. Wecalculate the value of SNR received at each user location and compare it with theSNRthreshold at time t = 0 second. If the SNR value from an AP is greater thanthe SNRthreshold, the user is connected to the nearest Wi-Fi AP. Since the usersare moving in the +X direction with a velocity of 36 Km/Hr (10 meters/second),so every user will move 10 meters in one second. Fig.2 shows the offloadingdecision at different instants of time. From the Fig. 2 as shown below, it canbe analysed that the users are moving in +X direction and different number ofusers are connected to each Wi-Fi AP at different instants of time. Users whichare connected to its nearest Wi-Fi AP are shown by a line between the user andthe Wi-Fi AP. The remaining users are assumed to get access from the cellularBS to fulfil its demand.
Fig. 3: Average Number of Users Connected to all Wi-Fi AP vs. TotalNumber of Users
Fig. 4: Blocking Ratio vs. Total Number of Users
Now, the performance of our offloading decision algorithm is evaluated in terms ofaverage number of users connected to all Wi-Fi APs, blocking ratio and averagenumber of users connected to each Wi-Fi AP. All these parameters are calculatedwith respect to different densities of users which is the total number of userspresent at a particular time in the urban area[13, 14]. The density of users in theurban area is varied as minimum number of users is 25 and maximum number ofusers is 300. Then the average number of users connected to the Wi-Fi APs indifferent area of environment i.e. for different values of path loss exponent will becalculated because the value of path loss exponent is different in different area ofenvironment. Then the average number of users connected for different numberof Wi-Fi APs deployed at fixed locations is calculated in the urban area. Fig. 3 shows the average number of users connected to Wi-Fi APs withrespect to the total number of vehicular users present in the urban region. Theaverage of the number of users connected is calculated after running the setup for10000 seconds for better reflection of the results. From the Fig.3, it can beanalysed that the average number of users connected to Wi-Fi APs increases asthe density of users increases in the urban area. It is so because the probabilityof presence of users near Wi-Fi AP will be more as the total number of users increases in urban region. Now, blocking ratio with respect to the total number of users is calculated in the urban region. Equation 10 is used to calculate the blocking ratio. Blockingratio specifies how many users are not able to connect to any Wi-Fi AP. ௩ே௨ௗ௦௦ (10) ݅ݐܴ݈ܽ݃݊݅݇ܿܤൌ ்௧ே௨௦௦௦
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Krishan Kumar et al. / Procedia Computer Science 125 (2018) 910–916 "Krishan Kumar" / Procedia Computer Science 00 (2018) 000–000
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where, the blocked users are those users which are not able to connect to anyWi-Fi AP. In other words blocked users are those users which are connected tocellular base station to fulfil their demands. Fig. 4 shows the blocking ratiowith respect to the total number of users present in the urban region. From thisfigure, it can be analysed that the value of blocking ratio is highest when thetotal number of users is 25 and lowest when the total number of users is 300 inthe urban region. A high value of blocking ratio denotes less number of vehicularusers will be able to offload their data via Wi-Fi network and vice-versa. As therequirement is that more number of users should be connected with the Wi-FiAP, the value of blocking ratio should be as small as possible. For this purpose,more number of Wi-Fi APs should be deployed in simulation area. Hence, the vehicular users will have more opportunity to meet a Wi-Fi AP on theirway and connect with it, which ultimately decreases value of blocking ratio. Now, average number of users connected to each Wi-Fi AP individually calculated. Five APs at fixed location have been and the number of users connectedto each Wi-Fi AP is different at different time of instants. It happens becausethe users are moving from one location to another location and they will meetdifferent Wi-Fi APs as they move on their way. Fig. 5 shows the averagenumber of vehicular users connected to the Wi-Fi AP individually with respectto total number of users.
Fig. 5: Average Number of Users Connected to Each Wi-Fi AP vs. Total Number of Users
Fig. 6: Average Number of Users Connected to Wi-Fi APs vs. Total Number of Users for Different Values of Ƞ
From Fig. 5, it can be analysed that different number of users are connectedto each Wi-Fi AP because the users are randomly located and they arecontinuously moving with a velocity. In the presented offloading decision algorithm,it is assumed that any number of users can be connected to any Wi-Fi AP,so the presented offloading approach does not consider the loading of Wi-Fi APs. Now, the results are shown for different values of path loss exponent. As itis known that path loss exponent (Ƞ) varies in different network environments.Table 1 gives the values of path loss exponent in different network environment.Fig. 6 shows the average number of vehicular users connected to Wi-Fi APwith respect to total number of users for different network environment. Thedifferent values of path loss exponent are 2.5, 3 and 4. From equation 6, it can beanalysed that as the value of path loss exponent is increased, the received powerfrom Wi-Fi AP at the vehicular users will get lower and we will get small valueof SNR at the vehicular user. So there will be less probability that a user will get sufficient coverage from Wi-Fi AP so that it can satisfy the condition of selectingWiFi as access network to fulfil its demands. From Fig. 6,it can be analysed that for smaller value of Ƞ, i.e. 2, theaverage number of users connected to Wi-Fi APs is high and as the value of Ƞ isincreased, the average number of users connected to Wi-Fi APs becomes smallerand at the same time it is shown that average increases as the number of usersincreases.Now, the performance of presented offloading decision algorithm is evaluated,when the number of Wi-Fi APs in the urban region is varied.Previously, five Wi-Fi APs have been taken in the simulation setup and calculatedthe average number of users connected to Wi-Fi APs. In Fig.7, theaverage number of users connected to Wi-Fi APs for three different numbers ofAPs, i.e. 5, 10 and 15 have been calculated and compared, deployed in urbanregion. As it is known that when the urban region is sparsely deployed with Wi-FiAPs, the vehicular users will have less opportunity that they will meet any Wi-FiAP on their way. On the other hand, in case of densely deployed urban regionwith Wi-Fi APs, the vehicular users will have more opportunity that they willmeet a Wi-Fi AP on their way. Fig.7 clearly shows that when the number ofAPs in the urban region is 5, less number of users are connected with Wi-FiAPs and as the number of APs increases i.e. 10 or 15, more number of users are connected with Wi-Fi APs.
KrishanKumar" Kumar/ et al. / Procedia Computer 125000–000 (2018) 910–916 "Krishan Procedia Computer ScienceScience 00 (2018)
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Fig. 7: Average Number of Users Connected to Wi-Fi APs vs. Total Numberof Users for Different No of AP
4. Conclusion In this paper, an SNR based offloading decision algorithm for Wi-Fi offloadingin vehicular network environment has been presented. The analysisresults of the well applicability of the offloading decision algorithm. The average number of vehicular users connected to all Wi-Fi APswith respect to the total number of users present has been estimated. Since the users are randomly located so the results are alsorandom in nature. Then the blocking ratio has been calculated, which gives idea about the number of users blocked from using Wi-Fi as access network tofulfil the users’ demands. Then, average number of users connected to eachWi-Fi AP individually has been calculated. Furthermore, the average number of users connected to Wi-Fi for different valuesof path loss exponent has been calculated to check the appropriateness of the results. It shows that the smaller the path loss exponent, SNR received from Wi-Fi AP will be high, which means more number ofusers will be connected to Wi-Fi Aps and vice versa.The results also clearly shows that as the number of APs increase in theconvergence area, more number of users are able to connect with the Wi-Fi AP,which indicates the appropriateness of presented algorithm. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14]
Wang B., and Liu K. J. R. (2011)“Advances in cognitive radio networks: A survey”IEEE Journal of Selected Topics in Signal Processing5(1):5-23. Karagiannis G., Altintas O., EkiciE., HeijenkG., JarupanB., LinK., and WeilT. (2011) “Vehicular Networking: A Survey and Tutorial on Requirements, Architectures, Challenges, Standards and Solutions” IEEE communicationssurveys & tutorials13(4):584–616. Trullols-Cruces O., Fiore M., and Barcelo-Ordinas J.(2012) “Cooperative Download in Vehicular Environments” IEEE Transactions on Mobile Computing11(4)4:663–678. Cheng N., N. Lu, Zhang N., Shen X. S., and Mark J. W.(2014), “Vehicular WiFiOffloading: Challenges and Solutions” Vehicular Communications1(1):13–21. Aijaz A., Aghvami H., and Amani M.(2013) “A Survey on Mobile Data Offloading: Technical and Business Perspectives” IEEE Wireless Communications20(2):104–112. Kumar K., Prakash A., and Tripathi R. (2017) “A Spectrum Handoff Scheme forOptimal Network Selection in NEMO Based Cognitive Radio Vehicular Networks”Wireless Communications and Mobile Computing2017:1-16. Kumar K., Prakash A., and Tripathi R. (2017) “Context Aware Spectrum HandoffScheme in Cognitive Radio Vehicular Networks” International Journal ofAd Hoc and Ubiquitous Computing, 24(1/2):101–116. Wada M., Yendo T., Fujii T., and Tanimoto M., “Road-to-Vehicle Communication using LED Traffic Light,” In: IEEE Proceeding of Intelligent Vehicles Symposium, 601–606 , (2005), June 05-06. Cheng L., Henty B. E., Stancil D. D., Bai F., and Mudalige P. (2007) “Mobile Vehicle-to-Vehicle Narrow-Band Channel Measurement and Characterization of the 5.9 GHz Dedicated Short Range Communication (DSRC) Frequency Band” IEEE Journal on Selected Areas in Communications25(8):1501-1516. Kenney J. B. (2011) “Dedicated Short-Range Communications (DSRC) Standardsin the United States” Proceedings of the IEEE99(7):1162–1182. Moustafa H. and Zhang Y.“Vehicular Networks: Techniques, Standards, andApplications” Auerbach publications2009. Rappaport T. S. “Wireless Communications: Principles and Practice” Pearson education2002. Luan T. H., Cai L. X., Chen J., Shen X. S., and Bai F. (2014) “Engineering aDistributed Infrastructure for Large-Scale Cost-Effective Content Disseminationover Urban Vehicular Networks” IEEE Transactions on VehicularTechnology 63(3):1419–1435. Thiagarajah S. P., Ting A., Chieng D., Alias M. Y., and Wei T. S., “UserData Rate Enhancement using Heterogeneous LTE-802.11n Offloading in Urban Area,” In: Proceeding of IEEE Symposium on Wireless Technology and Applications, 11–16, (2013), September 2225.
Scie enceDir rect Available online at www.sciencedirect.com Procedia Compputer Science 00 (2018) ( 000–000
ScienceDirect
www.elsevier..com/locate/proceedia
Procedia Computer Science 125 (2018) 910–916
6th Internaational Confference on Smart S Compputing and Communica C ations, ICSC CC 2017, 7--8 Decem mber 2017, Kurukshetra K a, India
Siignal to Noise Ratio R Bassed Wi-F Fi Offloaading Deecision A Algorith hm in Vehicula V ar Netwo orks a, Krishan Kumar K * , Ranjan R Kum mar Guptaa a
Nationaal Institute of Tecchnology, Hamirp pur, India
Absttract Now wadays, the rapid increase in mobile m data dem mand is faced byy the existing mobile m networks. It becomes cclear that the ex xisting celluular infrastructuure, along with its next generattion upgrades is i unable to meet the requirem ments of the useers to provide th hem a requiired level of thhroughput for thheir services. This T paper provvides an overvieew of the diffeerent standards and requirements in Wi-F Fi offloading syystem. In Het-N Nets, multi Raddio Access Techhnology may bee operating joinntly with the ceellular network. This schem me is aimed at evaluating the performance of incorporating Wi-Fi Access Points (APs) along with the ccellular network k. The decission is based onn the selection of best Wi-Fi AP A for offloadinng the data, forr which the Signnal to Noise Raatio value at thee user locattion is more thaan the thresholdd value in vehiccular environmeent. At the end, the numerical results evaluatee the performan nce of preseented scheme which w justify its application in vehicular v netwoorks. © 20018 The Authorrs. Published byy Elsevier B.V. Peer--review under responsibility r of the scientific committee of thhe 6th International Conferencce on Smart Com mputing and Com mmunications. Keyw words:Cognitive radio; r Wi-Fi; Acccess Points; SNR;; Offloading; Vehhicular networks.
1. In ntroduction Due to the rapid increase in thhe expansion of informatioon and commu unication techhnologies[1], it is a challen nging w comm munication ab bilities for thhe growth off future Intellligent task to fit-up thee moving vehhicles with wireless oticed in thiss area. Vehiccular networks are Trannsportation Syystems (ITS).. A consideraable progresss has been no efficcient and proovide road saafety to the vehicular ussers by using g wireless coommunicationn and inform mation technnologies withh the transportation system. The applicatiions originated d in vehicularr networks cann be categorizzed in two ways: safety applications, a w which includees collision avvoidance, remo ote vehicle diaagnosis and saafety warningss [2] __ ____________ ___ C Corresponding a author. Tel.: +91-9318811231 E-maail address:[email protected]
B 1877--0509© 2018 Thee Authors. Publisshed by Elsevier B.V.
Peer--review under responsibility r of the scientific committee of thhe 6th International Conferencce on Smart Com mputing and Com mmunications.
1877-0509 © 2018 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the 6th International Conference on Smart Computing and Communications 10.1016/j.procs.2017.12.116
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Krishan Kumar et al. / Procedia Science (2018) 910–916 "Krishan Kumar" / Procedia ComputerComputer Science 00 (2018)125 000–000
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Base Station
RSU
RSU
RSU
V2R communication V2V Communication V2I Communication
Fig. 1. System Model Showing Vehicular Offloading Networks
and entertainment application, which includes file downloading, E-mail forwarding, internet surfing and audio or video downloading [3]. To facilitate these types of applications, the bandwidth of 75 MHz in the 5.9 GHz band has been provided by United States Federal Communication Commission (UNFCC) for Dedicated Short Range Communications (DSRC). Apart from this, in Europe, the Car-to-Car Communication Consortium(C2C-CC) has been incorporated by the vehicle manufacturers, suppliers and research institutes aiming at the utilization of vehicle to vehicle communication for enhancement of efficiency and safety. From the literature survey, it is observed that vehicular networks have unique features in comparison to mobile ad hoc networks, which reflects different challenges on networking[4,5]. Vehicular networks are potentially large-scale mobile networks, which may cover the whole road network with a large number of vehicles and Road Side Units (RSUs). High mobility of users is also one of the features of vehicular networks because the users are extremely dynamic. The vehicle speed is very high on highways, which could be more than 140 km/h, while it could be more than 50 km/h, when the users are moving in the middle of the city and there may be very large number of vehicles during rush hour. The feature of vehicular networks is that it is partitioned network because in sparse scenarios, a large inter-vehicle distance may be there and it is because of the high mobility of vehicles. Therefore, the network is partitioned usually, which consists of isolated clusters of nodes. The other feature of vehicular networks is its network topology and connectivity. Vehicular networks are very dynamic since vehicles are moving from one position to other constantly. Hence, there is a constant change in the network topology and wireless links between the vehicles connect and disconnect continuously[6,7]. In addition to this, outdoor wireless channels also affect wireless link between vehicles. The other feature of vehicular networks is its varied application because vehicular networks have a large variety of applications having different QoS requirements. The features of vehicular networks discussed above causes difficulties in network protocol design, its implementation and performance evaluation. Vehicular networks as shown in Fig. 1 are categorized into three types of communication instances, which are Vehicle-to-Roadside (V2R) communications[8], Vehicle-to-Vehicle (V2V) communications[9] and Vehicle-toInfrastructure (V2I) communications [10]. In V2V communication, vehicles equipped with the On-Board Units (OBUs), can exchange their information with one another in ad hoc manner without the requirement of any existed infrastructure. By circulating the information such as vehicle location, speed and emergency warning messages to the neighboring vehicles using V2V communication, various applications are supported by vehicular networks,
Krishan Kumar et al. / Procedia Computer Science 125 (2018) 910–916 "Krishan Kumar" / Procedia Computer Science 00 (2018) 000–000
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which can be public safety applications, vehicular traffic coordination, road traffic management [11] and some entertainment applications, e.g., online gaming and file sharing etc [2]. The paper shows that presented offloading model with algorithm to make effective for vehicular networks.The paper makes following key research contributions • Due to the movement of vehicles, users will get short connection time withWi-Fi AP and will be able to deliver a small amount of data in each drive-thru. • If the data services are such that users can tolerate some delay for itsfulfilment, the offloading performance may be increased, as the vehicular userscan meet multiple APs on their way. 2. Offloading MathematicalModelling This section provides the path loss, noise and other environmental factors whichaffect the radio wave propagation. 2.2.1 Path LossEstimation:The two main factors which affect the radio wave propagation are path loss andshadowing. The effect of shadowing is neglected for simplicity reason. Free SpacePath Loss (FSPL) is mainly related with the distance between the transmitterand receiver and the frequency of radio wave [12]. ��� �
���� � � or
�
� (1)
���� �
� (2) ���� � � � where, λ is the radio signal wavelength in meters, f is the signal frequency in Hertz, d is the reference distance calculated from the transmitter end in meters,c is the speed of light in vacuum. Here we take the value of FSPL in decibels. So FSPL in decibels isgiven by following equations. when d is in meters and f is in megahertz [12], (3) �������� � �0 ����� � � �0 ����� � − ��.�� when“d is in meters and f is in kilohertz, (4) �������� � �0 ����� � � �0 ����� � − 8�.�� when d is in kilometers and f is in megahertz, (5) �������� � �0 ����� � � �0 ����� � � 3�.�� when d is in kilometers and f is in gigahertz, (6) �������� � �0 ����� � � �0 ����� � � ��.�� As”we have considered the area in which we want to model the offloading decisionalgorithm as in meters ×meters and the operating frequency is in megahertz,we will calculate the FSPL from equation 3.
2.2.2 Received Power Estimation:For SNR calculation at each user location from each AP, we first calculate thesignal strength at each user location, which is similar to received power calculationat a particular distance from APs. The received power in wireless communicationenvironment is calculated by Log distance path loss model. Power received Pr ata distance D from a Wi-Fi AP is given by the equation 7 [12] � �� ����� � �� ����� − �������� − 10Ƞ ��� − �(7) � where, Pt is the transmitted power from Wi-Fi AP in dBm, FSPL is thefreespace path loss in dB at a reference distance d from the transmitting antenna, which is calculated by the equation 3, D is the distance between the WiFiAP and the user, Ƞ is the path loss exponent andȠ is the zero mean Gaussianrandom variable.In our received power calculation, we have neglected the effectof Gaussian random variable.Since we are calculating Pr in urban area, we will take the valueof Ƞ as 3. The Noise power Pn in the receiver section is generated from the amplifier, whichis also known as thermal noise. This is the unwanted energy generated fromman-made and natural sources. It is calculated from the given equation 8 [12] �� � ���(8) where“K is the Boltzmann’s constant, which is numerically equal to 1.38 × 10��� , T is the temperature in Kelvin and B is the operational bandwidth in Hertz.The Signal to Noise Ratio (SNR) is in terms of the bandwidth and channel capacity of a communication channelare related by the Shannon-Hartley law as � � � � ���� �1 � �(9) ��
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where, C is the channel capacity, B is the bandwidth and Pn is the noisepower. Equation 9 is used to calculate the minimum value of SNR which isthe SNRthreshold required for comparison with the received value of SNRWi-Fi at each user location, where SNRWi-Fi is the SNR received from Wi-Fi AP [12]. 3. SNR Based Wi-Fi Offloading Decision Algorithm
Fig. 2: Offloading Decision at Different Time Instants
In the literature, several offloading decision algorithm are given to take decisionfor offloading the data to a Wi-Fi network originally targeted to cellular networkto reduce the loading of cellular network. The presented offloading decisionalgorithm is as follows: 1. Set numerical value of SNRthreshold, 2. Calculate Pr from each AP at each user location, 3. Calculate Pn, 4. Find the value of SNRWi-Fi from each AP at each user location, 5. Choose best Wi-Fi AP on the basis of SNR, 6.If SNRWi-Fi≥ SNRthreshold, user is connected to best Wi-Fi AP, 7.If SNRWi-Fi< SNRthreshold, user is connected to cellular network, 8. Repeat from step 2 for specified time interval of one second.. The target Offloading Scenario is as follows: The presented system consists of a region in urban area in which a BS and Wi-FiAPs are deployed at fixed locations. The users are distributed randomly in thisregion. The users are assumed to be moving in +X direction with a velocity inthis region. This region consists of single cellular BS at the centre of the regionand five Wi-Fi APs at the fixed location. It is assumed that coverage from cellularBS is present at each location in this region and all the moving users are able to get the cellular coverage from the BS. Each Wi-Fi AP has same coverage of area.No two Wi-Fi APs coverage area have overlapping region. All APs are assumedto transmit same power in all direction. But due to the path loss in environment,different signal strength is achieved at users. So the presented offloading decisionis based on received SNR at vehicular user location. As the users are
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moving fromone point to other, their location is not fixed at a point at a given time of instant,users get access from different Wi-Fi APs and the value of SNR at user locationchanges continuously. We set a threshold value of SNR termed as SNRthreshold,to select the particular Wi-Fi AP for offloading the data. This algorithm aimsat analyzing the performance of presented offloading model in terms of averagenumber of connected users to all Wi-Fi APs, average number of connected usersto each AP individually and blocking ratio in vehicular environments.Now it is elaborated that how SNR value at each user location is calculated.For this purpose, the received power from each AP is required at each user location.The received power calculation is based on path loss and other environmentaleffects in wireless communication environment. 4. Results and Discussion Firstly, it will be show that how the vehicular users are able to connect to thebest Wi-Fi AP based on the available SNR received from Wi-Fi APs. Here,300 numbers of users have been that are present in an urban area. Five WiFiAPs are deployed at fixed location and a cellular BS is present at the centre ofthe urban region. The users get the coverage from the nearest Wi-Fi APs. Wecalculate the value of SNR received at each user location and compare it with theSNRthreshold at time t = 0 second. If the SNR value from an AP is greater thanthe SNRthreshold, the user is connected to the nearest Wi-Fi AP. Since the usersare moving in the +X direction with a velocity of 36 Km/Hr (10 meters/second),so every user will move 10 meters in one second. Fig.2 shows the offloadingdecision at different instants of time. From the Fig. 2 as shown below, it canbe analysed that the users are moving in +X direction and different number ofusers are connected to each Wi-Fi AP at different instants of time. Users whichare connected to its nearest Wi-Fi AP are shown by a line between the user andthe Wi-Fi AP. The remaining users are assumed to get access from the cellularBS to fulfil its demand.
Fig. 3: Average Number of Users Connected to all Wi-Fi AP vs. TotalNumber of Users
Fig. 4: Blocking Ratio vs. Total Number of Users
Now, the performance of our offloading decision algorithm is evaluated in terms ofaverage number of users connected to all Wi-Fi APs, blocking ratio and averagenumber of users connected to each Wi-Fi AP. All these parameters are calculatedwith respect to different densities of users which is the total number of userspresent at a particular time in the urban area[13, 14]. The density of users in theurban area is varied as minimum number of users is 25 and maximum number ofusers is 300. Then the average number of users connected to the Wi-Fi APs indifferent area of environment i.e. for different values of path loss exponent will becalculated because the value of path loss exponent is different in different area ofenvironment. Then the average number of users connected for different numberof Wi-Fi APs deployed at fixed locations is calculated in the urban area. Fig. 3 shows the average number of users connected to Wi-Fi APs withrespect to the total number of vehicular users present in the urban region. Theaverage of the number of users connected is calculated after running the setup for10000 seconds for better reflection of the results. From the Fig.3, it can beanalysed that the average number of users connected to Wi-Fi APs increases asthe density of users increases in the urban area. It is so because the probabilityof presence of users near Wi-Fi AP will be more as the total number of users increases in urban region. Now, blocking ratio with respect to the total number of users is calculated in the urban region. Equation 10 is used to calculate the blocking ratio. Blockingratio specifies how many users are not able to connect to any Wi-Fi AP. ௩ே௨ௗ௦௦ (10) ݅ݐܴ݈ܽ݃݊݅݇ܿܤൌ ்௧ே௨௦௦௦
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where, the blocked users are those users which are not able to connect to anyWi-Fi AP. In other words blocked users are those users which are connected tocellular base station to fulfil their demands. Fig. 4 shows the blocking ratiowith respect to the total number of users present in the urban region. From thisfigure, it can be analysed that the value of blocking ratio is highest when thetotal number of users is 25 and lowest when the total number of users is 300 inthe urban region. A high value of blocking ratio denotes less number of vehicularusers will be able to offload their data via Wi-Fi network and vice-versa. As therequirement is that more number of users should be connected with the Wi-FiAP, the value of blocking ratio should be as small as possible. For this purpose,more number of Wi-Fi APs should be deployed in simulation area. Hence, the vehicular users will have more opportunity to meet a Wi-Fi AP on theirway and connect with it, which ultimately decreases value of blocking ratio. Now, average number of users connected to each Wi-Fi AP individually calculated. Five APs at fixed location have been and the number of users connectedto each Wi-Fi AP is different at different time of instants. It happens becausethe users are moving from one location to another location and they will meetdifferent Wi-Fi APs as they move on their way. Fig. 5 shows the averagenumber of vehicular users connected to the Wi-Fi AP individually with respectto total number of users.
Fig. 5: Average Number of Users Connected to Each Wi-Fi AP vs. Total Number of Users
Fig. 6: Average Number of Users Connected to Wi-Fi APs vs. Total Number of Users for Different Values of Ƞ
From Fig. 5, it can be analysed that different number of users are connectedto each Wi-Fi AP because the users are randomly located and they arecontinuously moving with a velocity. In the presented offloading decision algorithm,it is assumed that any number of users can be connected to any Wi-Fi AP,so the presented offloading approach does not consider the loading of Wi-Fi APs. Now, the results are shown for different values of path loss exponent. As itis known that path loss exponent (Ƞ) varies in different network environments.Table 1 gives the values of path loss exponent in different network environment.Fig. 6 shows the average number of vehicular users connected to Wi-Fi APwith respect to total number of users for different network environment. Thedifferent values of path loss exponent are 2.5, 3 and 4. From equation 6, it can beanalysed that as the value of path loss exponent is increased, the received powerfrom Wi-Fi AP at the vehicular users will get lower and we will get small valueof SNR at the vehicular user. So there will be less probability that a user will get sufficient coverage from Wi-Fi AP so that it can satisfy the condition of selectingWiFi as access network to fulfil its demands. From Fig. 6,it can be analysed that for smaller value of Ƞ, i.e. 2, theaverage number of users connected to Wi-Fi APs is high and as the value of Ƞ isincreased, the average number of users connected to Wi-Fi APs becomes smallerand at the same time it is shown that average increases as the number of usersincreases.Now, the performance of presented offloading decision algorithm is evaluated,when the number of Wi-Fi APs in the urban region is varied.Previously, five Wi-Fi APs have been taken in the simulation setup and calculatedthe average number of users connected to Wi-Fi APs. In Fig.7, theaverage number of users connected to Wi-Fi APs for three different numbers ofAPs, i.e. 5, 10 and 15 have been calculated and compared, deployed in urbanregion. As it is known that when the urban region is sparsely deployed with Wi-FiAPs, the vehicular users will have less opportunity that they will meet any Wi-FiAP on their way. On the other hand, in case of densely deployed urban regionwith Wi-Fi APs, the vehicular users will have more opportunity that they willmeet a Wi-Fi AP on their way. Fig.7 clearly shows that when the number ofAPs in the urban region is 5, less number of users are connected with Wi-FiAPs and as the number of APs increases i.e. 10 or 15, more number of users are connected with Wi-Fi APs.
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Fig. 7: Average Number of Users Connected to Wi-Fi APs vs. Total Numberof Users for Different No of AP
4. Conclusion In this paper, an SNR based offloading decision algorithm for Wi-Fi offloadingin vehicular network environment has been presented. The analysisresults of the well applicability of the offloading decision algorithm. The average number of vehicular users connected to all Wi-Fi APswith respect to the total number of users present has been estimated. Since the users are randomly located so the results are alsorandom in nature. Then the blocking ratio has been calculated, which gives idea about the number of users blocked from using Wi-Fi as access network tofulfil the users’ demands. Then, average number of users connected to eachWi-Fi AP individually has been calculated. Furthermore, the average number of users connected to Wi-Fi for different valuesof path loss exponent has been calculated to check the appropriateness of the results. It shows that the smaller the path loss exponent, SNR received from Wi-Fi AP will be high, which means more number ofusers will be connected to Wi-Fi Aps and vice versa.The results also clearly shows that as the number of APs increase in theconvergence area, more number of users are able to connect with the Wi-Fi AP,which indicates the appropriateness of presented algorithm. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14]
Wang B., and Liu K. J. R. (2011)“Advances in cognitive radio networks: A survey”IEEE Journal of Selected Topics in Signal Processing5(1):5-23. Karagiannis G., Altintas O., EkiciE., HeijenkG., JarupanB., LinK., and WeilT. (2011) “Vehicular Networking: A Survey and Tutorial on Requirements, Architectures, Challenges, Standards and Solutions” IEEE communicationssurveys & tutorials13(4):584–616. Trullols-Cruces O., Fiore M., and Barcelo-Ordinas J.(2012) “Cooperative Download in Vehicular Environments” IEEE Transactions on Mobile Computing11(4)4:663–678. Cheng N., N. Lu, Zhang N., Shen X. S., and Mark J. W.(2014), “Vehicular WiFiOffloading: Challenges and Solutions” Vehicular Communications1(1):13–21. Aijaz A., Aghvami H., and Amani M.(2013) “A Survey on Mobile Data Offloading: Technical and Business Perspectives” IEEE Wireless Communications20(2):104–112. Kumar K., Prakash A., and Tripathi R. (2017) “A Spectrum Handoff Scheme forOptimal Network Selection in NEMO Based Cognitive Radio Vehicular Networks”Wireless Communications and Mobile Computing2017:1-16. Kumar K., Prakash A., and Tripathi R. (2017) “Context Aware Spectrum HandoffScheme in Cognitive Radio Vehicular Networks” International Journal ofAd Hoc and Ubiquitous Computing, 24(1/2):101–116. Wada M., Yendo T., Fujii T., and Tanimoto M., “Road-to-Vehicle Communication using LED Traffic Light,” In: IEEE Proceeding of Intelligent Vehicles Symposium, 601–606 , (2005), June 05-06. Cheng L., Henty B. E., Stancil D. D., Bai F., and Mudalige P. (2007) “Mobile Vehicle-to-Vehicle Narrow-Band Channel Measurement and Characterization of the 5.9 GHz Dedicated Short Range Communication (DSRC) Frequency Band” IEEE Journal on Selected Areas in Communications25(8):1501-1516. Kenney J. B. (2011) “Dedicated Short-Range Communications (DSRC) Standardsin the United States” Proceedings of the IEEE99(7):1162–1182. Moustafa H. and Zhang Y.“Vehicular Networks: Techniques, Standards, andApplications” Auerbach publications2009. Rappaport T. S. “Wireless Communications: Principles and Practice” Pearson education2002. Luan T. H., Cai L. X., Chen J., Shen X. S., and Bai F. (2014) “Engineering aDistributed Infrastructure for Large-Scale Cost-Effective Content Disseminationover Urban Vehicular Networks” IEEE Transactions on VehicularTechnology 63(3):1419–1435. Thiagarajah S. P., Ting A., Chieng D., Alias M. Y., and Wei T. S., “UserData Rate Enhancement using Heterogeneous LTE-802.11n Offloading in Urban Area,” In: Proceeding of IEEE Symposium on Wireless Technology and Applications, 11–16, (2013), September 2225.