dynamic traffic prediction with adaptive sampling for 5g...

Research ArticleDynamic Traffic Prediction with Adaptive Sampling for5G HetNet IoT Applications

Shuangli Wu 12 Wei Mao13 Cong Liu 4 and Tao Tang4

1Computer Network Information Center Chinese Academy of Sciences Beijing China2University of Chinese Academy of Sciences Beijing China3Internet Domain Name System Beijing Engineering Research Center Beijing China4Beihang University Beijing China

Correspondence should be addressed to Shuangli Wu wushuanglicniccn

Received 1 April 2019 Accepted 12 June 2019 Published 26 June 2019

Guest Editor Bo Rong

Copyright copy 2019 Shuangli Wu et al This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

Due to the proliferation of global monitoring sensors the Internet of Things (IoT) is widely used to build smart cities and smarthomes 5GHetNets play an important role in the IoT video streamThis paper proposes an improved Call Session Control Function(CSCF) schemeThe improvedCSCF server contains additionalmodules to facilitate IoT traffic prediction and resource reservationWe highlight traffic prediction in this work and develop a compressed sensing based linear predictor to catch the traffic patternsExperimental results justify that our proposed scheme can forecast the traffic load with high accuracy but low sampling overhead

1 Introduction

The Internet of Things (IoT) makes it possible to connectvarious physical devices Vehicles buildings etc embeddedin sensors are connected through the Internet of Things anddata can be collected and exchanged via the Internet TheInternet of Things enables mutual communication betweendevices In the next decade the service targets in the InternetofThings will be improved to users in various industries andthe number of machine to machine (M2M) terminals willincrease dramatically and applications will be ubiquitous

In order to solve the problem of explosive data volumeof the IoT a heterogeneous networks (HetNets) technologyis proposed The problem of 5G communication is solvedby deploying a large number of small cells [1ndash3] Thereare several technical challenges hindering the IoT videostreaming over 5G HetNets For example the interactionsbetween HetNet and 5G optical core tremendously dependon the IoT traffic from the local HetNet which may changeperiodically for many reasons Consequently the systemmust have a forecast and reservation scheme to dynamicallybook necessary resource from optical core In addition aconnection admission control (CAC) mechanism plays anessential role in case the actually required resource overpasses

the reserved Traffic on future fifth-generation (5G) mobilenetworks is predicted to be dominated by challenging videoapplications such as mobile broadcasting remote surgeryand augmented reality demanding real-time and ultra-highquality delivery Two of themain expectations of 5Gnetworksare that they will be able to handle ultra-high-definition(UHD) video streaming and that they will deliver servicesthat meet the requirements of the end userrsquos perceived qualityby adopting quality of experience (QoE) aware networkmanagement approaches

To overcome the above challenges this paper proposesa scheme of improved CSCF server for 5G HetNets Theimproved CSCF can reside itself in a mobile edge computing(MEC) server with three extra components added to the orig-inal including IoT traffic prediction bandwidth negotiationand connection admission control [4]

The improved CSCF first forecasts the IoT traffic loadfrom a local HetNet to the 5G optical core After that itemploys Common Open Policy Service (COPS) protocol[5] to adaptively negotiate bandwidth resource Finally theconnection admission control serves to keep the system inshape and guarantee communication quality

The IoT is responsible for the communication betweendifferent devices providing better solutions and enhanced

HindawiWireless Communications and Mobile ComputingVolume 2019 Article ID 4687272 11 pageshttpsdoiorg10115520194687272

2 Wireless Communications and Mobile Computing

services for different information flows Video signal is veryimportant among multimedia services At the same time thevideo traffic on the Internet has increased dramatically anda lot of research work has been done to improve the viewingexperience of the audience

Video traffic between HetNet and optical core oftenfluctuates because of use casersquos characteristic and variationof observed object Therefore our proposed improved CSCFserver chooses a dynamic mechanism in a bid to bookbandwidth economically Apparently the traffic predictioncomponent of improved CSCF server has critical impact onthe performance of dynamic bandwidth negotiation In prac-tice sampling overhead is a real challenge as traffic predictormust talk to two other components of the improved CSCFserver during the sampling process For instance traffic pre-dictor needs the calling records from connection admissioncontrol component as the input After the prediction it hasto transfer the result to bandwidth negotiation componentwhich will further use COPS messages to negotiate with 5Gcore network The procedure above can strongly justify thesignificance of sampling rate reduction

Compressed sensing is a promising paradigm that usessignal sparsity to reduce the amount of data that needs to bemeasured [6 7] Compressed sensing (CS) theory indicatesthat the characteristics of sparse signals are not affected bythe signal sparse basis It can be obtained by a small amountof projection on another basis The reconstruction of thewideband signal can be obtained by solving the L1 normoptimization It is concluded that in the calculation processthe sparsity of the signals in the orthogonal basis determinesthe minimum projection number and sampling rate required[8]

In the literature the study of traffic prediction mainlyconcentrated on the least mean square (LMS) methods thattake sampling and prediction as two independent aspects [9ndash12] As a result the sampling often implements a constantinterval and is too prone to low efficiency in IoT environment

Other than previous work this paper focuses on theaggregated multiple streams in a HetNet instead of oneseparated video stream

Inspired by compressed sensing theory we develop anadaptive sampling rate (ASR) linear predictor to overcomethe shortcoming of traditional schemes ASR linear predictorutilizes the prediction error to control sampling interval sothat the sampling and predicting overheads are significantlyreduced

The rest of the paper is organized as the remainderSection 2 introduces 5G IoT applications in smart citiesSection 3 introduces 5G HetNets and IMS based IoT videostreaming Section 4 develops traffic prediction schemesusing adaptive sampling rate and compressed sensing theoryFinally Section 5 summarizes the advantages of our proposedimproved CSCF server and ASR linear predictor

2 5G IoT Applications in Smart Cities

21 Smart Cities The development of the IoTs and cloudcomputing technology has become an important symbol of

our modern information age especially in the city providingtechnical support for the commercial development of the cityThe comprehensive and multilevel processing of informationtechnology in the IoTs provides a service platform for datasystems Therefore in the smart city information systemthe digital information processing of the city can timelyunderstand the relevant information of the city

The use of IoT technology to improve and develop smartcity information technology shield bad information andguide and support the exchange of useful information enablesit to cover all corners of the city in a network and realizesthe management and control of the city as a whole asshown in Figure 1 At the same time with the help of theintelligent engine and the unified information service systemthe management efficiency of urbanization construction hasbeen effectively improved

For smart cities their construction and developmentare inseparable from the IoT communication technologyIt can be said that IoT communication technology is animportant component of its foundationOne of the importantapplications is intelligent building management which ismainly used in the following aspects

(a) The installation of intelligent building managementtechnology during the construction of the building willenable residents to conveniently manage and control remoteitems under the cooperation of the IoT

(b)The establishment of a large publicmonitoring systemthere are many public buildings in the city and there aremany equipment and facilities Therefore an emergencymonitoring system must be established to solve this problemquicklywith the assistance of the IoTwhenunexpected eventsoccur

(c) Adopt intelligent water coal and electricity metersThe RFID chip is built in the table of various facilities andthe data can be collected into the data processor through thetable and then transmitted to the background system throughthe 5G network to realize the function of automatic meterreading

(d) Processing of abnormal equipment remote moni-toring of air conditioners lights etc is carried out usingwireless sensors and local area network technology during theprocessing of this problem

22 IoT Intelligent Parking System in Smart City With thedevelopment of economy there are more and more vehiclesin the city and the problem of parking difficulty is followedSmart cities can bring effective solutions to this problem Inthe smart city an intelligent parking system based on InternetofThings is proposedThe framework of the system is shownin Figure 2

This system combines advanced IoT technology mobileinternet technology and big data technology As shown inthe figure the system is mainly composed of the videopile the base station the server the mobile client and themanagement cloud platform

The video pile is mainly composed of a camera aprocessor and a communication device While the systemis running the camera is used to capture images of eachparking space and monitor the status of each parking space

Wireless Communications and Mobile Computing 3

INDUSTRY

SECURITY RETAIL

SOCIETY HEALTHCARE

HOME ENERGY

MOBILITY

SMART CITY

Figure 1 5G IoT application in smart cities

BS

5G

Server

Mobile clientManagement

cloud platform

Figure 2 Smart parking system

in real time through target recognition technology Thenthe video pile transmits the information to the base stationvia the wireless network The base station forwards theinformation from different video pile to the server and thecloud management platformThe whole system adopts intel-ligent management and the big data technology is adoptedin the cloud management platform to carry out regionallevel parking space scheduling to prevent the problem ofuneven resource allocationTheuser can query the remainingnumber of parking spaces and make a parking space reser-vation in real time through the mobile phone client andif the user is not familiar with the terrain the system canrecommend the best parking space for the user and provideprecise navigation services Throughout the service process

the system performs automatic billing and supports mobileclient online payment

3 IoT Video Communication overMultilocation HetNets

31 5G Core Networks Bridged by MPLS VPN In the IoTsHetNets networks consist of eNode BS and low-power nodes(LPNs) aka access points a unit that makes it up Differentlayers are covered by the twonodes according to the transmis-sion power [13 14] This approach densifies the topology andimproves space utilization and spectrum reuseThe structureof the heterogeneous network satisfies the requirements of 5Gtechnology and greatly improves the spectral efficiency [15]


Cellular BS

Operator deployed smallcell

User deployed smallcell

Cloud

Macrocell link

Small cell link

Relaying

Gateway

DataCenter

Servers

Figure 3 A typical 5G IoT heterogeneous network

In order to solve this problem 5G wireless networkshave received attention and research in order to provideconnectivity and meet the requirements of different qualityof service (QoS) The key technologies of 5G enable thedeployment of heterogeneous networks (HetNets) to supporta large number of IoT data requirements by deploying a largenumber of small cells

Network density is themost effectivemeans inmanywaysto increase network capacity such as spectrum expansion andspectrum efficiency enhancement [16] By densely deploy-ing small cells indoors and outdoors network capacity isincreased and network latency is reduced Small cells include

microcells picocells and femtocells which are closer to theuser Figure 3 is a schematic diagram of deployment of a smallcell which can be widely deployed indoors and outdoorssuch as in offices intersections and plazas Among themthe data traffic of indoor users can be provided by Wi-FiThe next generation of Wi-Fi 80211ac is expected to growrapidly providing multigigabit transmission ratesThis led tothe concept of HetNets a multilayer network with multipleradio access technologies

A 5G core network could be enhanced by network slicingwhich allows different clients to bridge their private sites overa common provider-owned cloud Using MPLS or another


5G core networks5G HetNet

CE

PEPE

5G HetNet

5G HetNet

CE

CE

PE

PE

Figure 4 Network slicing based 5G core network connecting several local HetNets

CE PE

IMS

Improved CSCF Server

Figure 5 IMS based IoT video streaming over 5G HetNets

similar technology [17] network slicing becomes a noveltrend and has a wonderful capability to offer QoS guaran-teed services with flexibility Figure 4 shows an example ofnetwork slicing based 5G core that connects several localHetNets Using MPLS technology the core network containsthe following types of equipment

(a) customer edge (CE) router which serves to connectwith client local network directly

(b) provider edge (PE) router which serves as the inter-face between 5G HetNet and optical core

(c) provider (P) router which serves to forward the usertraffic arranged by CE and PE routers

32 IMS Based IoT Video Streaming Internet of things (IoT)is regarded recently as the most promising form of thewireless communication environments

In Internet devices a large amount of data is generatedand accumulated into a large amount of data streams overtime Therefore higher requirements are imposed on theprocessing and decomposition of online data In manyapplications a large amount of data storage is generated suchas monitoring the generated video stream data

5G HetNets transport the video streaming traffic bychanging an IoT sensor into an IP Multimedia Subsystem(IMS) terminal IMS realizes robust IMS service functionalityby working closely with Call Session Control Function(CSCF)TheCSCFnode facilitates Session Initiation Protocol(SIP) to operate session setup and teardown [18]

As a popular video streaming protocol CSCF conductssignaling and session management and enables connectioninformation to go across network boundaries

Figure 5 gives an example of IMS based IoT videostreaming over 5G HetNets The improved CSCF serveracts as an intermediate entity that receives and forwardssignaling messages Improved server offers a bunch of cru-cial functions such as authentication authorization androuting

33 Model of Improved CSCF Server By definition the mainmodule of the IMS system is call session control to allowvideovoice communication over the convergence of differentaccess networks It comprises of all the functional modulesrequired to handle all the signaling from end-user to servicesand other networks However conventional CSCF cannot


SIPampCOPSProtocol Stacks

Bandwidth

Negotiation

Traffic

Prediction

Connection

Admission

Control

Original CSCF Server


SIP Messages

COPS Messages

Enhanced Modules

Figure 6 Model of improved CSCF server

Traffic Load Sampling

Traffic Prediction Algorithm

Real Traffic Load

Predicted Traffic Load

Traffic Load Prediction Module

Figure 7 General framework of IoT traffic prediction

100 meet the requirement of IoT video streaming and bemodernized

Figure 6 shows that an improved CSCF server containsthree more modules than the original Our design has themerit that the improved CSCF server bargains with theoptical core on behalf of all IoT devices of the local HetNetin advance

For this reason the paths required by video streaming arepreconstructed before the stream even starts to reduce theunnecessary signaling delay

4 Traffic Prediction in IoT UsingCompressed Sensing

Traffic prediction in the IoT has been widely studied andapplied in recent years Sampling and prediction representusually two independent parts in the past For example mostconventional approaches keep the sampling rate unchangedwhich may result in low efficiency in IoT video streamingTo overcome this problem this paper develops a novelcompressed sensing based traffic prediction where error offorecast algorithm goes back to control the sampling intervalOur proposed scheme can significantly reduce the samplingoverhead without sacrificing the accuracy

41 Problem Formulation and Architecture Design Let 119861119899minus1119899be the sum of IoT video streaming loads from a local HetNetat time slot [119905119899minus1 119905119899](119899 = 0 1 2 ) Let improved CSCFserver be exactly aware of the value of 119861119899minus1119899 at time 119905119899minus1With this piece of information it will bargain with 5G corefor the amount of 119861119899minus1119899 bandwidth using COPS messagesAt the next time slot [119905119899 119905119899+1] IoT traffic may change to anew value of 119861119899119899+1 and the improved CSCF server will haveto rebargain with 5G core network again

It is nevertheless impossible for the improved CSCFserver to achieve the value of 119861119899minus1119899 before the time of 119905119899Thus at time 119905119899minus1 we can estimate the approximate value119861119899minus1119899 which is denoted by 1198611015840119899minus1119899 If 1198611015840119899minus1119899 lt 119861119899minus1119899 the localHetNet will suffer from the shortage of bandwidth resourceto accommodate all video streaming sessions As a solutionCAC component must reject some of connection requests inorder to guarantee overall quality of service

On the other hand if 1198611015840119899minus1119899 gt 119861119899minus1119899 part of bandwidthresource is simply overbooked and useless In this sense thetraffic prediction is critical to ensure a good performance ofour proposed scheme

Figure 7 presents the general framework of IoT trafficprediction module with the component of traffic load sam-pling and the component of traffic prediction algorithm


We will give more elaboration on these two componentsnext

42 IoT Traffic Sampling An IoT device initiates a videostream session by sending an INVITE message to theimproved CSCF server in a bid to indicate called URL andbandwidth requirement The improved CSCF server savesevery request in the record for IoT traffic sampling usageregardless the request is accepted by CAC or not

In this paper we use 119861119899minus1119899 to represent the averageIoT traffic interval Δ119905119899minus1119899 = 119905119899 minus 119905119899minus1 In practice thesystem achieves 119861119899minus1119899 from the record in the improved CSCFserver Conventional approach conducts traffic samplingwithconstant rate which rules

Δ11990501 = Δ11990512 = sdot sdot sdot = Δ119905119899minus1119899 = (119899 = 0 1 2 ) (1)

Let 119861(119905) be the traffic load function varying with time tand let 119861(119891) be the frequency counterpart of 119861(119905) with theupper bound of 119891max Nyquist theorem regulates the idea thatsampling ratemust be at least twice of119891max to prevent aliasing[19] In IoT video streaming 119891max could be high in somecomplicated scenarios which make constant sampling ratenot applicable at all

43 Compressed Sensing Compressed sensing theory is atheory that has been widely used in recent years and iswidely used in signal processing It is also called compressedsampling or sparse sampling CS wants to sample the signalat a sampling rate much lower than Shannons Law andfind a solution for underdetermined linear systems whilemaintaining the basic information of the signal [20ndash24]

In order to implement the compressed sensing theory thefollowing solution can be used to estimate the sparse vectorx isin C119873 using the observed measurement vector 119910 isin C119872A method based on measurement equations is applied Theformula is as follows

119910 = Φ119909 + 119908 (2)

The measurement matrix is represented by Φ isin C119872times119873119908 isin C119873 represents the unknown vector of measurementnoise and modeling error The reconstruction process canonly occur under ideal conditions ie only when the signalx is S sparse (119878 ≪ 119873 ) That is there can be at most S nonzeroitems During the operation the number of observations issignificantly smaller than the number of variables ie≫ 119872

In practice the signal we are dealing with is not a sparsesignalThe processingmethod in this time is to express Ψ119873119894=1on some basis with the corresponding sparse coefficient 120579119894Processing equation (1) yields

119910 = Φ119909 + 119908 = ΦΨ120579 + 119908 = Θ120579 + 119908 (3)

where 120579 isin 119862119871 is a L dimension sparse vector of coefficientsbased on the basismatrixΨ isin 119862119873times119871Θ = ΦΨ is a119872times119871matrix(119872 ≪ 119871 )

However because fewer measurements are applied thanthe entries in 120579 the resulting solution is uncertain In

order to recover the 119878 sparse signal 1198970 norm of the mostsparse sequence should be found from all feasible solutionsMinimization can be used for the reconstruction processTheformula is expressed as follows

120579 = min 1205790st 1003817100381710038171003817Θ120579 minus 11991010038171003817100381710038172 le Ξ (4)

where 120579 refers to the estimated vector for 120579 and w le Ξis noise tolerance However for practical operations the 1198970normminimization has huge computational complexity anda reconstruction algorithm with lower computational costmust be developed

Studies have shown that the reconstruction process of sig-nal 119909must satisfy a condition that matrix Θ cannot map twodifferent S sparse signals to the same set of samplesThereforethematrixΘmust satisfy the restricted equidistance property(RIP) [25 26]

44 Adaptive Sampling Rate (a)The definition of traditionalcompressive sensing sparse signals can be recovered from asmall number of nonadaptive linearmeasurements Let signal119909 be 119870 sparse in basisdictionary Ψ For example Ψ is DFTmatrix if the signal is frequency domain sparse Ψ can beexpressed as a matrix as shown in Figure 8(a) In Figure 9if signal 119909 is sparse then we can use compressive sensing by119910 = Φ times 119909

Φ is the measurement matrix and the theory of CS statesthat random Φ will work How to understand this Forexample if the signal is sparse in frequency domain then wemake a few random samplings in time domain In contrast ifthe signal is sparse in time domain we make a few randomsamplings in frequency domain

(b) Address the situation that the appearance time ofsparse signal can be predicted In this case Ψ is identitymatrix I as shown in Figure 8(b) Then measure matrix Φis part of I which involves the time points where the sparsesignal appears Φ is not random anymore In practice wepropose variable sampling rate scheme to predict these timepoints (or the measurement matrix)

If a fixed sampling interval applies as mentioned beforethe sampling rate must double the highest frequency of trafficcurve to achieve accurate prediction

Accordingly it is significant to develop an inconstantsampling rate scheme with much lower overhead

Variable sampling rate comes from the observation ofthe traffic curve in Figure 10 with combination of slow andfast changing periods The fluctuation of traffic significantlydepends on the timely feature of IoT devices For examplethe cameras monitor the parking carsrsquo moving and stoppingaccording to peoplersquos behavior which is obviously related tothe number of car arrivals and departures In Figure 8 fastchanging zone represents the period of rapid event varyingsuch as morning peak hour slow changing zone representsthe period of steady event numbers such as at night

With the goal of reducing the sampling overhead astraightforward adaptive rate approach is to utilize low ratein slow changing period and high rate in fast changing


(a) DFT matrix (b) Identity matrix I

Figure 8 The matrix involved in the compressive sensing

Mtimes 1

measurements

y

=

Mtimes N

K lt M ≪ N

xΦ

Ntimes 1

sparsesignal

K

nonzeroentries

Figure 9 The process of compressive sensing

Fast Changing ZoneFast Changing Zone

Traffic Load

Slow ChangingZone

0

500

1000

1500

2000

2500

Traffi

c Loa

d (M

bps)

2 4 6 8 10 12 14 16 18 20 22 240Time (Hours)

Figure 10 IoT traffic load curve of a parking monitor camera

period The adaptive sampling interval aims to sampleand reconstruct the traffic curve efficiently and effectivelywhile keeping the same prediction accuracy as constant rateapproach does

Next we study a typical scenario as follows Letrsquos dividean IoT traffic curve into a number of slow changing and fastchanging periods by using 119891119904max and 119891119891max to represent themaximum frequencies respectively Nyquist sampling theorystates that the sampling rate must be greater than 2119891119904max incase of slow changing zone and 2119891119891max in case of fast changingAfter that we can achieve the average rate of sampling 119877avg

119881119878119877by

119877119886V119892119881119878119877 =2119891119904max119879119904 + 2119891119891max119879119891

119879119904 + 119879119891 (5)

where 119879s and 119879119891 are time of slow and fast changing Since119891119904max lt 119891119891max lt 119891max we have 119877avg

119881119878119877 lt 2119891max implyingthat ASR approach has much less sampling overhead than itsconstant counterpart

45 Traditional Linear Predictor The traditional predictorused most often in practice is the least mean square (LMS)linear filter A k-step LPmakes a guess of the value of 119909(119899+119896)by a linear sum of the current and past x(n) Mathematicallythe pth-order k-step is given by

119909 (119899 + 119896) =119901minus1

sum119894=0

119908119894119909 (119899 minus 119894) = 119908119879119909 (119899) (6)

where 119909(119899 + 119896) is the estimated value of 119909(119899 + 119896) 119908 =[1199080 1199081 119908119901minus1]119879 is the weights and 119909(119899) = [119909(119899) 119909(119899 minus1) 119909(119899 minus 119901 + 1)]119879 is priori knowledge We further definethe prediction error as

119890 (119899) = 119909 (119899 + 119896) minus = 119909 (119899 + 119896) minus 119908119879119909 (119899) (7)Then LP updates 119908(119899) using the following recursive

equation

w (119899 + 1) = w (119899) + 120583119890 (119899) x (119899)x (119899)2 (8)

where 120583 is the step size that may keep constant (constant stepsize (CSS)) or adaptive (adaptive step size (ASS))


ASR Sampler w(n)x(n)

Linear Predictor

e(n)

Traffic Curve

+

-

x(n+k)

x(n+k)

Figure 11 ASR-LP working with variable sampling rate

46 Adaptive Sampling Rate Linear Predictor Realisticallythe traffic curve is unknown at the time of prediction andthus it is a mission impossible to chop it into slow andfast changing periods We therefore develop an adaptiveprediction scheme ASR-LP as shown in Figure 11

ASR-LP rules the sampling rate at time 119905119899 as 119877119904(119899) =1Δ119905119899 minus 1 and then update it in integration by

1198771199041015840 (119899 + 1) = 119877119904 (119899) [119902 + (1 minus 119902) |119890 (119899)|119864119887 ] (9)

with 0 lt 119902 lt 1 119864119887 gt 0 and

119877119904 (119899 + 1) =

119877max119904 119894119891 1198771199041015840 (119899 + 1) gt 119877max

119904

119877min119904 119894119891 1198771199041015840 (119899 + 1) lt 119877min

119904

1198771199041015840 (119899 + 1) 119900119905ℎ119890119903119908119894119904119890(10)

where 0 lt 119902 lt 1 is the remembering factor Eb is the targetedprediction error bound and 119877smax and 119877smax are the upperand lower bounds of sampling rate

The scheme of ASR-LP optimizes sampling interval bythe following principal The larger the prediction error isthe sharper the traffic curve changes As a result we mustincrease the sampling rate for satisfying prediction accuracyIn contrast the smaller the prediction error is the lowerthe sampling rate goes Particularly network operator candetermine the value of targeted prediction error bound Eb If|119890(119899)| gt 119864119887 the sampling rate should be increased if |119890(119899)| lt119864119887 the sampling rate should be decreased if |119890(119899)| = 119864119887 thesampling rate should be kept unchanged

In addition to Eb there are still three other parametersin (9) and (10) ie 119877smax is the upper bound of samplingrate which can lead to lower prediction error but largercomputational complexity as the value rises 119877smin is thelower bound of sampling rate which represents the bottomline responding time of a linear predictor Last but not leastq decides how much the current sampling rate is relying onits previous value rather than the prediction error We haveto leverage the value of remembering factor 119902 for the optimalperformance of predictor with 119902 = 70 as a typical case

In our research we have conducted simulation studieson a variety of traffic curves to evaluate the performance

Traffi

c Loa

d (M

bps)

0

500

1000

1500

2000

2500

Traffic Load

10 1505 20 25 30 35 4000

Time (Hours)

Figure 12 IoT Traffic load of four hours for parking monitorcamera

of our proposed ASR-LP scheme Due to the limit of spacewe demonstrate only one typical curve in Figure 10 as anexample However the simulation results from this exampleare also largely true to the general cases

Figure 10 is collected from Melbourne on street carparking sensor data [26] which reflects the typical timingfeature of IoT users It is worth noting that the curve of IoTtraffic is dramatically different from previously investigatedmobility models in existing literature such as Brownianmotion model

We use Figure 12 as prototype traffic curve to comparethe performance of three schemes as shown in Figure 12 Inaddition to its advantage in the running time VSR-NLMSdoes have another important virtue ie a lower averagesampling rate compared to FSS-NLMS and VSS-NLMS Itshows that with the same simulation configuration as shownin Figure 12 VSR-NLMS achieves an average sampling rateof 1371 Hz which is lower than 1420 Hz for FSS-NLMS andVSS-NLMS Clearly VSR-NLMS has the lowest sampling rateand thus the least sampling overhead

Figure 13 reveals thatASR-LP can basically tieASS-LP butfar outperform CSS-LP regarding prediction accuracy


40

35

30

25

20

15

10

5

000 05 10 15 20 25 30 35 40

Time (Hours)

Erro

r (M

bps)

CSSASRASS

Figure 13 Errors of different predicting methods in four hours

119860119878119877 minus 119871119875 119864119887 = 100119872119887119901119904 119877smax = 1 = 300119867z 119877smin =1800119867z 119877s(0) = 1420119867z and 119902 = 70

119862119878119878 minus 119871119875 119901 = 4 120583 = 01 119860119878119878 minus 119871119875 120583max = 025 120583min =005

Low overhead of average sampling is a significant advan-tage of ASR-LP compared to its counterpart Intensivesimulation study reveals that ASR-LP can save at least halfsampling rate over constant sampling rate linear predictorswith the same accuracy

5 Conclusions

This paper studies the deployment of IoT video streams on5G HetNets and proposes an improved CSCF server solutionto promote IoT traffic prediction and resource reservationWe further designed a linear predictor based on compressedsensing to capture traffic patterns greatly reducing samplingoverhead and computational complexity Intensive analysisand simulation results show that the proposed scheme caneffectively improve performance and save time and cost

Data Availability

The data used to support the findings of this study areincluded within the article

Conflicts of Interest

The authors declare that they have no conflicts of interest

References

[1] M Li Y Du X Ma and S Huang ldquoA 3-param networkselection algorithm in heterogeneous wireless networksrdquo inProceedings of the IEEE 9th International Conference onCommu-nication Software and Networks (ICCSN rsquo17) pp 392ndash396 IEEEGuangzhou China May 2017

[2] G Ma Y Yang X Qiu Z Gao and H Li ldquoFault-toleranttopology control for heterogeneous wireless sensor networks

using Multi-Routing Treerdquo in Proceedings of the 15th IFIPIEEEInternational Symposium on Integrated Network and ServiceManagement (IM rsquo17) pp 620ndash623 Lisbon Portugal May 2017

[3] S Han Y Huang W Meng C Li N Xu and D ChenldquoOptimal power allocation for SCMA downlink systems basedon maximum capacityrdquo IEEE Transactions on Communicationsvol 67 no 2 pp 1480ndash1489 2019

[4] A Montazerolghaem M H Moghaddam and A Leon-GarcialdquoOpenSIP toward software-defined SIP networkingrdquo IEEETransactions on Network and Service Management vol 15 no1 pp 184ndash199 2018

[5] A K Vimal S Pandit A K Godiyal S Anand S Luthraand D Joshi ldquoAn instrumented flexible insole for wireless COPmonitoringrdquo in Proceedings of the 8th International Conferenceon Computing Communications and Networking Technologies(ICCCNT rsquo17) pp 1ndash5 Delhi India July 2017

[6] A Mirrashid and A A Beheshti ldquoCompressed remote sensingby using deep learningrdquo in Proceedings of the 9th InternationalSymposium on Telecommunications (IST rsquo18) pp 549ndash552 IEEETehran Iran December 2018

[7] S RouabahM Ouarzeddine and B Souissi ldquoSAR images com-pressed sensing based on recovery algorithmsrdquo in Proceedingsof the 2018 IEEE International Geoscience and Remote SensingSymposium (IGARSS rsquo18) pp 8897ndash8900 IEEE Valencia SpainJuly 2018

[8] S Han Y Zhang W Meng and H Chen ldquoSelf-interference-cancelation-based SLNRprecoding design for full-duplex relay-assisted systemrdquo IEEE Transactions on Vehicular Technologyvol 67 no 9 pp 8249ndash8262 2018

[9] S Han S Xu W Meng and C Li ldquoDense-device-enabledcooperative networks for efficient and secure transmissionrdquoIEEE Network vol 32 no 2 pp 100ndash106 2018

[10] D Ghadiyaram J Pan and A C Bovik ldquoA subjective andobjective study of stalling events in mobile streaming videosrdquoIEEE Transactions on Circuits and Systems for Video Technologyvol 29 no 1 pp 183ndash197 2019

[11] Z Zheng L Pan and K Pholsena ldquoMode decomposition basedhybrid model for traffic flow predictionrdquo in Proceedings of the3rd IEEE International Conference onData Science inCyberspace(DSC rsquo18) pp 521ndash526 IEEE Guangzhou China June 2018

[12] S Fan and H Zhao ldquoDelay-based cross-layer QoS scheme forvideo streaming in wireless ad hoc networksrdquo China Communi-cations vol 15 no 9 pp 215ndash234 2018

[13] T Zhang L Feng P Yu S Guo W Li and X Qiu ldquoAhandover statistics based approach for cell outage detection inself-organized heterogeneous networksrdquo in Proceedings of the15th IFIPIEEE International Symposium on Integrated Networkand Service Management (IM rsquo17) pp 628ndash631 IEEE LisbonPortugal May 2017

[14] S Sun M Kadoch L Gong and B Rong ldquoIntegrating net-work function virtualization with SDR and SDN for 4G5Gnetworksrdquo IEEE Network vol 29 no 3 pp 54ndash59 2015

[15] M Lu Z Qu Z Wang and Z Zhang ldquoHete MESE multi-dimensional community detection algorithm based on multi-plex network extraction and seed expansion for heterogeneousinformation networksrdquo IEEE Access vol 6 pp 73965ndash739832018

[16] Z Zhang L Song ZHan andW Saad ldquoCoalitional gameswithoverlapping coalitions for interference management in smallcell networksrdquo IEEE Transactions on Wireless Communicationsvol 13 no 5 pp 2659ndash2669 2014


[17] K Zhang S Leng Y He S Maharjan and Y Zhang ldquoCoop-erative content caching in 5G networks with mobile edgecomputingrdquo IEEE Wireless Communications Magazine vol 25no 3 pp 80ndash87 2018

[18] J Rosenberg H Schulzrinne G Camarillo et al ldquoSIP sessioninitiation protocolrdquo RFC 3261 IETF June 2002

[19] K Zhang S Leng Y He S Maharjan and Y Zhang ldquoMobileedge computing and networking for green and low-latencyinternet of thingsrdquo IEEE CommunicationsMagazine vol 56 no5 pp 39ndash45 2018

[20] G Shrividya and S H Bharathi ldquoA study of optimum samplingpattern for reconstruction of MR images using compressivesensingrdquo in Proceedings of the Second International Conferenceon Advances in Electronics Computers and Communications(ICAECC rsquo18) pp 1ndash6 Bangalore India Feburary 2018

[21] Y Wu B Rong K Salehian and G Gagnon ldquoCloud transmis-sion a new spectrum-reuse friendly digital terrestrial broad-casting transmission systemrdquo IEEE Transactions on Broadcast-ing vol 58 no 3 pp 329ndash337 2012

[22] A Sammoud A Kumar M Bayoumi and T Elarabi ldquoReal-time streaming challenges in Internet of VideoThings (IoVT)rdquoin Proceedings of the 50th IEEE International Symposium onCircuits and Systems (ISCAS rsquo17) pp 1ndash4 Baltimore Md USAMay 2017

[23] N Eswara KManasa A Kommineni et al ldquoA continuous QoEevaluation framework for video streaming over HTTPrdquo IEEETransactions on Circuits and Systems for Video Technology vol28 no 11 pp 3236ndash3250 2018

[24] B Rong Y Qian K Lu H-H Chen and M Guizani ldquoCalladmission control optimization in WiMAX networksrdquo IEEETransactions on Vehicular Technology vol 57 no 4 pp 2509ndash2522 2008

[25] I D Irawati A B Suksmono and I Y M Edward ldquoLocalinterpolated compressive sampling for internet traffic recon-structionrdquo in Proceedings of the International Conference onAdvanced Computing and Applications (ACOMP rsquo17) pp 93ndash98Ho Chi Minh City Vietnam December 2017

[26] N Anselmi L Poli G Oliveri and A Massa ldquoThree dimen-sional imaging with the contrast source compressive samplingrdquoin Proceedings of the International Applied Computational Elec-tromagnetics Society Symposium - China (ACES rsquo18) pp 1-2IEEE Beijing China July 2018

International Journal of

AerospaceEngineeringHindawiwwwhindawicom Volume 2018

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Volume 2018

Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom

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Journal ofEngineeringVolume 2018

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Modelling ampSimulationin EngineeringHindawiwwwhindawicom Volume 2018


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Hindawi

wwwhindawicom Volume 2018

Advances in

Multimedia

Submit your manuscripts atwwwhindawicom


services for different information flows Video signal is veryimportant among multimedia services At the same time thevideo traffic on the Internet has increased dramatically anda lot of research work has been done to improve the viewingexperience of the audience

Video traffic between HetNet and optical core oftenfluctuates because of use casersquos characteristic and variationof observed object Therefore our proposed improved CSCFserver chooses a dynamic mechanism in a bid to bookbandwidth economically Apparently the traffic predictioncomponent of improved CSCF server has critical impact onthe performance of dynamic bandwidth negotiation In prac-tice sampling overhead is a real challenge as traffic predictormust talk to two other components of the improved CSCFserver during the sampling process For instance traffic pre-dictor needs the calling records from connection admissioncontrol component as the input After the prediction it hasto transfer the result to bandwidth negotiation componentwhich will further use COPS messages to negotiate with 5Gcore network The procedure above can strongly justify thesignificance of sampling rate reduction

Compressed sensing is a promising paradigm that usessignal sparsity to reduce the amount of data that needs to bemeasured [6 7] Compressed sensing (CS) theory indicatesthat the characteristics of sparse signals are not affected bythe signal sparse basis It can be obtained by a small amountof projection on another basis The reconstruction of thewideband signal can be obtained by solving the L1 normoptimization It is concluded that in the calculation processthe sparsity of the signals in the orthogonal basis determinesthe minimum projection number and sampling rate required[8]

In the literature the study of traffic prediction mainlyconcentrated on the least mean square (LMS) methods thattake sampling and prediction as two independent aspects [9ndash12] As a result the sampling often implements a constantinterval and is too prone to low efficiency in IoT environment

Other than previous work this paper focuses on theaggregated multiple streams in a HetNet instead of oneseparated video stream

Inspired by compressed sensing theory we develop anadaptive sampling rate (ASR) linear predictor to overcomethe shortcoming of traditional schemes ASR linear predictorutilizes the prediction error to control sampling interval sothat the sampling and predicting overheads are significantlyreduced

The rest of the paper is organized as the remainderSection 2 introduces 5G IoT applications in smart citiesSection 3 introduces 5G HetNets and IMS based IoT videostreaming Section 4 develops traffic prediction schemesusing adaptive sampling rate and compressed sensing theoryFinally Section 5 summarizes the advantages of our proposedimproved CSCF server and ASR linear predictor

2 5G IoT Applications in Smart Cities

21 Smart Cities The development of the IoTs and cloudcomputing technology has become an important symbol of

our modern information age especially in the city providingtechnical support for the commercial development of the cityThe comprehensive and multilevel processing of informationtechnology in the IoTs provides a service platform for datasystems Therefore in the smart city information systemthe digital information processing of the city can timelyunderstand the relevant information of the city

The use of IoT technology to improve and develop smartcity information technology shield bad information andguide and support the exchange of useful information enablesit to cover all corners of the city in a network and realizesthe management and control of the city as a whole asshown in Figure 1 At the same time with the help of theintelligent engine and the unified information service systemthe management efficiency of urbanization construction hasbeen effectively improved

For smart cities their construction and developmentare inseparable from the IoT communication technologyIt can be said that IoT communication technology is animportant component of its foundationOne of the importantapplications is intelligent building management which ismainly used in the following aspects

(a) The installation of intelligent building managementtechnology during the construction of the building willenable residents to conveniently manage and control remoteitems under the cooperation of the IoT

(b)The establishment of a large publicmonitoring systemthere are many public buildings in the city and there aremany equipment and facilities Therefore an emergencymonitoring system must be established to solve this problemquicklywith the assistance of the IoTwhenunexpected eventsoccur

(c) Adopt intelligent water coal and electricity metersThe RFID chip is built in the table of various facilities andthe data can be collected into the data processor through thetable and then transmitted to the background system throughthe 5G network to realize the function of automatic meterreading

(d) Processing of abnormal equipment remote moni-toring of air conditioners lights etc is carried out usingwireless sensors and local area network technology during theprocessing of this problem

22 IoT Intelligent Parking System in Smart City With thedevelopment of economy there are more and more vehiclesin the city and the problem of parking difficulty is followedSmart cities can bring effective solutions to this problem Inthe smart city an intelligent parking system based on InternetofThings is proposedThe framework of the system is shownin Figure 2

This system combines advanced IoT technology mobileinternet technology and big data technology As shown inthe figure the system is mainly composed of the videopile the base station the server the mobile client and themanagement cloud platform

The video pile is mainly composed of a camera aprocessor and a communication device While the systemis running the camera is used to capture images of eachparking space and monitor the status of each parking space


INDUSTRY

SECURITY RETAIL

SOCIETY HEALTHCARE

HOME ENERGY

MOBILITY

SMART CITY


BS

5G

Server


cloud platform







Cellular BS



Cloud

Macrocell link

Small cell link

Relaying

Gateway

DataCenter

Servers








CE

PEPE

5G HetNet

5G HetNet

CE

CE

PE

PE


CE PE

IMS















Bandwidth

Negotiation

Traffic

Prediction

Connection

Admission

Control



SIP Messages

COPS Messages

Enhanced Modules




Real Traffic Load





















119910 = Φ119909 + 119908 (2)



119910 = Φ119909 + 119908 = ΦΨ120579 + 119908 = Θ120579 + 119908 (3)




120579 = min 1205790st 1003817100381710038171003817Θ120579 minus 11991010038171003817100381710038172 le Ξ (4)













Mtimes 1

measurements

y

=

Mtimes N

K lt M ≪ N

xΦ

Ntimes 1

sparsesignal

K

nonzeroentries



Traffic Load

Slow ChangingZone

0

500

1000

1500

2000

2500

Traffi

c Loa

d (M

bps)

2 4 6 8 10 12 14 16 18 20 22 240Time (Hours)




119881119878119877by

119877119886V119892119881119878119877 =2119891119904max119879119904 + 2119891119891max119879119891

119879119904 + 119879119891 (5)




119909 (119899 + 119896) =119901minus1

sum119894=0

119908119894119909 (119899 minus 119894) = 119908119879119909 (119899) (6)



equation

w (119899 + 1) = w (119899) + 120583119890 (119899) x (119899)x (119899)2 (8)




Linear Predictor

e(n)

Traffic Curve

+

-

x(n+k)

x(n+k)




1198771199041015840 (119899 + 1) = 119877119904 (119899) [119902 + (1 minus 119902) |119890 (119899)|119864119887 ] (9)

with 0 lt 119902 lt 1 119864119887 gt 0 and

119877119904 (119899 + 1) =

119877max119904 119894119891 1198771199041015840 (119899 + 1) gt 119877max

119904

119877min119904 119894119891 1198771199041015840 (119899 + 1) lt 119877min

119904

1198771199041015840 (119899 + 1) 119900119905ℎ119890119903119908119894119904119890(10)





Traffi

c Loa

d (M

bps)

0

500

1000

1500

2000

2500

Traffic Load

10 1505 20 25 30 35 4000

Time (Hours)







40

35

30

25

20

15

10

5

000 05 10 15 20 25 30 35 40

Time (Hours)

Erro

r (M

bps)

CSSASRASS





5 Conclusions


Data Availability




References































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia



INDUSTRY

SECURITY RETAIL

SOCIETY HEALTHCARE

HOME ENERGY

MOBILITY

SMART CITY


BS

5G

Server


cloud platform







Cellular BS



Cloud

Macrocell link

Small cell link

Relaying

Gateway

DataCenter

Servers








CE

PEPE

5G HetNet

5G HetNet

CE

CE

PE

PE


CE PE

IMS















Bandwidth

Negotiation

Traffic

Prediction

Connection

Admission

Control



SIP Messages

COPS Messages

Enhanced Modules




Real Traffic Load





















119910 = Φ119909 + 119908 (2)



119910 = Φ119909 + 119908 = ΦΨ120579 + 119908 = Θ120579 + 119908 (3)




120579 = min 1205790st 1003817100381710038171003817Θ120579 minus 11991010038171003817100381710038172 le Ξ (4)













Mtimes 1

measurements

y

=

Mtimes N

K lt M ≪ N

xΦ

Ntimes 1

sparsesignal

K

nonzeroentries



Traffic Load

Slow ChangingZone

0

500

1000

1500

2000

2500

Traffi

c Loa

d (M

bps)

2 4 6 8 10 12 14 16 18 20 22 240Time (Hours)




119881119878119877by

119877119886V119892119881119878119877 =2119891119904max119879119904 + 2119891119891max119879119891

119879119904 + 119879119891 (5)




119909 (119899 + 119896) =119901minus1

sum119894=0

119908119894119909 (119899 minus 119894) = 119908119879119909 (119899) (6)



equation

w (119899 + 1) = w (119899) + 120583119890 (119899) x (119899)x (119899)2 (8)




Linear Predictor

e(n)

Traffic Curve

+

-

x(n+k)

x(n+k)




1198771199041015840 (119899 + 1) = 119877119904 (119899) [119902 + (1 minus 119902) |119890 (119899)|119864119887 ] (9)

with 0 lt 119902 lt 1 119864119887 gt 0 and

119877119904 (119899 + 1) =

119877max119904 119894119891 1198771199041015840 (119899 + 1) gt 119877max

119904

119877min119904 119894119891 1198771199041015840 (119899 + 1) lt 119877min

119904

1198771199041015840 (119899 + 1) 119900119905ℎ119890119903119908119894119904119890(10)





Traffi

c Loa

d (M

bps)

0

500

1000

1500

2000

2500

Traffic Load

10 1505 20 25 30 35 4000

Time (Hours)







40

35

30

25

20

15

10

5

000 05 10 15 20 25 30 35 40

Time (Hours)

Erro

r (M

bps)

CSSASRASS





5 Conclusions


Data Availability




References































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia



Cellular BS



Cloud

Macrocell link

Small cell link

Relaying

Gateway

DataCenter

Servers








CE

PEPE

5G HetNet

5G HetNet

CE

CE

PE

PE


CE PE

IMS















Bandwidth

Negotiation

Traffic

Prediction

Connection

Admission

Control



SIP Messages

COPS Messages

Enhanced Modules




Real Traffic Load





















119910 = Φ119909 + 119908 (2)



119910 = Φ119909 + 119908 = ΦΨ120579 + 119908 = Θ120579 + 119908 (3)




120579 = min 1205790st 1003817100381710038171003817Θ120579 minus 11991010038171003817100381710038172 le Ξ (4)













Mtimes 1

measurements

y

=

Mtimes N

K lt M ≪ N

xΦ

Ntimes 1

sparsesignal

K

nonzeroentries



Traffic Load

Slow ChangingZone

0

500

1000

1500

2000

2500

Traffi

c Loa

d (M

bps)

2 4 6 8 10 12 14 16 18 20 22 240Time (Hours)




119881119878119877by

119877119886V119892119881119878119877 =2119891119904max119879119904 + 2119891119891max119879119891

119879119904 + 119879119891 (5)




119909 (119899 + 119896) =119901minus1

sum119894=0

119908119894119909 (119899 minus 119894) = 119908119879119909 (119899) (6)



equation

w (119899 + 1) = w (119899) + 120583119890 (119899) x (119899)x (119899)2 (8)




Linear Predictor

e(n)

Traffic Curve

+

-

x(n+k)

x(n+k)




1198771199041015840 (119899 + 1) = 119877119904 (119899) [119902 + (1 minus 119902) |119890 (119899)|119864119887 ] (9)

with 0 lt 119902 lt 1 119864119887 gt 0 and

119877119904 (119899 + 1) =

119877max119904 119894119891 1198771199041015840 (119899 + 1) gt 119877max

119904

119877min119904 119894119891 1198771199041015840 (119899 + 1) lt 119877min

119904

1198771199041015840 (119899 + 1) 119900119905ℎ119890119903119908119894119904119890(10)





Traffi

c Loa

d (M

bps)

0

500

1000

1500

2000

2500

Traffic Load

10 1505 20 25 30 35 4000

Time (Hours)







40

35

30

25

20

15

10

5

000 05 10 15 20 25 30 35 40

Time (Hours)

Erro

r (M

bps)

CSSASRASS





5 Conclusions


Data Availability




References































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia




CE

PEPE

5G HetNet

5G HetNet

CE

CE

PE

PE


CE PE

IMS















Bandwidth

Negotiation

Traffic

Prediction

Connection

Admission

Control



SIP Messages

COPS Messages

Enhanced Modules




Real Traffic Load





















119910 = Φ119909 + 119908 (2)



119910 = Φ119909 + 119908 = ΦΨ120579 + 119908 = Θ120579 + 119908 (3)




120579 = min 1205790st 1003817100381710038171003817Θ120579 minus 11991010038171003817100381710038172 le Ξ (4)













Mtimes 1

measurements

y

=

Mtimes N

K lt M ≪ N

xΦ

Ntimes 1

sparsesignal

K

nonzeroentries



Traffic Load

Slow ChangingZone

0

500

1000

1500

2000

2500

Traffi

c Loa

d (M

bps)

2 4 6 8 10 12 14 16 18 20 22 240Time (Hours)




119881119878119877by

119877119886V119892119881119878119877 =2119891119904max119879119904 + 2119891119891max119879119891

119879119904 + 119879119891 (5)




119909 (119899 + 119896) =119901minus1

sum119894=0

119908119894119909 (119899 minus 119894) = 119908119879119909 (119899) (6)



equation

w (119899 + 1) = w (119899) + 120583119890 (119899) x (119899)x (119899)2 (8)




Linear Predictor

e(n)

Traffic Curve

+

-

x(n+k)

x(n+k)




1198771199041015840 (119899 + 1) = 119877119904 (119899) [119902 + (1 minus 119902) |119890 (119899)|119864119887 ] (9)

with 0 lt 119902 lt 1 119864119887 gt 0 and

119877119904 (119899 + 1) =

119877max119904 119894119891 1198771199041015840 (119899 + 1) gt 119877max

119904

119877min119904 119894119891 1198771199041015840 (119899 + 1) lt 119877min

119904

1198771199041015840 (119899 + 1) 119900119905ℎ119890119903119908119894119904119890(10)





Traffi

c Loa

d (M

bps)

0

500

1000

1500

2000

2500

Traffic Load

10 1505 20 25 30 35 4000

Time (Hours)







40

35

30

25

20

15

10

5

000 05 10 15 20 25 30 35 40

Time (Hours)

Erro

r (M

bps)

CSSASRASS





5 Conclusions


Data Availability




References































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia




Bandwidth

Negotiation

Traffic

Prediction

Connection

Admission

Control



SIP Messages

COPS Messages

Enhanced Modules




Real Traffic Load





















119910 = Φ119909 + 119908 (2)



119910 = Φ119909 + 119908 = ΦΨ120579 + 119908 = Θ120579 + 119908 (3)




120579 = min 1205790st 1003817100381710038171003817Θ120579 minus 11991010038171003817100381710038172 le Ξ (4)













Mtimes 1

measurements

y

=

Mtimes N

K lt M ≪ N

xΦ

Ntimes 1

sparsesignal

K

nonzeroentries



Traffic Load

Slow ChangingZone

0

500

1000

1500

2000

2500

Traffi

c Loa

d (M

bps)

2 4 6 8 10 12 14 16 18 20 22 240Time (Hours)




119881119878119877by

119877119886V119892119881119878119877 =2119891119904max119879119904 + 2119891119891max119879119891

119879119904 + 119879119891 (5)




119909 (119899 + 119896) =119901minus1

sum119894=0

119908119894119909 (119899 minus 119894) = 119908119879119909 (119899) (6)



equation

w (119899 + 1) = w (119899) + 120583119890 (119899) x (119899)x (119899)2 (8)




Linear Predictor

e(n)

Traffic Curve

+

-

x(n+k)

x(n+k)




1198771199041015840 (119899 + 1) = 119877119904 (119899) [119902 + (1 minus 119902) |119890 (119899)|119864119887 ] (9)

with 0 lt 119902 lt 1 119864119887 gt 0 and

119877119904 (119899 + 1) =

119877max119904 119894119891 1198771199041015840 (119899 + 1) gt 119877max

119904

119877min119904 119894119891 1198771199041015840 (119899 + 1) lt 119877min

119904

1198771199041015840 (119899 + 1) 119900119905ℎ119890119903119908119894119904119890(10)





Traffi

c Loa

d (M

bps)

0

500

1000

1500

2000

2500

Traffic Load

10 1505 20 25 30 35 4000

Time (Hours)







40

35

30

25

20

15

10

5

000 05 10 15 20 25 30 35 40

Time (Hours)

Erro

r (M

bps)

CSSASRASS





5 Conclusions


Data Availability




References































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia










119910 = Φ119909 + 119908 (2)



119910 = Φ119909 + 119908 = ΦΨ120579 + 119908 = Θ120579 + 119908 (3)




120579 = min 1205790st 1003817100381710038171003817Θ120579 minus 11991010038171003817100381710038172 le Ξ (4)













Mtimes 1

measurements

y

=

Mtimes N

K lt M ≪ N

xΦ

Ntimes 1

sparsesignal

K

nonzeroentries



Traffic Load

Slow ChangingZone

0

500

1000

1500

2000

2500

Traffi

c Loa

d (M

bps)

2 4 6 8 10 12 14 16 18 20 22 240Time (Hours)




119881119878119877by

119877119886V119892119881119878119877 =2119891119904max119879119904 + 2119891119891max119879119891

119879119904 + 119879119891 (5)




119909 (119899 + 119896) =119901minus1

sum119894=0

119908119894119909 (119899 minus 119894) = 119908119879119909 (119899) (6)



equation

w (119899 + 1) = w (119899) + 120583119890 (119899) x (119899)x (119899)2 (8)




Linear Predictor

e(n)

Traffic Curve

+

-

x(n+k)

x(n+k)




1198771199041015840 (119899 + 1) = 119877119904 (119899) [119902 + (1 minus 119902) |119890 (119899)|119864119887 ] (9)

with 0 lt 119902 lt 1 119864119887 gt 0 and

119877119904 (119899 + 1) =

119877max119904 119894119891 1198771199041015840 (119899 + 1) gt 119877max

119904

119877min119904 119894119891 1198771199041015840 (119899 + 1) lt 119877min

119904

1198771199041015840 (119899 + 1) 119900119905ℎ119890119903119908119894119904119890(10)





Traffi

c Loa

d (M

bps)

0

500

1000

1500

2000

2500

Traffic Load

10 1505 20 25 30 35 4000

Time (Hours)







40

35

30

25

20

15

10

5

000 05 10 15 20 25 30 35 40

Time (Hours)

Erro

r (M

bps)

CSSASRASS





5 Conclusions


Data Availability




References































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia





Mtimes 1

measurements

y

=

Mtimes N

K lt M ≪ N

xΦ

Ntimes 1

sparsesignal

K

nonzeroentries



Traffic Load

Slow ChangingZone

0

500

1000

1500

2000

2500

Traffi

c Loa

d (M

bps)

2 4 6 8 10 12 14 16 18 20 22 240Time (Hours)




119881119878119877by

119877119886V119892119881119878119877 =2119891119904max119879119904 + 2119891119891max119879119891

119879119904 + 119879119891 (5)




119909 (119899 + 119896) =119901minus1

sum119894=0

119908119894119909 (119899 minus 119894) = 119908119879119909 (119899) (6)



equation

w (119899 + 1) = w (119899) + 120583119890 (119899) x (119899)x (119899)2 (8)




Linear Predictor

e(n)

Traffic Curve

+

-

x(n+k)

x(n+k)




1198771199041015840 (119899 + 1) = 119877119904 (119899) [119902 + (1 minus 119902) |119890 (119899)|119864119887 ] (9)

with 0 lt 119902 lt 1 119864119887 gt 0 and

119877119904 (119899 + 1) =

119877max119904 119894119891 1198771199041015840 (119899 + 1) gt 119877max

119904

119877min119904 119894119891 1198771199041015840 (119899 + 1) lt 119877min

119904

1198771199041015840 (119899 + 1) 119900119905ℎ119890119903119908119894119904119890(10)





Traffi

c Loa

d (M

bps)

0

500

1000

1500

2000

2500

Traffic Load

10 1505 20 25 30 35 4000

Time (Hours)







40

35

30

25

20

15

10

5

000 05 10 15 20 25 30 35 40

Time (Hours)

Erro

r (M

bps)

CSSASRASS





5 Conclusions


Data Availability




References































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia




Linear Predictor

e(n)

Traffic Curve

+

-

x(n+k)

x(n+k)




1198771199041015840 (119899 + 1) = 119877119904 (119899) [119902 + (1 minus 119902) |119890 (119899)|119864119887 ] (9)

with 0 lt 119902 lt 1 119864119887 gt 0 and

119877119904 (119899 + 1) =

119877max119904 119894119891 1198771199041015840 (119899 + 1) gt 119877max

119904

119877min119904 119894119891 1198771199041015840 (119899 + 1) lt 119877min

119904

1198771199041015840 (119899 + 1) 119900119905ℎ119890119903119908119894119904119890(10)





Traffi

c Loa

d (M

bps)

0

500

1000

1500

2000

2500

Traffic Load

10 1505 20 25 30 35 4000

Time (Hours)







40

35

30

25

20

15

10

5

000 05 10 15 20 25 30 35 40

Time (Hours)

Erro

r (M

bps)

CSSASRASS





5 Conclusions


Data Availability




References































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia



40

35

30

25

20

15

10

5

000 05 10 15 20 25 30 35 40

Time (Hours)

Erro

r (M

bps)

CSSASRASS





5 Conclusions


Data Availability




References































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia















RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia


dynamic traffic prediction with adaptive sampling for 5g...

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