Anchor-Less Producer Mobility Management in Named Data ...downloads.hindawi.com/journals/misy/2019/3531567.pdf · requested. e data generated in Internet telephony, messaging, and

Research ArticleAnchor-Less Producer Mobility Management in Named DataNetworking for Real-Time Multimedia

Inayat Ali 12 and Huhnkuk Lim 12

1Korea Institute of Science and Technology Information (KISTI) Daejeon Republic of Korea2University of Science and Technology (UST) Daejeon Republic of Korea

Correspondence should be addressed to Huhnkuk Lim hklimkistirekr

Received 1 February 2019 Revised 5 April 2019 Accepted 17 April 2019 Published 13 May 2019

Academic Editor Yuh-Shyan Chen

Copyright copy 2019 Inayat Ali and Huhnkuk Lim +is is an open access article distributed under the Creative CommonsAttribution License which permits unrestricted use distribution and reproduction in anymedium provided the original work isproperly cited

Information-centric networking (ICN) is one of the promising solutions that cater to the challenges of IP-based networking ICNshifts the IP-based access model to a data-centric model Named Data Networking (NDN) is a flexible ICN architecture which isbased on content distribution considering data as the core entity rather than IP-based hosts User-generated mobile contents forreal-time multimedia communication such as Internet telephony are very common these days and are increasing both in qualityand quantity In NDN producer mobility is one of the challenging problems to support uninterrupted real-time multimediacommunication and needs to be resolved for the adoption of NDN as future Internet architecture We assert that mobile nodersquosfuture location prediction can aid in designing efficient anchor-less mobility management techniques In this article we show howlocation prediction techniques can be used to provide an anchor-less mobility management solution in order to ensure seamlesshandover of the producer during real-time multimedia communication +e results indicate that with a low level of locationprediction accuracy our proposed methodology still profoundly reduces the total handover latency and round trip time withoutcreating network overhead

1 Introduction

Mobile devices have transformed into multimedia com-puters owing to the multitude of mobile applications anddiverse features such as cameras messaging online con-tent sharing and online mobile gaming +ese smartfeatures of mobile devices and the human needs of creatingand sharing contents in real-time have enabled mobiledevices to play the roles of content providers and contentconsumers at the same time +e exponential growth indata production and its need for dissemination is becominga cumbersome issue in current network architecture +eexisting Internet model is not built to combat this growingproduction and dissemination of data Moreover manyattempts were made to withstand this issue which resultedin peer-to-peer (P2P) overlays on the IP network andcontent distribution network (CDN) [1] +ese attemptsare still very feeble to ensure smooth network operation

According to a prediction by CISCO Visual NetworkingIndex (VNI) [2] global traffic will inflate to 33 ZB per yearor 278 Exabytes per month by 2021 thus raising moredisputes for the current state of the practice network ar-chitecture and its protocols

Many proposals have been witnessed in recent years for ascalable and reliable future Internet architecture [3ndash6]Among these proposals information-centric networking(ICN) has gained much attention ICN is a data-orientednetwork where the information is retrieved from the Internetby naming the data not the end hosts Data objects are in-dependent of location unlike the IP address in the traditionalnetwork +is novel Internet architecture has addressed thechallenges faced by the current Internet architecture in-cluding scalability addressing name resolution securityprivacy routing and mobility Among the ICN approachesNamedData Networking (NDN) [7] is a very active agile andenterprising one NDN operation is based on InterestData

HindawiMobile Information SystemsVolume 2019 Article ID 3531567 12 pageshttpsdoiorg10115520193531567

packets where a consumer (data requester) sends an Interestpacket containing the name of the required data +e NDNForwarding Information Base (FIB) helps forward the Interestpacket towards the data named in the Interest packet andretrieve the data in one or more Data packets NDN also usesin-network caching for faster data access hence the data canbe retrieved from the producer or the cache of the NDNrouter in the network Many research challenges in NDNincluding naming name resolution routing security forlarge-scale data and mobility need to be resolved for NDN towin the race of future Internet architecture +e number ofmobile devices and the data they generated have drasticallyincreased over time [8 9] Mobility management is becomingmore salient because of the device transformation into a smartgadget that rapidly creates and shares content in real time+erefore device mobility management needs much atten-tion from the research community As a result of NDN designprinciples consumer mobility is natively supported by NDNas a change in the physical location of the consumer does notaffect the NDN data plane +ere is no need for signallingfrom network to heal from consumer mobility but only In-terest retransmission by the consumer for the data that havenot yet been received works well Handling producer mobilityis a challenging task and needs to update the network dataplane to successfully recover from outages due to producermobility

A few producer mobility management schemes for ICNhave been proposed for real-time services However thereis still room to design sophisticated algorithms to handleproducer mobility efficiently +e work in [10 11 12] hasreactive approaches towards mobility management wherethe packets are redirected after the handover completes andnotification is received from the producer +ese ap-proaches suffer from path stretch problem like in Mobile IPfor mobility management in IP networking Moreoverthese reactive approaches encounter more content retrievaldelay than proactive approaches+e concept of redirectingpackets toward the new location was first introduced in thetraditional IP network (Mobile IP [13]) to support mobilityHowever in mobile IP all the packets have to be redirectedby the old access point towards the new location until thecommunication session ends thus causing path stretchproblem for the entire session +e techniques used in[14 15] use a proactive caching approach to minimize theInterest retransmission during a handover +e scheme in[14] assumes that the request pattern is known and thefuture requested content is proactively pushed to thenetwork caches ahead of handover to satisfy the Interestduring the handover However the mechanism in [15]pushes the content to network cache based on contentpopularity However these approaches cannot be appliedin real-time communication where the contents are pro-duced in real-time after the Interest packets are receivedProactive caching techniques assume that the data arealready extant and pushed to the network cache beforereceiving an Interest for those data which is not the case inreal-time communication Real-time data are the data thatare generated and delivered immediately after they arerequested +e data generated in Internet telephony

messaging and online gaming are examples of real-timedata and the above proactive caching techniques formobility management will not support these applicationswhich account for a significant amount of Internet traffictoday

To solve the problems associated with the abovetechniques ie path stretch and long delay due to re-active approaches and lack of support for real-timecommunication in proactive caching approaches wein this article propose a proactive mechanism that is thefirst attempt to exploit location prediction techniques foranchor-less producer mobility management in order toensure seamless handover of the producer during real-time multimedia communication +e proposed meth-odology is based on the prediction of the future locationof a moving producer and proactively redirecting theInterests to the new access point (nAP) before thehandover completes +e proposed methodology is thefirst to exploit location prediction techniques in NDN formobility management Our scheme reduces both thehandover latency and the round trip time without causingnetwork overhead +e mechanism also does not sufferfrom the path stretch problem Moreover it does notassume that the data are already extant instead the dataare generated in real-time and forwarded after the In-terest packets are intelligently delivered to the producerafter the handover

+e rest of the paper is structured as follows In Section2 we briefly offer an insight into NDNmobility and relatedwork In Section 3 we describe the proposed locationprediction-based mobility management methodology indetails We provide a detailed discussion of the results andcomparison with two legacy schemes in Section 4 Finallywe conclude the paper in Section 5 and present futurework

2 Insight into NDN Mobility

In this section we give an overview of operational aspects ofNDN provide problems regarding mobility and presentsome related work on mobility management in NDN

21 Mobility in NDN NDN provides a new communicationparadigm that revolves around the contentdata +e dataare retrieved by data names rather than the IP addresses ofthe machines that host the data Once the data are forwardedto the network they are cached at network routers (in-network caching) to satisfy future requests for the same datawith small delays +is in-network caching results in fasterservice access and resists congestion Two data structures areused by NDN routers to aid packets forwardingie Forwarding Information Base (FIB) and Pending In-terest Table (PIT) FIB is populated by routing protocols anddifferent forwarding strategies and it is used to forwardInterest packets towards the data PIT keeps the records ofinterfaces on which the Interest packets are received and thedata names requested by those Interests PITensures that theData packets follow the reverse path of the Interest packets

2 Mobile Information Systems

A third data structure called Content Store (CS) is used atrouters to cache data for satisfying future Interests Uponreceiving an Interest in the router if the requested data areavailable in the router cache they will satisfy the Interestwithout forwarding it to the next hop based on FIB +eInterest packet is forwarded to the next hop based on FIBentry if the data are not available in router cache Beforeforwarding the Interest packets its incoming interface anddata name is stored in the PIT for data forwarding When aData packet is received at a router it will be forwarded to theinterface in the PIT entry against this data name +e datawill be dropped if there is no entry against this data name inthe PIT +e operation in the NDN router is explained inFigure 1

Mobility is an essential feature of future Internettechnology NDN supports mobility and reduces thecomplexities of mobility support in the IP-based networkMobility can be of two types ie consumer mobility andproducer mobility NDN naturally supports consumermobility by Interest retransmission and in-networkcaching If a consumer after requesting data moves toanother network the response will be delivered to therouter in the previous location which will be cachedthere Consumer at the new network will retransmit theInterest for the same data In which case the intermediaterouters will immediately respond to the Interest In casethe data are not available at any of the network caches theInterest will hit the data producer again and the data willbe retrieved with a longer delay Unlike consumer mo-bility producer mobility is a complicated problem +istype of mobility highly affects communication If aproducer moves to another network the Interests will notbe delivered to it unless network converges +is type ofmobility cannot be tackled with Interest retransmissionas the new name prefix of the producer will not be knownto any NDN router unless network converges +ecommunication session halts for at least the sum ofhandover time and the network reconvergence time asshown in Figure 2 +e Interest packets are discardedat the producerrsquos old point of attachment (oAP) (119 secin Figure 2) and start delivering again to the producer atthe new location after the routing update period expiresand the whole network converges (13 sec in Figure 2)To handle this mobility management issues few tech-niques have been proposed to ensure seamless handoverof producers during real-time streaming Mobilitymanagement algorithms are designed to guaranteeseamless handover from an old Access Point (oAP) to anew Access Point (nAP) and should have the followingcharacteristics

(i) +e time required to divert the route to nAP fromoAP should be minimum In other words thehandover latency should be the least possible

(ii) +e Interest and content loss during the handovertime should also be minimum

22 Related Work NDN relies on Interest retransmissionto heal from network outages owing to mobility However

Interest retransmission is not a scalable approach whenthere are a large number of mobility events also the end-to-end latency due to mobility in this approach is veryhigh +erefore some techniques have been proposed sofar to provide more robust and efficient mobility man-agement schemes +e schemes in the literature can bedivided into three categories rendezvous-based schemesare more like DNS system in the current IP-based net-working where a consumer sends a query to a particularnode called rendezvous server to find the location of theproducer before sending an Interest +e rendezvousserver keeps track of the location of all nodes in thenetwork +ese techniques are easy to implement but thedrawback is that they cause extra-overhead to maintain thelocation information of mobile nodes +e rendezvous-based schemes need the early binding of names that affectthe NDN data naming Anchor-based approaches used ananchor node that redirects the Interests to the producer atthe new location +e anchor node should always be keptinformed of the producerrsquos movement +ese schemespossess a single relay point (single point of failure) andsuffer from the path stretch problem Performance eval-uation of anchor and anchor-less approaches are given indetail in [10] +e anchor-less approach has no pre-specified node to handle mobility +is approach is hard toimplement but the schemes following such approaches arevery efficient in terms of performance +e schemes in[16 17] are anchor-based mobility management schemes+e schemes proposed in [10 11] are anchor-less mobilitymanagement schemes and suffer from the inheritedproblems from their corresponding approaches +eproposed methodology in this work is based on the pre-diction of the future location of a mobile node +eselocation prediction techniques can be exploited to provideanchor-less mobility management that highly reduces thehandover latency and round trip time even with lowprediction accuracy

3 Location Prediction-BasedMobility Management

Producer mobility causes degradation in the quality ofservice during real-time streaming +e services aredisrupted when a producer moves and goes out of theradio range of its oAP and connects to another accesspoint (nAP) +e Interest packets are dropped at the oAPas the new hierarchical name of the producer is notupdated in the network information +e communicationsession halts until the routing update period expires andthe whole network converges with this new producername information as shown in Figure 2 In NDN theInterest packets are forwarded using FIB entries that arebeing populated by routing protocols while the Datapackets follow the traces of Interest packet stored in thePIT to reach the consumer Hence the producer is notable to deliver Data packets of its ongoing streaming fromits new location right after the handover completesunless it receives an Interest packet at the new locationusing its new hierarchical name prefix +is drastically

Mobile Information Systems 3

maximizes the handover latency and hence round-triptime (RTT)

31 Preliminaries We use two types of Interest packets toimplement the mobility management scheme efficiently+eInterest types are as follows

(i) INTEREST_PU Interest path update message is sentby a mobile node (producer) to its AP to notify it

about its mobility and probable handover +epacket format is shown in Figure 3(a)

(ii) INTEREST_RED Interest redirection is an Interesttype in which the Interests are forwarded to theproducer at new location after the handover +epacket format is shown in Figure 3(b)

INTEREST_RED packet changes the content namefrom oAPale1 to nAPale1 (since the oAP havepredicted the nAP) and is forwarded based on the

Packet loss during producer mobility

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Figure 2 Handover latency without using any mobility management

Contentstore (CS)

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Forward

DataAdd incoming face

Return or drop the interest

Downstream Upstream

Pending InterestTable (PIT)

Contentstore (CS)

Data

Discard data

Cache

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Figure 1 Interest and Data forwarding at the NDN router


FIB entry corresponding to this new name When anINTEREST_RED packet is received at NDN router(eg Rtr-2 in Figure 4) it also updates the name and outgoing face in the previously recorded PITentry for contentdelivery toward the consumer It is also a very frugal way touse INTEREST_PU message to notify oAP about a mo-bility event

32 Algorithm Details +e mobility management can becontrolled at the network or mobile node However a moreeasy and efficient way is to make use of both to developmore efficient mobility management techniques +eproposed methodology manages mobility using co-operation from both the network and the mobile node(producer) +e location prediction scheme comes intoaction when the received signal strength (RSS) drops belowthe threshold (th minus77 dBm) As the RSS drops below theth the future location of the producer is calculated by theproducer itself using the speed and direction of movementobtained from its sensors +e producer sends its newlocation to the oAP in an INTEREST_PU message asshown in Figure 4 (Step 3) +e INTEREST_PU messagecontains the X and Y coordinates of the producerrsquos futurelocation +e pseudocode for this is given in Figure 5 +edetails of our prediction model are given in the followingsubsection

321 Location Prediction and New AP Selection +e se-lection of nAP is the very crucial and important part of thisscheme +ere are many possible ways to select nAPhowever we use the relative distances of the producerrsquospredicted future location from access points +e relativedistance of the producer from APs is used in this workbecause in our simulations all the APs and mobile nodesuse the same transmit and receive power Moreover we usedthe free-space propagation model where there are no pathlosses and it assumes one clear line-of-sight path from thetransmitter to the receiver In such settings the relativedistance is the fair method to be considered and increase theprobability of selecting the right new AP Upon receiving theINTEREST_PU message from the producer the oAP findsthe nAP using the Euclidean distance formula as explainedin the pseudocode in Figure 6 Here we assumed that all the

fixed routers and access points know each other geographiclocation To find the distance between producer and nAP at afuture time (Tf ) oAP should know the new location of theproducer at Tf +ere are many methods to predict thefuture location of a mobile node [18 19 20] with differentprediction accuracy ranging from 45 to 90 Our pre-diction model used in this paper is straightforward where topredict the location at a time (Tf ) the producer finds itsspeed and direction of movement and calculates its futurelocation coordinates at the time (Tf ) +e producer sends itsnew predicted coordinates to oAP in an INTEREST_PUmessage +e modern mobile devices come with lots ofadvanced hardware and software sensors like accelerometergyroscope digital compass GPS and speedometer +esesensors can easily calculate the speed and direction ofmovement of the mobile device (producer) +e producerconstantly checks its RSS after it starts a movement +econnection becomes unreliable after the signal strengthdrops than a predefined threshold (here thminus77 dBm) andthe node starts searching for reliable signals from other APsfor handover Before the handover the producer gets itsspeed and direction from the sensors at that particular timeand calculates its future coordinates and sends them in anINTEREST_PU message to the oAP +e new speed at aparticular time is determined by

Vnew min max vold + Δv 01113858 1113859 vmax1113864 1113865 if pleps

0 otherwise1113896 (1)

where vold is the old speed obtained from the producerdevice sensors and Δv is the change in speed after time (t)Δv vnew minus vold and it is uniformly distributed on [minus3 3]kmhr vmax is the prespecified maximum speed (30 kmhr inour simulation) of a mobile node p is the uniformly dis-tributed probability between [0 1] and ps is thresholdprobability (ps 05) which determines the probability ofproducer movement +e direction of the movement is alsouniformly distributed and is given by

ϕnew ϕold + Δϕ (2)

where ϕnew is the new direction after time (t) and ϕold is theold direction Δϕ is the change in the direction and isuniformly distributed on [minusπ4 π4] Euclidean distancebetween producer and AP is an efficient method to select

Name X-coordinates(new predicted position)

Y-coordinates(new predicted position) Nonce Interest_PU

(value = 0)

(a)

NameSelector

(order preference publisher filter etc)

Nonce Guiders(scope interest life time)

Interest_RED(value = 1)

(b)

Figure 3 Interest packet types (a) INTEREST_PU (b) INTEREST_RED


Finding and sending future location coordinates to oAPPossible event Received signal strength (RSS) drops than threshold (th = ndash77 dBm)

(1) Case event (2) If RSS le th(3) Get speed (vnew) and direction (ϕnew)(4) Calculate future coordinates (xp yp) using current speed and direction(5) Construct INTEREST_PU message(6) Send INTEREST_PU to oAP

Figure 5 Pseudocode for path update message generation

New AP (nAP) Selection by old AP (oAP) Possible events = Arrival of INTEREST_PU message at oAP part = arrival of nAP new reachable prefix notification from producer at oAP (wrong prediction)

(1) Case events(2) When (3) Get predicted coordinates (xp yp) from INTEREST_PU message(4) Predict nAP

(5) Disti = (APix ndash xp)2 + (APiy ndash yp)2

(6) APi = min (Disti)(7) nAP = APi

(8) Return nAP(9) Redirect Interests toward nAP(10) Store copies of Interests for small time (ts)(12) if part(13) Redirect Interests again based on nAP prefix information in part(14) Discard the stored Interests packets(15) else(16) Do nothing(17) Discard the stored packets after time ts

Figure 6 Pseudocode for new access point selection

Rtr-1 Rtr-2Cons-1

ale 1

ale 1

oAP

nAP

AP

1 Interest sent path before handover

2 Content received path before handover

3 RSS le thINTEREST_PU sent

(location update message sent to oAP)

4 Find nAPRedirect Interests to

nAP

5 Redirected Interests received

At producerrsquos new location

6 Content receivedafter handover

NDN router 1 NDN router 2

Figure 4 Location prediction-based mobility management scheme


the new access point in our simulation Because we haveconsidered free-space propagation model and sametransmit and receive power at all nodes however in arealistic scenario having dynamic path losses one can alsoconsider other parameters to improve the accuracy +eprediction accuracy can further be improved by leveragingmachine learning techniques to process the history data ofmobility and predict the possible handover +e perfor-mance of prediction can also be enhanced by consideringvarious other parameters in prediction decision such asreceived signal strength from each access point (AP) andthe number of available channels with each AP Con-sidering the other parameters may improve the selectionof nAP at a cost of network overhead that will be createdby the routing updates (to share RSS and channels in-formation) between the potential future new access points(eg nAP1 and nAP2 in Figure 7) and oAP as shown inFigure 7 Subsequently predicting the nAP of the pro-ducer the oAP continuously checks the reachability of theproducer +e Interests are delivered to the producer aslong as it is reachable and the producer satisfies eachInterest with the required content +e oAP redirects theInterest packets to nAP when the producer becomesunreachable (Step 4 in Figure 4) +e oAP also stores onecopy of each pending Interest for a small time ts to redirectit later in case the nAP prediction goes wrong Here ts isset a little more than the RTT in our simulation to ensurethe time required for nAP to notify the oAP after thehandover After the handover if the nAP does not receivean Interest packet it will notify its new reachable prefix tothe oAP via a broadcast If the oAP does not receive anynotification from the nAP it will assume that the nAP waspredicted right and the Interest packets have successfullybeen redirected to it or it will assume that the producerhas completely disconnected In both cases the storedInterest packets will be removed after time ts +e pseu-docode for nAP selection and redirecting the Intereststowards it is given in Figure 6 +e nAP is anticipated tohave producer connected to it after successful handover atlayer 2 We have set the layer 2 handover delay (L2) to100ms Layer 2 handover delay is the time it takes by anode to disassociate itself from one AP (eg oAP inFigure 7) and establish a connection with another AP(eg nAP1 in Figure 7)

322 Interest Redirection to nAP +e redirected Interestsare forwarded in a separate Interest packet type calledINTEREST_RED INTEREST_RED packet changes thecontent hierarchical name from oAPale1 to nAPale1 (since the oAP has predicted the nAP) and is for-warded based on the FIB entry corresponding to this newname +e intermediate NDN routers upon receiving theredirected Interest update the previous PIT entry for thatname prefix and also notify the FIB about the changes forupcoming Interests +e intermediate routers then forwardthe INTEREST_RED packets according to the longestprefix matching based on FIB towards the nAP+e INTEREST_RED packets after reaching the nAP

are buffered for some time and then forwarded to theproducer immediately after the handover completes andits connection establishes with the nAP If the nAP pre-diction goes wrong and the producer at the new locationdoes not receive the Interest packets it publishes itsnew name prefix by broadcasting it +e oAP after receivingthis new name prefix assumes that the nAP is predictedwrong and forwards the Interests to this correct new lo-cation of the producer according to the longestprefix matching based on FIB In this method the pre-handover Interest forwarding to the nAP highly reduces thehandover latency and overall RTT from consumer to theproducer +e Interest packets path to the producer beforehandover is Consumer⟶ AP⟶ Rtr1⟶ Rtr2⟶oAP⟶ Producer (Figure 4) +e Data packets followthe reverse path of the Interest packets +e path for theInterest packets that were in transit before updatingthe FIBs of the intermediate routers after handoveris Consumer⟶ AP⟶ Rtr1⟶ Rtr2⟶ oAP⟶ Rtr2⟶ nAP⟶ Producer +e Data packets path afterhandover will always be Producer⟶ nAP⟶ Rtr2⟶Rtr1⟶ AP⟶ Consumer +e scheme does not sufferfrom path stretch problem Only the Interest packets thatwere sent before forwarding the redirected Interests sufferfrom path stretch

4 Results and Discussion

We have simulated the following fat-tree backhaul net-work that resembles a real ISP topology as shown inFigure 8 using the NS-2 simulator +e scenario consists of4 mobile producers and two stationary consumers whereeach consumer is continuously sending requests for data tothe two producers for the entire duration of the simulationWe have repeated the simulation 100 times and the resultsare averaged over many runs +e producers randomlymove and handover can occur to any adjacent cell We

Prod

oAP

nAP2

nAP1

oAP Range

nAP1 Range

nAP2 Range

ConnectedStrong RSSWeak RSS

Figure 7 Access point selection


have 16 cells with full wireless coverage (WiFi 80211n)where the producers and consumers are located in dif-ferent cells initially +e 2D view of cells and the initialposition of the nodes are shown in the figure In theproposed location prediction-based mobility managementmethodology the nodersquos future expected location is pre-dicted before finding the nAP +e oAP does not drop theincoming Interests during the handover time InsteadoAP immediately forwards the Interests to nAP HereRTT2 ( asymp25ms) (delay experienced by INTEREST_REDfrom oAP to nAP) is completely eliminated by overlappingit with Layer 2 handover delay (L2) of 100ms as describedin Figure 9

It is clear that the Interest packets sent during the (L2)

handover delay time are delivered to the producer imme-diately after 100ms +e Interest packets are not waiting forany notification from producer or network after thehandover +e following equation gives the total handoverlatency in this scheme

Hp L2 + α

Hp asymp L2(3)

where α is very small and covers the propagation time ofInterests from nAP and producer after the handover and a

little delay in predicting the actual handover incident Hp isthe total handover latency and L2 is the layer 2 handoverdelay +e equation Hp L2 is the optimal mobility man-agement at the network layer as it eliminates the handoverdelay at the network layer+e handover latency we obtainedin our simulation is 109ms for location prediction-basedmethodology during accurate prediction as compared to134ms in zone flooding and 150ms in Interest forwarding[12] as shown in Figure 10(a) and 10(b) respectively +ebetter performance is because the Interest packets are for-warded to the new location (nAP) before the handovercompletes as the nAP is already predicted In the Interestforwarding scheme after the producer attached to the nAPit sends an update message to oAP and the oAP then

Core

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Figure 8 Fat-tree backhaul network topology

Layer 2 handover delay = 100ms

1 25 100

RTT2Interests redirection

Time in milliseconds

Figure 9 Overlapping of L2 handover delay and Interest re-direction delay


redirects the following Interests towards the producer at thenew location which causes the extra latency of RTT asshown in Table 1 In zone flooding scheme the APs aredivided into zones +e consumers start multicasting theInterest to all the APs in the zone after the handover occurs+e extra latency RTT2 is caused by sending handovernotification to consumers by the producer +e AP to whichthe producer is connected replies with the contents whilethe remaining APs ignore the received multicast

We also find the total RTT between consumer andproducer +e total RTT experienced by the packets sentexactly at the time of handover (RTTh) in the locationprediction based scheme is given by the equation below andshown by the peak value in Figure 11 RTT is defined as thetotal time it takes to send an Interest packet and receive thecorresponding Data packet

RTTh L2 + RTTt (4)

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Handover latency with location prediction-based schemeHandover latency with interest forwarding scheme

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Figure 10 Handover latency in (a) location prediction and zone flooding scheme and (b) location prediction and interest forwardingscheme


where RTTt is the average round trip time between con-sumer and producer before handover +e RTT duringhandover is highly reduced to 139ms and RTT in Interestforwarding scheme is 160ms while in zone flooding RTT is144ms almost similar to our scheme However the zoneflooding scheme creates a very high network overhead as it isbased on sending multiple copies of Interests to all APs in azone +erefore the zone flooding scheme is not scalable Onthe contrary the location prediction-based scheme createsno network overhead RTTdue to path stretch in the Interestforwarding scheme is shown in Figure 11 while the locationprediction-based scheme does not suffer from the pathstretch problem

Figures 12(a) and 12(b) show average handover latencyin different schemes with varying percentage of accuracy inprediction Since the prediction cannot be 100 rightFigure 12(a) shows that with a very low prediction accuracyof 50 location prediction based method substantiallyreduces the average handover latency yet keeping excellentcontent to the Interest ratio as shown in Figure 12(b) RTTand handover latency for zone flooding are quite small butits content to the Interest ratio is also very small because ofits multicasting nature and it cannot be regarded as thebest solution Moreover our proposed approach signifi-cantly reduces average handover latency RTT andmaintains an outstanding content to Interest ratio as ev-ident from the simulation results On the contrary Interest

forwarding scheme has a good Content to Interest ratio butit has big RTT handover latency and suffers from pathstretch

5 Conclusion and Future Work

In NDN the producer mobility is one of the strenuous andchallenging tasks during real-time streaming +e handoverlatency constitutes the delay caused by dissociation andreassociation of a mobile node to oAP and nAP respectively(L2 delay) and the delay caused by network convergencetime In this work we have exploited location prediction forproducer mobility management to show its impact on thedesign of such mobility management techniques +e sim-ulation results have shown that using location predictiontechniques for mobility management can significantly re-duce the total handover latency to approximately equal tolayer 2 handover delay by eliminating the delay caused bynetwork convergence With a minimal prediction accuracyof 50 the methodology tends to perform better whichshows the benefits of using location prediction for producermobility management It also reduced end-to-end RTTwithout creating any network overhead +e results of thismobility management approach may turn out as the baseknowledge for the design of a sophisticated location pre-diction technique for mobility management As of futurework we plan to design an efficient location prediction

Peak RTT in interestforwarding scheme Proposed location prediction-based scheme

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140

150

160

170

230 260 290 320 350 380Sequence number

Roun

d tr

ip ti

me (

ms)

410 440 470 500 530 560 590

End-to-end RTT of location prediction-based schemeEnd-to-end RTT of interest forwarding schemeEnd-to-end RTT of zone flooding scheme

Figure 11 Round trip time comparison of the three mobility management schemes

Table 1 Handover latency comparison

Scheme Handover latency Remarks

Interest forwarding Hp asymp L2 + RTTRTT between producer old location and new location is addedwhich varies with topology and other network parameters

Zone flooding Hp asymp L2 + RTT2 RTT2 is added here which also varies as RTT variesLocation prediction based (proposed) Hp asymp L2 RTT is completely eliminated with our proactive approach (Figure 9)


technique and apply it in our proposed methodology formobility management in NDN

Data Availability

No data were used to support this work Moreover thesimulation code is available from the authors upon request

Conflicts of Interest

+e authors declare that they have no conflicts of interest

References

[1] L Zhang A Afanasyev J Burke et al ldquoNamed data net-workingrdquo ACM SIGCOMM Computer Communication Re-view vol 44 no 3 pp 66ndash73 2014

[2] CISCO Visual Networking Index (VNI) Cisco Visual Net-working Index Forecast and Methodology 2016ndash2021 White

Paper CISCO Visual Networking Index (VNI) Cisco TXUSA 2017

[3] V Jacobson D K Smetters J D +ornton M F PlassN H Briggs and R L Braynard ldquoNetworking named con-tentrdquo in Proceedings of the 5th International Conference onEmerging Networking Experiments and Technologies pp 1ndash12ACM New York USA December 2009

[4] T Koponen M Chawla B-G Chun et al ldquoA data-oriented(and beyond) network architecturerdquo ACM SIGCOMMComputer Communication Review vol 37 no 4 pp 181ndash1922007

[5] httpswww4wardprojectcom[6] D Lagutin K Visala and S Tarkoma PublishSubscribe for

Internet Valencia FIA book Vol 4 PSIRP PerspectiveAmsterdam Netherlands 2010

[7] httpwwwnamed-datanet[8] H Sundmaeker P Guillemin P Friess and S Woelffle

ldquoVision and challenges for realizing the internet of thingscluster of European research projects on the internet ofthingsrdquo European Commision vol 3 no 3 pp 34ndash36 2010

[9] CISCO Visual Networking Index (VNI) Cisco Visual Net-working Index Forecast and Methodology 2014ndash2019 WhitePaper CISCO Visual Networking Index (VNI) Cisco TXUSA 2015

[10] D-H Kim J-H Kim Y-S Kim H-S Yoon and I YeomldquoEnd-to-end mobility support in content centric networksrdquoInternational Journal of Communication Systems vol 28no 6 pp 1151ndash1167 2015

[11] J Lee S Cho and D Kim ldquoDevice mobility management incontent-centric networkingrdquo IEEE Communications Maga-zine vol 50 no 12 pp 28ndash34 2012

[12] D Han M Lee K Cho T Kwon and Y Choi ldquoPublishermobility support in content-centric networksrdquo in Proceedingsof the International Conference on Information Networking2014 (ICOIN2014) pp 214ndash219 IEEE Phuket +ailandFebruary 2014

[13] C E Perkins ldquoMobile IPrdquo IEEE Communications Magazinevol 35 no 5 pp 84ndash99 1997

[14] H Farahat and H S Hassanein ldquoProactive caching forproducer mobility management in named data networksrdquo inProceedings of the IEEE 13th International Wireless Com-munications and Mobile Computing Conference (IWCMC)pp 171ndash176 Valencia Spain June 2017

[15] T Woo H Park S Jung and T Kwon ldquoProactive neighborpushing for enhancing provider mobility support inContent-Centric Networkingrdquo in Proceedings of the 2014Sixth International Conference on Ubiquitous and FutureNetworks (ICUFN) pp 158ndash163 IEEE Shanghai China July2014

[16] F Hermans E Ngai and P Gunningberg ldquoGlobal sourcemobility in the content-centric networking architecturerdquo inProceedings of the 1st ACM workshop on Emerging Name-Oriented Mobile Networking DesignmdashArchitecture Algo-rithms and Applications (NoM rsquo12) pp 13ndash18 Hilton HeadSC USA June 2012

[17] Y Luo J Eymann and A Timm-Giel ldquoMobility support forcontent centric networkingrdquo Telecommunication Systemsvol 59 no 2 pp 271ndash288 2015

[18] H Abu-Ghazaleh and A S Alfa ldquoApplication of mobilityprediction in wireless networks using markov renewal the-oryrdquo IEEE Transactions on Vehicular Technology vol 59no 2 pp 788ndash802 2010

[19] N Samaan and A Karmouch ldquoA user centric mobilityprediction approach based on spatial conceptual mapsrdquo in

160155150145140135130125120115110105

Aver

age h

ando

ver l

aten

cy (m

s)

10 20 30 40 50 60 70 80 90 100nAP prediction accuracy ()

Location prediction-based schemeInterest forwarding schemeZone flooding scheme

(a)

10 20 30 40 50

12

11

1

09

08

07

06

05

C to

l ra

tio

Handover events

Location prediction-based scheme (50 predictionaccuracy)


Interest forwarding schemeZone flooding scheme

(b)

Figure 12 (a) Average handover latency (b) Content to interestratio


Proceedings of the IEEE International Conference on Com-munication (ICC) vol 2 pp 1413ndash1417 Seoul Korea May2005

[20] H-W Ferng W-Y Kao S David C-L Liu H-Y Chen andH-Y Gu ldquoA dynamic resource reservation scheme withmobility prediction for wireless multimedia networksrdquo inProceedings of the IEEE 60th Vehicular Technology Conference(VTC2004-Fall 2004) vol 5 pp 3506ndash3510 Atlantic City NJUSA October 2004


Computer Games Technology

International Journal of

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom

Journal ofEngineeringVolume 2018

Advances in

FuzzySystems


Volume 2018


ReconfigurableComputing



Applied Computational Intelligence and Soft Computing

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Mathematical Problems in Engineering

Modelling ampSimulationin EngineeringHindawiwwwhindawicom Volume 2018

Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom

The Scientific World Journal

Volume 2018


Human-ComputerInteraction

Advances in


Scientic Programming

Submit your manuscripts atwwwhindawicom

packets where a consumer (data requester) sends an Interestpacket containing the name of the required data +e NDNForwarding Information Base (FIB) helps forward the Interestpacket towards the data named in the Interest packet andretrieve the data in one or more Data packets NDN also usesin-network caching for faster data access hence the data canbe retrieved from the producer or the cache of the NDNrouter in the network Many research challenges in NDNincluding naming name resolution routing security forlarge-scale data and mobility need to be resolved for NDN towin the race of future Internet architecture +e number ofmobile devices and the data they generated have drasticallyincreased over time [8 9] Mobility management is becomingmore salient because of the device transformation into a smartgadget that rapidly creates and shares content in real time+erefore device mobility management needs much atten-tion from the research community As a result of NDN designprinciples consumer mobility is natively supported by NDNas a change in the physical location of the consumer does notaffect the NDN data plane +ere is no need for signallingfrom network to heal from consumer mobility but only In-terest retransmission by the consumer for the data that havenot yet been received works well Handling producer mobilityis a challenging task and needs to update the network dataplane to successfully recover from outages due to producermobility

A few producer mobility management schemes for ICNhave been proposed for real-time services However thereis still room to design sophisticated algorithms to handleproducer mobility efficiently +e work in [10 11 12] hasreactive approaches towards mobility management wherethe packets are redirected after the handover completes andnotification is received from the producer +ese ap-proaches suffer from path stretch problem like in Mobile IPfor mobility management in IP networking Moreoverthese reactive approaches encounter more content retrievaldelay than proactive approaches+e concept of redirectingpackets toward the new location was first introduced in thetraditional IP network (Mobile IP [13]) to support mobilityHowever in mobile IP all the packets have to be redirectedby the old access point towards the new location until thecommunication session ends thus causing path stretchproblem for the entire session +e techniques used in[14 15] use a proactive caching approach to minimize theInterest retransmission during a handover +e scheme in[14] assumes that the request pattern is known and thefuture requested content is proactively pushed to thenetwork caches ahead of handover to satisfy the Interestduring the handover However the mechanism in [15]pushes the content to network cache based on contentpopularity However these approaches cannot be appliedin real-time communication where the contents are pro-duced in real-time after the Interest packets are receivedProactive caching techniques assume that the data arealready extant and pushed to the network cache beforereceiving an Interest for those data which is not the case inreal-time communication Real-time data are the data thatare generated and delivered immediately after they arerequested +e data generated in Internet telephony

messaging and online gaming are examples of real-timedata and the above proactive caching techniques formobility management will not support these applicationswhich account for a significant amount of Internet traffictoday

To solve the problems associated with the abovetechniques ie path stretch and long delay due to re-active approaches and lack of support for real-timecommunication in proactive caching approaches wein this article propose a proactive mechanism that is thefirst attempt to exploit location prediction techniques foranchor-less producer mobility management in order toensure seamless handover of the producer during real-time multimedia communication +e proposed meth-odology is based on the prediction of the future locationof a moving producer and proactively redirecting theInterests to the new access point (nAP) before thehandover completes +e proposed methodology is thefirst to exploit location prediction techniques in NDN formobility management Our scheme reduces both thehandover latency and the round trip time without causingnetwork overhead +e mechanism also does not sufferfrom the path stretch problem Moreover it does notassume that the data are already extant instead the dataare generated in real-time and forwarded after the In-terest packets are intelligently delivered to the producerafter the handover

+e rest of the paper is structured as follows In Section2 we briefly offer an insight into NDNmobility and relatedwork In Section 3 we describe the proposed locationprediction-based mobility management methodology indetails We provide a detailed discussion of the results andcomparison with two legacy schemes in Section 4 Finallywe conclude the paper in Section 5 and present futurework

2 Insight into NDN Mobility

In this section we give an overview of operational aspects ofNDN provide problems regarding mobility and presentsome related work on mobility management in NDN

21 Mobility in NDN NDN provides a new communicationparadigm that revolves around the contentdata +e dataare retrieved by data names rather than the IP addresses ofthe machines that host the data Once the data are forwardedto the network they are cached at network routers (in-network caching) to satisfy future requests for the same datawith small delays +is in-network caching results in fasterservice access and resists congestion Two data structures areused by NDN routers to aid packets forwardingie Forwarding Information Base (FIB) and Pending In-terest Table (PIT) FIB is populated by routing protocols anddifferent forwarding strategies and it is used to forwardInterest packets towards the data PIT keeps the records ofinterfaces on which the Interest packets are received and thedata names requested by those Interests PITensures that theData packets follow the reverse path of the Interest packets


















800

750

700

650

600

550

500

Sequ

ence

num

ber

1111

111

211

311

411

511

611

711

811

9 1212

112

2

Time (sec)

123

124

125

126

127

128

129 13

131

132

133

134

135



Contentstore (CS)


Forward



Downstream Upstream


Contentstore (CS)

Data

Discard data

Cache

Forward

Lookup miss

Lookup hit














(value = 0)

(a)

NameSelector




(b)












Rtr-1 Rtr-2Cons-1

ale 1

ale 1

oAP

nAP

AP






nAP












Prod

oAP

nAP2

nAP1

oAP Range

nAP1 Range

nAP2 Range







Hp L2 + α

Hp asymp L2(3)



Core

Backhaul

Edge

Access1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16


11

2

3 4

5 6

7 8

9 10

1612

13 14

15

1

Cons-1

Prod-2

Prod-4

Prod-1

Prod-3

2D view

ICN router

ICN AP

Consumer

Producer

Cons-2

Cons-2



1 25 100







RTTh L2 + RTTt (4)

800

750

700Se

quen

ce n

umbe

r

650

600

550

500


134ms109ms

11

111

112

113

114

115

116

117

118

119 12

121

122

Time (sec)

123

124

125

126

127

128

129 13


(a)

800

750

700

Sequ

ence

num

ber

650

600

550

500


150ms

109ms

11

111

112

113

114

115

116

117

118

119 12

121

122

Time (sec)

123

124

125

126

127

128

129 13


(b)












and zone flooding

20040

50

60

70

80

90

100

110

120

130

140

150

160

170

230 260 290 320 350 380Sequence number

Roun

d tr

ip ti

me (

ms)

410 440 470 500 530 560 590









Data Availability




References





















160155150145140135130125120115110105

Aver

age h

ando

ver l

aten

cy (m

s)



(a)

10 20 30 40 50

12

11

1

09

08

07

06

05

C to

l ra

tio

Handover events




(b)











Advances in

FuzzySystems


Volume 2018













Journal of

Journal of



Hindawi


Advances in

Multimedia


Biomedical Imaging





RoboticsJournal of









Volume 2018



Advances in




















800

750

700

650

600

550

500

Sequ

ence

num

ber

1111

111

211

311

411

511

611

711

811

9 1212

112

2

Time (sec)

123

124

125

126

127

128

129 13

131

132

133

134

135



Contentstore (CS)


Forward



Downstream Upstream


Contentstore (CS)

Data

Discard data

Cache

Forward

Lookup miss

Lookup hit














(value = 0)

(a)

NameSelector




(b)












Rtr-1 Rtr-2Cons-1

ale 1

ale 1

oAP

nAP

AP






nAP












Prod

oAP

nAP2

nAP1

oAP Range

nAP1 Range

nAP2 Range







Hp L2 + α

Hp asymp L2(3)



Core

Backhaul

Edge

Access1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16


11

2

3 4

5 6

7 8

9 10

1612

13 14

15

1

Cons-1

Prod-2

Prod-4

Prod-1

Prod-3

2D view

ICN router

ICN AP

Consumer

Producer

Cons-2

Cons-2



1 25 100







RTTh L2 + RTTt (4)

800

750

700Se

quen

ce n

umbe

r

650

600

550

500


134ms109ms

11

111

112

113

114

115

116

117

118

119 12

121

122

Time (sec)

123

124

125

126

127

128

129 13


(a)

800

750

700

Sequ

ence

num

ber

650

600

550

500


150ms

109ms

11

111

112

113

114

115

116

117

118

119 12

121

122

Time (sec)

123

124

125

126

127

128

129 13


(b)












and zone flooding

20040

50

60

70

80

90

100

110

120

130

140

150

160

170

230 260 290 320 350 380Sequence number

Roun

d tr

ip ti

me (

ms)

410 440 470 500 530 560 590









Data Availability




References





















160155150145140135130125120115110105

Aver

age h

ando

ver l

aten

cy (m

s)



(a)

10 20 30 40 50

12

11

1

09

08

07

06

05

C to

l ra

tio

Handover events




(b)











Advances in

FuzzySystems


Volume 2018













Journal of

Journal of



Hindawi


Advances in

Multimedia


Biomedical Imaging





RoboticsJournal of









Volume 2018



Advances in











800

750

700

650

600

550

500

Sequ

ence

num

ber

1111

111

211

311

411

511

611

711

811

9 1212

112

2

Time (sec)

123

124

125

126

127

128

129 13

131

132

133

134

135



Contentstore (CS)


Forward



Downstream Upstream


Contentstore (CS)

Data

Discard data

Cache

Forward

Lookup miss

Lookup hit














(value = 0)

(a)

NameSelector




(b)












Rtr-1 Rtr-2Cons-1

ale 1

ale 1

oAP

nAP

AP






nAP












Prod

oAP

nAP2

nAP1

oAP Range

nAP1 Range

nAP2 Range







Hp L2 + α

Hp asymp L2(3)



Core

Backhaul

Edge

Access1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16


11

2

3 4

5 6

7 8

9 10

1612

13 14

15

1

Cons-1

Prod-2

Prod-4

Prod-1

Prod-3

2D view

ICN router

ICN AP

Consumer

Producer

Cons-2

Cons-2



1 25 100







RTTh L2 + RTTt (4)

800

750

700Se

quen

ce n

umbe

r

650

600

550

500


134ms109ms

11

111

112

113

114

115

116

117

118

119 12

121

122

Time (sec)

123

124

125

126

127

128

129 13


(a)

800

750

700

Sequ

ence

num

ber

650

600

550

500


150ms

109ms

11

111

112

113

114

115

116

117

118

119 12

121

122

Time (sec)

123

124

125

126

127

128

129 13


(b)












and zone flooding

20040

50

60

70

80

90

100

110

120

130

140

150

160

170

230 260 290 320 350 380Sequence number

Roun

d tr

ip ti

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ms)

410 440 470 500 530 560 590









Data Availability




References





















160155150145140135130125120115110105

Aver

age h

ando

ver l

aten

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s)



(a)

10 20 30 40 50

12

11

1

09

08

07

06

05

C to

l ra

tio

Handover events




(b)











Advances in

FuzzySystems


Volume 2018













Journal of

Journal of



Hindawi


Advances in

Multimedia


Biomedical Imaging





RoboticsJournal of









Volume 2018



Advances in















(value = 0)

(a)

NameSelector




(b)












Rtr-1 Rtr-2Cons-1

ale 1

ale 1

oAP

nAP

AP






nAP












Prod

oAP

nAP2

nAP1

oAP Range

nAP1 Range

nAP2 Range







Hp L2 + α

Hp asymp L2(3)



Core

Backhaul

Edge

Access1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16


11

2

3 4

5 6

7 8

9 10

1612

13 14

15

1

Cons-1

Prod-2

Prod-4

Prod-1

Prod-3

2D view

ICN router

ICN AP

Consumer

Producer

Cons-2

Cons-2



1 25 100







RTTh L2 + RTTt (4)

800

750

700Se

quen

ce n

umbe

r

650

600

550

500


134ms109ms

11

111

112

113

114

115

116

117

118

119 12

121

122

Time (sec)

123

124

125

126

127

128

129 13


(a)

800

750

700

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ence

num

ber

650

600

550

500


150ms

109ms

11

111

112

113

114

115

116

117

118

119 12

121

122

Time (sec)

123

124

125

126

127

128

129 13


(b)












and zone flooding

20040

50

60

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80

90

100

110

120

130

140

150

160

170

230 260 290 320 350 380Sequence number

Roun

d tr

ip ti

me (

ms)

410 440 470 500 530 560 590









Data Availability




References





















160155150145140135130125120115110105

Aver

age h

ando

ver l

aten

cy (m

s)



(a)

10 20 30 40 50

12

11

1

09

08

07

06

05

C to

l ra

tio

Handover events




(b)











Advances in

FuzzySystems


Volume 2018













Journal of

Journal of



Hindawi


Advances in

Multimedia


Biomedical Imaging





RoboticsJournal of









Volume 2018



Advances in













Rtr-1 Rtr-2Cons-1

ale 1

ale 1

oAP

nAP

AP






nAP












Prod

oAP

nAP2

nAP1

oAP Range

nAP1 Range

nAP2 Range







Hp L2 + α

Hp asymp L2(3)



Core

Backhaul

Edge

Access1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16


11

2

3 4

5 6

7 8

9 10

1612

13 14

15

1

Cons-1

Prod-2

Prod-4

Prod-1

Prod-3

2D view

ICN router

ICN AP

Consumer

Producer

Cons-2

Cons-2



1 25 100







RTTh L2 + RTTt (4)

800

750

700Se

quen

ce n

umbe

r

650

600

550

500


134ms109ms

11

111

112

113

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119 12

121

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123

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128

129 13


(a)

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750

700

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ence

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ber

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150ms

109ms

11

111

112

113

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115

116

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118

119 12

121

122

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123

124

125

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127

128

129 13


(b)












and zone flooding

20040

50

60

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80

90

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110

120

130

140

150

160

170

230 260 290 320 350 380Sequence number

Roun

d tr

ip ti

me (

ms)

410 440 470 500 530 560 590









Data Availability




References





















160155150145140135130125120115110105

Aver

age h

ando

ver l

aten

cy (m

s)



(a)

10 20 30 40 50

12

11

1

09

08

07

06

05

C to

l ra

tio

Handover events




(b)











Advances in

FuzzySystems


Volume 2018













Journal of

Journal of



Hindawi


Advances in

Multimedia


Biomedical Imaging





RoboticsJournal of









Volume 2018



Advances in









Prod

oAP

nAP2

nAP1

oAP Range

nAP1 Range

nAP2 Range







Hp L2 + α

Hp asymp L2(3)



Core

Backhaul

Edge

Access1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16


11

2

3 4

5 6

7 8

9 10

1612

13 14

15

1

Cons-1

Prod-2

Prod-4

Prod-1

Prod-3

2D view

ICN router

ICN AP

Consumer

Producer

Cons-2

Cons-2



1 25 100







RTTh L2 + RTTt (4)

800

750

700Se

quen

ce n

umbe

r

650

600

550

500


134ms109ms

11

111

112

113

114

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117

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119 12

121

122

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123

124

125

126

127

128

129 13


(a)

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750

700

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ence

num

ber

650

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500


150ms

109ms

11

111

112

113

114

115

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119 12

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123

124

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127

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129 13


(b)












and zone flooding

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d tr

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ms)

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160155150145140135130125120115110105

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age h

ando

ver l

aten

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s)



(a)

10 20 30 40 50

12

11

1

09

08

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06

05

C to

l ra

tio

Handover events




(b)











Advances in

FuzzySystems


Volume 2018













Journal of

Journal of



Hindawi


Advances in

Multimedia


Biomedical Imaging





RoboticsJournal of









Volume 2018



Advances in







Hp L2 + α

Hp asymp L2(3)



Core

Backhaul

Edge

Access1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16


11

2

3 4

5 6

7 8

9 10

1612

13 14

15

1

Cons-1

Prod-2

Prod-4

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Prod-3

2D view

ICN router

ICN AP

Consumer

Producer

Cons-2

Cons-2



1 25 100







RTTh L2 + RTTt (4)

800

750

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quen

ce n

umbe

r

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122

Time (sec)

123

124

125

126

127

128

129 13


(b)












and zone flooding

20040

50

60

70

80

90

100

110

120

130

140

150

160

170

230 260 290 320 350 380Sequence number

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Data Availability




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(b)











Advances in

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Volume 2018













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Journal of



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Advances in

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Biomedical Imaging





RoboticsJournal of









Volume 2018



Advances in






RTTh L2 + RTTt (4)

800

750

700Se

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umbe

r

650

600

550

500


134ms109ms

11

111

112

113

114

115

116

117

118

119 12

121

122

Time (sec)

123

124

125

126

127

128

129 13


(a)

800

750

700

Sequ

ence

num

ber

650

600

550

500


150ms

109ms

11

111

112

113

114

115

116

117

118

119 12

121

122

Time (sec)

123

124

125

126

127

128

129 13


(b)












and zone flooding

20040

50

60

70

80

90

100

110

120

130

140

150

160

170

230 260 290 320 350 380Sequence number

Roun

d tr

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ms)

410 440 470 500 530 560 590









Data Availability




References





















160155150145140135130125120115110105

Aver

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(a)

10 20 30 40 50

12

11

1

09

08

07

06

05

C to

l ra

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Handover events




(b)











Advances in

FuzzySystems


Volume 2018













Journal of

Journal of



Hindawi


Advances in

Multimedia


Biomedical Imaging





RoboticsJournal of









Volume 2018



Advances in













and zone flooding

20040

50

60

70

80

90

100

110

120

130

140

150

160

170

230 260 290 320 350 380Sequence number

Roun

d tr

ip ti

me (

ms)

410 440 470 500 530 560 590









Data Availability




References





















160155150145140135130125120115110105

Aver

age h

ando

ver l

aten

cy (m

s)



(a)

10 20 30 40 50

12

11

1

09

08

07

06

05

C to

l ra

tio

Handover events




(b)











Advances in

FuzzySystems


Volume 2018













Journal of

Journal of



Hindawi


Advances in

Multimedia


Biomedical Imaging





RoboticsJournal of









Volume 2018



Advances in





Data Availability




References





















160155150145140135130125120115110105

Aver

age h

ando

ver l

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cy (m

s)



(a)

10 20 30 40 50

12

11

1

09

08

07

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05

C to

l ra

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Handover events




(b)











Advances in

FuzzySystems


Volume 2018













Journal of

Journal of



Hindawi


Advances in

Multimedia


Biomedical Imaging





RoboticsJournal of









Volume 2018



Advances in












Advances in

FuzzySystems


Volume 2018













Journal of

Journal of



Hindawi


Advances in

Multimedia


Biomedical Imaging





RoboticsJournal of









Volume 2018



Advances in









Advances in

FuzzySystems


Volume 2018













Journal of

Journal of



Hindawi


Advances in

Multimedia


Biomedical Imaging





RoboticsJournal of









Volume 2018



Advances in




Documents

Anchor-Less Producer Mobility Management in Named Data ...downloads.hindawi.com/journals/misy/2019/3531567.pdf · requested. e data generated in Internet telephony, messaging, and