Aalborg Universitet

Network aware dynamic context subscription management

Shawky, Ahmed; Olsen, Rasmus Løvenstein; Pedersen, Jens Myrup; Schwefel, Hans-Peter

Published in:Computer Networks

DOI (link to publication from Publisher):10.1016/j.comnet.2013.10.006

Publication date:2014

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Citation for published version (APA):Shawky, A., Olsen, R. L., Pedersen, J. M., & Schwefel, H-P. (2014). Network aware dynamic contextsubscription management. Computer Networks, 58, 239–253. https://doi.org/10.1016/j.comnet.2013.10.006

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Computer Networks 58 (2014) 239–253

Contents lists available at ScienceDirect

Computer Networks

journal homepage: www.elsevier .com/ locate/comnet

Network aware dynamic context subscription management

1389-1286/$ - see front matter � 2013 Elsevier B.V. All rights reserved.http://dx.doi.org/10.1016/j.comnet.2013.10.006

⇑ Corresponding author. Tel.: +45 27821040.E-mail addresses: asms@es.aau.dk, shawky84@gmail.com (A. Shawky).

A. Shawky ⇑, R. Olsen, J. Pedersen, H. SchwefelNetworks and Security, Department of Electronic Systems, Aalborg University, Denmark

a r t i c l e i n f o

Article history:Received 13 January 2013Received in revised form 31 July 2013Accepted 22 October 2013Available online 28 October 2013

Keywords:ContextAwarenessNetworkQoSOptimization

a b s t r a c t

Context-awareness is a key requirement in many of today’s networks, services and appli-cations. Context management systems are used to provide access to distributed, dynamiccontext information. The reliability of remotely accessed dynamic context information isimpacted by network delay, packet drop probability, its information dynamics and theaccess strategy used. Due to the characteristics of the different access strategies, differentlevels of reliability of context information can be ensured, but at the same time, these strat-egies lead to different access traffic which impacts also the network performance, andhence feeds back to the reliability of the information. Furthermore, different levels ofQoS may be available and used in order to mitigate the impact of network performancedegradation on the reliability of the dynamic context information. In this paper wedescribe a system and algorithms that are capable of configuring effectively context accessstrategies in order to maximize reliability of all accessed dynamic context information. Theframework utilizes and extends existing information reliability models, and it can utilizedifferent network performance models. Simulation results of scenarios in which the frame-work uses finite-buffer bottleneck performance models demonstrate the effectiveness ofour algorithm to increase reliability. Furthermore, the framework is applied to a scenariowith QoS classes that allows to trade off delay and loss via different buffer-sizeconfigurations.

� 2013 Elsevier B.V. All rights reserved.

1. Introduction

The ability of applications to adapt to the users’ envi-ronment is often referred to as context awareness, [1],and is becoming a key factor in today’s mobile networks,since users need to be able to efficiently interact withapplications and platforms in a highly dynamic world. Con-text awareness is achieved by accessing dynamic contextinformation provided by context agents in a context man-agement system and is a highly desirable feature for futuremobile applications. However, the access to dynamic con-text information distributed in the environment has to becarefully designed to achieve scalability and context reli-ability. European projects like MAGNET Beyond [10], SPICE,[11] or E-SENSE, [12], and others outside Europe, have been

researching and developing concepts for context manage-ment for some time, whereas reliability and accuracy indi-cators for context information just recently caught theattention e.g. in the project SENSEI, [13].

In those projects as well as within the research field, seee.g. [3,4], reliability of context information has beenacknowledged as an important meta data to contextinformation as the end user’s satisfaction obtained fromcontext-aware systems is directly depending on the reli-ability of the context information. In the respect of reliabil-ity, some work focuses on information reliability in termsof the uncertainty of the information source, e.g. using fuz-zy logic approaches, [5] or [6], in which reliability is relatedto inexactness and uncertainty of obtained informationand not the timely aspects of the information. Other workfocuses on reliability of the information either by consider-ing the age of the information, see e.g. [8,14] or [7].However, the information age needs to be related to the

240 A. Shawky et al. / Computer Networks 58 (2014) 239–253

temporal dynamics of the information in order to provide auseful notion of reliability.

The reliability of context information is challenged bynetwork delay, information dynamics, the context accessstrategy used and its parameter settings. The choice of thecontext access strategy on the other hand also influencesnetwork performance through the context access traffic.This paper provides a framework for automatic configura-tion of access to remote dynamic context informationwhich accounts for the interplay between the network,the access strategies and their configurations, while maxi-mizing the overall reliability of the information access.

The quality of context information plays an importantrole in adapting a system to continuously changing situa-tions. In order for these systems to function optimally, theyare required to implement measures that can resolve con-text conflicts in order to limit making of faulty context-aware adaptation decisions. Today there are several meansto measure context quality and there is a large amount ofliterature on context modeling and representation, yetmore needs to be done to insure context information ex-change with guaranteed quality levels [23] [24]. Thereare several methods and techniques that are able to mea-sure Quality of Context, amongst them are sensor trainingi.e (precision and correctness calculation), algorithms tocalculate QoC value of aggregated context and conflict res-olution algorithms. However most of the research done oncontext quality aims to develop a control scheme wherecontext information is gathered, then classified and thenis checked for quality by using one of the previous meth-ods. Most of these methods seem to neglect the fact thatthere are networking issues that need to be addressed suchas delay, overhead, jitter which directly context quality fordynamic context sources. While there is work [25] thatprovides networking QoS to context information, targetinghigher-level context quality metrics with a model-basedapproach is the main novel contribution of this paper.

2. Context management framework

For applications to be able to adapt to their environ-ment, access to dynamic context information [1], whichdescribes the current situation, is required. Context Man-agement systems offer flexible access often via dedicatedquery languages, e.g. [9,2], which allow applications easyaccess to distributed information.

A generic context management framework is illustratedin Fig. 1, which contains a set of entities called ContextAgents (CA). Each CA is responsible for collecting data fromits own local environment, as shown to the right in the fig-ure: one CA exists on each device collecting informationon, for instance, location, noise and temperature, respec-tively. A server in the network will act as context manager,which is responsible for maintaining the overlay networkof context agents, i.e. it performs agent discovery, mainte-nance of agent information, information (de)registrationand later on also access optimization for access to contextinformation. In our considered architecture, context sensi-tive applications access context information through thecontext manager. The node that operates the context man-ager, is called the Context Management Node (CMN).

As seen to the right of Fig. 1, context information at thesource changes according to external events, while the con-text-sensitive application would like to perform actionsmatching the true state of the context values at the source.When that information is dynamically changing, communi-cation and processing delays can lead to deviations of theknown context value at the application from the true valueat the source. In our earlier work, we introduced the notionof mismatch probability (mmPr), [17] that is defined as theprobability that at the time instant of using a certain informa-tion for processing in the context-sensitive application, thisinformation does not match the value at the (physical) source.The reliability of context information has previously beenacknowledged and considered an important part of the qual-ity of context, [3,4,14] or [8], but not used in any effectiveway. In this paper we use this quantitative context reliabilitymetric for intelligent choices of access strategies to improvereliability for all context sensitive applications requiring ac-cess to various dynamic distributed information elements.

The mismatch probability depends not only on the twostochastic processes (a) network delay and (b) informationchange process, but also on the strategy by which theinformation is accessed. The following three strategies willbe used as principle mechanisms to access remote dynamiccontext information:

� Reactive strategy: whenever the application intends toprocess a certain context value, the context managersends a request to the context providing agent, and getsa response with the information value in return. Fig. 2illustrates this access strategy.� Proactive, event driven: the context manager has setup a

subscription to the context providing agent, and eachtime information changes value, the context agentsends an update to the context manager. For continuousinformation types, such change events can be definedvia discretization intervals. The proactive event-drivenstrategy is illustrated in Fig. 3.� Proactive, periodic update: the context manager has

setup a subscription to the context providing agent,which after recurring time interval sends the currentvalue to the context manager. Fig. 4 illustrates this peri-odic strategy.

The goal of the developed framework and algorithms isthat the context manager is able to select and configure theaccess strategy by which it interacts with the various con-text agents to provide the information in such way that thereliability of the information is effectively increased.

The decision on which strategy to take is not trivial as itinvolves several stochastic processes and parameters thatmay be adjusted, and as each different strategy puts differ-ent loads to the network, and any decision effectively feedsback via an increased network delay to the mismatch prob-ability and hereby may render decisions not optimal.

The objective of the algorithm is therefore to decideupon one of the three access mechanisms that should beused, as well as parameter settings, while considering thatthe context access traffic also affects the network; so im-pact on network delay and loss caused by the higher loadis taken into account.

Fig. 1. Example of context management system – the context manager resides on the context management node.

CMN

CA E1

Req Res Req Res

E2 E3

2S1St

t

Fig. 2. Reactive strategy – the CMN sends request to the CA. The first request in the shown example leads to a matching information element, whereas thesecond ones leads to a mismatching information element. The delay for sending the response impacts the mismatch probability.

A. Shawky et al. / Computer Networks 58 (2014) 239–253 241

2.1. Interactions in the context management framework

The interactions between the different entities in theassumed context management framework are illustratedin Fig. 5. The different entities in the system are as follows:

� Context agents: Collect local context information, andprovide remote access to context information. Contextagents register at the CMN at start-up.� Context management node: maintains a repository of (a)

available context agents and (b) available context infor-mation in the network, and has possibility to control theinteractions for context access between the contextagents.� Network performance prediction: a component that

allows to obtain information of the network perfor-mance and provide information on how potential addi-tional traffic changes network performance.� Context-dependent application: This application uses

dynamic context information for real-time decisions.The correctness of these decisions affects the resultingapplication quality.

Fig. 5 shows the basic message flow that occurs duringthe registration and subscription phases. The basic princi-ples are:

� All CAs register at the CMN. They thereby provide infor-mation about the type of context elements they can offerand the relevant parameters. Particularly, they provideinformation on the temporal dynamics of the context ele-ment, which in our simplified setting are exponentiallydistributed inter-event times with a rate ki of the con-text-element i. Furthermore, also information on the sizeof update messages of the context element is provided.� When an application or middleware function on a node

is interested in a certain context element, it sends a sub-scription to the CMN. Along with other query relatedspecifications, the application also sends its expectedrequest rate and potentially also bounds on mismatchprobability of the context element or on access delays.� The CMN then evaluates which context access proce-

dure with what parameter setting is best for this newsubscription. In order to do so, it will interact with thenetwork performance prediction function in the net-work. Having determined the most suitable configura-tion, the CMN informs the subscriber and the CAabout this configuration and starts receiving contextupdates (in the proactive strategies) respectively relay-ing requests for context elements in the reactive strate-gies. All interaction between subscriber and CA isperformed via the CMN in order to allow abstractionand efficient processing.

CMN

CA E1 E2 E3

2S1St

t

U1 U2U3

Fig. 3. Proactive event driven strategy – in the shown example, reception of the update from Event 1 leads to a correct value at the CMN until Event 2happens at the CA. Update 2 will be filtered out at the CMN by the use of sequence numbers.

CMN

CA E1 E2

2S1St

t

T

2U1U

E4E3

U3

Fig. 4. Proactive periodic update – with a time interval T, an update of the most current value at the context agent is sent to the context manager. Reorderedoutdated updates are filtered out via sequence numbers.

242 A. Shawky et al. / Computer Networks 58 (2014) 239–253

� The CMN keeps track of all active subscriptions. Hence,as a variant, when a new subscription comes in, theCMN may not only determine an optimal access config-uration for this newly arriving subscription, but it mayin addition identify that it is beneficial to change someof the configurations of the already active subscriptions.Both cases will be specified and evaluated furtherbelow.

3. Analytic model representation

In order to calculate the impact of different access strat-egies on mismatch probability, different model parts needto be combined as illustrated in Fig. 6: Based on the param-eters of the context elements accessed (change event rate kand update size Ui) in the upper left and based on the re-quest rate, l, provided within the subscription request,the additionally created network traffic can be calculatedin the upper left block. These are straightforward calcula-tions, outlined further below for the individual strategies.The additional traffic is input to the network model, which

is used to calculate network performance parameters forthe paths between subscriber and context provider. Inthe example illustrated these parameters are mean delaysand packet loss probabilities, but more advanced networkmodels (beyond mean value calculations) are possible.

The delay and loss values are then used together withthe context and subscription parameters to calculate theresulting mismatch probability per context element for dif-ferent access configurations (lower left block). Finally, thecalculated individual mismatch values are combined in asingle metric called GmmPr as introduced further belowand the model part outputs the minimum GmmPr andthe corresponding strategy.

3.1. Utilization increase calculations

When a new subscription is executed by the CMN, addi-tional network traffic is generated. The required additionalthroughput, g, is calculated in the following for the threedifferent access strategies. U is thereby the size of the dif-ferent messages (requests or updates), l is the request

NetworkPerformancePrediction

DeviceRunningContext

Application

ContextManagement

NodeContext Agent

CA Registration

CA Discovery Info

New Subscription to CA

Delay & PLoss Request For Additional Subscription Load

Delay and Ploss Values for Additional Subscription

Subscribe Ok with Strategy (x)

Subscribe Strategy (X) Subscribe Strategy (X)

Subscription Configuration Algorithm

Fig. 5. Interactions with the CMN during establishment of a context subscription.

A. Shawky et al. / Computer Networks 58 (2014) 239–253 243

rate, k is the rate of the change events of the context infor-mation, and s is the rate of the periodic updates. Messagesthat are dropped in the network are included in thethroughput calculations.

Reactive strategy. The context access traffic generated bythe reactive strategy is depending on the request size, theresponse size and the rate of requests (assuming requestarrivals with rate l). The generated traffic is:

grea ¼ EðUrqsÞ þ EðUrspÞ� �

� l:

Proactive event driven update. For the proactive eventdriven strategy the traffic generated depends entirely onthe change rate of the information and the size of theupdates:

gevn ¼ EðUupdateÞ � k:

Proactive periodic update. For proactive periodic updatestrategy the context access traffic generated is directlydepending on the update rate and the size of the updates:

gper ¼ EðUupdateÞ � s

3.2. Network model

The Network model is used to translate utilization intodelay and packet loss probability. This paper uses two dif-ferent network models: (1) an M/M/1/K model, which hasthe advantage of a small parameter space, while still allow-ing to analyze trade-offs between delay and loss when

varying the buffer-size K. (2) A more complex networkmodel consisting of the convolution of a transmission de-lay with a queueing delay obtained from a bursty MMPP/M/1/K model.

The mean delay and packet drop probabilities at utiliza-tion q of the M/M/1/K queue are found in any standardqueuing theory book:

Packet Loss ¼ ðð1� qÞqKÞð1� qKþ1Þ

Queue Length ¼ qð1� qÞ �

ðK þ 1ÞðqKþ1Þð1� qKþ1Þ

Delay ¼ Queue LengthKð1� PlossÞ

K is hereby the overall offered load to the queue (inpackets per time unit), K the size of the buffer at the bottle-neck (measured in packets).

The analysis of the algorithms for context subscriptionhandling also uses a more complex network model whichis formed from the convolution of an end-to-end forward-ing delay and a queueing delay. The forwarding delay ismodelled by an exponential delay. The queueing delay isassuming bursty cross-traffic with exponential ON–OFFpattern superimposed to Poisson context access traffic.The cross-traffic represents a base-load, while the Poissonrate of the context traffic is a function of the number of ac-tive subscriptions, their context parameters and the se-lected access strategies, see Section 3.1. This traffic model

For NSubscriptions

StrategyUtilizationIncrease

Reactive Utilization Inc

Event Utilization Inc

Periodic Utilization Inc

NetworkModel

Packet LossDelay

Global mmPrCalculation

Reduced GlobalmmPr for Nsubscriptions

mmPrCalculation

mmPr Reactive

mmPr Event

mmPr Periodic

New Context Subscription

Current N-1 Subscriptions

Delay

Delay

Packet Loss

Packet Loss

Fig. 6. Model overview of internal parts of the algorithm that is being executed at the CMN.

244 A. Shawky et al. / Computer Networks 58 (2014) 239–253

can be represented by a 2-state Markov-modulated Pois-son Process (MMPP) and the performance metrics, meandelay and packet loss probability, of the correspondingMMPP/M/1/K queue can be calculated with matrix-ana-lytic methods [21]. In order to keep the complexity of themmPr calculations low, the queueing delay is then as-sumed to be exponentially distributed with a mean thatis obtained from the numerical solution of the MMPP/M/1/K queue. That way, the mismatch probability calcula-tions can be performed based on Erlangian-2 type distribu-tions for network delay. Such distribution is a specific caseof a general phase-type or matrix-exponential distribution,represented in the notation introduced by Lipsky [22] by avector/matrix pair hp;Bi, such that its cumulative distribu-tion function and density function can be described as

FðtÞ ¼ 1� p expð�tBÞe0; f ðtÞ ¼ pB expð�tBÞe0:

Here p is an entry row-vector to the delay model, B is thegenerator matrix for the delay model and e0 is a columnvector of ones. In the simplest case of an exponentially dis-tribution, p ¼ 1 and B ¼ m (the rate of the process), whichlater simplifies the used mismatch probability equations.The used Erlangian-type model for the network delay hasa Matrix exponential representation:

p ¼ ½1 0�; B ¼mt �mt

0 mq

� �ð1Þ

where mt is the transmission delay rate, and mq the queuingdelay rate calculated from the queueing model asdescribed above.With this, it is fairly simple to indepen-dently adjust the two types of delay occurring in the sys-tem we investigate. More complicated delay models caneasily be represented by more general matrix-exponentialdistributions.

3.3. Mismatch probability calculations

We extend the equations of [17] that describe the mis-match probabilities for the different access strategies. Themain extensions are to include packet loss, which are rele-vant when UDP transmissions are used for the context ac-cess. In case of context access traffic via TCP, packet losseslead to retransmissions, which can me mimicked by an ad-justed message delay distribution. We however assumeUDP context traffic for which losses are relevant to be in-cluded in the model.

Reactive strategy. In order to receive a correct contextinformation message, the reactive strategy requires suc-cessful transmission of two messages; otherwise the CMNdoes not receive a response which we assume to lead to amismatch. Under the condition of successful two way com-munication, i.e. the request and response are not lost, it isonly the delay of the response that may lead to mismatches.Extending the approach of [17] while assuming both the in-ter-event time, hpE;BEi, and the network delays, hpD;BDi, areMatrix-exponential distributed renewal processes, we canexpress the mismatch probability as follows:

mmPrrea;ME ¼ 1� ð1� plossÞ2

� ðpEB�1E Þ � ðpDBDÞpEB�1

E e0EBE � BD½ ��1e0dimðBEÞ�dimðBDÞ

ð2Þ

The symbols � and � represent the Kronecker-product andKronecker-sum, see [20]

When both the network delay and the time betweencontext change events are exponentially distributed withrates m and k, this equation simplifies to:

mmPrrea;exp ¼ 1� ð1� plossÞ2 mkþ m

:

Note that the mmPr increases with increasing contextchange rate k and with increasing network delays (which

A. Shawky et al. / Computer Networks 58 (2014) 239–253 245

correspond to decreasing rate, m, of the delay distribution).For recurrent context information types, the equationsfrom [17] can be analogously extended. In addition to thereactive strategy, it would also be possible to cache datathat has been received to be used as response forsubsequent request within some finite time interval. Thismechanism we evaluated in [18], however, to keep theconfiguration space low, we choose not to allow cachingtechniques to be applied.

Proactive event driven update. The mismatch probabilitywithout packet loss for this approach turns out to beexactly the same as for the reactive strategy, see [17]; incase of potential packet loss, a information match now re-quires that the last update message has not been lost. Weassume here that information updates are complete, i.e.each update completely replaces old information storedat the context manager. An alternative update approachdoes exist, where updates are incremental, but with anon-zero packet loss probability, this type of update isnot useful since one dropped message leads to subsequentmismatches. For the case of Matrix-exponential inter-event times and network delays, the results from [17] arehence extended to

mmPrevent;ME ¼ 1� ð1� plossÞ

� ðpEB�1E Þ � ðpDBDÞpEB�1

E e0EBE � BD½ ��1e0dimðBEÞ�dimðBDÞ;

ð3Þ

which reduces in case of exponential distributions to

mmPrevent;exp ¼ 1� ð1� plossÞm

kþ m�

Proactive periodic update. The mmPr model of theperiodic update case assumes an exponentially distributedupdate interval with rate s in order to simplify the mathe-matical calculations. If context providing processes arelowly prioritized by the operating system, stochastic fluc-tuations in the update period are not rare, even if timersare used. The basic approach and the resulting equationto calculate the mismatch probability is the same as in[17], and for Matrix Exponential distribution can be ex-pressed as

mmPrper;ME ¼esD

E

Z 1

0e�stexp �spDB�1

D expð�BDtÞe0Dh i

� pEexpð�BEtÞe0E� �

dt; ð4Þ

which can be simplified in case of exponentially distrib-uted processes

mmPrper;exp ¼ /ew Cð/þ wÞw/þw FCð/þw;wÞð1Þ:

The latter equation has the advantage that it is integralfree, but is limited to only the exponentially distributedcase. For the two stage delay, numerical integration is re-quired for solving the mmPr.

Including packet loss is done by considering the updateprocess as a thinned Poisson process with thinning proba-bility ð1� plossÞ, so that the parameter w now becomesw ¼ sð1� plossÞ=m (where s is the update rate, and m is the

delay rate). The other parameter remains as / ¼ k=m (ratioof event and delay rates). FCða;bÞ is the cdf of a gamma dis-tribution with parameters a and b.

Rejection of context requests. As context subscriptiontraffic in any of the above three configurations also influ-ences network delays and loss probabilities, it will increasethe mmPr for other subscriptions. Depending on the opti-mization target, it can hence be beneficial to reject contextsubscriptions to avoid this negative impact on other ongo-ing subscriptions. In case of rejection of a subscription, noadditional network traffic is generated, but the mismatchprobability for the context accesses of this rejected sub-scriptions are mmPr ¼ 1.

3.4. Global mismatch probability

When there are N context subscriptions, we define in asingle metric that we call the global mmPr (GmmPr). Forsimplicity in this work we use the average mmPr of theN sources:

GmmPr :¼XN

i¼1

mmPri

N:

Other definitions, e.g. using weighted averages based oncontext access frequency or based on context relevancecan be considered when demanded by the specificscenario.

4. Optimization of dynamic context subscriptions

The modelling framework of the previous section isnow utilized for configuration selection of context sub-scriptions within the context management frameworkintroduced in Section 2.

4.1. Algorithm descriptions

In the following, we present two algorithms that run atthe CMN. The first algorithm provides the base case, whena subscription is initiated by a context-dependent applica-tion or device middleware, the CMN determines the opti-mal configuration for this newly starting subscriptionwhile taking into account the network conditions and alsothe impact of this new subscription traffic on the alreadyongoing context subscriptions. The second algorithmincludes a reconfiguration of already active context sub-scriptions and as such is an extension of the first one.Although we do not consider this later in the evaluation,the second algorithm could also be triggered by changesin the network resources, e.g. by increase or decrease ofthe other traffic in the network, or by reduction of availablebandwidth/change of delays in wireless communicationsettings.

Algorithm 1: Configuration of new subscriptions. The firstalgorithm is executed by the CMN upon the reception of anew subscription. As our intention here is to evaluate thebenefit from an intelligent configuration choice, thealgorithm is not optimized for efficiency, but rather imple-ments a brute-force search over the possible configurationset – taking parameterized strategies (in our example the

246 A. Shawky et al. / Computer Networks 58 (2014) 239–253

periodic strategy with update rate s) via discretized searchspaces into account.

Algorithm 1. New subscription configuration algorithm

function determine_configuration_of_new_subscriptionInput: Current network load L and available network (bottleneck) resources R

in the path context agent - CMN - subscriber.Request Rate mu_i of new subscription.

Available Information (persistently maintained at CMN)

From registration: Context-value change rate lambda_i, and update size u_i of

context element of new subscription.

From previous subscriptions: Parameters and configurations of

currently active subscriptions.

Output: access strategy for new subscription

parameterizations of the access strategy

(here: rate of proactive periodic strategy).resulting new GmmPr best_mmPr.

Begin

best_mmPr = 1.0;best_strategy = not_defined;For all access strategies in (Reject, reactive, proactive event-driven,

proactive periodic_rate1, . . .proactive periodic_rateK);calculate additional network load L_inc created by NEW subscription

when using this access strategy (see Section III);

Call network model to calculate network performance metrics (D, p_l)at load L + L_inc for resources R;

Calculate GmmPr for these network performance metrics and

existing plus new subscriptions;

If calculated GmmPr < best_mmPr;Set currently considered access strategy and parameters

as best one;

Update best_mmPr;end-if

end for-loop;

end function;

Algorithm 2: Reconfigure all subscriptions. While theprevious algorithm only considers the newly incomingsubscription, it may also be worth to adapt the way al-ready existing subscriptions are executed. As the CMNkeeps track of all subscriptions, their access strategiesand their parameters, it can use this information to opti-mize the target metric further. As we are only interested

in a potential benefit of such reconfiguration, we also inthis case do not implement very sophisticated efficientsearch approaches, but rather an algorithm that firstdetermines the optimal configuration of the new sub-

scription, and then iterates over all existing subscriptionsto check if an improvement can be obtained by changingthe configuration. This obviously does not guarantee con-vergence to the true optimum, as it locally optimizes eachindividual subscription. Nevertheless, the algorithm isuseful in the evaluation part to investigate benefits fromreconfigurations.

A. Shawky et al. / Computer Networks 58 (2014) 239–253 247

Algorithm 2. Subscription reconfiguration algorithm

function iterate_over_existing_SubscriptionInput: Current network load L and available network (bottleneck)

Number of repetitions, rep, for iterative search.

Available Information (persistently maintained at CMN)

From previous subscriptions: Parameters and configurations of

currently active subscriptions.

Output: optimized access strategies for existing subscriptions

parameterizations of the access strategies

(here: rate of proactive periodic strategy).

resulting new GmmPr best_mmPr.

Begin

best_mmPr = 1.0;best_strategy = not_defined;

Repeat rep times,

for all previously existing subscriptions

subtract current load L_old caused by this subscription in

current access mode from total load L.

Take this subscription out of the currently active set and

consider it as ‘new’;

determine_configuration_of_new_subscription at load L-L_oldconsidering the just taken out old subscription;

Add subscription to the currently active set with potentially changed

access strategy;

L = L-L_old+(load created by reconfigured subscription)

Call network model to calculate network performance metrics (D, p_l)at load L + L_inc for resources R;

Calculate GmmPr for these network performance metrics and

existing plus new subscriptions;

If calculated GmmPr < best_mmPr;Set currently considered access strategy and parameters as best

one;

Update best_mmPr;end-if

end for-previously-existing;

end repeat-rep-times;

Note that the context access strategies are actually notupdated at every individual context request, but rather atestablishment of new subscriptions. Once a subscriptionhas been accepted, the CMN and CA communicate in thedetermined way for a number of context requests (orchange events or periodic update messages). Only whena new subscription is signaled to the CMN, a reconfigura-tion may happen. The network load created by the recon-figurations themselves is therefore small in case of longlasting subscriptions; the network models in this papertherefore do not consider the subscription managementand subscription reconfiguration traffic.

4.2. Evaluation methodology

To evaluate the algorithms’ performance a series of sim-ulations were carried out. These simulations will comparethe intelligent mmPr-based configuration optimizationalgorithms against strategies that assign a constant defaultaccess strategy to new subscriptions. The simulation con-siders the arrival of N context access requests at theCMN, the request parameters as well as the context param-eters are thereby stochastically varying (using uniformdistributions). All parameters and their values in thesimulation are listed in Table 1. The simulation evaluation

Table 2Network model parameters, M/M/1/K.

Service rate (l) 10 MBit/s (1250 pck/s)

Buffer size (K) 100 PacketsCross traffic 9 MBit/s

Table 1Context information parameters.

Context parameter Value

Context request rate [0.2–1] Requests/SecContext event rate [0.2–1] Events/SecContext update rate range [1–10] Updates/sContext request size 200 BytesContext update/response size [800–1200] Bytes/UpdatePacket size 1000 Bytes

Table 3Network model parameters, MMPP/M/1/K.

Service rate (l) 10 MBit/s (1250 pck/s)

Buffer Size (K) 100 PacketsCross traffic rate during OFF 0 MBit/sCross traffic rate during ON 10 MBit/s (1250 pck/s)Mean ON time 0.15 sMean OFF time 0.05 sExponential Transmission Delay, mean 500 ms

248 A. Shawky et al. / Computer Networks 58 (2014) 239–253

assumes that the parameters of the specifically addressedcontext elements (change rate, update size) and the result-ing individual request rate are known by the context agentrespectively the subscribing application and hence are cor-rectly specified (see Tables 2 and 3).

For the network, we assume that the context accesstraffic of all N subscriptions share the same network bot-tleneck, we consider a scenario where a CMN is runningon a wireless connected device, hence the CMN receivesand manages these N subscriptions over the same wirelessaccess interface. We use two different models: (1) anetwork model based on solely the queueing delay of anM/M/1/K model and (2) a network model which uses an

Fig. 7. Average global context mismatch probabilities when using an M/M/1/K mper second as comparison.

exponential forwarding delay in convolution with a queue-ing delay whose mean is calculated from a bursty MMPP/M/1/K model. The service rate used for both models ismotivated by a WLAN access scenario with a link capacityof 10 Mb/s. The buffer-size for the used bottleneck link isset to K ¼ 100 messages. It is furthermore assumed thatthe node is not exclusively running the CMN functionality,but other (non-context) traffic is utilizing the same wire-less interface, creating a ’base load’ taking two differentvalues, bursty traffic with 75% utilization for the MMPP/M/1/K model, and 90% for the M/M/1/K model.

The mmPr models are then used in the simulations tocalculate the resulting GmmPr of the context access aftereach incoming subscription, comparing the selections fromthe algorithms from the previous section with three de-fault constant access configurations. The comparison casesare: (1) always reactive access; (2) always event-driven ac-cess; and (3) periodic with a fixed update rate of s ¼ 0:2updates/s. Obviously, other heuristic methods of selectingaccess could be considered, but are not included here dueto space limitations.

The algorithm that minimizes the GmmPr based only onthe configuration of the newly incoming subscription (Alg1) is compared with the approach that iterates over all ac-tive subscriptions (Alg 2), where the number of iterationsis set to 2. Table 1 summarizes the context informationparameters and the M/M/1/K and MMPP/M/1/K parame-ters used for all six scenarios. The context parameters arethereby uniformly distributed in the range specified bythe given intervals in the table.

4.3. Simulation results

M/M/1/K network model. Fig. 7 shows the mean GmmProf 20 simulations each having 50 subscription requests.The corresponding dashed lines show the 95% confidenceintervals of this mean estimate from the 20 simulations.The graph represents a comparison of the fixed accessstrategies, periodic, reactive, and event-driven, with Algo-rithm 1 (optimized configuration of newly incomingsubscription only) and the iterative Algorithm 2, whichoptimizes the configuration of the newly incoming

odel with 90% cross-traffic utilization, using an update rate of 1/5 updates

A. Shawky et al. / Computer Networks 58 (2014) 239–253 249

subscription and subsequently makes two iterations overthe already established subscriptions to reconfigure in caseof better configuration options. Rejections of access re-quests (leading to mismatch equals 1 for this subscription)do not occur. After 50 subscriptions at the right end of thecurve, averaged over the 20 runs of Algorithm 1, the config-urations that result are: 36.4 event driven, 13.6 reactive, 0periodic. The reconfiguration of already previously ac-cepted subscriptions (Algorithm 2, black curve) shows aslightly lower average GmmPr compared to only selectinga configuration for the new subscription (Algorithm 1, pinkcurve). Algorithm 2 configuration results are: 31.45 eventdriven, 18.55 reactive and 0 periodic. The results show thatthe model-based optimizations lead to a substantially low-er GmmPr than the fixed strategies. The iteration with po-tential reconfigurations of already existing subscriptionsthereby shows a slight further gain, which is statisticallyrelevant judging from the detailed inspection of the confi-dence intervals at slightly lower confidence level; however,the additional computational effort and also communica-tion effort for reconfiguration of subscriptions appearsnot justified in the considered scenario.

Network model based on convolution of transmission timewith queueing delay from bursty model. Fig. 8 shows themean GmmPr of 20 simulations each having 50 subscrip-tion requests when using the more complex network mod-el, consisting of an exponentially distributed transmissiondelay convoluted by a queueing delay whose mean isdetermined from a MMPP/M/1/k model with increasingutilization upon accepted subscriptions. The service rateis 10 Mb/s and the Cross traffic is 7.5 MB/s. The corre-sponding dashed lines show the 95% confidence intervalsof this mean estimate from the 20 simulations. The graphagain shows a comparison of the fixed access strategieswith Algorithm 1 (optimization of newly incoming sub-scription) and the iterative Algorithm 2 with two optimiza-tion iterations (including potential reconfigurations ofpreviously accepted subscriptions). Rejections of access re-quests do not occur. After 50 subscriptions at the right endof the curve, averaged over the 20 runs of Algorithm 1, theconfigurations that result are: 44.85 event driven, 5.15reactive, 0 periodic. The reconfiguration of already previ-ously accepted subscriptions (Algorithm 2, black curve)

Fig. 8. Average global context mismatch probabilities when using themore complex network model with 75% utilization from bursty cross-traffic.

shows a slightly lower average GmmPr compared to onlyselecting a configuration for the new subscription (Algo-rithm 1, pink curve). Algorithm 2 configuration resultsare: 31.8 event driven, 18.2 reactive and 0 periodic.

It is of interest to notice the big difference in the numer-ical values of GmmPr in the two cases using the differentnetwork models. Adding the exponential transmission de-lay (which is independent of network traffic) in the secondscenario, the impact of increasing number of subscriptionsbecomes much lower. On the other hand, the absolute val-ues of the GmmPr are much higher than for the M/M/1/Kcase, despite the substantially lower utilization. The opti-mization algorithm is still effective in the scenario withcomplex network model, but in the chosen case, a fixedevent-driven strategy almost performs equally well. Notehowever, that this result is a consequence of the consid-ered scenario, and the algorithm would still be needed toidentify the choice of strategies in a general setting. Thebenefit from reconfigurations of already existing subscrip-tions is small in both scenarios. That algorithm however isstill useful as it can be applied also in settings when net-work behavior changes drastically (e.g. due to path failureand re-routing), since then existing subscriptions mayneed to be adapted.

4.4. Summary

The model-based optimization algorithms are executedon the Context-Management-Node (CMN), which is thenode that receives all context subscriptions and managesthe interaction with the context providers. We introducedtwo algorithm versions: (1) An algorithm that makes deci-sions on what context access strategy and configuration touse when handling a newly incoming subscription and (2)an algorithm that in addition considers reconfiguration ofalready established context subscriptions. The target inboth cases is a metric called Global mismatch Probability(GmmPr) that is the average over all subscriptions of theprobability that at the time of processing context informa-tion, the physical context source has changed value. Themodel used in both algorithms addresses the feedback loopof the impact of decisions on network traffic and hencenetwork performance. Simulations were made to test andcompare the algorithms. The simulation results showedthat selections of context access configurations based onthe mmPr model are able to reduce GmmPr, however,the level of improvements depends highly on the scenarioconsidered.

5. Subscription configuration for class-based trafficdifferentiation

Modern packet-switched network realizations allow fordifferentiated treatment of different traffic types, e.g. viathe DiffServ architecture [16]. That way it is possible toapply different scheduling strategies and to configure dif-ferent buffer-sizes for queues in bottleneck routers. Thelatter degree of freedom allows to trade-off delay forpacket-loss. In order to extend the context subscriptionconfiguration algorithms from the previous section to a

250 A. Shawky et al. / Computer Networks 58 (2014) 239–253

traffic differentiation scenario, we first motivate the ex-pected benefit from class-based traffic treatment by ananalysis of the trade-off of loss versus delay on the contextmismatch probability. We subsequently extend Algorithms1 and 2 to the class-based scenario and investigate theresulting context subscription performance, measured byglobal mismatch probability, in simulation experiments.

5.1. Delay versus loss trade-off

In a class-based QoS differentiation scheme, it is possi-ble to have different subscriptions treated in different clas-ses which potentially use different buffer-sizes. Usingshort buffers, lower transmission delays result at the costof increased packet loss. Using the mmPr models andassuming the M/M/1/K bottleneck buffer model, the im-pact of the buffer size changes on the resulting contextquality can be analyzed in the analytic model. Fig. 9 showsthe mmPr of the reactive access strategy, when it operatesin a network which is described by a bottleneck M/M/1/Kqueuing model with varying buffer size K. The differentcurves show from bottom to top different context changerates of k ¼ 1;2;5, while other parameters of the contextinformation (update size equals to one single packet) andthe context access rate (l ¼ 2) are constant, and the ser-vice rate of the M/M/1/K model maps a 10 Mb/s WLAN sce-nario with packet size of 1000 bytes, which here isassumed to run in overload at q ¼ 1:2. Note that in orderto analyze the delay-loss trade-off, in contrast to the previ-ous algorithms and evaluations, the additional (constant)context access traffic is here not taken into consideration,so the bottleneck utilization is exactly the same across allstrategies.

Fig. 9 shows that for the chosen example scenario, eachcurve reaches a minimum mmPr value, marked by thestars on the curves. As we are using analytic formulas,the buffer-size Kmin at which the minimum mmPr isachieved can be calculated from numerically searchingthe root of the mmPr derivative for the given parameters.Fig. 10 shows the resulting Kmin values for different event

function determine_QoS_class_and_access_configuraInput: Current load L_j on classes and available netw

in the path context agent - CMN - subsc

Request Rate mu_i of new subscription.

Available Information (persistently maintained at

From registration: Change rate lambda_i,context element of new subscription.

From previous subscriptions: Parameters

currently active subscriptions.

Output: QoS Class for new access strategy

access strategy for new subscr

parameterizations of the access strategy

(here: rate of proactive periodic strategy

rates (x-axis) and for different utilization of the bottleneckqueue. For the reactive strategy with the used scenarioparameters, scenarios with lower event rates and lowerutilization favor delay over loss, so larger buffers are better(Kmin grows). Note that this section is not intending to for-mally proof such behavior but rather acts as motivationthat DiffServ-like class differentiation with different buffersizes can be valuable and that there exist approaches todetermine optimal class parameters.

5.2. Algorithm

Basic approach for a class-based scenario is to extendthe algorithms of the previous section by also iteratingover the possible C DiffServ classes. The Network modelthen needs to be able to compute the resulting perfor-mance (mean delay and loss probability in our case) giventhat a certain context traffic is added to a specific class. Tokeep this part simple in our analysis, we assume a staticbandwidth assignment, i.e. the overall link capacity ofthe bottleneck is distributed to the C classes, so that foreach class i, an M/M/1/Ki model can be applied to calculatemean delay and loss within that class. The buffer-size Ki

and the service rate li can then be set independently foreach class, as long as

Pili ¼ l.

As before in Section 4, the extended algorithm is exe-cuted at the reception of a new subscription. The algorithmthen checks the available QoS classes and calculates themmPr over all access strategies, now also for all QoS clas-ses. The algorithm then selects the class and the strategythat results in the lowest GmmPr, taking into account alsothe impact of the additional context traffic on the alreadyexisting subscriptions (only the subscriptions in the sameclass as the new subscription are affected in the usedscheduling strategy). Analogously to Algorithm 2, subse-quently already existing subscriptions are reconsideredfor potential reconfiguration. The extended pseudo codeis found below.

Algorithm 3. Diffserv Subscription Classificationalgorithm

tion_of_new_subscriptionork (bottleneck) resources R_j for each class jriber.

CMN)

and update size u_i of

and configurations of

iption

)

Fig. 10. Buffer-size Kmin that minimizes the mmPr for different changerates of the context information (x-axis) and different utilization (differ-ent curves).

Fig. 9. Impact of buffer size in the M/M/1/K network model on mismatchprobability of different access strategies at high utilization.

resulting new GmmPr best_mmPr.Begin

best_mmPr = 1.0;best_class = NaN;best_strategy = NaN;for all classes j = 1, ,N;

For all access strategies in (Reject, reactive, proactive event-driven,

proactive periodic_rate1, . . .proactive periodic_rate);calculate additional class load Lj_inc created by NEW subscription

when using this access strategy (see Table I);

Call network model to calculate class performance metrics (Dj, Pj)

at class load Lj + Lj_inc for resources R;

Find Minimum N Class GmmPr

If calculated GmmPr < best_mmPr;Set currently considered access strategy and parameters

as best one;

best_Class = j;Update best_mmPr;

end-if

end for-loop-access strategies;

Calculate Link Global mmPr (GmmPr) for all classes;

Select class, strategy and parameter that minimizes Link GmmPr;

If Subscription_number > 1 for selected class

Run Reconfigure subscriptions and recalculate GmmPr;

end for-loop-classes;

end function;

A. Shawky et al. / Computer Networks 58 (2014) 239–253 251

5.3. Simulation model

In order to evaluate the algorithms, simulations as de-scribed in Section 4.2 were extended to the class-basedscenario enabling now to compare Algorithm 3 with theclass-less scenario using Algorithm 2. As in Section 4, thesimulation considers the arrival of N context access re-quests at the CMN. All parameters and their values in thesimulation are listed in Table 4. Same assumptions as forthe previous simulations hold: (1) The CMN is assumedto have perfect knowledge of the average request ratesfor the individual subscriptions. (2) For the network, we

assume that the context access traffic of all N subscriptionsshare the same network bottleneck. (3) A WLAN scenario isused to motivate the network parameters. The correspond-ing same packet rate is in the class-based scheme mappedto the larger Class 1, leading to class utilizations from back-ground traffic of q1 ¼ 1 (while Class 2 has no backgroundload, q2 ¼ 0) for the 90% utilization scenario. For the100% utilization scenario the resulting background utiliza-tion on Class 1 is q1 ¼ 1:11. Mapping all background trafficinto Class 1 corresponds to a scenario in which Class 2 isexclusively reserved for context traffic, while the subscrip-tion configuration algorithm is also allowed to use Class 1.

Table 4Context information parameters.

Context parameter Value

Context request rate (l) [0.2–5] Requests/sContext event rate (k) [0.2–5] Events/sContext request size (Urqs) 200 BytesContext update rate (s) [1–20] updates/sContext update/response size (Ursp) [800–1200] Bytes/

UpdateClassless buffer size (K) 100 PackestClass 1 buffer size (K1) 100 PacketsCalss 2 buffer size (K2) 30 PacketsClassless maximum link capacity (llink) 10 Mbit/sClass 1 maximum link capacity (l1) 9 Mbit/sClass 1 maximum link capacity (l2) 1 Mbit/s

Fig. 12. Classfull vs classless comparison at 100% background traffic inthe M/M/1/K network model.

Fig. 11. Classfull vs classless comparison at 90% background traffic in theM/M/1/K network model.

252 A. Shawky et al. / Computer Networks 58 (2014) 239–253

5.4. Simulation results

Scenario with 90% utilization from other traffic. Fig. 11shows the Comparison between classless context optimi-zation and class-based context optimization with a base-load of 9 Mbit/s, which in the class-based scheme is putonto Class 1. The results shows that the class distributionfor 20 simulation runs is: on average 3 out of 50 subscrip-tions are in Class 1, while Class 2 is used for on average 47out of 50 subscriptions; no subscriptions are rejected. Thesubscriptions show on average the following distributionin the class-full scenario: 18.75 using the reactive strategy,

on average 31.15 using the proactive event strategy, and0.1 periodic.

For the classless scenario the same results as shown inSection 4.3 apply. As it can be seen from the results, split-ting the link into two classes does not benefit the reductionof Global mmPr for active subscriptions in the shown sce-nario of 90% background traffic utilization.

Scenario with 100% utilization from other traffic. Fig. 12shows the comparison between classless context optimiza-tion and class-based context optimization with a base-loadof 10 Mbit/s which again is assigned to Class 1 in the class-based scheme. The results shows that for the class-basedscenario, the class distribution is on average 0.2 subscrip-tions for Class 1 and 49.8 subscriptions for Class 2. As forthe subscription distribution, for the class-based scenario,it is on average 0.7 rejects, 18.55 reactive, 30.55 proactiveevent driven and 0.2 for proactive periodic update. The re-sults for the classless scenario were presented in Sec-tion 4.3. In this highly saturated scenario it can be seenthat using a split link benefits the Global mmPr for all ac-tive subscriptions.

6. Summary

We investigated the possibility of increasing contextinformation reliability by configuration of access strategiesduring subscription establishment and by use of QoS clas-sification. The paper defines and evaluates an algorithmwhich is intended to be used as a part of a context QoS con-trol framework. The algorithm uses extensions of analyticcalculations of mismatch probabilities to scenarios withpacket loss, motivated by UDP based context access sce-narios. Furthermore, it uses a network model to computethe impact of additional context traffic on network perfor-mance metrics. The model-based algorithm is executed onthe Context-Management-Node (CMN), which is the nodethat receives all context subscriptions and manages theinteraction with the context providers. The algorithmwas evaluated for two network models; a simple M/M/1/K bottleneck queuing model and a more complicated net-work model that uses a convolution of transmission delaysand load-dependent queueing delays, in this case for bur-sty ON/OFF background traffic overlaid to Poisson contexttraffic. The evaluation results show the effectiveness ofthe approach. Evaluations of DiffServ like scenarios withtwo traffic classes allow to quantify the benefit of utilizingdedicated network resources for context traffic.

References

[1] A.K. Dey, Providing Architectural Support for Building Context-AwareApplications, Ph.D. thesis, Georgia Institute of Technology, 2000.

[2] Luis Sanchez, Rasmus L. Olsen, Martin Bauer, Marc Girod Genet: AGeneric Context Management Framework for Personal NetworkingEnvironments, First International Workshop on PersonalizedNetworks, 2006.

[3] T. Buchholz, A. Kupper, M. Schiffers, Quality of context: what it is andwhy we need it, in Proceedings of the 10th Workshop of theOpenView University Association: OVUA’03, Geneva, Switzerland,July 2003.

[4] C. Villalonga, D. Roggen, C. Lombriser, P. Zappi, G. Trster, Bringingquality of context into wearable human activity recognition systems,in: Proceedings of the First International Workshop on Quality ofContext (QuaCon 2009), LNCS 5786, 2009, pp. 164–173.

A. Shawky et al. / Computer Networks 58 (2014) 239–253 253

[5] G. Stoilos, G. Stamou, V. Tzouvaras, J.Z. Pan, I. Horrocks, Fuzzy OWL:uncertainty and the semantic web, IEEE Intelligent Systems 21 (5)(2006) 84–87. ISSN:1541-1672.

[6] D. Preuveneers, Y. Berbers, Quality extensions and uncertainty handlingfor context ontologies, in: Proceeding of Context and Ontologies:Theory Practice and Applications, Riva del Garda, Italy, August 28, 2006.

[7] K. Henricksen, J. Indulska, A. Rakotonirainy, Generating contextmanagement infrastructure from high-level context models, in:Proceedings of 4th International Conference on Mobile DataManagement, 2003.

[8] S. McKeever, J. Ye, L. Coyle, S. Dobson, A context quality model tosupport transparent reasoning with uncertain context, QuaCon 2009,LNCS 5786, Lecture notes published by Springer-Verlag BerlinHeidelberg, 2009, pp. 65-75.

[9] A. Devlic, E. Klintskog, Context retrieval and distribution in a mobiledistributed environment, in: Proceedings of 3rd Workshop on ContextAwareness for Proactive Systems, Guildford, United Kingdom, 2007.

[10] R. Prasad (Ed.), My Personal Adaptive Global Net (MAGNET): Signalsand Communication Technology, SpringerScience+Business Media,2010. doi:10.1007/978-90-481-3437-3.

[11] http://www.ist-spice.org/.[12] http://www.ist-e-sense.org/.[13] http://www.sensei-project.eu/.[14] Z. Abid, S. Chabridon, D. Conan, A Framework for Quality of Context

Management, QuaCon 2009, LNCS 5786, Lecture notes published bySpringer-Verlag Berlin Heidelberg, 2009, pp. 120–131.

[16] S. Blake et al., An Architecture for Differentiated Services, RFC 2475,IETF, December 1998.

[17] Martin Bogsted, Rasmus L. Olsen, Hans-Peter Schwefel, Probabilisticmodels for access strategies to dynamic information elements,Performance Evaluation 67 (2010) 43–60.

[18] H.P.Schwefel,M.B.Hansen,R.L.Olsen,Adaptivecachingstrategiesforcontextmanagement systems, in: Proceedings of PIMRC’07, Athens, Greece, 2007.

[20] Alexander Graham, Kronecker Products and Matrix Calculus withApplications, Ellis Horwood, 1981.

[21] Marcel Neuts, Structured Stochastic Matrices of M/G/1 Type andtheir Application, Dekker, New York, 1989.

[22] Lester Lipsky, Queueing Theory, A Linear Algebraic Approach, seconded., Springer, 2008.

[23] A. Corradi, M. Fanelli, L. Foschini, Adaptive context data distributionwith guaranteed quality for mobile environments, WirelessPervasive Computing (ISWPC) (2010).

[24] J.B. Filho, N. Agoulmine, A quality-aware approach for resolvingcontext conflicts in context-aware systems, Embedded andUbiquitous Computing (EUC) (2011).

[25] A. Toninelli, A. Corradi, R. Montanari, A quality of context-awareapproach to access control in pervasive environments mobilewirelessmiddleware, Operating Systems, and Applications (2009).

Further Reading

[15] K. Henricksen, J. Indulska, A. Rakotonirainy, Generating contextmanagement infrastructure from high-level context models, in:Proceedings of 4th International Conference on Mobile DataManagement, 2003.

[19] A. Shawky, R. Olsen, J. Pedersen, J.G. Rasmussen, Servicedegradation in context management frameworks, in: 2011Proceedings of 20th International Conference on ComputerCommunications and Networks (ICCCN), July 31 2011–August 4,2011, , pp. 1–6. http://dx.doi.org/10.1109/ICCCN.2011.6006060.

Ahmed Shawky received his bachelor degreein Computer Engineering from 6th of OctoberUniversity, Cairo, Egypt, in 2006. He receivedhis master degree from Aalborg University in2009 and is currently a PhD. Student in theDepartment of Electronic Systems at AalborgUniversity, Denmark. His research interestsare in the areas of wireless networks, mobilenetworks, and network planning.

Rasmus Løvenstein Olsen is an AssociateProfessor at Aalborg University working in theNetwork and security group. Rasmus receivedhis master degree from Aalborg University in2003 and has received his PhD degree on thetopic of Context Sensitive Service Discoveryand Context Management with focus onaccess to dynamic information in 2008. Since,Rasmus have been teaching, supervising andworking in various European projects andhave more than 40 publications in papers forinternational conferences, journals and book

chapters. Rasmus’ current research focus is on service migration based oncontext information, context quality metrics and quality of service incontext management systems, as well as usage of dynamic information

elements in smart grid scenarios. Previous Rasmus has previous beenguest visitor at National Institute of Communication Technology inYokosuka Research Park in Japan, cooperating with a team of researcherson next generation network technology. He is currently engaged inEuropean research projects doing research and leader of work packages.

Jens Pedersen is Associate Professor and headof the Networking and Security Section,Department of Electronic Systems, AalborgUniversity. His current research interestsinclude network planning, traffic monitoring,and network security. He obtained his MSc inMathematics and Computer Science fromAalborg University in 2002, and his PhD inElectrical Engineering also from Aalborg Uni-versity in 2005. He is author/co-author ofmore than 70 publications in internationalconferences and journals, and has participated

in Danish, Nordic and European funded research projects. He is also boardmember of a number of companies within technology and innovation.

Hans-Peter Schwefel is Scientific Director ofthe Forschungszentrum TelekommunikationWien (FTW), Austria; he defines and coordi-nates FTW’s overall research program onfuture communication technologies and theirapplication in Telecommunications, Intelli-gent Transport Systems, and IntelligentEnergy Distribution Grids. As a second affili-ation, Hans is part-time Professor for IP-basedwireless networks at Aalborg University,Denmark. Before Hans joint FTW in 2007, hehas been full-time Associate Professor at Aal-

borg University since 2003, and prior to that Project Manager for ResearchCooperations at Siemens Mobile Networks, Germany. Hans has beenoverall coordinator of the European FP6 project Highly-dependable IP-

based Networks and Services (HIDENETS) and has had a leading role inseveral European projects. He is Associate Editor of IEEE CommunicationsLetters and has been TPC co-chair of the International Symposium onReliable Distributed Systems (SRDS) 2013, IEEE SmartGridComm Com-munications and Networks for Smart Grids and Smart Metering Sympo-sium 2012, European Wireless 2011 and IEEE Network Computing andApplications 2010, and TPC member in many other relevant conferences.Hans obtained his doctoral degree (Dr. rer. nat.) in the area of IP trafficand performance modelling from the Technical University in Munich,Germany, in 2000.