Towards Low Cost Prototyping of Mobile Opportunistic Disconnection Tolerant … · · 2016-11-04manner via adaptive mobile delay/disconnection tolerant opportunistic networks

Received July 29, 2016, accepted August 26, 2016, date of publication September 7, 2016, date of current version September 28, 2016.

Digital Object Identifier 10.1109/ACCESS.2016.2606501

Towards Low Cost Prototyping of MobileOpportunistic Disconnection TolerantNetworks and SystemsMILENA RADENKOVIC1, JON CROWCROFT2, AND MUBASHIR HUSAIN REHMANI31School of Computer Science, The University of Nottingham, Nottingham, NG7 2RD, U.K.2Computer Laboratory, University of Cambridge, Cambridge, CB2 1TN, U.K.3COMSATS Institute of Information Technology, Islamabad 45550, Pakistan

Corresponding author: M. Radenkovic ([email protected])

This work was supported in part by the Project Health Monitoring and Life-Long Capability Management for SELf-SUStainingManufacturing Systems through the Commission of the European Communities under the 7th Framework Programme, under Grant 609382.

ABSTRACT Fast emerging mobile edge computing, mobile clouds, Internet of Things, and cyber physicalsystems require many novel realistic real-time multi-layer algorithms for a wide range of domains, suchas intelligent content provision and processing, smart transport, smart manufacturing systems, and mobileend-user applications. This paper proposes a low-cost open source platform, MODiToNeS, which usescommodity hardware to support prototyping and testing of fully distributed multi-layer complex algorithmsover real-world (or pseudoreal) traces. MODiToNeS platform is generic and comprises multiple interfacesthat allow real-time topology and mobility control, deployment and analysis of different self-organizedand self-adaptive routing algorithms, real-time content processing, and real-time environment sensingwith predictive analytics. Our platform also allows rich interactivity with the user. We show deploymentand analysis of two vastly different complex networking systems: a fault and disconnection-aware smartmanufacturing sensor network and cognitive privacy for personal clouds. We show that our platform designcan integrate both contexts transparently and organically and allows a wide range of analysis.

INDEX TERMS Disruption tolerant networking, mobile ad hoc networks, prototypes, wirelesscommunication, wireless sensor networks.

I. INTRODUCTIONOver the recent years there has been a growing interest indesigning and testing novel mobile wireless and opportunisticnetwork communication protocols and systems for a widerange of vastly different application scenarios, such as smartmanufacturing, mobile social networks and smart wellbeingdomains. Researchers increasingly aim to test their novel net-work architectures and protocols under realistic constraintsafter initially optimising theoretical models. Newly emergingservices and applications for Internet of Things (IoTs) andCyber Physical Systems (CPSs) require development of newintelligent communication, storage and processing architec-tures. We propose a novel platform where IoT ubiquitousdevices can host services and communicate in peer-to-peermanner via adaptive mobile delay/disconnection tolerantopportunistic networks. This paper describes a novel multi-layer intelligent Mobile Opportunistic and DisconnectionTolerant Networking (MODiToNeS) platform that supportsvarious mobility and connectivity patterns, adaptive

communication protocols, and a wide range of smart algo-rithms for intelligent content processing. We argue that ourplatform can be used to help research community test highlycomplex self-organised cognitive distributed protocols andarchitectures as well as serve as an educational resourcewhich uses open source software, low cost hardware, simplecontrol interfaces and modelling structures. We believe thatMODiToNeS will help advance the research and educationalopportunities available to the cognitive DTN and oppor-tunistic network communication communities by providinga ‘‘real-world’’ fully distributed platform where researchersand students can develop and test their cognitive protocolsand applications while being able to observe how they behavein a real world hybrid (wireless and wired, mobile andstatic) environments. We argue that it is very important toallow researchers to validate their core assumptions andhypothesis made when proposing new complex algorithmsand systems as early as possible to avoid building inaccurateand unusable protocols. MODiToNeS allows us to tackle

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M. Radenkovic et al.: Toward Low-Cost Prototyping of Mobile Opportunistic Disconnection Tolerant Networks and Systems

exactly that through incremental or evolutionary prototyping.Using simple open source interfaces, researchers are ableto rapidly develop distributed algorithms and deploy themin a real environment saving time in developing componentevaluation and simulation techniques while providing themwith valid feedback about how their components interactedwith different kinds of dynamic environments. Additionaladvantages of using MODiToNeS are multifold and include:first, low cost which refers to the total cost of the platformownership being less than the cost of a mid-high end laptop(around £2000), and second the platform being fundamentalto performing early feasibility tests and quick prototypingbefore embarking in complex simulations (e.g. NS-3 [29] orMininet [30]).In this paper, we describe two different example scenar-

ios prototyped in MODiToNeS: first, smart manufacturingFault Aware and Disconnection aware communication smartsensors protocol (FDASS) [10]); and second, privacy awaremobile personal clouds (CogPriv) [4]. More specifically, weshow how our open-source distributed platform allows build-ing, deploying and testing of 1) fault aware and disconnectionaware framework prototyping and testing for smart manufac-turing and 2) adaptive mobile privacy aware personal cloudsprototyping and testingwhen sharing various kinds of data viadifferent routing protocols via networks with different levelsof privacy leakage.The paper is organised as follows. Section 2 reviews related

work on state of the art testbeds for smart data communi-cation in mobile and wireless networks. Section 3 proposesthe multi-layer architecture of our MODiToNeS platform,introduces its key control planes and describes support forcross layer data communication that can on-the-fly adapt todynamic link properties and changing requirements of theusers. Section 4 describes smart manufacturing opportunis-tic and disconnection tolerant sensor network architecturescenario prototyping with smart adaptive routing protocolssuch as FDASS [10]. Section 5 describes peer to peer mobileclouds scenario which deploys smart protocols [4] for datasharing, monitoring and interaction. We show that bothCogPriv and FDASS outperform other competitive andbenchmark protocols across a range of metrics in line withpreviously done simulation based work in [4] and [10].In addition, we evaluate realistic resource costs for FDASSand CogPriv across a range of resource metrics in real time.Section 6 gives summary and future work directions.

II. RELATED WORKAs mobile edge computing and cognitive networks are stillemerging filed, there are limited simulation and testbedenvironments which allow prototyping and testing of newemerging applications, protocols and services. We review arange of state of the art testbeds for wireless networks andapplications, and identify how our proposal defers from eachone of them. Similarly, the majority of the current simulatorimplementations have limited number of control features asthey are based on the basic wireless sensor networks and

communication protocols. In this paper, we propose a novelmultilayer platform that uses low cost smart devices (e.g.such as Raspberry PIs) to prototype rich set of intelligentand interactive complex communication algorithms and dis-tributed network architectures.Radenkovic and Milic-Frayling [6] propose the design and

architecture for a low cost light weight testbed for a PersonalCloud based on Raspberry Pi with a range of sensors (RasPiP-Cloud). RasPiPCloud supports multiple on demand virtualcontainers to host different services and applications thatcan collect, store and share data with varying different lev-els of privacy. RasPiPCloud utilizes opportunistic networkscommunication to communicate with the heterogeneoussensors and other devices. RasPiPCloud can have multi-ple containers [5] such as: Healthcare, Finance, and SocialNetwork with additional container template ready for rapidon demand deployment. Each container gets installed andruns its purpose specific applications to ensure secure datafencing and protection. Radenkovic and Milic-Frayling [6]do not describe the support for multi-user communication.This paper focuses on multi clouds communication supportin MODiToNeS.Romero et al. [13] propose cognitive testbed for wireless

sensor networks as an emerging technology with a potentialto avoid traditional wireless problems such as reliability,interferences and spectrum scarcity in wireless sensor net-works. Romero et al. [13] argue that cognitive wireless sensornetworks testbeds are an important tool for future develop-ments, protocol strategy testing and algorithm optimizationin real scenarios. This paper focuses on sparse and potentiallydisconnected topologies in addition to large dense topologies.State of the art work in [16] proposes Haystack system whichaims to allow unobtrusive and comprehensive monitoring ofnetwork communications on mobile phones entirely fromuser space. Haystack correlates disparate contextual informa-tion to illuminate mobile phone app performance, privacy andsecurity. While Haystack runs locally on a user’s phone andcan provide highly useful real world data traces that we canuse in our platform, our platform is fully distributed and canrun different applications and contexts.TKN Wireless Indoor Sensor network Test-

bed (TWIST) [22], developed by the TKN at the TU Berlin,is one of the largest academic testbeds for experimentingwith WSN applications at indoor deployment scenarios.It provides basic services like node configuration, network-wide programming, out-of-band extraction of debug dataand gathering of application data. It also presents severalnovel features such as active control of the power supplyof the nodes. The testbed in [11] uses setup which con-sists of 102 TmoteSky nodes operating at 2.4 GHz and102 eyesIFX nodes at 868 MHz resulting in a fairly regu-lar grid deployment pattern with an inter-node distance of3 m. The Virginia Tech COgnitive Radio NEtwork Testbed(VT-CORNET) [21] is a collection of cognitive nodesdeployed in a building on the Virginia Tech campus. Thetestbed consists of a total of 48 static SDR nodes based on

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USRP210, located at the ceiling. In addition to the staticnodes, low-power mobile nodes are also available in orderto provide an environment that accommodates a wide varietyof research topics. All devices used in this testbed are basedon SDR and are not suitable for WSNs because of their highpower consumption. Despite their possibilities for frequencymobility, the solution implemented by this test-bed does notsupport CWSN implementation. Both [21] and [22] focuson the network layer communications and do not considerother IoT (middleware) layer and different application anddata types which MODiToNeS includes.While there has been extensive research and standard-

isation work being done in the areas of verification andvalidation for product lifecycle for different applicationareas [34], [35], in our paper we follow general guidelinesfor the physical prototyping which has been identified as anopen research problem for the intelligent mobile opportunis-tic research community and complex network protocols andsystems they aim to propose [8], [34]. Physical testing isstill an expected industry practice, frequently linked to prod-uct certification. Moreover, as we target complex systemsmodelled with complex temporal networks assuming likelyloss of connectivity and data, physical prototyping generatesvaluable knowledge and data that can be utilised to enhancethe design of future products or variants.

III. MOBILE OPPORTUNISTIC DELAY/DISCONNECTIONTOLERANT NETWORKS AND SYSTEMSPLATFORM (MODiToNeS)A. OVERVIEW OF THE MODiToNeS PLATFORMWepropose a novel platform,MODiTOnNeS,which is highlysuitable for fast prototyping of applications that are dis-tributed, cognitive (context-aware), intelligent (able to usevarious on demand self-organised and adaptive routing andmachine learning algorithms), interactive and driven by realworld connectivity and application traces. MODiToNsS plat-form contains five programmable layers for dynamic andon demand control including: 1) cognitive/smart hardwaredevices which are able to store and process real time data andare equipped with different heterogeneous sensors and differ-ent types of types of communication interfaces; 2) topologycontrol plane to enable rich diversity of mobile and fixednetwork topologies; 3) control plane for enabling differentintelligent routing protocols; 4) control plane to enable dif-ferent real time analytics and machine learning protocolswhich are suitable for different applications and 5) interac-tive real time user dashboard to allow user interaction andnotifications for different application types. This architectureis shown in Figure 1. We argue that it is important to enabledifferent control interfaces for different layers in order toenable a more complete and useful platform that promotesopportunistic disconnection tolerant networking and mobileedge computing research which is fundamental for pervasivecomputing, IoTs and CPSs research and services.While MODiToNeS draws inspiration from the work such

as Castalia [2], it focuses on different set of properties as

FIGURE 1. Overview of the layered architecture of MODiToNeS.

we target cross layer design, opportunistic smart communi-cation protocols in challenged networks and enabling highlevel analysis and visualisation accessible to the user or atthe edges. We provide a modular and simple open sourceimplementation (inspired by ONE [18]) for topology con-trol and monitoring, resources monitoring and analysis anddata monitoring and visualisation. Each of our smart nodeshas multiple communication interfaces which can be pro-grammed dynamically with different parameters to start orstop as well as to control and monitor different wired orwireless active channels which communicate different typesof sensor and user application data.MODiToNeS provides thedeveloper with simple open source functionality to change thedefault interface used to send data on demand as well as tothe change the active channel on the fly. In addition, our plat-form allows running of different real world and connectivitytraces in real time which can be mobile, static or hybrid net-works. We assume that connectivity traces are in accordancewith the syntax of the StandardEventsReader format usedin ONE. MODiToNeS also allows programmable topologieswhere the user can program the dynamic connectivity amongthe nodes. Our approach allows testing of various protocolsagainst multiple real world conditions by allowing real worldtopology information retrieval which is fed to the platformin order to mirror any real world topologies. We enable

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automated reconfiguration of the platform such as experi-ment repetitions with controlled parameter changing so thatour approaches can be tested against multiple real-life con-ditions. We also allow a user to in a simple way deploydifferent routing algorithms on the distributed MODiToNeSnodes to support various routing behaviour and topologies.Overview of the layered architecture of the platform is shownin Figure 1.Each MODiToNeS node is a low cost single-board com-

puter (Raspberry Pi model B) which provides good process-ing power, flexible storage, and has good software support.It can be integrated with a number of wired and wireless sen-sors using GPIO, I2C, RF modules, 802.11 Wifi, Bluetoothand USB. We currently have over 80 Raspberry Pi nodes.Figure 2 shows one Raspberry PI node which uses on-boardEthernet port, an 802.11n Wi-Fi dongle for wireless networkconnectivity, and uses IBR-DTN [3], [26] to provide P2PDTN capabilities. Figure 3 shows one hierarchical deploy-ment of over 20 Raspberry PIs with different low sensors suchas wireless temperature sensor, pressure sensor, magnetome-ter and 3-axis accelerometer.

FIGURE 2. DTNPi with 801.11 adapter and XRF receiver for wirelesssensors.

FIGURE 3. A hierarchical architecture Raspberry PI sensor networkexample.

B. MULTI-LAYER CONTROL PLANESThere is a growing need for generic network platforms thatcan combine real-world delay and other challenging networkconditions with the flexibility of simulators to support arange of different application domains. MODiToNeS enables

significantly lower time between the early prototype and pro-duction system deployment. Using Raspberry PIs (or similarhardware) allows having large number of hardware nodes ina relatively small physical space at a low cost. For example,influence of mobility on systems performance is complexand is usually evaluated in simulator environments. Contraryto this, our MODiToNeS platform enables the integration ofmany different distributed mobility patterns.We have had several diverse projects which benefited from

the design and deployment of a generic MODiToNeS plat-form that satisfies the following requirements:

• Allowing the coexistence of multiple independentprojects (e.g. nodes 1-17 for Project A, nodes 20-50 forProject B)

• Allowing experimental orchestration in terms of deter-mining how many run to have, selecting senders andreceivers, determining messages rates and sizes, deter-mining which protocols to run.

• Allowing for change of network environment within thescope of a single project to simulated different networktopologies for different simulation runs (e.g. nodes 1-5communicate with nodes 6-10, even ID nodes commu-nicate with odd ID nodes)

• Allowing for real-time change of network topologyemulating DTN [1], [27] or MANET where nodes cancome in and out of contact with each other within thespan of a few seconds.

We address these requirements by proposing real-timeprogrammable interface for network topology configura-tion. This control plane is used for fast automated and pro-grammable configuration of the network plane. The networklayer consists of the head node and variable number of workernodes directly connected e.g. via a wired Ethernet switch(see Figure 3, Figure 4 and Figure 5). The existence ofthe head node is fundamental for allowing integrated andholistic view of the design in order to be able to validatein an integrated manner. We have designed, developed anddeployed a set of tools on the head node that allow real timeconfiguration changes, command executions, as well as run-ning and deploying distributed smart protocol modules (suchas FDASS [10], MWCC [11], CogPriv [4], CafeREP [17],etc) to all nodes. All tools are based on PERL, C/C++,PYTHON and IBR-DTN suitable for our low cost hard-ware (Raspberry PIs). We use open source PNP4Nagios thatvisualise RRD files generated by sensor readings and othertime series data. Through combination of deploying differ-ent routing and content dissemination protocols as well asdynamic/programmable firewall configurations to all nodes,the network topology can be changed and certain nodes canbe included or excluded from it. For example configuring anode firewall to drop all incoming and outgoing packets willmake this node ‘‘invisible’’ to all other nodes. By changingfirewall rules we can achieve any traditional network topolo-gies (such as tree, star or full mash) or any complex temporalnetworks with our platform (by reading connectivity tracefiles from http://uk.crawdad.org which is a shared wireless

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FIGURE 4. (a) Algorithm for the control planes in the Master node.(b) Algorithm for a Worker node.

network data resources for the research community or gen-erating and capturing similarly formatted connectivity filesfrom other real work devices). We can also exclude a nodefrom the sensor topology while a particular experiment isin mid-run for cases when we want to test a node fault anddisconnection. Similarly, we enable real time configurationof different topologies across different experiments for vari-ous prototypes. For static topologies, the configured networktopology remains the same for the duration of the individualtrail. We use a simple format to design and describe thedesired network topology. It describes the tuples connectivitythat comprises the network topology as well as contains theconnection characteristics between the connected tuples asshown below:TimeStamp:Address1:Address2:<UP|DOWN>

[,RATE,DELAY,LOSS]

An example connectivity line describing only connectivityis given:0:10.0.10.2:10.0.10.17:UP

FIGURE 5. MODiTONeS topology used in the performance tests formanufacturing scenario.

This executes the below firewall and traffic rate configura-tion commands:For Node 10.0.10.2iptables -A INPUT -s 10.0.10.17 -j ACCEPT

iptables -A OUTPUT -d 10.0.10.17 -j ACCEPT

For Node 10.0.10.17:iptables -A INPUT -s 10.0.10.2 -j ACCEPT


An example connectivity line describing connectivity withnetwork characteristics is given below:0:10.0.10.2:10.0.10.17:UP:rate 2Mbit,delay

150ms,loss 7%



iptables -t mangle -A POSTROUTING -d

10.0.10.2 -j CLASSIFY -set-class 1:100

tc class add dev eth0 parent 1: classid

1:100 htb rate 2Mbit

tc qdisc add dev eth0 parent 1:100 handle

100: netem delay 150ms loss 3%



iptables -t mangle -A POSTROUTING -d

10.0.10.2 -j CLASSIFY -set-class 1:100

tc class add dev eth0 parent 1: classid

1:100 htb rate 10Mbit

tc qdisc add dev eth0 parent 1:100 handle

100: netem delay 150ms loss 3%

In addition to static topologies, MODiToNEs alsosupports dynamic topology configuration when the con-figured network topology is expected to change withinthe duration of the individual trail. We can use this toemulate DTN [1] and MANET network environments

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and run prototype experiments with real-world connectivityand data dissemination traces like Infocom [19], Rollernet[7], [9], [28] etc. We provide a mechanism that allows us toupdate the network configuration by using the above men-tioned connectivity/network topology format where entriesreflect changes in chronological order:TimeStamp:Address1:Address2:

<UP|DOWN> [,RATE,DELAY,LOSS]

7:10.0.10.2:10.0.10.17:UP

128:10.0.10.2:10.0.10.17:DOWN

or14:10.0.10.2:10.0.10.17:UP:rate 2Mbit,

delay 150ms,loss 7%

58:10.0.10.2:10.0.10.17:DOWN

Figures 4a and 4b give an overview of the pseudo codeof the master MODiToNeS node and distributed workingMODiToNeS nodes respectively.

IV. SMART MANUFACTURING SCENARIOSA. PROTOTYPING FAULT AND DISCONNECTION AWARESMART MANUFACTURINGWe describe the hardware, software and algorithms we useto prototype smart manufacturing sensor network that wehave used in smart manufacturing project EU Selsus [12] andfacilitate real world deployment of complex architectures andcommunication protocols.We describe the design and implementation for a sensor

network prototype in MODiToNEs that can reproduce a pro-duction floor sensor network environment and emulate vari-ous sensor network topologies and communication patternsthat we then integrate within the EU SelSus project [12].We assume that we have smart sensing nodes which oper-ate as MODiToNeS nodes and provide sensing, compu-tation, storage and communication together with allowingself-configuration, fault tolerance and self-monitoring. TheMODiToNeS platform allows development and evaluationof different novel and benchmark protocols FDASS [10],Prophet [24] and Epidemic/Flooding protocols [23] intendedfor use with smart sensors.A common and widely used production environment

monitoring sensor network topology is a tree topology ofdepth 2 – the lowest layer of nodes including heteroge-neous sensor nodes, the middle layer including gateways/aggregators/processors and the top layer referring to thehead/cloud/master node [30]–[34]. We build and demon-strate a logical depth 2 tree sensor network topology inMODiToNes comprising of four sensor nodes, two aggrega-tor nodes and one central cloud node (shown in Figure 5).The MODiToNeS sensor nodes are equipped with a range

of sensors including directly attached camera sensor, MEMSSensor Evaluation Board with Low power 3D magnetometer,3-Axis digital accelerometer, temperature / high precisionpressure sensor, and a remote ANT+ protocols compatiblesensor. The MODiToNeS sensor nodes are unable to detecteach other’s’ presence in the network and are only able to

communicate with the two MODiToNeS aggregator nodes.Each MODiToNeS aggregator node is able to communi-cate with the all sensors nodes and the central MODiToNeScloud node. The MODiToNeS aggregators are also unableto detect each other’s presence on the network. The cen-tral MODiToNeS node is able to communicate with any ofthe MODiToNeS aggregators. We assume that, during nor-mal operation of the MODiToNeS sensor node, it capturestheir corresponding sensors’ readings as well its real timelocal resources utilisation (including CPU load, memory,disk usage, I/O) at configured time intervals. Each node isable to store the measurements locally to be available forlocalised queries and also generates simple format messageswith sensor measurements which are sent to the central cloudnode. The only neighbours that anyMODiToNeS sensor nodedetects are the two MODiToNeS aggregators. The individualMODiToNeS sensor nodes can transmit their sensor mea-surements messages to any of the two MODiToNeS aggre-gators but in normal operation they ‘‘prefer’’ their localMODiToNeS aggregator. When a MODiToNeS aggregatorreceives a MODiToNeS sensor reading message, it storesthe sensor measurements locally to be available for localisedquery and also forwards the messages directly onto thecentral MODiToNeS cloud node. The MODiToNeS aggre-gator nodes also forward resource measurements to theMODiToNeS cloud node in the same way the MODiToNeSsensor nodes do. Each MODiToNeS aggregator stores thesensor measurements of its belonging sensor nodes and canprovide them if queried locally or remotely. In this way, thecentralMODiToNeS cloud node receivesmeasurements fromall MODiToNeS sensors within the sensor network includingresource utilisation readings (Fig 6 and Fig 7). All sensorreadings are being stored in RRD format as this format is wellsuited for time-series data like network bandwidth, tempera-tures, CPU load, etc. The data are stored in a circular bufferbased database, thus the system storage footprint remainsconstant over time. Note that this is distinct from the tradi-tional concept of round-robin scheduling. Readings for sep-arate sensors get allocated their individual RRD databases.Each Raspberry Pi node is running a web service that allowsreal-time queries of sensor states and reading as well ashistorical information and visualisation via PNP4Nagios.Figure 6 and Figure 7 show long termfile system utilisation

and long term memory utilisation for a MODiToNeS node.We observe that memory utilisation is firmly below the full

FIGURE 6. Long term file system utilisation for a MODiToNeS node.

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FIGURE 7. Long term memory utilisation for a MODiToNeS node.

utilisation and that the file system is not over-utilised as it getsmonthly archiving of experiment log to external long termstorage.

B. ROUTING PROTOCOL EVALUATIONIn order to better understand simulated performance offault aware disconnection tolerant smart sensor networkprotocol (FDASS) [10], we prototype and test FDASS againstFlooding and Prophet protocols in MODiToNeS. We havedeveloped an intelligent framework that aims to improve reli-ability of the manufacturing plant in the face of varying net-work connectivity and non-uniform distribution of differenttypes of faults in the network. Fault and Disconnection AwareSmart Sensing framework (FDASS) [10] is able to detect andidentify misperforming nodes in a fully distributed fashionin order to isolate them, reroute the traffic away from themand notify the sinks about the type, location and time of thefailures. FDASS builds on and extends multi-path transportapproaches to combine fault analytics layer with the complexnetwork topology and resources analytics layer into a com-plex heterogeneous network for manufacturing environmentsas shown below. As all MODiToNeS nodes run IBR-DTNon Raspberry PIs, we implemented the FDASS protocol asa IBR-DTN routing component written in C++. IBR-DTNalso includes other benchmark protocols (such as Epidemicand Prophet). This allows direct performance comparisonbetween different protocols in a real world environment withMODiToNeS.Each MODiToNeS node was augmented with a sensor

simulator capable of generating varying numbers of pseudorealistic sensor readings on demand. The simulated sensorreadings were chosen over real sensors to increase the diver-sity of sensing ranges and frequencies [31]–[33] compared tothe available low cost sensor types we have. The simulatedsensor readings were padded to 100 bytes to ensure eachreading had a consistent size. Each sensor reading was alsotimestamped as it was taken with millisecond precision. Thehead node also timestamped the bundles, with millisecondprecision, as they were received. These timestamps were usedto calculate the time taken for the bundle to propagate throughthe network.Each experimental prototype run lasted 60 minutes with

each sensor being polled once every second. Between eachrun the number of sensors per sensor node was increased byone until ten sensors per node was reached. All nodes were

FIGURE 8. (a) Delivery success with increasing numbers of sensors.(b) Delivery delays with increasing numbers of sensors.

rebooted between each run to ensure the nodes were in aknown state. Each experiment was repeated three times andthe averages of these three runs were recorded.Figure 8a shows the recorded bundle delivery success rate

achieved and Figure 8b shows the bundle delivery delayobserved as the number of sensors per sensor node increasesfrom one to ten. Figures 8a and 8b show that FDASS outper-forms both the Flooding and Prophet protocols.Figure 9 and 10 demonstrate FDASS robust functionality

in MODiToNeS by emulating faults of the MODiToNeSaggregators. Consider MODiToNeS Aggregator 2 fails firstby losing network connectivity. We observe in the Figure 9

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FIGURE 9. View of two MODiToNeS aggregators messages during failuredisconnections and recovery.

FIGURE 10. Centralized view of messages hops, delays, and deliverysuccess.

that all sensor messages get redirected via MODiToNeSAggregator 1. After a few minutes, MODiToNeS Aggre-gator 1 loses network connectivity as well. At this stagethere is no route between the MODiToNeS sensor nodes andthe central MODiToNeS node. During this time, all sensorreadings are being stored by the sensor nodes where theyare generated. After another few minutes both MODiToNeSAggregators recover their connectivity and we observe thepeak in traffic generated due to the instantaneous delivery ofall stored sensor readings.We aim to deploy MODiToNeS Raspberry PI nodes with

FDASS in the real world manufacturing shop floors to enablefurther validation with real users and improve reliability ofdiverse factory communications.

V. MOBILE CLOUDS SCENARIOA. PROTOTYPING MOBILE CLOUDS OVERVIEWAs an example of a different architecture that can be built andtested in MODiToNeS platform, we describe the design ofpredictive mobile clouds where mobile sensing and real time

predictive analytics algorithms are incorporated in dynamicmobile clusters of MODiToNeS nodes. Each smartMODiToNeS platform node allows intelligent real time deci-sion making that can predict (and change) the behaviourof the network communication of itself and other nodes’.Even though machine learning and analytics techniques havebeen widely recognised as important for context predictionin mobile computing and many theoretical and simulationbased works exist, real world implementations are still scarceand remain interesting future research challenge [8]. Themobile cloud (MC) prototype over MODiToNeS examplewe describe here supports new paradigm shift that combinesanticipatory systems [8] and adaptive collaborative propos-als [e.g. 17,20] where computer devices base their actionson the predictive models of themselves, the environmentand the other nodes. MODiToNeS support consideration ofmultiple criteria including different complex temporal graphscentrality predictions as well as resource, movement andbehaviour predictions.We viewmobile clouds (MC) as new approach that bridges

the gap between the device(s), environments and the user.In MODiToNeS platform, the prototype of each MC isequipped with a range of sensors (accelerometer, gyroscope,temperature, pressure, heart rate sensor) that can sense theenvironment and monitor the context, as well as run real timepredictive analytics (or other machine learning) algorithmsto develop models that predict occurrences of various events.Our MODiToNeS MC also allows rich real time interactionwith the users as well as sharing among MCs over differentintelligent protocols, different applications and data types.Additionally, each MODiToNeS MC is able to interact withthe environment and can adaptively change its behaviour indifferent situations.Of particular interest in this platform is to investigate the

performance characteristics of our MC smart data commu-nication algorithms in the face of different users’ require-ments for privacy in different contexts. In [11], we describe aMobile Wellbeing Cloud Companion (MWCC) testbed pro-totype which is able to continuously process accelerometerand gyroscope from the physical environment and processthe readings using various machine learning algorithms toidentify several user activity features. These are analysed inreal time and correlated with the heart rate signals to identifyif the heart rate is normal or not for the current user activity.In [4], we proposed CogPriv that explored through simula-tions how different levels of privacy can be supported viaadaptively changing network connectivity in both sparse anddense topologies. In this paper, we build CogPriv prototypein MODiToNeS and test it both in terms of quality of theexperience metrics (such as achieved end-to-end privacy anddelays) and the quality of service metrics (such as memory,I/O, CPU with resource limited devices.).CogPriv considers users who may be running a social

network that allows them to stay in contact with their friendsat the same time as regularly monitoring their long termmedical condition and being in contact with the hospital.

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These two types of applications have different privacyrequirements and need their data to be stored and sharedin different ways in order to adapt to each required privacyrequirements dynamically. Figure 11 shows example sensorintegration for mobile personal cloud (MPC) prototype inMODiToNeS on a Raspberry Pi with an Xtrinsic sensor boardwith temperature, pressure, and acceleration sensors.Figure 12 shows MODiToNeS Raspberry PI device that cap-tures, stores and processes a range of user and environmentdata such as heart rate and pedometer.

FIGURE 11. MODiToNeS Raspbery Pi B with a Xtrinsic sensor board and aWiPi wireless adapter.

FIGURE 12. MODiToNeS Raspberry Pi with Suunto and WiPi USB module,Garmin heartrate sensor and a smaprtphone displaying readings.

CogPriv inMODiToNeS extends the bundle protocol basedon RFC 5050 [25], [26] that provides API for DTN applica-tions to exchange and route bundles among distributed nodesin an intelligent P2P manner. CogPriv P2P DTN (IBR-DTN)module in MODiToNeS provides multi flow real time bundleforwarding based on a range of criteria such as source ID,Virtual Machine (VM) ID, application privacy requirements,destination ID so that different incoming bundles can bematched to the appropriate network interface in real time.At its core, CogPriv comprises multiple stages: it probes

local cellular network to identify the likelihood of any middleboxes that may compromise user traffic, requests the remotedestination nodes to provide their estimations of the cellu-lar network privacy levels, and collaborates and cooperateswith the local network nodes to determine the best localnext hop. CogPriv routing protocol can range dynamicallyand adaptively from providing fully cellular single hop endto end communication to fully localised multi hop mobileopportunistic communication. Through collaborations andcooperation in the local neighbourhoods, each node canunderstand its environment and neighbours better. Morespecifically, each CogPriv MODiToNeS node exchangestheir own cellular network privacy statistics and predictionsto negotiate feasibility of using cellular network for the par-ticular application, analytics of their own resource predic-tions and social connectivity analytics. Note that both socialconnectivity traces and middle boxes information are fed tothe MODiToNeS master node from external real world traces(e.g. utilising http://uk.crawdad.org/). In this paper, we showmeasured achieved end-to-end privacy, end-to-end delays,end-to-end number of hops and transitions, I/O, memory andCPU costs. Each CogPriv MODiToNeS node privacy level isimportant to consider as it is the core criteria for forwardingthe data and deciding on the next hop and via which interface.More detailed description of CogPriv Decision Algorithm isdescribed in [4].

B. COGNITIVE PRIVACY EXPERIMENT SCENARIOWe carry out evaluation of CogPriv in MODiToNeS againstfully cellular communication and fully local social oppor-tunistic networks across a range of different network condi-tions and user traffic types using a range of metrics. We showhow data can be shared with different levels of privacy inlight of untrusted infrastructure. We use findings identifiedin [14] and [15] that show widespread use of transparentmiddle boxes such as HTTP and DNS proxies in the cellularinfrastructure which are able to analyse and actively modifyuser traffic and thus compromise user privacy and security.In [4] we provided rich set of simulation based experimentswith real world traces of middle boxes [14], connectivity [7],interests [7] and friendships [7]. This paper addresses thesescenarios and proposes a way of integrating different layerswithin our MODiToNeS platform and exploring how differ-ent intelligent routing can exploit maximally trusted routesbased on the real time probes and collaboration with theMODiToNeS nodes that may be infrastructure nodes or fullyad hoc local nodes based on the local context sensing.We base our deployment on the real-world data traces of

different probes for mobile networks across 112 countriesand over 200 mobile providers obtained by netalyzrin [14] and [15].We select traces fromGermany as its numberof mobile networks providers best suits our real world usercommunication trace [7]. For every mobile node we obtainthe probability for the network spying on the web trafficby calculating the percentage of tests returning positive vsthe total number of tests performed. For every mobile

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network, we obtain the probability of it spying on web trafficby averaging the values obtained by all individual mobilenodes on this particular network. Based on the real cellularnetworks in Germany, we average privacy levels into fiveevenly distributed privacy threat levels .e.g. minimum (0%)such as ALICE and NETZCLUB, low (25%) such asM-NET,medium (50%) such as BASE, MEDION , high (75%) suchas CONGSTAR, maximum (100%) such as FYVE.While in our previous work, we developed extensions to

the ONE simulator [18] that utilise this data in order to returnmiddle boxes presence probability discovered when perform-ing probing of different cellular networks, in this paper wefeed this data toMODiToNeS to drive different testbed nodes’behaviour (to act as middle boxes or not). To enable dynamicreal world physical connectivity (and disconnections) amongMODiToNeS platform nodes, we drive theMODiToNeS fire-wall configuration for each MODiToNeS testbed node withthe of real world Facebook connectivity traces [7] duringthe whole time of the experiments. We range the privacylevels of the data being transmitted starting form maximumto minimum privacy requirements with three intermediarylevels. We run 5 randomly selected combinations of sourcesand receivers for each cellular network privacy level.

FIGURE 13. End-to-end privacy.

1) RESULTS

Figure 13 shows that end to end privacy levels remain higherfor MODiToNeS CogPriv approach than for cellular only andmobile social ad hoc communication independently of thelevel of presence of middle boxes in the cellular infrastructurei.e. ranging from no middle boxes to wide range of middleboxes, the performance of cognitive privacy drops from 100%privacy level to 80%. This is in contrast with the cellularnetwork which drops end to end privacy linearly with theamount of the middle boxes in the cellular network.

MODiToNeS CogPriv approach also outperforms fully localsocial ad hoc approach because the delays that are associatedwith the bundles time out and invoke the nodes to utilisecellular infrastructure that may have privacy leaks.

FIGURE 14. End-to-end number of hops.

Figure 14 shows statistical analyses of MODiToNeSCogPriv number of hops with increased number of middleboxes in the cellular architecture. We observe that the num-bers range between 1 and 4 across all levels of middle boxespresence.Figure 15 shows that MODiToNeS CogPriv delays

increase slowly until the infrastructure is fully compromisedat which point the delays become the same as the local adhoc approach. The cellular network approach has the lowest

FIGURE 15. End-to-end delays.

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delays but this is due to privacy being compromised and thetraffic taking single hop (direct) cellular link between the endnodes.Figure 15 shows delay distributions for highly private

traffic bundles when the cellular infrastructure contains dra-matically different amount of middle boxes. We observethat the delays are the lowest when the infrastructure is notcompromised as the MODiToNeS CogPriv approach takescellular single hope router to the destination. AsMODiToNeSCogPriv discovers increasing number of middle boxes inthe cellular networks, the delays will increase but still besignificantly lower than the local ad hoc approach. Eventhough there are some bundles that may take up to 27 minutesuntil 60% of surveillance of the cellular network overMODiToNeS, the average still remains low and below11 minutes. For the cellular network where there is 80%to 100% of middle box presence, the delays range from45 minutes to 79 minutes. These sorts of delays are appro-priate for non-emergency applications where the users valuetheir privacy and can tolerate delays such as regular dailychecks for users with long-term medical conditions.

FIGURE 16. End-to-end number of transitions.

In Figure 16 we show the number of transitions betweeni MODiToNeS nfrastructure and MODiToNeS local ad hocwhen the security of the cellular network decreases. It isinteresting to see that while the number of hops is relativelylow (reaching 4 for highly compromised cellular networks),up to 50% of these hops are transitions between the infras-tructure and local communication. This shows that supportingadaptive transitioning between infrastructure and local com-munication is highly beneficial.The previous figures have shown that delays and hop by

hop counts increase as MODiToNeS CogPriv moves adap-tively from fully cellularmode to the fully opportunisticmodewhile managing very high levels of end to end privacy. Morespecifically, we show that theMODiToNeS CogPriv achievesprivacy of end to end connections which is almost constant

FIGURE 17. Short and long term node resource utilisation visualisation.

while neither the delays nor the hop count is significantlyincreased.Figure 17 shows short term and long term CPU load,

memory usage and IO usage forMODiToNeSCogPriv nodes.We observe that, despite complex algorithm and low

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resources devices, MODiToNeS CogPriv memory usageremains firmly under the full usage. CPU load is in the lowerhalf of the total CPU utilisation for the majority of timewhile IO at the critical level for the majority of time (notethat this critical level has been administratively assigned tobe 2 K/sec).

VI. CONCLUSIONS AND FUTURE WORKWeproposed a novel platformMODiToNeS that supports realtime multi-layer and multi-dimensional communication andanalysis distributed architectures which can combine variousaspects of smart mobile social, transport and other CPS sys-tems with the particular focus on testing real world novelreliable and intelligent communications among potentiallylow resourced devices.We envisage increasing need for complex systems of

devices including vehicles, humans and infrastructure.Withinsuch systems, various communication paradigms need to besupported including the following: ad hoc communicationamong people, among vehicles (vehicle to vehicle), com-munication between vehicles and infrastructure (vehicles toroad side units and vice versa), human and the vehicle (vehi-cle notifying and guiding the driver as well as the driverproviding on the fly information that can potentially dif-fer from the vehicles information) and human and com-pany/home/hospital (human sharing information about theirtrip/health and getting information or instructions back).In this context, MODiToNeS platform can support the con-cept of Internet of Things joined with the concept of Internetof vehicles or mobile social networks representing futuretrends of smart transportation and mobility applications. Cur-rent research and services typically allow central remote realtime monitoring of various information while MODiToNeSallows users to interact in real time with the prototypes where,query and add additional information on any unexpectedevents. MODiToNeS builds on and extends existing researchto develop a prototype distributed system which allows richinteractivity with the end user and real time localised ana-lytics and predictions as well as remote data communicationfor non real time analysis. Capturing diverse collection ofinformation locally (which can include any environment andcontext data), providing real time data analysis and predic-tion which is visualised and fed back to the users is keyfor increasing reliability and efficiency of communicationin such environments. We envisage that MODiToNeS willplay an important role when integrating and testing humanbehaviour in the design and development of Cyber PhysicalSystems in mobile social, mobile health care and vehicularnetworks for critical safety applications.

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MILENA RADENKOVIC received the Ph.D.degree from The University of Nottingham, U.K.and the Dipl.-Ing. degree from the Universityof Nis, Serbia. Her research spans the areas ofmobile, delay and disconnection tolerant networksand services, intelligent P2P multimedia systems,mobile clouds, intelligent security and privacy, andtheir applications to different application domains.She has been the Principle Investigator of severalEPSRC and EU grants. She currently works on

a number of open research questions such as improving reliability andefficiency of mobile social and vehicular networks, designing new intelligentdata routing protocols, improving energy efficiency of continuous large scaleremote monitoring, and new adaptive security and privacy techniques fordata transmission in such environments. She has organized and chairedmultiple ACM and IEEE conferences, served on many ACM and IEEEconference program committees. She has been an Editor and a Guest Editorof many premium journals and published scientific papers in many premiumvenues including the IEEE TRANSACTIONS on VEHICULAR TECHNOLOGY, theIEEE TRANSACTIONS on PARALLEL and DISTRIBUTED COMPUTING, the ElsevierAd Hoc Networks, the ACM CHANTS/Mobicom, the IEEE Multimedia, theMIT Press PRESENCE, and the ACM Multimedia. She has authored oneinternational patent on real time saleable signal processing in the Internet andacted as a Scientific Expert for EU European Commission and EngineeringPhysics Scientific Research Council U.K. for over ten years.

JON CROWCROFT (F’04) received the degree inphysics from Trinity College, University of Cam-bridge in 1979, and theM.Sc. degree in computing,and the Ph.D. degree fromUCL, in 1981 and 1993,respectively. He has been the Marconi Professorof Communications Systems with the ComputerLaboratory since 2001. He has worked in the areaof Internet support for multimedia communica-tions for over 30 years. Three main topics of inter-est have been scalable multicast routing, practical

approaches to traffic management, and the design of deployable end-to-endprotocols. Current active research areas are opportunistic communications,social networks, and techniques and algorithms to scale infrastructure-freemobile systems. He is a fellow the Royal Society, a fellow of the ACM, afellow of the British Computer Society, and a fellow of the IET and the RoyalAcademy of Engineering.

MUBASHIR HUSAIN REHMANI (M’15–SM’16) received the B.Eng. degree in computersystems engineering from the Mehran Univer-sity of Engineering and Technology, Jamshoro,Pakistan, the M.S. degree from the Universityof Paris XI, Paris, France, and the Ph.D. degreefrom the University Pierre and Marie Curie, Paris,France, in 2004, 2008, and 2011, respectively.He is currently an Assistant Professor with theCOMSATS Institute of Information Technology,

Wah Cantonment, Pakistan. He was a Post-Doctoral Fellow with the Uni-versity of Paris Est, France, in 2012. His research interests include cognitiveradio ad hoc networks, smart grid, wireless sensor networks, and mobilead hoc networks. He served in the TPC for the IEEE ICC 2015, the IEEEWoWMoM2014, the IEEE ICC 2014, theACMCoNEXTStudentWorkshop2013, the IEEE ICC 2013, and the IEEE IWCMC 2013 conferences. He iscurrently an Editor of the IEEE COMMUNICATIONS SURVEYS and TUTORIALS

and an Associate Editor of the IEEE Communications Magazine, the IEEEACCESS, the Computers and Electrical Engineering (Elsevier), the Journal ofNetwork and Computer Applications (Elsevier), the Ad Hoc Sensor WirelessNetworks, the Wireless Networks (Springer) Journal, and the Journal ofCommunications and Networks. He is also serving as a Guest Editor ofthe Ad Hoc Networks (Elsevier), the Future Generation Computer Systems(Elsevier), the IEEEACCESS, thePervasive andMobile Computing (Elsevier),and theComputers and Electrical Engineering (Elsevier). He is the FoundingMember of IEEE Special Interest Group on Green and Sustainable Network-ing and Computing with Cognition and Cooperation. He received the cer-tificate of appreciation, an "Exemplary Editor of the IEEE COMMUNICATIONS

SURVEYS and TUTORIALS for the year 2015" from IEEE CommunicationsSociety.

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