Information-Centric Semantic Web of Things · M. Ruta, F. Scioscia: Information-Centric Semantic Web of Things human-objects interaction [32]. Wireless sensor networks (WSNs) monitor

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Open Access

Open Journal of Internet of Things (OJIOT)Volume 6, Issue 1, 2020

http://www.ronpub.com/ojiotISSN 2364-7108

Information-Centric Semantic Web of ThingsMichele Ruta, Floriano Scioscia

Department of Electrical and Information Engineering, Polytechnic University of Bari,Via Orabona 4, I-70125, Bari, Italy, {michele.ruta, floriano.scioscia}@poliba.it

ABSTRACT

In the Semantic Web of Things (SWoT) paradigm, a plethora of micro-devices permeates an environment. Storageand information processing are decentralized: each component conveys and even processes a (very) small amountof annotated metadata. In this perspective, the node-centric Internet networking model is inadequate. Thispaper presents a framework for resource discovery in semantic-enhanced pervasive environments leveragingan information-centric networking approach. Information gathered through different Internet of Things (IoT)technologies can be exploited by both ubiquitous and Web-based semantic-aware applications through a uniformset of operations. Experimental results and a case study support sustainability and effectiveness of the proposal.

TYPE OF PAPER AND KEYWORDS

Regular research paper: Semantic Web, Pervasive computing, Service discovery, Information-centric networking,Peer-to-peer protocols

1 INTRODUCTION

The Semantic Web of Things (SWoT) should enhancethe Internet of Things (IoT) capabilities and applicationsleveraging the Semantic Web theory and technology. Itaims to embed semantically rich and always-availableinformation fragments into the physical world, byenabling storage/retrieval of annotations in/from tinysmart objects. Traditional Internet-based applications(email, Web browsing, VoIP) adopt a node-centricconversational model since the TCP/IP protocol stackis based on a request/response approach. This modelis not sustainable in pervasive environments, whereuser agents may interact simultaneously with manysurrounding micro-devices. Retrieved informationshould be automatically processed to better supportcurrent activity of a user in an unobtrusive fashion, s/he

This paper is accepted at the International Workshop on VeryLarge Internet of Things (VLIoT 2020) in conjunction with theVLDB 2020 conference in Tokyo, Japan. The proceedings ofVLIoT@VLDB 2020 are published in the Open Journal of Internetof Things (OJIOT) as special issue.

even being not necessarily aware of individual deviceinteractions. Hence, the SWoT vision should be favoredby pervasive knowledge-based systems achieving highdegrees of autonomic capability in information storage,management and discovery, also providing transparentaccess to information sources. From this standpoint,information-centric (a.k.a. data-centric, content-centric)network infrastructures are more suitable, as theyare centered on information resources, and not oncomputer hosts. While research in sensor networks hasalready shown [18] that a content-centric approach ismore effective than a node-centric one for informationexchange in resource-constrained and volatile contexts,this is increasingly true even for the Internet itself.Trends in Internet and Web usage show that whatis being exchanged is becoming more important thanwho are exchanging information [46]. An exhaustivetechnical comparison between node-centric and contentcentric networking is in [20]. In information-centricnetworks, and particularly in dynamic environmentssuch as the SWoT, resource discovery is a pivotal feature.

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Open Journal of Internet of Things (OJIOT), Volume 6, Issue 1, 2020

Nevertheless, current paradigms employ elementary“string matching” to compare requests and encodedresource attributes. This is too simplistic to be useful foradvanced applications. Similarly, in most information-centric networking approaches in literature, retrieval isbased on structured “content addressing” schemes thatare rather arbitrary and need case-by-case agreementsamong involved actors to allow practical interoperability.Ideas and technologies borrowed from the Semantic Webvision may allow to overcome these limitations, sincethe formal Knowledge Representation (KR) foundationsprovide common vocabularies to describe resources andexpress requests.

In this paper a semantic content-centric discoveryframework is proposed, where autonomous objectscan be retrieved, queried and inventoried withoutrequiring a central control and coordination. Aproper dissemination protocol allows to exactly locatesuitable annotated descriptions directly on embeddedmicro-devices attached to items of interest. Theproposed framework includes a peer-to-peer distributedapplication-layer protocol allowing dissemination anddiscovery of knowledge. Information is gatheredthrough different identification and sensing technologiesto be exploited by inference engines and semantic-aware applications, in either pervasive Mobile Ad-hoc Networks (MANETs) or Web contexts, througha uniform set of operations. Mobile nodes capableof extracting information from embedded micro-devices work as cluster-heads w.r.t. resources intheir range. IEEE 802.11, along with IP (InternetProtocol) and UDP (User Datagram Protocol), isadopted as reference network infrastructure. ResourceDescription Framework [40] is the resource annotationlanguage w.r.t. RDF Schema (RDFS) [6] vocabularies,so allowing semantic-based applications to leveragequerying, reasoning and matchmaking tools, basedon formal logics. Noteworthy features of theproposed framework are: (i) peer-to-peer cooperationfor managing environments populated by autonomousobjects in a distributed fashion; (ii) semantic-enabledcontent-centric discovery; (iii) reactive disseminationto quickly locate semantic annotations on micro-devices attached to items in the field; (iv) backwardcompatibility w.r.t. standard identification and sensingtechnologies, thus allowing legacy applications to co-exist with novel ones; (v) dissemination and discoveryintegrated at the application level on top of IP andUDP, in order to preserve compatibility with standardprotocols and equipment for end-to-end routing in theInternet [20]. The feasibility of the approach hasbeen demonstrated by a simulation campaign exploitinga network simulator package assessing effectivenessof metadata dissemination and discovery protocol

architecture. Benefits of the proposed approach areillustrated in a case study for an IoT scenario aboutdisaster recovery.

The remaining of the paper is organized as follows:in the next section motivation and possible applicationscenarios are provided. In Section 3 relevant relatedwork is discussed. Architectural aspects are detailedin Section 4, along with the data propagation andservice retrieval protocol. The subsequent case studyclarifies the framework through an illustrative example.Simulation methods and results are presented andcommented in Section 5 before conclusion.

2 MOTIVATION

Each resource in the semantic-enabled Web -not onlydocuments and services, but any entity of interest suchas people, institutions, common knowledge topics- canbe annotated with metadata, using RDF, w.r.t. an RDFSor Web Ontology Language (OWL 2) [29] vocabulary.Information interlinking produces knowledge graphswhose basic unit is the (subject, predicate, object)statement. URIs (Uniform Resource Identifiers) areused to unambiguously identify both resources andpredicates that relate them. On top of RDF, RDFSand OWL enable KR based on formal model-theoreticsemantics, which allows the use of existing reasoningengines to infer new information from the one statedin the semantic annotations. In the majority ofcurrent applications -see Section 1.5 of [2] for asurvey- Knowledge Representation Systems (KRSs)play a role very much like Database ManagementSystems (DBMSs). Both are used as single fixedentities which are immediately available for informationqueries and updates. This approach is effective onlyas long as large computing resources and a stablenetwork infrastructure are granted. A different strategyis needed to adapt KR tools and technologies tofunctional and non-functional requirements of pervasivecomputing applications as the Internet of Things. Theyare characterized by user and device mobility anddependency on context. Furthermore, severe resourcelimits affect processing, storage, link bandwidth andpower consumption. As a consequence, knowledge-based systems conceived for wired networks arehardly adaptable, due to architectural differencesand performance issues. Anyway, several emergingidentification, monitoring and sensing technologies arenow suitable to connect physical and digital world alsoovercoming heterogeneity and evanescence of mobilead-hoc contexts. Radio Frequency IDentification (RFID)is the most widespread technology for product/objectidentification and tracking, but also for monitoring

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M. Ruta, F. Scioscia: Information-Centric Semantic Web of Things

human-objects interaction [32]. Wireless sensornetworks (WSNs) monitor environmental parameters,supporting both queries and automatic alerts triggeredby application-defined events [5]. Both technologiesare characterized by the dissemination of unobtrusive,inexpensive and disposable micro-devices in a givenenvironment. Due to space, power and cost constraints,they usually have very low storage, little or no processingcapabilities and short-range, low-throughput wirelesslinks. Each mobile host in the area can accessinformation only on micro-devices in its communicationrange. Consequently, approaches based on centralizedcontrol and information storage are utterly impractical.The goal of the framework proposed here is to allowobjects, places, events and phenomena to be easily andthoroughly described by means of semantic annotationsstored within an associated micro-device. Informationabout an object becomes structured and complete; it canfollow accurately the object history, being progressivelybuilt or updated during its life cycle. Tangiblebenefits can be so provided e.g., to the managementof supply chains and of the life cycle of industrialproducts, by improving the traceability of productionand distribution. Manufacturing and quality controlcan exploit accurate descriptions of raw materials,components and processes; supply chain managementcan benefit from improved item tracking; the verificationof multi-factor service level agreements betweencommercial partners can be automated. Furthermore,sale depots could obtain easier inventory managementand could provide u-commerce (ubiquitous commerce)capabilities [48]. Furthermore, smart post-sale servicescan be provided to purchasers, by integrating knowledgediscovery and reasoning capabilities in home and officeappliances [35]. In addition, asset management is greatlyimproved in those scenarios where retrieval shouldbe based on relevant object properties and purposes,rather than mere identification codes. In healthcareapplications, equipment, drugs and patients can bethoroughly and formally described and tracked, not onlyto ensure that appropriate treatments are given, but alsoto provide decision support in therapy management.This is an evident improvement w.r.t. infrastructureslacking support for formal semantics, such as [53].Likewise, in museums, libraries and archaeologicalsites, smart semantic-based content fruition can begranted to local visitors as well as to remote clientsconnected through the Web, leveraging the lightweightinfrastructure already deployed for internal inventoryand research. Finally, Wireless Semantic SensorNetworks are an emerging and challenging paradigm.Advanced solutions can be built for environmentalmonitoring, precision agriculture and disaster recovery,by means of semantic sensory data dissemination and

resource discovery features that are provided by theframework.

To clarify the rationale behind the proposed approachand its benefits with respect to classical resourcediscovery paradigms, this paper adopts a disasterrecovery mission planning case study as a runningexample. When a disaster occurs in urban settings,hostile environments can make search and rescue life-threatening even for experienced human operators.Robot teleoperation has several issues as well:environmental conditions (e.g., low visibility) maymake piloting unfeasible and continuous communicationrequires robust connectivity, which is often unavailablein a calamity. For this reason a team of autonomousrobots is dispatched as a wireless sensor and actornetwork with support for service/resource discovery,comprising both ground and aerial units [12]. The teamis coordinated by a robotic supervisor in contact with thecontrol station where human operators decide missiongoals and monitor progress.

3 RELATED WORK

Building the SWoT requires decentralized andcollaborative middleware, suitable for pervasiveand ubiquitous computing. Several proposals forsemantic mobile middleware infrastructures can befound in literature. Earlier solutions inherited designsfrom common stable networked infrastructures, relyingon centralized brokers for management and discoveryof devices, services and information [7, 28, 44, 14].The infrastructure in [47] for ubiquitous semantic-based resource discovery is related to the proposalpresented here due to the devised decentralizedcollaborative paradigm, but it was based on a directreuse of traditional Semantic Web technologies,particularly SPARQL [43] language for queries andHTTP (HyperText Transfer Protocol) for resourcetransfer. Significant performance overheads and highcomplexity of mobile node implementation are openissues when adopting protocols not optimized forpervasive computing environments.

More recent service-oriented architectures (SOA)for the IoT exploited Semantic Web technologies toannotate data, devices and services through standardvocabularies such as the Semantic Sensor Networkontology of the World Wide Web Consortium [9] andshare produced information as Linked (Open) Data(LOD) [16]. In order to facilitate interoperability andsolution design in SWoT contexts, ontology catalogshave been receiving increasing focus: the Linked OpenVocabularies for Internet of Things (LOV4IoT: https://lov4iot.appspot.com/) repository is among

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https://lov4iot.appspot.com/

https://lov4iot.appspot.com/


the most comprehensive and well-structured efforts [15].Sense2Web [4], Linked Stream Middleware (LSM) [24]and Ztreamy [13] are LOD platforms to publish sensordata, combine them and link them to existing resourceson the Semantic Web. In [45] KR was exploited tosupport automatic sensor composition: functional andnon-functional properties of sensors, as well as users’goals, were described w.r.t. an OWL ontology and anorchestrator combined sensors and processes to satisfya request. The proposal, however, did not outline acomplete system for IoT service discovery, focusing onlyon sensor description and composition methods. In [30],requests expressed in terms of device characteristicsundergo a matchmaking process with ontology-basedsensor descriptions: quantitative attributes and semantic-based reasoning are combined to improve the discoveryand select appropriate sensors through an exploratorysearch. The user-centric task-oriented IoT serviceframework in [23] adopts an ontology-based model ofactivities, user profiles and context. Elementary servicesare pre-filtered w.r.t. context and then composed inprocesses to satisfy user requests; processes are finallyranked via quality of service (QoS) estimation. Theapproach, however, is inherently centralized (requiringan on-premise or cloud-based support infrastructure),possibly limiting performance and manageability inlarge-scale IoT applications. Moreover, the role ofsemantics appears quite limited, with support for exactmatch of functional service attributes only. Theframework in [51] adopts a probabilistic approachinstead, building user-user, thing-thing and user-thingcorrelation models to support recommendation of themost interesting things to interact with for a given userand context.

The above proposals require a dependable Internetconnection and/or a support infrastructure to enableservice/resource discovery features. This impairs theirapplication to ad-hoc networks of resource-constrainedthings. Peer-to-peer overlay network techniques are usedto enable large-scale discovery within heterogeneousnetworks. In such cases, most approaches adopt onlya resource name resolution scheme, not allowing to usearticulate features for discovery, selection and ranking.

In order to improve upon the state of the art, thepresent paper shares core ideas with information-centricnetworking (a.k.a. named data networking, NDN)approaches for general-purpose computer networks.Basically, those approaches move to the network levelsome basic functions of Distributed Hash Tables (DHT),used in overlay networks (e.g., for file sharing) or ofIP-based resource discovery protocols such as ServiceLocation Protocol. Among other proposals, Content-Centric Networking (CCN) [20] stood out for its elegant,practical approach for progressive transition of the

Internet toward the CCN model. In CCN, only twopacket types exist: Interest and Data. They arecharacterized only by a content name field, entirelyreplacing source/destination host address fields. A node,interested in a data chunk, propagates an Interest packetin the network; a Data packet is transmitted only inresponse to an Interest (following the reverse route) and“consumes” that Interest. Further notable features areend-to-end security and the exploitation of the broadcastproperty of wireless channels, which makes the protocolsuitable to wireless ad-hoc networks. Lee et al. [25]analyzed benefits of content-centric frameworks fromthe energy efficiency standpoint, a relevant aspect formobile and pervasive computing. Nevertheless, aspointed out in [46], some open issues remain. Primarily,the unique name of each Data chunk is a binary string,so that only bitwise longest-prefix matching can beused for resource discovery. Furthermore, definingsuch structured names relies on application-specificshared policies, which appears as a limitation to broadinteroperability. Finally, experimental results in [20]and in subsequent work [19] concerned only networkswith very few nodes. Approaches proposed in latestyears explored different design decisions concerningarchitecture, caching, mobility and security in largerexperimental settings, highlighting their benefits andissues [50, 10]. In particular, scalability cannotbe neglected, as experiments reported in Section 5show. Content caching is the primary mechanism toreduce network load, improve availability and increasenetwork scale [52]. The IoT exacerbates scalability andmobility issues significantly w.r.t. conventional computernetworks, requiring novel approaches [1].

Efficient peer-to-peer data dissemination is akey technological issue to make information-centric paradigms successful. In latest years,epidemic protocols, a.k.a. gossip protocols [21],have received significant interest, as they require nonetwork configuration and provide a good trade-offbetween algorithmic complexity and performanceguarantees [22, 49]. They are also robust against highcommunication failure rates and data loss events, acrucial aspect in IoT scenarios [26].

Table 1 summarizes the main features of themost relevant related works w.r.t. the proposedapproach. The proposed approach is the only onecombining a distributed, peer-to-peer architecture forinformation dissemination and discovery based onlow-overhead transport protocols with semantic-basedservice discovery and composition.

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Table 1: Comparative table of information-centric IoT service discovery approaches

Reference Architecture Service discovery Service registration /advertisement

Transportprotocol Notes

GSD [7] –2006 Distributed, P2P

Hybrid (services group IDsand ontology-based servicedescriptions)

Controlled broadcastdissemination UDP

FlUF [28]–2007

Distributed,client/server Syntactic (XML)

Registration to zonedirectory service(“resource index” node)

UDP Multi-agent system

mRDP [47]– 2007

Distributed,client/server

Semantic(SPARQL-equivalent querylanguage over RDF)

None (request multicastonly)

UDP forrequests, TCP(HTTP) forreplies

AIDAS [44]– 2008 Centralized Semantic (matching of

OWL/RDF descriptions)

Registration to zonedirectory service(“context manager”)

UDP Ranking based on preferencepriority

CCN [20] –2009


Syntactic (longest-prefixstring matching)

None (request broadcastand reply caching only) UDP Integrability into current IP

routersSOCRADES[14] – 2010

Distributed,client/server Syntactic (keyword-based) WS-Discovery

(multicast) UDP RESTful service interfaces

Sense2Web[4] – 2010

Centralized (webapplication)

Semantic (SPARQL on RDFdescriptions) Linked Data TCP (HTTP)

Spatial, temporal andontology-based serviceattributes

Colitti et al.[8] – 2011

Centralized(gateway) Syntactic (CoAP)

None (resource changessent in unicast in observemode)

UDP (CoAP) HTTP-CoAP bridging

LSM [24] –2012

Centralized platform(Web application)

Semantic (SPARQL on RDFdescriptions) Registration to platform TCP (HTTP)

Both data sources andcontinuous queries supported asservices

NetInf [10] –2013 Distributed, P2P Syntactic (name-based,

hierarchical)Publish to nameresolution server

Agnostic (TCP,UDP or other) Embedded PKI-based security

Ztreamy[13] – 2014


Hybrid (URI prefix,SPARQL on RDFdescriptions, custom filters)

Publish to server TCP (HTTP) Data stream oriented

CASSARAM[30] – 2014


Hybrid (SPARQL queries,relational filters on contextattributes)

Unspecified TCP (HTTP) Attribute weights based on userpriorities

SoIoT [23] –2016 Centralized platform

Hybrid (ontology-based goaland context model,quantitative QoS attributes)

Register with theplatform TCP (HTTP) Semantic service composition

Yao et al.[51] – 2016

Centralized (Webapplication)

None (probabilistic proactiverecommendation) Web-based interface TCP (HTTP)

Probabilistic service-contextcorrelation, RESTful Webservices

This work Distributed, P2PHybrid (context attributesand semantic matchmakingof OWL descriptions)

Controlled broadcastdissemination UDP Semantic service composition

4 FRAMEWORK

Figure 1 shows the conceptualization of the proposedknowledge dissemination and discovery framework.IP is leveraged for basic addressing and routingin local (typically wireless and ad-hoc) networksand internetworking between autonomous networks(including wide area networks and the Internet).The semantic information-centric internetworking layergrants common access to information provided bysemantic-enhanced embedded devices and sensorspopulating a smart environment.

The framework provides interoperability with mobilead-hoc networks protocols of embedded devicesand sensors to allow the extraction of informationresources from the environment. In order to supportboth annotated information exchange and backwardcompatibility towards current application-level protocolsand interactions, each mobile identification and sensing

Semantic Webapplications

Semantic Webapplications

Remotereasoningservices

Remotereasoningservices

Ubiquitous andpervasive applications

Ubiquitous andpervasive applications

Localreasoningservices

Localreasoningservices

Semantic information-centric internetworkingSemantic information-centric internetworking

UDP / IPUDP / IP

Data link /physical

Data link /physical

Semantic support micro-layerSemantic support micro-layer

Embedded device and sensor network protocols: RFID, Bluetooth, ZigBee, KNX, ...

Embedded device and sensor network protocols: RFID, Bluetooth, ZigBee, KNX, ...

cross-layer interaction

Figure 1: Semantic information-centric framework

technology requires a semantic support micro-layer foradapting in the framework. Previous research workintroduced semantic support to widespread technologiessuch as Bluetooth [33], EPCglobal RFID [11], ZigBee[36], KNX protocol for home and building automation[34], Constrained Application Protocol (CoAP) [39] and

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Eddystone for Bluetooth Low Energy (BLE) beacons[38]. Further embedded device domains could beintegrated into the framework in a similar fashion.

Applications use the content-centric network todiscover resources/services, upon which they canexecute logic-based queries and reasoning services. InMANET environments, mobile hosts with embeddedreasoning capabilities [37] enable ubiquitous andpervasive semantic-aware applications. Furthermore, bymeans of the same protocol primitives, a gateway nodecan expose semantic annotations towards remote hostsas well as it can forward remote requests inside thelocal network. In this way, Semantic Web applications,powered by traditional query and inference engines forthe Web, are allowed access, reporting and monitoringcapabilities, so enabling the integration of pervasiveinformation into Linked Data on the Web [16].

With respect to the search and rescue case study,a reference example can be considered to understandthe proposed framework. An explosion has occurredin a campus laboratory. Environmental hazards mayinclude fire, toxic gases, debris, liquid pools and narrowtunnels. A robot rescue team is coordinated by amobile headquarter deployed near the disaster area.Communication is provided by an ad-hoc IEEE 802.11network. Robot units are highly heterogeneous w.r.t.movement mechanisms, provided capabilities/resourcesand functional requirements. The team must be ableto self-coordinate in the field, interacting with humancontrollers only for general mission directives. A teamleader robot will interface with the nearby headquarterand orchestrate features of team members dynamically.It will use semantic-based discovery upon wireless IEEE802.11 connectivity to select the best team configurationfor achieving the mission goal.

In the following subsections, the overall architectureis explained, as well as the dissemination and discoveryinteraction stages. Details about relevant protocolprimitives are provided.

4.1 Architecture

In KR approaches adopted by the Semantic Web, twokinds of knowledge are modeled:– conceptual knowledge, or general knowledge about theproblem domain;– factual knowledge, which is specific to a particularproblem.

Conceptual knowledge is represented in the form ofan ontology, describing general properties of conceptsand relationships among them. Factual knowledge isspecific to the individuals in the domain of discourse:current Semantic Web technologies such as RDF allowto describe individual resources and their existing

relationships. An ontology and a set of asserted factsform a Knowledge Base (KB) from which furtherentailed knowledge can be derived.

In the proposed approach, the classical KB (intendedas a fixed and centralized component) evolves towarda ubiquitous entity: ontology files can be managedby one or more hosts, while individual resources arescattered within a smart environment, because they arephysically tied to micro devices deployed in the field.For example, in RFID-based scenarios, each individualresource is a semantically annotated object/productdescription, stored within its RFID tag. Since severalobject classes, described w.r.t. different ontologies, canco-exist within a physical environment, they share thesystem infrastructure. Nevertheless, each individualresource annotation refers to one ontology providingthe conceptual knowledge for the particular domain.Ontology Universally Unique Identifier (OUUID) codes[33] are adopted to mark ontologies unambiguously andto associate each individual to the ontology w.r.t. whichit is described. In the Semantic Web technologicalstack, URIs are used to identify ontologies; furthermore,according to Linked Data best practices, URIs should beused as URLs (Uniform Resource Locators) pointing tothe actual ontology document via HTTP. Nevertheless,in this framework OUUIDs are preferred because:– URIs have variable length and are generally muchlonger than OUUIDs. That introduces overhead innetwork protocol fields, particularly in bandwidth-constrained mobile ad-hoc networks targeted by ourframework.– OUUID is easily mapped to data types for resourceclass identifiers adopted by most standard mobilediscovery protocols, e.g., UUID in Bluetooth ServiceDiscovery Protocol [33] and data types in messagesexchanged by ZigBee application objects [36].– URIs can be used to locate ontologies only as longas Internet connectivity is available. In many of thepervasive and ubiquitous contexts listed in Section 2,mobile devices cannot be connected to the Internet dueto cost, power and environmental constraints. In allthose cases, using URIs rather than OUUIDs providesno practical advantage while introducing overhead.– Whenever Internet connection is available, ontologyaccess is still granted by means of OUUID-to-URLmapping mechanisms. In [11], the ONS (Object NamingService) facility of the EPCglobal technological stackfor RFID, based on standard DNS (Domain NamingService) protocol, was exploited for this purpose. Inpervasive contexts not based on RFID, similar decodingmechanisms based on DNS or on HTTP redirection -already widely adopted on the Web, e.g., by popularURL shortening servicescan be trivially integratedinto the architecture in the same way. OUUID-to-URL

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mapping also allows to handle the case of composedontologies, consisting of several modules connectedby means of import relations. Hence, without lossof generality, hereafter it can be assumed that eachresource refers to only one OUUID (which is the oneof the “master” ontology in case of imports). Resourcesbelonging to the same domain will likely be described bymeans of the same ontology, while objects of differentcategories may refer to different ontologies. In detail, inthe proposed framework each resource is featured by:– 96-bit ID, globally unique item identifier (e.g., the 96-bit EPC code for an RFID tag or the 64-bit MAC address-padded to fit the space and to allow different MACprotocols to be distinguished- for a ZigBee sensor);– 64-bit OUUID;– a set of data-oriented attributes, which allow tointegrate and extend logic-based reasoning serviceswith application-specific and context-aware informationprocessing;– semantic annotation, stored as a compressed OWLdocument fragment.

For the compression of OWL annotations,a homomorphic encoding scheme for XML-based documents introduced in [41] is adopted.Homomorphism preserves XML document structureduring compression, so enabling query processingdirectly on encoded annotations, without requiringpreliminary decompression. Details about thecompression format and algorithm are not reportedhere, in order to keep the paper short and focused; thereader is referred to the above-mentioned work for athorough coverage.

In the proposed framework, the overall network can beseen as a two-level infrastructure, as depicted in Figure2. Pervasive identification and sensing technologies areexploited at the field layer (interconnecting embeddedmicro-devices dipped in the environment and hosts ableto receive the transmitted data) whereas the discoverylayer is related to the inter-host communication. Eachnetwork host –marked as (1) in Figure 2– acts asa cluster head (CH) for field devices in its directrange (2), using available communication interfaces.Resources acquired at the field layer through differentprotocols are exposed at the discovery layer in a uniformfashion (3), according to the structure described above.Interaction among hosts is performed by means of the IP-based information dissemination and resource retrievalframework outlined hereafter. If two nodes are inwireless range or share the same wired transmissionmedium, the interaction between them can happendirectly (4), otherwise it is necessary to adopt a multi-hop routing (5) by exploiting other nodes as intermediatelinks (6). However, a node does not depend onsome other ones to advertise/register object descriptions.

discovery layer Internet

discovery layerwireless ad-hoc network

query/reasoningengine

RFID reader/PDA

mobilequery/reasoning

engine

RFIDtag

RFIDtag

field layer RFID protocol

field layer wireless protocol

1

2

4

3

6

5

Figure 2: Field and Discovery layers in the proposedframework architecture

Resources are autonomously acquired from the fieldlayer and exposed. At the same time, nodes are ableto discover them thanks to the preliminary propagationof data each cluster head has seen in its range. Inshort, the information-centric framework is based onfour interaction stages:1. extraction of resource parameters (for carrying objectcharacteristics from field layer to discovery one);2. resource information dissemination (to make nearbynodes fully aware of the “network content”);3. resource discovery based on a peer-to-peercollaborative protocol (see later on);4. extraction of selected resource annotations (forcarrying semantic-based descriptions from field level tothe discovery one) to allow semantic-based queries andreasoning.

It should be pointed out that the proposed approachis fully decentralized. Address and main characteristicsof each resource/object are autonomously advertised bythe related cluster head, using small-sized messagesthroughout the network. Care has to be taken in the useof broadcasting mechanism to advertise object features,because an uncontrolled flooding could become largelyinefficient in terms of bandwidth usage and powerconsumption (both fundamental and precious resourcesin pervasive environments). In the proposed approach,only resource parameters are advertised in broadcastthroughout the network in order to unambiguouslyidentify both the location and the category of a resource.Then, if a node needs a specific resource, it will sendan explicit unicast request for its semantic annotation.Before starting whatever RDF query or reasoning task,a requester has to recompose also the ontology in aconsistent fashion. For this purpose, typical techniquesof hybrid peer-to-peer file sharing are exploited. Inparticular, the OUUID of the reference ontology will be

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used to retrieve ontology chunks from network hosts.When all the chunks become available, the ontology isready for use. Summarizing, the discovery procedureoccurs in two steps: the first one is syntax-based, thesecond one is semantic-based. The first phase aimsto select resource descriptions potentially interestingfor the requester via the OUUID matching and thecontextual parameters evaluation. The second one aimsto select the best available resources. In this secondstage the requester directly queries semantic annotationsof resources from the provider, so preparing the furtherreasoning. Semantic matchmaking allows ranking ofservice/resource descriptions w.r.t. a request as conceptexpressions w.r.t. a common ontology. The proposedapproach leverages two nonstandard inference servicesto provide fine-grained resource classification, logic-based ranking and outcome explanation (see [37] foralgorithms and further details):– Concept Contraction: if a request R and a suppliedresource S are in contrast, Contraction determines whichpart of R is conflicting with S. By retracting thispart, denoted as G (for Give up), a concept expressionK (for Keep) is obtained, representing a conflict-free(contracted) version of the original request;– Concept Abduction: if request and resource are not incontrast, but S does not completely satisfy R, Abductiondetermines what should be hypothesized in S in order toobtain a full match. The solution H (for Hypothesis) toAbduction can be interpreted as what is requested in Rand not specified in S.Both Contraction and Abduction have associated penaltyfunctions to measure the semantic distance of S from R.This enables a logic-based relevance ranking of a set ofresources w.r.t. a request;– Concept Covering: in IoT scenarios it is often usefulto aggregate several low-complexity devices and servicesin order to satisfy a specific request. The Abduction-based Concept Covering Problem (CCoP) aims to cover(i.e., satisfy) constraints expressed in a request as muchas possible, by means of the conjunction of serviceinstances, and to provide explanation of the possibleuncovered part H of the request.

The proposed hybrid, on-demand approach hasbeen chosen considering that semantic descriptions areneeded only in the last discovery phase, whereas thepreliminary ontology-based selection procedure tacklesontology-related interoperability issues. In this way,a significant reduction of the induced traffic can beobtained and hardware/software node complexity can bedecreased with respect to other semantic-based pervasivecomputing proposals. On the other hand, the full powerof semantic-based annotation and discovery is exploitedin the second step.

In the search and rescue case study, the mission

Figure 3: Disaster recovery KB class hierarchycreated for the case study

goal is the request for the resource discovery algorithm,which has to find the most suitable composition ofservices/resources provided by team units. Each robot ischaracterized by a set of operational preconditions thatmust be satisfied and a set of produced effects, whichmay comply preconditions for further team units. Apreliminary procedure is performed to select devicesmanaging the same “resource classes” using OUUIDs.Figure 3 shows the chunks of the OWL ontologythat has been modeled to express relevant conceptsand properties for the domain, grounding semanticsand inference tasks on the Attributive Languagewith unqualified Number restrictions (ALN ) logicallanguage of the Description Logics (DL) [2] family.

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<<2-hops host>> N3 : Node

<<1-hop host>> N2 : Node

T2: TagT1 : Tag <<resource provider>> N1 : Node

<<802.11 broadcast>> Advertisement PDU(ID,

seq+1) update cache

<<802.11 broadcast>> Advertisement PDU(ID, seq+1)

update cache

<<802.11 broadcast>> Advertisement PDU(ID, seq)

update cache

<<EPCglobal RFID>> read ID, OUUID, attributes

<<EPCglobal RFID>> read ID, OUUID, attributes

Figure 4: Information dissemination

0 4 8 12 16 20 21 25 37 byte

. . .

source

address

size

OUUID

lifetime

timestamp

traveled

hops

sequence

number

resource ID

resource description

Figure 5: Structure of a cache record

4.2 Information Dissemination

An efficient dissemination protocol is fundamentalto balance network resource usage and ease ofresource retrieval. Resource providers periodicallysend Advertisement PDUs (Protocol Data Units)also specifying the maximum number of hops thatthe advertisement must travel (MAX ADV DIAMETER).During such lifetime, advertisements are forwardedusing data-link-layer broadcasts and can be stored inthe cache memories of intermediate nodes. Figure4 shows the typical sequence of the informationdissemination phase and involved actors. In thedepicted example, embedded micro-devices consist inEPCglobal RFID tags storing semantically annotatedobject descriptions, while network nodes are PDAs(personal digital assistants) equipped with RFID readerand IEEE 802.11 transceiver.

All resources, detected at field layer, are advertised bymeans of a single advertisement PDU. Hence, the sizeof the frame increases proportionally with the numberof resources in the network host range. PDU fields areexplained in what follows.– TYPE: the kind of PDU (see Table 2).– FLAGS: it contains one status flag to distinguishthe kind of transmission (unicast or broadcast); theremaining flags are reserved for future purposes.– TRAVELED HOPS: the number of hops alreadytraversed by the frame. A node sets this value to 1 and itis increased every time a node forwards the frame.

Table 2: PDU types

Type Bit set PDUA 0 AdvertisementB 1 Cache entryC 2 SolicitD 3 RequestE ... L 4 ... 7 Reserved

– NUMBER OF RESOURCES: how many micro-devicesare in the Cluster Head (CH) radio range.– ADVERTISEMENT ID: the CH’s sequence number.– NODE SEQUENCE NUMBER: the sequence number ofthe node forwarding the frame. If the frame has been sentby a node, this value coincides with the previous one.– SOURCE ADDRESS: the IP address of the CH.– RESOURCE PARAMETERS: a composite, variable-length field depending on the number of advertisedresources. For each resource it contains: OUUID value,remaining life time, maximum hops number for theadvertisement travel, and resource ID.

A network node, which has detected one ormore micro-devices in its direct range, broadcastsan advertisement every DEFAULT RTIME milliseconds(values of exploited constants are reported in Table3). Nearby nodes forward the frame by broadcastingit to their neighbors; hence, the reader listens to theecho of the advertisement PDU it originally transmitted.Thus, it can obtain a confirmation of the presenceof other nodes in its neighborhood and update itsrouting table. If the reader does not receive anyecho within POLLING TIME milliseconds (less thanDEFAULT RTIME), it will retransmit the advertisement,presuming that a collision or a transmission error hasoccurred. After MAX RETRIES retries, it can bepresumed there are no neighbors, so the transmissionof the advertisement can be scheduled after a longertimeout in order to reduce power consumption.

When a node receives an advertisement, it extractsinformation about the resources and, in case of “new”elements, it adds cache entries; otherwise, beforeupdating stored data, the node verifies if the receivedinformation is more recent or has ran across a shorterpath than the existing one. In particular, the entry isupdated if its ADVERTISEMENT ID is smaller or itsTRAVELED HOPS field is greater or equal w.r.t. thereceived frame. This means that the arrived PDU is morerecent or has ran across a shorter path.

If the cache is updated and the maximumadvertisement diameter has not been reached, thenthe advertisement is forwarded; otherwise the wholeframe is silently discarded. This simple mechanismensures each node in the network sends the sameadvertisement at most once. Furthermore, each host

43


Table 3: Protocol constant parameter values

Name Meaning Value

DEFAULTRTIME

Time interval between twosubsequent advertisement frametransmissions

30000 ms

POLLINGTIME

Time a network node waits forthe echo of the advertisements 7500 ms

MAX ADVJITTER

Maximum value for random timewaited when advertisementframes are forwarded

600 ms

ONE HOPWAIT

Timer set by a requester nodeafter sending a solicit PDU,waiting for cache contentsreception

2000 ms

HOP TIMETime a node needs to process andforward a solicit PDU sent by aneighbor

50 ms

ACK RTTTimer set by a requester nodewaiting for acknowledgment aftera solicit has been sent

50 ms

DISCOVERYDIAMETER

Current search diameter (in hops)during discovery phase 4

MAXRETRIES

Maximum number ofretransmissions before a readerpresumes there are no neighbors

5

waits a random time t ∈ [0, MAX ADV JITTER] beforetransmitting. This aims to reduce collision probabilitywhen using data link layer protocols that do not provideacknowledgment for broadcast transmissions, such asIEEE 802.11 MAC protocol. After this check, the nodeverifies if the maximum advertisement diameter hasbeen reached and builds the new advertisement PDU tobe forwarded to its neighbors, according to the followingprocedure:1. The sequence number of the node is written into theNODE SEQUENCE NUMBER field of the new frame.2. For each advertised service, the TRAVELED HOPSvalue is compared against the MAX HOPS found in thereceived PDU.3. If TRAVELED HOPS equals MAX HOPS theinformation is discarded, because the maximumadvertisement diameter for that service has beenreached.4. Otherwise the TRAVELED HOPS value is increasedby 1 and the service information is included into thenew frame. In this case, both TRAVELED HOPS andNODE SEQUENCE NUMBER are increased.

Each reader manages a cache table where it storescharacteristics of both resources in its radio range andresources it has “seen” in the network. Figure 5 showsthe structure of a typical entry. Content of each field isas follows.– Source address: the address of the resourceprovider.– Size: the description size (in bytes).

Figure 6: Example of Wheels, Laser rangefinder andFire extinguisher resource descriptions

– OUUID: numeric identifier for the specific ontology.– Lifetime: remaining time of a resource/tag.– Timestamp: last reference to the entry (read/write).– Traveled hops: distance (hops number) betweenprovider and cache holder.– Sequence number: it is referred to the lastresource provider.– ID: the unique code of a resource.– Resource description: the annotation of aresource.

An entry is added to the cache whenever the nodereceives an advertisement or a cache content frame.

In the search and rescue case study, each robotadvertises its own description annotated in the ALN DL.Descriptions focus on provided and required capabilitiesrather than technically-oriented specifications of robotcomponents derived from datasheets, since a service-oriented approach is required for mission-drivenorchestration. Modeled capabilities include on-board sensing and acting devices, supported wirelesscommunication protocols and the kinds of terrain andobstacles that can be passed. OWL object properties(a.k.a. roles) are exploited to relate pairs of classes inservice instance descriptions. For example, as shown inFigure 6, the overcomes property relates a Movementtype with the types of Obstacles it can surmount,while measures property links a type of Sensor withthe Physical quantity it evaluates and performslinks an Actuator type with the Actions it can do.

44


No suitable resources found

Ready for reasoning

T2 : TagR1 : Tag<<1-hop host>> N2 : Node

N1 : Node

cache lookup

<<802.11 unicast>> Resource PDU

<<EPCglobal RFID>> read semantic annotation

<<EPCglobal RFID>> read semantic annotation

<<802.11 unicast>> Request PDU

<<802.11 unicast>> Cache content PDU

<<802.11 broadcast>> solicit PDU

cache lookup

Figure 7: Semantic-based resource discovery

4.3 Service/Resource discovery

When starting a resource retrieval process, a nodegenerally attempts to cover the request by using resourcedescriptions stored within its own cache memory. Ifsome description is missing, a Request PDU is sent inunicast. On the contrary, if a requester has no resourcedescriptions in its cache or if managed resources arenot enough to satisfy the request, the node broadcastsa Solicit PDU including the maximum travel diameter(TOTAL HOPS). When receiving a solicit, a node replies(in unicast) providing cache table entries matchingparameters contained within the solicit frame. If it doesnot manage any information satisfying the solicit, itwill reply with a “no matches” message. During theirtravel, replies to the request and solicit PDUs are used toupdate the cache memory of forwarding nodes. Figure 7depicts discovery procedure in case of RFID tag/readerinteraction. A node must firstly look within its localcache table for entries compatible with the request. Incase of a cache hit, it will require the correspondingannotated descriptions from their respective owners, bymeans of unicast Request PDUs. In what follows themeaning of introduced PDU fields is summarized.– TYPE: it is set to 3.– FLAGS: analogous to the corresponding field of theadvertisement PDU.– TRAVELED HOPS: how many hops the frame hasalready gone across.– LAST HOP SEQUENCE NUMBER: the sequencenumber of the last node processing the request.– DESTINATION SEQUENCE NUMBER: the sequencenumber of the destination node.– DESTINATION ADDRESS: the address of the lastnode processing the request.

– PROVIDER ADDRESS: the destination node.– OUUID: the ontology unique identifier.– NUMBER OF REQUESTS: the number of requestedresource descriptions.– DATA: the size of this field depends on the numberof requests; it contains the IDs of the tags of requireddescriptions. Each ID is 96 bit long, like in theadvertisement PDU.

If a requester has no resource descriptions in itscache or if managed resources do not satisfy therequest, it requires further descriptions. So it willtransmit a Solicit PDU to nearby nodes. Basically,the soliciting mechanism is analogous to the advertisingone, exploiting controlled broadcast of request in anexpanding ring fashion. A node generating a Solicitwaits for an acknowledgment from each neighbor forACK RTT seconds. In this way the requester elicitsinformation about nearby nodes: it can exactly knowthe number of neighbors. Each node located withinDISCOVERY DIAMETER hops from the requester, afterreceiving a solicit PDU, replies with a Cache contentPDU in unicast toward the node the solicit comes from.Nodes receiving the PDU update their own cache andrecursively send back the PDU, till the original requesternode receives the information. A Cache content PDU hasa variable length according to the number of containedresource handles. Hence the cache update could involvemore records. Other PDU fields are:– N: the number of resources handles (and then cacheentries) the packet transports.– REQUEST ID: the identifier of the original request.– LAST HOP SEQUENCE NUMBER: the sequencenumber of the node sending the packet.– DESTINATION ADDRESS: requester IP address.

The information dissemination/discovery frameworkand the inference procedures are both completely generaland suitable for any scenario where semantic-basedservice/resource discovery and composition could berequired. All specific aspects of a particular case studyare modeled within the knowledge base. This is clarifiedby means of the running example in the search andrescue scenario.

Algorithm resourceComposer is based on the ConceptCovering inference [42]. By limiting the expressivenessof resource descriptions to ALN , reasoning complexityis polynomial for standard and nonstandard inferenceservices [42], thus making the approach suitable tomobile and embedded computing. The algorithm takesas inputs: a set of services (resources) S, a requestD, a (possibly empty) set of initial preconditions anda reference ontology. It returns a composed serviceflow CS (Composite Service), possibly with the part ofthe request Duncovered which could not be covered (i.e.,satisfied) by available services or resources. For each

45


Algorithm 1: resourceComposer algorithmInput: Request D, Set S of cached service descriptions,

Minimum Threshold Covering Level (MTCL),maximum discovery range (Total hops MAX), aset of initial preconditions (P0), and theKnowledge Base B.

Output: A triple 〈CS, Duncovered, Covering Level〉;where CS is the composed service flow,Duncovered is the part of the request not covered,and Covering Level is the achieved requestcovering level.

1 Duncovered := D; Total hops := 0; continue := TRUE;2 repeat3 if Total hops 6= 0 then4 Update the cache and the set S with all

retrieved service descriptions;5 end6 〈CS, Duncovered, Covering Level〉 :=

resourceComposer(Duncovered, S, P0, B) ;7 if (Covering Level ≥ MTCL) or (Total hops =

Total hops MAX) then8 continue := FALSE;9 end

10 Total hops := Total hops +1 ;11 Broadcast a Solicit PDU with the new Total hops

value;12 until continue = TRUE;

element in S, if preconditions are satisfied, it is added tothe set EX(CS) of executable services. Maximal coveringof D is then performed by testing components of EX(CS)one at a time; if an element contributes to cover D, it isadded to CS. In the first iteration, the algorithm is runby composing resources in the cache of the requesterdevice itself. It outputs a temporary CS as well as theuncovered part of the request Duncovered. If the coveringlevel is under a given threshold, both Duncovered and CSare stored and the requester broadcasts a Solicit PDU inan expanding ring fashion to require more resources.

For the sake of readability, Manchester syntax [17]–as stylized by the Protege KB editor [27]– is usedto represent OWL concept expressions annotating therequest and resources in the following example in thesearch and rescue scenario. Let us suppose the missiongoal in the explosion area described in Section 2 is asfollows: Move through the debris, overcoming slopesand tunnels; detect, locate and extinguish fires; detect,locate and provide first aid to people. This request couldbe expressed in OWL 2 as:

Request: D = { measures some (owl:Thing) andmeasures only (Distance) and Air analysisand Imaging sensor and performssome (owl:Thing) and performs only(Removing debris and Detecting fire and

Extinguishing fire and Detecting presence)and overcomes some (owl:Thing) andovercomes only (Slope and Tunnel) andFirst aid kit }Initial preconditions: P0 = { WiFi and Fuel andBattery and DSP }

Preconditions and effects are referred to each availablerobot (mobile unit, m.u.). They are composed by eachm.u. to characterize its own state and capabilities byannotating information derived from embedded devicesand proximity sensors at the lowest layer in Figure 1.These field level annotations are exposed through thesemantic support micro-layer in Figure 1 to the semanticinformation-centric internetworking layer, as explainedin Section 4.2. Let us assume the following robots areavailable:

mu1: Environment unit = 〈 P1, E1 〉 = 〈 (WiFiand Internal power source), (GPS andAltimeter and Anemometer and Barometerand Hygrometer and Thermometer andAir analysis and Toxic gas analysis) 〉

mu2: Weather unit = 〈 P2, E2 〉 = 〈 (WiFiand Internal power source), (GPS andAltimeter and Anemometer and Barometerand Hygrometer and Thermometer) 〉

mu3: Mine unit = 〈 P3, E3 〉 = 〈 (Fuel), (Beacon andLoudspeaker and Metal detector) 〉

mu4: Fire victim detection unit = 〈 P4, E4 〉= 〈 (Batteryand GPS and Imaging sensor and Microphoneand Thermometer and Air Analysis and DSP),(Presence detector and Fire detector) 〉

mu5: Rescue unit = 〈 P5, E5 〉 = 〈(Presence detector and Fire detector),(Pincers and Fire extinguisher andFirst aid kit) 〉

mu6: Scout unit = 〈 P6, E6 〉 = 〈 (GPS andAltimeter and Anemometer and WiFi andFuel), (Wheels and Dozer blade and Forkliftand Videocamera and Laser rangefinder andMicrophone) 〉

In case of a supposed covering threshold of 90%, thefollowing covering steps are accomplished by means ofAlgorithm 1:

a. CS = ∅Duncovered = DCovering Level = 0%

b. EX(CS) = { mu1, mu2, mu3 }CS = ( mu1 )Duncovered = (measures some (owl:Thing)and measures only (Distance) andImaging sensor and performs some(owl:Thing) and performs only(Removing debris and Detecting fireand Extinguishing fire andDetecting presence) and overcomes some(owl:Thing) and overcomes only (Slope

46


and Tunnel) and First aid kit)Covering Level = 9.1%Among executable resources, only mu1 contributes tocover the request since it provides air analysis.

c. EX(CS) = { mu2, mu3, mu6 }CS = ( mu1, mu6 )Duncovered = (performs some (owl:Thing)and performs only (Detecting fireand Extinguishing fire andDetecting presence) and First aid kit)Covering Level = 63.6%mu6 is triggered, since its required preconditions P6 aresatisfied (by the conjunction of P0 and E1). Also noticethat mu2 would cause an effect duplication with mu1.

d. EX(CS) = { mu2, mu3, mu4 }CS = ( mu1, mu6, mu4 )Duncovered = { (performs some (owl:Thing)and performs only (Extinguishing fire)and First aid kit)Covering Level = 81.8%mu4 provides detection of fire sources and humanpresence.

e. EX(CS) = { mu2, mu3, mu5 }CS = ( mu1, mu6, mu4, mu5 )Duncovered = owl : ThingCovering Level = 100%

5 EXPERIMENTAL RESULTS

The proposed approach has been tested usingns-2 network simulator (http://nsnam.sourceforge.net/wiki/index.php/User_Information), to evaluate the effectiveness of theframework and find possible performance issues. Asimulation campaign of the full protocol stack has beenconducted for semantic-enhanced information-centricnetworking in complex MANET environments. Thefollowing performance metrics have been considered.1. Network load, assessed by means of the total packetsgenerated at discovery layer.2. Hit ratio, i.e., percentage of successful resourceretrieval, where a hit has to be intended as the delivery ofat least three complete descriptions referred to the sameontology. This reference value has been elicited frompreliminary tests, using different domain ontologies.Obviously, the greater the number of available resourcesper node, the greater the probability of success.3. Duration of resource discovery sessions.Furthermore, the sensitivity of such properties has beenassessed w.r.t. changes in network topology due tonode mobility. Several scenarios have been createdfor simulation, each comprising 50 nodes moving ina plain 1000 m x 1000 m area. Each simulated hostis equipped with an IEEE 802.11 transceiver with

0 50 250 500 900

3 providers, 15 requesters 4326,9 4133,5 3758,3 3375,9 3464,0








7providers, 45 requesters 8055,6 7770,0 7347,9 6565,0 5894,9

3000,0

4000,0

5000,0

6000,0

7000,0

8000,0

Net

wo

rk lo

ad (

PD

Us)

Pause time (s)

Figure 8: Network load of dissemination anddiscovery

omnidirectional antenna, 2 Mb/s nominal bandwidthand 250 m range. Two-way ground signal propagationmodel [3] was adopted. Scenarios were set up with3, 5 and 7 hosts detecting annotated resources in theirdirect range (hereafter providers) while 15, 30 and45 hosts act as requesters. Resources are available atthe beginning of each simulation, whereas requestsare generated at randomly chosen instants, uniformlydistributed within the simulation time. Moreover, foreach combination of reference parameters, 8 simulationswere run using different values for the seed of the ns-2random number generator. Obtained results have beenaveraged in order to filter out the bias deriving fromconditions of single scenarios (e.g., high link breakageratio or network partitions). The host motion followsthe random waypoint model [31], which is characterizedby two parameters: speed S and pause time P. Thesimulation starts with hosts remaining stationary for Pseconds, then each host selects a random destination andmoves toward it with a fixed speed, randomly chosenin the range [0, S]. After reaching the destination, thehost pauses again for P seconds, then selects anotherdestination and repeats the previous steps till the endof the simulation, which lasts 900 s. For each scenario,the value of S was set to 1 m/s while P varied from 0(non-stop motion) to 50, 250, 500 and 900 s (no motion).

In what follows, outcomes of performance evaluationare presented. Figure 8 reports on the overall networkpackets generated by dissemination and discoveryprotocols. Results show that traffic has higher correlationwith the number of providers rather than requesters.

47

http://nsnam.sourceforge.net/wiki/index.php/User_Information




0 50 250 500 900









7providers, 45 requesters 0,9972 0,9944 0,9861 0,9694 0,9667

0,9000

0,9100

0,9200

0,9300

0,9400

0,9500

0,9600

0,9700

0,9800

0,9900

1,0000

Hit

rat

io (

hit

s/re

qu

est

s)

Pause time (s)

Figure 9: Hit ratio

This happens because advertisements are regularly sentin a proactive way by providers, even if there are norequests. On the other hand, solicit, cache content andrequest PDUs are produced on-demand by requestersand their neighbors. Furthermore, the number ofgenerated packets decreases when pause time increases.As nodes remain stationary for more time, probabilitydecreases that radio links are lost and advertisements arenot echoed: hence advertisers do not schedule PDUs forretransmission, so decreasing the overall traffic. Whennormalizing the number of packets w.r.t. the number ofnodes and the simulation duration, values range from aminimum of 0.092 to a maximum of 0.364 packets pernode per second. Such data are comparable to results inFigure 14 of [7], which reported about [750, 850] packetsper node in 4500 s simulation runs with the most efficientprotocol variant, corresponding to [0.167, 0.189] packetsper node per second. The hit ratio is depicted in Figure9. It is very high in general, with values above 90% in alltests and above 95% in more than half the tests. Resultsare very close to the ones reported in Figure 15 of [7]and even to the perfect recall reported by [44], clearlyindicating the relevance of the proposed approach. Moreprecisely, hit ratio increases with the number of resourceproviders. This is motivated because: (i) with moreproviders the capillarity of dissemination is increased;(ii) the proposed discovery mechanism induces by itselfa load balancing of requests, since the closest hostswith available resources will be contacted first by arequester. Finally, Figure 10 reports on the time valuesfor a successful discovery. Analysis evidences that,for a given number of providers, the service time

0 50 250 500 900










1,0000

1,2000

1,4000

1,6000

1,8000

2,0000

2,2000

2,4000

2,6000

Du

rati

on

of

dis

cove

ry (

s)

Pause time (s)

Figure 10: Average duration of discovery

decreases as the number of clients increase. Thisis due because when a solicit is answered by cachecontent PDUs, intermediate nodes cache the resourcerecords. This reduces latency in the response to laterrequests. Results seem significantly worse than the[0.3, 0.7] s range reported in Figure 16 of [7], butactually they are not, because in that work a hit wasdefined as the successful retrieval of one resource whilehere it means three resources. Furthermore, [47]reported that in their approach “semantic matchmakingincreases exponentially as more data and ontologiesare provided” and “semantic reasoning is only carriedout at concrete moments of time when informationor ontologies change. Once the knowledge base hasbeen augmented via semantic reasoning, the queriescan be resolved against the data repository with [lineartime] lexical-like performance as long as the informationremains the same”. The proposed approach, onthe contrary, does not incur penalties in discoveryperformance when the size of KB or informationvolatility increase. Nevertheless, overall values arestill slightly high w.r.t. the requirements of pervasivecomputing. This may be influenced by the fact thatcurrent protocol implementation is not optimized forexecution speed. Further techniques should be devised,however, in order to reduce duration of discoverysessions. Based on the above results, the proposedapproach can be deemed as a step toward solving thecore issues of information-centric networking in theSemantic Web of Things. Effectiveness and relevanceseem evident, as well as the opportunity of furtheroptimization.

48


6 CONCLUSION

This paper presented a content-centric networkingapproach for the Semantic Web of Things. A peer-to-peer, collaborative and dynamic framework supportsinformation dissemination and semantic-based resourcediscovery. Information gathered in the field throughdifferent identification and sensing technologies isexploited at discovery layer. A thorough evaluationof the approach using ns-2 simulator test correctnessand effectiveness. An implementation of the systemis ongoing with real computing devices to provide amore significant assessment of real-world performance.Future work includes: (i) a wider validation of theapproach; (ii) an improvement of resource retrieval,through more advanced query and matchmakingschemes; (iii) an enhancement of ontology management,by means of semantic-aware fragmentation and on-the-fly rebuilding of TBoxes in the KB.

ACKNOWLEDGEMENTS

The authors acknowledge partial support of ItalianPON project RPASInAir - Remotely Piloted AircraftSystems Integration in non-segregated Air space forservices and Apulia Region POR project Sı-Ca.Re. -Integrated Monitoring and Care System for Patients withCardiorenal Syndrome.

REFERENCES

[1] M. Amadeo, C. Campolo, J. Quevedo, D. Corujo,A. Molinaro, A. Iera, R. L. Aguiar, and A. V.Vasilakos, “Information-centric networking for theInternet of Things: Challenges and opportunities,”IEEE Network, vol. 30, no. 2, pp. 92–100, 2016.

[2] F. Baader, D. Calvanese, D. L. McGuinness,D. Nardi, and P. Patel-Schneider, The DescriptionLogic Handbook. Cambridge University Press,2002.

[3] A. Bakre and B. Badrinath, “I-TCP: Indirect TCPfor mobile hosts,” in International Conference onDistributed Computing Systems, vol. 15. IEEEComputer Society Press, 1995, pp. 136–136.

[4] P. Barnaghi, M. Presser, and K. Moessner,“Publishing linked sensor data,” in Proceedings ofthe 3rd International Workshop on Semantic SensorNetworks (SSN), International Semantic WebConference, ser. CEUR Workshop Proceedings,vol. 668, 2010.

[5] L. M. Borges, F. J. Velez, and A. S. Lebres,“Survey on the characterization and classification

of wireless sensor network applications,” IEEECommunications Surveys & Tutorials, vol. 16,no. 4, pp. 1860–1890, 2014.

[6] D. Brickley and R. Guha, “RDF Schema 1.1,”W3C, W3C Recommendation, Feb. 2014,https://www.w3.org/TR/rdf-schema/.

[7] D. Chakraborty, A. Joshi, Y. Yesha, and T. Finin,“Toward distributed service discovery in pervasivecomputing environments,” IEEE Transactions onMobile Computing, vol. 5, no. 2, pp. 97–112, 2006.

[8] W. Colitti, K. Steenhaut, and N. De Caro,“Integrating Wireless Sensor Networks with theWeb,” Extending the Internet to Low powerandLossy Networks (IP+ SN 2011), 2011.

[9] M. Compton, P. Barnaghi, L. Bermudez, R. Garcıa-Castro, O. Corcho et al., “The SSN ontology of theW3C semantic sensor network incubator group,”Web Semantics: Science, Services and Agents onthe World Wide Web, vol. 17, pp. 25–32, Dec. 2012.

[10] C. Dannewitz, D. Kutscher, B. Ohlman, S. Farrell,B. Ahlgren, and H. Karl, “Network of Information(NetInf) – An information-centric networkingarchitecture,” Computer Communications, vol. 36,no. 7, pp. 721–735, 2013.

[11] R. De Virgilio, E. Di Sciascio, M. Ruta, F. Scioscia,and R. Torlone, “Semantic-based RFID DataManagement,” in Unique Radio Innovation for the21st Century: Building Scalable and Global RFIDNetworks. Springer, 2011, pp. 111–142.

[12] M. Erdelj, M. Krol, and E. Natalizio, “Wirelesssensor networks and multi-UAV systems for naturaldisaster management,” Computer Networks, vol.124, pp. 72–86, 2017.

[13] J. A. Fisteus, N. F. Garcia, L. S. Fernandez, andD. Fuentes-Lorenzo, “Ztreamy: A middleware forpublishing semantic streams on the Web,” WebSemantics: Science, Services and Agents on theWorld Wide Web, vol. 25, pp. 16–23, 2014.

[14] D. Guinard, V. Trifa, S. Karnouskos, P. Spiess,and D. Savio, “Interacting with the SOA-basedInternet of Things: Discovery, Query, Selection,and On-Demand Provisioning of Web Services,”IEEE Transactions on Services Computing, vol. 3,no. 3, pp. 223–235, 2010.

[15] A. Gyrard, A. Zimmermann, and A. Sheth,“Building IoT-based applications for smart cities:How can ontology catalogs help?” IEEE Internetof Things Journal, vol. 5, no. 5, pp. 3978–3990,2018.

49


[16] T. Heath and C. Bizer, “Linked data: Evolving theweb into a global data space,” Synthesis Lectures onthe Semantic Web: Theory and Technology, vol. 1,no. 1, pp. 1–136, 2011.

[17] M. Horridge and P. Patel-Schneider, “OWL2 Web Ontology Language Manchester Syntax(Second Edition),” W3C, W3C Note, Dec. 2012,http://www.w3.org/TR/owl2-manchester-syntax/.

[18] C. Intanagonwiwat, R. Govindan, and D. Estrin,“Directed diffusion: A scalable and robustcommunication paradigm for sensor networks,”in Proceedings of the 6th Annual InternationalConference on Mobile Computing and Networking.ACM, 2000, pp. 56–67.

[19] V. Jacobson, D. K. Smetters, N. H. Briggs, M. F.Plass, P. Stewart, J. D. Thornton, and R. L.Braynard, “VoCCN: Voice-over Content-centricNetworks,” in Proceedings of the 2009 Workshopon Re-architecting the Internet, ser. ReArch ’09.ACM, 2009, pp. 1–6.

[20] V. Jacobson, D. K. Smetters, J. D. Thornton,M. F. Plass, N. H. Briggs, and R. L. Braynard,“Networking Named Content,” in Proceedings ofthe 5th International Conference on EmergingNetworking Experiments and Technologies. ACM,2009, pp. 1–12.

[21] M. Jelasity, A. Montresor, and O. Babaoglu,“Gossip-based aggregation in large dynamicnetworks,” ACM Transactions on ComputerSystems (TOCS), vol. 23, no. 3, pp. 219–252, 2005.

[22] D. Kempe, J. Kleinberg, and A. Demers, “SpatialGossip and Resource Location Protocols,” Journalof ACM, vol. 51, no. 6, pp. 943–967, Nov. 2004.

[23] I.-Y. Ko, H.-G. Ko, A. J. Molina, and J.-H.Kwon, “SoIoT: Toward a user-centric IoT-basedservice framework,” ACM Transactions on InternetTechnology (TOIT), vol. 16, no. 2, p. 8, 2016.

[24] D. Le-Phuoc, H. Q. Nguyen-Mau, J. X. Parreira,and M. Hauswirth, “A middleware frameworkfor scalable management of linked streams,” WebSemantics: Science, Services and Agents on theWorld Wide Web, vol. 16, pp. 42–51, Nov. 2012.

[25] U. Lee, I. Rimac, and V. Hilt, “Greening theInternet with Content-centric Networking,” inProceedings of the 1st International Conferenceon Energy-Efficient Computing and Networking.ACM, 2010, pp. 179–182.

[26] A. Makhoul, C. Guyeux, M. Hakem, and J. M.Bahi, “Using an epidemiological approach tomaximize data survival in the Internet of Things,”ACM Transactions on Internet Technology (TOIT),vol. 16, no. 1, p. 5, 2016.

[27] M. A. Musen, “The Protege project: A look backand a look forward,” AI matters, vol. 1, no. 4, pp.4–12, 2015.

[28] G. Pallapa and S. Das, “Resource Discovery inUbiquitous Health Care,” in 21st InternationalConference on Advanced Information Networkingand Applications Workshops, vol. 2, 2007, pp. 1–6.

[29] B. Parsia, S. Rudolph, M. Krotzsch, P. Patel-Schneider, and P. Hitzler, “OWL 2 WebOntology Language Primer (Second Edition),”W3C, W3C Recommendation, Dec. 2012,http://www.w3.org/TR/owl2-primer.

[30] C. Perera, A. Zaslavsky, C. Liu, M. Compton,P. Christen, and D. Georgakopoulos, “SensorSearch Techniques for Sensing as a ServiceArchitecture for the Internet of Things,” IEEESensors Journal, vol. 14, no. 2, pp. 406–420, 2014.

[31] G. Resta and P. Santi, “An analysis of the nodespatial distribution of the random waypointmobility model for ad hoc networks,” inProceedings of the second ACM internationalworkshop on Principles of mobile computing.ACM New York, NY, USA, 2002, pp. 44–50.

[32] W. Ruan, Q. Z. Sheng, L. Yao, T. Gu, M. Ruta, andL. Shangguan, “Device-free indoor localizationand tracking through human-object interactions,”in 17th International Symposium on a World ofWireless, Mobile and Multimedia Networks. IEEE,2016, pp. 1–9.

[33] M. Ruta, T. Di Noia, E. Di Sciascio, and F. Donini,“Semantic-Enhanced Bluetooth DiscoveryProtocol for M-Commerce Applications,”International Journal of Web and Grid Services,vol. 2, no. 4, pp. 424–452, 2006.

[34] M. Ruta, F. Scioscia, E. D. Sciascio, and G. Loseto,“Semantic-Based Enhancement of ISO/IEC 14543-3 EIB/KNX Standard for Building Automation,”IEEE Transactions on Industrial Informatics,vol. 7, no. 4, pp. 731–739, Nov 2011.

[35] M. Ruta, T. Di Noia, E. Di Sciascio, F. Scioscia,and G. Piscitelli, “If objects could talk: Anovel resource discovery approach for pervasiveenvironments,” International Journal of InternetProtocol Technology (IJIPT), vol. 2, no. 3/4, pp.199–217, 2007.

[36] M. Ruta, F. Scioscia, T. Di Noia, and E. DiSciascio, “A hybrid ZigBee/Bluetooth approach tomobile semantic grids,” Computer Systems Scienceand Engineering, vol. 25, no. 3, pp. 235–249, 2010.

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[37] M. Ruta, F. Scioscia, E. Di Sciascio, F. Gramegna,and G. Loseto, “Mini-ME: the Mini MatchmakingEngine,” in OWL Reasoner Evaluation Workshop(ORE 2012), ser. CEUR Workshop Proceedings,I. Horrocks, M. Yatskevich, and E. Jimenez-Ruiz,Eds., vol. 858. CEUR-WS, 2012, pp. 52–63.

[38] M. Ruta, F. Scioscia, S. Ieva, G. Loseto,F. Gramegna, and A. Pinto, “Knowledge discoveryand sharing in the IoT: the Physical SemanticWeb vision,” in Proceedings of the Symposium onApplied Computing, 2017, pp. 492–498.

[39] M. Ruta, F. Scioscia, A. Pinto, E. Di Sciascio,F. Gramegna, S. Ieva, and G. Loseto, “Resourceannotation, dissemination and discovery inthe Semantic Web of Things: a CoAP-based framework,” in Green Computing andCommunications (GreenCom), 2013 IEEE andInternet of Things (iThings/CPSCom), IEEEInternational Conference on and IEEE Cyber,Physical and Social Computing. IEEE, 2013, pp.527–534.

[40] G. Schreiber and F. Gandon, “RDF 1.1 XMLsyntax,” W3C, W3C Recommendation, Feb. 2014,http://www.w3.org/TR/rdf-syntax-grammar/.

[41] F. Scioscia and M. Ruta, “Building a Semantic Webof Things: issues and perspectives in informationcompression,” in Semantic Web InformationManagement (SWIM’09). In Proceedings of the3rd IEEE International Conference on SemanticComputing (ICSC 2009). IEEE ComputerSociety, 2009, pp. 589–594.

[42] F. Scioscia, M. Ruta, G. Loseto, F. Gramegna,S. Ieva, A. Pinto, and E. Di Sciascio, “Mini-ME matchmaker and reasoner for the SemanticWeb of Things,” in Innovations, Developments,and Applications of Semantic Web and InformationSystems. IGI Global, 2018, pp. 262–294.

[43] The W3C SPARQL Working Group, “SPARQL 1.1Overview,” W3C, W3C Recommendation, Mar.2013, https://www.w3.org/TR/sparql11-overview/.

[44] A. Toninelli, A. Corradi, and R. Montanari,“Semantic-based discovery to support mobilecontext-aware service access,” ComputerCommunications, vol. 31, no. 5, pp. 935 –949, 2008.

[45] K.-N. Tran, M. Compton, and R. G. Jemma Wu,“Semantic Sensor Composition,” in 3rdInternational Workshop on Semantic SensorNetworks. Proceedings of the 9th InternationalSemantic Web Conference (ISWC 2010), ser.CEUR Workshop Proceedings, D. R. D. Taylor K.,Ayyagari A., Ed., vol. 668. CEUR-WS, nov 2010,pp. 33–48.

[46] D. Trossen, M. Sarela, and K. Sollins, “Argumentsfor an Information-centric InternetworkingArchitecture,” SIGCOMM Comput. Commun. Rev.,vol. 40, no. 2, pp. 26–33, 2010.

[47] J. I. Vazquez and D. L. de Ipina, “mRDP:An HTTP-based lightweight semantic discoveryprotocol,” Computer Networks, vol. 51, no. 16, pp.4529 – 4542, 2007.

[48] G. Venkataramani and P. Iyer, “Semantics awarerfid middleware for personalized web services toretail consumers,” 2008.

[49] S. Voulgaris, M. Jelasity, and M. van Steen,“A Robust and Scalable Peer-to-peer GossipingProtocol,” in Second International Conference onAgents and Peer-to-Peer Computing. Springer-Verlag, 2004, pp. 47–58.

[50] G. Xylomenos, C. N. Ververidis, V. A. Siris,N. Fotiou, C. Tsilopoulos, X. Vasilakos, K. V.Katsaros, and G. C. Polyzos, “A survey ofinformation-centric networking research,” IEEECommunications Surveys & Tutorials, vol. 16,no. 2, pp. 1024–1049, 2014.

[51] L. Yao, Q. Z. Sheng, A. H. Ngu, and X. Li,“Things of interest recommendation by leveragingheterogeneous relations in the Internet of Things,”ACM Transactions on Internet Technology (TOIT),vol. 16, no. 2, p. 9, 2016.

[52] M. Zhang, H. Luo, and H. Zhang, “A surveyof caching mechanisms in information-centricnetworking,” IEEE Communications Surveys &Tutorials, vol. 17, no. 3, pp. 1473–1499, 2015.

[53] R. Y. Zhong, G. Q. Huang, S. Lan, Q. Dai,X. Chen, and T. Zhang, “A big data approachfor logistics trajectory discovery from RFID-enabled production data,” International Journalof Production Economics, vol. 165, pp. 260–272,2015.

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AUTHOR BIOGRAPHIES

Michele Ruta received themaster’s degree in ElectronicsEngineering from PolytechnicUniversity of Bari in 2002 andthe Ph.D. in Computer Sciencein 2007. He is currently fullprofessor. His research interestsinclude pervasive computing

and ubiquitous web, knowledge representation systemsand applications for wireless ad-hoc contexts. On thesetopics, he has co-authored more than 110 papers ininternational journals, edited books and conferences.

Floriano Scioscia received themaster’s degree in InformationTechnology Engineering withhonours from PolytechnicUniversity of Bari in 2006,and the Ph.D. in InformationEngineering in 2010 from thesame institution. He is currently

an assistant professor at the Information SystemsLaboratory (SisInfLab) in the same university. Hisresearch interests include knowledge representationsystems and applications for pervasive computing,cyber-physical systems and the Internet of Things. Heco-authored over 80 papers in international journals,edited books and conferences.

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Documents

Information-Centric Semantic Web of Things · M. Ruta, F. Scioscia: Information-Centric Semantic Web of Things human-objects interaction [32]. Wireless sensor networks (WSNs) monitor