Knowledge discovery and sharing in the IoT: the PhysicalSemantic Web vision

Michele Ruta∗

Politecnico di Barivia E. Orabona 4

Bari (I-70125), Italymichele.ruta@poliba.it

Floriano SciosciaPolitecnico di Barivia E. Orabona 4

Bari (I-70125), Italyfloriano.scioscia@poliba.it

Saverio IevaPolitecnico di Barivia E. Orabona 4

Bari (I-70125), Italysaverio.ieva@poliba.it

Giuseppe LosetoPolitecnico di Barivia E. Orabona 4

Bari (I-70125), Italygiuseppe.loseto@poliba.it

Filippo GramegnaPolitecnico di Barivia E. Orabona 4

Bari (I-70125), Italyfilippo.gramegna@poliba.it

Agnese PintoPolitecnico di Barivia E. Orabona 4

Bari (I-70125), Italyagnese.pinto@poliba.it

ABSTRACTThe Physical Semantic Web is proposed as an enhancementto the Google Physical Web approach for the Internet ofThings. It allows associating beacons to semantic anno-tations (instead of trivial identifiers), in order to enablemore powerful expressiveness in human-things and things-things interactions. This paper presents a general frame-work for the Physical Semantic Web based on machine-understandable descriptions of physical resources and novellogic-guided resource discovery capabilities. A possible ap-plication scenario and early experiments are outlined to provebenefits of the induced enhancements and the effectivenessof theoretical solutions.

CCS Concepts•Human-centered computing→Ubiquitous comput-ing; •Computing methodologies → Knowledge rep-resentation and reasoning; •Information systems →Web Ontology Language (OWL);

KeywordsPhysical Web; Knowledge Discovery; Semantic Web of Things

1. INTRODUCTIONThe Physical Web is a paradigm in the Internet of Things(IoT) framework devised by Google Inc. to enhance the in-

∗Corresponding author. Tel.: +39-339-635-4949; fax: +39-080-596-3410

Permission to make digital or hard copies of all or part of this work forpersonal or classroom use is granted without fee provided that copiesare not made or distributed for profit or commercial advantage and thatcopies bear this notice and the full citation on the first page. To copyotherwise, or republish, to post on servers or to redistribute to lists,requires prior specific permission and/or a fee.SAC 2017,April 03-07, 2017, Marrakech, MoroccoCopyright 2017 ACM 978-1-4503-4486-9/17/04. . . $15.00http://dx.doi.org/10.1145/3019612.3019701

teraction capabilities with real-world objects. A discoveryutility running on a smartphone retrieves URLs (UniformResource Locators) of nearby things through the Eddys-tone Bluetooth beacon protocol1. This requires neither cen-tralized archives nor special-purpose applications. FindingURLs via Wi-Fi using multicast DNS (mDNS) and UniversalPlug and Play (uPnP) is also supported. The Google Phys-ical Web approach tends to preserve legacy applications: itmakes more powerful the human-thing interaction when na-tive software is unfeasible or even impractical to be used.Every smart object owns a web address, and this makespossible a simple and direct interaction bypassing dedicatedapps and on-line backends.

Although such an approach induces not-negligible enhance-ments in the object networks manageability, several issuesrestrain an effective adoption in even more complex IoT sce-narios. Particularly, things-things interaction is not enabledyet and discovery mechanisms are too simplistic with re-spect to what needed in really autonomic IoT scenarios.Basically, interoperability problems have to be taken intoaccount when coping with contexts evolving and modify-ing progressively. This paper proposes to extend the Physi-cal Web project exploiting the Semantic Web approach andtheory, so enabling advanced resource discovery features.Knowledge Representation (KR) has been acknowledged asa key technology for overcoming incompatibilities betweeninteracting entities. As of now, more and more studies indi-cate this could be also exploitable in the Internet of Things.The so-called Semantic Web of Things (SWoT) [13] refers toscenarios where intelligence is embedded into the environ-ment through the deployment in the field of a plethora ofheterogeneous micro-devices, each acting as dynamic knowl-edge micro-repository.

In the proposed approach, semantic annotations are sharedby beacons to enhance representability offered by the ob-jects in the Physical Web. This adds the possibility of morecomplex interactions: things become resources exposing theknowledge characterizing themselves without depending on

1http://github.com/google/eddystone

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any centralized actor and/or infrastructure. In addition,user agents running on mobile personal devices are ableto dynamically discover the best available objects accord-ing to user’s profile and preferences; not simply resourcesin the surroundings, but the ones better supporting user’stasks and needs. The proposed approach still maintains thereference URL-based mechanism detecting all Eddystone-URL beacons in a given environment. Legacy applicationsare preserved, any off-the-shelf beacon and mobile devicesupporting the base Eddystone protocol could be adopted.Hence, each URL could target: (i) a basic web page whereusers access the document via browser; (ii) an annotatedweb page, where users may either view the page or exploitfeatures allowed by metadata semantics; or (iii) a seman-tic annotation got and used by user agents. At the sametime, novel features are proposed to support peer-to-peerdevice interactions. Retrieved annotations are exploited ina semantic-based matchmaking [14] setting to compare a re-quest (e.g., user profile) with multiple beacon annotations(i.e., objects descriptions). Several resource domains (shop-ping, transportation, gaming, points of interest, work) canbe explored by simply selecting the conceptualization (i.e.,ontology) annotations are grounded on. For each pair <userprofile, resource>, a score value is the outcome of match-making: it assesses the affinity of the beacon with userpreferences. Moreover, exploiting Concept Abduction [14]non-standard inference, a full explanation about the score isgiven to the user evidencing compatible and/or missing ob-ject features determining the result. Analogously, in case ofincompatibility preferences/beacon, the Concept Contrac-tion inference [14] detects properties of the beacons causingthe mismatch.

The remainder of the paper is organized as follows. The nextsection frames the background of the proposal, while Section3 presents the envisioned Physical Semantic Web protocoland framework. Then Section 4 introduces an illustrativeexample to corroborate the comprehension of what proposedbefore, followed by an experimental evaluation in Section 5.Finally, Section 6 closes the paper sketching future work.

2. STATE OF THE ART

2.1 The Physical Web projectBasically, the Physical Web (PW) is grounded on two ele-ments: a wirelessly broadcast resource identifier (typically aURL) and a mobile device agent able to discover and showcollected nearby URLs to the user. Bluetooth Low Energy(BLE)2 beacons supporting the open Eddystone application-level protocol are used to expose identifiers. Over BLE,Eddystone protocol specification defines five different frameformats for proximity beacon messages: UID, URL, TLMUnencrypted, TLM Encrypted and EID. All messages sharea common PDU (Protocol Data Unit) format, reported inFigure 1a. It is composed by: (i) 3 bytes for flags as definedin [1, Part A]; (ii) 4 bytes for length, type and the EddystoneService UUID (Universally Unique IDentifier) (0xFEAA); (iii)resource data, comprising 4 bytes for data advertisement(also in this case the Eddystone Service UUID is included)

2https://www.bluetooth.com/what-is-bluetooth-technology/bluetooth-technology-basics/low-energy

and up to 20 bytes for the data message payload. Each Ed-dystone message type is identified by means of the four mostsignificant bits of the first octet in the data message. In par-ticular Eddystone-URL (0x10) exposes an encoded schemaprefix and a compressed and encoded URL (up to 17 bytes),fitting the message format reported in Figure 1b. The URLcan be decoded and used by clients to manage the related re-source (typically, open a Web page). Otherwise, Eddystone-UID (0x00) broadcasts a unique 16-byte beacon ID as shownin Figure 1c. Namespace ID can be used to group a set ofbeacons, while the instance ID identifies individual devicesin the group. This partition is useful to improve discoveryand filter beacons according to one or more namespaces.

(a) Eddystone Beacon PDUType 0x00

TX Power

16-bit UID

Type 0x00

TX Power

Namespace

1B 10B 6B

Instance RFU

1B 2B

Namespace ID

1B 10B 6B

Instance ID RFU

1B 2B

Type 0x10

TX Power

URL Schema

1B 0-17B

Encoded URL

1B 1B

(b) Eddystone-URL frame

Type 0x00

TX Power

16-bit UID

Type 0x00

TX Power

Namespace

1B 10B 6B

Instance RFU

1B 2B

Namespace ID

1B 10B 6B

Instance ID RFU

1B 2B

Type 0x10

TX Power

URL Schema

1B 0-17B

Encoded URL

1B 1B (c) Eddystone-UID frame

Figure 1: Format of Eddystone messages

In the current Physical Web proposal, any client support-ing BLE can scan the surrounding environment and dis-cover Eddystone-URL resources without requiring a direc-tory service or dedicated apps, which are deemed impracti-cal for simple interactions. Google provided reference clientimplementations for iOS, Android and Node.js. Eventu-ally, scanners should be implemented as background ser-vices in operating systems, so as to require no software in-stallation by users. Beacons broadcast URLs pointing toWeb pages for informative purposes or to Web apps usingadvanced technologies to offer interactive user experiences,e.g., push notifications, direct connection and remote con-trol of smart devices via Web Bluetooth3 specification, whichenables Web pages to access Bluetooth 4 devices using theGeneric ATTribute (GATT) profile to read/write attributevalues. When multiple beacons are detected in the samearea, ranking is based on beacon proximity. Previous useractions can be also taken into account by implementing thefollowing optional features: (i) history, a cache of recentlyvisited URLs; (ii) favorites, a list of bookmarked URLs; (iii)spam, a list of URLs marked as undesired.

2.2 Related workIn latest years, interesting approaches were developed to in-tegrate knowledge-based frameworks in the IoT. Resulting3Web Bluetooth, W3C Draft Community Group Re-port, 6 May 2016, https://webbluetoothcg.github.io/web-bluetooth/

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architectures usually aim to: (i) exploit ontologies to anno-tate data, devices and services; (ii) share sensor as Linked(Open) Data (LOD) [6]. Linked Stream Middleware (LSM)[9] and Ztreamy [5] are LOD platforms to publish sensordata, combine them and link them to existing resources onthe Semantic Web. [16] describes an application of knowl-edge representation to automatically create sensor compo-sitions: user goals, functional and non-functional propertiesof sensors are described w.r.t. an OWL (Web Ontology Lan-guage) ontology so that the envisioned orchestration systemis able to combine sensors and processes to satisfy a user re-quest. In [10], ontology-based sensor descriptions allow theusers to express requests in terms of device characteristics.Quantitative querying and semantic-based reasoning tech-niques are combined to improve the resource discovery andselect appropriate sensors through an exploratory search.

Other works in literature are based on Bluetooth Low En-ergy (BLE) and Eddystone. In [15], BLE beacons advertiseunique product codes, which are then associated to a seman-tic description via an Internet-based resolution procedure.Similarly, [7] creates a virtual counterpart of pervasive ob-jects in a centralized management platform, which exploitsontologies to map object categories to service types and en-able discovery and composition. Unfortunately, those ap-proaches require a support infrastructure and do not enabledirect semantic-based interactions between mobile agentsand beacons.

Valid and supported IoT protocol alternatives to BLE in-clude 6LoWPAN [8] at the network level and CoAP [2] at theapplication one. 6LoWPAN enables IPv6 packets to be car-ried on top of low-power wireless networks, while CoAP is anHTTP-like protocol for interconnected objects, designed formachine-to-machine interoperation of resource-constrainednodes. As an example, the SPITFIRE project [11] combinesSemantic Web and networking technologies to build a ser-vice infrastructure aiming to develop advanced applicationsexploiting Internet-connected sensors and lightweight pro-tocols, as CoAP. In such framework, sensors are describedas RDF triples and service discovery is based on metadata(referred for example to device features or location).

A major issue of most proposals is the requirement for astable Internet connection and/or a support infrastructureto enable discovery features. This makes them unsuitableto mobile ad-hoc networks of resource-constrained objects.As an example, the work in [3] proposes “IoT gateways” toexpose resources between CoAP and HTTP nodes. Peer-to-peer overlay network techniques are used to enable large-scale discovery among different networks. A further issue isthat the approach is based only on a resource name resolu-tion scheme, not allowing to use articulate resource featuresfor discovery, selection and ranking. Actually, all the aboveworks except [10] only allow elementary queries on annota-tions, and then only basic discovery is possible.

3. FROM THE PHYSICAL WEB TO THEPHYSICAL SEMANTIC WEB

Despite the benefits of a general-purpose and technologi-cally open approach, the Physical Web has several limits:(i) explicit interaction with the user is always required; (ii)

Table 1: PSW beacon frame type

Frame TypeHigh-Order

4 bitsLow-Order

4 bitsByteValue

UID 0000 0000 0x00

UID-PSW-BT 0000 0001 0x01

UID-PSW-WD 0000 0010 0x02

URL 0010 0000 0x10

URL-PSW 0010 0001 0x11

beacons can only broadcast a simple URL, not rich resourcedescriptions; (iii) beacon ranking is based on simplistic dis-tance criteria, without considering common and/or conflict-ing characteristics of the advertised resources w.r.t. a discov-erer’s profile or request. This paper proposes an extension ofPW vision, namely Physical Semantic Web (PSW), exploit-ing Semantic Web technologies to enable advanced resourcediscovery.

3.1 Protocol enhancementThe usage of Eddystone-URL beacons presumes an availableInternet connection to retrieve resources pointed by broad-cast URLs. In several real-world scenarios, MANETs (Mo-bile Ad-hoc NETworks) and point-to-point infrastructure-less connections provide a more flexible and effective solu-tion for wireless low-power networking. They can be partic-ularly useful in ubiquitous scenarios, where mobile objectsmust provide quick decision support and/or on-the-fly or-ganization in such intrinsically unpredictable environments.To overcome this PW limitation, the PSW enables peer-to-peer communication between BLE devices. To identify thesemantic-based beacons, three novel PSW frame types weredefined, as shown in Table 1. To ensure a full backwardcompatibility with basic Eddystone specifications, the pro-posed enhancement exploits the four low-order bits of frametype field currently reserved for future use and always set to0000.Eddystone-URL-PSW : similar to the basic URL beacon(reported in Figure 1b) but exposing an URL pointing to asemantic-based annotation rather than a simple web page.Eddystone-UID-PSW-BT : reuses the fields of an UIDbeacon, shown in Figure 1c, to transmit: (i) the MAC ad-dress of the local device exposing the resource (not neces-sarily the same device exposing the beacon) as a 6 B valueincluded in the instance ID field of the UID message; (ii)the unique ID of a specific semantic annotation providedby the object. The annotation ID is composed of an ontol-ogy ID (4 bytes) and an individual ID (6 bytes) embeddedin the namespace field of the message. Resource annotationwill be retrieved exploiting a Bluetooth File Transfer service.Eddystone-UID-PSW-WD : like Eddystone-UID-PSW-BTbut using the Wi-Fi Direct [18] protocol to retrieve the an-notation from the specified local device.

3.2 Framework architectureThe proposed PSW solution is based on four elements.A. Machine-understandable standard languages toexpress information with rich and unambiguous semantics.The devised PW extension supports both human-to-machineand machine-to-machine interactions. This grants flexibil-ity for accommodating a wider range of scenarios, including

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agent-based systems with implicit interaction patterns orwith no user involvement at all. The proposal relies on Se-mantic Web languages, and particularly on RDF (ResourceDescription Framework)4 and OWL 2 (Web Ontology Lan-guage)5, used for linking resources to domain ontologies. Anontology and a set of individuals build the Knowledge Base(KB) used for automated reasoning supporting discovery inthe reference domain.B. Objects exposing semantic annotations. Ontolo-gies are assumed as stable background knowledge, availableeither through direct wireless exchange between smart ob-jects or through the Web. Every object is able to: (i) ex-pose a semantic annotation describing its state, capabili-ties and/or factual knowledge collected from the context itis dipped in; (ii) update and enrich its annotation duringits lifecycle, in order to reflect evolution in its perceptions,goals and functionalities. Objects materialize structuredinformation with rigorous semantics and make it discover-able to nearby devices through BLE. Exploiting Eddystone-UID frames, knowledge exchange can occur through point-to-point connectivity, even without Internet connection asshown in Figure 2a. Whenever Internet access is available,they also publish the same knowledge fragments on the Weband advertise them via Eddystone-URL frames (Figure 2b),in order to make them retrievable through the standard PWmechanism.C. Knowledge discovery and sharing. The discovereragent collects UIDs and URLs from neighboring devices andpreselects only the ones corresponding to semantic annota-tions having the same reference ontology as the request. Notrelevant knowledge fragments are excluded, so reducing thecommunication and computational burden of the subsequentmatchmaking step. The matchmaking outcome is a list ofannotations ranked by relevance. In classical PW scenarios,the user is in control of the discovery process: she selectsone of the returned results pointing to a Web page withhuman-readable information and possible actions. In moreadvanced scenarios, the discovery process is performed au-tonomously by an agent device, equipped with knowledgerepresentation tools to select the best result(s) and guideautomatic interactions between objects.D. Semantic matchmaking. Knowledge discovery is sup-ported by a rigorous semantic matchmaking framework torank a set of resources according to relevance with respect toa request/profile. In order to produce a finer resource rank-ing and a logic-based explanation of outcomes, the frame-work exploits Concept Abduction and Concept Contractionnon-standard inference services [14]. Given a request R in-compatible with an available resource S, Contraction detectswhat part G (for Give up) of R is conflicting with S. If oneretracts G from R, K (for Keep) is obtained, which repre-sents a contracted version of the original request, such thatit is compatible with S. On the other hand, if R and S arecompatible but S does not match R completely, Abductionidentifies what additional features H (for Hypothesis) should

4RDF 1.1 Primer, W3C Working Group Note,24 June 2014, https://www.w3.org/TR/2014/NOTE-rdf11-primer-20140624/5OWL 2 Web Ontology Language Document Overview (Sec-ond Edition), W3C Recommendation, 11 December 2012,https://www.w3.org/TR/owl2-overview/

PSW Client URL-PSW Beacon

PW Web Service

BLE

retrieveAnnotation FromInternet()

advertiseURL()

retrieveAnnotation(url)

HTTP

HTTP

ANNOTATION

resolveShortURL()

PSW Client UID-PSW Beacon

Local Device

BLE

retrieveLocal Annotation()

advertiseUID() retrieveAnnotation

(ontoID, indID)

BT/Wi-Fi Direct ANNOTATION

BT/Wi-Fi Direct

(a) Eddystone-UID-PSW beacon

PSW Client URL-PSW Beacon

PW Web Service

BLE

retrieveAnnotation FromInternet()

advertiseURL()

retrieveAnnotation(url)

HTTP

HTTP

ANNOTATION

resolveShortURL()

PSW Client UID-PSW Beacon

Local Device

BLE

retrieveLocal Annotation()

advertiseUID() retrieveAnnotation

(ontoID, indID)

BT/Wi-Fi Direct ANNOTATION

BT/Wi-Fi Direct

(b) Eddystone-URL-PSW beacon

Figure 2: Discovery of PSW Eddystone beacons

be assumed in S in order to reach a full match. A penaltyfunction computes a semantic distance metric, as summa-rized in Figure 3. Efficient implementations of the aboveinferences exist for mobile and embedded computing archi-tectures on moderately expressive Description Logics (DLs)[14]. The final ranking score integrates semantic distancewith context-aware data-oriented attributes (i.e., distanceand user’s preference):

f(R,S) = 100[1− penalty(R,S)

penalty(R,>)(1 + dist(R,S))]

where penalty(R,S) is the penalty induced by Abductionand Contraction from request R and resource S; this valueis normalized dividing by the penalty between R and theuniversal concept (a.k.a. Top or Thing), which depends ex-clusively on axioms in the reference ontology. The dist(R,S)term is the physical distance between the discoverer agentand the discovered resource’s owner. The formula for ftranslates the semantic distance measure into a relevance0-100% ascending scale.

Request R, resource S

compatibility? Concept Abduction: find what part H of R

is missing in S

Concept Contraction: find incompatible part G

and compatible part K of R YES NO

G, K A K H

penaltyc(K) penaltya(H) context attributes

Utility function

rank

Figure 3: Semantic context-aware matchmaking

4. ILLUSTRATIVE EXAMPLEA case study in the field of Precision Agriculture (PA) isnow outlined to exemplify practical applications of the pro-posed approach. PA is a cyclical process of observation (datagathering), data interpretation and decision of actions to be

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(B) Wheat

(C) Rice

M1

A2A3

A1

M2M3

(A) Beans

Figure 4: Reference scenario

implemented. Let us consider a simple example where anagricultural land is divided into several fields, farmed withdifferent type of products each characterized by a set of fea-tures (e.g., age, growth stage). As shown in Figure 4, fieldsare managed autonomously by a team of robots (sensors andactuators), able to process data and produce shareable use-ful knowledge. Monitor robots (in red circles) collect datain the field and create a semantic-based annotation. Thesedescriptions are then processed by a mobile matchmaker toidentify most suitable actuators (in blue circles) able to per-form required actions (e.g., irrigation, fertilization). Anno-tations of both required actions and provided services aredefined with respect to a reference ontology. Particularly,ONTAgri [17] is a scalable service-oriented agriculture on-tology, organized in two main parts: (i) system concepts,defining hardware components such as physical sensing andactuation units; (ii) agriculture concepts, containing coreelements like soil characteristics, crop stages and service de-scriptions. ONTAgri has been extended for our case studyto support more detailed annotations of service and contextentities. An excerpt is in Figure 5. For example, a light ir-rigation intervention in a rice field is described like in panel(b), inheriting general information for light irrigation fromannotation in panel (a) and combining it with description ofsensed data in panel (d).

Processing is organized in two subsequent stages aiming to(i) identify all farming services required in each area and(ii) detect the most suitable actuators for each service. Thediscovery procedure can be summarized as in what follows:(1) Periodically, each monitor robot stops in a zone, gath-ers data and builds the corresponding annotation based onsensed parameters (type and stage of crop, soil moisturelevel, weather conditions, fertilizer concentration). This de-scription will become the request for a specific set of ser-vices; for the sake of brevity only irrigation and fertilizationare considered in the following example.(2) Each monitor robot identifies the needed actions for itszone by means of a matchmaking procedure exploiting theembedded Mini-ME reasoner [14]. The reasoner ranks all ac-tions modeled in the reference KB, exploiting the inferenceservices in Section 3.2, according to observed conditions.(3) The detected activity annotation is then exposed as abeacon description transmitted via Bluetooth. The beacondescription represents services required in the area and cap-

WaterSprinkler

ShortThrowSprinkler

LowCapacity

hasCapacity

LowJetLength

hasJetLength

LowRainfallPerHour

hasRainfallPerHour

(c) Actuator description

LowIrrigatedArea

hasIrrigatedArea

Sensed data annotation

RainhasWeatherCondition

BelowThresholdhasSoilMoisture

(d) Sensed description

Rice hasCropType

ActuatorSystem

Light Irrigation

LowCapacityhasCapacity

Irrigation

LowNozzleDiameter

hasNozzleDiameter

LowRainfallPerHour

hasRainfallPerHour

(a) Beacon annotation

Light IrrigationRice

RicehasCropType

BelowThreshold

hasSoilMoisture

Rain

hasWeatherCondition

(b) Service description

Service Fertilization

Figure 5: Precision agricolture u-KB excerpt

tures the actions to be implemented with the correspondingactuator specifications. Each description also contains oneor more annotation properties indicating the specific geo-graphical point or area in which the action is required.(4) Each actuator robot in the field retrieves the annotationsfrom nearby monitors, as described in Section 3. Also in thiscase, exploiting the Mini-ME matchmaker each actuator willrank the zone descriptions with the aim to identify the mostpromising one for intervention.For example, consider a field divided into 3 zones assigned tobeans (Zone A), wheat (Zone B) and rice (Zone C). Threeactuator robots provide irrigation and fertilization services.The weather is clear and each monitor robot gathers soil pa-rameters in the observed zone. Sensors acquire data androbots compose the context-aware annotations Rx, usingconcepts defined in the domain ontology. For instance, inzone C both low nitrogen fertilization and heavy irrigationservices are detected as required, because the soil moisturelevel is low, nitrogen concentration is high and rice plantsare flowering. Monitor robots now broadcast the requested

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Table 2: Actuator robots discovery results

ZoneRobot Zone A Zone B Zone C

Robot A1 72.9% 33.3% 37.2%Robot A2 55.3% 66.7% 62.9%Robot A3 55.3% 53.3% 80.0%

services through Eddystone-UID beacons, each containingthe semantic-based annotations for a specific zone. For ex-ample, zone C is described in DL formalism as follows:

ZoneC ≡ Watering u ∃ hasRainfallPerHour u∀ hasRainfallPerHour.(HighRainfallPerHour) u∀ hasCapacity.(HighWaterCapacity) u∃ hasCapacity u ∃ hasNozzleDiameter u∀ hasNozzleDiameter.(HighNozzleDiameter) uNitrogenFertilization u ∃ hasNitrogenQty u∀ hasNitrogenQty.(LowNitrogen) u∃ hasPhosphorusQty u ∃ hasPotassiumQty u∀ hasPhosphorusQty.(MediumPhosphorus) u∀ hasPotassiumQty.(MediumPotassium).

Each actuator robot in proximity discovers and collects themand exploits matchmaking to rank the retrieved beacon an-notations (what is required) w.r.t. the provided capabilities(what it offers). For example, Robot A3 in Figure 4 has thefollowing features expressed in a semantic-based annotation(full DL descriptions for other actuators are not reported forthe sake of brevity):

RobotA3 ≡ ∀ hasCapacity.(HighWaterCapacity) u∀ hasRainfallPerHour.(HighRainfallPerHour) u∀ hasNozzleDiameter.(HighNozzleDiameter) u∀ hasJetLenght.(HighJetLenght) u∀ hasNitrogenQty.(LowNitrogen) u∀ hasPhosphorusQty.(MediumPhosphorus) u∀ hasPotassiumQty.(MediumPotassium) uWaterSprinkler.

According to their characteristics, the actuators self-schedule. Robot A3 selects zone C, where a heavy irrigationand a light nitrogen fertilization service are required. In thesame way, robot A1 only provides the moderate irrigationservice, suitable for the zone A, and robot A2 capabilitiesbest fit the light irrigation and high nitrogen fertilization ser-vice required in zone B. Matchmaking scores are reported inTable 2; they take into account also the physical distance ofthe actuator from the specific area of intervention.

5. EXPERIMENTAL EVALUATIONA prototypical testbed implemented the above case study.It has three basic components:(i) Eddystone Beacons: three PSW beacons were used tobroadcast data about monitor robot annotations, accord-ing to the protocol enhancements described in Section 3.1.Eddystone messages were shared exploiting Bluvision iBeekBeacon devices6. However, a modified version of node-eddystone-beacon library was also implemented to advertisePSW resources using Node.js.(ii) Robot Platform: actuator robots were implemented us-ing ROS (Robot Operating System) Indigo [12] running on a

6http://bluvision.com/wp-content/uploads/2015/12/Specs-iBEEK1.6

UDOO Quad7 board equipped with UDOObuntu 2.0 Mini-mal Edition8 and a Java9 runtime to manage all robot tasks.It consists of the following packages:- it.poliba.sisinflab.ros.turtlebot: implements basicrobot functionalities (e.g., movement, sensor management).- it.poliba.sisinflab.psw.ble: includes classes for bea-con discovery. A PSW scanner was implemented to han-dle beacons detected through the node-eddystone-beacon-scanner project. This package also includes Java classesmodeling low-level Eddystone protocol data.- it.poliba.sisinflab.owl: provides basic functionalitiesto manage the reference KB and retrieved OWL annotationsand to invoke the Mini-ME reasoner [14] implementing non-standard inference services.(iii) Simulation Environment : robot sensors/actuators andthe farming environment were simulated exploiting Gazebo10

simulator, as shown in Figure 4.Source code of all developed projects can be found on thereference GitHub repository11.

Experiments were conducted exploiting two distinct setsof three beacons, exposing Eddystone-UID-PSW andEddystone-URL-PSW frames, respectively. For both Ed-dystone types, tests were divided in five different steps toidentify and evaluate main processing tasks and their rela-tive performance. Each test was repeated three times andaverage values were taken. As reported in Figure 6, theoverall processing of an actuator robot can be divided in thefollowing tasks: load and initialize the reference KB; dis-cover nearby beacons and retrieve the annotation exposedby the monitor robots; load retrieved annotations into theKB; perform the matchmaking and select the most suitablearea. Only the second task is different in case of UID andURL beacons. In the first case, the annotation is retrievedfrom the local device (i.e., the monitor robot) via Bluetoothafter establishing a direct P2P connection (tasks 2a and 3ain Figure 6); otherwise, the actuator robot has to resolvethe short URL exposed by the beacon (step 2b) and thenretrieve the annotation from the web using an HTTP con-nection (step 3b). In both cases, time reported for tasks 2and 3 refer to a single beacon exposing an annotation of 1.75kB. As KB loading occurs once per session, required time isnot a problem. On the other hand, connection and anno-tation retrieval via Bluetooth file transfer is slightly longerthan via Internet. This issue can be mitigated by resort-ing to annotation compression algorithms and/or exploitingfaster point-to-point protocols such as Wi-Fi Direct.Memory usage was also evaluated. The robot software re-quires very low memory and the type of detected beaconshas no influence on this value. It used only 10.8 MB on aver-age with a peak of 13.3 MB. These are reasonable values forembedded computers/boards used to build mobile robots.

6. CONCLUSIONThe paper proposed a framework enabling the Physical Se-

7http://www.udoo.org/udoo-dual-and-quad8Ubuntu OS 14.04 LTS customized for UDOO boards,http://www.udoo.org/udoobuntu-2-minimal-edition932-bit ARM Java 8 SE Runtime Env. (build 1.8.0 91-b14)

10http://gazebosim.org/11http://github.com/sisinflab-swot/psw-robotics

497

2237

664

123

117

64

32

29

10 100 1000 10000

Load KB (1) Connect to local BT device (2a)

Retrieve file from local device (3a) Short-URL Resolution (2b)

Retrieve file from Internet (3b) Load annotation (4)

Matchmaking (5)

Processing Time (ms)

Figure 6: Time of the main processing tasks

mantic Web, a novel paradigm enhancing the Physical Webprogram by Google. It basically applies the Semantic Webof Things vision to the real world. Knowledge sharing anddiscovery have been extended to fully autonomic environ-ments populated by smart objects. A possible illustrativeapplication and early experiments allowed evaluating bene-fits of the approach. Future work will validate the approachthrough a more extensive experimentation and object inter-action schemes will be further investigated in order to enablemore effective data management. In addition, main securityissues facing IoT devices [4] will be taken into consideration.

7. ACKNOWLEDGMENTSThe authors acknowledge partial support of Apulia regionproject PERSON (PERvasive game for perSOnalized treat-ment of cognitive and functional deficits associated withchronic and Neurodegenerative diseases) and Google corp.,granting the Google IoT Research Award, to sustain bothresearch and experiments on the theme.

8. ADDITIONAL AUTHORSAdditional authors: Eugenio Di Sciascio (Politecnico diBari, via E. Orabona 4 - Bari (I-70125), Italy – email: eu-

genio.disciascio@poliba.it).

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