A Ubiquitous Knowledge-based System to enable RFID
Object Discovery in Smart Environments
M. Ruta, T. Di Noia, E. Di Sciascio, F. Scioscia and E. Tinelli
Politecnico di Bari, via Re David 200, I-70125, Bari, Italy
Abstract. This paper presents an extended framework supported by a suitable
dissemination protocol to enable ubiquitous Knowledge Bases (u-KBs) in perva-
sive RFID environments. A u-KB is a distributed and decentralized knowledge
base where the factual knowledge (i.e., individuals) is scattered among objects
disseminated within the environment, with no centralized repository and coordi-
1 Introduction
In pervasive contexts intelligence is embedded into a physical environment by means
of a relatively large number of heterogeneous micro devices such as RFID tags and
wireless sensors, each conveying a small amount of useful information. Due to power
and cost constraints, they are usually endowed with very low storage space, little or
no processing capability and short-range, low-throughput wireless links. Each mobile
host in the area can access information only on micro devices in its communication
range. Consequently, approaches based on centralized control and information storage
are utterly impractical in such scenarios.
In our previous work [1], we devised solutions for the integration of semantic-
enhanced EPCglobal RFID into Mobile Ad-hoc NETworks (MANETs). Nevertheless,
we were still forced to use a fixed central component for reasoning over a Knowledge
Base (KB). This led to expensive information duplication within the environment: se-
mantic annotations were placed simultaneously on tags and within the KB; moreover,
the reasoning engine was a single point of failure. Here we want to show how these
problems can be mitigated if a more distributed approach is followed. We present a
general framework to carry out an advanced matchmaking using metadata stored in
RFIDs lacking unique and fixed knowledge bases. An advanced resource discovery is
supported by a dissemination protocol allowing to exactly locate suitable descriptions
directly on tags attached to objects. The proposed framework comprises the specifica-
tion of components and operations of a u-KB (ubiquitous Knowledge Base), as well as
a distributed application-layer protocol for dissemination and discovery of knowledge
embedded within RFID tags in a MANET-based computing environment. RFID readers
are adopted as cluster-heads w.r.t. tags in their radio range and they are able to automat-
ically build up a multi-hop communication infrastructure when placed in the same area
[2]. For lower network layers we adopt IEEE 802.11 [3], IP and UDP, while for knowl-
edge representation we exploit DL-based ontology languages originally conceived for
Ruta M., Di Noia T., Di Sciascio E., Scioscia F. and Tinelli E. (2008).
A Ubiquitous Knowledge-based System to enable RFID Object Discovery in Smart Environments.
In Proceedings of the 2nd International Workshop on RFID Technology - Concepts, Applications, Challenges, pages 87-100
DOI: 10.5220/0001741900870100
the Semantic Web effort, particularly DIG [4], which is a more compact equivalent of
The remaining of the paper is organized as follows: in the next Section we present
motivation of our paper; in Section 3 some significant theoretical aspects of the pro-
posed approach are revised; in Section 4 we outline the proposed data propagation and
retrieval framework, whereas in Section 5 our proposal is explained and motivated by
means of a case study. Section 6 closes the paper.
2 Motivation
Motivation for this paper stems from our experience in using Knowledge Representa-
tion (KR) approaches –based on Description Logics (DLs)– in pervasive contexts, and
particularly in semantic-based discovery frameworks for objects equipped with RFID
tags [1]. In traditional applications (see [5, 1.5] for a survey) a Knowledge Represen-
tation System (KRS) plays an architectural role very much like a Database Manage-
ment System (DBMS). Both are used as central repositories where knowledge of the
problem domain can be inserted (forming a KB) and upon which automated inference
procedures can be executed in order to extract implicit knowledge. Hence, in traditional
KRSs, a KB is seen as a single large entity which is immediately available, either in
local storage or via a high-throughput network link. This approach is effective only as
long as large computing resources and a stable network infrastructure are granted. A
different approach is needed to adapt KR tools and technologies to functional and non-
functional requirements of mobile and ubiquitous computing applications. They are
characterized by user (and device) mobility, dependency on context, severe resource
limitations. Hence, knowledge-based systems designed for wired networks are hardly
adaptable, due to architectural differences and performance issues.
As Figure 1 shows, in our vision a u-KB layer provides access to knowledge em-
bedded into semantic-enhanced EPCglobal RFID tags populating a smart environment.
Discoveryand reasoning tasks can then be performed either by hosts in the local MANET
or by a remote entity through a gateway exposing a high-level interface (e.g., Web
Sevices of RPC (Remote Procedure Call) or REST (REpresentational State Transfer)
type) and translating remote methods into operations on the u-KB. This paper focuses
on the definition of the components of a u-KB layer, while parallel research effort is
being spent into the adaptation of reasoning procedures to resource-constrained mobile
2.1 Application Scenarios
All application areas of RFID technology [6] can be enhanced by a semantically rich
description and discovery layer, without depending on a centralized infrastructure. In
the lifecycle of industrial products, manufacturing and quality control can exploit accu-
rate descriptions of raw materials, components and processes. Sale depots benefit from
OWL Web Ontology Language, W3C Recommendation, February 10th 2004, available at
Fig.1. Architecture of the proposed approach.
easier inventory management and can introduce ubiquitous commerce [7] capabilities
like in [8], withouth expensive investments in infrastructure. Finally, smart post-sale
services can be provided to purchasers, by integrating knowledge discovery in home
and office appliances [1].
Asset management is greatly improved in those scenarios where retrieval should
depend on object properties and purposes, rather than mere identification codes. In
healthcare applications, equipment, drugs and patients can be thoroughly and formally
described and tracked, enabling to provide not only monitoring but also decision sup-
port to clinicians. Wireless pervasive technologies help break barriers between patient
management in the hospital and at home. Likewise, in museums and archaeological
sites, smart semantic-based content fruition can be granted to local visitors as well as to
remote clients connected through the Internet, leveraging the lightweight infrastructure
already deployed for internal inventory.
The u-KB approach can be extended to other monitoring and sensing technologies
beyond RFID. Wireless semantic sensor networks [9] are an emerging yet challeng-
ing technology. Semantic-based sensory data dissemination and query processing are
needed to enable advanced solutions for e.g., precision agriculture and disaster recov-
3 From KBs to u-KBs
3.1 KB Components and Operations
A DL knowledge base has two components [5]: a TBox, containing intensional knowl-
edge in the form of an ontology describing general concepts and properties of the ref-
erence domain; an ABox containing extensional knowledge that is specific to the indi-
viduals of a particular problem within the domain.
Current KRSs are characterized in terms of what functions they provide to appli-
cations, instead of exposing system data structures and low-level operations [10]. Two
basic functions were identified for KB management. (1) Tell: build the TBox and the
ABox by explicit assertions of terminological knowledge and information about indi-
viduals. (2) Ask: extracting (implicit) knowledge by using inference procedures that
determine if the meaning of the query is implied by the information that has been told
to the system. This paradigm has led to detailed and formal interface specifications
for Knowledge Representation Systems (such as KRSS [11] and, more recently, DIG
[4]), implemented by most KRSs. The ability to remove information from a KB is also
desirable. Due to technical reasons, most systems allow to Un-Tell (i.e., retract) only
information that has been previously told explicitly [12]. Nevertheless, experience has
shown that, for the vast majority of applications, in production environments the TBox
seldom or never changes after an initial knowledge acquisition phase (see [5, ch. 8] for
a review).
3.2 u-KB Components
In our approach we preserve the distinction between TBox and ABox. The TBox is
contained in an ontology file, which can be managed by one or more mobile hosts.
We hypothesize that ontologies are defined before object annotation and u-KB deploy-
ment and do not change during normal system activity. We adopt Ontology Universally
Unique Identifiers (OUUIDs) [13] to mark ontologies unambiguously and to associate
each individual to the ontology w.r.t. it is described.
The ABox is scattered within a smart environment, as KB individuals are physically
tied to micro devicesdeployed in the field. In RFID-based scenarios, each individual is a
semantically annotated object/product description, stored within the RFID transponder
the object is clung to. Each annotation refers to an ontology providing the intensional
knowledge for a particular domain. In detail, each individual is characterized by: glob-
ally unique item identifier (the EPC code in the case of RFID tags); OUUID; semantic
annotation, stored as a compressed document fragment in the DL-based language DIG;
a set of data-oriented attributes, which allow to integrate and extend logic-based reason-
ing services with application-specific and context-aware information processing. Since
several object categories can co-exist within the same physical space, multiple u-KBs
can actually populate the same environment and share the system infrastructure.
3.3 u-KB Operations
In our u-KB approach, we adhere to the classic Tell/Ask model. These functions, how-
ever, are implemented in a novel way, coping with the characteristics of pervasive com-
puting scenarios. Section 4 defines in detail the data structures and protocol devised to
build a u-KB system.
Tell/Un-tell operations are transparent to users, i.e., no explicit knowledge declara-
tion/retraction is required. The system allows autonomic creation and maintenance of a
“virtual” knowledge base, by means of a data alignment protocol between caches of the
various mobile hosts. Each host advertises individuals it sees in its proximity via RFID.
Other hosts store advertisements in their cache and forward them to nearby nodes. The
protocol keeps track of the freshness of advertised individuals through sequence num-
bers, so that only updates will be automatically propagated. Each individual has also
a limited Time-To-Live (TTL), so that it will be automatically removed (un-told) from
the u-KB if not renewed. The system tends toward a steady state where every host is
aware of all individuals in the environment.
Ask operations require a preliminary retrieval phase. The requester host specifies
the ontology identifier and a range for each particular attribute it is interested in. Ac-
cess to local cache tables provides IP addresses of hosts owning all the individuals that
meet the specified criteria. Pre-filtered on-demand” provisioning of KB individuals
avoids unnecessary data transfers, minimizes transmissions for data alignment and pre-
vents propagation issues in the case of description update or individual removal. The
requester can fetch ontology file and filtered individuals from their respective providers,
so as to reconstruct a local subset of the whole KB, containing only the TBox and indi-
viduals which are actually needed. Then it is able to submit any Ask-type request to a
local or remote reasoning engine. In the present work we aim at assuring “on-demand”
knowledge availability, so we will not specify further how reasoning is carried out.
4 How to Build a u-KB
The proposed framework presents a two level infrastructure where RFID is exploited at
the field layer (able to interconnect tags dipped in the environment and readers able to
receive the transmitted data) whereas the discovery layer is related to the inter-reader
ad-hoc communication (see Figure 2). The communication between the tag field and
readers exploits the semantic-enhanced EPCglobal RFID protocol data exchange [1],
whereas the data propagation among readers is performed following a data dissemina-
tion paradigm in 802.11 [3] proposed here.
Thus, the resource discovery is based on three stages: (1) the extraction of good’s
parameters (for carrying object characteristics from field layer to discovery one); (2)
resource data dissemination (to make the overall nodes fully aware of the “network
content”); (3) the extraction of resource annotations (for carrying semantic-based de-
scriptions from field level to the discovery one) for the further matchmaking. Each
reader involved in the data propagation and/or in the object discovery, maintains a cache
containing the advertisements which will be matched against requests. It plays a central
role in the whole service oriented architecture as it advertises contextual parameters
discovery layer
RFID reader/Wi-Fi PDA
RFID reader/Wi-Fi PDA
RFID tag
RFID tag
field layer
RFID tag
RFID tag
field layer
Fig.2. Field and Discovery layers in the proposed framework architecture.
referred to tags in its radio range (at the field layer) and during the further phase (at the
discovery layer), it will receive requests from nearby nodes (via 802.11) and in case it
will extract semantic annotations from tags in range (via RFID EPCglobal) so replying
to the requester.
We hypothesize each resource in the MANET is labeled by means of the triple
[SOURCE ADDRESS, OUUID, EPC], where the first value is the IP address of the
RFID reader which has “seen” the resource, the second one marks the specific reference
ontology the resource is associated with, the last one is the Electronic Product Code.
Initially, each reader will advertise, for each resource, the managed reference OUUID
as well as some context-aware parameter (i.e., the resource life time). So the initial
selection allows to choose only semantically compatible services (OUUID matching)
which have suitable values for context aware parameters.
To perform the data dissemination, resource providers periodically send advertise-
ment packets also specifying the maximum number of hops for the advertisement
travel (MAX ADV DIAMETER). The cyclic advertisement diffusion allows to cope with
tag and reader mobility. During their travel, the advertisements are forwarded using
MAC broadcasts and can be stored in the cache memories of the nodes they go through.
When starting a resource matchmaking, a node generally attempts to cover the request
by using resource descriptions stored within its own cache memory. If some seman-
tically annotated description is missing, it can be retrieved in unicast using apposite
demand PDUs. On the contrary, if a requester has no resource descriptions in its cache
or if managed resources are considered insufficient to satisfy the request, the node can
send a solicit PDU with a specified maximum travel diameter (MAX REQ DIAMETER)
in order to get new resource locators. A node receiving a solicit, replies (in unicast) pro-
viding cache table entries matching parameters contained within the solicit frame. If it
does not manage any information satisfying the solicit, it will reply with a “no matches”
message. During their travel, replies to the demand and solicit PDUs are used to update
the cache memory of forwarding nodes. Figure 3 and Figure 4 show the typical se-
quence and involved actors of the data dissemination phase and of semantic annotations
retrieval, respectively.
Fig.3. Tag data dissemination phase.
Fig.4. Retrieval of advertised semantic annotations from tags.
Table 1. PDU types exploited in the proposed framework.
A 0 Advertisement
B 1 Cache entry
C 2 Solicit
D 3 Demand
E..L 4..7 reserved
4.1 Advertisement PDU
In Figure 5 the structure of an advertisement PDU is sketched. All the resources inven-
toried by a reader are advertised by means of a unique advertisement PDU and then the
size of the packet increases proportionally with the number of tags in the reader range.
In what follows we outline PDU fields.
TYPE: the kind of PDU (see Table 1).
FLAGS: it contains one status flag to distinguish the kind of transmission (uni or broadcast);
the remaining flags are reserved for future purposes.
TRAVELED HOPS: the number of hops already traversed by the packet. A reader sets this
value to 1 and it is increased every time a node forwards the packet.
NUMBER OF RESOURCES: how many tags are in the reader radio range.
ADVERTISEMENT ID: the reader’s sequence number.
NODE SEQUENCE NUMBER: the sequence number of the node forwarding the packet. If
the packet has been sent by a reader this field value coincides with the previous one.
SOURCE ADDRESS: the IP address of the reader.
RESOURCE PARAMETERS: a composite, variable length field depending on the number of
advertised resources. In particular, it contains the OUUID value, the remaining life time of a
resource, the maximum hops number for the advertisement travel, and finally the EPC code
of the single tag.
Table 2. Constant values used in the framework.
DEFAULT RTIME Time interval between two consecutive advertisement packet
30000 ms
POLLING TIME Time a reader node waits for the echo of the advertisements 7500 ms
MAX ADV JITTER Maximum value for random time waited when forwarding
advertisement packets
600 ms
ONE HOP WAIT Timer set by a requester node after sending a solicit packet, waiting for
cache contents reception
HOP TRAVERSAL TIME Time a node needs to process and forward a solicit packet sent by a
ACK RTT Timer set by a requester node waiting for ack after a solicit has been
50 ms
DISCOVERY DIAMETER Current search diameter (in hops) during discovery phase 4
MAX RETRIES Maximum number of retransmissions before a reader assumes there are
no neighbors
A reader which has inventoried a tag series, broadcasts an advertisement every
DEFAULT RTIME milliseconds (see Table 2 for constant values). Nearby nodes for-
ward the packet by broadcasting it to their neighbors; as a consequence, the reader lis-
tens to the echo of the advertisement packet it originally transmitted. Thus, it can obtain
a confirmation of the presence of other nodes in its neighborhood. If the reader does not
receive any echo within POLLING TIME milliseconds (less than DEFAULT RTIME),
it will retransmit the advertisement, assuming that a collision or a transmission error
has occurred. After MAX RETRIES retries it can be assumed there are no neighbors, so
the transmission of the advertisement can be scheduled after a longer timeout in order
to reduce power consumption.
When a node receives an advertisement, it extracts information about the resources
and, in case of “new” items, it adds cache entries; otherwise, before updating stored
data, the node verifies if the received information is more recent or has ran across a
shorter path than the existing one. This happens also when multiple readers attempt
to scan the same tag. If the cache is updated and the maximum advertisement diam-
eter has not been reached, then the advertisement is forwarded; otherwise the whole
packet is silently discarded. This simple mechanism grants each mobile node in the
network sends the same advertisement at most once. Furthermore, in order to reduce
the collision probability (recall that MAC 802.11 protocol does not provide any ac-
knowledgment frame for broadcast transmission), each host waits a random time t
(t [0, MAX ADV JITTER]) before transmitting.
We developed an analytical model of the data dissemination protocol (not reported
here due to lack of space) to estimate whether the inclusion of the 96-bit EPC code
in each resource advertisement could lead to an unacceptable network overhead. Let
us consider a typical scenario with a partition-free network of 30 readers and 1000
tags per reader. Since typical read rates are not above 100 tags/s for EPCglobal Gen-2
UHF RFID [14], we deem that reasonably the advertisement period DEFAULT R TIME
should not be set below 30 s; moreover, let us set MAX ADV DIAMETER=4. In that
case, total traffic generated by data dissemination has an upper bound of 590 kB/s,
i.e., 20 kB/s per reader. Furthermore, generated traffic is a linear function of tag
population size, thus granting a theoretically acceptable scalability to the system.
Fig.5. Advertisement PDU.
Fig.6. Demand PDU.
4.2 Demand PDU
When starting a matchmaking, a node must firstly look within its cache table for entries
compatible with the request and, in case, it must require in unicast the corresponding
semantically annotated descriptions from their respective owners. A demand PDUs as
the one sketched in Figure 6 is used. In what follows the meaning of introduced PDU
fields is summarized.
TYPE: it is set to 3.
FLAGS: analogous to the corresponding field of the advertisement PDU.
TRAVELED HOPS: how many hops the frame has already gone across.
LAST HOP SEQUENCE NUMBER: the sequence number of the last node processing the
DESTINATION SEQUENCE NUMBER: the sequence number of the destination node.
DESTINATION ADDRESS: the address of the last node processing the request.
PROVIDER ADDRESS: is the address of the destination node.
OUUID: the ontology’s unique identifier.
NUMBER OF REQUESTS: the number of requested resource descriptions.
DATA: the size of this field depends on the number of requests; it contains the EPCs of the
tags whose descriptions are required.
4.3 Solicit PDU
Basically, the soliciting mechanism is analogous to the advertising one. Figure 8 shows
the format of a solicit packet. Not reported PDU fields are analogous to the previous
0 4 10 148 byte18 19 23 35
OUUID timestamp
resource descriptionsequence
size lifetime
Fig.7. The structure of a record in the reader cache.
TYPE: it is set to 2.
FLAGS: it maintains the ordinary structure and functionality.
TRAVELED HOPS: hops the packet has already gone across.
TOTAL HOPS: total hops the PDU has to skip. Together with the TRAVELED HOPS eld,
it regulates the frame travel.
REQUEST ID: unambiguously labels the PDU in order to distinguish different solicit re-
A node generating a solicit packet waits for an acknowledgment from each neigh-
bor for ACK RTT seconds. By means of this frame the requester elicits information
about nearby nodes: it can exactly know the number of neighbors. Each node located
at DISCOVERY DIAMETER hops from the requester, after receiving a solicit PDU,
replies with a cache content PDU in unicast toward the node the solicit cames from.
Readers receiving a cache content PDU update their own cache and recursively sends
back a cache content PDU, till the original requester node receives information it needs.
4.4 Cache Table Management
Each reader manages a cache table where it stores characteristics of both tags in its
radio range and tags it has “seen” in the network. Figure 7 shows the structure of a
typical entry. Here the content of each field is reported.
Source address: the address of the resource provider.
Size: the description size (in byte).
OUUID: a numeric identifier for the specific ontology.
Lifetime: the remaining time to live of a resource/tag.
Timestamp: it marks the last reference to the entry (read/write).
Traveled hops: distance (hops number) between provider and cache holder.
Sequence number: it is referred to the last resource provider.
EPC: the Electronic Product Code of a specific resource/tag.
Resource description: the semantically annotated description of a resource. It will
have a variable length, but in some case there could be a pointer to the compressed file
containing the DIG description.
An entry could be added to the cache table whenever the node receives an adver-
tisement or a cache content frame arrives. Note that if a node receives an advertisement
frame, the corresponding entry of the cache table is updated either if the sequence num-
ber within the packet is higher than the one stored or if the route the PDU suggests is
shorter than the previously stored one.
Fig.8. Solicit PDU.
Fig.9. Cache content PDU.
A cache content packet (whose format is depicted in Figure 9) has a variable length
according to the number of resource handles the PDU transports. PDU fields are out-
lined in what follows.
TYPE: it is set to 1.
FLAGS: it maintains the ordinary meaning.
N: the number of resources handles (and then cache tuples) the packet transports.
REQUEST ID: is the identifier of the original request.
OUUID: the identifier of the reference ontology.
LAST HOP SEQUENCE NUMBER: the sequence number of the node sending the packet.
DESTINATION ADDRESS: requester IP address.
Last fields are the resource records.
5 Case Study
5.1 RFID-based u-KB
The proposed framework has been studied specifically in pervasive computing envi-
ronments where a wide range of objects/products are endowed with RFID transpon-
ders conforming to the EPCglobal standard for class I - second generation UHF tags
[15]. Mobile RFID readers equipped with IEEE 802.11 wireless connectivity (hereafter
hosts) are responsible for u-KB creation and management. Tagged objects represent
KB individuals in our system. In our previous work [1] the EPCglobal standard was en-
hanced to support storage and retrieval on RFID tags of an ontology identifier, a set of
attributes and a compressed semantic-based annotation, in addition to the EPC identi-
fier. These basic elements recur in our definition of an individual in a u-KB, as explained
above. For the sake of conciseness, here we omit details of the semantic-enhancedRFID
protocol proposed in [1] so assuming the reader be familiar with it.
Table 3. SELECT command able to detect only semantic enabled tags.
PARAMETER Target Action MemBank Pointer Length Mask
DESCRIPTION SL flag set in case of match, EPC memory initial address number of bits bit mask
clear otherwise bank to compare
Table 4. READ command able to extract OUUID from the TID memory bank.
PARAMETER MemBank WordPtr WordCount
DESCRIPTION TID memory bank starting address read up to 2 words (32 bits)
5.2 Interactions with EPCglobal RFID Technology
1. Dissemination. After each advertisementperiod DEFAULT TIME, a host scans RFID
tags in its range. Only semantic enabled tags are preselected, by means of a Select com-
mand with parameters as shown in Table 3.
EPC codes of semantic based tags are then scanned individually and TTL of cor-
responding cache table entries are refreshed. If the host detects a new EPC code not
present in its cache table, it will read its OUUID and contextual attributes, exploiting
two Read commands, as shown in Table 4 and Table 5 respectively. Data extracted from
the RFID tag will be stored in a new cache entry with a fresh sequence number. Con-
versely, if the EPC code is not detected for an existing local entry in the cache table, the
host will wait for the TTL to expire before removing the entry. This prevents the well-
known issue of “RFID event flickering
[16] from causing incorrect removal/addition
of u-KB individuals. At the end of the loop, the cache table is fully updated and the host
can issue an advertisement PDU to notify individuals to neighboring hosts.
2. Discovery. When a host receives a demand PDU for which it is the destination, it
starts an RFID scan of semantic enabled tags only, as seen above. During inventory, for
each detected EPC among those listed in the PDU payload, it reads the semantically
annotated compressed object description stored in the User memory bank of the tag,
with a Read command as in Table 6. Finally, the host replies to the requester.
3. Ontology Provisioning. As already suggested in [1], the EPCglobal Object Naming
Service (ONS)
can be used as a fallback mechanism for ontology support in ubiquitous
computing contexts if an Internet connection is available.
4. System Evaluation. After a feasibility study using IBM WebSphere RFID middle-
ware, we are currently performing an extensive simulation campaign in ns-2
of the full
protocol stack, in scenarios with both fixed and moving readers. We expect valuable
insight for a thorough evaluation of the proposed approach, in particular w.r.t. network
load, duration of service discovery sessions, hit ratio (percentage of successful resource
retrieval) and sensitivity to topology variations (due to RFID reader and tag mobility).
Due to lack of space, we are not able to report here our early findings.
Due to collisions, a tag might not be detected in every consecutive scan. This phenomenon can
trigger spurious leave-enter event pairs.
Object Naming Service (ONS - v. 1.0), EPCglobal Ratified Specification, Oct. 4, 2005,
ns-2, the network simulator, http://www.isi.edu/nsnam
Table 5. READ command to extract contextual attributes from User memory bank.
PARAMETER MemBank WordPtr WordCount
DESCRIPTION user memory bank starting address read up to 8 words (16 bytes)
Table 6. READ command to extract the compressed semantic annotation from User memory
PARAMETER MemBank WordPtr WordCount
DESCRIPTION user memory bank starting address read up to the end
6 Conclusions
Building on our previous work, we have presented an approach to carry out an ad-
vanced matchmaking using semantic metadata stored in RFID tags without unique and
fixed knowledge bases. An advanced resource discovery framework is supported by a
knowledge dissemination protocol, so allowing an “on-demand” retrieval of suitable
descriptions directly from tags located on the objects. An analytical model has been de-
veloped for a preliminary assessment of resource requirements, while functionality tests
have been carried out in a reduced simulation environment. We are currently working
on a thorough evaluation of the approach using ns-2 simulation environment.
We wish to acknowledge support of Apulia project PE 074 “IC Technologies for track-
ing of agricultural and food products equipped with RFID tags”.
1. Ruta, M., Di Noia, T., Di Sciascio, E., Scioscia, F., Piscitelli, G.: If objects could talk: A novel
resource discovery approach for pervasive environments. International Journal of Internet
and Protocol Technology (IJIPT), Special issue on RFID: Technologies, Applications, and
Trends 2 (2007) 199–217
2. Ramanathan, R., Redi, J.: A brief overview of ad hoc networks: Challenges and directions.
IEEE Communications Magazine 40 (2002) 20–22
3. 802.11, I.: Information Technology Telecommunications and Information Exchange be-
tween Systems Local and Metropolitan Area Networks Specific Requirements Part 11:
Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications.
ANSI/IEEE Std. 802.11, ISO/IEC 8802-11. First edn. (1999)
4. Bechhofer, S., M¨oller, R., Crowther, P.: The DIG Description Logic Interface. In: Proceed-
ings of the 16th International Workshop on Description Logics (DL’03). Volume 81 of CEUR
Workshop Proceedings. (2003)
5. Baader, F., Calvanese, D., Mc Guinness, D., Nardi, D., Patel-Schneider, P.: The Description
Logic Handbook. Cambridge University Press (2002)
6. Weinstein, R.: Rfid: A technical overview and its application to the enterprise. IT Profes-
sional 07 (2005) 27–33
7. Watson, R., Pitt, L., Berthon, P., Zinkhan, G.: U-Commerce: Expanding the Universe of
Marketing. Journal of the Academy of Marketing Science 30 (2002) 333–347
8. Di Noia, T., Di Sciascio, E., Donini, F., Ruta, M., Scioscia, F., Tinelli, E.: Semantic-based
bluetooth-rfid interaction for advanced resource discovery in pervasive contexts. Interna-
tional Journal on Semantic Web and Information Systems (IJSWIS) 4 (2008) 50–74
9. Ni, L., Zhu, Y., Ma, J., Li, M., Luo, Q., Liu, Y., Cheung, S., Yang, Q. In: Semantic Sensor
Net: An Extensible Framework. Springer Berlin / Heidelberg (2005) 1144–1153
10. Levesque, H.: Foundations of a Functional Approach to Knowledge Representation. Artifi-
cial Intelligence 23 (1984) 155–212
11. Patel-Schneider, P., Swartout, B.: Description-Logic Knowledge Representation System
Specification. (KRSS Group of the ARPA Knowledge Sharing Effort)
12. Brachman, R., McGuinness, D., Patel-Schneider, P., Resnick, L., Borgida, A.: Living with
CLASSIC: When and how to use a KL-ONE-like language. Principles of Semantic Networks
(1991) 401–456
13. Ruta, M., Di Noia, T., Di Sciascio, E., Donini, F.: Semantic-Enhanced Bluetooth Discovery
Protocol for M-Commerce Applications. International Journal of Web and Grid Services 2
(2006) 424–452
14. Kawakita, Y., Mistugi, J.: Anti-collision performance of Gen2 air protocol in random error
communication link. In: Proceedings of the International Symposium on Applications and
the Internet Workshops - SAINT 2006. (2006) 68–71
15. Traub, K., Allgair, G., Barthel, H., Bustein, L., Garrett, J., Hogan, B., Rodrigues, B., Sarma,
S., Schmidt, J., Schramek, C., Stewart, R., Suen, K.: EPCglobal Architecture Framework.
Technical report, EPCglobal (2005)
16. R¨omer, K., Schoch, T., Mattern, F., D¨ubendorfer, T.: Smart Identification Frameworks for
Ubiquitous Computing Applications. Wireless Networks 10 (2004) 689–700