A simulation model for localization of pervasive objects using heterogeneous wireless networks
Introduction
Localization of pervasive objects is a relevant advanced facility in ubiquitous contexts, which aims at providing context-aware services to mobile users. The recent interworking between the third generation (3G) of wireless wide area communication networks (e.g., Universal Mobile Telecommunications System – UMTS), and the current generation of wireless local area networks (i.e., IEEE 802.11), has allowed to leverage wireless networks for localization purposes [2]. Proprietary architectural solutions, that integrate heterogeneous technologies, have been designed to support identification and positioning of mobile devices in outdoor scenarios [3]. The spreading of wireless hot-spots into many public and private places, as well as the new generation of mobile devices (that support several positioning technologies, e.g., Bluetooth, Wi-Fi, RFID), has fostered the development of integrated positioning systems for indoor scenarios [4]. Moreover, in order to encourage a multi-vendor deliverable framework for leveraging the interoperability among different location platforms, open-standards based solutions have been proposed [5].
However, location services availability is always related to the accessibility and the reliability of the positioning infrastructure, as well as the capabilities of the device/object to exploit such an infrastructure for being located. There are application contexts which are not always compliant with these conditions. For instance, in post-disaster scenarios implemented positioning solutions may be compromised, when the effects of disastrous phenomena, like an earthquake or a tsunami, affect the working condition of network infrastructure, in part or in total. Moreover, in ordinary situations users could require location of objects/things (e.g., digital projectors, books, work tools), which can be unable to infer their position. In general, beyond the availability of wide area wireless communication infrastructures, different issues can affect identification and localization capability, such as the availability of positioning infrastructures, and/or the limited resources and equipments of the mobile devices, which could not support the required positioning technologies (e.g., GPS and/or Wi-Fi interfaces).
Pervasive computing paradigm enables new solutions that can be used to overcome the mentioned deficiencies. For example, after an earthquake or an avalanche, personal pervasive things or objects (e.g., mobile phones, keyrings equipped with radio frequency identification tags) can be used to localize lost peoples. These objects can be able to interact with neighbouring smart devices, or at least, they can be discoverable via radio frequency technologies, including Bluetooth and Radio Frequency Identification (RFID), in order to notify their identity.
In the following, we present three application scenarios, and we explore the deficiencies of the current positioning systems in these scenarios. Then, we introduce a solution for inferring location information about discoverable pervasive objects, that is based on collaboration of mobile agents, in heterogeneous wireless networks (i.e., 3G, WLAN) and ad-hoc networks (e.g., Bluetooth). In particular, the proposed solution allows to provide location-based services (LBSs), that support the localization of pervasive objects, by different technologies (e.g., Bluetooth or RFID sensors), in critical conditions. When users look for any kinds of object, a position request, in the form of mobile agent, is forwarded to all smart devices, which are moving in the area of interest. Mobile agents [15] are software components able to move on mobile devices through peer-to-peer connections. They are in charge of cooperating to discover and identify surrounding components, in order to distribute and multiply the search action as well as to compute their position and to notify it to the requesting application.
We present an architectural model that exploits a layered infrastructure compliant with emerging standards. In particular, the mobile agents based proposed solution is integrated as a layer of an infrastructure that implements the OMA Secure User Plane Location (SUPL) protocol [5]. It acts as an enabler, which uses existing standards, where feasible, to exchange positioning information between the mobile terminal and the application. The use of multiple levels in the architecture provides appropriate abstractions in order to support an extensible framework, so that new positioning technologies, supported by the network or by the mobile device, can be added without requiring any changes to the existing architecture.
Finally, a simulation tool has been developed in order to evaluate the proposed solution in different application scenario.
The paper is organized as follows. In Section 2 we present three application scenarios. In Section 3 we discuss the elements have to be blended in positioning system design. In Section 4 we describe how agents perform localization of discoverable objects. Section 5 Architecture, 6 The mobile agent framework for context-awareness provisioning present the location architecture model. The communication scenario is described in Section 7. Section 8 presents the simulation tool and model adopted during the experimental tests. Preliminary results are presented in Section 9. In Section 10 we discuss related work. Finally, Section 11 provides conclusions.
Section snippets
Application scenarios
It is very easy to find, in vicissitudes of every-day life, application scenarios which highlight the weakness of current solutions for positioning. In the following tree possible scenarios are presented.
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Supervision of safety in constructing sites: In constructing sites workers must behave according to defined rules in order to prevent accidents. For example, a rule can require that a worker does not forget safety belt when it is near a scaffold. Dangerous situations, like that one shown in
Pervasive objects localization through mobile smart devices cooperation
In order to locate pervasive objects through heterogeneous networks and scenarios, like those previously described, new elements have to be blended in the different components involved in positioning system design, including wireless communication networks, positioning systems, location architectures and protocols, open location standards, and mobile devices. The wireless network infrastructure allows delivering of LBSs as well as transferring of service requests and location information. The
Extended positioning as an emergent property of a multi-agents system
To overcome the limitations of the presented scenarios, devices capabilities are exploited to complement and/or recover the location awareness of the positioning service. On-board interfaces (e.g., GPS receiver, Wi-Fi antennas), available context information (e.g., access points, received signal strength, street address, close points of interest, and so on), and location awareness of neighbour devices represent sources of heterogeneous information to be collected and inferred for positioning.
Architecture
In order to provide ubiquitous position information through different inter-working pervasive environments, a hierarchical SUPL-based architecture, showed in Fig. 4, supports the integration of heterogeneous facilities at different levels by abstraction of their interfaces. This architecture acts as an enabler to support several kinds of networks with different and complementary location-sensing features: The classical 3G/4G infrastructure at the first level, and WLAN at the second level (i.e.,
The mobile agent framework for context-awareness provisioning
As previous stated, we exploited mobile agents for provision of context awareness to applications, which need to get information about mobile devices and session parameters in order to optimize the delivery of their facilities.
A positioning service, developed According to the Service Oriented Architecture (SOA) model, exposes a Web Service interface [24]. This supports the interoperability with any kinds of applications, which are not aware about agent technology. The context is described by a
Overall interaction protocol
When an MLS application requests the position of a PO, which is supposed to be located in a specific area, covered by WWAN and identified by symbolic name (e.g., a city, a street) or geographic coordinates, the following interaction protocol is performed.
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Step A: A MLS application requests the position of the PO to the H-SLP. It consists of a message that contains the parameters of the location request (i.e., the identifier of the object it intends to locate, the identifier of the area in which
The simulation tool
In order to evaluate the proposed solution in the application scenarios described in Section 2, we implemented a prototype tool to simulate the movements of pervasive devices in a workspace area. The proposed solution is composed of tree main software modules:
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Drawing module: It allows drawing a bit map of the working area, and placing obstacles into the drawn workspace.
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Simulation module: It implements the model of movements of the pervasive device in the workspace, as well as the propagation of
Preliminary experiments
The objective of the following preliminary simulations are to evaluate the proposed solution in real case studies. In particular, preliminary experiments aim to evaluate the probability to find the pervasive object, within a maximum time, and on the base of smart devices to be involved during the discovery process.
The configuration parameters of the considered model are:
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h_SD: Number of SDs;
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s_SD: Number of SDs that are initially informed to search;
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n_PO: Number of POs (each PO is identifies by
Related work
A lot of contributions can be found in literature, which use information and facilities available in ubiquitous and pervasive environments, to provide added value services, and support users to exploit them. This increasing interest is due to the feasibility of effective utilization of low-cost, low-power, multi-functional sensor nodes that are small in size and communicate in short distances [16]. The ever-increasing capabilities of these tiny sensor nodes, which consist of sensing, data
Conclusion
We explored the deficiencies of current solutions for positioning in some usual or special scenarios of every-day life. To overcome the highlighted limitations an effective architectural model that exploits a layered infrastructure can be extended with an ad-hoc network of mobile smart devices. At this layer localization of discoverable objects can be based on collaboration of mobile agents. Agents are able to exploit peripherals/sensors of mobile hosting terminals in order to discover
Acknowledgment
This work has been supported by PRIST 2009, Fruizione assistita e context aware di siti archelogici complessi mediante terminali mobile, founded by Second University of Naples.
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