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  1. Bibliografia Basilare dell'AT - Old Testament Basic Bibliography
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Patwari, I. Hero, A. Perkins, N. Correal, and R. Bergamo and G. Ha inoltre recente esperienza attiva nel campo della localizzazione in ambienti chiusi e di tecnologie relative al controllo in contesti di AAL Ambient Assisted Living. Abbiamo in programma l'acquisto di altri di questi dispositivi per costruire reti di ancore complesse e per poterli situare sul corpo.

Le misure produrranno registrazioni di letture RSSI ottenute da un sensore mobile durante la trasmissione di diverse ancore. Saranno considerate diverse situazioni ambientali, partendo dal caso semplice di un nodo mobile statico is una stanza isolata. Le letture RSSI saranno registrate con tempi precisi, in maniera da permettere precise analisi delle serie temporali, e le registrazioni saranno periodicamente inviate a un ricevitore di dati. Questi verranno perturbati secondo gli errori misurati al passo precedente, per costruire una piattafomra capace sia di valutare le prestazioni di un dato ambiente e algoritmi, sia di stabilire generici limiti superiori di prestazione per algoritmi di localizzazione e identificazione della postura.

Nella prossima sezione descriviamo il nostro progetto di usare la simulazione e l'analisi numerica per calcolare limiti superiori per le prestazioni di un qualunque algoritmo dato l'ambiente e le fonti di informazione. Alcuni risultati preliminari mostrano la robustezza dell'uso del criterio di massima verosimiglianza per stimare la posizione del mobile partendo da simulazioni. Definite quindi le fonti di informazione, come l'RSSI ricevuto da un numero fisso di ancore senza memoria delle posizioni passate e un solo sensore mobile, saremo in grado di calcolare la massima precisione ottenibile da un algoritmo di localizzazione.

Alcuni primi risultati ci fanno ritenere che questa procedura possa funzionare con ogni insieme di fonti informative, per sistemi sia con che senza memoria, al fine della localizzazione e dell'identificazione della postura. Messi insieme, questi risultati daranno una solida base per creare e valutare algoritmi che funzionino in condizioni relistiche, segnando uno stacco da quella letteratura che si fonda solo su ipotesi propagative o semplicistiche simulazioni.

Gli algoritmi scelti per la valutazione saranno tratti dalla letteratura ed eventualmente inventati ex novo. Testo inglese The Pisa CNR research unit has gained considerable experience in measurement, analysis, simulation and modelling of wireless signals and statistical methods. Additionally, it has recent active experience in indoor localisation and technologies related to monitoring and control in AAL Ambient Assisted Living frameworks. It is currently involved in two international projects related to localisation. It is centred on Audio Video systems and applications convergence, in particular in home environments with homes as spaces of convergence, and for nomadic users with advanced mobile devices as points of convergence.

The aim is to evolve from the home-centric or device-centric approaches to a user-centric convergence, where the knowledge of the user position has a fundamental role. It aims at developing a scalable open standard technological platform for building a broad range of AAL services. The main technical challenge is the design of a self-organising middleware infrastructure that guarantees ad hoc extensibility of component and device ensembles.

One of its roles is to gather localisation information of users from sensors and make it accessible to other network elements and applications. By building on this expertise, this research unit will concentrate on a series of initial steps, namely Measurements, Simulation and Numerical Analysis.

Bibliografia Basilare dell'AT - Old Testament Basic Bibliography

The results of these activities, together with the initial results from the other research units, will be the building base for studying New Or Improved Localisation And Posture Identification Algorithms and evaluating them. Measurements Measurements will be made primarily by using the Micaz motes produced by Crossbow, with which this research unit has had experience in the recent past and which are available to us. We plan to buy more of these devices in order to build even complex anchor networks and in order to be able to place several of them on body.

Measurements will produce logs of RSSI readings registered by a mobile when several to many anchors are transmitting. Different environment conditions will be considered, starting from the simple case of a static mobile node in an isolated room. The simple case will be then gradually made more complex, by switching to an office not isolated room and several rooms; by using a number of mobile nodes; by introducing the effect of the body; by considering a moving body in a controlled pattern and eventually a moving body in uncontrolled pattern.

RSSI readings will be logged with precise time stamping, so to allow accurate time series analysis, and the logs will be periodically transmitted to a data sink. The data sink may be a portable computer attached to the mobile sensors, a computer connected to the mobile sensors via the IEEE As soon as first results from the Pisa University research unit are available, we plan to use specific antennas and to position the sensors on the body according to them.

Simulation Simulation studies will be made in parallel with the measurement activity and require strong interaction with the Pisa University unit since the very beginning. They will be based on received signal power as computed by the ray-tracing model developed by the Pisa University research unit. Simulation will be used in two steps. As the first step, we will assess how much the measured trace logs can be predicted by using the ray-tracing simulation, which necessarily simplifies the environment.

From preliminary results, we expect to find that the measured traces are highly sensitive to small errors, both in the exact location where the measurement is done, in the RSSI readings and in the transmitted power level. We want to quantify this sensitivity and additionally to assess sensitivity with respect to antenna modelling and body presence, orientation and posture. This assessment will help set higher bounds on the precision attainable using a given sensor configuration and given algorithms in the subsequent step.

The second step in simulation is to start from the ray-tracing results, assumed to be a high-precision special case, to perturbate them according to the errors measured in the previous step, and thus build a platform which is able to both evaluate the performance of given environment and algorithms and to establish generic upper bounds on performance of localisation and body tracking algorithms.

At the current stage, we suppose that simulation will not be directly useful to build algorithms for localisation and body posture identification. However, we anticipate that it will be precious to compare them. In the next section we detail how we envision that simulation coupled with numerical analysis can be used to compute upper bounds on the performance of any conceivable algorithm with given environment and sources of information. Numerical analysis We need to extract statistics from the measured data and from the simulation data.


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Data logs from measured RSSI readings will be analysed to assess stability, reproducibility, presence of interfering patterns, sensitivity to several sources of error as above detailed. The most significant application of numerical analysis will be devoted to statistics on simulation data. Preliminary results indicate that usage of a maximum-likelihood criterion to estimate the mobile position based on simulation results is very robust.

One objective is to estimate, based on these findings, upper bounds of performance for a given environment and given sources of information. In the simplest case, we draw a map of the environment and we simulate the RSSI distribution using the ray-tracing model. We then define the sources of information, for example RSSI readings received by fixed number of anchors without memory of past positions and a single mobile node receiver.

Given those constraints we will be able to compute the maximum precision that any localisation algorithm can attain. The objective is to change or expand the sources of information and compare the maximum theoretical performance one can attain. From preliminary results, we are confident that this procedure will work for any set of information sources, for both localisation and posture identification, and for memoryless systems as well as systems with memory.

This approach is very interesting and general, because it promises to be able to exploit any source of information to increase the precision of the result. Knowledge of the history of the system or a priori statistical information on the movements of the mobile can be accounted for by using a Bayesian approach.

The use of Kalman filters may also be investigated for this purpose. Notice that being able to exploit any source of information implies that results relative to localisation alone can be complemented with those relative with body posture alone, enriching both. As an example, the body posture can complement the localisation information by suggesting whether small changes in the received RSSI are due to noise or to the mobile moving. And localisation can complement body posture identification by suggesting that in a given position in a room there are only few possible postures such as on a bed or behind a desk.

In summary, the described simulation framework coupled with numerical analysis can be generally used as a benchmark for possible localisation and body posture identification algorithms. Searching for new or improved localisation and posture identification algorithms The last research item has the highest potential for new and better applications in the fields of AAL frameworks, monitoring and control in pervasive and ubiquitous computing and in general all environments benefiting from indoor localisation and body posture identification.

This activity is based on common work of all research units, and will build on previous results gathered by each of them. The results used will be the measurement results produced by the CNR unit and the antenna and propagation results produced by the two other units: together, these results will be a solid foundation for devising and evaluating algorithms that work in realistic conditions, marking a difference from much literature that only relies on propagation hypotheses or unsophisticated simulation.

The algorithms elected for consideration will be taken from the literature and possibly invented ex novo. The measurements and the models developed by all units will be used to compare them, keeping real-world sources of error into account. The ray-tracing model will provide a means to compare different algorithms, by analysing their behaviour in realistically simulated environments.

Additionally, the performance of known, improved and possibly brand new algorithms developed during this project will be compared with the maximum attainable performance computed using the maximum likelihood approach mentioned in the previous section. As far as the kind of algorithms to be considered, there is a whole range of possible methods to be examined, starting from likelihood-based, Bayesian and Kalman filtering methods and possibly considering neural networks and fuzzy logic.

Most of these methods make most sense when exploiting knowledge of the history of the system, that is, when the system has memory. The coupling of localisation and body posture identification proposed in this project is the ideal stage to investigate and develop methods using memory information, for example when tracking the user's movements. We may also consider introducing adaptive capabilities, which provide a sort of dynamic calibration of the algorithm's parameters, and exploiting proximity information gathered from domotic environments where many devices are interconnected and can provide information of the moment they are operated on, thus signalling when the user is close to the device.

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