An emotional and context-aware model for adapting RSS news to users and groups

The exhibition of information does not always attend to the preferences and characteristics of the users, nor the context that involves the user. With the aim of overcoming this gap, we propose an emotional context-aware model for adapting information contents to users and groups. The proposed model is based on OCC and Big Five models to handle emotion and personality respectively. The idea is to adapt the representation of the information in order to maximize the positive emotional valences and minimize the negatives. To evaluate the proposed model it was developed a prototype for adapting RSS news to users and group of users.

RSS and LEA adaptation for indoor location using fingerprinting

Fingerprinting is an indoor location technique, based on wireless networks, where data stored during the offline phase is compared with data collected by the mobile device during the online phase. In most of the real-life scenarios, the mobile node used throughout the offline phase is different from the mobile nodes that will be used during the online phase. This means that there might be very significant differences between the Received Signal Strength values acquired by the mobile node and the ones stored in the Fingerprinting Map. As a consequence, this difference between RSS values might contribute to increase the location estimation error. One possible solution to minimize these differences is to adapt the RSS values, acquired during the online phase, before sending them to the Location Estimation Algorithm. Also the internal parameters of the Location Estimation Algorithms, for example the weights of the Weighted k-Nearest Neighbour, might need to be tuned for every type of terminal. This paper focuses both approaches, using Direct Search optimization methods to adapt the Received Signal Strength and to tune the Location Estimation Algorithm parameters. As a result it was possible to decrease the location estimation error originally obtained without any calibration procedure.

Visualização personalizada sensível ao contexto e às emoções

Mestrado em Engenharia Informática

BANMAC: an opportunistic MAC protocol for reliable communications in body area networks

We consider reliable communications in Body Area Networks (BAN), where a set of nodes placed on human body are connected using wireless links. In order to keep the Specific Absorption Rate (SAR) as low as possible for health safety reasons, these networks operate in low transmit power regime, which however, is known to be error prone. It has been observed that the fluctuations of the Received Signal Strength (RSS) at the nodes of a BAN on a moving person show certain regularities and that the magnitude of these fluctuations are significant (5 - 20 dB). In this paper, we present BANMAC, a MAC protocol that monitors and predicts the channel fluctuations and schedules transmissions opportunistically when the RSS is likely to be higher. The MAC protocol is capable of providing differentiated service and resolves co-channel interference in the event of multiple co-located BANs in a vicinity. We report the design and implementation details of BANMAC integrated with the IEEE 802.15.4 protocol stack. We present experimental data which show that the packet loss rate (PLR) of BANMAC is significantly lower as compared to that of the IEEE 802.15.4 MAC. For comparable PLR, the power consumption of BANMAC is also significantly lower than that of the IEEE 802.15.4. For co-located networks, the convergence time to find a conflict-free channel allocation was approximately 1 s for the centralized coordination mechanism and was approximately 4 s for the distributed coordination mechanism.