Current language processing technologies allow the creation of conversational chatbot platforms. Even though artificial intelligence is still too immature to support satisfactory user experience in many mass market domains, conversational interfaces have found their way into ad hoc applications such as call centres and online shopping assistants. However, they have not been applied so far to social inclusion of elderly people, who are particularly vulnerable to the digital divide. Many of them relieve their loneliness with traditional media such as TV and radio, which are known to create a feeling of companionship. In this paper we present the EBER chatbot, designed to reduce the digital gap for the elderly. EBER reads news in the background and adapts its responses to the user's mood. Its novelty lies in the concept of “intelligent radio”, according to which, instead of simplifying a digital information system to make it accessible to the elderly, a traditional channel they find familiar -background news- is augmented with interactions via voice dialogues. We make it possible by combining Artificial Intelligence Modelling Language, automatic Natural Language Generation and Sentiment Analysis. The system allows accessing digital content of interest by combining words extracted from user answers to chatbot questions with keywords extracted from the news items. This approach permits defining metrics of the abstraction capabilities of the users depending on a spatial representation of the word space. To prove the suitability of the proposed solution we present results of real experiments conducted with elderly people that provided valuable insights. Our approach was considered satisfactory during the tests and improved the information search capabilities of the participants.

Automatic identification based on radio frequency identification (RFID) is progressively being introduced into industrial environments, enabling new applications and processes. In the context of communications, RFID rely mostly on Frame Slotted Aloha (FSA) anticollision protocols. Their goal is to reduce the time required to detect all the tags within range (identification time). Using FSA, the maximum identification rate is achieved when the number of contending tags equals the number of contention slots available in the frame. Therefore, the reader must estimate the number of contenders and allocate that number of slots for the next frame. This paper introduces the new MFML-DFSA anticollision protocol. It estimates the number of contenders by means of a maximum-likelihood estimator, which uses the statistical information from several frames (multiframe estimation) to improve the accuracy of the estimate. Based on this expected number of tags, the algorithm determines the best frame length for the next reading frame, taking into account the constraints of the EPCglobal Class-1 Gen-2 standard. The MFML-DFSA algorithm is compared with previous proposals and found to outperform these in terms of (lower) average identification time and computational cost, which makes it suitable for implementation in commercial RFID readers.

In this paper, we propose and evaluate the possibility of extending vehicular controller-area network (CAN) fieldbuses with delay-tolerant networks (DTNs) based on the multihop car2car (C2C)-or vehicle2vehicle-paradigm. Previous research has focused on single-hop wireless communications, which are inadequate in many real-life scenarios [1]. We demonstrate that efficient network layers, such as DTNs, are necessary to overcome those limitations. As a proof of concept, we present a working prototype of a system that extends the standard diagnostics interface of a CAN bus.

With the shortage of spectrum in conventional cellular frequencies, millimeter-wave (mmWave) bands are being widely considered for use in next-generation networks. Multihop relaying is likely to play a significant role in mmWave cellular systems for self backhauling, range extension and improved robustness from path diversity. However, designing scheduling policies for these systems is challenging due to the need to account for both adaptive directional transmissions and dynamic time-division duplexing schedules, which are key enabling features of mmWave systems. This paper considers the problem of joint scheduling and congestion control in a multihop mmWave network using a Network Utility Maximization (NUM) framework. Interference is modeled with an exact model and two auxiliar simplified models: actual interference (AI), with a graph-based calculation of the Signal to Interference plus Noise Ratio (SINR) depending on dynamic link activity and directivity, as well as upper and lower bounds computed from worst-case interference (WI) and interference free (IF) approximations. Throughput and utility optimal policies are derived for all interference models (AI, WI and IF) with both deterministic Maximum Weighted and randomized Pick and Compare scheduling algorithms, jointly with decentralized Dual Congestion Control. Results are evaluated with numerical simulations, using accurate mmWave channel and beamforming gain approximations based on measurement campaigns.