FASOR Retransmission Timeout and Congestion Control Mechanism for CoAP

The Constrained Application Protocol (CoAP) has been designed to be used on constrained devices such as Internet of Things (IoT) devices. The existing congestion control algorithms for CoAP have known shortcomings in addressing congestion and retaining a good level of performance when link errors occur. In this paper, we propose Fast-Slow RTO (FASOR) mechanism that takes into account special needs in wireless environments while still properly addressing congestion. We run a series of experiments to confirm that FASOR is able to successfully cope with challenging network conditions such as bufferbloat, high level of congestion, and high link-error rates unlike the default and CoCoA congestion control that have severe problems with bufferbloated congestion.Peer reviewe

Is CoAP Congestion Safe?

A huge number of Internet of Things (IoT) devices are expected to be connected to the Internet in the near future. The Constrained Application Protocol (CoAP) has been increasingly deployed for wide-area IoT communication. It is crucial to understand how the specified CoAP congestion control algorithms perform. We seek an answer to this question by performing an extensive evaluation of the existing IETF CoAP Congestion Control proposals. We find that they fail to address congestion properly, particularly in the presence of a bufferbloated bottleneck buffer. We also fix the problem with a few simple modifications and demonstrate their effectiveness.Peer reviewe

Performance Evaluation of Constrained Application Protocol over TCP

The Constrained Application Protocol (CoAP) is specifically designed for constrained IoT devices and is being rapidly deployed for the communication needs of the IoT devices. CoAP has been specified with its own congestion control algorithms because it runs on top of UDP that does not include any congestion control measures. These algorithms aim at taking into account the specific needs of the IoT communication. The need of running CoAP also over TCP has arised recently and is expected to be increasingly deployed alongside with CoAP over UDP. To understand the benefits and shortcomings of both CoAP over TCP and CoAP over UDP, we run an extensive set of experiments in different network settings and compare the performance of CoAP over TCP to the existing congestion control algorithms for CoAP over UDP. Our results reveal that even though CoAP over TCP has its known limitations it scales well and performs even better than expected in certain wireless settings that CoAP over UDP algorithms are specifically designed for, often even outperforming CoAP over UDP.Peer reviewe

The role of visual spatial attention in audiovisual speech perception

International audienc

Using hidden Markov model to uncover processing states from eye movements in information search tasks

We study how processing states alternate during information search tasks. Inference is carried out with a discriminative hidden Markov model (dHMM) learned from eye movement data, measured in an experiment consisting of three task types: (i) simple word search, (ii) finding a sentence that answers a question and (iii) choosing a subjectively most interesting title from a list of ten titles. The results show that eye movements contain necessary information for determining the task type. After training, the dHMM predicted the task for test data with 60.2% accuracy (pure chance 33.3%). Word search and subjective interest conditions were easier to predict than the question-answer condition. The dHMM that best fitted our data segmented each task type into three hidden states. The three processing states were identified by comparing the parameters of the dHMM states to literature on eye movement research. A scanning type of eye behavior was observed in the beginning of the tasks. Next, participants tended to shift to states reflecting reading type of eye movements, and finally they ended the tasks in states which we termed as the decision states. (C) 2008 Elsevier B.V. All rights reserved

User Models from Implicit Feedback for Proactive Information Retrieval

Our research consortium develops user modeling methods for proactive applications. In this project we use machine learning methods for predicting users' preferences from implicit relevance feedback. Our prototype application is information retrieval, where the feedback signal is measured from eye movements or user's behavior. Relevance of a read text is extracted from the feedback signal with models learned from a collected data set. Since it is hard to define relevance in general, we have constructed an experimental setting where relevance is known a priori