We consider a dynamic multichannel access problem, where multiple correlated channels follow an unknown joint Markov model. A user at each time slot selects a channel to transmit data and receives a reward based on the success or failure of the transmission. The objective is to find a policy that maximizes the expected long-term reward. The problem is formulated as a partially observable Markov decision process (POMDP) with unknown system dynamics. To overcome the challenges of unknown system dynamics as well as prohibitive computation, we apply the concept of reinforcement learning and implement a Deep Q-Network (DQN) that can deal with large state space without any prior knowledge of the system dynamics. We provide an analytical study on the optimal policy for fixed-pattern channel switching with known system dynamics and show through simulations that DQN can achieve the same optimal performance without knowing the system statistics. We compare the performance of DQN with a Myopic policy and a Whittle Index-based heuristic through both simulations as well as real-data trace and show that DQN achieves near-optimal performance in more complex situations. Finally, we propose an adaptive DQN approach with the capability to adapt its learning in time-varying, dynamic scenarios. [3] has shown that dynamic spectrum access is one of the keys to improving the spectrum utilization in wireless networks and meeting the increasing need for more capacity, particularly in the presence of other networks operating in the same spectrum. In the context of cognitive radio research, a standard assumption has been that secondary users may search and use idle channels that are not being used by their primary users (PU). Although there are many existing works that focus on the algorithm design and implementation in this field, nearly all of them assume a simple independent-channel (or PU activity) model, that may not hold in practice. For instance, the operation of a low power wireless sensor network (WSN) is based on IEEE 802.15.4-radios, which uses the globally available 2.4 GHz and 868/900 MHz bands. These bands are shared by various wireless technologies (e.g. Wi-Fi, Bluetooth, RFID), as well as industrial/scientific equipment and appliances (e.g. micro-wave ovens) whose activities can affect multiple IEEE 802.15.4 channels. Thus, external interference can cause the channels in WSNs to be highly correlated, and the design of new algorithms and schemes in dynamic multichannel access is required to tackle this challenge.Motivated by such practical considerations, we consider in this work a multichannel access problem with N correlated channels. Each channel has two possible states: good or bad, and their joint distribution follow a 2 N -states Markovian model. There is a single user (wireless node) that selects one channel at each time slot to transmit a packet. If the selected channel is in the good state, the transmission is successful; otherwise, there is a transmission failure. The goal is to obtain as many successful transmissi...

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Emerging Industrial Internet of Things applications, such as smart factories, require reliable communication and robustness against interference from colocated wireless systems. To address these challenges, frequency‐hopping spread spectrum has been used by different protocols, including IEEE802.15.4‐2015 TSCH. Frequency‐hopping spread spectrum can be improved with the aid of blacklists to avoid bad frequencies. The quality of channels in most environments shows significant spatial‐temporal variation, which limits the effectiveness of simple blacklisting schemes. In this article, we propose an enhanced blacklisting solution to improve the TSCH protocol. The proposed algorithms work in a distributed fashion, where each pair of receiver/transmitter nodes negotiates a local blacklist, based on the estimation of packet delivery ratio. We model the channel quality estimation as a multiarmed bandit problem and show that it is possible to create blacklists that provide results close to optimal without any separate learning phase. The proposed algorithms are implemented in OpenWSN and evaluated through simulations in 2 different scenarios with about 40 motes and experiments using an indoor testbed with 40 TelosB motes.

Food allergy is an immunologically mediated adverse reaction to food protein. Cow's milk protein allergy (CMPA) is the most frequent type and is the one that is most difficult to diagnose. This study had the objective of analyzing the accuracy of hypersensitivity and specific IgE skin tests among children with CMPA and predominantly gastrointestinal clinical manifestations. The participants in this study were 192 children aged one and five (median of 2 yr). Among these, 122 underwent open oral challenge to the suspected food. After evaluating the sensitivity, specificity and positive and negative predictive values (respectively, PPV and NPV) of skin and specific IgE tests in relation to the gold standard (open oral challenge); all the children underwent the skin prick test (SPT), specific IgE test and atopy patch test (APT) for cow's milk, eggs, wheat and peanuts and the open oral challenge for the food to which the child was sensitive or had suspected sensitivity. Presence of food allergy was confirmed for 50 children (40.9%). Among these cases, 44/50 (88%) were of allergy to cow's milk protein. Children who presented a positive response to an oral challenge to cow's milk protein were considered to be cases, while the controls were children with negative response. Twenty-two of the 44 cases (50.0%) presented symptoms within the first 4 h after the challenge. The SPT presented 31.8% sensitivity, 90.3% specificity, 66.7% PPV and 68.4% NPV. The APT presented 25.0% sensitivity, 81.9% specificity, 45.8% PPV and 64.1% NPV. The specific IgE test presented, respectively, 20.5%, 88.9%, 52.9% and 64.6%. Despite the operational difficulty and the possible exposure risk, oral challenge is the best method for diagnosing CMPA, because of the low sensitivity and PPV of skin and specific IgE tests.

FLZ, at the bioavailable concentration present in saliva, interferes with the development of C. albicans biofilms, but does not interfere with the development of C. glabrata biofilms.

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