An On-Demand TDMA Approach Optimized for Low-Latency IoT Applications.

The never-ending evolution of the Internet of Things ecosystem is reshaping the arena of wireless communications and competing against conventional networking solutions in fields such as battery life, device and deployment cost, coverage, and support for an immense number of devices. Inspired by this phenomenon, this paper presents a novel Medium Access Control protocol utilizing long-range technology, based on a Time Division Multiple Access communication protocol variant, adjusted to make better use of each device's hardware. Focusing on Low Power Wide Area Network applications, this implementation improves data latency and offers amplified performance due to better network awareness and dynamic time slot rescheduling. Various simulation scenarios were contrived to evaluate the protocol's performance. The results instate the proposed algorithm as a promising access scheme for the IoT field.

Towards the Definition of an Intelligent Triage and Continuous Monitoring System for Hospital Emergency Departments and Clinics.

Recent statistics have demonstrated that Emergency Departments (EDs) in Greece lack in organization and service. In most cases, patient prioritization is not automatically implemented. The main objective of this paper is to present IntelTriage, a smart triage system, that dynamically assigns priorities to patients in an ED and monitors their vital signs and location during their stay in the clinic through wearable biosensors. Initital scenarios and functional requirements are presented as preliminary results.

A new class of epsilon-optimal learning automata.

A new class of P-model absorbing learning automata is introduced. The proposed automata are based on the use of a stochastic estimator in order to achieve a rapid and accurate convergence when operating in stationary random environments. According to the proposed stochastic estimator scheme, the estimates of the reward probabilities of actions are not strictly dependent on the environmental responses. The dependence between the stochastic estimates and the deterministic ones is more relaxed for actions that have been selected only a few times. In this way, actions that have been selected only a few times, have the opportunity to be estimated as "optimal," to increase their choice probability and consequently, to be selected. In this way, the estimates become more reliable and consequently, the automaton rapidly and accurately converges to the optimal action. The asymptotic behavior of the proposed scheme is analyzed and it is proved to be epsilon-optimal in every stationary random environment. Furthermore, extensive simulation results are presented that indicate that the proposed stochastic estimator scheme converges faster than the deterministic-estimator-based DP(RI) and DGPA schemes when operating in stationary P-model random environments.