Messages prioritization in IEEE 802.15.4 based WSNs for roadside infrastructure

Wireless Sensor Networks (WSNs) have gained wide-scale popularity for real-time events monitoring and detection due to their high accuracy and ease of deployment. Therefore, they have become increasingly prevalent solutions in several application domains such as health-care, transportation, etc. Many studies in the literature have focused on optimizing the energy consumption of the wireless sensors in order to ensure their autonomous operation for longer periods. Some applications of WSNs, however, have strict delay requirements due to the nature of the information being transmitted. This delay should be kept as short as possible to ensure timely and efficient reaction of the system, such as a Traffic Management System (TMS) dealing with an incident on the road. In this context, we propose in this work, two novel schemes to reduce the transmission delay of prioritized messages in WSNs deployed in road networks to report regular traffic as well as event-driven (i.e. incidents) information. These schemes improve the backoff computation mechanism at the MAC layer of IEEE 802.15.4 standard protocol, by providing an original dual mode operation mechanism which speeds up the transmission of event-driven messages, while keeping the transmission delay of periodic messages reasonably low. Simulation results show that our schemes succeed in reducing the transmission delay of event-driven messages and achieve a very high packets delivery ratio

Planning & Acting: Optimal Markov Decision Scheduling of Aggregated Data in WSNs by Genetic Algorithm

Data aggregation techniques have emerged as promising solutions for extending Wireless Sensor Networks (WSNs) lifetime. However, this approach suffers from a design issue in delivering the strict requirements needed by some monitoring applications. Carefully balancing Energy, Delay and Accuracy is essential for achieving these requirements. In this work, we focus on distributed data aggregation, where a sensor estimates the network information by the exchange of readings with different priority levels. We then propose an optimal decision policy for scheduling the transmission of the aggregated data at the node level. To model the investigated problem, we first adopt Markov Decision Process (MDP) whereby we define the reward function. Then, we apply a Genetic Algorithm (GA) to find a set of optimal decisions that ensures the best trade-off between energy saving, delay and accuracy of the received data based on their priority level. The simulation results yield excellent performance and our optimization shows a significant enhancement up to 20% compared to the other policies

Gastrointestinal decontamination in the acutely poisoned patient

ObjectiveTo define the role of gastrointestinal (GI) decontamination of the poisoned patient.Data sourcesA computer-based PubMed/MEDLINE search of the literature on GI decontamination in the poisoned patient with cross referencing of sources.Study selection and data extractionClinical, animal and in vitro studies were reviewed for clinical relevance to GI decontamination of the poisoned patient.Data synthesisThe literature suggests that previously, widely used, aggressive approaches including the use of ipecac syrup, gastric lavage, and cathartics are now rarely recommended. Whole bowel irrigation is still often recommended for slow-release drugs, metals, and patients who "pack" or "stuff" foreign bodies filled with drugs of abuse, but with little quality data to support it. Activated charcoal (AC), single or multiple doses, was also a previous mainstay of GI decontamination, but the utility of AC is now recognized to be limited and more time dependent than previously practiced. These recommendations have resulted in several treatment guidelines that are mostly based on retrospective analysis, animal studies or small case series, and rarely based on randomized clinical trials.ConclusionsThe current literature supports limited use of GI decontamination of the poisoned patient