Imola: A decentralised learning-driven protocol for multi-hop White-Fi

In this paper we tackle the digital exclusion problem in developing and remote locations by proposing Imola, an inexpensive learning-driven access mechanism for multi-hop wireless networks that operate across TV white-spaces (TVWS). Stations running Imola only rely on passively acquired neighbourhood information to achieve scheduled-like operation in a decentralised way, without explicit synchronisation. Our design overcomes pathological circumstances such as hidden and exposed terminals that arise due to carrier sensing and are exceptionally problematic in low frequency bands. We present a prototype implementation of our proposal and conduct experiments in a real test bed, which confirms the practical feasibility of deploying our solution in mesh networks that build upon the IEEE 802.11af standard. Finally, the extensive system level simulations we perform demonstrate that Imola achieves up to 4x\u97 more throughput than the channel access protocol defined by the standard and reduces frame loss rate by up to 100%

e-Doctor: A Web based Support Vector Machine for Automatic Medical Diagnosis

AbstractThis paper proposes e-doctor; a web-based application that makes automatic diagnoses about health problems. The whole procedure is based on Support Vector Machines (SVMs), which are supervised learning models that analyze data and proceed to decisions, based on their knowledge. System administrators define specific characteristics for each health problem that can be diagnosed, and educate the SVM by entering sample files of statistical data. After that, medical staff can enter exam information about patients, and e-doctor makes an automatic diagnosis/prediction by means of answering if the patient has (or may have in the future) a specific health problem. The application can be used in cases where statistical information plays a vital role on deciding about a patient's condition. A prototype was developed and the system trained and tested for the case of heart symptoms. The results were satisfactory

A Resource Reservation and Traffic Categorization Agent for QoS in Medical Ad Hoc Networks

AbstractOne of the most sensitive and demanding types of network traffic is the one concerning medical information. Unlike other cases, medical data are distinguished in many different types, and the traffic generated by each one of them has different resource demands and requires different levels of reliability. On the other hand, in ad hoc networks, which constitute the most rapid and straight solution for communication and data exchange in conditions of crisis, bandwidth availability is limited, while QoS guarantees are difficult to be provided. Given this context, an intelligent agent is designed in this paper, suitable for ad hoc networks, which categorizes medical traffic in classes, and reserves bandwidth according to each node's needs. The whole operation is based on a sophisticated algorithm that calculates the optimal paths in each case and reconfigures the nodes’ routing tables. Experimental results through simulation show that the agent can guarantee strict limits of bandwidth reservation and high levels of reliability, for all types of medical traffic