Broadcast or flooding is a dissemination technique of paramount importance in wireless ad hoc networks. The broadcast scheme is widely used within routing protocols by a wide range of wireless ad hoc networks such as mobile ad hoc networks, vehicular ad hoc networks, and wireless sensor networks, and used to spread emergency messages in critical scenarios after a disaster scenario and/or an accidents. As the type broadcast scheme used plays an important role in the performance of the network, it has to be selected carefully. Though several types of broadcast schemes have been proposed, probabilistic broadcast schemes have been demonstrated to be suitable schemes for wireless ad hoc networks due to a range of benefits offered by them such as low overhead, balanced energy consumption, and robustness against failures and mobility of nodes. In the last decade, many probabilistic broadcast schemes have been proposed by researchers. In addition to reviewing the main features of the probabilistic schemes found in the literature, we also present a classification of the probabilistic schemes, an exhaustive review of the evaluation methodology including their performance metrics, types of network simulators, their comparisons, and present some examples of real implementations, in this paper

Internet of Things (IoT) can help healthcare facilities to transform the experience for both the staff and patients. IoT can facilitate novel solutions for a classic problem, which is the real-time tracking of patients and staff. In this work, an architecture for real-time tracking using Bluetooth Low Energy (BLE) and iBeacons in hospitals is proposed. The proposed system is to locate not only humans but also other facilities such as medicines. It consists of four different frameworks: server communication, user interaction, cross-platform communication and indoor localization. A wearable BLE-enabled device or any smartphone can be used in our system. The first stage of the tracking system is setting up and calibrating the BLE iBeacons for initialization. Then the Received Signal Strength is collected from the BLE-enabled devices carried by users. These data are analyzed and calculated using our improved Least Square Estimation approach to estimate the actual location of users. Experimental results show that in a crowded environment, our tracking system achieves resolutions of the order of 12cm. This will be further integrated with other sensors such as accelerometer or heart rate monitor to improve the real-time healthcare monitoring experience.