In this work, we are interested in periodic beacons transmission, the main cause of the Control Channel (CCH) congestion and the major obstacle delaying the progress of safety messages dissemination in VANETs. In order to offload the network, solutions that range from transmit rate to transmit power adaptations including hybrid solutions have been proposed. Although some of these solutions have managed to successfully reduce the load on the wireless channel, none, to the best of our knowledge, have considered the impact of the applied adaptation scheme on the overall level of awareness among vehicles and its quality. ETSI TS released a technical specification stating a limit for the minimum beacons transmit rate in order to maintain a good level of awareness among vehicles and ensure a certain accuracy in VANET applications. In this paper, we propose to jointly adapt both transmit rate and power in a new smart way that guarantees a strict beaconing frequency as well as a good level of awareness in closer ranges, while maintaining a marginal beacons collision rate and a good level of channel utilisation. First, the transmit rate is adapted to meet the channel requirements in terms of collision rate and channel load; then, once the minimum beacon transmit rate, set by ETSI, has been reached, transmit power is adapted in a way that guarantees a good level of awareness for closer neighbours. The simulation results show a significant enhancement in terms of the quality as well as the level of awareness.

Periodic Beacon Messages are one of the building blocks that enable the operation of VANET applications. In vehicular networks environments, congestion and awareness control mechanisms are key for a reliable and efficient functioning of vehicular applications. In order to control the channel load, a reliable mechanism allowing real time measurements of parameters like the local density of vehicles is a must. These measurements can then serve as an input to perform a fast adaptation of the transmit parameters. In this context, considerable efforts have been directed in the recent years towards designing flexible yet robust protocols solving this problem; yet, very few have considered a proactive adaptation of the transmit parameters as a preventive measure from channel load peaks. To this end, we take the opportunity to introduce P&A-A, a new congestion control protocol that performs a joint adaptation of the transmit rate and power, relying on an altruistic short-term prediction algorithm that estimates the vehicular density around a given vehicle within the next short while. Additionally, P&A-A adapts the transmit parameters in a way that guarantees the strict beaconing requirements and satisfies the level of awareness required for the operation of most critical VANET applications. The results of the simulations performed in a realistic scenario justify our theoretical considerations and confirm the efficiency and the effectiveness of our protocol by showing significant improvements in terms of network performance (up to 8% and 14% improvement in collision rate; and up to 10% and 20% increase in busy ratio compared to our previous scheme and the ETSI schemes respectively) as well as the achieved level of awareness (higher coverage with higher transmission rate and power in dense scenarios, and up to 8% and 55% improvement in density perception accuracy compared to our previous scheme and the ETSI schemes respectively).

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Human occupancy measurement has become a topic of increasing interest in the past few years, due to the important role it plays in controlling a number of demand-driven applications like smart lighting and smart heating, as well as improving the energy efficiency of these applications in a broader sense. Office occupancy monitoring in commercial buildings can yield huge savings and improvements in terms of thermal, visual, and air quality. However, this is often impeded due to the lack of fine-grained occupancy information. This paper explores the use of low-priced environmental (temperature and humidity) sensor data for measuring occupancy in an office space. The idea behind this work is to leverage the variation divergence between humidity and temperature caused by human presence. We used a Raspberry Pi with a daughterboard called Sense Hat, which is equipped with the environmental sensors used in this study. The results are compared with occupancy data obtained from camera feeds in order to assess the effectiveness and the accuracy of the combined occupancy measurements, and show up to 87% accuracy.

It is foreseeable that in the few upcoming years, real time traffic information, including road incidents notifications, will be collected and disseminated by mobile vehicles, thanks to their plethora of embedded sensors. Each vehicle can thus actively participate in sharing the collected information with the other peers forming an infrastructure-less self-organizing network of vehicles. However, the fast development of applications in ITS field may result in an excessive load on such a network; therefore an efficient use of the available bandwidth is highly required. Not only should the size of the data inserted in the network be properly controlled, but also the extent of each message should be accurately defined. In this paper, we propose a distributed dissemination protocol for safety messages in urban areas, dubbed "Road-Casting Protocol (RCP)", which is based on a novel cooperative forwarding mechanism. Moreover, an accurate definition of the Region of Interest (RoI) (i.e. the geographical scope) of each broadcasted safety message is also devised to ensure better control of the network load. We have evaluated the efficiency of the RCP along with the proposed RoI definition using realistic simulations, based on an accurate propagation loss model for urban vehicular ad hoc network communications, and the obtained results show a substantial improvement, compared to state of the art schemes, in terms of enhanced packet delivery ratio up to higher than 95%, lower end-to-end delay and reduced network load.