Evaluation of Epidemic-Based Information Dissemination in a Wireless Network Testbed

Information dissemination is an integral part of modern networking environments, such as Wireless Sensor Networks (WSNs). Probabilistic flooding, a common epidemic-based approach, is used as an efficient alternative to traditional blind flooding as it minimizes redundant transmissions and energy consumption. It shares some similarities with the Susceptible-Infected-Recovered (SIR) epidemic model, in the sense that the dissemination process and the epidemic thresholds, which achieve maximum coverage with the minimum required transmissions, have been found to be common in certain cases. In this paper, some of these similarities between probabilistic flooding and the SIR epidemic model are identified, particularly with respect to the epidemic thresholds. Both of these epidemic algorithms are experimentally evaluated on a university campus testbed, where a low-cost WSN, consisting of 25 nodes, is deployed. Both algorithm implementations are shown to be efficient at covering a large portion of the network’s nodes, with probabilistic flooding behaving largely in accordance with the considered epidemic thresholds. On the other hand, the implementation of the SIR epidemic model behaves quite unexpectedly, as the epidemic thresholds underestimate sufficient network coverage, a fact that can be attributed to implementation limitations.

Adaptive Probabilistic Flooding for Information Hovering in VANETs

Information hovering is an information dissemination concept over a mobile set of peers which has not been investigated to the extent that other information dissemination paradigms have. It naturally appears in many vehicular network applications where information must be made available to vehicles within a confined geographical area for during some time period. One elementary strategy is to flood the area with data. Even in this case, some vehicles may never receive the content due to potential partitions created by low traffic density. In order to address this issue, in this work we propose a strategy based on epidemic routing in the hovering area, and probabilistic flooding outside it. Vehicles outside the hovering area serve as bridges towards partitions, leading to high reachability. We highlight the adaptive feature of the protocol, where the rebroadcast probability in partitions is adaptively regulated based on estimates of the density of vehicles in the hovering area. The performance of the proposed scheme is evaluated in VISSIM, using as the reference model in all simulation experiments a section of the road network in cities of Washington. The proposed protocol is shown to achieve the set design goals.

Coastal Flood Assessment due to Sea Level Rise and Extreme Storm Events: A Case Study of the Atlantic Coast of Portugal’s Mainland

Portugal’s mainland has hundreds of thousands of people living in the Atlantic coastal zone, with numerous high economic value activities and a high number of infrastructures that must be adapted and protected from natural coastal hazards, namely, extreme storms and sea level rise (SLR). In the context of climate change adaptation strategies, a reliable and accurate assessment of the physical vulnerability to SLR is crucial. This study is a contribution to the implementation of flooding standards imposed by the European Directive 2007/60/EC, which requires each member state to assess the risk associated to SLR and floods caused by extreme events. Therefore, coastal hazard on the Atlantic Coast of Portugal’s mainland was evaluated for 2025, 2050, and 2100 over the whole extension due to SLR, with different sea level scenarios for different extreme event return periods. A coastal probabilistic flooding map was produced based on the developed probabilistic cartography methodology using geographic information system (GIS) technology. The Extreme Flood Hazard Index (EFHI) was determined on probabilistic flood bases using five probability intervals of 20% amplitude. For a given SLR scenario, the EFHI is expressed, on the probabilistic flooding maps for an extreme tidal maximum level, by five hazard classes ranging from 1 (Very Low) to 5 (Extreme).

Adapting Probabilistic Flooding in Energy Harvesting Wireless Sensor Networks

Technological advantages in energy harvesting have been successfully applied in wireless sensor network environments, prolonging network’s lifetime, and, therefore, classical networking approaches like information dissemination need to be readdressed. More specifically, Probabilistic Flooding information dissemination is revisited in this work and it is observed that certain limitations arise due to the idiosyncrasies of nodes’ operation in energy harvesting network environments, resulting in reduced network coverage. In order to address this challenge, a modified version of Probabilistic Flooding is proposed, called Robust Probabilistic Flooding, which is capable of dealing with nodes of about to be exhausted batteries that resume their operation after ambient energy collection. In order to capture the behavior of the nodes’ operational states, a Markov chain model is also introduced and—based on certain observations and assumptions presented here—is subsequently simplified. Simulation results based on the proposed Markov chain model and a solar radiation dataset demonstrate the inefficiencies of Probabilistic Flooding and show that its enhanced version (i.e., Robust Probabilistic Flooding) is capable of fully covering the network on the expense of increased termination time in energy harvesting environments. Another advantage is that no extra overhead is introduced regarding the number of disseminated messages, thus not introducing any extra transmissions and therefore the consumed energy does not increase.