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In December 2016, the wastewater treatment plant of Baarle-Nassau, Netherlands, failed. The failure was caused by the illegal disposal of high volumes of acidic waste into the sewer network. Repairs cost between 80,000 and 100,000 EUR. A continuous monitoring system of a utility network such as this one would help to determine the causes of such pollution and could mitigate or reduce the impact of these kinds of events in the future. We have designed and tested a data fusion system that transforms the time-series of sensor measurements into an array of source-localized discharge events. The data fusion system performs this transformation as follows. First, the time-series of sensor measurements are resampled and converted to sensor observations in a unified discrete time domain. Second, sensor observations are mapped to pollutant detections that indicate the amount of specific pollutants according to a priori knowledge. Third, pollutant detections are used for inferring the propagation of the discharged pollutant downstream of the sewage network to account for missing sensor observations. Fourth, pollutant detections and inferred sensor observations are clustered to form tracks. Finally, tracks are processed and propagated upstream to form the final list of probable events. A set of experiments was performed using a modified variant of the EPANET Example Network 2. Results of our experiments show that the proposed system can narrow down the source of pollution to seven or fewer nodes, depending on the number of sensors, while processing approximately 100 sensor observations per second. Having considered the results, such a system could provide meaningful information about pollution events in utility networks.

This paper presents a new traffic engineering load balancing taxonomy, classifying several publications and including their objective functions, constraints and proposed heuristics. Using this classification, a novel Generalized Multiobjective Multitree model (GMM-model) is proposed. This model considers for the first time multitree-multicast load balancing with splitting in a multiobjective context, whose mathematical solution is a whole Pareto optimal set that can include several results than it has been possible to find in the publications surveyed. To solve the GMMmodel, a multi-objective evolutionary algorithm (MOEA) inspired by the Strength Pareto Evolutionary Algorithm (SPEA) is proposed. Experimental results considering up to 11 different objectives are presented for the well-known NSF network, with two simultaneous data flows.

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Internet access can improve people's life quality by helping them to reduce and overcome the poverty and educational gaps. However, most rural communities in the world, specially in underdeveloped countries, do not have access to the Internet. Delay/Disruption Tolerant Networking (DTN) is a recent low-cost technology now being used to provide connectivity to rural towns were some transportation means periodically arrive. DTNs can be implemented to connect communities to Internet, since this technology takes advantage of the existing people's transportation infrastructure using it to move packets and messages to and from Internet. This paper proposes a DTN mathematical optimization model that maximizes the availability probabilities of the paths from sources to destinations. We also present an opportunistic forwarding algorithm that takes into account the availability probability of a node's neighbors to decide if a node should forward a message or store the message until a node with a higher availability probability contacts it. This algorithm was tested in five different scenarios and in all of them it found a path to the destination.