We present and evaluate an IoT-enabled sensing and actuating system for localizing illegal industrial harsh discharges of polluting wastewater in sewer networks. The special conditions of the sewer environment bring special challenges for the design of an IoT system and of its real-time algorithm for anomaly detection and localization in wastewater networks. The proposed design fulfills these requirements by using a new IoT architecture pattern, which we generalize and name Hop-by-hop Anomaly Detection and Actuation (HADA). The distributed anomaly detection and localization algorithm makes predictions over previous sensor measurements, while taking into account seasonality effects of wastewater and noise of the sensors. Based on simulations in a large network with three common illegal industrial wastewater pollutants, the advantages and limitations of the proposed wastewater anomaly localization system are discussed. The IoT system, including its anomaly detection and localization algorithm, was implemented using in a low-power microcontroller and tested in flowing wastewater with different harsh industrial waste.

Lightpath reconfiguration is a networking task that can be performed in order to improve resource utilization. The lightpath reconfiguration problem becomes nontrivial when a new set of lightpaths requires the release of resources previously seized by the (working) lightpaths currently in place, but, in order to ensure continuity of the traffic flow, the working lightpaths cannot be torn down before the new ones are set up. Under this condition the reconfiguration can fall into a deadlock state, and deadlocks can only be solved by temporary disruption of some connections. At this point, traffic disruptions are necessary, and network operators must compensate customers with penalty fees for the service disruption. In this paper we focus on minimizing the number of simultaneously disrupted connections at any time during the reconfiguration process. In this paper, we propose a mixed-integer program (MIP) model, an exact algorithm, and a heuristic for solving the problem considering our objective.

Harsh pollutants that are illegally disposed in the sewer network may spread beyond the sewer network—e.g., through leakages leading to groundwater reservoirs—and may also impair the correct operation of wastewater treatment plants. Consequently, such pollutants pose serious threats to water bodies, to the natural environment and, therefore, to all life. In this article, we focus on the problem of identifying a wastewater pollutant and localizing its source point in the wastewater network, given a time-series of wastewater measurements collected by sensors positioned across the sewer network. We provide a solution to the problem by solving two linked sub-problems. The first sub-problem concerns the detection and identification of the flowing pollutants in wastewater, i.e., assessing whether a given time-series corresponds to a contamination event and determining what the polluting substance caused it. This problem is solved using random forest classifiers. The second sub-problem relates to the estimation of the distance between the point of measurement and the pollutant source, when considering the outcome of substance identification sub-problem. The XGBoost algorithm is used to predict the distance from the source to the sensor. Both of the models are trained using simulated electrical conductivity and pH measurements of wastewater in sewers of a european city sub-catchment area. Our experiments show that: (a) resulting precision and recall values of the solution to the identification sub-problem can be both as high as 96%, and that (b) the median of the error that is obtained for the estimation of the source location sub-problem can be as low as 6.30 m.

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.

Most network operators have considered reducing LSR label spaces (number of labels used) as a way of simplifying management of underlaying virtual private networks (VPNs) and therefore reducing operational expenditure (OPEX). The IETF outlined the label merging feature in MPLS-allowing the configuration of multipoint-to-point connections (MP2P)-as a means of reducing label space in LSRs. We found two main drawbacks in this label space reduction scheme: a) it should be separately applied to a set of LSPs with the same egress LSR-which decreases the options for better reductions, and b) LSRs close to the edge of the network experience a greater label space reduction than those close to the core. The later implies that MP2P connections reduce the number of labels asymmetrically.