Online monitoring of organic micropollutants (OMPs) in the aquatic environment at high temporal resolution is an upcoming technique that provides insights into their dynamics and has the potential to bring water research and management to a new level. An online monitoring setup was developed to quantify OMPs in wastewater treatment plant (WWTP) influent and effluent using automated and continuous sampling, sample preparation, online solid-phase extraction-liquid chromatography-tandem mass spectrometry analysis and data evaluation. This online monitoring setup provided high selectivity and sensitivity (limit of quantification down to 1 ng/L) as well as a stable performance during one week of constant operation whilst using a high sampling frequency of 10 min (>1000 samples). Custom automated data evaluation enabled quantification within seconds after each measurement and results were comparable to those from a commercial software. Additionally, an alarm tool was included in the evaluation application, which automatically notified the user in case a substance exceeded a predefined threshold. The online monitoring setup was applied to WWTP influent and effluent, where 57 sub-stances were monitored over a period of one week and two days, respectively. High temporal resolution enabled the observation of periodic patterns of pharmaceuticals as well as pollution by OMPs originating from point and diffuse sources, while dynamics of OMPs in WWTP effluent were less pronounced. These new insights into the dynamics of OMPs in WWTP influent, which would not be observable using 24 h composite samples, will be a starting point for new stormwater and wastewater research and management strategies.

Wireless sensor networks are fundamental for technologies related to the Internet of Things. This technology has been constantly evolving in recent times. In this paper, we consider the problem of minimising the cost function of covering a sewer network. The cost function includes the acquisition and installation of electronic components such as sensors, batteries, and the devices on which these components are installed. The problem of sensor coverage in the sewer network or a part of it is presented in the form of a mixed-integer programming model. This method guarantees that we obtain an optimal solution to this problem. A model was proposed that can take into account either only partial or complete coverage of the considered sewer network. The CPLEX solver was used to solve this problem. The study was carried out for a practically relevant network under selected scenarios determined by artificial and realistic datasets.

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.

Wireless sensor networks (WSNs) are fundamental to the ever-evolving technologies associated with the broader Internet of Things (IoT). In this paper, we consider the problem of coverage and cost function minimization in a sewer network. The problem of sensor coverage in a sewer network is presented as a mixed-integer programming problem. This approach guarantees that we obtain the optimal solution to the problem under consideration. To solve this problem we used a CPLEX solver. The study is performed for a practically relevant network within selected scenarios determined by realistic data sets.

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.

In this article, we design and evaluate several algorithms for the computation of the optimal Rice coding parameter. We conjecture that the optimal Rice coding parameter can be bounded and verify this conjecture through numerical experiments using real data. We also describe algorithms that partition the input sequence of data into sub-sequences, such that if each sub-sequence is coded with a different Rice parameter, the overall code length is minimised. An algorithm for finding the optimal partitioning solution for Rice codes is proposed, as well as fast heuristics, based on the understanding of the problem trade-offs.

In previous works, a multi-objective traffic engineering scheme (MHDB-S model) using different distribution trees to multicast several flows were proposed. Because the flow assignment cannot be mapped directly into MPLS architecture, in this paper, we propose a liner system equation to create multiple point-2-multipoint LSPs based on the optimum sub-flow values obtained with our MHDB-S model.

Extensive research in the field of telecommunications has been done on the techniques of multipath routing, as they offer many advantages over conventional single-path routing methods. Some of these techniques make use of the so-called Destination-Oriented Directed Acyclic Graphs (DODAGs) which are constructed on the networks, usually in a distributed way. However, while defining methods of forming DODAGs, the authors of multipath algorithms tend to overlook a possibly significant issue which could, in a way, define the quality of a given DODAG in the context of multipath routing, namely, providing an equitable distribution of the paths between the nodes in the newly created DODAG. In this paper, a few requirements for constructing a "fair" DODAG are identified in the context of multipath routing. An optimization algorithm that tries to find an equitable solution according to these requirements is also presented. Three DODAG-creation algorithms that appear in the literature are simulated and compared against this equitable solution, and none of them is getting close to it in terms of fairness in the distribution of the paths. Moreover, two interesting properties of equitable solutions are revealed in the simulations.

Network re-optimization is a process that must be triggered periodically in order to improve the inefficient resource allocation of online routing heuristics due to the uncertainty of online lightpath demand arrivals and departures. Network re-optimization involves two tasks: a) finding new lightpaths for a (sub)set of current demands, i.e. rerouting, and b) migrating the current traffic to the new configuration diminishing traffic disruptions, i.e. lightpath reconfiguration. If not controlled, excessive traffic disruptions may be a cause of violations of clients' Service Level Agreement, which should be compensated by the network operator with penalization fees.So far, rerouting and reconfiguration tasks of a re-optimization process have been done separately, hence, efforts in trying to achieve the best network performance (rerouting) yields to solutions incurring on unacceptable traffic disruptions (reconfiguration) and vice-versa. In this paper, given a time-disruption threshold for reconfiguring every demand, we present a novel methodology consisting on two procedures that collaboratively find the best network performance without incurring on penalization fees.Our numerical results are extremely encouraging: in our scenarios, it is always possible to achieve an optimal routing performance without incurring on penalization fees.

All-Optical Label Switching (AOLS) is a new technology that performs packet forwarding without any optical–electrical–optical conversions. In this paper, we study the problem of routing a set of requests in AOLS networks using GMPLS technology, with the aim of minimizing the number of labels required to ensure the forwarding. We first formalize the problem by associating to each routing strategy a logical hypergraph, called a hypergraph layout, whose hyperarcs are dipaths of the physical graph, called tunnels in GMPLS terminology. We define a cost function for the hypergraph layout, depending on its total length plus its total hop count. Minimizing the cost of the design of an AOLS network can then be expressed as finding a minimum cost hypergraph layout. We prove hardness results for the problem, namely for general directed networks we prove that it is NP-hard to find a Clogn-approximation, where C is a positive constant and n is the number of nodes of the network. For symmetric directed networks, we prove that the problem is APX-hard. These hardness results hold even if the traffic instance is a partial broadcast. On the other hand, we provide approximation algorithms, in particular an O(logn)-approximation for symmetric directed networks. Finally, we focus on the case where the physical network is a directed path, providing a polynomial-time dynamic programming algorithm for a fixed number k of sources running in O(nk+2) time.

All-Optical Label Switching (AOLS) is a new technology that performs packet forwarding without any Optical-Electrical-Optical (OEO) conversions. In this paper, we study the problem of routing a set of requests in AOLS networks using GMPLS technology, with the aim of minimizing the number of labels required to ensure the forwarding. We first formalize the problem by associating to each routing strategy a logical hypergraph whose hyperarcs are dipaths of the physical graph, called tunnels in GMPLS terminology. Such a hypergraph is called a hypergraph layout, to which we assign a cost function given by its physical length plus the total number of hops traveled by the traffic. Minimizing the cost of the design of an AOLS network can then be expressed as finding a minimum cost hypergraph layout. We prove hardness results for the problem, namely for general directed networks we prove that it is NP-hard to find a C logn-approximation, where C is a a positive constant and n is the number of nodes of the network. For symmetric directed networks, we prove that the problem is APX-hard. These hardness results hold even is the traffic instance is a partial broadcast. On the other hand, we provide an $\mathcal{O}(\log n)$-approximation algorithm to the problem for a general symmetric network. Finally, we focus on the case where the physical network is a path, providing a polynomial-time dynamic programming algorithm for a bounded number of sources, thus extending the algorithm given in [1] for a single source.

This chapter is devoted to the analysis and modeling of some problems related to the optimal usage of the label space in label switching networks. Label space problems concerning three different technologies and architectures - namely Multi-protocol Label Switching (MPLS), Ethernet VLAN-Label Switching (ELS) and All-Optical Label Switching (AOLS) - are discussed in this chapter. Each of these cases yields to different constraints of the general label space reduction problem. We propose a generic optimization model and, then, we describe some adaptations aiming at modeling each particular case. Simulation results are briefly discussed at the end of this chapter.

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.

All-Optical Label Switching (AOLS) is a new technology that performs forwarding without any Optical-Electrical-Optical conversions. The most promising scheme to manage the control plane of these optical networks is Generic MultiProtocol Label Switching (GMPLS). In this paper, we study the problem of routing a set of requests in GMPLS networks with the aim of minimizing the number of labels required to ensure the forwarding. In order to spare the label space, we consider label stacking, allowing the configuration of GMPLS tunnels. We study particularly this network design problem when the network is a line. We provide an exact algorithm for the case in which all the requests have a common source and present some approximation algorithms and heuristics when an arbitrary number of sources are distributed over the line. We analyze by simulations the performance of our proposed algorithms and compare them with previous ones.

All-optical packet switching designs have evolved in the last years. However, all-optical packet forwarding functionalities are still limited. Several label assignation methods have been studied in order to efficiently use the small label space of the designs. In this paper, we present a novel scheme for label switched path protection for networks with small label spaces, such as all-optical packet switching technologies based on all-optical label swapping. With the purpose of using as fewest labels as possible, the scheme has as basis using the same working paths as backup paths. The scheme is named self-protection. Properties and requirements of the schemes in mention are given in this paper. A brief analysis of the trade-off of the scheme is also provided.

With the development of All-Optical Label Switching (AOLS) network, nodes are capable of forwarding labeled packets without performing Optical-Electrical-Optical (OEO) conversions, speeding up the forwarding. However, this new technology also brings new constraints and, consequently, new problems have to be adressed. We study in this paper the problem of routing a set of demands in such a network, considering that routers have limited label space, preventing from the usage of label swapping techniques. Label stripping is a solution that ensures forwarding, concerning these constraints, of all the paths at expenses of increasing the stack size and wasting bandwith. We propose an intermediate feasible solution that keeps the GMPLS stack size smaller than label stripping, in order to gain bandwidth resources. After proposing an heuristic for this problem, we present simulations that show the performance of our solution.

Wavelength Division Multiplexing (WDM)networks havebeenadopted asanear-future solution forthe broadband Internet. Inprevious workweproposed anewarchitecture, namedEnhanced Grooming (G+), that extends thecapabilities oftraditional optical routes (lightpaths). Inthis paper, we compare theoperational expenditures incurred byrouting asetofdemands using lightpaths withthat oflighttours. Thecomparison is donebysolving anInteger Linear Programming (ILP) problem based onapathformulation. Results showthat, undertheassumption ofsingle-hop routing, almost 15%oftheoperational costcanbereduced withour architecture. Inmulti-hop routing theoperation costisreduced in7.1%andatthesametimetheratio of operational costtonumberofOptical-Electro-