Joint optimization for wireless sensor networks in critical infrastructures

Energy optimization represents one of the main goals in wireless sensor network design where a typical sensor node has usually operated by making use of the battery with limited-capacity. In this thesis, the following main problems are addressed: first, the joint optimization of the energy consumption and the delay for conventional wireless sensor networks is presented. Second, the joint optimization of the information quality and energy consumption of the wireless sensor networks based structural health monitoring is outlined. Finally, the multi-objectives optimization of the former problem under several constraints is shown. In the first main problem, the following points are presented: we introduce a joint multi-objective optimization formulation for both energy and delay for most sensor nodes in various applications. Then, we present the Karush-Kuhn-Tucker analysis to demonstrate the optimal solution for each formulation. We introduce a method of determining the knee on the Pareto front curve, which meets the network designer interest for focusing on more practical solutions. The sensor node placement optimization has a significant role in wireless sensor networks, especially in structural health monitoring. In the second main problem of this work, the existing work optimizes the node placement and routing separately (by performing routing after carrying out the node placement). However, this approach does not guarantee the optimality of the overall solution. A joint optimization of sensor placement, routing, and flow assignment is introduced and is solved using mixed-integer programming modelling. In the third main problem of this study, we revisit the placement problem in wireless sensor networks of structural health monitoring by using multi-objective optimization. Furthermore, we take into consideration more constraints that were not taken into account before. This includes the maximum capacity per link and the node-disjoint routing. Since maximum capacity constraint is essential to study the data delivery over limited-capacity wireless links, node-disjoint routing is necessary to achieve load balancing and longer wireless sensor networks lifetime. We list the results of the previous problems, and then we evaluate the corresponding results

Hybrid Evolutionary Routing Optimisation for Wireless Sensor Mesh Networks

Battery powered wireless sensors are widely used in industrial and regulatory monitoring applications. This is primarily due to the ease of installation and the ability to monitor areas that are difficult to access. Additionally, they can be left unattended for long periods of time. However, there are many challenges to successful deployments of wireless sensor networks (WSNs). In this thesis we draw attention to two major challenges. Firstly, with a view to extending network range, modern WSNs use mesh network topologies, where data is sent either directly or by relaying data from node-to-node en route to the central base station. The additional load of relaying other nodes’ data is expensive in terms of energy consumption, and depending on the routes taken some nodes may be heavily loaded. Hence, it is crucial to locate routes that achieve energy efficiency in the network and extend the time before the first node exhausts its battery, thus improving the network lifetime. Secondly, WSNs operate in a dynamic radio environment. With changing conditions, such as modified buildings or the passage of people, links may fail and data will be lost as a consequence. Therefore in addition to finding energy efficient routes, it is important to locate combinations of routes that are robust to the failure of radio links. Dealing with these challenges presents a routing optimisation problem with multiple objectives: find good routes to ensure energy efficiency, extend network lifetime and improve robustness. This is however an NP-hard problem, and thus polynomial time algorithms to solve this problem are unavailable. Therefore we propose hybrid evolutionary approaches to approximate the optimal trade-offs between these objectives. In our approach, we use novel search space pruning methods for network graphs, based on k-shortest paths, partially and edge disjoint paths, and graph reduction to combat the combinatorial explosion in search space size and consequently conduct rapid optimisation. The proposed methods can successfully approximate optimal Pareto fronts. The estimated fronts contain a wide range of robust and energy efficient routes. The fronts typically also include solutions with a network lifetime close to the optimal lifetime if the number of routes per nodes were unconstrained. These methods are demonstrated in a real network deployed at the Victoria & Albert Museum, London, UK.Part of this work was supported by a knowledge transfer partnership (KTP) awarded to the IMC Group Ltd. and the University of Exeter (KTP008748).University of Exeter has provided financial support for the student

A survey of network lifetime maximization techniques in wireless sensor networks

Emerging technologies, such as the Internet of things, smart applications, smart grids and machine-to-machine networks stimulate the deployment of autonomous, selfconfiguring, large-scale wireless sensor networks (WSNs). Efficient energy utilization is crucially important in order to maintain a fully operational network for the longest period of time possible. Therefore, network lifetime (NL) maximization techniques have attracted a lot of research attention owing to their importance in terms of extending the flawless operation of battery-constrained WSNs. In this paper, we review the recent developments in WSNs, including their applications, design constraints and lifetime estimation models. Commencing with the portrayal of rich variety definitions of NL design objective used for WSNs, the family of NL maximization techniques is introduced and some design guidelines with examples are provided to show the potential improvements of the different design criteri

Static, dynamic, and adaptive heterogeneity in distributed smart camera networks

We study heterogeneity among nodes in self-organizing smart camera networks, which use strategies based on social and economic knowledge to target communication activity efficiently. We compare homogeneous configurations, when cameras use the same strategy, with heterogeneous configurations, when cameras use different strategies. Our first contribution is to establish that static heterogeneity leads to new outcomes that are more efficient than those possible with homogeneity. Next, two forms of dynamic heterogeneity are investigated: nonadaptive mixed strategies and adaptive strategies, which learn online. Our second contribution is to show that mixed strategies offer Pareto efficiency consistently comparable with the most efficient static heterogeneous configurations. Since the particular configuration required for high Pareto efficiency in a scenario will not be known in advance, our third contribution is to show how decentralized online learning can lead to more efficient outcomes than the homogeneous case. In some cases, outcomes from online learning were more efficient than all other evaluated configuration types. Our fourth contribution is to show that online learning typically leads to outcomes more evenly spread over the objective space. Our results provide insight into the relationship between static, dynamic, and adaptive heterogeneity, suggesting that all have a key role in achieving efficient self-organization

Topology Control Multi-Objective Optimisation in Wireless Sensor Networks: Connectivity-Based Range Assignment and Node Deployment

The distinguishing characteristic that sets topology control apart from other methods, whose motivation is to achieve effects of energy minimisation and an increased network capacity, is its network-wide perspective. In other words, local choices made at the node-level always have the goal in mind of achieving a certain global, network-wide property, while not excluding the possibility for consideration of more localised factors. As such, our approach is marked by being a centralised computation of the available location-based data and its reduction to a set of non-homogeneous transmitting range assignments, which elicit a certain network-wide property constituted as a whole, namely, strong connectedness and/or biconnectedness. As a means to effect, we propose a variety of GA which by design is multi-morphic, where dependent upon model parameters that can be dynamically set by the user, the algorithm, acting accordingly upon either single or multiple objective functions in response. In either case, leveraging the unique faculty of GAs for finding multiple optimal solutions in a single pass. Wherefore it is up to the designer to select the singular solution which best meets requirements. By means of simulation, we endeavour to establish its relative performance against an optimisation typifying a standard topology control technique in the literature in terms of the proportion of time the network exhibited the property of strong connectedness. As to which, an analysis of the results indicates that such is highly sensitive to factors of: the effective maximum transmitting range, node density, and mobility scenario under observation. We derive an estimate of the optimal constitution thereof taking into account the specific conditions within the domain of application in that of a WSN, thereby concluding that only GA optimising for the biconnected components in a network achieves the stated objective of a sustained connected status throughout the duration.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

Random Neural Networks and Optimisation

In this thesis we introduce new models and learning algorithms for the Random Neural Network (RNN), and we develop RNN-based and other approaches for the solution of emergency management optimisation problems. With respect to RNN developments, two novel supervised learning algorithms are proposed. The first, is a gradient descent algorithm for an RNN extension model that we have introduced, the RNN with synchronised interactions (RNNSI), which was inspired from the synchronised firing activity observed in brain neural circuits. The second algorithm is based on modelling the signal-flow equations in RNN as a nonnegative least squares (NNLS) problem. NNLS is solved using a limited-memory quasi-Newton algorithm specifically designed for the RNN case. Regarding the investigation of emergency management optimisation problems, we examine combinatorial assignment problems that require fast, distributed and close to optimal solution, under information uncertainty. We consider three different problems with the above characteristics associated with the assignment of emergency units to incidents with injured civilians (AEUI), the assignment of assets to tasks under execution uncertainty (ATAU), and the deployment of a robotic network to establish communication with trapped civilians (DRNCTC). AEUI is solved by training an RNN tool with instances of the optimisation problem and then using the trained RNN for decision making; training is achieved using the developed learning algorithms. For the solution of ATAU problem, we introduce two different approaches. The first is based on mapping parameters of the optimisation problem to RNN parameters, and the second on solving a sequence of minimum cost flow problems on appropriately constructed networks with estimated arc costs. For the exact solution of DRNCTC problem, we develop a mixed-integer linear programming formulation, which is based on network flows. Finally, we design and implement distributed heuristic algorithms for the deployment of robots when the civilian locations are known or uncertain

Optimisation de réseaux de capteurs sans fil pour le suivi de cibles mobiles

Wireless sensor networks have received a particular attention during the last years, involving many applications, such as vehicle tracking or battlefield monitoring.A set of sensors is randomly dispatched in a region in order to monitor moving targets.Each sensor has a limited battery lifetime and two states: active or inactive.An active sensor is able to monitor targets inside its sensing radius, which consumes energy.In this thesis, the studied problems consist in deciding an optimal schedule of sensing activities, in order to cover all the targets at any instant of the mission.First, we study a robust scheduling problem.A target such that the spatial trajectory is exactly known is subject to temporal uncertainties.This context is met for a plane flying in an airline route, a train running on a railway, or any vehicle following a predetermined path.The objective is to compute a schedule of activities able to resist to the largest uncertainties.This first problem is solved using an exact pseudo-polynomial algorithm, relying on a dichotomy.Second, we study a problem aiming at preserving enough sensor network capacity in order to perform further missions.For this problem, the targets are subject to spatial uncertainties, i.e. their actual position may be at a distance $\delta$ of their expected position.This second problem is solved using an exact algorithm based on column generation, accelerated by a metaheuristic.All the proposed methods have a common phase, called discretization, that leads to reformulate the original problems as activity scheduling problems.The monitored area is split into faces, that are defined as sets of points covered by the same set of sensors.Computing the stay duration of targets inside each face leads to split the mission duration into time windows, so the moving target tracking problem can be seen as a sequence of static target tracking problems.The proposed algorithms are tested on many instances, and the analysis of the results is provided.Numerous open perspectives of this work are also given.Les réseaux de capteurs sans fil suscitent une attention croissante depuis quelques années, tant les applications sont nombreuses, incluant notamment le suivi de véhicules ou la surveillance de champs de bataille.Un ensemble de capteurs disséminé aléatoirement a pour but de surveiller des cibles se déplaçant dans une région donnée.Chaque capteur a une durée de vie limitée et deux états : actif ou inactif.Un capteur actif peut surveiller des cibles dans son rayon de portée, au prix d'une consommation d'énergie.Dans cette thèse, les problèmes étudiés consistent à déterminer un ordonnancement optimal d'activités de surveillance, afin de couvrir toutes les cibles à tout instant de la mission.Nous abordons en premier lieu un problème d'ordonnancement robuste.Une cible dont on connaît la trajectoire spatiale avec précision est sujette à incertitude temporelle.Cette situation est rencontrée lorsqu'un avion vole dans un couloir aérien, qu'un train circule sur une voie ferrée, ou que de tout autre véhicule suit un itinéraire pré-déterminé.L'objectif est de calculer un ordonnancement d'activités capable de résister au plus grand écart par rapport aux dates prévisionnelles de passage de la cible.Ce premier problème est résolu à l'aide d'un algorithme exact pseudo-polynomial, reposant sur une dichotomie.En second lieu, nous étudions le problème visant à préserver la capacité de surveillance du réseau de capteurs dans un contexte multi-missions.Les cibles sont maintenant sujettes à une incertitude spatiale, c'est-à-dire susceptibles de se trouver à une distance inférieure à un seuil $\delta$ de leur position prévisionnelle.Ce second problème est résolu à l’aide d’un algorithme exact basé sur la génération de colonnes, et accéléré par une métaheuristique.Les méthodes de résolution proposées ont en commun une étape préliminaire, appelée discrétisation, qui conduit à reformuler les problèmes originaux comme des problèmes d'ordonnancement d'activités de surveillance.L'espace de surveillance est découpé en faces, ensembles de points couverts par un même sous-ensemble de capteurs.Le calcul des durées de séjour des cibles dans chaque face permet de découper la durée de la mission en fenêtres de temps, et d'envisager le problème de couverture de cibles mobiles comme une séquence de problèmes de couverture de cibles immobiles.Les algorithmes proposés pour aborder ces problèmes sont testés sur de nombreuses instances, et leurs résultats sont analysés.De nombreuses perspectives ouvertes par ces travaux sont également présentées