Home health care routing and scheduling in densely populated communities considering complex human behaviours

This study focuses on the home health care routing problem (HHCRP) in the scenario of high population density areas where many elders live closely together. This study considers two main objectives. The first is to reduce travel and wait times for nurses or elders. The second concerns socially related objectives in scheduling problems, such as ‘quality of life’ and empowerment, by considering assumptions related to the acquaintanceship and mutual preferences of nurses and elders. This study models the effects of mutual preferences and acquaintanceship on service time in HHCRP. We use the Markov decision process and chance-constrained programming (CCP) to model the system to conserve the sequential service provision parameters and better represent the influence of stochastic service times. Because traditional deterministic algorithms cannot solve such a model, we apply a model-free reinforcement learning algorithm, Q-learning (QL), as well as the ant colony optimisation (ACO) algorithm. Thus, we tackle this problem by developing a model and algorithm to solve complex, large-scale systems. This study’s theoretical and practical contributions are verified by feedback from researchers and practitioners

IoT in smart communities, technologies and applications.

Internet of Things is a system that integrates different devices and technologies, removing the necessity of human intervention. This enables the capacity of having smart (or smarter) cities around the world. By hosting different technologies and allowing interactions between them, the internet of things has spearheaded the development of smart city systems for sustainable living, increased comfort and productivity for citizens. The Internet of Things (IoT) for Smart Cities has many different domains and draws upon various underlying systems for its operation, in this work, we provide a holistic coverage of the Internet of Things in Smart Cities by discussing the fundamental components that make up the IoT Smart City landscape, the technologies that enable these domains to exist, the most prevalent practices and techniques which are used in these domains as well as the challenges that deployment of IoT systems for smart cities encounter and which need to be addressed for ubiquitous use of smart city applications. It also presents a coverage of optimization methods and applications from a smart city perspective enabled by the Internet of Things. Towards this end, a mapping is provided for the most encountered applications of computational optimization within IoT smart cities for five popular optimization methods, ant colony optimization, genetic algorithm, particle swarm optimization, artificial bee colony optimization and differential evolution. For each application identified, the algorithms used, objectives considered, the nature of the formulation and constraints taken in to account have been specified and discussed. Lastly, the data setup used by each covered work is also mentioned and directions for future work have been identified. Within the smart health domain of IoT smart cities, human activity recognition has been a key study topic in the development of cyber physical systems and assisted living applications. In particular, inertial sensor based systems have become increasingly popular because they do not restrict users’ movement and are also relatively simple to implement compared to other approaches. Fall detection is one of the most important tasks in human activity recognition. With an increasingly aging world population and an inclination by the elderly to live alone, the need to incorporate dependable fall detection schemes in smart devices such as phones, watches has gained momentum. Therefore, differentiating between falls and activities of daily living (ADLs) has been the focus of researchers in recent years with very good results. However, one aspect within fall detection that has not been investigated much is direction and severity aware fall detection. Since a fall detection system aims to detect falls in people and notify medical personnel, it could be of added value to health professionals tending to a patient suffering from a fall to know the nature of the accident. In this regard, as a case study for smart health, four different experiments have been conducted for the task of fall detection with direction and severity consideration on two publicly available datasets. These four experiments not only tackle the problem on an increasingly complicated level (the first one considers a fall only scenario and the other two a combined activity of daily living and fall scenario) but also present methodologies which outperform the state of the art techniques as discussed. Lastly, future recommendations have also been provided for researchers

Machine Learning for Unmanned Aerial System (UAS) Networking

Fueled by the advancement of 5G new radio (5G NR), rapid development has occurred in many fields. Compared with the conventional approaches, beamforming and network slicing enable 5G NR to have ten times decrease in latency, connection density, and experienced throughput than 4G long term evolution (4G LTE). These advantages pave the way for the evolution of Cyber-physical Systems (CPS) on a large scale. The reduction of consumption, the advancement of control engineering, and the simplification of Unmanned Aircraft System (UAS) enable the UAS networking deployment on a large scale to become feasible. The UAS networking can finish multiple complex missions simultaneously. However, the limitations of the conventional approaches are still a big challenge to make a trade-off between the massive management and efficient networking on a large scale. With 5G NR and machine learning, in this dissertation, my contributions can be summarized as the following: I proposed a novel Optimized Ad-hoc On-demand Distance Vector (OAODV) routing protocol to improve the throughput of Intra UAS networking. The novel routing protocol can reduce the system overhead and be efficient. To improve the security, I proposed a blockchain scheme to mitigate the malicious basestations for cellular connected UAS networking and a proof-of-traffic (PoT) to improve the efficiency of blockchain for UAS networking on a large scale. Inspired by the biological cell paradigm, I proposed the cell wall routing protocols for heterogeneous UAS networking. With 5G NR, the inter connections between UAS networking can strengthen the throughput and elasticity of UAS networking. With machine learning, the routing schedulings for intra- and inter- UAS networking can enhance the throughput of UAS networking on a large scale. The inter UAS networking can achieve the max-min throughput globally edge coloring. I leveraged the upper and lower bound to accelerate the optimization of edge coloring. This dissertation paves a way regarding UAS networking in the integration of CPS and machine learning. The UAS networking can achieve outstanding performance in a decentralized architecture. Concurrently, this dissertation gives insights into UAS networking on a large scale. These are fundamental to integrating UAS and National Aerial System (NAS), critical to aviation in the operated and unmanned fields. The dissertation provides novel approaches for the promotion of UAS networking on a large scale. The proposed approaches extend the state-of-the-art of UAS networking in a decentralized architecture. All the alterations can contribute to the establishment of UAS networking with CPS

Recent Developments in Smart Healthcare

Medicine is undergoing a sector-wide transformation thanks to the advances in computing and networking technologies. Healthcare is changing from reactive and hospital-centered to preventive and personalized, from disease focused to well-being centered. In essence, the healthcare systems, as well as fundamental medicine research, are becoming smarter. We anticipate significant improvements in areas ranging from molecular genomics and proteomics to decision support for healthcare professionals through big data analytics, to support behavior changes through technology-enabled self-management, and social and motivational support. Furthermore, with smart technologies, healthcare delivery could also be made more efficient, higher quality, and lower cost. In this special issue, we received a total 45 submissions and accepted 19 outstanding papers that roughly span across several interesting topics on smart healthcare, including public health, health information technology (Health IT), and smart medicine

Méthodes exactes et approchées pour le problème de planification des soins à domicile

RÉSUMÉ: De par le vieillissement de la population ainsi que le souhait des patients de rester le plus longtemps possible chez eux, auprès de leur famille, la dernière décennie a vu émerger la démocratisation des soins à domicile. Ces services peuvent prendre différentes formes telles que des soins infirmiers (piqûres, changement de pansement), de l’aide à la personne (pour prendre un bain, pour manger) ou encore du soutien psychologique. Au-delà du confort de vie qu’ils permettent chez les patients, ces soins à domicile donnent aussi la possibilité aux gouvernements de réduire le flux de patient dans les hôpitaux, de décentraliser les décisions de soins et de réduire le coût de prise en charge des patients. Néanmoins, afin de prendre en compte un maximum de patients tout en gardant un haut niveau de service, il a été montré qu’une planification des visites faite à la main était sousoptimale. Pour parer à cela, de nombreux outils d’aide à la décision ont été développés durant les vingt dernières années. Ces outils, capables de prendre en compte les nombreuses contraintes métier rencontrées par les agences de soins à domicile, permettent de créer en quelques secondes ou quelques minutes, des horaires hebdomadaires optimisés pour des dizaines d’employés. Cette thèse porte sur l’élaboration de ces outils d’aide à la décision et sur l’amélioration des processus opérationnels des agences de soins à domicile. Ces améliorations permettent alors de prendre en charge plus de patients, tout en conservant un haut niveau de service et de bonnes conditions de travail pour le personnel infirmier. Dans la première partie de cette thèse, nous présentons un travail réalisé en collaboration avec une compagnie montréalaise, Alayacare. Dans ce projet, nous listons l’ensemble des contraintes métier rencontrées pour les agences de soins à domicile et nous développons une modélisation du problème sous la forme d’un partitionnement d’ensemble. Pour résoudre le problème, nous développons une matheuristique, se décomposant en deux grandes parties. Tout d’abord un algorithme à voisinage large (LNS) est développé afin d’itérativement générer de nouvelles solutions réalisables et déterminer de nouveaux horaires hebdomadaires possibles pour les soignants. Ensuite, une résolution de la relaxation linéaire du problème de partitionnement d’ensemble, basée sur les horaires trouvés précédemment, est appelée. Sur des instances réelles issues de notre partenaire industriel, cette méthode de résolution a montré que l’on pouvait réduire de 37% le temps de trajet total, mais aussi augmenter de 16% la continuité des soins entre les patients et le personnel soignant. Dans la seconde partie de cette thèse, nous mettons l’emphase sur l’importance d’avoir une régularité dans les heures et jours de visites des patients. Pour cela, nous prenons en compte le fait que les patients restent plusieurs semaines dans le système des agences de soins à domicile et donc, lors de l’acceptation de nouveaux patients, il faut prendre en compte les contraintes associées aux patients existants (jours et heures de visite, personne soignante affectée). L’objectif est alors d’accepter le plus de nouveaux patients possibles, tout en gardant les horaires des patients existants inchangés. Afin de résoudre ce problème, nous reprenons et améliorons une décomposition de Benders et nous développons l’idée d’utiliser des patterns de visites pour les patients (comprenant les jours et heures de visite ainsi que l’employé affecté). Les expérimentations faites sur des instances réelles de la littérature montrent que notre nouvelle formulation permet de réduire drastiquement les temps de calcul. Enfin, nous montrons que pour les instances les plus difficiles à résoudre, nous pouvons adapter la LNS présentée dans l’article 1 afin d’obtenir les solutions optimales pour un temps de calcul ne dépassant pas les 20 secondes. Enfin, le troisième projet de cette thèse consiste à prendre en compte l’aspect dynamique du problème. En effet, nous avons expliqué précédemment que certains patients restaient dans le système durant plusieurs semaines, conservant leurs jours et heures de visites ainsi que leur personnel soignant affecté. Dans cette dernière partie, nous prenons un horizon roulant sur plus d’un an et étudions l’impact des décisions d’acceptation et de planification prises chaque semaine, sur le nombre de visites moyen. Dans ce contexte, nous recevons donc plusieurs offres de patients chaque jour et nous devons décider si le patient peut être accepté et si oui, qui le visitera, quels jours et à quelle heure. Pour cela, nous développons différentes heuristiques et mettons l’emphase sur les effets positifs que permet la flexibilité lors de la planification des visites. Cette flexibilité vient dans un premier temps du moment auquel nous prenons la décision pour l’acceptation des patients (à la réception de l’offre, à la fin de la journée, à la fin de la semaine). L’autre flexibilité vient du fait que l’on va non pas attribuer une heure exacte de visite au patient pour l’ensemble de son plan de soin, mais plutôt une fenêtre de temps, de soixante minutes par exemple, dans laquelle il sera visité. Les résultats de ces différentes heuristiques ainsi que des différentes flexibilités montrent que, sans modifications massives des processus de décision des agences, il est possible d’accepter jusqu’à 12% de visites en plus chaque semaine.----------ABSTRACT: Due to the population’s aging and people’s will to stay at home with family and friends, the last decade has been the decade of home health care services democratization. Those home care services have different aspects such as nursing acts (injection, band-aid replacement),personnal support (bathing, cooking) or social work for the psychological support of the patients. Beyond the fact that those services positively impact patients’ life, they also give governments the possibility of reducing flows of patients in the hospitals, decentralize the decisions and reduce the costs. Nevertheless, keeping up a high level of service for the patients is challenging and it has been shown that the manual scheduling of the visits by the head nurses usually leads to sub-optimal solutions. To cope with this issue, decision-making tools have been developed during the last decades in order to help the home care agencies in this scheduling task. These tools, capable to take into account a large set of practical constraints, allow the users to quickly (in a few seconds or minutes) and efficiently design weekly visit schedules for dozens of nurses. This thesis focuses on the elaboration of efficient decision-making tools and resolution methods in the context of home health care services. In the first part of this thesis, we present a work realised in colalboration with a company from Montréal, Alayacare. In this project, we list the different practical constraints met by their users (worldwide home care agencies) and we propose a set partitioning-based formulation. In order to solve the problem, we propose a matheuristic, composed of two main elements. Firstly, a large neighborhood search (LNS) method is implemented, allowing to iteratively generate new feasible solutions and retrieve a set of feasible weekly schedules for the different nurses. Secondly, a relaxed version of the set partitioning is solved using the weekly schedules previously found. On real instances provided by our industrial partner, experiments show that our method allows to reduce by 37% the travel time and increase by 16% the continuity of care between the patients and the nurses. In the second part of this thesis, we focus on the patients’ visits’ recurrency aspect. To do so, we take into account the fact that patients stay multiple weeks in home care agencies’ system and so, when we accept new patients, we have to take into account resource constraints from the existing patients (visit time and days, assigned nurse). The objective is then to maximize the number of new patients accepted without modifying old patients’ assignment and scheduling. In order to solve this problem, we extend a Benders decomposition and propose a new decomposition using visit patterns (composed of visit time and days and an assigned caregiver). Computational experiments show that our new decomposition allows to dramatically reduce the computation times on benchmark instances. For the largest instances, we show that we can adapt the LNS proposed in the first paper using visit patterns and solve optimally all the instances in less than 20 seconds. Finally, the third research projet consists in taking into account the dynamic aspect of the home health care services. Indeed, we previously presented the fact that patients stay multiple weeks in the system and so have to be taken as constraints when accepting new patients. In this last part of the thesis, we take into account the rolling horizon aspect of the problem (on more than a year) and we study the impact of the weekly decisions over time. The metric corresponds to the maximization of the average number of weekly visits. In this context, we receive multiple patient offers per day and we have to decide which patients we can accept and how they will be scheduled. To solve this problem, we propose different heuristics and focus on the impact of flexibility during the acceptance and scheduling process. On the one hand, this flexibility corresponds to the moment the decision is taken (when the offer is received, at the end of the day, at the end of the week). On the other hand, we also study flexibility on the visit time and propose not to assign the patients an exact visit time, but rather a visit time window. Results show that those heuristics and the flexiblity we propose allow the home care agencies, without drastic modification of their processes, to dramatically increase the average number of weekly visits with up to 12%

Integrating Blockchain and Fog Computing Technologies for Efficient Privacy-preserving Systems

This PhD dissertation concludes a three-year long research journey on the integration of Fog Computing and Blockchain technologies. The main aim of such integration is to address the challenges of each of these technologies, by integrating it with the other. Blockchain technology (BC) is a distributed ledger technology in the form of a distributed transactional database, secured by cryptography, and governed by a consensus mechanism. It was initially proposed for decentralized cryptocurrency applications with practically proven high robustness. Fog Computing (FC) is a geographically distributed computing architecture, in which various heterogeneous devices at the edge of network are ubiquitously connected to collaboratively provide elastic computation services. FC provides enhanced services closer to end-users in terms of time, energy, and network load. The integration of FC with BC can result in more efficient services, in terms of latency and privacy, mostly required by Internet of Things systems

OptPlatform: metaheuristic optimisation framework for solving complex real-world problems

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonWe optimise daily, whether that is planning a round trip that visits the most attractions within a given holiday budget or just taking a train instead of driving a car in a rush hour. Many problems, just like these, are solved by individuals as part of our daily schedule, and they are effortless and straightforward. If we now scale that to many individuals with many different schedules, like a school timetable, we get to a point where it is just not feasible or practical to solve by hand. In such instances, optimisation methods are used to obtain an optimal solution. In this thesis, a practical approach to optimisation has been taken by developing an optimisation platform with all the necessary tools to be used by practitioners who are not necessarily familiar with the subject of optimisation. First, a high-performance metaheuristic optimisation framework (MOF) called OptPlatform is implemented, and the versatility and performance are evaluated across multiple benchmarks and real-world optimisation problems. Results show that, compared to competing MOFs, the OptPlatform outperforms in both the solution quality and computation time. Second, the most suitable hardware platform for OptPlatform is determined by an in-depth analysis of Ant Colony Optimisation scaling across CPU, GPU and enterprise Xeon Phi. Contrary to the common benchmark problems used in the literature, the supply chain problem solved could not scale on GPUs. Third, a variety of metaheuristics are implemented into OptPlatform. Including, a new metaheuristic based on Imperialist Competitive Algorithm (ICA), called ICA with Independence and Constrained Assimilation (ICAwICA) is proposed. The ICAwICA was compared against two different types of benchmark problems, and results show the versatile application of the algorithm, matching and in some cases outperforming the custom-tuned approaches. Finally, essential MOF features like automatic algorithm selection and tuning, lacking on existing frameworks, are implemented in OptPlatform. Two novel approaches are proposed and compared to existing methods. Results indicate the superiority of the implemented tuning algorithms within constrained tuning budget environment

Smart Monitoring and Control in the Future Internet of Things

The Internet of Things (IoT) and related technologies have the promise of realizing pervasive and smart applications which, in turn, have the potential of improving the quality of life of people living in a connected world. According to the IoT vision, all things can cooperate amongst themselves and be managed from anywhere via the Internet, allowing tight integration between the physical and cyber worlds and thus improving efficiency, promoting usability, and opening up new application opportunities. Nowadays, IoT technologies have successfully been exploited in several domains, providing both social and economic benefits. The realization of the full potential of the next generation of the Internet of Things still needs further research efforts concerning, for instance, the identification of new architectures, methodologies, and infrastructures dealing with distributed and decentralized IoT systems; the integration of IoT with cognitive and social capabilities; the enhancement of the sensing–analysis–control cycle; the integration of consciousness and awareness in IoT environments; and the design of new algorithms and techniques for managing IoT big data. This Special Issue is devoted to advancements in technologies, methodologies, and applications for IoT, together with emerging standards and research topics which would lead to realization of the future Internet of Things