A Taxonomy for Management and Optimization of Multiple Resources in Edge Computing

Edge computing is promoted to meet increasing performance needs of data-driven services using computational and storage resources close to the end devices, at the edge of the current network. To achieve higher performance in this new paradigm one has to consider how to combine the efficiency of resource usage at all three layers of architecture: end devices, edge devices, and the cloud. While cloud capacity is elastically extendable, end devices and edge devices are to various degrees resource-constrained. Hence, an efficient resource management is essential to make edge computing a reality. In this work, we first present terminology and architectures to characterize current works within the field of edge computing. Then, we review a wide range of recent articles and categorize relevant aspects in terms of 4 perspectives: resource type, resource management objective, resource location, and resource use. This taxonomy and the ensuing analysis is used to identify some gaps in the existing research. Among several research gaps, we found that research is less prevalent on data, storage, and energy as a resource, and less extensive towards the estimation, discovery and sharing objectives. As for resource types, the most well-studied resources are computation and communication resources. Our analysis shows that resource management at the edge requires a deeper understanding of how methods applied at different levels and geared towards different resource types interact. Specifically, the impact of mobility and collaboration schemes requiring incentives are expected to be different in edge architectures compared to the classic cloud solutions. Finally, we find that fewer works are dedicated to the study of non-functional properties or to quantifying the footprint of resource management techniques, including edge-specific means of migrating data and services.Comment: Accepted in the Special Issue Mobile Edge Computing of the Wireless Communications and Mobile Computing journa

A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities

Mobile crowdsensing (MCS) has gained significant attention in recent years and has become an appealing paradigm for urban sensing. For data collection, MCS systems rely on contribution from mobile devices of a large number of participants or a crowd. Smartphones, tablets, and wearable devices are deployed widely and already equipped with a rich set of sensors, making them an excellent source of information. Mobility and intelligence of humans guarantee higher coverage and better context awareness if compared to traditional sensor networks. At the same time, individuals may be reluctant to share data for privacy concerns. For this reason, MCS frameworks are specifically designed to include incentive mechanisms and address privacy concerns. Despite the growing interest in the research community, MCS solutions need a deeper investigation and categorization on many aspects that span from sensing and communication to system management and data storage. In this paper, we take the research on MCS a step further by presenting a survey on existing works in the domain and propose a detailed taxonomy to shed light on the current landscape and classify applications, methodologies, and architectures. Our objective is not only to analyze and consolidate past research but also to outline potential future research directions and synergies with other research areas

Studying user behavior through a participatory sensing framework in an urban context

A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Geographic Information SystemsThe widespread use of mobile devices has given birth to participatory sensing, a data collection approach leveraging the sheer number of device users, their mobility, intelligence and device’s increasingly powerful computing and sensing capabilities. As a result, participatory sensing is able to collect various types of information at a high spatial and temporal resolution and it has many applications ranging from measuring cellular signal strength or road condition monitoring to observing the distribution of birds. However, in order to achieve better results from participatory sensing, some issues needed to be dealt with. On a high level, this thesis addressed two issues: (1) the design and development of a participatory sensing framework that allows users to flexibly create campaigns and at the same time collect different types of data and (2) the study of different aspects of the user behaviors in the context of participatory sensing. In particular, the first contribution of the thesis is the design and development of Citizense, a participatory sensing framework that facilitates flexible deployments of participatory sensing campaigns while at the same time providing intuitive interfaces for users to create sensing campaigns and collect a variety of data types. During the real-world deployments of Citizense, it has shown its effectiveness in collecting different types of urban information and subsequently received appreciation from different stakeholders. The second contribution of the thesis is the in-depth study of user behavior under the presence of different monetary incentive mechanisms and the analysis of the spatial and temporal user behavior when participants are simultaneously exposed to a large number of participatory sensing campaigns. Concerning the monetary incentive, it is observed that participants prefer fixed micro-payment to other mechanisms (i.e., lottery, variable micro-payment); their participation was increased significantly when they were given this incentive. When taking part in the participatory sensing process, participants exhibit certain spatial and temporal behaviors. They tend to primarily contribute in their free time during the working week, although the decision to respond and complete a particular participatory sensing campaign seems to be correlated to the campaign’s geographical context and/or the recency of the participants’ activities. Participants can be divided into two groups according to their behaviors: a smaller group of active participants who frequently perform participatory sensing activities and a larger group of regular participants who exhibit more intermittent behaviors

Resource Allocation and Service Management in Next Generation 5G Wireless Networks

The accelerated evolution towards next generation networks is expected to dramatically increase mobile data traffic, posing challenging requirements for future radio cellular communications. User connections are multiplying, whilst data hungry content is dominating wireless services putting significant pressure on network's available spectrum. Ensuring energy-efficient and low latency transmissions, while maintaining advanced Quality of Service (QoS) and high standards of user experience are of profound importance in order to address diversifying user prerequisites and ensure superior and sustainable network performance. At the same time, the rise of 5G networks and the Internet of Things (IoT) evolution is transforming wireless infrastructure towards enhanced heterogeneity, multi-tier architectures and standards, as well as new disruptive telecommunication technologies. The above developments require a rethinking of how wireless networks are designed and operate, in conjunction with the need to understand more holistically how users interact with the network and with each other. In this dissertation, we tackle the problem of efficient resource allocation and service management in various network topologies under a user-centric approach. In the direction of ad-hoc and self-organizing networks where the decision making process lies at the user level, we develop a novel and generic enough framework capable of solving a wide array of problems with regards to resource distribution in an adaptable and multi-disciplinary manner. Aiming at maximizing user satisfaction and also achieve high performance - low power resource utilization, the theory of network utility maximization is adopted, with the examined problems being formulated as non-cooperative games. The considered games are solved via the principles of Game Theory and Optimization, while iterative and low complexity algorithms establish their convergence to steady operational outcomes, i.e., Nash Equilibrium points. This thesis consists a meaningful contribution to the current state of the art research in the field of wireless network optimization, by allowing users to control multiple degrees of freedom with regards to their transmission, considering mobile customers and their strategies as the key elements for the amelioration of network's performance, while also adopting novel technologies in the resource management problems. First, multi-variable resource allocation problems are studied for multi-tier architectures with the use of femtocells, addressing the topic of efficient power and/or rate control, while also the topic is examined in Visible Light Communication (VLC) networks under various access technologies. Next, the problem of customized resource pricing is considered as a separate and bounded resource to be optimized under distinct scenarios, which expresses users' willingness to pay instead of being commonly implemented by a central administrator in the form of penalties. The investigation is further expanded by examining the case of service provider selection in competitive telecommunication markets which aim to increase their market share by applying different pricing policies, while the users model the selection process by behaving as learning automata under a Machine Learning framework. Additionally, the problem of resource allocation is examined for heterogeneous services where users are enabled to dynamically pick the modules needed for their transmission based on their preferences, via the concept of Service Bundling. Moreover, in this thesis we examine the correlation of users' energy requirements with their transmission needs, by allowing the adaptive energy harvesting to reflect the consumed power in the subsequent information transmission in Wireless Powered Communication Networks (WPCNs). Furthermore, in this thesis a fresh perspective with respect to resource allocation is provided assuming real life conditions, by modeling user behavior under Prospect Theory. Subjectivity in decisions of users is introduced in situations of high uncertainty in a more pragmatic manner compared to the literature, where they behave as blind utility maximizers. In addition, network spectrum is considered as a fragile resource which might collapse if over-exploited under the principles of the Tragedy of the Commons, allowing hence users to sense risk and redefine their strategies accordingly. The above framework is applied in different cases where users have to select between a safe and a common pool of resources (CPR) i.e., licensed and unlicensed bands, different access technologies, etc., while also the impact of pricing in protecting resource fragility is studied. Additionally, the above resource allocation problems are expanded in Public Safety Networks (PSNs) assisted by Unmanned Aerial Vehicles (UAVs), while also aspects related to network security against malign user behaviors are examined. Finally, all the above problems are thoroughly evaluated and tested via a series of arithmetic simulations with regards to the main characteristics of their operation, as well as against other approaches from the literature. In each case, important performance gains are identified with respect to the overall energy savings and increased spectrum utilization, while also the advantages of the proposed framework are mirrored in the improvement of the satisfaction and the superior Quality of Service of each user within the network. Lastly, the flexibility and scalability of this work allow for interesting applications in other domains related to resource allocation in wireless networks and beyond

Opportunistic Service Provisioning in Mobile Clouds of Users' Personal Devices

Opportunistic computing is the recent application of delay-tolerant networking to the creation of networks of mobile devices that give users the capability to share and access services provided by other (mobile) devices in proximity without using any cellular infrastructures. The importance of this paradigm becomes apparent given the ubiquitous proliferation of personal mobile devices in recent years. Opportunistic computing can also be used to realise service offloading, a recent trend in mobile networking research where resources on the edge of the cellular network are used in synergy with the cloud infrastructure. The importance of this application of opportunistic computing comes from the data traffic generated by mobile devices that, in the last few years, has been steadily increasing. While the development of LTE and LTE-A will boost cellular network capacity, it is unclear whether this would be enough to support the expected exponential increase in traffic demands in the medium term. Opportunistic techniques can contribute to solve this problem by offloading computation and data access to locally available devices, exploiting unused resources and balancing allocation of users requests to obtain an increase in service provisioning performances and avoiding network congestion. This thesis brings contributions in two different scenarios: the first one is purely opportunistic with the detailing of a distributed system for the establishment and self-organization of mobile service provisioning. The system is established by each device autonomously collecting and using context information to individuate sequential compositions of resources for service provisioning and, thanks to a stochastic model, find the alternative that is expected to result in the lowest service provisioning time. In the second scenario, this thesis presents a solution for the integration of the opportunistic paradigm into a mobile edge system, where service provisioning is orchestrated between mobile devices and a remote cloud system thanks to the collaboration with network base stations local to the mobile devices. In the first scenario, experiments are presented to validate the decision algorithms and the stochastic model they rely on, while in the second scenario, we evaluate the performance gains obtained by using the opportunistic paradigm for service offloading in respect to traditional remote cloud systems

De la Routine Humaine vers des Réseaux Mobiles Plus Efficaces

The proliferation of pervasive communication caused a recent boost up on the mobile data usage, which network operators are not always prepared for. The main origin of the mobile network demands are smartphone devices. From the network side those devices may be seen as villains for imposing an enormous traffic, but from the analytical point of view they provide today the best means of gathering users information about content consumption and mobility behavior on a large scale. Understanding users' mobility and network behavior is essential in the design of efficient communication systems. We are routinary beings. The routine cycles on our daily lives are an essential part of our interface with the world. Our habits define, for instance, where we are going Saturday night, or what is the typical website for the mornings of Monday. The repetitive behavior reflects on our mobility patterns and network activities. In this thesis we focus on metropolitan users generating traffic demands during their normal daily lives. We present a detailed study on both users' routinary mobility and routinary network behavior. As a study of case where such investigation can be useful, we propose a hotspot deployment strategy that takes into account the routine aspects of people's mobility.We first investigate urban mobility patterns. We analyze large-scale datasets of mobility in different cities of the world, namely Beijing, Tokyo, New York, Paris, San Francisco, London, Moscow and Mexico City. Our contribution is this area is two-fold. First, we show that there is a similarity on people's mobility behavior regardless the city. Second, we unveil three characteristics present on the mobility of typical urban population: repetitiveness, usage of shortest-paths, and confinement. Those characteristics undercover people's tendency to revisit a small portion of favorite venues using trajectories that are close to the shortest-path. Furthermore, people generally have their mobility restrict to a dozen of kilometers per day.We then investigate the users' traffic demands patterns. We analyze a large data set with 6.8 million subscribers. We have mainly two contributions in this aspect. First, a precise characterization of individual subscribers' traffic behavior clustered by their usage patterns. We see how the daily routine impacts on the network demands and the strong similarity between traffic on different days. Second, we provide a way for synthetically, still consistently, reproducing usage patterns of mobile subscribers. Synthetic traces offer positive implications for network planning and carry no privacy issues to subscribers as the original datasets.To assess the effectiveness of these findings on real-life scenario, we propose a hotspot deployment strategy that considers routine characteristics of mobility and traffic in order to improve mobile data offloading. Carefully deploying Wi-Fi hotspots can both be cheaper than upgrade the current cellular network structure and can concede significant improvement in the network capacity. Our approach increases the amount of offload when compared to other solution from the literature.L’omniprésence des communications a entraîné une récente augmentation des volumes de données mobiles, pour laquelle les opérateurs n’étaient pas toujours préparés. Les smartphones sont les plus gros consommateurs de données mobiles. Ces appareils peuvent être considérés comme méchants à cause d’un tel traffic, mais d’un point de vue analytique ils fournissent, aujourd’hui un des meilleurs moyens afin de collecter les données sur le comportement de consommation et de mobilité de grande échelle. Comprendre le comportement des utilisateurs sur leur mobilité et leur connectivité est nécessaire à la création d’un système de communication effectifs. Nous sommes routiniers. Ces cycles routiniers sont une grande partie de nos interactions avec le monde. Par exemple, nos habitudes definissent ce que l’on va faire le samedi ou les sites que nous consultons le lundi matin. Ces comportements répétés reflètent nos déplacements et activités en ligne. Dans cette thèse, nous allons nous concentrer sur les demandes de traffic générées par les usagers métropolitains durant leurs activités quotidiennes. Nous présentons une étude détaillée des usagers selon les comportements routiniers de mobilité ou d’activité sur internet. Dans une étude de cas, ou cette enquête serait utile, nous proposons une stratégies de déploiement de points de accès qui prendra en compte les aspects routiniers de la mobilités des utilisateurs.Nous étudirons en premier lieu, les modèles de mobilité en milieu urbain. Nous analyserons les données de mobilité à grande échelle dans de grandes villes comme Beijing, Tokyo, New York, Paris, San Francisco, London, Moscow, Mexico City. Cette contribution se fait en deux étapes. Premièrement, nous observerons les similitudes des déplacements peu importe la ville concernée. Ensuite, nous mettrons en évidence trois caractéristiques présentes dans les déplacements d’une population urbaine typique: Répétivité, utilisation de raccourcis, confinement. Ces caractéristiques sont dues à la tendance qu’ont les personnes à revisiter les même rues en utilisant les trajectoires proches du chemin le plus court. D’ailleurs, les personnes ont une mobilité quotidienne inférieure à dix kilomètres par jour.Nous avons ensuite étudié les modèles de demandes de traffic en utilisant une base de données comprenant les données de 6.8 millions d’utilisateurs. Pour cela nous avons principalement deux contributions. Premièrement, une caractérisation précise des comportements de consommation des utilisateurs agrégés par modèle. Nous pouvons voir comment les routines quotidiennes impactent nos demandes de connections et la similarité de ce traffic en fonction des jours. En suite, nous fournirons un moyen de reproduire artificiellement mais avec cohérence les modèles des utilisateurs de données mobiles. Ces données synthétisées ont l’avantage de permettre la planification du réseau sans information sur la vie privées de utilisateurs comme les bases de données d’origine.Afin d’évaluer l’efficacité de ces informations dans un scénario grandeur nature, nous proposerons une stratégie de deploiement de points de accès qui prend en compte les caractéristiques routinières en terme de déplacement et de demande de trafic dans le but d’améliorer la décharge de données mobile. Déployer correctement des points de accès WiFi peut être moins cher que d’améliorer l’infrastructure de réseaux mobiles, et peut permettre d’améliorer considérablement la capacité du réseau. Notre approche améliore l’évacuation de trafic comparée aux autres solutions disponibles dans la littérature

Towards low complexity matching theory for uplink wireless communication systems

Millimetre wave (mm-Wave) technology is considered a promising direction to achieve the high quality of services (QoSs) because it can provide high bandwidth, achieving a higher transmission rate due to its immunity to interference. However, there are several limitations to utilizing mm-Wave technology, such as more extraordinary precision hardware is manufactured at a higher cost because the size of its components is small. Consequently, mm-Wave technology is rarely applicable for long-distance applications due to its narrow beams width. Therefore, using cell-free massive multiple input multiple output (MIMO) with mm-Wave technology can solve these issues because this architecture of massive MIMO has better system performance, in terms of high achievable rate, high coverage, and handover-free, than conventional architectures, such as massive MIMO systems’ co-located and distributed (small cells). This technology necessitates a significant amount of power because each distributed access point (AP) has several antennas. Each AP has a few radio frequency (RF) chains in hybrid beamforming. Therefore more APs mean a large number of total RF chains in the cell-free network, which increases power consumption. To solve this problem, deactivating some antennas or RF chains at each AP can be utilized. However, the size of the cell-free network yields these two options as computationally demanding. On the other hand, a large number of users in the cell-free network causes pilot contamination issue due to the small length of the uplink training phase. This issue has been solved in the literature based on two options: pilot assignment and pilot power control. Still, these two solutions are complex due to the cell-free network size. Motivated by what was mentioned previously, this thesis proposes a novel technique with low computational complexity based on matching theory for antenna selection, RF chains activation, pilot assignment and pilot power control. The first part of this thesis provides an overview of matching theory and the conventional massive MIMO systems. Then, an overview of the cell-free massive MIMO systems and the related works of the signal processing techniques of the cell-free mm-Wave massive MIMO systems to maximize energy efficiency (EE), are provided. Based on the limitations of these techniques, the second part of this thesis presents a hybrid beamforming architecture with constant phase shifters (CPSs) for the distributed uplink cell-free mm-Wave massive MIMO systems based on exploiting antenna selection to reduce power consumption. The proposed scheme uses a matching technique to obtain the number of selected antennas which can contribute more to the desired signal power than the interference power for each RF chain at each AP. Therefore, the third part of this thesis solves the issue of the huge complexity of activating RF chains by presenting a low-complexity matching approach to activate a set of RF chains based on the Hungarian method to maximize the total EE in the centralized uplink of the cell-free mm-Wave massive MIMO systems when it is proposed hybrid beamforming with fully connected phase shifters network. The pilot contamination issue has been discussed in the last part of this thesis by utilizing matching theory in pilot assignment and pilot power control design for the uplink of cell-free massive MIMO systems to maximize SE. Firstly, an assignment optimization problem has been formulated to find the best possible pilot sequences to be inserted into a genetic algorithm (GA). Therefore, the GA will find the optimal solution. After that, a minimum-weighted assignment problem has been formulated regarding the power control design to assign pilot power control coefficients to the quality of the estimated channel. Then, the Hungarian method is utilized to solve this problem. The simulation results of the proposed matching theory for the mentioned issues reveal that the proposed matching approach is more energy-efficient and has lower computational complexity than state-of-the-art schemes for antenna selection and RF chain activation. In addition, the proposed matching schemes outperform the state-of-the-art techniques concerning the pilot assignment and the pilot power control design. This means that network scalability can be guaranteed with low computational complexity

State of the Art and Future Perspectives in Smart and Sustainable Urban Development

This book contributes to the conceptual and practical knowledge pools in order to improve the research and practice on smart and sustainable urban development by presenting an informed understanding of the subject to scholars, policymakers, and practitioners. This book presents contributions—in the form of research articles, literature reviews, case reports, and short communications—offering insights into the smart and sustainable urban development by conducting in-depth conceptual debates, detailed case study descriptions, thorough empirical investigations, systematic literature reviews, or forecasting analyses. This way, the book forms a repository of relevant information, material, and knowledge to support research, policymaking, practice, and the transferability of experiences to address urbanization and other planetary challenges

Continuous evaluation framework for software architectures: an IoT case

Context: Design-time evaluation is essential to build the initial software architecture to be deployed. However, experts’ design-time assumptions are unlikely to remain true indefinitely in systems characterized by scale, heterogeneity, and dynamism (e.g. IoT). Experts’ design-time decisions can be thus challenged at run-time. A continuous architecture evaluation that systematically intertwines design-time and run-time evaluation is necessary. However, the literature lacks examples on how continuous evaluation can be realized and conducted. Objective: This thesis proposes the first continuous architecture evaluation framework. Method: The framework is composed of two phases: design-time and run-time evaluation. The design-time evaluation enables the necessary initial step of system design and deployment. Run-time evaluation assesses to what extent the architecture options adopted at design-time and other potential options, perform well at run-time. For that, the framework leverages techniques inspired by finance, reinforcement learning, multi-objective optimisation, and time series forecasting. The framework can actively track and proactively forecast the performance of architecture decisions and detect any detrimental changes. It can then inform deployment, refinement, and/or phasing-out decisions. We use an IoT case study to show how continuous evaluation can fundamentally guide the architect and influence the outcome of the decisions. A series of experiments is conducted to demonstrate the applicability and effectiveness of the framework. Results: The design-time evaluation was able to evaluate the architecture options under uncertainty and shortlist candidates for further refinement at run-time. The run-time evaluation has shown to be effective. In particular, it enabled a significant improvement in overall quality (about 40-70% better than reactive and state-of-the-art approaches in some scenarios), with enhanced architecture’s stability. It was also shown to be scalable and robust to various noise levels. In addition, it provides the architect with flexibility to set a monitoring interval to profile the quality of candidates and has parameters that enable the architect to manage the trade-off between architecture stability and learning accuracy. Conclusion: The proposed continuous evaluation framework could potentially aid the architect in evaluating complex design decisions in dynamic environments

Development of mathematical models to improve road freight movements for tunnel infrastructure using connected and autonomous vehicles

Road freight transportation is considered the backbone of country’s socio-economic framework and thus its vital to ensure it is working optimally. The research detailed in this thesis is focused on improving the movement of road freight, especially for hazardous goods vehicles via a road tunnel, with the help of Connected and Autonomous Freight Vehicles (CAV-F). The study analyses real-world Dartford Crossing tunnel data to identify the impact of existing check and allow procedures for Dangerous Goods Vehicles (DGVs) and Abnormal Load Vehicles (ALVs) at a tunnel. A near realistic traffic simulation model is developed as part of analysis and is validated against an independent Highways England’s Motorway Incident Detection and Automatic Signalling (MIDAS) data. The effectiveness of CAV-F in improving road traffic conditions is measured using different simulation scenarios involving mixed traffic (i.e. CAV-F and conventional vehicles alongside) and different real-world tunnel closure conditions. Once the effective performance of CAV-F is established, this research develops a novel mathematical model aimed at automating the check and allow procedures for DGVs at the tunnel. The mathematical model calculates the geo-reference locations for the placement of cooperative communications between the vehicles and road infrastructure to generate dynamic vehicular gaps. This will allow desired safety gaps between the platoon of DGVs and its preceding and following vehicles enabling isolated travel via the road tunnel to ensure safe and secure passage. The mathematical model is verified for different road layouts determined based on geo-referenced locations, approaching a road tunnel. Using traffic simulation, the results determine if the modulation of vehicles’ speeds at identified geo-referenced locations are suitable for desired gap generation. Finally, to conclude the research questions, the second mathematical model is developed to help uninterrupted traffic merging at the junctions, as was observed after the successful gap generation. This model could also be generalised to optimise the traffic merge sequence at a motorway junction. The approach is inspired by the noise cancellation technique which utilises destructive wave interference patterns, where vehicular flow on two merging roads is considered as traffic waves. By analysing the merge sequence of vehicles at the junction from fixed equidistant positions on separate roads, the dynamic phase shifting is applied by modulating the speeds of the identified vehicles which would otherwise approach at the junction simultaneously, leading to queue formation (or collision). The performance of the approach is then measured using a traffic simulation model and are determined against existing real-world traffic flow on motorways for improvements in travel time, and traffic throughput and reduction in congestion, with increasing traffic density