An optimized computational model for multi-community-cloud social collaboration

PublishedCommunity Cloud Computing is an emerging and promising computing model for a specific community with common concerns, such as security, compliance and jurisdiction. It utilizes the spare resources of networked computers to provide the facilities so that the community gains services from the cloud. The effective collaboration among the community clouds offers a powerful computing capacity for complex tasks containing the subtasks that need data exchange. Selecting the best group of community clouds that are the most economy-efficient, communication-efficient, secured, and trusted to accomplish a complex task is very challenging. To address this problem, we first formulate a computational model for multi-community-cloud collaboration, namely MG3. The proposed model is then optimized from four aspects: minimizing the sum of access cost and monetary cost, maximizing the security-level agreement and trust among the community clouds. Furthermore, an efficient and comprehensive selection algorithm is devised to extract the best group of community clouds in MG3. Finally, the extensive simulation experiments and performance analysis of the proposed algorithm are conducted. The results demonstrate that the proposed algorithm outperforms the minimal set coverings based algorithm and the random algorithm. Moreover, the proposed comprehensive community clouds selection algorithm can guarantee good global performance in terms of access cost, monetary cost, security level and trust between user and community clouds

Virtual sensor networks: collaboration and resource sharing

This thesis contributes to the advancement of the Sensing as a Service (SeaaS), based on cloud infrastructures, through the development of models and algorithms that make an efficient use of both sensor and cloud resources while reducing the delay associated with the data flow between cloud and client sides, which results into a better quality of experience for users. The first models and algorithms developed are suitable for the case of mashups being managed at the client side, and then models and algorithms considering mashups managed at the cloud were developed. This requires solving multiple problems: i) clustering of compatible mashup elements; ii) allocation of devices to clusters, meaning that a device will serve multiple applications/mashups; iii) reduction of the amount of data flow between workplaces, and associated delay, which depends on clustering, device allocation and placement of workplaces. The developed strategies can be adopted by cloud service providers wishing to improve the performance of their clouds. Several steps towards an efficient Se-aaS business model were performed. A mathematical model was development to assess the impact (of resource allocations) on scalability, QoE and elasticity. Regarding the clustering of mashup elements, a first mathematical model was developed for the selection of the best pre-calculated clusters of mashup elements (virtual Things), and then a second model is proposed for the best virtual Things to be built (non pre-calculated clusters). Its evaluation is done through heuristic algorithms having such model as a basis. Such models and algorithms were first developed for the case of mashups managed at the client side, and after they were extended for the case of mashups being managed at the cloud. For the improvement of these last results, a mathematical programming optimization model was developed that allows optimal clustering and resource allocation solutions to be obtained. Although this is a computationally difficult approach, the added value of this process is that the problem is rigorously outlined, and such knowledge is used as a guide in the development of better a heuristic algorithm.Esta tese contribui para o avanço tecnológico do modelo de Sensing as a Service (Se-aaS), baseado em infraestrutura cloud, através do desenvolvimento de modelos e algoritmos que resolvem o problema da alocação eficiente de recursos, melhorando os métodos e técnicas atuais e reduzindo os tempos associados `a transferência dos dados entre a cloud e os clientes, com o objetivo de melhorar a qualidade da experiência dos seus utilizadores. Os primeiros modelos e algoritmos desenvolvidos são adequados para o caso em que as mashups são geridas pela aplicação cliente, e posteriormente foram desenvolvidos modelos e algoritmos para o caso em que as mashups são geridas pela cloud. Isto implica ter de resolver múltiplos problemas: i) Construção de clusters de elementos de mashup compatíveis; ii) Atribuição de dispositivos físicos aos clusters, acabando um dispositivo físico por servir m´ múltiplas aplicações/mashups; iii) Redução da quantidade de transferência de dados entre os diversos locais da cloud, e consequentes atrasos, o que dependente dos clusters construídos, dos dispositivos atribuídos aos clusters e dos locais da cloud escolhidos para realizar o processamento necessário. As diferentes estratégias podem ser adotadas por fornecedores de serviço cloud que queiram melhorar o desempenho dos seus serviços.(…

Allocation of resources in SAaaS Clouds managing thing mashups

The sensing and actuation as-a-service is an emerging business model to make sensors, actuators and data from the Internet of Things more attainable to everyday consumer. With the increase in the number of accessible Things, mashups can be created to combine services/data from one or multiple Things with services/data from virtual Web resources. These may involve complex tasks, with high computation requirements, and for this reason cloud infrastructures are envisaged as the most appropriate solution for storage and processing. This means that cloud-based services should be prepared to manage Thing mashups. Mashup management within the cloud allows not only the optimization of resources but also the reduction of the delay associated with data travel between client applications and the cloud. In this article, an optimization model is developed for the optimal allocation of resources in clouds under the sensing and actuation as-a-service paradigm. A heuristic algorithm is also proposed to quickly solve the problem.FCT (Foundation for Science and Technology) from Portugal within CEOT (Center for Electronic, Optoelectronic and Telecommunications) [UID/MULTI/00631/2020]info:eu-repo/semantics/publishedVersio

Resource allocation model for sensor clouds under the sensing as a service paradigm

The Sensing as a Service is emerging as a new Internet of Things (IoT) business model for sensors and data sharing in the cloud. Under this paradigm, a resource allocation model for the assignment of both sensors and cloud resources to clients/applications is proposed. This model, contrarily to previous approaches, is adequate for emerging IoT Sensing as a Service business models supporting multi-sensing applications and mashups of Things in the cloud. A heuristic algorithm is also proposed having this model as a basis. Results show that the approach is able to incorporate strategies that lead to the allocation of fewer devices, while selecting the most adequate ones for application needs.FCT (Foundation for Science and Technology) from Portugal within CEOT (Center for Electronic, Optoelectronic and Telecommunications) UID/MULTI/00631/2019info:eu-repo/semantics/publishedVersio

3D Robotic Sensing of People: Human Perception, Representation and Activity Recognition

The robots are coming. Their presence will eventually bridge the digital-physical divide and dramatically impact human life by taking over tasks where our current society has shortcomings (e.g., search and rescue, elderly care, and child education). Human-centered robotics (HCR) is a vision to address how robots can coexist with humans and help people live safer, simpler and more independent lives. As humans, we have a remarkable ability to perceive the world around us, perceive people, and interpret their behaviors. Endowing robots with these critical capabilities in highly dynamic human social environments is a significant but very challenging problem in practical human-centered robotics applications. This research focuses on robotic sensing of people, that is, how robots can perceive and represent humans and understand their behaviors, primarily through 3D robotic vision. In this dissertation, I begin with a broad perspective on human-centered robotics by discussing its real-world applications and significant challenges. Then, I will introduce a real-time perception system, based on the concept of Depth of Interest, to detect and track multiple individuals using a color-depth camera that is installed on moving robotic platforms. In addition, I will discuss human representation approaches, based on local spatio-temporal features, including new “CoDe4D” features that incorporate both color and depth information, a new “SOD” descriptor to efficiently quantize 3D visual features, and the novel AdHuC features, which are capable of representing the activities of multiple individuals. Several new algorithms to recognize human activities are also discussed, including the RG-PLSA model, which allows us to discover activity patterns without supervision, the MC-HCRF model, which can explicitly investigate certainty in latent temporal patterns, and the FuzzySR model, which is used to segment continuous data into events and probabilistically recognize human activities. Cognition models based on recognition results are also implemented for decision making that allow robotic systems to react to human activities. Finally, I will conclude with a discussion of future directions that will accelerate the upcoming technological revolution of human-centered robotics

From cellular networks to mobile cloud computing: security and efficiency of smartphone systems.

In my first year of my Computer Science degree, if somebody had told me that the few years ahead of me could have been the last ones of the so-called PC-era, I would have hardly believed him. Sure, I could imagine computers becoming smaller, faster and cheaper, but I could have never imagined that in such a short time the focus of the market would have so dramatically shifted from PCs to personal devices. Today, smartphones and tablets have become our inseparable companions, changing for the better numerous aspects of our daily life. The way we plan our days, we communicate with people, we listen to music, we search for information, we take pictures, we spend our free time and the way we note our ideas has been totally revolutionized thanks to them. At the same time, thanks also to the rapid growth of the Cloud Computing based services, most of our data and of the Internet services that we use every day are just a login-distance away from any device connected to the Internet that we can find around us. We can edit our documents, look our and our friends’ pictures and videos, share our thoughts, access our bank account, pay our taxes using a familiar interface independently from where we are. What is the most fascinating thing is that all these new possibilities are not anymore at the hand of technically-savvy geeks only, but they are available to newer and older generations alike thanks to the efforts that recently have been put into building user interfaces that feel more natural and intuitive even to totally unexperienced users. Despite of that, we are still far from an ideal world. Service providers, software engineers, hardware manufacturers and security experts are having a hard time in trying to satisfy the always growing expectations of a number of users that is steadily increasing every day. People are always longing for faster mobile connectivity at lower prices, for longer lasting batteries and for more powerful devices. On top of that, users are more and more exposed to new security threats, either because they tend to ignore even the most basic security-practices, or because virus writers have found new ways to exploit the now world-sized market of mobile devices. For instance, more people accessing the Internet from their mobile devices forces the existing network infrastructure to be continuously updated in order to cope with the constantly increase in data consumption. As a consequence, AT&T’s subscribers in the United States were getting extremely slow or no service at all because of the mobile network straining to meet iPhone users’ demand [5]. The company switched from unlimited traffic plans to tiered pricing for mobile data users in summer 2010. Similarly, Dutch T-Mobile’s infrastructure has not been able to cope with intense data traffic, thus forcing the company to issue refunds for affected users [6]. Another important aspect is that of mobile security. Around a billion of people today have their personal information on Facebook and half of them access Facebook from their mobile phone [7]; the size of the online-banking in America has almost doubled since 2004, with 16% of the American mobile users conducting financial-related activities from their mobile device [8]; on 2010, customers spent one billion of dollars buying products on Amazon via mobile devices [9]. These numbers give an idea of the amount of people that today could find themselves in trouble by not giving enough care into protecting their mobile device from unauthorized access. A distracted user who loses his phone, or just forgets it in a public place, even if for a short time only, could allow someone else to get unrestrained access to his online identity. By copying the contents of the phone, including passwords and access keys, an attacker could steal money from the user’s bank account, read the user’s emails, steal the user’s personal files stored on the cloud, use the user’s personal information to conduct scams, frauds, and other crimes using his name and so on. But identity theft is not the only security problem affecting mobile users. Between 2011 and 2012, the number of unique viruses and malwares targeting mobile devices has increased more than six times, according to a recent report [10]. Typically, these try to get installed in the target device by convincing the user to download an infected app, or by making them follow a link to a malicious web site. The problems just exposed are major issues affecting user’s experience nowadays. We believe that finding effective, yet simple and widely adoptable solutions may require a new point of view, a shift in the way these problems are tackled. For these reasons, we evaluated the possibility of using a hybrid approach, that is, one where different technologies are brought together to create new, previously unexplored solutions. We started by considering the issues affecting the mobile network infrastructure. While it is true that the usage of mobile connectivity has significantly increased over the past few years, it is also true that socially close users tend to be interested in the same content, like, the same Youtube videos, the same application updates, the same news and so on. By knowing that, operators, instead of spending billions [11] to update their mobile network, could try an orthogonal approach and leverage an ad-hoc wireless network between the mobile devices, referred to in literature as Pocket Switched Networks [12]. Indeed, most of the smartphones on the market today are equipped with short-ranged radio interfaces (i.e., Bluetooth, WiFi) that allow them to exchange data whenever they are close enough to each other. Popular data could be then stored and transferred directly between devices in the same social context in an ad-hoc fashion instead of being downloaded multiple times from the mobile network. We therefore studied the possibility of channeling traffic to a few, socially important users in the network called VIP delegates, that can help distributing contents to the rest of the network. We evaluated VIP selection strategies that are based on the properties of the social network between mobile devices users. In Chapter 2, through extensive evaluations with real and synthetic traces, we show the effectiveness of VIP delegation both in terms of coverage and required number of VIPs – down to 7% in average of VIPs are needed in campus-like scenarios to offload about 90% of the traffic. These results have also been presented in [1]. Next we moved to the security issues. On of the highest threats to the security of mobile users is that of an identity theft performed using the data stored on the device. The problem highlighted by this kind of attacks is that the most commonly used authentication mechanisms completely fail to distinguish the honest user from somebody who just happens to know the user’s login credentials or private keys. To be resistant to identity theft attacks, an authentication mechanism should, instead, be built to leverage some intrinsic and difficult to replicate characteristic of each user. We proposed the Personal Marks and Community Certificates systems with this aim in mind. They constitute an authentication mechanism that uses the social context sensed by the smartphone by means of Bluetooth or WiFi radios as a biometric way to identify the owner of a device. Personal Marks is a simple cryptographic protocol that works well when the attacker tries to use the stolen credentials in the social community of the victim. Community Certificates works well when the adversary has the goal of using the stolen credentials when interacting with entities that are far from the social network of the victim. When combined, these mechanisms provide an excellent protection against identity theft attacks. In Chapter 3 we prove our ideas and solutions with extensive simulations in both simulated and real world scenarios—with mobility traces collected in a real life experiment. This study appeared in [2]. Another way of accessing the private data of a user, other than getting physical access to his device, could be by means of a malware. An emerging trend in the way people are fooled into installing malware-infected apps is that of exploiting existing trust relationships between socially close users, like those between Facebook friends. In this way, the malware can rapidly expand through social links from a small set of infected devices towards the rest of the network. In our quest for hybrid solutions to the problem of malware spreading in social networks of mobile users we developed a novel approach based on the Mobile Cloud Computing paradigm. In this new paradigm, a mobile device can alleviate the burden of computationally intensive tasks by offloading them to a software clone running on the cloud. Also, the clones associated to devices of users in the same community are connected in a social peer-to-peer network, thus allowing lightweight content sharing between friends. CloudShield is a suite of protocols that provides an efficient way stop the malware spread by sending a small set of patches from the clones to the infected devices. Our experiments on different datasets show that CloudShield is able to better and more efficiently contain malware spreading in mobile wireless networks than the state-of-the-art solutions presented in literature. These findings (which are not included in this dissertation) appeared in [3] and are the result of a joint work with P.h.D student S. Kosta from Sapienza University. My main contribution to this work was in the simulation of both the malware spreading and of the patching protocol schemes on the different social networks datasets. The Mobile Cloud Computing paradigm seems to be an excellent resource for mobile systems. It alleviates battery consumption on smartphones, it helps backing up user’s data on-the-fly and, as CloudShield proves, it can also be used to find new, effective, solutions to existing problems. However, the communication between the mobile devices and their clones needed by such paradigm certainly does not come for free. It costs both in terms of bandwidth (the traffic overhead to communicate with the cloud) and in terms of energy (computation and use of network interfaces on the device). Being aware of the issues that heavy computation or communication can cause to both the battery life of the devices [13], and to the mobile infrastructure, we decided to study the actual feasibility of both mobile computation offloading and mobile software/data backups in real-life scenarios. In our study we considered two types of clones: The off-clone, whose purpose is to support computation offloading, and the back-clone, which comes to use when a restore of user’s data and apps is needed. In Chapter 5 we give a precise evaluation of the feasibility and costs of both off-clones and back-clones in terms of bandwidth and energy consumption on the real device. We achieved this by means measurements done on a real testbed of 11 Android smartphones and on their relative clones running on the Amazon EC2 public cloud. The smartphones have been used as the primary mobile by the participants for the whole experiment duration. This study has been presented in [4] and is the result of a collaboration with P.h.D. Student S. Kosta from Sapienza University. S. Kosta mainly contributed to the experimental setup, deployment of the testbed and data collection

Extending 3-DoF Metrics to Model User Behaviour Similarity in 6-DoF Immersive Applications

Immersive reality technologies, such as Virtual and Augmented Reality, have ushered a new era of user-centric systems, in which every aspect of the coding-delivery-rendering chain is tailored to the interaction of the users. Understanding the actual interactivity and behaviour of the users is still an open challenge and a key step to enabling such a user-centric system. Our main goal is to extend the applicability of existing behavioural methodologies for studying user navigation in the case of 6 Degree-of-Freedom (DoF). Specifically, we first compare the navigation in 6-DoF with its 3-DoF counterpart highlighting the main differences and novelties. Then, we define new metrics aimed at better modelling behavioural similarities between users in a 6-DoF system. We validate and test our solutions on real navigation paths of users interacting with dynamic volumetric media in 6-DoF Virtual Reality conditions. Our results show that metrics that consider both user position and viewing direction better perform in detecting user similarity while navigating in a 6-DoF system. Having easy-to-use but robust metrics that underpin multiple tools and answer the question "how do we detect if two users look at the same content?" open the gate to new solutions for a user-centric syste

Extending 3-DoF Metrics to Model User Behaviour Similarity in 6-DoF Immersive Applications

Immersive reality technologies, such as Virtual and Augmented Reality, have ushered a new era of user-centric systems, in which every aspect of the coding--delivery--rendering chain is tailored to the interaction of the users. Understanding the actual interactivity and behaviour of the users is still an open challenge and a key step to enabling such a user-centric system. Our main goal is to extend the applicability of existing behavioural methodologies for studying user navigation in the case of 6 Degree-of-Freedom (DoF). Specifically, we first compare the navigation in 6-DoF with its 3-DoF counterpart highlighting the main differences and novelties. Then, we define new metrics aimed at better modelling behavioural similarities between users in a 6-DoF system. We validate and test our solutions on real navigation paths of users interacting with dynamic volumetric media in 6-DoF Virtual Reality conditions. Our results show that metrics that consider both user position and viewing direction better perform in detecting user similarity while navigating in a 6-DoF system. Having easy-to-use but robust metrics that underpin multiple tools and answer the question ``how do we detect if two users look at the same content?" open the gate to new solutions for a user-centric system