Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

The Internet of Things (IoT) shall be able to incorporate transparently and seamlessly a large number of different and heterogeneous end systems, while providing open access to selected subsets of data for the development of a plethora of digital services. Building a general architecture for the IoT is hence a very complex task, mainly because of the extremely large variety of devices, link layer technologies, and services that may be involved in such a system. In this paper, we focus specifically to an urban IoT system that, while still being quite a broad category, are characterized by their specific application domain. Urban IoTs, in fact, are designed to support the Smart City vision, which aims at exploiting the most advanced communication technologies to support added-value services for the administration of the city and for the citizens. This paper hence provides a comprehensive survey of the enabling technologies, protocols, and architecture for an urban IoT. Furthermore, the paper will present and discuss the technical solutions and best-practice guidelines adopted in the Padova Smart City project, a proof-of-concept deployment of an IoT island in the city of Padova, Italy, performed in collaboration with the city municipality.

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Internet of Things for Smart Cities

Mobile-edge cloud computing is a new paradigm to provide cloud computing capabilities at the edge of pervasive radio access networks in close proximity to mobile users. In this paper, we first study the multi-user computation offloading problem for mobile-edge cloud computing in a multi-channel wireless interference environment. We show that it is NP-hard to compute a centralized optimal solution, and hence adopt a game theoretic approach for achieving efficient computation offloading in a distributed manner. We formulate the distributed computation offloading decision making problem among mobile device users as a multi-user computation offloading game. We analyze the structural property of the game and show that the game admits a Nash equilibrium and possesses the finite improvement property. We then design a distributed computation offloading algorithm that can achieve a Nash equilibrium, derive the upper bound of the convergence time, and quantify its efficiency ratio over the centralized optimal solutions in terms of two important performance metrics. We further extend our study to the scenario of multi-user computation offloading in the multi-channel wireless contention environment. Numerical results corroborate that the proposed algorithm can achieve superior computation offloading performance and scale well as the user size increases.

Efficient Multi-User Computation Offloading for Mobile-Edge Cloud Computing

https://www.social-iot.org/d/SIoT_ComNet.pdf

The Social Internet of Things (SIoT) - When social networks meet the Internet of Things: Concept, architecture and network characterization

The cloud seems to be an excellent companion of mobile systems, to alleviate battery consumption on smartphones and to backup user's data on-the-fly. Indeed, many recent works focus on frameworks that enable mobile computation offloading to software clones of smartphones on the cloud and on designing cloud-based backup systems for the data stored in our devices. Both mobile computation offloading and data backup involve communication between the real devices and the cloud. This communication does certainly not come for free. It costs 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). In this work we study the fmobile software/data backupseasibility of both mobile computation offloading and mobile software/data backups in real-life scenarios. In our study we assume an architecture where each real device is associated to a software clone on the cloud. We consider 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. 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 achieve this through measurements done on a real testbed of 11 Android smartphones and an equal number of software 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.

To offload or not to offload? The bandwidth and energy costs of mobile cloud computing

In this paper we describe SANE, the first forwarding mechanism that combines the advantages of both social-aware and stateless approaches in pocket switched network routing. SANE is based on the observation“that we validate on real-world traces”that individuals with similar interests tend to meet more often. In our approach, individuals (network members) are characterized by their interest profile, a compact representation of their interests. Through extensive experiments, we show the superiority of social-aware, stateless forwarding over existing stateful, social-aware and stateless, social-oblivious forwarding. An important byproduct of our interest-based approach is that it easily enables innovative routing primitives, such as interest-casting. An interest-casting protocol is also described, and extensively evaluated through experiments based on both real-world and synthetic mobility traces.

/pdf/social-aware-stateless-forwarding-in-pocket-switched-4swmsmk5fs.pdf

Social-aware stateless forwarding in pocket switched networks

This paper presents small world in motion (SWIM), a new mobility model for ad-hoc networking SWIM is relatively simple, is easily tuned by setting just a few parameters, and generates traces that look real—synthetic traces have the same statistical properties of real traces SWIM shows experimentally and theoretically the presence of the power law and exponential decay dichotomy of inter-contact time, and, most importantly, our experiments show that it can predict very accurately the performance of forwarding protocols Index Terms—Mobility model, small world, simulations, for- warding protocols in mobile networks I INTRODUCTION it has been established clearly that models like RWP are not good to simulate human mobility, raising the need of new, more realistic mobility models for mobile ad-hoc networking In this paper we present small world in motion (SWIM), a simple mobility model that generates small worlds The model is very simple to implement and very efficient in simulations The mobility pattern of the nodes is based on a simple intuition on human mobility: People go more often to places not very far from their home and where they can meet a lot of other people By implementing this simple rule, SWIM is able to raise social behavior among nodes, which we believe to be the base of human mobility in real life We validate our model using real traces and compare the distribution of inter-contact time, contact duration and number of contact distributions between nodes, showing that synthetic data that we generate match very well real data traces Furthermore, we show that SWIM can predict well the performance of forwarding protocols We compare the performance of two forwarding protocols— epidemic forwarding (9) and (a simplified version of) del- egation forwarding (10)—on both real traces and synthetic traces generated with SWIM The performance of the two protocols on the synthetic traces accurately approximates their performance on real traces, supporting the claim that SWIM is an excellent model for human mobility The rest of the paper is organized as follows: Section II briefly reports on current work in the field; in Section III we present the details of SWIM and we prove theoretically that the distribution of inter-contact time in SWIM has an exponential tail, as recently observed in real life experiments; Section V compares synthetic data traces to real traces and shows that the distribution of inter-contact time has a head that decays as a power law, again like in real experiments; in Section VI we show our experimental results on the behavior of two forwarding protocols on both synthetic and real traces; lastly, Section VII present some concluding remarks

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SWIM: A Simple Model to Generate Small Mobile Worlds

Big data stream mobile computing is proposed as a paradigm that relies on the convergence of broadband Internet mobile networking and real-time mobile cloud computing. It aims at fostering the rise of novel self-configuring integrated computing-communication platforms for enabling in real time the offloading and processing of big data streams acquired by resource-limited mobile/wireless devices. This position article formalizes this paradigm, discusses its most significant application opportunities, and outlines the major challenges in performing real-time energy-efficient management of the distributed resources available at both mobile devices and Internet-connected data centers. The performance analysis of a small-scale prototype is also included in order to provide insight into the energy vs. performance tradeoff that is achievable through the optimized design of the resource management modules. Performance comparisons with some state-of-the-art resource managers corroborate the discussion. Hints for future research directions conclude the article.

Energy-efficient dynamic traffic offloading and reconfiguration of networked data centers for big data stream mobile computing: review, challenges, and a case study

The ever increasing ubiquitousness of WiFi access points, coupled with the diffusion of smartphones, suggest that Internet every time and everywhere will soon (if not already has) become a reality. Even in presence of 3G connectivity, our devices are built to switch automatically to WiFi networks so to improve user experience. Most of the times, this is achieved by recurrently broadcasting automatic connectivity requests (known as Probe Requests) to known access points (APs), like, e.g., "Home WiFi", "Campus WiFi", and so on. In a large gathering of people, the number of these probes can be very high. This scenario rises a natural question: "Can significant information on the social structure of a large crowd and on its socioeconomic status be inferred by looking at smartphone probes?". In this work we give a positive answer to this question. We organized a 3-months long campaign, through which we collected around 11M probes sent by more than 160K different devices. During the campaign we targeted national and international events that attracted large crowds as well as other gatherings of people. Then, we present a simple and automatic methodology to build the underlying social graph of the smartphone users, starting from their probes. We do so for each of our target events, and find that they all feature social-network properties. In addition, we show that, by looking at the probes in an event, we can learn important sociological aspects of its participants---language, vendor adoption, and so on.

/pdf/signals-from-the-crowd-uncovering-social-relationships-5dfe49xv5t.pdf

Julinda Stefa

Papers

To offload or not to offload? The bandwidth and energy costs of mobile cloud computing

Social-aware stateless forwarding in pocket switched networks

SWIM: A Simple Model to Generate Small Mobile Worlds

Energy-efficient dynamic traffic offloading and reconfiguration of networked data centers for big data stream mobile computing: review, challenges, and a case study

Signals from the crowd: uncovering social relationships through smartphone probes