Cristian Chilipirea

Energy-Aware Social-Based Routing in Opportunistic Networks

In particular types of Delay-Tolerant Networks (DTN) such as Opportunistic Mobile Networks, node connectivity is transient. For this reason, traditional routing mechanisms are no longer suitable. New approaches use social relations between mobile users as a criterion for the routing process. We argue that in such an approach, nodes with high social popularity may quickly deplete their energy resources - and, therefore, might be unwilling to participate in the routing process. We show that social-based routing algorithms such as BUBBLE Rap are prone to this behavior, and introduce energy awareness as an important criterion in the routing decision. We present experimental results showing that our approach delivers performances similar to BUBBLE Rap, whilst balancing the energy consumption between nodes in the network.

Energy efficiency and robustness for IoT: Building a smart home security system

Internet of Things (IoT) represents a new paradigm in computing in which devices are connected to the internet and directly communicate with each other. Because these devices are generally thought to be wireless, small and cheap, in other words not very reliable, it is vital that we address the robustness problems in IoT. Applying standard fault tolerance models in IoT is impossible. The devices are not only heterogeneous, but unlike compute nodes, different devices have completely different capabilities and serve completely different functions (they have different sensors). We propose a model in which we describe the capabilities of each device and use this information to dynamically replace faulty devices with other, not-directly-compatible ones. Furthermore, our model uses the overlap between device characteristics in order to temporarily disable part of them and preserve energy. We show how the model can be applied on an IoT home security system, where robustness is critical. To offer a concrete example, a system based on our model would use a WiFi scanner, a heat sensor and a door opening sensor in order to replace a faulty security camera.

Predicting Encounters in Opportunistic Networks Using Gaussian Process

In particular types of Delay-Tolerant Networks (DTN) such as Opportunistic Mobile Networks, node connectivity is transient, and connections are sparse and small in length. For this reason, traditional routing mechanisms are no longer suitable. Routing algorithms designed for such networks try to maximize the probability of successful message delivery. The most popular approach is to compute the probability of delivering a message using information such as node contacts and location knowledge, thus using past encounters to predict future ones. In this paper we investigate the predictability of human mobility and interactions patterns. We propose the use of supervised learning techniques together with Gaussian process modeling to predict future encounters based on historical patterns of individual nodes. We analyze their accuracy compared to previous prediction techniques, using real-world mobility data traces.

An integrated architecture for future studies in data processing for smart cities

Abstract Data processing for Smart Cities become more challenging, facing with different handling steps: data collection from different heterogeneous sources, processing sometimes in real-time and then delivered to high level services or applications used in Smart Cities. Applications used for intelligent transportation systems, crowd management, water resources management, noise and air pollution management, require different data processing techniques. The main subject of this paper is to propose an architecture for data processing in Smart Cities. The architecture is oriented on the flow of data from the source to the end user. We describe seven steps of data processing: collection of data from heterogeneous sources, data normalization, data brokering, data storage, data analysis, data visualization and decision support systems. We consider two case studies on crowd management in smart cities and on Intelligent Transportation Systems (ITS) and we provide experimental highlights.

Filters for Wi-Fi Generated Crowd Movement Data

Cities represent large groups of people that share a common infrastructure, common social groups and/or common interests. With the development of new technologies current cities aim to become what is known as smart cities, in which all the small details of these large constructs are controlled to better improve the quality of life of its inhabitants. One of the important gears that powers a city is given by traffic, be it vehicular or pedestrian. As such traffic is closely related to all other activities that take place inside of a city. Understanding traffic is still a difficult process as we have to be able to not only measure it in the sense of how many people are using a particular path but also in analyzing where people are going and when, while still maintaining individual privacy. And all this has to be done at a scale that would cover most if not all individuals in a city. With the high increase in smartphones adoption we can reliably assume that a large part of the population in cities are carrying with them, at all times, at least one Wi-Fi enabled device. Because Wi-Fi devices are regularly transmitting signals we can rely on these devices to detect individuals movements unobtrusively without identifying or tracking any particular individual. Special sensors that monitor Wi-Fi frequencies can be placed around a city to gather data that can later be used to identify patterns in the traffic flows. We present a set of filters that can be used to minimize the amount of data needed for processing and without negatively impacting the result or the information that can be extracted from this data. Part of the filters we present can be deployed at the sensor level, making the entire system more scalable, while a different part can be executed before data processing thus enabling real time information extraction and a broader temporal and spatial range for data analysis. Some of these filters are particular to Wi-Fi but some of them can be applied to any detection system.

Presumably Simple: Monitoring Crowds Using WiFi

Crowd Monitoring is receiving much attention. An increasingly popular technique is to scan for mobile devices, notably smartphones. We take a look at scanning for such devices by recording WiFi packets. Although research on capturing crowd patterns using WiFi detections has been done, there are not many published results when it comes to tracking movements. This is not surprising when realizing that the data provided by WiFi scanners is susceptible to many seemingly erroneous and missed detections, caused by the use of randomized network addresses, overlap between scanners, high variance in WiFi detection ranges, among other sources. In this paper, we investigate various techniques for cleaning up sets of raw detections to sets that can subsequently be used for crowd analytics. To this end, we introduce two different quality metrics to measure the effects of applying the various techniques. We test our approach using a data set collected from 27 WiFi scanners spread across the downtown area of a Dutch city where at that time a 3-day multi-stage festival took place attended by some 130,000 people.

Enabling Mobile Cloud Wide Spread Through an Evolutionary Market-Based Approach

Mobile clouds are an ongoing research topic that has yet to become ubiquitous as the now popular cloud paradigm. This is because of a number of issues with mobile clouds that still need to be addressed such as: incentives, security, privacy, context, data management, usability, and cost benefits. Out of these issues, the most important one that needs to be addressed is the issue of incentives, without which mobile clouds cannot gain enough users for the concept to be useful. Unlike public, company-owned cloud systems, in mobile clouds, the amount of resources or processing power is directly dependent on mobile cloud users that are in the proximity of the individual that requires extra resources. With an increase in the number of mobile cloud users willing to share resources or willing to use the service offered by others, comes an increase in the likeliness that enough mobile-cloud-enabled devices will be available. In this paper, we study incentives for mobile cloud systems and consider as a solution an evolutionary market-based approach to create these incentives. Creating a market for these systems is particularly difficult because of the large number of individuals that need to be involved and their high mobility.

A simulator for opportunistic networks

When mobile devices involved in a communication process are unable to establish a direct connection, or when communication should be offloaded to cope with large throughputs, mobile collaboration can be used to enable communication through opportunistic networks. These types of networks are formed when mobile devices communicate only using short‐range transmission protocols, usually when users are close. Routes are built dynamically, because each mobile device is acting according to the store‐carry‐and‐forward paradigm. Thus, contacts are seen as opportunities to move data towards the destination. In such networks, the routing protocol is of vital importance, and today, we witness quite a number of routing algorithms that have been proposed to maximize the success rate of message delivery whilst minimizing the communication cost. Such protocols take advantage of the devices history of contacts, or information about users carrying the mobile devices, to make their forwarding decision. This paper extends our previous work with the following: First, we describe a new simplified, fast simulator, designed to minimize the work needed to conduct extensive tests for opportunistic routing algorithm on multiple traces; next, we analyze extensively several of the most popular routing algorithms through extensive simulations conducted using our simulation platform. We highlight their pros and cons in different scenarios, considering different real‐world mobility data traces, such as Global Positioning System traces. The raw Global Positioning System traces are converted to a format based on encounters between participating entities. Copyright

A Comparison of Private Cloud Systems

Cloud Computing represents one of the most popular new paradigms making utility computing a reality. In recent years, this concept has received a lot of attention because of the enormous benefits it brings: for instance the capability known as elasticity permits users to change the number of machines they use according to their needs and in a timely manner. Many large companies deployed public cloud systems such as Amazon EC2 or Microsoft Azure. Even though the popularity and usefulness of cloud systems is clear, there are still concerns around the use of public clouds: availability, data safety or privacy. Because of these concerns there is a need to deploy private cloud systems. Systems that run on machines owned by the organization that requires them, offering similar services as public clouds to different parts of the organization while maintaining a large degree of trust. With the need for private cloud systems a number of software solutions appeared. We concentrate here on Infrastructure as a Service, cloud systems where users are given full access to virtual machines. These are generally Open Source and capture large interest from the public. In this paper we make a through comparison between three of the most popular open source cloud software systems and describe our experience with installing and maintaining small deployments using these three software suits.

Tele-Monitoring System for Water and Underwater Environments Using Cloud and Big Data Systems

Recent research in communications and computer science has been considered to advance the performances of monitoring water environments. However, constrains produced by the water environments, caused by the specific channel propagation and harsh operating conditions must be taken into account. The purpose of this paper is to define and describe a monitoring system for the water environments, based on a previous study regarding both the underwater, but also technologies that are appropriate for such surroundings. The system is based on an underwater sensors network which is connected to a cloud platform by means of a reconfigurable wireless transceiver. The sensor network integrates several low cost sensors that can measure different parameters such as water level, the water flow, temperature, pressure etc. The measured parameters will be transmitted through an operational communication node, which should be able to ensure a reliable communication with timing and variation delay constraints. Finally, the paper describes the platform interface available to end users, providing real time visualization of the water environment events.