Rafael Oliveira Vasconcelos

A DDS-based middleware for scalable tracking, communication and collaboration of mobile nodes

Applications such as transportation management and logistics, emergency response, environmental monitoring and mobile workforce management employ mobile networks as a means of enabling communication and coordination among a possibly very large set of mobile nodes. The majority of those systems may thus require real-time tracking of the nodes and interaction with all participant nodes as well as a means of adaptability in a very dynamic scenario. In this paper, we present a middleware communication service based on the OMG DDS standard that supports on-line tracking and unicast, groupcast and broadcast with several thousand mobile nodes. We then show a Fleet Tracking and Management application built using or middleware, and present the performance results in LAN and WAN settings to evaluate our middleware in terms of scalability and robustness.

The Mobile Hub concept: Enabling applications for the Internet of Mobile Things

Few studies have investigated and proposed a middleware solution for the Internet of Mobile Things (IoMT), where the smart things (Smart Objects) can be moved, or else can move autonomously, and yet remain accessible and controllable remotely from any other computer over the Internet. Examples of mobile Smart Objects include vehicles of any kind, wearable devices, sensor tags, mobile robots, etc, anyhing with embedded sensors and/or actuators. In this context of general mobility of objects, mobile personal devices (smart phones, tablets, etc.) are well suited as the universal providers of Internet connectivity and location information for simpler smart objects that lack location sensors and have only short-range wireless interfaces. This paper describes Mobile Hub (M-Hub), a generic mobile middleware for IoMT, its design and prototype implementation for Android and Bluetooth. The Mobile Hub extends our previous mobile-cloud communication middleware SDDL, so that it is able to provide scalable and reliable mobile communication and data processing capabilities to mobile smart objects. Preliminary experiments have shown that our implementation of M-Hub delivers good mobility responsiveness and that the concept is suitable for IoT applications that require opportunistic discovery and connection to a variety of mobile Smart Objects.

A Communication Middleware for Scalable Real-Time Mobile Collaboration

Applications such as transportation management and logistics, emergency response, environmental monitoring or mobile workforce management, employ mobile networks as means of enabling communication and coordination among a possibly very large set of mobile nodes. The majority of those systems thus may require real-time tracking of the nodes, interaction with all participant nodes, as well as means of adaptability in a very dynamic scenario. In this paper we present a middleware communication service that supports real-time tracking of several thousands of mobile nodes, demand adaptability, as well as three modes of communication between the nodes: unicast, group cast and broadcast. We then show Fleet Tracking and Management system and use it to evaluate our middleware.

ContextNet: context reasoning and sharing middleware for large-scale pervasive collaboration and social networking

In this paper, we describe ContextNet, a middleware architecture with context services for wide- and large-scale pervasive collaborative applications. It also provides context-based reasoning over data and events generated by autonomously moving users or their vehicles.

Towards efficient group management and communication for large-scale mobile applications

Applications such as fleet management and logistics, emergency response, public security and surveillance or mobile workforce management use geo-positioning and mobile networks as means of enabling real-time monitoring, communication and collaboration among a possibly large set of mobile nodes. The majority of those systems require real-time tracking of mobile nodes (e.g. vehicles, people or mobile robots), reliable communication to/from the nodes, as well as group communication among the mobile nodes. In this paper we describe a distributed middleware with focus on management of context-defined groups of mobile nodes, and group communication with large sets of nodes. We also present a prototype Fleet Tracking and Management system based on our middleware, give an example of how context-specific group communication can enhance the nodes mutual awareness, and show initial performance results that indicate small overhead and latency of the group communication and management.

A middleware for managing dynamic software adaptation

The design and development of adaptive systems brings new challenges since the dynamism of such systems is a multifaceted concern that range from mechanisms to enable the adaptation on the software level to the (self-) management of the entire system using adaptation plans or system administrator, for instance. Networked and mobile embedded systems are examples of systems where dynamic adaptation become even more necessary as the applications must be capable of discovering the computing resources in their near environment. While most of the current research is concerned with low-level adaptation techniques (i.e., how to dynamically deploy new components or change parameters), we are focused in providing management of distributed dynamic adaptation and facilitating the development of adaptation plans. In this paper, we present a middleware tailored for mobile embedded systems that supports distributed dynamic software adaptation, in transactional and non-transactional fashion, among mobile devices. We also present results of initial evaluation.

Design and Evaluation of an Autonomous Load Balancing System for Mobile Data Stream Processing Based On a Data Centric Publish Subscribe Approach

Several new applications of mobile computing environments, such as Intelligent Transportation Systems, Fleet Management and Logistics, and integrated Industrial Process Automation share the requirement of remote monitoring and high performance processing of huge data streams produced by large sets of mobile nodes. Two key requirements for the deployment and operation of such mobile infrastructures are the handling of large and variable numbers of wireless connections to the monitored mobile nodes regardless of their current use or locations, and to automatically adapt to variations in the volume of the mobile data streams. This article describes the design, implementation, and evaluation of an autonomic mechanism for load balancing of mobile data streams. The autonomic capability has been incorporated into a scalable middleware system based on a Data Centric Publish Subscribe approach using the OMG Data Distribution Service (DDS) standard and aimed at real-time and adaptive handling of mobile connectivity and data stream processing for great sets of mobile nodes. A significant amount of evaluation experiments of the proposed infrastructure is presented, reinforcing its viability and the benefits arising from the use of an autonomic approach to handle the requirements of high variability and scalability.

A security infrastructure for massive mobile data distribution

Many modern mobile applications have to address the challenge of enabling communication and managing a very large amount of mobile nodes. Examples of those applications include fleet management, workforce coordination, Intelligent Transportation Systems, cooperative mobile robots, rescue and emergency management, and environmental surveillance. Data distribution is an essential part of those systems, and the data traffic often contains sensitive information - thus enforcing the need of the CIA triad (confidentiality, integrity and availability). This work presents a data distribution layer based on the OMG DDS standard, developed to enforce CIA for data distribution through the usage of PKI and symmetric cryptography. This layer provides all required PKI-related functionality and symmetric key management, offering mobile devices an affordable data distribution solution.

An Adaptive Middleware for Opportunistic Mobile Sensing

The current ubiquity of smart phones with mobile Internet and several short-range wireless interfaces (NFC, Bluetooth, Bluetooth Smart) and the fact that these devices are carried almost anytime and anywhere by users, enables potentially new pervasive sensing applications where smartphones can act as universal hubs for interaction with sensors (or sensor networks) that have only short-range wireless connectivity. Thus, in next years we can expect an increasing number of long-term and large-scale deployments for various crowd-sourced monitoring applications, such as environment monitoring, domestic utility meter reading, urban monitoring, etc. In this paper, we present the implementation and initial performance results with our mobile-cloud middleware that enables such opportunistic mobile sensing. One of the singular features of our middleware is the capability to discover, dynamically download and install sensor-specific Transco ding modules on the mobile phone according to the encountered sensor type and make.

Smartphone-based outlier detection: a complex event processing approach for driving behavior detection

The majority of fatal car crashes are caused by reckless driving. With the sophistication of vehicle instrumentation, reckless maneuvers, such as abrupt turns, acceleration, and deceleration, can now be accurately detected by analyzing data related to the driver-vehicle interactions. Such analysis usually requires very specific in-vehicle hardware and infrastructure sensors (e.g. loop detectors and radars), which can be costly. Hence, in this paper, we investigated if off-the-shelf smartphones can be used to online detect and classify the driver’s behavior in near real-time. To do so, we first modeled and performed an intrinsic evaluation to assess the performance of three outlier detection algorithms formulated as a data stream processing network which receives as input and processes data streams of smartphone and vehicle sensors. Next, we implemented a novel scoring mechanism based on online outlier detection to quantitatively evaluate drivers’ maneuvers as either cautious or reckless. Thus, we adapted a data mining mechanism which takes into account a sensor’s data rates and power to determine driver behavior in the scoring process. Finally, as the intrinsic evaluation does not necessarily reveal how well an algorithm will perform in a real-world scenario, we evaluated the algorithm that achieved the best result in a real-world case study to assess drivers’ driving behavior. Our results indicate that the algorithm performs quickly and accurately; the algorithm classifies driver behavior with 95.45% accuracy. Moreover, such results are obtained within 100 milliseconds of processing time on average.