Esunly Medina

A communication infrastructure to ease the development of mobile collaborative applications

Mobile workers doing loosely coupled activities typically perform on demand collaboration in the physical workplace. Communication services available in such work scenarios are uncertain, therefore mobile collaborative applications supporting those activities must provide ad hoc communication mechanisms in order to use each cooperation opportunity. Typically, the complexity of implementing such mobile ad hoc communication mechanisms becomes a challenge that jeopardizes the development of mobile collaborative solutions. This article presents a communication infrastructure named HLMP API dealing with that challenge. HLMP API intends to ease the development of such applications through the reuse of communication services. The infrastructure is an application programming interface that implements the HLMP routing protocol and also some awareness mechanisms that are required for mobile loosely coupled work. Developers using this infrastructure do not have to perform low-level programming.

Building real-world ad-hoc networks to support mobile collaborative applications: lessons learned

Mobile collaboration is required in several work scenarios, i.e. education, healthcare, business and disaster relief. The features and capabilities of the communication infrastructure used by mobile collaborative applications will influence the type of coordination and collaboration that can be supported in real work scenarios. Developers of these applications are typically unaware of the constraints the communication infrastructure imposes on the collaborative system. Therefore, this paper presents an experimental study of how ad-hoc networks can effectively support mobile collaborative work. The article analyzes several networking issues and it determines how they influence the collaborative work. The paper also presents the lessons learned and it provides recommendations to deal with the networking issues intrinsic to adhoc networks.

Sharing hardware resources in heterogeneous computer-supported collaboration scenarios

There currently are many mobile computing devices with various properties and capabilities. These devices may need to collaborate among them to allow nomad workers to perform a common activity. Unfortunately software developers in charge of creating infrastructures or applications allowing these devices to cooperate among them, do not count with clear guidelines to design such software components; particularly when these components must work in a scenario involving heterogeneous devices. This paper presents a study that tries to understand how to address collaboration among heterogeneous mobile devices, by exploring several variables affecting the process. In particular, this study explores various strategies to borrow CPU slots from peer mobile computing devices and return the favor back later on. The study outcomes indicate there is a short list of computing and network variables affecting the collaboration capability of the mobile devices. These findings have been verified using data mining techniques. Based on these findings and the lessons learned, the article presents a simulation method of computing scenarios that can help developers to determine which computing configuration would be suitable to be used in each particular work scenario. Software designers can take advantage of this simulation method and the design guidelines reported in this paper in order to develop applications able to work appropriately in heterogeneous computing scenarios.

HLMP API: A software library to support the development of mobile collaborative applications

Mobile collaborative applications are usually deployed in work scenarios where the existence of fixed communication infrastructure is hard to predict. For that reason, these applications use Mobile Ad hoc Networks (MANETs) to support communication between mobile users. The complexity involved in such communication infrastructures make that developers avoid developing software for mobile work scenarios. However, it is possible to provide a reusable abstraction of such communication mechanisms, in order to avoid that developers have to deal with low-level programming. This article presents HLMP API, which is an application programming interface that provides access to a HLMP implementation. This API is organized as a fully distributed mobile communication infrastructure, able to run on MANETs. This infrastructure provides an important set of services, which are required to support mobile collaboration. The reuse of these services allows developers to reduce the complexity, times and cost of these development projects.

Group Prediction in Collaborative Learning

We propose an approach for predicting group formations, to address the problem of automating the incorporation of group awareness into CSCL applications. Contextual information can enable the construction of applications that effectively assist the group members to automatically communicate in synchronous and collocated collaborative learning activities. We used data traces collected from the study of students’ behavior to train and test an intelligent system. Results have shown that context-information can be effectively used as a basis for a middleware for a dynamic group management. Inferring group membership is technically viable and can be used in real world settings.

OLSRp: Predicting Control Information to Achieve Scalability in OLSR Ad Hoc Networks

Scalability is a key design challenge that routing protocols for ad hoc networks must properly address to maintain the network performance when the number of nodes increases. We focus on this issue by reducing the amount of control information messages that a link state proactive routing algorithm introduces to the network. Our proposal is based on the observation that a high percentage of those messages is always the same. Therefore, we introduce a new mechanism that can predict the control messages that nodes need for building an accurate map of the network topology so they can avoid resending the same messages. This prediction mechanism, applied to OLSR protocol, could be used to reduce the number of messages transmitted through the network and to save computational processing and energy consumption. Our proposal is independent of the OLSR configuration parameters and it can dynamically self-adapt to network changes.

OIoT: A Platform to Manage Opportunistic IoT Communities

Opportunistic Internet of Things (IoT) extends the concept of opportunistic networking combining human users carrying mobile devices and smart things. It explores the relationships between humans and the opportunistic connection of smart objects. This paper presents a software infrastructure, named Opportunistic IoT Platform (OIoT), which helps developers to create and manage opportunistic IoT communities between smart devices. The platform enables the creation of opportunistic IoT communities that support the AllJoyn communications framework, for IoT devices and applications. Results from a preliminary evaluation of the OIoT platform indicate that this infrastructure is useful to manage and share data across opportunistic IoT communities.

COMMUNICATION SUPPORT FOR MOBILE COLLABORATIVE WORK: AN EXPERIMENTAL STUDY

Advances in mobile computing and wireless communication are easing the evolution from traditional nomadic work to computer-mediated mobile collaborative work. Technology allows efficient and effective interaction among mobile users and also provides access to shared resources available to them. However, the features and capabilities of the communication infrastructure supporting these activities influence the type of coordination and collaboration employed by mobile collaborative applications in real work scenarios. Developers of these applications are typically unaware of the constraints the communication infrastructure imposes on mobile collaborative systems, because they are not easy to foresee. That leads to a high probability of communication problems in otherwise fully functional mobile collaborative support applications. This paper presents an experimental study with real devices and networks on a realistic physical environment that shows how ad hoc networks can effectively support mobile collaborative work and the practical limitations. The paper analyzes several networking issues and determines how they influence mobile collaborative work in various interaction scenarios. The paper also presents the lessons learned in the study and provides recommendations to deal with some networking issues related to real-world ad hoc networks.

Characterizing the effects of sharing hardware resources in mobile collaboration scenarios

Advances in wireless communication systems and mobile devices allow nomad users to participate in mobile collaborative activities. However the availability of hardware resources in the mobile devices participating in the collaboration process enhances or jeopardizes such activity. This paper studies how the network topology and the hardware resources distributed into a mobile network influence the collaboration activities among the participants. Several simulations were done to try to understand this issue. The obtained results show that in mobile collaboration scenarios involving a high number of resources-constraint mobile devices (e.g. handhelds), the maximum cooperation among node is obtained in a small world network topology. The results also show that another factor that encourages cooperation among nodes is the network size.

Mobile Autonomous Sensing Unit (MASU): A Framework That Supports Distributed Pervasive Data Sensing.

Pervasive data sensing is a major issue that transverses various research areas and application domains. It allows identifying people’s behaviour and patterns without overwhelming the monitored persons. Although there are many pervasive data sensing applications, they are typically focused on addressing specific problems in a single application domain, making them difficult to generalize or reuse. On the other hand, the platforms for supporting pervasive data sensing impose restrictions to the devices and operational environments that make them unsuitable for monitoring loosely-coupled or fully distributed work. In order to help address this challenge this paper present a framework that supports distributed pervasive data sensing in a generic way. Developers can use this framework to facilitate the implementations of their applications, thus reducing complexity and effort in such an activity. The framework was evaluated using simulations and also through an empirical test, and the obtained results indicate that it is useful to support such a sensing activity in loosely-coupled or fully distributed work scenarios.