An Adaptive Middleware for Opportunistic Mobile Sensing

Vasconcelos, Rafael Oliveira; Talavera, L. E.; Vasconcelos, Igor; Roriz, Marcos; Endler, Markus; Gomes, Berto de Tácio Pereira; Silva, Francisco José da Silva e

doi:10.1109/dcoss.2015.12

Cited by 7 publications

(11 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In respect to power management, the work Rahmani et al, 23 Cubo et al, 24 Balasubramanan and Stranieri 25 Beyond this comparison with other research groups initiatives, it is important to explain that the SDDL and M-Hub have been the subject of discussion and evaluation in some of our previously published work in opportunistic mobile sensing 16,29 and scalable data distribution fields. [9][10][11] However, these work were not motivated by Ambient Assisted Living scenarios.…”

Section: Discussionmentioning

confidence: 99%

“…This led us to propose the concept of Mobile Hub (M-Hub),16 a general-purpose middleware that extends the SDDL, responsible for discovering and opportunistically connecting a myriad of simple M-objects accessible only through short-range WPAN technologies to the Internet. This led us to propose the concept of Mobile Hub (M-Hub),16 a general-purpose middleware that extends the SDDL, responsible for discovering and opportunistically connecting a myriad of simple M-objects accessible only through short-range WPAN technologies to the Internet.…”

mentioning

confidence: 99%

See 1 more Smart Citation

A comprehensive and scalable middleware for Ambient Assisted Living based on cloud computing and Internet of Things

Gomes

Muniz

Silva

et al. 2016

Concurrency and Computation

Self Cite

View full text Add to dashboard Cite

Ambient Assisted Living (AAL) main goal is the development of health monitoring systems for patients with chronic diseases and elderly people through the use of body, home, and environmental sensors that increase their degree of independence and mobility. A comprehensive software infrastructure for AAL systems should be able to cover scenarios involving several patient mobility levels, locations, and physical and cognitive abilities. Cloud computing can provide to AAL systems the ability to extend the limited processing power of mobile devices, but its main role is to integrate all stakeholders through the storage and processing of health data and the orchestration of healthcare business logic. On the other hand, the Internet of Things (IoT) provides the ability to connect sensors and actuators, integrating and making them available through the Internet. This paper presents the Mobile-Hub/Scalable Data Distribution Layer, a middleware for AAL based on cloud computing and IoT. We discuss how this middleware can handle the requirements of the main health monitoring scenarios and present results that demonstrate the ability to opportunistically discover and connect with sensors in a timely manner and the scalability necessary for handling the connection and data processing of many connected patients. KEYWORDSambient assisted living, cloud computing, health monitoring systems, internet of things (IoT) 1 range of other applications for AAL systems, such as rescue and emer-gency response systems, fall detection, video surveillance systems, etc.Nowadays, AAL systems are regarded as a trend in a context of increasing awareness of how the Internet can be used to personal healthcare. Ambient Assisted Living systems are composed of several technologies: sensors and actuators, portable/wearable devices, heterogeneous wireless networks, medical applications executing on mobile devices (handhelds), personal computers, or in a cloud computing infrastructure. Among the variety of low-level sensors that can be applied in AAL systems, there are the wearable medical sensors, able to collect data from physiological signals (e.g., Electrocardiogram [ECG], Electromyogram, heart rate, and oxygen consumption) or data reflecting the body movement (e.g., accelerometer). Personal mobile devices, such as smartphones, are also usually equipped with motion and location sensors (e.g., accelerometer and GPS). Environmental sensors can also be used, as they collect information that helps determine if environmental conditions (e.g., temperature, light, humidity, and carbon dioxide levels) favor or not the patient's health. In addition to gathering data Concurrency Computat: Pract Exper. 2017;29:e4043. wileyonlinelibrary.com/journal/cpe

show abstract

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

A comprehensive and scalable middleware for Ambient Assisted Living based on cloud computing and Internet of Things

Gomes

Muniz

Silva

et al. 2016

Concurrency and Computation

Self Cite

View full text Add to dashboard Cite

show abstract

“…In this work is enhanced to create a middleware for Ambient Assisted Living. In Vasconcelos et al (2015) there is another middleware based on M-Hub. There is no support for security or privacy described.…”

Section: Non-secured Systemsmentioning

confidence: 99%

A survey of secure middleware for the Internet of Things

Fremantle

Scott

2017

PeerJ Computer Science

View full text Add to dashboard Cite

The rapid growth of small Internet connected devices, known as the Internet of Things (IoT), is creating a new set of challenges to create secure, private infrastructures. This paper reviews the current literature on the challenges and approaches to security and privacy in the Internet of Things, with a strong focus on how these aspects are handled in IoT middleware. We focus on IoT middleware because many systems are built from existing middleware and these inherit the underlying security properties of the middleware framework. The paper is composed of three main sections. Firstly, we propose a matrix of security and privacy threats for IoT. This matrix is used as the basis of a widespread literature review aimed at identifying requirements on IoT platforms and middleware. Secondly, we present a structured literature review of the available middleware and how security is handled in these middleware approaches. We utilise the requirements from the first phase to evaluate. Finally, we draw a set of conclusions and identify further work in this area.

show abstract

“…This middleware was developed by the Laboratory of Intelligent Distributed Systems (LSDi) of the Federal University of Maranhão (UFMA), in partnership with the Laboratory for Advanced Collaboration (LAC) of the Pontifical Catholic University of Rio de Janeiro (PUC-Rio). The proposed solution combines a mobile gateway (the Mobile Hub [ 4 , 57 ]) for the acquisition of raw data from heterogeneous physical sensors with a CDDL. This layer is also responsible for registering and discovering the available context services, as well as for provisioning and monitoring context information and for ensuring the context data distribution service quality.…”

Section: Proposed Solution: M-hub/cddlmentioning

confidence: 99%

“…In previous work [ 4 , 57 , 58 , 59 , 60 , 61 , 62 , 63 ], M-Hub has used the Scalable Data Distribution Layer (SDDL) as the default mechanism for the dissemination and processing of context data. SDDL uses two distinct communication protocols in the path between context producers and consumers: Mobile Reliable UDP (MR-UDP) and Data Distribution Service (DDS) [ 64 ].…”

Section: Proposed Solution: M-hub/cddlmentioning

confidence: 99%

A Middleware with Comprehensive Quality of Context Support for the Internet of Things Applications

Gomes

Muniz

Silva

et al. 2017

Sensors

Self Cite

View full text Add to dashboard Cite

Context aware systems are able to adapt their behavior according to the environment in which the user is. They can be integrated into an Internet of Things (IoT) infrastructure, allowing a better perception of the user’s physical environment by collecting context data from sensors embedded in devices known as smart objects. An IoT extension called the Internet of Mobile Things (IoMT) suggests new scenarios in which smart objects and IoT gateways can move autonomously or be moved easily. In a comprehensive view, Quality of Context (QoC) is a term that can express quality requirements of context aware applications. These requirements can be those related to the quality of information provided by the sensors (e.g., accuracy, resolution, age, validity time) or those referring to the quality of the data distribution service (e.g, reliability, delay, delivery time). Some functionalities of context aware applications and/or decision-making processes of these applications and their users depend on the level of quality of context available, which tend to vary over time for various reasons. Reviewing the literature, it is possible to verify that the quality of context support provided by IoT-oriented middleware systems still has limitations in relation to at least four relevant aspects: (i) quality of context provisioning; (ii) quality of context monitoring; (iii) support for heterogeneous device and technology management; (iv) support for reliable data delivery in mobility scenarios. This paper presents two main contributions: (i) a state-of-the-art survey specifically aimed at analyzing the middleware with quality of context support and; (ii) a new middleware with comprehensive quality of context support for Internet of Things Applications. The proposed middleware was evaluated and the results are presented and discussed in this article, which also shows a case study involving the development of a mobile remote patient monitoring application that was developed using the proposed middleware. This case study highlights how middleware components were used to meet the quality of context requirements of the application. In addition, the proposed middleware was compared to other solutions in the literature.

show abstract

An Adaptive Middleware for Opportunistic Mobile Sensing

Cited by 7 publications

References 18 publications

A comprehensive and scalable middleware for Ambient Assisted Living based on cloud computing and Internet of Things

A comprehensive and scalable middleware for Ambient Assisted Living based on cloud computing and Internet of Things

A survey of secure middleware for the Internet of Things

A Middleware with Comprehensive Quality of Context Support for the Internet of Things Applications

Contact Info

Product

Resources

About