Hybrid Aspect of Context-Aware Middleware for Pervasive Smart Environment: A Review

Abstract

Pervasive computing has made almost every device we see today to be communicated and function in collaboration with one another. Since the portable devices have become a part of our everyday life, people are more involved in a pervasive computing environment. They engage with many computational devices simultaneously without knowing the availability of their existence. The current world is being filled with more and more smart environments. These smart environments make them to be attracted towards the new technological emergence in the field of pervasive computing. Various researches are being carried out to improve the smart environment and their applications. Middleware plays a vital role in building the pervasive applications. The pervasive devices act based on the context of the situation, that is, they do their actions according to the environment of the application. They react to the situations smartly as they can take their own decisions based on the context developed for that specific application. Most of the pervasive applications were using its own middleware that is specific towards their need. As today, most of the applications are using their own middleware with their specific requirement, which leads us to unearth out their common features and their scope of using it. In this paper, a survey on the various hybrid aspects of the different context-aware middleware has been done. This middleware is classified based on service, context, and device aspects. Merits and demerits are identified from the existing smart environments, and future perspective of their development such as generic context-aware middleware need has been discussed.

A generic and smart automation system for home using internet of things

“…A real-time control scheme is suggested [11] for the management of home/office equipment from mobile devices via the general packet radio services (GPRS) network. An authorization system is proposed for the message queuing telemetry transport-based (MQTT-based) IoT to build to enforce an authorization framework and the architecture is based on OAuth 1.0a, an open authorization standard for web applications [12], [13]. The home automation framework analysis was suggested by [14] in which the IoT is a technology that provides the tremendous capacity for the advancement of numerous technologies, namely: e-government, environmental control, military applications, infrastructure maintenance, manufacturing applications, energy management, health monitoring, smart home, and transportation systems.…”

Towards an Approach for Intelligent Adaptation Decision-Making of Pervasive Middleware

“…After surveying the chosen set of pervasive middleware, it is valuable to analyze and evaluate each of them. It bears noting that the selection of the pervasive middleware that is surveyed and assessed in this section is based on the surveys [Baldauf et al, 2007, Perera et al, 2013, Madhusudanan et al, 2018, Pradeep and Krishnamoorthy, 2019. However, these surveys do not cover all objectives that are identified in section 1.2.…”

“…As illustrated in Table 3.5, a common weakness that can be found in the majority of existing approaches mainly stands on the use of traditional association rule mining algorithms, such as FP-Growth and Apriori. This weakness is caused by the narrow applicability of these traditional algorithms due to the huge number of generated rules [Mahmood et al, 2013]. However, generating a redundant and large number of rules leads to a situation where most of the rules are in fact irrelevant, uninteresting and useless in making decisions at runtime.…”

“…The present section is structured into two parts. The first part surveys relevant middleware from several surveys [Baldauf et al, 2007, Perera et al, 2013, Madhusudanan et al, 2018, Pradeep and Krishnamoorthy, 2019 (see subsection 3.2.1). The second part provides a discussion on the surveyed middleware solutions (see subsection 3.2.2).…”

“…By going through the literature and several surveys in the ontology learning area [Asim et al, 2018, Lehmann and Voelker, 2014, Ma and Molnár, 2020, limited approaches have been proposed to support ontology evolution through ontology learning from data sources.…”