An osmotic computing infrastructure for urban pollution monitoring

Abstract

Summary Urban pollution control systems suffer from the presence of fixed stations in a greater number than mobile monitoring devices. Data gathered from such stations provide detailed and reliable information, thanks to equipment quality and effective measuring protocols, but these sampled data are gathered from very limited areas and through discontinuous monitoring campaigns. Fortunately, the spread of technologies for mobility has fostered the development of new approaches like mobile crowdsensing (MCS), increasing the chances of using mobile devices, even personal ones, as suitable sensors for the urban monitoring scenario. Nevertheless, one of the open challenges is the management of integrated heterogeneous data flows, differing in terms of typology, technical specifications (eg, transmission protocols), and semantics. The osmotic computing paradigm aims at creating an abstract level between mobile devices/Internet‐of‐Things devices and a cloud platform, which enables opportunistic filtering and the addition of metadata for improving the data processing flow. This work focuses on the design and development of a middleware that integrates data coming from mobile and Internet‐of‐Things devices specifically deployed in urban contexts using the osmotic computing paradigm. Moreover, a component of the osmotic membrane has been developed for security management.

A Systematic Review on Osmotic Computing

“…Osmotic computing enables tunable configuration of resources based on application requirements and resource availability that makes it distinguishable from the chemical osmotic process (P28) [28]. This distinct feature helps in determining the direction of microservice movement, i.e., from cloud to edge and in reverse.…”

“…They validated the middleware by carrying out unit, integration, and load tests on Agento, kubernetes, and kafka filtering applications. Longo et al (P28) [28] designed and developed a prototype to explicitly address the connection between conventional network monitors, IoT-fixed devices (Laptops), and mobile devices (Smartphones). A middleware was developed for the interaction amongst cloud, edge, IoT, as well as MCS layers with message brokering facilities.…”

“…Virtualization Initial work Sharma et al[46] 2018 To design an osmotic-based framework for exploring 802.21a-2012 MIH in IoT-Villari et al[52] 2018 To introduce concept of microelements (MELs) to support development of future internet services.TheoreticalInitial Kaur et al[23] 2019 To develop an osmotic-based framework to guarantee energy-aware dynamic management of resources.Hyper Heuristics Gamal et al[19] 2019 To design an osmotic-based hybrid meta-heuristics for cloud computing by exploiting bio-inspired load balancing algorithms.Ant Colony and Artificial Bee Colony OptimizationFramework Carnevale et al[12] 2019 To develop an osmotic-based architecture incorporating a distributed multi-agent sys-Buzachis et al[7] 2019 To design FaaS-based osmotic flow model for smart city video surveillance. Longo et al[28] 2019 To design and develop an osmotic-based middleware for urban pollution monitoring in a hybrid network. Sharma et al[45] 2020 To develop an osmotic-based framework to ensure service migration and efficient resource scheduling in an augmented reality system.…”

Osmotic management of distributed complex systems: A declarative decentralised approach

“…Recently, Forti and Brogi 42 have proposed a declarative centralised solution to support the adaptive placement of multiservice osmotic application, considering the optimisation of latency, cost and flavour‐related constraints, both for long‐ and short‐lived application instances. Finally, Longo et al 43 proposed an osmotic computing architecture for monitoring urban pollution, encompassing fixed and opportunistic resources by relying on Kubernetes orchestration capabilities and leaving more sophisticate autonomic management of devices and MELs in the scope for future work.…”

Trustworthiness of Context-Aware Urban Pollution Data in Mobile Crowd Sensing

“…First of all, contributed data must be collected and managed through a dedicated mobile app. This app has to sense, (possibly) pre-process locally and (optionally) send sensed data to remote/cloud-based servers [14]. Sensors data should be associated to location-based and georeferenced data (i.e., situated data creation), without any significant time-related or space-related restriction (i.e., time continuity).…”