Prem Prakash Jayaraman

Big Data Reduction Methods: A Survey

AbstractnResearch on big data analytics is entering in the new phase called fast data where multiple gigabytes of data arrive in the big data systems every second. Modern big data systems collect inherently complex data streams due to the volume, velocity, value, variety, variability, and veracity in the acquired data and consequently give rise to the 6Vs of big data. The reduced and relevant data streams are perceived to be more useful than collecting raw, redundant, inconsistent, and noisy data. Another perspective for big data reduction is that the million variables big datasets cause the curse of dimensionality which requires unbounded computational resources to uncover actionable knowledge patterns. This article presents a review of methods that are used for big data reduction. It also presents a detailed taxonomic discussion of big data reduction methods including the network theory, big data compression, dimension reduction, redundancy elimination, data mining, and machine learning methods. In addition, the open research issues pertinent to the big data reduction are also highlighted.

Internet of Things and Edge Cloud Computing Roadmap for Manufacturing

The manufacturing industry is exploring the use of Internet of Things and cloud computing to enhance the efficiency of manufacturing plant operations, improve product quality, increase manufacturers ability to respond to changing customer demands, and expand to new markets. Productivity, quality, safety, and the ability to respond quickly to changing conditions are essential to maintaining the industrys competitiveness.

Remote health care cyber-physical system : quality of service (QoS) challenges and opportunities

There is a growing emphasis to find alternative non-traditional ways to manage patients to ease the burden on health care services largely fuelled by a growing demand from sections of population that is ageing. In-home remote patient monitoring applications harnessing technological advancements in the area of Internet of things (IoT), semantic web, data analytics, and cloud computing have emerged as viable alternatives. However, such applications generate large amounts of real-time data in terms of volume, velocity, and variety thus making it a big data problem. Hence, the challenge is how to combine and analyse such data with historical patient data to obtain meaningful diagnoses suggestions within acceptable time frames (considering quality of service (QoS)). Despite the evolution of big data processing technologies (e.g. Hadoop) and scalable infrastructure (e.g. clouds), there remains a significant gap in the areas of heterogeneous data collection, real-time patient monitoring, and automated decision support (semantic reasoning) based on well-defined QoS constraints. In this study, the authors review the state-of-the-art in enabling QoS for remote health care applications. In particular, they investigate the QoS challenges required to meet the analysis and inferencing needs of such applications and to overcome the limitations of existing big data processing tools.

Contextualised service delivery in the Internet of Things: Parking recommender for smart cities

The Internet of Things (IoT) plays an important role in the development of smart cities. In this paper we focus on the development of IoT-based smart services for solving urban problems that involve IoT-enabled Observation, Orientation, Decision, and Action (OODA) loops. We also focus on how to efficiently support such OODA loops in situations where such loops involve internet-scale data. More specifically, IoT supports Observation via the discovery of sensors and the integration of their data. It supports Orientation via a contextualisation process that refines such data to include only those that are relevant to the situation and/or activities of each specific individual or group. As IoT contextualisation potentially involves internet-scale data, performing this process efficiently allows for fast decision making, and this in turn permits carrying out a timely Action. In this paper we propose an approach and related techniques for performing internet-scale data contextualisation. In particular, we propose IoT-based contextualisation techniques that effectively consider the entire range of data that is being collected in smart cities and use such data to provide hyper-personalised information to each user, i.e., information that best suits the context of each user in the Smart City. We exemplify the proposed contextualisation solution in a smart parking space recommender application/service, and provide an experimental evaluation of this service to illustrate the benefits of our solution.

Data Ingestion and Storage Performance of IoT Platforms: Study of OpenIoT

Internet of Things is a very active research area with great commercialisation potential. The number of IoT platforms is already exceeding 300 and still growing. However, performance evaluation and benchmarking of IoT platforms are still in their infancy. As a step towards developing a performance benchmarking approach for IoT platforms, this paper analyses and compares a number of popular IoT platforms from data ingestion and storage capability perspectives. In order to test the proposed approach, we use the widely used open source IoT platform, OpenIoT. The results of the experiments and the lessons learnt are presented and discussed. While having a great research promise and pioneering contribution to semantic interoperability of IoT silos, the experimental results indicate OpenIoT platform needs more development effort to be ready for any substantial deployment in commercial IoT applications.

Analytics-as-a-Service in a Multi-Cloud Environment through Semantically-enabled Hierarchical Data Processing

A large number of cloud middleware platforms and tools are deployed to support a variety of internet‐of‐things (IoT) data analytics tasks. It is a common practice that such cloud platforms are only used by its owners to achieve their primary and predefined objectives, where raw and processed data are only consumed by them. However, allowing third parties to access processed data to achieve their own objectives significantly increases integration and cooperation and can also lead to innovative use of the data. Multi‐cloud, privacy‐aware environments facilitate such data access, allowing different parties to share processed data to reduce computation resource consumption collectively. However, there are interoperability issues in such environments that involve heterogeneous data and analytics‐as‐a‐service providers. There is a lack of both architectural blueprints that can support such diverse, multi‐cloud environments and corresponding empirical studies that show feasibility of such architectures. In this paper, we have outlined an innovative hierarchical data‐processing architecture that utilises semantics at all the levels of IoT stack in multi‐cloud environments. We demonstrate the feasibility of such architecture by building a system based on this architecture using OpenIoT as a middleware, and Google Cloud and Microsoft Azure as cloud environments. The evaluation shows that the system is scalable and has no significant limitations or overheads. Copyright

Monitoring Internet of Things Application Ecosystems for Failure

For Internet of Things (IoT) application ecosystems to excel, end-to-end components including the cloud, network, and edge devices must be highly dependable and resilient. This dependability must be verifiable by continuously monitoring the constituent components for conformance to defined behavior in terms of functional and nonfunctional requirements. However, the authors contend that current techniques and frameworks for monitoring the performance of hardware and application resources in distributed systems are not capable of monitoring and detecting root causes of failure and performance degradation for entire end-to-end IoT ecosystems. Motivated by this finding, they discuss their vision of future research into developing formal approaches for monitoring end-to-end IoT ecosystems.

Opportunistic Data Collection for IoT-Based Indoor Air Quality Monitoring

Opportunistic sensing advance methods of IoT data collection using the mobility of data mules, the proximity of transmitting sensor devices and cost efficiency to decide when, where, how and at what cost collect IoT data and deliver it to a sink. This paper proposes, develops, implements and evaluates the algorithm called CollMule which builds on and extends the 3D kNN approach to discover, negotiate, collect and deliver the sensed data in an energy- and cost-efficient manner. The developed CollMule software prototype uses Android platform to handle indoor air quality data from heterogeneous IoT devices. The CollMule evaluation is based on performing rate, power consumption and CPU usage of single algorithm cycle. The outcomes of these experiments prove the feasibility of CollMule use on mobile smart devices.

CDQL: A Generic Context Representation and Querying Approach for Internet of Things Applications

As the standardization efforts for IoT is fast progressing, we will quickly get to a point where context derived from IoT data and relations will be the underpinning factor to enable interaction between smart things. Therefore, having a generic approach for describing and querying context is crucial for the future of IoT applications. In this paper, we propose Context Definition and Query Language (CDQL), an advanced approach that enables things to exchange context. CDQL consists of two main parts: Context Definition Model, which is designed to describe the contextual attributes and context related capabilities of each thing; and Context Query Language (CQL), which is a flexible query language to express contextual information requirements without considering details of the underlying data structure. We exemplify the usage of the proposed CDQL, via a smart city use case study that highlight how CDQL can be utilized to deliver context information to IoT applications.

Classification and Annotation of Open Internet of Things Datastreams

The Internet of Things (IoT) is springboarding novel applications and has led to the generation of massive amounts of data that can offer valuable insights across multiple domains: Smart Cities, environmental monitoring, healthcare etc. In particular, the availability of open IoT data streaming from heterogeneous sources constitute a novel powerful knowledge base. However, due to the inherent distributed, heterogeneous and open nature of such data, metadata that describe the data is generally lacking. This happens especially in contexts where IoT data is contributed by users via cloud-based open data platforms, in which even the information about the type of data measured is often missing. Since metadata is of paramount importance for data reuse, there is a need to develop intelligent techniques that can perform automatic annotation of heterogeneous IoT datastreams. In this paper, we propose two novel IoT datastream classification algorithms: CBOS and TKSE for the task of metadata annotation. We validate our proposed techniques through extensive experiments using public IoT datasets and comparing the outcomes with state-of-the-art classification methods. Results show that our techniques bring significant improvements to classification accuracy.