Data Fusion and IoT for Smart Ubiquitous Environments: A Survey

Abstract:

 The Internet of Things (IoT) is set to become one of the key technological developments of our times provided we are able to realize its full potential. The number of objects connected to IoT is expected to reach 50 billion by 2020 due to the massive influx of diverse objects emerging progressively. IoT hence is expected to be a major producer of big data.Sharing and collaboration of data and other resources would be the key for enabling sustainable ubiquitous environments such as smart cities and societies. A timely fusion andanalysis of big data, acquired from IoT and other sources, to enable highly efficient, reliable and accurate decision making and management of ubiquitous environments would be a grand future challenge. Computational intelligence would play a key role in this challenge.Anumber of surveys exist on data fusion. However, these are mainly focused on specific application areas orclassifications.

Existing system:

IoT is expected to be a major producer of big data. This data would be produced by various vendors giving rise to data as a service. Sharing and collaboration of data and other resources would be the key for enabling sustainable ubiquitous environments such as smart cities and societies [6]. The fusion of various types and forms of data, i.e. data fusion, to enhance data quality and decision making there-fore would be of prime importance in ubiquitous environments. Data fusion is defined as “the theory, techniques and tools which are used for combining sensor data, or data derived from sensory data, into a common represen-tational format” [7].A timely fusion and analysis of big data (volume, velocity, variety, and veracity), acquired from IoT and other sources, to enable highly efficient, reliable and accurate decision making and management of ubiquitous environments would be a grand future challenge. Computational intelligence would play a key role in this challenge.

 Proposed system:

 An enormous amount of data is produced in a quick span of time in the IoT environment. How to make this large volume of data precise and highly accurate is an open problem which needs to be solved because the quality of information plays an important role in decision making. Reliable and accurate information is critical. This can be achieved by data fusion or information fusion (terms which can be used interchangeably). Data fusion is an effective way for the optimum utilization of large volumes of data from multiple sources [17].Multi-sensor data fu-sionseeks to combine information from multiple sensors and sources to achieve inferences that are not feasible from a single sensor or source[21]. The fusion of information from sensors with different physical characteristics enhances the understanding of our surroundings and pro-vides the basis for planning, decision-making, and the control of autonomous and intelligent machines. IoT middleware is an interface that integrates and facilitates the interaction between the various elements called ‘Things’ and internet. A very critical part of IoT middle-ware is event processing. The predictive fusion analytics.

Conclusion:

 The Internet of Things (IoT) is set to become one of the key technological developments of our times provided we are able to realize its full potential. The number of objects con-nected to IoT is expected to reach 50 billion by 2020 due to the massive influx of diverse objects emerging progres-sively. IoT hence is expected to be a major producer of big data. Sharing and collaboration of data and other resources would be the key for enabling sustainable ubiquitous en-vironments such as smart cities andsocieties. A timely fu-sion and analysis of big data, acquired from IoT and other sources, to enable highly efficient, reliable and accurate de-cision making and management of ubiquitous environ-ments would be a grand future challenge. Computational intelligence would play a key role in this challenge. A number of surveys exist on data fusion. However, these are mainly focused on specific application areas or classifications.

References:

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