EUREC⁴A's <i>HALO</i>

As part of the EUREC4A (Elucidating the role of cloud–circulation coupling in climate) field campaign, the German research aircraft HALO (High Altitude and Long Range Research Aircraft), configured as a cloud observatory, conducted 15 research flights in the trade-wind region east of Barbados in January and February 2020. Narrative text, aircraft state data, and metadata describing HALO's operation during the campaign are provided. Each HALO research flight is segmented by timestamp intervals into standard elements to aid the consistent analysis of the flight data. Photographs from HALO's cabin and animated satellite images synchronized with flight tracks are provided to visually document flight conditions. As a comprehensive product from the remote sensing observations, a multi-sensor cloud mask product is derived and quantifies the incidence of clouds observed during the flights. In addition, to lower the threshold for new users of HALO's data, a collection of use cases is compiled into an online book, How to EUREC4A, included as an asset with this paper. This online book provides easy access to most of EUREC4A's HALO data through an intake catalogue. Code and data are freely available at the locations specified in Table 6.

Enabling Internet of Things through Sensor Cloud: A Review

With the inception of the Internet of Things (IoT), wireless technology found a new outlook where the physical objects can interact with each other and can sense the environment. The IoT has found its way in the real world and has connected billions of devices throughout the world. However, its limitations, such as limited processing capability, storage capability, security and privacy issues, and energy constraints prevent the IoT system to be properly utilized by the real-world applications. Hence, the integration of IoT with various emerging technologies like big data, software defined networks, machine learning, fog computing, sensor cloud, etc., will make the IoT system a more powerful technology. The sensor cloud provides an open, secure, flexible, large storage and a computational capable infrastructure which makes the ensemble architecture of IoT and sensor cloud more efficient. An extensive review of the IoT system enabled sensor cloud is presented in the paper, and with this context, the paper attempts to summarize the sensor cloud infrastructure along with its challenges. In addition, the paper presents the possible integrated architecture of the IoT and the sensor cloud which enables the network to be properly utilized. Further, the importance of integrating these two promising technologies and research challenges associated with it is also identified. Finally, the paper analyses and discusses the motivation behind the ensemble system along with future research direction.

Energy Efficient Opportunistic Sensing Management in Fog Cloud Environment

With so many people now wearing mobile devices with sensors (such as smartphones), utilizing the immense capabilities of these business mobility goods has become a prospective skill to significant behavioural and ecological sensors. A potential challenge for pervasive context assessment is opportunistic sensing, has been effectively used to a wide range of applications. The sensor cloud combines cloud technology with a wireless sensor, resulting in a scalable and cost-effective computing platform for real-time applications. Because the sensor's battery power is limited and the data centre’s servers consume a significant amount of energy to supply storage, a sensor cloud must be energy efficient. This study provides a Fog-based semantic for enabling these kinds of technologies quickly and successfully. The suggested structure is comprised of fundamental algorithms to help set up and coordinate the fog sensing jobs. It creates effective multihop routes for coordinating relevant devices and transporting acquired sensory data to fog sinks. It was claimed that energy-efficient sensor cloud approaches were categorized into different groups and that each technology was examined using numerous characteristics. The outcomes of a series of thorough test simulation in NS3 to define the practicality of the created console, as well as the proportion of each parameter utilized for each technology, are computed.

Recommended System for Cluster Head Selection in a Remote Sensor Cloud Environment Using the Fuzzy-Based Multi-Criteria Decision-Making Technique

Clustering is an energy-efficient routing algorithm in a sensor cloud environment (SCE). The clustering sensor nodes communicate with the base station via a cluster head (CH), which can be selected based on the remaining energy, the base station distance, or the distance from the neighboring nodes. If the CH is selected based on the remaining energy and the base station is far away from the cluster head, then it is not an energy-efficient selection technique. The same applies to other criteria. For CH selection, a single criterion is not sufficient. Moreover, the traditional clustering algorithm head nodes keep changing in every round. Therefore, the traditional algorithm energy consumption is less, and nodes die faster. In this paper, the fuzzy multi-criteria decision-making (F-MCDM) technique is used for CH selection and a threshold value is fixed for the CH selection. The fuzzy analytical hierarchy process (AHP) and the fuzzy analytical network process (ANP) are used for CH selection. The performance evaluation results exhibit a 5% improvement compared to the fuzzy AHP clustering method and 10% improvement compared to the traditional method in terms of stability, energy consumption, throughput, and control overhead.

Quasi-Identifier Recognition Algorithm for Privacy Preservation of Cloud Data Based on Risk Reidentification

Cloud computing plays an essential role as a source for outsourcing data to perform mining operations or other data processing, especially for data owners who do not have sufficient resources or experience to execute data mining techniques. However, the privacy of outsourced data is a serious concern. Most data owners are using anonymization-based techniques to prevent identity and attribute disclosures to avoid privacy leakage before outsourced data for mining over the cloud. In addition, data collection and dissemination in a resource-limited network such as sensor cloud require efficient methods to reduce privacy leakage. The main issue that caused identity disclosure is quasi-identifier (QID) linking. But most researchers of anonymization methods ignore the identification of proper QIDs. This reduces the validity of the used anonymization methods and may thus lead to a failure of the anonymity process. This paper introduces a new quasi-identifier recognition algorithm that reduces identity disclosure which resulted from QID linking. The proposed algorithm is comprised of two main stages: (1) attribute classification (or QID recognition) and (2) QID dimension identification. The algorithm works based on the reidentification of risk rate for all attributes and the dimension of QIDs where it determines the proper QIDs and their suitable dimensions. The proposed algorithm was tested on a real dataset. The results demonstrated that the proposed algorithm significantly reduces privacy leakage and maintains the data utility compared to recent related algorithms.