Massive Number Of Sensors Research Articles

A Brief Systematics Review and Visualization of Smart Cities based on Fog and Big Data Analysis Technology

The fog computing is based on IoT, and same as clouds are on the edge of a network between a device and the cloud, which can provide very close service to the device in real time. cloud computing frameworks also extend support to emerging application paradigms such as IoT, Fog computing, Edge, and Big Data through service and infrastructure. the ubiquitous deployment of sensors in smart cities requires a high-performance computing paradigm to support big data analysis with smart technologies and communications in IoT, providing location-awareness and latency-sensitive computing near the data sources (i.e., at the edge of the network). Cloud Computing paradigms widely used in enterprises to address the emerging challenges of big data analysis because of its scalable and distributed data management scheme. However, data centers in the Cloud faces great challenges on the burden of exploding amount of big data and the additional requirements of location awareness and low latency at the edge of network necessary for smart cites. the widely distributed sensor networks generate massive volume of data, which leads to a “Big Data “analysis challenge. The machine-to-machine(M2M) communication among massive numbers of sensors will dominate future communication network traffic, namely Internet of Things, instead of traditional Internet of Contents (IoC) in human-to-human and human-to-machine communication. In this review system where we took the research article around 33 from the different database and also taken 1987 datasets form the PubMed database from 1959 to 2024 (02 June 2024). In addition, depending on the different requirement which is partially processed the data. Then the gathered data is stored at the ledger unit through the smart cities based on the fog computing and big data technology. The hashing identity-based data encryption is a public key encryption which uses a digital signature to encrypt the data to prevent the information from the third party.

Dynamic Load Regulation of Robots with Multi-sensor Fusion

Multi-sensor fusion technology has been extensively deployed for the autonomous navigation and control of robots. Theoretically, by increasing the number of sensors, more effective data on the surrounding environment and obstacle information can be obtained. However, in practical applications, a massive number of sensors will not only increase the cost of using sensors in the system but also add to the difficulty and cost of data processing, leading to unnecessary resource consumption. To solve this problem, first, the robot navigation area is divided according to the importance of the navigation area and the characteristics of sensor parameters. The navigation non-core area adopts the single lidar sensor, while the core area adopts the multi-sensor fusion of “lidar +” to reduce the number of sensors used in the fusion and improve the performance of multi-sensor fusion after the division. Then, in consideration of the higher requirement for computing capacity of the device imposed by deep learning, the multi-sensor fusion algorithm is introduced into the dynamic load regulation mechanism to solve the problem from the algorithm level so that the outperformance of deep learning in feature extraction and other aspects can be utilized to reduce the performance requirements of the device and the overall power consumption of the system. Finally, by building the ROS experimental platform, it is verified that the robot can use autonomous navigation in complex and diversified home scenes.

5G Magnetic Resonance Coupling Planar Spiral Coil Wireless Power Transfer

Wireless Power Transfer in the 5G frequency band is the most promising technology to power up ubiquitous small electronic devices as well as IoT devices. A strongly coupled magnetic resonance WPT technique that focuses on near-field electromagnetic energy has been proposed in this paper. However, most Magnetic Resonance Coupling Wireless Power Transfer (MRC WPT) applications have been designed in kHz and MHz frequency spectrum. This paper demonstrates Planar Spiral Coil Magnetic Resonance Coupling (PSC MRC) WPT designs at 5G (GHz) frequencies. Also, the transformation technique of the low frequency (kHz and MHz) magnetic resonance circuit model equations to high frequency (GHz) circuit model equations to achieve a high-efficiency power transfer. PSC MRC WPT designs structure antennas are designed at 3.4 - 3.5 GHz in the form of circular and square shapes with 1 turn coil. The proposed antenna structures are firstly being optimized in a full-wave electromagnetic simulator, CST Microwave Studio to resonate at the 3.4 - 3.5 GHz band. Then, the close-loop equations to determine the efficiency of 5G Magnetic Resonance Coupling Planar Spiral Coil Wireless Power Transfer is designed. Lastly, the results are compared with the simulation and calculated parts. The highest efficiency of the PSC MRC circular antenna is 31.58 % when the distance is at 2 mm, and 31.26 and 31.02 % when the distance is at 3 and 4 mm, respectively. The efficiency of circular PSC MRC is found to be 25 % better than the efficiency of square shape design. HIGHLIGHTS IoT devices need to be charged and maintained as the network system has massive numbers of sensors. Wireless power transfer is a promising technology to power up IoT devices This paper proposed, a WPT technique based on strongly coupled magnetic resonance which focuses on the near-field electromagnetic energy. This technique can extend the distance of the power transfer between the transmitter and the receiver The transformation technique of the low frequency (kHz and MHz) magnetic resonance circuit model equations to high frequency (GHz) circuit model equations to achieve a high-efficiency PSC MRC WPT design structure is introduced in this paper This novel technique can be applied to effectively design PSC MRC Antenna WPT for 5G applications. Most reported Magnetic Resonance Coupling Wireless Power Transfer (MRC WPT) applications have been designed in kHz and MHz frequency spectrum GRAPHICAL ABSTRACT

Guest Editorial: Introduction to the Special Section on Data-Driven IoT for Smart Cities

The papers in this special section focus on data-driven Internet of Things (IoT) for smart cities. Developing smart cities to better support the growing urban population is a global and complex challenge and involves interdisciplinary fields. Instead of the traditional Internet of Contents in human-to-human and human-to-machine communications, the Internet of Things with communications among massive numbers of sensors will dominate the network traffic. Every day, these smart things generate a massive amount of data in the magnitude of ZettaBytes. Due to inherent characteristics of geo-distribution in big data generated by networked sensors deployed for IoT applications, the “smartness” of infrastructure in future cities requires intelligent data sensing and mining for monitoring and actuation to ensure infrastructure reliability and public safety.

A LoRaWAN Testbed Design for Supporting Critical Situations: Prototype and Evaluation

The Internet of Things is one of the hottest topics in communications today, with current revenues of $151B, around 7 billion connected devices, and an unprecedented growth expected for next years. A massive number of sensors and actuators are expected to emerge, requiring new wireless technologies that can extend their battery life and can cover large areas. LoRaWAN is one of the most outstanding technologies which fulfill these demands, attracting the attention of both academia and industry. In this paper, the design of a LoRaWAN testbed to support critical situations, such as emergency scenarios or natural disasters, is proposed. This self-healing LoRaWAN network architecture will provide resilience when part of the equipment in the core network may become faulty. This resilience is achieved by virtualizing and properly orchestrating the different network entities. Different options have been designed and implemented as real prototypes. Based on our performance evaluation, we claim that the usage of microservice orchestration with several replicas of the LoRaWAN network entities and a load balancer produces an almost seamless recovery which makes it a proper solution to recover after a system crash caused by any catastrophic event.

D-DSC: Decoding Delay-based Distributed Source Coding for Internet of Sensing Things.

Spatial correlation between densely deployed sensor nodes in a wireless sensor network (WSN) can be exploited to reduce the power consumption through a proper source coding mechanism such as distributed source coding (DSC). In this paper, we propose the Decoding Delay-based Distributed Source Coding (D-DSC) to improve the energy efficiency of the classical DSC by employing the decoding delay concept which enables the use of the maximum correlated portion of sensor samples during the event estimation. In D-DSC, network is partitioned into clusters, where the clusterheads communicate their uncompressed samples carrying the side information, and the cluster members send their compressed samples. Sink performs joint decoding of the compressed and uncompressed samples and then reconstructs the event signal using the decoded sensor readings. Based on the observed degree of the correlation among sensor samples, the sink dynamically updates and broadcasts the varying compression rates back to the sensor nodes. Simulation results for the performance evaluation reveal that D-DSC can achieve reliable and energy-efficient event communication and estimation for practical signal detection/estimation applications having massive number of sensors towards the realization of Internet of Sensing Things (IoST).

Statistical Process Monitoring for IoT-Enabled Cybermanufacturing: Opportunities and Challenges

Initiated from services and consumer products industries, there is a growing interest in using Internet of Things (IoT) technologies in various industries. In particular, IoT-enabled cybermanufacturing starts to draw increasing attention. Because IoT devices such as IoT sensors are usually much cheaper and smaller than the traditional sensors, there is a potential for instrumenting manufacturing systems with massive number of sensors. The premise is that the big data subsequently collected from IoT sensors can be utilized to advance manufacturing. Therefore, data-driven statistical process monitoring (SPM) is expected to contribute significantly to the advancement of cybermanufacturing. In this work, the state-of-the-art in cybermanufacturing is reviewed; an IoT-enabled manufacturing technology testbed (MTT) was built to explore the potential of IoT sensors for manufacturing, as well as to understand the characteristics of data produced by the IoT sensors; finally, the potentials and challenges associated with big data analytics presented by cybermanufacturing systems is discussed; and we propose statistics pattern analysis (SPA) as a promising SPM tool for cybermanufacturing.

A Scalable Sensor Management Architecture Using BDI Model for Pervasive Surveillance

Recent world events have amplified the need for improved safety and security to contend with natural and man-made threats. The universality and unpredictability of such threats have stimulated intense interest in smart pervasive surveillance systems. They are built by adopting smart sensor networks that cover large areas and can perform self-contained assessments of situations in the environment. However, such systems rely on a massive number of sensors with diverse capabilities but limited resources, e.g., power, processing, and storage. Thus, successful management of tasks hinges on the systems architecture. Sensor management architectures (SMAs) coordinate the sensor nodes and their resources in a manner that improves system control and situation awareness. This paper introduces a scalable and flexible SMA for many sensor management (SM) applications, particularly, pervasive surveillance. This novel SMA is called the extended hybrid architecture for SM (E-HASM), an architecture that combines the advantages of the holonic, federated, and market-based paradigms. The E-HASM models each node as an intelligent sensor by using the beliefs, desires, and intentions model and defines the interaction and cooperation among the nodes. The simulation results illustrate the performance of the E-HASM over a variety of security threats, background targets, and network sizes. The results prove that the proposed architecture is significantly more scalable and flexible than centralized architectures.

Extreme Big Data (EBD): Next Generation Big Data Infrastructure Technologies Towards Yottabyte/Year

Our claim is that so-called Data will evolve into a new era with proliferation of data from multiple sources such as massive numbers of sensors whose resolution is increasing expo- nentially, high-resolution simulations generating huge data results, as well as evolution of social infrastructures that allow for opening up of data silos, i.e., data sources being abundant across the world instead of being confined within an institution, much as how scientific data are being handled in the modern era as a common asset openly accessible within and across disciplines. Such a situation would create the need for not only petabytes to zetabytes of capacity and beyond, but also for extreme scale computing power. Our new project, sponsored under the Japanese JST- CREST program is called Extreme Big Data, and aims to achieve the convergence of extreme supercomputing and big data in order to cope with such explosion of data. The project consists of six teams, three of which deals with defining future EBD convergent SW/HW architecture and system, and the other three the EBD co-design applications that represent di?erent facets of big data, in metagenomics, social simulation, and climate simulation with real-time data assimilation. Although the project is still early in its lifetime, started in Oct. 2013, we have already achieved several notable results, including becoming world #1 on the Green Graph 500, a benchmark to measure the power e?ciency of graph processing that appear in typical big data scenarios.

Direct integration of solid state stress sensors with single crystal microelectromechanical systems

Unique displacement sensors made of pn diodes and MOS transistors are integrated at the root of silicon micro-beams. Displacement induced stress excites the sensors by modulating the silicon bandgap energy. Owing to their simplicity, these sensors are useful for microelectromechanical systems requiring a massive number of sensors for multiple degree of freedom sensing.