Minimizing the Number of Deployed UAVs for Delay-bounded Data Collection of IoT Devices

2021 ◽  
Author(s):  
Junqi Zhang ◽  
Zheng Li ◽  
Wenzheng Xu ◽  
Jian Peng ◽  
Weifa Liang ◽  
...  
Keyword(s):  
Data Collection ◽  
Bounded Data ◽  
Iot Devices

scholarly journals Monitoring Passive Wireless Devices

2021 ◽  
Author(s):  
Zohar Naor
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Collection ◽  
Data Gathering ◽  
Geographic Area ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Wireless Devices ◽  
Iot Devices ◽  
Passive Rfid

Abstract This study suggests using a user-initiated detecting and data gathering from power-limited and even passive wireless devices, such as passive RFID tags, wireless sensor networks (WSNs), and Internet of Things (IoT) devices, that either power limitation or poor cellular coverage prevents them from communicating directly with wireless networks. While previous studies focused on sensors that continuously transmit their data, the focus of this study is on passive devices. The key idea is that instead of receiving the data transmitted by the sensor nodes, an external device (a reader), such as an unnamed aerial vehicle (UAV), or a smartphone is used to detect IoT devices and read the data stored in the sensor nodes, and then to deliver it to the cloud, in which it is stored and processed. While previous studies on UAV-aided data collection from WSNs focused on the UAV path planning, the focus of this study is on the rate at which the passive sensor nodes should be polled. That is, to find the minimal monitoring rate that still guarantees accurate and reliable data collection. The proposed scheme enables us to deploy wireless sensor networks over a large geographic area (e.g., for agricultural applications), in which the cellular coverage is very poor if any. Furthermore, the usage of initiated data collection can enable the deployment of passive WSNs. Thus, can significantly reduce both the operational cost, as well as the deployment cost, of the WSN.

scholarly journals Ping-Pong Free Advanced and Energy Efficient Sensor Relocation for IoT-Sensory Network

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5654
Author(s):  
Moonseong Kim ◽  
Sooyeon Park ◽  
Woochan Lee
Keyword(s):  
Big Data ◽  
Data Collection ◽  
Energy Efficient ◽  
Essential Role ◽  
Sensor Nodes ◽  
Ping Pong ◽  
Iot Devices ◽  
Artificial Network ◽  
Specific Cluster ◽  
Big Data Technology

With the growing interest in big data technology, mobile IoT devices play an essential role in data collection. Generally, IoT sensor nodes are randomly distributed to areas where data cannot be easily collected. Subsequently, when data collection is impossible (i.e., sensing holes occurrence situation) due to improper placement of sensors or energy exhaustion of sensors, the sensors should be relocated. The cluster header in the sensing hole sends requests to neighboring cluster headers for the sensors to be relocated. However, it can be possible that sensors in the specific cluster zones near the sensing hole are continuously requested to move. With this knowledge, there can be a ping-pong problem, where the cluster headers in the neighboring sensing holes repeatedly request the movement of the sensors in the counterpart sensing hole. In this paper, we first proposed the near-uniform selection and movement scheme of the sensors to be relocated. By this scheme, the energy consumption of the sensors can be equalized, and the sensing capability can be extended. Thus the network lifetime can be extended. Next, the proposed relocation protocol resolves a ping-pong problem using queues with request scheduling. Another crucial contribution of this paper is that performance was analyzed using the fully-customed OMNeT++ simulator to reflect actual environmental conditions, not under over-simplified artificial network conditions. The proposed relocation protocol demonstrates a uniform and energy-efficient movement with ping-pong free capability.

scholarly journals PurExt: Automated Extraction of the Purpose-Aware Rule from the Natural Language Privacy Policy in IoT

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Lu Yang ◽  
Xingshu Chen ◽  
Yonggang Luo ◽  
Xiao Lan ◽  
Li Chen
Keyword(s):  
Data Collection ◽  
Natural Language ◽  
Recall Rate ◽  
Semantic Role Labeling ◽  
Privacy Policies ◽  
Novel Approach ◽  
High Recall Rate ◽  
Iot Devices ◽  
Machine Readable ◽  
The Internet Of Things

The extensive data collection performed by the Internet of Things (IoT) devices can put users at risk of data leakage. Consequently, IoT vendors are legally obliged to provide privacy policies to declare the scope and purpose of the data collection. However, complex and lengthy privacy policies are unfriendly to users, and the lack of a machine-readable format makes it difficult to check policy compliance automatically. To solve these problems, we first put forward a purpose-aware rule to formalize the purpose-driven data collection or use statement. Then, a novel approach to identify the rule from natural language privacy policies is proposed. To address the issue of diversity of purpose expression, we present the concepts of explicit and implicit purpose, which enable using the syntactic and semantic analyses to extract purposes in different sentences. Finally, the domain adaption method is applied to the semantic role labeling (SRL) model to improve the efficiency of purpose extraction. The experiments that are conducted on the manually annotated dataset demonstrate that this approach can extract purpose-aware rules from the privacy policies with a high recall rate of 91%. The implicit purpose extraction of the adapted model significantly improves the F1-score by 11%.

AIoT Data Management, Analytics and Decision Making (Artificial Intelligence of Things Data Management, Analytics and Decision Making)

2021 ◽  
Vol 11 (5) ◽  
pp. 496-497
Author(s):  
Ivan Izonin
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Data Collection ◽  
Data Management ◽  
High Tech ◽  
The Internet ◽  
Huge Amount ◽  
Infrastructure Systems ◽  
Fast Development ◽  
Iot Devices

Nowadays, the fast development of hardware for IoT-based systems creates appropriate conditions for the development of services for different application areas. As we know, the large number of multifunctional devices, which are connected to the Internet is constantly increasing. Today, most of the IoT devices just only collect and transmit data. The huge amount of data produced by these devices requires efficient and fast approaches to its analysis. This task can be solved by combining Artificial Intelligence and IoT tools. Essentially, AI accelerators can be used as a universal sensor in IoT systems, that is, we can create Artificial Intelligence of Things (AIoT). AIoT can be considered like a movement from data collection to knowledge aggregation. AIoT-based systems are being widely implemented in many high-tech industrial and infrastructure systems. Such systems are capable of providing not only the ability to collect but also analyse various aspects of data for identification, planning, diagnostics, evaluation, monitoring, optimization, etc., at the lower level in the entire system's hierarchy. That is, they are able to work more efficiently and effectively by generating the knowledge that is needed for real-time analytics and decision-making in some application areas.

scholarly journals Design and Implementation of an Energy-Efficient Weather Station for Wind Data Collection

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3831
Author(s):  
Padma Balaji Leelavinodhan ◽  
Massimo Vecchio ◽  
Fabio Antonelli ◽  
Andrea Maestrini ◽  
Davide Brunelli
Keyword(s):  
Data Collection ◽  
Precision Agriculture ◽  
Climatic Factors ◽  
Wind Data ◽  
Weather Data ◽  
Weather Station ◽  
Weather Patterns ◽  
Weather Stations ◽  
Iot Devices ◽  
Local Weather

Agriculture faces critical challenges caused by changing climatic factors and weather patterns with random distribution. This has increased the need for accurate local weather predictions and weather data collection to support precision agriculture. The demand for uninterrupted weather stations is overwhelming, and the Internet of Things (IoT) has the potential to address this demand. One major challenge of energy constraint in remotely deployed IoT devices can be resolved using weather stations that are energy neutral. This paper focuses on optimizing the energy consumption of a weather station by optimizing the data collected and sent from the sensor deployed in remote locations. An asynchronous optimization algorithm for wind data collection has been successfully developed, using the development lifecyle specifically designed for weather stations and focused on achieving energy neutrality. The developed IoT weather station was deployed in the field, and it has the potential to reduce the power consumption of the weather station by more than 60%.

Multi-UAV Collaborative Data Collection for IoT Devices Powered by Battery

2020 ◽  
Author(s):  
Yue Wang ◽  
Xiangming Wen ◽  
Zhiqun Hu ◽  
Zhaoming Lu ◽  
Jiansong Miao ◽  
...  
Keyword(s):  
Data Collection ◽  
Iot Devices ◽  
Multi Uav

scholarly journals Open Sensor Manager for IIoT

2020 ◽  
Vol 9 (2) ◽  
pp. 30 ◽  
Author(s):  
Riku Ala-Laurinaho ◽  
Juuso Autiosalo ◽  
Kari Tammi
Keyword(s):  
Data Collection ◽  
Application Programming Interface ◽  
Sensor Nodes ◽  
Authentication Code ◽  
Representational State Transfer ◽  
Software Updating ◽  
Additional Data Collection ◽  
Iot Devices ◽  
Rest Api ◽  
User Friendly

Data collection in an industrial environment enables several benefits: processes and machinery can be monitored; the performance can be optimized; and the machinery can be proactively maintained. To collect data from machines or production lines, numerous sensors are required, which necessitates a management system. The management of constrained IoT devices such as sensor nodes is extensively studied. However, the previous studies focused only on the remote software updating or configuration of sensor nodes. This paper presents a holistic Open Sensor Manager (OSEMA), which addresses also generating software for different sensor models based on the configuration. In addition, it offers a user-friendly web interface, as well as a REST API (Representational State Transfer Application Programming Interface) for the management. The manager is built with the Django web framework, and sensor nodes rely on ESP32-based microcontrollers. OSEMA enables secure remote software updates of sensor nodes via encryption and hash-based message authentication code. The collected data can be transmitted using the Hypertext Transfer Protocol (HTTP) and Message Queuing Telemetry Transport (MQTT). The use of OSEMA is demonstrated in an industrial domain with applications estimating the usage roughness of an overhead crane and tracking its location. OSEMA enables retrofitting different sensors to existing machinery and processes, allowing additional data collection.

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