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 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.

AggieAir: An Integrated and Effective Small Multi-UAV Command, Control and Data Collection Architecture

2009 ◽  
Author(s):  
Calvin Coopmans ◽  
Yiding Han
Keyword(s):  
Data Collection ◽  
System Architecture ◽  
System Level ◽  
Flight System ◽  
Multiple Uavs ◽  
Small Uav ◽  
Multi Uav

Small UAV performance depends on an effective and efficient command system architecture. Based on an existing UAV system called Paparazzi, AggieAir is a full flight system capable of handling single or multiple UAVs with single or multiple payloads per airframe. System-level block diagrams are presented and specific details about implementation and results are provided.

scholarly journals Energy-Efficient Multi-UAV Data Collection for IoT Networks with Time Deadlines

2020 ◽  
Author(s):  
Oussama Ghdiri ◽  
Wael Jaafar ◽  
Safwan Alfattani ◽  
Jihene Ben Abderrazak ◽  
Halim Yanikomeroglu
Keyword(s):  
Data Collection ◽  
Energy Efficient ◽  
Multi Uav

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.

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