scholarly journals Fine-Grained Pests Recognition Based on Truncated Probability Fusion Network via Internet of Things in Forestry and Agricultural Scenes

Algorithms ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 290
Author(s):  
Kai Ma ◽  
Ming-Jun Nie ◽  
Sen Lin ◽  
Jianlei Kong ◽  
Cheng-Cai Yang ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Insect Pests ◽  
Accurate Identification ◽  
Technical Application ◽  
Network Mining ◽  
Fine Grained ◽  
Feature Expression ◽  
The Internet Of Things ◽  
Different Levels ◽  
Better Than

Accurate identification of insect pests is the key to improve crop yield and ensure quality and safety. However, under the influence of environmental conditions, the same kind of pests show obvious differences in intraclass representation, while the different kinds of pests show slight similarities. The traditional methods have been difficult to deal with fine-grained identification of pests, and their practical deployment is low. In order to solve this problem, this paper uses a variety of equipment terminals in the agricultural Internet of Things to obtain a large number of pest images and proposes a fine-grained identification model of pests based on probability fusion network FPNT. This model designs a fine-grained feature extractor based on an optimized CSPNet backbone network, mining different levels of local feature expression that can distinguish subtle differences. After the integration of the NetVLAD aggregation layer, the gated probability fusion layer gives full play to the advantages of information complementarity and confidence coupling of multi-model fusion. The comparison test shows that the PFNT model has an average recognition accuracy of 93.18% for all kinds of pests, and its performance is better than other deep-learning methods, with the average processing time drop to 61 ms, which can meet the needs of fine-grained image recognition of pests in the Internet of Things in agricultural and forestry practice, and provide technical application reference for intelligent early warning and prevention of pests.

Sustainable Security for the Internet of Things Using Artificial Intelligence Architectures

2021 ◽  
Vol 21 (3) ◽  
pp. 1-22
Author(s):  
Celestine Iwendi ◽  
Saif Ur Rehman ◽  
Abdul Rehman Javed ◽  
Suleman Khan ◽  
Gautam Srivastava
Keyword(s):  
Internet Of Things ◽  
Detection System ◽  
Experimental Testing ◽  
Cyber Attacks ◽  
Internet Technology ◽  
The Internet ◽  
Business Operations ◽  
Private Data ◽  
The Internet Of Things ◽  
Better Than

In this digital age, human dependency on technology in various fields has been increasing tremendously. Torrential amounts of different electronic products are being manufactured daily for everyday use. With this advancement in the world of Internet technology, cybersecurity of software and hardware systems are now prerequisites for major business’ operations. Every technology on the market has multiple vulnerabilities that are exploited by hackers and cyber-criminals daily to manipulate data sometimes for malicious purposes. In any system, the Intrusion Detection System (IDS) is a fundamental component for ensuring the security of devices from digital attacks. Recognition of new developing digital threats is getting harder for existing IDS. Furthermore, advanced frameworks are required for IDS to function both efficiently and effectively. The commonly observed cyber-attacks in the business domain include minor attacks used for stealing private data. This article presents a deep learning methodology for detecting cyber-attacks on the Internet of Things using a Long Short Term Networks classifier. Our extensive experimental testing show an Accuracy of 99.09%, F1-score of 99.46%, and Recall of 99.51%, respectively. A detailed metric representing our results in tabular form was used to compare how our model was better than other state-of-the-art models in detecting cyber-attacks with proficiency.

scholarly journals Predictive maintenance as an internet of things enabled business model: A taxonomy

2020 ◽  
Author(s):  
Jens Passlick ◽  
Sonja Dreyer ◽  
Daniel Olivotti ◽  
Lukas Grützner ◽  
Dennis Eilers ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Internet Of Things ◽  
Manufacturing Industry ◽  
Business Models ◽  
Predictive Maintenance ◽  
Sensor Data ◽  
Visualization Technique ◽  
Hardware Development ◽  
The Internet Of Things ◽  
Different Levels

Abstract Predictive maintenance (PdM) is an important application of the Internet of Things (IoT) discussed in many companies, especially in the manufacturing industry. PdM uses data, usually sensor data, to optimize maintenance activities. We develop a taxonomy to classify PdM business models that enables a comparison and analysis of such models. We use our taxonomy to classify the business models of 113 companies. Based on this classification, we identify six archetypes using cluster analysis and discuss the results. The “hardware development”, “analytics provider”, and “all-in-one” archetypes are the most frequently represented in the study sample. For cluster analysis, we use a visualization technique that involves an autoencoder. The results of our analysis will help practitioners assess their own business models and those of other companies. Business models can be better differentiated by considering the different levels of IoT architecture, which is also an important implication for further research.

scholarly journals Exploiting Smart Contracts for Capability-Based Access Control in the Internet of Things

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1793 ◽  
Author(s):  
Yuta Nakamura ◽  
Yuanyu Zhang ◽  
Masahiro Sasabe ◽  
Shoji Kasahara
Keyword(s):  
Internet Of Things ◽  
Access Control ◽  
Human Life ◽  
The Internet ◽  
Monetary Cost ◽  
Fine Grained ◽  
Smart Contract ◽  
Gas Consumption ◽  
Access Rights ◽  
The Internet Of Things

Due to the rapid penetration of the Internet of Things (IoT) into human life, illegal access to IoT resources (e.g., data and actuators) has greatly threatened our safety. Access control, which specifies who (i.e., subjects) can access what resources (i.e., objects) under what conditions, has been recognized as an effective solution to address this issue. To cope with the distributed and trust-less nature of IoT systems, we propose a decentralized and trustworthy Capability-Based Access Control (CapBAC) scheme by using the Ethereum smart contract technology. In this scheme, a smart contract is created for each object to store and manage the capability tokens (i.e., data structures recording granted access rights) assigned to the related subjects, and also to verify the ownership and validity of the tokens for access control. Different from previous schemes which manage the tokens in units of subjects, i.e., one token per subject, our scheme manages the tokens in units of access rights or actions, i.e., one token per action. Such novel management achieves more fine-grained and flexible capability delegation and also ensures the consistency between the delegation information and the information stored in the tokens. We implemented the proposed CapBAC scheme in a locally constructed Ethereum blockchain network to demonstrate its feasibility. In addition, we measured the monetary cost of our scheme in terms of gas consumption to compare our scheme with the existing Blockchain-Enabled Decentralized Capability-Based Access Control (BlendCAC) scheme proposed by other researchers. The experimental results show that the proposed scheme outperforms the BlendCAC scheme in terms of the flexibility, granularity, and consistency of capability delegation at almost the same monetary cost.

scholarly journals PRICHAIN: A Partially Decentralized Implementation of UbiPri Middleware Using Blockchain

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4483 ◽  
Author(s):  
Iago Sestrem Ochôa ◽  
Luis Augusto Silva ◽  
Gabriel de Mello ◽  
Bruno Alves da Silva ◽  
Juan Francisco de Paz ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Data Storage ◽  
Personal Information ◽  
Distributed Storage ◽  
The Internet ◽  
Storage Security ◽  
User Data ◽  
And Performance ◽  
The Internet Of Things ◽  
Different Levels

With the popularization of the Internet-of-Things, various applications have emerged to make life easier. These applications generate a large amount of user data. Analyzing the data obtained from these applications, one can infer personal information about each user. Considering this, it is clear that ensuring privacy in this type of application is essential. To guarantee privacy various solutions exist, one of them is UbiPri middleware. This paper presents a decentralized implementation of UbiPri middleware using the Ethereum blockchain. Smart contracts were used in conjunction with a communication gateway and a distributed storage service to ensure users privacy. The results obtained show that the implementation of this work ensures privacy at different levels, data storage security, and performance regarding scalability in the Internet of Things environments.

Data’s Intimacy

2020 ◽  
pp. 76-113
Author(s):  
Sun-ha Hong
Keyword(s):  
Internet Of Things ◽  
The Self ◽  
The Internet ◽  
Care Of The Self ◽  
The People ◽  
The World ◽  
Smart Machines ◽  
Market Driven ◽  
The Internet Of Things ◽  
Better Than

Today, machines observe, record, and sense the world—not just for us but also often instead of us and indifferently to our meaning. The intertwined problems of technological knowledge and (our) knowledge of technology manifest in the growing industry of smart machines, the Internet of Things, and other means for self-tracking. The automation of the care of the self is buoyed by a popular fantasy of data’s intimacy, of machines that know you better than yourself. Yet as the technology becomes normalized, the hacker ethic gives way to a market-driven shift in which more and more of “my” personal truth is colonized by machines (and the people behind the machines) that I cannot question.

Transitioning from testbeds to ships: an experience study in deploying the TIPPERS Internet of Things platform to the US Navy

2020 ◽  
pp. 154851292095638 ◽  
Author(s):  
Dave Archer ◽  
Michael A August ◽  
Georgios Bouloukakis ◽  
Christopher Davison ◽  
Mamadou H Diallo ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Situational Awareness ◽  
Differential Privacy ◽  
Fall Detection ◽  
Information Warfare ◽  
Privacy And Security ◽  
Fine Grained ◽  
The Us ◽  
Privacy Enhancing Technologies ◽  
The Internet Of Things

This paper describes the collaborative effort between privacy and security researchers at nine different institutions along with researchers at the Naval Information Warfare Center to deploy, test, and demonstrate privacy-preserving technologies in creating sensor-based awareness using the Internet of Things (IoT) aboard naval vessels in the context of the US Navy’s Trident Warrior 2019 exercise. Funded by DARPA through the Brandeis program, the team built an integrated IoT data management middleware, entitled TIPPERS, that supports privacy by design and integrates a variety of Privacy Enhancing Technologies (PETs), including differential privacy, computation on encrypted data, and fine-grained policies. We describe the architecture of TIPPERS and its use in creating a smart ship that offers IoT-enabled services such as occupancy analysis, fall detection, detection of unauthorized access to spaces, and other situational awareness scenarios. We describe the privacy implications of creating IoT spaces that collect data that might include individuals’ data (e.g., location) and analyze the tradeoff between privacy and utility of the supported PETs in this context.

Precise Correlation Extraction for IoT Fault Detection With Concurrent Activities

2021 ◽  
Vol 20 (5s) ◽  
pp. 1-21
Author(s):  
Gyeongmin Lee ◽  
Bongjun Kim ◽  
Seungbin Song ◽  
Changsu Kim ◽  
Jong Kim ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Fault Detection ◽  
Time Window ◽  
The Internet ◽  
Fine Grained ◽  
Detection And Identification ◽  
Extraction Scheme ◽  
Iot Devices ◽  
The Internet Of Things

In the Internet of Things (IoT) environment, detecting a faulty device is crucial to guarantee the reliable execution of IoT services. To detect a faulty device, existing schemes trace a series of events among IoT devices within a certain time window, extract correlations among them, and find a faulty device that violates the correlations. However, if a few users share the same IoT environment, since their concurrent activities make non-correlated devices react together in the same time window, the existing schemes fail to detect a faulty device without differentiating the concurrent activities. To correctly detect a faulty device in the multiple concurrent activities, this work proposes a new precise correlation extraction scheme, called PCoExtractor. Instead of using a time window, PCoExtractor continuously traces the events, removes unrelated device statuses that inconsistently react for the same activity, and constructs fine-grained correlations. Moreover, to increase the detection precision, this work newly defines a fine-grained correlation representation that reflects not only sensor values and functionalities of actuators but also their transitions and program states such as contexts. Compared to existing schemes, PCoExtractor detects and identifies 40.06% more faults for 4 IoT services with concurrent activities of 12 users while reducing 80.3% of detection and identification times.

IoT Limitations and Concerns Relative to 5G Architecture

2020 ◽  
pp. 45-69
Author(s):  
Ohood Saud Althobaiti
Keyword(s):  
Internet Of Things ◽  
The Internet ◽  
Transmission Power ◽  
Research Issues ◽  
Fifth Generation ◽  
Power Frequency ◽  
Device Lifetime ◽  
Distinguishing Features ◽  
The Internet Of Things ◽  
Different Levels

Several current computer science applications, implemented within specific paradigms, work at different levels to solve various challenges facing particular sectors. The potential of the internet of things (IoT) in the context of fifth-generation networks (5G) is envisioned as suggesting several beneficial opportunities for companies, industries, and users to exploit this technology's applications. This chapter establishes how the IoT works, considering its 5G architecture. The emphasis is on the infrastructural characteristic in terms of transmission power, frequency, speed, security, localization, device lifetime, and others. Additionally, the chapter illustrates what the IoT entails, discussing its workability and efficiency. Furthermore, it highlights a range of newly distinguishing features that would give it much-touted success in comparison with other technologies. It also presents research issues and challenges.

An Anti-Collision Algorithm Based on Slot Location and Tag Estimation

2013 ◽  
Vol 756-759 ◽  
pp. 3394-3398
Author(s):  
Yong Liu ◽  
Xing Zhong Xiong
Keyword(s):  
Internet Of Things ◽  
The Internet ◽  
Test Results ◽  
Traditional Algorithm ◽  
The Internet Of Things ◽  
Better Than

RFID is one of the most important technologies in the Internet of Things (IoT), which has been widely used in all kinds of fields. However, a key problem is the tag collision when multiple tags reflect simultaneously to a reader. In traditional algorithm, slot location is always ignored. We present an anti-collision algorithm based on tag estimation and slot location in this paper. The key technique can be divided into two parts: precisely tag estimation and slot location. Simulation and test results show that the proposed anti-collision algorithm performs better than the traditional algorithm despite its simplicity.

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