Spatial-temporal Basis of Sensory Data for 5G Internet of Things Networks

2021 ◽  
Author(s):  
Xiangping Gu ◽  
Mingxue Zhu ◽  
Liyun Zhuang
Keyword(s):  
Internet Of Things ◽  
Sensory Data ◽  
Temporal Basis

scholarly journals Human Activity Recognition with Deep Learning: Overview, Challenges & Possibilities

2021 ◽  
Author(s):  
Pranjal Kumar
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Activity Recognition ◽  
Human Activity ◽  
Recognition System ◽  
The Internet ◽  
Learning Approaches ◽  
The Public ◽  
Sensory Data ◽  
The Internet Of Things

The growing use of sensor tools and the Internet of Things requires sensors to understand the applications. There are major difficulties in realistic situations, though, that can impact the efficiency of the recognition system. Recently, as the utility of deep learning in many fields has been shown, various deep approaches were researched to tackle the challenges of detection and recognition. We present in this review a sample of specialized deep learning approaches for the identification of sensor-based human behaviour. Next, we present the multi-modal sensory data and include information for the public databases which can be used in different challenge tasks for study. A new taxonomy is then suggested, to organize deep approaches according to challenges. Deep problems and approaches connected to problems are summarized and evaluated to provide an analysis of the ongoing advancement in science. By the conclusion of this research, we are answering unanswered issues and providing perspectives into the future.

scholarly journals A Review of Semantic Sensor Technologies in Internet of Things Architectures

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-21 ◽  
Author(s):  
Gergely Marcell Honti ◽  
Janos Abonyi
Keyword(s):  
Internet Of Things ◽  
Essential Role ◽  
Contextual Information ◽  
Data Sources ◽  
Intelligent Sensor ◽  
Semantic Metadata ◽  
Sensory Data ◽  
Intelligent Sensors ◽  
High Level

Intelligent sensors should be seamlessly, securely, and trustworthy interconnected to enable automated high-level smart applications. Semantic metadata can provide contextual information to support the accessibility of these features, making it easier for machines and humans to process the sensory data and achieve interoperability. The unique overview of sensor ontologies according to the semantic needs of the layers of IoT solutions can serve a guideline of engineers and researchers interested in the development of intelligent sensor-based solutions. The explored trends show that ontologies will play an even more essential role in interlinked IoT systems as interoperability and the generation of controlled linkable data sources should be based on semantically enriched sensory data.

scholarly journals Towards Efficient Data Collection in Space-Based Internet of Things

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5523 ◽  
Author(s):  
Changjiang Fei ◽  
Baokang Zhao ◽  
Wanrong Yu ◽  
Chunqing Wu
Keyword(s):  
Data Collection ◽  
Internet Of Things ◽  
Weather Data ◽  
Data Set ◽  
Sensory Data ◽  
Spatiotemporal Correlation ◽  
Exact Data ◽  
Efficient Data ◽  
Global Coverage ◽  
Efficient Data Collection

Due to the strong anti-destructive ability, global coverage, and independent infrastructure of the space-based Internet of Things (S-IoT), it is one of the most important ways to achieve a real interconnection of all things. In S-IoT, a single satellite can often achieve thousands of kilometers of coverage and needs to provide data transmission services for massive ground nodes. However, satellite bandwidth is usually low and the uplink and downlink bandwidth is extremely asymmetric. Therefore, exact data collection is not affordable for S-IoT. In this paper, an approximate data collection algorithm is proposed for S-IoT; that is, the sampling-reconstruction (SR) algorithm. Since the uplink bandwidth is very limited, the SR algorithm samples only the sensory data of some nodes and then reconstructs the unacquired data based on the spatiotemporal correlation between the sensory data. In order to obtain higher data collection precision under a certain data collection ratio, the SR algorithm optimizes the sampling node selection by leveraging the curvature characteristics of the sensory data in time and space dimensions. Moreover, the SR algorithm innovatively applies spatiotemporal compressive sensing (ST-CS) technology to accurately reconstruct unacquired sensory data by making full use of the spatiotemporal correlation between the sensory data. We used a real-weather data set to evaluate the performance of the SR algorithm and compared it with two existing representative approximate data collection algorithms. The experimental results show that the SR algorithm is well-suited for S-IoT and can achieve efficient data collection under the condition that the uplink bandwidth is extremely limited.

Composite Identity of Things (CIDoT) on Permissioned Blockchain Network for Identity Management of IoT Devices

2021 ◽  
pp. 59-80
Author(s):  
Anang Hudaya Muhamad Amin ◽  
Fred N. Kiwanuka ◽  
Nabih T. J. Abdelmajid ◽  
Saif Hamad AlKaabi ◽  
Sultan Khalid Abdulqader Rashed Ahli
Keyword(s):  
Energy Efficiency ◽  
Internet Of Things ◽  
Complex Systems ◽  
Identity Management ◽  
Autonomous Systems ◽  
Green Technology ◽  
Comprehensive Approach ◽  
Device Identification ◽  
Sensory Data ◽  
Iot Devices

Internet of things (IoT) is in the forefront of many existing smart applications, including autonomous systems and green technology. IoT devices have been commonly used in the monitoring of energy efficiency and process automation. As the application spreads across different kinds of applications and technology, a large number of IoT devices need to be managed and configured, as they are capable of generating massive amount of sensory data. Looking from this perspective, there is a need for a proper mechanism to identify each IoT devices within the system and their respective applications. Participation of these IoT devices in complex systems requires a tamper-proof identity to be generated and stored for the purpose of device identification and verification. This chapter presents a comprehensive approach on identity management of IoT devices using a composite identity of things (CIDoT) with permissioned blockchain implementation. The proposed approach described in this chapter takes into account both physical and logical domains in generating the composite identity.

scholarly journals A Randomized Watermarking Technique for Detecting Malicious Data Injection Attacks in Heterogeneous Wireless Sensor Networks for Internet of Things Applications

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4346 ◽  
Author(s):  
Arwa Alromih ◽  
Mznah Al-Rodhaan ◽  
Yuan Tian
Keyword(s):  
Internet Of Things ◽  
Early Stage ◽  
Homomorphic Encryption ◽  
Original Data ◽  
Sensory Data ◽  
Iot Applications ◽  
Injection Attacks ◽  
Power Resources ◽  
Heterogeneous Wireless Sensor Networks ◽  
The Military

Using Internet of Things (IoT) applications has been a growing trend in the last few years. They have been deployed in several areas of life, including secure and sensitive sectors, such as the military and health. In these sectors, sensory data is the main factor in any decision-making process. This introduces the need to ensure the integrity of data. Secure techniques are needed to detect any data injection attempt before catastrophic effects happen. Sensors have limited computational and power resources. This limitation creates a challenge to design a security mechanism that is both secure and energy-efficient. This work presents a Randomized Watermarking Filtering Scheme (RWFS) for IoT applications that provides en-route filtering to remove any injected data at an early stage of the communication. Filtering injected data is based on a watermark that is generated from the original data and embedded directly in random places throughout the packet’s payload. The scheme uses homomorphic encryption techniques to conceal the report’s measurement from any adversary. The advantage of homomorphic encryption is that it allows the data to be aggregated and, thus, decreases the packet’s size. The results of our proposed scheme prove that it improves the security and energy consumption of the system as it mitigates some of the limitations in the existing works.

scholarly journals IoT-Stream: A Lightweight Ontology for Internet of Things Data Streams and Its Use with Data Analytics and Event Detection Services

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 953 ◽  
Author(s):  
Tarek Elsaleh ◽  
Shirin Enshaeifar ◽  
Roonak Rezvani ◽  
Sahr Thomas Acton ◽  
Valentinas Janeiko ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Data Streams ◽  
System Architecture ◽  
System Approach ◽  
Data Analytics ◽  
Common Knowledge ◽  
Semantic Model ◽  
Stream Data ◽  
Sensory Data ◽  
Stream Model

With the proliferation of sensors and IoT technologies, stream data are increasingly stored and analysed, but rarely combined, due to the heterogeneity of sources and technologies. Semantics are increasingly used to share sensory data, but not so much for annotating stream data. Semantic models for stream annotation are scarce, as generally, semantics are heavy to process and not ideal for Internet of Things (IoT) environments, where the data are frequently updated. We present a light model to semantically annotate streams, IoT-Stream. It takes advantage of common knowledge sharing of the semantics, but keeping the inferences and queries simple. Furthermore, we present a system architecture to demonstrate the adoption the semantic model, and provide examples of instantiation of the system for different use cases. The system architecture is based on commonly used architectures in the field of IoT, such as web services, microservices and middleware. Our system approach includes the semantic annotations that take place in the pipeline of IoT services and sensory data analytics. It includes modules needed to annotate, consume, and query data annotated with IoT-Stream. In addition to this, we present tools that could be used in conjunction to the IoT-Stream model and facilitate the use of semantics in IoT.

scholarly journals Opportunistic Mobile Sensing in the Fog

2018 ◽  
Vol 2018 ◽  
pp. 1-18 ◽  
Author(s):  
Rolando Menchaca-Mendez ◽  
Brayan Luna-Nuñez ◽  
Ricardo Menchaca-Mendez ◽  
Arturo Yee-Rendon ◽  
Rolando Quintero ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Distributed Algorithms ◽  
Mobile Sensing ◽  
Sensory Data ◽  
Sensing Applications ◽  
Sensing Platforms ◽  
Simulation Based ◽  
Detailed Simulation ◽  
Software Platforms ◽  
Personal Devices

The increasing adoption of mobile personal devices and Internet of Things devices is leveraging the emergence of a wide variety of opportunistic sensing applications. However, the designers of this type of applications face a set of technical challenges related to the limitations and heterogeneity of the hardware and software platforms and to the dynamics of the scenarios where they are deployed. In this paper, we introduce a Semantic-Centric Fog-based framework aimed at effectively and efficiently supporting this type of applications. The proposed framework is composed of local and distributed algorithms that support the establishment and coordination of sensing tasks in the Fog. First, it performs ontology-driven in-network processing to locate the most adequate devices to carry out a given sensing task and then, it establishes efficient multihop routes that are used to coordinate relevant devices and to transport the collected sensory data to Fog sinks. We present a set of theorems that prove that the proposed algorithms are correct and the results of a series of detailed simulation-based experiments in NS3 that characterize the performance of the proposed platform. The results show that the proposed framework outperforms traditional sensing platforms that are based on centralized services.

scholarly journals Generic framework for industrial 4.0 applications based on internet of things

2020 ◽  
Vol 3 (SI1) ◽  
pp. First
Author(s):  
Nhan Trong Le ◽  
Nguyen Tran Huu Nguyen ◽  
Pham Le Song Ngan
Keyword(s):  
Operating System ◽  
Internet Of Things ◽  
Low Cost ◽  
Complete Solution ◽  
Sensor Nodes ◽  
Smart Devices ◽  
Sensory Data ◽  
Generic Framework ◽  
Serial Connection ◽  
Smart Agriculture

The Internet of Things (IoTs) is a network of interconnected devices, transportations, home appliances, and other devices. They are functionally embedded in electronics, software, sensors, actuators, and connectivity that allows them to connect and exchange information. On the basis of the IoT concept, implementations are gradually being proposed in a range of areas, ranging from smart house, smart office and smart agriculture. In this research paper, a generic framework for smart monitoring applications based on the IoTs network is proposed. In this framework, low-powered sensor nodes are based on the micro:bit platform, providing a multiple footprints for different sensor connections. The wireless capability on micro:bit provides a complete solution to deploy the system in such places that wire is impractical to draw. The data is wirelessly gathered by a basestation node that is powered by Android Things operating system provided by Google. This operating system is based on the Android platform for smart devices and Internet of Things products. The approach to this framework indicates a low cost and minimum setup and especially amenable for applications control. To support many applications with minimum modifications, the framework is designed for easy expansion by supporting popular serial connection ports, including the Universal Asynchronous Receiver/Transmitter and Serial Peripheral Interface. With these connections, on one line data bus, several sensors can be added to match the different application requirements. In this paper, our platform is validated for an automatic water monitoring in aquaculture based on the temperature, pH and dissolved oxygen sensory data. Through our framework, the data is uploaded to a cloud for remote monitoring and providing alarms for users whenever the data is out of a predefined safe domain.

scholarly journals Big Data and Its V’s with IoT to Develop Sustainability

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Xiao Chu ◽  
Shah Nazir ◽  
Kunhao Wang ◽  
Zeqi Leng ◽  
Wajeeha Khalil
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Information Technologies ◽  
Modern Society ◽  
Automatic Monitoring ◽  
Smart Devices ◽  
Comprehensive Overview ◽  
Sensory Data ◽  
Sustainable Environment ◽  
Iot Devices

Internet of Things (IoT) has been considered as one of the emerging network and information technologies that can comprehend automatic monitoring, identification, and management through a network of smart IoT devices. The effective use of IoT in different areas has improved efficiency and reduced errors. The rapid growth of smart devices such as actuators, sensors, and wearable devices has made the IoT enable for smart and sustainable developments in the area. Physical objects are interlinked with these smart devices for the progression to analyse, process, and manage the surroundings data. Such data can then be further utilised for smarter decisions and postanalysis for different purposes. However, with the limited IoT resources, the management of data is difficult due to the restrictions of transmission power place and energy consumption, and the processing can put pressure on these smart devices. The network of IoT is connected with big data through Internet for manipulating and storing huge bulk of data on cloud storage. The secure framework based on big data through IoT is the awful need of modern society which can be energy efficient in a sustainable environment. Due to the intrinsic characteristics of sensors nodes in the IoT, like data redundancy, constrained energy, computing capabilities, and limited communication range, the issues of data loss are becoming among the main issues which mostly depend on the completeness of data. Various approaches are in practice for the recovery problem of data, such as spatiotemporal correlation and interpolation. These are used for data correlation and characteristics of sensory data. Extracting correlation data became difficult specifically as the coupling degree between diverse perceptual attributes is low. The current study has presented a comprehensive overview on big data and its V’s with Internet of Things to describe the research into the area with in-depth review of existing literature.

scholarly journals A Randomized Watermarking Technique for Detecting Malicious Data Injection Attacks in Heterogeneous Wireless Sensor Networks for Internet of Things Applications

2018 ◽  
Author(s):  
Arwa Alromih ◽  
Mznah Al-Rodhaan ◽  
Yuan Tian
Keyword(s):  
Internet Of Things ◽  
Early Stage ◽  
Homomorphic Encryption ◽  
Original Data ◽  
Sensory Data ◽  
Iot Applications ◽  
Injection Attacks ◽  
Power Resources ◽  
Heterogeneous Wireless Sensor Networks ◽  
Main Factor

Using Internet of Things (IoT) applications has been a growing trend in the last few years. They have been deployed in several areas of life including secure and sensitive sectors like military and health. In these sectors, sensory data is the main factor in any decision-making process. This introduces the need to ensure the integrity of data. Secure techniques are needed to detect any data injection attempt before catastrophic effects happen. Sensors have limited computational and power resources. This limitation creates a challenge to design a security mechanism that is both secure and energy-efficient. This work presents a Randomized Watermarking Filtering Scheme (RWFS) for IoT applications that provides en-route filtering to get rid of any injected data at an early stage of the communication. Filtering injected data is based on a watermark that is generated from the original data and embedded directly in random places throughout the packet's payload. The scheme uses homomorphic encryption techniques to conceal the report's measurement from any adversary. The advantage of homomorphic encryption is that it allows the data to be aggregated and, thus, decreases the packet's size. The results of our proposed scheme proved that it improves the security and energy consumption of the system as it mitigates some of the limitations in the existing works.

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