Development of a Low-Cost Internet-of-Things (IoT) System for Monitoring Soil Water Potential Using Watermark 200SS Sensors

Soil moisture monitoring is one of the methods that farmers can use for irrigation scheduling. Many sensor types and data logging systems have been developed for this purpose over the years, but their widespread adoption in practical irrigation scheduling is still limited due to a variety of factors. Important factors limiting adoption of soil moisture sensing technology by farmers include high cost and difficulties in timely data collection and interpretation. Recent developments in open source microcontrollers (such as Arduino), wireless communication, and Internet-of-Things (IoT) technologies offer opportunities for reducing cost and facilitating timely data collection, visualization, and interpretation for farmers. Therefore, the objective of this study was to develop and test a low-cost IoT system for soil moisture monitoring using Watermark 200SS sensors. The system uses Arduino-based microcontrollers and data from the field sensors (End Nodes) are communicated wirelessly using LoRa radios to a receiver (Coordinator), which connects to the Internet via WiFi and sends the data to an open-source website (ThingSpeak.com) where the data can be visualized and further analyzed using Matlab. The system was successfully tested under field conditions by installing Watermark sensors at four depths in a wheat field. The system described here could contribute to widespread adoption of easy-to-use and affordable moisture sensing technologies among farmers.

Towards machine-learning-driven effective mashup recommendations from big data in mobile networks and the Internet-of-Things

A large number of Web APIs have been released as services in mobile communications,but the service provided by a single Web API is usually limited.To enrich the services in mobile communications,developers have combined Web APIs and developed a new service,which is known as a mashup.The emergence of mashups greatly increases the number of services in mobile communications,especially in mobile networks and the Internet-of-Things(IoT),and has encouraged companies and individuals to develop even more mashups,which has led to the dramatic increase in the number of mashups.Such a trend brings with it big data,such as the massive text data from the mashups themselves and continually-generated usage data.Thus,the question of how to determine the most suitable mashups from big data has become a challenging problem.In this paper,we propose a mashup recommendation framework from big data in mobile networks and the IoT.The proposed framework is driven by machine learning techniques,including neural embedding,clustering,and matrix factorization.We employ neural embedding to learn the distributed representation of mashups and propose to use cluster analysis to learn the relationship among the mashups.We also develop a novel Joint Matrix Factorization(JMF)model to complete the mashup recommendation task,where we design a new objective function and an optimization algorithm.We then crawl through a real-world large mashup dataset and perform experiments.The experimental results demonstrate that our framework achieves high accuracy in mashup recommendation and performs better than all compared baselines.

Distributed Consensus for Blockchains in Internet-of-Things Networks

In recent years,due to the wide implementation of mobile agents,the Internet-of-Things(IoT) networks have been applied in several real-life scenarios,servicing applications in the areas of public safety,proximity-based services,and fog computing.Meanwhile,when more complex tasks are processed in IoT networks,demands on identity authentication,certifiable traceability,and privacy protection for services in IoT networks increase.Building a blockchain system in IoT networks can greatly satisfy such demands.However,the blockchain building in IoT brings about new challenges compared with that in the traditional full-blown Internet with reliable transmissions,especially in terms of achieving consensus on each block in complex wireless environments,which directly motivates our work.In this study,we fully considered the challenges of achieving a consensus in a blockchain system in IoT networks,including the negative impacts caused by contention and interference in wireless channel,and the lack of reliable transmissions and prior network organizations.By proposing a distributed consensus algorithm for blockchains on multi-hop IoT networks,we showed that it is possible to directly reach a consensus for blockchains in IoT networks,without relying on any additional network layers or protocols to provide reliable and ordered communications.In our theoretical analysis,we showed that our consensus algorithm is asymptotically optimal on time complexity and is energy saving.The extensive simulation results also validate our conclusions in the theoretical analysis.

Reinforcement learning based energy-efficient internet-of-things video transmission

The video transmission in the Internet-of-Things(IoT)system must guarantee the video quality and reduce the packet loss rate and the delay with limited resources to satisfy the requirement of multimedia services.In this paper,we propose a reinforcement learning based energy-efficient IoT video transmission scheme that protects against interference,in which the base station controls the transmission action of the IoT device including the encoding rate,the modulation and coding scheme,and the transmit power.A reinforcement learning algorithm state-action-reward-state-action is applied to choose the transmission action based on the observed state(the queue length of the buffer,the channel gain,the previous bit error rate,and the previous packet loss rate)without knowledge of the transmission channel model at the transmitter and the receiver.We also propose a deep reinforcement learning based energy-efficient IoT video transmission scheme that uses a deep neural network to approximate Q value to further accelerate the learning process involved in choosing the optimal transmission action and improve the video transmission performance.Moreover,both the performance bounds of the proposed schemes and the computational complexity are theoretically derived.Simulation results show that the proposed schemes can increase the peak signal-to-noise ratio and decrease the packet loss rate,the delay,and the energy consumption relative to the benchmark scheme.

Design Framework of Unsourced Multiple Access for 6G Massive IoT

In this paper,ambient IoT is used as a typical use case of massive connections for the sixth generation(6G)mobile communications where we derive the performance requirements to facilitate the evaluation of technical solutions.A rather complete design of unsourced multiple access is proposed in which two key parts:a compressed sensing module for active user detection,and a sparse interleaver-division multiple access(SIDMA)module are simulated side by side on a same platform at balanced signal to noise ratio(SNR)operating points.With a proper combination of compressed sensing matrix,a convolutional encoder,receiver algorithms,the simulated performance results appear superior to the state-of-the-art benchmark,yet with relatively less complicated processing.

Multi-UAV Network Assisted Intelligent Edge Computing:Challenges and Opportunities

Introducing multi-UAV network with flexible deployment into mobile edge computing(MEC)can effectively improve the quality of service of Internet-of-Things services,reduce the coverage cost and resource waste rate of edge nodes,and also bring some challenges.This paper first introduces the current situation and pain points of mobile edge computing,then analyzes the significance and value of using multi-UAV network to assist mobile edge computing,and summarizes its key technologies and typical applications.In the end,some open research problems and technology prospects of multi-UAV network assisted intelligent edge computing are put forward,which provide new ideas for the future development of this field.

Artificial intelligence in seismology:Advent,performance and future trends

Realistically predicting earthquake is critical for seismic risk assessment,prevention and safe design of major structures.Due to the complex nature of seismic events,it is challengeable to efficiently identify the earthquake response and extract indicative features from the continuously detected seismic data.These challenges severely impact the performance of traditional seismic prediction models and obstacle the development of seismology in general.Taking their advantages in data analysis,artificial intelligence(AI) techniques have been utilized as powerful statistical tools to tackle these issues.This typically involves processing massive detected data with severe noise to enhance the seismic performance of structures.From extracting meaningful sensing data to unveiling seismic events that are below the detection level,AI assists in identifying unknown features to more accurately predicting the earthquake activities.In this focus paper,we provide an overview of the recent AI studies in seismology and evaluate the performance of the major AI techniques including machine learning and deep learning in seismic data analysis.Furthermore,we envision the future direction of the AI methods in earthquake engineering which will involve deep learning-enhanced seismology in an internet-of-things(IoT) platform.

Real-time air pollution monitoring with sensors on city bus

This paper presents an experimental study on real-time air pollution monitoring using wireless sensors on public transport vehicles.The study is part of the GreenIoT project in Sweden,which utilizes Internet-of-Things to measure air pollution level in the city center of Uppsala.Through deploying low-cost wireless sensors,it is possible to obtain more fine-grained and real-time air pollution levels at different locations.The sensors on public transport vehicles complement the readings from stationary sensors and the only ground level monitoring station in Uppsala.The paper describes the deployment of wireless sensors on Uppsala buses and the integration of the mobile sensor network with the GreenIoT testbed.Extensive experiments have been conducted to evaluate the communication quality and data quality of the system.

An Arduino-Based Wireless Sensor Network for Soil Moisture Monitoring Using Decagon EC-5 Sensors

It is undeniable that wireless communication technology has become a very important component of modern society. One aspect of modern society in which application of wireless communication technologies has tremendous potential is in agricultural production. This is especially true in the area of sensing and transmission of relevant farming information such as weather, crop development, water quantity and quality, among others, which would allow farmers to make more accurate and timely farming decisions. A good example would be the application of wireless communication technology to transmit soil moisture data in real time to help farmers make irrigation scheduling decisions. Although many systems are commercially available for soil moisture monitoring, there are still many important factors, such as cost, limiting widespread adoption of this technology among growers. Our objective in this study was, therefore, to develop and test an affordable wireless communication system for monitoring soil moisture using Decagon EC-5 sensors. The new system uses Arduino-compatible microcontrollers and communication systems to sample and transmit values from four Decagon EC-5 soil moisture sensors. Developing the system required conducting lab calibrations for the EC-5 sensors for the microcontroller operating in either 10-bit or 12-bit analog-to-digital converter (ADC) resolution. The system was successfully tested in the field and reliably collected and transmitted data from a wheat field for more than two months.

A Distributed ADMM Approach for Collaborative Regression Learning in Edge Computing

With the recent proliferation of Internet-of-Things(IoT),enormous amount of data are produced by wireless sensors and connected devices at the edge of network.Conventional cloud computing raises serious concerns on communication latency,bandwidth cost,and data privacy.To address these issues,edge computing has been introduced as a new paradigm that allows computation and analysis to be performed in close proximity with data sources.In this paper,we study how to conduct regression analysis when the training samples are kept private at source devices.Specifically,we consider the lasso regression model that has been widely adopted for prediction and forecasting based on information gathered from sensors.By adopting the Alternating Direction Method of Multipliers(ADMM),we decompose the original regression problem into a set of subproblems,each of which can be solved by an IoT device using its local data information.During the iterative solving process,the participating device only needs to provide some intermediate results to the edge server for lasso training.Extensive experiments based on two datasets are conducted to demonstrate the efficacy and efficiency of our proposed scheme.

Joint optimization for secure ambient backscatter communication in NOMA-enabled IoT networks

Non-Orthogonal Multiple Access(NOMA)has emerged as a novel air interface technology for massive connectivity in Sixth-Generation(6G)era.The recent integration of NOMA in Backscatter Communication(BC)has triggered significant research interest due to its applications in low-powered Internet of Things(IoT)networks.However,the link security aspect of these networks has not been well investigated.This article provides a new optimization framework for improving the physical layer security of the NOMA ambient BC system.Our system model takes into account the simultaneous operation of NOMA IoT users and the Backscatter Node(BN)in the presence of multiple EavesDroppers(EDs).The EDs in the surrounding area can overhear the communication of Base Station(BS)and BN due to the wireless broadcast transmission.Thus,the chief aim is to enhance link security by optimizing the BN reflection coefficient and BS transmit power.To gauge the performance of the proposed scheme,we also present the suboptimal NOMA and conventional orthogonal multiple access as benchmark schemes.Monte Carlo simulation results demonstrate the superiority of the NOMA BC scheme over the pure NOMA scheme without the BC and conventional orthogonal multiple access schemes in terms of system secrecy rate.

Voting Classifier and Metaheuristic Optimization for Network Intrusion Detection

Managing physical objects in the network’s periphery is made possible by the Internet of Things(IoT),revolutionizing human life.Open attacks and unauthorized access are possible with these IoT devices,which exchange data to enable remote access.These attacks are often detected using intrusion detection methodologies,although these systems’effectiveness and accuracy are subpar.This paper proposes a new voting classifier composed of an ensemble of machine learning models trained and optimized using metaheuristic optimization.The employed metaheuristic optimizer is a new version of the whale optimization algorithm(WOA),which is guided by the dipper throated optimizer(DTO)to improve the exploration process of the traditionalWOA optimizer.The proposed voting classifier categorizes the network intrusions robustly and efficiently.To assess the proposed approach,a dataset created from IoT devices is employed to record the efficiency of the proposed algorithm for binary attack categorization.The dataset records are balanced using the locality-sensitive hashing(LSH)and Synthetic Minority Oversampling Technique(SMOTE).The evaluation of the achieved results is performed in terms of statistical analysis and visual plots to prove the proposed approach’s effectiveness,stability,and significance.The achieved results confirmed the superiority of the proposed algorithm for the task of network intrusion detection.

Smart all-time vision:The battery-free video communication for urban administration and law enforcement

The Chinese government is dedicated to enhancing the level of informatization in administrative law enforcement to ensure fairness and increase credibility.Currently,law enforcement has exposed such problems as insufficient force,difficulties in collecting evidence,and low evidential power.These problems contradict the principles of rule of law and standardization.Many local governments have implemented advanced information technologies for urban administration and law enforcement,including big data,artificial intelligence,and IoT.Among these technologies,IoT is the core communication infrastructure for innovative law enforcement platforms.However,traditional video communications rely on batteries or dedicated power sources,leading to maintenance challenges and high power consumption.In this paper,we propose AT-Eye,a new video communication paradigm for all-time law enforcement video monitoring,which is battery-free and high-throughput.The key enabler of AT-Eye is ambient backscatter technology,which enables energy harvesting and video communication simultaneously.Unlike previous methods,our proposal introduces a novel CCK-based modulation for tag cameras and achieves 4-way concurrency.The modulation is simple because it uses phase difference and it is energyefficient because generating square waves is adequate.Moreover,to ensure full compatibility with COTS WiFi,we modulate the physical service data unit.Therefore,data recovery is possible using only commercial NICs.We conduct comprehensive experiments to examine our proposal and experiment results show that AT-Eye achieves a throughput of 10.8 Mbps with COTS radios.We also simulate a 16-way battery-free tag camera system,demonstrating AT-Eye’s feasibility of high-definition video communication.

Network Intrusion Detection Based on Feature Selection and Hybrid Metaheuristic Optimization

Applications of internet-of-things(IoT)are increasingly being used in many facets of our daily life,which results in an enormous volume of data.Cloud computing and fog computing,two of the most common technologies used in IoT applications,have led to major security concerns.Cyberattacks are on the rise as a result of the usage of these technologies since present security measures are insufficient.Several artificial intelligence(AI)based security solutions,such as intrusion detection systems(IDS),have been proposed in recent years.Intelligent technologies that require data preprocessing and machine learning algorithm-performance augmentation require the use of feature selection(FS)techniques to increase classification accuracy by minimizing the number of features selected.On the other hand,metaheuristic optimization algorithms have been widely used in feature selection in recent decades.In this paper,we proposed a hybrid optimization algorithm for feature selection in IDS.The proposed algorithm is based on grey wolf(GW),and dipper throated optimization(DTO)algorithms and is referred to as GWDTO.The proposed algorithm has a better balance between the exploration and exploitation steps of the optimization process and thus could achieve better performance.On the employed IoT-IDS dataset,the performance of the proposed GWDTO algorithm was assessed using a set of evaluation metrics and compared to other optimization approaches in 2678 CMC,2023,vol.74,no.2 the literature to validate its superiority.In addition,a statistical analysis is performed to assess the stability and effectiveness of the proposed approach.Experimental results confirmed the superiority of the proposed approach in boosting the classification accuracy of the intrusion in IoT-based networks.

A Model Transformation Approach for Detecting Distancing Violations in Weighted Graphs

This work presents the design of an Internet of Things(IoT)edge-based system based on model transformation and complete weighted graph to detect violations of social distancing measures in indoor public places.Awireless sensor network based on Bluetooth Low Energy is introduced as the infrastructure of the proposed design.A hybrid model transformation strategy for generating a graph database to represent groups of people is presented as a core middleware layer of the detecting system’s proposed architectural design.A Neo4j graph database is used as a target implementation generated from the proposed transformational system to store all captured real-time IoT data about the distances between individuals in an indoor area and answer user predefined queries,expressed using Neo4j Cypher,to provide insights from the stored data for decision support.As proof of concept,a discrete-time simulation model was adopted for the design of a COVID-19 physical distancing measures case study to evaluate the introduced system architecture.Twenty-one weighted graphs were generated randomly and the degrees of violation of distancing measures were inspected.The experimental results demonstrate the capability of the proposed system design to detect violations of COVID-19 physical distancing measures within an enclosed area.

IoT Testbed Business Model

The Internet-of-Things (IoT) is today one of the hypes in the technological world but despite the enormous attention and research investment, the clear business value is still hard to perceive. IoT deployments are costly to be installed, managed and maintained, and need to provide a very clear value to justify the investments. For another viewpoint, IoT technologies need to be proven before deployment, which implies the need to test and assess IoT solutions in real settings and involve the actual target users. And as such, this presents an opportunity to have IoT deployments with a clear business model mainly focused on real-life large-scale research and technological experimentation. This would mean having a sustainable IoT infrastructure in-place based on the provision of experimentation services and a trial environment to industry and research, which then could also present an opportunity to establish added-value (business) services. This is the exact idea of the flagship SmartSantander testbed facility and especially its major deployment in the city of Santander, Spain. The SmartSantander facility business model is built around experimentally-driven research and technology development thus attracting many experimenters from industry and European research projects. This model makes it possible to sustain an outstanding large-scale IoT deployment of around 12,000 sensors and on top of it the development of new the development of new services and applications especially targeting the needs of users (citizens, businesses, authorities) in smart-cities. This paper studies the business model of outstanding SmartSantander facility in order to provide a generic Business Model for IoT testbeds that can provide guidance and be adapted by owners (or owners to-be) wishing to exploit their IoT deployments as facilities supporting experimentation and trials of IoT solutions.

StableFP:NN-Based Hardware Fingerprint Extractor for LoRa Device Identification

Hardware fingerprint is a new dimension of security mechanisms in low power wide area networks(LPWANs).It is hard to emulate for attackers and does not increase the computing and energy burden of transmitters.long range(LoRa)is a long-range communication technology designed for battery-powered devices.In practice,LoRa is vulnerable to malicious attacks such as replace attack.Therefore,the hardware fingerprint is an excellent supplementary mechanism of LoRa security.However,the variable wireless environment contaminates the extracted fingerprints.The long wireless channel adds a large amount of the environment dependent information to the hardware features extracted from LoRa devices.In this paper,we propose StableFP which is a neural network(NN)based device identifier for long range wide area network(LoRaWAN).StableFP extracts stable and representative hardware features from channel frequency response(CFR)as the fingerprint,and it eliminates the environment dependent information caused by wireless environments.We implement StableFP on a software defined radio(SDR)testbed which consists of 4 commercial LoRa nodes.The result demonstrates that StableFP achieves over 90%identification accuracy in unseen environments under an over 5 dB signal to noise ratio(SNR).

Approximate Data Aggregation in Sensor Equipped IoT Networks

As Internet-of-Things(IoT) networks provide efficient ways to transfer data, they are used widely in data sensing applications. These applications can further include wireless sensor networks. One of the critical problems in sensor-equipped IoT networks is to design energy efficient data aggregation algorithms that address the issues of maximum value and distinct set query. In this paper, we propose an algorithm based on uniform sampling and Bernoulli sampling to address these issues. We have provided logical proofs to show that the proposed algorithms return accurate results with a given probability. Simulation results show that these algorithms have high performance compared with a simple distributed algorithm in terms of energy consumption.

What Is Semantic Communication?A View on Conveying Meaning in the Era of Machine Intelligence

In the 1940s,Claude Shannon developed the information theory focusing on quantifying the maximum data rate that can be supported by a communication channel.Guided by this fundamental work,the main theme of wireless system design up until the fifth generation(5G)was the data rate maximization.In Shannon’s theory,the semantic aspect and meaning of messages were treated as largely irrelevant to communication.The classic theory started to reveal its limitations in the modern era of machine intelligence,consisting of the synergy between Internet-of-things(IoT)and artificial intelligence(AI).By broadening the scope of the classic communication-theoretic framework,in this article,we present a view of semantic communication(SemCom)and conveying meaning through the communication systems.We address three communication modalities:human-to-human(H2H),human-to-machine(H2M),and machine-to-machine(M2M)communications.The latter two represent the paradigm shift in communication and computing,and define the main theme of this article.H2M SemCom refers to semantic techniques for conveying meanings understandable not only by humans but also by machines so that they can have interaction and“dialogue”.On the other hand,M2M SemCom refers to effective techniques for efficiently connecting multiple machines such that they can effectively execute a specific computation task in a wireless network.The first part of this article focuses on introducing the SemCom principles including encoding,layered system architecture,and two design approaches:1)layer-coupling design;and 2)end-to-end design using a neural network.The second part focuses on the discussion of specific techniques for different application areas of H2M SemCom[including human and AI symbiosis,recommendation,human sensing and care,and virtual reality(VR)/augmented reality(AR)]and M2M SemCom(including distributed learning,split inference,distributed consensus,and machine-vision cameras).Finally,we discuss the approach for designing SemCom systems based on knowledge graphs.We believe that this comprehensive introduction will provide a useful guide into the emerging area of SemCom that is expected to play an important role in sixth generation(6G)featuring connected intelligence and integrated sensing,computing,communication,and control.

Reality over Web:Pervasive Computing Meets the Web

Reality over Web （ROW） is a novel concept, where a window on the web corresponds to a window onto a real space. Once the correspondence is established, users should be able to interact or manipulate the objects or people in the real space through the web window. In this paper, we introduce the RoW concept and highlight the principles that govern its design and implementation. A system architecture for realizing the RoW concept is described along with a proof-of-concept prototype that implements portions of the RoW concept. One essential part of an RoW implementation is accurate Iocationing of objects and people in a video frame. The Iocationing problem becomes particularly challenging because we want to reuse existing infrastructure as much as possible. We developed a high-frequency sound-based Iocationing scheme and implemented it on the prototype. The results from initial experiments performed on the Iocationing scheme are reported here.