Efficient IoT Research Articles

VOICE CONTROLLED VEHICLE USING MQTT SERVER

The voice-controlled vehicle was created to make human work easier, since we live in an artificial intelligence-driven world where robots perform many tasks. The human voice is used to drive the vehicle. A stable android mobile application built with android studio software transmits the speech. It's essentially a Wi-Fi link. Using the mobile application, we can operate the vehicle with our voice from anywhere. The NodeMCU IoT framework is free and open source. It includes firmware for the ESP8266 Wi-Fi module, a Espressif Systems SoC, and ESP-12 module hardware. With the application that has been created, human work may become simpler. Since the vehicle will be linked to Wi-Fi, we will be able to access it from any location in our project. He or she will use the Android application to send commands or voice commands such as forward, backward, left, right, left forward, left backward, right forward, right backward, and right forward, right backward. The pins have been connected to the NodeMCU esp8266, and the code to control the car has been written. When an object or vehicle inhibits the car's movement, an ultrasonic sensor is used to stop it. The voice is recognised and forwarded to the HiveMQ server, where it is processed. HiveMQ's MQTT broker is optimised for cloud native deployments to take advantage of cloud resources. The use of MQTT reduces the amount of bandwidth used to transport data across the network. Low overall operating costs are a result of efficient IoT solutions. The hivemq server receives the command from the Android application and sends it to the NodeMCU microcontroller, which programmes the code to drive the vehicle. The voicecontrolled robot vehicle project has military, surveillance, and human applications in scope. It's a voice-activated wireless robot vehicle. The project's main goal is to guide the robotic vehicle to a specific location. In addition, the project's main goal is to use voice to control the robot. It is now possible to have

Energy Efficient Downlink Resource Allocation in Cellular IoT Supported H-CRANs

The cloud computing supported heterogeneous cloud radio access network (H-CRAN) is one of the promising solutions to support cellular IoT devices with the legacy cellular systems. However, the dense deployment of small cells with fractional frequency reuse in orthogonal frequency division multiple access (OFDMA) based H-CRANs increases intra- and inter-cell interference, turning the resource allocation into a more challenging problem. In general, the macro cell users are considered as the legacy users, whereas the cellular IoT devices and small cell users share the macro cell users' resource blocks in an underlaid approach. In this paper, we investigate an underlaid approach of resource allocation for small and macro cell users to improve the energy efficiency (EE) in H-CRANs. The solution approaches are derived with the Dinkelbach, Lagrange multiplier and Alternating Direction Method of Multipliers (ADMM) methods by considering the maximum power, resource block allocation, fronthaul capacity and quality of service (QoS) constraints of macro cell users. A two-step energy efficient underlaid cellular IoT (UC-IoT) supported H-CRAN method is proposed and evaluated with the overlaid cellular IoT (OC-IoT) supported H-CRAN and underlaid H-CRAN without cellular IoT devices. The proposed method is evaluated in terms of energy efficiency and the Jain's fairness index, considering the effect of number of cellular IoT density in each small cell of the H-CRAN. The simulation results demonstrate the effectiveness of the proposed approach compared to earlier approaches.

Bandwidth Efficient IoT Traffic Shaping Technique for Protecting Smart Home Privacy from Data Breaches in Wireless LAN

Bandwidth Efficient IoT Traffic Shaping Technique for Protecting Smart Home Privacy from Data Breaches in Wireless LAN

Efficient IOT based Water Quality Prediction Using Cat Swarm Optimized Neural Network classification

Water is the most significant sources for human life, but, it is in serious threat of contamination by life itself. The protection and availability of drinking-water are major worries throughout the globe. In this work,anIOT based solution isintroduced to check and predict the water quality and alert the user before the water gets polluted. The proposed system uses IoT and optimized neural network for prediction. It consists of various embedded sensors like conductivity, pH, turbidity and color. The measured sensor values are stored in the database and further directed for prediction analysis. The Cat swarm optimization (CSO) based neural network algorithm is used for forecasting thequality result. The proposed system alerts the user when any of themeasured parameters are lesser than the fixed thresholds. This technique can also be implemented in water plants, rivers and industries.

A Survey on Long-Range Wide-Area Network Technology Optimizations

Long-Range Wide-Area Network (LoRaWAN) enables flexible long-range service communications with low power consumption which is suitable for many IoT applications. The densification of LoRaWAN, which is needed to meet a wide range of IoT networking requirements, poses further challenges. For instance, the deployment of gateways and IoT devices are widely deployed in urban areas, which leads to interference caused by concurrent transmissions on the same channel. In this context, it is crucial to understand aspects such as the coexistence of IoT devices and applications, resource allocation, Media Access Control (MAC) layer, network planning, and mobility support, that directly affect LoRaWAN’s performance. We present a systematic review of state-of-the-art works for LoRaWAN optimization solutions for IoT networking operations. We focus on five aspects that directly affect the performance of LoRaWAN. These specific aspects are directly associated with the challenges of densification of LoRaWAN. Based on the literature analysis, we present a taxonomy covering five aspects related to LoRaWAN optimizations for efficient IoT networks. Finally, we identify key research challenges and open issues in LoRaWAN optimizations for IoT networking operations that must be further studied in the future.

Massive connectivity with machine learning for the Internet of Things

Driven by the need to ensure the connectivity of an unprecedentedly huge number of IoT devices with no human intervention the issues of massive connectivity have recently become one of the main research areas in IoT studies. Conventional wireless communication technologies are designed for Human-to-Human (H2H) communication which leads to major problems in primary access, channel utilization and spectrum efficiency when massive numbers of devices require connectivity. Current random access procedures are based on a four-step handshaking with control messages which contradicts the requirements of IoT applications in terms of small data payloads and low complexity. Targeted channel utilization and spectrum efficiency cannot be achieved using traditional orthogonal approaches. Thus the goal of our work is to review the most recent developments and critically evaluate the existing work related to the evolution of network access methods in the new communication era. The paper covers three major aspects: first the primary random access procedures, proposed for IoT communications are discussed. The second aspect focuses on the approaches for integration of existing random multiple access schemes with non-orthogonal multiple access methods (NOMA). This integration of random access procedures with NOMA opens a new research trend in the field of massive connectivity. Operating on space domains additional to the physical domain such as code and power domains, NOMA integration targets increased channel utilization and spectrum efficiency to complement the flexibility of random access. On the other hand, the design of efficient algorithms for massive connectivity in IoT is also challenged by the highly application and environmentally dependent traffic model. A new angle of tackling this problem has emerged thanks to the extensive developments in machine learning and the possibilities of their incorporation in communication networks. Thus, the final aspect this review paper addresses are the newly emerging research directions of incorporating machine learning (ML) methods for providing efficient IoT connectivity. Breakthrough ML techniques allow wireless networking devices to perform transmissions by learning and building knowledge about the communication and networking environment. A critical evaluation of the large body of work accumulated in this area in the most recent years and outlining of some major open research issues concludes the paper.

A Two-Class Data Transmission Method Using a Lightweight Blockchain Structure for Secure Smart Dust IoT Environments.

In smart dust IoT environments, a large number of devices with low computing power/resources are deployed to collect surrounding information. There are many issues to consider for an efficient and secure smart dust IoT environment. Sometimes the urgent sensed data needs to be transmitted immediately. In addition, there are potential problems related to security issues since the smart dust IoT systems may be deployed in hard-to-access areas. In this paper, we propose an effective transmission method for two-class sensed data for secure smart IoT systems. We divide the sensed data into two classes which consist of the urgent sensed data class (requiring urgent data transmission) and the normal sensed data class (with a slight transmission delay due to yielding to the urgent data transmission). In addition, for security reasons, the proposed transmission method uses two kinds of blockchains with the following two ledgers: (1) the urgent sensed data ledger, which is a ledger of data that needs urgent transmission; and (2) the normal sensed data ledger, which is a ledger of data that allows some delay. To be specific, the lightweight blockchain based on our earlier work is used for the normal sensed data transmission, whereas the modified conventional blockchain is used for the normal sensed data transmission. The experiments show that the performance of the proposed transmission method is better than the conventional transmission method in almost all sections. There is a 53% performance increase on average with regard to the transmission time. When the ratio of urgent sensed data is 0% (i.e., no urgent sensed data at all), the proposed transmission method is greater improved by as much as about 96%. This means that the lightweight blockchain scheme used in the proposed transmission method for the normal sensed data is very efficient.

Secure and Efficient Cloud-based IoT Authenticated Key Agreement scheme for e-Health Wireless Sensor Networks

With the advancement and innovations in mobile and wireless communication technologies including cloud computing and Internet of Things, the paradigms of dispensing heath-based services have massively transformed. The telecare medical information system (TMIS) or wireless body area networks technology frameworks facilitate patients to remotely receive medical treatment from physicians through the Internet without paying a visit to the hospital. In order to ensure the medical privacy of the patients and the verification of authenticity of all entities in TMIS-based system before exchange of sensitive credentials and diagnosis reports, many TMIS-based authentication protocols have been presented so far; however, there is a still need of more efficient and secure key agreements. Recently, Karthegaveni et al. demonstrated a remote health care monitoring protocol with the use of elliptic curve cryptography crypto-primitives. However, we discover many drawbacks in their protocol including replay attack, denial of service attack, lacking mutual authenticity between the client and server. Moreover, the scheme has several technical limitations. We propose an efficient and secure TMIS-based protocol employing lightweight symmetric key operations. The contributed model is proven by rigorous formal security analysis, while its security features are also validated under automated ProVerif tool. The proposed scheme supports 38% more security features on average than compared schemes. The performance evaluation also depicts that the demonstrated and comparative findings are in the favour of the proposed model.

An efficient IoT based smart farming system using machine learning algorithms

This paper suggests an IoT based smart farming system along with an efficient prediction method called WPART based on machine learning techniques to predict crop productivity and drought for proficient decision support making in IoT based smart farming systems. The crop productivity and drought predictions is very important to the farmers and agriculture’s executives, which greatly help agriculture-affected countries around the world. Drought prediction plays a significant role in drought early warning to mitigate its impacts on crop productivity, drought prediction research aims to enhance our understanding of the physical mechanism of drought and improve predictability skill by taking full advantage of sources of predictability. In this work, an intelligent method based on the blend of a wrapper feature selection approach, and PART classification technique is proposed for crop productivity and drought predicting. Five datasets are used for estimating the proposed method. The results indicated that the projected method is robust, accurate, and precise to classify and predict crop productivity and drought in comparison with the existing techniques. From the results, the proposed method proved to be most accurate in providing drought prediction as well as the productivity of crops like Bajra, Soybean, Jowar, and Sugarcane. The WPART method attains the maximum accuracy compared to the existing supreme standard algorithms, it is obtained up to 92.51%, 96.77%, 98.04%, 96.12%, and 98.15% for the five datasets for drought classification, and crop productivity respectively. Likewise, the proposed method outperforms existing algorithms with precision, sensitivity, and F Score metrics.

AIoT with PUF: A Concrete Security

Artificial Intelligence in contrast to Natural Intelligence also known as Machine Intelligence is intelligence revealed by machine. It is the science and engineering of making machines intelligent. Therefore, it is a technique that makes a machine work like humans. The IOT Internet of Things is a network of internet-connected objects which can connect and exchange data. The combination of AI and IoT called AIoT is the combination of Artificial Intelligence and Internet of Things to achieve more efficient IoT operations. When Artificial Intelligence is added to IoT it means that the devices can analyze data and make decisions and act accordingly without the intervention of humans. The combination of AI and IOT has several advantages like saving money, building deeper customer relationships, increased operational efficiency and productivity and enhanced security and safety. This research paper focuses on what is AIoT, its applications and challenges and further, it also focuses on AIoT security concern and how can we solve the security problem with the use of PUF which is hardware security which is a simple and fast solution for security purpose. PUF is also more compatible with AIoT gadgets. Attacks on IoT devices are on the upsurge. Physical Unclonable functions (PUFs) are recognized as a robust and mild-weight way for AIoT.

Joint Optimization in Cached-Enabled Heterogeneous Network for Efficient Industrial IoT

In the era of industrial 4.0, industrial Internet of Things (IIoT) has brought essential changes to human society. For IIoT, communication in network can be defined as the basic condition for further development and integrated information exchange. In this way, cached-enabled heterogeneous industrial network is necessary to be optimized. In this paper, we consider the optimal geographical placement of contents in cache-enabled heterogeneous networks to minimize the total missing probability. And the probability represents that typical user cannot find requested file in the nearby base stations (BSs). In contract to existing works which only concern content placement, we jointly optimize content placement at BSs and activation densities of BSs of different tiers subject to the cache size limits and the constraint on the BSs energy consumption cost. In addition, the user distribution in this work is modeled by a homogeneous Poisson Point Process. We prove that the original optimization problem can be transformed to a convex problem. The convexity of the optimization problem allows us to apply the KKT conditions to derive useful analytical results of the optimal solution. Based on this, we propose a low-complexity near-optimal algorithm to find the approximated content placement probabilities. We further extend the optimization to heterogeneous networks with the user distribution modeled by the modified Cluster Process. Extensive simulation results show the superior performance of joint optimization of content placement and BSs activation densities compared to only optimizing content placement.

Efficient and Compressive IoT based Health Care System for Parkinson’s Disease Patient

There are various diseases arising in the world with new unexpected areas. Each disease has its own symptoms and cures. Some people are capable to fight against these diseases but most of them are a part of the attacks done by the diseases. This paper introduces the Parkinson’s disease patient with the idea to work their body normally with the help of artificial life to their neurons by adding a chip known as Electroencephalography (EEG) into their brains that work in the form of wearable devices. This is required for those people who are suffering from this disease and could not able to communicate with another people just because they cannot able to send their signals properly to the brain because they do not get the signals on time because of dead neurons. This paper is going to introduce architecture to work on the brain by using several chips, which help to send signals and become a mediator/ transmitter who sends to do work.

3D node deployment strategies prediction in wireless sensors network

ABSTRACT3D Deployment plays a fundamental role in setting up efficient wireless sensor networks (WSNs) and IoT networks. In general, WSN are widely utilised in a set of real contexts such as monitoring smart houses and forest fires with parachuted sensors. This study focus on planned 3D deployment in which the sensor nodes must be accurately positioned at predetermined locations to optimise one or more design objectives under some given constraints. The purpose of planned deployment is to identify the type, the number, and the locations of nodes to optimise the coverage, the connectivity and the network lifetime. There have been a large number of studies that proposed algorithms resolving the premeditated problem of 3D deployment. The objective of this study is twofold. The first one is to present the complexity of 3D deployment and then detail the types of sensors, objectives, applications and recent research that concerns the strategy used to solve this problem. The second one is to present a comparative survey between the recent optimisation strategies solving the problem of 3D deployment in WSN. Based on our extensive review, we discuss the strengths and limitations of each proposed solution and compare them in terms of WSN design factors.

Protocol translation middleware for scalable and efficient IoT communications

Protocol translation middleware for scalable and efficient IoT communications

High Efficient IoT based modern soil composition identifier for smart yield farming

In Systematic agriculture the accurate readings of soil nutrients are needed, which includes specific crop management. The nutrients applications are adjusted accordingly on requirement basis. The fertility of the soil decides the essential amount of nutrients and water adequacy for proper yield. In soil chemical testing, analytical laboratories provide insufficient data, only few testing are carried out and other essentials were ignored. To enhances from this, our idea deals with complete soil.

Cloud-assisted green IoT-enabled comprehensive framework for wildfire monitoring

Wildfires are one of the most destructive disasters that have the ability of causing enormous loss to life and nature. Moreover, with its capability to spread abruptly over huge sectors of land, the loss to mankind is unimaginable. Global warming around the world has led to increase in the wildfires, therefore demands immediate attention of the concerned organizations. Conspicuously, this research aims at predicting the forest fires to minimize the loss and immediate actions in the direction of safety. Specifically, this research proposes an energy efficient IoT framework backed by fog-cloud computing technology for early prediction of wildfires. Initially, Jaccard similarity analysis is used to determine the redundant data acquired from IoT devices in real-time. This data is analyzed at fog computing layer and reduces multi-dimensional data to single value termed as Vulnerability Index. Finally, Artificial Neural Network is used to predict the vulnerability on forest region based on Wildfire Causing Parameters. ANN model is appended with Self-Organized mapping technique for effective visualization of geographical region with respect to wildfire vulnerability. Implementation simulation is performed over different datasets. Results are compared with several state-of-the-art techniques for overall performance estimation.

Integrating IoT Modern Communication Architectures into the New Generation of VR/MR Environments

This paper presents how modern IoT communication architectures can be successfully used in building both virtual and mixed reality scenarios. The IoT architecture enables the use of LPWAN technologies with a limited Maximum Transmission Unit for an efficient IoT data provision. Based on the IoT communication architecture, this paper will present two study cases, where IoT data can be used in developing complex Mixed Reality (MR) / Virtual Reality (VR) environments. First study case will present how Mixed Reality can be applied for the development of an application based on Microsoft HoloLens© headset. The MR application will allow mixing virtual and real elements for creating an improved context related to possible indoor emergencies getting information at real-time from distributed IoT devices. The MR application that will be presented will cover four topics: virtual evacuation drills, media content display, real-time sensors information access and domotic actions. Second study case will present a solution for building VR environments used for overhead crane training operations based on shipping ports sensors information This paper will emphasize on the fact that based on IoT sensor information it is possible to move from the development of static MR/VR environments, where real, external conditions don’t influence at all the virtual context, to the creation of dynamically generated environments, based on IoT data. This work is developed in the ongoing Smart City 3D simulation and monitoring platform (CITISIM) Itea3 project where MR/VR services are being developed in the smart city domain.

A short-term energy prediction system based on edge computing for smart city

Abstract The development of Internet of Things technologies has provided potential for real-time monitoring and control of environment in smart cities. In the field of energy management, energy prediction can be carried out by sensing and analyzing dynamic environmental information of the energy consumption side, and provide decision support for energy production to avoid excess or insufficient energy supply and achieve agile production. However, due to the complexity and diversity of the IoT data, it is difficult to build an efficient energy prediction system that reflects the dynamics of the IoT environment. To address this problem, a short-term energy prediction system based on edge computing architecture is proposed, in which data acquisition, data processing and regression prediction are distributed in sensing nodes, routing nodes and central server respectively. Semantics and stream processing techniques are utilized to support efficient IoT data acquisition and processing. In addition, an online deep neural network model adapted to the characteristics of IoT data is implemented for energy prediction. A real-world case study of energy prediction in a regional energy system is given to verify the feasibility and efficiency of our system. The results show that the system can provide support for real-time energy prediction with high precision in a promising way.

Data Transmission Reduction Schemes in WSNs for Efficient IoT Systems

Spatial and temporal correlation among the generated traffic in wireless sensor networks (WSNs) can be exploited in reducing the energy consumption of continuous sensor data collection. Dual prediction (DP) and data compression (DC) schemes rely on the spatio-temporal correlation to reduce the number of transmissions across WSNs, which leads to conserving energy and bandwidth. In this paper, we present both schemes in a two-tier data reduction framework. The DP scheme is used to reduce transmissions between cluster nodes and cluster heads, while the DC scheme is used to reduce traffic between cluster heads and sink nodes. For both schemes, various algorithms will be studied and compared in terms of accuracy, delay, and transmission reduction percentage. For the DP scheme, neural networks (NNs) and long short-term memory networks (LSTMs) are proposed to perform predictions. The training phase of the NNs and LSTMs is done online which is necessary in the DP scheme. The performance will be compared to popular least-mean-square approaches. Regarding the DC scheme, principal component analysis, non-negative matrix factorization, truncated-singular value decomposition, and discrete wavelet transform will be discussed and compared. This paper focuses on comparative analysis of various data reduction algorithms alongside the proposed ones. Finally, design challenges and open research areas for having more transmission reductions will be presented.

Smart application-aware IoT data collection

We present and experimentally evaluate procedures for efficient IoT data collection while achieving target requirements in terms of data accuracy, response time, energy, and privacy protection. Different strategies are considered because different IoT applications can have different requirements. Specifically, the accuracy-driven strategy adjusts the time period between consecutive measurements following an additive increase and multiplicative decrease (AIMD) scheme based on a target data accuracy, while the time-driven strategy adjusts the time period between measurement requests to achieve delay less than a given maximum delay between consecutive measurements. The energy-driven strategy considers both the data accuracy and the energy costs for the corresponding measurements. Finally, the privacy-driven strategy adds noise to measurements using differential privacy techniques. The experimental evaluation involves real temperature, humidity, and ozone (O3) measurements obtained from three testbeds through the FIESTA-IoT platform. Our results show that the AIMD adaptation of the measurement period is robust to different types of measurements from different testbeds, without having any tuning parameters. Also, the experimental results show the trade-offs between the target data accuracy and the number of measurements and between the target data accuracy and the corresponding energy costs. For the privacy-driven strategy, the results show that the addition of noise to the sensor measurements using differential privacy has a negligible effect on the aggregate statistics.