Important Internet Of Things Research Articles

Genetic Algorithm based Secure Hybrid Routing Technique for IoT Framework

Internet is gaining new heights in today’s technological environment. Internet of things (IoT) is a new class of Internet-basedheterogeneous networked application systems that uses various types of sensors/detectors and devices for the exchange and collection of data. Due to their applications in situations such as building home automation, emergencies, crisis management, energy management, and healthcare, path optimization and message security becomes of topmost importance in IoT. An optimized routing scheme using intelligent mathematical techniques including Genetic Algorithms (GA) and Analytical Hierarchy Process (AHP), is proposed here and an optimized route can be encrypted using cryptographic techniques. From the simulation results, it has been found that the overall efficiency of the IoT system can be greatly improved with the proposed model. A comparison is also provided in the discussion section which demonstrates that hybrid algorithms developed for IoT systems perform much better than traditional routing algorithms.

IoT Approach for Ensuring Safety in smart Cities for Energy Saving

The security of city dwellers is of the utmost importance due to the increasing number of smart cities and the speed with which they are being developed. As a whole, smart city safety can be improved through the use of the Internet of Things (IoT), which is the focus of this article. Several safety concerns, such as public safety, emergency management, and disaster preparedness, can be monitored and addressed with the help of the Internet of Things (IoT) framework's network of interconnected devices, sensors, and data analytics. In order to improve situational awareness, reaction times, and overall safety results, this research dives into important Internet of Things (IoT) applications such smart sensors, real-time analytics, and surveillance systems. In addition to looking at potential energy savings, the study analyses the pros and cons of using an Internet of Things (IoT) safety infrastructure in smart cities.

Tensor Recurrent Neural Network With Differential Privacy

Recurrent neural network (RNN), a branch of deep learning, is a powerful model for sequential data that has outstanding performance on a wide range of important Internet of Things (IoT) tasks. This unprecedented growth of RNN model has however encountered both heterogeneous IoT data and privacy issues. Existing RNN model can not deal with heterogeneous sequential data; often the larger datasets used in training of RNN model contain sensitive information. To tackle these challenges and for the first time, this research proposes a novel differentially private tensor-based RNN (DPTRNN) that can be applied in many challenging deep learning sequence tasks for IoT systems. Specifically, to process heterogeneous sequential data, we propose a tensor-based RNN model. To guarantee privacy, we develop a tensor-based back-propagation through time algorithm with perturbation to avoid exposing the sensitive information for training the tensor-based RNN model within the framework of differential privacy. Thorough security analysis shows that the differential private tensor-based RNN efficiently protects the confidentiality of sensitive user information for IoT. Our results from extensive experiments on two challenging large video datasets suggest that our proposed scheme is practical with guarantee of data privacy preservation and acceptable accuracy loss.

A Genetic Algorithm Based Hybrid Routing Technique for IOT Systems

Internet is gaining new heights in the today’s technological environment. Internet of things (IoT) is a new class of Internet based heterogeneous networked application systems which uses various types of sensor/detectors and devices for the exchange and collection of data. Devices need to be in the radio range and must remain continuously connected to the internet. IoT can sense and control the objects remotely across existing networked architecture and builds chances for direct combination among the physical world and computer-based systems. It is believed that, in future many of the household application will be based on IoT. Due to their applications in situations such as building home automation, emergencies, crisis management, energy management and healthcare, message security becomes of top importance in IoT. An optimized routing scheme using the intelligent mathematical techniques, which includes Genetic Algorithms(GA) and Analytical Hierarchy Process(AHP), is proposed here and an optimized route can be encrypted using cryptanalytic techniques. Simulation results of GA and AHP are also presented here for the proposed network. It has been found that overall efficiency of the IoT system can be greatly improved with the proposed model. A comparison is also provided in discussion section which demonstrates that hybrid algorithms developed for IoT systems performs much better than traditional routing algorithms. Keywords: IoT; AHP;GA; Cryptanalytic Techniques; WSN

Evaluating dynamics, security, and performance metrics for smart manufacturing

The role of the Internet of Things (IoT) in Smart Manufacturing, aimed to illuminate its transformative impact on operational efficiency, responsiveness, and environmental sustainability. The aim of the investigation was to explore IoT's pivotal significance in reshaping manufacturing processes towards heightened efficiency, responsiveness, and environmental consciousness. The study presents results from performance metric assessments, visualizations, and data simulations. It contains information about way IoT data is shown in manufacturing environments, the clear relationship between temperature and pressure, the distribution of security risks and related safety measures, and the dynamic behaviour of important IoT components. The importance of IoT in real-time environmental management, process optimization, and security enhancement within paradigms of smart manufacturing are the key points of observation. The comparative analysis of Conventional Analytical Models and Composite Models highlights the choice between stability and adaptability, providing crucial insights for modeling approaches tailored to distinct manufacturing requirements. IoT's transformative potential within Smart Manufacturing, emphasizing data integrity, security, sensor dynamics, analytics, and sustainability.

DAC4SH: A Novel Data Access Control Scheme for Smart Home Using Smart Contracts

Smart home (s-home) is an important Internet of Things (IoT)-based application in improving the living environment. Many cryptography-based protection schemes have been proposed to guarantee data confidentiality in s-home. Access control is a promising method to protect generated data from unauthorized access, and it is also urgently needed in s-home. Current centralized access control schemes are not excellent in security and performance. For instance, it still faces the problems of a single point of failure, low reliability, and poor scalability. This study proposes a decentralized and reliable access control scheme for s-home using smart contracts, named DAC4SH. To be specific, this proposed framework consists of an access policy management contract (APMC), a data attribute management contract (DAMC), a subject attribute management contract (SAMC), and a data access control contract (DACC) for realizing the fine-grained data access control. Meanwhile, we record all the access activities into the immutable distributed ledgers for auditing. To verify the feasibility of our DAC4SH, we construct an Ethereum-based prototype system and evaluate the performance of DAC4SH in terms of computational and communication costs. According to the experimental results, we can conclude that the performance of DAC4SH is appropriate.

Using Security Centre in Indoor Internet of Things

The Internet of things (IoT) facilitated various aspects of people’s daily lives. In simple terms, the IoT refers to the connection of sensors, actuators, and devices to a network by which they can interact with each other and their users. It is a practical model that works on connecting a group of sensors and actuators that form a network that works to carry out several tasks, such as collecting information from the surrounding environment and processing it and providing the beneficiary of the network with this critical information is used in many cases in making important decisions and missions. Over time, the importance of IoT will increase due to its use in many fields. This technology is expected to become an essential milestone in developing many sectors and sciences. Therefore, to benefit from IoT technology, security in the IoT must focus on raising its efficiency. The security of IoT is a big challenge because of its complexity, heterogeneity, and a large number of related resources. It is necessary to improve security protocols to guarantee IoT connection and ensure all devices in this network are trusted and prevent an attacker from accessing any device in the network because these attacks penetrate privacy and expose the personal property to damage. This research introduces a new framework to improve the confidentiality, integrity, authenticity, and availability of IoT devices in indoor environments, by forcing new devices (IoT-MD), middleware (Security Centre SC), and protocols (add a new authorized device and IoT Traffic Control Protocol IoT-TCP). Security in IoT will be guaranteed by using this framework which enforces isolation between the IoT devices and networks to prevent unauthorized IoT devices. Moreover, prevent attacks in the IoT indoor environment.

Grouping based radio frequency identification anti-collision protocols for dense internet of things application

<span lang="EN-US">Radio frequency identification (RFID) is an important internet of things (IoT) enabling technology. In RFIDs collision occur among tags because tags share communication channel. This is called tag collision problem. The problem becomes catastrophic when dense population of tags are deployed like in IoT. Hence, the need to enhance existing dynamic frame slotted ALOHA (DFSA) based electronic product code (EPC) C1G2 media access control (MAC) protocol. Firstly, this paper validates through simulation the DFSA theory that efficiency of the RFID system is maximum when the number tags is approximately equal to the frame size. Furthermore, literature review shows tag grouping is becoming popular to improving the efficiency of the RFID system. This paper analyzes selected grouping-based algorithms. Their underlining principles are discussed including their tag estimation methods. The algorithms were implemented in MATLAB while extensive Monte Carlo simulation was performed to evaluate their strengths and weaknesses. Results show that with higher tag density, fuzzy C-means based algorithm (FCMBG) outperformed traditional DFSA by over 40% in terms of throughput rate. The results also demonstrate FCMBG bettered other grouping-based algorithms (GB-DFSA and GBSA) whose tag estimation method are based on collision slots in terms slot efficiency by over 10% and also in terms of identification time.</span>

Privacy leakage of LoRaWAN smart parking occupancy sensors

Development of smart cities is enabled by its core concepts of smart and sustainable mobility, where Low Power Wide Area Network (LPWAN) such as Long Range Wide Area Network (LoRaWAN) became one of the most important Internet of Things (IoT) technologies. Due to its low power consumption, simple setup, and large communication range, LoRaWAN smart parking devices are already employed to reduce congestion and improve quality of life. This paper studies privacy leakage of LoRaWAN smart parking communication devices. Namely, when a vehicle as a metallic obstacle obscures the LoRaWAN smart parking device, the signal strength will be significantly reduced on the receiver side. Consequently, the variation in the signal strength of LoRaWAN parking systems transmits information about parking space occupancy, allowing the implementation of a passive side-channel attack at large distances. Using supervised machine learning techniques based on Neural Network, the attacker can estimate parking lot occupancy with accuracy up to 97%, while Random Forrest approach reaches the accuracy over 98%.

A Multiscale CNN Framework for Wireless Technique Classification in Internet of Things

Wireless technique classification (WTC) is of crucial importance in Internet of Things for realizing efficient spectrum sharing and interference management. However, the existing deep-learning-based methods have low classification accuracy, especially at low signal-to-noise ratio levels. In this article, a multiscale convolutional neural network framework is proposed for WTC. A multiscale module is exploited to capture the higher abstraction features. Simulation results demonstrate that our proposed scheme can achieve a better classification performance and a higher convergence speed compared to the state-of-the-art schemes.

Towards an efficient big data management schema for IoT

Internet of things (IoT) is an essential technology in our life; the importance of IoT is yearly increasing because of the excellent usage value. IoT management can help stakeholders in analyzing and making the right decisions based on previous historical sensed data. However, some challenges emerge while using the IoT that will be more complicated in the future. Data management is one of the significant challenges that is facing IoT technology. The growth of the number of sensors will increase the generated data (Big Data). In a few years, the problem of analyzing, processing, and storing such data will become a highly complex process. Due to the mentioned challenges, in this paper, we propose a new schema to efficiently store the structured IoT data to improve the performance of analyzing and retrieving the data. The main idea about the proposed schema is performed in the data preprocessing step by grouping the data into different levels without losing any single value (lossless compression). We evaluate our proposed schema using eight other datasets in terms of storage size and processing time; our results show that the proposed schema outperforms the traditional storing method for all datasets.

Role of IoT-Cloud Ecosystem in Smart Cities : Review and Challenges

Smart Cities is one of the most important Internet of Things (IoT) applications. Billions of smart devices on IoT produce volumes of data directed to cloud for storage and processing. Sending complete data to cloud is least preferred from resource utilization perspective comprising of bandwidth and storage. Therefore, cloud computing paradigm limitations conquered by fog computing, acting as a bridge between IoT and cloud. Further, the limited computational capacity of end-devices in IoT infrastructure and inherited pros and cons of cloud and fog computing necessitates for all three paradigms to work together to full fill the needs of sustainable infrastructure for smart city. Keeping in view the need of integrating fog computing paradigm (due to its limited storage and computational capabilities), with IoT and cloud infrastructure, this article reviews the literature on role of IoT & cloud ecosystem in smart cities along with parameters of evaluation and future research directions in smart cities.

Comparative Study of Speaker Recognition Techniques in IoT Devices for Text Independent Negative Recognition

Speaker recognition (SR) or identification is the subset of broad area of Pattern recognition. Given the features of the voice print, the recognition system identifies the speaker from the knowledge of the speaker models stored in the database. In today’s world when many of our works are done through voice, recognition of the speaker is necessary.Recently, SR has also gained importance in Internet of Things (IoT) like setting up of smart environments for home, industries or educational and commercial applications. The race for high accuracy needs making the devices used in these smart environments as close to human hearing capacity as possible. Speaker identification is mostly used to establish negative recognition. Negative recognition is when thesystem decides whether a person is who he disagrees to be thus preventing a person from exploiting multiple identities. Only biometrics will be suitable to establish such identification. The feature extraction of voice sample along with comparative analysis of its methods is of fundamental interest in this paper. We try to compare the performance of features which are used in state of art speaker recognition models and analyse variants of Mel frequency cepstrum coefficients (MFCC) predominantly used in feature extraction which can be further incorporated and used in various smart devices.

ABC-RuleMiner: User behavioral rule-based machine learning method for context-aware intelligent services

This paper formulates the problem of a rule-based machine learning method to discover the behavioral rules of individual smartphone users to provide context-aware intelligent services. Smartphones nowadays are considered as one of the most important Internet-of-Things (IoT) devices for providing various context-aware personalized services. These devices can record individuals' contextual data - for example, temporal, spatial, or social contexts, and their daily behavioral activity records. Association rule mining (ARM) is the most popular rule-based machine learning method for discovering rules for a particular constraint preference utilizing a given dataset. However, it generates numerous uninteresting contextual associations which lead to generate huge number of redundant rules that become useless in making context-aware decisions. This redundant generation makes not only the rule-set unnecessarily large but also makes the context-aware decision making process more complex and ineffective. To minimize these issues, in this paper, we propose a rule-based machine learning method “ABC-RuleMiner” that effectively identifies the redundancy in associations, and discovers a set of non-redundant behavioral rules(IF-THEN) for individual users by taking into account the precedence of relevant contexts. Our experiments on individuals’ contextual smartphone datasets show that this rule discovery approach is more effective while comparing with traditional rule-based methods.

Internet of Things-Based Smart Drip Irrigation Using Arduino

Agriculture is one of the primary sectors in the Indian economy. Over the past several years, agricultural scientific techniques and agricultural implements have developed in the region, replacing the traditional method of farming. By which the economic condition of the farmers has improved. In addition, there are still some small farmers in India who use the old traditional methods of farming of agriculture because they do not have the resources to use modern methods. Besides, it is the only region that has contributed not only to the development of itself but also to other areas of the country. Agriculture has a major contribution to the country’s GDP and national income. In addition, it requires a huge manpower and labour which constitutes about 80% of the total workforce. Employees work not only directly but indirectly in the agricultural sector. Irrigation is the use of controlled amounts of water at the required intervals to the plants. Effective irrigation will affect seed development, germination, root growth, utilization of nutrients, plant growth and revival, yield and quality of the entire development process. The farmer knows how much water he has to give to the crop and when crop is to be planted, but the best system of irrigation it is necessary to have the information about use of equipment, plant species, soil structure, soil preparation and It is also important to be aware of all these that at what stage to watering the plant. The aim of this research paper is to describe how the Internet of Things (IoT) framework plays an important role in the field of agriculture. Here to show the importance of IoT in agriculture, we choose “Smart Drip Irrigation System.”

Role and Importance of IoT in the Smart City and E-Governance

In the recent years, developments growth have been rising in utilizing of recent technologies; Smart cities have been furnished with various electronic devices concentrated on the Internet of Things (IoT) to be more smarter than before. The Internet of Things is empowered by various emerging technologies such as smart cameras, sensors, wireless communication devices etc. Smart cities are huge systems connected with countless sub-systems, and these systems are depending on the electricity to move the human, things, and data to share information. Without electric power and Internet of Things, the smart city cannot be achieved and services cannot be provided. This paper aims to provide a comprehensive concept of IoT, the smart city as well as the relationship of Smart Cities to IoT. Moreover, an extensive review of the relationship of IoT to the smart city discussed with few instances of services provided by the government using IoT technologies for the citizens. Therefore the objects and applications which come under the IoT technology that can be used to meet the objective of having a smart city. In this paper, a thorough literature on the concept of IoT with Smart City and a brief comparison of it in terms of services, challenges, and issues are discussed. Also the discussion on the issues faced by service providers with regards to technologies used to equip the citizens with up to date services.

Use of IoT Technology to Drive the Automotive Industry from Connected to Full Autonomous Vehicles

The automotive industry has been around for quite some time and it has evolved ever since, but the major transformation that is happening now from vehicles driven by humans to vehicles driven by themselves will have a long term impact on society. Today's cars are already connected and have been connected for some time, since they can link to smartphones, offer emergency roadside assistance, register real-time traffic alerts etc., but this evolution is about to change. The automobile industry is on the brink of a revolution, to move to self-driving automobile industry, and the driving force behind this is the fast developing technology, the Internet of Things (IoT). IoT will transform the automobile industry and at the same time, the automobile industry will provide a big boost to IoT. The potential and the prospects of this technology is astonishing. This paper examines the market and technical trends towards Autonomous Vehicles, evolution stages from early cars to fully autonomous, the importance of IoT in driving this industry ecosystem, advantages and disadvantages of Autonomous Vehincles, key issues and challenges faced by the industry, standards activities around this industry and finally the deployment use cases. The focus of this paper is more based on an industrial push to identify issues and challenges of Autonomous Vehicles and less on any academic research activity. The intention of this paper is to bring these issues and challenges to the attention of IFAC technical committee and trigger some debate on the opportunities for IFAC research in international stability.

프로세스 기반 사물인터넷 객체 협업 모델

최근 정보통신 기술과 다양한 스마트 디바이스들의 성능이 빠르게 발전하고 실생활에 적용됨에 따라, 사물인터넷(Internet of things, IoT)의 중요성이 강조되고 있다. 본 논문에서는 IoT 서비스를 프로세스 기반으로 명세하고 이를 실행하기 위한 프로세스 기반 사물인터넷 객체 협업 모델을 제안한다. 즉, 본 논문의 목적은 프로세스 개념을 적용하여 IoT 서비스를 일련의 실행 순서를 가지는 태스크들과 이의 실행을 담당하는 IoT 객체간의 협업으로 정형적으로 명세하기 위한 방법을 제안하는 것이다. 결론적으로 제안 모델을 통해 IoT 서비스는 프로세스 기반 사물인터넷 컴퓨팅 환경에서 자동적으로 실행되며, 이에 대한 분석, 모니터링, 재사용이 가능할 것으로 기대된다. In recent years, the importance of IoT (Internet of Things) is emphasized by information communication technologies and the performance of various smart devices are rapidly developed and applied in the real world. In this paper, we propose a process-driven IoT-object collaboration model to specify and execute a IoT service based on processes. That is, the purpose of this paper is to suggest a formal method in order to describe a IoT service into a group of tasks having execution order and collaboration between IoT-objects in charge of the enactment of a task. Conclusively, through the proposed model, we expect that IoT services will be automatically executed, analyzed, monitored and reused in the process-driven IoT computing environment.

A Petri Net Processing Model of STeCEQL

The internet of vehicles is an important internet of things. It is an urgent problem that how to real-time processes these spatial and temporal data of the internet of vehicles. The complex event processing technology can filter the concerned data to the event by the complex event query language (EQL) and the system can responds effectively. The STeCEQL is a complex event language for the internet of vehicles, which constraint with spatial and temporal. The processing model of the complex event language is a core issue of the complex event processing technology. In this paper, we established a Petri Net processing model of STeCEQL. We give all kind of processing model of STeCEQL expressions. And these processing models are synthesized by two types of basic Petri Net model: the sequence structure and the logic and structure. Finally, we proved the Petri Net processing model of STeCEQL is structural boundedness and structural conservativeness, but it is not structural repetitiveness.