An IoT Agricultural System For Harumanis Farm

Since the Internet of Things (IoT) will be entwined with everything we use in our daily life, the consequence of security flaws escalates. Smart objects will govern most of the home appliances and car engines yielding potential disaster scenarios. In this context, successful attacks could lead to chaos and scary scenarios (www.darkreading.com). Unprotected personal information may expose sensitive and embarrassing data to the public and attacks may threaten not only our computers and smart devices, but our intimacy and perhaps our lives too. Because persons and objects will be bonded with each other, user consent becomes critical. Therefore, thing, object, and user Identity will be the focus of future IoT security solutions, yielding a Trust, Security, and Privacy (TSP) paradigm, which may constitute the Achilles' heel of IoT. While security issues are quite straightforward, mainly from background knowledge, privacy issues are far more complex. Privacy constitutes a rather challenging task, even for the m...

In modern times, there are many types of agricultural and fish farming systems, one of which is the modern aquaculture system which has been recognized as an innovative method of sustainable food production that combines fish cultivation with agriculture simultaneously. Modern aquaponics is able to overcome problems in urban areas which require land for growing crops and cultivating fish. Good fish cultivation means always monitoring the growth and health of fish to reduce the risk of crop failure, therefore this research aims to implement an Internet of Things (IoT) system in modern aquaponics to increase the growth of tilapia. IoT parameters consisting of pH sensors, Total Dissolved Solids (TDS) sensors, and temperature sensors are used to monitor water quality conditions in modern aquaponics. Through IoT systems, data collected in real-time enables more effective environmental monitoring. The creation of an IoT system in modern aquaponics shows that the implementation of IoT can increase the efficiency of modern aquaponic environmental management, resulting in better fish growth and plant productivity. This was verified through the pH sensor test results with a value of 7.2 indicating optimal conditions of acid-base balance in the water, which is essential for the health of tilapia fish. The TDS sensor test with a value of 300 ppm confirmed that the concentration of solid particles in the water was at an optimal level, which is also an indicator of the health of the tilapia fish. Temperature measurement using a temperature sensor with a value of 28°C, shows that the water temperature is within the ideal range for comfort for tilapia, which is usually comfortable at temperatures between 28-30°C

Herein, an Internet of Things (IoT) system with remote reconfigurable wireless sensor network (WSN) nodes is presented and its application to measure group work activity in a classroom is discussed. This IoT system consists of wiki pages, bots, (defined as remotely controlled devices), gateways, and remotely reconfigurable WSN Nodes. The bots/gateways and WSN nodes of the IoT system are controlled by scripts written on wiki pages that are read by the bot/gateways, which then forward the commands contained in the scripts to the WSN nodes. Each WSN node then executes the commands, obtains the resulting sensor values, and then returns those values to the bot/gateways. The bot/gateways relay the values and write them onto the wiki page. Each WSN node is flexible and reconfigurable because its behavior can be changed by the script commands written onto the wiki page. Using this IoT system, we created a measurement system to evaluate activity levels of group work in a classroom. After developing the measurement system, we found it was possible to adjust the sampling rate for obtaining sensor values simply by rewriting the script on the wiki page without directly manipulating the WSN.

This paper is a survey about the uprising of the Internet of Things (IoT) technologies, their evolution, their variety of the application domains of the IoT systems, which includes, but not limits to healthcare domain, smart cities, smart objects, integration of smart sensors, smart home appliances control, etc. and all the technology revolution that this evolution implies and forces to happen. The IoT system's revolution and fast-phased evolution will create and/or cause positive effects in the whole society and technology, like in the case when the IoT domain is redesigning the applied modern healthcare systems, promising and later offering technological, economic, and social benefits. This paper also reviews the state-of-the-art network architectures/platforms employed in the IoT systems, presents different applications, and also the future trends. IoT security and privacy features, including security requirements will be also discussed and presented. Ambient intelligence, and wearables can be leveraged in the IoT healthcare context, sensors are also becoming ubiquitous and very important in everyday life and also generates data at an unprecedented, unseen rate and scale. This article focuses on the current/existing trends in the domain of IoT and on the current stage of development of research for hardware and software solutions, possible future communication capabilities between human beings and machines, and any new technologies that will employ IoT. Future researches on IoT-based systems are based on a set of open issues and challenges; also, there is the vital step from current "Big Data" to "Big Information" that can be improved and faster created with the help and implication of the current and future IoT systems/setups. With the proliferation of IoT, humans will be able to interact with machines/computers and also control a variety of networked electronic devices, including sensors and actuators. Also, the emergence and evolution of wearable Brain-Machine Interfaces (BMIs), employing directly Electroencephalographyc (EEG) signals, can be a key catalyser of this interaction and evolution of IoT. As smart home appliances, smart sensors, smart glasses and wearable EEG/EMG headsets and any other smart objects are appearing in our lives every day, all of them connected or being a member of the IoT systems, it is important to be able to foresee what is lying ahead of us, as a society in view of the fact that their number grows every day. As the gathered data is growing each day, the computation power from the available IoT devices and infrastructure are becoming more important and more utilized.

LoRaWAN based internet of things (IoT) system for precision irrigation in plasticulture fresh-market tomato

A survey of trust computation models for service management in internet of things systems

Privacy preservation in blockchain based IoT systems: Integration issues, prospects, challenges, and future research directions

Advanced digital signatures for preserving privacy and trust management in hierarchical heterogeneous IoT: Taxonomy, capabilities, and objectives

Wireless communication in Internet of Things (IoT) systems involves several devices communicating in the limited available frequency spectrum using different wireless communication standards. To achieve efficient spectrum usage in the IoT systems, it is desired to have the ability of dynamic spectrum allocation. Cognitive radios (CR) enable efficient spectrum utilization by performing spectrum sensing to gain the knowledge of current spectrum occupancy and dynamically reconfiguring the communication parameters, making them highly suitable for IoT systems. In this paper, we present the efficient hardware implementation of a spectrum sensing scheme for CR based IoT applications that require large bandwidth. We show that the proposed implementation on field programmable gate arrays (FPGAs) can achieve over 1.56x improvement in performance, while consuming 77% less generic resources and at a fractionally higher power consumption.

With the continuous development of the Internet of Things (IoT) and the needs of the times, the number of IoT edge devices is rapidly increasing, and to be able to provide users with more reliable information services and decision-making, this paper constructs an IoT system based on cloud-edge collaboration. Based on cloud-edge collaboration, data mining technology is used to categorize data, and regression models and branch neural networks are combined to further improve the IoT system based on cloud-edge collaboration. Finally, the particle swarm algorithm is used to optimize reinforcement learning, and the framework of an IoT system built on cloud-edge partnership is established. Through the data processing experiments on IoT edge computing, the data mining performance of the IoT edge computing-based data mining method is improved by 0.0295, 0.1870, and 0.2059 on the large datasets of Dataset4 and Dataset5, respectively. the average turnaround time is lower than 6.5 for different edge computing power levels. The system rewards are gradually improved from 5.3 to 8.2, and the number of packet loss of the algorithm is less than 5. The feasibility and effectiveness of the IoT data mining method and system based on cloud-edge collaboration are confirmed by this study, and a feasible method for data mining can be applied to IoT systems under cloud-edge collaboration.

Computational notebooks to support developers in prototyping IoT systems

Internet of Things (IoT) system provides personalized services to users by analyzing the amount of data collected from billions of sensors. It is important to ensure the trustworthiness of the heterogeneous IoT data. To address this requirement, the data provenance needs to be used in the IoT system. In this article, we integrate the PROV-DM model into the IoT architecture and propose an IoT data provenance model. To avoid corrupting and forging the provenance information, we record the provenance data onto the blockchain. In addition, we use the IoT secure and computable data sharing system to illustrate that the proposed model can construct a provenance graph to detect which agent operates the IoT data abnormally. Finally, we implement and evaluate the proposed model on a public IoT database.

The rapid growth of Internet of Things (IoT) devices, including smartwatches, home assistants, and connected appliances, has brought significant convenience to daily life, but it has also introduced serious security challenges. These devices often transmit sensitive data, making them vulnerable to theft, misuse, and unauthorized access. Current security measures are insufficient to address the complex and evolving nature of IoT systems, leaving many of them exposed to potential breaches and cyberattacks. This review explores recent developments in IoT security, focusing on how advanced technologies, such as machine learning, can be utilized to enhance the protection of IoT systems. The main objective of this paper is to examine potential solutions to the security problems that arise in IoT environments. It includes a thorough analysis of recent research and technological innovations in the field, with a particular emphasis on how different security methods are applied across IoT systems. By identifying the most common security vulnerabilities and outlining their impact on IoT networks, the review suggests improved methods to safeguard IoT data and ensure privacy. The findings aim to support researchers, developers, and businesses in designing more secure IoT solutions, and contribute to the establishment of stronger data protection policies. Ultimately, the review serves as a resource for those seeking to enhance the security of IoT devices and systems in an increasingly interconnected world.

This chapter discusses the architecture of Internet of Things (IoT) systems. IoT system security takes a team of experts in network security, computer security, hardware and software security, cloud infrastructure security, secure protocols, and cryptography. The security of an IoT system is extremely complex and crosses many security disciplines. Various industries and standards groups have developed and proliferated system architectures in order to better communicate the issues and requirements relating to systems for which they are concerned. The Internet of Things community is no different from any other architectural community. The primary difference is that IoT spans many different industries and ecosystems, and has a diverse set of concerns. The cloud is essential to an IoT system because it houses centralized applications and contains the data model of the IoT system, making those applications accessible to its users wherever they are, and is wholly independent of the actual location of the IoT devices themselves.

Internet of Things (IoT) systems are characterized by being highly heterogeneous, distributed, and scalable, made up of a variety of devices that operate in uncertain and ubiquitous settings. In this sense, models@runtime emerges as a promising software development approach to address IoT systems’ intrinsic characteristics. This study presents a systematic review to know the state of the art using of models@runtime to support IoT systems’ operation. The methodology proposed by Kitchenham has been used to carry out this research. The systematic review answers the following research questions: i) How are models@runtime being used in the IoT domain? And ii) How is addressed the research in studies related to the use of models@runtime in the IoT domain? Initially, 692 primary studies were retrieved, where 50 relevant studies related to approaches based on models@runtime for IoT were selected after applying the corresponding inclusion and exclusion criteria. Those primary studies were classified based on the extraction criteria. Among review’s main results are that the models@runtime have been widely used to support the monitoring and self-adaptation mechanisms in IoT systems, as opposed to the few studies found aimed at interoperability, quality assurance, self-organization, and self-optimization of IoT systems, consisting of essential research opportunities.