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Peoples are naturally communicators but devices are not. In the Internet of Things (IoT) architecture, the smart devices (SDs), sensors, programs and association of smart objects are connected together to transfer information among them. The SD is designed as physical device linked with computing resources that are capable to connect and communicat...
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... the whole world is becoming more and more depending on the mobility services and wireless communication. The drawback of wireless communication is now clear as wireless networking is growing. According to statistica [20] website, by 2020, It is expected that the total number of smart devices connected together will reach up to 50 billion. According to Siemens research, up to 2020, near about 26 billion physical objects will be connected together on the internet (See figure 1). That time is not far away when billions of physical things linked together in real time. They can communicate each other and forwarding and process required data on the cloud. But there is a lack of technical standardization security perspective on the internet of smart thing. According to Statistica [20] report, in 2025, the total number of connected devices in the world will be approximately 75.44 billion. See figure 1. The main factor of this growth is not the population of the world but the smart devices. The integrated technologies are playing big role to connect the physical things together and exchange the information among them [11], [12]. This environment where the machine can talk to another machine (Machine-to-machine) and human can talk to machine. The IoT is integration of physical things, smart devices, smart buildings, smart vehicles, embedded objects including electronics, programs, sensors, actuators and network connections to exchange information among each other [13]. The The IoT represents the interconnected physical things that are uniquely identified with sensors [4]. Many researchers moved in the area of security and reliability in IoT day by day. The reliability can be measured through smart device integrity. The IoT requires an understanding of the connectivity between devices, the development of standards for the transmission of information and tools that enable the autonomous behavior of objects according to the functions to be met and the instructions received from the network that interconnects [16]. Transport and logistics have already long incorporated these technologies, particularly to improve service delivery, and the next evolution is towards the personal and professional environment. The following is the observation that can be happen in next few ...
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p>Peoples are naturally communicators but devices are not. In the Internet of Things (IoT) architecture, the smart devices (SDs), sensors, programs and association of smart objects are connected together to transfer information among them. The SD is designed as physical device linked with computing resources that are capable to connect and communic...
The reliability of memory devices is affected by radiation induced soft errors. Multiple cell upsets (MCUs) caused by radiation corrupt data stored in multiple cells within memories. Error correction codes (ECCs) are typically used to mitigate the effects of MCUs. Single error correction-double error detection (SEC-DED) codes are not the right choi...
p>Peoples are naturally communicators but devices are not. In the Internet of Things (IoT) architecture, the smart devices (SDs), sensors, programs and association of smart objects are connected together to transfer information among them. The SD is designed as physical device linked with computing resources that are capable to connect and communic...
Citations
... The architecture of IoT is determined by how it functions and is used in different sectors. Smart gadgets, not the world's population, are the primary driver of this growth (Alam et al., 2018) [12]. Integrated technologies are essential for linking physical objects and facilitating information transmission between them (Aljohani et al., 2015). ...
A comprehensive search for primary research published between 2014 and 2023 was carried across several databases. Studies that describe the application of machine learning (ML) and deep learning techniques for if they was carried out across several databases. Studies that described the application of deep learning (DL) and machine learning (ML) methods for IoT botnet attack detection. Numerous facets of contemporary life have been transformed by the Internet of Things (IoT), including home automation, industrial control systems, healthcare, and transportation. On the other hand, as more devices become connected, security risks have also increased, especially from botnets. IoT Botnet attack detection techniques utilizing ML and DL have been developed in order to reduce these dangers. The best DL and ML techniques for IoT botnet attack detection are identified by a detailed examination of evaluation criteria, and performance measures in this systematic review. Performance metrics from well-known machine learning models are used to illustrate how well these machine learning techniques detect and stop Botnet attacks. When it comes to detecting Botnet assaults, deep learning (DL) and traditional machine learning (ML) methods perform similarly well. Furthermore, traditional machine learning systems still have challenges with real-time monitoring, timely detection and adaptability to novel attack approaches.
... The number of Internet of Things (IoT) devices worldwide is anticipated to experience a significant increase, nearly doubling from 15.9 billion in 2023 to over 32.1 billion by 2030, according to data from Statista (2023). They produce a massive amount of data, and it is expected to reach around 85 zettabytes (ZB) by 2025 [1,2,23]. All these are driven by the widespread utilization of IoT technology and services across various industries, from environmental monitoring [22] to smart city management [10]. ...
In recent years, Edge AI has become more prevalent with applications across various industries, from environmental monitoring to smart city management. Edge AI facilitates the processing of Internet of Things (IoT) data and provides privacy-enabled and latency-sensitive services to application users using Machine Learning (ML) algorithms, e.g., Time Series Classification (TSC). However, existing TSC algorithms require access to full raw data and demand substantial computing resources to train and use them effectively in runtime. This makes them impractical for deployment in resource-constrained Edge environments. To address this, in this paper, we propose an Adaptive Brownian Bridge-based Symbolic Aggregation Vector Space Model (ABBA-VSM). It is a new TSC model designed for classification services on Edge. Here, we first adaptively compress the raw time series into symbolic representations, thus capturing the changing trends of data. Subsequently, we train the classification model directly on these symbols. ABBA-VSM reduces communication data between IoT and Edge devices, as well as computation cycles, in the development of resource-efficient TSC services on Edge. We evaluate our solution with extensive experiments using datasets from the UCR time series classification archive. The results demonstrate that the ABBA-VSM achieves up to 80% compression ratio and 90-100% accuracy for binary classification. Whereas, for non-binary classification, it achieves an average compression ratio of 60% and accuracy ranging from 60-80%.
... The ICT sector stands as a beacon of progress and innovation, exerting profound technological and economic influence on society at large. Central to its significance is the sector's propensity for developing transformative technologies that permeate and redefine societal and economic structures [1]. ICT practices are not confined solely to their own domain but extend to diverse sectors, adapting and influencing their operational frameworks. ...
The research presented in this paper originated from my master's thesis, and I have chosen to publish it together with my supervisor, who is the second author, to contribute to the existing body of knowledge. The technology known as the Internet of Things (IoT) continues to expand the current Internet infrastructure by facilitating connections and interactions between the physical and cyber worlds. IoT and its associated applications have significantly enhanced the quality of life on Earth. Advanced wireless sensor networks and their revolutionary computing capabilities have paved the way for various IoT applications to explore new frontiers, impacting nearly every aspect of daily life. Concurrently, the imperative of energy optimization has emerged as a major concern, driving the adoption of sustainable practices and green technologies. The fusion of Artificial Intelligence (AI) with IoT represents a potent combination, enabling the realization of unique projects and innovative solutions. The potential impact of IoT and AI is vast, promising transformative changes in the future landscape. Recognizing the magnitude of these advancements, the European Commission is committed to collaborating with partners and authorities in the Western Balkans to fully implement the digital agenda. To this end, the EU and Western Balkans ICT Dialogue Initiative, established by the Commission in cooperation with regional partners, will oversee the implementation of the Digital Agenda.
... There was a total of 16 billion devices connected to the internet in 2016 of which approximately 6 billion were identified as IoT devices (Suomi, 2018). This amount is expected to increase rapidly in future and according to different forecasts, the number of connected IoT nodes by 2025 will be around 75.44 billion (Alam, 2018). This represents approximately 6 billion annual growths in a number of connections. ...
... Estimated growth rate of IoT connected devices(Alam, 2018). ...
The Electrical Secondary Distribution Networks (ESDN) are very complex with high user density, making detection of defects and faults very challenging. In many developing countries, faults in ESDN have been reported mainly by customers and visual inspection by utility personnel. This process is time-consuming, costly, and among the causes of inefficient power supply to the end-users. The existing systems using Supervisory Control and Data Acquisition (SCADA) and Phasor Measurement Units (PMU) in the transmission and primary distribution networks are not efficient for fault detection in ESDN. The PMUs and SCADA systems relied mainly on centralized processing that is inefficient and relatively expensive for the secondary distribution network. This study proposes the architecture for fault detection and classification in the ESDN using Internet of Things (IoT) based architecture on distributed processing. The deployed IoT based sensor nodes were designed using the raspberry-pi and micro-controllers. The algorithms for fault detection and classification were designed and deployed in the prototype. The results show that the deployed sensor node obtained 98% accuracy and 18 ms faults detection time. The results implies that the deployed architecture using the IoT based sensor nodes, which is based on distributed processing, can be used for fault detection and classification in the ESDN.
... The positioning module uses a GPS [14], with parameters listed in Table 1, which meet the high-precision excavation positioning requirements. The sensor module integrates an elevation sensor and an inclination sensor [15]. The elevation sensor provides the elevation data of the collection device, while the inclination sensor measures the angle between the arm and the vertical plane. ...
In recent years, with advancements in machine control automation and intelligent systems, both domestic and international research has increasingly focused on the automation and semi-automation of excavator control. This study introduces an auxiliary control system for excavator fleets, which utilizes sensor and wireless communication technologies. The research investigates the excavation errors that arise when operators rely on personal vision and experience in complex working environments, as well as the challenges of managing large fleets of excavators. By assisting operators in controlling the excavators, the system significantly reduces the operator’s experience requirements and work intensity and provides higher precision, consistency, and efficiency for excavation equipment. This method not only further improves the operating efficiency and excavation accuracy, but also saves the overall construction cost and improves the sustainability of the project.
... Machine Learning algorithms can further analyze the data retrieved to predict and synthesize information, thus improving the autonomous capabilities and efficiency of the drone networks. A huge surge in the number of connected devices can be observed within the last decade, and projected numbers will rise to 75.44 billion by 2025 [2]. This huge boost in the number of devices is due to huge advancements in sensor technology and wireless communication. ...
The integration of machine learning algorithms into drone networks offers significant opportunities to enhance the processing power and security of Unmanned Aerial Vehicles (UAVs). This chapter provides a comprehensive overview of how leveraging IoT, and collaborative learning techniques can enable drones to operate autonomously, adapt to changing environments, and perform complex tasks with greater efficiency. By analyzing large datasets, drones can achieve improved situational awareness, predictive maintenance, and optimized mission planning through adaptive reinforcement learning and deep reinforcement learning. Techniques like federated learning, collaborative swarm intelligence , and ensemble learning contribute to the accuracy and effectiveness of drone operations, making them valuable in applications such as traffic management , disaster response, smart city infrastructure, and agricultural monitoring. The chapter also addresses critical issues such as privacy concerns, data security, regulatory compliance, and transparency in data usage. Additionally, it explores future directions, including the development of advanced machine learning algorithms , edge computing, interoperability, ethical frameworks, multi-domain integration , resilience, and human-drone interaction. By continuing to explore these areas, researchers and industry professionals can drive innovation and fully harness the potential of machine learning to advance drone networks for both civilian and military uses.
... As businesses deploy more IoT devices, the risk of vulnerabilities being targeted and exploited increases [11]. As shown in Fig. 1, By the year 2025, it is projected that the IoT will reach a staggering number of 75.44 billion devices, resulting in an enormous data output of 79 zettabytes [12]. The IoT has been recognized as a crucial factor in digitization for societal transformation [13,14]. ...
... To enhance the effectiveness of existing systems, it is essential to extract the most significant features. To address these issues, this paper proposes an attention-based convolutional neural network (ABCNN) for intrusion Fig. 1 Projected growth of IoT devices from 2018 to 2025 [12] Content courtesy of Springer Nature, terms of use apply. Rights reserved. ...
This paper proposes an attention-based convolutional neural network (ABCNN) for intrusion detection in the Internet of Things (IoT). The proposed ABCNN employs an attention mechanism that aids in the learning process for low-instance classes. On the other hand, the Convolutional Neural Network (CNN) employed in the ABCNN framework converges toward the most important parameters and effectively detects malicious activities. Furthermore, the mutual information technique is employed during the pre-processing stage to filter out the most significant features from the datasets, thereby improving the effectiveness of the ABCN model. To assess the effectiveness of the ABCNN approach, we utilized the Edge-IoTset, IoTID20, ToN_IoT, and CIC-IDS2017 datasets. The performance of the proposed architecture was assessed using various evaluation metrics, such as precision, recall, F1-score, and accuracy. Additionally, the performance of the proposed model was compared to multiple ML and DL methods to evaluate its effectiveness. The proposed model exhibited impressive performance on all the utilized datasets, achieving an average accuracy of 99.81%. Furthermore, it demonstrated excellent scores for other evaluation metrics, including 98.02% precision, 98.18% recall, and 98.08% F1-score, which outperformed other ML and DL models.
... Parallel to the increase in the number of mobile users, there has been an increase in demand for higher data rates (at least 100 times higher than 4G LTE networks), lower latency (around one millisecond), lower energy consumption, improved reliability and security, and higher scalability. Hundreds of Gigabit-per-second (Gbps) and even Terabit-persecond (Tbps) lines are projected to become a reality in the near future [3,4]. High data rates result in large modulation bandwidths. ...
This study presents a highly efficient Doherty Power Amplifier (DPA). The design uses 10W GaN High-Electron-Mobility Transistors (CG2H40010F) for their characteristics, such as high breakdown voltage and power density. Advanced
Design Software (ADS) was used to conduct the design. The design configuration employed a pair of individual Power Amplifiers
(PAs) and connected them via a Wilkinson Power divider (WPD), which also facilitates the transmission of power towards the
charge. The Doherty Power Amplifier (DPA) has been designed to offer high efficiency, output power, and wide bandwidth, in
addition to expanding power back-off levels. It operates within the 2.0–2.8 GHz frequency range. The DPA topology replaces
the previous quarter-wave transformer with a Wilkinson Power Combiner (WPD). Simulation results show a fractional
bandwidth of 33.33%, a saturated output power of 44 dBm, and a higher gain of approximately 15 dB. Furthermore, Drain
efficiency (Deff) and Power-Added Efficiency (PAE) stand at approximately 85% and 95%, respectively. After linearization, the
design produced an output power of 39.171 dBm using a 100 MHz, 6.5 dB PAPR 5G NR DL signal at 2.4 GHz. Additionally, it
achieved an ACLR of -56.88 dB for the adjacent channel. The outcomes of this study indicate that the proposed DPA achieves
excellent drain efficiency, providing a solution for increasing DPA bandwidth while maintaining linearity. The intrinsic features
of GaN devices, which allow for higher frequency operation and wider bandwidth, make this design ideal for 5G applications.
Keywords - Doherty Power Amplifier, GaN HEMTs transistor, Power added efficiency, Drain efficiency, Wilkinson Power
Divider.
... The author proposed establishing reliable communication between physical objects at the Transmission Control Protocol (TCP) layer. Reliable communication between smart devices requires the TCP layer to detect errors, correct errors, and provide confirmation for information transfer [9]. The author proposed a routing solution for IoT systems that combines MANET protocol and WSN routing code. ...
Mobile Ad Hoc Networks (MANET) are crucial for the next generation of computing in the Internet of Things (IoT). All devices in a MANET can transfer from one location to another in any direction. Data processing and resource management can be implemented in all components by providing cloud services to users in MANET to access smart devices within the IoT framework. However, security is a major challenge for the growth of Cloud Computing (CC). Establishing a secure network connection takes time and effort. Nevertheless, embracing CC includes frequent outages, improper management, lack of resources, interoperability issues, privacy concerns, and reliability problems. Therefore, to solve this problem, they initially selected the Cluster Head (CH) to calculate the parameters of a mobility and energy node using a weighted metric. Furthermore, the Load Balancing Cluster Head (LBCH) algorithm can reduce the delay in sending and receiving packets through CH. In addition, the variation between the communication workload of each mobile node can be assessed. Then, the Velocity of the IoT node can be calculated using the Cloud Data Transfer Rate (CDTR) approach at the base of the cloud-maintained IoT integration. Finally, the proposed technique can detect the new positions using a Smart Device-Machine-To-Machine (SD-M2M) approach and enhance communication network security. Implementing the proposed method can enhance secure communication in MANET's IoT structure systematically and efficiently. Simulation results evaluate the algorithm's performance regarding channel number, throughput, data transmission rate, energy consumption, energy efficiency, network security and packet delivery rate. The proposed method attains a throughput performance of 79% and security performance of 83% in MANET..
... For the Internet of Things, these dynamic sets of images provide unique challenges. Standby spares are frequently used for important IoT devices to improve fault tolerance and availability (Maratha and Gupta, 2019, 2023. Three alternative standby modes-cold, hot, and warm-are available based on the amount of recovery time required and . ...
... This strategy, meanwhile, disregarded the chance that malware or other danger may spread across the network or that certain IoT components could suddenly stop working and start producing inaccurate data. A technique that can warn the user of system defects before crucial decisions are taken is required to appropriately display dependability (Alam, 2018). To forecast the dependability needs of an IoT system, a Markov model was suggested. ...
The Internet of Things (IoT) proposes to transform human civilization so that it is smart, practical, and highly efficient, with enormous potential for commercial as well as social and environmental advantages. Reliability is one of the major problems that must be resolved to enable this revolutionary change. The reliability issues raised with specific supporting technologies for each tier according to the layered IoT reliability are initially described in this research. The research then offers a complete review and assessment of IoT reliability. In this paper, various types of reliability on the IoT have been analyzed with each layer of IoT to solve the issues of failure rates, latency, MTTF, and MTBF. Each parameter has a certain classification and perception as well as enhancement in efficiency, accuracy, precision, timeliness, and completeness. Reliability models provide efficient solutions for different IoT problems, which are mirrored in the proposed study and classified with four types of reliabilities. The field of IoT reliability exploration is still in its initial phases, despite a sizable research record. Furthermore, the recent case study of CHISS is elaborated with discovered behaviors including brand-new aspects such as the multifaceted nature of evolving IoT systems, research opportunities, and difficulties.
