The increasing popularity of specialized Internet-connected devices and appliances, dubbed the Internet-of-Things (IoT), promises both new conveniences and new privacy concerns. Unlike traditional web browsers, many IoT devices have always-on sensors that constantly monitor fine-grained details of users' physical environments and influence the devices' network communications. Passive network observers, such as Internet service providers, could potentially analyze IoT network traffic to infer sensitive details about users. Here, we examine four IoT smart home devices (a Sense sleep monitor, a Nest Cam Indoor security camera, a WeMo switch, and an Amazon Echo) and find that their network traffic rates can reveal potentially sensitive user interactions even when the traffic is encrypted. These results indicate that a technological solution is needed to protect IoT device owner privacy, and that IoT-specific concerns must be considered in the ongoing policy debate around ISP data collection and usage.
Abstract Integration of all smart systems (such as smart home, smart parking, etc.) and the IoT devices (such as sensors, actuators, and smartphones) in the city can play a vital role to develop the urban services by building their city digital and smarter. However, interconnection of lots of IoT objects to collect urban data over the Internet to launch a smart digital city, effects vast volume of data generation, termed as Big Data. Thus, it is a challenging task to integrate IoT devices and smart systems in order to harvest and process such big amount of real-time city data in an effective manner aimed at creating a Smart Digital City. Therefore, in this paper, we have established an IoT-based Smart City by using Big Data analytics while harvesting real-time data from the city. We used sensors’ deployment including sensors at smart home, smart parking, vehicular networking, surveillance, weather and water monitoring system, etc., for real time data collection. The complete system is described by its proposed architecture and implementation prototype using Hadoop ecosystem in a real environment. In addition, the Smart Digital City services are extended by developing the intelligent Smart Transportation System by means of big graph processing to facilitate citizens while providing real-time traffic information and alerts. The proposed system consists of number of stages including data generation and collection, aggregation, filtration, classification, preprocessing, computing, and decision making. The efficiency of the system is extended by applying Big Data processing using Apache Spark over Hadoop. Whereas, the big city graph processing is achieved by using Giraph over Hadoop. The system is practically implemented by taken existing smart systems and IoT devices as city data sources to develop the Smart Digital City. The proposed system is evaluated with respect to efficiency in terms of scalability and real-time data processing.
Abstract The deployment of Internet of Things (IoT) results in an enlarged attack surface that requires end-to-end security mitigation. IoT applications range from mission-critical predicaments (e.g., Smart Grid, Intelligent Transportation Systems, video surveillance, e-health) to business-oriented applications (e.g., banking, logistics, insurance, and contract law). There is a need for comprehensive support of security in the IoT, especially for mission-critical applications, but also for the down-stream business applications. A number of security techniques and approaches have been proposed and/or utilized. Blockchain mechanisms (BCMs) play a role in securing many IoT-oriented applications by becoming part of a security mosaic, in the context of a defenses-in-depth/Castle Approach. A blockchain is a database that stores all processed transactions – or data – in chronological order, in a set of computer memories that are tamperproof to adversaries. These transactions are then shared by all participating users. Information is stored and/or published as a public ledger that is infeasible to modify; every user or node in the system retains the same ledger as all other users or nodes in the network. This paper highlights some IoT environments where BCMs play an important role, while at the same time pointing out that BCMs are only part of the IoT Security (IoTSec) solution.
Technology has become ubiquitous, it is all around us and is becoming part of us. Together with the rise of the Internet-of-Things (IoT) paradigm and enabling technologies (e.g., Augmented Reality (AR), Cyber-Physical Systems, Artificial Intelligence (AI), blockchain or edge computing), smart wearables and IoT-based garments can potentially have a lot of influence by harmonizing functionality and the delight created by fashion. Thus, smart clothes look for a balance among fashion, engineering, interaction, user experience, cybersecurity, design and science to reinvent technologies that can anticipate needs and desires. Nowadays, the rapid convergence of textile and electronics is enabling the seamless and massive integration of sensors into textiles and the development of conductive yarn. The potential of smart fabrics, which can communicate with smartphones to process biometric information such as heart rate, temperature, breathing, stress, movement, acceleration, or even hormone levels, promises a new era for retail. This article reviews the main requirements for developing smart IoT-enabled garments and shows smart clothing potential impact on business models in the medium-term. Specifically, a global IoT architecture is proposed, the main types and components of smart IoT wearables and garments are presented, their main requirements are analyzed and some of the most recent smart clothing applications are studied. In this way, this article reviews the past and present of smart garments in order to provide guidelines for the future developers of a network where garments will be connected like other IoT objects: the Internet-of-Smart-Clothing.
The emergence of massive Internet of Things (IoT) mobile devices (MDs) and the deployment of ultradense 5G cells have promoted the evolution of IoT toward ultradense IoT networks. In order to meet the diverse quality-of-service and quality of experience demands from the ever-increasing IoT applications, the ultradense IoT networks face unprecedented challenges. Among them, a fundamental one is how to address the conflict between the resource-hungry IoT mobile applications and the resource-constrained IoT MDs. By offloading the IoT MDs’ computation tasks to the edge servers deployed at the radio access infrastructures, including macro base station (MBS) and small cells, mobile-edge computation offloading (MECO) provides us a promising solution. However, note that available MECO research mostly focused on single-tier base station scenario and computation offloading between the MDs and the edge server connected to the MBS. Little works can be found on performing MECO in ultradense IoT networks, i.e., a multiuser ultradense edge server scenario. Toward this end, we provide this paper to study the MECO problem in ultradense IoT networks, and propose a two-tier game-theoretic greedy offloading scheme as our solution. Extensive numerical results corroborate the superior performance of conducting computation offloading among multiple edge servers in ultradense IoT networks.
The Industrial Revolution 4.0 (IR4.0) introduces transformative technologies like the Internet of Things (IoT), Artificial Intelligence (AI), and Blockchain, offering small and medium enterprises (SMEs) opportunities to revolutionize operations and competitiveness. However, existing IR4.0 readiness assessment tools are manufacturing-centric, time-consuming, and lack adaptability across sectors. This study aims to develop a novel, web-based self-assessment tool that enables Malaysian SMEs across both manufacturing and non-manufacturing sectors to evaluate their IR4.0 readiness independently and efficiently. To date, no such self-evaluation system exists in Malaysia that empowers SMEs to independently assess their IR4.0 readiness without the need for third-party audits. Unlike prior tools, this system integrates recommendations for training and funding, enhancing both usability and actionability. A three-phase methodology was applied. The initial phase involved a systematic literature review of 10,428 articles, filtered to 13 high-quality studies, and qualitative content analysis, which identified 23 readiness items within five dimensions: Organizational, Data, Infrastructure, Analytics, and IT, Development, and Operations. Field observations from IR4.0 programs further contextualized these factors. The second phase focused on model design and tool development, incorporating expert-weighted scoring through a Delphi process and automated recommendations for national support programs. In the final phase, the tool was tested with 52 SMEs and validated through interviews with Malaysian industry experts. Findings show an average readiness of 69 %, with gaps in infrastructure, analytics, and organizational commitment. The study concludes by offering an empirically grounded, sector-neutral tool that benchmarks SME digital readiness and informs both enterprise strategies and policymaking.
The rise in popularity of the Internet of Things (IoT) has increased the need to power devices wirelessly, a process called Wireless Power Transfer (WPT), to avoid the usage of batteries, which present limited lifespans. In particular, Microwave Power Transfer (MPT), both Near-field (NF) and Far-field (FF), use Electromagnetic (EM) waves to transfer power between two points. However, these systems still present some downsides, mainly efficiency-wise. This paper explores the usage of Multibeam Antennas (MBAs), specifically Beamforming Network (BFN)-based ones, to improve the capabilities of traditional MPT and Radio Frequency Energy Harvesting (RFEH) systems. The paper starts by introducing the usage of MPT in IoT applications and how MBAs could help solve some of them or at least mitigate them. Afterward, a general explanation of the typical MBAs architectures, including Passive Multibeam Antennas (PMBAs), Multibeam Phased-Array Antennas (MBPAAs), and Digital Multibeam Antennas (DMBAs) is presented, along with their advantages, drawbacks, and some emerging trends. After introducing the typical architectures of MBAs, a comprehensive literature survey is done around rectennas and MPT Transmitters (TXs). This approach allows us to understand better why some architectures are more present than others in both applications, highlighting the exclusive usage of PMBAs in rectennas due to them not using energy. To finalize the paper, using the literature survey done, some challenges associated with integrating MBAs in MPT and RFEH are presented, along with some works presenting ways to mitigate them.
Telecommunication, Electric apparatus and materials. Electric circuits. Electric networks
Parisa Kanani, Mohammad Javad Omidi, Mahmoud Modarres-Hashemi
et al.
This paper introduces a novel high altitude platform station (HAPS)-based integrated sensing and communication (ISAC) system, referred to as HAPS-ISAC, designed to enhance the capabilities of future 6G networks by simultaneously optimizing communication and sensing functions. HAPS operates as a super-macro base station in the stratosphere, utilizing advanced beamforming techniques within a multiple-input multiple-output (MIMO) architecture, supplemented by multiple-input single-output (MISO) configurations, effectively enabling the system to serve ground communication users (CUs) while conducting high-resolution sensing of potential targets. A Rician channel model is employed to capture both line-of-sight (LoS) and non-line-of-sight (NLoS) conditions. The performance of the system is optimized through a non-convex optimization problem that maximizes the minimum beampattern gain towards desired sensing angles while ensuring that the signal-to-interference-plus-noise ratio (SINR) requirements for CUs are satisfied, all under the power constraints of the HAPS. Compared to the traditional terrestrial and UAV-based ISAC systems, HAPS-ISAC delivers sustained and reliable service over extensive areas, leading to significantly improved overall performance. Simulation results show that HAPS-ISAC significantly improves SINR, resource allocation, sensing accuracy, and fairness, outperforming existing technologies. This establishes HAPS-ISAC as a key enabler for 6G networks and advances intelligent infrastructures like IoT and smart cities.
Telecommunication, Transportation and communications
Athanasios Papanikolaou, Athanasios Tziouvaras, George Floros
et al.
The early detection of plant diseases is critical to improving agricultural productivity and ensuring food security. However, conventional centralized deep learning approaches are often unsuitable for large-scale agricultural deployments, as they rely on continuous data transmission to cloud servers and require high computational resources that are impractical for Internet of Things (IoT)-based field environments. In this article, we present a distributed deep learning framework based on Federated Learning (FL) for the diagnosis of plant diseases in IoT sensor networks. The proposed architecture integrates multiple IoT nodes and an edge computing node that collaboratively train an EfficientNet B0 model using the Federated Averaging (FedAvg) algorithm without transferring local data. Two training pipelines are evaluated: a standard single-model pipeline and a hierarchical pipeline that combines a crop classifier with crop-specific disease models. Experimental results on a multicrop leaf image dataset under realistic augmentation scenarios demonstrate that the hierarchical FL approach improves per-crop classification accuracy and robustness to environmental variations, while the standard pipeline offers lower latency and energy consumption.
Theodore Tochuckwu Chiagunye, Somtochukwu Francis Ilo, Godspower Ikechukwu Ndukwe
et al.
This paper presents the development of an automated aquarium monitoring system with an IOT interface using google sheets; the system autonomously monitors key water quality parameters temperature, pH, and turbidity while automating fish feeding and water replacement functions. An ESP32 microcontroller serves as the system’s core, control unit which is programmed using C++ to transmit environmental data to a cloud-based Google Sheet. A servo motor dispenses feed precisely every 12 hours, while two DC pumps are triggered automatically when turbidity exceeds 50 NTU, ensuring proactive water quality management. The designed system is powered by a 30W solar panel and a charge controller coupled with a 12V lead-acid battery, allowing continuous operation in off-grid locations. The system performance test was conducted over a period of five days and was validated by comparing the sensor outputs with results of the manual measurements obtained by using laboratory-grade instruments. The results demonstrated high accuracy, with average deviations of only 1.95% for temperature, 2.09% for pH, and 1.96% for turbidity when compared with the result obtained from the manual measurement. Also the automated feeding and water replacement mechanisms operated with 100% reliability by being able dispense the feed from the hoper at every 12 hours interval and changing the water once the turbidity is equals or above 50 NTU. Hence the proposed system successfully enhanced automation, real-time cloud integration, and renewable power supply for improved fish aquarium management, thereby offering a compelling alternative to labour-intensive and manually operated systems while laying the groundwork for intelligent, data-driven fish farming practices.
Anurag Verma, S. Prakash, Vishal Srivastava
et al.
In this review paper, we have discussed the existing state-of-the-art practices of improved intelligent features, controlling parameters and Internet of things (IoT) infrastructure required for smart building. The main focus is on sensing, controlling the IoT infrastructure which enables the cloud clients to use a virtual sensing infrastructure using communication protocols. The following are some of the intelligent features that usually make building smart such as privacy and security, network architecture, health services, sensors for sensing, safety, and overall management in smart buildings. As we know, the Internet of Things (IoT) describes the ability to connect and control the appliances through the network in smart buildings. The development of sensing technology, control techniques, and IoT infrastructure give rise to a smart building more efficient. Therefore, the new and problematic innovation of smart buildings in the context of IoT is to a great extent and scattered. The conducted review organized in a scientific manner for future research direction which presents the existing challenges, and drawbacks.
Incorporating Internet of Things (IoT) technology into indoor kale cultivation holds significant promise for revolutionizing organic farming methodologies. While numerous studies have investigated the impact of environmental factors on kale growth in IoT-based smart agricultural systems, such as temperature, humidity, and nutrient levels, indoor ultraviolet (UV) LED light’s operational efficiencies and advantages in organic farming still need to be explored. This study assessed the efficacy of 15 UV light-controlling indoor experiments in three distinct lighting groups: kale cultivated using conventional household LED lights, kale cultivated using specialized indoor UV lights designed for plant cultivation, and kale cultivated using hybrid household and LED grow lights. The real-time IoT-based monitoring of light, soil, humidity, and air conditions, as well as automated irrigation using a water droplet system, was employed throughout the experiment. The experimental setup for air conditioning maintained temperatures at a constant 26 degrees Celsius over the 45-day study period. The results revealed that a combination of daylight household lights and indoor 4000 K grow lights scored the highest, indicating optimal growth conditions. The second group exposed to warm white household and indoor grow red light exhibited slightly lower scores but larger leaf size than the third group grown under indoor grow red light, likely attributable to reduced light intensity or suboptimal nutrient levels. This study highlights the potential of indoor UV LED light farming to address challenges posed by urbanization and climate change, thereby contributing to efforts to mitigate agricultural carbon emissions and enhance food security in urban environments. This research contributes to positioning kale as a sustainable organic superfood by optimizing kale cultivation.
Deep learning models are increasingly deployed on edge Internet of Things (IoT) devices. However, these models typically operate under supervised conditions and fail to recognize unseen classes different from training. To address this, zero-shot learning (ZSL) aims to classify data of unseen classes with the help of semantic information. Foundation models (FMs) trained on web-scale data have shown impressive ZSL capability in natural language processing and visual understanding. However, leveraging FMs' generalized knowledge for zero-shot IoT sensing using signals such as mmWave, IMU, and Wi-Fi has not been fully investigated. In this work, we align the IoT data embeddings with the semantic embeddings generated by an FM's text encoder for zero-shot IoT sensing. To utilize the physics principles governing the generation of IoT sensor signals to derive more effective prompts for semantic embedding extraction, we propose to use cross-attention to combine a learnable soft prompt that is optimized automatically on training data and an auxiliary hard prompt that encodes domain knowledge of the IoT sensing task. To address the problem of IoT embeddings biasing to seen classes due to the lack of unseen class data during training, we propose using data augmentation to synthesize unseen class IoT data for fine-tuning the IoT feature extractor and embedding projector. We evaluate our approach on multiple IoT sensing tasks. Results show that our approach achieves superior open-set detection and generalized zero-shot learning performance compared with various baselines. Our code is available at https://github.com/schrodingho/FM\_ZSL\_IoT.