Design and Evaluate Low-Cost Wireless Sensor Network Infrastructure to Monitor the Jetty Docking Area in Rural Areas

Internet of Things (IoT) deployments in rural and remote areas often struggle to provide reliable real-time data due to intermittent connectivity, geographical barriers, and limited device power budgets. These limitations create a critical challenge to ensure low-latency connectivity and sustainable energy consumption. With the advent of communication technologies in remote and rural areas, people increasingly rely on connected devices for activities such as agriculture and infrastructure management. However, the application of communication technologies and IoT solutions faces several challenges in these areas, which typically have natural constraints mainly due to their geography. Among others, realtime communication and energy consumption are two of the main challenges to address in several scenarios, raising significant difficulties to the implementation of technology in these environments. This paper provides a literature review on real-time data transmission and power management strategies for IoT solutions in rural or remote scenarios. Specifically, it details state-of-the-art solutions addressing real-time data transmission and energy efficiency, and identifies key research gaps that merit further attention. Moreover, it also centers on efficient energy consumption approaches. By studying communication protocols and power management, this survey offers valuable insights for researchers, practitioners, and policymakers concerned with IoT deployment in resource-constrained environments.

Recent growth in the Internet of Things (IoT) looks promising for realizing a smart environment of the future. However, concerns about the security of IoT devices are escalating as they are inherently constrained by limited resources, heterogeneity, and lack of standard security controls or protocols. Due to their inability to support state-of-the-art secure network protocols and defense mechanisms, standard security solutions are unsuitable for dynamic IoT environments that require large and smart IoT infrastructure deployments. At present, the IoT based smart environment deployments predominantly use cloud-centric approaches to enable continuous and on-demand data exchange that leads to further security and privacy risks. While standard security protocols, such as Virtual Private Networks (VPNs), have been explored for certain IoT environments recently, the implementation models reported have several variations and are not practically scalable for any dynamically scalable IoT deployment. This paper addresses current drawbacks in providing the required flexibility, interoperability, scalability, and low-cost practical viability of a secure IoT infrastructure. We propose an adaptive end-to-end security model that supports the defense requirements for a scalable IoT infrastructure. With low-cost embedded controllers, such as the Raspberry Pi, allowing for the convergence of more sophisticated networking protocols to be embedded at the IoT monitoring interface, we propose a scalable IoT security model integrating both the IoT devices and the controller as one embedded device. Our approach is unique, with a focus on the integration of a security protocol at the embedded interface. In addition, we demonstrate a prototype implementation of our IoT security model for a smart water monitoring system. We believe that our modest first step would instill future research interests in this direction.

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...

Assessment of factors affecting implementation of IoT based smart skin monitoring systems

Background: The idea behind the Internet of Things is to bring the virtual world into the physical one by connecting commonplace items. With the help of the Internet of Things (IoT), it is possible to remotely sense or control objects through preexisting network infrastructure. This opens up possibilities for computer-based systems to integrate with the physical world, which in turn improves efficiency, accuracy, and economic benefit while reducing the need for human intervention. Objective: The purpose of this patent study is to investigate how a (NSGA-II) multi-objective genetic algorithm might be utilized to optimize the execution of an Internet of Things (IoT) temperature monitoring Box-Type Solar Cooker (BTSC). To determine the best set of output parameters for an IoT temperature monitoring box-type solar cooker, (NSGA-II) multi-objective genetic algorithms are used to perform optimizations of the figure of merits (F2), cooking power, cooker efficiency, and final water temperature. Methods: The present research work involves the development of a Wi-Fi module system integrated with a smart temperature monitoring system for a BTSC. Keeping track of the temperature data from different locations in the BTSC through the IoT system was the primary objective of this project. A waterproof temperature sensor (DS18B20) was used to keep monitoring. After that, the data was shown on an LCD, stored on a microSD card, and made available through a smartphone. The Blynk Applications' IoT was employed. Using existing data, regression-based computational models are developed to describe the complex correlations between the decision-processing parameters and the input parameters of an IOT-based solar cooker. These models are applied in the objective functions after determining that a genetic algorithm is more appropriate for the problem. To forecast the optimal values about the figure of merits (F2), cooking power, cooker efficiency, and final water temperature, the Pareto fronts have been developed. Results: We compare the values of response variables that were gathered experimentally with the values that were predicted by NSGA-II. The predicted values are found to be quite close to experimental values. This indicates that the multi-objective optimization method, as used in this study, has very good prediction performance. The test results are graphically shown using the error bar. Therefore, it is clear that the optimization process used to adjust the parameters of the solar cooker's performance has been quite effective. According to the findings of the experiment, the temperature at which a cooking pot remained stagnant on average was 158°C. It was determined that the cooker was of class A based on the values of the first figure of merit (F1), the second figure of merit (F2), and the cooking power (P), which were respectively 0.132, 0.359, and 86.108 W. Therefore, the thermal efficiency of the IoT-base temperature monitoring box type solar cooker is 39.99 %. Conclusion: The findings of this inquiry furthermore produced the outcome that the model provided can be applied conveniently with a confidence level of 95% to calculate the figure of merits (F2), cooking power, cooker efficiency, and final water temperature value of an Internet of Things-based temperature monitoring BTSC. The performance of IoT-based BTSC is optimized by providing real- time monitoring and data visualization, ultimately improving their efficiency and reliability. This research provides an educational tool to promote awareness and understanding of renewable energy sources and their potential benefits.

Air pollution has become a serious threat to human health, especially in densely populated urban regions such as Delhi NCR. The increasing number of vehicles, industries, and construction activities has resulted in high levels of harmful gases and airborne particles. To overcome this challenge, this work presents a low- cost decentralized smart air purification and monitoring system based on Internet of Things (IoT) and blockchain technology. The proposed system uses DHT11, MQ-7, and MQ-135 sensors to continuously measure temperature, humidity, carbon monoxide, and overall air quality. The sensed data is transmitted to a cloud platform and securely recorded using blockchain to ensure data integrity and transparency. When pollution levels exceed safe limits, a low-cost air purification unit is automatically activated to reduce contaminants. The system is affordable, scalable, and suitable for deployment in smart city environments, providing both real-time monitoring and localized pollution control.

Groundwater has provided a reliable source of high-quality water for human use. After India, USA and China, Pakistan is the fourth largest groundwater user in the world and around 60x109 m3 of groundwater is extracted annually. The situation in Pakistan has further exacerbated when government subsidized electricity for agricultural purposes – paving the way for installation of myriad tube wells across the country which resulted in excessive withdrawal of groundwater. The major challenges in sustainable groundwater management system are twofold. First, increasing withdrawals to meet growing human needs have led to significant groundwater depletion, which is usually not monitored due to high cost of monitoring system. Second, data limitations and the application of regional groundwater models for future prediction.In this research, Internet of Things (IoT) enabled smart groundwater monitoring system has been developed and tested to monitor in-situ real-time dynamics of groundwater depletion. Each groundwater monitoring sensor is connected to an embedded module that consists of a microcontroller and a wireless transceiver based on Long Range Radio (LoRa) technology. The readings from each LoRa enabled module is aggregated at one (or more) gateways which is then connected to a central server typically through an IP connection. Sensors of the smart groundwater monitoring system were calibrated in the lab by fluctuation water levels in a 3-meter water column. A network of the low-cost groundwater sensors was installed in managed aquifer recharge wells to provide real-time assessment of groundwater level measurement remotely. The smart and resource efficient groundwater monitoring system help to reduce number of physical visits to the field and also enhance stakeholders participation to get social benefits (promote equity among groundwater users), economic benefit (optimize pumping, which reduces energy cost) and technical benefit (better estimates of groundwater abstraction) for sustainable groundwater management.

Energy efficient clustering with disease diagnosis model for IoT based sustainable healthcare systems

This study investigates the deployment of artificial intelligence (AI)–supported structural health monitoring (SHM) systems for in-service bridges using Internet of Things (IoT) sensor networks, a rapidly advancing domain that merges cutting-edge sensing, communication, and data analytics to improve infrastructure safety and durability. Traditional bridge inspections and early wired SHM systems have faced persistent limitations, including manual data interpretation, latency in damage detection, and scalability barriers due to cost and complexity. Recent breakthroughs in wireless IoT technologies, multi-modal sensor fusion, and AI-based analytics now offer the potential for continuous, automated, and highly reliable monitoring of structural integrity across diverse bridge typologies and environmental conditions. To critically synthesize the state of knowledge, this systematic review analyzed 146 peer-reviewed papers published between 2000 and 2022 spanning civil engineering, computer science, and information systems. The review explored key dimensions: (a) the historical evolution and objectives of SHM in bridge engineering; (b) IoT sensor modalities and network architectures enabling large-scale monitoring; (c) AI techniques for damage classification, anomaly detection, and signal feature engineering; (d) system reliability and data integrity strategies including calibration, drift compensation, and cybersecurity; (e) deployment challenges and scalability considerations across steel and concrete bridges; (f) comparative field case studies and lessons from global smart infrastructure programs; and (g) emerging research directions such as digital twins, blockchain data provenance, climate-resilient AI, and hybrid human–AI decision systems. The findings indicate that AI-enhanced SHM significantly improves predictive damage detection, reduces false alarms, and supports timely maintenance decisions, especially when combined with multi-sensor IoT networks and robust data governance. However, challenges remain in standardization, model retraining under concept drift, cost–benefit justification for large-scale deployment, and regulatory acceptance of AI-informed safety decisions. The review also highlights knowledge gaps around extreme climate resilience, secure and scalable data management, and human oversight frameworks for trustworthy AI. By consolidating insights across 146 studies, this work provides an integrated roadmap for researchers, engineers, and policymakers aiming to advance the next generation of smart, resilient, and cost-effective bridge monitoring systems.

Condition monitoring systems conduct important roles in manufacturing process lines by monitoring condition status of machines to prevent accident faults of them. Those systems need to be improved in their functionalities by adapting more intellectual, self-reconfigurable, and self-decisional mechanisms for supporting smart manufacturing operations. Whereas, many enterprises in manufacturing industry have in trouble with well-designing these smart condition monitoring systems for fitting their own purpose in their specific domains. In this paper, we propose a systems engineering approach to designing smart condition monitoring systems. The systems engineering is powerful methodology to design complex systems in many multi-disciplinary domains, so we have tailored the systems engineering processes for designing the system architecture of our system-of-interest (i.e. smart condition monitoring systems). The deliverables of this research work can contribute to provide a technical solution for well-designing other smart manufacturing systems in several industrial domains. In the end, our research works can resolve the market domains such as high-productivity and production of high-quality products in manufacturing process lines.

A smart farming concept based on smart embedded electronics, internet of things and wireless sensor network

Technological developments, especially in the IoT (Internet of Things), play a significant role in various fields. In addition to achieving a modern and efficient work environment, IoT can also play a role in the security sector, which has now become a significant need. One of the roles of IoT in the security sector is that it can be applied to calibrated TMWE (Measuring, Dosing, Weighing, and Equipment). So that it can guarantee the correctness of the measurement results from the TMWE, continuous monitoring can be carried out so that fraud does not occur. One of the innovations in this regard is the smart seal. The use of smart seals is prioritized for fixed TMWE, which is vulnerable to theft and breaches. This system is designed based on IoT, which is integrated with cloud services and GPS systems that allow the use of web-based applications for online monitoring of seal conditions by users. Data from the seal will be sent to the server via wifi network and stored in a database, to be displayed in web service with a user, admin, and signer access. In addition to providing information to users about the seal’s location, the use of the GPS also aims to make it easier for admins to detect the seal’s location in the event of a violation.

In this paper, we present the design and implementation of a smart water quality monitoring system for the Tharparkar region of Pakistan, where access to clean water is limited. The system utilizes Internet of Things (IoT) sensors and machine learning algorithms to assess and predict water quality. Parameters such as pH, turbidity, and total dissolved solids were continuously monitored using IoT sensors deployed in three strategically selected groundwater wells in Tharparkar. The collected data was transmitted wirelessly to a central server, where a Support Vector Regression model was applied to analyze water quality trends and classify samples as polluted or unpolluted. The results demonstrate the system's effectiveness in providing accurate, timely, and location-specific information, enabling early detection of contamination, and supporting proactive water resource management. This work highlights the potential of integrating IoT and artificial intelligence to address water scarcity and quality challenges in an underdeveloped region.

- ICRADLE is a smart baby monitoring system leveraging the Internet of Things (IoT) to ensure infant safety and comfort. The system integrates real-time sensors to track vital parameters such as temperature, humidity, motion, and sound, alerting parents instantly via a mobile application. ICRADLE offers enhanced parental awareness by enabling remote monitoring and automated responses to abnormal conditions. Utilizing cloud storage and data analytics, it provides historical insights into the baby's health and environment. Designed for modern parenting, ICRADLE enhances caregiving efficiency while reducing stress and uncertainty, making it a reliable, intelligent companion in early childhood care. Key Words: Smart Baby Monitor, IoT (Internet of Things), Infant Safety, Real-Time Monitoring, Sensor Integration, Mobile Application, Health Tracking, Remote Monitoring, Environmental Sensors, Data Analytics, Cloud Storage, Parental Alerts, Childcare Technology, Automated Response, Smart Parenting

Objective: The objective of this paper is to review the literature on the role of technological innovations in mobile communication systems in expanding connectivity in rural areas, with a focus on the challenges and solutions for implementing the Internet of Things (IoT) in the agricultural sector. Methodology/Approach: The bibliometric study carried out to identify trends and main advances in technological innovations in mobile communication systems, with a focus on the implementation of the Internet of Things (IoT) and improving connectivity in rural areas. Originality/Relevance: This research stands out by addressing a gap in the literature, focusing on the integration of technologies associated with Industry 4.0 and their applications in the digital transformation of agriculture. The study provides an innovative analysis of the barriers and opportunities related to the implementation of these technologies in rural areas, with particular emphasis on the crucial role of connectivity and mobile communication innovations. Key Findings: The findings highlight that the adoption of Agriculture 4.0 technologies faces significant challenges due to high implementation costs and a lack of basic infrastructure in rural areas. However, the research also underscores the considerable benefits of these technologies in improving productivity efficiency, which can be achieved with lower operational costs and reduced environmental impact. The study emphasizes the critical importance of robust investments in rural broadband infrastructure, which are essential for enabling hyperconnectivity between devices, machines, and individuals. This level of connectivity is fundamental to advancing and fully implementing Agriculture 4.0, thus requiring strategic alignment with public policies aimed at overcoming cost and infrastructure barriers. Theoretical/Methodological Contributions: This study provides a conceptual framework for the intersections between Agriculture 4.0 technologies and their implementation in rural areas, highlighting how connectivity and innovations in mobile communication are key to advancing this agricultural revolution. By mapping emerging trends and adoption challenges, the research provides a solid foundation for future strategic public and private policy decisions, as well as guiding managers in allocating investments to maximize competitiveness in the agricultural sector. These contributions are essential to understanding the transformative role of 4.0 technologies in modern agriculture.