This article presents a secure framework for remote healthcare monitoring in the context of home isolation, thereby addressing the concerns related to untrustworthy client connections to a hospital information system (HIS) within a secure network. Our proposed solution leverages a public blockchain network as a secure distributed database to buffer and transmit patient vital signs. The framework integrates an algorithm for the secure gathering and transmission of vital signs to the Ethereum network. Additionally, we introduce a publish/subscribe paradigm, thus enhancing security using the TLS channel to connect to the blockchain network. An analysis of the maintenance cost of the distributed database underscores the cost-effectiveness of our approach. In conclusion, our framework provides a highly secure and economical solution for remote healthcare monitoring in home isolation scenarios.

Home delivery (B2C) experienced rapid growth during the COVID-19 pandemic, especially food delivery. Nonrepudiation is a problem in security and affects delivery. Blockchain technology is a new technology that addresses this issue. This paper proposes preventing nonrepudiation in home delivery through blockchain technology. We also design a data structure and smart contract for distributed application. In addition, we evaluate the performance of the proposed framework in terms of transaction fees and response times. We experimented on the blockchain emulator, stored data in RDBMS, and implemented a client with a mobile application. The data in the smart contract were directly impacted by the gas used and the response time. Primarily, the function processes the large data size and involves high transaction fees and long response times. The gas used accounts for 9061 times of data length, while the response time accounts for 2.84 times of data length. Finally, we propose a security policy for the proposed framework.

This study investigates the impact of Zlib compression on gas consumption within blockchain systems, focusing particularly on Ethereum transactions. By employing the Ethereum simulator Ganache, we simulate 100 realistic home delivery system datasets to evaluate the performance of compressed versus uncompressed data. The methodology encompasses rigorous statistical analysis to ensure robust results. Our findings reveal that using the Zlib algorithm to compress textual data exceeding 141 bytes before submitting transactions on the Ethereum network reduces the gasUsed while maintaining the system time unchanged. This demonstrates the effectiveness of data compression in optimizing transaction costs without affecting operational efficiency. Additionally, our research extends to analyzing real gasPrice trends on the Ethereum network. We propose a non-linear regression model that accurately predicts hourly gasPrice variations based on the day of the week and the specific time. This provides a valuable tool for users to plan transactions strategically. These insights enhance the understanding of blockchain dynamics and offer practical solutions for improving economic and system efficiency in blockchain operations.

Email spam and phishing detection is typically evaluated using accuracy-centric metrics under implicitly unconstrained computational settings. However, in practical deployment scenarios—particularly in real-time and resource-constrained environments—models with comparable predictive performance may differ substantially in inference latency and resource usage, directly affecting their operational feasibility. This paper introduces TERA, a deployment-aware evaluation framework that formulates model assessment as a constraint-aware decision problem. Instead of aggregating performance and efficiency into a single objective, TERA treats predictive performance as a feasibility requirement that defines an admissible set of models. Within this feasible region, operational factors such as latency and resource usage are used to differentiate among candidates through structured, multi-dimensional analysis. Experiments on benchmark email datasets show that multiple models achieve comparable detection performance, forming a region of predictive equivalence. Within this region, significant variations in latency and resource consumption are observed, indicating that predictive equivalence does not imply deployment equivalence. These findings demonstrate that accuracy-based evaluation alone may provide limited guidance for deployment-oriented model selection. By explicitly separating feasibility constraints from preference-based trade-offs, TERA enables transparent and deployment-aligned model evaluation. The framework supports consistent comparison and selection among accuracy-comparable models without altering the role of detection effectiveness as a primary requirement, thereby complementing existing evaluation practices with a structured decision-oriented perspective.

This study investigates the optimization of a low-cost IoT-based weather station designed for disaster monitoring, focusing on minimizing power consumption. The system architecture includes application, middleware, communication, and sensor layers, with solar power as the primary energy source. A novel task scheduling algorithm was developed to reduce power usage by efficiently managing the sensing and data transmission periods. Experiments compared the energy consumption of polling and deep sleep techniques, revealing that deep sleep is more energy-efficient (4.73% at 15 s time intervals and 16.45% at 150 s time intervals). Current consumption was analyzed across different test scenarios, confirming that efficient task scheduling significantly reduces power consumption. The energy consumption models were developed to quantify power usage during the sensing and transmission phases. This study concludes that the proposed system, utilizing affordable hardware and solar power, is an effective and sustainable solution for disaster monitoring. Despite using non-low-power devices, the results demonstrate the importance of adaptive task scheduling in extending the operational life of IoT devices. Future work will focus on implementing dynamic scheduling and low-power routing algorithms to enhance system functionality in resource-constrained environments.

The network monitoring system (NMS) monitors network service and system, resource capacity plan, statistics and accounting, fault management and performance; such as throughput, latency and round trip time. Audit log is a importance data source that are used for network behaviour analysis. There are a terabyte of traffic and event log that needed any database system to store and reply any query. This paper study the traditional log database (text file), MySQL and MongoDB to be implemented in network monitoring system. The storage growth and execution time are the performance metrics of this experimental. The result show that MongoDB consume storage more that other database system. However, the storage grown as stepwise function. In the execution time perspective, MongoDB process and reply query result within ps that less than MySQL and Text File. Because MongoDB file management is B-Tree, it result in searching time grown as O(log(n)). Whereas big O of searching in MySQL without indexing is O(n).