The Kingdom of Saudi Arabia is known for its extreme climate where temperatures can exceed 50 °C, especially in summer. Improving agricultural production can only be achieved using innovative environmentally suitable solutions and modern agricultural technologies. Using Internet of Things (IoT) technologies in greenhouse farming allows reduction of the immediate impact of external climatic conditions. In this paper, a highly scalable intelligent system controlling, and monitoring greenhouse temperature using IoT technologies is introduced. The first objective of this system is to monitor the greenhouse environment and control the internal temperature to reduce consumed energy while maintaining good conditions that improve productivity. A Petri Nets (PN) model is used to achieve both monitoring of the greenhouse environment and generating the suitable reference temperature which is sent later to a temperature regulation block. The second objective is to provide an Energy-Efficient (EE) scalable system design that handles massive amounts of IoT big data captured from sensors using a dynamic graph data model to be used for future analysis and prediction of production, crop growth rate, energy consumption and other related issues. The design tries to organize various possible unstructured formats of raw data, collected from different kinds of IoT devices, unified and technology-independent fashion using the benefit of model transformations and model-driven architecture to transform data in structured form.

Network security and energy consumption are deemed to be two important components of wireless and mobile ad hoc networks (WMANets). There are various routing attacks which harm Ad Hoc networks. This is because of the unsecure wireless communication, resource constrained capabilities and dynamic topology. In order to cope with these issues, Ad Hoc On-Demand Distance Vector (AODV) routing protocol can be used to remain the normal networks functionality and to adjust data transmission by defending the networks against black hole attacks. The proposed system, in this work, identifies the optimal route from sender to collector, prioritizing the number of jumps, the battery life, and security, which are fundamental prerequisites. Researches have proposed various plans for detecting the shortest route, as well as ensuring energy conversions and defense against threats and attacks. In this regard, the packet drop attack is one of the most destructive attack against WMANet communication and hence merits special attention. This type of attack may allow the attacker to take control of the attacked hubs, which may lost packets or transmitted information via a wrong route during the packets journey from a source hub to a target one. Hence, a new routing protocol method has been proposed in this study. It applies the concept of energy saving systems to conserve energy that is not required by the system. The proposed method for energy aware detection and prevention of packet drop attacks in mobile ad hoc networks is termed the Ad Hoc On-Demand and Distance Vector–Packet Drop Battling Mechanism (AODV–PDBM).

Heart disease (HD) has surpassed all other causes of death in recent years. Estimating one’s risk of developing heart disease is difficult, since it takes both specialized knowledge and practical experience. The collection of sensor information for the diagnosis and prognosis of cardiac disease is a recent application of Internet of Things (IoT) technology in healthcare organizations. Despite the efforts of many scientists, the diagnostic results for HD remain unreliable. To solve this problem, we offer an IoT platform that uses a Modified Self-Adaptive Bayesian algorithm (MSABA) to provide more precise assessments of HD. When the patient wears the smartwatch and pulse sensor device, it records vital signs, including electrocardiogram (ECG) and blood pressure, and sends the data to a computer. The MSABA is used to determine whether the sensor data that has been obtained is normal or abnormal. To retrieve the features, the kernel discriminant analysis (KDA) is used. By contrasting the suggested MSABA with existing models, we can summarize the system’s efficacy. Findings like accuracy, precision, recall, and F1 measures show that the suggested MSABA-based prediction system outperforms competing approaches. The suggested method demonstrates that the MSABA achieves the highest rate of accuracy compared to the existing classifiers for the largest possible amount of data.

This paper presents a conceptual architecture, design, and recommendation for the IoT Edge-based healthcare management system. The suggested architecture aims at distributing the workload of system performance (electronic healthcare services), including monitoring, diagnosis, prediction, as well as managing and archiving medical data of patients across different points of the system (at the edge and on the cloud). The proposed system design consists of two main subsystems (one for monitoring tasks and another one for medical record management activities). Both subsystems interact with multiple kinds of database systems (SQL and NoSQL). Transformational-based system for data migration is presented as a contribution of this paper. Two styles of transformation compositions are considered in the architectural design of transformation agents.

The incorporation of sustainability principles during the requirements engineering phase of the development life cycle constitutes greening software requirements. This incorporation can have a variety of effects on the software design employed in modern and cutting-edge information technology (IT) systems. When sustainability principles are incorporated into requirements engineering, software design priorities can change and address current design issues such as energy and resource consumption, modularity, maintainability, and adaptability. In contrast to other green approaches that consider sustainable development, there is a further need to investigate the relationship between software development and the relevant green principles of sustainability during the requirements engineering phase. We present a new mechanism for mapping software nonfunctional requirements (NFRs) to defined dimensions of green software sustainability, consisting of two mapping steps: 1) between NFRs and sustainability dimensions; and 2) between sustainability dimensions and two clusters of green IT aspects defined in this work. The overall architecture of the promising approach is based on the use of the Bidirectional Encoder Representations from Transformers (BERT) language model with an expanded dataset. We consider transfer learning and domain-specific fine-tuning capabilities for constructing and evaluating a model specifically tailored for developing a proof of concept of the greening software requirements engineering task, as language models have recently emerged as a potent technique in the field of software engineering, with numerous applications in code analysis, automated documentation, and code generation. In addition, we test the model’s performance using an extended version of the PROMISE_exp dataset after adding a new binary classification column for categorizing sustainability dimensions into two defined clusters: Eco-technical and Socioeconomic, and having a selected domain expert label the raw data. The model’s efficiency is evaluated using four matrices—1) accuracy; 2) precision; 3) recall; and 4) F1 score—across a variety of epoch and batch sizes. Our numerical results demonstrate the viability of the approach in text classification tasks via performing well in mapping NFRs to software sustainability dimensions. This acts as a proof of concept for automating the sustainability measurement of software awareness at the early development stage. In addition, the results emphasize the importance of domain-specific fine-tuning and transfer learning for obtaining high performance in classification tasks in requirements engineering.

Purpose: This paper aims to introduce the goal-oriented requirements extraction approach (GOREA). It is an elicitation approach that uses, specifically, healthcare business goals to derive the requirements of e-health system to be developed.Design/methodology/approach: GOREA consists of two major phases: (1) modelling e-health business requirements phase and (2) modelling e-health information technology (IT) and systems requirements phase. The modelling e-health business requirements phase is divided into two main stages: (1) model e-health business strategy stage and (2) model e-health business environment stage. The modelling e-health IT and systems requirements phase illustrates the process of obtaining requirements of e-health system from the organizational goals that are determined in the previous phase. It consists of four main steps that deal with business goals of e-health system: (1) modelling e-health business process (BP) step; (2) modelling e-health business goals step; (3) analysing e-health business goals step; and (4) eliciting e-health system requirements step. A case study based on the basic operations and services in hospital emergency unit for checking patient against COVID-19 virus and taking its diagnostic testing has been set and used to examine the validity of the proposed approach by achieving the conformance of the developed system to the business goals.Findings: The results indicate that (1) the proposed GOREA has a positive influence on the system implementation according to e-health business expectations; and (2) it can successfully fulfil the need of e-health business in order to save the citizens life by checking them against COVID-19 virus.Research limitations/implications: The proposed approach has some limitations. For example, it is only validated using one e-health business goal and thus it has to be authenticated with different e-health business goals in order to address different e-health problems.Originality/value: Many e-health projects and innovations are not established based on robust system requirements engineering phase. In order to ensure the success delivery of e-health services, all characteristics of e-health systems and applications must be understood in terms of technological perspectives as well as the all system requirements.