Solid-state data storage is becoming a widely accepted technology and is looking for new ways to provide cost-effective solutions across various information systems. Solid-state drives (SSDs), existing in different types and models, have several sustainable features: storage, dimensions, volume, etc. Due to the wide range of attributes, designing a robust method can easily select from the purchaser/retailer/wholesaler point of view. This work offers a joint multi-criteria decision-making (MCDM) to rank SSD alternatives, and a newly developed approach, namely Measurement Alternatives and Ranking according to the Compromise Solution (MARCOS) technique, is utilised, and a comparative investigation has also been achieved with other MCDM methods. Data of separate SSDs have been collected from the Indian market with twenty-six different models of eleven brands. The Bonferroni operator (BFO) allocates and compiles the objective weights using the Entropy weights technique (EWT), the Criteria Importance through Inter criteria Correlation (CRITIC) and the Method based on the Removal Effects of Criteria (MEREC). The sensitivity analysis using objective weights considering 18 scenarios was performed, and analysis with the Standard deviation shows that the joint MCDM possesses high accuracy and robustness. The results achieved have been tested with Spearman’s rank and Wojciech-Salabun (WS) coefficient, and the first rank goes to SSD-7. The presented results benefit the manufacturers to understand the market requirement better and for the consumer to make a wise decision while purchasing SSD. It also offers future scope for applying the proposed methodology in similar areas, social sciences and engineering, to make complex decisions.

From the 18th century, the Industrial Revolutions led to the creation of a base for the development and progress of civilization and business. Different business models that relied on the new trends defined by the industrial revolutions significantly transformed the ecosystems of companies. This resulted in the accelerated development and entrance into the digital age, followed by the digitization of the environment and establishing a new system of values. Information and communication technologies are the backbone of the development and mapping of the physical domain into cyber-physical, leading to the emergence of new processes, knowledge, and skills that have been established in the context of Digital Transformation and the digital era. The key part of Digital transformation process is the Smart Systems concept, which is present in almost all industries and domains. Smart Agriculture is one of the most common applications of Smart Systems solutions. It aims to answer important questions regarding food production while, at the same time, taking care of the preservation of the environment, the health of the population and global development. In this paper, the key concepts of the digital transformation and smart systems are presented. In addition, the design and development of the GoGrow, commercial solution for smart agriculture is described.

From the 18th century, the Industrial Revolutions led to the creation of a base for the development and progress of civilization and business. Different business models that relied on the new trends defined by the industrial revolutions significantly transformed the ecosystems of companies. This resulted in the accelerated development and entrance into the digital age, followed by the digitization of the environment and establishing a new system of values. Information and communication technologies are the backbone of the development and mapping of the physical domain into cyber-physical, leading to the emergence of new processes, knowledge, and skills that have been established in the context of Digital Transformation and the digital era. The key part of Digital transformation process is the Smart Systems concept, which is present in almost all industries and domains. Smart Agriculture is one of the most common applications of Smart Systems solutions. It aims to answer important questions regarding food production while, at the same time, taking care of the preservation of the environment, the health of the population and global development. In this paper, the key concepts of the digital transformation and smart systems are presented. In addition, the design and development of the GoGrow, commercial solution for smart agriculture is described. Keywords-Industry 4.0; Digital Transformation; Smart Systems; Smart Agriculture;

The rapid advancements in information and communications technologies (ICT) and the increasing number of smart things shift an old-fashioned healthcare system to a model better suited for a population of the 21st century. New healthcare approaches based on Internet of Things (IoT)/Internet of Medical Things (IoMT) powered systems make health monitoring, diagnostics and treatment more personalized, timely and convenient, enabling a global approach to the healthcare system infrastructure development. Commercial systems in this area exist in various forms but usually do not fit the general patient needs, and those that do are usually economically unacceptable due to the high operational and development costs. Do It Yourself (DIY) healthcare, including mobile applications and consumer medical devices, nowadays is the top healthcare trend. Therefore, this paper, based on well-known low-cost technologies, presents a DIY IoMT solution for observing human vital parameter as well as environmental factors affecting health.

Development of new technologies, particularly the Internet and Sensor Networks, creates a completely new paradigm of the Internet utilization, commonly known as “The Internet of Things (IoT)”. The IoT can be defined as a worldwide network of “smart things” enabled to interact and communicate to each other, as well as with the environment, empowering better understanding of the “real/physical world” and discovering and extracting information about objects and actions that drive that world. Sensor nodes today can be looked as smart objects and therefore they can produce significant computational power which can be used for manipulating and processing collected information. Energy and power efficiency are essential factors in the design and operation of sensor nodes and there are a number of initiatives and tendencies to improve the power efficiency in variety of areas. Relying on the fact that the choice of control algorithm and location of the computational logic may strongly influence power efficiency, in this paper a prototype sensor node, empowered by using fuzzy logic in decision making process, is built and tested in real case environment scenario. Used fuzzy logic processing algorithm is based on predefined rules and can detect a temperature changes in order to ensure accurate and timely response in the case of fire presence. Comparative analysis of power efficiency has been done, and was carried out for best, worst and average case of timely depended temperature changes. The aim of the experiment is to show which solution is the optimal in the sense of energy consumption – implementation of computational logic on sensor node or on a remote host.