Internet of things security implementation using blockchain for wireless technology

Blockchain is a new security system which group many data into a block or so called classifying the data into a block. The block can have many types and each of them content data and security code. By using a decentralize mechanism, one security code protect all the data. That could happen at the server. In this research, a network of wireless sensor technology is proposed. The transmission of sensor data is via the Internet of things (Internet of Thing) technology. As many data transmitted, they have to classified and group them into a block. All the blocks are then send to the central processing unit, like a microcontroller. The block of data is then processed, identified and encrypted before send over the internet network. At the receiver, a GUI or Apps is developed to open and view the data. The Apps or GUI have an encrypted data or security code. User must key in the password before they can view the data. The password used by the end user at the Apps or GUI must be equivalent to the one encrypted at the sensor nodes. This is to satisfy the decentralized concept used in the Blockchain. To demonstrate the Blockchain technology applied to the wireless sensor network, a MATLAB Simulink function is used. The expected results should show a number of block of data in cryptography manner and chain together. The two set of data. Both have the data encrypted using hash. The black dots indicate the data has been encrypted whereas the white dot indicate indicates the data is not encrypted. The half white and half black indicates the data is in progress of encrypted. All this data should arrange in cryptography order and chain together in a vertical line. A protocol called block and chain group the data into the block and then chain then. The data appears in the blocks and send over the network. As seen in the simulation results, the yellow color represents the user data. This data has a default amplitude as 1 or 5. The data is chained and blocked to produce the Blockchain waveform Keywords: Blockchain, Internet of things, Wireless Sensor Network and MATLAB Simulin

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

The Strategic Supply Chain Management in the Digital Era, Tactical vs Strategic

The perspective of procurement and supply chain management is changing dramatically; traditionally, it was seen as a support function; however, the procurement function is receiving increased attention and investment as an essential contributor to the strategic success and a business enabler. While an end-to-end digital supply chain is an opportunity as it unleashes the next level of strategic growth and involves minimal investment in infrastructure, it is still a challenge to optimize and transform. Furthermore, the recent pandemics and geopolitical disruptions of Covid-19, the Ukraine-Russian war, Brexit and the US-China trade war; have structurally changed the global economy and revealed a new risk assessment that will result in the re-introduction of buffers, boundaries across industries and a partial return to regionalization with sort of de-globalization in which existing just-in-time getting replaced by just-in-case strategy

The complex interaction between Global Production Networks, Digital Information Systems and International Knowledge Transfers

Traditionally many studies of knowledge in economics have focused on localized networks and intra-regional collaborations. However, the rising frequency by which firms collaborate within the context of global networks of production and innovation, the increasingly intricate divisions of labor involved and the extensive use of the Internet to facilitate interaction are all relatively novel trends that underline the importance of knowledge creation and flows across different locations. Focusing on this topic, the present chapter examines the complex interactions between global production networks (GPN), digital information systems (DIS) and knowledge transfers in information technology industries. It seeks to disentangle the various conduits through which different kinds of knowledge are transferred within such networks, and investigate how recent generations of DIS are affecting those knowledge transfers. The paper concludes that the dual expansion of GPN and DIS is adding new complexity to the practice of innovation: To access knowledge necessary for sustained creativity firms often have to link up with remote partners in GPN, but to be able to absorb and utilize this knowledge, they also frequently have to engage in local interactive learning processes. These local- global linkages - and the various skills necessary to operate them - are strongly interdependent, mutually reinforcing and critical for the development and maintenance of innovation-based competitiveness.

TEN STEPS TO INCREASE THE KNOWLEDGE FOR AN EFFICIENT MANAGEMENT OF THE INTELLECTUAL CAPITAL IN THE ENTERPRISE BUSINESS INTELLIGENCE

In order to develop intelligent business for become competitive, the enterprises must increase the quality and technologic level of products and services conform with applicable codes and standards, to have permanent new products or to make old products bettering, to respect the market rules, the applicable laws and to have a good price politic. These activities request a large amount of date, information and knowledge collecting from all sources and then transferring at each enterprise level. This work analyses the state of the art of the knowledge management and it propose a methodological model, based on the occurrence of conversion types of the knowledge to be used during the product development process.Business intelligence, knowledge transfer, knowledge applications, knowledge capitalization, knowledge management

TEN STEPS TO INCREASE THE KNOWLEDGE FOR AN EFFICIENT MANAGEMENT OF THE INTELLECTUAL CAPITAL IN THE ENTERPRISE BUSINESS INTELLIGENCE

In order to develop intelligent business for become competitive, the enterprises must increase the quality and technologic level of products and services conform with applicable codes and standards, to have permanent new products or to make old products bettering, to respect the market rules, the applicable laws and to have a good price politic. These activities request a large amount of date, information and knowledge collecting from all sources and then transferring at each enterprise level. This work analyses the state of the art of the knowledge management and it propose a methodological model, based on the occurrence of conversion types of the knowledge to be used during the product development process.Business intelligence, knowledge transfer, knowledge applications, knowledge capitalization, knowledge management

TEN STEPS TO INCREASE THE KNOWLEDGE FOR AN EFFICIENT MANAGEMENT OF THE INTELLECTUAL CAPITAL IN THE ENTERPRISE BUSINESS INTELLIGENCE

In order to develop intelligent business for become competitive, the enterprises must increase the quality and technologic level of products and services conform with applicable codes and standards, to have permanent new products or to make old products bettering, to respect the market rules, the applicable laws and to have a good price politic. These activities request a large amount of date, information and knowledge collecting from all sources and then transferring at each enterprise level. This work analyses the state of the art of the knowledge management and it propose a methodological model, based on the occurrence of conversion types of the knowledge to be used during the product development process.Business intelligence, knowledge transfer, knowledge applications, knowledge capitalization, knowledge management

Managing logistics outsourcing to China: business problems and solutions for Australian firms

This study aims to investigate the outsourcing relationships between firms in Australia and China by using a problem-solution approach, and find out practical solutions. Empirical data from Australian firms were collected by a questionnaire survey and interviews. The survey data were analysed by descriptive statistics and correlation to find the most common problems and related solutions, and then eight case studies were compiled. The results agree that outsourcing can achieve many significant benefits, though it presents a variety of new risks. Solutions found include increasing communication levels, trust and management control