Federated learning (FL) has attracted increasing attention as a promising approach to driving a vast number of end devices with artificial intelligence. However, it is very challenging to guarantee the efficiency of FL considering the unreliable nature of end devices while the cost of device-server communication cannot be neglected. In this paper, we propose SAFA, a semi-asynchronous FL protocol, to address the problems in federated learning such as low round efficiency and poor convergence rate in extreme conditions (e.g., clients dropping offline frequently). We introduce novel designs in the steps of model distribution, client selection and global aggregation to mitigate the impacts of stragglers, crashes and model staleness in order to boost efficiency and improve the quality of the global model. We have conducted extensive experiments with typical machine learning tasks. The results demonstrate that the proposed protocol is effective in terms of shortening federated round duration, reducing local resource wastage, and improving the accuracy of the global model at an acceptable communication cost.

The internet of things (IoT) is a technology that has the capacity to revolutionise the way that we live, in sectors ranging from transport to health, from entertainment to our interactions with government. This fantastic opportunity also presents a number of significant challenges. The growth in the number of devices and the speed of that growth presents challenges to our security and freedoms as we battle to develop policies, standards, and governance that shape this development without stifling innovation. This paper discusses the evolution of the IoT, its various definitions, and some of its key application areas. Security and privacy considerations and challenges that lie ahead are discussed both generally and in the context of these applications.

Internet of Things-enabled Intelligent Transportation Systems (ITS) are gaining significant attention in academic literature and industry, and are seen as a solution to enhancing road safety in smart cities. Due to the ever increasing number of vehicles, a significant rise in the number of road accidents has been observed. Vehicles embedded with a plethora of sensors enable us to not only monitor the current situation of the vehicle and its surroundings but also facilitates the detection of incidents. Significant research, for example, has been conducted on accident rescue, particularly on the use of Information and Communication Technologies (ICT) for efficient and prompt rescue operations. The majority of such works provide sophisticated solutions that focus on reducing response times. However, such solutions can be expensive and are not available in all types of vehicles. Given this, we present a novel Internet of Things-based accident detection and reporting system for a smart city environment. The proposed approach aims to take advantage of advanced specifications of smartphones to design and develop a low-cost solution for enhanced transportation systems that is deployable in legacy vehicles. In this context, a customized Android application is developed to gather information regarding speed, gravitational force, pressure, sound, and location. The speed is a factor that is used to help improve the identification of accidents. It arises because of clear differences in environmental conditions (e.g., noise, deceleration rate) that arise in low speed collisions, versus higher speed collisions). The information acquired is further processed to detect road incidents. Furthermore, a navigation system is also developed to report the incident to the nearest hospital. The proposed approach is validated through simulations and comparison with a real data set of road accidents acquired from Road Safety Open Repository, and shows promising results in terms of accuracy.

Supply chains (SC) present performance bottlenecks that contribute to a high level of costs, infiltration of product quality, and impact productivity. Examples of such inhibitors include the bullwhip effect, new product lines, high inventory, and restrictive data flows. These bottlenecks can force manufacturers to source more raw materials and increase production significantly. Also, restrictive data flow in a complex global SC network generally slows down the movement of goods and services. The use of distributed ledger technologies (DLT) in SC management (SCM) demonstrates the potentials to reduce these bottlenecks through transparency, decentralization, and optimizations in data management. These technologies promise to enhance the trustworthiness of entities within the SC, ensure the accuracy of data-driven operations, and enable existing SCM processes to migrate from a linear to a fully circular economy. This article presents a comprehensive review of 111 articles published in the public domain in the use and efficacy of DLT in SC. It acts as a roadmap for current and future researchers who focus on SC security management to better understand the integration of digital technologies such as DLT. We clustered these articles using standard descriptors linked to trustworthiness, namely, immutability, transparency, traceability, and integrity.

Digital twins (DT) have been proposed to support and enhance manufacturing processes of the industries. The outcome of adopting DT is so encouraging that it is hoped that more than 50% of the large industries will benefit from DT by the end of 2021. Unfortunately, DT lacks a single publicly accepted narrative. In order to help researchers for building a common narrative about DT, we present an elaborated structure of DT, namely, spiral DT-framework. Furthermore, for a secure and reliable management of the DT data, we propose using the blockchain technology rather than cloud or fog. As the classical blockchain suffers from transaction confirmation delays and is vulnerable to the quantum attacks, therefore, we propose a new variant of blockchain, namely twinchain, which is quantum-resilient and offers immediate transaction confirmation. This article also presents a framework for deployment of twinchain for manufacturing of a robot surgical machine.