How next generation digital technologies are transforming the pharmaceutical industry

The introduction of new digital technologies in various business sectors leads to numerous changes in the business environment. One of the most tangible such changes is the transformation in the pharmaceutical industry produced by the expanded application of cloud systems, artificial intelligence, and machine learning.The use of autonomous devices and systems is vital in producing high-quality medicines at lower cost and in less time. The research and development phase for developing a new pharmaceutical product is very long and expensive compared to conventional products, and the use of advanced technologies in this process can bring huge benefits to the pharmaceutical industry, while providing an opportunity for regulatory authorities to track all stages of the process with greater transparency and less effort

Design and Development IoT based Smart Energy Management Systems in Buildings through LoRa Communication Protocol

Energy management is a vital tool for reducing significant supply-side deficits and increasing the efficiency of power generation. The present energy system standard emphasizes lowering the total cost of power without limiting consumption by opting to lower electricity use during peak hours. The previous problem necessitates the development and growth of a flexible and mobile technology that meets the needs of a wide variety of customers while preserving the general energy balance. In order to replace a partial load decrease in a controlled manner, smart energy management systems are designed, according to the preferences of the user, for the situation of a full power loss in a particular region. Smart Energy Management Systems incorporate cost-optimization methods based on human satisfaction with sense input features and time of utilization. In addition to developing an Internet of Things (IoT) for data storage and analytics, reliable LoRa connectivity for residential area networks is also developed. The proposed method is named as LoRa_bidirectional gated recurrent neural network (LoRa_ BiGNN) model which achieves 0.11 and 0.13 of MAE, 0.21 and 0.23 of RMSE, 0.34 and 0.23 of MAPE for heating and cooling loads

Resilient IoT-based Monitoring System for Crude Oil Pipelines

International audiencePipeline networks dominate the oil and gas mid-stream sector, and although the safest means of transportation for oil and gas products, they are susceptible to failures. These failures are due to manufacturing defects, environmental effects, material degradation, or third party interference through sabotage and vandalism. Internet of Things (IoT)-based solutions are promising to address these by monitoring and predicting failures. However, some challenges remain in the deployment of industrial IoT-based solutions, as the reliability, the robustness, the maintainability, the scalability, the energy consumption, etc. This paper is therefore aimed at highlighting potential solutions for detection and mitigation of pipeline failures while addressing the robustness, the cost and scalability issues of such approach efficiently across the network infrastructure, data and service layers

REMIND: A Framework for the Resilient Design of Automotive Systems

In the past years, great effort has been spent on enhancing the security and safety of vehicular systems. Current advances in information and communication technology have increased the complexity of these systems and lead to extended functionalities towards self-driving and more connectivity. Unfortunately, these advances open the door for diverse and newly emerging attacks that hamper the security and, thus, the safety of vehicular systems. In this paper, we contribute to supporting the design of resilient automotive systems. We review and analyze scientific literature on resilience techniques, fault tolerance, and dependability. As a result, we present the REMIND resilience framework providing techniques for attack detection, mitigation, recovery, and resilience endurance. Moreover, we provide guidelines on how the REMIND framework can be used against common security threats and attacks and further discuss the trade-offs when applying these guidelines

A Logic for Monitoring Dynamic Networks of Spatially-distributed Cyber-Physical Systems

Cyber-Physical Systems (CPS) consist of inter-wined computational (cyber) and physical components interacting through sensors and/or actuators. Computational elements are networked at every scale and can communicate with each other and with humans. Nodes can join and leave the network at any time or they can move to different spatial locations. In this scenario, monitoring spatial and temporal properties plays a key role in the understanding of how complex behaviors can emerge from local and dynamic interactions. We revisit here the Spatio-Temporal Reach and Escape Logic (STREL), a logic-based formal language designed to express and monitor spatio-temporal requirements over the execution of mobile and spatially distributed CPS. STREL considers the physical space in which CPS entities (nodes of the graph) are arranged as a weighted graph representing their dynamic topological configuration. Both nodes and edges include attributes modeling physical and logical quantities that can evolve over time. STREL combines the Signal Temporal Logic with two spatial modalities reach and escape that operate over the weighted graph. From these basic operators, we can derive other important spatial modalities such as everywhere, somewhere and surround. We propose both qualitative and quantitative semantics based on constraint semiring algebraic structure. We provide an offline monitoring algorithm for STREL and we show the feasibility of our approach with the application to two case studies: monitoring spatio-temporal requirements over a simulated mobile ad-hoc sensor network and a simulated epidemic spreading model for COVID19

Towards a Secure and Resilient Vehicle Design: Methodologies, Principles and Guidelines

The advent of autonomous and connected vehicles has brought new cyber security challenges to the automotive industry. It requires vehicles to be designed to remain dependable in the occurrence of cyber-attacks. A modern vehicle can contain over 150 computers, over 100 million lines of code, and various connection interfaces such as USB ports, WiFi, Bluetooth, and 4G/5G. The continuous technological advancements within the automotive industry allow safety enhancements due to increased control of, e.g., brakes, steering, and the engine. Although the technology is beneficial, its complexity has the side-effect to give rise to a multitude of vulnerabilities that might leverage the potential for cyber-attacks. Consequently, there is an increase in regulations that demand compliance with vehicle cyber security and resilience requirements that state vehicles should be designed to be resilient to cyber-attacks with the capability to detect and appropriately respond to these attacks. Moreover, increasing requirements for automotive digital forensic capabilities are beginning to emerge. Failures in automated driving functions can be caused by hardware and software failures as well as cyber security issues. It is imperative to investigate the cause of these failures. However, there is currently no clear guidance on how to comply with these regulations from a technical perspective.In this thesis, we propose a methodology to predict and mitigate vulnerabilities in vehicles using a systematic approach for security analysis; a methodology further used to develop a framework ensuring a resilient and secure vehicle design concerning a multitude of analyzed vehicle cyber-attacks. Moreover, we review and analyze scientific literature on resilience techniques, fault tolerance, and dependability for attack detection, mitigation, recovery, and resilience endurance. These techniques are then further incorporated into the above-mentioned framework. Finally, to meet requirements to hastily and securely patch the increasing number of bugs in vehicle software, we propose a versatile framework for vehicle software updates

On the Secure and Resilient Design of Connected Vehicles: Methods and Guidelines

Vehicles have come a long way from being purely mechanical systems to systems that consist of an internal network of more than 100 microcontrollers and systems that communicate with external entities, such as other vehicles, road infrastructure, the manufacturer’s cloud and external applications. This combination of resource constraints, safety-criticality, large attack surface and the fact that millions of people own and use them each day, makes securing vehicles particularly challenging as security practices and methods need to be tailored to meet these requirements.This thesis investigates how security demands should be structured to ease discussions and collaboration between the involved parties and how requirements engineering can be accelerated by introducing generic security requirements. Practitioners are also assisted in choosing appropriate techniques for securing vehicles by identifying and categorising security and resilience techniques suitable for automotive systems. Furthermore, three specific mechanisms for securing automotive systems and providing resilience are designed and evaluated. The first part focuses on cyber security requirements and the identification of suitable techniques based on three different approaches, namely (i) providing a mapping to security levels based on a review of existing security standards and recommendations; (ii) proposing a taxonomy for resilience techniques based on a literature review; and (iii) combining security and resilience techniques to protect automotive assets that have been subject to attacks. The second part presents the design and evaluation of three techniques. First, an extension for an existing freshness mechanism to protect the in-vehicle communication against replay attacks is presented and evaluated. Second, a trust model for Vehicle-to-Vehicle communication is developed with respect to cyber resilience to allow a vehicle to include trust in neighbouring vehicles in its decision-making processes. Third, a framework is presented that enables vehicle manufacturers to protect their fleet by detecting anomalies and security attacks using vehicle trust and the available data in the cloud