Examining Modern Data Security and Privacy Protocols in Autonomous Vehicles

A fully automated, self-driving car can perceive its environment, determine the optimal route, and drive unaided by human intervention for the entire journey. Connected autonomous vehicles (CAVs) have the potential to drastically reduce accidents, travel time, and the environmental impact of road travel. Such technology includes the use of several sensors, various algorithms, interconnected network connections, and multiple auxiliary systems. CAVs have been subjected to attacks by malicious users to gain/deny control of one or more of its various systems. Data security and data privacy is one such area of CAVs that has been targeted via different types of attacks. The scope of this study is to present a good background knowledge of issues pertaining to different attacks in the context of data security and privacy, as well present a detailed review and analysis of eight very recent studies on the broad topic of security and privacy related attacks. Methodologies including Blockchain, Named Data Networking, Intrusion Detection System, Cognitive Engine, Adversarial Objects, and others have been investigated in the literature and problem- and context-specific models have been proposed by their respective authors.

Geocognitive Technologies Improve Basin- and Petroleum-System Analyses

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 197610, “Application of Geocognitive Technologies to Basin- and Petroleum-System Analyses,” by Paolo Ruffo, Marco Piantanida, SPE, and Floriana Bergero, Eni, et al., prepared for the 2019 Abu Dhabi International Petroleum Exhibition and Conference, Abu Dhabi, 11-14 November. The paper has not been peer reviewed. Eni and IBM developed a cognitive engine exploiting a deep-learning approach to scan documents searching for basin geology concepts, extracting information about petroleum system elements (e.g., formation name; geological age; and lithology of source rocks, reservoirs, and seals), and enabling basin geologists to perform automated queries to collect all information related to a basin of interest. The cognitive engine succeeded in identifying the correct formations, lithologies, and geological ages of the petroleum systems with an accuracy in the range of 75 to 90%. Introduction While commercial databases often provide summary information about basins that can be extracted easily with queries or even interactive tools, the explorationist needs to integrate such information with more up-to-date and in-depth descriptions of structural and sedimentary events occurring in the basin, descriptions that can be found only in unstructured documents. Key information about basins can be scattered across paragraphs, tables, and image captions of hundreds of technical articles, or can be embedded within pictures. Even when exploiting a traditional search engine with the name of the desired basin, the results can be unsatisfactory: first, not all the results might be relevant; second, many different variants of the basin name are often used within publications. In the optimistic hypothesis that the subset of relevant documents is found by the search engine, all key concepts related to a basin need to be understood by the geologist by careful examination of the paper text and images. Moreover, even if the published information (structured and unstructured) on a basin is found, there are different opinions expressed by different authors, in addition to the uncertainty of the data itself (such as the age of a formation or the details of the geological evolution of the basin), so that multiple conceptual models of the basin can be drawn from the uncertain and scarce information available. Some of these conceptual models are more probable while others are less probable, but sometimes the latter happen to be economically more valuable. Therefore, recovery of all relevant information about a basin is crucial, but also important is the preservation of differing opinions about the data - what might be termed minority reports.

AI Business Model: An Integrative Business Approach

Artificial Intelligence is the ecosphere’s prevalent and most comprehensive general knowledge and common-sense cognitive engine. The Artificial Intelligence (AI) business platform model is virtually at affluence with cloud SaaS model. It concerns AI solutions that can work together on top layer of the other digital systems, like a Customer Relationship Management (CRM) and Enterprise Resource Planning (ERP) business system. AI admittances in digital data fluid through these coordination, fueling business enhancements over phase. In this business model, the business will safekeeping a recurrent subscription. This study endeavors to emphasis on the preemptive side of the use of AI and Machine learning (ML) technology to enterprise digital platform business model innovation and business dynamics.

Applying Case-Based Reasoning to Tactical Cognitive Sensor Networks for Dynamic Frequency Allocation

In this paper, a cognitive radio engine platform is proposed for exploiting available frequency channels for a tactical wireless sensor network while aiming to protect incumbent communication devices, known as the primary user (PU), from undesired harmful interference. In the field of tactical communication networks, there is an urgent need to identify available frequencies for opportunistic and dynamic access to channels on which the PU is active. This paper introduces a cognitive engine platform for determining the available channels on the basis of a case-based reasoning technique deployable as a core functionality on a cognitive radio engine to enable dynamic spectrum access (DSA) with high fidelity. To this end, a plausible learning engine to characterize the channel usage pattern is introduced to extract the best channel candidate for the tactical cognitive radio node (TCRN). The performance of the proposed cognitive engine was verified by simulation tests that confirmed the reliability of the functional aspect, which includes the learning engine, as well as the case-based reasoning engine. Moreover, the efficacy of the TCRN with regard to the avoidance of collision with the PU operation, considered the etiquette secondary user (SU), was demonstrated.

Applying Case-Based Reasoning to Tactical Cognitive Sensor Networks for Dynamic Frequency Allocation

This paper proposes a cognitive radio engine platform for making exploitation of available frequency channels usable for a tactical wireless sensor network in presence of incumbent communication devices known as the primary user (PU) required to be protected from undesired harmful interference. In the field of tactical communication networks, it is desperate to find available frequencies for opportunistic and dynamic access to channels in which PU is in active. This paper introduces a cognitive engine plaform for determining available channels on the basis of case-based reasoning technique deployable as core functionality on cognitive radio engine to enable dynamic spectrum access (DSA) with high fidelity. Towards this, this paper introduces a plausible learning engine to characterize channel usage pattern to extract best channel candiates for the tactical cognitive radio node (TCRN). Performance of the proposed cognitive engine is verified by conducting simulation tests which confirm the reliability in functional aspect of the proposed cognitive engine covering the learning engine as well as the case-based reasoning engine with showing how well TCRN can avoid the collision against the PU operation considered as the etiquette secondary user (SU) should have.