Identification of different species of animals has become an important issue in biology and ecology. Ornithology has made alliances with other disciplines in order to establish a set of methods that play an important role in the birds’ protection and the evaluation of the environmental quality of different ecosystems. In this case, the use of machine learning and deep learning techniques has produced big progress in birdsong identification. To make an approach from AI-IoT, we have used different approaches based on image feature comparison (through CNNs trained with Imagenet weights, such as EfficientNet or MobileNet) using the feature spectrogram for the birdsong, but also the use of the deep CNN (DCNN) has shown good performance for birdsong classification for reduction of the model size. A 5G IoT-based system for raw audio gathering has been developed, and different CNNs have been tested for bird identification from audio recordings. This comparison shows that Imagenet-weighted CNN shows a relatively high performance for most species, achieving 75% accuracy. However, this network contains a large number of parameters, leading to a less energy efficient inference. We have designed two DCNNs to reduce the amount of parameters, to keep the accuracy at a certain level, and to allow their integration into a small board computer (SBC) or a microcontroller unit (MCU).

The utilization of Internet of Things (IoT) devices in various smart city and industrial applications is growing rapidly. Within a trusted authority (TA), such as an industry or smart city, all IoT devices are closely monitored in a controlled infrastructure. However, in cases where an IoT device from one TA needs to communicate with another IoT device from a different TA, the trust establishment between these devices becomes extremely important. Obtaining a digital certificate from a certificate authority for each IoT device can be expensive. To solve this issue, a group authentication framework is proposed that can establish trust between group IoT devices owned by different entities. The Chebyshev polynomial has many important properties, semigroup is one of the most important. These properties make the Chebyshev polynomial a good candidate for the proposed group authentication mechanism. The secure exchange of information between trusted authorities is supported by Blockchain technology. The proposed framework was implemented and tested using Python and deployed on Blockchain using Ethereum’s Goerli’s testnet. The results show that the proposed framework can reasonably use Chebyshev polynomials with degrees up to four digits in length. The values of various parameters related to Blockchain are also discussed to understand the usability of the proposed framework.

The Internet of Things (IoT) is the network of multiple devices known as “things” which includes sensors, security cameras, smart lights, smart TV, traffic lights etc. in the smart home or industrial environment. In many applications, these IoT devices are installed in open areas for example traffic lights/ security cameras in a smart city. Strong authentication and authorisation for these devices need to be deployed to ensure trust among IoT networks. IoT devices produce and forward security-sensitive data and hence confidentiality, authentication and proper authorisation should be the primary priority of an IoT system. Implementing Certificate Authority-based digital certificate solutions is costly because of the number of devices involved in IoT networks. Blockchain is a decentralized ledger-based technology which can help to provide seamless yet cost-effective solutions for confidentiality, authentication, and authorisation for IoT environments. A blockchain-based system for device registration, authentication, authorisation, and data confidentiality is proposed. The paper shows the methodological and procedural details of the proposed security scheme.

The motivation of this paper is to be able to generate high-quality (Structured Query Language) SQL language sentences in terms of syntax and semantics so that they are intended to achieve a concrete predefined and well-known aim. For example, generating SQL sentences that are capable of detecting a cyber-attack from a set of metrics available in a database table. Two solutions are needed to achieve so, a tool that enables and performs the syntactically valid generation of SQL sentences and an (Artificial intelligence) AI algorithm able to guide the semantics of such generations to the achievement of the best sentences for the intended purpose. The main contribution of this manuscript is the first of these solutions. To be concrete, this paper proposes a tool to enable and generate syntactic-valid language sentences. The tool can deal with any language defined as an ANTLR4 EBNF (Extended Backus-Naur Form) grammar. The paper also provides a methodology to help achieve an EBNF grammar suitable for addressing concerns related to ambiguity and recursion as a direct result of the generation process. The paper further implements a prototype utilizing ANTLR4’s recognizer and its Augmented Transition Network for language generation using EBNF grammars. In-depth design and logic implementation are provided, showcasing areas of interest for AI integration. The achieved prototype showed an ability to easily generate syntactically valid SQL statements at various depths, with observable problems becoming more apparent during the exponential recursive growth. Our mitigation controls for such scenarios proved to be successful and were able to complete the recursion whilst also moving the push-down automata forward until query completion. Experimental validation was performed against a SQL EBNF grammar feeding the generated SQL statement into an SQL parser to validate the syntax.

Background: Improving the accuracy of bird population estimation is crucial for determining their species concern. In Scotland, several species, including the Eurasian Bittern and the Corn Crake, pose challenges as their choice of habitat and behaviour makes it extremely difficult for researchers to obtain accurate population numbers. Aims: To identify the targeted species individuals and aid researchers in pinpointing their location. Method: A low-cost open-source Edge-based passive acoustic monitoring system was designed (PANDI). Results: The PANDI system design and initial validation are presented in this paper. Future work: Include increasing classification accuracy and scope and commencement of long-term field-trials.