The world’s current linear economic model is unsustainable. This model encourages improper use of limited natural resources and causes abundant waste production resulting in severe harm to the environment. A circular economy (CE) is a sustainable, restorative, and regenerative alternative to the current linear economy and is gaining popularity worldwide. Amongst various digital technologies, Artificial intelligence (AI) is a crucial enabler for CE and can aid significantly with the adoption and implementation of CE in real-world applications. In this paper, we describe the intersection of AI and CE and policies around implementing CE principles using AI. As a means of grounding the discussion, we discuss some initiatives taken by the Irish government to adopt circularity and explore the role AI plays in these. We present a number of practical examples of AI and CE from Ireland. We argue that digitalisation has potential in CE and it has a major role to play in the transition towards CE. We close the paper by reflecting on future steps around practical implementations of AI-based CE processes.

A mobile ad-hoc network (MANET) is a temporary network of wireless mobile nodes. In a MANET, it is assumed that all of the nodes cooperate with each other to transfer data packets in a multi-hop fashion. However, some malicious nodes don’t cooperate with other nodes and disturb the network through false routing information. In this paper, we propose a prominent technique, called dual attack detection for black and gray hole attacks (DDBG), for MANETs. The proposed DDBG technique selects the intrusion detection system (IDS) node using the connected dominating set (CDS) technique with two additional features; the energy and its nonexistence in the blacklist are also checked before putting the nodes into the IDS set. The CDS is an effective, distinguished, and localized approach for detecting nearly-connected dominating sets of nodes in a small range in mobile ad hoc networks. The selected IDS nodes broadcast a kind of status packet within a size of the dominating set for retrieving the complete behavioral information from their nodes. Later, IDS nodes use our DDBG technique to analyze the collected behavioral information to detect the malicious nodes and add them to the blacklist if the behavior of the node is suspicious. Our experimental results show that the quality of the service parameters of the proposed technique outperforms the existing routing schemes.