Energy autonomous sensors for I4.0 applications powered by kinetic energy harvesters (KEHs) are widely discussed – especially in terms of vibration harvesting. Typically, industrial linear stages offer weak vibrations, so other inertia-based harvesting methods are investigated. This study investigates the usability of human motion energy harvesters in industrial linear motion technology for the first time. Two KEHs – harvesting swing or shocks, respectively – are tested while controlling the parameters velocity, acceleration, and jerk-limitation according to the real applications’ parameter ranges. The swing-KEH and the shock-KEH harvested up to 106 and 124 mW, respectively. Furthermore, a parameter study is performed assuming constant driving lengths with optimised stroke rates to obtain a generalised power and energy profile for each harvester. The analytically obtained overall average power is 22 mW for the swing-KEH and 14 mW for the shock-KEH. The analytical investigation revealed that a reciprocal dependency of performance and velocity exists for both KEHs, respectively. Both experimental and analytical parts show that the wireless sensor node for I4.0 on industrial linear stages can be powered by harvesters made for human motions.

This study presents a near-domain-based diagnosis algorithm for wireless sensor networks nodes. The whole network is clustered to adapt to dynamic topologies, then self-diagnosis procedure is started simultaneously on all nodes when a new cycle is initiated, or an abnormal behaviour is detected. Matching with neighbour nodes at most twice, the status of a node can be diagnosed. Wireless link fault between nodes is particularly considered in this study which may lead to the erroneous diagnosis of node status. Theoretical analysis and simulation results show that the proposed method has a high accuracy of fault diagnosis and a less communication traffic between nodes, thus greatly reducing energy consumption. At the same time, a high robustness is maintained in this novel fault diagnosis algorithm.

Wireless sensor network (WSN) is considered as the resource constraint network, in which the entire nodes have limited resources. In WSN, prolonging the lifetime of the network remains as the unsolved point. Accordingly, this study intends to propose a hybrid GGWSO (Grouped Grey Wolf Search Optimisation) algorithm to improve the performance of a cluster head selection in WSN, so that the network's lifetime can be extended. The proposed method concerns the main constraints associated with distance, delay, energy, and security. This study compares the performance of the proposed GGWSO with several traditional algorithms like artificial bee colony (ABC), fractional ABC, group search optimisation and Grey Wolf optimisation-based cluster head selection. During the performance analysis, the various ranges of risk, such as 20, 60, and 100% are added to validate the performance variations, by evaluating the number of alive nodes, and normalised network energy remained in the network. The simulation results have shown that there is a need for a hybrid model for attaining the superior results.

This study proposes a routing algorithm to enhance throughput and spectrum utilisation efficiency for underwater cognitive acoustic sensor networks (UCASNs). In a UCASN, each cognitive acoustic (CA) user would sense the medium to detect available spectrum resources. However, CA users have no exact knowledge about the communication mechanism of the primary users (PUs) such as sonar users or sonar interference sources. In addition, because of the frequency-dependent attenuation, the available frequencies in water are severely limited and are still underutilised. Therefore, by analysing the behaviour of the CA users based on the ON–OFF process in the primary channel, an optimisation problem is derived to maximising the spectrum utilisation taking into account the bandwidth requirement of CA users. In this way, an efficient routing algorithm for UCASNs is proposed to enable throughput improvement of the network while reducing interference to PUs. Finally, simulation results are shown to verify the effectiveness of the authors proposed scheme.

Nowadays, with the rapid increase of Internet users, the Internet services dominate a primary part of our lifestyle. Moreover, the evolution of the internet of things has introduced new insights into smart platforms and devices that leads to the new vision of ‘smart homes’. The idea of smart homes is not a recent concept; it has been in high interest by both academia and industry to make smart homes a more convenient technology for human's comfort. In this study, the authors propose a new hybrid prediction system based on the frequent pattern (FP)-growth and ontology graphs for home automation systems. Their proposed system simulates the human prediction actions by adding common sense data by utilizing the advantages of the ontology graph and the FP-growth to find a better solution in predicting home user actions for automated systems. For the evaluation of the proposed system, two ontology graphs are introduced with FP-growth to achieve the best results. Both graphs are tested through multiple weight values with the results of FP-growth. As a result, the best weight distribution selected in this study is (70, 30) for time and location ontology graphs respectively. Their results showed that the proposed prediction system achieved an accuracy of 79% for all weekdays and 81% excluding weekend days.