The domain of underwater wireless sensor networks (UWSNs) had received a lot of attention recently due to its significant advanced capabilities in the ocean surveillance, marine monitoring and application deployment for detecting underwater targets. However, the literature have not compiled the state-of-the-art along its direction to discover the recent advancements which were fuelled by the underwater sensor technologies. Hence, this paper offers the newest analysis on the available evidences by reviewing studies in the past five years on various aspects that support network activities and applications in UWSN environments. This work was motivated by the need for robust and flexible solutions that can satisfy the requirements for the rapid development of the underwater wireless sensor networks. This paper identifies the key requirements for achieving essential services as well as common platforms for UWSN. It also contributes a taxonomy of the critical elements in UWSNs by devising a classification on architectural elements, communications, routing protocol and standards, security, and applications of UWSNs. Finally, the major challenges that remain open are presented as a guide for future research directions.

A comprehensive review is presented on the latest approaches to solutions focusing on multi-hop and mesh LoRa networks through the evaluation of simulations and real-world experiments, based on papers published between 2015 and 2023. The approaches are systematically classified into four (4) aspects: energy awareness, concurrent access and duty cycle regulations, routing protocols, and security. The first aspect encompasses parameter selection, distance, and clustering. The second aspect focuses on network segregation and time synchronisation. The third aspect covers proactive, reactive, and hybrid routing protocols. The aspect of security includes privacy and Denial-of-Service (DoS). Findings show a consensus that multi-hop networks and adherence to radio duty cycle are able to lower average power consumption. Concurrent access method can improve network performance, but more research is needed due to different conclusions. Reactive protocols have a slight advantage over proactive protocols in terms of node deployment scalability and lower power consumption due to less frequent routing table updates. Hybrid protocols are only beneficial in specific topology deployments. Security is still a major concern for LoRa networks, particularly on attacks such as Man-In-The-Middle (MITM), spoofing, wormholes, flooding, packet replay, and information disclosure. The upcoming trend of implementing machine learning algorithms to multi-hop and mesh LoRa networks opens up a wide range of possibilities, ranging from airspace efficiency, efficient route selection, and improving data throughput. A detailed discussion of the aspects’ issues and recommended industry applications that will benefit from multi-hop and mesh LoRa networks are also provided.

A smart grid (SG) is an emerging technology that provides electricity in a cost-efficient and eco-friendly way. SG combined with distributed energy resources (DERs) plays a crucial role in extending the existing grid’s capacity while mitigating carbon emissions. The potential sources of DERs include solar, wind, and tidal energy. Usually, these DERs are located far away from the grid and not necessarily tied to the grid system. However, the energy trading capabilities of a grid-tied DERs are getting attention, both from academia and industry. This bonding of grid-tied DERs helps to decrease the loss of surplus energy, build an energy storage capacity, and other operational charges. Energy-consuming flexible home tasks can be optimized coordinately with the operations of DERs to minimize the economic cost and CO2 emissions. In this work, our problem is multi-objective and we aim to reduce both electricity price and CO2 emission. We proposed a multi-objective self-adaptive multi-population based Jaya algorithm (PMO-SAMP-Jaya) to schedule the operations of flexible home tasks. Different pricing schemes have been applied to uncover the correlation between CO2 emission, economic cost, and pricing schemes. We assume a smart building, including 30 smart homes with PV and energy storage system (ESS) as DERs. Promising results have shown the effectiveness of our proposed scheme.

Underwater wireless sensor networks have grown considerably in recent years and now contribute substantially to ocean surveillance applications, marine monitoring and target detection. However, the existing deployment solutions struggle to address the deployment of mobile underwater sensor nodes as a stochastic system. The system faces internal and external environment problems that must be addressed for maximum coverage in the deployment region while minimizing energy consumption. In addition, the existing traditional approaches have limitations of improving simultaneously the objective function of network coverage and the dissipated energy in mobility, sensing and redundant coverage. The proposed solution introduced a hybrid adaptive multi-parent crossover genetic algorithm and fuzzy dominance-based decomposition approach by adapting the original non-dominated sorting genetic algorithm II. This study evaluated the solution to substantiate its efficacy, particularly regarding the nodes’ coverage rate, energy consumption and the system’s Pareto optimal metrics and execution time. The results and comparative analysis indicate that the Multi-Objective Optimisation Genetic Algorithm based on Adaptive Multi-Parent Crossover and Fuzzy Dominance (MOGA-AMPazy) is a better solution to the multi-objective sensor node deployment problem, outperforming the non-dominated sorting genetic algorithm II, SPEA2 and MOEA/D algorithms. Moreover, MOGA-AMPazy ensures maximum global convergence and has less computational complexity. Ultimately, the proposed solution enables the decision-maker or mission planners to monitor effectively the region of interest.

The rapid growth of Android applications has led to a continuous influx of Android malware. Numerous research has been undertaken to tackle that issue. Existing research has indicated that leveraging machine learning is a highly effective and promising approach for Android malware detection. This paper presents a review of Android malware detection methodologies that rely on machine learning. We commence by providing a brief overview of the background context related to Android applications, including insights into the Android system architecture, security mechanisms, and the categorization of Android malware. Subsequently, with machine learning as the central focus, we methodically examine and condense the current state of research, encompassing crucial perspectives such as sample acquisition, data pre-processing, feature selection, machine learning models, algorithms, and the assessment of detection effectiveness. The aim of this review is to equip scholars with a holistic understanding of Android malware detection through the lens of machine learning. It is intended to serve as a foundational resource for future researchers embarking on new endeavours in this field, while also providing overarching guidance for research endeavours within the broader domain.