The rapid increase in the number of Internet of Things (IoT) devices has led to the development of advanced networking technologies such as bluetooth low energy (BLE) standardized by special interest group (SIG) mesh and Wirepas mesh networks. Each of these technologies offers unique features and capabilities. BLE is a widely used short-range technology that has made a significant impact on the IoT paradigm development thanks to its simplicity, low power consumption, robustness, and low cost. In contrast, Wirepas mesh is a decentralized wireless communication protocol, meaning that each node in the network selects its own role while maintaining and optimizing the connection automatically based on its environment. This paper provides a comparative analysis of BLE SIG mesh and Wirepas mesh, focusing on their security, scalability, power, and memory efficiency, as well as application suitability in diverse ad hoc IoT environments. The study highlights that while BLE SIG mesh benefits from easy adoption, low power consumption, and ease of integration into the consumer IoT ecosystem, Wirepas mesh excels in high-density, large-scale industrial applications due to its robust management and decentralized control. Security is realized in BLE SIG mesh with standard authentication and encryption, whereas Wirepas mesh utilizes deployment-configurable security mechanisms to handle diverse network topologies. This comparative review enables IoT industrialists and researchers to select appropriate mesh technologies based on their specific requirements and deployment constraints.

The Internet of Things (IoT) bridges the gap between the physical and digital worlds, enabling seamless interaction with real-world objects via the Internet. However, IoT systems face significant challenges in ensuring efficient data generation, collection, and management, particularly due to the resource-constrained and unreliable nature of connected devices, which can lead to data loss. This paper presents DRACO (Data Replication and Collection), a framework that integrates a distributed hop-by-hop data replication approach with a routing-free mobile sink-based data collection strategy. DRACO enhances data availability, optimizes replica placement, and ensures efficient data retrieval even under node failures and varying network densities. Extensive ns-3 simulations demonstrate that DRACO outperforms state-of-the-art techniques, improving data availability by up to 15% and 34%, and replica creation by up to 18% and 40%, compared to greedy and random replication techniques, respectively. DRACO also ensures efficient data dissemination through optimized replica distribution and achieves superior data collection efficiency under varying node densities and failure scenarios as compared to commonly used uncontrolled sink mobility approaches namely random walk and self-avoiding random walk. By addressing key IoT data management challenges, DRACO offers a scalable and resilient solution well-suited for emerging use cases including industrial IoT device monitoring, smart city environmental sensing, agricultural IoT data collection, and disaster response networks, where maintaining data availability under device failures or intermittent connectivity is critical.

Enhancing reliability in dense underwater networks with hefty data traffic often raises energy consumption due to constant packet listening and retransmissions caused by packet loss. To address the challenging energy demand in Underwater Acoustic Sensor Networks (UASNs), we propose a Reinforce-ment learning (RL)-based Energy-efficient and Adaptive Multi-modal routing protocol abbreviated as REAM, that integrates Q-learning with multi-modal communication to enhance energy efficiency in underwater networks by adaptively selecting the optimal mode for packet transmission. We compare the performance of two variants of the proposed REAM protocol-REAM-MM, which uses two modems, and REAM-SM, which uses a single modem, against two other state-of-the-art protocols namely QELAR and MARLIN-Q. Our results demonstrate the effective-ness of using multiple modems instead of a single modem in reducing energy consumption and improving reliability. REAM-MM reduces energy consumption per bit by up to 81.2%, 79.6%, and 72.9% under low, medium, and high traffic scenarios, respec-tively, compared to the best-performing alternative, MARLIN-Q. REAM-MM achieves a consistently comparable Packet Delivery Ratio (PDR) to MARLIN-Q and a higher PDR than QELAR and REAM-SM. Additionally, it maintains the lowest energy consumption under all traffic conditions and dense networks.

In the last decade, the field of wireless optical communication has gathered immense interest due to its adoption in growing bandwidth-hungry underwater applications. The expensive and non-standardized on-field research measurements call for a reliable simulation tool that allows researchers to realistically design and assess the performance of Underwater Optical Communication (UOC) systems before conducting actual underwater experiments. In this paper, we present a UOC module as an extension to the network simulator ns-3. The module can study the impact of different water conditions on underwater optical networks from the physical layer to the network layer. The proposed UOC module realizes physical layer models of the UOC channels where the added noise and interference effects are modeled as Additive White Gaussian Noise (AWGN). Results show the capability of our module to facilitate large underwater optical network design and optimization. Since ns-3 is an open-source software, the module has the flexibility and reusability to be further developed by the worldwide research community.

Technology is continually undergoing a constituent development caused by the appearance of billions new interconnected “things” and their entrenchment in our daily lives. One of the underlying versatile technologies, namely wearables, is able to capture rich contextual information produced by such devices and use it to deliver a legitimately personalized experience. The main aim of this paper is to shed light on the history of wearable devices and provide a state-of-the-art review on the wearable market. Moreover, the paper provides an extensive and diverse classification of wearables, based on various factors, a discussion on wireless communication technologies, architectures, data processing aspects, and market status, as well as a variety of other actual information on wearable technology. Finally, the survey highlights the critical challenges and existing/future solutions.