Characterization of RF signals in Different Types of Water

Underwater communication at microwave frequencies overcome the data rate and bandwidth limitations of acoustic communication and the environmental problems associated with optical-based underwater networks. It has several applications in underwater sensors networks. In view of potential applications to 5G, we conducted experimental tests and computer simulations to investigate the propagation losses encountered by microwave signals in the licensed and license-free frequencies (up to 2.6 GHz) in freshwater and seawater. Our results indicate the feasibility of microwave communication in freshwater at the licensed mobile frequencies

The role of artificial intelligence driven 5G networks in COVID-19 outbreak: opportunities, challenges, and future outlook

There is no doubt that the world is currently experiencing a global pandemic that is reshaping our daily lives as well as the way business activities are being conducted. With the emphasis on social distancing as an effective means of curbing the rapid spread of the infection, many individuals, institutions, and industries have had to rely on telecommunications as a means of ensuring service continuity in order to prevent complete shutdown of their operations. This has put enormous pressure on both fixed and mobile networks. Though fifth generation mobile networks (5G) is at its infancy in terms of deployment, it possesses a broad category of services including enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC), and massive machine-type communications (mMTC), that can help in tackling pandemic-related challenges. Therefore, in this paper, we identify the challenges facing existing networks due to the surge in traffic demand as a result of the COVID-19 pandemic and emphasize the role of 5G empowered by artificial intelligence in tackling these problems. In addition, we also provide a brief insight on the use of artificial intelligence driven 5G networks in predicting future pandemic outbreaks, and the development a pandemic-resilient society in case of future outbreaks

Energy Optimisation Through Path Selection for Underwater Wireless Sensor Networks

This paper explores energy-efficient ways of retrieving data from underwater sensor fields using autonomous underwater vehicles (AUVs). Since AUVs are battery-powered and therefore energy-constrained, their energy consumption is a critical consideration in designing underwater wireless sensor networks. The energy consumed by an AUV depends on the hydrodynamic design, speed, on-board payload and its trajectory. In this paper, we optimise the trajectory taken by the AUV deployed from a floating ship to collect data from every cluster head in an underwater sensor network and return to the ship to offload the data. The trajectory optimisation algorithm models the trajectory selection as a stochastic shortest path problem and uses reinforcement learning to select the minimum cost path, taking into account that banked turns consume more energy than straight movement. We also investigate the impact of AUV speed on its energy consumption. The results show that our algorithm improves AUV energy consumption by up to 50% compared with the Nearest Neighbour algorithm for sparse deployments

DEKCS: a dynamic clustering protocol to prolong underwater sensor networks

Energy consumption is a critical issue in the design of wireless underwater sensor networks (WUSNs). Data transfer in the harsh underwater channel requires higher transmission powers compared to an equivalent terrestrial-based network to achieve the same range. However, battery-operated underwater sensor nodes are energy-constrained and require that they transmit with low power to conserve power. Clustering is a technique for partitioning wireless networks into groups where a local base station (cluster head) is only one hop away. Due to the proximity to the cluster head, sensor nodes can lower their transmitting power, thereby improving the network energy efficiency. This paper describes the implementation of a new clustering algorithm to prolong the lifetime of WUSNs. We propose a new protocol called distance- and energy-constrained k-means clustering scheme (DEKCS) for cluster head selection. A potential cluster head is selected based on its position in the cluster and based on its residual battery level. We dynamically update the residual energy thresholds set for potential cluster heads to ensure that the network fully runs out of energy before it becomes disconnected. Also, we leverage the elbow method to dynamically select the optimal number of clusters according to the network size, thereby making the network scalable. Our evaluations show that the proposed scheme outperforms the conventional low-energy adaptive clustering hierarchy (LEACH) protocol by over 90% and an optimised version of LEACH based on k-means clustering by 42%

Dynamic Clustering and Data Aggregation for the Internet-of-Underwater-Things Networks

Advances in semiconductor technology have made it possible to have high processing powers in cheap microcontrollers, which is spawning off a revolution in the range of applications of the Internet-of-Things (IoT) and its underwater counterpart, the Internet-of-Underwater-Things (IoUT). As a result, it has now become possible and cost effective to implement powerful data processing algorithms on very cheap microcontrollers and achieve network intelligence on edge devices. In this paper, we evaluate the impact of implementing an unsupervised machine learning technique based on the k-means algorithm, as well as data aggregation, on the performance of a wireless underwater sensor network. A clustering algorithm based on the k-means algorithm is used to divide the network into clusters and to select cluster heads based on network topology and residual energy. Each cluster head collects and aggregates data from nodes within its cluster's coverage and forwards the data to the sink. The network is deployed in a shallow seabed, and it is assumed that the nodes can reach the sink using their full transmission powers. Hence, the performance evaluation compares the sum-throughput, energy efficiency and coverage probability for direct transmissions to the sink against transmissions using the cluster heads. We also propose a special consideration for disaster early warning data, which packets are assigned priority delivery and handled with minimum delay. The evaluation is performed through computer simulations and the results show over a 100% improvement in throughput for clusterbased transmissions compared to direct transmissions

A Survey on Energy Optimization Techniques in UAV-Based Cellular Networks: From Conventional to Machine Learning Approaches

Wireless communication networks have been witnessing an unprecedented demand due to the increasing number of connected devices and emerging bandwidth-hungry applications. Albeit many competent technologies for capacity enhancement purposes, such as millimeter wave communications and network densification, there is still room and need for further capacity enhancement in wireless communication networks, especially for the cases of unusual people gatherings, such as sport competitions, musical concerts, etc. Unmanned aerial vehicles (UAVs) have been identified as one of the promising options to enhance the capacity due to their easy implementation, pop up fashion operation, and cost-effective nature. The main idea is to deploy base stations on UAVs and operate them as flying base stations, thereby bringing additional capacity to where it is needed. However, because the UAVs mostly have limited energy storage, their energy consumption must be optimized to increase flight time. In this survey, we investigate different energy optimization techniques with a top-level classification in terms of the optimization algorithm employed; conventional and machine learning (ML). Such classification helps understand the state of the art and the current trend in terms of methodology. In this regard, various optimization techniques are identified from the related literature, and they are presented under the above mentioned classes of employed optimization methods. In addition, for the purpose of completeness, we include a brief tutorial on the optimization methods and power supply and charging mechanisms of UAVs. Moreover, novel concepts, such as reflective intelligent surfaces and landing spot optimization, are also covered to capture the latest trend in the literature.Comment: 41 pages, 5 Figures, 6 Tables. Submitted to Open Journal of Communications Society (OJ-COMS