A smart city is a geographical area that uses modern technologies to facilitate the lives of its residents. Wireless sensor networks (WSNs) are important components of smart cities. Deploying IoT sensors in WSNs is a challenging aspect of network design. Sensor deployment is performed to achieve objectives like increasing coverage, strengthening connectivity, improving robustness, or increasing the lifetime of a given WSN. Therefore, a sensor deployment method must be carefully designed to achieve such objective functions without exceeding the available budget. This study introduces a novel deployment algorithm, called the Evaluated Delaunay Triangulation-based Deployment for Smart Cities (EDTD-SC), which targets not only sensor distribution, but also sink placement. Our algorithm utilizes Delaunay triangulation and k-means clustering to find optimal locations to improve coverage while maintaining connectivity and robustness with obstacles existence in sensing area. The EDTD-SC has been applied to real-world areas and cities, such as Midtown Manhattan in New York in the United States of America. The results show that the EDTD-SC outperforms random and regular deployments in terms of area coverage and end-to-end-delay by 29.6% and 29.7%, respectively. Further, it exhibits significant performance in terms of resilience to attacks.

Fifth-generation (5G) cellular networks are a promising technology to meet the rapid growth in wireless traffic. Small cells are critical in fulfilling the requirements of 5G networks. A heterogeneous ultra-dense network (HUDN) is an enabling technology consisting of several types of small cells to enhance the performance of 5G networks effectively. A critical issue of HUDN is the cell selection method because the traditional technique for cell selection is inapplicable in such a network. This study proposes a novel adaptive cell selection (ADA-CS) scheme. It adapts to various characteristics of HUDNs and vehicle movements. It performs six phases to select the best base station with which to be associated. Simulation results show that, with low- and medium-speed vehicles, the ADA-CS scheme outperforms the traditional protocol in terms of the average number of handovers by 42.39%. In addition, it is superior to some relevant recent schemes by up to 36.53%. The adaptation feature of the proposed protocol provides additional improvements regarding the average number of handovers with high-speed vehicles. Therefore, it achieves superiority in terms of the average number of handover failures and unnecessary handovers. In addition, the ADA-CS scheme enhances the average achievable downlink data rates and spectral efficiency per vehicle by 3.98% and 2.79% compared with the traditional and the relevant recent schemes.

In Underwater Sensor Networks (UWSNs), the energy sources of sensor nodes are limited and difficult to recharge and solar energy cannot be used in that environment. The power issue is one of the most significant constraints in underwater sensor networks and energy balancing is essential to prolong the network lifetime. The MAC/routing protocols that are used in other types of networks may not be suitable for UWSNs due to their unique characteristics. This paper aims to overcome the energy problem by developing a new MAC/routing protocol for UWSNs called the Energy-Efficient protocol for UWSNs (EE-UWSNs). It is based on five principles to save sensor energy and to prolong the lifetime of UWSNs. These principles are using finite levels of power, applying the multi-hops transmission, narrowing the scope of transmission, applying inactivation mode, and balancing energy consumption. Using the AUVNetSim simulator, which is a Python project developed by the Massachusetts Institute of Technology (MIT), the proposed EE-UWSNs protocol was compared with well-known protocols. Simulation results proved that the proposed protocol reduces the average energy consumption of sensors by up to 68.49% compared with the other protocols. Furthermore, the average number of collisions and the end-to-end delay are enhanced.

The ultra-dense network (UDN) is one of the key technologies in fifth generation (5G) networks. It is used to enhance the system capacity issue by deploying small cells at high density. In 5G UDNs, the cell selection process requires high computational complexity, so it is considered to be an open NP-hard problem. Internet of Vehicles (IoV) technology has become a new trend that aims to connect vehicles, people, infrastructure and networks to improve a transportation system. In this paper, we propose a machine-learning and IoV-based cell selection scheme called Artificial Neural Network Cell Selection (ANN-CS). It aims to select the small cell that has the longest dwell time. A feed-forward back-propagation ANN (FFBP-ANN) was trained to perform the selection task, based on moving vehicle information. Real datasets of vehicles and base stations (BSs), collected in Los Angeles, were used for training and evaluation purposes. Simulation results show that the trained ANN model has high accuracy, with a very low percentage of errors. In addition, the proposed ANN-CS decreases the handover rate by up to 33.33% and increases the dwell time by up to 15.47%, thereby minimizing the number of unsuccessful and unnecessary handovers (HOs). Furthermore, it led to an enhancement in terms of the downlink throughput achieved by vehicles.

The fifth generation of wireless networks is expected to provide high capacity, low latency, high reliability, and massive connectivity services. Ultra-dense network (UDN) is a clear trend for enhancing capacity, coverage, and load balancing. In UDNs, the cell selection issue for moving vehicles should be addressed. In this paper, a cell selection strategy known as a dwell time estimation (DTE) scheme is proposed, which is based on Vehicle-to-Everything (V2X) communications. It selects small cells that have the longest dwell time in a vehicle’s direction to decrease handover (HO) rate. The proposed DTE scheme is evaluated using two datasets, which are 5G small cells and vehicles datasets, that were collected in the city of Los Angeles in California. The simulation result shows that our DTE algorithm outperforms other recent related schemes in terms of the mean number of HOs by up to 36.26% because it prolongs the dwell time of the vehicle within the serving small cell. Consequently, it reduces the average number of HO failures and unnecessary HOs. In addition, it gives improvements in terms of the average achievable downlink throughput and network energy efficiency. Furthermore, it has superiority over the other schemes in terms of the mean packet delay by up to 16.49%.

Heterogeneous networks (HetNets) are one of the key enabling technologies for next-generation networks. They aim to provide high capacity, low installation cost, and distributed traffic loads. The cell selection issue is an open research problem in HetNets, due to the different characteristics of base stations and the existence of a large number of them. In this paper, a novel software-defined networking (SDN)/machine learning (ML)-based adaptive algorithm is proposed, called adaptive two-tier, based on the K-nearest neighbor (A2T-KNN) algorithm. It is designed for millimeter wave (mmWave)-based HetNets and it has the ability to adapt to the various movement features of moving vehicles, as well as the different characteristics of the base stations. A real-world case is considered in the center of London. Simulation results demonstrate that A2T-KNN achieves high prediction performance in association with different vehicle features and configuration information. It outperforms other related schemes in terms of average number of handovers by up to 45.83%. Moreover, it was found to enhance the average achievable downlink data rate and network energy efficiency achieved by vehicles by up to 17.18% and 16.86%, respectively.