Connectivity In Wireless Networks Research Articles

Unlocking the Potential of Mobile-Edge Cloud: A Comprehensive Review and Future Directions

With the rapid expansion of the Internet of Things (IoT), the demand for computational resources continues to soar, necessitating innovative solutions to address the needs of resource-constrained IoT users. Mobile edge computing (MEC) emerges as a promising remedy, mitigating the strain imposed by resource-intensive mobile applications. Concurrently, leveraging unmanned aerial vehicles (UAVs) as aerial platforms presents an enticing opportunity to enhance connectivity in wireless networks, owing to their on-demand deployment capabilities, high cruising altitudes, and maneuverability in three-dimensional space. This paper presents a comprehensive examination of UAV-enabled aerial MEC, elucidating its advantages, challenges, and recent advancements across various domains. Topics explored include joint optimization of UAV trajectory, computation offloading, and resource allocation, UAV deployment strategies, task scheduling, load balancing, interplay with other technologies, and machine learning-driven optimizations. Additionally, the paper outlines key avenues for future research endeavors.

Unlocking the Potential of Mobile-Edge Cloud: A Comprehensive Review and Future Directions

With the rapid expansion of the Internet of Things (IoT), the demand for computational resources continues to soar, necessitating innovative solutions to address the needs of resource-constrained IoT users. Mobile edge computing (MEC) emerges as a promising remedy, mitigating the strain imposed by resource-intensive mobile applications. Concurrently, leveraging unmanned aerial vehicles (UAVs) as aerial platforms presents an enticing opportunity to enhance connectivity in wireless networks, owing to their on-demand deployment capabilities, high cruising altitudes, and maneuverability in three-dimensional space. This paper presents a comprehensive examination of UAV-enabled aerial MEC, elucidating its advantages, challenges, and recent advancements across various domains. Topics explored include joint optimization of UAV trajectory, computation offloading, and resource allocation, UAV deployment strategies, task scheduling, load balancing, interplay with other technologies, and machine learning-driven optimizations. Additionally, the paper outlines key avenues for future research endeavors.

Network partition detection and recovery with the integration of unmanned aerial vehicle

SummaryWireless sensor and actor networks (WSANs) consist of nodes associated in an ad hoc manner to perform sensing tasks for information gathering and acting functions on the basis of gathered information. Connectivity is an essential requirement of large‐scale wireless networks, and WSANs are supposed to stay connected. The nodes in hostile environments are prone to failures such as battery depletion, physical damage, or hardware malfunction. The failure of some nodes, like cut vertex nodes, can partition the network into multiple network segments. Most of the solutions for network partition recovery proposed in the literature depend on the assumption that the network is obstacle‐free. However, an obstacle‐free environment is not possible in real‐life situations. In the last few decades, UAVs or drones have been engaged in various applications such as industrial inspections, remote sensing, agriculture, military, disaster relief, and so forth, UAVs can be employed to strengthen the connections in wireless networks by coordinating with ground nodes since they can render services in rough areas where ground nodes cannot provide services. Thus, our research is based on using UAVs as relay nodes to reconnect the disjoint partitions. This paper proposes two algorithms: Drone assisted partition recovery algorithm (DAPRA) and drone assisted detection and partition recovery algorithm (DADPRA). In both algorithms, partitions are detected by the sink node. In DAPRA sink node determines the failed cut‐vertex node and sends UAV to the location of the failed cut‐vertex node. In DADPRA algorithm, UAV identifies the failed cut‐vertex node and reconnects the disjoint network segments. DAPRA and DADPRA are analyzed according to the state‐of‐the‐art parameters, that is, recovery and detection time, UAV's travel distance, and the total messages transmitted. The proposed algorithms are compared with similar Distributed Partition Detection and Recovery using UAV (DPDRU) approach. The simulation results show the proposed algorithms detect network partitioning in less time as compared to DPDRU approach.

Bulk Schottky Junctions-Based Flexible Triboelectric Nanogenerators to Power Backscatter Communications in Green 6G Networks.

This work introduces a novel method to construct Schottky junctions to boost the output performance of triboelectric nanogenerators (TENGs). Perovskite barium zirconium titanate (BZT) core/metal silver shell nanoparticles are synthesized to be embedded into electrospun polyvinylidene fluoride-co-hexafluoropropylene (PVDF-HFP) nanofibers before they are used as tribo-negative layers. The output power of TENGs with composite fiber mat exhibited >600% increase compared to that with neat polymer fiber mat. The best TENG achieved 1339V in open-circuit voltage, 40µA in short-circuit current and 47.9Wm-2 in power density. The Schottky junctions increased charge carrier density in tribo-layers, ensuring a high charge transfer rate while keeping the content of conductive fillers low, thus avoiding charge loss and improving performance. These TENGs are utilized to power radio frequency identification (RFID) tags for backscatter communication (BackCom) systems, enabling ultra-massive connectivity in the 6G wireless networks and reducing information communications technology systems' carbon footprint. Specifically, TENGs are used to provide an additional energy source to the passive tags. Results show that TENGs can boost power for BackCom and increase the communication range by 386%. This timely contribution offers a novel route for sustainable 6G applications by exploiting the expanded communication range of BackCom tags.

Modeling and Analyzing Urban Sensor Network Connectivity Based on Open Data

The optimization of network topology is crucial to achieve efficient data transmission in wireless sensor networks. Recently it has been proven that emerging open data sources can be used for modeling the structures of heterogeneous urban sensor networks. With this, leveraging real location data of various networked and sensing devices became feasible and essential. This approach enables the construction and analysis of more accurate representations based on frequently updated actual network infrastructure topology data, as opposed to using synthetic models or test environments. The presented modeling method serves as the basis for the designed architecture and implemented research environment. This paper introduces a set of algorithms which transform devices’ location data into graph-based wireless network connectivity models. Each algorithm is thoroughly discussed and evaluated. Moreover, static (momentary) and dynamic (time-spanning) network topologies are constructed in four large Polish cities based on publicly available data. Multidimensional simulation-based analysis is conducted to investigate the characteristics of the modeled structures. Directions for further research are suggested as well.

Perancangan Keamanan Jaringan Metode Authentication Login Hotspot Menggunakan Router Mikrotik di PT. Nusindo Rekatama Semesta

Nusindo Rekatama Semesta is a company located in the Sleman Regency area, Jombor Lor, Sinduadi. PT. Nusindo Semesta is a company in the corporate management industry that has been operating since February 2011. PT. Nusindo Rekatama Semesta provides wireless hotspot facilities where employees and visitors who come can take advantage of an internet connection that can be accessed via laptops, smartphones, or other devices such as those that support wireless technology. However, the implementation of hotspots needs to be corrected; for example, per-client bandwidth still needs to be focused. It aims to overcome per-client bandwidth usage and create a captive login portal as a security system so that those who do not have access rights cannot use internet access in the company. Using the hotspot login authentication system within the company. With the application of Mikrotik Router OS as a router in the company's network, it can overcome the instability of wireless network connections, and the captive portal login feature on Mikrotik can prevent users who do not have access rights from being able to use the internet access in the company.

EcoSync: An Energy-Efficient Clock Discipline Data Synchronization in Wi-Fi IoMT Systems

The growth of the Internet of Medical Things (IoMT) and healthcare data analytics allows wearable Wireless Body Area Networks (WBANs) and ambient sensors to collect the large quantities of physiological signals necessary for better patient diagnostics and treatments. Artificial intelligence and machine learning algorithms frequently require precisely synchronized signals from multiple sensors, which in turn require time-consuming and energy-inefficient synchronization methods with constant wireless network connectivity. We propose ecoSync, a highly energy-efficient time synchronization algorithm for Wi-Fi devices in IoMT applications. We demonstrated that ecoSync can correct the time difference error to ±42 µs with an hour between resynchronizations, using only 658 millijoules of energy. This is an 87% improvement in time difference error and a 99.93% reduction in energy usage over using TSF for synchronization alone over a 1 h period. Wireless synchronization of sensors allows placement of physiological sensors on objects of everyday use (Smart Stuff), which in turn allows seamless collection of physiological status data every time we interact with smart objects in an IoMT environment.

Fuzzy logic and cooperative hybrid least squares for improving network capacity and connectivity in high-density wireless networks

Fuzzy logic and cooperative hybrid least squares for improving network capacity and connectivity in high-density wireless networks

Analysis of Long-Range Wireless Area Network Enabled Smart Agriculture for Sustainable Food Production

Technology advancements and increased connection are bolstering the movement toward digital transformation in agricultural and food systems, which might lead to the proliferation of robots, fully automated farms and tractors, and spraying drones. The next-generation (4G) wireless network connection, known as 5G, will be necessary provide the massive data transmission volumes and low latency that may have many positive effects on IoT and blockchain applications in the agriculture and food industries. This study makes use of LoRaWAN (Long Range Wireless Area Network) for its energy-harvesting capabilities in remote agriculture monitoring. According to the findings of the experiments, LoRaWAN is the best technology to utilize in a system for monitoring agriculture when network lifespan and power consumption are important factors. Following application criteria, the experimental findings may be utilized to pick wireless technology for agricultural monitoring.

UAV-Aided RF Mapping for Sensing and Connectivity in Wireless Networks

The use of unmanned aerial vehicles (UAV) as flying radio access network (RAN) nodes offers a promising complement to traditional fixed terrestrial deployments. More recently yet still in the context of wireless networks, drones have also been envisioned for use as radio frequency (RF) sensing and localization devices. In both cases, the advantage of using UAVs lies in their ability to navigate themselves freely in 3D and in a timely manner to locations of space where the obtained network throughput or sensing performance is optimal. In practice, the selection of a proper location or trajectory for the UAV very much depends on local terrain features, including the position of surrounding radio obstacles. Hence, the robot must be able to map the features of its radio environment as it performs its data communication or sensing services. The challenges related to this task, referred here as radio mapping, are discussed in this paper. Its promises related to efficient trajectory design for autonomous radio-aware UAVs are highlighted, along with algorithm solutions. The advantages induced by radio-mapping in terms of connectivity, sensing, and localization performance are illustrated.

Discrete Data Rate Adaptation for Wireless Body Area Networks

eHealth services require continuous data streaming and a stable level of quality of service. However, wireless network connections can be characterized by variable bandwidths. This requires continuous adaptation of systems, including adapting the bit rates of data streamed by sensors. Assigning appropriate rates to the data represents a main task in congestion control. Most of the current methods look for proper sensor data rates within continuous domains. We examine the case when sensors can generate data streams with several different qualities (e.g., sampling rates, sampling accuracies, etc.). For this reason, the domain of the data rate values can be restricted to the discrete values representing the data rates of the possible quality variations. This paper examines the optimization of the utility of the delivered data under resource constraints by selecting an appropriate variation of the provided data from a discrete set. We provide a formal model for delivering data streams in WBANs and recommend an optimization algorithm to solve the problem. Our recommended solutions are related to the multiple-choice multidimensional knapsack problem. By comparing the proposed algorithms, we found that the greedy method closely approximates the optimum in a short running time.

Adaptive Recursive Sliding Mode Control (ARSMC)-Based UAV Control for Future Smart Cities

The rapid expansion of the Internet and communication technologies is leading to significant changes in both society and the economy. This development is driving the evolution of smart cities, which utilize cutting-edge technologies and data analysis to optimize efficiency and reduce waste in their infrastructure and services. As the number of mobile devices and embedded computers grows, new technologies, such as fifth-generation (5G) cellular broadband networks and the Internet of Things (IoT), are emerging to extend wireless network connectivity. These cities are often referred to as unmanned aerial vehicles (UAVs), highlighting their innovative approach to utilizing technology. To address the challenges posed by continuously varying perturbations, such as unknown states, gyroscopic disturbance torque, and parametric uncertainties, an adaptive recursive sliding mode control (ARSMC) has been developed. The high computational cost and high-order nonlinear behavior of UAVs make them difficult to control. The controller design is divided into two steps. First, a confined stability analysis is performed using controllability and observability to estimate the system’s stability calculation. Second, a Lyapunov-based controller design analysis is systematically tackled using a recursive design procedure. The strategy design aims to enhance robustness through Lyapunov stability-based mathematical analysis in the presence of considered perturbations. The ARSMC introduces new variables that depend on state variables, controlling parameters, and stabilizing functions to minimize unwanted signals and compensate for nonlinearities in the system. The paper’s significant contribution is to improve the controlled output’s rise time and stability time while ensuring efficient robustness.

Exploring the effects of directional antennas on the secure connectivity and hop count of secure multi-hop ad-hoc wireless networks

Directional antennas can increase the received power in specific directions while reducing it in other directions. Utilizing this unique feature, we configure directional antennas at legitimate users to obtain twofold benefits for secure multi-hop ad-hoc wireless networks (AHWNs), i.e., enhancing the secure connectivity among legitimate users and lowering the hop count of the multi-hop paths. For the quantitative evaluation of these benefits, we first use a stochastic geometry graph to model the multi-hop AHWNs under the coexistence of eavesdroppers. Then, we introduce a simulation-based analysis method comprised of two algorithms: (a) the first algorithm provides the connectivity and the hop count of the shortest path between random source–destination pairs; (b) the second algorithm utilizes the results of the first one to return the average path connectivity, the probability mass function (pmf) of the hop count distribution, and the average hop count. This simulation-based analysis method helps to exhaustively study the behaviors of connectivity, hop count distribution, average hop count, and their mutual relationship in different scenarios. To the best of our knowledge, this paper is the first research work that studies the effects of directional antennas equipped at legitimate users on both secure connectivity and hop count of secure AHWNs in four combinations of two types of practical directional antennas, i.e., uniform circular array (UCA) antenna and uniform linear array (ULA) antennas, and two beamforming schemes, i.e., centralized and randomized beamforming. Moreover, comparisons with the case of using omnidirectional antennas are also provided. From the obtained simulation results, we demonstrate that using directional antennas does not always benefit the secure connectivity and hop count of secure AHWNs, i.e., only UCA-random gives higher secure connectivity and lower hop count than the omnidirectional antenna among four combinations of antenna type — beamforming scheme. Moreover, in the cases of high-density legitimate users and low-density eavesdroppers, lowering the average hop count is more remarkable than improving secure connectivity. The critical findings in this paper are valuable for deploying secure multi-hop AHWNs with directional antennas in practice.

Intelligent Beam Blockage Prediction for Seamless Connectivity in Vision-Aided Next-Generation Wireless Networks

The upsurge in wireless devices and real-time service demands force the move to a higher frequency spectrum. Millimetre-wave (mmWave) and terahertz (THz) bands combined with the beamforming technology offer significant performance enhancements for future wireless networks. Unfortunately, shrinking cell coverage and severe penetration loss experienced at higher spectrum render mobility management a critical issue in high-frequency wireless networks, especially optimizing beam blockages and frequent handover (HO). Mobility management challenges have become prevalent in city centres and urban areas. To address this, we propose a novel mechanism driven by exploiting wireless signals and on-road surveillance systems to intelligently predict possible blockages in advance and perform timely HO. This paper employs computer vision (CV) to determine obstacles and users’ location and speed. In addition, this study introduces a new HO event, called block event (BLK), defined by the presence of a blocking object and a user moving towards the blocked area. Moreover, the multivariate regression technique predicts the remaining time until the user reaches the blocked area, hence determining best HO decision. Compared to conventional wireless networks without blockage prediction, simulation results show that our BLK detection and proactive HO algorithm achieves 40% improvement in maintaining user connectivity and the required quality of experience (QoE).

A General Analysis and Optimization Framework of Time Index Modulation for Integrated Data and Energy Transfer

Integrated data and energy transfer (IDET) has been considered as a key technology to tackle the energy shortage in the densely connected wireless networks. In this paper, a novel time index modulation (TIM) assisted IDET system is studied, where data information is delivered not only by the conventional modulation scheme, but also by the time index of the activated symbol durations for either wireless data transfer (WDT) or wireless energy transfer (WET). With the aid of theoretical analysis, the average energy harvesting performance as well as the achievable data rate between the transmitter and the receiver are both derived in the semi-closed form. Further, a population-based optimizer is conceived to obtain the power allocation of WDT and WET signals, in order to maximize the energy harvesting performance by satisfying the constraints of the achievable data rate. Simulation results validate our theoretical analysis, while they also demonstrate that the TIM assisted system can substantially increase the IDET performance.

Generating more Realistic Packet Loss Patterns for Wireless links using Neural Networks

Simulations of wireless network connections are essential forthe development of new technologies because they are farmore scalable than real-world experiments and reproducible.Modeling packet loss realistically provides a highly abstractyet powerful tool for the simulation of wirelesses links. Typi-cally, simple statistical models or replaying of recorded tracesare used for the simulation. For a proper parametrization ofsimple statistical models, recorded traces are required, too.Both approaches have drawbacks: replaying traces is limitedto the length of the traces, a repetition may lead to unwantedeffects in the simulation. The statistical models solve this, butthe resulting packet loss patterns significantly differ from realones. In this paper, we propose using a neural network in-stead. It takes the same kind of input, i.e., a real-world trace,but it can generate longer traces with more realistic loss pat-terns. We share pre-trained neural networks for multiple linksin office and industry scenarios with the community for usein future research.

Design of Switching Controller for Connected Vehicles Platooning With Intermittent Communication via Mode-Dependent Average Dwell-Time Approach

In real life, due to the influence of environment and physical equipments, C-V2X wireless communication network connection is prone to be intermittent. When intermittent communication occurs, the vehicle cannot make correct control decisions because it cannot receive the required information from neighboring vehicles, which will lead to the deterioration of the performance of the vehicle platoon. Considering the intermittent information connection of the leading vehicle through C-V2X communication network, a robust control method is proposed to realize vehicle platoon control. The platoon control strategy of a connected vehicle system is designed based on the mode-dependent average dwell time (MDADT) method. The designed strategy has better performance in reducing conservatism and improving flexibility, by comparison with the average dwell time (ADT) method. Moreover, based on the designed switching strategy, the sufficient conditions for the platoon control system to meet the exponential stability and exponential <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{2}$ </tex-math></inline-formula> performance are analyzed, and then, the linear matrix inequalities for solving the controller gain are given. Furthermore, the designed method has an advantage in applying to a variety of communication topologies. Finally, the effectiveness of the proposed robust control method in the case of intermittent communication in leading vehicle’s information transmission is verified by simulation.

Honeynet Implementation in Cyber Security Attack Prevention with Data Monitoring System Using AI Technique and IoT 4G Networks

Cyber Physical Systems (CPS) comprises of the ubiquitous object concept those are connected with Internet to provide ability of data transmission and sensing over network. The smart appliances transmits the data through CPS devices with the implementation of Internet of Things (IoT) exhibits improved performance characteristics with significant advantages such as time savings, reduced cost, higher human comfort and efficient electricity utilization. In the minimal complexity sensor nodes cyber physical system is adopted for the heterogeneous environment for the wireless network connection between clients or hosts. However, the conventional security scheme uses the mechanisms for desktop devices with efficient utilization of resources in the minimal storage space environment, minimal power processing and limited energy backup. This paper proposed a Secure Honeynet key authentication (SHKA) model for security attack prevention through effective data monitoring with IoT 4G communication. The proposed SHKA model uses the lightweight key agreement scheme for authentication to provide security threats and confidentiality issues in CPS applications. With the implementation of SHKA HoneyNet model the data in IoT are monitored for security mechanism in IoT environment. The middleware module in SHKA scheme uses the Raspberry platform to establish internetworking between CPS device to achieve dynamic and scalability. The secure IoT infrastructure comprises of flexible evaluation of user-centric environment evaluation for the effectiveness. The developed SHKA model perform mutual authentication between CPS devices for minimal computation overhead and efficiency. The wireless channel uses the dynamic session key for the secure communication for cyber-attacks security with lightweight security in CPS system. The SHKA model demonstrate the effectiveness based on consideration of three constraints such as low power processing, reduced storage and minimal backup energy. Experimental analysis stated that proposed SHKA scheme provides lightweight end-to-end key establishment in every session. The CPS devices generates the session key of 128 bit long. The minimum key size is implemented to provide effective security in IoT 4G communication with minimal execution time. The simulation results demonstrated that SHKA model exhibits effective cyber-attacks for the constraint devices to improve performance of IoT network.

Efficient target monitoring with fault‐tolerant connectivity in wireless sensor networks

AbstractSensing coverage with fault‐tolerant connectivity is considered as a recent research challenge, which measures the Quality of Service (QoS) in wireless sensor networks. QoS is determined based on the set of deployed sensor nodes that monitor all available discrete targets lying at the given point of interest and at the same time have to retain reliable connectivity with the base station. In this article, we focus on two phases namely, monitoring targets in the given sensing area by splitting the sensor network into set covers, which is considered as a nondeterministic polynomial‐time complete problem and optimal deployment of intelligent nodes. At first, achieving full coverage with deployed sensors by covering all discrete targets to prolong the network lifetime. On the other hand, a novel S2 (sensors‐spine) node approach was used to place an optimal number of intelligent nodes using a nature‐inspired algorithm named moth flame optimization to overcome the fault among the disjoints sets such that the entire network was fully connected and ensure that sensed data from each target points are collected by the sink node. To evaluate our proposed design, extensive simulations were conducted to demonstrate the efficacy of our proposed mechanism that outperforms other existing techniques found in the literature in terms of prolonged network lifetime and there is no failure in the transmission path during data transmission to the sink node at every t s, such that the entire network was fully connected.

Remote Surgical Training (ReST): The Road to a Fully-fledged Remote Robotic Surgery

Introduction: Remote control of surgical robots represents a valuable opportunity for computer-assisted surgery. Since signal latency is still not yet compatible with safe clinical practice, we explored a field of application that is free from patient-safety issues, namely training. Our research aims to demonstrate the feasibility and effectiveness of Remote Surgical Training (ReST) in the cadaver lab or on alternative models. Methods: ReST tests were carried out over a short distance, using the Xi da Vinci dual console and a cable connection. A trainee performed surgical tasks assisted by a trainer, and a tutor was dislocated remotely in an adjacent OR. ReST tests were compared with control events with tutors and trainees in the same robotic OR. Trainee’s performance was evaluated by supervisors using the Global Evaluative Assessment of Robotic Skills (GEARS) score. At the end of each event, all subjects filled in an online Likert scale-based questionnaire to evaluate the educational process. Results: Fifteen ReST tests were successfully completed and compared with fifteen control events. Statistical analysis of questionnaire data showed no significant differences in the educational process between the ReST group and the control group. At GEARS assessment, trainees achieved similar mean performance scores in both groups. Conclusion: Remote surgical training appears to be feasible and effective. Our results support further investigation on more advanced forms of remote training, including remotely displacing the trainee and testing by cable or wireless network connection over a long distance. This research is the first step towards remote robotic surgery.