Accurate and cost-effective vehicle localization is essential for fleet management and logistics services. Although GPS-based tracking is widely used, it suffers from high power consumption and signal attenuation in transport vehicles, resulting in high cost and loss of precise location. This research proposes a machine learning-driven GPS-free localization approach using a Low-Power Wide-Area Network (LPWAN) communication technology i.e., LoRaWAN, which is already used to send location and other data like temperature and status of goods to the operator. The location is calculated using signal characteristics from a mobile vehicle transmitting data to multiple LoRaWAN gateways (GWs) and LoRaWAN Network Server (LNS). A LoRaWAN device records radio signal parameters like RSSI, SNR, spreading factor, and gateway parameters, with ground truth GPS coordinates for validation. The device's latitude and longitude serve as targets, while machine learning models predict location. Four experimental scenarios are evaluated: raw data, polynomial feature engineering, feature selection, and their combination. The performance is assessed via Mean Distance Error (MDE) and Cumulative Distribution Function (CDF) analysis. Results indicate that feature selection improves accuracy, while polynomial transformations further enhance the Gradient Boost, achieving 209.20 meters of localization accuracy. This work provides a practical and scalable solution for logistics applications, enabling low-power, cost-effective, and accurate vehicle tracking without GPS, where energy efficiency and robust localization are critical for operational efficiency.

Millimeter-wave (mmWave) fifth-generation (5G) networks play a crucial role in facilitating communication and connectivity for unmanned aerial vehicles (UAVs) within the$\mathbf{5G}$landscape. These networks leverage advanced 5G technology to ensure rapid data transfer speeds, minimal latency, and dependable wireless communication, offering vital support for diverse services and applications in the realm of UAV operations. This research introduces a pioneering approach focusing on the development and evaluation of a novel, compact, and lightweight mmWave antenna specifically optimized to enhance communication capabilities in 5G UAV communications. The proposed antenna is designed on an ultrathin 0.254 mm RO5880 substrate with a relative permittivity of 2.3. It features ring structures stacked vertically and a partial ground plane with bent corners and square slots. The single-element antenna, measuring$12 \times 15 ~\text{mm}^{2}$, operates across two frequency bands. The first band spans from 23 to 33 GHz with a 10 GHz impedance bandwidth, while the second band covers 37.75 to 41 GHz with a 3.25 GHz impedance bandwidth, offering gains of 5.69 dBi and 5.01 dBi at 28 GHz and 38 GHz, respectively. The total and radiated efficiency exceeds$>90 \%$, making it suitable for practical mmWave devices. Moreover, the fabricated prototype aligns well with the simulated results, validating the antenna design. The proposed single-element antenna is further expanded into an$8 \times 1$Multiple Input Multiple Output (MIMO) linear array ($96 \times 12 \times 0.254 ~\text{mm}^{3}$). Noteworthy isolation levels exceeding -20 dB at both bands signify high isolation levels among radiating elements, providing gains of 13.4 dBi at 28 GHz and 12.2 dBi at 38 GHz. The fabricated prototype of the eight-element MIMO system aligns well with the simulations, confirming the effectiveness of the MIMO antenna design configuration. Furthermore, the proposed antenna undergoes testing on a CAD model of an UAV, yielding promising results. These characteristics position the proposed antenna as a compelling choice for integration into mmWave 5G networks, especially in applications related to 5G UAV Communications.

machine learning (ML) with 5G technology has revolutionized smart healthcare. It has helped improve the quality of care, such as real-time analysis, decision-making, patient monitoring, and personalized treatments. In this article, a 5G aware real-time diabetes prediction framework is proposed using optimized bidirectional long short-term memory (Bi-LSTM) with deep reinforcement learning (DRL). Bi-LSTM can analyze time-series data in forward and backward passes on patient health metrics, such as blood glucose (BG) levels of a diabetic patient, to identify patterns and trends and make predictions about future health outcomes. A local dataset using a wearable sensor is collected of ten Type-2 diabetic patients, encompassing daily BG levels at different times, alongside additional parameters, such as blood pressure and body weight. The proposed framework leverages a dataset of 1830 data points to forecast glucose levels for the following day. It also harnesses DRL to improve and optimize the model’s future predictive performance. The obtained results are evaluated with other ML algorithms to validate the effectiveness of the proposed framework, which shows an improvement from 93.1% to 98.6% accuracy. The patient’s diabetic condition is categorized using the surveillance error grid (SEG) to increase the clinical impact of glucose prediction and make informed decisions. The results show that Bi-LSTM-DRL is an effective approach to predicting glucose levels in real-time and can adapt to health-related changes and optimize its predictions accordingly.

Millimeter-wave (mmWave) fifth-generation (5G) vehicular networks refer to the application of 5G technology. It plays a crucial role in facilitating vehicle communication and connectivity. The primary objective of these networks is to offer wireless communication characterized by fast data transfer rates, low latency, and consistent reliability to enable various services and applications in the automotive industry. This article presents a design and analysis of novel, super-compact, light-weight mmWave antenna design that is specifically designed for 5G vehicular networks. The proposed antenna is designed on an ultra-thin 0.254mm RO5880 substrate with a relative permittivity of 2.3. The design incorporates a tri-circular ring patch, along with a partial ground plane that includes a square slot. Each circular ring is parallel to each other and placed in a vertical direction. The overall dimensions of the antenna are 7 ×11mm2, and it operates within a wide frequency range of 26–40 GHz with an impedance bandwidth of 14 GHz. The antenna shows impressive performance, achieving over a peak efficiency >89% and a gain of 3.8 dBi at 28 GHz and 5.36 dBi at 38 GHz, respectively. The proposed antenna is fabricated, and through validation and testing of the prototype, the results align well with the initial simulations. The single-element design is transformed into a six-element linear array system, featuring dimensions of 54 × 12 ×0.254 mm3. The isolation levels between the radiating elements are greater than 20 dB, indicating high isolation levels. Furthermore, the proposed antenna is tested using a vehicle model, and the outcomes show potential results. These features make the proposed antenna an attractive candidate for use in mmWave 5G networks, particularly in applications related to 5G vehicular networks.

Millimeter-wave (mmWave) fifth-generation (5G) vehicular networks are essential for vehicle communication and connectivity, leveraging 5G technology. These networks aim to provide fast data transfer rates, low latency, and reliable wireless communication to support various services and applications in the automotive industry. This research presents the design and analysis of a novel, compact, lightweight mmWave antenna tailored explicitly for 5G vehicular networks. The proposed antenna is designed on an ultrathin 0.787 mm RO5880 substrate with a relative permittivity of 2.3. It incorporates a bubbleshaped patch and a partial ground plane with a square slot. The antenna has overall dimensions of 12×12 mm2and operates within a wide frequency range of 24-32 GHz, providing an impedance bandwidth of 8 GHz. The antenna’s performance is truly impressive, with a peak efficiency greater than >90% and gains of 4.4 dBi at 26 GHz and 4.5 dBi at 28 GHz. The fabricated prototype aligns well with the initial simulations, validating its design. The single-element design is expanded into a four-element linear array system measuring 48×12×0.787 mm3. The isolation levels between the radiating elements exceed 20 dB, indicating high isolation performance. Furthermore, the proposed antenna undergoes testing using a vehicle model, yielding promising results. These features position the proposed antenna as an attractive candidate for mmWave 5G networks, particularly in applications related to 5G vehicular networks.

As the Internet of Things (IoT) is poised to become a global phenomenon, it is imperative to schedule the transmissions of IoT devices effectively and in a fair way. Leveraging Long Range (LoRa) technology, we can achieve transmissions that consume minimal power while covering vast distances, aligning with the requirements of IoT devices. However, the proximity of multiple devices within the same area often leads to packet interference and collisions. To address this, our study introduces a pioneering scheduling method utilizing a constellation of Low Earth Orbit (LEO) satellites to manage and streamline the transmission of data from End Devices (EDs). This method employs two LEO satellites: the first satellite assigns the sequence for EDs to dispatch their packets, and the second collects these packets in the predetermined sequence before forwarding them to the LoRa Network Server (LNS). For urgent (URG) communications, EDs can alert the first satellite, which then coordinates with the LNS to schedule these priority transmissions. The LNS generates a schedule that is relayed to the second satellite, informing EDs with URG packets of their specific transmission times and channels. This scheduling approach is designed to optimize channel usage effectively while accommodating the transmission of urgent data.

Fifth-generation (5G) cellular networks have led to the implementation of beyond 5G (B5G) networks, which are capable of incorporating autonomous services to swarm of unmanned aerial vehicles (UAVs). They provide capacity expansion strategies to address massive connectivity issues and guarantee ultra-high throughput and low latency, especially in extreme or emergency situations where network density, bandwidth, and traffic patterns fluctuate. On the one hand, 6G technology integrates AI/ML, IoT, and blockchain to establish ultra-reliable, intelligent, secure, and ubiquitous UAV networks. 6G networks, on the other hand, rely on new enabling technologies such as air interface and transmission technologies, as well as a unique network design, posing new challenges for the swarm of UAVs.Keeping these challenges in mind, this article focuses on the security and privacy, intelligence, and energy-efficiency issues faced by swarms of UAVs operating in 6G mobile network. In this state-of-the-art review, we integrated blockchain and AI/ML with UAV networks utilizing the 6G ecosystem. The key findings are then presented, and potential research challenges are identified. We conclude the review by shedding light on future research in this emerging field of research.

With a plethora of Internet of Things (IoT) applications for smart cities, encompassing and supporting several enabling technologies for real-time performance, an enormous amount of network packets faces the challenge of timely delivery. The IEEE 802.15.4 standard is one of the most popular and extensively adopted networking specifications for implementing different IoT applications and catering to several application-specific Quality of Service (QoS) requirements. Deterministic Synchronous Multi-channel Extension (DSME) is one of the Medium Access Control (MAC) protocols of IEEE 802.15.4 standard that facilitates stringent QoS through the allocation of DSME-Guaranteed Time Slots (GTSs) between a pair of devices. Interestingly, the standard does not define any mechanism for scheduling the DSME-GTSs, thereby opening several research opportunities. In this paper, we propose a Distributed Delay Aware Scheduling (DDAS) mechanism to increase the efficiency of the DSME MAC by using priority-based guaranteed time slots scheduling. DDAS assigns priority to the devices according to the criticality of time and number of associated devices, i.e., it identifies various flow deadlines and assigns GTS slots accordingly. The DDAS scheme aims to satisfy and adhere to various delay deadlines in the data flows of an IoT application. The proposed scheduling mechanism is shown to outperform other closely related schemes in terms of latency as well as energy consumption.

The new era of the Internet of Things (IoT) applications is driving the evolution of conventional Vehicle Ad-hoc Networks into the Internet of Vehicles (IoV). In the IoV networks, vehicles and the embedded sensory devices are the smart objects that are connected with other similar smart/IoT devices for data sharing and communication. The current specification of the IEEE 802.15.4 standard supports IoV/IoT application-specific Quality of Service (QoS) requirements. Specifically, the Deterministic and Synchronous Multi-channel Extension (DSME) MAC mode facilitates stringent latency and throughput performance through the use of DSME-Guaranteed Time Slots (GTS) allocation between two communicating devices. However, the standard does not explore DSME-GTS scheduling across the available channels, thereby making it an active research issue. This paper addresses the problem of DSME-GTS scheduling by proposing a distributed scheduling mechanism incurring minimal network overhead. The main challenge is to optimally use the available resources (channels and timeslots) and adhere to different flow deadlines. The proposed scheduling mechanism considers a 2-hop collision domain for channel assignment, and number of child devices for DSME-GTS allocations. The proposed scheduling mechanism's performance evaluation shows its efficiency in terms of energy consumption, transmission overhead, and channel utilization compared to other closely related schemes.

In ad hoc networks, the communication is usually made through multiple hops by establishing an environment of cooperation and coordination among self-operated nodes. Such nodes typically operate with a set of finite and scarce energy, processing, bandwidth, and storage resources. Due to the cooperative environment in such networks, nodes may consume additional resources by giving relaying services to other nodes. This aspect in such networks coined the situation of noncooperative behavior by some or all the nodes. Moreover, nodes sometimes do not cooperate with others due to their social likeness or their mobility. Noncooperative or selfish nodes can last for a longer time by preserving their resources for their own operations. However, such nodes can degrade the network's overall performance in terms of lower data gathering and information exchange rates, unbalanced work distribution, and higher end-to-end delays. This work surveys the main roots for motivating nodes to adapt selfish behavior and the solutions for handling such nodes. Different schemes are introduced to handle selfish nodes in wireless ad hoc networks. Various types of routing techniques have been introduced to target different types of ad hoc networks having support for keeping misbehaving or selfish nodes. The major solutions for such scenarios can be trust-, punishment-, and stimulation-based mechanisms. Some key protocols are simulated and analyzed for getting their performance metrics to compare their effectiveness.

The last decade has witnessed a steep growth in multimedia traffic due to real‐time content delivery such as in online games and video conferencing. In some contexts, MANETs play a key role in the hyperconnectivity of everything in multimedia services. In this context, this work proposes a new scheduling approach based on context‐aware mobile nodes for their connectivity. The contribution relies on reporting not only the locations of devices in the network but also their movement identified by sensors. In order to illustrate this approach, we have developed a novel agent‐based simulator called MASEMUL for illustrating the proposed approach. The results show that a movement‐aware scheduling strategy defined with the proposed approach has decreased the ratio of channel interruptions over another common strategy in mobile networks.

Small cell technology is expected to be an integral part of future 5G networks in order to meet the increasingly high user demands for traffic volume, frequency efficiency, and energy and cost reductions. Small cell networks can play an important role in enhancing the Quality of Service (QoS) and Quality of Experience (QoE) in m-health applications, and in particular, in medical video streaming. In this paper, we propose a hybrid medical QoE prediction model based on a Fuzzy Inference System (FIS) that correlates the network QoS (NQoS) and application QoS (AQoS) parameters to the QoE. The model is tested on the transmission of medical ultrasound video over small cell technology. The results show that the predicted QoE scores of our proposed model have a high correlation with the subjective scores of medical experts.

In this paper, we propose a strategy for resource allocation for different traffic classes at the Medium Access Control (MAC) layer of wireless systems based on Orthogonal Frequency Division Multiple Access (OFDMA), such as the recent Long-Term Evolution (LTE) wireless standard. In order to achieve inter-class fairness, we propose a modification of the Virtual Token Modified Largest Weighted Delay First (VT-M-LWDF) and Modified Largest Weighted Delay First (M-LWDF) rules. Through simulation, we show that the proposed scheduler introduces remarkable multi-objective improvement of the Quality of Service (QoS) performance parameters, i.e., Packet Loss Rate (PLR), average throughput, fairness index and system spectral efficiency, among different classes of traffic such as video, VoIP and best-effort.