Packet Error Research Articles

Scalability Analysis of LoRa and Sigfox in Congested Environment and Calculation of Optimum Number of Nodes.

Low-power wide area network (LPWAN) technologies as part of IoT are gaining a lot of attention as they provide affordable communication over large areas. LoRa and Sigfox as part of LPWAN have emerged as highly effective and promising non-3GPP unlicensed band IoT technologies while challenging the supremacy of cellular technologies for machine-to-machine-(M2M)-based use cases. This paper presents the design goals of LoRa and Sigfox while throwing light on their suitability in congested environments. A practical traffic generator of both LoRa and Sigfox is introduced and further interpolated for understanding simultaneous operation of 100 to 10,000 such nodes in close vicinity while establishing deep understanding on effects of collision, re-transmissions, and link behaviour. Previous work in this field have overlooked simultaneous deployment, collision issues, effects of re-transmission, and propagation profile while arriving at a number of successful receptions. This work uses packet error rate (PER) and delivery ratio, which are correct metrics to calculate successful transmissions. The obtained results show that a maximum of 100 LoRa and 200 Sigfox nodes can be deployed in a fixed transmission use case over an area of up to 1 km. As part of the future scope, solutions have been suggested to increase the effectiveness of LoRa and Sigfox networks.

Knowledge hierarchy-based dynamic multi-objective optimization method for AUV path planning in cooperative search missions

This study focuses on path planning for Autonomous Underwater Vehicles (AUVs) in underwater cooperative search missions. The complexities of the ocean environment, the uncertainty of target movements, and limited communication capabilities render underwater cooperative search missions dynamic multi-objective optimization challenges. Studies focusing on communication-constrained search strategies still lack reliable metrics for assessing underwater communication quality and do not consider communication dead zones. Addressing these deficiencies, this paper introduces a novel communication optimization strategy utilizing packet error rate as a metric for assessing communication efficacy, complemented by a potential field-based method for navigating out of communication dead zones. To tackle the inherently dynamic nature of cooperative search missions for mobile underwater targets, we propose a dynamic multi-objective optimization algorithm that employs a knowledge hierarchy strategy. This method enhances the NSGA-II algorithm's efficiency by extracting effective gene segments from historical Pareto solution set and generating a new initial population through recombination, hierarchy, and prediction. Distinct from other advanced dynamic multi-objective optimization approaches that are limited to theoretical problems, our approach is directly applicable to practical scenarios. The effectiveness and practicality of the proposed method are validated through a series of simulation experiments considering the impact of underwater acoustic communication. These results demonstrate that this research is not only innovative in theory but also holds significant engineering value and practical prospects in real-world applications.

ANALIZA STATYSTYK SIECI KOMPUTEROWYCH W CELU IDENTYFIKACJI PRZEPŁYWÓW INFORMACJI ZAKŁÓCAJĄCYCH STABILNOŚĆ W WOJSKOWYCH SIECIACH LOKALNYCH

The increasing complexity and scope of military computer networks necessitate robust methods to ensure network stability and security. This study presents a comprehensive analysis of computer network statistics in military local networks to develop a method for detecting information flows that disrupt stability. By leveraging advanced statistical techniques and machine learning algorithms, this research aims to enhance the cybersecurity posture of military local networks globally. Military networks are vital for communication, data exchange, and operational coordination. However, the dynamic nature of network traffic and the persistent threat of cyberattacks pose significant challenges to maintaining network stability. Traditional monitoring techniques often fail to meet the unique requirements of military networks, which demand high levels of security and rapid response capabilities. This study employs a multi-faceted approach to detect anomalies in network traffic, utilizing statistical methods such as Z-score analysis, Principal Component Analysis (PCA), and Autoregressive Integrated Moving Average (ARIMA) models. Machine learning techniques, including Support Vector Machines (SVM), Random Forests, Neural Networks, K-means clustering, and Reinforcement Learning, are also applied to identify patterns indicative of stability-disrupting information flows. The integration of statistical and machine learning methods forms a hybrid model that enhances anomaly detection, providing a robust framework for network security. The research problem is formulated as follows: does data collection include comprehensive network traffic data from various segments of military local area networks, including packet flows, transmission rates, and error rates over a specified period? Statistical analysis identifies patterns in the network traffic, which are then used to train machine learning models to classify normal and abnormal traffic. The research hypothesis states that machine learning models achieve high accuracy in detecting stability-disrupting information flows, with a precision rate exceeding 90%. The models identified several instances of stability-disrupting events, correlating these with known security incidents to validate the effectiveness of the detection method. This study underscores the importance of continuous monitoring and analysis of network statistics to ensure stability and security. The proposed method can be integrated with existing network monitoring and intrusion detection systems, providing a comprehensive approach to network security. Future research can build on these findings to develop more sophisticated models and explore additional factors influencing network stability, including the incorporation of advanced machine learning techniques, such as deep learning, and the exploration of other network metrics, like latency and packet loss. This comprehensive approach aims to enhance the security and operational reliability of military local networks.

5G Networks Based on Software Defined Network

A software-defined network (SDN) based 5G architecture that utilizes three controllers to improve network performance is proposed in this paper. The controllers are responsible for managing functions related to access mechanism, core connectivity, and network transport. The proposed architecture aims to address solutions for the challenges of traditional 5G networks such as scalability, flexibility, and resource allocation. The paper investigates two networks: 5G without SDN and 5G with SDN. The performance of these networks is evaluated by simulation tests using OMNeT++. The results show a considerable improvement in performance measures such as throughput, packet error rate (PER), and energy consumption. Under favorite test conditions, improvements of 46%, 88% and 94%, in throughput, PER, and energy consumption are achieved. The main finding of the paper is that the proposed SDN-based 5G architecture with multi-controllers is a promising arrangement to overcome the challenges of 5G networks.

RTTV-TCP: Adaptive congestion control algorithm based on RTT variations for mmWave networks

Internet applications such as video gaming virtual/ augmented reality necessitate efficient fifth-generation (5G) millimeter-wave (mmWave) cellular networks. The Transmission Control Protocol (TCP) is an essential protocol for network connectivity. However, TCP faces challenges in efficiently utilizing the available bandwidth of 5G mmWave cellular networks while maintaining low latency, mainly due to constraints like Non-Line of Sight (NLoS) conditions. This paper introduces Round-Trip-Time Variations-TCP (RTTV-TCP), enhancing TCP performance in 5G mmWave cellular networks. Simulation scenarios for a 5G mmWave cellular network have been conducted to evaluate RTTV-TCP’s performance, comparing it to legacy TCP variants such as NewReno, HighSpeed, CUBIC, Bottleneck Bandwidth and Round-trip propagation time (BBR), FB-TCP (Fuzzy Based-TCP). The results demonstrate that RTTV-TCP achieves higher average throughput than these TCP variants while maintaining the same level of delay in 5G mmWave cellular networks. RTTV-TCP outperforms NewReno and CUBIC by a very significant margin, demonstrating a 208% improvement compared to HighSpeed and a 6% increase compared to BBR protocol in the worst Packet Error Rate (PER) scenario and when the buffer size matches the Bandwidth Delay Product (BDP).

Blockchain Technology and Smart Contract Application in Security Management of Intelligent Chemical Plants

As blockchain technology and smart contracts develop, computer technology is constantly integrating with smart chemical plants. Due to the continuous development of intelligent chemical plants, their systems have gradually become large and dispersed, posing a threat to safety management. In order to improve the performance of intelligent security management systems, the study first explores the principles of blockchain and smart contract technology, and then combined with the requirements of intelligent chemical plant security management systems, designs an intelligent security management system based on blockchain and smart contract technology. The experimental results showed that compared to systems without smart contract support, the communication success rate between nodes was lower. The error rates of blockchain-based encryption systems, deep learning-based encryption systems and improved data encryption systems proposed in the study were 0.22, 0.07 and 0.09, respectively. The packet loss rates were 0.13, 0.04 and 0.05, respectively. The lower the bit error rate and packet loss rate of the encryption system, the clearer the illegal eavesdropping information. The experimental results indicate that the intelligent security management system designed in this study has good encryption performance and a higher communication success rate. The results have certain reference value in security management application in intelligent chemical plants.

Ultra-Low-Power Sensor Nodes for Real-Time Synchronous and High-Accuracy Timing Wireless Data Acquisition.

This paper presents an energy-efficient and high-accuracy sampling synchronization approach for real-time synchronous data acquisition in wireless sensor networks (saWSNs). A proprietary protocol based on time-division multiple access (TDMA) and deep energy-efficient coding in sensor firmware is proposed. A real saWSN model based on 2.4 GHz nRF52832 system-on-chip (SoC) sensors was designed and experimentally tested. The obtained results confirmed significant improvements in data synchronization accuracy (even by several times) and power consumption (even by a hundred times) compared to other recently reported studies. The results demonstrated a sampling synchronization accuracy of 0.8 μs and ultra-low power consumption of 15 μW per 1 kb/s throughput for data. The protocol was well designed, stable, and importantly, lightweight. The complexity and computational performance of the proposed scheme were small. The CPU load for the proposed solution was <2% for a sampling event handler below 200 Hz. Furthermore, the transmission reliability was high with a packet error rate (PER) not exceeding 0.18% for TXPWR ≥ -4 dBm and 0.03% for TXPWR ≥ 3 dBm. The efficiency of the proposed protocol was compared with other solutions presented in the manuscript. While the number of new proposals is large, the technical advantage of our solution is significant.

Throughput fairness in backscatter-assisted cognitive radio networks with short packets

This work studies the throughput fairness among multiple Internet of Things (IoT) nodes in a backscatter assisted cognitive radio network, where the primary transmitter conveys its long-packet information to its receiver while multiple IoT nodes alternate backscattering their short-packet information to the information receiver via backscatter communication (BackCom). Specifically, we devise a non-convex problem aimed at ensuring the throughput fairness among IoT nodes concerning their transmitted data bits, by jointly optimizing the short-packet blocklength, packet error rate, and power reflection coefficient of each IoT node. Employing the block-coordinated-decent (BCD), the original problem is decoupled into two subproblems, both of which are solved by the proposed golden section based iterative algorithm and the proposed successive convex approximation (SCA) based iterative algorithm, respectively. Then a BCD based iterative algorithm is developed to solve the original problem. Simulations demonstrate the rapid convergence and superiority of the proposed algorithm over several baseline schemes in achieving the fairness of transmission bits.

Evaluation of Radio Access Protocols for V2X in 6G Scenario-Based Models

The expansion of mobile connectivity with the arrival of 6G paves the way for the new Internet of Verticals (6G-IoV), benefiting autonomous driving. This article highlights the importance of vehicle-to-everything (V2X) and vehicle-to-vehicle (V2V) communication in improving road safety. Current technologies such as IEEE 802.11p and LTE-V2X are being improved, while new radio access technologies promise more reliable, lower-latency communications. Moreover, 3GPP is developing NR-V2X to improve the performance of communications between vehicles, while IEEE proposes the 802.11bd protocol, aiming for the greater interoperability and detection of transmissions between vehicles. Both new protocols are being developed and improved to make autonomous driving more efficient. This study analyzes and compares the performance of the protocols mentioned, namely 802.11p, 802.11bd, LTE-V2X, and NR-V2X. The contribution of this study is to identify the most suitable protocol that meets the requirements of V2V communications in autonomous driving. The relevance of V2V communication has driven intense research in the scientific community. Among the various applications of V2V communication are Cooperative Awareness, V2V Unicast Exchange, and V2V Decentralized Environmental Notification, among others. To this end, the performance of the Link Layer of these protocols is evaluated and compared. Based on the analysis of the results, it can be concluded that NR-V2X outperforms IEEE 802.11bd in terms of transmission latency (L) and data rate (DR). In terms of the packet error rate (PER), it is shown that both LTE-V2X and NR-V2X exhibit a lower PER compared to IEEE protocols, especially as the distance between the vehicles increases. This advantage becomes even more significant in scenarios with greater congestion and network interference.

LOW POWER DESIGN AND ANALYSIS OF ADAPTIVE RADIOS

This undertaking presents a noteworthy low-power procedure, RF Power Gating, proposing a unique change of the Dynamic Time Proportion (ATR) inside the RF front end at an image time scale. Adapting receiver power consumption to performance requirements without altering the fundamental architecture is the unique challenge that this method addresses. The effect of changing the ATR on the performance of the Bit Error Rate (BER) is carefully examined, with a focus on minimum-shift keying signaling and a basic estimator. To gauge power decrease unequivocally, a complete framework level energy model is determined, considering the qualities and power utilization of individual blocks. This model gives experiences into the particular supporters of force decrease. The review consolidates BER results with the energy model to pinpoint the ideal ATR meeting plan requirements. When this method is applied to the IEEE 802.15.4 standard, it can be seen that an ATR of 20% is a sensible compromise that meets the requirements for the packet error rate while also achieving the highest possible energy reduction ratio. Further investigation utilizing run of the mill block power utilizations uncovers a reachable energy decrease proportion of around 20%. Quite, while power-gating is widely applied, significantly more noteworthy energy decrease proportions (∼60%) are achievable. The proposed procedure is executed in Verilog, displaying its functional materialness, and consistently coordinated into Xilinx apparatuses. The outcomes highlight not just the viability of RF Power Gating in accomplishing a critical decrease in energy utilization yet additionally its versatility to different situations, making it a promising methodology for low-power plan in versatile radios. Key Words: Active Time Ratio (ATR) Blunder Rate (BER) and IEEE 802.15.4 standard.

IRS-aided NOMA-based communication architecture for 6G wireless networks: An enhanced error-control and reliable data transmission

Intelligent reflecting surface (IRS) is a key technology for sixth-generation (6G) wireless communication to obtain high energy and spectral efficiency with a minimum cost. On the other hand, non orthogonal multiple access (NOMA) enhances the spectrum efficiency of the network. The combining schemes are implemented to obtain error-free data at the receiver. This paper proposes an IRS-aided NOMA-based packet combining (PC) and aggressive packet combining (APC) schemes-assisted communication architecture to enhance service quality and achieve sustainable and resilient connectivity among users for 6G wireless networks. In this paper, the mathematical model has been derived for the instantaneous achievable data rate, bit error rate, outage probability, and packet correction capability. The derived analytical expressions are validated through detailed Monte Carlo simulations. The obtained results show that the proposed schemes outperform the conventional NOMA with PC and NOMA with APC schemes and facilitate enhanced error control and reliable data transmission capabilities. Moreover, the proposed schemes have been compared with the IRS-aided orthogonal multiple access (OMA) schemes, where IRS-aided OMA schemes are considered benchmarks of the proposed schemes. It is presented that the proposed schemes are superior to the conventional OMA with PC and APC schemes and IRS-aided OMA with PC and APC schemes.

Review of LoRaWAN: Performance, Key Issues and Future Perspectives

In the last few years, the Low-powered wide area networks (LPWAN) have gained popularity and massively deployed, especially in smart cities and agriculture, due to their advantages, such as energy efficiency, extensive coverage, and low cost. The long-range wide area networks (LoRaWAN) protocol is a new technology. It attracts the attention of many research centers worldwide as it allows data transmission at a low cost across long distances. This article reviews the performance of LoRa and LoRaWAN for both investigations in indoor and outdoor environments. Moreover, a performance analysis of this technology is made by focusing on five main indicators, which are coverage, time on air (TOA), packet error rate (PER), received signal strength indication (RSSI), and signal-to-noise ratio (SNR) while considering technical characteristics is included. The investigation settings were divided into two categories: simulation and testbed in a real-world application. Consequently, a table of summary of indicators used by the researcher was made to make it easier for other researchers to find references for their topics relevant to this review. Next, the identified key issues and solutions are discussed in this review. The issues discussed significantly affect the performance of various monitoring technologies regarding energy management, quality of service, coverage, signal interference, and data handling. Finally, this review will discuss the future perspective, provide valuable suggestions for future research works, and lead toward improving current LoRaWAN technology.

Internet of medical things and blockchain-enabled patient-centric agent through SDN for remote patient monitoring in 5G network

During the COVID-19 pandemic, there has been a significant increase in the use of internet resources for accessing medical care, resulting in the development and advancement of the Internet of Medical Things (IoMT). This technology utilizes a range of medical equipment and testing software to broadcast patient results over the internet, hence enabling the provision of remote healthcare services. Nevertheless, the preservation of privacy and security in the realm of online communication continues to provide a significant and pressing obstacle. Blockchain technology has shown the potential to mitigate security apprehensions across several sectors, such as the healthcare industry. Recent advancements in research have included intelligent agents in patient monitoring systems by integrating blockchain technology. However, the conventional network configuration of the agent and blockchain introduces a level of complexity. In order to address this disparity, we present a proposed architectural framework that combines software defined networking (SDN) with Blockchain technology. This framework is specially tailored for the purpose of facilitating remote patient monitoring systems within the context of a 5G environment. The architectural design contains a patient-centric agent (PCA) inside the SDN control plane for the purpose of managing user data on behalf of the patients. The appropriate handling of patient data is ensured by the PCA via the provision of essential instructions to the forwarding devices. The suggested model is assessed using hyperledger fabric on docker-engine, and its performance is compared to that of current models in fifth generation (5G) networks. The performance of our suggested model surpasses current methodologies, as shown by our extensive study including factors such as throughput, dependability, communication overhead, and packet error rate.

Age of Information for Partial Earliest Relay Aided Short Packet Status Update With Energy Harvesting

This paper focuses on the timely delivery of status updates for a multi-relay assisted energy harvesting (EH) Internet of Things system with short packet communications (SPC), where an EH-powered source transmits status updates to a destination with the aid of multiple EH-powered relays. To improve the energy efficiency and information freshness, an earliest-ℓ partial relay selection scheme is proposed, in which the earliest ℓ relays that successfully receive the status update from the source will be selected to forward the status update to the destination, and the selection combining (SC) and maximal-ratio combining (MRC) schemes are used at the destination. Firstly, we investigate the update packet delivery schemes of source-relay (S-R) and relay-destination (R-D) links, and present the packet error probabilities under the SC and MRC schemes based on SPC theory. Secondly, after characterizing the full charge time durations of both the source and relay, we investigate the statistical description of the number of the packet transmission attempts from the source to relay as well as the one from the relay to destination by using order statistics. With the analysis of the evolution of the instantaneous age of information (AoI), the expression for average AoI is derived. Finally, we investigate the average AoI minimization, which is formulated as a coupled triplet problem of the packet lengths at the source and relays and the value of ℓ. To overcome the coupling, we present the convexity approximation of the average AoI as well as the concise proof. Then, an efficient gradient-based two-step optimal search algorithm is proposed to solve the optimization problem, which searches the local optimal packet lengths for S-R and R-D links by iteratively updating the value of ℓ until the global optimal objective parameters are found. Numerical analyses give the effect of the key parameters on the average AoI, and the comparison demonstrates that the proposed gradient-based method converges quickly than the conventional greedy search method.

Reliability Analysis of Cyber–Physical Energy Hubs: A Monte Carlo Approach

In this paper, the reliability of cyber-physical energy hubs (CPEHs) is evaluated based on the Monte Carlo simulation (MCS) method. Previous works in the related literature are generally focused on the physical layer of energy hubs (EHs). However, the security of EHs heavily relies on the reliable and safe performance of the cyber system. Therefore, this study considers the failure of cyber-physical components, data packet errors, and information transmission delay to improve the reliability assessment accuracy. The physical layer of the proposed CPEH is divided into two sections, namely heat and power sections, and reliability indices are calculated for both sections. Various scenarios are suggested to precisely analyze the effects of the cyber layer consideration on the CPEH reliability results. Sensitivity analysis on the failure rate of the components is also conducted to understand better the performance of the CPEH in situations with a higher risk of heat components failure. The notable impacts of the cyber layer disturbances on the CPEH reliability evaluation are demonstrated through the simulation results.

Performance Analysis of 6TiSCH Networks Using Discrete Events Simulator

The Internet of Things (IoT) empowers small devices to sense, react, and communicate, with applications ranging from smart ordinary household objects to complex industrial processes. To provide access to an increasing number of IoT devices, particularly in long-distance communication scenarios, a robust low-power wide area network (LPWAN) protocol becomes essential. A widely adopted protocol for this purpose is 6TiSCH, which builds upon the IEEE 802.15.4 standard. It introduces time-slotted channel hopping (TSCH) mode as a new medium access control (MAC) layer operating mode, in conjunction with IEEE 802.15.4g, which also defines both MAC and physical layer (PHY) layers and provides IPv6 connectivity for LPWAN. Notably, 6TiSCH has gained adoption in significant standards such as Wireless Intelligent Ubiquitous Networks (Wi-SUN). This study evaluates the scalability of 6TiSCH, with a focus on key parameters such as queue size, the maximum number of single-hop retries, and the slotframe length. Computational simulations were performed using an open-source simulator and obtained the following results: increasing the transmission queue size, along with adjusting the number of retries and slotframe length, leads to a reduction in the packet error rate (PER). Notably, the impact of the number of retries is particularly pronounced. Furthermore, the effect on latency varies based on the specific combination of these parameters as the network scales.

A Dispersed Federated Learning Framework for 6G-Enabled Autonomous Driving Cars

Sixth-Generation (6G)-based Internet of Everything applications (e.g. autonomous driving cars) have witnessed a remarkable interest. Autonomous driving cars using federated learning (FL) has the ability to enable different smart services. Although FL implements distributed machine learning model training without the requirement to move the data of devices to a centralized server, it its own implementation challenges such as robustness, centralized server security, communication resources constraints, and privacy leakage due to the capability of a malicious aggregation server to infer sensitive information of end-devices. To address the aforementioned limitations, a dispersed federated learning (DFL) framework for autonomous driving cars is proposed to offer robust, communication resource-efficient, and privacy-aware learning. A mixed-integer non-linear programming (MINLP) optimization problem is formulated to jointly minimize the loss in federated learning model accuracy due to packet errors and transmission latency. Due to the NP-hard and non-convex nature of the formulated MINLP problem, we propose the Block Successive Upper-bound Minimization (BSUM) based solution. Furthermore, the performance comparison of the proposed scheme with three baseline schemes has been carried out. Extensive numerical results are provided to show the validity of the proposed BSUM-based scheme.

Improved Performance of 5G Based Software Defined Networks

With the growth of processed data for wireless networks and the establishment of new applications and services, mobile operators keep searching for solutions to deal with the issues raised by nextgeneration networks (NGN). The fifth generation (5G) mobile networks, for example, should enhance their performance without consuming a lot of energy. For these reasons, software-defined networks (SDN) appear to be the promising technology that will make achieving the architectural agility required for the upcoming 5G mobile networks easier. SDN is a clever solution for providing innovation and enforcing the primary drivers in 5G mobile networks, such as flexibility, dependability, service-oriented management, and cost reduction through the control and management of the 5G core network. In this paper, the integration of SDN and multiple controllers with the 5G core network is investigated to determine how these two technologies can affect mobile IP network performance. An energy model suitable for the proposed network architecture has been developed. A widely used Diamond network is being considered for the core network. Open-Flow controllers were used to improve the routing performance by considering load balancing of IP traffic. According to such a model, reduced energy consumption is experienced with SDN. The addition of an SDN controller results in a 11% reduction in consumed energy. The results also show that the use of SDN and multi-controller with NGN can improve the performance further. Using SDN reduced the average delay of the network by 14%. A great reduction in packet error rate (PER) is also achieved when SDN is used.

A Novel Fuzzy Based Beluga Whale Optimization for Enhancing Link Stability and Optimal Route discovery in MANET

The networking field plays a major role and developed significantly over the past decades. The identification of best route in Mobile Ad hoc Networks (MANETs) is a complex problem in the multipath routing method. Besides, multipath discovery is regarded as the other difficult task for improving the existence of the network. Through optimal route discovery, the routing protocol considers several parameters like link failures, topological variations, obstacles, and extended power. In this paper, based on optimal route discovery, a novel Fuzzy Beluga Whale Optimization (FBWO) is proposed for presenting effective routing and enhanced link stability. The Beluga Whale Optimization (BWO) algorithm determines multiple routes in the network for communicating data packets to their destination. However, the discovered routes may be susceptible to link failures due to the complete energy exhaustion of nodes during continuous data transmission. Therefore, the stability of links is assured by evaluating link stability indicators namely probabilistic link reliable time, link expiration time, residual battery power, link received signal strength, and link packet error rate. Moreover, optimal routes in the network are established using a fuzzy rule system by adopting fuzzy rules. Thus, the proposed FBWO approach maximizes the overall performance of the network by establishing optimal routes to broadcast packets along with enhanced link stability and signal strength. The experimental results manifest the FBWO model and achieve superior accuracy in terms of all measures than other existing methods.

The Environmental Impacts of Radio Frequency and Power Line Communication for Advanced Metering Infrastructures in Smart Grids.

In the neighborhood area network (NAN), the advanced metering infrastructure (AMI) enables a bidirectional connection between the smart meter (SM) and the data concentrator (DC). Sensors, such as smart meter nodes or environmental sensor nodes, play a crucial role in measuring and transmitting data to central units for advanced monitoring, management, and analysis of energy consumption. Wired and wireless communication technologies are used to implement the AMI-NAN. This paper delves into a novel approach for optimizing the choice of communication medium, air for radio frequency (RF) or power lines for power line communication (PLC), between the SM and DC in the context of the AMI-NAN. The authors methodically select the specific technologies, RF and NB-PLC (narrowband power line communication), and meticulously characterize their attributes. Then, a comparative analysis spanning rural, urban, and industrial settings is conducted to evaluate the proposed method. The overall reliability performance of the AMI-NAN system requires a packet error rate (PER) lower than 10%. To this end, an efficient approach is introduced to assess and enhance the reliability of NB-PLC and RF for AMI-NAN applications. Simulation results demonstrate that wireless communication is the optimal choice for the rural scenario, especially for a signal-to-noise ratio (SNR) lower than 25 dB. However, in urban environments characterized by higher SNR values and moderately dense networks, NB-PLC gains prominence. In denser networks, it outperforms wireless communication, exhibiting a remarkable 10 dB gain for a bit error rate (BER) of 10-3. Moreover, in industrial zones characterized by intricate network topologies and non-linear loads, the power line channel emerges as the optimal choice for data transmission.