Energy Efficiency Coverage Research Articles

A Comprehensive Strategy for Implementing Adaptive Data Rate (ADR) in LoRaWAN Technology: Optimizing IoT Networks

The Internet of Things (IoT) is increasingly essential for creating smart environments by establishing long range connectivity among devices. LoRaWAN is a prominent technology choice for these applications due to its energy efficiency and extensive coverage. Central to LoRaWAN’s functionality is the Adaptive Data Rate (ADR) mechanism, which optimizes network performance by adjusting each device’s data rate and transmission power dynamically. This study delves into ADR’s operational principles, benefits, and implementation challenges in LoRaWAN networks. The proposed simulation model, featuring configurable parameters like the spreading distribution factor (SF), the adjustable number of devices, and the SNR threshold, denotes enhanced network performance after implementing the ADR algorithm. With an SNR threshold of 15 dB, ADR reduced the average transmission power of 200 devices from 7 dBm to 3 dBm after twenty iterations, underscoring ADR’s role in minimizing energy consumption while improving network efficiency.

Effective capacity of STAR-RIS assisted RSMA network with imperfect SIC

The proliferation of various Internet of Everything applications places urgent demand for high energy efficiency, massive connection and wide coverage for the future communication era. Motivated by this, rate-splitting multiple access (RSMA) and simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) has attracted wide publicity from industry and academia. In this paper, we consider a STAR-RIS assisted two-user RSMA network with imperfect successive interference cancellation, and all channels are modeled as Nakagami-m distribution. By adopting effective capacity (EC) as the metric, we analyze the system performance under certain quality-of-service constraint. Specifically, analytical expressions of EC for a pair of RSMA users are provided. To explore further insights, high signal-to-noise ratio (SNR) conditions are also considered. Furthermore, the effect of some specific elements on system EC performance are analyzed, and some significant conclusions are obtained as follows: (1) As SNR grows, the effective capacities of two users improve, but finally converge to constant at high SNR. (2) The increase in the amount of the STAR-RIS elements can obviously increase the effective capacities. (3) Compared to traditional STAR-RIS assisted non-orthogonal multiple access networks, STAR-RIS assisted RSMA network has distinct advantages to improve system EC performance.

Enhancing IoT Network Reliability: Evaluating LoRa Module Susceptibility to Interference

The Internet of Things (IoT) is gaining popularity, leading to the widespread use of remote communication modules (LoRa), known for their energy efficiency and wide coverage range. However, as the number of LoRa modules used in IoT networks grows, the possibility of interference from third-party devices operating at the same frequency becomes a concern. This study aimed to examine the vulnerability of LoRa modules to electromagnetic interference (EMI) when transmitting text messages and images. Radiation emission conditions were measured in the test area for evaluating LoRa module performance, and susceptibility to interference was assessed under non-line-of-sight (NLOS) conditions. The study's outcomes reveal that interference with LoRa transmitters has no noticeable effect on the range within a distance of up to 50 meters. In contrast, the interference power required to disrupt the LoRa receiver decreases with increasing distance. Additionally, interference from frequencies outside the designated LoRa working frequency (915 MHz) has no discernible impact on module performance. Introducing a delivery delay check demonstrates consistent performance even in interference. These findings deepen our understanding of the susceptibility of LoRa modules to tampering, emphasizing the importance of implementing effective disruption management strategies in IoT deployments. By considering the potential impact of electromagnetic interference (EMI) on LoRa modules, developers can design more robust IoT networks, ensuring reliable communication and improved system performance. Overall, the research focuses on the interference characteristics of LoRa modules, providing insights for developing resilient and interference-resistant IoT solutions. It underscores the necessity of addressing interference issues to ensure the reliable operation of IoT devices across diverse environments.

Low-Cost Computer-Vision-Based Embedded Systems for UAVs

Unmanned Aerial Vehicles (UAVs) are versatile, adapting hardware and software for research. They are vital for remote monitoring, especially in challenging settings such as volcano observation with limited access. In response, economical computer vision systems provide a remedy by processing data, boosting UAV autonomy, and assisting in maneuvering. Through the application of these technologies, researchers can effectively monitor remote areas, thus improving surveillance capabilities. Moreover, flight controllers employ onboard tools to gather data, further enhancing UAV navigation during surveillance tasks. For energy efficiency and comprehensive coverage, this paper introduces a budget-friendly prototype aiding UAV navigation, minimizing effects on endurance. The prototype prioritizes improved maneuvering via the integrated landing and obstacle avoidance system (LOAS). Employing open-source software and MAVLink communication, these systems underwent testing on a Pixhawk-equipped quadcopter. Programmed on a Raspberry Pi onboard computer, the prototype includes a distance sensor and basic camera to meet low computational and weight demands.Tests occurred in controlled environments, with systems performing well in 90% of cases. The Pixhawk and Raspberry Pi documented quad actions during evasive and landing maneuvers. Results prove the prototype’s efficacy in refining UAV navigation. Integrating this cost-effective, energy-efficient model holds promise for long-term mission enhancement—cutting costs, expanding terrain coverage, and boosting surveillance capabilities.

A TDMA-Based Access Protocol for Dense Networks with Moving Nodes for IoT Applications

Low-power wide-area (LPWA) technologies have gained popularity in accordance with the explosive growth of the Internet of Things (IoT). Among others, LoRa is considered as the leading standard that can meet the needs of modern wireless networking, mainly offering energy efficiency and broad coverage as well as a massive amount of device support. In addition to the ALOHA protocol, which is the default channel access mechanism used by the standard, a number of alternatives have been proposed in the literature in an effort to ameliorate the overall network performance. Furthermore, with moving nodes gaining ground more and more in the IoT realm and the research being at a relatively premature stage, it is imperative to create innovative algorithms that support highly dense networks with fast moving nodes. Motivated by these reasons, this work proposes a novel medium access protocol that takes advantage of the increased capabilities of modern wake up radio (WuR) technology in order to achieve low latency and mitigate the risk of lost packets in IoT networks with moving nodes based on the LoRa technology. A number of simulation scenarios have been devised and the findings suggest that the proposed protocol achieves the set goals and improves existing solutions.

BIM-supported scan and flight planning for fully autonomous LiDAR-carrying UAVs

The Unmanned Aerial Vehicle (UAV) equipped with a Light Detection and Ranging (LiDAR) scanner provides a versatile and efficient platform for mobile laser scanning in construction-related scenarios. Recent studies on UAVs have demonstrated point-to-point navigation, yet there has been sparse investigation on scan coverage planning to fully explore a construction site, and on kinodynamic motion planning to ensure energy-efficient trajectories. This study develops a Building Information Model (BIM)-supported framework to facilitate scan planning and motion planning of autonomous LiDAR-carrying UAVs. The proposed framework selectively integrates the geometry and semantics from BIM to construct a probabilistic 3D voxel map. Then, a greedy algorithm is developed to iteratively generate waypoints with optimized coverage. After that, a collision-free guiding path is computed for traversing all the waypoints before it is further transformed into a high-degree polynomial trajectory. The proposed framework was validated in a simulated construction scenario of water treatment facilities using MATLAB and Unreal Engine 4 (UE4). The planned trajectory demonstrated smoothness, energy efficiency, and sufficient coverage. It reduced 86.17% of required moments from motors over the regular A* algorithm and achieved 91.67% scan coverage on the target facility.

IoE: Towards application-specific technology selection

Determining the suitability of any technology for an Internet of Everything (IoE) application is essential in the presence of diverse technologies and application requirements. Some of the IoE applications include smart metering, wearables, healthcare, remote monitoring, inventory management and industrial automation. Energy efficiency, scalability, security, low-cost deployment and network coverage are some of the requirements that vary from one application to another. Wireless technologies such as WiFi, ZigBee, Bluetooth, LTE, NB-IoT, LoRa and SigFox will play crucial roles in enabling these applications. Some of the technological features are transmission range, bandwidth, data rate, security schemes and infrastructure requirements. As there is no one-size-all network solution available, the key is to understand the diverse requirements of different IoE applications and specific features offered by different IoE enabling technologies. Application-specific technology selection will ensure the best possible utilization of any technology and the quality of service requirements. An overview of network performance expectations from various IoE applications and enabling technologies, their features and potential applications are presented in this paper.

5G for Verticals: From Theory to Practice and Beyond [Guest Editorial

The 3rd Generation Partnership Project (3GPP) completed its Fifth Generation (5G) New Radio (NR) Release 16 and approved the OpenAPI specification in July 2020. The 5G NR technology is expected to support numerous future applications in one or a mix of three usage scenarios: enhanced Mobile Broadband (eMBB), massive Machine Type Communications (mMTC), and Ultra Reliable Low Latency Communications (URLLC). However, 5G is still lacking its killer applications. Which vertical applications will fully utilize the capabilities offered by 5G that cannot be provided by 4G? What is the unprecedented performance gain these applications can achieve with 5G in practice? To answer these and similar questions, the 3GPP started to focus on specific applications and field performance evaluations in Release 17, which includes communication service requirements for critical medical applications, enhancements for unmanned aerial vehicles, enhancements for cyber-physical control applications in vertical domains, enhanced relays for energy efficiency and extensive coverage.

Modeling, Analysis, and Optimization of Grant-Free NOMA in Massive MTC via Stochastic Geometry

Massive machine-type communications (mMTC) is a crucial scenario to support booming Internet of Things (IoTs) applications. In mMTC, although a large number of devices are registered to an access point (AP), very few of them are active with uplink short packet transmission at the same time, which requires novel design of protocols and receivers to enable efficient data transmission and accurate multi-user detection (MUD). Aiming at this problem, grant-free non-orthogonal multiple access (GF-NOMA) protocol is proposed. In GF-NOMA, active devices can directly transmit their preambles and data symbols altogether within one time frame, without grant from the AP. Compressive sensing (CS)-based receivers are adopted for non-orthogonal preambles (NOP)-based MUD, and successive interference cancellation is exploited to decode the superimposed data signals. In this paper, we model, analyze, and optimize the CS-based GF-MONA mMTC system via stochastic geometry (SG), from an aspect of network deployment. Based on the SG network model, we first analyze the success probability as well as the channel estimation error of the CS-based MUD in the preamble phase and then analyze the average aggregate data rate in the data phase. As IoT applications highly demands low energy consumption, low infrastructure cost, and flexible deployment, we optimize the energy efficiency and AP coverage efficiency of GF-NOMA via numerical methods. The validity of our analysis is verified via Monte Carlo simulations. Simulation results also show that CS-based GF-NOMA with NOP yields better MUD and data rate performances than contention-based GF-NOMA with orthogonal preambles and CS-based grant-free orthogonal multiple access.

A Low Energy MCL-Based Clustering Routing Protocol for Wireless Sensor Networks

Wireless sensor networks (WSNs) have attracted increasing attention during the two last decades. Indeed, their applications target a wide range of fields such as healthcare, industrial control, environmental monitoring, etc. The main challenge of WSNs is the limitation of resources requiring efficient management of energy consumption. Coverage is also considered as one of the critical indicators of the quality of service (QoS) of WSNs. Therefore, the design of routing protocols should focus on energy efficiency and network coverage. In this paper, the authors propose a novel energy-efficient clustering protocol that is more efficient than some prominent routing protocols and offers better coverage of the network than LEACH. The protocol combines the MCL algorithm for cluster formation and a new cluster head selection strategy based on location and residual energy of sensors. Simulation results demonstrate that the proposed protocol is better than other protocols in terms of energy management and extending the lifetime of the network, whereas, it achieves good coverage than LEACH.

RSSI Fingerprinting-Based Localization Using Machine Learning in LoRa Networks

The scale of wireless technologies' penetration in our daily lives, primarily triggered by Internet of Things (IoT)-based smart cities, is bea-coning the possibilities of novel localization and tracking techniques. Recently, low-power wide-area network (LPWAN) technologies have emerged as a solution to offer scalable wireless connectivity for smart city applications. LoRa is one such technology, which provides energy efficiency and wide-area coverage. This article explores the use of intelligent machine learning techniques, such as support vector machines, spline models, decision trees, and ensemble learning, for received signal strength indicator (RSSI)-based ranging in LoRa networks on a training dataset collected in two different environments: indoors and outdoors. The suitable ranging model is then used to experimentally evaluate the accuracy of localization and tracking using trilateration in the studied environments. Later, we present the accuracy of a LoRa-based positioning system (LPS) and compare it with the existing ZigBee, WiFi, and Bluetooth-based solutions. In the end, we discuss the challenges of satellite-independent tracking systems and propose future directions to improve accuracy and provide deployment feasibility.

OFDM-IM Based Dual-Hop System Using Fixed-Gain Amplify-and-Forward Relay With Pre-Processing Capability

Orthogonal frequency-division multiplexing with index modulation (OFDM-IM) has recently attracted many researchers’ attention due to its superior spectrum efficiency and reliability compared to the traditional OFDM. Cooperative decode-and-forward relaying has been incorporated with OFDM-IM, which provides a higher energy efficiency and better network coverage. However, it might not be feasible in realistic applications, owing to the high system complexity and transmission delay rendered by complex decoding and channel estimation procedures. Therefore, in this paper, we propose a fixed-gain (FG) amplify-and-forward (AF) relay-assisted OFDM-IM system, which does not need to perform complex decoding and channel estimation at the relay, but only requires a pre-processing capability at the relay, e.g., cyclic prefix removal and re-insertion. Therefore, the system complexity can be reduced and the forwarding delay as well as power consumption caused by processing at the relay also decline. We analyze the average outage probability, block error rate, and achievable rate of the proposed system and verify all analysis by numerical results. The proposed FG AF relay-assisted OFDM-IM provides a simple solution to the implementation of OFDM-IM in new network paradigms, where nodes are simple, power limited, and/or complexity limited.

Optimized and low-complexity power allocation and beamforming with full duplex in massive MIMO and small-cell networks

By combining massive multiple-input and multiple-output (Ma-MIMO) and small-cell approaches, it is possible to improve the capacity of the network, with the features like energy efficiency and high coverage. Ma-MIMO helps in advancing the wireless communication, to incorporate it in the wireless broadband standards such as Wi-Fi and LTE. The use of Ma-MIMO is expected to increase the spectral efficiency for the mobile networks. In this paper, these two different technologies are incorporated for improving the 5G wireless communication. The concern of the whole paper is to satisfy the user with high quality of service. The effective technologies like Ma-MIMO and small cell are analyzed by employing beamforming and power allocation while operated in full-duplex transmission mode. The simulation is conducted to prove the optimal as well as low-complexity beamforming and power allocation, which is required for improving the total power consumption.

Energy-Efficient Vehicular Heterogeneous Networks for Green Cities

With the evolutionary development of automobile industry, modern transportation systems cause a series of critical problems, such as increased energy consumption and air pollution. To make green cities a reality, an ever expanding and evolving vehicular heterogeneous network infrastructure is required to enable fine-granularity data collection and reliable service delivery. In this paper, we investigate how to realize energy-efficient vehicular heterogeneous networks for green cities by exploring cooperative two-hop device-to-device-based vehicle-to-vehicle (D2D–V2V) transmission. We propose a two-stage energy-efficient resource allocation algorithm. In the first stage, an auction-matching-based joint relay selection, spectrum allocation, and power control algorithm is derived, which employs an English-auction approach for matching preference updating and conflict avoidance, and optimizes the energy efficiency of two-hop D2D–V2V and cellular links simultaneously in an iterative fashion. In the second stage, a nonlinear fractional programming based power control algorithm is developed to maximize the energy efficiency of the base station. Theoretical properties in terms of convergence, stability, and complexity are analyzed. Finally, the proposed algorithm is evaluated based on real-world road topology and realistic vehicular traffic. Numerical results demonstrate that the proposed algorithm achieves superior performance in terms of energy efficiency and network coverage compared to other heuristic algorithms.

Updates to the China Design Standard for Energy Efficiency in public buildings

The China Design Standard for Energy Efficiency in public buildings (GB 50189) debuted in 2005 when China completed the 10th Five-Year Plan. GB 50189-2005 played a crucial role in regulating the energy efficiency in Chinese commercial buildings. The standard was recently updated in 2014 to increase energy savings targets by 30% compared with the 2005 standard. This paper reviews the major changes to the standard, including expansion of energy efficiency coverage and more stringent efficiency requirements. The paper also discusses the interrelationship of the design standard with China's other building energy standards. Furthermore, comparisons are made with ASHRAE Standard 90.1-2013 to provide contrasting differences in efficiency requirements. Finally recommendations are provided to guide the future standard revision, focusing on three areas: (1) increasing efficiency requirements of building envelope and HVAC systems, (2) adding a whole-building performance compliance pathway and implementing a ruleset based automatic code baseline model generation in an effort to reduce the discrepancies of baseline models created by different tools and users, and (3) adding inspection and commissioning requirements to ensure building equipment and systems are installed correctly and operate as designed.

Two-tier particle swarm optimization protocol for clustering and routing in wireless sensor network

Many cluster-based routing techniques for Wireless Sensor Networks (WSNs) have been proposed in the literature. However, most of the proposed protocols emphasized on the Cluster Head (CH) selection ignoring how the CHs will send the aggregated data back to the Base Station (BS). Furthermore, they tend to use non-realistic parameters and assumptions. Such examples include the use of infinite transmission range and location awareness. They also used an energy model that is fundamentally flawed for modelling radio power consumption in sensor networks. In this paper, two Linear Programming (LP) formulations to the problems of clustering and routing are presented followed by two proposed algorithms for the same based on Particle Swarm Optimization (PSO). The clustering algorithm finds the optimal set of CHs that maximize the energy efficiency, cluster quality and network coverage. The routing algorithm is developed with a novel particle encoding scheme and fitness function to find the optimal routing tree that connects these CHs to the BS. These two algorithms are then combined into a two-tier protocol to provide a complete and practical clustering model. The effect of using a realistic network and energy consumption model in cluster-based communication for WSN will be investigated. Extensive simulations on 50 homogeneous and heterogeneous WSN models are evaluated and compared against well-known cluster-based sensor network protocols. The results demonstrate that the proposed protocol performs better than such protocols in terms of various performance metrics such as scalability, Packet Delivery Rate (PDR) at the CHs and delivery of total data packets to the BS.

A Novel Distributed Clustering Protocol Using Fuzzy Logic

Energy resource constraint is an important issue in wireless sensor networks. An efficient solution to this issue is clustering approach; which markes the network nodes as cluster heads (CHs) and cluster members (CMs). In the clustering method cluster members send packets to the cluster head and CHs forward the received packets to the sink. CH selection mechanism has direct effect on the energy efficiency and network coverage of WSN. In this paper a new distributed clustering approach using fuzzy logic is proposed. Our proposed method uses Fuzzy logic to assign CH selection chance to network nodes in order to choose tentative and final CHs. simulation results demonstrate that our approach reachs to better network lifetime, energy efficiency and network coverage in comparison with UCFIA, GCA, and SCP.

An Energy-Efficient Dense PicoStation Deployment and Power Control Strategy for Heterogeneous Networks

Deploying PicoStations in traditional Macro-cellular networks, especially in hot-spot areas, is a promising strategy to boost system throughput and enhance indoor coverage. However, PicoStations’ introduction brings some new problems, such as severe interference and energy consumption, which may result in performance degradation. In order to improve energy efficiency, throughput fairness and PicoStation coverage, a practical energy-efficient dense PicoStation deployment and power control algorithm is proposed. The proposed scheme maximizes both the number of active PicoStations that can achieve the required capacity and the sum cell capacity under individual capacity constraints in two steps. Finally, simulation results demonstrate that the proposed algorithm outperforms the traditional cell capacity maximum scheme in terms of user fairness and energy saving.