Search Results (9)

The seamless handover of mobile devices is critical for maximizing the potential of smart city applications, which demand uninterrupted connectivity, ultra-low latency, and performance in diverse environments. Fifth-generation (5G) and beyond-5G networks offer advancements in massive connectivity and ultra-low latency by leveraging advanced technologies like millimeter wave, massive machine-type communication, non-orthogonal multiple access, and beam forming. However, challenges persist in ensuring smooth handovers in dense deployments, especially in higher frequency bands and with increased user mobility. This paper presents an adaptive handover management scheme that utilizes reinforcement learning to optimize handover decisions in dynamic environments. The system selects the best target cell from the available neighbor cell list by predicting key performance indicators, such as reference signal received power and the signal–interference–noise ratio, while considering the fixed time-to-trigger and hysteresis margin values. It dynamically adjusts handover thresholds by incorporating an offset based on real-time network conditions and user mobility patterns. This adaptive approach minimizes handover failures and the ping-pong effect. Compared to the baseline LIM2 model, the proposed system demonstrates a 15% improvement in handover success rate, a 3% improvement in user throughput, and an approximately 6 sec reduction in the latency at 200 km/h speed in high-mobility scenarios. Full article

In cloud-based Distributed Acoustic Sensing (DAS) sensor data management, we are confronted with two primary challenges. First, the development of efficient storage mechanisms capable of handling the enormous volume of data generated by these sensors poses a challenge. To solve this issue, we propose a method to address the issue of handling the large amount of data involved in DAS by designing and implementing a pipeline system to efficiently send the big data to DynamoDB in order to fully use the low latency of the DynamoDB data storage system for a benchmark DAS scheme for performing continuous monitoring over a 100 km range at a meter-scale spatial resolution. We employ the DynamoDB functionality of Amazon Web Services (AWS), which allows highly expandable storage capacity with latency of access of a few tens of milliseconds. The different stages of DAS data handling are performed in a pipeline, and the scheme is optimized for high overall throughput with reduced latency suitable for concurrent, real-time event extraction as well as the minimal storage of raw and intermediate data. In addition, the scalability of the DynamoDB-based data storage scheme is evaluated for linear and nonlinear variations of number of batches of access and a wide range of data sample sizes corresponding to sensing ranges of 1–110 km. The results show latencies of 40 ms per batch of access with low standard deviations of a few milliseconds, and latency per sample decreases for increasing the sample size, paving the way toward the development of scalable, cloud-based data storage services integrating additional post-processing for more precise feature extraction. The technique greatly simplifies DAS data handling in key application areas requiring continuous, large-scale measurement schemes. In addition, the processing of raw traces in a long-distance DAS for real-time monitoring requires the careful design of computational resources to guarantee requisite dynamic performance. Now, we will focus on the design of a system for the performance evaluation of cloud computing systems for diverse computations on DAS data. This system is aimed at unveiling valuable insights into performance metrics and operational efficiencies of computations on the data in the cloud, which will provide a deeper understanding of the system’s performance, identify potential bottlenecks, and suggest areas for improvement. To achieve this, we employ the CloudSim framework. The analysis reveals that the virtual machine (VM) performance decreases significantly the processing time with more capable VMs, influenced by Processing Elements (PEs) and Million Instructions Per Second (MIPS). The results also reflect that, although a larger number of computations is required as the fiber length increases, with the subsequent increase in processing time, the overall speed of computation is still suitable for continuous real-time monitoring. We also see that VMs with lower performance in terms of processing speed and number of CPUs have more inconsistent processing times compared to those with higher performance, while not incurring significantly higher prices. Additionally, the impact of VM parameters on computation time is explored, highlighting the importance of resource optimization in the DAS system design for efficient performance. The study also observes a notable trend in processing time, showing a significant decrease for every additional 50,000 columns processed as the length of the fiber increases. This finding underscores the efficiency gains achieved with larger computational loads, indicating improved system performance and capacity utilization as the DAS system processes more extensive datasets. Full article

Advanced Metering Infrastructure (AMI) plays a crucial role in enabling the efficient functioning of Smart Electrical Grids, but its successful implementation hinges on robust cybersecurity measures. To uphold data confidentiality and integrity, the deployment of an effective key management scheme (KMS) for multiple Smart Meters (SMs) and devices is imperative. The AMI exhibits unique characteristics, including storage and computation constraints in SMs, hybrid message transmission techniques, and varying participation levels in Demand Response (DR) projects, necessitating a tailored approach to security compared to other systems. In this research, we propose a KMS that is designed to address the specific security concerns of the AMI. The scheme comprises three key management procedures catering to the unicast, broadcast, and multicast modes of hybrid transmission. Given the resource limitations of SMs, we adopted simple cryptographic techniques for key creation and refreshing policies, ensuring efficiency without compromising on security. Furthermore, considering the variability of participants in DR projects, we established key refreshing policies that adapted to changing involvement. The effectiveness and security of the proposed KMS were rigorously evaluated, demonstrating its practical applicability and ability to safeguard the AMI ecosystem. The results of the evaluation indicate that our approach provides a viable and robust solution to the security challenges faced by AMI systems. By employing the proposed KMS, stakeholders can confidently deploy and manage AMI, ensuring the protection of sensitive data and maintaining the integrity of the Smart Electrical Grid. Full article

The novel coronavirus disease-2019 (COVID-19) has transformed into a global health concern, which resulted in human containment and isolation to flatten the curve of mortality rates of infected patients. To leverage the massive containment strategy, fifth-generation (5G)-envisioned unmanned aerial vehicles (UAVs) are used to minimize human intervention with the key benefits of ultra-low latency, high bandwidth, and reliability. This allows phased treatment of infected patients via threefold functionalities (3FFs) such as social distancing, proper sanitization, and inspection and monitoring. However, UAVs have to send massive recorded data back to ground stations (GS), which requires a real-time device connection density of 10${}^7$/km², which forms huge bottlenecks on 5G ecosystems. A sixth-generation (6G) ecosystem can provide terahertz (THz) frequency bands with massive short beamforming cells, intelligent deep connectivity, and physical- and link-level protocol virtualization. The UAVs form a swarm network to assure 3FFs which requires high-end computations and are data-intensive; thus, these computational tasks can be offloaded to nearby edge servers, which employ local federated learning to train the global models. It synchronizes the UAV task formations and optimizes the network functions. Task optimization of UAV swarms in 6G-assisted channels allows better management and ubiquitous and energy-efficient seamless communication over ground, space, and underwater channels. Thus, a data-centric 3FF approach is essential to fight against future pandemics, with a 6G backdrop channel. The proposed scheme is compared with traditional fourth-generation (4G) and 5G-networks-based schemes to indicate its efficiency in traffic density, processing latency, spectral efficiency, UAV mobility, radio loss, and device connection density. Full article

Dynamic wireless charging (DWC) is a promising technology to charge Electric Vehicles (EV) using on-road charging segments (CS), also known as DWC pads. In order to ensure effective utilization of this on-the-road charging service, communication and coordination need to be established between the EVs and the different network entities, thereby forming an Internet of Electric Vehicles (IoEV). In an IoEV, EVs can utilize different V2X communication modes to enable charging scheduling, load management, and reliable authentication and billing services. Yet, designing an authentication scheme for dynamic EV charging presents significant challenges given the mobility of the EVs and the short contact time between the EVs and the charging segments. Accordingly, this work proposes a fast, secure and lightweight authentication scheme that allows only authentic EVs with valid credentials to charge their batteries while ensuring secure and fair payments. The presented scheme starts with a key pre-distribution phase between the charging service company (CSC) and the charging pad owner (PO), followed by a hash chain and digital signature-based registration and authentication phase between the EV and the CSC, before the EV reaches the beginning of the charging lane. These preliminary authentication phases allow the authentication between the EVs and the charging segments to be performed using simple hash key verification operations prior to charging activation, which reduces the computational cost of the EVs and the CS. Symmetric and asymmetric key cryptography are utilized to secure the communication between the different network entities. Analysis of the computational and transmission time requirements of the proposed authentication scheme shows that, for an EV traveling at 60 km/h to start charging at the beginning of the charging lane, the authentication process must be initiated at least $1.35$ m ahead of the starting point of the lane as it requires ≃81 ms to be completed. Full article

In recent years, in which a considerable increase in the road traffic volumes has been witnessed, traffic calming has become one the key issues in the area of road engineering. This concerns, in particular, trunk roads passing through small villages with a population of up to 500 and the road section length within the village limits of ca. 1400–1700 m. A successful traffic calming scheme must involve primarily effective reduction in inbound traffic speed. A review of the data from various countries revealed that chicanes installed in the transition zones may have a determining effect on the success of the traffic calming project. The effectiveness of such chicanes depends mainly on the type of chicane, its location on the carriageway, its shape and the size of the lateral deflection imposed by the chicane on the inbound lane. The purpose of this study was to identify the speed reduction determinants in traffic calming schemes in village transition zones, based on a central island horizontally deflecting one lane of a two-lane two-way road with 50 km/h speed restriction. As part of the study, vehicle speeds were measured just before and after the chicanes under analysis. Furthermore, the inbound lane traffic volumes were measured in field and a number of factors were identified, including the applied traffic management scheme, road parameters, view of the road ahead and of the village skyline, isolated buildings, road infrastructure and adjacent roadside developments. The obtained data were analysed with a method employing tautologies of the selected 32 factors affecting the drivers’ perception. A single aggregate parameter was proposed for assessing the coincidence of the influence of selected factors on speed reduction. The analysis of the existing schemes and the results of statistical analyses carried out in this study confirmed the authors’ hypothesis that the combined selected factors produce a desirable effect and that they should be additionally enhanced by the application of solar powered devices. Full article

A string inverter converts the low voltage direct current coming from the string of its Photovoltaic (PV) panels into alternating current to be exported to the grid. In today Smart Grid’s context, PV plants feature clusters of cooperating smart string inverters that exchange information in a multicast fashion (typically) over the Internet Protocol (IP). However, IP multicast does not provide any mechanism to limit the access to multicast data to authorized subjects only. A security infringement may cause a cluster either into exporting no energy into the grid (zero energy attack) or more energy than the limit set (energy overflow attack). Both the attacks can lead to potential severe consequences. In this regard, we are the first addressing those issues. Particularly, we propose a Key Management Service (KMS) for group key generation and distribution. The KMS provides forward secrecy and periodic refresh. We implement a prototype on a cluster of Power-One Italy S.p.A. a member of FIMER Group smart string inverters and evaluate the performance. Experimental results indicate that the scheme scales up to clusters composed of 50 inverters with an efficiency of 90.5% in terms of latency for group key distribution and 99% in terms of memory overhead. Full article

Evapotranspiration (ET) is one of the components in the water cycle and the surface energy balance systems. It is fundamental information for agriculture, water resource management, and climate change research. This study presents a scheme for regional actual evapotranspiration estimation using multi-source satellite data to compute key land and meteorological variables characterizing land surface, soil, vegetation, and the atmospheric boundary layer. The algorithms are validated using ground observations from the Heihe River Basin of northwest China. Monthly data estimates at a resolution of 1 km from the proposed algorithms compared well with ground observation data, with a root mean square error (RMSE) of 0.80 mm and a mean relative error (MRE) of −7.11%. The overall deviation between the average yearly ET derived from the proposed algorithms and ground-based water balance measurements was 9.44% for a small watershed and 1% for the entire basin. This study demonstrates that both accuracy and spatial depiction of actual evapotranspiration estimation can be significantly improved by using multi-source satellite data to measure the required land surface and meteorological variables. This reduces dependence on spatial interpolation of ground-derived meteorological variables which can be problematic, especially in data-sparse regions, and allows the production of region-wide ET datasets. Full article

Increase in irrigated area, driven by demand for more food production, in the semi-arid regions of Asia and Africa is putting pressure on the already strained available water resources. To cope and manage this situation, monitoring spatial and temporal dynamics of the irrigated area land use at basin level is needed to ensure proper allocation of water. Publicly available satellite data at high spatial resolution and advances in remote sensing techniques offer a viable opportunity. In this study, we developed a new approach using time series of Landsat 8 (L8) data and Random Forest (RF) machine learning algorithm by introducing a hierarchical post-processing scheme to extract key Land Use Land Cover (LULC) types. We implemented this approach for Mashhad basin in Iran to develop a LULC map at 15 m spatial resolution with nine classes for the crop year 2015/2016. In addition, five irrigated land use types were extracted for three crop years—2013/2014, 2014/2015, and 2015/2016—using the RF models. The total irrigated area was estimated at 1796.16 km², 1581.7 km² and 1578.26 km² for the cropping years 2013/2014, 2014/2015 and 2015/2016, respectively. The overall accuracy of the final LULC map was 87.2% with a kappa coefficient of 0.85. The methodology was implemented using open data and open source libraries. The ability of the RF models to extract key LULC types at basin level shows the usability of such approaches for operational near real time monitoring. Full article