Eurasip Journal on Wireless Communications and Networking最新文献

The increasing frequency and intensity of forest fires demand innovative technologies to support firefighting and mitigate their impact on ecosystems and communities. This paper explores the application of untethered and tethered uncrewed aerial vehicles (UAVs) as aerial base station (ABS) in the context of forest fire management. We propose an ABS placement algorithm to serve UAV-user equipment (UE) in forest environments. The novelty of the proposed approach is to optimize the strategic ABS placement based on throughput requirements while serving multiple drone forest monitoring areas. We analyze and compare the performances of two aerial base stations (ABSs) such as an untethered ABS (UTABS) and a tethered ABS (TABS) for forest surveillance. We evaluate UTABS and TABS performances across single and multiple UAV-UE monitoring zones in forest environments under different network throughputs, wind effects, payload configurations, monitoring zone areas, communication frequencies, operating costs, etc. Our results indicate that while a UTABS offers superior flexibility, a TABS provides a cost-effective (78.2% reduction) and reliable solution to ensure long-term continuous forest fire surveillance operations.

In this paper, we present a signaling design for secure integrated sensing and communication (ISAC) systems comprising a dual-functional multi-input multi-output base station that simultaneously communicates with multiple users while detecting targets present in their vicinity, which are regarded as potential eavesdroppers. In particular, assuming that the distribution of each parameter to be estimated is known a priori, we focus on optimizing the targets' sensing performance. To this end, we derive and minimize the Bayesian Cramér-Rao bound, while ensuring certain communication quality of service by exploiting constructive interference. The latter scheme enforces that the received signals at the eavesdropping targets fall into the destructive region of the signal constellation, to deteriorate their decoding probability, thus enhancing the ISAC's system physical layer security capability. To tackle the nonconvexity of the formulated problem, a tailored successive convex approximation method is proposed for its efficient solution. Our extensive numerical results verify the effectiveness of the proposed secure ISAC design showing that the proposed algorithm outperforms block-level precoding techniques.

Edge computing is an emerging approach that enables applications to run closer to users, accommodating their specific execution time requirements. Edge computing systems typically consist of heterogeneous processing and networking components, resulting in inconsistent task performance. To improve the consistency of edge computing applications, this study presents a method to identify the factors that affect variability in task execution time. We deploy a set of single-particle analysis algorithms, designed for an electron microscopy use case, running on a Kubernetes cluster monitored by Prometheus. This specific usecase was chosen because it encompasses a diverse set of time-sensitive and privacy-sensitive applications, with a wide range of resource requirements. Our experiments revealed a significant increase in the variability of round-trip time when tasks share resources. The proposed approach identifies the most relevant monitoring metrics from a larger set of collected ones (provided by Prometheus), with correlations up to 87%. This process reduces the number of metrics to 90, achieving a reduction of 80%. As a result, the overhead of the monitoring system is decreased, and the use of these metrics for further processing, such as predictive modeling and scheduling, is simplified. These selected metrics not only help to understand the causes of performance variability, but also possess predictive value, enabling more efficient scheduling. The prediction power of these metrics is shown using SHapley Additive exPlanations analysis.

Objectives: Globally, problem gambling prevalence is estimated at between 0.1% and 5.8%. Problem gambling can have many negative consequences; including on physical, and psychological health, and social functioning. There is a need to better understand treatment uptake as only a small proportion seek treatment. This is the first Irish national study using routinely gathered health surveillance data to describe treated problem gambling. Results will inform service policy and planning.

Methods: An analysis of episodes treated for problem gambling collected by the National Drug Treatment Reporting System was undertaken. Included were episodes entering treatment between 2008 and 2019 (n = 2999). Variables of interest included service types accessed, demographics, socioeconomic information, referral and assessment details, current problems (up to five) and treatment history.

Results: The majority (93.8%) were male. One fifth (20.9%) lived with dependent children, 7.4% were homeless. There were high levels of employment (35.4%) and formal education qualifications; half (53.8%) had completed second or third level education. Problem gambling frequently co-occurred with problem use of other substances (47.3%), which was most commonly alcohol (85.6%), followed by cannabis (32.3%), cocaine (28.0%) and benzodiazepines (10.9%). The majority were treated at inpatient settings (56.1%) with many self-referrals (46.3%).

Conclusions: This study provides insights into treated problem gambling nationally. Monitoring and surveillance can play a crucial role in measuring the successful efforts and help inform planning and treatment. The findings may have implications for treatment pathways.