ABSTRACT Due to the low gamma-ray fluxes from pulsars above 50 GeV and the small collecting area of space-based telescopes, the gamma-ray emission discovered by the Fermi Large Area Telescope (LAT) ...in ∼150 pulsars is largely unexplored at these energies. In this regime, the uncertainties on the spectral data points and/or the constraints from upper limits are not sufficient to provide robust tests of competing emission models in individual pulsars. The discovery of power-law-type emission from the Crab pulsar at energies exceeding 100 GeV provides a compelling justification for exploration of other pulsars at these energies. We applied the method of aperture photometry to measure pulsar emission spectra from Fermi-LAT data and present a stacked analysis of 115 pulsars selected from the Second Fermi-LAT catalog of gamma-ray pulsars. This analysis, which uses an average of ∼4.2 yr of data per pulsar, aggregates low-level emission which cannot be resolved in individual objects but can be detected in an ensemble. We find no significant stacked excess at energies above 50 GeV. An upper limit of 30% of the Crab pulsar level is found for the average flux from 115 pulsars in the 100-177 GeV energy range at the 95% confidence level. Stacked searches exclusive to the young pulsar sample, the millisecond pulsar sample, and several other promising sub-samples also return no significant excesses above 50 GeV.
Modulation and Multiple Access for 5G Networks Yunlong Cai; Zhijin Qin; Fangyu Cui ...
IEEE Communications surveys and tutorials,
2018-Firstquarter, Letnik:
20, Številka:
1
Journal Article
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Fifth generation (5G) wireless networks face various challenges in order to support large-scale heterogeneous traffic and users, therefore new modulation and multiple access (MA) schemes are being ...developed to meet the changing demands. As this research space is ever increasing, it becomes more important to analyze the various approaches, therefore, in this paper we present a comprehensive overview of the most promising modulation and MA schemes for 5G networks. Unlike other surreys of 5G networks, this paper focuses on multiplexing techniques, including modulation techniques in orthogonal MA (OMA) and various types of non-OMA (NOMA) techniques. Specifically, we first introduce different types of modulation schemes, potential for OMA, and compare their performance in terms of spectral efficiency, out-of-band leakage, and bit-error rate. We then pay close attention to various types of NOMA candidates, including power-domain NOMA, code-domain NOMA, and NOMA multiplexing in multiple domains. From this exploration, we can identify the opportunities and challenges that will have the most significant impacts on modulation and MA designs for 5G networks.
The increasing pervasiveness of wireless sensor networks (WSNs) in diverse application domains including critical infrastructure systems, sets an extremely high security bar in the design of WSN ...systems to exploit their full benefits, increasing trust while avoiding loss. Nevertheless, a combination of resource restrictions and the physical exposure of sensor devices inevitably cause such networks to be vulnerable to security threats, both external and internal. While several researchers have provided a set of open problems and challenges in WSN security and privacy, there is a gap in the systematic study of the security implications arising from the nature of existing communication protocols in WSNs. Therefore, we have carried out a deep-dive into the main security mechanisms and their effects on the most popular protocols and standards used in WSN deployments, i.e., IEEE 802.15.4, Berkeley media access control for low-power sensor networks, IPv6 over low-power wireless personal area networks, outing protocol for routing protocol for low-power and lossy networks (RPL), backpressure collection protocol, collection tree protocol, and constrained application protocol, where potential security threats and existing countermeasures are discussed at each layer of WSN stack. This paper culminates in a deeper analysis of network layer attacks deployed against the RPL routing protocol. We quantify the impact of individual attacks on the performance of a network using the Cooja network simulator. Finally, we discuss new research opportunities in network layer security and how to use Cooja as a benchmark for developing new defenses for WSN systems.
Autonomic Computing is a concept that brings together many fields of computing with the purpose of creating computing systems that self-manage. In its early days it was criticised as being a “hype ...topic” or a rebadging of some Multi Agent Systems work. In this survey, we hope to show that this was not indeed ‘hype’ and that, though it draws on much work already carried out by the Computer Science and Control communities, its innovation is strong and lies in its robust application to the specific self-management of computing systems. To this end, we first provide an introduction to the motivation and concepts of autonomic computing and describe some research that has been seen as seminal in influencing a large proportion of early work. Taking the components of an established reference model in turn, we discuss the works that have provided significant contributions to that area. We then look at larger scaled systems that compose autonomic systems illustrating the hierarchical nature of their architectures. Autonomicity is not a well defined subject and as such different systems adhere to different degrees of Autonomicity, therefore we cross-slice the body of work in terms of these degrees. From this we list the key applications of autonomic computing and discuss the research work that is missing and what we believe the community should be considering.
Pipeline networks have been widely utilised in the transportation of water, natural gases, oil and waste materials efficiently and safely over varying distances with minimal human intervention. In ...order to optimise the spatial use of the pipeline infrastructure, pipelines are either buried underground, or located in submarine environments. Due to the continuous expansion of pipeline networks in locations that are inaccessible to maintenance personnel, research efforts have been ongoing to introduce and develop reliable detection methods for pipeline failures, such as blockages, leakages, cracks, corrosion and weld defects. In this paper, a taxonomy of existing pipeline failure detection techniques and technologies was created to comparatively analyse their respective advantages, drawbacks and limitations. This effort has effectively illuminated various unaddressed research challenges that are still present among a wide array of the state-of-the-art detection methods that have been employed in various pipeline domains. These challenges include the extension of the lifetime of a pipeline network for the reduction of maintenance costs, and the prevention of disruptive pipeline failures for the minimisation of downtime. Our taxonomy of various pipeline failure detection methods is also presented in the form of a look-up table to illustrate the suitability, key aspects and data or signal processing techniques of each individual method. We have also quantitatively evaluated the industrial relevance and practicality of each of the methods in the taxonomy in terms of their respective deployability, generality and computational cost. The outcome of the evaluation made in the taxonomy will contribute to our future works involving the utilisation of sensor fusion and data-centric frameworks to develop efficient, accurate and reliable failure detection solutions.
Autonomous outdoor moving objects like cars, motorcycles, bicycles, and pedestrians present different risks to the safety of Visually Impaired People (VIPs). Consequently, many camera-based VIP ...mobility assistive solutions have resulted. However, they fail to guarantee VIP safety in practice, i.e., they cannot effectively prevent collisions with more dangerous threats moving at higher speeds, namely, Critical Moving Objects (CMOs). This paper presents the first practical camera-based VIP mobility assistant scheme, ARAware, that effectively identifies CMOs in real-time to give the VIP more time to avoid danger through simultaneously addressing CMO identification, CMO risk level evaluation and classification, and prioritised CMO warning notification. Experimental results based on our real-world prototype demonstrate that ARAware accurately identifies CMOs (with 97.26% mAR and 88.20% mAP) in real-time (with a 32 fps processing speed for 30 fps incoming video). It precisely classifies CMOs according to their risk levels (with 100% mAR and 91.69% mAP), and warns in a timely manner about high-risk CMOs while effectively reducing false alarms by postponing the warning of low-risk CMOs. Compared to the closest state-of-the-art approach, DEEP-SEE, ARAware achieves significantly higher CMO identification accuracy (by 42.62% in mAR and 10.88% in mAP), with a 93% faster end-to-end processing speed.
Low-Power Wide-Area Networks for Sustainable IoT Qin, Zhijin; Li, Frank Y.; Li, Geoffrey Ye ...
IEEE wireless communications,
2019-June, 2019-6-00, 20190601, Letnik:
26, Številka:
3
Journal Article
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LPWA networks are attracting extensive attention because of their ability to offer low-cost and massive connectivity to IoT devices distributed over wide geographical areas. This article provides a ...brief overview of the existing LPWA technologies and useful insights to aid the large-scale deployment of LPWA networks. In particular, we first review the currently competing candidates of LPWA networks, such as NB-IoT and LoRa, in terms of technical fundamentals and large-scale deployment potential. Then we present two implementation examples of LPWA networks. By analyzing the field-test results, we identify several challenges that prevent LPWA technologies from moving from theory to wide-spread practice.
The modernization of logistics through the use of Wireless Sensor Network (WSN) Internet of Things (IoT) devices promises great efficiencies. Sensor devices can provide real-time or near real-time ...condition monitoring and location tracking of assets during the shipping process, helping to detect delays, prevent loss, and stop fraud. However, the integration of low-cost WSN/IoT systems into a pre-existing industry should first consider security within the context of the application environment. In the case of logistics, the sensors are mobile, unreachable during the deployment, and accessible in potentially uncontrolled environments. The risks to the sensors include physical damage, either malicious/intentional or unintentional due to accident or the environment, or physical attack on a sensor, or remote communication attack. The easiest attack against any sensor is against its communication. The use of IoT sensors for logistics involves the deployment conditions of mobility, inaccesibility, and uncontrolled environments. Any threat analysis needs to take these factors into consideration. This paper presents a threat model focused on an IoT-enabled asset tracking/monitoring system for smart logistics. A review of the current literature shows that no current IoT threat model highlights logistics-specific IoT security threats for the shipping of critical assets. A general tracking/monitoring system architecture is presented that describes the roles of the components. A logistics-specific threat model that considers the operational challenges of sensors used in logistics, both malicious and non-malicious threats, is then given. The threat model categorizes each threat and suggests a potential countermeasure.
We have shown previously that T cells are required for the full development of angiotensin II-induced hypertension. However, the specific subsets of T cells that are important in this process are ...unknown. T helper 17 cells represent a novel subset that produces the proinflammatory cytokine interleukin 17 (IL-17). We found that angiotensin II infusion increased IL-17 production from T cells and IL-17 protein in the aortic media. To determine the effect of IL-17 on blood pressure and vascular function, we studied IL-17(-/-) mice. The initial hypertensive response to angiotensin II infusion was similar in IL-17(-/-) and C57BL/6J mice. However, hypertension was not sustained in IL-17(-/-) mice, reaching levels 30-mm Hg lower than in wild-type mice by 4 weeks of angiotensin II infusion. Vessels from IL-17(-/-) mice displayed preserved vascular function, decreased superoxide production, and reduced T-cell infiltration in response to angiotensin II. Gene array analysis of cultured human aortic smooth muscle cells revealed that IL-17, in conjunction with tumor necrosis factor-alpha, modulated expression of >30 genes, including a number of inflammatory cytokines/chemokines. Examination of IL-17 in diabetic humans showed that serum levels of this cytokine were significantly increased in those with hypertension compared with normotensive subjects. We conclude that IL-17 is critical for the maintenance of angiotensin II-induced hypertension and vascular dysfunction and might be a therapeutic target for this widespread disease.
The increasing usage of interconnected devices within the Internet of Things (IoT) and Industrial IoT (IIoT) has significantly enhanced efficiency and utility in both personal and industrial settings ...but also heightened cybersecurity vulnerabilities, particularly through IoT malware. This paper explores the use of one-class classification, a method of unsupervised learning, which is especially suitable for unlabeled data, dynamic environments, and malware detection, which is a form of anomaly detection. We introduce the TF-IDF method for transforming nominal features into numerical formats that avoid information loss and manage dimensionality effectively, which is crucial for enhancing pattern recognition when combined with n-grams. Furthermore, we compare the performance of multi-class vs. one-class classification models, including Isolation Forest and deep autoencoder, that are trained with both benign and malicious NetFlow samples vs. trained exclusively on benign NetFlow samples. We achieve 100% recall with precision rates above 80% and 90% across various test datasets using one-class classification. These models show the adaptability of unsupervised learning, especially one-class classification, to the evolving malware threats in the IoT domain, offering insights into enhancing IoT security frameworks and suggesting directions for future research in this critical area.
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