Estimation of displacement for internet of things applications with kalman filter

In the vision of the Internet of Things, an object embedded in the physical world is recognizable and becomes smart by communicating data about itself and by accessing aggregate information from other devices. One of the most useful types of information for interactions among objects regards their movement. Mobile devices can infer their position by exploiting an embedded accelerometer. However, the double integration of the acceleration may not guarantee a reliable estimation of the displacement of the device (i.e., the difference in the new location). In fact, noise and measurement errors dramatically affect the assessment. This paper investigates the benefits and drawbacks of the use of the Kalman filter as a correction technique to achieve more precise estimation of displacement. The approach is evaluated with two accelerometers embedded in commercial devices: A smartphone and a sensor platform. The results show that the technique based on the Kalman filter dramatically reduces the percentage error, in comparison to the assessment made by double integration of the acceleration data; in particular, the precision is improved by up to 72%. At the same time, the computational overhead due to the Kalman filter can be assumed to be negligible in almost all application scenarios

How to improve CSMA-based MAC protocol for dense RFID reader-to-reader Networks?

International audienceDue to the dedicated short range communication feature of passive radio frequency identification (RFID) and the closest proximity operation of both tags and readers in a large-scale dynamic RFID system, when nearby readers simultaneously try to communicate with tags located within their interrogation range, serious interference problems may occur. Such interferences may cause signal collisions that lead to the reading throughput barrier and degrade the system performance. Although many efforts have been done to maximize the throughput by proposing protocols such as NFRA or more recently GDRA, which is compliant with the EPCglobal and ETSI EN 302 208 standards. However, the above protocols are based on unrealistic assumptions or require additional components with more control packet and perform worse in terms of collisions and latency, etc. In this paper, we explore the use of some well-known Carrier Sense Multiple Access (CSMA) backoff algorithms to improve the existing CSMA-based reader-to-reader anti-collision protocol in dense RFID networks. Moreover, the proposals are compliant with the existing standards. We conduct extensive simulations and compare their performance with the well-known state-of-the-art protocols to show their performance under various criteria. We find that the proposals improvement are highly suitable for maximizing the throughput, efficiency and for minimizing both the collisions and coverage latency in dense RFID Systems

A hierarchical key pre-distribution scheme for fog networks

Security in fog computing is multi-faceted, and one particular challenge is establishing a secure communication channel between fog nodes and end devices. This emphasizes the importance of designing efficient and secret key distribution scheme to facilitate fog nodes and end devices to establish secure communication channels. Existing secure key distribution schemes designed for hierarchical networks may be deployable in fog computing, but they incur high computational and communication overheads and thus consume significant memory. In this paper, we propose a novel hierarchical key pre-distribution scheme based on “Residual Design” for fog networks. The proposed key distribution scheme is designed to minimize storage overhead and memory consumption, while increasing network scalability. The scheme is also designed to be secure against node capture attacks. We demonstrate that in an equal-size network, our scheme achieves around 84% improvement in terms of node storage overhead, and around 96% improvement in terms of network scalability. Our research paves the way for building an efficient key management framework for secure communication within the hierarchical network of fog nodes and end devices. KEYWORDS: Fog Computing, Key distribution, Hierarchical Networks

Trust in Food

Trust is important in the food sector. This is primarily because households entrust some of the tasks related to food preparation to food processors. The public is concerned about pesticides, food additives, preservatives, and processed foods that may harbor unwanted chemicals or additives. After numerous food scandals, consumers expect food processing industries and retailers to take responsibility for food safety. Meanwhile, the food industry focuses on profit growth and costs reduction to achieve higher production efficiency and competitiveness. It means that they introduce innovations, such as new production methods, processing techniques, and additives. Consumers have to delegate the responsibility for ensuring food safety to food producers, retailers, and regulatory authorities who ensure that the foods are safe, healthy, and pose no risks. For consumers, trusting these actors can reduce feelings of uncertainty. It is helpful for companies to be responsible for their activities through transparency and traceability. In turn, the food industry tries to gain consumers’ trust by providing objective information, such as ranked brands or labels on food packages

Pregnancy outcomes and vaccine effectiveness during the period of omicron as the variant of concern, INTERCOVID-2022: a multinational, observational study.

BACKGROUND: In 2021, we showed an increased risk associated with COVID-19 in pregnancy. Since then, the SARS-CoV-2 virus has undergone genetic mutations. We aimed to examine the effects on maternal and perinatal outcomes of COVID-19 during pregnancy, and evaluate vaccine effectiveness, when omicron (B.1.1.529) was the variant of concern. METHODS: INTERCOVID-2022 is a large, prospective, observational study, involving 41 hospitals across 18 countries. Each woman with real-time PCR or rapid test, laboratory-confirmed COVID-19 in pregnancy was compared with two unmatched women without a COVID-19 diagnosis who were recruited concomitantly and consecutively in pregnancy or at delivery. Mother and neonate dyads were followed until hospital discharge. Primary outcomes were maternal morbidity and mortality index (MMMI), severe neonatal morbidity index (SNMI), and severe perinatal morbidity and mortality index (SPMMI). Vaccine effectiveness was estimated, adjusted by maternal risk profile. FINDINGS: We enrolled 4618 pregnant women from Nov 27, 2021 (the day after WHO declared omicron a variant of concern), to June 30, 2022: 1545 (33%) women had a COVID-19 diagnosis (median gestation 36·7 weeks [IQR 29·0-38·9]) and 3073 (67%) women, with similar demographic characteristics, did not have a COVID-19 diagnosis. Overall, women with a diagnosis had an increased risk for MMMI (relative risk [RR] 1·16 [95% CI 1·03-1·31]) and SPMMI (RR 1·21 [95% CI 1·00-1·46]). Women with a diagnosis, compared with those without a diagnosis, also had increased risks of SNMI (RR 1·23 [95% CI 0·88-1·71]), although the lower bounds of the 95% CI crossed unity. Unvaccinated women with a COVID-19 diagnosis had a greater risk of MMMI (RR 1·36 [95% CI 1·12-1·65]). Severe COVID-19 symptoms in the total sample increased the risk of severe maternal complications (RR 2·51 [95% CI 1·84-3·43]), perinatal complications (RR 1·84 [95% CI 1·02-3·34]), and referral, intensive care unit (ICU) admission, or death (RR 11·83 [95% CI 6·67-20·97]). Severe COVID-19 symptoms in unvaccinated women increased the risk of MMMI (RR 2·88 [95% CI 2·02-4·12]) and referral, ICU admission, or death (RR 20·82 [95% CI 10·44-41·54]). 2886 (63%) of 4618 total participants had at least a single dose of any vaccine, and 2476 (54%) of 4618 had either complete or booster doses. Vaccine effectiveness (all vaccines combined) for severe complications of COVID-19 for all women with a complete regimen was 48% (95% CI 22-65) and 76% (47-89) after a booster dose. For women with a COVID-19 diagnosis, vaccine effectiveness of all vaccines combined for women with a complete regimen was 74% (95% CI 48-87) and 91% (65-98) after a booster dose. INTERPRETATION: COVID-19 in pregnancy, during the first 6 months of omicron as the variant of concern, was associated with increased risk of severe maternal morbidity and mortality, especially among symptomatic and unvaccinated women. Women with complete or boosted vaccine doses had reduced risk for severe symptoms, complications, and death. Vaccination coverage among pregnant women remains a priority. FUNDING: None