Implementing ZigBee assisted power management for delay bounded communication on mobile devices

Over the years WiFi has gained immense popularity in networking devices to transfer data over short distances. WiFi communication can consume a lot of energy on battery powered devices like mobile phones. The Standard Power Saving Management(SPSM) which is part of the standard specification for wireless LAN technology has been applied widely. However, it may not deliver satisfactory energy effiiciency in many cases as the wakeup strategy adopted by it cannot adapt dynamically to traffic pattern changes. Motivated by the fact that it has been more and more popular for a mobile device to have both WiFi and other low-power wireless interfaces such as Bluetooth and ZigBee, we propose an implementation of a ZigBee-assisted Power Saving Management (ZPSM) scheme, leveraging the ZigBee interface to wake up WiFi interface based on the delay bound to improve energy efficiency. The results obtained by applying this scheme on a Linux environment shows that ZPSM can save energy significantly without violating delay requirements in various scenarios

A Machine Learning Approach to Edge Type Prediction in Internet AS Graphs

The Internet consists of a large number of interconnected autonomous systems (ASes). ASes engage in two types of business relationships to exchange traffic: provider-to-customer (p2c) relationship and peer-to-peer (p2p) relationship. Internet AS-level topology can be represented by AS graphs where nodes represent autonomous systems (ASes) and edges represent connectivity between ASes. While researchers have derived AS graphs using various data sources, inferring the types of edges (p2c or p2p) in AS graphs remains an open problem. In this paper we present a new machine learning approach to edge type inference in AS graphs. Our method uses the AdaBoost machine learning algorithm to train a model that predicts the edge types in a given AS graph using two node attributes - degree and minimum distance to a Tier-1 node. We train a model for a BGP graph and validate the model using ground truth AS relationships and CAIDA\u27s inferred AS relationship dataset. Our results show that the model achieves over 92% accuracy on a number of BGP graphs

Computing Observed Autonomous System Relationships in the Internet

Autonomous Systems (ASes) in the Internet use BGP to perform interdomain routing. BGP routing policies are mainly determined by the business relationships between neighboring ASes, which can be classified into three types: provider-to-customer, peer-to-peer, and sibling-to-sibling. ASes usually do not export provider routes and peer routes to providers or peers. It has been proved that if all ASes conform to this common export policy then all AS paths are valley-free. Since AS relationships are not publicly available, several studies have proposed heuristic algorithms for inferring AS relationships using publicly available BGP data. Most of these algorithms rely on the valley-free property of AS paths. However, not all AS paths are valley-free because some ASes do not conform to the common export policy. As a result, inferred AS relationship are inaccurate. Instead of inferring AS relationships, we propose an algorithm for computing observed AS relationships based on transit relationships between ASes that are revealed by BGP data. We analyze the types of mismatches between observed AS relationships and actual AS relationships and show that the mismatches can be used to identify ASes that violate the common export policy

Effect of Self-monitoring and Medication Self-titration on Systolic Blood Pressure in Hypertensive Patients at High Risk of Cardiovascular Disease

IMPORTANCE: Self-monitoring of blood pressure with self-titration of antihypertensives (self-management) results in lower blood pressure in patients with hypertension, but there are no data about patients in high-risk groups. OBJECTIVE: To determine the effect of self-monitoring with self-titration of antihypertensive medication compared with usual care on systolic blood pressure among patients with cardiovascular disease, diabetes, or chronic kidney disease. DESIGN, SETTING, AND PATIENTS: A primary care, unblinded, randomized clinical trial involving 552 patients who were aged at least 35 years with a history of stroke, coronary heart disease, diabetes, or chronic kidney disease and with baseline blood pressure of at least 130/80 mm Hg being treated at 59 UK primary care practices was conducted between March 2011 and January 2013. INTERVENTIONS: Self-monitoring of blood pressure combined with an individualized self-titration algorithm. During the study period, the office visit blood pressure measurement target was 130/80 mm Hg and the home measurement target was 120/75 mm Hg. Control patients received usual care consisting of seeing their health care clinician for routine blood pressure measurement and adjustment of medication if necessary. MAIN OUTCOMES AND MEASURES: The primary outcome was the difference in systolic blood pressure between intervention and control groups at the 12-month office visit. RESULTS: Primary outcome data were available from 450 patients (81%). The mean baseline blood pressure was 143.1/80.5 mm Hg in the intervention group and 143.6/79.5 mm Hg in the control group. After 12 months, the mean blood pressure had decreased to 128.2/73.8 mm Hg in the intervention group and to 137.8/76.3 mm Hg in the control group, a difference of 9.2 mm Hg (95% CI, 5.7-12.7) in systolic and 3.4 mm Hg (95% CI, 1.8-5.0) in diastolic blood pressure following correction for baseline blood pressure. Multiple imputation for missing values gave similar results: the mean baseline was 143.5/80.2 mm Hg in the intervention group vs 144.2/79.9 mm Hg in the control group, and at 12 months, the mean was 128.6/73.6 mm Hg in the intervention group vs 138.2/76.4 mm Hg in the control group, with a difference of 8.8 mm Hg (95% CI, 4.9-12.7) for systolic and 3.1 mm Hg (95% CI, 0.7-5.5) for diastolic blood pressure between groups. These results were comparable in all subgroups, without excessive adverse events. CONCLUSIONS AND RELEVANCE: Among patients with hypertension at high risk of cardiovascular disease, self-monitoring with self-titration of antihypertensive medication compared with usual care resulted in lower systolic blood pressure at 12 months

Self management of patients with mild COPD in primary care : randomised controlled trial

Objective: To evaluate the effectiveness of nurse-led telephone health coaching to encourage self-management in a primary care population with mild symptoms of COPD. Design: Pragmatic, multi-centre randomised controlled trial. Setting: 71 general practices in four areas of England. Participants: 577 people, with MRC dyspnoea grade 1 or 2, recruited from primary care COPD registers with spirometry confirmed diagnosis, were randomised to the intervention (n=289) or usual care (n=288). Interventions: Nurse-delivered telephone health coaching intervention, underpinned by Social Cognitive Theory, promoting: accessing smoking cessation services, increasing physical activity, medication management and action planning (4 sessions over 11 weeks; postal information at weeks 16 and 24). Nurses received two days of training. The usual care group received a leaflet about COPD. Main outcome measures: The primary outcome was health related quality of life at 12 months using the short version of the St Georges Respiratory Questionnaire (SGRQ-C). Results: The intervention was delivered with good fidelity: 86% of scheduled calls were delivered; 75% of participants received all four calls. 92% participants were followed-up at six months and 89% at 12 months. There was no difference in SGRQ-C total score at 12 months (mean difference -1.3, 95%CI -3.6 to 0.9; p=0.2). Compared to usual care participants, at six months follow-up, the intervention group reported significantly greater physical activity, more had received a care plan (44% v 30%), rescue packs of antibiotics (37% v 29%) and inhaler technique check (68% v 55%). There were no differences in other secondary outcomes (dyspnoea, smoking cessation, anxiety, depression, self-efficacy, objectively measured physical activity). Conclusions A novel telephone health coaching intervention to promote behaviour change in primary care patients with mild symptoms of dyspnoea did lead to changes in self-management activities, but did not improve health related quality of life

Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P < 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

Implementing ZigBee assisted power management for delay bounded communication on mobile devices

Over the years WiFi has gained immense popularity in networking devices to transfer data over short distances. WiFi communication can consume a lot of energy on battery powered devices like mobile phones. The Standard Power Saving Management(SPSM) which is part of the standard specification for wireless LAN technology has been applied widely. However, it may not deliver satisfactory energy effiiciency in many cases as the wakeup strategy adopted by it cannot adapt dynamically to traffic pattern changes. Motivated by the fact that it has been more and more popular for a mobile device to have both WiFi and other low-power wireless interfaces such as Bluetooth and ZigBee, we propose an implementation of a ZigBee-assisted Power Saving Management (ZPSM) scheme, leveraging the ZigBee interface to wake up WiFi interface based on the delay bound to improve energy efficiency. The results obtained by applying this scheme on a Linux environment shows that ZPSM can save energy significantly without violating delay requirements in various scenarios.</p

A Machine Learning Approach to Edge Type Prediction in Internet AS Graphs

The Internet consists of a large number of interconnected autonomous systems (ASes). ASes engage in two types of business relationships to exchange traffic: provider-to-customer (p2c) relationship and peer-to-peer (p2p) relationship. Internet AS-level topology can be represented by AS graphs where nodes represent autonomous systems (ASes) and edges represent connectivity between ASes. While researchers have derived AS graphs using various data sources, inferring the types of edges (p2c or p2p) in AS graphs remains an open problem. In this paper we present a new machine learning approach to edge type inference in AS graphs. Our method uses the AdaBoost machine learning algorithm to train a model that predicts the edge types in a given AS graph using two node attributes - degree and minimum distance to a Tier-1 node. We train a model for a BGP graph and validate the model using ground truth AS relationships and CAIDA's inferred AS relationship dataset. Our results show that the model achieves over 92% accuracy on a number of BGP graphs.</p