A Survey on Behavioral Pattern Mining from Sensor Data in Internet of Things

The deployment of large-scale wireless sensor networks (WSNs) for the Internet of Things (IoT) applications is increasing day-by-day, especially with the emergence of smart city services. The sensor data streams generated from these applications are largely dynamic, heterogeneous, and often geographically distributed over large areas. For high-value use in business, industry and services, these data streams must be mined to extract insightful knowledge, such as about monitoring (e.g., discovering certain behaviors over a deployed area) or network diagnostics (e.g., predicting faulty sensor nodes). However, due to the inherent constraints of sensor networks and application requirements, traditional data mining techniques cannot be directly used to mine IoT data streams efficiently and accurately in real-time. In the last decade, a number of works have been reported in the literature proposing behavioral pattern mining algorithms for sensor networks. This paper presents the technical challenges that need to be considered for mining sensor data. It then provides a thorough review of the mining techniques proposed in the recent literature to mine behavioral patterns from sensor data in IoT, and their characteristics and differences are highlighted and compared. We also propose a behavioral pattern mining framework for IoT and discuss possible future research directions in this area. © 2013 IEEE

Trends in maternal health services in Bangladesh before, during and after COVID-19 lockdowns: Evidence from national routine service data

Bangladesh instituted a national lockdown to contain community transmission of COVID-19, initially for ten days, from March 26 to April 4, 2020, then extended through May 30. During the lockdown, the pandemic and its mitigation measures’ impacts on social, economic, and financial aspects of life in Bangladesh were widely documented. Disruptions to the health system, particularly critical maternal health services, however, have received relatively less attention. This brief provides details on a study that analyzed potential impacts of COVID-19 and its related mitigation measures on maternal health services in Bangladesh, examining national and district trends in antenatal care (ANC), institutional delivery, and postnatal care (PNC). Monthly service statistics from January through July 2020 from the Directorate General of Family Planning of Bangladesh’s Ministry of Health and Family Welfare were examined to determine ANC, institutional delivery, and PNC service trends. Analysis did not include statistics from the Directorate General of Health Service, which are not publicly available

Trends in family planning services in Bangladesh before, during and after COVID-19 lockdowns: Evidence from national routine service data

Bangladesh instituted a national lockdown to contain community transmission of COVID-19 initially for ten days, from March 26 to April 4, 2020, then extended through May 30. During the lockdown, the pandemic and its mitigation measures’ impacts on social, economic, and financial aspects of life in Bangladesh were widely documented. Disruptions to the health system, however, have received relatively less attention. This brief provides details on a study that analyzed the impacts of COVID-19 and its related mitigation measures on family planning (FP) services in Bangladesh, examining national and district trends for distribution and use of short-acting, long-acting and reversible, and permanent contraception, utilizing publicly available service statistics from before, during, and after the lockdowns. Monthly service statistics from the Directorate General of Family Planning (DGFP) of the Ministry of Health and Family Welfare of Bangladesh were examined to determine trends in distribution and provision of short-acting (pill, condom, injectables), long-acting and reversible (intrauterine device and implant), and permanent contraceptive methods from January to July 2020, utilizing service statistics from both public- and private-sector providers that constitute DGFP data

Early outcome of radiculopathy with local application of steroid in perineural space in lumbar discectomy

Lumbar disc herniation resulting in compression and inflammation of nerve roots causes low back pain and radiculopathy. Per-operative use of steroids may help reduce inflammatory reaction and scar formation, causing less postoperative pain. The study aimed to assess the early outcome of radiculopathy with local application of steroids in peri-neural space after lumbar discectomy. This experimental study was carried out in the Department of Neurosurgery of the National Institute of Neuroscience and Hospital (NINS&H), Dhaka from March 2019 to August 2020. A total of 68 patients operated for prolapsed lumbar intervertebral disc (PLID) at L4/L5 and /or L5/S1 levels were divided into two groups. Patients who did not receive steroids (n=34) and those who received steroids (n=34) in peri-neural space were considered group A and group B, respectively. Patients were examined on the 1st, 2nd and 14th postoperative days to measure the pain intensity by the Visual Analogue Scale (VAS). Pre-operatively mean (standard deviation, sd) VAS was 7.41 (1.28) in Group A and 7.91 (0.9) in Group B (p-value >.05). Mean (sd) improvement of pain intensity on day 1, was 58.82 (17.55)% in Group A and 70.59 (12.26)% in Group B from pre-operative VAS. On day 2, 71.69 (12.43)% improvement was seen in Group A and 79.78 (9.74)% in Group B. On day 14, 75.37 (9.96)% improvement was seen in Group A and 83.46 (7.36)% in Group B from pre-operative. The improvements of VAS in all 1st, 2nd and 14th days were statistically significant (p-value <.05) between the two groups. Local application of steroids in peri-neural space found effective in reducing early postoperative radiculopathy following lumbar discectomy. BSMMU J 2022; 15(2): 107-10

A survey on behavioral pattern mining from sensor data in Internet of Things

The deployment of large-scale wireless sensor networks (WSNs) for the Internet of Things (IoT) applications is increasing day-by-day, especially with the emergence of smart city services. The sensor data streams generated from these applications are largely dynamic, heterogeneous, and often geographically distributed over large areas. For high-value use in business, industry and services, these data streams must be mined to extract insightful knowledge, such as about monitoring (e.g., discovering certain behaviors over a deployed area) or network diagnostics (e.g., predicting faulty sensor nodes). However, due to the inherent constraints of sensor networks and application requirements, traditional data mining techniques cannot be directly used to mine IoT data streams efficiently and accurately in real-time. In the last decade, a number of works have been reported in the literature proposing behavioral pattern mining algorithms for sensor networks. This paper presents the technical challenges that need to be considered for mining sensor data. It then provides a thorough review of the mining techniques proposed in the recent literature to mine behavioral patterns from sensor data in IoT, and their characteristics and differences are highlighted and compared. We also propose a behavioral pattern mining framework for IoT and discuss possible future research directions in this area

Dependable large scale behavioral patterns mining from sensor data using Hadoop platform

Wireless sensor networks (WSNs) will be an integral part of the future Internet of Things (loT) environment and generate large volumes of data. However, these data would only be of benefit if useful knowledge can be mined from them. A data mining framework for WSNs includes data extraction, storage and mining techniques, and must be efficient and dependable. In this paper, we propose a new type of behavioral pattern mining technique from sensor data called regularly frequent sensor patterns (RFSPs). RFSPs can identify a set of temporally correlated sensors which can reveal significant knowledge from the monitored data. A distributed data extraction model to prepare the data required for mining RFSPs is proposed, as the distributed scheme ensures higher availability through greater redundancy. The tree structure for RFSP is compact requires less memory and can be constructed using only a single scan through the dataset, and the mining technique is efficient with low runtime. Current mining techniques in the literature on sensor data employ a single memory-based sequential approach and hence are not efficient. Moreover, usage of the. MapReduce model for the distributed solution has not been explored extensively. Since MapReduce is becoming the de facto model for computation on large data, we also propose a parallel implementation of the RFSP mining algorithm, called RFSP on Hadoop (RFSP-H), which uses a MapReduce-based framework to gain further efficiency. Experiments conducted to evaluate the compactness and performance of the data extraction model, RFSP-tree and RFSP-H mining show improved results. (C) 2016 Elsevier Inc. All rights reserved