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The integration of medical signal processing capabilities and advanced sensors into Internet of Things (IoT) devices plays a key role in providing comfort and convenience to human lives. As the number of patients is increasing gradually, providing healthcare facilities to each patient, particularly to the patients located in remote regions, not only has become challenging but also results in several issues, such as: (i) increase in workload on paramedics, (ii) wastage of time, and (iii) accommodation of patients. Therefore, the design of smart healthcare systems has become an important area of research to overcome these above-mentioned issues. Several healthcare applications have been designed using wireless sensor networks (WSNs), cloud computing, and fog computing. Most of the e-healthcare applications are designed using the cloud computing paradigm. Cloud-based architecture introduces high latency while processing huge amounts of data, thus restricting the large-scale implementation of latency-sensitive e-healthcare applications. Fog computing architecture offers processing and storage resources near to the edge of the network, thus, designing e-healthcare applications using the fog computing paradigm is of interest to meet the low latency requirement of such applications. Patients that are minors or are in intensive care units (ICUs) are unable to self-report their pain conditions. The remote healthcare monitoring applications deploy IoT devices with bio-sensors capable of sensing surface electromyogram (sEMG) and electrocardiogram (ECG) signals to monitor the pain condition of such patients. In this article, fog computing architecture is proposed for deploying a remote pain monitoring system. The key motivation for adopting the fog paradigm in our proposed approach is to reduce latency and network consumption. To validate the effectiveness of the proposed approach in minimizing delay and network utilization, simulations were carried out in iFogSim and the results were compared with the cloud-based systems. The results of the simulations carried out in this research indicate that a reduction in both latency and network consumption can be achieved by adopting the proposed approach for implementing a remote pain monitoring system.

Background: Iron deficiency is the most common nutritional disorder in the world. The aim of this questionnaire based survey study was to determine the prevalence of iron deficiency anemia in reproductive age women, and their relation to variables such as age, marital status, education with those attending obstetrics and gynecology outpatient of King Faisal University Health Centre in Al-Ahsa in eastern region of Kingdom of Saudi Arabia. Materials and Methods: This study was conducted for the period of 6 month staring from September 2012 to February 2013. The questionnaire had three sections on personal information: their educational indicators, gynecological clinical history, and hematological indices. Results: The average age was 25.97±7.17 years. According to the gynecological clinical history of the respondents, 15 (48.4%) respondents were pregnant while 16 (51.6%) were not pregnant. There was significant effect of pregnancy status on Hb level. Majority of the anemic respondents 15/17 were married. Moreover 14/17 anemic women were experiencing severe menstrual bleeding, 11/17 respondents were pregnant. 54.8% of respondents were hemoglobin deficient while 77.4% were found to have low Hct. In 87.1 % of the respondents, transferrin saturation was found to be abnormal. Conclusion: In this study iron deficiency anemia is quite prevalent in the university community especially among pregnant women. The fetus's and newborn infant's iron status depends on the iron status of the pregnant woman and therefore, iron deficiency in the mother-to-be means that growing fetus probably will be iron deficient as well. Thus iron deficiency anemia during pregnancy in well-educated set up needs more attention by the concerned authorities.

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CRSN or Cognitive Radio Sensor Network is a network of wireless cognitive radio sensor nodes, these sensor nodes sense data and transmit over available spectrum bands. The energy of the nodes decreases gradually while transmitting the data, and ultimately the system expires. Traditional routing algorithms are not suitable for CRSN because these algorithms rely only on the shortest hops whereas in cognitive radio network the status of each link is totally different. In the following paper, we have introduced a three-stage routing algorithm for efficient performance. In the first stage, we calculate the transmission delay for every link in the form of link cost. In the second stage, the cost of each link is adjusted between primary radio sensor nodes and cognitive radio sensor nodes according to the state of primary users. In the third stage, a load sharing algorithm is applied to improve the life of whole node sensors in such a way to obtained maximum output. We have modified the traditional technique of routing by rescuing the power of each node. Computer simulation results clearly indicate the improvement in the lifetime of a network.