No abstract

Emergencies, by definition, are unpredictable and rapid response is a key requirement in emergency management. Globally, a significant number of deaths occur each year, caused by excessive delays in rescue activities. Vehicles embedded with sophisticated technologies, along with roads equipped with advanced infrastructure, can play a vital role in the timely identification and notification of roadside incidents. However, such infrastructure and technologically-rich vehicles are rarely available in less developed countries. Hence, in such countries, low-cost solutions are required to address the issue. Systems based on the Internet of Things (IoT) have begun to be used to detect and report roadside incidents. The majority of the systems designed for this purpose involve the use of the cloud to compute, manage, and store information. However, the centralization and remoteness of cloud resources can result in an increased delay that raises serious concerns about its feasibility in emergency situations; in life-threatening situations, all delays should be minimized where feasible. To address the problem of latency, fog computing has emerged as a middleware paradigm that brings the cloud-like resources closer to end devices. In light of this, the research proposed here leverages the advantages of sophisticated features of smartphones and fog computing to propose and develop a low-cost and delay-aware accident detection and response system, which we term Emergency Response and Disaster Management System (ERDMS). An Android application is developed that utilizes smartphone sensors for the detection of incidents. When an accident is detected, a plan of action is devised. Initially, a nearby hospital is located using the Global Positioning System (GPS). The emergency department of the hospital is notified about the accident that directs an ambulance to the accident site. In addition, the family contacts of the victim are also informed about the accident. All the required computation is performed on the nearby available fog nodes. Moreover, the proposed scheme is simulated using iFogSim to evaluate and compare the performance using fog nodes and cloud data centers. INDEX TERMS Accident detection, fog computing, mobile edge computing, cloud computing, Internet of Things, emergency alerts, disaster management system.

Background: Global Health Estimates 2015 has shown IHD as second leading global cause of death and 3rd leading global cause for DALYs for 2015. The objectives of this study were to determine frequency, distribution and determinants of DM in adult acute coronary syndrome (ACS) population of D.I.Khan Division, Pakistan. Materials & Methods: This cross-sectional study was conducted in Departments of Ophthalmology & Community Medicine, Gomal Medical College, D.I.Khan, from February 1, 2017 to April 30, 2017. 331 cases were selected with margin of error 4.511%, 90%CL and 25% prevalence of DM in 73,438 adults assumed to have IHD. All indoor adult patients of ACS were eligible. Sex, age groups, and residence and presence of DM were variables. Frequency and distribution were analyzed by count and percentage. Hypotheses for distribution were substantiated by chi-square goodness-of-fit and of association by chi-square test of association. Results: Out of 331 patients with ACS, 225 (68.0%) were men and 106 (32.0%) women, 221 (66.8%) ≤60 years and 110 (33.2%) >60 years, and 210 (63.4%) urban and 121 (35.6%) rural. Frequency of DM was 79/331 (23.87%). Out of 79 patients with DM, men were 44 (13.29%), women 35 (10.57%), age group ≤60 years 57 (17.22%), >60 years 22 (6.65%), urban 53 (16.01%) and rural 60 (7.85%). Our prevalence of DM was lower than expected (p=.00214), our distribution by sex was similar to expected (p=.4993) while our distribution for age groups (p=.01209) and residence (p=.00005) were not similar to expected. Presence of DM was associated to sex (p=.011) but not to age groups (p=.0304) and residence (p=.5241). Conclusion: Prevalence of DM in adult ACS population of D.I.Khan Division, Pakistan was found lower than expected. The prevalence was more in men than women, more in younger age group (≤60 years) than older age group (>60 years) and more in urban than rural population. Our prevalence of DM was lower than expected, our distribution by sex was similar to expected while our distribution for age groups and residence were not similar to expected. The presence of DM was associated to sex but not to age groups and residence.

With opportunities brought by Internet of Things (IoT), it is quite a challenge to assure privacy preservation when a huge number of resource-constrained distributed devices is involved. Blockchain has become popular for its benefits, including decentralization, persistence, immutability, auditability and consensus. With the implementation of blockchain in IoT, the benefits provided by blockchain can be derived in order to make IoT more efficient and maintain trust. In this paper, we discuss some applications of IoT in different fields and privacy-related issues faced by IoT in resource-constrained devices. We discuss some applications of blockchain in vast majority of areas, and the opportunities it brings to resolve IoT privacy limitations. We, then, survey different researches based on the implementation of blockchain in IoT. The goal of this paper is to survey recent researches based on the implementation of blockchain in IoT for privacy preservation. After analyzing the recent solutions, we see that the blockchain is an optimal way for preventing identity disclosure, monitoring, and providing tracking in IoT.

The Internet of Things (IoT) has emerged as a promising paradigm to enhance the living standard of human life by employing varied smart devices including smart phones, smart watches, sensors, on-board units and other networking equipment. However, these devices consume a considerable amount of energy to perform their operations that has a significant impact on the environment, product cost and life of the device. Given this fact, energy-efficient solutions for smart environments have gained great attention from researchers and the industrial community. In this context, a novel fog-based multi-level energy-efficient framework for IoT-enabled smart environments has been proposed. To achieve this, the proposed framework adds additional two layers in the existing IoT-fog-cloud architecture -sensors-based energy-efficient hardware layer and policy layer, to monitor the energy consumption and to enable the energy-aware decision making. Initially, the main sources of energy consumption in an IoT-enabled smart environment are identified. Further, the energy requirements of a device to perform a specific task are estimated. Moreover, the alternative devices to perform the same task using less energy are searched out. Finally, a device or a set of devices, to process the job consuming lower energy while ensuring the job requirements, is selected. To validate the proposed framework, four case studies are considered -smart parking, smart hospital specifically ICU, smart agriculture and smart airport. Simulations are conducted using iFogsim toolkit and results show that a significant amount of energy can be conserved by employing the proposed framework.