The ubiquity of smartphones and the growth of computing resources, such as connectivity, processing, portability, and power of sensing, have greatly changed people’s lives. Today, many smartphones contain a variety of powerful sensors, including motion, location, network, and direction sensors. Motion or inertial sensors (e.g., accelerometer), specifically, have been widely used to recognize users’ physical activities. This has opened doors for many different and interesting applications in several areas, such as health and transportation. In this perspective, this work provides a comprehensive, state of the art review of the current situation of human activity recognition (HAR) solutions in the context of inertial sensors in smartphones. This article begins by discussing the concepts of human activities along with the complete historical events, focused on smartphones, which shows the evolution of the area in the last two decades. Next, we present a detailed description of the HAR methodology, focusing on the presentation of the steps of HAR solutions in the context of inertial sensors. For each step, we cite the main references that use the best implementation practices suggested by the scientific community. Finally, we present the main results about HAR solutions from the perspective of the inertial sensors embedded in smartphones.

The rise in the number and intensity of natural disasters is a serious problem that affects the whole world. The consequences of these disasters are significantly worse when they occur in urban districts because of the casualties and extent of the damage to goods and property that is caused. Until now feasible methods of dealing with this have included the use of wireless sensor networks (WSNs) for data collection and machine-learning (ML) techniques for forecasting natural disasters. However, there have recently been some promising new innovations in technology which have supplemented the task of monitoring the environment and carrying out the forecasting. One of these schemes involves adopting IP-based (Internet Protocol) sensor networks, by using emerging patterns for IoT. In light of this, in this study, an attempt has been made to set out and describe the results achieved by SENDI (System for dEtecting and forecasting Natural Disasters based on IoT). SENDI is a fault-tolerant system based on IoT, ML and WSN for the detection and forecasting of natural disasters and the issuing of alerts. The system was modeled by means of ns-3 and data collected by a real-world WSN installed in the town of São Carlos - Brazil, which carries out the data collection from rivers in the region. The fault-tolerance is embedded in the system by anticipating the risk of communication breakdowns and the destruction of the nodes during disasters. It operates by adding intelligence to the nodes to carry out the data distribution and forecasting, even in extreme situations. A case study is also included for flash flood forecasting and this makes use of the ns-3 SENDI model and data collected by WSN.

As urban populations grow, cities need new strategies to maintain a good standard of living while enhancing services and infrastructure development. A key area for improving city operations and spatial layout is the transportation of people and goods. While conventional transportation systems (i.e., fossil fuel based) are struggling to serve mobility needs for growing populations, they also represent serious environmental threats. Alternative-fuel vehicles can reduce emissions that contribute to local air pollution and greenhouse gases as mobility needs grow. However, even if alternative-powered vehicles were widely employed, road congestion would still increase. This paper investigates ridesharing as a mobility option to reduce emissions (carbon, particulates and ozone) while accommodating growing transportation needs and reducing overall congestion. The potential of ridesharing to reduce carbon emissions from personal vehicles in Changsha, China, is examined by reviewing mobility patterns of approximately 8,900 privately-owned vehicles over two months. Big data analytics identify ridesharing potential among these drivers by grouping vehicles by their trajectory similarity. The approach includes five steps: data preprocessing, trip recognition, feature vector creation, similarity measurement and clustering. Potential reductions in vehicle emissions through ridesharing among a specific group of drivers are calculated and discussed. While the quantitative results of this analysis are specific to the population of Changsha, they provide useful insights for the potential of ridesharing to reduce vehicle emissions and the congestion expected to grow with mobility needs. Within the study area, ridesharing has the potential to reduce total kilometers driven by about 24% assuming a maximum distance between trips less than 10 kilometers, and schedule time less than 60 minutes. For a more conservative maximum trip distance of 2 kilometers and passenger schedule time of less than 40 minutes, the reductions in traveled kilometers could translate to the equivalent of approximately 4.0 tons CO2 emission reductions daily.