A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> 1) DRIVE SENSING <s> Traffic delays and congestion are a major source of inefficiency, wasted fuel, and commuter frustration. Measuring and localizing these delays, and routing users around them, is an important step towards reducing the time people spend stuck in traffic. As others have noted, the proliferation of commodity smartphones that can provide location estimates using a variety of sensors---GPS, WiFi, and/or cellular triangulation---opens up the attractive possibility of using position samples from drivers' phones to monitor traffic delays at a fine spatiotemporal granularity. This paper presents VTrack, a system for travel time estimation using this sensor data that addresses two key challenges: energy consumption and sensor unreliability. While GPS provides highly accurate location estimates, it has several limitations: some phones don't have GPS at all, the GPS sensor doesn't work in "urban canyons" (tall buildings and tunnels) or when the phone is inside a pocket, and the GPS on many phones is power-hungry and drains the battery quickly. In these cases, VTrack can use alternative, less energy-hungry but noisier sensors like WiFi to estimate both a user's trajectory and travel time along the route. VTrack uses a hidden Markov model (HMM)-based map matching scheme and travel time estimation method that interpolates sparse data to identify the most probable road segments driven by the user and to attribute travel times to those segments. We present experimental results from real drive data and WiFi access point sightings gathered from a deployment on several cars. We show that VTrack can tolerate significant noise and outages in these location estimates, and still successfully identify delay-prone segments, and provide accurate enough delays for delay-aware routing algorithms. We also study the best sampling strategies for WiFi and GPS sensors for different energy cost regimes. <s> BIB001 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> 1) DRIVE SENSING <s> In the recent past, great attention has been directed toward road surface condition monitoring. As a matter of fact, this activity is of critical importance in transportation infrastructure management. In response, multiple solutions have been proposed which make use of mobile sensing, more specifically contemporary applications and architectures that are used in both crowdsensing and vehicle-based sensing. This has allowed for automated control as well as analysis of road surface quality. These innovations have thus encouraged and showed the importance of cloud to provide reliable transport services to clients. Nonetheless, these initiatives have not been without challenges that range from mobility support, locational awareness, low latency, as well as geo-distribution. As a result, a new term has been coined for this novel paradigm, called, fog computing. In this paper, we propose a privacy-preserving protocol for enhancing security in vehicular crowdsensing-based road surface condition monitoring system using fog computing. At the onset, this paper proposes a certificateless aggregate signcryption scheme that is highly efficient. On the basis of the proposed scheme, a data transmission protocol for monitoring road surface conditions is designed with security aspects such as information confidentiality, mutual authenticity, integrity, privacy, as well as anonymity. In analyzing the system, the ability of the proposed protocol to achieve the set objectives and exercise higher efficiency with respect to computational and communication abilities in comparison to existing systems is also considered. <s> BIB002
Traffic monitoring is a significant participatory sensing application, where GPS enabled smartphones can offer priceless information about traffic conditions. It can sense driver/vehicle activities and behavior, sudden traffic events and risky/aggressive driving. It can also sense the traffic status (dynamic travel time, traffic congestions, etc.). Eventually, it will monitor the road conditions including potholes, road bumps, and slippery roads (using sensors attached to vehicles), vehicle fuel consumption, and emissions. Moreover, authorities can perform analysis of the collected data in terms of the detection of real-time traffic events, dynamic black spots, and can generate profiles for bad driving habits BIB001 , [26] . Other recent work, Basudan et al. BIB002 suggest a new idea for privacy preserving for vehicular crowdsensing. The idea is to introduce a certificateless aggregate signcryption scheme, (technique to accomplish both encryption and signature in one logical step), which is highly efficient in term of low communication overhead and fast verification. The authors developed a road surface condition monitoring system consisting of a control center, smart devices, and a cloud server.