Pavement roughness is an expression of the irregularities in a pavement surface that adversely affect the ride quality of a vehicle. Roughness also affects vehicle delay costs, fuel consumption, tires, and maintenance costs. Roughness is predominantly characterized by the international roughness index (IRI), which is often measured with inertial profilers. Inertial profilers are equipped with sensitive accelerometers, a height-measuring laser, and a distance-measuring instrument for measuring vehicle vertical acceleration data and the pavement profile. Modern smartphones are equipped with several sensors including a three-axis accelerometer, which was used in this project to collect vehicle acceleration data with an Android-based application. In the study, acceleration data were double integrated numerically to obtain a pavement profile, which was input into the software program ProVAL. The pavement roughness was then calculated. For the initial validation, pavement profile and acceleration data were collected with both an inertial profiler and the newly developed smartphone application from three test sites. The initial validation results suggest that the newly developed smartphone application can measure IRI with good correspondence to the inertial profiler and with good repeatability between measurement replications. However, calibration is needed for rougher pavement sections because the current analysis techniques do not directly account for acceleration damping resulting from vehicle suspension systems. With improvements in analysis that consider the vehicle suspension effects and additional validation, the approach could be used to reduce the cost of acquiring pavement roughness data for agencies and to reduce user costs for the traveling public by providing more robust feedback about route choice and its effect on estimated vehicle maintenance cost and fuel efficiency.

After the construction of a pavement system, deterioration occurs because of traffic loading and weathering action and results in the formation of various types of distresses and an increase in pavement roughness. “Roughness” can be defined as irregularities of pavement surface that affect driver safety and increase user costs, including fuel consumption, repair and maintenance, depreciation, and tire costs. In this study, pavement roughness was predicted with the use of the newly released Mechanistic–Empirical Pavement Design Guide for levels of initial roughness condition. Four alternative maintenance and rehabilitation (M&R) strategies were used to estimate the life-cycle cost of pavement over a 35-year analysis period. Various categories of user costs were calculated on the basis of different cost models and from data reported in the literature. From this analysis, pavement roughness was found to affect user costs dramatically. A comparison was made between agency investment and user costs related to pavement roughness. The results of this analysis showed that agency costs were small compared with roughness-related user costs over the life of the pavement (less than 4% of total costs) and that agency investment in increased rehabilitation activities could have a 50-fold return in the form of reduced user costs. A strong case is made for the critical importance of investing in enhanced M&R activities to reduce pavement roughness. This case is strengthened by hypothesized benefits in pavement system sustainability through reduced user fuel costs and reduced tire wear and increased remaining life of pavement.

No abstract

The cloud-users are getting impatient by experiencing the delays in loading the content of the web applications over the internet, which is usually caused by the complex latency while accessing the cloud datacenters distant from the cloud-users. It is becoming a catastrophic situation in availing the services and applications over the cloud-centric network. In cloud, workload is distributed across the multiple layers which also increases the latency. Time-sensitive Internet of Things (IoT) applications and services, usually in a cloud platform, are running over various virtual machines (VM’s) and possess high complexities while interacting. They face difficulties in the consolidations of the various applications containing heterogenetic workloads. Fog computing takes the cloud computing services to the edge-network, where computation, communication and storage are within the proximity to the end-user’s edge devices. Thus, it utilizes the maximum network bandwidth, enriches the mobility, and lowers the latency. It is a futuristic, convenient and more reliable platform to overcome the cloud computing issues. In this manuscript, we propose a Fog-based Spider Web Algorithm (FSWA), a heuristic approach which reduces the delays time (DT) and enhances the response time (RT) during the workflow among the various edge nodes across the fog network. The main purpose is to trace and locate the nearest f-node for computation and to reduce the latency across the various nodes in a network. Reduction of latency will enhance the quality of service (QoS) parameters, smooth resource distribution, and services availability. Latency can be an important factor for resource optimization issues in distributed computing environments. In comparison to the cloud computing, the latency in fog computing is much improved.

No abstract