Fog computing is a technology that brings computing and storage resources from cloud near to the end user. Being in its infancy, Fog computing lacks standardization in terms of architectures and simulation platforms. There are a number of Fog simulators available today, among which a few are open-source, whereas rest are commercially available. The existing Fog simulators mainly focus on a number of devices that can be simulated. Generally, the existing simulators are more inclined toward sensors’ configurations, where sensors generate raw data and fog nodes are used to intelligently process the data before sending to back-end cloud or other nodes. Most of the simulators lack network properties and assume reliable and error-free delivery. Moreover, to simulate the optimum utilization of Fog nodes and modeling the handover is not fully supported in Fog simulators. In this paper, we propose a new Fog simulator called FogNetSim++ that provides users with a detailed configuration options to simulate a Fog network, as well as enable researchers to incorporate their own mobility models, Fog node scheduling algorithms, and handovers modeling. Our evaluations demonstrated the flexibility and effectiveness of proposed simulator in terms of energy, CPU, and execution delay.

With population growth, the demand for vehicles has increased tremendously, which has created an alarming situation in terms of traffic hazards and road accidents. The road accidents percentage is growing exponentially and so are the fatalities caused due to accidents. However, the primary cause of the increased rate of fatalities is due to the delay in emergency services. Many lives could be saved with efficient rescue services. The delay happens due to traffic congestion or unstable communication to the medical units. The implementation of automatic road accident detection systems to provide timely aid is crucial. Many solutions have been proposed in the literature for automatic accident detection. The techniques include crash prediction using smartphones, vehicular ad-hoc networks, GPS/GSM based systems, and various machine learning techniques. With such high rates of deaths associated with road accidents, road safety is the most critical sector that demands significant exploration. In this paper, we present a critical analysis of various existing methodologies used for predicting and preventing road accidents, highlighting their strengths, limitations, and challenges that need to be addressed to ensure road safety and save valuable lives.

The number of vehicles on the roads has increased proportionally over the last couple of years and this number is likely to rise due to the increase in population growth and the number of vehicles that are manufacturing every day. This high traffic density leads to several problems, from which effectively disseminating the emergency messages is a major concern. Keeping in view the dynamic characteristics of VANETs, significant challenges are faced in disseminating the message across the network. The major challenges are the broadcast storm problem, hidden node problem and the packet collision. Many studies have been performed to devise an effective and reliable mechanism for disseminating emergency messages in a Vehicular ad-Hoc Network (VANET). Researchers have proposed different models to tackle various types of scenarios for emergency message dissemination. This paper not only reviews some recent contributions to emergency message dissemination in vehicular networks but also discusses various proposed methods based on Intelligent Transportation System (ITS), Internet of Things (IoT), Priority messaging, Clustering approach, Software Defined Network (SDN) and Fog Computing. We have also tried to explore the latest developments in emergency message dissemination using 5G networks.

With the advancement in technology and inception of smart vehicles and smart cities, every vehicle can communicate with the other vehicles either directly or through ad-hoc networks. Therefore, such platforms can be utilized to disseminate time-critical information. However, in an ad-hoc situation, information coverage can be restricted in situations, where no relay vehicle is available. Moreover, the critical information must be delivered within a specific period of time; therefore, timely message dissemination is extremely important. The existing data dissemination techniques in VANETs generate a large number of messages through techniques such as broadcast or partial broadcast. Thus, the techniques based on broadcast schemes can cause congestion as all the recipients re-broadcast the message and vehicles receive multiple copies of same messages. Further, re-broadcast can degrade the coverage delivery ratio due to channel congestion. Moreover, the traditional cluster-based approach cannot work efficiently. As clustering schemes add additional delays due to communication with cluster head only. In this paper, we propose a data dissemination technique using a time barrier mechanism to reduce the overhead of messages that can clutter the network. The proposed solution is based on the concept of a super-node to timely disseminate the messages. Moreover, to avoid unnecessary broadcast which can also cause the broadcast storm problem, the time barrier technique is adapted to handle this problem. Thus, only the farthest vehicle rebroadcasts the message which can cover more distance. Therefore, the message can reach the farthest node in less time and thus, improves the coverage and reduces the delay. The proposed scheme is compared with traditional probabilistic approaches. The evaluation section shows the reduction in message overhead, transmission delay, improved coverage, and packet delivery ratio.

Energy consumption in the residential sector is 25% of all the sectors. The advent of smart appliances and intelligent sensors have increased the realization of home energy management systems. Acquiring balance between energy consumption and user comfort is in the spotlight when the performance of the smart home is evaluated. Appliances of heating, ventilation and air conditioning constitute up to 64% of energy consumption in residential buildings. A number of research works have shown that fuzzy logic system integrated with other techniques is used with the main objective of energy consumption minimization. However, user comfort is often sacrificed in these techniques. In this paper, we have proposed a Fuzzy Inference System (FIS) that uses humidity as an additional input parameter in order to maintain the thermostat set-points according to user comfort. Additionally, we have used indoor room temperature variation as a feedback to proposed FIS in order to get the better energy consumption. As the number of rules increase, the task of defining them in FIS becomes time consuming and eventually increases the chance of manual errors. We have also proposed the automatic rule base generation using the combinatorial method. The proposed techniques are evaluated using Mamdani FIS and Sugeno FIS. The proposed method provides a flexible and energy efficient decision-making system that maintains the user thermal comfort with the help of intelligent sensors. The proposed FIS system requires less memory and low processing power along with the use of sensors, making it possible to be used in the IoT operating system e.g., RIOT. Simulation results validate that the proposed technique reduces energy consumption by 28%.

The recent development of deep neural networks attempts to go deeper through the layered architecture to solve complex problems. As expected, the deepening impacts the processing times for inferences in tasks like object classification and localization. This demands solutions for balancing computational resource requirements and extend the versatility of a given model to suit a variety of applications. Often the solutions rely on architectural modifications to ease training times while achieving more accurate results. In this work, we propose a multistage algorithm with an initial stage adopting the YOLOv3 network for object detection and HART approach for object tracking. This is followed by an adaptive post-localization stage shift system taking into consideration the processing times of stage inferences, which are the number of located objects in image sequences. The goal is to change the localization strategy to achieve optimal processing time performance. For evaluation, we present results in terms of precision and processing times in varying traffic conditions. The results demonstrate the effectiveness of the proposed adaptive multistage model in comparison to other real-time state-of-theart detection strategies, as it achieves a frame rate gain of 49% over YOLOv3 while maintaining competitive task accuracy.