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Vehicular ad hoc networks (VANETs) play an important role as enabling technology for future cooperative intelligent transportation systems (CITSs). Vehicles in VANETs share real-time information about their movement state, traffic situation, and road conditions. However, VANETs are susceptible to the cyberattacks that create life threatening situations and/or cause road congestion. Intrusion detection systems (IDSs) that rely on the cooperation between vehicles to detect intruders, were the most suggested security solutions for VANET. Unfortunately, existing cooperative IDSs (CIDSs) are vulnerable to the legitimate yet compromised collaborators that share misleading and manipulated information and disrupt the IDSs’ normal operation. As such, this paper proposes a misbehavior-aware on-demand collaborative intrusion detection system (MA-CIDS) based on the concept of distributed ensemble learning. That is, vehicles individually use the random forest algorithm to train local IDS classifiers and share their locally trained classifiers on-demand with the vehicles in their vicinity, which reduces the communication overhead. Once received, the performance of the classifiers is evaluated using the local testing dataset in the receiving vehicle. The evaluation values are used as a trustworthiness factor and used to rank the received classifiers. The classifiers that deviate much from the box-and-whisker plot lower boundary are excluded from the set of the collaborators. Then, each vehicle constructs an ensemble of weighted random forest-based classifiers that encompasses the locally and remotely trained classifiers. The outputs of the classifiers are aggregated using a robust weighted voting scheme. Extensive simulations were conducted utilizing the network security laboratory-knowledge discovery data mining (NSL-KDD) dataset to evaluate the performance of the proposed MA-CIDS model. The obtained results show that MA-CIDS performs better than the other existing models in terms of effectiveness and efficiency for VANET.

Air pollution risk assessment is complex due to dynamic data change and pollution source distribution. Air quality index concentration level prediction is an effective method of protecting public health by providing the means for an early warning against harmful air pollution. However, air quality index-based prediction is challenging as it depends on several complicated factors resulting from dynamic nonlinear air quality time-series data, such as dynamic weather patterns and the verity and distribution of air pollution sources. Subsequently, some minimal models have incorporated a time series-based predicting air quality index at a global level (for a particular city or various cities). These models require interaction between the multiple air pollution sensing sources and additional parameters like wind direction and wind speed. The existing methods in predicting air quality index cannot handle short-term dependencies. These methods also mostly neglect the spatial correlations between the different parameters. Moreover, the assumption of selecting the most recent part of the air quality time series is not valid considering that pollution is cyclic behavior according to various events and conditions due to the high possibility of falling into the trap of local minimum and poor generalization. Therefore, this paper proposes a new air pollution global risk assessment (APGRA) prediction model for an air quality index of spatial correlations to address these issues. The APGRA model incorporates an autoregressive integrated moving average (ARIMA), a Monte Carlo simulation, a collaborative multi-agent system, and a prediction algorithm for reducing air quality index prediction error and processing time. The proposed APGRA model is evaluated based on Malaysia and China real-world air quality datasets. The proposed APGRA model improves the average root mean squared error by 41%, mean and absolute error by 47.10% compared with the conventional ARIMA and ANFIS models.

The electronic devices that are targeted to the end users have become day to day essential parts. Traditional methodologies have changed drastically resulting in efficient mode of communication and fast information retrieval. As the demand and the production are exponentially growing, patterns of sales, storage and their destruction and then again, their collection have also been changed. This paper analyses many such behaviors of (electronic) waste management and recommends solutions like recycling management, different directives and policies required to be followed. Authors have emphasized on providing substantial information that can be useful to the regulating authorities responsible for waste management or the manufacturers of various electronic products and then the policy makers. With an extensive review of electronic wastages, authors have emphasized three variables (sales, stock and lifespan) for replacing/upgrading the older products with advanced versions. The root causes of electronic wastages are found in industrializing countries like India, China, Vietnam, Pakistan, the Philippines, Ghana and Nigeria whereas industrialized countries also play equally important role for its generation. This paper signifies the importance of e-waste management practice to reduce the emerging electronic waste hazards. Authors focus on today’s demand of electronic devices, importance of e-waste management and management practices. The paper recommends key findings based on surveying data regarding the lack of regulation to manage the e-waste. The review concludes that the lack of regulation and improper awareness are the basic factors responsible for e-wastage and requires major focus to manage the e-waste.