Wireless sensor networks (WSNs) have been recognized as one of the most essential technologies of the twenty-first century. The applications of WSNs are rapidly increasing in almost every sector because they can be deployed in areas where cable and power supply are difficult to use. In the literature, different methods have been proposed to minimize the energy consumption of sensor nodes to prolong WSNs utilization. In this article, we propose an efficient routing protocol for data transmission in WSNs; it is called energy-efficient hierarchical routing protocol for wireless sensor networks based on fog computing. Fog computing is integrated into the proposed scheme due to its capability to optimize the limited power source of WSNs and its ability to scale up to the requirements of the Internet of things applications. In addition, we propose an improved ant colony optimization algorithm that can be used to construct an optimal path for efficient data transmission for sensor nodes. The performance of the proposed scheme is evaluated in comparison with P-SEP, EDCF, and RABACO schemes. The results of the simulations show that the proposed approach can minimize sensor nodes’ energy consumption, data packet losses, and extends the network lifetime. We are aware that in WSNs, the certainty of the sensed data collected by a sensor node can vary due to many reasons such as environmental factors, drained energy, and hardware failures.

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Identifying overloaded vehicles on a highway is essential for the safety of vehicles on the road as well as for the performance monitoring of highway infrastructure and planning. Traffic enforcement uses various weigh-in-motion (WIM) methods. Since Vehicular Telematics (VT) is favoured in the transport industry, using it for building a new WIM system to infer the payload of a vehicle at any road segment would be beneficial for the transport industry. This paper presents the effort taken to use VT data from onboard diagnostics modules and smartphones to infer the payload of a vehicle. The experiment done to find the correlation between VT data and the payload of a vehicle is discussed. Feature engineering was done; nine different settings were tested to find the best regression model. A multiple nonlinear regression model produced significant a p value of 6.322e-08 and an R-squared value of 0.8736. Results support the notion of using the VT data for nonintrusive measurement of the weight of a vehicle in motion.

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Wireless sensor networks (WSNs) are becoming more popular in recent times due to the availability of low cost, the short transmission range of sensor nodes and their easy deployment in different application areas; however, security is crucial for WSNs due to the nature of data they transmit in most cases in their locations. Sensor nodes transmit through an open medium and due to the short communication range of each sensor node, a node may need to transmit through many hops to a final destination. Denial-ofservice (DoS) is one of the attacks that can be launched against WSNs to prevent legitimate nodes from transmitting/receiving data packets. Developing an attack detection and countermeasure system to mitigate the effect of DoS is not an easy task. This work proposes lightweight models for DoS attack detection in WSNs. We developed different lightweight models and algorithms for the detection and elimination of DoS in WSNs and experimented the same. The performance of data delivery ratio, delay, detection rate, energy consumption, and false-positive was carried out. The experimental results show that the proposed scheme achieved a high true-positive detection rate, low false-positive detection rate, energy-efficient, and high data reliability. Numerical examples are presented to further validate the effectiveness of the proposed scheme. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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