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The vehicular network is a collection of vehicles and other components facilitated with versatile sensors to communicate, which is backboned by authentication. Present authentication protocols focus on either the lightweight feature or security. On the other hand, privacy during authentication is essential. Moreover, existing schemes are dependent on the trusted authority for checking the legitimacy of a communicating node, which suffers from the infrastructure-less scenario. Alternatively, existing protocols are dependent on a secure communication channel during the registration phase. To address these concerns, an elliptic curve cryptography-based authentication protocol has been designed. Moreover, a secure key establishment is shown in this paper. Pseudo-id-based authentication is used to provide the privacy-preservation on the scheme. On the other hand, symmetric-key cryptography with a session key is used for message encryption during communication. A security analysis on the proposed scheme using the BAN logic and AVISPA shows the protocol’s resilience capabilities on various attacks. Alternatively, the mathematical proof of the protocol represents the proof of correctness. Moreover, extensive performance analysis with the existing authentication schemes shows that the proposed algorithm is better in computation cost, communication cost, and energy cost. Lastly, the NS3 simulation shows the packet data transfer among the nodes in the network.

Security and privacy are among the most critical challenges on the internet of drones (IoD) network. The communication entities of the IoD network can communicate securely with the use of authenticated key agreement (AKA) based techniques. However, the design of such techniques must balance the tradeoff between security and lightweight features. Recently, Chen et al. proposed an authentication and key sharing scheme for IoD deployment. It is, however, realized after scrutiny that the proposed technique is vulnerable to security attacks under the well-accepted Canetti-Krawczyk (CK) adversary model. Moreover, the scheme applies to the IoD network with only one drones’ flying zone. To solve these challenges, this paper proposed a secure lightweight proven authenticated key agreement (SLPAKA) technique for IoD deployment. The technique is free from all the problems identified in the scheme of Chen et al . To ensure the reliability of the SLPAKA, the security of the technique has been assessed from a theoretical method and formal way using the ProVerif cryptographic protocol verification tool. Apart from comparing the performance of SLPAKA with the benchmarking schemes in terms of security, computational cost, and communication cost, the SLPAKA and the technique proposed by Chen et al. are implemented using a python programming language to evaluate and compare their performance in terms of energy consumption and computational time metrics. The results show that the SLPAKA outperforms the technique of Chen et al. and all the other benchmarking techniques in terms of security and lightweight features.

Internet of Drones (IoD) is a decentralized network and management framework that links drones’ access to the controlled airspace and provides inter-location navigation services. The interconnection of drones in the IoD network is through the Internet of Things (IoT). Hence the IoD network is vulnerable to all the security and privacy threats that affect IoT networks. It is highly required to safeguard a good atmosphere free from security and privacy threats to get the desired performance from IoD applications. Security and privacy issues have significantly restricted the overall influence of the IoD paradigm. There are existing survey studies that helped lay a vital foundation for understanding the IoD security and privacy issues. However, not all have thoroughly investigated the level of security and privacy threats associated with the various drone categories. Besides, most existing review studies do not examine secured IoD architecture. This paper aims to assess the recent trends in the security and privacy issues that affect the IoD network. We investigate the level of security and privacy threats of the various drone categories. We then highlight the need for secured IoD architecture and propose one. We also give a comprehensive taxonomy of the attacks on the IoD network. Moreover, we review the recent IoD attack mitigating techniques. We also provide the performance evaluation methods and the performance metrics employed by the techniques. Finally, we give research future direction to help researchers identify the latest opportunities in IoD research.

The implementation of efficient security mechanisms for Radio Frequency Identification (RFID) system has always been a continuous challenge due to its limited computing resources. Previously, hash-based, symmetric-key cryptography-based and elliptic curve cryptography based security protocols were proposed for RFID system. However, these protocols are not suitable because some of them failed to fulfil the RFID security requirements, and some of them produce high computational overhead. Recently researchers have focused on developing an efficient security mechanism based on Hyper Elliptic Curve Cryptography (HECC) which provides high security with 80 bits lower-key size. In this paper, we propose an efficient RFID authentication scheme (RFID-AS) based on hyperelliptic curve Signcryption. The proposed RFID-AS provides the required security features for the RFID system as well as security from potential attacks. We validated the security of proposed RFID-AS by using formal security analysis techniques, such as the Real-Or-Random (ROR) model and Automated Validation of Internet Security Protocols and Applications (AVISPA). Furthermore, the results reveal that the computational, communication and storage overheads of the proposed RFID-AS is much less than the other recently proposed schemes. Compared to the most recently published work based on ECC Signcryption, our scheme is 70% efficient in terms of computational overhead, 42.7% efficient in terms of communication overhead, and 57.7% efficient in terms of storage overhead. Therefore, the proposed RFID-AS is more efficient as compared to the recently published work in this domain. Hence, it is an attractive solution for resource-limited devices like RFID tags.

The strength of Vehicular Ad hoc Networks (VANETs) and the rapid deployment capability, can be used in many situations where the network should be arranged in a short time and there is a need to collect sensitive information. We consider cluster-based attack detection in data compilation wherever the neighbor nodes give the important information to the cluster head. Moreover, evidence is obtainable in the cluster head may possibly be accumulated by some vehicular nodes and executes numerous responsibilities such as decision making about delivering information. The existence of malicious nodes threatens determination making through transmitting malevolent information, which is not appropriate to the VANET categorized data and might send a substantial number of packets to the vehicles or Road Side Unit (RSU). To overcome this issue, we have proposed a Stream Position Performance Analysis (SPPA) approach. This approach monitors the position of any field station in sending the information to perform a Distributed Denial of Service (DDoS) attack. The method computes various factors like Conflict field, Conflict data and Attack signature sample rate (CCA). Using all these factors, the method identifies the trustworthiness of the packet and includes it in decision making. The proposed approach increases the performance of a Distributed Denial of Service (DDoS) attack detection in a VANET environment.

Public transportation is a vital service provided to enable a community to carry out daily activities. One of the mass transportations used in an area is a bus. Moreover, the smart transportation concept is an integrated application of technology and strategy in the transportation system. Using smart idea is the key to the application of the Internet of Things. The ways to improve the management transportation system become a bottleneck for the traditional data analytics solution, one of the answers used in machine learning. This paper uses the Artificial Neural Network (ANN) and Support Vector Machine (SVM) algorithm for the best prediction of travel time with a lower error rate on a case study of a university shuttle bus. Apart from predicting the travel time, this study also considers the fuel cost and gas emission from transportation. The analysis of the experiment shows that the ANN outperformed the SVM. Furthermore, a recommender system is used to recommend suitable routes for the chosen scenario. The experiments extend the discussion with a range of future directions on the stipulated field of study.

Optimization of data usability and privacy protection is a challenging goal. Although the released data are sanitized with the upgraded model of data semantic changer, always face privacy and scalability issues. On the other hand, local anonymization may not be suitable in a multi-nodal environment such as a vehicular network. Alternatively, hiding sensitive attributes are essential alongside discreet data privacy. In this paper, we proposed a collaborative privacy-preserving data anonymization technique for the vehicular network. Moreover, the method is capable of achieving a desirable level of data sanitization in a group. The use of k-anonymity, l-diversity and t-closeness make the proposed technique more strong to protect data privacy in different stages of communication. Furthermore, the performed analysis of the proposed scheme ensures usability and practicality. Lastly, the future direction of the stipulated field shows the research path in collaborative data anonymization in the vehicular environment.

Vehicle ad-hoc network (VANET) faces heavy traffic with communication burden to connect with various nodes in the network, uses clustering techniques with neighboring vehicles, and elects a powerful and efficient vehicle as a cluster head. Cluster head vehicle collects all the information and pass them out and transmit outside data to other members in the cluster. However, clustering faces issues such as the inability of choosing feasible and stable clusters for a long time. On the other hand, the nomination of cluster heads fails to adhere to real-world parameters. To address these issues, in this paper, a k-means algorithm and direction vector-based clustering techniques and a cluster head selection method with the utilization of fuzzy-based PROMETHEE multi-criteria decision techniques are proposed. Moreover, mathematical and rational analysis shows that the method is implementable and achievable. Finally, a range of future directions of the study is shown.

This work is based on the vehicle positioning system using the statistical approaches. Moreover, the method does not depend on the real time devices such as GPS, or networking technologies.

The impact of the accurate estimated time of arrival (ETA) is often overlooked by bus operators. By providing accurate ETA to riders, it gives them the impression of bus services is efficient and reliable and this promotes higher ridership in the long run. This research project aims to predict bus arrival time by using the Support Vector Regression (SVR) model which is based on the same theory as the Support Vector Machine (SVM). Urban City Bus data covering part of the Petaling Jaya area (route name PJ03) is used in this research work. Features related to traffic such as travel duration, a distance of the road, weather and operation at peak or non-peak hour have been used as input in the training of the SVR model. By using kernel trick and specifying optimum parameters, all the features in higher dimensions are efficiently calculated and the SVR model achieves convergence. The model is evaluated with the test set of data split from the original dataset. The experimental result indicates the SVR model displays good prediction ability with its low average error on the prediction result. However, weather data has not been influential to the prediction model as the results of the model trained with and without weather data show a negligible difference.

With the development of an intelligent transport system (ITS), security becomes an increasingly important issue. The Vehicle ad-hoc network (VANET) security researchers have shown enormous development on the field. Among all, the intrusion detection system (IDS) is creating the highest attraction. Additionally, a way of multidisciplinary researches has proved the importance of IDS over VANET. In this paper, a trust-based collaborative intrusion detection system (T-BICDS) is proposed. In which, each car maintains a score table of other cars in the network to identify their previous pattern of network behavior. Additionally, the vehicles gather real-time network traffic can analysis based on local IDS agent, which is equipped with k-nearest neighbors kNN nonlinear classifier. Besides, a vehicle can collaborate with other neighboring vehicles and update the score table to identify intruders in real-time as well as use the table for future prediction. Finally, we have concluded with potential future works out of this effort.