In this paper, a review of the main actions and policies that can be implemented to promote sustainable mobility is proposed. The work aims to provide a broad, albeit necessarily not exhaustive, analysis of the main studies and research that from different points of view have focused on sustainable mobility. The structure of the paper enables the reader to easily identify the topics covered and the studies related to them, so as to guide him/her to the related in-depth studies. In the first part of the paper, there is a preliminary analysis of the concept of sustainable mobility, the main transport policies implemented by the European Union and the USA, and the main statistical data useful to analyze the problem. Next, the main policies that can promote sustainable mobility are examined, classifying them into three topics: Environmental, socio-economic, and technological. Many of the policies and actions examined could be classified into more than one of the three categories used; for each of them, there is a description and the main literature work on which the topic can be analyzed in more detail. The paper concludes with a discussion on the results obtained and the prospects for research.

Sustainable Mobility: A Review of Possible Actions and Policies

Internet of Things (IoT) is a paradigm in digital technology which is prevalently revolutionizing various sectors like healthcare, military, business and more. However, the incremental deployment of this advanced technology has also caused critical security issues simultaneously. In particular, IoT networks are continuing to grow vulnerable to security attacks due to exponential connectivity of ‘things’ with each other in the smart infrastructure. Due to this increased vulnerability, it has become crucial to address the issue of insecure routing in these IoT devices. IoT uses RPL, which is a specially designed standard for networking that caters to the resource-constrained and lightweight nature of IoT devices, for information broadcast. It is equally prone to routing attacks like any other class of protocols in wireless networks. Various solutions have been proposed by researchers to counter them including version, rank, sinkhole and wormhole attacks since last decade. However, given the huge impact, neither detection nor mitigation method has been found which addresses rank and wormhole attacks when they are initiated at the same time on an IoT network. In this paper, a rank and wormhole attack detection framework is proposed, by employing machine learning approaches, which address the stated issue. This research aims to contribute toward design and development of high-performing and effective solutions for routing attacks in RPL-based IoT networks.

Proposing a Rank and Wormhole Attack Detection Framework using Machine Learning

Energy efficient thwarting rank attack from RPL based IoT networks: A review

-Security is the main concern in wireless sensor network. Attack can be occurred in different ways in WSN depending upon the types of malicious activity through this research work we are identifying the location of malicious node using Stack based approach. Cooja simulator is used to implement the RPL attack and considering the Average Power Consumption as a bench mark for identifying the malicious activity. We have verified the impact of the attack by varying the number of malicious node and the network size. The Stack based approach for finding the location is novel one and simulation results shows the efficiency of the approach.

Stack based location identification of malicious node in RPL attack using average power consumption

Over the past decades, brain-computer interfaces (BCIs) have been developed to provide individuals with an alternative communication channel toward external environment. Although the primary target users of BCI technologies include the disabled or the elderly, most newly developed BCI applications have been tested with young, healthy people. In the present study, we developed an online home appliance control system using a steady-state visual evoked potential (SSVEP)-based BCI with visual stimulation presented in an augmented reality (AR) environment and electrooculogram (EOG)-based eye tracker. The performance and usability of the system were evaluated for individuals aged over 65. The participants turned on the AR-based home automation system using an eye-blink-based switch, and selected devices to control with three different methods depending on the user’s preference. In the online experiment, all 13 participants successfully completed the designated tasks to control five home appliances using the proposed system, and the system usability scale exceeded 70. Furthermore, the BCI performance of the proposed online home appliance control system surpassed the best results of previously reported BCI systems for the elderly.

Brain-Controlled, AR-Based Home Automation System Using SSVEP-Based Brain-Computer Interface and EOG-Based Eye Tracker: A Feasibility Study for the Elderly End User

The routing protocol for low-power and lossy networks (RPL) is an IPv6-based routing protocol optimised for internet of things (IoT) environments. However, it is susceptible to malicious packet dropping attacks. If a node with a lower rank that is closer to the root node attempts a malicious packet dropping, it may disrupt basic data transmission or even the entire IoT application service. In this paper, we present a novel detection method for malicious packet dropping attacks against RPL-based networks. The proposed method is based on the anomaly intrusion detection system and detects malicious packet dropping in the presence of normal packet losses. We evaluate the performance of the method on Contiki's network simulator, Cooja. The evaluation results show that the method has good performance in detecting malicious packet dropping attacks. In every case, the successful detection rate is greater than 94% and the false alarm rate is less than 3%.

Detection of malicious packet dropping attacks in RPL-based internet of things

Carsharing is becoming one of the fastest growing industries. In contrast to a round-trip system, the one-way carsharing system permits to return the rented vehicle to another station. Inevitably, a one-way system results in vehicle inventory imbalance issues and periodical vehicle relocations are required as a result. This paper deals with the issue using two optimization models: CA rsharing NO relocation with MA ximal demand satisfaction (CANOMA) and C arsharing RE location with MA ximal demand satisfaction (CREMA ). The CANOMA model looks into the optimal solution of vehicle inventory and allocation without vehicle relocation, while the CREMA model searches for optimal solution of vehicle inventory, allocation and relocation with periodic vehicle relocation. Various experiments are performed to prove the vehicle relocation. In comparison with CANOMA, CREMA shows a lower level of vehicle inventory, a higher level of vehicle allocation and utilization required for meeting same customer demand via efficient vehicle relocation.

KyoungHoon Kim

Papers

Detection of malicious packet dropping attacks in RPL-based internet of things

Vehicle-relocation optimization for one-way carsharing

Usability and Security Analysis of Authentication Methods for Mobile Fin-Tech Services

An Analysis of Password Meters for Domestic Web Sites