The Internet of Things (IoT) is a heterogeneous network of constrained devices connected both to each other and to the Internet. Since the significance of IoT has risen remarkably in recent years, a considerable amount of research has been conducted in this area, and especially on, new mechanisms and protocols suited to such complex systems. Routing Procotol for Lower-Power and Lossy Networks (RPL) is one of the well-accepted routing protocols for IoT. Even though RPL has defined some specifications for its security, it is still vulnerable to insider attacks. Moreover, lossy communication links and resource-constraints of devices introduce a challenge for developing suitable security solutions for such networks. Therefore, in this study, a new intrusion detection system based on neural networks is proposed for detecting specific attacks against RPL. Besides features collected from the routing layer, the effects of link layer-based features are investigated on intrusion detection. To the best of our knowledge, this study presents the first cross-layer intrusion detection system in the literature.
An effort is being made by authorities worldwide to improve the safety of the transportation of goods while preserving efficiency. Vehicle inspections are important for safety but not very frequent. When they do happen, vehicles are selected on the roadside and authorities spend a long time retrieving the relevant information while the vehicle is stopped. In this paper, we present and evaluate STOP, a road transportation vehicle inspection support system with tamper-proof records to prevent location spoofing attacks. To the best of our knowledge, it is the first such system described in literature. The STOP system uses mobile devices and a central server to allow authorities to select and notify vehicles for inspection while retrieving the needed information to prepare the procedure beforehand. The location chain for each vehicle can be verified and signed by the inspectors. We implemented a prototype in the Android platform and tested it with real users. We evaluated the system’s location retrieval accuracy, response times, and Bluetooth communication during inspection.
Conserving energy is probably the most important requirement in wireless sensor networks. In TSCH, this goal is obtained by subdividing time into slots, and by switching the communication interface of Internet of Things devices (frequently referred to as motes) off when, at any given time, neither transmissions nor receptions are scheduled for them. Nevertheless, in this kind of networks a considerable amount of energy may still be wasted due to idle listening. This occurs every time a cell is scheduled for frame reception but no transmissions are performed in the related slot and channel. In this paper, Proactive Reduction of Idle Listening (PRIL) techniques are introduced, which aim at lowering the energy wasted because of the above phenomenon. In particular, here we focus on a simplified mechanism that only considers the first hop (PRIL-F). A relevant feature of this kind of techniques is that they cannot worsen performance in any way. On the contrary, in those cases where they can be applied, they may only bring benefits. Results obtained through a simulation campaign show a tangible reduction in energy consumption, especially for periodic traffic generation, in application contexts based on either a star topology (wireless sensor and actuator networks) or a two-level topology (wireless sensor networks).
The Internet of Drones (IoD) is an emerging network architecture that leverages bidirectional communications between Unmanned Aerial Vehicles (UAVs) and a ground infrastructures. Given its versatility, the IoD is a key enabler for the evolution of sophisticated verticals, thus including Smart Cities, Industry 4.0 and Smart Transportation, to name a few. As a matter of fact, a swarm of drones can carry out real-time data gathering, industrial plants monitoring and wide area surveying. Since drones are battery-supplied systems, energy efficiency, communication reliability, and flight formation management routines become critical aspects of the IoD design. This work discusses the communication facets in a swarm of drones, leveraging the joint adoption of Bluetooth and Millimeter-Wave technologies. The proposal enables the exchange of heterogeneous data with a ground infrastructure while facilitating coordinated flight of the swarm. The resulting dual mode system optimizes energy resources, while maximizing reliability and effectiveness of drones’ communications.
In recent years, the scientific community has found increasing interest in technological systems for the evaluation of the mobility performance of the elderly population. The reduced quantity of dataset for gait and balance analysis of elderly people is a serious issue in studying the link between cognitive impairment and motor dysfunction, particularly in people suffering from neurodegenerative diseases. In this context, this work aims to provide a dataset with skeletal information of people aged 60 years and older, while they perform well-established tests for stability assessment. 27 healthy people and 20 patients affected by neurodegenerative diseases, housed at two different nursing institutes, have been selected for the stability analysis. Subjects have been observed and evaluated by clinical therapists while executing three motion tests, namely balance, sit-to-stand and walking. The stability postural and gait control of each subject has been analyzed using a video-based system, made of three low-cost cameras, without the need for wearable and invasive sensors. The dataset provided in this work contains the skeletal information and highly-discriminant features of the balance, sit-to-stand and walking tests performed by each subject. To evaluate the efficiency of the balance dataset, the estimated risk of fall of the subjects has been processed considering the extracted features, and compared with the expected one. Final results have proven a good estimation of the risk of fall of the people under analysis, underlining the effectiveness of the dataset.
