Machine learning (ML) can aid the challenging beam management operations in millimeter-wave (mmWave) communication systems. In this paper, we investigated the performance of four ML algorithms (i.e., K-Nearest Neighbour (KNN), Support Vector Machine (SVM), Decision Tree (DT) and Naïve Bayes (NB)) on position-aided beam prediction, using beam prediction accuracy as the metric. We used the KNN, SVM, DT, and NB algorithms to investigate the effects of beam codebook sizes and dataset sample sizes on beam prediction accuracy performance in various vehicle-to-infrastructure (V2I) scenarios using real-world datasets from extensive mmWave V2I measurements. We also illustrated the results using confusion matrices to reveal the misclassification statistics across the different beams. For the four algorithms, the results show that the larger the beam codebook size, the lower the beam prediction accuracy. The results also show that the dataset split ratio does not significantly impact the beam prediction accuracy for the four algorithms. The results point to the need for multimodal approaches that employ a combination of sensor and communication data to improve the beam prediction performance.

Crowdsensing requires scalable privacy-preserving authentication that allows users to send anonymously sensing reports, while enabling eventual anonymity revocation in case of user misbehavior. Previous research efforts already provide efficient mechanisms that enable conditional privacy through pseudonym systems, either based on Public Key Infrastructure (PKI) or Group Signature (GS) schemes. However, previous schemes do not enable users to self-generate an unlimited number of pseudonyms per user to enable users to participate in diverse sensing tasks simultaneously, while preventing the users from participating in the same task under different pseudonyms, which is referred to as sybil attack. This paper addresses this issue by providing a scalable privacy-preserving authentication solution for crowdsensing, based on a novel pseudonym-based signature scheme that enables unlinkable-yet-accountable pseudonymity. The paper provides a detailed description of the proposed scheme, the security analysis, the performance evaluation, and details of how it is implemented and integrated into a real crowdsensing platform.

In the emerging industrial Internet of Things (IIoT) era, machine-to-machine (M2M) communication technology is considered as a key underlying technology for building IIoT environments, where devices (e.g., sensors, actuators, and gateways) are enabled to exchange information with each other in an autonomous way without human intervention. However, most of the existing M2M protocols that can be also used in the IIoT domain provide security mechanisms based on asymmetric cryptography resulting in high computational cost. As a consequence, the resource-constrained IoT devices are not able to support them appropriately and thus, many security issues arise for the IIoT environment. Therefore, lightweight security mechanisms are required for M2M communications in IIoT in order to reach its full potential. As a step toward this direction, in this paper, we propose a lightweight authentication mechanism, based only on hash and XOR operations, for M2M communications in IIoT environment. The proposed mechanism is characterized by low computational cost, communication, and storage overhead, while achieving mutual authentication, session key agreement, device's identity confidentiality, and resistance against the following attacks: replay attack, man-in-the-middle attack, impersonation attack, and modification attack.

An Intrusion Detection System (IDS) aims at protecting a network against attacks intended to exposing and/or vandalizing it. To build and test an IDS, network data are usually acquired containing attacks and normal behavior. The objective of this work is to use machine learning techniques to build IDSs and to investigate their reliability. To build and test the IDSs, KDDCUP99 has been used. The data contain a training set and a testing set with 4,898,430 samples (∼700MB) and 311,032 samples (∼45MB), respectively. However, the cleaned dataset via using SQL commands show that KDDCUP99 is highly redundant. The cleaned/distinct data are nearly one fifth of the original. Subsequently, experimental results have been performed using neural networks based IDSs. Some IDSs give low and median performances when tested using the redundant data and the distinct data, respectively, but other IDSs gave high and median performances using the redundant and the distinct data, respectively. Thus, there is a fluctuation in the performance when the data are redundant, which shows that an IDS built using a redundant dataset has unstable performance. The goal of preparing a balanced dataset is to only use it in testing the realistic performance of the IDS and has no relation to IDS generalization and implementation in real-world scenarios.

MoSHCA is a mHealth project designed to improve patient-doctor interaction and to promote the self-management of chronic diseases by the patients themselves. The number of people with a chronic disease is dramatically increasing worldwide. This is becoming a major obstacle for economic stability and growth and the sustainability of national health care systems. The introduction of self-management by patients with a chronic disease seems inevitable as a countermeasure against these developments. MoSHCA provides intelligent, user-friendly and secure, medical and well-being decision support through embedded software in mobile devices by utilizing specific sensors and data from customized information systems.

Human movement is strongly affected by the needs of humans to socialize or cooperate with each other, in one form or another, which results in some degree of clustering and correlation with their movements. In this paper, we exploit the movement correlations, and propose a new community-based sequential paging algorithm to reduce the paging signaling overhead. Our simulation results demonstrate that given the same location update scheme employed in the LTE-A network, the proposed algorithm is capable of reducing 20% of the paging messages in the best scenario when having high moving rate.

It is necessary to re-think the current access selection polices to the simultaneously available wireless networks, whose number is growing, and the appropriate management mechanisms, to enhance the experience for the final user, for example in terms of QoS and energy efficiency. The IEEE 802.21 standard introduces link layer intelligence as well as related network information to upper layers in order to optimize handovers between networks of different technologies (WiMAX, Wi-Fi, 3GPP, etc.). As the integration of multiple wireless interfaces in mobile terminals becomes ordinary it is important to efficiently manage those interfaces. Not only to conveniently provide the requested services to the user, but also to do that in an energy efficient way in order to allow higher mobility by extending the battery life of its mobile terminal. In this paper the IEEE 802.21 standard is used while considering handovers between WiMAX and Wi-Fi, through an implementation in ns-2 introducing a simple, but effective, location-assisted energy saving technique.

The growing presence of concurrent heterogeneous wireless access networks, together with the increasing service demands from the end-users, require re-thinking of current access selection polices and appropriate management mechanisms, namely concerning quality of service, energy efficiency, etc. The recent IEEE 802.21 standard introduces link layer intelligence as well as related network information to upper layers in order to optimise handovers between networks of different technologies, such as WiMAX, Wi-Fi and 3GPP. With the massification of mobile terminals with multiple wireless interfaces it is important to efficiently manage those interfaces not only to appropriately provide the requested services to the user, but also to do that in an energy efficient way in order to allow higher mobility to the user by extending the battery life of its terminal. The study the IEEE 802.21 standard is briefly introduced, presented in the signalling in a handover between WiMAX and Wi-Fi, and exploited through an implementation in ns-2 introducing a simple, but effective, energy-saving approach.

Radio frequency spectrum is a highly expensive commodity. However, UMTS UL bands capacity has been mainly underutilized due typical Internet traffic asymmetry. In this paper we consider a secondary wireless system that operates over the UMTS UL bands in an opportunistic way. The opportunistic radios (OR) sense the path loss between its location and the UMTS base station. With this sensing information the ORs adapts its power to avoid harmful interference with the UMTS system. Sensing is performed exploiting cyclostationary features of the UMTS signal and no cooperation between the two networks is assumed. Spectrum opportunities are computed and coexistence between the two wireless networks is analyzed.

In beyond 3G systems, one of the important factors is to address radio access technology (RAT) selection and load balancing between heterogeneous networks to ensure high spectral efficiency in an era where spectral resources are at a premium. This work aims to address the feasibility of utilizing WiFi as complementary service for HSDPA, to prevent quality of service deterioration in the event of network overload in HSDPA during the busy period. The proposed RAT selection algorithm is based on the load of each system, and the results show that the outage probability can be improved by up to 45% relative to a stand-alone HSDPA system.