Intrusion Detection Systems for Flying Ad-hoc Networks

Unmanned Aerial Vehicles (UAVs) are becoming more dependent on mission success than ever. Due to their increase in demand, addressing security vulnerabilities to both UAVs and the Flying Ad-hoc Networks (FANET) they form is more important than ever. As the network traffic is communicated through open airwaves, this network of UAVs relies on monitoring applications known as Intrusion Detection Systems (IDS) to detect and mitigate attacks. This paper will survey current IDS systems that include machine learning techniques when combating various vulnerabilities and attacks from bad actors. This paper will be concluded with research challenges and future research directions in finding an effective IDS system that can handle cyber-attacks while meeting performance requirements.Comment: 5 Pages, 1 figure, 1 table, 41 Reference

PERFORMANCE OF UPLINK-NOMA WITH USER PAIRING AND DATA RATE-BASED POWER SCHEME

This paper analyzes a performance of uplink power-domain non-orthogonal multiple access (NOMA) system with 2K users in which a resource allocation is taken into consideration. Since the power allocation and user pairing are tightly intertwined, they are considered as a hybrid issue. Accordingly, High-High/High-Low user pairing process precedes date rate-based power allocation. Derived closed-form expressions for the outage probabilities and the sum data rate for uplink power-domain NOMA system over a composite Fisher-Snedecor (F) fading channel are used for an extensive performance evaluation. The impact of different fading/shadowing channel conditions, various users’ positions and their number on the performance metrics is examined. Presented results have high level of generality since the F fading model provides accurate characterization of the multipath/shadowing conditions in numerous communication scenarios of interest

The 3D indoor deployment in DL-IoT with experimental validation using a particle swarm algorithm based on the dialects of songs

The 3D indoor redeployment of connected objects in IoT collection networks is a complex problem that influences the overall performance of the network. In this paper, we aim to resolve this problem using a real prototyping system based on a real-world deployment. The aim is to choose the best positions to add a set of connected objects while optimizing a set of objectives. The used approach is based on a new hybrid optimization algorithm that combines a strategy of incorporation of user preferences (PI-EMO-VF) with a many-objective variant of the genetic algorithms (NSGA-III). The obtained numerical results and the real experiments on our testbeds prove the effectiveness of the proposed approach compared with another recent optimization algorithm (MOEA/DD)

A smartwater metering deployment based on the fog computing paradigm

In this paper, we look into smart water metering infrastructures that enable continuous, on-demand and bidirectional data exchange between metering devices, water flow equipment, utilities and end-users. We focus on the design, development and deployment of such infrastructures as part of larger, smart city, infrastructures. Until now, such critical smart city infrastructures have been developed following a cloud-centric paradigm where all the data are collected and processed centrally using cloud services to create real business value. Cloud-centric approaches need to address several performance issues at all levels of the network, as massive metering datasets are transferred to distant machine clouds while respecting issues like security and data privacy. Our solution uses the fog computing paradigm to provide a system where the computational resources already available throughout the network infrastructure are utilized to facilitate greatly the analysis of fine-grained water consumption data collected by the smart meters, thus significantly reducing the overall load to network and cloud resources. Details of the system's design are presented along with a pilot deployment in a real-world environment. The performance of the system is evaluated in terms of network utilization and computational performance. Our findings indicate that the fog computing paradigm can be applied to a smart grid deployment to reduce effectively the data volume exchanged between the different layers of the architecture and provide better overall computational, security and privacy capabilities to the system

A MPTCP-based RTT-aware packet delivery prioritisation algorithm in AR/VR scenarios

This work proposes, describes and performs performance analysis of a Round-Trip Time (RTT)-aware packet delivery prioritisation algorithm (RDPA) for networked Augmented Reality/Nirtual Reality (ARNR) content distribution. In this approach, the proposed algorithm uses the built-in multipath delivery feature of MPTCP. RDPA tracks, identifies and redirects the priority packets through the subflow that presents best opportunities to deliver the content with the lowest latency in the next transmission interval. This subflow selection is based on a linear regression that analyses each subflow behaviour and identifies the best subflow in terms of latency. The assessment of this algorithm is performed in a Network Simulator 3-based simulation environment and indicates performance improvements varying from 8% to 36% (peak performance) when compared with the MPTCP default operation

An Analytical Study on the Implementation of a Healthcare App to Assist People with Disabilities Using Cloud Computing and IoT

This study targets a group of people who require care, that is, people with special needs. The significance of this study lies in addressing the main problem that this group suffers from, which is the lack of awareness and information that leads to the acceptance of that group in society. This work aims to create a mobile application that contributes to spreading knowledge among people with special needs and enhancing their skills to help them become accepted by community members. This application supports people with special needs with training resources, education, suitable jobs, and other services helping them in developing their experiences and knowledge to be active in society. In addition, an evaluation questionnaire has been developed to collect data from both the private and public sectors to classify the building blocks necessary for KSA to incorporate the Internet of Things (IoT) and cloud computing into the healthcare sector. As a result, most respondents acknowledge the importance of a streamlined data-gathering process, the IoT, and cloud-based computing to meet their healthcare needs. Lastly, six main blocks for checking suppliers and the public to accept IoT and cloud healthcare applications are then acknowledged in this paper

TSCH and RPL Joining Time Model for Industrial Wireless Sensor Networks

[EN] Wireless sensor networks (WSNs) play a key role in the ecosystem of the Industrial Internet of Things (IIoT) and the definition of today's Industry 4.0. These WSNs have the ability to sensor large amounts of data, thanks to their easy scalability. WSNs allow the deployment of a large number of self-configuring nodes and the ability to automatically reorganize in case of any change in the topology. This huge sensorization capacity, together with its interoperability with IP-based networks, allows the systems of Industry 4.0 to be equipped with a powerful tool with which to digitalize a huge amount of variables in the different industrial processes. The IEEE 802.15.4e standard, together with the access mechanism to the Time Slotted Channel Hopping medium (TSCH) and the dynamic Routing Protocol for Low-Power and Lossy Networks (RPL), allow deployment of networks with the high levels of robustness and reliability necessary in industrial scenarios. However, these configurations have some disadvantages in the deployment and synchronization phases of the networks, since the time it takes to synchronize the nodes is penalized compared to other solutions in which access to the medium is done randomly and without channel hopping. This article proposes an analytical model to characterize the behavior of this type of network, based on TSCH and RPL during the phases of deployment along with synchronization and connection to the RPL network. Through this model, validated by simulation and real tests, it is possible to parameterize different configurations of a WSN network based on TSCH and RPL.This work has been supported by the MCyU (Spanish Ministry of Science and Universities) under the project ATLAS (PGC2018-094151-B-I00), which is partially funded by AEI, FEDER and EU.Vera-Pérez, J.; Silvestre-Blanes, J.; Sempere Paya, VM. (2021). TSCH and RPL Joining Time Model for Industrial Wireless Sensor Networks. Sensors. 21(11):1-17. https://doi.org/10.3390/s21113904117211

Gafor : Genetic algorithm based fuzzy optimized re-clustering in wireless sensor networks

Acknowledgments: The authors are grateful to the Deanship of Scientific Research at King Saud University for funding this work through Vice Deanship of Scientific Research Chairs: Chair of Pervasive and Mobile Computing. Funding: This research was funded by King Saud University in 2020.Peer reviewedPublisher PD