Securing IoT Networks from DDoS Attacks Using a Temporary Dynamic IP Strategy.

The progression of the Internet of Things (IoT) has brought about a complete transformation in the way we interact with the physical world. However, this transformation has brought with it a slew of challenges. The advent of intelligent machines that can not only gather data for analysis and decision-making, but also learn and make independent decisions has been a breakthrough. However, the low-cost requirement of IoT devices requires the use of limited resources in processing and storage, which typically leads to a lack of security measures. Consequently, most IoT devices are susceptible to security breaches, turning them into "Bots" that are used in Distributed Denial of Service (DDoS) attacks. In this paper, we propose a new strategy labeled "Temporary Dynamic IP" (TDIP), which offers effective protection against DDoS attacks. The TDIP solution rotates Internet Protocol (IP) addresses frequently, creating a significant deterrent to potential attackers. By maintaining an "IP lease-time" that is short enough to prevent unauthorized access, TDIP enhances overall system security. Our testing, conducted via OMNET++, demonstrated that TDIP was highly effective in preventing DDoS attacks and, at the same time, improving network efficiency and IoT network protection.

Advanced Topics and Smart Systems for Wireless Communications and Networks.

Telecommunication has shaped our civilization and fueled economic growth significantly throughout human history [...].

LoRaCog: A Protocol for Cognitive Radio-Based LoRa Network.

In this paper, we propose a new protocol called LoRaCog to introduce the concept of Cognitive Radio (CR) in the LoRa network. LoRaCog will enable access to a wider spectrum than that of LoRaWAN by using the unutilized spectrum and thus has better efficiency without impacting the end devices' battery consumption. LoRa networks are managed by LoRaWAN protocol and operate on the unlicensed Industrial, Scientific and Medical (ISM) band. LoRaWAN is one of thriving protocols for Low-Power Wide-Area Networks (LPWAN) implemented for the Internet of Things (IoT). With the growing demand for IoT, the unlicensed spectrum is expected to be congested, unlike the licensed spectrum, which is not fully utilized. This can be fairly balanced by applying CR to the LoRa network, where the End Devices (EDs) may change the operating channel opportunistically over the free/available licensed spectrum. Spectrum sensing, channel selection and channel availability relevance become essential features to be respected by the proposed protocol. The main objective of adding CR to LoRaWAN is reducing the congestion and maintaining LoRaWAN's suitability for battery-operated devices. This is achieved by modifying LoRaWAN components such as the ED receive window RX2 rearrangement, spectrum sensing functionality by gateway (GW) for identifying unused channels, and reaching a decision on the unused channels by network server (NS). These changes will create LoRaCog meeting spectrum efficiency and maintain the same level of battery consumption as in LoRaWAN. Numerical simulations show a significant decrease in the rejected packet rate (more than 50%) with LoRaCog when more EDs use cognitive channels. As the results prove, LoRaWAN can reach above 50% rejected packets for the simulated environment versus 24% rejection for LoRaCog using only one additional channel (means total two channels). This means that the system can eliminate rejected packets almost completely when operating over the possible many channels. As well, these results show the flexibility in the system to utilize the available frequencies in an efficient and fair way. The results also reveal that a lower number of GWs is needed for LoRaCog from LoRaWAN to cover the same area.

In-Network Data Aggregation for Ad Hoc Clustered Cognitive Radio Wireless Sensor Network.

In cognitive radio wireless sensor networks (CRSN), the nodes act as secondary users. Therefore, they can access a channel whenever its primary user (PU) is absent. Thus, the nodes are assumed to be equipped with a spectrum sensing (SS) module to monitor the PU activity. In this manuscript, we focus on a clustered CRSN, where the cluster head (CH) performs SS, gathers the data, and sends it toward a central base station by adopting an ad hoc topology with in-network data aggregation (IDA) capability. In such networks, when the number of clusters increases, the consumed energy by the data transmission decreases, while the total consumed energy of SS increases, since more CHs need to perform SS before transmitting. The effect of IDA on CRSN performance is investigated in this manuscript. To select the best number of clusters, a study is derived aiming to extend the network lifespan, taking the SS requirements, the IDA effect, and the energy consumed by both SS and transmission into consideration. Furthermore, the collision rate between primary and secondary transmissions and the network latency are theoretically derived. Numerical results corroborate the efficiency of IDA to extend the network lifespan and minimize both the collision rate and the network latency.

Simultaneous up- and down-frequency mixing based on a cascaded SOA-MZIs link.

In this paper, a simultaneous frequency up- and down-conversion is performed using a cascaded semiconductor optical amplifier Mach-Zehnder interferometers (SOA-MZIs) link for radio-over-fiber (RoF) applications. The intermediate frequency (IF) signal carrying quadratic phase shift keying (QPSK) data at a frequency f1 is up-converted at the SOA-MZI1 output at nfs±f1, where n is the harmonic rank of the first sampling signal. In addition, this up-converted signal is concurrently up- and down-converted at the SOA-MZI2 output at mfs+nfs±f1 and |nfs±f1-mfs|, respectively, where m is the harmonic rank of the second sampling signal. Using the virtual photonics integrated (VPI) simulator, it has been shown that the optical transmission system based on a cascaded SOA-MZIs link has a better efficiency and more endurable quality of the frequency mixing of QPSK signals and the higher frequency range for up- and down-conversions. Positive conversion gains are obtained at the highest mixing frequency of 101.9 and 86.3 GHz for up- and down-conversions, respectively. The best error vector magnitude provided using a cascaded SOA-MZIs link is 15.5% at the mixing frequency of 101.9 GHz for up-conversion and 18% at 86.3 GHz for down-conversion at the bit rate of 40,500 Mbit/s. The maximum bit rates of 40.5, 81, and 121.5 Gbit/s for QPSK, 16-QAM, and 64-QAM modulations, respectively, that meet the forward error correction limit is fulfilled by using a cascaded SOA-MZIs link. Another advantage of using the cascaded SOA-MZIs link is the up- and down-conversion simultaneously achieved at the second stage.

Spectrum Sensing for Cognitive Radio: Recent Advances and Future Challenge.

Spectrum Sensing (SS) plays an essential role in Cognitive Radio (CR) networks to diagnose the availability of frequency resources. In this paper, we aim to provide an in-depth survey on the most recent advances in SS for CR. We start by explaining the Half-Duplex and Full-Duplex paradigms, while focusing on the operating modes in the Full-Duplex. A thorough discussion of Full-Duplex operation modes from collision and throughput points of view is presented. Then, we discuss the use of learning techniques in enhancing the SS performance considering both local and cooperative sensing scenarios. In addition, recent SS applications for CR-based Internet of Things and Wireless Sensors Networks are presented. Furthermore, we survey the latest achievements in Spectrum Sensing as a Service, where the Internet of Things or the Wireless Sensor Networks may play an essential role in providing the CR network with the SS data. We also discuss the utilisation of CR for the 5th Generation and Beyond and its possible role in frequency allocation. With the advancement of telecommunication technologies, additional features should be ensured by SS such as the ability to explore different available channels and free space for transmission. As such, we highlight important future research axes and challenging points in SS for CR based on the current and emerging techniques in wireless communications.

A Hadoop-Based Platform for Patient Classification and Disease Diagnosis in Healthcare Applications.

Nowadays, the increasing number of patients accompanied with the emergence of new symptoms and diseases makes heath monitoring and assessment a complicated task for medical staff and hospitals. Indeed, the processing of big and heterogeneous data collected by biomedical sensors along with the need of patients' classification and disease diagnosis become major challenges for several health-based sensing applications. Thus, the combination between remote sensing devices and the big data technologies have been proven as an efficient and low cost solution for healthcare applications. In this paper, we propose a robust big data analytics platform for real time patient monitoring and decision making to help both hospital and medical staff. The proposed platform relies on big data technologies and data analysis techniques and consists of four layers: real time patient monitoring, real time decision and data storage, patient classification and disease diagnosis, and data retrieval and visualization. To evaluate the performance of our platform, we implemented our platform based on the Hadoop ecosystem and we applied the proposed algorithms over real health data. The obtained results show the effectiveness of our platform in terms of efficiently performing patient classification and disease diagnosis in healthcare applications.