Search

This paper explores the integration of Location Information-based Routing (LIBRO) protocol into Energy Harvesting-based Cognitive Radio Wireless Sensor Networks (EH-CWSNs) to address energy consumption challenges and ensure sustainable operation. LIBRO has been recently proposed as an efficient routing algorithm for traditional WSN networks. In this paper, we extend its application to EH-CWSNs, in which the WSN nodes act as Secondary Users (SUs). By proposing LIBRO-based EH-CWSN, we aim to eliminate energy constraints on the nodes and reduce the need for frequent battery replacements in EH-CWSN, thus enabling deployment in challenging environments. Additionally, LIBRO’s unique feature of minimizing nodes’ cooperation during routing offers advantages for protecting Primary Users (PUs) against interference caused by secondary transmissions. The paper’s primary goal is to adapt LIBRO to the CWSN functionalities, especially the spectrum sensing operation. Then, EH-CWSN network parameters are optimized to ensure lower energy consumption compared to the harvested energy from EH units, establishing a sustainable network while respecting PU collision limits. Through a multi-objective optimization approach, we seek to strike a balance between collision probability with PUs, energy consumption, packet delivery latency, and network throughput, creating a self-powered network that meets various constraints. Numerical findings demonstrate the effectiveness of LIBRO in enhancing energy and spectral efficiencies in EH-CWSNs compared to state-of-the-art routing protocols.

Concurrent transmission of the opportunistic Secondary User (SU) and the licensed Primary User (PU) is a promising approach to increasing secondary network capacity in Cognitive Radio (CR) networks. In future telecommunication generations, PU can be expected to adopt directional transmission. This allows SU to exploit the spatial dimension in addition to the time-frequency dimensions, as in classical CR. However, sensing the beam of the PU and optimizing the transmission parameters of SU become crucial in this scenario to avoid interference between the SU and PU. In this paper, we propose a concurrent transmission strategy, where SU independently and passively senses the position and beamwidth of PU. Once the PU’s beam is detected, SU becomes allowed to direct its transmit power in the unoccupied space, leading to improved spectrum efficiency of the CR network. The proposed strategy aims to optimize the transmission power directed toward the secondary receiver so that the interference coming from the secondary lobes does not exceed an interference threshold. Given the role of transmit power and the number of antenna elements in determining the transmission characteristics of the SU, we formulate the allowed transmission power that respects tolerable interference to the PU. Additionally, we investigate the number of antenna elements at the SU by developing a non-convex optimization problem and proposing a method to address it. Finally, we formulate the capacity of the SU network and compare it to the traditional opportunistic CR approach. Numerical results investigate the functionality of the proposed strategy in relation to the primary network transmission conditions and potential errors in estimating these conditions. Simulation results demonstrate a significant capacity increase compared to conventional CR, where SUs only transmit during channel idle periods.

Unmanned aerial vehicles (UAVs) play pivotal roles in various applications, from surveillance to delivery services. Efficient path planning for UAVs in dynamic environments with obstacles and moving landing stations is essential to ensure safe and reliable operations. In this study, we propose a novel approach that combines the A* algorithm with the grey wolf optimizer (GWO) for path planning, referred to as GW-A*. Our approach enhances the traditional A algorithm by incorporating weighted nodes, where the weights are determined based on the distance from obstacles and further optimized using GWO. A simulation using dynamic factors such as wind direction and wind speed, which affect the quadrotor UAV in the presence of obstacles, was used to test the new approach, and we compared it with the A* algorithm using various heuristics. The results showed that GW-A* outperformed A* in most scenarios with high and low wind speeds, offering more efficient paths and greater adaptability.

Theoretical mathematics is a key evolution factor of artificial intelligence (AI). Nowadays, representing a smart system as a mathematical model helps to analyze any system under development and supports different case studies found in real life. Additionally, the Markov chain has shown itself to be an invaluable tool for decision-making systems, natural language processing, and predictive modeling. In an Internet of Things (IoT), Machine-to-Machine (M2M) traffic necessitates new traffic models due to its unique pattern and different goals. In this context, we have two types of modeling: (1) source traffic modeling, used to design stochastic processes so that they match the behavior of physical quantities of measured data traffic (e.g., video, data, voice), and (2) aggregated traffic modeling, which refers to the process of combining multiple small packets into a single packet in order to reduce the header overhead in the network. In IoT studies, balancing the accuracy of the model while managing a large number of M2M devices is a heavy challenge for academia. One the one hand, source traffic models are more competitive than aggregated traffic models because of their dependability. However, their complexity is expected to make managing the exponential growth of M2M devices difficult. In this paper, we propose to use a Markov-Modulated Poisson Process (MMPP) framework to explore Human-to-Human (H2H) traffic and M2M heterogeneous traffic effects. As a tool for stochastic processes, we employ Markov chains to characterize the coexistence of H2H and M2M traffic. Using the traditional evolved Node B (eNodeB), our simulation results show that the network’s service completion rate will suffer significantly. In the worst-case scenario, when an accumulative storm of M2M requests attempts to access the network simultaneously, the degradation reaches 8% as a completion task rate. However, using our “Coexistence of Heterogeneous traffic Analyzer and Network Architecture for Long term evolution” (CHANAL) solution, we can achieve a service completion rate of 96%.

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.

Although significant progress has been achieved in the development of spatio-temporal models that capture real-world phenomena, more interaction between phenomenon-based and business-based modeling approaches is still required. This represents a much-needed step forward in balancing the management and scientific aspects of many urban applications. The main goal of this paper is to provide an integrated modeling framework that combines MADS (Modeling of Application Data with Spatio-temporal Features), a specific conceptual spatiotemporal model that successfully represents geographical data, with BPMN (Business Process Model Notation), which models and determines how business processes interact with geographic objects. The integration of MADS and BPMN guarantees clarity, extensibility, and autonomy between data and management operations, as it portrays spatio-temporal abstractions on a conceptual level. This integration offers a unique opportunity to accurately represent, understand, and explain the rationale behind the changes occurring in space and time. The approach has been implemented within a PostgreSQL/PostGIS database management system. A case study implementation shows the practical applicability and potential of this modeling approach.

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

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.

Simulation and experimental performance analyses of simultaneous up-converted signals, for the first time, were investigated utilizing a semiconductor optical amplifier Mach–Zehnder interferometer (SOA-MZI) sampling mixer in co- and counter-directions for standard and differential modulation modes. An optical pulse source at a sampling frequency of fs = 15.6 GHz was used as a sampling signal. The IF signal channels carrying quadrature phase shift keying (QPSK) data at frequencies fm were up-converted at different mixing frequencies up to 195.5 GHz. Using the Virtual Photonics Inc. (VPI) simulator, we realized mixed QPSK signals and studied their characteristics through a conversion gain and an error vector magnitude (EVM). Simulations of up mixing operated in a frequency range up to 158 GHz. For the standard modulation in the co-direction, the conversion gain decreased from 43.3 dB at the mixing frequency of 16.6 GHz to 21.8 dB at 157 GHz for the first channel and from 43 dB at 17.6 GHz to 21 dB at 158 GHz for the second channel. The use of the differential modulation principle improved the conversion gain by about 10 dB at 195.5 GHz compared to standard modulations in co- and counter-directions. The EVM reached, respectively, 15.5 and 17.5% for the differential modulation in both configurations, at the bit rate of 100 Gbit/s at 195.5 GHz. The benefit provided by the differential modulation was that EVM values were shifted by 20% for all channels in both configurations at 100 Gbit/s. In the real measurement, we confirmed that co-directional conversion exhibited a better performance than the counter-directional operation. In addition, the real mixed signal exhibited lower efficiency and quality in comparison with simulated signals due to the sensitivity of the receiver.

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.

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.

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.