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Accurate solar power forecasting is pivotal for the global transition towards sustainable energy systems. This study conducts a meticulous comparison between Quantum Long Short-Term Memory (QLSTM) and classical Long Short-Term Memory (LSTM) models for solar power production forecasting. The primary objective is to evaluate the potential advantages of QLSTMs, leveraging their exponential representational capabilities, in capturing the intricate spatiotemporal patterns inherent in renewable energy data. Through controlled experiments on real-world photovoltaic datasets, our findings reveal promising improvements offered by QLSTMs, including accelerated training convergence and substantially reduced test loss within the initial epoch compared to classical LSTMs. These empirical results demonstrate QLSTM’s potential to swiftly assimilate complex time series relationships, enabled by quantum phenomena like superposition. However, realizing QLSTM’s full capabilities necessitates further research into model validation across diverse conditions, systematic hyperparameter optimization, hardware noise resilience, and applications to correlated renewable forecasting problems. With continued progress, quantum machine learning can offer a paradigm shift in renewable energy time series prediction, potentially ushering in an era of unprecedented accuracy and reliability in solar power forecasting worldwide. This pioneering work provides initial evidence substantiating quantum advantages over classical LSTM models while acknowledging present limitations. Through rigorous benchmarking grounded in real-world data, our study illustrates a promising trajectory for quantum learning in renewable forecasting.

The concept of the smart city relies on connectivity and is expected to revolutionize urban living and create sustainable cities. However, while smart cities continue to evolve, small towns, suburbs, and villages lack basic communication infrastructure. This phenomenon exacerbates inequality and perpetuates a growing digital divide. Hence, it is imperative to also concentrate on these communities by designing strategies to extend essential services to them and drive the development of smart communities. High-altitude platform stations (HAPS) are a key enabling technology for such a concept. They fly in the stratosphere and offer extensive connectivity coverage, are cost-effective, and powered by solar energy and hydrogen. To demonstrate the potential of HAPS in enabling smart communities, the world’s first-ever fifth-generation (5G) trial using HAPS was conducted. The experiment showed that HAPS provided high data rates and ubiquitous 5G coverage from the stratosphere in different scenarios. The main requirements of smart communities, such as ensuring equity and inclusion, intelligent transportation systems, and a blue economy were successfully addressed. We also highlight the main benefits of HAPS in enabling smart communities, as well as the involvement of communities in the co-design of such solutions. Finally, additional applications are presented, along with the main key enabling technologies.

Short-reach optical communication networks have various applications in areas where high-speed connectivity is needed, for example, inter- and intra-data center links, optical access networks, and indoor and in-building communication systems. Machine learning (ML) approaches provide a key solution for numerous challenging situations due to their robust decision-making, problem-solving, and pattern-recognition abilities. In this work, our focus is on utilizing deep learning models to minimize symbol error rates in short-reach optical communication setups. Various channel impairments, such as nonlinearity, chromatic dispersion (CD), and attenuation, are accurately modeled. Initially, we address the challenge of modeling a nonlinear channel. Consequently, we harness a deep learning model called autoencoders (AEs) to facilitate communication over nonlinear channels. Furthermore, we investigate how the inclusion of a nonlinear channel within an autoencoder influences the received constellation as the optical fiber length increases. Another facet of our work involves the deployment of a deep neural network-based receiver utilizing a channel influenced by chromatic dispersion. By gradually extending the optical length, we explore its impact on the received constellation and, consequently, the symbol error rate. Finally, we incorporate the split-step Fourier method (SSFM) to emulate the combined effects of nonlinearities, chromatic dispersion, and attenuation in the optical channel. This is accomplished through a neural network-based receiver. The outcome of this work is an evaluation and reduction of the symbol error rate as the length of the optical fiber is varied.

In this paper, we present a simulation-based study for the coexistence of an International Mobile Telecommunication (IMT) system with a Digital Terrestrial Television Broadcasting (DTTB) system. The study considers the coexistence of the two systems in the sub-700 MHz band and presents interference analysis results for locations in the Kingdom of Saudi Arabia (KSA). The study includes different terrains in which each of the two systems is deployed, e.g., mountains, sea, desert, etc. Using an advanced terrain-aware simulation software (HTZ from Advanced Topographic and Digital Imaging (ATDI), the coexistence problem is studied in these cases which represent some of the most common types of terrains. The goal is to quantify the level of interference in each of the case studies and compare it with the protection ratio in terms of either the carrier-to-interference-plus noise $C/(I+N)$ or interference-to-noise $(I/N)$ ratios, depending on the simulation parameters and following the standard approach used in such studies. These two measures help in providing the proper recommendations on the coexistence of the IMT and DTTB systems. This study is structured into two main parts. The first part focuses on studying the interference induced by IMT base station (BS) and IMT user equipment (UE) on the DTTB receiver in the sub−700 MHz band (from 614 to 694 MHz) which corresponds to the 5G N71 band, whereas the second part considers the interference induced by DTTB system on IMT BS and IMT UE. The investigation has revealed that in most of those situations, separation distances (exclusion zones) are found to be relatively within a reasonable range from the border compared with the size of the whole network. This indicates that IMT network could be deployed in many locations close to the DTTB network with tolerable effect. The most challenging case is the effect of DTTB system on the up-link (UL) of IMT network, which necessitates the use of additional mitigation techniques to reduce the interference.

For energy management and billing purposes, advanced metering infrastructure (AMI) requires periodic transmission of consumer power consumption readings by smart meters to the electric utility (EU). An efficient way for collecting readings is the change-and-transmit approach (CAT AMI) whereby readings are only transmitted if there is an enough change in consumption readings. CAT AMI, however, is plagued by malicious consumers who hack their smart meters to illegally lower their electricity bills by falsifying their readings. These attacks on the AMI could have bad economic consequences and impair the performance of the power grid if these readings are used for managing the grids. Machine learning models can be used to detect false readings but this requires disclosing consumers’ CAT readings to the EU to evalaute the model. However, disclosing the consumers’ readings jeopardizes consumers’ privacy due to the fact that these readings can reveal sensitive information about consumers’ lifestyles, e.g., their presence or absence, the appliances they use, etc. The problems of detecting power theft while protecting the consumers’ privacy in CAT AMI is investigated in this paper. First, a dataset of actual readings to generate a benign dataset is developped followed by proposing new cyber-attacks tailored for CAT AMI to generate malicious samples. Then, two deep-learning detectors using a baseline model (CNN) and a CNN-GRU model are trained to detect power thefts in CAT AMI. To preserve consumers’ privacy, the paper develops an approach to enable the EU to evaluate the detector using encrypted data without being able to learn the readings. Extensive experiments were carried out to assess our proposal, and the results indicate that our proposal is capable of accurately identifying malicious consumers with acceptable overhead while preserving the privacy of the consumers. Specifically, comparing to CNN model, our CNN-GRU model increases the detection rate from 93.85% to 97.14% and $HD$ from to 87.7% to 94.28%, respectively.

Automatic Target Recognition (ATR) using Correlation Pattern Recognition (CPR) in IoT-based applications encounters limitations like limited memory and inadequate computational resources. One reason is the required quantity of reference templates for each target/object to cover all features of a target/object. To mitigate the issue of reference templates per target/object without accuracy degradation, this paper proposes energy compaction-based CPR autoencoder-training. Additionally, a newly proposed performance metric known as Peak Energy Gain (PEG) estimates the quality of the correlation plane and the feature compression capability CPR methods. The proposed, composite filtering strategy, Eigen Maximum Average Correlation Height (EMACH), and Extended Eigen Maximum Average Correlation Height ( $E^{2}$ MACH) are vigorously validated using publicly available biometric and object image databases. By training a single reference template, the proposed training method achieves 97.97% mean accuracy with the second-best approach of $E^{2}$ MACH that attains 53.04% mean accuracy on the Pose Estimation Database. For bio-metric fingerprint verification, the mean Equal Error Rate (EER) of the proposed approach and the composite strategy is 3% and 29.69%, respectively on the FVC2002DB1A database. Similarly, the mean EER of the proposed approach and the composite strategy is 10.55% and 26.32%, respectively on the FVC2006IA database. For FEI faces dataset, the proposed method achieves 1.41% mean EER, and the composite filtering approach achieves 21.43% mean EER. On the University of Tehran Iris database, the proposed autoencoder-based methodology obtains 19.07%, and 18.07% mean EER on the left and right side iris instances, respectively. The comparative results for each dataset demonstrate superiority of AE-based method over the state-of-the-art CPR methods.

A multi-hop free space optical link based on all-optical relays is presented. All-optical relays are either composed of simple optical amplifiers or optical regenerators. It has been shown that after a certain number of hops, the amplifier based relay cannot extend the range of the free space optical link. Therefore, we have investigated the use of a self phase modulation based regenerator as a substitute for simple amplifier based relay. By employing the regenerative relay, a 10 Gbps optical signal having pulse width of 7 ps is transmitted over a distance of 8 km of free space optical link. The free space optical link is modeled on the basis of Gamma-Gamma channel model in the strong turbulence regime. The performance of the link is observed on the basis of bit error rate analysis. It has been shown that under strong turbulence and accumulation of amplifier noise, the range of the link can only be extended by employing an optical regenerator.

In the advanced metering infrastructure, smart meters are deployed at the consumers’ side to regularly transmit fine-grained electricity consumption readings to the system operator (SO) for billing and real-time load monitoring and energy management. However, fraudulent consumers may compromise their meters to launch electricity-theft cyberattacks by reporting low-consumption readings to reduce their bills. These false readings not only cause financial losses but also degrade the grid’s performance because they are used for energy management and load estimate. The existing solutions in the literature focus only on securing the billing, so they are not designed to detect the attacks in real time, and thus the SO may use false readings for a long period of time in load monitoring and energy management until they are identified. In this paper, we propose a general ensemble-based deep-learning detector that enables the SO to detect false readings in real time. To do that, we first train several deep learning models on samples generated from a sliding window of the readings. Then, we use the best-performing model to train several models on different ratios of false readings and use them in our ensemble-based detector. Extensive experiments are conducted, and the results indicate that comparing to the literature, our detector can detect the false readings after sending a few false readings (around 15) comparing to the existing daily and weekly detection approaches that need 144 and 1,008 readings, respectively.

Abstract A free space optics based ring topology that transmits full‐duplex data to four different nodes in the ring is proposed. The link is designed such that the four nodes do not require a local optical source to transmit the uplink data. A wavelength division multiplexed signal composed of four different wavelengths each modulated by the downlink baseband data using differential phase shift keying is transmitted towards the nodes. At each node, the downlink baseband data is extracted and the received optical pulses are remodulated by the uplink baseband data using on‐off keying modulation format. Each node has a data rate of 10 Gbps and is at a distance of 400 m from the consecutive node. The free space optical link is modelled on the basis of Gamma‐Gamma channel model under different turbulence conditions by considering the refractive index structure parameter values of 5×10−15, 5×10−13 and 5×10−12m−2/3. Bit error rate results are obtained for both the downlink and uplink channels. Finally, power budget analysis is presented to demonstrate the robustness of the link under different weather conditions.

In this research, we have suggested a combined strategy to calculate and determine the solutions for problems originating in combustion theory and heat transfer, that are known as Bratu differential equations. We aim to suggest and test a soft computing technique using an efficient meta-heuristic the Symbiotic Organism Search (SOS) algorithm and Artificial neural network (ANN) architecture to obtain better solutions for Bratu differential equations by utilizing fewer computational resources and minimal time. We have simulated our computing approach for different cases, and we compare the outcome of our experiments with solutions obtained by the existing state-of-the-art methods. For novelty, we have found an accurate critical value of λc by using SOS algorithm. Values of TIC, MAD, and NSE confirm that our method is a convenient and potential candidate for handling real-application problems. We found that this ANN-SOS algorithm takes less time and is accurate in getting results of the expected standard.

Lead halide perovskite solar cell shows an enormous potential for the next generation renewable energy, but it also offers some environmental hazards due to their inherent lead-based toxicity. On the other hand, if lead based perovskite is efficiently replaced by other non-toxic metal based perovskite then their toxicity can remarkably be reduced for photovoltaic applications. In this study, a novel lead free compound Cs2TiBr6 based photovoltaic device is proposed as Au/PEDOT:PSS/ Cs2TiBr6/TiO2/AZO, where each layer is optimized and analysed through a series of simulations for maximum power-conversion efficiency using SCAPS-1D software. Comprehensive optimization of the thickness and doping density of each layer leads to the maximum power-conversion efficiency up to 17.83% for our proposed solar cell. Therefore, we believe that these results will provide a route heading towards the progress of lead-free and highly efficient perovskite photovoltaic devices.

A cost efficient and reliable single mode fiber and free space optics based hybrid 32×5Gbps ultra dense wavelength division multiplexed-passive optical network architecture employing optical comb and polarization multiplexing is proposed. The proposed architecture is a step forward towards shifting the next generation wireless network traffic over existing passive optical network to achieve the optimum bandwidth, capacity, quality of service and cost efficiency for mobile subscribers. Self-restorable passive optical networks having fiber based feeder and distribution paths are widely researched in various studies in the past. However, to the best of our knowledge, a self-restorable passive optical network architecture employing free space optics based protection path has not been proposed so-far. The effect of varying polarization mode dispersion from 0.02 to 0.06ps.km-0.5 and the refractive index structure parameter of the Gamma-Gamma channel model from 5×10-16 to 5×10-12m-2/3 is evaluated. A techno-economic performance analysis of the architecture for downstream is performed by considering both the distribution and protection paths. It has been shown that the receiver sensitivity for a polarization mode dispersion of 0.06ps.km-0.5 is around -22dBm and for strong turbulence is around -20dBm.

Real application problems in physics, engineering, economics, and other disciplines are often modeled as differential equations. Classical numerical techniques are computationally expensive when we require solutions to our mathematical problems with no prior information. Hence, researchers are more interested in developing numerical methods that can obtain better solutions with fewer efforts and computational time. Heuristic algorithms are considered suitable candidates for such type of problems. In this research, we have developed a new neuroevolutionary algorithm that combines the power of feed- forward artificial neural networks (ANNs) and a modern metaheuristic, the Symbiotic Organism Search (SOS) algorithm. With our new approach, we have analyzed the simultaneous surface convection and radiation during heat transfer in different models of fins/heat exchangers. Longitudinal fins are considered with concave parabolic, rectangular and trapezoidal shapes. We have analyzed our problem in two scenarios and six sub-cases. Our solutions are of high quality, with minimum residual errors in all cases. We have established the quality of our results by calculating values of different performance indicators like Root-mean-square error (RMSE), Absolute error (AE), Generational distance (GD), Mean absolute deviation (MAD), Nash-Sutcliffe efficiency (NSE), Error in Nash-Sutcliffe efficiency (ENSE). Statistical and graphical analysis of our results suggests that our approach is suitable for handling real application problems. We have compared our results with state-of-the- art results, and the outcome of our analysis points to the superiority of our approach.

Distributed Source Coding (DSC) schemes rely on separate encoding but joint decoding of statistically dependent sources, which exhibit correlation. More specifically, Distributed Joint Source-Channel coding (DJSC) is associated with the scenario, where the correlated source signals are transmitted through a noisy channel. On one hand, employing DSC or DJSC schemes exploits the existing correlation between sensors resulting in minimising the transmission energy required by the sources, while maintaining reliable communication. On the other hand, Network Coding (NC) is an efficient data transport technique leveraging network efficiency, by allowing Relay Nodes (RNs) in a communication network to combine multiple data packets received via the incoming links before transmitting them to the Destination Node (DN). In this paper, the bandwidth-efficient Distributed Joint Turbo Trellis-Coded Modulation (DJTTCM) aided by both Dynamic Network Coding (DNC) and Adaptive DNC (ADNC)-based cooperative transmission schemes are proposed. Both systems are proposed for supporting correlated source transmissions over hostile channels experiencing both small-scale and large-scale fading in which the RNs dynamically transmit its non-binary linear combinations to the DN. A substantial gain of 19.5 dB was attained at a correlation coefficient of ρ = 0.8 over its counterpart dispensing with NC.

Most of the existing smart parking systems threaten the drivers' privacy by revealing information about their visited locations. Moreover, they are centralized making them vulnerable to a single point of failure and attack, which threatens the availability of the parking service. They also suffer from a lack of transparency, where the centralized service organizer may favor some parking lots by booking their parking slots first. To tackle these concerns, we propose a blockchain-based smart parking system with privacy preservation and reputation management. In our system, a consortium blockchain is created by different parking lots to run the parking system securely and transparently, where the parking offers are recorded on a shared and immutable ledger. We use a commitment technique during the submission of the offers to ensure fair parking rates. Then, we use a private information retrieval technique during the offers retrieval to preserve the drivers' location privacy. Furthermore, to anonymously and efficiently authenticate drivers during the reservation process, we use a short randomizable signature. We also use a time-locked anonymous payment technique to discourage drivers from not committing to their parking reservations and provide a secure and privacy-preserving payment method for parking service. Finally, we integrate a blockchain-based anonymous reputation management scheme into our system, where drivers can anonymously rate the parking service to ensure high quality of service. Our evaluations demonstrate that our smart parking system is secure and capable of preserving drivers' privacy with low communication, computation, and storage overheads.

In this article, a low-cost 16-port non-planar Multiple-Input-Multiple Output (MIMO) antenna system is proposed for future 5G applications. The non-planar MIMO antenna system is established around a 3D-octagonal-shape polystyrene block. The MIMO elements are arranged on the eight-sides of octagonal-shape block, whereas bottom and top faces of polystyrene block are left void. The single antenna element comprises of slotted microstrip patch with a stepped chamfered feed line and defected ground plane. Each MIMO element is designed on FR-4 substrate with a size of 22 mm × 20 mm, to cover the frequency band of 3.35 GHz to 3.65 GHz for the fifth-generation (5G) applications. The isolation between array elements is improved by using a meander-lines based near-zero-index epsilon-negative (NZI-ENG) metamaterial decoupling structure. The array elements are placed on the top-layer, whereas common connected ground plane and decoupling structure is placed on the bottom-layer. The metamaterial-based decoupling structure offers an isolation of more than 28 dB for antenna elements arranged in across and side-by-side configuration. Moreover, simulated and measured MIMO performance parameters i.e. Total Active Reflection Coefficient (TARC)

The flow of fluids in multi-phase porous media results due to many interesting natural phenomena. The counter-current water imbibition phenomena, that occur during oil extraction through a cylindrical well is an interesting problem in petroleum engineering. During the secondary oil recovery process, water is injected into a porous media having heterogenous and homogenous characteristics. Due to the difference in viscosities of fluids in oil wells, the counter-current imbibition phenomenon occurs. At that moment, the imbibition equation Vi=-Vn is satisfied by the viscosities of oil and water. In this article, we have analyzed the governing mathematical model of the imbibition phenomenon occurring during the secondary oil recovery process. A new soft computing algorithm is designed and adapted to analyze the mathematical model of dual-phase flow in detail. Weighted Legendre polynomials based artificial neural networks are hybridized with an efficient global optimizer the Whale Optimization Algorithm (WOA) and a local optimizer the Nelder-Mead algorithm. It is established, that our algorithm LeNN-WOA-NM is efficient and reliable in calculating high-quality solutions in less time. We have compared our experimental outcome with state-of-the-art results. The quality of our solutions is judged based on values of absolute errors, MAD, TIC, and ENSE. It is obvious that LeNN-WOA-NM algorithm can solve real application problems efficiently and accurately.

Because charging coordination is a solution for avoiding grid instability by prioritizing charging requests, electric vehicles may lie and send false data to illegally receive higher charging priorities. In this article, we first study the impact of such attacks on both the lying and honest electric vehicles. Our evaluations indicate that lying electric vehicles have a higher chance of charging, whereas honest electric vehicles may not be able to charge or may charge late. Then, an anomaly-based detector based on a deep neural network is devised to identify lying electric vehicles. The idea is that since each electric vehicle driver has a particular driving pattern, the data reported by the corresponding electric vehicle should follow this pattern, and any deviation due to reporting false data can be detected. To train the detector, we first create an honest dataset for the charging coordination application using real driving traces and information provided by an electric vehicle manufacturer, and we then propose a number of attacks as a basis for creating malicious data. We train and evaluate a gated recurrent unit model using this dataset. Our evaluations indicate that our detector can detect lying electric vehicles with high accuracy and a low false alarm rate even when tested on attacks that are not represented in the training dataset.

Full-duplex (FD) communication has been shown to provide an increased achievable rate, while millimeter wave (mmWave) communications benefit from a large available bandwidth that further improves the achievable rate. On the other hand, the concept of multi-set space-time shift keying (MS-STSK) has been proposed to provide a flexible design trade-off between throughput and performance. Hence, in this work, we consider the design of an FD-aided MS-STSK transceiver for millimeter wave communications. However, a major challenge is that channel reciprocity is not valid in mmWave communications due to shorter channel coherence time. Thus, the uplink (UL) pilots cannot be utilized to estimate the downlink (DL) channel. To overcome this challenge, we propose a beamforming technique based on channel statistics without assuming channel reciprocity. For this purpose, a closed-form expression for the outage probability of the system is derived by employing the characterization of the ratio of the Indefinite Quadratic Form (IQF). The derived analytical expression is then utilized to design optimum beamforming weights using the Sequential Quadratic Programming (SQP)-based heuristic method. Moreover, an Iterative Statistical Method (ISM) of joint transmit and receive beamforming algorithm is also developed by utilizing Principle Eigenvector (PE) and Generalized Rayleigh Quotient (G-RQ) optimization techniques. Finally, we verify our simulation results with the theoretical analysis.

COVID-19 tracing applications have been launched in several countries to track and control the spread of viruses. Such applications utilize Bluetooth Low Energy (BLE) transmissions, which are short range and can be used to determine infected and susceptible persons near an infected person. The COVID-19 risk estimation depends on an epidemic model for the virus behavior and Machine Learning (ML) model to classify the risk based on time series distance of the nodes that may be infected. The BLE technology enabled smartphones continuously transmit beacons and the distance is inferred from the received signal strength indicators (RSSI). The educational activities have shifted to online teaching modes due to the contagious nature of COVID-19. The government policy makers decide on education mode (online, hybrid, or physical) with little technological insight on actual risk estimates. In this study, we analyze BLE technology to debate the COVID-19 risks in university block and indoor class environments. We utilize a sigmoid based epidemic model with varying thresholds of distance to label contact data with high risk or low risk based on features such as contact duration. Further, we train multiple ML classifiers to classify a person into high risk or low risk based on labeled data of RSSI and distance. We analyze the accuracy of the ML classifiers in terms of F-score, receiver operating characteristic (ROC) curve, and confusion matrix. Lastly, we debate future research directions and limitations of this study. We complement the study with open source code so that it can be validated and further investigated.

Distributed source coding (DSC) schemes rely on separate encoding but joint decoding of statistically dependent sources, which exhibit correlation. DSC has numerous promising applications ranging from reduced-complexity handheld video communications to onboard hyperspectral image coding under computational limitations. The concept of separate encoding at the first sight compromises the attainable encoding performance. However, the DSC theory proves that independent encoding can in fact be designed as efficiently as joint encoding, as long as joint decoding is allowed. More specifically, distributed joint source-channel coding (DJSC) is associated with the scenario, where the correlated source signals are transmitted through a noisy channel. In this paper, we present a concise historic background of DSC concerning both its theory and its practical aspects. In addition, a series of turbo trellis-coded modulation (TTCM)-aided DJSC-based cooperative transmission schemes are proposed. DJSC scheme is conceived for the transmission of a pair of correlated sources to a destination node (DN). The first source sequence is TTCM encoded, and then, it is compressed before it is transmitted both over a Rayleigh fading channel, where the second source signal is assumed to be perfectly decoded side-information at the DN for the sake of improving the achievable decoding performance of the first source. The proposed scheme is capable of performing reliable communications for various levels of correlation near to the theoretical Slepian-Wolf/Shannon (SW/S) limit. Pursuing our objective of designing practical DJSC schemes, we further extended the above-mentioned arrangement to a more realistic cooperative communication system, where the pair of correlated sources are transmitted to a DN with the aid of a relay node (RN). Explicitly, the pair of correlated source sequences are TTCM encoded and compressed before transmission over a Rayleigh fading multiple access channel, where our proposed scheme is capable of operating within 0.55 dB from the SW/S limit for a correlation coefficient value of ρ = 0.8, and within 0.07 bits of the minimum SW compression rate. The RN transmits both users' signal with the aid of a powerful superposition modulation technique that judiciously allocates the transmit power between the two signals. The correlation is beneficially exploited at both the RN and the DN using our powerful iterative joint decoder, which is optimized using extrinsic information transfer characteristics charts.

Wireless networks are vulnerable to jamming attacks. Jamming in wireless communication becomes a major research problem due to ease in Unmanned Aerial Vehicle (UAV) launching and blocking of communication channels. Jamming is a subset of Denial of Service Attack (DoS) and an intentional interference where the malicious node disrupts the wireless communication by increasing the noise at the receiver node through transmission interference signal towards the target channel. In this work, the considered jammer is a UAV hovering around the target area to block the communication channel between two transceivers. We proposed a three-dimensional (3-D) UAV jamming localization scheme to track and detect the jammer position at each time step by employing a single boundary node observer. For this purpose, we developed two distributed Extended Kalman Filter (EKF) based schemes: (1) the Distributed EKF (DEKF) scheme using the information of the received power from the jammer at a single nearby boundary node only and (2) Distance Ratio aided Distributed EKF (DEKF-DR) based scheme utilizing an edge node in addition to a single boundary node. Extensive simulations are conducted in order to evaluate the performance of the proposed distributed algorithms for a 3-D trajectory and compared with that of the conventional Centralized EKF (EKF-Centr) based method (which is also modified for the 3-D scenario). The results show the clear supremacy of the proposed distributed algorithms with much lesser complexity in contrast to the conventional EKF-Centr technique.

Among various photovoltaic devices, the poly 3, 4-ethylenedioxythiophene:poly styrenesulfonate (PEDOT:PSS) and silicon nanowire (SiNW)-based hybrid solar cell is getting momentum for the next generation solar cell. Although, the power-conversion efficiency of the PEDOT:PSS–SiNW hybrid solar cell has already been reported above 13% by many researchers, it is still at a primitive stage and requires comprehensive research and developments. When SiNWs interact with conjugate polymer PEDOT:PSS, the various aspects of SiNW array are required to optimize for high efficiency hybrid solar cell. Therefore, the designing of silicon nanowire (SiNW) array is a crucial aspect for an efficient PEDOT:PSS–SiNW hybrid solar cell, where PEDOT:PSS plays a role as a conductor with an transparent optical window just-like as metal-semiconductor Schottky solar cell. This short review mainly focuses on the current research trends for the general, electrical, optical and photovoltaic design issues associated with SiNW array for PEDOT:PSS–SiNW hybrid solar cells. The foremost features including the morphology, surface traps, doping of SiNW, which limit the efficiency of the PEDOT:PSS–SiNW hybrid solar cell, will be addressed and reviewed. Finally, the SiNW design issues for boosting up the fill-factor, short-circuit current and open-circuit voltage will be highlighted and discussed.