Grating lobes pose significant limitations to antenna array directivity and radiation efficiency, affecting the performance of wireless communication systems. This paper introduces a novel 2 × 2 phased microstrip patch antenna array integrated with a non-uniform metasurface acting as a Partially Reflective Surface (PRS) for grating lobe suppression. The non-uniform metasurface, composed of 11 × 11 unit cells, is placed above the array to form a Fabry–Perot Cavity (FPC), enabling constructive interference in the main beam direction and attenuation of undesired lobes. The non-uniform metasurface design, inspired by current-density tapering, offers a novel approach to grating-lobe problems. With an element spacing of 1.04 at 12.5 GHz, rigorous design optimization shows Grating Lobe Levels (GLLs) of -20.22 dB and peak gain increases from 10.3 dB to 19.1 dB, highlighting the design's effectiveness. This design offers a simple, low-profile solution for 5G base stations, radar, and satellite applications.

As real-time access and high-capacity requirements in wireless communication networks increase rapidly, solutions must balance the complex relationship between Spectral Efficiency (SE), and Energy Efficiency (EE) metrics. The proposed approach emphasizes the importance of combining Transmit Power Control (TPC), and Optimal Power Allocation (OPA) methods to achieve optimal results. The basic premise is that the hybrid algorithm will boost EE while retaining an acceptable level of SE. The CF mMIMO technology is first tested in a controlled setting without TPC and OPA. A hybrid algorithm combining TPC (Max-min EE) and OPA (Sum SE maximization) is then created, and EE and SE are optimized in the hybrid algorithm. The mixed technique is found to outperform the individual TPC and OPA algorithms. With an unparalleled 33,263,040.4068 bits/Joule, the hybrid algorithm boosts average EE. The hybrid algorithm also exceeds the targeted SE of 21 bits/s/Hz, demonstrating its capacity to balance EE and SE. This study advances the theory of CF mMIMO systems and offers practical insight into energy-efficient wireless communication. Future research and development for sustainable and high-performing wireless networks can build on these insights.

This study proposes a novel hybrid Firefly Algorithm, Genetic Algorithm, and Ant Colony Optimization Algorithm (FAGAACO) for spectrum allocation in TV White Space (TVWS) networks. The Genetic Algorithm (GA) was used in the design to provide cross-over chromosomes to both the Firefly Algorithm (FA) and the Ant Colony Optimization Algorithm (ACO), thereby improving the exploration abilities of FA and ACO and preventing FA and ACO from becoming trapped in local optimum. The proposed algorithm was implemented using MATLAB R2018a. Simulation results show that in comparison with a hybrid of the Firefly Algorithm and Genetic Algorithm (FAGA), the proposed algorithm achieved 13.03% higher throughput, 1.3% improved objective function value and 5.03% higher runtime due to the good accuracy of the proposed algorithm. Based on these improvements, the proposed algorithm is therefore an efficient spectrum allocation technique in TVWS networks.

Sensor nodes are devices deployed in target environments to sense and forward data to base stations for different applications. These form wireless sensor networks (WSNs) applied in agriculture, health systems, underground and underwater monitoring, and body area networks. The nodes are battery-powered and, in most applications, not rechargeable. Consequently, the nodes no longer participate in the network once the energy is depleted. The small size of these nodes poses a challenge of limited energy storage, thus making research on energy efficiency a critical area in wireless sensor networks. Clustering techniques are a popular strategy in energy management, and recent research has utilized fuzzy logic in cluster head selection. One-tier and two tier fuzzy systems used in cluster head selection are compared. Four input variables, residual energy, centrality, move speed, and pause time, determine the chance of a node being elected as cluster head. The one-tier feeds all four inputs to one fuzzy inference system. In contrast, the two-tier feeds the speed and time into the first fuzzy inference system, giving an output of the mobility factor, then feeds this as input in addition to the residual energy and centrality to the second fuzzy inference system. Performance is measured based on the network lifetime, energy consumption, and fuzzy system complexity. The one-tier system is found to have higher accuracy for four input variables, leading to lower energy consumption but at the expense of higher design complexity.

Wireless sensor networks deploy sensor nodes to different areas for data collection. The small size of these sensor nodes allows limited energy storage capacity, and most applications of the networks do not support recharging the batteries once their energy is depleted. Research on energy efficiency in wireless sensor networks is thus an active area that seeks to minimize energy consumption so that the sensor nodes can live longer. Clustering, one of the energy consumption optimization techniques, is employed in this research. It splits the network into smaller groups for data collection and forwards the data to the base station via appointed cluster heads. A fuzzy-based cluster head election strategy is proposed here to improve energy efficiency in wireless sensor networks. The input parameters of the fuzzy inference system are chosen as the residual energy, the node centrality, and the mobility factor. The system generates an output of the chance of a node being selected as a cluster head based on the combination of the values of the given inputs. The simulation results show that the proposed model reduces the network’s overall energy consumption and extends the sensor nodes’ lifetime.

The use of machine learning and neural networks in classification of images and recognition of objects has been applied to various datasets available in the public domain. Recognition of animals on camera trap images is an essential process in monitoring animals. In this paper, animal detection and recognition system is developed and applied to images from camera traps. A power control circuit is used to power the system during the day and shut it down during the night to save on energy consumption. The key information extracted from the images is the species of the animals and the count of each species. The species data and the GPS location coordinates of the camera trap is sent to The Things Network (TTN) via a LoRa transceiver. The system uses two models in series. The first stage use a Unet model to perform image segmentation and outputs a binary mask. The binary mask is then used to generate bounding boxes around the areas an animal is detected. The second stage uses a classifier model to classify each of the regions inside a bounding box. The classifier models used are the SqueezeNet, MobileNetV2 and NASNetMobile. The models are trained with images collected by Centre for Data Science and Artificial Intelligence (DSAIL) camera traps and other images sourced from Labeled Information Library of Alexandria: Biology and Conservation (LILA-BC). After deployment, a set of 145 images were analysed which resulted to a counting accuracy of 75.12%. The classification accuracies of the system containing SqueezeNet, MobileNetV2 and NASNetMobile were 32.68%, 55.61% and 62.93% respectively. The resolution of images collected by the system was much lower than the larger set of training images obtained from LILA-BC. This was a major cause of observed low accuracies. The real time data was accessible at The Things Network site. They whole monitoring system has demonstrated its usefulness in real-time wildlife conservation through animal recognition.

Collaborative Beamforming (CB) is an essential tool towards achieving long-range transmission in Wireless Sensor Networks (WSNs). In some instances, there may be multiple intended data destinations (sinks) in a WSN. This calls for multi-CB. In comparison to sink-by-sink CB, multi-CB implies improved data rates and decreased co-channel interference; and consequently increased network capacity. In current literature, there is no research in multi-CB particularly in 3-dimension WSNs. In this paper, a novel multi-CB mechanism is brought to the fore. This is from the point of view of a random arrangement of sensor nodes in a 3-dimension manner. It is assumed that all sinks' directions are known at the CB cluster head. Node transmit amplitude and phase are optimized using a Particle Swarm Optimization (PSO) algorithm variant to concurrently achieve balanced multiple narrow beams and minimal radiation in undesired directions. The performance of the proposed scheme is checked against that of a pure multiple beam steering approach (without beam power balancing and minimization of radiation in undesired directions). Moreover, an analysis of beam power, width and steering accuracy is done upon varying the number of collaborating nodes and the collaborating cluster radius. Increasing the count of collaborating nodes yields improved beam precision/accuracy, lower radiation in undesired directions and appreciable stability in beam power performance Increasing the collaborating nodes' cluster radius yields narrow beams, improved beam precision and appreciably lower radiation in undesired directions. The contributions of this work to current literature include: (i) formulation and analysis of a multiple beamforming scheme in the realm of 3-dimension WSNs; (ii) design of a multi-CB scheme taking into account minimization of radiation in undesired directions; (iii) a statistical multi-CB performance analysis upon varying collaborating nodes' cluster radius and collaborating node count.

Null steering is essential in collaborative beamforming (CB) in wireless sensor networks (WSNs) to ensure minimal radiation power and interference in the direction of unintended receivers. Current research in null steering in CB in WSNs is mainly from the perspective of planar arrangements of sensor nodes and sink(s). Furthermore, there is no research dedicated to the formation of multiple wide nulls during CB in 3-dimension WSNs. Wide nulls are ideal in scenarios featuring mobile unintended sink(s). A new multiple and wide null steering scheme applicable to CB in WSNs is presented in this work (from the perspective of a 3-dimensional random arrangement of static sensor nodes). It is assumed that desired nulling directions are implicitly known at a CB cluster head. A particle swarm optimization (PSO) algorithm variant is applied in concurrent node transmit amplitude and phase perturbation with an aim of achieving beam steering alongside multiple and wide null steering. The performance of the proposed null steering scheme is validated against a basic null steering approach (with reference to current literature). Furthermore, a comparative null depth, width, and nulling accuracy analysis are done upon varying the count of collaborating nodes and the collaborating cluster radius. An increase in the number of collaborating nodes is found to increase nulling depth at an exponentially decaying rate. An increase in the collaborating nodes’ cluster radius yields a reduction in null width. The contributions of this work to the existing literature are as follows: (i) the design and investigation of a null steering scheme from the perspective of a 3-dimension random arrangement of sensor nodes; (ii) the design of a concurrent beam steering and multiple wide null steering scheme on the basis of concurrent node transmit amplitude and phase perturbation whilst ensuring null depth uniformity; (iii) a statistical analysis of the impact of a count of collaborating nodes and collaborating cluster radius on nulling performance; (iv) investigation of capacity improvement at unintended receivers upon null steering.

Currently, it is evident that the use of wireless systems is growing at an unprecedented rate more so in relation to Internet of Things (IoT). Because of this fact, there is need for accelerated studies on these systems to improve on the quality of service (QoS) provided to the users of these systems. In the process of wireless signal prediction modelling, a number of methods have been used that include deterministic and empirical models among others. This study is aimed at explaining the use Adaptive Neural Fuzzy Inference System (ANFIS) in wireless signal propagation modelling. It gives details on step-by-step use of ANFIS in the modelling process. It also shows how the improved ANFIS, that is, the six layered LOG10D-ANFIS is used to increase its performance for a single input. The root mean square error (RMSE), mean error (ME) and standard deviation (SD) of the predicted signal were used in the process of training where the LOG10D-ANFIS model had very low values in the range of 10−6 to 10−8 for the RMSE. The data used in this research is based on the one slope model (OSM) and multiwall model (MWM) though all the other theoretical models' data showed similar results.

A typical outcome of Collaborative Beamforming (CB) in Wireless Sensor Networks (WSNs) is the presence of relatively high radiation in undesired directions, an aspect attributed to the usual random arrangement of collaborating sensor nodes.High radiation in undesired directions and prominent sidelobes are bound to result in interference in adjacent co-channel networks.Research towards suppression of radiation in undesired directions in CB is active with a number of proposals already in place.Most of the proposals are in the domain/perspective of 2-dimension WSN configuration with a focus on suppressing the highest-leveled (peak) sidelobe only.Commonly, peak sidelobe suppression is achieved through nodes' transmission amplitude perturbation after a conventional phase steering based beamsteering procedure.In this paper, concurrent amplitude and phase perturbation at collaborating nodes has been utilized towards achieving concurrent beamsteering and suppression of radiation in an elaborate set of undesired directions.A variant of the Particle Swarm Optimization (PSO) algorithm has been applied in the node transmit amplitude and phase perturbation process.Selection of radiation suppression directions is done uniformly from the set of all possible undesired radiation directions.A WSN featuring planar node arrangement with the sink at an elevated plane has been used as the analysis platform.The proposed scheme outperforms the peak sidelobe suppression approach in terms of observed radiation in undesired directions and average sidelobe levels.It has also been established that increasing the number of collaborating nodes and/or the number of selected undesired radiation directions in the proposed CB scheme leads to undesired radiation performance improvement although at an exponentially decaying rate.

This paper presents a High Gain, enhanced Bandwidth Patch antenna for 5G operations. The dual-band is achieved using an inset-fed feeding technique for the microstrip patch antenna, which operates at the 28/38GHz millimeter-wave band. The high gain of the patch is achieved by inserting two rectangular slots on the radiating element of the patch. The designed antenna Bandwidth is improved by incorporating three steps at the edge of the rectangular patch. The substrate used for the format is Rogers RT Duroid 5880, with a thickness of 0.508mm, loss tangent of 0.0009, and a relative permittivity constant of 2.2. Ansys HFSS software is used for the simulation. The design attained a maximum gain of 8.2dB and 7.8dB at 27.84GHz and 39.32GHz. The impedance bandwidth response of 1.46 and 4.27GHz at the respective resonating frequencies below the -10dB line of the parameters are achieved. A compact antenna is proposed with a size of 3.2x4.9x0.508 and has a high Gain with a wide Bandwidth at both bands. The proposed antenna has achieved a good performance within the operating bands, making it suitable for 5G applications.

Intelligent Transport System (ITS) has emerged as the most probable technology for improved transport experience more so in environments with high vehicular density. Effective vehicular communication is however hindered by spectrum scarcity due to the already crowded licensed spectrum. This has led to the emergence of Cognitive Radio (CR) systems as a solution to the spectrum scarcity problem. A crucial component in CR is spectrum sensing. Various spectrum sensing techniques including Cyclostationary, Matched Filter, and Energy detection have been proposed and applied with varied outcomes. In this paper, the above mentioned detection techniques are discussed and an improved energy detection based cooperative spectrum sensing scheme is proposed for improved communication in vehicular ad hoc networks (VANET). The proposed scheme showed an improvement in the performance of a network which in turn could lead to more efficient utilization of spectrum.

Collaborative Beamforming (CBF) is an essential tool towards increasing transmission range in Wireless Sensor Networks (WSNs). Owing to the random and complex nature of WSNs, development and use of improved metaheuristic algorithms in CBF is of essence. Particle Swarm Optimization (PSO) algorithm is a good candidate for use in CBF owing to its simplicity and low computation complexity. However, the basic PSO algorithm suffers from premature convergence particularly in highly multimodal functions (typical of CBF). This paper delves into the development and application of an improved Particle Swarm Optimization (PSO) algorithm in CBF. A new fuzzy-logic based confidence and inertia weight parameters adaptation scheme has been developed with an aim of enhancing exploration and exploitation capabilities of the PSO algorithm. Normalized particle quality and iteration count have been used as the inputs to the designed fuzzy-logic inference system. The fuzzy logic based parameters adaptation scheme has been implemented in the form of a lookup table to minimize "on-line" computation complexity. Furthermore, a particle culling/ re-initialization procedure is utilized at half the number of maximum iterations to enhance overall swarm diversity. The modified PSO algorithm has been christened Culled Fuzzy Adaptive Particle Swarm Optimization (CFAPSO) algorithm. The developed CFAPSO algorithm is noted to outperform other metaheuristic algorithms in a statistical performance analysis procedure (on the basis of a set of standard unimodal and multimodal functions). Upon application to CBF, the CFAPSO algorithm is found to generate a beamsteering outcome statistically identical to that of conventional beamsteering.

Monitoring of wild animals has taken different approaches with an aim to provide vital information used in animal protection in their natural habitats. To recognize animal species without human trackers requires machine learning models that extract specie's features from an image. This project proposes a method of counting animals in an image and specifying the species of each animal using Unet and a variant of the SqueezeNet model. To train the Unet model, images and corresponding masks are used as the training data. Different optimizers are applied to each model. During inference, Unet outputs a binary mask with ones where an animal is detected and zeros elsewhere. SqueezeNet model is trained with images corresponding to six classes: bushbuck, impala, llama, warthog, waterbuck, and zebra. Three variants of the SqueezeNet model have been trained. The first contains the original backbone while the other two have the original backbone with an additional fire module. In one model the Fire module is similar to the Fire modules of the original backbone while in the other model, the extra fire module contains batch normalization layers. The trained models show that Unet trained with Nadam optimizer achieves the highest dice coefficient while the SqueezeNet with an extra Fire module containing batch norm layers and RMSprop optimizer achieves the highest training accuracy. The combined system containing the two models takes an image and outputs the image with bounding boxes around each animal and the corresponding animal species. The system achieves both counting and recognition of the species for each image placed at the input.

Collaborative beamforming (CBF) has been proposed as a viable tool towards increasing node transmission range in Wireless Sensor Networks (WSNs). This is highly essential in scenarios featuring highly elevated airborne sinks (Unmanned Aerial Vehicles (UAVs)) where multi-hop transmission is inconceivable. CBF yields radiation in unintended directions (sidelobes). A high sidelobe is bound to lead to interference at an UAV in the sidelobe’s direction (taking into consideration a case of multiple and mobile UAV sinks). A direct consequence of increased interference is reduction in communication capacity at the affected UAV. This paper presents a generalized sidelobe minimization scheme using Gravitational Search Algorithm (GSA). The effect of sidelobe minimization on the communication capacity at unintended UAVs is analyzed. Appreciable capacity increase is observed in comparison to a scheme featuring peak sidelobe minimization only.

Device-to-Device (D2D) communication is one of the revolutionary technologies in the 5G mobile communication systems, it can provide high connection speed and massive device connectivity. However, the inclusion of D2D Users (DUs) into the cellular network interferes with (Cellular Users) CUs, thus hindering CU’s communication. Therefore, efficient resource allocation techniques for D2D communication need to be formulated to enhance system capacity while limiting interference experienced by CUs. In this article, resource allocation scheme for D2D communications underlay network is proposed. First, a resource allocation optimization problem is formulated to maximize the system sum rate, which is modelled into a maximum weight bipartite problem and solved using the Hungarian method. Then, a fair resource allocation optimization problem is formulated into a max min utility problem, which is solved using the bisection method together with the Hungarian method. The max sum algorithm leads to a higher sum rate compared to max min algorithm. However, the max min algorithm enhances fairness among DUs compared to max sum algorithm, by maximizing the minimum achievable rate of the DUs and ensuring uniformity in data rates among the D2D users.

One of the viable solutions for effective spectrum management is cognitive radio. Single sensing systems are prone to interference; thus, the use of cooperative spectrum sensing. This paper aims to determine the required number of cognitive radios that would optimize the performance of a communication network in terms of energy utilization and bandwidth requirement. The cognitive sensing technique used was energy detection due to its reduced energy, computational, and communication resources requirement. The channel noise variance was set to −25 dB. Spectrum sensing was carried out at a frequency of 936 MHz and bandwidth of 200 kHz. Machine learning was first used to enhance the specificity of detection to minimize interference. Genetic Algorithm (GA) and Simulated Annealing (SA) were used to optimize the number of cognitive radios putting into consideration all constraints in the network. Genetic Algorithm gave a better result of two optimization techniques used. It gave an overall reduction of 40.74

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