Electricity theft is a major source of non-technical losses and poses a significant operational challenge for distribution system operators. While advanced metering infrastructure offers large-scale electricity consumption (EC) data, existing deep learning methods remain computationally expensive, prone to overfitting, and sensitive to class imbalance and high-dimensional inputs. This work proposes a computationally efficient surrogate-activated spiking neural network (SSNet) for electricity theft detection (ETD). SSNet employs leaky integrate-and-fire (LIF) neurons with surrogate gradients to capture temporal patterns in discrete EC measurements while substantially reducing computational overhead compared with conventional deep neural networks. To mitigate class imbalance, the proximity-weighted synthetic oversampling (ProWSyn) technique is integrated to generate informative minority-class samples. In addition, a set of discriminative time-series features is extracted using the time series feature extraction library (TSFEL) to address high-dimensionality and enhance the separability of theft and non-theft profiles. SSNet is evaluated against several deep learning baselines and demonstrates superior performance, achieving 96.64% accuracy, 99.34% area under the curve (AUC), and 93.28% Matthews Correlation Coefficient (MCC). Moreover, SSNet operations per second (OPs) comparison with the floating-point operations per second (FLOPs) of deep learning networks demonstrates the sparsity and energy efficiency of the proposed network. The results highlight the potential of spiking neural architectures as efficient and high-performance solutions for large-scale ETD.

Federated learning (FL) is a privacy‐preserving method for short‐term load forecasting in energy networks. However, current defense mechanisms against adversarial attacks often depend on supplementary machine learning frameworks, such as anomaly detection models or Byzantine‐robust aggregators. These frameworks add significant computational overhead, straining edge devices such as smart meters and IoT systems with limited processing power. To solve this issue, we propose a new defense‐free framework called federated random layer aggregation (FedRLA). By aggregating only one randomly chosen neural network layer per communication round, FedRLA limits adversarial influence to isolated layers. This reduces attack surfaces by 66% compared to full‐model aggregation (FedAvg). Using 8‐bit quantization, FedRLA cuts data transmission by 92.97% without accuracy loss (MAE: 0.08 kWh vs. FedAvg’s 0.076 kWh). Under four model poisoning attacks, it reduces forecasting errors by 19%–35% compared to FedAvg. FedRLA also uses 24% less CPU and 13% less memory than frameworks such as FedProx, while training 58% faster. It combines communication efficiency (0.195 MB/round), adversarial robustness (MAE ≤ 0.11 kWh under ϵ = 0.2 DP), and low resource consumption, offering a scalable solution for secure FL in resource‐constrained energy networks.

Federated Learning (FL) enables collaborative model training across distributed clients while preserving data privacy, yet its robustness under fine-grained data perturbations remains insufficiently characterised. Pixel-level manipulations, such as the One Pixel Attack, are known to degrade model performance in centralized settings, but their behaviour under federated aggregation is less understood. This paper investigates the optimisation-level impact of pixel-level perturbations in FL through a diagnostic framework termed Federated One Pixel Perturbation. By systematically varying perturbation intensity, the number of compromised clients, data heterogeneity, and aggregation rules, we examine how localised input disturbances propagate through aggregation and influence global training dynamics. To contextualise these effects, the proposed perturbation is compared with random pixel perturbation, label flipping, FGSM, and PGD baselines. Experiments on CIFAR-10, CIFAR-100, and FEMNIST reveal a consistent divergence between final accuracy and convergence behaviour. Under moderate pixel-level perturbations, FL can maintain relatively stable final accuracy while requiring substantially more communication rounds. In 100-client non-IID settings, update divergence, gradient variance, and cosine similarity further show that structured perturbations alter client-update dynamics even when accuracy changes remain limited. Adversarial training improves final accuracy, but does not fully restore stable update behaviour. Overall, structured pixel-level perturbations primarily disrupt optimisation dynamics before visible accuracy degradation occurs. The results reveal a threshold-like robustness boundary and highlight the limitation of accuracy-centric robustness evaluation in federated learning.

Human Activity Recognition (HAR) using radar signals has gained significant attention due to its non-intrusive nature and robustness in various environments. However, the impact of radar signal preprocessing techniques on the performance of deep learning (DL) models remains an active area of research. This study investigates how different radar domain representations affect HAR accuracy by evaluating four preprocessing methods: Time-Range (TR) maps generated via Range-Fast Fourier Transform (FFT), Range-Doppler (RD) maps obtained through sequential FFTs, and Time-Doppler (TD) features extracted using Short Time Fourier Transform (STFT) and Smoothed Pseudo Wigner Ville Distribution (SPWVD). We employ a baseline Convolutional Neural Network (CNN) and state-of-the-art Transfer Learning (TL) models to assess whether advanced preprocessing or increased model complexity yields greater performance gains. The results reveal that high-resolution TD analysis using SPWVD does not significantly enhance classification performance and incurs substantial computational overhead, limiting its real-time applicability. Conversely, the TR representation offers computational efficiency but struggles to classify complex activities with the baseline CNN accurately. RD and STFT methods provide a favorable balance between classification accuracy and computational efficiency. Notably, transitioning from the baseline CNN to TL models leads to substantial improvements in recognition accuracy: up to 29.36% for TR, 21.42% for RD, 16.66% for STFT, and 11.11% for SPWVD representations. Overall, our findings demonstrate that TL models, when combined with computationally efficient radar preprocessing techniques like RD or STFT, significantly improve recognition accuracy and generalize well across datasets, as confirmed by evaluation on two publicly available radar-based HAR datasets. Among these, the RD representation combined with VGG-19 yielded the best trade-off between accuracy and latency, achieving a total processing time of 0.91 s per sample for a 10 s activity duration, making it highly suitable for latency-sensitive HAR applications.

A significant proportion of the world's population speaks Urdu and Hindi, with many individuals being bilingual in both English and these languages. Still, no multilingual spoofing dataset exists to capture the conversational style of bilingual speakers who frequently code-switch while communicating. This paper presents a multilingual spoofed speech (MSS) dataset comprising 472,486 utterances from 154 speakers. We specifically considered bona fide utterances from Urdu and Hindi speakers, where language alternation occurs within a single audio. Spoofed samples are generated using voice conversion techniques to preserve the speaking accents and conversation styles of bilingual individuals. Further, we propose and evaluate an anti-spoofing framework called WavSpeech-AASIST, which incorporates self-supervised models (wav2vec and UniSpeech) into the AASIST network. Our comparative analysis underscores the significance of the MSS dataset and demonstrates the effectiveness of WavSpeech-AASIST for audio spoofing detection.

The advent of Federated Learning (FL) empowers IoT devices to collectively train a shared model without local data exposure. In order to address the issue of Non-IID that causes model performance degradation, the recently proposed federated codistillation framework has shown great potential. However, due to the system heterogeneity of devices, the federated codistillation framework still faces a synchronization barrier issue, resulting in a non-negligible waiting time with a fixed computation amount (epoch or batch size) assigned. In this paper, we propose Adaptive Computation Amount Allocation (ACAA) to accelerate federated codistillation. Specifically, we leverage a criterion, solution inexactness, to quantify the computation amount. We dynamically adjust the solution inexactness of devices based on their computing power and bandwidth to enable them nearly simultaneous completion of training, reducing synchronization waiting time without sacrificing the training performance. The minimum required computation amount is determined by the coefficient of the distillation term and the gradient dissimilarity bound of Non-IID. We theoretically analyze the convergence of ACAA. Extensive experiments show that, compared to benchmark algorithms, ACAA can accelerate training by up to 5×.

Predicting signal blockages in millimetre-wave and terahertz networks is essential for enabling proactive handover (PHO) and ensuring seamless connectivity. Existing approaches utilising deep learning, multi-modal vision and wireless sensing data primarily depend on centralised model training. Although these techniques are effective, they come with high communication costs, inefficient bandwidth usage, and latency issues, which restrict their real-time applicability. This paper proposes a Semantic-Aware Federated Blockage Prediction (SFBP) framework, leveraging the lightweight computer vision technique MobileNetV3 for edge-based semantic extraction, lowering communication and computation costs. Furthermore, we introduce a Similarity-Driven Federated Averaging (SD-FedAVG) mechanism to enhance the robustness of the model aggregation process, effectively mitigating the impact of noisy updates and adversarial attacks. Our proposed SFBP framework achieves 97.1% blockage prediction accuracy, closely matching centralised learning methods, while reducing communication costs by 88.75% compared to centralised learning and by 57.87% compared to FL without semantic extraction. Moreover, on-device inference reduces the latency by 23% compared to centralised learning and 18% compared to FL without semantic extraction, improving real-time decision-making for PHO. Additionally, the SD-FedAVG mechanism improves prediction accuracy under noisy conditions, directly impacting the PHO by reducing the handover failure rate by 7%.

Human Activity Recognition (HAR) using Channel State Information (CSI) enables energy-efficient and non-invasive healthcare monitoring. However, conventional HAR systems rely on centralised model training, which requires the sharing of raw data, leading to privacy risks, excessive bandwidth usage, and high communication latency that limit scalability. This paper proposesFedFusionQuant (FFQ), a federated learning (FL) framework that jointly performs feature fusion, adaptive aggregation, and quantisation-aware compression during training. A novelfederated distance (FedDist)mechanism dynamically adjusts parameter updates using neuron dissimilarity metrics, enhancing generalisation across heterogeneous clients. Meanwhile,quantisation-aware training (QAT)reduces model size and transmission cost while preserving accuracy. Extensive experiments on real CSI data from 30 participants demonstrate that FFQ improves multi-class HAR accuracy by4.29%and binary fall detection by5.55%compared to raw fusion models. Furthermore, model compression with QAT achieves a47% reduction in communication overheadwhile maintaining accuracy comparable to state-of-the-art methods.

Federated learning (FL) has gained prominence in energy forecasting applications. Despite its advantages, FL remains vulnerable to adversarial attacks that threaten the reliability of predictive models. This study introduces a stealth attack, Federated Communication Round Attack (Fed-CRA), which increases communication rounds without affecting forecasting accuracy. Increased communication rounds can delay decisionmaking, reducing system responsiveness and cost-effectiveness in dynamic energy forecasting scenarios. Experimental validation on two datasets demonstrated that Fed-CRA increased communication rounds by 574% (from 72 to 485) in the AEP dataset and by 237% (from 92 to 310) in the COMED dataset. This led to a corresponding rise in energy consumption by 573% (from 41.04 kWh to 276.35 kWh) and 237% (from 52.44 kWh to 176.65 kWh), respectively, while preserving forecasting accuracy. To counter this attack, we proposed Federated Incentivized Averaging (Fed-InA), a game theory-inspired framework that rewards honest clients and penalizes dishonest ones based on their contributions. Results showed that Fed-InA reduced the additional communication rounds caused by Fed-CRA by 85% in the AEP dataset and 70% in the COMED dataset, while maintaining forecasting performance. Fed-InA achieves resource efficiency comparable to Federated Averaging (FedAvg) and demonstrates robustness in handling non-IID data.

Millimeter wave (mmWave) bands come with a deployment challenge of signal degradation when an obstacle blocks the line of sight (LOS) link. This paper proposes an intra-cell proactive handover (PHO) framework utilizing links from an intelligent reflective surface (IRS) to assist a 60GHz mmWave transmitter. Empowered by vision-aided wireless communication (VAWC) the PHO is aiming to replace a blocked LOS link with an IRS-assisted link. Evaluation of IRS-assisted link performance during the HO scenario is conducted to establish a solid understanding of deployment requirements and limitations. Estimations of the received signal strength indicator (RSSI) were performed to compare IRS-assisted links in the blocked area and LOS links in the absence of a blockage event. Results showed that for an IRS to provide a comparable signal level to the original LOS link, beam focusing must be the operating mode. IRS-assisted PHO scenarios were evaluated based on a range of IRS elements (64, 100 and 1000) to compare between the two links. Signal drop was between 30 to 15 dBm depending on the number of IRS elements and user location. The gap in signal level was further reduced to 10–5 dBm by increasing the number of antenna elements in the uniform linear array (ULA) sector transmitting to the IRS. Finally, the results showed that a HO to a 30GHZ IRS-assisted link with 64 ULA antenna elements and 1000 IRS elements will perform comparably to the LOS signal strength.

The millimetre-wave (mmWave) based frequency-modulated continuous wave (FMCW) radar is a pioneering technique for non-invasive vital sign monitoring, specifically designed for integration within 5G/Beyond 5G (B5G) network environments. In this study, we focus on evaluating the accuracy of the FMCW radar in measuring heart rate (HR) and respiratory rate (RR) at various heights relative to the subject's chest. To harness the low-latency benefits of advanced wireless technologies, the mmWave radar operates in tandem with the real-time data-capture adapter (DCA1000) evaluation module, facilitating the high-speed transmission of vital sign data. We employed two signal-processing methodologies, fast fourier transform (FFT) and peak count, to analyze and validate the radar's precision and consistency against reference sensors. Our results underscore the effectiveness of the peak count method, which demonstrates superior accuracy, with mean absolute error (MAE) values of 5.33 breaths/min for RR and 4.03 beats/min for HR, along with root mean square error (RMSE) values of 5.30 breaths/min for RR and 4.30 beats/min for HR, consistent across all tested heights. The proposed system significantly enhances the reliability and responsiveness of next-generation healthcare systems, offering a robust solution for patient monitoring in various interactive and intelligent real-time settings.

This article introduces a new method to improve the dependability of millimeter-wave (mmWave) and terahertz (THz) network services in dynamic outdoor environments. In these settings, line-of-sight (LoS) connections are easily interrupted by moving obstacles like humans and vehicles. The proposed approach, coined as Radar-aided Dynamic blockage Recognition (RaDaR), leverages radar measurements and federated learning (FL) to train a dual-output neural network (NN) model capable of simultaneously predicting blockage status and time. This enables determining the optimal point for proactive handover (PHO) or beam switching, thereby reducing the latency introduced by 5G new radio procedures and ensuring high quality of experience (QoE). The framework employs radar sensors to monitor and track object movement, generating range-angle and range-velocity maps that are useful for scene analysis and predictions. Moreover, FL provides additional benefits such as privacy protection, scalability, and knowledge sharing. The framework is assessed using an extensive real-world dataset comprising mmWave channel information and radar data. The evaluation results show that RaDaR substantially enhances network reliability, achieving an average success rate of 94% for PHO compared to existing reactive HO procedures that lack proactive blockage prediction. Additionally, RaDaR maintains a superior QoE by ensuring sustained high throughput levels and minimising PHO latency.

The rapid evolution of drone communication systems necessitates the development of novel approaches for optimal beam management in millimetre wave (mmWave) 6G networks. Beamforming is used to improve signal quality and enhance the signal-to-noise ratio (SNR); however, the existing beam management performs an exhaustive search over the pre-defined codebook, resulting in higher latency due to training overhead that makes it impractical for high-mobility applications. Therefore, this paper introduces an innovative technique for mmWave beam prediction, considering practical visual and communication scenarios. The approach proposed in this study utilizes computer vision (CV) and ensemble learning via stacking, combining multi-modal vision sensing and positional data to achieve accurate estimations of drone positions and orientations. The developed framework first fine-tunes "you look only once" version 5 (YOLO-v5), a CV model to obtain the bounding box (location) of the drone from RGB images. This filtered vision sensing information and position data are used to train two different sets of neural networks, and the output of each model is stacked to train a meta-learner, used for the prediction of K-beams from a pre-defined codebook. The proposed method outperforms with the top-1 accuracy of approximately 90% compared to 86% and 60% for vision and position models, respectively. Furthermore, top-3 and top-5 accuracies are approximately 100%, resulting in a significant receive signal strength.

Federated Learning (FL) is susceptible to backdoor adversarial attacks during the training process, which poses a significant threat to the model's performance. Existing adversarial mitigation solutions mainly rely on the neural network (NN) model statistics and discard an entire client model if attacked. This approach is not feasible as it results in suboptimal performance. Hence, it is crucial to develop lightweight backdoor attack mitigation solutions that efficiently utilize clients' model statistics. To address this issue, we propose (Layer Based Anomaly Aware) LBAA-FedAVG, a modified version of the common aggregation mechanism FedAVG. Our proposed framework employs a clustering-based technique and addresses each NN layer individually. Depending on the type of adversarial attack, this method selectively eliminates one or multiple layers of the NN during the aggregation process. Furthermore, we focused on the model inversion attack and varied the percentage of compromised clients from 10% to 50%. Our experimental findings demonstrate that LBAA-FedAVG outperforms Federated Averaging (FedAVG) in reducing the negative effects of backdoor adversarial attacks. The complexity analysis suggests that the extra training time is the only additional resource limitation in LBAA-FedAVG, which is 19% greater than that of FedAVG. Additionally, we conducted experiments on short-term load forecasting using grid-level datasets to show the effectiveness of LBAA-FedAVG in lightweight backdoor attack mitigation in FL settings, offering a trade-off between time efficiency and enhanced defense.

Line of sight (LoS) links that use high frequencies are sensitive to blockages, making it challenging to scale future ultra-dense networks (UDN) that capitalise on millimetre wave (mmWave) and potentially terahertz (THz) networks. This paper embraces two novelties; Firstly, it combines machine learning (ML) and computer vision (CV) to enhance the reliability and latency of next-generation wireless networks through proactive identification of blockage scenarios and triggering proactive handover (PHO). Secondly, this study adopts federated learning (FL) to perform decentralised model training so that data privacy is protected, and channel resources are conserved. Our vision-aided PHO framework localises users using object detection and localisation (ODL) algorithm that feeds a multiple-output neural network (NN) model to predict possible blockages. This involves analysing images captured from the video cameras co-located with the base stations (BSs) in conjunction with wireless parameters to predict future blockages and subsequently trigger PHO. Simulation results show that our approach performs remarkably well in highly dynamic multi-user environments where vehicles move at different speeds, and achieves 93.6% successful PHO. Furthermore, the proposed framework outperforms the reactive-HO methods by a factor of 3.3 in terms of latency while maintaining a high quality of experience (QoE) for the users.

Recently, the use of unmanned aerial vehicles (UAVs) for wireless communications has attracted much research attention. However, most applications of UAVs for wireless communication provisioning are not feasible as researchers fail to consider some vital aspects of their deployment, especially the energy requirements of both the UAV and communication system. The considerable energy consumption overhead involved in flying or hovering UAVs makes them less appealing for green wireless communications. Therefore, in this work, we examine the feasibility of an alternative energy-efficient deployment scheme where UAVs can be made to land-on designated locations, also known as landing stations (LSs). The idea of LS makes the UAV-based wireless communication more durable and advantageous, since the total energy consumption is reduced by minimizing the flying/hovering energy consumption, which, in turn, enables diverse set of applications including emergency and pop-up networking. We evaluate the impact of the separation distance between these LSs and the Optimal Hovering Position (OHP) on the network performance. Specifically, we develop mathematical frameworks to model the relationship between UAV power consumption, coverage probability, throughput, and separation distance. Numerical results reveal that a significant energy reduction can be achieved when the LS concept is exploited with a slight compromise in coverage probability and throughput. However, the choice of a suitable LS location depends on the users’ service requirements, transmit power, and frequency band utilized.

Smart energy performance monitoring and optimisation at the supplier and consumer levels is essential to realising smart cities. In order to implement a more sustainable energy management plan, it is crucial to conduct a better energy forecast. The next-generation smart meters can also be used to measure, record, and report energy consumption data, which can be used to train machine learning (ML) models for predicting energy needs. However, sharing energy consumption information to perform centralised learning may compromise data privacy and make it vulnerable to misuse, in addition to incurring high transmission overhead on communication resources. This study addresses these issues by utilising federated learning (FL), an emerging technique that performs ML model training at the user/substation level, where data resides. We introduce FedraTrees, a new, lightweight FL framework that benefits from the outstanding features of ensemble learning. Furthermore, we developed a delta-based FL stopping algorithm to monitor FL training and stop it when it does not need to continue. The simulation results demonstrate that FedraTrees outperforms the most popular federated averaging (FedAvg) framework and the baseline Persistence model for providing accurate energy forecasting patterns while taking only 2% of the computation time and 13% of the communication rounds compared to FedAvg, saving considerable amounts of computation and communication resources.

The upsurge in wireless devices and real-time service demands force the move to a higher frequency spectrum. Millimetre-wave (mmWave) and terahertz (THz) bands combined with the beamforming technology offer significant performance enhancements for future wireless networks. Unfortunately, shrinking cell coverage and severe penetration loss experienced at higher spectrum render mobility management a critical issue in high-frequency wireless networks, especially optimizing beam blockages and frequent handover (HO). Mobility management challenges have become prevalent in city centres and urban areas. To address this, we propose a novel mechanism driven by exploiting wireless signals and on-road surveillance systems to intelligently predict possible blockages in advance and perform timely HO. This paper employs computer vision (CV) to determine obstacles and users’ location and speed. In addition,this study introduces a new HO event, called block event (BLK), defined by the presence of a blocking object and a user moving towards the blocked area. Moreover, the multivariate regression technique predicts the remaining time until the user reaches the blocked area, hence determining best HO decision. Compared to conventional wireless networks without blockage prediction, simulation results show that our BLK detection and proactive HO algorithm achieves 40% improvement in maintaining user connectivity and the required quality of experience (QoE).

Education for sustainable development (ESD) is one of the key UN strategies, which aims to encourage the development of competencies such as critical thinking and decision making. To ensure that students benefit from this education, there is general consensus that teaching methods need to focus on experiential learning. This may be challenging during a pandemic, or when experimental resources are limited. Therefore, the purpose of this paper is to showcase our approach in teaching solar energy systems design to higher education students using a game-based virtual reality approach. Our approach enables students to immerse themselves in a virtual environment, which is safe for both students and their teachers. The game consists of two levels, where students are invited to solve an energy-related task in two different homes. Based on user feedback, our interactive learning tool improved student awareness of solar energy systems and how they can be used to satisfy domestic energy demands.

The outbreak of the COVID-19 pandemic has caused unprecedented disruption to education and progressed remote teaching as a predominant model for delivering educational content. However, the online teaching and learning model has its challenges, such as the lack of technological tools to quantity the student attention and engagement with the learning content. This paper focuses on developing an e-learning framework for capturing and analysing the students’ attention during remote teaching sessions and subsequently profiling their learning behaviour leveraging eye-tracking data. Our proposed eye-tracking solution deploys a webcam to capture and track raw gaze points that grant the user the freedom of natural head movement and scalability compared to conventional eye-tracking approaches. We derived various gaze metrics in conjunction with state-of the-art machine learning (ML) models like logistic regression, support vector machine and polynomial regression to classify the student attention with an accuracy above 91%. Furthermore, our findings can help in the early detection and diagnosis of attention deficit hyperactivity disorder (ADHD) among students, thus supporting their learning journeys by creating an adaptive learning environment tailored to their needs.

The COVID-19 pandemic has accelerated our transition to an online and self-directed learning environment. In an effort to design better e-learning materials, we investigated the effectiveness of collecting psychophysiological eye-tracking data from participants in response to visual stimuli. In particular, we focused on collecting fixation data since this is closely related to human attention. Current wearable devices allow the measurement of visual data unobtrusively and in real-time, leading to new applications in wearable technology. Despite their accuracy, head-mounted eye trackers are too expensive for deployment on large-scale deployment. Therefore, we developed a low-cost, webcam-based eye tracking solution and compared its performance with a commercial head-mounted eye tracker. Four-minute lecture slides on the 3rdyear electronic engineering course were presented as stimuli to eight learners for data collection. Their eye movement was collected within the pre-defined area of interest (AOI). Our results demonstrate that a low-cost webcam-based eye-tracking solution, combined with machine learning algorithms, can achieve similar accuracy to the head-worn tracker. Based on these results, learners can use the eye tracker for attention guidance. Our work also demonstrates that these webcam-based eye trackers can be scaled up and used in large classrooms to provide real-time information to instructors regarding student attention and behaviour.

Abstract The health status of an older or vulnerable person can be determined by looking into the additive effects of aging as well as any associated diseases. This status can lead the person to a situation of ‘unstable incapacity’ for normal aging and is determined by the decrease in response to the environment and to specific pathologies with apparent decrease of independence in activities of daily living (ADL). In this paper, we use micro-Doppler images obtained using a frequency-modulated continuous wave radar (FMCW) operating at 5.8 GHz with 400 MHz bandwidth as the sensor to perform assessment of this health status. The core idea is to develop a generalized system where the data obtained for ADL can be portable across different environments and groups of subjects, and critical events such as falls in mature individuals can be detected. In this context, we have conducted comprehensive experimental campaigns at nine different locations including four laboratory environments and five elderly care homes. A total of 99 subjects participated in the experiments where 1453 micro-Doppler signatures were recorded for six activities. Different machine learning, deep learning algorithms and transfer learning technique were used to classify the ADL. The support vector machine (SVM), K-nearest neighbor (KNN) and convolutional neural network (CNN) provided adequate classification accuracies for particular scenarios; however, the autoencoder neural network outperformed the mentioned classifiers by providing classification accuracy of ~ 88%. The proposed system for fall detection in elderly people can be deployed in care centers and is application for any indoor settings with various age group of people. For future work, we would focus on monitoring multiple older adults, concurrently in indoor settings using continuous radar sensor data stream which is limitation of the present system.

The proliferation of mobile devices and big data has made it possible to understand the human movements and forecasts of precise and intelligent short and long-term data consumption of services like call, sms, or internet data which has interesting and promising applications in modern cellular networks. Human nature and moods are known to be synonymous with the physical attributes of mother nature such as temperature. The change in those physical features affects the human routines and activities such as cellular data consumptions. The future of telecommunication lies in the exploration of heap of information and data available to companies and inferring the valuable results through extensive analysis. In this paper, we analyze three main traits of cellular activity: sms, call, and internet. This paper investigates whether the relationship between the temperature and the cellular data consumption exits or not. This work introduces a novel approach to identify the strength of relationship between the temperature and cellular activity (sms, call, internet) and discuss the methods to quantify the relationship using correlation method. The real network CDR big data set - Milano Grid data set is used to analyze the behavior of the cellular activity with respect to temperature.

Cell densification is being perceived as the panacea for the imminent capacity crunch. However, high aggregated energy consumption and increased inter-cell interference (ICI) caused by densification, remain the two long-standing problems. We propose a novel network orchestration solution for simultaneously minimizing energy consumption and ICI in ultra-dense 5G networks. The proposed solution builds on a big data analysis of over 10 million CDRs from a real network that shows there exists strong spatio-temporal predictability in real network traffic patterns. Leveraging this, we develop a novel scheme to pro-actively schedule radio resources and small cell sleep cycles yielding substantial energy savings and reduced ICI, without compromising the users QoS. This scheme is derived by formulating a joint Energy Consumption and ICI minimization problem and solving it through a combination of linear binary integer programming, and progressive analysis based heuristic algorithm. Evaluations using: 1) a HetNet deployment designed for Milan city where big data analytics are used on real CDRs data from the Telecom Italia network to model traffic patterns, 2) NS-3 based Monte-Carlo simulations with synthetic Poisson traffic show that, compared to full frequency reuse and always on approach, in best case, the proposed scheme can reduce energy consumption in HetNets to 1/8th while providing same or better QoS.

Automatic detection of cells which are in outage has been identified as one of the key use cases for Self Organizing Networks (SON) for emerging and future generations of cellular systems. A special case of cell outage, referred to as Sleeping Cell (SC) remains particularly challenging to detect in state of the art SON because in this case cell goes into outage or may perform poorly without triggering an alarm for Operation and Maintenance (O&M) entity. Consequently, no SON compensation function can be launched unless SC situation is detected via drive tests or through complaints registered by the affected customers. In this paper, we present a novel solution to address this problem that makes use of minimization of drive test (MDT) measurements recently standardized by 3GPP and NGMN. To overcome the processing complexity challenge, the MDT measurements are projected to a low-dimensional space using multidimensional scaling method. Then we apply state of the art k-nearest neighbor and local outlier factor based anomaly detection models together with pre-processed MDT measurements to profile the network behaviour and to detect SC. Our numerical results show that our proposed solution can automate the SC detection process with 93% accuracy.