In this paper, we introduce a framework for multi-class novelty detection using structural vibration signals. Structural vibration-based person identification is a promising soft-biometric approach with potential applications in elderly care and access control. However, current research faces two key challenges. The first challenge is the lack of large-scale datasets necessary for thorough evaluation in structural vibration gait recognition. To address this, we created a new dataset with recordings from fifty individuals. The second challenge lies in the limited exploration of deep learning methods for large-scale multi-class novelty detection in structural vibration data. To fill this gap, we propose the energy-shifted contrastive loss function, specifically designed for this task. Our results demonstrate that the proposed framework achieves 96.57% accuracy in multi-class classification. For novelty detection, it achieves an Receiver Operating Characteristic-Area Under the Curve (ROC-AUC) score of 89.15% for single footsteps, which improves to 93.83% with five consecutive footsteps.

Structural vibration-based gait recognition has emerged as a promising soft-biometric modality, particularly for privacy-sensitive monitoring and access control. Despite its potential, current research is largely limited to proof-of-concept studies that rely on hand-crafted features, with minimal exploration of deep learning methodologies. This gap reduces the potential for integrating structural vibration-based gait recognition with existing modalities, such as camera-based systems. In this study, we propose a multi-modal gait recognition system that integrates both vision and structural vibration modalities. We address two key challenges: (a) lack of studies exploring outdoor gait recognition using both vision and structural vibration, and (b) absence of a multi-modal training scheme that combines these two modalities. To tackle the first challenge, we curated a dataset comprising five minutes of walking data from ten individuals captured simultaneously by two cameras and a geophone sensor. To address the second challenge, we developed a joint training framework that uses data from both modalities. Our methods achieve an accuracy of 96.03% (±1.12) using structural vibration signals alone, and this improves to 98.27% (±0.06) when both modalities are combined.

Scheduling precedence-constrained workflows on heterogeneous multiprocessor systems is pivotal for throughput, responsiveness, and energy efficiency. However, many existing approaches do not yield deadline-feasible schedules that jointly optimize makespan and energy during mapping with predictable runtime, often favoring a single metric or deferring energy decisions to a post-mapping stage. To address this, we introduce Beam Exploration for Makespan and Energy Optimization Scheduling (BEMOS), a bounded-width beam exploration that scores partial schedules by current makespan, an energy-aware proxy, and communication cost; uses per-task finish-time caps to guide mapping and applies a lightweight DVFS pass, achieving consistent gains over state-of-the-art methods, cutting energy by$\approx 5 \times-8 \times$while finishing from equal to within$\approx 17 \%$of the fastest under tight deadlines, maintaining$\approx 1.9 \times-4.6 \times$energy reduction with completion times from equal to within$\approx 15 \%$of the fastest at moderate deadlines, and sustaining$\approx 1.9 \times-13 \times$lower EDP as problem size grows.

Gait recognition based on structural vibration signals is an emerging area in soft biometrics and healthcare. It is valued for its privacy-preserving features, making it ideal for continuous monitoring in healthcare environments. In this work, we propose a method for simultaneous person identification and gait-abnormality detection using structural vibration signals. We have experimented on a dataset of eight people. The system uses shared feature extraction layers with separate branches for identification and abnormality detection. Our framework achieves a person identification accuracy of ~89.00% and ~90.00% accuracy in detecting abnormality in gait patterns.

Battery swapping is becoming a feasible method to tackle charging issues in the expanding Electric Vehicle (EV) markets, especially in areas such as India, where two-wheelers are prevalent and three-wheelers depend on prompt service periods. In contrast to conventional methods that focus on cost efficiency and throughput, our research emphasizes Quality of Service (QoS) and sustainability through enhanced battery stock management. We represent a Battery Swapping Station (BSS) as a tandem queuing process, where congestion primarily arises during battery recharge, resulting in delays that affect swapping operations. Such delays may result in service disruptions and premature client withdrawals. To support various BSS designs, we utilize MATLAB's SimEvents toolbox to develop a customized, drag-and-drop modeling environment based on queueing theory. We make the performance measurements more realistic by integrating reneging phenomena into a Discrete Event Simulation framework. Our method implements the Q-learning algorithm with strategic reward engineering to meet a wide range of optimization goals in a variety of demand situations. This approach optimizes the usage of a limited battery stock and exhibits resilience despite limited historical data. The results indicate massive improvement in idle stock of swappable batteries (SB), enhancing stock usage and sustainability while preserving low reneging rates and attaining increased throughput with limited battery stock.

Efficient content distribution to end-users poses a significant challenge in content distribution networks (CDNs). Traditional CDNs rely on cloud-based architectures, which may not be optimal for delivering content in densely populated areas due to increased network latency and bandwidth limitations. These problems can be mitigated by adopting fog/edge computing, which deploys computing nodes near end-users to reduce latency and improve content delivery. Although factors such as deployment and distribution are often considered separately, there are relatively few studies on how node deployment affects content distribution. Moreover, current research on fog-based content distribution networks (fog-based CDN) does not often address formal methods for key challenges such as (R1) optimal fog node placement; (R2) providing efficient content distribution to end-users; (R3) minimizing the overall fog-based CDN cost. Therefore, we propose an algorithm to jointly optimize the placement of fog nodes and the content distribution, called the Joint Optimization Algorithm for the Fog-node Placement and Content distribution (JFnP-CDA). The algorithm uses a two-step procedure including clustering and Voronoi method, and nonlinear programming to optimize R1, R2, and R3. We consider four parameters for this: a given geographical region, locations of open public Wi-Fi access points (OPWAPs) in that region, quality of service (QoS, with delivery in delay as the measure), and cost to generate optimal service sub-regions (and, thereby, distribute content to edge-network hot spots). We evaluated the effectiveness of the proposal by implementing real-world OPWAP data, and the results show that the algorithm JFnP-CDA outperforms the baseline methods.

Received signal strength (RSS)-based localization has been popular as it can achieve reasonable accuracy without the need for additional hardware. In this work, we consider RSS-based cooperative and non-cooperative localization when the transmit power of the nodes is unknown. The maximum likelihood (ML) formulation of the RSS-based localization is non-linear, non-convex, and discontinuous and cannot be solved using conventional techniques. Firstly, we linearize the relation between pairwise distance between nodes and received power using a least squares-based linearization technique. Next, we propose two RSS-based localization techniques, referred to as SDP-URSS and SDP-RSS, that convert the ML into a constrained convex optimization problem using the proposed linearization technique and semidefinite relaxation. SDP-URSS assumes the transmit powers are unknown and estimates them along with the location of the nodes, whereas SDP-RSS uses the transmit power information to improve the location estimates. Extensive performance evaluation of the proposed technique considering various performance metrics under non-cooperative and cooperative scenarios demonstrates the superiority of the proposed localization techniques over the existing methods.

In biometrics, footstep-based seismic signals for person identification have become increasingly popular. Person identification is a crucial aspect because it serves as the foundation for personalized services like elderly monitoring, tailored access control, etc. However, these systems may not have enough data for specific individuals, especially those with limited mobility. This lack of data can be a significant challenge, making achieving accurate and reliable results difficult. We used Generative Adversarial Networks (GAN) to develop a novel technique for generating a footstep-based seismic signal of a person. In this paper, we have addressed two challenges. The first one involves generating synthetic footstep signals that closely mimic the significant statistical properties of an individual's footstep signal. The second challenge is filtering the generated data to achieve high classification accuracy. To overcome the first challenge, we have developed a Wasserstein distance-based One-Dimensional Generative Adversarial Network(1DGAN) with gradient penalty, to generate synthetic footstep data of a person. To address the second challenge we formulated a screening process based on statistical error metrics. Our experimentation on ten people achieved an overall test identification accuracy of 98.44%. Furthermore, it achieved an average identification accuracy of 91.23% from just ten footsteps when tested with unseen real data

Received signal strength (RSS)-based localization techniques have attracted a lot of interest as they are easy to implement and do not require any localization-specific hardware. However, maximum likelihood (ML) formulation of RSS-based localization problem is non-convex, non-linear, and discontinuous, and cannot be solved using standard optimization techniques. We propose techniques that converts the ML objective function into an invex (invariant convex) function and solve them using gradient descent. We also employ coordinate descent to solve the invex problem in a completely distributed manner without any synchronization requirements. The coordinate descent-based technique can be implemented on the sensor nodes as it has low computational complexity and scales very well to large networks. We prove the convergence theoretically, derive the convergence rate, and provide a detailed computational complexity and communication overhead analysis of the techniques. We perform extensive performance analysis and compare our techniques with centralized and distributed localization methods, and demonstrate the superior performance of the proposed techniques in terms of convergence rate, localization accuracy, and execution time.

System-on-chip (SoC) has migrated from single core to multi core architectures to adapt the expanding intricacy of real time applications. Network-on-chip (NoC) is appeared as an alternative to deal with the communication issues in embedded system-on-chip architectures. In network-on-chip (NoC) design, application mapping plays a significant role. In this research paper, a modified 2-D mesh NoC architecture is introduced and proposed an effective mapping algorithm, which maps the cores in the modified NoC architecture based on a core efficient region (CER) to enhance the processor performance and reduces the communication energy. The outcomes of the simulation illustrate that the proposed strategy is outperformed comparing with the other mapping techniques in terms of communication energy and performance. Moreover, the proposed algorithm is relevant to both random and distributed core graphs.

With the large number of processors in the chip, the design of a well-organized communication framework is crucial to satisfy the energy and bandwidth of multi-core systems. Network-on-Chip (NoC) has become the standard communication outline to replace the bus networks. Wireless NoC is becoming well known to be an auspicious upcoming on-chip communication framework because of low latency and high bandwidth provided by this emerging technology. Mapping vertices on various cores of the network is a critical segment in Wireless NoC because it decides the communication energy and latency. To diminish the communication energy of application core graph on multi-processors architecture, we propose an efficient application core mapping algorithm for Wireless NoC, that maps the application cores on Wireless NoC platform based on preliminaries. Which has three key steps: finding the efficient mapping region, selecting the first vertex to be mapped and choosing the suitable core on the Wireless NoC platform. Our empirical evaluation shows that, the proposed efficient algorithm averagely reduces packet latency 12%, 18% and 25%, and communication energy 17%,23%,28% over the RRM [14], DAMA [13] and MCDM [11].

Network-on-Chips (NoCs) are suitable communication framework for on-chip multiprocessors. NoC performance parameters, such as execution time and energy consumption, affect overall processor performance. The execution time of NoC simulator mapping applications increases with the enhancement of the NoC size. To provide efficient mapping for performance improvement. In this paper, focus on an efficient mapping algorithm and applied machine learning techniques to predict the execution time and energy consumption of the mapped NoC. The experimental outcomes exhibit the proposed mapping algorithm can achieves approximately 80% and 75% accuracy for execution time and energy consumption prediction, respectively. This type of performance prediction can be constructive for ongoing processors.

In this article, we present a person identification system for Internet-of-Things-based smart home applications that utilizes footstep induced structural vibrations as biometric modality. Footfall events are generated by the rhythmic contact of the heel and toe on the floor while walking. In the case of such biometric systems, there is no disturbance of the natural movement of the individuals and, thus, they provide an advantage over the existing systems that deal with human intervention. Another key advantage is that footfall signals are not subjected to spoofing attacks as they are nearly impossible to mimic unlike other biometric traits (fingerprint, face, and voice). We propose a 3-layer computing architecture, for the decentralized implementation of the biometric system. We also propose a basis pursuit-based data compression technique (DS8BP) to reduce power and bandwidth for the wireless transmission of footfall events. DS8BP achieves a compression ratio of 108 and increases the scalability of the system. We performed extensive experimentation to evaluate the proposed biometric system using indigenous databases containing 100,000+ footfall events of eight individuals collected in three types of surfaces (concrete tile floor, carpet floor, and wooden floor). The proposed system achieves a prediction accuracy of 93%, 98%, and 96% in three surface types when features from seven footsteps are considered.

The probability of error of a new two‐dimensional (2D) code, one‐coincidence frequency hopping code/quadratic congruence code, atmospheric optical code division multiple access (OCDMA) system is evaluated with single hard limiter and double hard limiter in presence of weak, moderate and strong turbulence. In the atmospheric channel, weak turbulence is characterised by lognormal probability density function (pdf) and moderate and strong turbulence by gamma‐gamma pdf. Further, aperture averaging and spatial diversity techniques are used to improve the error probability ( ) of double hard limited 2D atmospheric OCDMA system. The increment in the receiver aperture diameter from 2 to 10 cm improves performance in presence of weak, moderate and strong turbulence. This improvement in performance is due to aperture averaging. In addition, spatial diversity also improves performance in presence of turbulence and different weather conditions, i.e. light fog, light mist, haze and clear air. For an eight user system, is achievable with light mist, haze and clear air in presence of weak, moderate and strong turbulence. With light fog, eight users can communicate (can achieve ) in presence of weak turbulence and not in moderate and strong turbulence.

Geotagging of sensor data in a wireless sensor network is important in many applications. It may not always be possible to record locations of the sensor nodes during deployment. Localization techniques provide the node location information, however most of these techniques rely on the neighboring nodes for localization. Thus it is essential that the information from neighbors be trustworthy and/or the localization techniques be robust to some of the nodes turning malicious. In this paper, we address the scenario where the malicious node(s) attempt to disrupt the localization process of a target node in an uncoordinated manner. We propose a secure localization technique, called the weighted least square (WLS) localization, in a network with one or more compromised anchor nodes (nodes with known positions are referred to as anchor nodes). The WLS technique assigns larger weights to the anchor nodes that are closer to the target node and is shown to offer significant advantages over existing techniques. The Cramer-Rao lower bound (CRLB) on the root mean square error (RMSE) of the position estimate for the uncoordinated attack is also derived. The proposed technique is shown to provide better localization accuracy than existing algorithms.

Social Internet of Things (SIoT), an amalgamation of Social Networking concepts to the Internet of Things (IoT), is a strong architectural alternative for IoT solutions. A lot of research work in SIoT has proposed the use of social networking data for community and trust management in SIoT networks. While it seems like an interesting choice, it is important to analyze the effectiveness of social networking data for application to SIoT. In this paper, we analyze the accuracy of using tie information from the Facebook Friend Graph to mimic real-world SIoT network ties. We also discuss a method for ranking the strength of ties in a SIoT network by analyzing the structure of the Facebook Friend Graph. A similar analysis can be performed on data available from other Social Networking platforms, like Twitter, LinkedIn etc.

Economical, low-power, and easy to deploy indoor localization schemes have always been a challenge. We propose a technique that utilizes the analog output of pyroelectric (or passive) infrared (PIR) sensors for indoor localization. We propose two models for distance estimation based on the characterization of the analog sensor output in terms of the peak to peak value and its relationship with distance from the sensor. The proposed distance estimation models are based on bijective functions: Mbhusing a hyperbolic function and Mplusing a piecewise linear function. Once distances of the subject from the PIR sensors (or anchors) are estimated, multilateration or support vector regression (SVR) based techniques are used for computing the location coordinates of the subject. Using four PIR sensors, we demonstrate the localization of a human subject in a 7 m × 7.5 m area with the regression based technique outperforming the other techniques in terms of accuracy and achieving an RMS localization error of 0.65 m using the Mplmodel for distance estimation. We also compare the computational complexity and memory or storage requirements of the proposed techniques which are an important consideration for distributed implementation on resource constrained devices such as sensor nodes.

Atmospheric turbulence causes severe impairment of FSO communication link. Existing model underestimates the beam wander effect in high turbulence regime. In this paper, we model each turbulent eddy as a thin dielectric lens with Gaussian shaped refractive index profile and assume there are several sheets of eddies throughout the propagation path. We consider uniformly distributed eddy positions in a laminar sheet with Gamma distributed eddy sizes and refractive index fluctuations. We calculate mean beam wander and link availability for a given aperture size in high turbulence regime. Our simulation results show good concordance with the analytical results.

In this study, the authors compare the performance of one‐dimensional (1D) and two‐dimensional (2D) optical code division multiple access (OCDMA) systems in presence of turbulence and various weather conditions. Lognormal fading model is used for analysis of weak turbulence and gamma–gamma model for moderate and strong turbulence. It is observed that presence of moderate and strong turbulence may lead to a complete link failure in 2D OCDMA system depending upon the number of users and turbulence level. The various weather conditions considered are very clear air, light fog and thick fog. The results show that the performance degradation due to thick fog is more than the light fog and very clear air in all turbulence regimes.

In this study, power spectral densities (PSDs) of intensity modulation schemes used in free space optical communications namely, on‐off keying (OOK), pulse‐position modulation (PPM), digital pulse interval modulation (DPIM), pulse amplitude and position modulation (PAPM) and differential amplitude pulse interval modulation (DAPIM) have been derived. The bandwidth requirements of these schemes are determined from the occurrence of the first null in the PSDs. For comparison we have considered OOK, 8‐PPM, 8‐DPIM, 2 × 4‐PAPM and 2 × 4‐DAPIM. Among all the schemes considered, the bandwidth requirement of 2 × 4‐DAPIM is the least. Although 8‐PPM has the least effect of baseline wander and the maximum detected optical power among all the schemes considered, the bandwidth requirement is very high. This may not be desirable in certain cases. The PSD curves are quite important in the link design as these will give information on how the signal power is changing with the bandwidth. If the noise PSD is also known then these curves can be used to determine the optimum bandwidth for maximum signal‐to‐noise ratio. The results are helpful in the selection of modulation scheme for free space optical links and to determine how the power content of the signals is affected by filters and other devices in the link.

The authors study the effect of multi‐input multi‐output (MIMO) spatial diversity schemes when used in a free space optical (FSO) communication link in the presence of turbulence and varied weather conditions such as very clear air, drizzle, haze, fog etc. The performance is evaluated in terms of the bit error rate (BER) and outage probability P out . We show that MIMO schemes cause a decrease in the BER and P out which, at low signal‐to‐noise ratios, is more significant in presence of very clear air and clear air as compared with haze and fog weather conditions. The diversity gain at a BER of 10 −6 is evaluated and observed to increase as the number of transmit/receive apertures increase. However, it remains almost constant over all the weather conditions for a given MIMO scheme and turbulence strength. The performance of FSO link with MIMO schemes is compared with a FSO link using aperture averaging. Owing to constraint on the receiver aperture diameter, the aperture averaging technique fails to give the same kind of performance as provided by the higher order MIMO schemes.

In this study, the authors evaluate the performance of an earth‐to‐satellite free‐space optical link in terms of bit error rate (BER) for three intensity modulation (IM) schemes [on–off keying (OOK), M ‐ary pulse position modulation ( M ‐PPM) and M ‐ary differential PPM ( M ‐DPPM)] and direct detection receiver. The performance is analysed in the presence of atmospheric turbulence, beam‐wander induced pointing error and weather conditions. Turbulence with beam wander effect is modelled using gamma‐gamma distribution and the weather effects are incorporated using the Beer‐Lambert law. They obtain closed form BER expressions for the above IM schemes using combined channel state probability density functions. They show that for the same average power, M ‐PPM offers the best performance, followed by M ‐DPPM and OOK schemes. Link performance degrades for all IM schemes with increase in the value of ground level refractive index structure parameter and bit rate. Presence of weather conditions like moderate, light and thin fog increase signal attenuation and this increase may be very high in case of thick and dense fog or clouds and can lead to link failure.