Accurate classification of encrypted IoT traffic remains challenging due to evolving applications and distribution shifts. This work presents a self-updating hybrid meta-learning framework that integrates Bayesian Neural Networks (BNN) for uncertainty-aware update triggering with a Random Forest meta-classifier for robust decision fusion. The proposed design improves scalability and interpretability through feature-importance analysis and lightweight ensemble learning. Prediction instability is quantified using the Hellinger distance, avoiding normalization overhead and enabling an adaptive familiarity score via a tunable parameter α. Experimental results on encrypted traffic datasets demonstrate significant gains in reliability, achieving up to 95.7% accuracy and 0.95 macro-F1, and effective selective retraining under distribution shifts.

A Wireless Sensor Network (WSN) is a major component of any Internet of Things (IoT)-based system. In WSNs, a Mobile Sink (MS) collects sensed data from deployed sensor nodes by visiting Rendezvous Points (RPs) in an energy-efficient manner. The presence of mobile obstacles in WSNs significantly reduces network performance by increasing data transmission delay and degrading overall operation. This paper proposes an Energy-Efficient Intelligent Obstacle Avoidance Data Routing Scheme (EEIOADRS) for IoT-enabled WSNs. It effectively identifies and avoids mobile obstructions with minimal message passing and low delay. A heuristic-based Minimum Spanning Tree algorithm is used to find an optimal path among all Grid Cell Heads (GCHs) for MS-based data gathering. Furthermore, Gain-Based Dynamic Ant Colony Optimization is used to construct an optimal mobile obstacle-free path for MS-based data collection. It significantly reduces data transmission delay and enhances overall network performance. Extensive simulations demonstrate that the proposed scheme significantly improves network performance. The proposed approach improves network lifetime by 50.84% relative to OMCSO, 42.34% relative to CSOBUG, and 39.83% relative to OASPP. Additionally, simulation results indicate that the proposed scheme enhances network throughput by 47.15% compared to OMCSO, 36.69% compared to CSOBUG, and 33.76% compared to OASPP.

Recently, the Internet of Things (IoT) has found widespread applications in diverse fields, including environmental monitoring, Industry 4.0, smart cities, and smart agriculture. In these applications, sensor nodes form Wireless Sensor Networks (WSNs) and collect data from the monitoring environment. Sensor nodes are vulnerable to various faults, including battery depletion and hardware malfunctions. These faulty nodes cut/partition the network into several isolated segments. Therefore, several non-faulty nodes become disconnected from the Base Station (BS)/Sink and are unable to transmit their data to the BS. It is subject to the early demise of the network. Network cuts also significantly degrade overall network performance. Once the network is divided into isolated segments, it is very difficult to detect and collect data from them. Therefore, this paper proposes a Mobile Data Collector (MDC)-based data-gathering approach for WSNs to collect data from isolated segments. This paper proposes a novel MDC-based network cut detection algorithm that identifies the formation of network cuts in WSNs. A network recovery algorithm is also proposed to enable data collection from the isolated segment. Furthermore, this paper proposes a Reinforcement learning Brain Storm Optimization (RLBSO) algorithm for optimal selection of Rendezvous Points (RPs) and optimal MDC path design. It significantly reduces data-gathering time across isolated network segments. The simulation and testbed results show that the proposed approach outperforms existing state-of-the-art approaches in terms of network lifetime, data collection ratio, energy consumption, and latency.

Even though large language models (LLMs) have demonstrated remarkable performance across various natural language processing tasks, their application in speech-related tasks has largely remained underexplored. This work addresses this gap by incorporating acoustic features into an LLM which can be fine-tuned for downstream direct speech-to-text translation and automatic speech recognition tasks. To address the computational demands associated with fine-tuning LLMs, a novel self and semi-supervised knowledge distillation technique is proposed to implement a lightweight LLM having 50% lesser parameters. Validated on the MuST-C and Librispeech datasets, this technique achieves over 92% of the performance of the larger LLM, demonstrating both robust performance and computational efficiency.

Pixel failures on Reconfigurable Intelligent Surfaces (RISs) cause major challenges in wireless communication systems. These failures are especially problematic for Internet of Things (IoT) networks, where reliability and Quality-of-Service (QoS) are critical parameters. Failed RIS pixels can significantly impact system efficiency. These failures disrupt signal quality, reduce Signal-to-Interference-Plus-Noise-Ratio (SINR), lower the sum rate, and affect energy efficiency. As a result, maintaining stable communication links becomes challenging. The primary function of RIS is to improve the connectivity and signal strength between the IoT devices and the Base Station (BS). However, when a large number of pixels fail, this function is compromised. Motivated by the aforementioned challenges, this paper focuses on severe RIS pixel failures. The method uses a Deep Neural Network (DNN) to detect failed RIS pixels and a mathematical model to optimize the configuration of the affected regions, including the failed pixels and their neighboring healthy elements. To achieve this, a dynamically adapting mask is employed, ensuring that the total failed pixels within the mask always remain at 25 % or less. The remaining functional elements retain their standard configuration, allowing for minimal and targeted adjustments. This selective reconfiguration focuses on improving the SINR and sum rate at the BS, while minimizing adjustments to ensure optimal performance. The effectiveness of the proposed method is validated through comprehensive experimental evaluations in various scenarios and time instances. The results consistently demonstrate that the proposed Pixel and Configuration (P&C) RIS method improves the SINR and sum-rate performance at the BS, outperforming conventional RIS systems. This adaptive approach offers a resilient and scalable solution to address the challenges posed by faulty RIS elements, ensuring high-quality communication in dynamic and real-world IoT applications.

The proliferation of Internet of Things (IoT) devices has raised considerable difficulties in the identification of users, optimization of power, and load balancing in hybrid RF/LiFi networks. As interconnection among devices increases, ensuring optimal performance while managing network resources efficiently becomes quite complex. This complexity arises due to accommodating the possibly diverse users’ needs, fluctuating channel conditions, and varying interference levels, all necessitating sophisticated management solutions to provide seamless connectivity and dependable communication. To tackle these issues, a deep joint hybrid system (DJHS) technique is presented, which employs proximal policy optimization (PPO), a cutting-edge deep reinforcement learning (DRL) algorithm. DJHS aims to effectively handle the intricate problems surrounding user association and load balancing while optimizing power usage in dynamic contexts. DJHS continuously updates its approach based on real-time network data through adaptive learning methods, allowing it to make intelligent decisions that improve overall system performance regarding data throughput and power optimization. Simulation results demonstrate that DJHS outperforms existing approaches such as sac, a2c, td3, and trpo regarding crucial metrics, including data rate and power transmission. Notably, DJHS’s ability to adjust to variations in signal-to-interference-plus-noise ratio (SINR) allows for enhanced resource allocation and network stability. This flexibility ensures that users receive optimal service even in changing conditions, enhancing the overall user experience.

Node localization in time-varying Internet of Things (IoT) networks is an essential problem due to increased delay and poor Signal-to-Interference plus Noise Ratio (SINR) at the Base Station (BS). To improve the received signal strength at the BS, Reconfigurable Intelligent Surface (RIS) has recently been used between transmitter and receiver. Additionally, novel phase prediction methods and optimal weight assignment frameworks have been proposed over RIS and BSs, respectively. Nevertheless, these methods suffer from poor performance due to their heuristic approach, resulting in more time consumption and poor SINR. Motivated by the aforementioned challenges, we propose a novel node localization method over a RIS-assisted time-varying IoT network using Machine Learning (ML) frameworks in this work. Firstly, the method computes the optimal phase configuration over the RIS corresponding to each element using coeff2phaseNN, which has been trained on channel coefficients among the transmitter, receiver, and RIS. Subsequently, the weight of the individual antenna element at the BS is optimized using the proposed VectorSync model. The results confirm that the coeff2phaseNN method demonstrates a reduction of 89.79% in total MSE loss compared to the Artificial Neural Network-RIS (ANN-RIS) method. Additionally, it demonstrates a 71.04% reduction in the absolute RIS phase prediction deviation from the optimal phase compared to the ANN-RIS method. Moreover, the proposed VectorSync method attains a 79.28% and 92.29% reduction in time required for optimal weight assignment compared to the Bartlett and Capon methods, respectively. Finally, the Localization Error(LR) using the proposed method is compared to conventional methods in a time-varying experimental scenario and found to be the minimum, i.e., 6.156%.

The Internet of Things (IoT) has grown explosively with wireless technology integration. Several IoT applications require high data throughput, low data transmission latency, and high data gathering reliability. Since, the IoT network (IoTN) is generally dynamic and utilizes a multi-hop data transmission scheme for such applications, the throughput, latency, and network lifetime tend to degrade as the hops increase. Moreover, IoT devices (IoD) are low-cost, less computationally capable, and battery-limited, further impacting performance. A faulty IoD worsens network lifetime and throughput. Predicting faulty nodes and re-routing data can significantly enhance performance. This work proposes a node fault prediction framework to enhance data routing in dynamic IoTN, maximizing throughput and lifetime. The network is represented as a graph in which the IoD are the nodes. Then a novel deep learning model is proposed utilizing various node and edge features to predict the faulty IoDs. Particularly, the proposed edge and node features-accumulation deep learning (ENADL) method exploits features, such as Euclidean distance between nodes, residual energy level of nodes, and type and number of messages passed between edges to predict the forthcoming faulty IoD. Thereafter, data routing is performed over the updated network topology. Furthermore, to improve the network lifetime, the node's degree and betweenness centrality measures-based energy allocation method is also proposed. Finally, numerical results on simulated and real-field testbeds demonstrate the ENADL method.s effectiveness in predicting faulty nodes and re-routing data packets. This results in maximized network throughput and lifetime as compared to several existing methods.

In wireless networks, automatic modulation classification (AMC) is crucial for enabling intelligent signal demodulation, thereby enhancing the system’s adaptability across various applications. Concurrently, the rapid expansion of the Internet of Things (IoT) necessitates scalable network solutions with limited power consumption. Moreover, addressing the Nonline-of-Sight (NLoS) effects in IoT networks, intelligent reflecting surface (IRS) emerges as a promising, cost-effective technology. This article introduces a novel transformer-based deep recurrent architecture (TDRA) for AMC, tailored for IRS-assisted IoT networks, which significantly improves IoT Device (IoTD) performance in NLoS scenarios. In TDRA, the existing recurrent models, long-short-term memory (LSTM), and gated-recurrent-unit (GRU) are suitably revamped with a transformer-based approach and termed as transformer-based LSTM (T-LSTM) and transformer-based GRU (T-GRU). Numerical data sets are generated for IoT applications considering the seven widely used modulation types to train and test the proposed models. Comparative analysis with seven state-of-the-art deep learning models and five machine learning models for AMC demonstrates the superior performance of the proposed models across multiple metrics, including accuracy, R-squared-score, mean-square error, mean-absolute error, precision, recall, and F1-score. Further, the proposed models exhibit notable improvements under various conditions, such as optimized and random IRS phase shifts, with and without IRS-assisted IoT networks, different modulation sequence lengths, and fading channels. Additionally, the time complexity and processing time of the proposed models have been studied to test their suitability for IoTD. The simulation results indicate that the TDRA for AMC in IRS-assisted IoT networks achieves up to 87% higher accuracy compared to without IRS-assisted IoT networks. This significant enhancement underscores the potential of TDRA to revolutionize IoT networks by providing robust, efficient, and scalable solutions for real-world applications.

By integrating the merits of aerial, terrestrial, and satellite communications, the space-air-ground integrated network (SAGIN) is an emerging solution that can provide massive access, seamless coverage, and reliable transmissions for global-range applications. In SAGINs, the uplink connectivity from ground users (GUs) to the satellite is essential because it ensures global-range data collections and interactions, thereby paving the technical foundation for practical implementations of SAGINs. In this article, we aim to establish an accurate analytical model for the uplink connectivity of SAGINs in consideration of the global distributions of both GUs and aerial vehicles (AVs). Particularly, we investigate the uplink path connectivity of SAGINs, which refers to the probability of establishing the end-to-end path from GUs to the satellite with or without AV relays. However, such an investigation on SAGINs is challenging because all GUs and AVs are approximately distributed on a spherical surface (instead of the horizontal surface), resulting in the complexity of network modeling. To address this challenge, this paper presents a new analytical approach based on spherical stochastic geometry. Based on this approach, we derive the analytical expression of the path connectivity in SAGINs. Extensive simulations confirm the accuracy of the analytical model.

In order to support the recent explosive growth in the applications of Internet of Things (IoT), networking technologies are evolving, resulting in high data throughput, low-latency data transfer, and improved lifetime of Internet of Things Devices (IoDs). These technologies work fairly for static conditions as the devices have fixed locations. However, in several practical IoT networks, including intelligent transportation networks and mobile health monitoring systems, the devices change their locations with time, resulting in time-varying network topologies. Dynamic networks generally operate on multi-hop data transmission schemes. However, due to its dynamic nature, these networks are susceptible to poor performance as a consequence of the inaccurate selection of relay IoD. In this context, the selection of optimal relay IoD towards data transfer is an important problem in time-varying IoT networks. To address such a critical issue, in this work, we consider a dynamic IoT network in which the devices select an optimal relay IoD at various discrete time instants to improve network performance. Thereafter, a novel reinforcement learning-based data routing algorithm in the time-varying multi-hop IoT network is proposed for optimum data routing. The proposed algorithm, Optimal Relay IoD Selection Using Q-Learning (OptRISQL), selects the optimum relay IoD for data routing using Q-learning. The proposed method maximizes the aggregate reward value between specified device-gateway pairs by adjusting the network’s Q-matrix at discrete time instants to identify optimal relay IoD. The proposed method’s applicability and effectiveness are demonstrated using a simulated IoT testbed and real-field datasets. Moreover, when compared to various existing methods, the acquired findings indicate the proposed method’s improved network performance in terms of Energy-Efficiency (EE) and Quality-of-Service (QoS).

Mobile sensors, such as smart wearables, autonomous cars, cognitive healthcare devices, and intelligent drones, draw great attention due to their ability to provide a wide-range of services. Hence, a consistent connection of each sensor node to the access point is a crucial requirement to obtain high data throughput in such mobile wireless sensor networks (MWSN). Data interference among such densely populated mobile sensor nodes (MSN) must also be minimized to enhance the data gathering reliability at individual MSN. In this context, a novel socially aware network clustering and interference management technique for MWSN is proposed in this work. The proposed method considers the current and prediction of future encounters among MSN to compute the social relationship index (SRI)-factor in developing a novel clustering algorithm. Moreover, a frequency-separation (FS) distance between clusters is utilized to form the non-overlapping clusters. The FS distance aids in reducing the network interference. For further interference management and reliable data transfer, beamforming is also utilized over the clustered MWSN. Subsequently, an optimization problem is formulated to maximize the data throughput over the clustered MWSN with respect to antenna downtilt angle while accounting for the high-density mobile behavior of MSN. Finally, experiments are conducted to evaluate the performance of the proposed method over a time-varying MWSN. The obtained results demonstrate the effectiveness of the proposed when compared to the benchmark methods. The results also validate the utilization of the proposed method over medium and large-scale network applications.

Recently, drones have attracted considerable attention for sensing hostile areas. Multiple drones are deployed to communicate and coordinate sensing and data transfer in the Internet of Drones (IoD) network. Traditionally, multi-hop routing is employed for communication over long distances to increase the network’s lifetime. However, multi-hop routing over large-scale networks leads to energy imbalance and higher data latency. Motivated by this, in this paper, a novel framework of energy-efficient and latency-aware data routing is proposed for Small-World (SW)-IoD networks. We started with an optimization problem formulation in terms of network delay, energy consumption, and reliability. Then, the formulated mixed integer problem is solved by introducing the Small-World Characters (SWC) into the conventional IoD network to form the SW-IoD network. Here, the proposed framework introduces SWC by removing a few existing edges with the least edge weight from the traditional network and introducing the same number of long-range edges with the highest edge weight. We present the simulation results corresponding to packet delivery ratio, network lifetime, and network delay for the performance comparison of the proposed framework with state-of-the-art approaches such as the conventional SWC method, LEACH, Modified LEACH, Canonical Particle Multi-Swarm (PMS) method, and conventional shortest path routing algorithm. We also analyze the effect of the location of the ground control station, the velocity of the drones, and the different heights of layers on the performance of the proposed framework. Through experiments, the superiority of the proposed method is proven to be better when compared to other methods. Finally, the performance evaluation of the proposed model is tested on a network simulator (NS3).

At present, the centralized learning models, used for IoT applications generating large amount of data, face several challenges such as bandwidth scarcity, more energy consumption, increased uses of computing resources, poor connectivity, high computational complexity, reduced privacy, and large latency towards data transfer. In order to address the aforementioned challenges, Blockchain-Enabled Federated Learning Networks (BFLNs) emerged recently, which deal with trained model parameters only, rather than raw data. BFLNs provide enhanced security along with improved energy-efficiency and Quality-of-Service (QoS). However, BFLNs suffer with the challenges of exponential increased action space in deciding various parameter levels towards training and block generation. Motivated by aforementioned challenges of BFLNs, in this work, we are proposing an actor-critic Reinforcement Learning (RL) method to model the Machine Learning Model Owner (MLMO) in selecting the optimal set of parameter levels, addressing the challenges of exponential grow of action space in BFLNs. Further, due to the implicit entropy exploration, actor-critic RL method balances the exploration-exploitation trade-off and shows better performance than most off-policy methods, on large discrete action spaces. Therefore, in this work, considering the mobile scenario of the devices, MLMO decides the data and energy levels that the mobile devices use for the training and determine the block generation rate. This leads to minimized system latency and reduced overall cost, while achieving the target accuracy. Specifically, we have used Proximal Policy Optimization (PPO) as an on-policy actor-critic method with it's two variants, one based on Monte Carlo (MC) returns and another based on Generalized Advantage Estimate (GAE). We analyzed that PPO has better exploration and sample efficiency, lesser training time, and consistently higher cumulative rewards, when compared to off-policy Deep Q-Network (DQN).

In recent years, several applications of Internet of Things (IoT) have been observed in various areas including environmental monitoring, healthcare systems, cognitive smart agriculture, industrial control, smart homes , intelligent transportation systems , and traffic management. For such applications, wireless sensor networks (WSNs) are generally deployed to gather the sensed data from the targeted application field. In order to transfer the sensor node data to the gateway (sink node), novel routing protocols need to be developed, leading to reduced data transmission delay, high data throughput , and improved energy efficiency across the network. In this context, geographical routing protocol has been considered as a promising approach for the path selection in WSNs. This approach is full of scalability and multi-hop routing is performed using local decisions. However, geographical routing protocols suffer from the void node problem (VNP) i.e., a region where active nodes are not available in the direction closer to the destination. Numerous protocols have been designed to get recovery from VNP in 2D networks which cannot be directly applied to 3D networks. The 3D routing includes the networks deployed in the hilly area, high buildings, airborne region, underground, underwater and so forth. On applying the 2D routing protocols on complex 3D topology, the network may face additional problems like packet looping, routing failure, ambiguity, or increased data latency due to longer path. Further, the majority of geographical routing protocols follow the boundary of void which leads to a longer path. In order to address the aforementioned challenges, this paper presents a novel anchor-based void detouring routing (AVDR) protocol where anchor node is treated as a sub-destination which provides the direct smaller path between source and gateway nodes. The proposed method bypasses the void boundaries and directly connects source to anchor, anchor to destination, or two successive anchors. Further, anchor information is distributed to the desired region to reduce the periodic anchor advertisement process. The effectiveness of the proposed method has been tested over both, real field data set and simulated testbed with OMNET++ simulator. The results obtained over real field data set claim that the proposed method takes only 29.09 ms (ms) for transferring the data on an average. However, this value is 32.37 ms, 34.32 ms, 33.61 ms, 37.20 ms, and 38.73 ms, respectively, using A3DR, EDGR, GPSR-3D, BSMH, and RPL methods. Moreover, it is also noted that the proposed method achieves an improvement of 8.2%, 7.54%, 7.66%, 8.49%, and 8.22%, in routing stretch when compared to aforementioned methods, respectively. This improvement with respect to network overhead is 30.25%, 57.45%, 51.05%, 75.56%, and 58.89% using the proposed method.

The widespread use of the Internet of Things (IoT) necessitates large-scale communication among smart IoT devices (IoDs) across a wide geographical area. However, due to the limited radio range and scalability issues of traditional wireless sensor networks, wide-area communication among IoDs is not feasible. As a solution, a low-power wide-area network (LPWAN) is emerging as one of the techniques that can provide long-range communication with minimal power consumption. Nevertheless, the direct data transmission approach will no longer be viable due to its short network lifetime. As such, multihop data routing strategies for LPWANs are proposed in the literature. However, multihop data transmission has several challenges, including increased data latency, energy imbalance, poor bandwidth utilization, and low data throughput. To address these challenges, we propose a novel method that uses the machine learning technique for an energy-efficient and Quality-of-Service (QoS)-aware data transfer based on a recent breakthrough in social networks known as small-world characteristics (SWC). The network having SWC (i.e., low average path length and high average clustering coefficient) uses long-range links to reduce the number of intermediate hops for data transmission. In particular, a$Q$-learning framework is utilized for introducing optimal long-range links between the selected IoDs, resulting in the development of a small-world LPWAN (SW-LPWAN). Furthermore, the performance of the proposed method is computed in terms of energy efficiency and QoS. Moreover, the results are compared with existing data routing techniques, such as low-energy adaptive clustering hierarchy (LEACH), modified LEACH, conventional multihop, and direct data transmission. Specifically, the proposed method maintains 29% more alive nodes, 18% higher residual energy, and 22% higher data throughput compared to the second-best-performing method. As such, the obtained experimental results validate that the proposed method outperforms other existing methods in the context of energy consumption and QoS.

Multihop data routing over a large-scale Internet of Things (IoT) network results in energy imbalance and poor data throughput performance. In addition, data transmission using a large number of hops causes more delay. In light of this, in this work, a novel method of energy and throughput management in a delay-constrained small-world unmanned aerial vehicle (UAV)-IoT network is proposed. The proposed small-world framework optimizes the number of hops required for the data transmission leading to improved energy efficiency and quality of service. The method introduces optimal long-range links between device pairs resulting in low average path length and high clustering coefficient which are called as small-world characteristics. Therefore, in this work, UAVs are deployed to collect the data from IoT devices and forward it to the ground station (GS) utilizing the small world framework. It is shown through results that the network delays corresponding to the proposed method, conventional routing method, low-energy adaptive clustering hierarchy (LEACH) protocol, modified LEACH protocol, and canonical particle multiswarm (CPMS) method are 789.39, 1602.53, 1000.92, 873.63, and 999.79 s, respectively. It is also observed that the number of dead UAVs in case of the proposed method is reduced when compared to other existing methods. It is also noticed that the proposed method results in 100% packet delivery ratio (PDR) dominating LEACH and modified LEACH protocols. Thus, it is shown that the proposed method outperforms the other shortest path methods in terms of network latency, lifetime, and PDR. Further, the effect of location of GS, velocities of UAVs, and hovering heights of UAVs is considered for the performance evaluation of the proposed method. The obtained results validate the significance of utilization of the proposed method over various network scenarios.

Environmental sound classification (ESC) is an important research problem with a broad range of applications including audio-based surveillance, audio-visual systems, smart homes, and robotics, among others. The recently proposed vision multi-layer perceptron-mixer (MLP-mixer) has outperformed traditional deep models (CNN or ResNet) and attained new state-of-the-art performances for several computer vision applications (image/video classification and image segmentation). Following the success of MLP-mixer, in this paper, we propose a novel audio MLP-mixer (AMM) network that classifies the different types of environmental sounds. Despite the higher performance, the high computational cost (number of trainable parameters and floating point operations) prohibits deployment of the AMM model on edge for designing real-life applications. To alleviate the aforementioned issue, in this work, we present three different knowledge distillation (KD) strategies to train a compact deep network for ESC. The proposed strategies divide the input Mel-spectrogram into patches and a lightweight deep ESC model is trained in the presence of three teacher networks under the offline KD training framework. Additionally, we have designed two novel loss functions for KD that are free from a temperature parameter that need to be set manually by a user as in the case of the traditional vanilla KD technique. We conducted our experiments on three benchmark ESC datasets namely ESC-10, Urbansound8k (US8K), and DCASE-2019 Task-1(A). The obtained results demonstrate the significance of utilization of proposed methods over other existing KD methods in terms of classification accuracy.

Internet of Things (IoT) enables important data collection of the sensor nodes at desired location through device-to-device communication. It helps us in various applications including tracking of physical objects, human health monitoring, climate smart agriculture, smart manufacturing, field surveillance, and intelligent transportation. Since, these applications utilize multi-hop data transmission framework, Quality-of-Service (QoS) such as data transmission delay, data throughput, and data gathering reliability degrades at the destination nodes. In addition, multi-hop data routing causes more data interference and reduced network lifetime due to non-uniform energy consumption across the network. Moreover, in an IoT network, faulty nodes further reduce the performance of data collection and network lifetime. In order to address the aforementioned challenges, various fault detection and fault tolerant-data routing methods have been proposed in the literature. However, predicting the faulty nodes a prior can result in improved network lifetime and QoS. Hence, in this work, a novel joint node fault prediction based optimal data routing method is proposed in an IoT network. The method utilizes a novel unsupervised learning based Local Outlier Factor (LOF) method for predicting forthcoming faults. The method classifies the faulty state of a sensor node as an outlier when plotted with normal state of the sensor node. Subsequently, a novel data routing method is proposed which utilizes Q-learning framework towards multi-hop data routing. Here, the Q-values decide the optimal routing path, where, data transmission path is altered based on predictions made on the faulty nodes. The performance of the proposed methods is evaluated over both, simulated IoT testbed and real-field dataset. The obtained results demonstrate that the proposed methods are able to successfully predict the faulty nodes and avoid data transmission through them, resulting in improved energy-efficiency and QoS over the network.

The expeditious growth of the Internet of Things (IoT) has accelerated the evolution of multi-access edge computing (MEC). MEC alleviates the challenges of conventional cloud computing, such as high data latency, poor data gathering reliability, increased network cost, and lack of network robustness. The primary objective of MEC is to facilitate a hierarchy of edge servers to address these quality-of-service (QoS) challenges, especially the information propagation issue due to the mobility of IoT devices (IoD). Further, social-relationship among mobile IoD is a critical parameter used to reduce the data transmission delay and queue size at the MEC. Specifically, in this work, a novel socially-aware radio map generation method is proposed to compute the fine-grained and accurate locations of QoS-deprived areas. Firstly, a novel method to compute the social relationship index (SRI) factor is proposed on the basis of current and future encounters among moving IoDs. Then the obtained SRI factor is used to form clusters of mobile IoD. The clusters’ signal to interference plus noise ratio (SINR) is then used to generate the socially-aware radio map. Following that, unmanned aerial vehicles (UAV) use this radio map, which contains rich and serviceable channel information, for 3D beamforming towards the mobile clusters. Using the obtained radio map, Kalman filter-based offline path planning of UAVs is proposed to minimize the UAVs flying distance from the initial to final locations. Furthermore, an optimization problem is formulated to assess the performance of the proposed method. Finally, the performance of the proposed method is compared with the existing methods, taking into account various network parameters such as optimum number of UAVs needed to cover the deployed area, data transmission delay, and received SINR.

The spectrum cartography finds applications in several areas such as cognitive radios , spectrum aware communications, machine-type communications, Internet of Things , connected vehicles, wireless sensor networks , and radio frequency management systems, etc. This paper presents a survey on state-of-the-art of spectrum cartography techniques for the construction of various radio environment maps (REMs). Following a brief overview on spectrum cartography, various techniques considered to construct the REMs such as channel gain map, power spectral density map, power map, spectrum map, power propagation map, radio frequency map, and interference map are reviewed. In this paper, we compare the performance of the different spectrum cartography methods in terms of mean absolute error , mean square error , normalized mean square error, and root mean square error . The information presented in this paper aims to serve as a practical reference guide for various spectrum cartography methods for constructing different REMs. Finally, some of the open issues and challenges for future research and development are discussed.

With millions of devices connected together, the Internet of Things (IoT) has become an emerging technology for future wireless networks. The ever-increasing number of smart devices and data hungry applications demand a high Quality-of-Service (QoS) for IoT. In conventional networks, data being sent to cloud for computational purpose leads to poor QoS. In order to address QoS challenges, mobile edge networks have emerged as a promising solution. In edge networks, bringing the networks resources closer to the end devices results in improved QoS. The maneuverability and the ease of versatile deployment coupled with cost efficiency makes unmanned aerial vehicles (UAVs) a promising candidate for future edge networks. The UAVs can act as edge servers to provide computational capabilities and improved services to the edge devices. Due to the flying ability, UAVs can establish better line-of-sight link with the ground devices. In this paper, we consider that the edge devices in the area of interest have to be facilitated with a certain desired QoS, which is based on the notion of outage probability of the wireless link between the UAV and the edge devices. In this context, we first propose a novel method that computes the optimum height at which UAV should hover, resulting in maximum coverage radius with sufficiently small outage probability. Then the geographical area is divided in optimal number of clusters using a novel algorithm based on K-means clustering. The method computes the optimum number of UAVs required for covering the area of interest. Each of the UAVs utilizes 3D beamforming in order to cover its own coverage area. For this purpose, we are taking coordinate transformation of the original area and forming a wide beam to cover the desired area. The obtained results demonstrate the effectiveness of the proposed method when compared to existing methods, which validate the utilization of the proposed method over large scale network applications.

Recent technology advancement has resulted in optimistic view toward the practicability of wireless sensor networks (WSNs) in the context of Internet of Things (IoT) and Cyber Physical Systems (CPS). However, to realize their full benefits in a broad range of commercial applications, there are still many technical hitches that need to be overcome. In this paper, we address three vital technical issues in a WSN: (1) distributed event detection, (2) distributed parameter estimation, and (3) network's robustness. We make use of a recent development in social networks called small world characteristics and propose novel fault-resilient distributed detection and estimation methods over a small world WSN (SW-WSN). In particular, a small world WSN has been developed by mounting antenna arrays on sensor nodes for the purpose of beamforming. A low-complexity optimization problem for beamforming is formulated by introducing a new parameter Flow between node pairs. Additionally, a new beamforming algorithm is also proposed which optimizes this flow, leading to optimal beam parameters. The proposed method yields a lower average path length and a higher average clustering coefficient of the network. Experiments are conducted using simulations and real node deployments over a WSN testbed. Analysis and experimental results obtained demonstrate that the proposed SW-WSN model achieves faster convergence rates for both distributed detection and distributed estimation while being resilient to node failures when compared to results obtained using state-of-the-art methods.