Modern quantum computing technologies endanger the security mechanisms of RSA and ECC used to protect IoT-based healthcare systems. This research introduces a privacy-protecting healthcare data infrastructure that uses post-quantum lattice cryptography along with the Paillier scheme in order to secure IoT exchange and protected data processing. Vital signs of IoT devices reach a central server through a secure system which also allows data encryption and processing outside the server. Our simulation models a medical institution containing 200 IoT devices to test the framework’s operation regarding encryption delays together with aggregated data precision and quantum resistance capabilities. Experiment results demonstrate that the framework raises quantum breach resistance from R ≈ 0.985 to R′≈ 0.3. Future work will focus on real-time optimization to improve the system performance despite the existing performance bottlenecks from Paillier’s encryption.

The emergence of smart healthcare has transformed the delivery of medical services. It increased the reliability issues related to telesurgery system. Robotic technologies integrate to achieve surgical precision in countless medical fields. But, this increased connectivity has raised concerns about vulnerability to denial of service (DoS) attacks that can disrupt operations and affect users' safety. In this paper, we propose an explainable machine learning (ML) framework for detecting DoS attacks in telesurgery environments. Our framework utilizes several ML algorithms, including K-Nearest Neighbors (KNN), Decision Tree (DT), XGBoost (XGB), and Logistic Regression (LR), to analyze network traffic and distinguish between legitimate and malicious signals. Here, XGB achieves the highest accuracy at 0.8967, making it a good choicefor this application. We also used the concepts of Explainable AI (XAI) such as SHAP and LIME to enhance decision-making process of our models. We aim to build trust among healthcare providers and ensures the safe conduct of robotic telesurgery operations and smart healthcare solutions by improving the interpretation of research and providing information on performance metrics such as accuracy, precision, recall, F1 scores, and ROC curves.

Unmanned aerial vehicles (UAVs) and reconfigurable intelligent surface (RIS) are the emerging technologies for 5G and beyond networks. These two techniques reduce inter-user interference and enhance the network's coverage performance. Despite this advantage, these two techniques are not able to satisfy the diversified quality of service (QoS) requirements of cellular mobile users under the presence of existing multiple access schemes. To tackle this issue, we integrate non-orthogonal multiple access (NOMA) with both these techniques. In this paper, our goal is to maximise the energy efficiency (EE) of the overall network by optimising the powers of UAVs and the phase shift matrix of RIS. The formulated problem is in a mixed-integer non-convex programming form. So, to solve this problem, a deep deterministic policy gradient (DDPG) approach is used in a centralised manner under a time-varying channel. The proposed NOMA-RIS scheme for multi-UAV networks achieves higher EE than the orthogonal multiple access (OMA)-RIS and random selection schemes, according to numerical results.

In this paper,Interference Reduction Scheme for Device-to-Device (D2D) Mobile Groups Using Uplink NOMA (ISHU)is proposed to maximize the throughput of the network. To achieve this goal, we integrated uplink non-orthogonal multiple access (NOMA) in the D2D mobile groups (DMGs). DMGs improve the spectral efficiency by sharing the resources with cellular mobile users (CMUs) whereas, uplink NOMA in DMGs associate the large number of D2D mobile users (DMUs) with the D2D transmitter (DDT). ISHU jointly optimizes the user group association and resource allocation in the uplink NOMA-enabled DMGs. The problem of joint user association and resource allocation is formulated as a mixed integer non-linear programming. To address this problem, we divided the problem of interference mitigation in two sub-problems and solved it independently. First, for joint user group association and sub-carrier assignment, a 3-D matching game is designed between the DMUs, DDT, and sub-carriers, respectively. Second, to optimize the power of DMUs across each sub-carriers, the branch and bound (BB) technique is used. Also, to reduce the complexity of ISHU scheme, the successive convex approximation low complexity (SCALE) technique is used across each sub-carrier. Simulated results demonstrated that ISHU provides 3.846 and 26.92 percent superior throughout as compared to the existing uplink conventional NOMA and OFDMA schemes.

Device-to-device (D2D) communication is one of the promising technology of the fifth-generation (5G) network. In D2D, the devices are in close proximity to each other communicate directly with or without depending upon the base station (BS), resulting in large gain, low latency, and high energy efficiency. Also, it improves the spectral efficiency by sharing the spectrum resources with cellular mobile users (CMUs). Despite these advantages, co-channel interference and eavesdropping attack on the D2D links are two major challenges. To overcome these issues, we used the power domain non orthogonal multiple access (PDNOMA) techniques with the D2D mobile groups (DMGs) under the social-domain scenario. The successive interference cancellation technique of PD-NOMA in the DMGs mitigate the intra-user and co-channel interference among the D2D receivers (DDRs), resulting in an increase in signal to interference noise ratio (SINR) and better quality of services. Furthermore, to improve the spectral efficiency, and reduce the security risk of the eavesdropper on the DMGs over each resource block (RB) in the presence of dynamic channel environment of imperfect channel state information, we used the coalition game approach. The simulated results show the proposed scheme achieves 5.5% and 27.77% higher sum rate and ensure 8.3% and 41.6% higher information secrecy as compared to first-order algorithm (FOA) and orthogonal frequency division multiple access (OFDMA) schemes.

In the last few years, we have witnessed the usage of billions of Internet-of-Things (IoT)-enabled devices in different applications starting from e-healthcare, transportation, agriculture, etc., across the globe. These interconnected devices share information using the Internet to improve the Quality of Service of the end users. There is a requirement of synchronization among the devices to provide scalability, reliability, and connectivity. Despite these advantages, proximity gain, interference, and fairness are various challenges for these devices in IoT which need to be resolved. To overcome these issues, we propose deep reinforcement learning (DRL)-based control scheme in the underlay of device-to-device (D2D) communication. D2D communication reuses the spectrum resources with cellular user equipment (CUE) to improve spectral efficiency. We propose the joint resource block (RB) scheduling and power control scheme to improve the sum rate of the network while considering the users' fairness among all the links. To solve this problem, first, we transform the nonconvex optimization problem into a multiagent reinforcement learning formulation using the Markov decision process (MDP). Then, to solve the RB allocation, we used the multiagent deep Q-network (DQN) framework to reduce the output dimension and improve the learning efficiency. Then, to convert the stochastic policy into deterministic policy, and to improve the fairness we combine the DQN with deep deterministic policy gradient to form the distributed deep deterministic policy gradient (DDDPG) scheme. Finally, to control the power of both the CUEs and D2D transmitters (DTs), we integrated the conventional optimization scheme with the DDDPG (CO-DDDPG). This combination enhances the convergence speed and reduces the computational complexity of the overall network. Numerical results show that the proposed scheme improves the network sum rate of 11.76% and the fairness 4.21% as compared to the state-of-the-art existing distributed DRL schemes.

Ambient Assisted Living (AAL) is becoming a necessity in today's world. It provides care to the elderly patients who are under observation. With the advancements in the technology, the ability of health systems to indulge in the patient's life and remote monitoring has proven useful to prevent catastrophes. Automatic sensing based on sensors and computer vision enabled devices has taken up the field of AAL a notch ahead. Motivated from the aforementioned discussion, in this paper, we propose, a fretwork named as HRIDaaY (an architecture for remote monitoring of the heart rate of a patient) by using a ballistocardiogram sensor and fog computing (FC). We further demonstrate a data compression technique at the fog layer to reduce the bandwidth utilization. Then, a comparison is drawn using alone-Cloud and as fog- cloud combination implementation. Finally, the simulation results demonstrate that HRIDaaY has better accuracy of heart rate monitoring in comparison to the state-of-art schemes.

Few decades ago, wellness management systems were not in the position to give salutary to their users. One of the possible reasons is inefficient resources and minimum technological infrastructure which do not allow a comprehensive structure pertaining to specific user. Sleep apnea is one such problem which is related to the permanent condition that involve stagnation of breathing. Although incurable, it can be minimized by maintaining a healthy lifestyle. Motivated from this, in this paper, we propose a health manager directive system to investigate the precise medical condition of sleep apnea. A recommender system is used which suggests the healthy lifestyle schedule to reduce the apnea severity in a patient. A Probabilistic Markov model (PMM) is used to adhere the activities based on time consumption in different activities performed by the patient. We evaluated the recommendation cycle on three patients to demonstrate the reductions in apnea cycles by indicating sound sleep patterns. Numerical results show that the proposed recommendation System suggest a relative improvement in sleep quality for all patients as compared to pre-existing expensive detection and relief schemes for sleep apnea patients.

To reduce the delay for accessing real-time data access from various applications (healthcare, transportations, virtual reality etc.), there is an exponential increase in the usage of Tactile Internet (TI) technology in recent era. Motivated from this, in this paper, we propose a TI-based random forest (RF) learning algorithm for heart disease predictions. The aim of this paper is to monitor and analyse the human activities for real-time data collection. The proposed approach is an analysis of heart ailments and can be used regularly for the health measure. For this purpose, the RF model is trained to map the collected sensor data features to output normal and abnormal states of the patient suffering from heart disorientation. Moreover, it removes excessive dependence on input values and cover possible alternate paths. Simulated results demonstrate that the proposed approach reduces the average delay and provides less training time in comparison to the pre existing conventional techniques.

Energy management of mobile devices is a crucial issue in fifth generation (5G) network due to their limited battery capacity. Simultaneous Wireless Information and Power Transfer (SWIPT) is an emerging technique which allows mobile devices to harvest energy from radio frequency (RF) signals. Moreover, Non-Orthogonal Multiple Access (NOMA) serves multiple users simultaneously using the same subchannel inter-user interference mitigation. By considering the aforementioned issues, in this paper, we propose a subchannel assignment scheme for SWIPT-NOMA based pico base station/femto base station with macro-cellular networks. The energy-efficient subchannel assignment is a probabilistic mixed non-convex optimization problem by considering imperfect channel state information (CSI). To address this problem, many-to-many matching theory is used in the proposal. Numerical results show that the proposed algorithm performs better in terms of numbers of PUs/FUs, average energy efficiency (EE) of the Picocells/Femtocells, in comparison to the orthogonal frequency division access scheme and conventional NOMA.

In the last few years, there has been an exponential increase in the deployment of 5G-based test beds across the globe with an aim to reduce the latency for accessing various applications. The integration of generic services such as enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), critical machine-type communication (cMTC), and ultra-reliable low-latency communications (URLLC) can improve the performance of 5G-based applications. This service heterogeneity can be achieved by network slicing for an optimized resource allocation and an emerging technology, Tactile Internet, to achieve low latency, high bandwidth, service availability, and end-to-end security. In this paper, we discuss the application-specific nonorthogonal multiple access (NOMA)-based communication architecture for Tactile Internet which allows nonorthogonal resource sharing from a pool of eMBB, mMTC, cMTC, and URLLC devices to a shared base station. We summarize various variants of NOMA and their suitability for future low latency Tactile-Internet-based applications.

Device-to-device (D2D) two-hop cooperative communication improves the network coverage and throughput to provide the quality of service and quality of experience to the end users. Nonorthogonal multiple access (NOMA) can be used at the D2D transmitter to improve the spectral efficiency of the network. But, two-hop transmission with NOMA suffers from delay and interference from the neighboring nodes. To resolve the aforementioned issues, in this paper, we propose Tactile Internet (TI) driven delay assessment for D2D communication (DIYA) scheme, which works in two phases. In the first phase, a full duplex communication at relays (intermediate nodes) is used to have the first- and second-hop transmission simultaneously in the same time slot. Then, TI-based communication is used at D2D transmitter to increase the speed of transmission. In the second phase, pricing-based three-dimensional (3-D) matching is proposed to improve the throughput of the cell edge users along with the mitigation of cochannel interference. Also, the power of the D2D transmitter is optimized using successive convex approximation with low complexity, which converts the nonconvex optimization problem of subchannel allocation and power control into convex problem. Numerical results demonstrate that DIYA achieves higher throughput with reduced delay in comparison to other existing orthogonal multiple access (OMA) and NOMA-based schemes.

In the last few years, we have witnessed an exponential increase in the popularity of Internet-enabled smart devices. In this era, various smart devices generate a huge amount of data during computing and communication. However, fixed infrastructure, especially in the dense population, have the issues of coverage and connectivity in this environment. But, 5G technology based femtocell emerges as one of the solutions for the aforementioned issues. Hence, in this paper, we investigate the non-orthogonal multiple access (NOMA) transmission with 5G enabled cognitive femtocell to attain higher spectral efficiency and to maximize the sum rate of femto users (FUs) with guaranteed QoS. A paring algorithm between strong and weak users has been proposed to reduce the NOMA interference between different FUs. To achieve higher data rates, the calculation of sum rate for an even/odd number of FUs in a femtocell is also proposed. Numerical results show that the proposed schemes effectively improve the sum rate of the cognitive femtocell under NOMA transmission to minimize the interference.

With the widespread popularity of low cost sensors, the data collection and processing on real-time from different geographical regions becomes easy. Agriculture is one such areas where sensors can be deployed to get real-time data of different regions. In agriculture field, temperature monitoring, soil moisture monitoring and plants growth monitoring can be obtained using sensor nodes. Obtaining the maximum growth in agricultural domain under optimized resources availability through Precise Irrigation (PI) is called as Precision Agriculture (PA). In this paper, we propose PI-Cloud, which is a sensorcloud based measurement to management (M2M) system, which can be used in an agricultural field to maintain the moisture level of soil above a predefined threshold in real-time. We used a cluster based hierarchical architecture of sensor-cloud, where deployed sensors, called as mobile sensor robot (MSR) can be recharged/replaced as and when required. In order to maintain the real-time monitoring of moisture level of soil, replacement mechanism of energy depleted MSR and cluster heads is also proposed. Management to maintain the PI is done using the control action matrix prepared from sensor cloud. Two novel parameters have been selected as, number of replacements required (NRR) for energy depleted MSR and first replacement analysis (FRA) are used for validation of proposed scheme. The results obtained show that with the availability of energy and recharging of MSR, PI can be done on real-time basis with PICloud based architecture system.

From the last few years, Wireless body area networks (WBANs) have attracted a lot of attention from both academia and industry due to an increase in real-time data capturing and processing for patient monitoring. This has become possible due to the technological advancements in which high computing and communication facilities are available for most of the modern handheld devices. In this environment, computing resources are available close to the proximity of the end users using the most popular technology called as Fog computing (devices used in the fog computing are called as fog devices). Most of the solutions reported in the literature for this purpose have used the traditional cloud-based infrastructure in which there may occur a long delay for getting the response even for data which is not of very huge amount which may cause a performance degradation for most of the implemented solutions (such as for treatment of neurological diseases where a real-time monitoring is required) in this environment. Hence, to cope up these issues, in this paper, we proposed a fog computing based patient monitoring system for ambient assisted living (FAAL). Data traces of the movement of the patients (for neurological diseases) are collected using sensor nodes using body area networks (BANs) and are passed using the fog gateways. To reduce the load on the communication infrastructure, an efficient clustering algorithm for data transmission is also presented in the paper. Performance of the proposed solution has been evaluated using the parameters such as-latency, and data overloading. Results obtained clearly show the superior performance of the proposed scheme as compared to the non-fog computing based environment.

Physical therapy has a lot of importance for the well being and a better quality of living for an elderly patient. One integral constituent of any patient regime is the home-based exercise that a patient works on in a much comfortable environment. Although the benefits are well known, there is a big lag between the exercises prescribed by the therapists and the ones actually done by the patient. There is no cost effective and non-complex methods available to quantify the exercises performed by the patient. In this paper, a study was performed to check the validity and efficiency of a system consisting of a Smart IoT enabled speaker, which contains an orchestrator. Which is speech learning unit, an exercise database at the edge, and connected to the cloud, where the generated reports are stored and transferred for further analysis, if required. We report the efficiency of the system compared to the ratings of a physical therapist, a standard currently being used.

Wireless Sensor Networks (WSNs) have gained a lot of popularity in recent years because these are being used in wide range of applications. A collection of randomly/planned deployed tiny Sensor Nodes (SNs) can perform the task according to the need of a specific application. Utilization of energy of SNs is one of the key issues in these networks as nodes are battery operated and recharge or replacement of battery is a difficult task to be achieved. To address this issue, we propose a Bayesian Coalition Game-based optimized clustering in WSNs. To formulate the game, we propose a new Hybrid Homogeneous LEACH (HHO-LEACH) protocol for SNs in WSNs. We have used the concepts of Learning Automata (LA), and Bayesian Coalition Game (BCG) in which SNs are assumed as the players in the game with dynamic thresholds-based coalition formation among themselves, i.e., coalition among the nodes are formed using distance-based thresholds which makes a partition of the network field. SNs near to base station use direct communication for data transfer with or without single hop to the Base Station(BS) after interacting with the environment. During this process, each player may get a reward, or a penalty with respect to the finite number of actions performed. Performance of the proposed protocol is evaluated using extensive simulations by selecting various evaluation metrics. The results obtained show that proposed coalition game achieved better stability, and network lifetime in comparison to other existing protocols such as LEACH, and DD.