The vision of the Internet of Things (IoT) is to enable systems across the globe to share data using advanced communication technologies. With the recent technological advancements, IoT-based ...solutions are no longer a challenging vision. IoT will offer numerous and potentially revolutionary benefits to today's digital world. Future personalized and connected healthcare is one of the promising areas to see the benefits of IoT. This paper surveys emerging healthcare applications, including detailed technical aspects required for the realization of a complete end-to-end solution for each application. The survey explores the key application-specific requirements from the perspective of communication technologies. Furthermore, a detailed exploration from the existing to the emerging technologies and standards that would enable such applications is presented, highlighting the critical consideration of short-range and long-range communications. Finally, the survey highlights important open research challenges and issues specifically related to IoT-based future healthcare systems.
Healthcare data is highly sensitive and must be safeguarded. Personal and sensitive data, such as names and addresses, is stored in Encrypted Electronic Health Records (EHRs). This paper proposes a ...Blockchain-based distributed application platform for Bangladesh's public and private healthcare service providers. The proposed application framework enables users to create secure digital agreements for commerce or collaboration by leveraging data immutability and smart contracts. As a result, all stakeholders can collaborate securely over the same Blockchain network, taking advantage of their data's openness and read/write nature. The proposed application is made up of various application interfaces for various stakeholders. The proposed solution employs Hyperledger Fabric and Blockchain to ensure data integrity, privacy, permissions, and service availability. In the application portal, each user has a profile. The creation of a unique identity for each user, as well as the establishment of digital information centers across the country, has greatly aided the process. This application collects health data from each user in a systematic manner, which is useful for research institutes and healthcare-related organizations. For this application, a national data warehouse in Bangladesh is feasible, and various healthcare-related analyses can be performed using the collected data, assisting the strategy and planning department in making informed decisions regarding the healthcare sector in Bangladesh. Because Bangladesh has both public and private healthcare providers, a simple digital strategy is essential for all organizations to accomplish their services. This study proposes a solution to achieve this goal.
In scenarios, like critical public safety communication networks, On-Scene Available (OSA) user equipment (UE) may be only partially connected with the network infrastructure, e.g., due to physical ...damages or on-purpose deactivation by the authorities. In this work, we consider multi-hop Device-to-Device (D2D) communication in a hybrid infrastructure where OSA UEs connect to each other in a seamless manner in order to disseminate critical information to a deployed command center. The challenge that we address is to simultaneously keep the OSA UEs alive as long as possible and send the critical information to a final destination (e.g., a command center) as rapidly as possible, while considering the heterogeneous characteristics of the OSA UEs. We propose a dynamic adaptation approach based on machine learning to improve a joint energy-spectral efficiency (ESE). We apply a Q-learning scheme in a hybrid fashion (partially distributed and centralized) in learner agents (distributed OSA UEs) and scheduler agents (remote radio heads or RRHs), for which the next hop selection and RRH selection algorithms are proposed. Our simulation results show that the proposed dynamic adaptation approach outperforms the baseline system by approximately 67% in terms of joint energy-spectral efficiency, wherein the energy efficiency of the OSA UEs benefit from a gain of approximately 30%. Finally, the results show also that our proposed framework with C-RAN reduces latency by approximately 50% w.r.t. the baseline.
Device-to-device (D2D) communication is an essential feature for the future cellular networks as it increases spectrum efficiency by reusing resources between cellular and D2D users. However, the ...performance of the overall system can degrade if there is no proper control over interferences produced by the D2D users. Efficient resource allocation among D2D User equipments (UE) in a cellular network is desirable since it helps to provide a suitable interference management system. In this paper, we propose a cooperative reinforcement learning algorithm for adaptive resource allocation, which contributes to improving system throughput. In order to avoid selfish devices, which try to increase the throughput independently, we consider cooperation between devices as promising approach to significantly improve the overall system throughput. We impose cooperation by sharing the value function/learned policies between devices and incorporating a neighboring factor. We incorporate the set of states with the appropriate number of system-defined variables, which increases the observation space and consequently improves the accuracy of the learning algorithm. Finally, we compare our work with existing distributed reinforcement learning and random allocation of resources. Simulation results show that the proposed resource allocation algorithm outperforms both existing methods while varying the number of D2D users and transmission power in terms of overall system throughput, as well as D2D throughput by proper Resource block (RB)-power level combination with fairness measure and improving the Quality of service (QoS) by efficient controlling of the interference level.
Multi-access Edge Computing (MEC) allows a mobile user to access a service on a computing device called MEC Host (MEH), enabling lower latency by running the service closer to the users. When the ...user moves away from the serving MEH, the latency increases, which may cause a disruption of the user experience and of the service continuity. Moreover, the serving MEH may also fail, making the service unavailable. We propose a solution to a service migration problem that maximizes the MEC service availability by jointly deciding (i) migration timing and (ii) target MEH based on latency constraint, resource constraint, and availability status of a MEH. We solve the problem by using Deep Reinforcement Learning (DRL). The experiment shows that our proposed solution can successfully maintain a high service availability (more than 94%) in the presence of different failure probabilities, while another algorithm gives unstable service availability.
Abstract
In the fifth-generation (5G) of mobile networks, Multi-Access Edge Computing (MEC) refers to the deployment of computing resources closer to the end-users for improved service delivery. In ...the context of 5G MEC, the slice broker plays a crucial role in managing the allocation of resources among the different network slices, which are logical networks on top of a shared infrastructure. The slice broker is a business entity that acts as an intermediary between the slice tenants and the infrastructure provider and is responsible for allocating resources (such as CPU, memory, and network bandwidth) required to set up the network. The slice broker must ensure that resources are allocated in a way that the revenue is maximized. In a dynamic environment, the slice broker must learn the revenue model adaptively and online. Adversaries can significantly reduce the revenue by misleading the system about the resources pretending to be selfish nodes, or creating noise. The slice broker should learn the revenue model in the presence of adversaries. We apply cooperative deep reinforcement learning with consensus mechanism and consensus deep learning to learn the revenue model adaptively. We also compare our proposed methods with the reference solution. Simulation results show that our proposed methods, especially the cooperative version, outperform the reference solution.
Wireless sensor networks (WSNs) are an attractive platform for monitoring and measuring physical phenomena. WSNs typically consist of hundreds or thousands of battery-operated tiny sensor nodes which ...are connected via a low data rate wireless network. A WSN application, such as object tracking or environmental monitoring, is composed of individual tasks which must be scheduled on each node. Naturally the order of task execution influences the performance of the WSN application. Scheduling the tasks such that the performance is increased while the energy consumption remains low is a key challenge. In this paper we apply online learning to task scheduling in order to explore the tradeoff between performance and energy consumption. This helps to dynamically identify effective scheduling policies for the sensor nodes. The energy consumption for computation and communication is represented by a parameter for each application task. We compare resource-aware task scheduling based on three online learning methods: independent reinforcement learning (RL), cooperative reinforcement learning (CRL), and exponential weight for exploration and exploitation (Exp3). Our evaluation is based on the performance and energy consumption of a prototypical target tracking application. We further determine the communication overhead and computational effort of these methods.
Wireless sensor networks are creating a new era of pervasive computing applications, such as various monitoring and tracking system. The sensor network consists of so many tiny sensor nodes that have ...so many critical challenges, since they are battery operated and have limited processing capabilities. Binary sensor networks are modeled in a way that the sensor nodes can communicate with the only 1 b of information. One of the challenges in a binary sensor network is to localize the multiple sources. Very few works have been done considering this challenge. Localization failure may cause the whole system useless. We propose a multiple source localization method. We convert the localization problem into an optimization problem, and we solve that optimization problem using primal dual interior point method. Simulation results show that our proposed method provides better performance in every perspective compared with the existing works.
A wireless sensor network (WSN) is composed of a large number of tiny sensor nodes. Sensor nodes are very resource-constrained, since nodes are often battery-operated and energy is a scarce resource. ...In this paper, a resource-aware task scheduling (RATS) method is proposed with better performance/resource consumption trade-off in a WSN. Particularly, RATS exploits an adversarial bandit solver method called exponential weight for exploration and exploitation (Exp3) for target tracking application of WSN. The proposed RATS method is compared and evaluated with the existing scheduling methods exploiting online learning: distributed independent reinforcement learning (DIRL), reinforcement learning (RL), and cooperative reinforcement learning (CRL), in terms of the tracking quality/energy consumption trade-off in a target tracking application. The communication overhead and computational effort of these methods are also computed. Simulation results show that the proposed RATS outperforms the existing methods DIRL and RL in terms of achieved tracking performance.
One of the challenging issues in a distributed computing system is to reach on a decision with the presence of so many faulty nodes. These faulty nodes may update the wrong information, provide ...misleading results and may be nodes with the depleted battery power. Consensus algorithms help to reach on a decision even with the faulty nodes. Every correct node decides some values by a consensus algorithm. If all correct nodes propose the same value, then all the nodes decide on that. Every correct node must agree on the same value. Faulty nodes do not reach on the decision that correct nodes agreed on. Binary consensus algorithm and average consensus algorithm are the most widely used consensus algorithm in a distributed system. We apply binary consensus and average consensus algorithm in a distributed sensor network with the presence of some faulty nodes. We evaluate these algorithms for better convergence rate and error rate.
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