Nearby Devices Research Articles

Design and Analysis of Smart Shoe

The purpose of this report is to provide a new product to make life much and a lot simpler, and we as engineer this is one of our responsibilities, a smart shoe that would comfort the user. Our task would the generation of reliable amount of power by using the conversion of mechanical energy into electrical energy using piezoelectric materials and stored it, to use many purposes such as (Mobile charging, lighting, etc.). A model for a shoe that enabled with the obstacle sensors which are help to walking of blind people and Lighting arrangement for its better and advanced applications for differently able and rural users. The shoe will be also could pair location and navigation which identifies the location of place and Listen to the sounds & audio nearby device anytime secretly without making any sound. It has also, with help of SOS Panic button feature. Press Panic/SOS button will call to relative/family number and alert them for help. so, it provides safety for the people.

IoT Based Water Quality Monitoring System

Water pollution is the biggest problem nowadays. It is increasing as new technology evolves and human population increases. Evolving technologies makes human life easy. But they have huge impact on the environment, and one of the main problems is water pollution, which causes various diseases and other issues in living beings. So, it has to be monitored continuously and reduced as much as possible to ensure the provision of clean drinking water. This research presents the solution to monitor the water quality parameters like water conductivity, turbidity, pH continuously and send these values to the LCD and nearby devices using Wi-Fi module. Moreover, this system is designed to measure the quantity of water being monitored using water flow sensor. Water quality parameters are measured by different sensors such as pH, TDS, and Turbidity, which are connected to microcontroller (Arduino Uno). The proposed system uses GSM module to send the message to the officials during abnormal conditions. Hardware results of the proposed system are also provided in this work.

Collaborative AI-Enabled Intelligent Partial Service Provisioning in Green Industrial Fog Networks

With the evolutionary development of the latency-sensitive industrial Internet-of-Things (IIoT) applications, delay restriction becomes a critical challenge, which can be resolved by distributing IIoT applications on nearby fog devices. Besides that, efficient service provisioning and energy optimization are confronting serious challenges with the ongoing expansion of large-scale IIoT applications. However, due to insufficient resource availability, a single fog device cannot execute large-scale applications completely. In such a scenario, a partial service provisioning strategy provides a promising outcome to enable the services on multiple fog devices or collaboration with cloud servers. By motivating this scenario, in this article, we introduce a new deep reinforcement learning (DRL)-enabled partial service provisioning strategy in the green industrial fog networks. With this strategy, multiple fog devices share the excessive workload of an application among themselves. To reflect this, a task partitioning policy is introduced to partition the requested applications into a set of independent or interdependent tasks. Furthermore, we develop an intelligent partial service provisioning strategy to utilize maximum fog resources in the network. The experimental results express the significance of the proposed strategy over the traditional baseline algorithms in terms of energy consumption and latency up to 25% and 16%, respectively.

Evaluation of Conducted Electromagnetic Interference Behavior of Copper Vapor Laser

High power and high repetition rate electrically excited copper vapor laser (CVL) is a strong source of radiated and conducted electromagnetic interference (EMI). The thermal energy required to raise the temperature (∼1600°C) to generate copper vapor and the required voltage pulse for population inversion is provided by the pulse generating unit (PGU). The PGU generates a voltage pulse of the order of ∼13 kV with a rise time of less than 100 ns and a current pulse in the range of ∼500 A with a similar rise time. The high voltage and current generated by PGU along with the high voltage pulse discharge in the laser tube serves as a prominent source of EMI and affects the performance of nearby electronic devices. This article presents a comprehensive study of conducted EMI from a high-power and high voltage discharge inside the CVL system and its impact on laser performance. The conducted noise from the laser is a function of laser impedance and is proportional to the magnitude of load mismatch. A simple and robust technique is presented to reduce the conducted noise from CVL. The correlation between the laser electrode pin configurations and the conducted noise is also presented.

An Auction-Based Bid Prediction Mechanism for Fog-Cloud Offloading Using Q-Learning

In the fog computing paradigm, if the computing resources of an end device are insufficient, the user’s tasks can be offloaded to nearby devices or the central cloud. In addition, due to the limited energy of mobile devices, optimal offloading is crucial. The method presented in this paper is based on the auction theory, which has been used in recent studies to optimize computation offloading. We propose a bid prediction mechanism using Q-learning. Nodes participating in the auction announce a bid value to the auctioneer entity, and the node with the highest bid value is the auction winner. Then, only the winning node has the right to offload the tasks on its upstream (parent) node. The main idea behind Q-learning is that it is stateless and only considers the current state to perform an action. The evaluation results show that the bid values predicted by the Q-learning method are near-optimal. On average, the proposed method consumes less energy than traditional and state-of-the-art techniques. Also, it reduces the execution time of tasks and leads to less consumption of network resources.

A New Task Scheduling Framework for Internet of Things based on Agile VNFs On-demand Service Model and Deep Reinforcement Learning Method

Recent innovations in the Internet of Things (IoT) have given rise to IoT applications that require quick response times and low latency. Fog computing has proven to be an effective platform for handling IoT applications. It is a significant challenge to deploy fog computing resources effectively because of the heterogeneity of IoT tasks and their delay sensitivity. To take advantage of idle resources in IoT devices, this paper presents an edge computing concept that offloads edge tasks to nearby IoT devices. The IoT-assisted edge computing should meet two conditions, edge services should exploit the computing resources of IoT devices effectively and edge tasks offloaded to IoT devices do not interfere with local IoT tasks. Two main phases are included in the proposed method: virtualization of edge nodes, and task scheduling based on deep reinforcement learning. The first phase offers a layered edge framework. In the second phase, we applied deep reinforcement learning (DRL) to schedule tasks taking into account the diversity of tasks and the heterogeneity of available resources. According to simulation results, our proposed task scheduling method achieves higher levels of task satisfaction and success than existing methods.

A lightweight deep learning approach for COVID-19 detection using X-ray images with edge federation.

This study aims to develop a lightweight convolutional neural network-based edge federated learning architecture for COVID-19 detection using X-ray images, aiming to minimize computational cost, latency, and bandwidth requirements while preserving patient privacy. The proposed method uses an edge federated learning architecture to optimize task allocation and execution. Unlike in traditional edge networks where requests from fixed nodes are handled by nearby edge devices or remote clouds, the proposed model uses an intelligent broker within the federation to assess member edge cloudlets' parameters, such as resources and hop count, to make optimal decisions for task offloading. This approach enhances performance and privacy by placing tasks in closer proximity to the user. DenseNet is used for model training, with a depth of 60 and 357,482 parameters. This resource-aware distributed approach optimizes computing resource utilization within the edge-federated learning architecture. The experimental results demonstrate significant improvements in various performance metrics. The proposed method reduces training time by 53.1%, optimizes CPU and memory utilization by 17.5% and 33.6%, and maintains accurate COVID-19 detection capabilities without compromising the F1 score, demonstrating the efficiency and effectiveness of the lightweight convolutional neural network-based edge federated learning architecture. Existing studies predominantly concentrate on either privacy and accuracy or load balancing and energy optimization, with limited emphasis on training time. The proposed approach offers a comprehensive performance-centric solution that simultaneously addresses privacy, load balancing, and energy optimization while reducing training time, providing a more holistic and balanced solution for optimal system performance.

Identification and Visualization of a Patient’s Medical Record via Mobile Devices without an Internet Connection

Nowadays, people’s medical records are crucial when it comes to providing treatments, discovering pathologies, or keeping track of health status. Advances in technology have allowed these records to be increasingly digitized, to the point that they can be consulted by specialists from anywhere. This also allows people to report their health status, allergies, or treatments. However, knowing a person’s medical history is a delicate and complex process, and accessing these digitized data is not always possible due to problems with network connectivity or physical communication with the person. In this work, we propose a solution for medical staff to obtain a patient’s medical history through facial recognition, and without the need for an internet connection. This proposal is based on the development of an architecture that allows connecting nearby mobile devices of doctors and patients without an internet connection to obtain the desired information using facial recognition as an authentication method. The architecture has been validated with the development of a mobile application that, by focusing on the patient’s face with the camera of the doctor’s mobile device, makes it possible to obtain the patient’s medical information. With this proposal, it is possible to obtain the medical history of a person in dangerous situations or where the connectivity is limited or non-existent, in a simple and fast way.

Fuzzy Q-Learning-Based Opportunistic Communication for MEC-Enhanced Vehicular Crowdsensing

This study focuses on MEC-enhanced, vehicle-based crowdsensing systems that rely on devices installed on automobiles. We investigate an opportunistic communication paradigm in which devices can transmit measured data directly to a crowdsensing server over a 4G communication channel or to nearby devices or so-called Road Side Units positioned along the road via Wi-Fi. We tackle a new problem that is how to reduce the cost of 4G while preserving the latency. We propose an offloading strategy that combines a reinforcement learning technique known as Q-learning with Fuzzy logic to accomplish the purpose. Q-learning assists devices in learning to decide the communication channel. Meanwhile, Fuzzy logic is used to optimize the reward function in Q-learning. The experiment results show that our offloading method significantly cuts down around 30-40% of the 4G communication cost while keeping the latency of 99% packets below the required threshold.

A blockchain-orchestrated deep learning approach for secure data transmission in IoT-enabled healthcare system

The integration of the Internet of Things (IoT) with traditional healthcare systems has improved quality of healthcare services. However, the wearable devices and sensors used in Healthcare System (HS) continuously monitor and transmit data to the nearby devices or servers using an unsecured open channel. This connectivity between IoT devices and servers improves operational efficiency, but it also gives a lot of room for attackers to launch various cyber-attacks that can put patients under critical surveillance in jeopardy. In this article, a Blockchain-orchestrated Deep learning approach for Secure Data Transmission in IoT-enabled healthcare system hereafter referred to as “BDSDT” is designed. Specifically, first a novel scalable blockchain architecture is proposed to ensure data integrity and secure data transmission by leveraging Zero Knowledge Proof (ZKP) mechanism. Then, BDSDT integrates with the off-chain storage InterPlanetary File System (IPFS) to address difficulties with data storage costs and with an Ethereum smart contract to address data security issues. The authenticated data is further used to design a deep learning architecture to detect intrusion in HS network. The latter combines Deep Sparse AutoEncoder (DSAE) with Bidirectional Long Short-Term Memory (BiLSTM) to design an effective intrusion detection system. Experiments on two public data sources (CICIDS-2017 and ToN-IoT) reveal that the proposed BDSDT outperformed state-of-the-arts in both non-blockchain and blockchain settings and have obtained accuracy close to 99% using both datasets.

Edge AR X5: An Edge-Assisted Multi-User Collaborative Framework for Mobile Web Augmented Reality in 5G and Beyond

Multi-user mobile Augmented Reality (AR) has been successfully used in various fields as a novel visual interaction technology. But current mainstream wearable device-based and app-based solutions are still facing cross-platform, real-time communication, and intensive computing requirements. Mobile Web technology is envisioned to be a promising supporting technology for cross-platform application of mobile AR especially in 5G networks, which provide pervasive communication and computing resources thereby forming a formidable framework for the practical application of multi-user mobile Web AR. However, the problem of how to use these new techniques properly to achieve efficient communication and computing collaboration is obviously paramount in order for multi-user mobile Web AR to be realized in 5G networks. In this article, we propose the first edge-assisted multi-user collaborative framework for mobile Web AR in the 5G era. First, we propose a heuristic mechanism BA-CPP for efficient communication planning, which allows multi-user interaction synchronization to be achieved. Second, we introduce a motion-aware key frame selection mechanism called Mo-KFP to optimize the computational efficiency of the edge system, and simultaneously alleviate the initialization problem by collaborating with nearby mobile devices using the Device-to-Device (D2D) communication technique. Experiments are conducted in a real-world 5G network, and the results demonstrate the superiority of our proposed collaborative framework.

Scalable Dew Computing

Dew computing differs from the classical cloud and edge computing by bringing devices closer to the end-users and adding autonomous processing independent from the Internet, but it is still able to collaborate with other devices to exchange information on the Internet. The difference is expressed also on scalability, since edge and cloud providers can provide (almost endless) resources, and in the case of dew computing the scalability needs to be realized on the level of devices, instead of servers. In this paper, we introduce an approach to provide deviceless and thingless computing and ensure scalable dew computing. The deviceless approach allows functions to be executed on nearby devices found closer to the user, and the thingless approach goes even further, providing scalability on a low-level infrastructure that consists of multiple things, such as IoT devices. These approaches introduce the distribution of computing to other smart devices or things on a lower architectural level. Such an approach enhances the existing dew computing architectural model as a sophisticated platform for future generation IoT systems.

D2D-Assisted Multi-User Cooperative Partial Offloading in MEC Based on Deep Reinforcement Learning.

Mobile edge computing (MEC) and device-to-device (D2D) communication can alleviate the resource constraints of mobile devices and reduce communication latency. In this paper, we construct a D2D-MEC framework and study the multi-user cooperative partial offloading and computing resource allocation. We maximize the number of devices under the maximum delay constraints of the application and the limited computing resources. In the considered system, each user can offload its tasks to an edge server and a nearby D2D device. We first formulate the optimization problem as an NP-hard problem and then decouple it into two subproblems. The convex optimization method is used to solve the first subproblem, and the second subproblem is defined as a Markov decision process (MDP). A deep reinforcement learning algorithm based on a deep Q network (DQN) is developed to maximize the amount of tasks that the system can compute. Extensive simulation results demonstrate the effectiveness and superiority of the proposed scheme.

Efficient and Privacy-Preserving Certificate Activation for V2X Pseudonym Certificate Revocation

Vehicle to everything (V2X) technology allows the broader development of driving safety, efficiency, and comfort. Because the vehicles can quickly send and receive frequent messages from other vehicles and nearby devices, e.g., cooperative awareness message applications on the intelligent transport system (ITS), V2X requires a good security and privacy protection system to make the messages reliable for the ITS requirements. The existing standards developed in the US and Europe use many short valid period pseudonym certificates to meet the security and privacy requirements. However, this method has difficulty ensuring that revoked pseudonym certificates are treated as revoked by any vehicles because distributing revocation information on a wireless vehicular network with intermittent and rapidly changing topology is demanding. A promising approach to solving this problem is the periodic activation of released pseudonym certificates. Initially, it releases all required pseudonym certificates for a certain period to the vehicle, and pseudonym certificates can be used only after receiving an activation code. Such activation-code-based schemes have a common problem in the inefficient use of network resources between the road-side unit (RSU) and vehicles. This paper proposes an efficient and privacy-preserving activation code distribution strategy solving the problem. By adopting the unicast distribution model of modified activation code for pseudonym certificate (ACPC), our scheme can obtain benefits of efficient activation code distribution. The proposed scheme provides small communication resource usage in the V2X network with various channel options for delivering activation codes in a privacy preserved manner.

Socially-Aware Energy-Efficient Task Partial Offloading in MEC Networks With D2D Collaboration

The future wireless network will face demands of massive connectivity and intensive computation with the increase of mobile devices. Mobile edge computing (MEC) and Device-to-Device (D2D) have emerged as promising technologies to address the above challenges, and implementing social relationships in D2D-MEC networks can improve the reliability of D2D links. Exploiting these benefits, we investigate the energy-efficient task offloading problem in socially-aware D2D-assisted MEC networks, where the user devices can offload tasks to the nearby device or further forward to the MEC server based on social relationships. Specifically, we design a task partial offloading scheme of joint D2D connection selection, transmit power control and task allocation, to maximize the long-term network utility with considering dynamic system status and random task arrival. First, the social relationship among users is quantified into a social trust matrix. As the formulated socially-aware energy-efficient problem is a long-term stochastic optimization problem that is directly intractable, we thus employ the Lyapunov optimization to transform it into a series of short-term problems, each of which can be solved by the Karush-Kuhn-Tucker method and a pricing-based matching algorithm. Finally, we verify the performance optimality and the long-term network stability through numerical simulations as well as theoretical analysis.

Split computing: DNN inference partition with load balancing in IoT-edge platform for beyond 5G

In the era of beyond 5G technology, it is expected that more and more applications can use deep neural network (DNN) models for different purposes with minimum inference time which is highly important for providing a great user experience in the Internet of Things (IoT) use-cases. However, due to hardware limitations and low computational capabilities, it is almost impossible to execute complex DNN models on IoT devices. To provide a better user experience and mitigate these problems, we introduce split computing technology where the DNN model inference task is partially offloaded from an IoT device to a nearby dependable device called edge. While splitting the DNN between IoT-edge, it is unclear how much the DNN model needs to be partitioned according to varying network conditions to ensure a satisfactory user experience. To address these issues, we propose the following mechanisms that provide a trade-off between the computation and communication, namely: (1) dynamic split computation (DSC) mechanism: finds an optimal partitioning of the DNN model to be executed between IoT and edge to reduce the computation overhead (i.e., overall inference time); (2) reliable communication network switching (RCNS) mechanism: intelligently finds a suitable network to connect to either Wi-Fi/Cellular/Bluetooth to provide reliable data transport between IoT and edge. Further, as the edge devices perform DNN tasks extensively, it is possible that they run out of battery, or get interrupted by high-priority user applications executing on them due to which edge devices cannot complete the assigned DNN task in the desired time. To address these issues, we also propose a load-balancing mechanism between IoT and edge called task load balancing with prioritization (TLBP) model for single and multiple DNN task scenarios. We conduct extensive experiments with Raspberry Pi as an IoT device and Samsung Galaxy S20 smartphones as edge devices to test the proposed mechanism. The results show that the proposed algorithms substantially reduce the inference time of DNN models as compared to the on-device inference time and balance the tasks across the edge devices to minimize excessive battery drainage.

A Systems Analysis of Energy Usage and Effectiveness of a Counter-Unmanned Aerial System Using a Cyber-Attack Approach

Existing counter-unmanned aerial system (C-UAS) defensive mechanisms rely heavily on radio frequency (RF) jamming techniques that require a large amount of energy to operate. The effects of RF jamming result in undesirable consequences, such as the jamming of other nearby friendly radio devices as well as the increase in RF footprint for local operators. Current cybersecurity analysis of commercial off-the-shelf (COTS) UASs have revealed multiple vulnerabilities that give rise to opportunities to conduct C-UAS operations in the cyber domain. This is achieved by performing cyber-attacks on adversarial UASs through hijacking the device-specific communication’s link on a narrow RF band and without the need for broad-spectrum RF energy bursts during C-UAS operations, which can result in lower energy usage to accomplish the same outcome. This article validates the cyber-attack C-UAS (CyC-UAS) concept through reviewing recent C-UAS operational experimental scenarios and conducting analysis on the collected data. Then, a simulation model of a defense facility is constructed to analyze and validate specific mission scenarios of interest and several proposed concepts of operation. A comparison of the energy requirements between CyC-UAS and existing C-UAS techniques is performed to assess energy efficiency and trade-offs of different C-UAS approaches. In this article, the comparison of energy requirements between the CyC-UAS prototype and existing C-UAS products that utilize RF jamming methods reveals that CyC-UAS achieves significant energy savings while not affecting other telecommunication devices operating at the same frequencies. While both the C-UAS techniques adopt the denial-of-service strategy, the CyC-UAS is able to achieve the same mission by consuming much less energy. Therefore, the CyC-UAS concept shows promise as a new, lower energy, and lower collateral damage approach to defending against UAS.

Joint Computation Offloading and Resource Allocation for NOMA-Enabled Multitask D2D System

Due to the limited computing capacity of mobile device and high network-accessing delay, user in the area where mobile terminals are densely distributed (e.g., schools, malls, and hospitals) will experience high latency when processing multiple computation-intensive tasks. In this paper, a computation offloading scheme based on Device-to-Device (D2D) communication is proposed to deal with the problem that users have multiple tasks to process. Exploiting nonorthogonal multiple access (NOMA), user can offload tasks to multiple nearby devices that have available idle computing resource. We aim to minimize the user’s total cost including time latency, energy consumption, and offloading charge, which is formulated as a mixed integer nonlinear programming (MINLP) problem. We use decomposition approach to solve our problem and propose a two-layer optimization scheme named Multitask Joint Computation Offloading and Resource Allocation (MT-JCORA). In the inner layer, the NOMA-transmission time optimization problem in given task offloading decision is proved as a strictly convex problem. In the outer layer, we design a heuristic algorithm based on GA algorithm to obtain the optimal task offloading decision. Simulation results demonstrate that MT-JCORA can effectively reduce the total cost of user compared with related schemes.

Latency-aware computation offloading and DQN-based resource allocation approaches in SDN-enabled MEC

The proposed mobile edge computing can transfer the computing tasks in mobile applications to the nearby edge devices, effectively reducing the processing pressure of local servers and avoiding delays in backhaul and core networks, thus better solving the problem that cloud computing cannot effectively handle resource allocation. However, the complexity of the wireless environment during the communication process leads to the fact that the computing tasks are easily caused by the increase in the number of traffic and packet loss when they are uploaded to the MEC side through the wireless link, which cannot guarantee a lower total system energy consumption and shorter total delay. To address the problem that mobile devices cannot handle many computationally intensive tasks in a timely manner, this paper proposes a task offloading optimization scheme for SDN-enabled MEC environments. Modeling the computation offloading problem based on Lyapunov optimization, and then analyzes the offloading delay with respect to the offloading gain. In order to guarantee application requirements, minimize energy consumption and latency, and better satisfy user QoS requests, this paper proposes a resource allocation strategy based on deep reinforcement learning. The strategy designs a DQN-based resource allocation algorithm to deploy a joint optimal offloading decision and resource allocation scheme in a mobile edge computing environment under the limited computational resources and the latency constraints of the computational tasks. Based on the experimental results, it is shown that the proposed task offloading strategy can reduce the overall latency; the proposed resource allocation strategy can reduce the total energy consumption and total latency of the system and improve the successful execution rate of tasks.

Compute With Nearby Mobile Devices Using Work Stealing Algorithm – Cloud Technology

Abstract: As mobile devices evolve to be powerful and pervasive computing tools, their usage also continues to increase rapidly. However, mobile device users frequently experience problems when running intensive applications on the device itself, or offloading to remote clouds, due to resource shortage and connectivity issues. Ironically, most users‟ environments are saturated with devices with significant computational resources. This paper argues that nearby mobile devices can efficiently be utilized as a crowd-powered resource cloud to complement the remote clouds. Node heterogeneity, unknown worker capability, and dynamism are identified as essential challenges to be addressed when scheduling work among nearby mobile devices. We present a work-sharing model, called Honeybee, using an adaptation of the well-known work stealing method to load balance independent jobs among heterogeneous mobile nodes, able to accommodate nodes randomly leaving and joining the system [3].The overall strategy of Honeybee is to focus on short-term goals, taking advantage of opportunities as they arise, based on the concepts of proactive workers and opportunistic delegator.