Internet Of Everything Devices Research Articles

A systematic literature review for load balancing and task scheduling techniques in cloud computing

Cloud computing is an emerging technology composed of several key components that work together to create a seamless network of interconnected devices. These interconnected devices, such as sensors, routers, smartphones, and smart appliances, are the foundation of the Internet of Everything (IoE). Huge volumes of data generated by IoE devices are processed and accumulated in the cloud, allowing for real-time analysis and insights. As a result, there is a dire need for load-balancing and task-scheduling techniques in cloud computing. The primary objective of these techniques is to divide the workload evenly across all available resources and handle other issues like reducing execution time and response time, increasing throughput and fault detection. This systematic literature review (SLR) aims to analyze various technologies comprising optimization and machine learning algorithms used for load balancing and task-scheduling problems in a cloud computing environment. To analyze the load-balancing patterns and task-scheduling techniques, we opted for a representative set of 63 research articles written in English from 2014 to 2024 that has been selected using suitable exclusion-inclusion criteria. The SLR aims to minimize bias and increase objectivity by designing research questions about the topic. We have focused on the technologies used, the merits-demerits of diverse technologies, gaps within the research, insights into tools, forthcoming opportunities, performance metrics, and an in-depth investigation into ML-based optimization techniques.

Stabilizing Schottky junction in conjugated polymer diodes enables long-term reliable radio-frequency energy harvesting on plastic

Due to their inherent flexibility, solution-processable conjugated polymers are increasingly being considered for the cost-effective production of thin-film semiconductor devices used in Internet of Everything (IoE) applications. With considerable improvements in charge carrier mobilities, the final challenge impeding the commercialization of conjugated polymers may be improving their environmental and electrical stabilities. Recent studies have improved the stability of computing devices (i.e., transistors) by eliminating interface traps and water molecules within conjugated polymers. However, the stability issue of Schottky diodes, which play a crucial role in configuring thin-film IoE devices used in wireless communication and energy harvesting, has been largely overlooked. This study reveals that aluminum, which is commonly used as a cathode metal in polymer Schottky diodes, creates a nonstoichiometric effect when deposited on conjugated polymers, thereby leading to the formation of charge traps over time, which reduces the rectification ratio of the Schottky diodes and induces a significant bias stress effect during operation. To address this issue, we introduce a zinc-oxide sacrificial interlayer between the conjugated polymer and cathode. This interlayer effectively eliminates the penetrated Al metal or ionized Al-induced nonstoichiometric effect without reducing the charge injection efficiency, achieving exceptional environmental and operational stability. The printed polymer Schottky diodes demonstrate consistent rectifying operation at 13.56 MHz for several months with negligible changes in electrical characteristics.

A Channel-aware FL Approach for Virtual Machine Placement in 6G Edge Intelligent Ecosystems

This article deals with an artificial intelligence (AI) framework to support Internet-of-everything (IoE) applications over sixth-generation wireless (6G) networks. An integrated IoE-Edge Intelligence ecosystem is designed to effectively face the problems of Virtual Machines (VMs) placement based on their popularity, computation offloading optimization, and system reliability improvement predicting compute nodes faults. The main objective of the article is to increase performance in terms of minimization of worst end-to-end (e2e) delay, percentage of requests in outage, and the enhancement of reliability. The article focuses on the following main issues: (i) proposal of a channel-aware federated learning (FL) approach to forecast the popularity of the VMs required by IoE devices; (ii) use of an AI-based channel conditions forecasting module at the benefits of the FL process; (iii) development of a suitable VMs placement on the basis of their popularity and of an efficient tasks allocation technique based on a modified version of the auction theory (AT) and a proper matching game; (iv) enhancement of the system reliability by an echo-state-network (ESN), located on each computation node and running in the background to predict failures and anticipate tasks migration. Numerical results validate the effectiveness of the proposed strategy for IoE applications over 6G networks.

CETS: Enabling Sustainable IoT with Cooperative Energy Transfer Schedule towards 6G Era.

The large scale of the Internet of Things necessitates using long-lasting physical layer devices for data collection. Deploying large numbers of Wi-Fi-enabled devices is expensive, so the Internet of Everything (IoE) is equipped with multiple communication modules to collect data where Wi-Fi is unavailable. However, because of their extended communication capabilities, IoE devices face energy limitations. As a result, IoE devices must be provided with the necessary energy resources. This paper introduces a novel multi-hop cooperation communication mechanism for Wireless Energy Transfer (WET) in the Wireless Powered-Internet of Everything (WP-IoE). IoE devices are outfitted here with various communication devices such as RF, Bluetooth, and Wi-Fi. This research proposes a two-phase energy transmission schedule to address the energy requirements. For data collection, the first phase provides a distributed tree-based data communication plan. The proposed model’s second phase used the reverse data collection protocol to implement wireless energy transmission. By combining these two phases, an optimized WET framework was created without unmanned aerial vehicles or robots. The experimental findings show that the proposed method in this research increases the average lifetime of the network and has a more significant charge latency and average charge throughput than other models.

OAM-SWIPT for IoE-Driven 6G

Simultaneous wireless information and power transfer (SWIPT), which achieves both wireless energy transfer (WET) and information transfer, is an attractive technique for future Internet of Everything (IoE) in 6G mobile communications. With SWIPT, battery-less IoE devices can be powered while communicating with other devices. Line-of-sight (LoS) RF transmission and near-field inductive-coupling-based transmission are typical SWIPT scenarios, which are both LoS channels and without enough degree of freedom for high spectrum efficiency as well as high energy efficiency. Due to the orthogonal wavefronts, orbital angular momentum (OAM) can facilitate SWIPT in LoS channels. In this article, we introduce OAM-based SWIPT and discuss some of its basic advantages and challenges. After introducing OAM-based SWIPT for IoE, we first propose an OAM-based SWIPT system model with the OAM mode-assisted dynamic power splitting. Then four basic advantages of OAM-based SWIPT are reviewed with some numerical analyses to further demonstrate the advantages. Next, four challenges regarding integrating OAM into SWIPT and possible solutions are discussed. OAM technology provides multiple orthogonal streams to increase both spectrum and energy efficiencies for SWIPT, thus creating many opportunities for future WET and SWIPT researches.

Guest Editorial: Wireless Energy Transfer in Future Networks

With 5G starting to be commercially deployed, research on 6G mobile networks has started. One of the major key performance indicators for 6G is its extremely massive connectivity for small devices to enable the so-called Internet of Everything (IoE). Most of these IoE devices will be either battery-powered or battery-less. How to prolong the lifetime of these IoE devices becomes a key challenge.

Resource Allocation and Task Off-Loading for 6G Enabled Smart Edge Environments

There has been an enormous increase in information flow and communication data due to the rapid rise in the number of Internet of Everything (IoE) devices and the development of cutting-edge technologies such as the rollout of the Sixth Generation (6G) network. The rising and inevitable off-loading requirements of IoE devices have resulted in an unprecedented increase in the reliance on edge and cloud paradigms. However, such a reliance on far-end technologies to access already scarce resources can often result in increased latency and unstable connection issues due to limited bandwidth. In this paper, we investigate the solution for such a stringent network design by presenting a conceptual cloud architecture based on key components such as resource allocation, scheduling and task off-loading for IoE devices. The IoE devices utilise a scheduler to access resources from nearby higher resourced IoE devices for their task computation, where the scheduler allocates incoming requests according to the availability of resources within a cluster of devices or to other devices in nearby clusters. Motivated by these design characteristics, we propose a design of a novel Main Task Off-loading Scheduling Algorithm (MTOSA) for efficient task allocation and dissemination. We present a theoretical analysis of five different scheduling policies namely Round Robin (RR), Strongest Channel (SC), Max Rate (MR), Proportional Fair (PF) and Priority Base (PB) scheduling to find an optimal technique for task off-loading in futuristic networks. Furthermore, we compare the performance of these five scheduling policies with the two existing scheduling policies from the literature. It is shown through various experiments that the proposed MTOSA algorithm performs better when compared with the existing schemes for different performance parameters.

Decentralized adaptive resource-aware computation offloading & caching for multi-access edge computing networks

Decentralized computation offloading and caching in Multi-Access Edge Computing (MEC) is a promising approach to evolve the forthcoming network generation. MEC is the emerging technology that provides adaptive micro cloud services to the edge of proximity resource-constrained smart communication and Internet of Everything (IoE) devices for cellular subscribers. Nowadays, Massive IoE devices are exponentially connected to the global ecosystem. As a result, the backhaul network traffic grows enormously and users’ ultra-reliable low latency communications are challenging as well. In this paper, we explored decentralized adaptive resource-aware communication, computing, & caching framework which can orchestrate the dynamic network environments based on Deep Reinforcement Learning (DRL). Subsequently, the framework can perform augmented decision-making capabilities to enhance users’ connectivity and resource utilization requirements. Basically, every IoE device user are attempting to capitalize their own utilities. Hence, the problem is formulated using Non-cooperative game theory which is non-deterministic polynomial to solve the structural property of the MEC networks. We analyze and show that the game admits a Nash Equilibrium. Moreover, we have introduced a decentralized cognitive scheduling algorithm by exploiting DRL technology to leverage the utility of IoE & smart communication devices. Therefore, numerical results and theoretical analysis revealed that the proposed algorithm outperform, ultra-reliable low latency, and scalable than the baseline schemes.

Energy self-sustainability in 6G

With 5G being commercially deployed worldwide, research into 6G mobile communication networks has started. One of the major key performance indicators for 6G is its extremely massive connectivity for small devices to enable so-called Internet of Everything (IoE), which is a scale up from Internet of Things (IoT). Most of these IoE devices will be either battery-powered or battery-less. How to prolong the lifetime of these IoE devices becomes a key challenge. This calls for effort from two complementary aspects: 1) energy efficiency to reduce IoE device's energy consumption on one hand and 2) energy self-supply to open up new energy supply to IoE devices on the other. It is envisioned that 6G will provide energy self-sustainability (ESS) to massive IoE devices.

Securing the Internet of Everything through Adaptive and Resource-Efficient Mechanisms

The Internet of Everything (IoE) is the intelligent connection of people, process, data and things. In addition to machine-to-machine communications as facilitated in traditional networks and the Internet of Things, IoE enables machine-to-people and technology-assisted people-to-people interactions. An IoE local network may comprise various devices, including resource-constrained devices such as sensors, actuators, and embedded and wearable devices. The protection of such a network (and the data collected by and transmitted between network devices) against security attacks is highly challenging. First, the devices may be deployed in open environments without physical protection. More severely, the security protocols developed for traditional computer networks are ineffective or even inapplicable due to the resource-constrained nature of the end devices in IoE. In this article, we discuss the security challenges and requirements for data communications between IoE devices. We show that a suite of security protocols developed recently may provide an adaptive and resource-ef-ficient solution to IoE security.

Energy Self-Sustainability in Full-Spectrum 6G

Full-spectrum ranging from sub-6 GHz to THz and visible light will be exploited in 6G in order to reach unprecedented key-performance-indicators. However, an extraordinary amount of energy will be consumed by network infrastructure, while functions of massively deployed Internet of Everything (IoE) devices are limited by embedded batteries. Therefore, energy self-sustainable (ESS) 6G is proposed in this article. First of all, it may achieve network-wide energy efficiency by exploiting cell-free and airborne access networks as well as by implementing intelligent reflecting surfaces (IRSs). Second, by exploiting radio-frequency/visible-light signals for on-demand wireless information and energy provision (WIEP) and for enabling passive backscatter communication, “zero-energy” IoE devices may become a reality. Furthermore, IoE devices actively adapt their transceivers for better performance to a dynamic environment. Case studies of cell-free and IRS based WIEP are provided for demonstrating the advantage of our proposed architecture in terms of energy self-sustainability. This article aims to provide a first glance at primary designing principles of ESS-6G.

QoS-aware service provisioning in fog computing

Fog computing has emerged as a complementary solution to address the issues faced in cloud computing. While fog computing allows us to better handle time/delay-sensitive Internet of Everything (IoE) applications (e.g. smart grids and adversarial environment), there are a number of operational challenges. For example, the resource-constrained nature of fog-nodes and heterogeneity of IoE jobs complicate efforts to schedule tasks efficiently. Thus, to better streamline time/delay-sensitive varied IoE requests, the authors contributes by introducing a smart layer between IoE devices and fog nodes to incorporate an intelligent and adaptive learning based task scheduling technique. Specifically, our approach analyzes the various service type of IoE requests and presents an optimal strategy to allocate the most suitable available fog resource accordingly. We rigorously evaluate the performance of the proposed approach using simulation, as well as its correctness using formal verification. The evaluation findings are promising, both in terms of energy consumption and Quality of Service (QoS).

A job scheduling algorithm for delay and performance optimization in fog computing

SummaryDue to an ever‐increasing number of Internet of Everything (IoE) devices, massive amounts of data are produced daily. Cloud computing offers storage, processing, and analysis services for handling of such large quantities of data. The increased latency and bandwidth consumption is not acceptable to real‐time applications like online gaming, smart health, video surveillance, etc. Fog computing has emerged to overcome the increase in latency and bandwidth consumption in Cloud computing. Fog Computing provides storage, processing, networking, and analytical services at the edge of a network. As Fog Computing is still in its infancy, its significant challenges include resource‐allocation and job‐scheduling. The Fog devices at the edge of the network are resource‐constrained. Therefore, it is important to decide the assignment and scheduling of a job on a Fog node. An efficient job scheduling algorithm can reduce energy consumption and response time of an application request. In this paper, we propose a novel Fog computing scheduler that supports service‐provisioning for Internet of Everything, which optimizes delay and network usage. We present a case study to optimally schedule the requests of Internet of Everything devices on Fog devices and efficiently address their demands on available resources on every Fog device. We consider delay and energy consumption as performance metrics and evaluate the proposed scheduling algorithm using iFogSim in comparison with existing approaches. The results show that the delay and network usage of the proposed scheduler improve by 32% and 16%, respectively, in comparison with FCFS approach.

A ReRAM Macro Using Dynamic Trip-Point-Mismatch Sampling Current-Mode Sense Amplifier and Low-DC Voltage-Mode Write-Termination Scheme Against Resistance and Write-Delay Variation

Many cost-aware IoE devices require embedded nonvolatile memory (eNVM) to achieve high-speed read and low-power write operations for serving as code and data storage unit. Resistive random access memory (ReRAM) is a good candidate for eNVM of Internet-of-Everything (IoE) but suffers low read yield and require long read latency ( $T_{\text {CD}}$ ) against small $R$ -ratio, large cell-resistance variations, and device-mismatch induced input offset at current-mode sense amplifier (CSA). The wide distribution in write time also causes large wasted write power ( $E_{\text {W}}$ ) and long NVM-stress-time ( $T_{\text {STRS}}$ ). This paper proposes a bitline-current-aware small-offset CSA, using dynamic trip-point-mismatch sampling (DTPMS) scheme, to improve read yield and shorten $T_{\text {CD}}$ . This paper also proposes a low dc-current voltage-mode write termination (LDC-VWT) module, including SET and RESET termination circuits, to suppress $E_{\text {W}}$ and $T_{\text {STRS}}$ . A fabricated 65-nm 2-Mb ReRAM macro achieved $T_{\text {CD}}= 2.6$ ns and confirm the write-termination operations for reduction in $T_{\text {STRS}}$ and $E_{\text {W}}$ .

Firework: Data Processing and Sharing for Hybrid Cloud-Edge Analytics

Now we are entering the era of the Internet of Everything (IoE) and billions of sensors and actuators are connected to the network. As one of the most sophisticated IoE applications, real-time video analytics is promising to significantly improve public safety, business intelligence, and healthcare & life science, among others. However, cloud-centric video analytics requires that all video data must be preloaded to a centralized cluster or the cloud, which suffers from high response latency and high cost of data transmission, given the scale of zettabytes of video data generated by IoE devices. Moreover, video data is rarely shared among multiple stakeholders due to various concerns, which restricts the practical deployment of video analytics that takes advantages of many data sources to make smart decisions. Furthermore, there is no efficient programming interface for developers and users to easily program and deploy IoE applications across geographically distributed computation resources. In this paper, we present a new computing framework, <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Firework</i> , which facilitates distributed data processing and sharing for IoE applications via a virtual shared data view and service composition. We designed an easy-to-use programming interface for <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Firework</i> to allow developers to program on <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Firework</i> . This paper describes the system design, implementation, and programming interface of Firework. The experimental results of a video analytics application demonstrate that <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Firework</i> reduces up to 19.52 percent of response latency and at least 72.77 percent of network bandwidth cost, compared to a cloud-centric solution.

Fabrication of package embedded spiral inductors with two magnetic layers for flexible SIP point of load converters in Internet of Everything devices

Inductive DC-DC converters are key elements in power delivery units for the development of Internet of Everything (IoE) architectures made of interconnected networks of smart devices, self-powered from different energy sources. The ultra-low power levels involved in IoE devices in addition to cost reduction of final products requires the development of new technologies for the miniaturization of all the active and passive components. In this paper, a low temperature, cheap and simple two-steps fabrication process of embedded planar inductors with two magnetic layers on a very thin, flexible and lightweight FR4 organic substrate is demonstrated for flexible System in package (SIP) based point of load DC-DC converters in IoE devices. The process uses standard printed wiring board (PWB) copper etching to define the square geometry of the planar inductors and trenches underneath the inductor copper traces. Very thick top and bottom magnetic layers are deposited using stencil printing of an FR4 compatible epoxy-NiZn ferrite composite magnetic material on top of the copper traces and into the trenches. At 30MHz, the permittivity is 3.4 and 6.23 while the dielectric loss tangent is 0.014 and 0.009 for the epoxy and composite magnetic material respectively. The composite material has a permeability of 8.46, a loss tangent value of 0.1 at a frequency of 30MHz and saturates around 0.13 Tesla. The DC resistance varies from 136mΩ to 386mΩ while at 30MHz the AC resistance varies from 1.9Ω to 10.1Ω for an inductance value from 180nH to 715nH, corresponding to an inductance density between 5.69nH/mm2 and 12.47nH/mm2 and a quality factor between 13 and 17. Flexibility and its effect on the electrical parameters of the fabricated inductors is demonstrated through bending tests. A variation of <3% for the DC resistance is obtained for 5mm bend radius. At 30MHz and for a bend radius (both upward and downward) from 3.7mm to 5mm, the flat state (unbent) AC resistance and inductance values (1.9Ω to 10.1Ω for 180nH to 715nH) decrease by 6.5% to 18% and by 2% to 4.4% respectively which increase the self-resonant frequency by 3% to 3.4% and the quality factor by 2.2% to 20%. The characterized inductors are modeled in ANSYS HFSS electromagnetic simulator and simulation results show a good correspondence with the measured inductances and quality factors, which allows a reliable prediction of the inductors behavior for DC-DC converter design and implementation for IoE devices.