IoT Tasks Research Articles

TEBDS: A Trusted Execution Environment-and-Blockchain-supported IoT data sharing system

Data sharing services based on massive IoT data have been widely used in various fields such as health monitoring and image recognition, providing users with more reliable, efficient, and flexible data services and significantly improving the user service quality. With the popularization of IoT applications, the usage rate of IoT data is getting higher and higher. Due to the dangerous network environment, the security of IoT data faces challenges. On the one hand, attacks such as data tampering can lead to the failure of IoT tasks, and on the other hand, the intrusion of malicious users can lead to the collapse of the entire IoT network. In recent years, many protection schemes used for IoT data security have been proposed. However, security flaws still exist in these schemes. Therefore, to solve the data security and identity security issues in the IoT data sharing process, in this paper, we propose a TEE-and-Blockchain-supported IoT data sharing architecture(TEBDS), which combines on-chain and off-chain methods to meet the security requirements of the IoT data sharing framework. Therein, the consortium blockchain realizes the protection of on-chain IoT data and the access control of IoT users. An Intel SGX-based Distributed Storage System (SDSS) is proposed to secure off-chain data. Besides, an incentive mechanism is developed to facilitate the whole system. Security analysis shows that TEBDS meets the requirements of data security and identity security. Experimental results show that TEBDS has better performance than the centralized method SPDS.

Processing capacity‐based decision mechanism edge computing model for IoT applications

AbstractThe handling of complex tasks in IoT applications becomes difficult due to the limited availability of resources in most IoT devices. There arises a need to offload the IoT tasks with huge processing and storage to resource enriched edge and cloud. In edge computing, factors such as arrival rate, nature and size of task, network conditions, platform differences and energy consumption of IoT end devices impacts in deciding an optimal offloading mechanism. A model is developed to make a dynamic decision for offloading of tasks to edge and cloud or local execution by computing the expected time, energy consumption and processing capacity. This dynamic decision is proposed as processing capacity‐based decision mechanism (PCDM) which takes the offloading decisions on new tasks by scheduling all the available devices based on processing capacity. The target devices are then selected for task execution with respect to energy consumption, task size and network time. PCDM is developed in the EDGECloudSim simulator for four different applications from various categories such as time sensitiveness, smaller in size and less energy consumption. The PCDM offloading methodology is experimented through simulations to compare with multi‐criteria decision support mechanism for IoT offloading (MEDICI). Strategies based on task weightage termed as PCDM‐AI, PCDM‐SI, PCDM‐AN, and PCDM‐SN are developed and compared against the five baseline existing strategies namely IoT‐P, Edge‐P, Cloud‐P, Random‐P, and Probabilistic‐P. These nine strategies are again developed using MEDICI with the same parameters of PCDM. Finally, all the approaches using PCDM and MEDICI are compared against each other for four different applications. From the simulation results, it is inferred that every application has unique approach performing better in terms of response time, total task execution, energy consumption of device, and total energy consumption of applications.

Delay-Aware Resource Allocation in Fog-Assisted IoT Networks Through Reinforcement Learning

Fog nodes in the vicinity of IoT devices are promising to provision low-latency services by offloading tasks from IoT devices to them. Mobile IoT is composed by mobile IoT devices, such as vehicles, wearable devices, and smartphones. Owing to the time-varying channel conditions, traffic loads, and computing loads, it is challenging to improve the Quality of Service (QoS) of mobile IoT devices. As task delay consists of both the transmission delay and computing delay, we investigate the resource allocation (i.e., including both radio resource and computation resource) in both the wireless channel and fog node to minimize the delay of all tasks while their QoS constraints are satisfied. We formulate the resource allocation problem into an integer nonlinear problem, where both the radio resource and computation resource are taken into account. As IoT tasks are dynamic, the resource allocation for different tasks are coupled with each other and the future information is impractical to be obtained. Therefore, we design an online reinforcement learning algorithm to make the suboptimal decision in real time based on the system’s experience replay data. The performance of the designed algorithm has been demonstrated by extensive simulation results.

Fragmented Task Scheduling for Load-Balanced Fog Computing Based on Q-Learning

5G and beyond (B5G) applications generate tremendous computing-intensive, latency-sensitive, and privacy-sensitive tasks, which differ from the legacy cloud computing tasks, requiring more sophisticated scheduling strategies. We must satisfy the stringent service requirements, particularly privacy preservation that has not been sufficiently considered in the past. Meanwhile, we need to balance the tasks offloaded to different edge nodes to avoid overwhelming some fog nodes, which may degrade the overall performance. To appropriately schedule the privacy-sensitive tasks while balancing the traffic load, we define IoT tasks according to their security need, processing time, and real-time requirement and segment IoT tasks into smaller pieces based on their privacy levels. The sliced tasks are scheduled to multiple fog nodes with satisfactory security reputations to avoid a compromised fog node handling a whole task. Meanwhile, we consider the constraint of the response time of all available fog nodes before scheduling IoT tasks to avoid chaos task scheduling that may overwhelm some fog nodes. Regarding this, we propose a reinforcement learning (RL) model in which the agent tends to satisfy the required latency and security requirements while avoiding overloading some fog nodes to minimize the average delay. The numerical results demonstrate that the proposed approach performs well in a better-balanced load and less performance violation in latency and security.

SMRETO: Stable Matching for Reliable and Efficient Task Offloading in Fog-Enabled IoT Networks

Fog computing is a key technology that supports timely and efficient computation of different tasks in IoT networks. By using the nearby fog nodes for quick task computation, application related decisions by IoT devices can be taken within the delay requirements. Resource allocation in terms of task placement on the free computing resources of the fog nodes is a major challenge in IoT networks. In this paper, we consider task offloading from IoT devices to the logically partitioned fog computing resources known as Virtual Resource Units (VRUs) to reduce the number of task outages and energy consumption of the IoT and fog nodes. We propose a two phased task offloading algorithm to minimize the number of task outages. In the first phase, we utilize the task deadline to compute the minimum number of resources required for a task from the fog nodes. To meet the heterogeneous task computing requirements, we introduce the concept of variable sized VRUs in the fog nodes. Moreover, we propose a modified Deferred Acceptance Algorithm (DAA) for stable matching between IoT tasks and variable sized VRUs. In the second phase of the algorithm, the unmatched fog node resources are distributed among the previously matched IoT tasks. Simulation results show that the proposed algorithm outperforms available techniques in the literature in terms of task outages and energy efficiency.

Lightweight Direct Acyclic Graph Blockchain for Enhancing Resource-Constrained IoT Environment

Blockchain technology is regarded as the emergent security solution for many applications related to the Internet of Things (<i>IoT</i>). In concept, blockchain has a linear structure that grows with the number of transactions entered. This growth in size is the main obstacle to the blockchain, which makes it unsuitable for resource-constrained IoT environments. Moreover, conventional consensus algorithms such as PoW, PoS are very computationally heavy. This paper solves these problems by introducing a new lightweight blockchain structure and lightweight consensus algorithm. The Multi-Zone Direct Acyclic Graph (DAG) Blockchain (<i>Multizone-DAG-Blockchain</i>) framework is proposed for the fog-based IoT environment. In this context, fog computing technology is integrated with the IoT to offload IoT tasks to the fog nodes, thus preserving the energy consumption of the IoT devices. Both IoT and fog nodes are initially authenticated using a non-cloneable physical function- based validation mechanism (<i>DPUF-VM</i>) in which multiple authentication certificates are verified in the blockchain. Each transaction is stored in a hash function in the blockchain using the lightweight CubeHash algorithm and signed by the Four-Q- Curve algorithm. In the cloud, sensitive data is stored as ciphertext. Fog nodes provide data security to avoid the energy consumption and complexity of IoT nodes. The fog node first performs a redundancy analysis using the Jaccard Similarity (JS) measure and sensitivity analysis using the Neutrosophic Neural Intelligent Network (<i>N2IN</i>) algorithm. A lightweight proof-of-authentication (<i>PoAh</i>) algorithm is presented and executed by the optimal consensus node selected by the bi- objective spiral optimization (<i>BoSo</i>) algorithm for transaction validation. The proposed work is modeled in Network Simulator 3.26 (ns-3.26), and the performance is evaluated in terms of energy consumption, storage cost, response time, and throughput.

IoT Task Management Mechanism Based on Predictive Optimization for Efficient Energy Consumption in Smart Residential Buildings

Energy-saving is a global challenge and one of the hot research topics of this decade. The need for sustainable technologies and solutions for energy-saving dramatically increased in residential buildings due to population growth, quality of indoor environment, and climate change. Recently, IoT based applications have been developed in smart homes, smart cities, smart hospitals, and other smart environments. The goals of sustainable technologies in residential buildings incorporate maximization of thermal comfort and minimizing energy consumption. The challenges and problems of residential buildings can be solved using consumer behavior models and integrating their inference into residential problem solutions. This paper proposes an IoT task management mechanism based on predictive optimization for energy consumption minimization in smart residential buildings. The proposed task management mechanism has a predictive optimization module based on prediction and an optimization module for solving energy consumption minimization problems. The energy data is obtained from different appliances to evaluate the proposed predictive optimization approach. The proposed approach results are compared with prediction and optimization modules. The performance is evaluated in terms of regression performance metrics. The case study results show that the predictive optimization mechanism based on task management performs better than standalone prediction and optimization-based energy consumption mechanisms in residential buildings.

Optimal Cloud Orchestration Model of Containerized Task Scheduling Strategy Using Integer Linear Programming: Case Studies of IoTcloudServe@TEIN Project

As a playground for cloud computing and IoT networking environment, IoTcloudServe@TEIN has been established in the Trans-Eurasia Information Network (TEIN). In the IoTcloudServe@TEIN platform, a cloud orchestration for conducting the flow of IoT task demands is imperative for effectively improving performance. In this paper, we propose the model of optimal containerized task scheduling in cloud orchestration that maximizes the average payoff from completing tasks within the whole cloud system with different levels of cloud hierarchies. Based on integer linear programming, the model can take into account demand requirement and resource availability in terms of storage, computation, network, and splittable task granularity. To show the insights obtainable from the proposed model, the edge-core cluster of IoTcloudServe@TEIN and its peer-to-peer federated cloud scenario with OF@TEIN+ are numerically experimented and herein reported. To evaluate the model’s performance, payoff level and task completion time are considered by comparing with a well-known round-robin scheduling algorithm. The proposed ILP model can be a guideline for the cloud orchestration in IoTcloudserve@TEIN because of the lower task completion time and the higher payoff level especially upon the large demand growth, which is the major operation range of concerns in practice. Moreover, the proposed model illustrates mathematically the significance of implementing cloud architecture with refined splittable task granularity via the light-weighted container technology that has been used as the basis for IoTcloudServe@TEIN clustering design.

Load balancing of IoT tasks in the cloud computing by using sparrow search algorithm

Load balancing of IoT tasks in the cloud computing by using sparrow search algorithm

Dynamically Controlling Offloading Thresholds in Fog Systems.

Fog computing is a potential solution to overcome the shortcomings of cloud-based processing of IoT tasks. These drawbacks can include high latency, location awareness, and security—attributed to the distance between IoT devices and cloud-hosted servers. Although fog computing has evolved as a solution to address these challenges, it is known for having limited resources that need to be effectively utilized, or its advantages could be lost. Computational offloading and resource management are critical to be able to benefit from fog computing systems. We introduce a dynamic, online, offloading scheme that involves the execution of delay-sensitive tasks. This paper proposes an architecture of a fog node able to adjust its offloading threshold dynamically (i.e., the criteria by which a fog node decides whether tasks should be offloaded rather than executed locally) using two algorithms: dynamic task scheduling (DTS) and dynamic energy control (DEC). These algorithms seek to minimize overall delay, maximize throughput, and minimize energy consumption at the fog layer. Compared to other benchmarks, our approach could reduce latency by up to 95%, improve throughput by 71%, and reduce energy consumption by up to 67% in fog nodes.

Process Automation in an IoT–Fog–Cloud Ecosystem: A Survey and Taxonomy

The number of IoT sensors and physical objects accommodated on the Internet is increasing day by day, and traditional Cloud Computing would not be able to host IoT data because of its high latency. Being challenged of processing all IoT big data on Cloud facilities, there is not enough study on automating components to deal with the big data and real-time tasks in the IoT–Fog–Cloud ecosystem. For instance, designing automatic data transfer from the fog layer to cloud layer, which contains enormous distributed devices is challenging. Considering fog as the supporting processing layer, dealing with decentralized devices in the IoT and fog layer leads us to think of other automatic mechanisms to manage the existing heterogeneity. The big data and heterogeneity challenges also motivated us to design other automatic components for Fog resiliency, which we address as the third challenge in the ecosystem. Fog resiliency makes the processing of IoT tasks independent to the Cloud layer. This survey aims to review, study, and analyze the automatic functions as a taxonomy to help researchers, who are implementing methods and algorithms for different IoT applications. We demonstrated the automatic functions through our research in accordance to each challenge. The study also discusses and suggests automating the tasks, methods, and processes of the ecosystem that still process the data manually.

Energy Efficient Task Scheduling in Fog Environment using Deep Reinforcement Learning Approach

The users of cloud span to several types of tasks for various purposes, such as users who need to accomplish tasks that utilize cloud based on as Infrastructure as a Service. These tasks are usually of high latency. There are some tasks that require immediate response. That is, ultra-low latency tasks like IoT device requirements. It is not feasible to always depend on a far cloud datacenter. Unlike traditional cloud computing, it is possible to place edge and fog nodes can be placed close to the IoT devices provides noticeable reduction in latency. The emerging fog computing technology is characterized by ultra-low latency response, which benefits several time-sensitive services and applications. Nodes in fog are deployed in less centralized. In a fog layer, the computing equipment dedicates parts of its limited resources to process IoT application tasks. Therefore, efficient utilization of computing resources is essential and requires an optimal and intelligent strategy for task scheduling. This paper focuses on scheduling IoT tasks in a fog-based environment with the aim to minimizing energy, cost, and service delay. Towards this end, a deep reinforcement learning based algorithm named Clipped Double Deep Q-learning using target networks and experience replay techniques is proposed. To ensure there is no lag in using resources optimally, a parallel queueing is utilized. One of the important factors in cloud (and fog) computing research is to address the problem of long waiting time of the task in the virtual machine queue. This paper proposes a dual queue method.

An evolutionary game approach to IoT task offloading in fog-cloud computing

Developing the edge and fog computing has been the result of the fast growth of cloud-based IoT applications. These new paradigms define new resource management problems such as IoT task offloading challenge. Despite the many conducted effort in the IoT task offloading, this issue remains a research gap partly because this problem is NP-hard inherently, and also because many proposed solutions are based on unrealistic assumptions. Indeed, many proposed solutions suffer from the lack of a real set of tasks for a comprehensive evaluation. Thus, we prepare a collection of Python tasks to provide a realistic context for the assessment of the proposed task offloading methods. Also, this paper proposes a four-tier architecture to determine the specific decision-maker for the task offloading. Then, we formulate this problem as a population (evolutionary) game that is solved by Maynard replicator dynamics. The time and energy consumption are the main optimization objectives in this work. Finally, we simulate the proposed scheme in MATLAB by emphasizing the use of realistic values and parameters. The experimental results indicate that our scheme is a practical approach partly because it contributes to the core traffic decreases and because its convergence time is less than 6 s to solve the problem with 100 tasks.

A Workflow-Aided Internet of Things Paradigm with Intelligent Edge Computing

In this article, we propose a workflow-aided Internet of things (WIoT) paradigm with intelligent edge computing (IEC) to automate the execution of IoT applications with dependencies. Our design primarily targets at reducing the latency of the IoT systems from two perspectives. To reduce the latency from an application perspective, we develop a WIoT paradigm to orchestrate various IoT applications in a programming way. To reduce the latency from a computation perspective, we propose a novel IEC framework to execute latency-sensitive IoT tasks at the edge network. We put forth a deep reinforcement learning algorithm to adaptively allocate the edge resources to the dynamic requests, aiming to provide the best quality of service for terminal users in real-time. Furthermore, we design a software platform to implement the proposed WIoT with IEC. Experimental results demonstrate that WIoT with IEC can significantly reduce the service latency and improve the network throughput, compared with the traditional cloud-based IoT systems.

A task orchestration approach for efficient mountain fire detection based on microservice and predictive analysis in IoT environment

Mountains are attraction spots for tourists, and tourism contributes to the country’s gross domestic product. Mountains have many benefits such as biodiversity, tourism, and the supplication of food, to name a few. However, there are challenges to protect mountain lives from hazards such as fire caused by tourist activities in mountains. The in-time fire detection and notification to the authorities have always been the central point in literature studies, and different studies have been carried out to optimize the notification time. In this paper, we model the fire detection and notification as a real-time internet of things application and uses task orchestration and task scheduling mechanism to provide scalability along with optimal latency. The proposed fire detection and prediction mechanism detect mountain fire at the earliest stage and provide predictive analysis to prevent damage to mountain life and tourists. The architecture is based on microservice-based IoT task orchestration mechanism and device virtualization, which is not only lightweight but also handles a single problem in parallel chunks, thus optimizes the latency. The in-time information about the fire is used for predictive analysis and notified to safety authorities which helps them to make a more informed decisions to minimize the damage caused by mountain fire. The performance of the proposed mechanism is evaluated in terms of different measures such as RMSE, MAPE, MSE, and MAPE. The proposed work approaches the fire detection and notification as a collection of tasks, and thus those tasks are selected for deployment which are guaranteed to be executed and have minimum latency. This idea of pre-planing the latency and task execution is the first attempt to the best of the authors’ knowledge.

Towards the Design of a Formal Verification and Evaluation Tool of Real-Time Tasks Scheduling of IoT Applications

Real-Time Internet of Things (RT-IoT) is a newer technology paradigm envisioned as a global inter-networking of devices and physical things enabling real-time communication over the Internet. The research in Edge Computing and 5G technology is making way for the realisation of future IoT applications. In RT-IoT tasks will be performed in real-time for the remotely controlling and automating of various jobs and therefore, missing their deadline may lead to hazardous situations in many cases. For instance, in the case of safety-critical and mission-critical IoT systems, a missed task could lead to a human loss. Consequently, these systems must be simulated, as a result, and tasks should only be deployed in a real scenario if the deadline is guaranteed to be met. Numerous simulation tools are proposed for traditional real-time systems using desktop technologies, but these relatively older tools do not adapt to the new constraints imposed by the IoT paradigm. In this paper, we design and implement a cloud-based novel architecture for the formal verification of IoT jobs and provide a simulation environment for a typical RT-IoT application where the feasibility of real-time remote tasks is perceived. The proposed tool, to the best of our knowledge, is the first of its kind effort to support not only the feasibility analysis of real-time tasks but also to provide a real environment in which it formally monitors and evaluates different IoT tasks from anywhere. Furthermore, it will also act as a centralised server for evaluating and tracking the real-time scheduled jobs in a smart space. The novelty of the platform is purported by a comparative analysis with the state-of-art solutions against attributes which is vital for any open-source tools in general and IoT in specifics.

Energy Efficient IoT Virtualization Framework With Peer to Peer Networking and Processing

In this paper, an energy efficient IoT virtualization framework with peer-to-peer (P2P) networking and edge processing is proposed. In this network, the IoT task processing requests are served by peers. The peers in our work are represented by IoT objects and relays that host virtual machines (VMs). We have considered three scenarios to investigate the saving in power consumption and the system capabilities in terms of task processing. The first scenario is a ‘relays only’ scenario, where the task requests are processed using relays only. The second scenario is an ‘objects only’ scenario, where the task requests are processed using the IoT objects only. The last scenario is a hybrid scenario, where the task requests are processed using both IoT objects and VMs. We have developed a mixed integer linear programming (MILP) model to maximize the number of processing tasks served by the system, and minimize the total power consumed by the IoT network. Based on the MILP model principles, we developed an energy efficient virtualized IoT P2P networks heuristic (EEVIPN). Our results show that the hybrid scenario serves up to 77% (57% on average) processing task requests, but with higher energy consumption compared to the other scenarios. The relays only scenario serve 74% (57% on average) of the processing task requests with 8% saving in power consumption compared to the hybrid scenario. In contrast, 28% (22% on average) of task requests can be handled by the objects only scenario with up to 62% power saving compared to the hybrid scenario.