Service Scheduling Based on Edge Computing for Power Distribution IoT

Li, Zhu; Qiu, Xuesong; Zhang, Shuai; Deng, Siyang

doi:10.32604/cmc.2020.07334

Cited by 8 publications

(7 citation statements)

References 13 publications

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“…In addition, the authors have given and analyzed an overview of the study in the energy efficiency problems and possible solutions for 5G broadband wireless access networks. Some more 5G-related work has been introduced in [15][16][17][18][19][20][21][22][23][24][25]. It generates an energy-saving challenge that combines storage and data transmission costs.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Routing Approach to Maximize the Lifetime of IoT-Based Wireless Sensor Networks in 5G and Beyond

Jothikumar

Ramana²,

Chakravarthy

et al. 2021

Mobile Information Systems

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The Internet of Things grew rapidly, and many services, applications, sensor-embedded electronic devices, and related protocols were created and are still being developed. The Internet of Things (IoT) allows physically existing things to see, hear, think, and perform a significant task by allowing them to interact with one another and exchange valuable knowledge when making decisions and caring out their vital tasks. The fifth-generation (5G) communications require that the Internet of Things (IoT) is aided greatly by wireless sensor networks, which serve as a permanent layer for it. A wireless sensor network comprises a collection of sensor nodes to monitor and transmit data to the destination known as the sink. The sink (or base station) is the endpoint of data transmission in every round. The major concerns of IoT-based WSNs are improving the network lifetime and energy efficiency. In the proposed system, Optimal Cluster-Based Routing (Optimal-CBR), the energy efficiency, and network lifetime are improved using a hierarchical routing approach for applications on the IoT in the 5G environment and beyond. The Optimal-CBR protocol uses the k-means algorithm for clustering the nodes and the multihop approach for chain routing. The clustering phase is invoked until two-thirds of the nodes are dead and then the chaining phase is invoked for the rest of the data transmission. The nodes are clustered using the basic k-means algorithm during the cluster phase and the highest energy of the node nearest to the centroid is selected as the cluster head (CH). The CH collects the packets from its members and forwards them to the base station (BS). During the chaining phase, since two-thirds of the nodes are dead and the residual energy is insufficient for clustering, the remaining nodes perform multihop routing to create chaining until the data are transmitted to the BS. This enriches the energy efficiency and the network lifespan, as found in both the theoretical and simulation analyses.

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Section: Related Workmentioning

confidence: 99%

An Efficient Routing Approach to Maximize the Lifetime of IoT-Based Wireless Sensor Networks in 5G and Beyond

Jothikumar

Ramana²,

Chakravarthy

et al. 2021

Mobile Information Systems

View full text Add to dashboard Cite

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“…He et al [17] propose a dynamic network slicing mechanism including virtual network mapping and reconfiguration to provide network slices for services. Liu et al [18] present a service scheduling method, that includes the architecture, components and functional requirements, to balance the business load of Power Distribution Internet of Things based on edge computing. Boulakbech et al [19] propose a new mashup service model, called IoT big services.…”

Section: Related Work and Comparison A Iot Service Allocation And Compositionmentioning

confidence: 99%

Optimized Composition for Multiple User Service Requests Based on Edge-Cloud Collaboration

Tang

Lin

Wang³

et al. 2021

IEEE Access

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The Internet of Things (IoT) has become the infrastructure to widely support ubiquitous services. In this setting, a large number of smart object devices provide IoT services for people to carry on day-to-day activities. These services are easy to access, and thus, can be easily used by different users with different requirements through service allocation and composition. Nevertheless, with the increasing adoption and presence of IoT services, it becomes more difficult to find the appropriate IoT service compositions that can maximize the service utility of all the users' requests. Moreover, the IoT devices are usually resource constrained and cannot effectively finish some complex task of service requests, thereby reducing the service quality. To address this problem, we develop a novel IoT service allocation mechanism in collaborative device-edge-cloud architecture. First, prevalence of frequent function interaction is mined to help construct IoT service allocation index. Second, service requests' priority and their preferences towards branch nodes are further set and estimated to help maximize the service composition utility of multiple user requests. Third, edge device is exploited to help service execution and guarantee service allocation utility by offloading task from IoT devices. The results are demonstrated by conducting a set of extensive experiments, which show that the proposed method performs better than the alternative techniques in reducing the total energy consumption and promoting the service utility maximum.

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“…In this way, part of the processing can be done at the edge, close to the data source, which in turn results in costs savings related to data transmission, latency and bandwidth usage among other benefits. Examples of tasks in an edge computing scenario can include task-based resource allocation [ 11 ], service scheduling for power distribution [ 12 ], task offloading mechanisms [ 13 ], local sentiment analysis [ 14 ], charging, and discharging networking system algorithms for electric vehicles [ 15 ], etc.…”

Section: Introductionmentioning

confidence: 99%

Embedded Data Imputation for Environmental Intelligent Sensing: A Case Study

Erhan

Mauro

Anjum

et al. 2021

Sensors

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Recent developments in cloud computing and the Internet of Things have enabled smart environments, in terms of both monitoring and actuation. Unfortunately, this often results in unsustainable cloud-based solutions, whereby, in the interest of simplicity, a wealth of raw (unprocessed) data are pushed from sensor nodes to the cloud. Herein, we advocate the use of machine learning at sensor nodes to perform essential data-cleaning operations, to avoid the transmission of corrupted (often unusable) data to the cloud. Starting from a public pollution dataset, we investigate how two machine learning techniques (kNN and missForest) may be embedded on Raspberry Pi to perform data imputation, without impacting the data collection process. Our experimental results demonstrate the accuracy and computational efficiency of edge-learning methods for filling in missing data values in corrupted data series. We find that kNN and missForest correctly impute up to 40% of randomly distributed missing values, with a density distribution of values that is indistinguishable from the benchmark. We also show a trade-off analysis for the case of bursty missing values, with recoverable blocks of up to 100 samples. Computation times are shorter than sampling periods, allowing for data imputation at the edge in a timely manner.

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Service Scheduling Based on Edge Computing for Power Distribution IoT

Cited by 8 publications

References 13 publications

An Efficient Routing Approach to Maximize the Lifetime of IoT-Based Wireless Sensor Networks in 5G and Beyond

An Efficient Routing Approach to Maximize the Lifetime of IoT-Based Wireless Sensor Networks in 5G and Beyond

Optimized Composition for Multiple User Service Requests Based on Edge-Cloud Collaboration

Embedded Data Imputation for Environmental Intelligent Sensing: A Case Study

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