Priority-based Task Pre-processing in IoT Sensory Environments

2019 ◽  
Vol 13 ◽  
Author(s):  
U.N.V.P. Rajendranath ◽  
V. Berlin Hency
Keyword(s):  
Internet Of Things ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Residual Energy ◽  
Sensor Nodes ◽  
Resource Utilisation ◽  
The Internet ◽  
Network Overload ◽  
Task Sets ◽  
The Internet Of Things

Background: The motive of the internet of things (IoT) is to monitor and to control the devices that are connected to the internet. In IoT sensory environments, the application queries for the physical quantities in the spatiotemporal domain. The interaction between the sensors and the applications from the internet is the next big thing in the era of the internet of things. To minimize the resource utilisation, task scheduling mechanisms are implemented to the network. The survey on various patents of task scheduling is revised. Method: The PRITRAPS (Priority-based Task aware Pre-processing and Scheduling) is a mechanism that is employed in real time scenarios of industries. In which different applications units are accessing the gateway unit to measure and monitor the parameters of different service types. PRITRAPS employs priority among the tasks to reduce the network load. Results: The QoS parameters of the system are analysed and compared with the previous methodologies. The PRITRAPS mechanism consists of a task pre-processor unit, Scheduler and EMS module within the gateway unit. The scheduling algorithm employs in PRITRAPS is EDF (Earliest Deadline First) algorithm. The pre-processing task unit decreases the number of tasks by choosing the tasks having similar spatial and temporal requirements. The residual energy of the sensor nodes can help the scheduler for deciding the sensor nodes in respective of task requirements. The scheduler finds the best potential nodes and assigns them to the task for processing. Conclusion: To reduce the tasks arrivals at the wireless sensor unit, a priority based CCTs (Critical Covering Task sets) is proposed, and it effectively reduces the packet congestion and network overload. The results obtained are satisfactory and proven that PRITRAPS outperform TRAPS in energy consumption of a node by processing the tasks on the node. PRITRAPS require only 50% of the time that has been taken by TRAPS for serving the tasks. The PRITRAPS mechanism is implemented in NS3 simulator and tested for different task sets.

scholarly journals Fog Task Scheduling using Clustering based Randomized Round Robin

2021 ◽  
Vol 22 (3) ◽  
pp. 295-302
Author(s):  
Shahid Sultan Hajam ◽  
Shabir Ahmad Sofi
Keyword(s):  
Internet Of Things ◽  
Task Scheduling ◽  
Clustering Algorithm ◽  
Scheduling Algorithm ◽  
Fog Computing ◽  
Round Robin ◽  
The Internet ◽  
Delay Sensitive ◽  
Task Scheduling Algorithm ◽  
The Internet Of Things

Fog computing serves the delay-sensitive applications of the Internet of Things (IoT) in more efficient means than the cloud. The heterogeneity of the tasks and the limited fog resources make task scheduling a complicated job. This paper proposes a clustering based task scheduling algorithm. Specifically, the K-Means++ clustering algorithm is used for clustering the fog nodes. Randomized round robin, a task scheduling algorithm is applied to each cluster. The results show that the proposed algorithm reduces the system's average waiting time.

scholarly journals Development of Metal Polishing Dust Monitoring System using the Internet of Things and Cloud Server

2019 ◽  
Vol 15 (04) ◽  
pp. 53
Author(s):  
Zhiyao Fan ◽  
Tianhong Pan ◽  
Li Ma
Keyword(s):  
Internet Of Things ◽  
Monitoring System ◽  
Remote Monitoring ◽  
Environmental Information ◽  
Sensor Nodes ◽  
The Internet ◽  
Sink Node ◽  
Remote Monitoring System ◽  
Cloud Server ◽  
The Internet Of Things

In order to increase the management efficiency and decrease the maintenance costs in the traditional dust monitoring system, a novel real-time remote monitoring system using the Internet of Things and cloud server is proposed in this paper. The system includes several sensor nodes, a sink node and Cloud Server. The high-precision dust probe, temperature and humidity sensors, water flow sensors and hydrogen transmitters are integrated together into a sensor node to access the metal polished environmental information. Then, the collected information is transmitted to sink-node using the 2.4G wireless network. The sink-node uploads data to the Cloud Server through the 4G network and TCP Socket. Based on the Browser/Server (B/S) model, a remote monitoring system is developed by using Tencent Cloud Server, C# language, and SQL database. As a result, the on-site metal polishing environmental information is obtained via the App and Web page.

Detection of False Data Injection Attack in the Internet of Things

2012 ◽  
Vol 452-453 ◽  
pp. 932-936
Author(s):  
Xiang Dong Hu ◽  
Peng Qin Yu
Keyword(s):  
Internet Of Things ◽  
Rapid Development ◽  
Sensor Nodes ◽  
The Internet ◽  
Malicious Nodes ◽  
High Detection ◽  
Detection Ratio ◽  
False Data Injection Attack ◽  
The Internet Of Things ◽  
Ubiquitous Network

With the rapid development of ubiquitous network and its applications, the key technologies of the Internet of things are actively researched all over the world. The Internet of things has tremendous attraction for adversaries, and it is easily attacked due to poor resource and non-perfect distribution of sensor nodes, then false data maybe be injected into network. Security is one of the most important demands for applications in the Internet of things, an algorithm of malicious nodes detection is proposed to protect the network from destruction based on weighted confidence filter, namely, the cluster heads take charge of collecting messages from nodes and computing their average of confidence in cluster-based network, then they aggregate data from nodes with higher confidence than average and ignore the others, they update confidence of each node by comparing the aggregation value and the received data, and regard it as the weight of exactness of message from node. A sensor node is judged to be a malicious one if its weight is lower than the set threshold. The simulation results show that the algorithm can detect malicious nodes with high detection ratio, low false alarm ratio and outstanding scalability.

scholarly journals A Novel Multitask Scheduling and Distributed Collaborative Computing Method of Edge Nodes in the Internet of Things

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Yong Wang ◽  
Siyu Tang ◽  
Xiaorong Zhu ◽  
Yonghua Xie
Keyword(s):  
Internet Of Things ◽  
Scheduling Algorithm ◽  
Multiple Edge ◽  
The Internet ◽  
Collaborative Computing ◽  
Single Edge ◽  
Thread Scheduling ◽  
Processing Delay ◽  
Computing Method ◽  
The Internet Of Things

In this paper, we propose a novel multitask scheduling and distributed collaborative computing method for quality of service (QoS) guaranteed delay-sensitive services in the Internet of Things (IoT). First, we propose a multilevel scheduling framework combining the process and thread scheduling for reducing the processing delay of multitype services of a single edge node in IoT, where a preemptive static priority process scheduling algorithm is adopted for different types of services and a dynamic priority-based thread scheduling algorithm is proposed for the same type of services with high concurrency. Furthermore, for reducing the processing delay of computation-intensive services, we propose a distributed task offloading algorithm based on a multiple 0-1 knapsack model with value limitation with the collaboration of multiple edge nodes to minimize the processing delay. Simulation results show that the proposed method can significantly reduce not only the scheduling delay of a large number of time-sensitive services in single edge node but also the process delay of computation-intensive service collaborated by multiple edge nodes.

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