Summary
Task clustering process in distributed systems is used to improve the performance of executing workflow applications. In cloud‐assisted Internet of things (CIoT) framework, performing efficient task clustering is highly challenging because of the dynamic nature of cloud, availability of various billing models, and billing granularity options. This work aims at reducing the security overhead among the dependent tasks by using data transfer aware grouping technique (DTAGT), which takes into consideration the amount of data transferred among dependent tasks to obtain a cluster that reduces the security overhead in the network by optimizing the data transfer rate. Collecting the data in a CIoT environment is difficult, and the redundancy among the data from the sensors tends to decrease the lifetime of the network and also increases the latency has been resolved using proposed DTAGT. The overall objective of the proposed grouping algorithm is to reduce the total completion time and cost of executing workflow application with high reliability in CIoT platform. The proposed system is implemented, and the result analysis shows DTWCT (Dual‐tree complex wavelet transform) outperforms the other related works.
Recently, the water resource becomes the most important conditions for living in safe. This is due to the shortage in groundwater, river resources and rain level. This project design a complete sensor network structure to monitor the water levels and water qulity of the considered water wellfield and the consumed electrical power of the installed pump. The utilized sensors use the energy saving techniques and allocate the controller sensor to work as a PAN coordinator. The designed structure has been implemented and tested using MIXIM framework under the environment of OMNET++ simulator. The obtained results from the simulator appear the efficient of the system in monitoring the wells factors.
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