Agriculture productivity can be enhanced by IoT- enabled real-time monitoring of weather and soil parameters. Increased volume sensor data demands a significant amount memory power. It also overloads the network, making parameter very difficult. A large reduces lifetime latency decreasing overall throughput. Hence, reduction in overload becomes necessary for energy optimization these energy-con...

To solve data aggregation problems, several algorithms have developed to operate efficient networks. However, a current data aggregation still includes a design weakness. This paper proposes to design distributed aggregation for a confidential data using Software Robot (SR) in cluster sensor networks. Software robot of smart software is based on homomorphic encryption to aggregate secure sensin...

Analytical processing of large relation data demands for a shared compact representation of data in multiple resolutions in order to efficiently facilitate the incurring data aggregation, data cube, and range queries. This paper addresses technical problems of multi-resolution data aggregation and investigates enabling technologies for efficient analytical processing of large data sets. In part...

Energy consumption is considered as a critical issue in wireless sensor networks (WSNs). Batteries of sensor nodes have limited power supply which in turn limits services and applications that can be supported by them. An efcient solution to improve energy consumption and even trafc in WSNs is Data Aggregation (DA) that can reduce the number of transmissions. Two main challenges for DA are: (i)...

The two biggest problems with wireless sensor networks are security and energy usage. In sensing devices, malicious nodes could be found in large numbers. researchers have proposed several methods to find these rogue nodes. To prevent assaults on data transmission, the must secured. Data aggregation aids reducing number of messages transmitted within network, which turn lowers total network con...

How can we combine datasets collected under different conditions? This is the well-known problem of meta-analysis, for which Bayesian methods have long been used to achieve partial pooling. Here we consider the challenge when one dataset is given as raw data while the second dataset is given as averages only. In such a situation, common meta-analytic methods can only be applied when the model i...

Many scientific computing tasks are too large to be processed quickly on one computer. Instead, the grid has become popular for a wide range of disciplines. Efficiently using the all the resources of the grid is a challenging, NP problem though. The authors introduced a new method for scheduling that accounted for data dependencies, a topic often ignored in job scheduling algorithms. They also ...