Kamel Abbassi

By Kamel Abbassi, Mohamed Hechmi Jeridi and Tahar Ezzedine

In the 20th century, computers were senseless brains, but today, thanks to sensor networks, they can feel things for themselves. This major trend has given rise to many wireless sensor networks with the ability to sense the environment, deliver findings and process those data appropriately. Within this trend, this chapter outlines deployment and routing strategies as well as data handling practices. For convenience, the most encompassing application to consider is that of event detection.

Part of the book: Wireless Sensor Networks

By Kamel Abbassi and Tahar Ezzedine

For the super-excellence applications used to control the water level in rivers, temperature handles a very large volume of information and does not stop constantly changing. These spatio-temporal data collected by a network of sensors form a set of thematic, integrated, non-volatile and historical data organized to help decision-making. Usually this process is performed with temporal, spatial and spatiotemporal queries. This in turn increases the execution time of the query load. In the literatures, several techniques have been identified such as materialized views (MV), indexes, fragmentation, scheduling, and buffer management. These techniques do not consider the update of the request load and the modification at the database level. In this chapter, we propose an optimal dynamic selection solution based on indexes and VMs. the solution is optimal when it meets the entire workload with a reasonable response time. The proposed approach supports modification at the database level and at the workload level to ensure the validity of the optimal solution for this the knapsack algorithm was used.

Part of the book: Wireless Sensor Networks