Abstract-the prime objective of deploying large-scale wireless sensor networks is to collect information from to control systems associated with these networks. Wireless sensor networks are widely used in application domains such as security and inspection, environmental monitoring, warfare, and other situations especially where immediate responses are required such as disasters and medical emergency. Whenever there is a growth there are challenges and to cope with these challenges strategies and solutions must be developed. This paper discusses the recently addressed issues of data aggregation through presenting a comparative study of different research work done on minimizing delay in different structures of wireless sensor networks. Finally we introduce our proposed method to minimize both delay and power consumption using a tree based clustering scheme with partial data aggregation.Keywords-Wireless sensor networks; Data aggregation; Delay; Energy consumption; IntroductionWireless sensor networks, primarily used for disasters detection and, in best cases, prevention consist of autonomous sensors spatially distributed. A sensor may be grouped with other sensors to make a node. These nodes collect and cooperatively pass data to a management base or center which usually a powerful computer managing a huge database. Sensors in nodes monitor physical and/or environmental conditions such as images, sound, temperature, pressure, etc. Each wireless sensor network consists from few to hundreds or thousands of nodes depending on the project and each node mainly consists of a microcontroller, an interface circuit, a radio transceiver with antenna, and an energy source. There is a revolutionary and ongoing research in this area and in particular on data aggregation and the reliability and efficiency of data transmission process from sources to destination(s). Data aggregation in wireless sensor networks is the amalgamation of data coming from different sources while eliminating redundancy and thus minimizing the number of transmissions in order to save energy using datacentric networking rather than Address-centric networking (Krishnamachari et al 2002). A major challenge for effective data aggregation is the management of energy consumption and delay. Most of the applications of wireless sensor networks where immediate response is crucial, such as disaster response, require minimum delays something that cannot be effectively achieved when measures for energy saving are implemented. The purpose for this research paper is to present a comparative study of research work done by examining these two important factors of data aggregation. The work will examine different event based data aggregation in different types of wireless sensor networks like cluster-and tree-based sensor networks. We will also discuss the process of partial aggregation and how to implement it with these parameters for future research work. 2. review of related research work 2.1 Methods of Data Aggregation This section covers detailed literature revie...

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the prime objective of deploying large-scale wireless sensor networks is to collect information from to control systems associated with these networks. Wireless sensor networks are widely used in application domains such as security and inspection, environmental monitoring, warfare, and other situations especially where immediate responses are required such as disasters and medical emergency. Whenever there is a growth there are challenges and to cope with these challenges strategies and solutions must be developed. This paper discusses the recently addressed issues of data aggregation through presenting a comparative study of different research work done on minimizing delay in different structures of wireless sensor networks. Finally we introduce our proposed method to minimize both delay and power consumption using a tree based clustering scheme with partial data aggregation.

With the economic crisis going around the world, a new approach, “build back better”, has been adopted as a recovery package for various systems. The tsunami detection and warning system is one such system, crucial for saving human lives and infrastructure. While designing a tsunami detection system, the social, economic, and geographical circumstances are considered to be vital. This research is focused on designing a low-cost early warning system mainly for underdeveloped countries, which are more prone to tsunami damage due to a lack of any reliable early warning and detection systems. Such countries require proper cost-effective solutions to address these issues. Previous research has shown that the existing systems are either very costly or hard to implement and manage. In this study, we present a wireless sensor networking model, which is an optimized model in terms of cost, delay, and energy consumption. This research contemplates the techniques and advantages of the intelligence of marine animals. We propose a fuzzy logic-based approach for early tsunami detection, using electromagnetic and pressure sensors, based on the behavioral attributes of turtles and real-time values of earthquakes and water levels.