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A Muthu Krishnan et al ,Int.J.Computer Technology & Applications,Vol 4 (1), 51-57 ISSN:2229-6093 required. Therefore, it is essential to make a tradeoff between accuracy level and resource consumption. Data-driven approaches can be divided according to the problem they address. Specifically, data-reduction schemes address the case of unneeded samples, while energy-efficient data acquisition schemes are mainly aimed at reducing the energy spent by the sensing subsystem. However, some of them can reduce the energy spent for communication as well. Also in this case, it is worth discussing here one more classification level related to data-reduction schemes. All these techniques aim at reducing the amount of data to be delivered to the sink node. However the principles behind them are rather different. In-network processing consists in performing data aggregation (e.g., computing average of some values) at intermediate nodes between the sources and the sink. In this way, the amount of data is reduced while traversing the network towards the sink. The most appropriate innetwork processing technique depends on the specific application and must be tailored to it. Data compression can be applied to reduce the amount of information sent by source nodes. This scheme involves encoding information at nodes which generate data, and decoding it at the sink. There are different methods to compress data. As compression techniques are general (i.e. not necessarily related to WSNs), we will omit a detailed discussion of them to focus on other approaches specifically tailored to WSNs. In this paper, we propose a hybrid data-gathering protocol that dynamically switches between the eventdriven data reporting scheme and the data-driven datareporting scheme. The proposed protocol behaves as event-driven, meaning that an event of interest triggers data dissemination by sensor nodes. However, from the point at which an event occurs to the point at which the event becomes invalid, the sensor nodes detecting the event continuously send data to an observer, thereby enabling accurate analysis of the environment. The novel aspect of our approach is that not only the sensor nodes that are detecting an event of interest but also those nodes that will potentially detect the event in the near future become engaged in the time-driven data-reporting process. This capability enables data from potentially relevant areas to be proactively gathered without requiring observer intervention. As such, the proposed protocol accurately analyzes the environment being monitored using only moderate consumption of valuable resources. We envision that many real-life WSN applications will benefit from the proposed data-gathering protocol. For instance, consider a forest fire detection system. As soon as a fire breaks out, it is immediately reported to an observer because the sensor nodes in the burning area react to the rapid increase in temperature. By continuously receiving raw temperature data from not only the burning area but also the neighboring areas in which no fire has yet been detected but may soon travel, an observer is able to accurately identify where the fire originated, forecast where it may be heading, and determine where the fire extinguishing operation should focus. Similarly, an earthquake detection system is able to gather advance readings of seismic waves produced both at the epicenter IJCTA | Jan-Feb 2013 Available online@www.ijcta.com and in surrounding areas and quickly raise an alarm for earthquake-prone buildings. The contributions of this paper are twofold. First, we have developed an adaptive hybrid data-gathering protocol that allows a WSN to dynamically switch between the datadriven data-reporting scheme and the event-driven data reporting scheme based on node context; thus, it is able to more accurately analyze environments than the eventdriven data-reporting scheme and use less energy than the time driven data-reporting scheme. Second, we have evaluated the proposed protocol using significant simulation studies. One may argue that a combination of reactive and proactive data-reporting schemes may not be new in the literature. However, the proposed protocol differs from those of previous works in that time and space domain variables are taken into account when a WSN adapts its data-gathering process. Related Works: Hybrid routing protocol (APTEEN) is proposed [4], which allows for comprehensive information retrieval. The nodes in such a network not only react to time-critical situations, but also give an overall picture of the network at periodic intervals in a very energy efficient manner. Such a network enables the user to request past, present and future data from the network in the form of historical, one-time and persistent queries respectively. But here every sensor node in the network participates in a proactive data-reporting process regardless of its relevance to an event of interest, which leads to energy wastage and higher band width requirement, which in turn will increase the deployment cost. An Efficient Hybrid Data Gathering Scheme in WSN’, For time-sensitive applications requiring frequent data gathering from a remote WSN, it is a challenging task to design an efficient routing scheme that can minimize delay and also offer good performance in energy efficiency and network lifetime. A new data gathering scheme is proposed, which is a combination of clustering and shortest hop pairing of the sensor nodes. The cluster heads and the super leader are rotated every round for ensuring an evenly distributed energy consumption among all the nodes. The major drawback of this system is in clustering the nodes. Clustering needs to be done periodically to ensure the nodes connectivity to their cluster heads. Clustering is an energy consuming, bandwidth requiring overhead in denser networks. Directed diffusion is data centric in that all communication is for named data. All nodes in a directed diffusion-based network are application aware. This enables diffusion to achieve energy savings by selecting empirically good paths and by caching and processing data in-network. Though directed diffusion has high significance in energy conservation, the frame work is highly complex in nature, which may not be applicable for different types of WSN. Being highly static in nature, involves higher overheads when applied in mobile networks, but the application demands protocol that endures well with MANETs. HEED is proposed to prolong the network lifetime and energy efficiency. In this paper [5], the impact of 52
A Muthu Krishnan et al ,Int.J.Computer Technology & Applications,Vol 4 (1), 51-57 ISSN:2229-6093 heterogeneity in terms of node energy in wireless sensor networks have been mentioned. It introduces different level of heterogeneity: 2-level, 3-level and multilevel in terms of the node energy. HEED is a clustering based data collection protocol, where the residual energy of the node is used for the selection of cluster head. This method keeps the node from getting drained due to high data processing task. HEED only helps in reducing the excessive load, but the regular data transmission burden remains same. So significant energy saving is not possible. Moreover clustering burdens also may not reduced. HEED can achieve energy conservation only if the network is static. But our proposed scheme proved to be efficient in both the static and dynamic environments. PEGASIS (Power-Efficient Gathering in Sensor Information Systems) [6], a near optimal chain-based protocol that is an improvement over LEACH. In PEGASIS, each node communicates only with a close neighbor and takes turns transmitting to the base station, thus reducing the amount of energy spent per round. In this paper, we describe PEGASIS, a greedy chain protocol that is near optimal for a data-gathering problem in sensor networks. PEGASIS outperforms LEACH by eliminating the overhead of dynamic cluster formation, minimizing the distance non leader-nodes must transmit, limiting the number of transmissions and receives among all nodes, and using only one transmission to the BS per round. Nodes take turns to transmit the fused data to the BS to balance the energy depletion in the network and preserves robustness of the sensor web as nodes die at random locations. Distributing the energy load among the nodes increases the lifetime and quality of the network. PEGASIS again a cluster based protocol where an improvement of leach is obtained. PEGASIS communicates with its close neighbor and reaches the cluster head. SINA introduces a sensor information networking architecture that facilitates querying, monitoring, and tasking of sensor networks [7]. SINA plays the role of a middleware that abstracts a network of sensor nodes as a collection of massively distributed objects. The SINA's execution environment provides a set of configuration and communication primitives that enable scalable and energy-efficient organization of and interactions among sensor objects. On top the execution environment is a programmable substrate that provides mechanisms to create associations and coordinate activities among sensor nodes. Users then access information within a sensor network using declarative queries, or perform tasks using programming scripts. SINA does not support dynamic switching between data-reporting schemes. Whereas the proposed scheme involves Dynamic switching that enables context aware energy saving. So that unwanted sample transmission can be reduced during static duration and information loss can be avoided during dynamic situations. The current systems are designed either based on time driven data gathering or based on event driven data gathering. Time driven data gathering guarantees high data accuracy at the cost higher energy. On the other hand event driven data gathering results in maximum energy conservation with a significant loss in data accuracy. So the existing WSN’s can assure either data accuracy or energy conservation. But in the users perspective both data accuracy and energy are the two IJCTA | Jan-Feb 2013 Available online@www.ijcta.com most important Quality of Service parameters. The existing systems do not have the flexibility to switch between time driven data gathering and event driven data gathering, which results in unwanted or redundant data transmissions during time driven data gathering and information and data pattern loss during event driven data gathering. Existing systems are mostly centralized systems, where decision making is done the central entity. This system suffers from destabilization problems during communication loss. There are some systems, which are cluster based data gathering systems. These systems also experience higher communication overheads during cluster forming and cluster head selection. A new hybrid system for data gathering is the need of the time. In the proposed method Nodes that detect an event of interest or those nodes that are likely to detect the event in the near future become engaged in a proactive data reporting through spatio-temporal correlation of data. Since no clustering is required, the over heads are completely avoided. The proposed work doesn’t rely on another node to reach the cluster. This will further improve the energy efficiency. Here also the clustering overheads are reduced to a great extent. Methodology: The essential elements of the hybrid data-gathering protocol proposed [9] in this paper are that: 1) it switches dynamically between the event-driven data-reporting scheme and the time driven data-reporting scheme, and 2) sensor nodes that will detect the events in the near future, which typically are in close proximity to those nodes detecting the events, are also engaged in the time-driven data-reporting process. Fig.1. nodes in data reporting processs charecteristics Figure (1) illustrates the key protocol characteristics. Under normal conditions, sensor nodes respond only when the measured temperature is above 100 °C. However, once sensor nodes realize that the abnormal phenomenon is not transient (e.g., at tp and tx), they switch to the time-driven data-reporting scheme and continuously disseminate temperature data to an observer. Furthermore, they notify other nodes of their changes so that neighboring nodes continuously disseminate data as well. Similarly, when the temperature goes below 100 °C (e.g., at tq and ty), the nodes switch back to the eventdriven data-reporting scheme. 53
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