Query Optimisation in Wireless Sensor Networks Database - Literature review Example

Query Optimisation in Wireless Sensor Networks Database Query Optimisation in Wireless Sensor Networks Database According to Itl Education Solutions Limited (2010), query optimisation is a procedure through which numerous plans for the execution of the query aimed at meeting the requirements of a query are assessed and the plan of the query that is most suitable selected for the execution. There exists systems for the management of databases, which control big quantities of the data that can be recovered through stipulating several queries that are articulated in a query language. When considering the database, wireless sensor networks (WSN) are taken into contemplation for this paper. Singh, et al. (2010) note that when wireless sensor networks are taken into consideration, they come up as one of the most vital technologies that had a great impact in the 21st century. It is evidence by the great attention it has commanded all over the globe from technologists. The authors demystify the wireless sensor network and state that it is made up of sensor nodes that consume little power, they perform multiple functions and that are lowly priced. These sensor nodes have the capabilities to compute, undertake communication over a wireless network, and sense. Essentially, the sensor nodes conduct communication over a short range through a medium that is wireless. They perform this function by functioning together. Thus, the authors note that the principle that guides the sensor functions is that though a single sensor is restricted in its ability, the power of all the sensors combined is enough to undertake the needed function. Sendra, et al. (2011) are in agreement with Singh et al. (2010). Sendra et al. (2010) note that the communication in a wireless network follows an ad hoc conformation. As presented by Venkata (2014), there have been numerous research studies that have been undertaken on wireless sensor databases in regard to query optimisation techniques for queries using SQL. As the author says, the foundational concept of these studies is to decrease the price of a single query. Due to this, varying query optimisation techniques are been used so as to assess the best means of formulating a query that will bring down its corresponding price. The author opines that query optimisation techniques are used to cut down the query processing cost, which is fundamental because information around the assembly of the wireless network along with the interactions that exist in between and among the sensor nodes is needed. It has also been noted that query optimisation is important in the reduction of the amount of consumed power (Xu, et al., 2012). Itl Education Solutions Limited (2010) notes that database systems are judged by their capability to spontaneously locate the assessment strategies for queries that are declaratively stated. Depending on the intricacy of a query, the availed query is processed in a variety of techniques. Hence, the query optimiser in use identifies and builds the best query evaluation plan, say on the basis of the cost. So, depending on the expression that has been provided, the query optimiser produces alternate strategies, which yield a similar outcome. The query optimiser then identifies the one with the minimal cost or the most cost effective. Hence, there is the cost-based query optimisation technique. As it is indicated, under the cost based query optimisation technique, from the availed query, the optimiser produces several query evaluation plans. It does this through the use of several equivalence guidelines. Based on these guidelines, the most cost effective query is selected (Itl Education Solutions Limited, 2010). There exists several query execution costs, they include the cost of storage, accessing the subordinate storage, memory utilisation, computation, and the communication. The produced subordinate and primary catalogues along with the relation magnitudes is obtained from the database management system (DBMS). An evaluation of the statistical data concerning these relations helps approximate the cost of the query evaluation plan. Robinson, et al. (2014) proposes a cost-based query optimisation through artificial intelligence planning. Under this technique, they argue that for queries that are relational, artificial intelligence programmed planning can be applied to the query optimisation issue. Most importantly, the technique suggested by Robinson et al. (2014) concentrates on join-order assortment. The focus is so for queries that are conjunctive. The authors note that it is the responsibility of the join-order assortment to select the data to be utilised in the attainment of the data on the disk. The purpose of this is to ensure that the query organisation is planned in a manner that reduces the execution cost of the plan. Xiang, et al. (2007) suggest a technique they term as Two-Tier Multiple Query Optimisation. The technique as its name suggests constitutes two tiers. Base station optimisation is the initial tier. The methodology utilised in this initial tier takes cost into consideration. Using this methodology, a group of queries are rewritten into a group that is optimised on the basis of similarity while doing away with any excesses that may have been there in the initial queries. In-network optimisation is the following tier. In this tier, the authors state that the outcomes of query are efficiently availed. The provision of the query outcomes is done through distributing the readings from the sensors in queries that are alike. It is also done through radio channel broadcasting (Xiang, et al., 2007). Kumar, et al. (2011) offer a query optimisation technique that is based on heuristic approach. The costs the study seeks to tackle are those arising from computations such as the time of communication and processing. As explained by the authors, they have suggested an algorithm whereby, the query is evaluated through the utilisation of a file that had been stored before. The reason for this evaluation is to look and indicate any enhancements that have been made to the query in regard to the optimisation techniques that had been used on the prior query. They argue that there is a boost to the enhancement that had been made to the query due to the increased intricacy. The researchers go ahead to explain how their suggested query optimisation based on heuristics functions. They say that once the binary tree is formed by the query optimiser, all the variables that are dependent are placed on one flank of the binary tree’s branch (Kumar, et al., 2011). They indicate that a weight is allocated to every variable, which enables them to compute the cost on the basis of weight. Kumar et al. (2011), the period that the operation or variable uses determines the amount of weight it is allocated. The initiation of the query search triggers the quest for the required entry. At that moment every entry will be at leaf (Kumar et al., 2011). To explain their idea, the researchers give the example of a nested query (Kumar et al., 2011). In the example, they explain that if the cost for running the query is “x” units and in the nested query they run an estimate for twenty times. It gives a cost of 20*”x” units (Kumar et al., 2011). To show how their proposition reduced the cost, they explain that their prognosis is run only once. Hence, the cost will only be 1*”x” units (Kumar et al., 2011). Based on heuristics, the researchers opine that in the initial stages there might only be a slight enhancement; however, after a certain period, a greater enhancement will be shown once the information to be used fills the memory (Kumar et al., 2011). They say that in any database system, similar queries are executed after a specific period (Kumar, et al., 2011). Hence, if they have prior knowledge where the query search is going to occur, then they immediately head there. This saves on the cost and the time (Kumar et al., 2011). Hatmode and Rangdale (2014) also talks of a query optimisation technique that is based on heuristic. The researchers in this study utilise what they call a Heuristic optimiser (Hatmode & Rangdale, 2014). As they specify, the Heuristic optimiser seeks to reduce the amount of accesses made. This it does by cutting down the amount of columns and tuples that shall be examined. The study uses Heuristic regulations. These rules are the early execution of the projections and the selection. Through the early execution of the projections, the amount of attributes is decreased, while the prior execution of the selection diminishes the tuple number (Hatmode & Rangdale, 2014). Hatmode and Rangdale (2014), note that their query optimisation technique aims at interpreting queries in SQL to similar greatly optimised queries in SQL that can be located in numerous databases used for commercial purposes. Their query optimisation is tested in replica database to examine the execution time and speed (Hatmode & Rangdale, 2014). Bharaktya and Reddy (2012) are of the idea that query optimisation can be done through emphasis on the base station. As per their idea, they seek to evoke energy efficacy using optimisation of the queries. Their idea considers the cost of communicating and sensing the data. In their explanation, they allude that once a query is received, it is first evaluated. This first evaluation aims at determining if the query that has been delivered to the base station can be assessed using the outcomes of other queries that are still in process. If the query is found to be assessable, then a novel query is reconstituted at the base station utilising the ones that are running at that moment. The step is undertaken without having to inject the query into the sensor network (Bharaktya and Reddy, 2012). Hence, they opine that this optimises the processing of query networks in the wireless sensor network (Bharaktya and Reddy, 2012). Further, they note that the decrease of the queries in the wireless sensor network used in assessing the queries that have been inputted cuts down on the energy costs as per their idea. Besides, using the outcomes of the queries in process at that moment does away with the data requests that match (Bharaktya and Reddy, 2012). They also say that if the queries with expression such as WHERE, that is alike are to be evaluated, then they can be combined. It reduces energy utilisation by the wireless sensor network (Bharaktya and Reddy, 2012). Malik, et al. (2011) has a novel and interesting query optimisation technique that has to do with the sensory features. In the technique, the researchers signify that they have used principal component analysis (PCA) and a statistical method (Malik, et al., 2011). Under the PCA, they assess the sensory data for any historical traces (Malik, et al., 2011). By doing so, they are able to spontaneously recognise vital features that exist among those traces that are interlinked (Malik, et al., 2011); hence, the overhead of the procedure is decreased (Malik, et al., 2011). They do it in several stages. In the first stage, the SQL is fed into the system where the management of the data and optimisation is to occur (Malik, et al., 2011). In this system, there is the optimisation of the query. The principal component analysis algorithm is used to mine the components that are considered to be of high variation from the historical data (Malik, et al., 2011). Once this is done, the query that has been optimised is entered into the wireless sensor network. It is entered in order to mine the sensory data (Malik, et al., 2011). The researchers note that after this is done, the features that originally constituted the data can be obtained from those that have been optimised by reversing the principal component analysis procedure (Malik, et al., 2011). Energy consumption is reduced by up to one quarter using this query optimisation technique. References Bharaktya, A. & Reddy, S. G., 2012. Energy Efficient Query Optimization in Wireless Sensor Networks. International Journal of Engineering and Advanced Technology, 1(6), pp. 99-103. Hatmode, V. & Rangdale, S., 2014. Heuristic Based Query Optimization. International Journal of Advanced Research in Computer and Communication Engineering, 3(6), pp. 7041-7046. Itl Education Solutions Limited, 2010. Introduction to Database Systems. Mumbai: Pearson Education India. Kumar, S., Khandelwal, G., Varshney, A. & Arora, M., 2011. Cost-Based Query Optim. International Journal of Scientific & Engimeering Research, 2(9), pp. 1-6. Malik, H., Malik, A. S. & Roy, C. K., 2011. A methodology to optimize query in wireless sensor networks using historical data. Journal of Ambient Intelligence and Humanized Computing, 2(3), pp. 227-238. Robinson, N., McIIraith, S. A. & Toman, D., 2014. Cost-based Query Optimization via AI Planning. Proceedings of the Twenty-Eighth AAAI Confrence on Artificial Intelligence, pp. 2344-2351. Sendra, S., Lloret, J., Garcia, M. & Toledo, J. F., 2011. Power saving and energy optimization techniques for Wireless Sensor Networks. Journal of Communications, 6(6), pp. 439-459. Singh, S. K., Singh, M. P. & Singh, D. K., 2010. Routing Protocols in Wireless Sensor Networks- A Survey. International Journal of Computer Science & Engineering Survey, 1(2), pp. 63-83. Venkata, K. P., 2014. Challenges, Opportunities, and Dimensions of Cyber-Physical Systems. New York: IGI Global. Xiang, S., Lim, H. B., Tan, K. & Zhou, Y., 2007. Two-Tier Multiple Query Optimization for Sensor Networks. [Online] Available at: http://www.dblab.ntua.gr/~gtsat/collection/multi-query%20optimization/2T-MQUery.pdf [Accessed 29 May 2015]. Xu, Z., Tu, Y. & Wang, X., 2012. PET: Reducing Database Energy Cost via Query Optimization. Proceeding of the VLDB Endowment, 5(12), pp. 1954-1957. Read More