A Honey-Hive based Efficient Data Aggregation in Wireless Sensor Networks
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- Journal of Electrical Engineering and Technology
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- v.13 no.2
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- pp.998-1007
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- 2018
The advent of Wireless Sensor Networks (WSN) has led to their use in numerous applications. Sensors are autonomous in nature and are constrained by limited resources. Designing an autonomous topology with criteria for economic and energy conservation is considered a major goal in WSN. The proposed honey-hive clustering consumes minimum energy and resources with minimal transmission delay compared to the existing approaches. The honey-hive approach consists of two phases. The first phase is an Intra-Cluster Min-Max Discrepancy (ICMMD) analysis, which is based on the local honey-hive data gathering technique and the second phase is Inter-Cluster Frequency Matching (ICFM), which is based on the global optimal data aggregation. The proposed data aggregation mechanism increases the optimal connectivity range of the sensor node to a considerable degree for inter-cluster and intra-cluster coverage with an improved optimal energy conservation.
'Bowl Phenomenon' refers to allocating the work loads to middle stages slightly less than the outer ones in a series production system. Millier and Boling(1966) first discovered that the output rate of a production line were obtained by deliberately unbalancing, like a bowl-shape, under certain circumstances. So far quite a many researches have been studied either theory-oriented or simulation-oriented on this topic. However the papers concerning assemble production line are rather rare possibly due to the system complexity. In this paper, a simulation work on a 6-node assembly line has been conducted with the help of SLAMSYSTEM software. The simulation results have been turned out that 1) the Bowl phenemenon is still valid in the given system, 2) buffer storage between the work stations are critical measure for determining the degree of work-load unbalancing.
A three dimensional finite element generation code has been developed attaching simple blocks. Block can be either a quadrature or a cube depending on the dimension of a subject considered. Finite element serendipity basis functions are employed to map elements between the computational domain and the physical domain. Elements can be generated with wser defined progressive ratio for each block. For blocks to be connected properly, a block should have a consistent numbering scheme for vertices, side nodes, edges and surfaces. In addition the edge information such as the number of elements and the progressive ratio for each direction should also be checked for interfaces to have unique node numbers. Having done so, user can add blocks with little worry about the orientation of blocks, Since the present the present code has been written by a Visual Basic language, it can be developed easily for a user interactive manner under a Windows environment.
Recommender system has become one of the most important technologies in e-commerce in these days. The ultimate reason to shop online, for many consumers, is to reduce the efforts for information search and purchase. Recommender system is a key technology to serve these needs. Many of the past studies about recommender systems have been devoted to developing and improving recommendation algorithms and collaborative filtering (CF) is known to be the most successful one. Despite its success, however, CF has several shortcomings such as cold-start, sparsity, gray sheep problems. In order to be able to generate recommendations, ordinary CF algorithms require evaluations or preference information directly from users. For new users who do not have any evaluations or preference information, therefore, CF cannot come up with recommendations (Cold-star problem). As the numbers of products and customers increase, the scale of the data increases exponentially and most of the data cells are empty. This sparse dataset makes computation for recommendation extremely hard (Sparsity problem). Since CF is based on the assumption that there are groups of users sharing common preferences or tastes, CF becomes inaccurate if there are many users with rare and unique tastes (Gray sheep problem). This study proposes a new algorithm that utilizes Social Network Analysis (SNA) techniques to resolve the gray sheep problem. We utilize 'degree centrality' in SNA to identify users with unique preferences (gray sheep). Degree centrality in SNA refers to the number of direct links to and from a node. In a network of users who are connected through common preferences or tastes, those with unique tastes have fewer links to other users (nodes) and they are isolated from other users. Therefore, gray sheep can be identified by calculating degree centrality of each node. We divide the dataset into two, gray sheep and others, based on the degree centrality of the users. Then, different similarity measures and recommendation methods are applied to these two datasets. More detail algorithm is as follows: Step 1: Convert the initial data which is a two-mode network (user to item) into an one-mode network (user to user). Step 2: Calculate degree centrality of each node and separate those nodes having degree centrality values lower than the pre-set threshold. The threshold value is determined by simulations such that the accuracy of CF for the remaining dataset is maximized. Step 3: Ordinary CF algorithm is applied to the remaining dataset. Step 4: Since the separated dataset consist of users with unique tastes, an ordinary CF algorithm cannot generate recommendations for them. A 'popular item' method is used to generate recommendations for these users. The F measures of the two datasets are weighted by the numbers of nodes and summed to be used as the final performance metric. In order to test performance improvement by this new algorithm, an empirical study was conducted using a publically available dataset - the MovieLens data by GroupLens research team. We used 100,000 evaluations by 943 users on 1,682 movies. The proposed algorithm was compared with an ordinary CF algorithm utilizing 'Best-N-neighbors' and 'Cosine' similarity method. The empirical results show that F measure was improved about 11% on average when the proposed algorithm was used