5 research outputs found
Target coverage through distributed clustering in directional sensor networks
Maximum target coverage with minimum number of sensor nodes, known as an MCMS problem, is an important problem in directional sensor networks (DSNs). For guaranteed coverage and event reporting, the underlying mechanism must ensure that all targets are covered by the sensors and the resulting network is connected. Existing solutions allow individual sensor nodes to determine the sensing direction for maximum target coverage which produces sensing coverage redundancy and much overhead. Gathering nodes into clusters might provide a better solution to this problem. In this paper, we have designed distributed clustering and target coverage algorithms to address the problem in an energy-efficient way. To the best of our knowledge, this is the first work that exploits cluster heads to determine the active sensing nodes and their directions for solving target coverage problems in DSNs. Our extensive simulation study shows that our system outperforms a number of state-of-the-art approaches
Identifying reputation collectors in community question answering (CQA) sites: Exploring the dark side of social media
YesThis research aims to identify users who are posting as well as encouraging others to post low-quality
and duplicate contents on community question answering sites. The good guys called Caretakers and
the bad guys called Reputation Collectors are characterised by their behaviour, answering pattern and
reputation points. The proposed system is developed and analysed over publicly available Stack
Exchange data dump. A graph based methodology is employed to derive the characteristic of
Reputation Collectors and Caretakers. Results reveal that Reputation Collectors are primary sources
of low-quality answers as well as answers to duplicate questions posted on the site. The Caretakers
answer limited questions of challenging nature and fetches maximum reputation against those
questions whereas Reputation Collectors answers have so many low-quality and duplicate questions
to gain the reputation point. We have developed algorithms to identify the Caretakers and Reputation
Collectors of the site. Our analysis finds that 1.05% of Reputation Collectors post 18.88% of low quality answers. This study extends previous research by identifying the Reputation Collectors and 2 how they collect their reputation points