Partial gathering of mobile agents in asynchronous unidirectional rings

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• 作者：Masahiro Shibata ; ^{m-sibata@ist.osaka-u.ac.jp" class="auth_mail" title="E-mail the corresponding author} ; Shinji Kawai ; Fukuhito Ooshita ; ^{f-oosita@ist.osaka-u.ac.jp" class="auth_mail" title="E-mail the corresponding author} ; Hirotsugu Kakugawa ; ^{kakugawa@ist.osaka-u.ac.jp" class="auth_mail" title="E-mail the corresponding author} ; Toshimitsu Masuzawa ; ^{masuzawa@ist.osaka-u.ac.jp" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Distributed system ; Mobile agent ; Gathering problem ; Partial gathering
• 刊名：Theoretical Computer Science
• 出版年：2016
• 出版时间：29 February 2016
• 年：2016
• 卷：617
• 期：Complete
• 页码：1-11
• 全文大小：481 K

文摘

In this paper, we consider the partial gathering problem of mobile agents in asynchronous unidirectional rings equipped with whiteboards on nodes. The partial gathering problem is a new generalization of the total gathering problem. The partial gathering problem requires, for a given integer g, that each agent should move to a node and terminate so that at least g   agents should meet at the same node. The requirement for the partial gathering problem is weaker than that for the (well-investigated) total gathering problem, and thus, we have interests in clarifying the difference on the move complexity between them. We propose three algorithms to solve the partial gathering problem. The first algorithm is deterministic but requires unique ID of each agent. This algorithm achieves the partial gathering in class="mathmlsrc">class="formulatext stixSupport mathImg" data-mathURL="/science?_ob=MathURL&_method=retrieve&_eid=1-s2.0-S0304397515008270&_mathId=si1.gif&_user=111111111&_pii=S0304397515008270&_rdoc=1&_issn=03043975&md5=91c996ced85e35319abce2ab9288acd9" title="Click to view the MathML source">O(gn)class="mathContainer hidden">class="mathCode"> total moves, where n   is the number of nodes. The second algorithm is randomized and requires no unique ID of each agent (i.e., anonymous). This algorithm achieves the partial gathering in expected class="mathmlsrc">class="formulatext stixSupport mathImg" data-mathURL="/science?_ob=MathURL&_method=retrieve&_eid=1-s2.0-S0304397515008270&_mathId=si1.gif&_user=111111111&_pii=S0304397515008270&_rdoc=1&_issn=03043975&md5=91c996ced85e35319abce2ab9288acd9" title="Click to view the MathML source">O(gn)class="mathContainer hidden">class="mathCode"> total moves. The third algorithm is deterministic and requires no unique ID of each agent. For this case, we show that there exist initial configurations in which no algorithm can solve the problem and agents can achieve the partial gathering in class="mathmlsrc">class="formulatext stixSupport mathImg" data-mathURL="/science?_ob=MathURL&_method=retrieve&_eid=1-s2.0-S0304397515008270&_mathId=si150.gif&_user=111111111&_pii=S0304397515008270&_rdoc=1&_issn=03043975&md5=75b425aeaefb7ffd8738bfa79c2f9e27" title="Click to view the MathML source">O(kn)class="mathContainer hidden">class="mathCode"> total moves for solvable initial configurations, where k   is the number of agents. Note that the total gathering problem requires class="mathmlsrc">class="formulatext stixSupport mathImg" data-mathURL="/science?_ob=MathURL&_method=retrieve&_eid=1-s2.0-S0304397515008270&_mathId=si3.gif&_user=111111111&_pii=S0304397515008270&_rdoc=1&_issn=03043975&md5=1b369deea6244358b884d3aefb5be3ab" title="Click to view the MathML source">Ω(kn)class="mathContainer hidden">class="mathCode"> total moves, while the partial gathering problem requires class="mathmlsrc">class="formulatext stixSupport mathImg" data-mathURL="/science?_ob=MathURL&_method=retrieve&_eid=1-s2.0-S0304397515008270&_mathId=si4.gif&_user=111111111&_pii=S0304397515008270&_rdoc=1&_issn=03043975&md5=e1eb166fb6974170b07ad94cdd456419" title="Click to view the MathML source">Ω(gn)class="mathContainer hidden">class="mathCode"> total moves in each model. Hence, we show that the move complexity of the first and second algorithms is asymptotically optimal.