{"title":"Robust distributed incremental LMS for parameter estimation using heterogeneous adaptive networks","authors":"M. Farhid, M. Shamsi, M. Sedaaghi","doi":"10.1109/IKT.2015.7288759","DOIUrl":"https://doi.org/10.1109/IKT.2015.7288759","url":null,"abstract":"Adaptive networks include a set of nodes with adaptation and learning abilities for modeling various types of self-organized and complex activities encountered in the real world. This paper presents the effect of heterogeneous distributed incremental LMS algorithm with ideal links on the quality of unknown parameter estimation. In heterogeneous adaptive networks, a fraction of the nodes, defined based on previously calculated SNR, is assumed to be the informed nodes that collect data and perform in-network processing, while the remaining nodes are assumed to be uninformed and only participate in the processing tasks. As our simulation results show, the proposed algorithm not only considerably improves the performance of the Distributed Incremental LMS algorithm in a same condition, but also proves a good accuracy of estimation in cases where some of the nodes make unreliable observations (noisy nodes).","PeriodicalId":338953,"journal":{"name":"2015 7th Conference on Information and Knowledge Technology (IKT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132229199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Multi-metric clusterhead selection using classification in wireless sensor networks","authors":"Parinaz Eskandarian, J. Bagherzadeh","doi":"10.1109/IKT.2015.7288769","DOIUrl":"https://doi.org/10.1109/IKT.2015.7288769","url":null,"abstract":"Multi-metric clusterhead selection is a multidimensional problem in wireless networks whose optimum solution cannot be found in real time. In this paper, we design an approximation algorithm called MMCSC for this problem using SOM classification techniques. SOM (Self Organizing Map) converts the multidimensional problem into a one-dimensional problem, thus makes it fast to solve. MMCSC considers multiple metrics in clusterhead selection including remaining energy, number of neighbors, and distance to sink. Our evaluations show that MMCSC surpasses the existing algorithms in terms of shorter execution duration, higher remaining energy of clusterheads, and achieving unequal clustering.","PeriodicalId":338953,"journal":{"name":"2015 7th Conference on Information and Knowledge Technology (IKT)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126070762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}