Khaled Abid, Ghada Jaber, Hicham Lakhlef, A. Lounis, A. Bouabdallah
{"title":"An Energy Efficient Architecture of self-sustainable WSN based on Energy Harvesting and Wireless Charging with Consideration of Deployment Cost","authors":"Khaled Abid, Ghada Jaber, Hicham Lakhlef, A. Lounis, A. Bouabdallah","doi":"10.1145/3416013.3426450","DOIUrl":"https://doi.org/10.1145/3416013.3426450","url":null,"abstract":"Energy efficiency is a major issue in self-sustainable Wireless Sensor Networks (WSNs) since they operate without battery replacement.Recent works introduce the Energy Harvesting (EH) technique that limits battery dependency of sensors to benefit from green and renewable energy. Furthermore, Wireless Charging (WC) techniques provide reliable energy sources for sensors. The combination of these two technologies solves many problems in WSNs. However, the cost of such deployment must be taken into consideration. In this paper, we propose an energy-efficient architecture of self-sustainable WSN based on an Energy Harvesting Base Station(EHBS) and a Mobile Charger (MC) with consideration of deployment cost. Through extensive simulations, we compare different solutions and we demonstrate that the proposed solution maximizes the network lifetime while taking into account the deployment cost.","PeriodicalId":103892,"journal":{"name":"Proceedings of the 16th ACM Symposium on QoS and Security for Wireless and Mobile Networks","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133759695","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":"Session details: Session 4: IoT Systems","authors":"Samir Chouali","doi":"10.1145/3436360","DOIUrl":"https://doi.org/10.1145/3436360","url":null,"abstract":"","PeriodicalId":103892,"journal":{"name":"Proceedings of the 16th ACM Symposium on QoS and Security for Wireless and Mobile Networks","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132002735","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":"A Vertex-centric Markov Chain Algorithm for Network Clustering based on b-Coloring","authors":"Jianyi Lin, C. Mio","doi":"10.1145/3416013.3426459","DOIUrl":"https://doi.org/10.1145/3416013.3426459","url":null,"abstract":"The massive size and complexity of big datasets such as those coming from social, natural and sensor environments raise utmost challenges to unsupervised cluster analysis methods in terms of performance scalability in designing algorithms, also considering parallel and distributed networking context. To cope with these hindrances, the parallelization of clustering techniques, also benefiting from GPU-centered computation, can contribute to fill the gap in applicative areas such as optimization of network routing or management of large-scale IoT networks, thus enabling the extraction, processing and policy making relying on rich network information that are typically represented in the form of graphs. One established approach to clustering graphs is through the coloring techniques, and indeed, graph clustering and graph coloring can be viewed as tied. We devise a graph clustering technique based on a Markov Chain method aimed at b-coloring the data points, that works in efficient vertex-centric parallel manner and produces a valid clustering with reduced number of color classes. We assess our algorithm against synthetic data encapsulating group structure characteristics and present a brief convergence analysis of the method.","PeriodicalId":103892,"journal":{"name":"Proceedings of the 16th ACM Symposium on QoS and Security for Wireless and Mobile Networks","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126338184","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}