Kasthuri Jayarajah, Shuochao Yao, Raghava Mutharaju, Archan Misra, Geeth de Mel, Julie Skipper, T. Abdelzaher, Michael A. Kolodny
The US Army Research Laboratory (ARL) and the Air Force Research Laboratory (AFRL) have established a collaborative research enterprise referred to as the Situational Understanding Research Institute (SURI). The goal is to develop an information processing framework to help the military obtain real-time situational awareness of physical events by harnessing the combined power of multiple sensing sources to obtain insights about events and their evolution. It is envisioned that one could use such information to predict behaviors of groups, be they local transient groups (e.g., Protests) or widespread, networked groups, and thus enable proactive prevention of nefarious activities. This paper presents a vision of how social media sources can be exploited in the above context to obtain insights about events, groups, and their evolution.
{"title":"Social Signal Processing for Real-Time Situational Understanding: A Vision and Approach","authors":"Kasthuri Jayarajah, Shuochao Yao, Raghava Mutharaju, Archan Misra, Geeth de Mel, Julie Skipper, T. Abdelzaher, Michael A. Kolodny","doi":"10.1109/MASS.2015.89","DOIUrl":"https://doi.org/10.1109/MASS.2015.89","url":null,"abstract":"The US Army Research Laboratory (ARL) and the Air Force Research Laboratory (AFRL) have established a collaborative research enterprise referred to as the Situational Understanding Research Institute (SURI). The goal is to develop an information processing framework to help the military obtain real-time situational awareness of physical events by harnessing the combined power of multiple sensing sources to obtain insights about events and their evolution. It is envisioned that one could use such information to predict behaviors of groups, be they local transient groups (e.g., Protests) or widespread, networked groups, and thus enable proactive prevention of nefarious activities. This paper presents a vision of how social media sources can be exploited in the above context to obtain insights about events, groups, and their evolution.","PeriodicalId":436496,"journal":{"name":"2015 IEEE 12th International Conference on Mobile Ad Hoc and Sensor Systems","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126672698","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}
The first comparison of the performance of name-based content routing protocols based on distance vectors and link-states is presented. The protocols used for this comparison are the Named-data Link State Routing (NLSR) protocol, which is the main representative of name-based content routing based on link states, and the Distance-based Content Routing (DCR) protocol, which is the first name-based content routing protocol based on distance vectors. In the simulation of NLSR, the signaling of NLSR is simplified to minimize the overhead it incurs sending link state advertisements (LSAs), such that a single transmission is need to send an LSA, rather than multiple transmission as is the case with NLSR. The results of simulations show that the ideal version of NLSR requires fewer control messages to react to changes of name prefixes when the number of replicas is very small, and DCR incurs less signaling overhead to react to topology changes or changes in name prefixes when the number of replicas is large.
{"title":"A Comparison of Name-Based Content Routing Protocols","authors":"E. Hemmati, J. Garcia-Luna-Aceves","doi":"10.1109/MASS.2015.52","DOIUrl":"https://doi.org/10.1109/MASS.2015.52","url":null,"abstract":"The first comparison of the performance of name-based content routing protocols based on distance vectors and link-states is presented. The protocols used for this comparison are the Named-data Link State Routing (NLSR) protocol, which is the main representative of name-based content routing based on link states, and the Distance-based Content Routing (DCR) protocol, which is the first name-based content routing protocol based on distance vectors. In the simulation of NLSR, the signaling of NLSR is simplified to minimize the overhead it incurs sending link state advertisements (LSAs), such that a single transmission is need to send an LSA, rather than multiple transmission as is the case with NLSR. The results of simulations show that the ideal version of NLSR requires fewer control messages to react to changes of name prefixes when the number of replicas is very small, and DCR incurs less signaling overhead to react to topology changes or changes in name prefixes when the number of replicas is large.","PeriodicalId":436496,"journal":{"name":"2015 IEEE 12th International Conference on Mobile Ad Hoc and Sensor Systems","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133284391","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}
A novel dynamic radio-cooperation strategy is proposed for Cloud Radio Access Networks (C-RANs) consisting of multiple Remote Radio Heads (RRHs) connected to a central Virtual Base Station (VBS) pool. In particular, the key capabilities of C-RANs in computing-resource sharing and real-time communication among the VBSs are leveraged to design a joint dynamic radio clustering and cooperative beam forming scheme that maximizes the downlink weighted sum-rate system utility (WSRSU). Due to the combinatorial nature of the radio clustering process and the non-convexity of the cooperative beam forming design, the underlying optimization problem is NP-hard, and is extremely difficult to solve for a large network. Our approach aims for a suboptimal solution by transforming the original problem into a Mixed-Integer Second-Order Cone Program (MI-SOCP), which can be solved efficiently using a proposed iterative algorithm. Numerical simulation results show that our low-complexity algorithm provides close-to-optimal performance in terms of WSRSU while significantly outperforming conventional radio clustering and beam forming schemes. Additionally, the results also demonstrate the significant improvement in computing-resource utilization of C-RANs over traditional RANs with distributed computing resources.
{"title":"Dynamic Radio Cooperation for Downlink Cloud-RANs with Computing Resource Sharing","authors":"Tuyen X. Tran, D. Pompili","doi":"10.1109/MASS.2015.21","DOIUrl":"https://doi.org/10.1109/MASS.2015.21","url":null,"abstract":"A novel dynamic radio-cooperation strategy is proposed for Cloud Radio Access Networks (C-RANs) consisting of multiple Remote Radio Heads (RRHs) connected to a central Virtual Base Station (VBS) pool. In particular, the key capabilities of C-RANs in computing-resource sharing and real-time communication among the VBSs are leveraged to design a joint dynamic radio clustering and cooperative beam forming scheme that maximizes the downlink weighted sum-rate system utility (WSRSU). Due to the combinatorial nature of the radio clustering process and the non-convexity of the cooperative beam forming design, the underlying optimization problem is NP-hard, and is extremely difficult to solve for a large network. Our approach aims for a suboptimal solution by transforming the original problem into a Mixed-Integer Second-Order Cone Program (MI-SOCP), which can be solved efficiently using a proposed iterative algorithm. Numerical simulation results show that our low-complexity algorithm provides close-to-optimal performance in terms of WSRSU while significantly outperforming conventional radio clustering and beam forming schemes. Additionally, the results also demonstrate the significant improvement in computing-resource utilization of C-RANs over traditional RANs with distributed computing resources.","PeriodicalId":436496,"journal":{"name":"2015 IEEE 12th International Conference on Mobile Ad Hoc and Sensor Systems","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132539449","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}
Measurement samples are often taken in various monitoring applications. To reduce the sensing cost, it is desirable to achieve better sensing quality while using fewer samples. Compressive Sensing (CS) technique finds its role when the signal to be sampled meets certain sparsity requirements. In this paper we investigate the possibility and basic techniques that could further reduce the number of samples involved in conventional CS theory by exploiting learning-based non-uniform adaptive sampling. Based on a typical signal sensing application, we illustrate and evaluate the performance of two of our algorithms, Individual Chasing and Centroid Chasing, for signals of different distribution features. Our proposed learning-based adaptive sampling schemes complement existing efforts in CS fields and do not depend on any specific signal reconstruction technique. Compared to conventional sparse sampling methods, the simulation results demonstrate that our algorithms allow 46% less number of samples for accurate signal reconstruction and achieve up to 57% smaller signal reconstruction error under the same noise condition.
{"title":"Pushing Towards the Limit of Sampling Rate: Adaptive Chasing Sampling","authors":"Ying Li, Kun Xie, Xin Wang","doi":"10.1109/MASS.2015.30","DOIUrl":"https://doi.org/10.1109/MASS.2015.30","url":null,"abstract":"Measurement samples are often taken in various monitoring applications. To reduce the sensing cost, it is desirable to achieve better sensing quality while using fewer samples. Compressive Sensing (CS) technique finds its role when the signal to be sampled meets certain sparsity requirements. In this paper we investigate the possibility and basic techniques that could further reduce the number of samples involved in conventional CS theory by exploiting learning-based non-uniform adaptive sampling. Based on a typical signal sensing application, we illustrate and evaluate the performance of two of our algorithms, Individual Chasing and Centroid Chasing, for signals of different distribution features. Our proposed learning-based adaptive sampling schemes complement existing efforts in CS fields and do not depend on any specific signal reconstruction technique. Compared to conventional sparse sampling methods, the simulation results demonstrate that our algorithms allow 46% less number of samples for accurate signal reconstruction and achieve up to 57% smaller signal reconstruction error under the same noise condition.","PeriodicalId":436496,"journal":{"name":"2015 IEEE 12th International Conference on Mobile Ad Hoc and Sensor Systems","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124045423","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：