Vehicle-to-Everything (V2X) assisted proximity marketing is one of the most promising V2X services due to its huge potential, and has attracted a lot of research efforts recently. In proximity marketing, roadside merchants rely on third-party ad networks to target their advertisements to nearby vehicles or pedestrians with related interests, and pay ad networks according to some pricing mechanisms, such as cost per-view. It is therefore important for merchants to learn ad conversion rate (how much of their revenue can be attributed to proximity marketing) such that merchants can adjust their advertising strategy. For ad conversion, two-party private set intersection (PSI) technique has been widely adopted, where ad networks and merchants can jointly compute ad conversion rate without leaking sensitive customer information. However, state-of-art literature on PSI either assumes the involved two parties honestly follow the protocol or only tolerates limited adversarial behaviors. In this paper, we first design a novel and efficient PSI scheme that is secure in the presence of malicious adversaries, where two parties can arbitrarily deviate from the scheme. By integrating an efficient input certification mechanism into the designed PSI scheme, we propose a privacy-preserving ad conversion protocol for V2X-assisted proximity marketing, that can achieve input privacy, unlinkability, unforgeability, and output verifiability. Security analysis demonstrates that the proposed ad conversion protocol is secure under cryptographic assumptions. Finally, we show that the proposed ad conversion protocol outperforms the state-of-art approaches when considering both security strength and computation complexity.
{"title":"Efficient and Privacy-Preserving Ad Conversion for V2X-Assisted Proximity Marketing","authors":"Dongxiao Liu, Jianbing Ni, Hongwei Li, Xiaodong Lin, Xuemin Shen","doi":"10.1109/MASS.2018.00014","DOIUrl":"https://doi.org/10.1109/MASS.2018.00014","url":null,"abstract":"Vehicle-to-Everything (V2X) assisted proximity marketing is one of the most promising V2X services due to its huge potential, and has attracted a lot of research efforts recently. In proximity marketing, roadside merchants rely on third-party ad networks to target their advertisements to nearby vehicles or pedestrians with related interests, and pay ad networks according to some pricing mechanisms, such as cost per-view. It is therefore important for merchants to learn ad conversion rate (how much of their revenue can be attributed to proximity marketing) such that merchants can adjust their advertising strategy. For ad conversion, two-party private set intersection (PSI) technique has been widely adopted, where ad networks and merchants can jointly compute ad conversion rate without leaking sensitive customer information. However, state-of-art literature on PSI either assumes the involved two parties honestly follow the protocol or only tolerates limited adversarial behaviors. In this paper, we first design a novel and efficient PSI scheme that is secure in the presence of malicious adversaries, where two parties can arbitrarily deviate from the scheme. By integrating an efficient input certification mechanism into the designed PSI scheme, we propose a privacy-preserving ad conversion protocol for V2X-assisted proximity marketing, that can achieve input privacy, unlinkability, unforgeability, and output verifiability. Security analysis demonstrates that the proposed ad conversion protocol is secure under cryptographic assumptions. Finally, we show that the proposed ad conversion protocol outperforms the state-of-art approaches when considering both security strength and computation complexity.","PeriodicalId":146214,"journal":{"name":"2018 IEEE 15th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132115328","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}
Johannes Kässinger, Mohamed Abdelaal, Frank Dürr, K. Rothermel
Recently, mobile crowdsensing has become an appealing paradigm thanks to the ubiquitous presence of powerful mobile devices. Indoor mapping, as an example of crowdsensingdriven applications, is essential to provide many indoor locationbased services, such as emergency response, security, and tracking/navigation in large buildings. In this realm, 3D point clouds stand as an optimal data type which can be crowdsensed—using currently-available mobile devices, e.g. Google Tango, Microsoft Hololens and Apple ARKit—to generate floor plans with different levels of detail, i.e. 2D and 3D mapping. However, collecting such bulky data from "resources-limited" mobile devices can significantly harm their energy efficiency. To overcome this challenge, we introduce GreenMap, an energy-aware architectural framework for automatically mapping the interior spaces using crowdsensed point clouds with the support of structural information encoded in formal grammars. GreenMap reduces the energy overhead through projecting the point clouds to several filtration steps on the mobile devices. In this context, GreenMap leverages the potential of approximate computing to reduce the computational cost of data filtering while maintaining a satisfactory level of modeding accuracy. To this end, we propose two approximation strategies, namely DyPR and SuFFUSION. To demonstrate the effectiveness of GreenMap, we implemented a crowdsensing Android App to collect 3D point clouds from two different buildings. We show that GreenMap achieves significant energy savings of up to 67.8%, compared to the baseline methods, while generating comparable floor plans.
{"title":"GreenMap: Approximated Filtering Towards Energy-Aware Crowdsensing for Indoor Mapping","authors":"Johannes Kässinger, Mohamed Abdelaal, Frank Dürr, K. Rothermel","doi":"10.1109/MASS.2018.00069","DOIUrl":"https://doi.org/10.1109/MASS.2018.00069","url":null,"abstract":"Recently, mobile crowdsensing has become an appealing paradigm thanks to the ubiquitous presence of powerful mobile devices. Indoor mapping, as an example of crowdsensingdriven applications, is essential to provide many indoor locationbased services, such as emergency response, security, and tracking/navigation in large buildings. In this realm, 3D point clouds stand as an optimal data type which can be crowdsensed—using currently-available mobile devices, e.g. Google Tango, Microsoft Hololens and Apple ARKit—to generate floor plans with different levels of detail, i.e. 2D and 3D mapping. However, collecting such bulky data from \"resources-limited\" mobile devices can significantly harm their energy efficiency. To overcome this challenge, we introduce GreenMap, an energy-aware architectural framework for automatically mapping the interior spaces using crowdsensed point clouds with the support of structural information encoded in formal grammars. GreenMap reduces the energy overhead through projecting the point clouds to several filtration steps on the mobile devices. In this context, GreenMap leverages the potential of approximate computing to reduce the computational cost of data filtering while maintaining a satisfactory level of modeding accuracy. To this end, we propose two approximation strategies, namely DyPR and SuFFUSION. To demonstrate the effectiveness of GreenMap, we implemented a crowdsensing Android App to collect 3D point clouds from two different buildings. We show that GreenMap achieves significant energy savings of up to 67.8%, compared to the baseline methods, while generating comparable floor plans.","PeriodicalId":146214,"journal":{"name":"2018 IEEE 15th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)","volume":"194 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132396014","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}
Chenyang Wang, Shanjia Wang, Ding Li, Xiaofei Wang, Xiuhua Li, Victor C. M. Leung
Caching at the edge of mobile networks can significantly offload network traffic while satisfying content requests from mobile users locally. The contents can be requested from the proximity users via Device-to-device (D2D) communications while proactive caching the popular content to local users. However, the assumptions that content popularity is equal to user preference in several existing studies, which are invalid and not rigorous due to the fact that content popularity is calculated by the statistic of user requests within a certain period while user preference reflects the probability of a content requested by the individual user. Motivated by this, in this paper, we study the edge caching optimization of hierarchical wireless networks. Our aiming is to maximize the size of content offload by D2D communications. In particular, the edge caching policy with D2D sharing model based on the analysis of user mobility and social relationship is derived. We first prove the problem is NP-hard and then formulate it as a Markov Decision Process (MDP) problem, finally a Q-learning based distributed content replacement strategy is proposed. The large-scale real trace based experiment results show the effectiveness of our proposed framework.
{"title":"Q-Learning Based Edge Caching Optimization for D2D Enabled Hierarchical Wireless Networks","authors":"Chenyang Wang, Shanjia Wang, Ding Li, Xiaofei Wang, Xiuhua Li, Victor C. M. Leung","doi":"10.1109/MASS.2018.00019","DOIUrl":"https://doi.org/10.1109/MASS.2018.00019","url":null,"abstract":"Caching at the edge of mobile networks can significantly offload network traffic while satisfying content requests from mobile users locally. The contents can be requested from the proximity users via Device-to-device (D2D) communications while proactive caching the popular content to local users. However, the assumptions that content popularity is equal to user preference in several existing studies, which are invalid and not rigorous due to the fact that content popularity is calculated by the statistic of user requests within a certain period while user preference reflects the probability of a content requested by the individual user. Motivated by this, in this paper, we study the edge caching optimization of hierarchical wireless networks. Our aiming is to maximize the size of content offload by D2D communications. In particular, the edge caching policy with D2D sharing model based on the analysis of user mobility and social relationship is derived. We first prove the problem is NP-hard and then formulate it as a Markov Decision Process (MDP) problem, finally a Q-learning based distributed content replacement strategy is proposed. The large-scale real trace based experiment results show the effectiveness of our proposed framework.","PeriodicalId":146214,"journal":{"name":"2018 IEEE 15th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116819808","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}
Bluetooth low energy (BLE) based devices are already deployed in massive quantity as Internet-of-things (IoT) becomes prominent in the last two decades. In order to lower the energy consumption, BLE devices have to compromise with security and privacy problems. Existing research work shows that BLE devices can be easily spoofed and leveraged to gain access to a networking system. In this paper, we propose BF-IoT, the first IoT secure communication framework for BLE-based networks that guards against device spoofing via monitoring the work-life cycles of devices. We dig into the BLE protocol stack and extract the unique network-flow features from the link layer and ATT/GATT service layer so as to generate the fingerprints for device authentication. BF-IoT provides two-phase defense against malicious entities: continuously authenticating device identity before the connection setup and during session establishment. We build a customized system to validate the effectiveness of our mechanism. We extensively evaluate BF-IoT with a dozen of different off-the-shelf commodity IoT devices which shows that the devices can be accurately authenticated via only sniffing the transmission characteristics.
{"title":"BF-IoT: Securing the IoT Networks via Fingerprinting-Based Device Authentication","authors":"Tianbo Gu, P. Mohapatra","doi":"10.1109/MASS.2018.00047","DOIUrl":"https://doi.org/10.1109/MASS.2018.00047","url":null,"abstract":"Bluetooth low energy (BLE) based devices are already deployed in massive quantity as Internet-of-things (IoT) becomes prominent in the last two decades. In order to lower the energy consumption, BLE devices have to compromise with security and privacy problems. Existing research work shows that BLE devices can be easily spoofed and leveraged to gain access to a networking system. In this paper, we propose BF-IoT, the first IoT secure communication framework for BLE-based networks that guards against device spoofing via monitoring the work-life cycles of devices. We dig into the BLE protocol stack and extract the unique network-flow features from the link layer and ATT/GATT service layer so as to generate the fingerprints for device authentication. BF-IoT provides two-phase defense against malicious entities: continuously authenticating device identity before the connection setup and during session establishment. We build a customized system to validate the effectiveness of our mechanism. We extensively evaluate BF-IoT with a dozen of different off-the-shelf commodity IoT devices which shows that the devices can be accurately authenticated via only sniffing the transmission characteristics.","PeriodicalId":146214,"journal":{"name":"2018 IEEE 15th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114621391","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}
Beomsu Kim, Ki-Il Kim, Bongsoo Roh, Hyungseok Choi
In this paper, we present dynamic clustering scheme and hierarchical routing for Unmanned Aerial Vehicle (UAV) relayed tactical ad hoc networks. The simulation results are given to prove the higher packet deliver ratio and shorter end-to-end delay through the proposed scheme than typical routing protocol in tactical ad hoc networks.
{"title":"Hierarchical Routing for Unmanned Aerial Vehicle Relayed Tactical Ad Hoc Networks","authors":"Beomsu Kim, Ki-Il Kim, Bongsoo Roh, Hyungseok Choi","doi":"10.1109/MASS.2018.00034","DOIUrl":"https://doi.org/10.1109/MASS.2018.00034","url":null,"abstract":"In this paper, we present dynamic clustering scheme and hierarchical routing for Unmanned Aerial Vehicle (UAV) relayed tactical ad hoc networks. The simulation results are given to prove the higher packet deliver ratio and shorter end-to-end delay through the proposed scheme than typical routing protocol in tactical ad hoc networks.","PeriodicalId":146214,"journal":{"name":"2018 IEEE 15th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132733190","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}
Guoju Gao, Mingjun Xiao, Jie Wu, He Huang, Guoliang Chen
Recently, influence maximization in social networks has attracted great attention. In this paper, we consider that a company intends to select some users to promote its multiple products (called influences) in online social network consisting of many communities, in which each user has different preferences for each influence. We focus on the Minimum Cost Seed Selection (MCSS) problem for multiple influences, that is, how to select some seeds with minimum cost so that the average influenced probability of all users in each community is not less than a threshold. To solve the MCSS problem, we design a submodular utility function, based on which we turn our problem into a non-trivial set cover problem with non-linear constraints. After proving the NP-hardness of MCSS, we propose a greedy algorithm, called G-MCSS, to solve it. We analyze the approximation ratio of G-MCSS. Additionally, we extend the MCSS problem to a complex case, where the number of acceptable influences for each user is limited, and the cost is proportional to the number of allocated influences. We further propose another greedy algorithm to solve the extended problem. Finally, we demonstrate the significant performances of our algorithms through extensive experiments based on real social network traces.
{"title":"Minimum Cost Seed Selection for Multiple Influences Diffusion in Communities","authors":"Guoju Gao, Mingjun Xiao, Jie Wu, He Huang, Guoliang Chen","doi":"10.1109/MASS.2018.00048","DOIUrl":"https://doi.org/10.1109/MASS.2018.00048","url":null,"abstract":"Recently, influence maximization in social networks has attracted great attention. In this paper, we consider that a company intends to select some users to promote its multiple products (called influences) in online social network consisting of many communities, in which each user has different preferences for each influence. We focus on the Minimum Cost Seed Selection (MCSS) problem for multiple influences, that is, how to select some seeds with minimum cost so that the average influenced probability of all users in each community is not less than a threshold. To solve the MCSS problem, we design a submodular utility function, based on which we turn our problem into a non-trivial set cover problem with non-linear constraints. After proving the NP-hardness of MCSS, we propose a greedy algorithm, called G-MCSS, to solve it. We analyze the approximation ratio of G-MCSS. Additionally, we extend the MCSS problem to a complex case, where the number of acceptable influences for each user is limited, and the cost is proportional to the number of allocated influences. We further propose another greedy algorithm to solve the extended problem. Finally, we demonstrate the significant performances of our algorithms through extensive experiments based on real social network traces.","PeriodicalId":146214,"journal":{"name":"2018 IEEE 15th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130515683","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}
An effective web attack detection method appears as a natural solution to protect web security, as they help to protect web applications. The traditional method of detecting web attacks is to encode the attack features manually into corresponding rules for detection. With the diversification of web attack methods, the demerits of the traditional methods have become increasingly noticeable. With the rapid development of high-performance computing and expansion of data volume, machine learning methods can obtain more efficient and accurate web attacks detection. In this paper, we exploit a bag of words based (BOW) model to extract features and further efficiently detect web attacks with hidden Markov algorithms. The experimental results show that, compared with the previous experiments of N-gram extraction feature algorithm, BOW has higher detection rate and lower false alarm rate with a lower cost. Finally, satisfactory results in the real environment are also achieved.
{"title":"A Web Attack Detection Technology Based on Bag of Words and Hidden Markov Model","authors":"Xin Ren, Yupeng Hu, Wenxin Kuang, Mohamadou Ballo Souleymanou","doi":"10.1109/MASS.2018.00081","DOIUrl":"https://doi.org/10.1109/MASS.2018.00081","url":null,"abstract":"An effective web attack detection method appears as a natural solution to protect web security, as they help to protect web applications. The traditional method of detecting web attacks is to encode the attack features manually into corresponding rules for detection. With the diversification of web attack methods, the demerits of the traditional methods have become increasingly noticeable. With the rapid development of high-performance computing and expansion of data volume, machine learning methods can obtain more efficient and accurate web attacks detection. In this paper, we exploit a bag of words based (BOW) model to extract features and further efficiently detect web attacks with hidden Markov algorithms. The experimental results show that, compared with the previous experiments of N-gram extraction feature algorithm, BOW has higher detection rate and lower false alarm rate with a lower cost. Finally, satisfactory results in the real environment are also achieved.","PeriodicalId":146214,"journal":{"name":"2018 IEEE 15th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130305702","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}
Urban roads tend to cause traffic congestion for a long time after the occurrence of traffic accidents, which greatly affects daily transportations. Therefore, the prediction of the duration of traffic jams caused by traffic accidents can allocate traffic resources more reasonably and effectively, release induced traffic information, avoid secondary congestion, and quickly handle traffic accidents. It is of great significance to the rapid rescue of traffic accidents and to eliminate traffic safety hazards. In response to this hot issue, many scholars have done a lot of researches through numerous models, such as probability distribution and time series, and artificial neural networks. However, these models usually only consider temporal features or are based on shallow networks. Therefore, this work adopts a hybrid deep spatial-temporal residual neural network HD-SP-ResNet to predict the traffic volume and velocity, as well as the road congestion duration after traffic accident, so as to monitor and dispatch real-time traffic, response to the postaccidental congestion in time, in order to reduce the various losses incurred by congestion and improve people's satisfaction with traffic on the road. To verify the effectiveness of the proposed model, we conduct extensive experiments based on the taxi trajectory data and road accident data in Shanghai. The experiment results show that the proposed model can achieve a relatively accurate prediction on traffic volume and velocity, as well as the post-accidental congestion duration.
{"title":"Deep Learning Based Urban Post-Accidental Congestion Prediction","authors":"Mingming Lu, Kunfang Zhang, Junyan Wu, D. Tan","doi":"10.1109/MASS.2018.00035","DOIUrl":"https://doi.org/10.1109/MASS.2018.00035","url":null,"abstract":"Urban roads tend to cause traffic congestion for a long time after the occurrence of traffic accidents, which greatly affects daily transportations. Therefore, the prediction of the duration of traffic jams caused by traffic accidents can allocate traffic resources more reasonably and effectively, release induced traffic information, avoid secondary congestion, and quickly handle traffic accidents. It is of great significance to the rapid rescue of traffic accidents and to eliminate traffic safety hazards. In response to this hot issue, many scholars have done a lot of researches through numerous models, such as probability distribution and time series, and artificial neural networks. However, these models usually only consider temporal features or are based on shallow networks. Therefore, this work adopts a hybrid deep spatial-temporal residual neural network HD-SP-ResNet to predict the traffic volume and velocity, as well as the road congestion duration after traffic accident, so as to monitor and dispatch real-time traffic, response to the postaccidental congestion in time, in order to reduce the various losses incurred by congestion and improve people's satisfaction with traffic on the road. To verify the effectiveness of the proposed model, we conduct extensive experiments based on the taxi trajectory data and road accident data in Shanghai. The experiment results show that the proposed model can achieve a relatively accurate prediction on traffic volume and velocity, as well as the post-accidental congestion duration.","PeriodicalId":146214,"journal":{"name":"2018 IEEE 15th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114190566","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}
Nowadays, breakthroughs in wireless power transfer make it possible to transfer energy over a long distance. Existing works mainly focused on maximizing network lifetime, optimizing charging efficiency, and optimizing charging quality. All these works use a charging model with the linear superposition, which may not be the most accurate in a real life situation. We use a concurrent charging model, which has a nonlinear superposition, and we consider the Fast Charging Scheduling problem (FCS): given multiple chargers and a group of sensor nodes, how can the chargers be optimally scheduled over the time dimension so that the total charging time is minimized and each sensor node has at least energy E? We prove that FCS is NP-complete and propose algorithms to solve the problem in 1D line and 2D plane respectively. Unlike other algorithms, our algorithm does not need to calculate the combined energy of every possible combination of chargers in advance, which greatly reduces the complexity. We obtain a bound in 2D cases when chargers and sensors are uniformly distributed. Extensive simulations demonstrate that the performance of our algorithm is almost as good as the optimal algorithm when the distribution of chargers is not very dense.
{"title":"Fast Interference-Aware Scheduling of Multiple Wireless Chargers","authors":"Zhi Ma, Jie Wu, S. Zhang, Sanglu Lu","doi":"10.1109/MASS.2018.00057","DOIUrl":"https://doi.org/10.1109/MASS.2018.00057","url":null,"abstract":"Nowadays, breakthroughs in wireless power transfer make it possible to transfer energy over a long distance. Existing works mainly focused on maximizing network lifetime, optimizing charging efficiency, and optimizing charging quality. All these works use a charging model with the linear superposition, which may not be the most accurate in a real life situation. We use a concurrent charging model, which has a nonlinear superposition, and we consider the Fast Charging Scheduling problem (FCS): given multiple chargers and a group of sensor nodes, how can the chargers be optimally scheduled over the time dimension so that the total charging time is minimized and each sensor node has at least energy E? We prove that FCS is NP-complete and propose algorithms to solve the problem in 1D line and 2D plane respectively. Unlike other algorithms, our algorithm does not need to calculate the combined energy of every possible combination of chargers in advance, which greatly reduces the complexity. We obtain a bound in 2D cases when chargers and sensors are uniformly distributed. Extensive simulations demonstrate that the performance of our algorithm is almost as good as the optimal algorithm when the distribution of chargers is not very dense.","PeriodicalId":146214,"journal":{"name":"2018 IEEE 15th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115689754","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}
W. Meng, Kaiping Xue, Jie Xu, Jianan Hong, Nenghai Yu
With an advancement of mobile communication technology, the space information network (SIN) has been proposed to meet the increasing demands of mobile communication due to its advantage of providing great expanding access services. In SIN, authentication is significant for the security to prevent the network resource from unauthorized access. However, the features of highly exposed links and extremely high propagation delay make it difficult to design a secure and fast authentication scheme for SIN. Although some existing researches have tried to design authentication protocols for SIN, they haven't taken the intolerable authentication delay and the risk of satellite compromising into consideration. Faced with these problems, we design a proxy signature-based authentication scheme for SIN, in which, the interaction process of authentication can be only implemented between the mobile user and the satellite node, thus reducing the long authentication implementation delay. Furthermore, we utilize the proxy signature to mitigate the risk of satellites being attacked. The results of security and performance analysis show that the proposed scheme can provide the required security and largely reduce the authentication latency.
{"title":"Low-Latency Authentication Against Satellite Compromising for Space Information Network","authors":"W. Meng, Kaiping Xue, Jie Xu, Jianan Hong, Nenghai Yu","doi":"10.1109/MASS.2018.00045","DOIUrl":"https://doi.org/10.1109/MASS.2018.00045","url":null,"abstract":"With an advancement of mobile communication technology, the space information network (SIN) has been proposed to meet the increasing demands of mobile communication due to its advantage of providing great expanding access services. In SIN, authentication is significant for the security to prevent the network resource from unauthorized access. However, the features of highly exposed links and extremely high propagation delay make it difficult to design a secure and fast authentication scheme for SIN. Although some existing researches have tried to design authentication protocols for SIN, they haven't taken the intolerable authentication delay and the risk of satellite compromising into consideration. Faced with these problems, we design a proxy signature-based authentication scheme for SIN, in which, the interaction process of authentication can be only implemented between the mobile user and the satellite node, thus reducing the long authentication implementation delay. Furthermore, we utilize the proxy signature to mitigate the risk of satellites being attacked. The results of security and performance analysis show that the proposed scheme can provide the required security and largely reduce the authentication latency.","PeriodicalId":146214,"journal":{"name":"2018 IEEE 15th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115771135","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}
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