Short-lived tasks have a large impact on mobile computer's battery life. In executing such tasks, the whole system transitions in and out of the deep sleep mode. This suspend/resume procedure is controlled by the operating system (OS), which consumes a dominating portion of energy. Through characterizing the Linux kernel on a variety of modern system-on-chips (SoCs), we show that the OS suspend/resume mechanism is fundamentally slowed down by various IO devices, which frequently keep CPU waiting. To minimize energy consumption, we advocate offloading the OS suspend/resume to a miniature processor that waits more efficiently. To this end, we propose a new virtual executor that runs on a miniature core and directly executes the unmodified kernel binary of the main CPU. We construct the virtual executor centering on software-only, cross-ISA binary translation, an approach previously considered prohibitively expensive. Through novel designs and optimizations, we reduce the translation overhead by 5×. The preliminary benchmarks show promising energy efficiency.
{"title":"Decelerating Suspend and Resume in Operating Systems","authors":"Shuang Zhai, Liwei Guo, Xiangyu Li, F. Lin","doi":"10.1145/3032970.3032975","DOIUrl":"https://doi.org/10.1145/3032970.3032975","url":null,"abstract":"Short-lived tasks have a large impact on mobile computer's battery life. In executing such tasks, the whole system transitions in and out of the deep sleep mode. This suspend/resume procedure is controlled by the operating system (OS), which consumes a dominating portion of energy. Through characterizing the Linux kernel on a variety of modern system-on-chips (SoCs), we show that the OS suspend/resume mechanism is fundamentally slowed down by various IO devices, which frequently keep CPU waiting. To minimize energy consumption, we advocate offloading the OS suspend/resume to a miniature processor that waits more efficiently. To this end, we propose a new virtual executor that runs on a miniature core and directly executes the unmodified kernel binary of the main CPU. We construct the virtual executor centering on software-only, cross-ISA binary translation, an approach previously considered prohibitively expensive. Through novel designs and optimizations, we reduce the translation overhead by 5×. The preliminary benchmarks show promising energy efficiency.","PeriodicalId":309322,"journal":{"name":"Proceedings of the 18th International Workshop on Mobile Computing Systems and Applications","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124805374","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}
Vision- and LIDAR-based Simultaneous Localization and Mapping (SLAM) techniques can build indoor building floorplans with ease, but require line-of-sight into every room of a building. We are developing a radio microwave-based system for building indoor floor plans using SLAM techniques, but without the requirement that the mapping robot has access to every room in the building. Our system uses multiple antennas to direct radio energy through walls in particular directions, and joint time- and angle-of-arrival estimation techniques to estimate their backscatter returns from the walls of the building. Wide bandwidth (120 MHz) transmissions combined with an iterative transmit nulling and receive cancellation strategy allows ThruMapper to isolate individual walls and measure the location of a non-line-of-sight wall hidden behind another wall and office clutter to within a 50-centimetre RMS error.
{"title":"ThruMapper: Through-Wall Building Tomography with a Single Mapping Robot","authors":"Bo Tan, K. Chetty, K. Jamieson","doi":"10.1145/3032970.3032973","DOIUrl":"https://doi.org/10.1145/3032970.3032973","url":null,"abstract":"Vision- and LIDAR-based Simultaneous Localization and Mapping (SLAM) techniques can build indoor building floorplans with ease, but require line-of-sight into every room of a building. We are developing a radio microwave-based system for building indoor floor plans using SLAM techniques, but without the requirement that the mapping robot has access to every room in the building. Our system uses multiple antennas to direct radio energy through walls in particular directions, and joint time- and angle-of-arrival estimation techniques to estimate their backscatter returns from the walls of the building. Wide bandwidth (120 MHz) transmissions combined with an iterative transmit nulling and receive cancellation strategy allows ThruMapper to isolate individual walls and measure the location of a non-line-of-sight wall hidden behind another wall and office clutter to within a 50-centimetre RMS error.","PeriodicalId":309322,"journal":{"name":"Proceedings of the 18th International Workshop on Mobile Computing Systems and Applications","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116697092","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}
U. Moravapalle, A. Deshpande, Ashish Kapoor, R. Ramjee, Priya Ravi
Low-cost lenses with magnifications of 150-200x are being sold in the market today as accessories for mobile smartphones. Attaching these lenses to a smartphone camera creates low-cost, ultra-portable digital microscopes, with a potential for significant impact on applications in a variety of fields such as healthcare, agriculture, education etc. In this paper, we consider a low-cost do-it-yourself Complete Blood Count (CBC) application using a smartphone microscope. We discuss several unique challenges that come up in implementing this application that include preparing the blood sample, correcting the small field of view and blur of the lens, and automating the cell counting procedure. We present our approach to overcome these challenges and report early promising results on counting red blood cells.
{"title":"Blood Count on a Smartphone Microscope: Challenges","authors":"U. Moravapalle, A. Deshpande, Ashish Kapoor, R. Ramjee, Priya Ravi","doi":"10.1145/3032970.3032986","DOIUrl":"https://doi.org/10.1145/3032970.3032986","url":null,"abstract":"Low-cost lenses with magnifications of 150-200x are being sold in the market today as accessories for mobile smartphones. Attaching these lenses to a smartphone camera creates low-cost, ultra-portable digital microscopes, with a potential for significant impact on applications in a variety of fields such as healthcare, agriculture, education etc. In this paper, we consider a low-cost do-it-yourself Complete Blood Count (CBC) application using a smartphone microscope. We discuss several unique challenges that come up in implementing this application that include preparing the blood sample, correcting the small field of view and blur of the lens, and automating the cell counting procedure. We present our approach to overcome these challenges and report early promising results on counting red blood cells.","PeriodicalId":309322,"journal":{"name":"Proceedings of the 18th International Workshop on Mobile Computing Systems and Applications","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127835701","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}
Mobile devices, such as smartphones and tablets, use notifications to inform their users of events. Some security- and privacy-related events are time-sensitive: the user must be notified immediately. In this paper, we perform a user study with 40 participants to understand the properties of such time-sensitive notifications. We specifically focus on sensor notifications that notify the users when one of the sensitive sensors, such as camera, microphone, or location is being accessed. We show that none of the notification channels available on mobile devices, i.e., LED, vibration, sound, and display, can grab the user's attention in more than 24% of the time. Among them, vibration achieves the best success rates on average. Moreover, our results show that less intrusive channels, e.g., display, can achieve significantly better results if the device's physical context, i.e., ambient light intensity, is considered. Based on our findings, we suggest that display notification is the best option for camera while different vibration patterns are best options for microphone and location sensor.
{"title":"Understanding Sensor Notifications on Mobile Devices","authors":"Zongheng Ma, S. Mirzamohammadi, A. A. Sani","doi":"10.1145/3032970.3032978","DOIUrl":"https://doi.org/10.1145/3032970.3032978","url":null,"abstract":"Mobile devices, such as smartphones and tablets, use notifications to inform their users of events. Some security- and privacy-related events are time-sensitive: the user must be notified immediately. In this paper, we perform a user study with 40 participants to understand the properties of such time-sensitive notifications. We specifically focus on sensor notifications that notify the users when one of the sensitive sensors, such as camera, microphone, or location is being accessed. We show that none of the notification channels available on mobile devices, i.e., LED, vibration, sound, and display, can grab the user's attention in more than 24% of the time. Among them, vibration achieves the best success rates on average. Moreover, our results show that less intrusive channels, e.g., display, can achieve significantly better results if the device's physical context, i.e., ambient light intensity, is considered. Based on our findings, we suggest that display notification is the best option for camera while different vibration patterns are best options for microphone and location sensor.","PeriodicalId":309322,"journal":{"name":"Proceedings of the 18th International Workshop on Mobile Computing Systems and Applications","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126151766","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}
Shashank Jain, V. Tiwari, A. Balasubramanian, Niranjan Balasubramanian, Supriyo Chakraborty
Personalized services such as news recommendations are becoming an integral part of our digital lives. The problem is that they extract a steep cost in terms of privacy. The service providers collect and analyze user's personal data to provide the service, but can infer sensitive information about the user in the process. In this work we ask the question "How can we provide personalized news recommendation without sharing sensitive data with the provider?" We propose a local private intelligence assistance framework (PrIA), which collects user data and builds a profile about the user and provides recommendations, all on the user's personal device. It decouples aggregation and personalization: it uses the existing aggregation services on the cloud to obtain candidate articles but makes the personalized recommendations locally. Our proof-of-concept implementation and small scale user study shows the feasibility of a local news recommendation system. In building a private profile, PrIA avoids sharing sensitive information with the cloud-based recommendation service. However, the trade-off is that unlike cloud-based services, PrIA cannot leverage collective knowledge from large number of users. We quantify this trade-off by comparing PrIA with Google's cloud-based recommendation service. We find that the average precision of PrIA's recommendation is only 14% lower than that of Google's service. Rather than choose between privacy or personalization, this result motivates further study of systems that can provide both with acceptable trade-offs.
{"title":"PrIA: A Private Intelligent Assistant","authors":"Shashank Jain, V. Tiwari, A. Balasubramanian, Niranjan Balasubramanian, Supriyo Chakraborty","doi":"10.1145/3032970.3032988","DOIUrl":"https://doi.org/10.1145/3032970.3032988","url":null,"abstract":"Personalized services such as news recommendations are becoming an integral part of our digital lives. The problem is that they extract a steep cost in terms of privacy. The service providers collect and analyze user's personal data to provide the service, but can infer sensitive information about the user in the process. In this work we ask the question \"How can we provide personalized news recommendation without sharing sensitive data with the provider?\" We propose a local private intelligence assistance framework (PrIA), which collects user data and builds a profile about the user and provides recommendations, all on the user's personal device. It decouples aggregation and personalization: it uses the existing aggregation services on the cloud to obtain candidate articles but makes the personalized recommendations locally. Our proof-of-concept implementation and small scale user study shows the feasibility of a local news recommendation system. In building a private profile, PrIA avoids sharing sensitive information with the cloud-based recommendation service. However, the trade-off is that unlike cloud-based services, PrIA cannot leverage collective knowledge from large number of users. We quantify this trade-off by comparing PrIA with Google's cloud-based recommendation service. We find that the average precision of PrIA's recommendation is only 14% lower than that of Google's service. Rather than choose between privacy or personalization, this result motivates further study of systems that can provide both with acceptable trade-offs.","PeriodicalId":309322,"journal":{"name":"Proceedings of the 18th International Workshop on Mobile Computing Systems and Applications","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126985137","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}
Ashutosh Dhekne, Mahanth K. Gowda, Romit Roy Choudhury
This paper explores a future in which drones serve as extensions to cellular networks. Equipped with a WiFi interface and a (LTE/5G) backhaul link, we envision a drone to fly in and create a WiFi network in a desired region. Analogous to fire engines, these drones can offer on-demand network service, alleviating unpredictable problems such as sudden traffic hotspots, poor coverage, and natural disasters. While realizing such a vision would need various pieces to come together, we focus on the problem of "drone placement". We ask: when several scattered users demand cellular connectivity in a particular area, where should the drone hover so that the aggregate demands are optimally satisfied? This is essentially a search problem, i.e., the drone needs to determine a 3D location from which its SNR to all the clients is maximized. Given the unknown environmental conditions (such as multipath, wireless shadows, foliage, and absorption), it is not trivial to predict the best hovering location. We explore the possibility of using RF ray tracing as a hint to narrow down the scope of search. Our key idea is to use 3D models from Google Earth to roughly model the terrain of the region, and then simulate how signals would scatter from the drone to various clients. While such simulations offer coarse-grained results, we find that they can still be valuable in broadly guiding the drone in the right direction. Once the drone has narrowed down the 3D search space, it can then physically move to quickly select the best hovering location. Measurement results from a WiFi mounted drone, communicating with 7 clients scattered in the UIUC campus, are encouraging. Our early prototype, DroneNet, reports 44% throughput gain with only 10% measurement overhead compared to a full scan of the entire region.
{"title":"Extending Cell Tower Coverage through Drones","authors":"Ashutosh Dhekne, Mahanth K. Gowda, Romit Roy Choudhury","doi":"10.1145/3032970.3032984","DOIUrl":"https://doi.org/10.1145/3032970.3032984","url":null,"abstract":"This paper explores a future in which drones serve as extensions to cellular networks. Equipped with a WiFi interface and a (LTE/5G) backhaul link, we envision a drone to fly in and create a WiFi network in a desired region. Analogous to fire engines, these drones can offer on-demand network service, alleviating unpredictable problems such as sudden traffic hotspots, poor coverage, and natural disasters. While realizing such a vision would need various pieces to come together, we focus on the problem of \"drone placement\". We ask: when several scattered users demand cellular connectivity in a particular area, where should the drone hover so that the aggregate demands are optimally satisfied? This is essentially a search problem, i.e., the drone needs to determine a 3D location from which its SNR to all the clients is maximized. Given the unknown environmental conditions (such as multipath, wireless shadows, foliage, and absorption), it is not trivial to predict the best hovering location. We explore the possibility of using RF ray tracing as a hint to narrow down the scope of search. Our key idea is to use 3D models from Google Earth to roughly model the terrain of the region, and then simulate how signals would scatter from the drone to various clients. While such simulations offer coarse-grained results, we find that they can still be valuable in broadly guiding the drone in the right direction. Once the drone has narrowed down the 3D search space, it can then physically move to quickly select the best hovering location. Measurement results from a WiFi mounted drone, communicating with 7 clients scattered in the UIUC campus, are encouraging. Our early prototype, DroneNet, reports 44% throughput gain with only 10% measurement overhead compared to a full scan of the entire region.","PeriodicalId":309322,"journal":{"name":"Proceedings of the 18th International Workshop on Mobile Computing Systems and Applications","volume":"137 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115791743","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: Reimagining Virtual Reality","authors":"David Chu","doi":"10.1145/3257164","DOIUrl":"https://doi.org/10.1145/3257164","url":null,"abstract":"","PeriodicalId":309322,"journal":{"name":"Proceedings of the 18th International Workshop on Mobile Computing Systems and Applications","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133496281","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}
K. Zeng, Yuanchao Shu, Shinan Liu, Yanzhi Dou, Yaling Yang
High value of GPS location information and easy availability of portable GPS signal spoofing devices incentivize attackers to launch GPS spoofing attacks against location-based applications. In this paper, we propose an attack model in road navigation scenario, and develop a complete framework to analyze, simulate and evaluate the spoofing attacks under practical constraints. To launch an attack, the framework first constructs a road network, and then searches for an attack route that smoothly diverts a victim without his awareness. In extensive data-driven simulations in College Point, New York City, we managed to navigate a victim to locations 1km away from his original destination.
{"title":"A Practical GPS Location Spoofing Attack in Road Navigation Scenario","authors":"K. Zeng, Yuanchao Shu, Shinan Liu, Yanzhi Dou, Yaling Yang","doi":"10.1145/3032970.3032983","DOIUrl":"https://doi.org/10.1145/3032970.3032983","url":null,"abstract":"High value of GPS location information and easy availability of portable GPS signal spoofing devices incentivize attackers to launch GPS spoofing attacks against location-based applications. In this paper, we propose an attack model in road navigation scenario, and develop a complete framework to analyze, simulate and evaluate the spoofing attacks under practical constraints. To launch an attack, the framework first constructs a road network, and then searches for an attack route that smoothly diverts a victim without his awareness. In extensive data-driven simulations in College Point, New York City, we managed to navigate a victim to locations 1km away from his original destination.","PeriodicalId":309322,"journal":{"name":"Proceedings of the 18th International Workshop on Mobile Computing Systems and Applications","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116120508","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}
Hang Qiu, Fawad Ahmad, R. Govindan, M. Gruteser, F. Bai, Gorkem Kar
Like today's autonomous vehicle prototypes, vehicles in the future will have rich sensors to map and identify objects in the environment. For example, many autonomous vehicle prototypes today come with line-of-sight depth perception sensors like 3D cameras. These cameras are used for improving vehicular safety in autonomous driving, but have fundamentally limited visibility due to occlusions, sensing range, and extreme weather and lighting conditions. To improve visibility and performance, not just for autonomous vehicles but for other Advanced Driving Assistance Systems (ADAS), we explore a capability called Augmented Vehicular Reality (AVR). AVR broadens the vehicle's visual horizon by enabling it to share visual information with other nearby vehicles, but requires careful techniques to align coordinate frames of reference, and to detect dynamic objects. Preliminary evaluations hint at the feasibility of AVR and also highlight research challenges in achieving AVR's potential to improve autonomous vehicles and ADAS.
{"title":"Augmented Vehicular Reality: Enabling Extended Vision for Future Vehicles","authors":"Hang Qiu, Fawad Ahmad, R. Govindan, M. Gruteser, F. Bai, Gorkem Kar","doi":"10.1145/3032970.3032976","DOIUrl":"https://doi.org/10.1145/3032970.3032976","url":null,"abstract":"Like today's autonomous vehicle prototypes, vehicles in the future will have rich sensors to map and identify objects in the environment. For example, many autonomous vehicle prototypes today come with line-of-sight depth perception sensors like 3D cameras. These cameras are used for improving vehicular safety in autonomous driving, but have fundamentally limited visibility due to occlusions, sensing range, and extreme weather and lighting conditions. To improve visibility and performance, not just for autonomous vehicles but for other Advanced Driving Assistance Systems (ADAS), we explore a capability called Augmented Vehicular Reality (AVR). AVR broadens the vehicle's visual horizon by enabling it to share visual information with other nearby vehicles, but requires careful techniques to align coordinate frames of reference, and to detect dynamic objects. Preliminary evaluations hint at the feasibility of AVR and also highlight research challenges in achieving AVR's potential to improve autonomous vehicles and ADAS.","PeriodicalId":309322,"journal":{"name":"Proceedings of the 18th International Workshop on Mobile Computing Systems and Applications","volume":"59 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114022494","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}
Jun Han, M. Harishankar, Xiao Wang, Albert Jin Chung, P. Tague
Truck platooning is emerging as a promising solution with many economic incentives. However, securely admitting a new vehicle into a platoon is an extremely important yet difficult task. There is no adequate method today for verifying physical arrangements of vehicles within a platoon formation. Specifically, we address the problem of a platoon ghost attack wherein an attacker spoofs presence within a platoon to gain admission and subsequently execute malicious attacks. To address such concerns, we present Convoy, a novel autonomous platoon admission scheme which binds the vehicles' digital certificates to their physical context (i.e., locality). Convoy exploits the findings that vehicles traveling together experience similar context to prove to each other over time that they are co-present. Specifically, they experience similar road (e.g., bumps and cracks) and traffic (e.g., acceleration and steering) conditions. Our approach is based on the ability for vehicles to capture this context, generate fingerprints to establish shared keys, and later bind these symmetric keys to their public keys. We design and implement the Convoy protocol and evaluate it with real-world driving data. Our implementation demonstrates that vehicles traveling in adjacent lanes can be sufficiently distinguished by their context and this can be utilized to thwart platoon ghost attacks and similar misbehavior.
{"title":"Convoy: Physical Context Verification for Vehicle Platoon Admission","authors":"Jun Han, M. Harishankar, Xiao Wang, Albert Jin Chung, P. Tague","doi":"10.1145/3032970.3032987","DOIUrl":"https://doi.org/10.1145/3032970.3032987","url":null,"abstract":"Truck platooning is emerging as a promising solution with many economic incentives. However, securely admitting a new vehicle into a platoon is an extremely important yet difficult task. There is no adequate method today for verifying physical arrangements of vehicles within a platoon formation. Specifically, we address the problem of a platoon ghost attack wherein an attacker spoofs presence within a platoon to gain admission and subsequently execute malicious attacks. To address such concerns, we present Convoy, a novel autonomous platoon admission scheme which binds the vehicles' digital certificates to their physical context (i.e., locality). Convoy exploits the findings that vehicles traveling together experience similar context to prove to each other over time that they are co-present. Specifically, they experience similar road (e.g., bumps and cracks) and traffic (e.g., acceleration and steering) conditions. Our approach is based on the ability for vehicles to capture this context, generate fingerprints to establish shared keys, and later bind these symmetric keys to their public keys. We design and implement the Convoy protocol and evaluate it with real-world driving data. Our implementation demonstrates that vehicles traveling in adjacent lanes can be sufficiently distinguished by their context and this can be utilized to thwart platoon ghost attacks and similar misbehavior.","PeriodicalId":309322,"journal":{"name":"Proceedings of the 18th International Workshop on Mobile Computing Systems and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131864881","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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