2020 IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)最新文献

{"title":"Game Theoretic Storage Outsourcing in the Mobile Blockchain Mining Network","authors":"Suhan Jiang, Jie Wu","doi":"10.1109/MASS50613.2020.00045","DOIUrl":"https://doi.org/10.1109/MASS50613.2020.00045","url":null,"abstract":"Besides the computation limitation, the requirement of storing the entire blockchain is another challenge for blockchain mining in mobile environments, and thus has hindered the development of blockchain-powered mobile applications. Storage outsourcing to a cloud service provider (CSP) is a viable solution. An individual miner can store his blockchain in the cloud and then validate transactions by querying the CSP. However, validation outsourcing to a remote CSP incurs delay and damages a miner’s winning probability in the mining competitions. To shorten such an unwanted delay, miners can also cache the unspent transaction output (UTXO) set in a nearby edge service provider (ESP) for fast transaction validations, which definitely brings extra costs. In this paper, we consider a two-layer outsourcing paradigm to solve storage shortage for mobile miners. Due to the delay-cost tradeoff when selecting service providers, we can model interactions among miners as a non-cooperative game and formulate a Nash equilibrium problem to investigate the effects of outsourcing on miners’ utilities. We also study the access probability of UTXOs with different generation times. This will guide miners on how to select unspent transaction outputs if they decide only to cache the partial UTXO set in the edge. We further extend our game by modeling multiple mining rounds as a one-shot game to see how the cache update frequency affects miners’ strategies. Numerical evaluation is conducted to show the feasibility of storage outsourcing and to validate the proposed models and theoretical results.","PeriodicalId":105795,"journal":{"name":"2020 IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121353691","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":"Secure and User Efficient EAP-based Authentication Protocol for IEEE 802.11 Wireless LANs","authors":"Awaneesh Kumar Yadav, M. Misra, Madhusanka Liyanage, G. Varshney","doi":"10.1109/MASS50613.2020.00076","DOIUrl":"https://doi.org/10.1109/MASS50613.2020.00076","url":null,"abstract":"Wireless Local Area Networks (WLANs) have experienced significant growth in the last two decades due to the extensive use of wireless devices. Security (especially authentication) is a staple concern as the wireless medium is accessible to everybody. Extensible Authentication Protocol (EAP) is the widely used authentication framework in WLANs to secure communication. The authentication mechanism designed on EAP is called EAP method. There are numerous EAP based and non-EAP based authentication protocols for WLANs, but there is no protocol that fulfills all the security requirements, as mentioned in RFC-4017 and other additional requirements like perfect forward secrecy, Denial-of-service (DoS) attack protection, and lightweight computation. Hence, it is fair to infer that there is an impelling need to design a protocol that can meet all the security requirements. In this paper, we propose a secure and user efficient EAP-based authentication protocol for IEEE 802.11 WLANs. The proposed protocol has been formally validated by BAN logic and the AVISPA tool [18]. The simulation results depict that the proposed protocol achieves all security requirements, as mentioned in RFC-4017 along with perfect forward secrecy, Denial-of-service (DoS) attack protection, and lightweight computation. The proposed protocol outperforms the existing protocols in terms of computation cost by reducing the computation cost by $approx 99.9956$%, 99.991%, 27.27%, 22.705% in comparison to EAP-TLS, EAP-TTLS, EAP-Ehash, EAP-SELUA, respectively.","PeriodicalId":105795,"journal":{"name":"2020 IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115397294","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":"Reinforcement Learning based Scheduling for Cooperative EV-to-EV Dynamic Wireless Charging","authors":"Li Yan, Haiying Shen, Liuwang Kang, Juanjuan Zhao, Chengzhong Xu","doi":"10.1109/MASS50613.2020.00056","DOIUrl":"https://doi.org/10.1109/MASS50613.2020.00056","url":null,"abstract":"Previous Electric Vehicle (EV) charging scheduling methods and EV route planning methods require EVs to spend extra waiting time and driving burden for a recharge. With the advancement of dynamic wireless charging for EVs, Mobile Energy Disseminator (MED), which can charge an EV in motion, becomes available. However, existing wireless charging scheduling methods for wireless sensors, which are the most related works to the deployment of MEDs, are not directly applicable for the scheduling of MEDs on city-scale road networks. We present MobiCharger: a Mobile wireless Charger guidance system that determines the number of serving MEDs, and the optimal routes of the MEDs periodically (e.g., every 30 minutes). Through analyzing a metropolitan-scale vehicle mobility dataset, we found that most vehicles have routines, and the temporal change of the number of driving vehicles changes during different time slots, which means the number of MEDs should adaptively change as well. Then, we propose a Reinforcement Learning based method to determine the number and the driving route of serving MEDs. Our experiments driven by the dataset demonstrate that MobiCharger increases the medium state-of-charge and the number of charges of all EVs by 50% and 100%, respectively.","PeriodicalId":105795,"journal":{"name":"2020 IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117042348","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 Self-adaptive Low Delay MAC Protocol For Event-driven Industrial Wireless Networks","authors":"Yida Xu, Qi Wang, Yongjun Xu","doi":"10.1109/MASS50613.2020.00022","DOIUrl":"https://doi.org/10.1109/MASS50613.2020.00022","url":null,"abstract":"In many industrial wireless networks, sensor nodes sense an event and then generate related data packets to transmit. In such event-driven networks, contention medium access control (MAC) protocols such as 802.11 DCF are widely used to address the burst traffic. However, due to the fixed probability distribution of contention time-slot selection (PDCS), traditional methods may cause severe collisions when a great number of packets are being transmitted in a short period. To deal with this problem, a MAC protocol that dynamically adjusts PDCS and contention window simultaneously is proposed in this paper. This method exploits the spatial and temporal relationship of packet generation among source nodes in industrial wireless networks. In this method, sensor nodes related to the same event source converge to the optimal PDCS quickly and collisions are significantly reduced. Delay performances of this method are evaluated through simulations. According to the simulation, our protocol outperforms current MAC protocols in access delay by about 20% in event-driven networks.","PeriodicalId":105795,"journal":{"name":"2020 IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125875584","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":"Deep Reinforcement Learning for Computation Offloading and Caching in Fog-Based Vehicular Networks","authors":"Dapeng Lan, Amirhosein Taherkordi, F. Eliassen, Lei Liu","doi":"10.1109/MASS50613.2020.00081","DOIUrl":"https://doi.org/10.1109/MASS50613.2020.00081","url":null,"abstract":"The role of fog computing in future vehicular networks is becoming significant, enabling a variety of applications that demand high computing resources and low latency, such as augmented reality and autonomous driving. Fog-based computation offloading and service caching are considered two key factors in efficient execution of resource-demanding services in such applications. While some efforts have been made on computation offloading in fog computing, a limited amount of work has considered joint optimization of computation offloading and service caching. As fog platforms are usually equipped with moderate computing and storage resources, we need to judiciously decide which services to be cached when offloading computation tasks to maximize the system performance. The heterogeneity, dynamicity, and stochastic properties of vehicular networks also pose challenges on optimal offloading and resource allocation. In this paper, we propose an intelligent computation offloading architecture with service caching, considering both peer-pool and fog-pool computation offloading. An optimization problem of joint computation offloading and service caching is formulated to minimize the task processing time and long-term energy utilization. Finally, we propose an algorithm based on deep reinforcement learning to solve this complex optimization problem. Extensive simulations are undertaken to verify the feasibility of our proposed scheme. The results show that our proposed scheme exhibits an effective performance improvement in computation latency and energy consumption compared to the chosen baseline.","PeriodicalId":105795,"journal":{"name":"2020 IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122271878","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":"Robust, Fine-Grained Occupancy Estimation via Combined Camera & WiFi Indoor Localization","authors":"Anuradha Ravi, Archan Misra","doi":"10.1109/MASS50613.2020.00074","DOIUrl":"https://doi.org/10.1109/MASS50613.2020.00074","url":null,"abstract":"We describe the development of a robust, accurate and practically-validated technique for estimating the occupancy count in indoor spaces, based on a combination of WiFi & video sensing. While fusing these two sensing-based inputs is conceptually straightforward, the paper demonstrates and tackles the complexity that arises from several practical artefacts, such as (i) over-counting when a single individual uses multiple WiFi devices and under-counting when the individual has no such device; (ii) corresponding errors in image analysis due to real-world artefacts, such as occlusion, and (iii) the variable errors in mapping image bounding boxes (which can include multiple possible types of human views: {head, torso, full-body}) to location coordinates. We develop statistical techniques to overcome these practical challenges, and finally propose a novel fusion algorithm, based on inexact bipartite matching of these two streams of independent estimates, to estimate the occupancy in complex, multi-inhabitant indoor spaces (such as university labs). We experimentally demonstrate that this estimation technique is robust and accurate, achieving less than 20% error, in an approx. 85m2 lab space (with the error staying below 30% in a smaller 25m2 area), across a wide variety of occupancy conditions.","PeriodicalId":105795,"journal":{"name":"2020 IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122432512","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":"An Advanced Black-Box Adversarial Attack for Deep Driving Maneuver Classification Models","authors":"Ankur Sarker, Haiying Shen, Tanmoy Sen, Hua Uehara","doi":"10.1109/MASS50613.2020.00032","DOIUrl":"https://doi.org/10.1109/MASS50613.2020.00032","url":null,"abstract":"Connected and autonomous vehicles (CAV) have been introduced to increase roadway safety and traffic flow efficiency. In CAV scenarios, an autonomous vehicle shares its current and near-future driving maneuvers in terms of different driving signals (e.g., speed, brake pedal pressure) with its nearby vehicles using wireless communication technologies. Deep neural network (DNN) models are usually used to process the driving maneuver time-series data over other machine learning algorithms due to the high prediction accuracy of DNN models. In this scenario, an attacker can send false driving maneuver signals to fool the DNN model to misclassify an input. The existing black-box adversarial attacks (which are for image datasets) require many queries to the DNN model to check if a generated attack will be successful (hence long time) or high amount of perturbation (low imperceptibility), and thus cannot be applied to the time-sensitive CAV scenarios featured by multi-dimensional time series driving data. In this paper, we present an Advanced black-box Adversarial Attack $({mathrm {A}}^{3})$ for the deep driving maneuver classification models. We first formulate an optimization problem for the attack generation with continuous search space to reduce the search time. To solve the optimization problem, A3 innovatively combines the binary search and optimization algorithm to improve the time-efficiency of searching the optimal solution. It first uses a binary partition technique to reduce the perturbation search space in solving the problem to improve time-efficiency. It then uses the zeroth-order stochastic gradient descent approach, which is featured by searching a solution faster for high-dimensional datasets, thus further improving time-efficiency. We evaluate the proposed A3 attack in terms of different metrics using two real driving datasets. The experimental results show that the A3 attack requires up to 84.12% fewer queries and 57.67% less perturbation with 94.87% higher success rates than the existing black-box adversarial attacks.","PeriodicalId":105795,"journal":{"name":"2020 IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132778770","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":"Station Correlation Attention Learning for Data-driven Bike Sharing System Usage Prediction","authors":"Xi Yang, Suining He, Huiqun Huang","doi":"10.1109/MASS50613.2020.00083","DOIUrl":"https://doi.org/10.1109/MASS50613.2020.00083","url":null,"abstract":"After years of development, bike sharing has been one of the major choices of transportation for urban residents worldwide. However, efficient use of bike sharing resources is challenging due to the unbalanced station-level demands and supplies, which causes the maintenance of the bike sharing systems painstaking. To achieve system efficiency, efforts have been made on accurate prediction of bike traffic (demands/pick-ups and returns/drop-offs). Nonetheless, bike station traffic prediction is difficult due to the spatio-temporal complexity of bike sharing systems. Moreover, such level of prediction over the entire bike sharing systems is also challenging due to the large number of bike stations.To fill this gap, we propose BikeGAAN, a graph adjacency attention neural network to predict station-level bike traffic for entire bike sharing systems. The proposed prediction system consists of a graph convolutional network with an attention mechanism differentiating the spatial correlations between features of bike stations in the system and a long short-term memory network capturing temporal correlations. We have conducted extensive data analysis upon bike usage, weather, points of interest and event data, and derived the graph representation of the bike sharing networks. Through experimental study on over 27 millions trips of bike sharing systems of four metropolitan cities in the U.S., New York City, Chicago, Washington D.C. and Los Angeles, our network design has shown high accuracy in predicting the bike station traffic in the cities, outperforming other baselines and state-of-art models.","PeriodicalId":105795,"journal":{"name":"2020 IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124268803","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":"Dynamic Scheduling of an Autonomous PTZ Camera for Effective Surveillance","authors":"Pratibha Kumari, Nikhil Nandyala, Allu Krishna Sai Teja, Neeraj Goel, Mukesh Saini","doi":"10.1109/MASS50613.2020.00060","DOIUrl":"https://doi.org/10.1109/MASS50613.2020.00060","url":null,"abstract":"PTZ cameras can be an effective replacement for multiple camera networks with their pan-tilt-zoom capability. However, the state of the art scheduling method for the PTZ cameras focuses mainly on tracking, not on coverage. In this paper, we aim to maximize coverage as well as information gain, thus, leading to effective surveillance. Towards this goal, we define an information map that represents the sensitivity of a region. We propose a scheduling algorithm in which the camera visits those states more often that are likely to be more important than others, thus, maximizing information gain. A probabilistic framework is used to maximize information gain and coverage simultaneously. Currently, there are no existing datasets and methods to evaluate PTZ camera scheduling methods. We build a real multi-camera dataset and develop a performance measure for this purpose. Experimental results show that the proposed stochastic scheduling algorithm based on adaptive information gain probability is better than traditional as well as other variants proposed in the paper in terms of information gain as well as coverage.","PeriodicalId":105795,"journal":{"name":"2020 IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124976289","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":"Message from the SP-DLT 2020 Workshop Chairs","authors":"","doi":"10.1109/mass50613.2020.00008","DOIUrl":"https://doi.org/10.1109/mass50613.2020.00008","url":null,"abstract":"","PeriodicalId":105795,"journal":{"name":"2020 IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)","volume":"13 10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125650291","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}