Pub Date : 2018-04-11DOI: 10.1109/ICCPS.2018.00009
Omar Inverso, A. Bemporad, M. Tribastone
We propose an approach to either certify that a given control system is safe under possible cyber-attacks on the measured data used for feedback and/or the commanded control signals, or alternatively synthesise a particular spoofing attack that corrupts the signals to make the closed-loop system unsafe. We assume that a (possibly nonlinear) dynamical model of the physical plant is available along with the control law, but that no on-line diagnosis is in place to detect attacks. After converting the model to a piecewise polynomial discrete-time form, we interpret the synthesis of the spoofing attack as a software verification query by means of an encoding into a Boolean satisfiability problem. Using a prototype implementation of our verification engine, we demonstrate its effectiveness on a case study of cyber-attack to a chemical reactor.
{"title":"SAT-Based Synthesis of Spoofing Attacks in Cyber-Physical Control Systems","authors":"Omar Inverso, A. Bemporad, M. Tribastone","doi":"10.1109/ICCPS.2018.00009","DOIUrl":"https://doi.org/10.1109/ICCPS.2018.00009","url":null,"abstract":"We propose an approach to either certify that a given control system is safe under possible cyber-attacks on the measured data used for feedback and/or the commanded control signals, or alternatively synthesise a particular spoofing attack that corrupts the signals to make the closed-loop system unsafe. We assume that a (possibly nonlinear) dynamical model of the physical plant is available along with the control law, but that no on-line diagnosis is in place to detect attacks. After converting the model to a piecewise polynomial discrete-time form, we interpret the synthesis of the spoofing attack as a software verification query by means of an encoding into a Boolean satisfiability problem. Using a prototype implementation of our verification engine, we demonstrate its effectiveness on a case study of cyber-attack to a chemical reactor.","PeriodicalId":199062,"journal":{"name":"2018 ACM/IEEE 9th International Conference on Cyber-Physical Systems (ICCPS)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117151599","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}
Pub Date : 2018-04-11DOI: 10.1109/ICCPS.2018.00015
Yukun Yuan, Desheng Zhang, Fei Miao, J. Stankovic, T. He, George J. Pappas, Shan Lin
An integrated urban transportation system usually consists of multiple transport modes that have complementary characteristics of capacities, speeds, and costs, facilitating smooth passenger transfers according to planned schedules. However, such an integration is not designed to operate under disruptive events, e.g., a signal failure at a subway station or a breakdown of a bus, which have rippling effects on passenger demand and significantly increase delays. To address these disruptive events, current solutions mainly rely on a substitute service to transport passengers from and to affected areas using ad-hoc schedules and static routes, e.g., sending shuttles to closed subway stations. These solutions are highly inefficient and do not utilize real-time data to estimate dynamic passenger demand. To fully utilize heterogeneous transportation systems under disruptive events, we design a service called eRoute based on a hierarchical receding horizon control framework to automatically reroute, reschedule, and reallocate multi-mode transportation systems based on real-time and predicted demand and supply. Focusing on an integration of subway and bus, we implement and evaluate eRoute with large datasets including (i) a bus system with 13,000 buses, (ii) a subway system with 127 subway stations, (iii) an automatic fare collection system with a total of 16,840 readers and 8 million card users from a metropolitan city. The data-driven evaluation results show that our solution improves the ratio of served passengers (RSP) by up to 11.5 times and reduces the average traveling time by up to 82.1% compared with existing solutions.
{"title":"Dynamic Integration of Heterogeneous Transportation Modes under Disruptive Events","authors":"Yukun Yuan, Desheng Zhang, Fei Miao, J. Stankovic, T. He, George J. Pappas, Shan Lin","doi":"10.1109/ICCPS.2018.00015","DOIUrl":"https://doi.org/10.1109/ICCPS.2018.00015","url":null,"abstract":"An integrated urban transportation system usually consists of multiple transport modes that have complementary characteristics of capacities, speeds, and costs, facilitating smooth passenger transfers according to planned schedules. However, such an integration is not designed to operate under disruptive events, e.g., a signal failure at a subway station or a breakdown of a bus, which have rippling effects on passenger demand and significantly increase delays. To address these disruptive events, current solutions mainly rely on a substitute service to transport passengers from and to affected areas using ad-hoc schedules and static routes, e.g., sending shuttles to closed subway stations. These solutions are highly inefficient and do not utilize real-time data to estimate dynamic passenger demand. To fully utilize heterogeneous transportation systems under disruptive events, we design a service called eRoute based on a hierarchical receding horizon control framework to automatically reroute, reschedule, and reallocate multi-mode transportation systems based on real-time and predicted demand and supply. Focusing on an integration of subway and bus, we implement and evaluate eRoute with large datasets including (i) a bus system with 13,000 buses, (ii) a subway system with 127 subway stations, (iii) an automatic fare collection system with a total of 16,840 readers and 8 million card users from a metropolitan city. The data-driven evaluation results show that our solution improves the ratio of served passengers (RSP) by up to 11.5 times and reduces the average traveling time by up to 82.1% compared with existing solutions.","PeriodicalId":199062,"journal":{"name":"2018 ACM/IEEE 9th International Conference on Cyber-Physical Systems (ICCPS)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116392413","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}
Pub Date : 2018-04-11DOI: 10.1109/ICCPS.2018.00030
Radoslav Ivanov, James Weimer, Insup Lee
This paper considers the problem of incorporating context in medical cyber-physical systems (MCPS) applications for the purpose of improving the performance of MCPS detectors. In particular, in many applications additional data could be used to conclude that actual measurements might be noisy or wrong (e.g., machine settings might indicate that the machine is improperly attached to the patient); we call such data context. The first contribution of this work is the formal definition of context, namely additional information whose presence is associated with a change in the measurement model (e.g., higher variance). Given this formulation, we developed the context-aware parameter-invariant (CA-PAIN) detector; the CA-PAIN detector improves upon the original PAIN detector by recognizing events with noisy measurements and not raising unnecessary false alarms. We evaluate the CA-PAIN detector both in simulation and on real-patient data; in both cases, the CA-PAIN detector achieves roughly a 20-percent reduction of false alarm rates over the PAIN detector, thus indicating that formalizing context and using it in a rigorous way is a promising direction for future work.
{"title":"Context-Aware Detection in Medical Cyber-Physical Systems","authors":"Radoslav Ivanov, James Weimer, Insup Lee","doi":"10.1109/ICCPS.2018.00030","DOIUrl":"https://doi.org/10.1109/ICCPS.2018.00030","url":null,"abstract":"This paper considers the problem of incorporating context in medical cyber-physical systems (MCPS) applications for the purpose of improving the performance of MCPS detectors. In particular, in many applications additional data could be used to conclude that actual measurements might be noisy or wrong (e.g., machine settings might indicate that the machine is improperly attached to the patient); we call such data context. The first contribution of this work is the formal definition of context, namely additional information whose presence is associated with a change in the measurement model (e.g., higher variance). Given this formulation, we developed the context-aware parameter-invariant (CA-PAIN) detector; the CA-PAIN detector improves upon the original PAIN detector by recognizing events with noisy measurements and not raising unnecessary false alarms. We evaluate the CA-PAIN detector both in simulation and on real-patient data; in both cases, the CA-PAIN detector achieves roughly a 20-percent reduction of false alarm rates over the PAIN detector, thus indicating that formalizing context and using it in a rigorous way is a promising direction for future work.","PeriodicalId":199062,"journal":{"name":"2018 ACM/IEEE 9th International Conference on Cyber-Physical Systems (ICCPS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122328682","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}
Pub Date : 2018-04-11DOI: 10.1109/ICCPS.2018.00032
Chunhui Guo, Zhicheng Fu, Zhenyu Zhang, Shangping Ren, L. Sha
Improving patient care safety is an ultimate objective for medical cyber-physical systems. A recent study shows that the patients' death rate is significantly reduced by computerizing medical best practice guidelines [16]. Recent data also show that some morbidity and mortality in emergency care are directly caused by delayed or interrupted treatment due to lack of medical resources [15]. However, medical guidelines usually do not provide guidance on medical resource demands and how to manage potential unexpected delays in resource availability. If medical resources are temporarily unavailable, safety properties in existing executable medical guideline models may fail which may cause increased risk to patients under care. The paper presents a separately model and jointly verify (SMJV) architecture to separately model medical resource available times and relationships and jointly verify safety properties of existing medical best practice guideline models with resource models being integrated in. The separated modeling approach also allows different domain professionals to make independent model modifications, facilitates the management of frequent resource availability changes, and enables resource statechart reuse in multiple medical guideline models. A simplified stroke scenario is used as a case study to investigate the effectiveness and validity of the SMJV architecture. The case study indicates that the SMJV architecture is able to identify unsafe properties caused by unexpected resource delays.
{"title":"Model and Integrate Medical Resource Available Times and Relationships in Verifiably Correct Executable Medical Best Practice Guideline Models","authors":"Chunhui Guo, Zhicheng Fu, Zhenyu Zhang, Shangping Ren, L. Sha","doi":"10.1109/ICCPS.2018.00032","DOIUrl":"https://doi.org/10.1109/ICCPS.2018.00032","url":null,"abstract":"Improving patient care safety is an ultimate objective for medical cyber-physical systems. A recent study shows that the patients' death rate is significantly reduced by computerizing medical best practice guidelines [16]. Recent data also show that some morbidity and mortality in emergency care are directly caused by delayed or interrupted treatment due to lack of medical resources [15]. However, medical guidelines usually do not provide guidance on medical resource demands and how to manage potential unexpected delays in resource availability. If medical resources are temporarily unavailable, safety properties in existing executable medical guideline models may fail which may cause increased risk to patients under care. The paper presents a separately model and jointly verify (SMJV) architecture to separately model medical resource available times and relationships and jointly verify safety properties of existing medical best practice guideline models with resource models being integrated in. The separated modeling approach also allows different domain professionals to make independent model modifications, facilitates the management of frequent resource availability changes, and enables resource statechart reuse in multiple medical guideline models. A simplified stroke scenario is used as a case study to investigate the effectiveness and validity of the SMJV architecture. The case study indicates that the SMJV architecture is able to identify unsafe properties caused by unexpected resource delays.","PeriodicalId":199062,"journal":{"name":"2018 ACM/IEEE 9th International Conference on Cyber-Physical Systems (ICCPS)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126988395","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}
Pub Date : 2018-04-11DOI: 10.1109/ICCPS.2018.00046
Zhenyong Zhang, Junfeng Wu, David K. Y. Yau, Peng Cheng, Jiming Chen
Recently, the security of state estimation has been attracting significant research attention due to the need for trustworthy situation awareness in emerging (e.g., industrial) cyber-physical systems. In this paper, we investigate secure estimation based on Kalman filtering (SEKF) using partially homomorphically encrypted data. The encryption will enhance the confidentiality not only of data transmitted in the communication network, but also key system information required by the estimator. We use a multiplicative homomorphic encryption scheme, but with a modified decryption algorithm. SEKF is able to conceal comprehensive information (i.e., system parameters, measurements, and state estimates) aggregated at the sink node of the estimator, while retaining the effectiveness of normal Kalman filtering. Therefore, even if an attacker has gained unauthorized access to the estimator and associated communication channels, he will not be able to obtain sufficient knowledge of the system state to guide the attack, e.g., ensure its stealthiness. We present an implementation structure of the SEKF to reduce the communication overhead compared with traditional secure multiparty computation (SMC) methods. Finally, we demonstrate the effectiveness of the SEKF on an IEEE 9-bus power system.
{"title":"Secure Kalman Filter State Estimation by Partially Homomorphic Encryption","authors":"Zhenyong Zhang, Junfeng Wu, David K. Y. Yau, Peng Cheng, Jiming Chen","doi":"10.1109/ICCPS.2018.00046","DOIUrl":"https://doi.org/10.1109/ICCPS.2018.00046","url":null,"abstract":"Recently, the security of state estimation has been attracting significant research attention due to the need for trustworthy situation awareness in emerging (e.g., industrial) cyber-physical systems. In this paper, we investigate secure estimation based on Kalman filtering (SEKF) using partially homomorphically encrypted data. The encryption will enhance the confidentiality not only of data transmitted in the communication network, but also key system information required by the estimator. We use a multiplicative homomorphic encryption scheme, but with a modified decryption algorithm. SEKF is able to conceal comprehensive information (i.e., system parameters, measurements, and state estimates) aggregated at the sink node of the estimator, while retaining the effectiveness of normal Kalman filtering. Therefore, even if an attacker has gained unauthorized access to the estimator and associated communication channels, he will not be able to obtain sufficient knowledge of the system state to guide the attack, e.g., ensure its stealthiness. We present an implementation structure of the SEKF to reduce the communication overhead compared with traditional secure multiparty computation (SMC) methods. Finally, we demonstrate the effectiveness of the SEKF on an IEEE 9-bus power system.","PeriodicalId":199062,"journal":{"name":"2018 ACM/IEEE 9th International Conference on Cyber-Physical Systems (ICCPS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126237331","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}
Pub Date : 2018-04-11DOI: 10.1109/ICCPS.2018.00010
Fardin Abdi, Chien-Ying Chen, M. Hasan, Songran Liu, Sibin Mohan, M. Caccamo
Physical plants that form the core of the Cyber-Physical Systems (CPS) often have stringent safety requirements. Recent attacks have shown that cyber intrusions can result in the safety of such plants being compromised – thus leading to physical damage. In this paper, we demonstrate how to ensure safety of the plant even when the system gets compromised. We leverage the fact that due to inertia, an adversary cannot destabilize the physical system (even with complete control of the software) in an instantaneous manner; in fact, it often takes finite (even considerable time). This property, coupled with em system-wide restarts is used to enforce a secure (and safe) operational window for the system. A hardware root-of-trust, further decreases the ability for attackers to compromise our mechanisms. We demonstrate our approach using two realistic systems – a 3 degree of freedom (3-DoF) helicopter and a simulated warehouse temperature control unit. We also show that our system is robust against multiple emulated attacks – essentially the attackers are not able to compromise the safety of the CPS.
{"title":"Guaranteed Physical Security with Restart-Based Design for Cyber-Physical Systems","authors":"Fardin Abdi, Chien-Ying Chen, M. Hasan, Songran Liu, Sibin Mohan, M. Caccamo","doi":"10.1109/ICCPS.2018.00010","DOIUrl":"https://doi.org/10.1109/ICCPS.2018.00010","url":null,"abstract":"Physical plants that form the core of the Cyber-Physical Systems (CPS) often have stringent safety requirements. Recent attacks have shown that cyber intrusions can result in the safety of such plants being compromised – thus leading to physical damage. In this paper, we demonstrate how to ensure safety of the plant even when the system gets compromised. We leverage the fact that due to inertia, an adversary cannot destabilize the physical system (even with complete control of the software) in an instantaneous manner; in fact, it often takes finite (even considerable time). This property, coupled with em system-wide restarts is used to enforce a secure (and safe) operational window for the system. A hardware root-of-trust, further decreases the ability for attackers to compromise our mechanisms. We demonstrate our approach using two realistic systems – a 3 degree of freedom (3-DoF) helicopter and a simulated warehouse temperature control unit. We also show that our system is robust against multiple emulated attacks – essentially the attackers are not able to compromise the safety of the CPS.","PeriodicalId":199062,"journal":{"name":"2018 ACM/IEEE 9th International Conference on Cyber-Physical Systems (ICCPS)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133137317","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}
Pub Date : 2018-04-11DOI: 10.1109/ICCPS.2018.00031
Taisa Kushner, D. Bortz, D. Maahs, S. Sankaranarayanan
This paper presents a case study of a data driven approach to verification and parameter synthesis for artificial pancreas control systems which deliver insulin to patients with type-1 diabetes (T1D). We present a new approach to tuning parameters using non-deterministic data-driven models for human insulin-glucose regulation, which are inferred from patient data using multiple time scales. Taking these equations as constraints, we model the behavior of the entire closed loop system over a five-hour time horizon cast as an optimization problem. Next, we demonstrate this approach using patient data gathered from a previously conducted outpatient clinical study involving insulin and glucose data collected from 50 patients with T1D and 40 nights per patient. We use the resulting data-driven models to predict how the patients would perform under a PID-based closed loop system which forms the basis for the first commercially available hybrid closed loop device. Futhermore, we provide a re-tuning methodology which can potentially improve control for 82% of patients, based on the results of an exhaustive reachability analysis. Our results demonstrate that simple nondeterministic models allow us to efficiently tune key controller parameters, thus paving the way for interesting clinical translational applications.
{"title":"A Data-Driven Approach to Artificial Pancreas Verification and Synthesis","authors":"Taisa Kushner, D. Bortz, D. Maahs, S. Sankaranarayanan","doi":"10.1109/ICCPS.2018.00031","DOIUrl":"https://doi.org/10.1109/ICCPS.2018.00031","url":null,"abstract":"This paper presents a case study of a data driven approach to verification and parameter synthesis for artificial pancreas control systems which deliver insulin to patients with type-1 diabetes (T1D). We present a new approach to tuning parameters using non-deterministic data-driven models for human insulin-glucose regulation, which are inferred from patient data using multiple time scales. Taking these equations as constraints, we model the behavior of the entire closed loop system over a five-hour time horizon cast as an optimization problem. Next, we demonstrate this approach using patient data gathered from a previously conducted outpatient clinical study involving insulin and glucose data collected from 50 patients with T1D and 40 nights per patient. We use the resulting data-driven models to predict how the patients would perform under a PID-based closed loop system which forms the basis for the first commercially available hybrid closed loop device. Futhermore, we provide a re-tuning methodology which can potentially improve control for 82% of patients, based on the results of an exhaustive reachability analysis. Our results demonstrate that simple nondeterministic models allow us to efficiently tune key controller parameters, thus paving the way for interesting clinical translational applications.","PeriodicalId":199062,"journal":{"name":"2018 ACM/IEEE 9th International Conference on Cyber-Physical Systems (ICCPS)","volume":"303 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132131631","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}
Pub Date : 2018-04-11DOI: 10.1109/ICCPS.2018.00019
W. Saab, Maaz M. Mohiuddin, S. Bliudze, J. Boudec
We consider cyber-physical systems (CPSs) comprising a central controller that might be replicated for high-reliability, and one or more process agents. The controller receives measurements from process agents, causing it to compute and issue setpoints that are sent back to process agents. The implementation of these setpoints causes a change in the state of the controlled physical process, and the new state is communicated to the controllers through resulting measurements. To ensure correct operation, the process agents must implement only those setpoints that were caused by their most recent measurements. However, in the presence of replication of the controller, network or computation delays, setpoints and measurements do not necessarily succeed in causing the intended behavior. To capture the dependencies among events associated with measurements and setpoints, we introduce the intentionality relation among such events in a CPS and illustrate its differences with respect to the happened-before relation. We propose a mechanism, intentionality clocks, and the design of controllers and process agents that can be used to guarantee the strong clock-consistency condition under the intentionality relation. Moreover, we prove that our design ensures correct operation despite crash, delay, and network faults. We also demonstrate the practical application of our abstraction through an illustration with a real-world CPS for electrical vehicles.
{"title":"Ordering Events Based on Intentionality in Cyber-Physical Systems","authors":"W. Saab, Maaz M. Mohiuddin, S. Bliudze, J. Boudec","doi":"10.1109/ICCPS.2018.00019","DOIUrl":"https://doi.org/10.1109/ICCPS.2018.00019","url":null,"abstract":"We consider cyber-physical systems (CPSs) comprising a central controller that might be replicated for high-reliability, and one or more process agents. The controller receives measurements from process agents, causing it to compute and issue setpoints that are sent back to process agents. The implementation of these setpoints causes a change in the state of the controlled physical process, and the new state is communicated to the controllers through resulting measurements. To ensure correct operation, the process agents must implement only those setpoints that were caused by their most recent measurements. However, in the presence of replication of the controller, network or computation delays, setpoints and measurements do not necessarily succeed in causing the intended behavior. To capture the dependencies among events associated with measurements and setpoints, we introduce the intentionality relation among such events in a CPS and illustrate its differences with respect to the happened-before relation. We propose a mechanism, intentionality clocks, and the design of controllers and process agents that can be used to guarantee the strong clock-consistency condition under the intentionality relation. Moreover, we prove that our design ensures correct operation despite crash, delay, and network faults. We also demonstrate the practical application of our abstraction through an illustration with a real-world CPS for electrical vehicles.","PeriodicalId":199062,"journal":{"name":"2018 ACM/IEEE 9th International Conference on Cyber-Physical Systems (ICCPS)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116731195","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}
Pub Date : 2018-04-11DOI: 10.1109/ICCPS.2018.00016
Praveen Venkateswaran, Qing Han, R. Eguchi, N. Venkatasubramanian
Community water networks have become increasingly prone to failures due to aging infrastructure, resulting in an increased effort to instrument and monitor networks using IoT (Internet of Things) sensors. However, identifying optimal locations to instrument these sensors to detect and localize failures such as leaks is challenging due to the growing scale and complexity of water networks. Current sensor placement algorithms use heuristics that focus mainly on enabling network coverage. In this paper, we propose a multilevel approach to model and quantify the real-world impact of a failure on a community using various geospatial, infrastructural and societal factors. We present techniques to integrate failure impact, IoT sensing data, and simulation based analytics to drive two novel sensor placement algorithms with the objective of reducing community-scale impact. We evaluate our proposed algorithms on various failure scenarios using multiple real-world water networks at different scales and compare them to existing solutions. The experimental results show that the proposed algorithms result in sensor placements that can achieve an 80% reduction in impact while using a comparable number of sensors for diverse real-world networks.
{"title":"Impact Driven Sensor Placement for Leak Detection in Community Water Networks","authors":"Praveen Venkateswaran, Qing Han, R. Eguchi, N. Venkatasubramanian","doi":"10.1109/ICCPS.2018.00016","DOIUrl":"https://doi.org/10.1109/ICCPS.2018.00016","url":null,"abstract":"Community water networks have become increasingly prone to failures due to aging infrastructure, resulting in an increased effort to instrument and monitor networks using IoT (Internet of Things) sensors. However, identifying optimal locations to instrument these sensors to detect and localize failures such as leaks is challenging due to the growing scale and complexity of water networks. Current sensor placement algorithms use heuristics that focus mainly on enabling network coverage. In this paper, we propose a multilevel approach to model and quantify the real-world impact of a failure on a community using various geospatial, infrastructural and societal factors. We present techniques to integrate failure impact, IoT sensing data, and simulation based analytics to drive two novel sensor placement algorithms with the objective of reducing community-scale impact. We evaluate our proposed algorithms on various failure scenarios using multiple real-world water networks at different scales and compare them to existing solutions. The experimental results show that the proposed algorithms result in sensor placements that can achieve an 80% reduction in impact while using a comparable number of sensors for diverse real-world networks.","PeriodicalId":199062,"journal":{"name":"2018 ACM/IEEE 9th International Conference on Cyber-Physical Systems (ICCPS)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129895285","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}
Pub Date : 2018-04-11DOI: 10.1109/ICCPS.2018.00037
Yiran Zhao, Shuochao Yao, Huajie Shao, T. Abdelzaher
This paper presents the design and evaluation of CoDrive, a cooperative speed advice system aiming at vehicular fuel savings by reconciling speeds of different vehicles with the timing of signalized intersections. Existing systems for speed coordination and platoon management primarily focus on safety, stability, and security issues. In the authors' own prior work, speed optimizations are discussed for minimizing fuel consumption by exploiting signalized intersection timing. In this paper, we recognize that vehicles whose paths diverge after the next intersection have different fuel-optimal speeds. Since slower vehicles will block faster ones from meeting their optimal speed in heavy traffic or on single-lane roads, we develop an algorithm for speed re-negotiation that arrives at a compromise speed for all vehicles involved. The resulting cooperative speed advice scheme minimizes the total fuel consumption of the involved vehicles, leading to a global optimum. An accounting scheme offers incentives that compensate for resulting inequity in savings distribution across individual vehicles. For evaluation, we use the SUMO simulator. We show that our cooperative scheme saves up to 38.2% in fuel over the baseline where no speed advice is provided, and saves up to 7.9% over prior work GreenDrive.
{"title":"CoDrive: Cooperative Driving Scheme for Vehicles in Urban Signalized Intersections","authors":"Yiran Zhao, Shuochao Yao, Huajie Shao, T. Abdelzaher","doi":"10.1109/ICCPS.2018.00037","DOIUrl":"https://doi.org/10.1109/ICCPS.2018.00037","url":null,"abstract":"This paper presents the design and evaluation of CoDrive, a cooperative speed advice system aiming at vehicular fuel savings by reconciling speeds of different vehicles with the timing of signalized intersections. Existing systems for speed coordination and platoon management primarily focus on safety, stability, and security issues. In the authors' own prior work, speed optimizations are discussed for minimizing fuel consumption by exploiting signalized intersection timing. In this paper, we recognize that vehicles whose paths diverge after the next intersection have different fuel-optimal speeds. Since slower vehicles will block faster ones from meeting their optimal speed in heavy traffic or on single-lane roads, we develop an algorithm for speed re-negotiation that arrives at a compromise speed for all vehicles involved. The resulting cooperative speed advice scheme minimizes the total fuel consumption of the involved vehicles, leading to a global optimum. An accounting scheme offers incentives that compensate for resulting inequity in savings distribution across individual vehicles. For evaluation, we use the SUMO simulator. We show that our cooperative scheme saves up to 38.2% in fuel over the baseline where no speed advice is provided, and saves up to 7.9% over prior work GreenDrive.","PeriodicalId":199062,"journal":{"name":"2018 ACM/IEEE 9th International Conference on Cyber-Physical Systems (ICCPS)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131136263","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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