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We consider the problem of data collection from a continental-scale network of mobile sensors, specifically applied to wildlife tracking. Our application constraints favor a highly asymmetric solution, with heavily duty-cycled sen...
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We consider the problem of data collection from a continental-scale network of mobile sensors, specifically applied to wildlife tracking. Our application constraints favor a highly asymmetric solution, with heavily duty-cycled sensor nodes communicating with a network of powered base stations. Individual nodes move freely in the environment, resulting in low-quality radio links and hot-spot arrival patterns with the available data exceeding the radio link capacity. We propose a novel scheduling algorithm, κ-Fair Scheduling Optimization Model (κ-FSOM), that maximizes the amount of collected data under the constraints of radio link quality and energy, while ensuring a fair access to the radio channel. We show the problem is NP-complete and propose a heuristic to approximate the optimal scheduling solution in polynomial time. We use empirical link quality data to evaluate the κ-FSOM heuristic in a realistic setting and compare its performance to other heuristics. We show that κ-FSOM heuristic achieves high data reception rates, under different fairness and node lifetime constraints.
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摘要 :
We consider the problem of data collection from a continentalscale network of mobile sensors, specifically applied to wildlife tracking. Our application constraints favor a highly asymmetric solution, with heavily duty-cycled sens...
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We consider the problem of data collection from a continentalscale network of mobile sensors, specifically applied to wildlife tracking. Our application constraints favor a highly asymmetric solution, with heavily duty-cycled sensor nodes communicating with a network of powered base stations. Individual nodes move freely in the environment, resulting in low-quality radio links and hot-spot arrival patterns with the available data exceeding the radio link capacity.We propose a novel scheduling algorithm, κ-Fair Scheduling Optimization Model (κ-FSOM), that maximizes the amount of collected data under the constraints of radio link quality and energy,while ensuring a fair access to the radio channel.We show the problem is NP-complete and propose a heuristic to approximate the optimal scheduling solution in polynomial time.We use empirical link quality data to evaluate the κ-FSOMheuristic in a realistic setting and compare its performance to other heuristics.We showthat κ-FSOMheuristic achieves high data reception rates, under different fairness and node lifetime constraints.
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Interaction patterns among individuals play vital roles in spreading infectious diseases. Understanding these patterns and integrating their impact in modeling diffusion dynamics of infectious diseases are important for epidemiolo...
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Interaction patterns among individuals play vital roles in spreading infectious diseases. Understanding these patterns and integrating their impact in modeling diffusion dynamics of infectious diseases are important for epidemiological studies. Current network-based diffusion models assume that diseases transmit through interactions where both infected and susceptible individuals are co-located at the same time. However, there are several infectious diseases that can transmit when a susceptible individual visits a location after an infected individual has left. Recently, we introduced a diffusion model called same place different time (SPDT) transmission to capture the indirect transmissions that happen when an infected individual leaves before a susceptible individual's arrival along with direct transmissions. In this paper, we demonstrate how these indirect transmission links significantly enhance the emergence of infectious diseases simulating airborne disease spreading on a synthetic social contact network. We denote individuals having indirect links but no direct links during their infectious periods as hidden spreaders. Our simulation shows that indirect links play similar roles of direct links and a single hidden spreader can cause large outbreak in the SPDT model which causes no infection in the current model based on direct link. Our work opens new direction in modeling infectious diseases.
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摘要 :
Interaction patterns among individuals play vital roles in spreading infectious diseases. Understanding these patterns and integrating their impact in modeling diffusion dynamics of infectious diseases are important for epidemiolo...
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Interaction patterns among individuals play vital roles in spreading infectious diseases. Understanding these patterns and integrating their impact in modeling diffusion dynamics of infectious diseases are important for epidemiological studies. Current network-based diffusion models assume that diseases transmit through interactions where both infected and susceptible individuals are co-located at the same time. However, there are several infectious diseases that can transmit when a susceptible individual visits a location after an infected individual has left. Recently, we introduced a diffusion model called same place different time (SPDT) transmission to capture the indirect transmissions that happen when an infected individual leaves before a susceptible individual's arrival along with direct transmissions. In this paper, we demonstrate how these indirect transmission links significantly enhance the emergence of infectious diseases simulating airborne disease spreading on a synthetic social contact network. We denote individuals having indirect links but no direct links during their infectious periods as hidden spreaders. Our simulation shows that indirect links play similar roles of direct links and a single hidden spreader can cause large outbreak in the SPDT model which causes no infection in the current model based on direct link. Our work opens new direction in modeling infectious diseases.
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Future smart vehicles will employ automotive over-the-air updates to update the soft ware in the embedded electronic control units. The update process can affect the safety of the involved users, thus requires a comprehensive and ...
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Future smart vehicles will employ automotive over-the-air updates to update the soft ware in the embedded electronic control units. The update process can affect the safety of the involved users, thus requires a comprehensive and elaborate security architecture ensuring the confidentiality and the integrity of the exchanged data, as well as protecting the privacy of the involved users. In this paper, we propose an automotive security architecture employing Blockchain to tackle the implicated security and privacy challenges. We describe our proof-of-concept implementation of a Blockchain-based software update system, use it to show the applicability of our architecture for automotive systems, and evaluate different aspects of our architecture.
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Future smart vehicles will employ automotive over-the-air updates to update the soft ware in the embedded electronic control units. The update process can affect the safety of the involved users, thus requires a comprehensive and ...
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Future smart vehicles will employ automotive over-the-air updates to update the soft ware in the embedded electronic control units. The update process can affect the safety of the involved users, thus requires a comprehensive and elaborate security architecture ensuring the confidentiality and the integrity of the exchanged data, as well as protecting the privacy of the involved users. In this paper, we propose an automotive security architecture employing Blockchain to tackle the implicated security and privacy challenges. We describe our proof-of-concept implementation of a Blockchain-based software update system, use it to show the applicability of our architecture for automotive systems, and evaluate different aspects of our architecture.
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Long-term tracking of small-size animals with wireless sensor networks remains a challenge as only limited energy harvesting and storage is possible due to stringent size and weight constraints for animal collars. We present first...
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Long-term tracking of small-size animals with wireless sensor networks remains a challenge as only limited energy harvesting and storage is possible due to stringent size and weight constraints for animal collars. We present first experiences towards a perpetual monitoring system for free-living flying foxes. The high mobility of flying foxes requires a delay tolerant wireless network for data gathering: GPS positions and sensor data have to be stored locally until a wireless gateway deployed in bat congregation areas, so called roosting camps, comes within radio range. In this chapter, we present the system architecture and discuss our design decisions towards sustainable and reliable monitoring of flying foxes with a limited energy budget for sensing, storage and communication. Using empirical data from three free-living flying foxes, we characterize the overall system performance in terms of energy consumption and latency.
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摘要 :
Long-term tracking of small-size animals with wireless sensor networks remains a challenge as only limited energy harvesting and storage is possible due to stringent size and weight constraints for animal collars. We present first...
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Long-term tracking of small-size animals with wireless sensor networks remains a challenge as only limited energy harvesting and storage is possible due to stringent size and weight constraints for animal collars. We present first experiences towards a perpetual monitoring system for free-living flying foxes. The high mobility of flying foxes requires a delay tolerant wireless network for data gathering: GPS positions and sensor data have to be stored locally until a wireless gateway deployed in bat congregation areas, so called roosting camps, comes within radio range. In this chapter, we present the system architecture and discuss our design decisions towards sustainable and reliable monitoring of flying foxes with a limited energy budget for sensing, storage and communication. Using empirical data from three free-living flying foxes, we characterize the overall system performance in terms of energy consumption and latency.
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Buildings are amongst the largest consumers of electrical energy in developed countries. Building efficiency can be improved by adapting building systems to a change in the environment or user context. Appropriate action, however,...
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Buildings are amongst the largest consumers of electrical energy in developed countries. Building efficiency can be improved by adapting building systems to a change in the environment or user context. Appropriate action, however, can only be taken if the building control system has access to reliable real-time data. Sensors providing this data need to be ubiquitous, accurate, have low maintenance cost, and should not violate privacy of building occupants. We conducted a 3 year study in a mid-size office space with 15 offices and 25 people. Specifically, we focused on sensing modalities that can help improve energy efficiency of buildings. We have deployed 25 indoor climate sensor nodes and 41 wireless power meters, submetered 12 electric loads in circuit breaker boxes, logged data from our building control system and tracked activity on 40 desktop computers. We summarize our experiences with the cost, data yields, and user privacy concerns of the different sensing modalities and evaluate their accuracy using ground-truth experiments.
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Buildings are amongst the largest consumers of electrical energy in developed countries. Building efficiency can be improved by adapting building systems to a change in the environment or user context. Appropriate action, however,...
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Buildings are amongst the largest consumers of electrical energy in developed countries. Building efficiency can be improved by adapting building systems to a change in the environment or user context. Appropriate action, however, can only be taken if the building control system has access to reliable real-time data. Sensors providing this data need to be ubiquitous, accurate, have low maintenance cost, and should not violate privacy of building occupants. We conducted a 3 year study in a mid-size office space with 15 offices and 25 people. Specifically, we focused on sensing modalities that can help improve energy efficiency of buildings. We have deployed 25 indoor climate sensor nodes and 41 wireless power meters, submetered 12 electric loads in circuit breaker boxes, logged data from our building control system and tracked activity on 40 desktop computers. We summarize our experiences with the cost, data yields, and user privacy concerns of the different sensing modalities and evaluate their accuracy using ground-truth experiments.
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