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In this paper, we investigate the impacts of location on the spectral distribution of received light, i.e., the intensity of light for different wavelengths, in indoor environments. Our findings show that, even when using the same light source, different locations exhibit slightly different spectral distribution due to reflections from their localised environment containing different materials or colours. Based on this observation, we present Spectral-Loc, a novel indoor localization method that employs light spectrum information to detect the device's position. Because spectrum sensors are increasingly being used in new products and applications, such as white balance in smartphone photography, Spectral-Loc can be quickly implemented without the need for extra hardware or infrastructure. We used a commercially available light spectrum sensor, the AS7265x, to prototype Spectral-Loc, which can measure light intensity over 18 different wavelength sub-bands.We benchmark the localization accuracy of Spectral-Loc against the conventional light intensity sensors that provide only a single intensity value. Our evaluations in two indoor areas, a meeting room and a large office, show that using light spectral information considerably decreases the localization error for different percentiles. [ABSTRACT FROM AUTHOR]

We design a system, SolarGest, which can recognize hand gestures near a solar-powered device by analyzing the patterns of the photocurrent. SolarGest is based on the observation that each gesture interferes with incident light rays on the solar panel in a unique way, leaving its distinguishable signature in harvested photocurrent. Using solar energy harvesting laws, we develop a model to optimize design and usage of SolarGest. To further improve the robustness of SolarGest under non-deterministic operating conditions, we combine dynamic time warping with Z-score transformation in a signal processing pipeline to pre-process each gesture waveform before it is analyzed for classification. We evaluate SolarGest with both conventional opaque solar cells as well as emerging see-through transparent cells. Our experiments with 6,960 gesture samples for 6 different gestures reveal that even with transparent cells, SolarGest can detect 96% of the gestures while consuming 44% less power compared to light sensor based systems. [ABSTRACT FROM AUTHOR]

Cellular operators are considering large-scale small cell deployment in urban traffic hot spots to combat the looming capacity crisis in their networks. However, connecting a large number of small cells to the network core using current backhaul technology is costly. In this paper, we propose message ferrying as a low-cost backhaul solution that uses mobile phones of vehicle occupants as an army of ferries to transfer network data between small cell base stations and nearby switching centres. There are a number of challenges in the design and deployment of such a system. Use of phone memory for network data transfer without interfering with user applications is one such challenge that we study in this paper. Although the memory capacity in mobile phones has increased significantly over the years, it is considered a volatile ferrying resource as it is claimed and released dynamically by user applications. If message ferrying is to be transparent to the user, base stations must be able to predict memory availability over a sufficiently long horizon to ensure that data carried in the phone do not get wiped out during transit due to dynamic memory claim by user applications. We experiment with real phones by logging memory availability for extended hours under different usage scenarios. By applying autoregressive models on memory usage traces, we are able to predict the minimum memory available over a 7.5-minute horizon with 94% accuracy. [ABSTRACT FROM AUTHOR]

While bandwidth predictability has been well studied in static environments, it remains largely unexplored in the context of mobile computing. To gain a deeper understanding of this important issue in the mobile environment, we conducted an eight-month measurement study consisting of 71 repeated trips along a 23Km route in Sydney under typical driving conditions. To account for the network diversity, we measure bandwidth from two independent cellular providers implementing the popular High-Speed Downlink Packet Access (HSDPA) technology in two different peak access rates (1.8 and 3.6Mbps). Interestingly, we observe no significant correlation between the bandwidth signals at different points in time within a given trip. This observation eventually leads to the revelation that the popular time series models, e.g. the Autoregressive and Moving Average, typically used to predict network traffic in static environments are not as effective in capturing the regularity in mobile bandwidth. Although the bandwidth signal in a given trip appears as a random white noise, we are able to detect the existence of patterns by analyzing the distribution of the bandwidth observed during the repeated trips. We quantify the bandwidth predictability reflected by these patterns using tools from information theory, entropy in particular. The entropy analysis reveals that the bandwidth uncertainty may reduce by as much as 46% when observations from past trips are accounted for. We further demonstrate that the bandwidth in mobile computing appears more predictable when location is used as a context. All these observations are consistent across multiple independent providers offering different data transfer rates using possibly different networking hardware. [ABSTRACT FROM AUTHOR]

The Dedicated Short Range Communication (DSRC) technology is currently being standardized by the IEEE to enable a range of communication-based automotive safety applications. However, for DSRC to be cost-effective, it is important to accommodate commercial non-safety use of the spectrum as well. The co-existence of safety and non-safety is achieved through a periodic channel switching scheme whereby access to DSRC alternates between these two classes of applications. In this paper, we propose a framework that links the non-safety share of DSRC as effected by the channel switching to the performance requirements of safety applications. Using simulation experiments, we analyze the non-safety opportunity in the DSRC under varied road traffic conditions. We find that non-safety use of DSRC may have to be severely restricted during peak hours of traffic to insure that automotive safety is not compromised. Our study also provides interesting insights into how simple strategies, e.g., optimizing the message generation rate of the safety applications, can significantly increase the commercial opportunities of DSRC. Finally, we find that adaptive schemes that can dynamically adjust the switching parameters in response to observed traffic conditions may help in maximizing the commercial use of DSRC. [ABSTRACT FROM AUTHOR]

The Dedicated Short Range Communication (DSRC) technology is currently being standardized by the IEEE to enable a range of communication-based automotive safety applications. However, for DSRC to be cost-effective, it is important to accommodate commercial non-safety use of the spectrum as well. The co-existence of safety and non-safety is achieved through a periodic channel switching scheme whereby access to DSRC alternates between these two classes of applications. In this paper, we propose a framework that links the non-safety share of DSRC as effected by the channel switching to the performance requirements of safety applications. Using simulation experiments, we analyze the non-safety opportunity in the DSRC under varied road traffic conditions. We find that non-safety use of DSRC may have to be severely restricted during peak hours of traffic to insure that automotive safety is not compromised. Our study also provides interesting insights into how simple strategies, e.g., optimizing the message generation rate of the safety applications, can significantly increase the commercial opportunities of DSRC. Finally, we find that adaptive schemes that can dynamically adjust the switching parameters in response to observed traffic conditions may help in maximizing the commercial use of DSRC. [ABSTRACT FROM AUTHOR]

While bandwidth predictability has been well studied in static environments, it remains largely unexplored in the context of mobile computing. To gain a deeper understanding of this important issue in the mobile environment, we conducted an eight-month measurement study consisting of 71 repeated trips along a 23 Km route in Sydney under typical driving conditions. To account for the network diversity, we measure bandwidth from two independent cellular providers implementing the popular High-Speed Downlink Packet Access (HSDPA) technology in two different peak access rates (1.8 and 3.6 Mbps). Interestingly, we observe no significant correlation between the bandwidth signals at different points in time within a given trip. This observation eventually leads to the revelation that the popular time series models, e.g. the Autoregressive and Moving Average, typically used to predict network traffic in static environments are not as effective in capturing the regularity in mobile bandwidth. Although the bandwidth signal in a given trip appears as a random white noise, we are able to detect the existence of patterns by analyzing the distribution of the bandwidth observed during the repeated trips. We quantify the bandwidth predictability reflected by these patterns using tools from information theory, entropy in particular. The entropy analysis reveals that the bandwidth uncertainty may reduce by as much as 46% when observations from past trips are accounted for. We further demonstrate that the bandwidth in mobile computing appears more predictable when location is used as a context. All these observations are consistent across multiple independent providers offering different data transfer rates using possibly different networking hardware. [ABSTRACT FROM AUTHOR]