Abstract-Internet-of-Things (IoT) deployments increasingly incorporate long range communication technologies. To support this transition, wide area IoT deployments are employing LoRa as their communication technology of choice due to its low power consumption and long range. The security of LoRa networks and devices is currently being put to the test in the wild, and has already become a major challenge. New features and characteristics of LoRa technology also intorduce new vulnerabilities against security attacks. In this paper, we investigate potential security vulnerabilities in LoRa. In particular, we analyze the LoRa network stack and discuss the possible susceptibility of LoRa devices to different types of attacks using commercial-off-the-shelf hardware. Our analysis shows that the long range transmissions of LoRa are vulnerable to multiple security attacks.

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BackgroundMost studies on the local food environment have used secondary sources to describe the food environment, such as government food registries or commercial listings (e.g., Reference USA). Most of the studies exploring evidence for validity of secondary retail food data have used on-site verification and have not conducted analysis by data source (e.g., sensitivity of Reference USA) or by food outlet type (e.g., sensitivity of Reference USA for convenience stores). Few studies have explored the food environment in American Indian communities. To advance the science on measuring the food environment, we conducted direct, on-site observations of a wide range of food outlets in multiple American Indian communities, without a list guiding the field observations, and then compared our findings to several types of secondary data.MethodsFood outlets located within seven State Designated Tribal Statistical Areas in North Carolina (NC) were gathered from online Yellow Pages, Reference USA, Dun & Bradstreet, local health departments, and the NC Department of Agriculture and Consumer Services. All TIGER/Line 2009 roads (>1,500 miles) were driven in six of the more rural tribal areas and, for the largest tribe, all roads in two of its cities were driven. Sensitivity, positive predictive value, concordance, and kappa statistics were calculated to compare secondary data sources to primary data.Results699 food outlets were identified during primary data collection. Match rate for primary data and secondary data differed by type of food outlet observed, with the highest match rates found for grocery stores (97%), general merchandise stores (96%), and restaurants (91%). Reference USA exhibited almost perfect sensitivity (0.89). Local health department data had substantial sensitivity (0.66) and was almost perfect when focusing only on restaurants (0.91). Positive predictive value was substantial for Reference USA (0.67) and moderate for local health department data (0.49). Evidence for validity was comparatively lower for Dun & Bradstreet, online Yellow Pages, and the NC Department of Agriculture.ConclusionsSecondary data sources both over- and under-represented the food environment; they were particularly problematic for identifying convenience stores and specialty markets. More attention is needed to improve the validity of existing data sources, especially for rural local food environments.

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