The broad spectrum of interconnected sensors and actuators, available on various mobile devices and smartphones and collectively defined as the Internet of Things (IoT), have evolved into platforms with ability to both collect personal sensory data and also change the users' immediate environment. The continuous streams of richly annotated sensory data on these IoT devices have also enabled the emergence of a new class of context-aware apps that use the data to infer user context and accordingly customize their responses in real-time. However, this growth in the number of apps has not been complemented with adequate system support on the IoT devices resulting in monolithic apps that each implement and execute their own customized sensing pipelines. In this paper, we outline our vision of a sensing stack, akin to a networking stack, that can facilitate the development and execution of context-aware apps on IoT devices. There are several advantages to building a rich sensing stack. First, it allows apps to reuse stages of the sensing pipeline easing their development. Second, the layers of the stack allow for both in- and cross-layer resource optimization. Finally, it allows better control over the shared data as instead of raw-sensor data, higher-level semantic abstractions, such as inferences can now be shared with apps. We describe our initial efforts towards creating the different building blocks of such a sensing stack.

Continuous collection of sensory information using smartphones and body-worn sensors is now feasible with recent advancement of technologies. Sharing such personal information enables many useful applications such as medical behavioral studies, personal health-care, and participatory sensing. However, sharing such information along with inferences that can be drawn from the data increases user's various privacy concerns. This paper proposes SensorSafe, an application framework that enables users to share adequate amounts of their private data and supports obfuscation of sensitive information to protect user privacy. Our framework provides rule-based sharing with context-awareness and conflicting rule detection. In addition, our framework includes several optimization techniques for database processing of rule-based sharing and data obfuscation. We evaluate the optimization techniques with a large amount of accelerometer data from the fine-grained posture recognition application, which is about 6.25 GB.

We propose a 6LoWPAN-SNMP that enables transmission of SNMP messages over IPv6-enabled low-power wireless personal area networks (6LoWPAN). The 6LoWPAN-SNMP is an extended modification of the Simple Network Management Protocol (SNMP). Compared to traditional IP-networks, 6LoWPAN is a severely resource-constrained network; hence, existing SNMP protocols need to be modified to meet the goals in RFC 4919, ldquo6LoWPAN Problems and Goalsrdquo. The proposed 6LoWPAN-SNMP provides for native communication of SNMP messages on LoWPAN networks. The proposed mechanism is resource-efficient and fully compatible with the standard SNMP. It utilizes SNMP header compression and provides extended protocol operations to reduce the number of SNMP messages generated among the SNMP entities. Compatibility with the standard SNMP is achieved by a proxy forwarder on the 6LoWPAN gateway. The proposed mechanism is implemented on actual hardware platforms using the open-source Net-SNMP library and the Berkeley 6LoWPAN on the TinyOS 2.1. The feasibility and effectiveness of 6LoWPAN-SNMP is evaluated. The experimental results show that 6LoWPAN can be effectively supported with network management functionality based on SNMP.