Air quality monitoring has attracted a lot of attention from governments, academia and industry, especially for PM2.5due to its significant impact on our respiratory systems. In this paper, we present the design, implementation, and evaluation of Mosaic, a low cost urban PM2.5 monitoring system based on mobile sensing. In Mosaic, a small number of air quality monitoring nodes are deployed on city buses to measure air quality. Current low-cost particle sensors based on light-scattering, however, are vulnerable to airflow disturbance on moving vehicles. In order to address this problem, we build our air quality monitoring nodes, Mosaic-Nodes, with a novel constructive airflow-disturbance design based on a carefully tuned airflow structure and a GPS-assisted filtering method. Further, the buses used for system deployment are selected by a novel algorithm which achieves both high coverage and low computation overhead. The collected sensor data is also used to calculate the PM2.5of locations without direct measurements by an existing inference model. We apply the Mosaic system in a testing urban area which includes more than 70 point-of-interests. Results show that the Mosaic system can accurately obtain the urban air quality with high coverage and low cost.

Wireless communications are inherently unreliable, especially for low-power wireless networks like 802.15.4. Packet corruptions typically occur because of interference, fading or noise. In this paper, we investigate the problem of corruption aware retransmission with adaptive coding in commercial-off-the-shelf (COTS) low-power devices. We propose an accurate corruption detection algorithm for identifying the number of corrupted bytes in a packet based on the RSSI time series during packet reception. Based on the corruption level, we design an adaptive coding scheme based on Reed Solomon (RS) codes. We carefully select the coding parameters so as to optimize the network performance. Finally, we design and implement CARE, a Corruption-Aware REtransmission protocol by incorporating corruption detection and adaptive coding. We conduct extensive experiments based on COTS low-power devices. Results show that CARE significantly improves the performance for weak and highly interfered links while incurring no additional overhead for good links.

Time synchronization is crucial for cyber-physical systems (CPSs), e.g., wireless sensor and actuator networks. Cyber physical security is an important yet challenging problem. In particular, attacks to the time synchronization service may incur data distortion or even malfunction of the whole system. Sybil attack is one of the most common attack types for sensor networks, where a node illegitimately claims multiple identities. Existing secure time-synchronization protocols; however, cannot well address Sybil attacks in the time synchronization process. We propose a robust and secure time-synchronization protocol (RTSP) to defend against Sybil attacks. Different from previous secure timesync protocols, RTSP employs a novel graph theoretical approach, which is able to perform anomaly detection at the message level instead of the node level. This fine-grained detection ability enables RTSP to become robust against Sybil attacks, as well as node compromise and message manipulation attacks. Extensive experimental results show the effectiveness of our proposed protocol.