Embracing Collisions to Increase Fidelity of Sensing Systems with COTS Tags

RFID techniques have been extensively used in sensing systems due to their low cost. However, limited by the structural simplicity, collision is one key issue which is inevitable in RFID systems, thus limiting the accuracy and scalability of such sensing systems. Existing anti-collision techniques try to enable parallel decoding without sensing based applications in mind, which can not operate on COTS RFID systems. To address the issue, we propose COFFEE, which enables parallel channel estimation of COTS passive tags by harnessing the collision. We revisit the physical layer design of current standard. By exploiting the characteristics of low sampling rate and channel diversity of RFID tags, we separate the collided data and extract the channels of the collided tags. We also propose a tag identification algorithm which explores history channel information and identify the tags without decoding. COFFEE is compatible with current COTS RFID standards which can be applied to all RFID-based sensing systems without any modification on tag side. To evaluate the real world performance of our system, we build a prototype and conduct extensive experiments. The experimental results show that we can achieve up to 7.33x median time resolution gain for the best case and 3.42x median gain on average.

Novel Extensions to Enhance Scalability and Reliability of the IEEE 802.15.4-DSME Protocol

The Deterministic and Synchronous Multichannel Extension (DSME) of the IEEE 802.15.4 standard was designed to fulfill the requirements of commercial and industrial applications. DSME overcomes the IEEE 802.15.4 limitation on the maximum number of Guaranteed Time Slots (GTS) in a superframe and it also exploits channel diversity to increase the communication reliability. However, DSME suffers from scalability problems, as its multi-superframe structure does not efficiently handle GTS in networks with a high number of nodes and periodic flows. This paper proposes the enhanceD DSME (D-DSME), which consists of two extensions that improve the DSME scalability and reliability exploiting a GTS within the multi-superframe to accommodate multiple flows or multiple retransmissions of the same flow. The paper describes the proposed extensions and the performance results of both OMNeT simulations and experiments with real devices implementing the D-DSME.

In-orbit AIS performance of the Norwegian microsatellites NorSat-1 and NorSat-2

Two Norwegian AIS-satellites, NorSat-1 and NorSat-2, were launched in July 2017. Both are equipped with the ASR x50, the latest space-AIS receiver developed by Kongsberg Seatex AS, offering advanced signal processing and continuous operation on all four AIS channels. The NorSat-satellites collect ~ 1.5 million messages from ~ 50,000 ships per day (24 h) each, which is a factor ~ 2.8 increase in the number of messages compared to the ASR 100 on-board AISSat-1 and AISSat-2. The improvements of the AIS-satellites can be attributed to three developments: the performance of the receiver, the use of antenna diversity, and the use of frequency channel diversity. Daily statistics for February 2018 over the Mediterranean Sea illustrate the improvements: The median value of the number of messages received with NorSat-1 using only one antenna is 2.3 times higher than for AISSat-1. When both NorSat-1 antennas are used, the improvement factor becomes 4.1, and finally, when two additional receiver channels are used to collect long-range AIS messages, the total improvement becomes 8.2 times. In terms of ships detected, the factors are 1.8, 2.7, and 4.4 for the respective steps. Long-range AIS messages amount to just 5% of the total AIS messages received by NorSat-1 in August 2017, but it allows to detect 20% more ships on a global scale, and as much as 10 times more ships in a the high-traffic area in the North Sea.

Enhancing the Accuracy and Robustness of a Compressive Sensing Based Device-Free Localization by Exploiting Channel Diversity

As an emerging and promising technique, device-free localization (DFL) estimates target positions by analyzing their shadowing effects. Most existing compressive sensing (CS)-based DFL methods use the changes of received signal strength (RSS) to approximate the shadowing effects. However, in changing environments, RSS readings are vulnerable to environmental dynamics. The deviation between runtime RSS variations and the data in a fixed dictionary can significantly deteriorate the performance of DFL. In this paper, we introduce ComDec, a novel CS-based DFL method using channel state information (CSI) to enhance localization accuracy and robustness. To exploit the channel diversity of CSI measurements, the DFL problem is formulated as a joint sparse recovery problem that recovers multiple sparse vectors with common support. To solve this problem, we develop a joint sparse recovery algorithm under the variational Bayesian inference framework. In this algorithm, dictionaries are parameterized based on the saddle surface model. To adapt to the environmental changes and different channel characteristics, dictionary parameters are modelled as tunable parameters. Simulation results verified the superior performance of ComDec as compared with other state-of-the-art CS-based DFL methods.