Bluetooth Low Energy (BLE) devices such as iBeacons have been popularly deployed for Location Based Services (LBS), including indoor infrastructure monitoring, positioning, and navigation. In these applications, the positions of iBeacons are assumed to be known. However, the location information is often unavailable or inaccurate as most iBeacons were deployed by different external parties. In addition, manual localizing the already-deployed iBeacons is costly and even impractical, especially in large-scale and complex indoor environments. Therefore, we propose a novel method, namely SomeBe, which can localize deployed iBeacons with a minimal effort and invasiveness to existing infrastructures. Specifically, our approach uses cooperative multilateration based on Received Signal Strength (RSS) of available smartphones and WiFi access points (APs) in the environment. Both Bluetooth signal strengths (between smartphones and iBeacons) and WiFi signal strengths (between smartphones and APs) are jointly employed in a single optimization cost function to surpass the local minima. Requiring that the positions of the APs are known only, the proposed cost function can also localize the iBeacons without knowing the positions of smartphones. To improve the localization accuracy, we employ a clustering method based on the RSS values for the coarse estimation of iBeacons' positions. SomBe also can be used to simplify iBeacon deployment as it can localize the iBeacons with a minimal effort. The performance evaluation results of our testbed experiments as well as realistic simulations show that SomBe outperforms non-cooperative approaches with 85% better in terms of accuracy.

Localization, a process of determining the position of a blind node, can be used in various applications. Signal-strength localization provides a low-cost and low-power solution to positioning. Signal-strength positioning approaches using fingerprinting or calibrated approaches require a time-consuming calibration phase. Existing self-calibrating approaches, which do not require a priori calibration, use a least-squares fitting model to determine both the position of the blind node as well as the optimal environmental parameters. In this paper, we propose an approach using the Product-Moment correlation between the measured signal strength and the estimated signal strengths. Such approach does not require estimation of the environmental parameters or prior calibration and outperforms existing self-calibrating least-squares approaches. We compare our approach to existing least-squares calibration-free positioning approaches. Moreover, we look at the Cramer-Rao Bound (CRB) of signal-strength localization and using simulations we show that the product-moment correlation outperforms least-squares approaches and follows the CRB closely. Simulation and evaluation using a real-world experiment dataset show the product-moment approach significantly outperforms least-squares approaches. The product-moment approach follows the CRB much more closely and achieves up to twice more accurate positions in certain scenarios. When the error ratio increases and the number of reference positions stays fixed at 6, the product-moment approach scores 20% more accurate positions. In the cooperative localization scenario, the product-moment correlation performs 40% better.

Performing real world experiments with underwater communication is difficult and time-consuming. Input for evaluation of localization and time-synchronization derived from experiments is not readily available.

In this paper we describe a smart way to apply dynamic wireless sensor networks (WSN) in logistics. Especially in the temperature controlled supply chain (cold chain), perishable goods like fruits and pharmaceuticals greatly benefit from real-time quality monitoring during storage and transport in order to avoid quality degradation and spoilage. In our system, wireless sensor nodes called SmartPoints monitor the environmental conditions and generate alarms when specific events are detected. Additionally, they calculate the remaining shelf life of the perishable goods they travel with. When there is an Internet-connected WSN available during travel, the shelf-life prediction and associated alarms are directly sent to a back-end server. Alternatively they are logged on the SmartPoints and flushed upon arrival, such that the remaining shelf-life and alarms are immediately clear and a full history will be available later. Our dynamic WSN supports a number of protocols that enable support for the dynamic processes in logistic processes. The Ambient middleware supports real-time monitoring and remote maintenance across the Internet via wired and mobile wireless network access technologies. Additionally, the middleware offers easy integration with third-party applications. Ambient Studio utilizes the middleware for remote WSN configuration and monitoring.