Bus company operators are interested in obtaining knowledge about the number of passengers on their buses—preferably doing so at low deployment costs and in an automated manner, while keeping accuracy high. One solution, widely used in practice, involves deploying a light sensor-based system, counting the people entering and leaving the bus. The light sensor system is simple, but errors accumulate over time, because it is not capable of error correcting. For this reason, the light sensor-based system is compared to a WLAN probe-based system, which has entirely different characteristics. Inaccuracy with the WLAN estimator comes from a need to filter out mobile devices outside the bus and to map the number of detected devices to a number of people. The comparison is performed based on data collected from a real-life deployment in a medium sized German city. The comparison shows the trade-off in selecting either of the two methods. Furthermore, a novel approach for fusion of the light sensor and WLAN estimators is proposed which has a big potential in improving accuracy of both estimators. A fusion approach is proposed that utilizes the different error characteristics for error compensation by calculating compensation terms. The knowledge of Ground Truth is not required as part of this fusion approach for calibration; results show that the approach can find the optimal parameter settings and that it makes this occupancy estimation approach scalable and automated.

Decentralized cooperative resource allocation schemes for robotic swarms are essential to enable high reliability in high throughput data message exchanges. These cooperative schemes require control signaling to avoid half-duplex problems at the receiver and mitigate interference. We propose two cooperative resource allocation schemes, device sequential and group scheduling, and introduce a control signaling design. We observe that failure in the reception of these control signals lead to non-cooperative behavior and to significant performance degradation. The cause of these failures is identified, and specific countermeasures are proposed and evaluated through extensive system level simulations. The key performance indicators are data message reliability and the packet inter-reception metric. As our main reference, we compare the proposed resource allocation schemes against the NR sidelink mode 2 resource allocation, and show that despite signaling has a significant impact on resource allocation performance, our proposed device sequential and group scheduling resource allocation schemes improve reliability by an order of magnitude compared to sidelink mode 2.

This paper is investigating the energy saving potential of Bluetooth. Using the asynchronous connectionless link (ACL) for data transmission, Bluetooth provides different packet types, namely DM and DH packets. Each packet has a different payload size and in the actual implementation the packet selection is based on the channel conditions. In this paper we present and discuss results of a measurements campaign that we have made on commercial mobile phones. We show that energy savings can be achieved by selecting the appropriate packet type in an energy aware manner, by forcing the phone to use a specific packet type.

Relaying can improve the coverage and performance of wireless access networks. In presence of a localisation system at the mobile nodes, the use of such location estimates for relay node selection can be advantageous as such information can be collected by access points in linear effort with respect to number of mobile nodes (while the number of links grows quadratically). However, the localisation error and the chosen update rate of location information in conjunction with the mobility model affect the performance of such location-based relay schemes; these parameters also need to be taken into account in the design of optimal policies. This paper develops a Markov model that can capture the joint impact of localisation errors and inaccuracies of location information due to forwarding delays and mobility; the Markov model is used to develop algorithms to determine optimal location-based relay policies that take the aforementioned factors into account. The model is subsequently used to analyse the impact of deployment parameter choices on the performance of location-based relaying in WLAN scenarios with free-space propagation conditions and in an measurement-based indoor office scenario.