Recognizing the transportation modes of people's daily living is an important research issue in the pervasive computing. Prior research in this field mainly uses Global Positioning System (GPS), Global System for Mobile Communications (GSM) or their combination with accelerometer to recognize transportation modes, such as walking, driving, etc. In this paper, we will introduce transportation mode recognition on mobile phones only using embedded accelerometer. In order to deal with uncertainty of position and orientation of mobile phone, acceleration synthesization based method and acceleration decomposition based method are introduced. Performance comparison indicates that acceleration synthesization based method outperforms acceleration decomposition based method. We will discuss the factors affect the recognition accuracy of acceleration decomposition based method and present potential improvements.

In this letter, an analytical model is proposed for 3-channel-based neighbor discovery in Bluetooth Low Energy (BLE) networks. The model can be used to determine some important performance metrics, such as average latency or average energy consumption during the course of discovering neighbors. Since intermittent connections are frequently encountered in practical scenarios of BLE, the modeling results can provide a beneficial guidance to customize advertising or scanning behavior towards user desired performance.Index Terms-Bluetooth Low Energy, neighbor discovery, wireless body area networks.

Sink mobility has attracted much research interest in recent years because it can improve network performance such as energy efficiency and throughput. An energy-unconscious moving strategy is potentially harmful to the balance of the energy consumption among sensor nodes so as to aggravate the hotspot problem of sensor networks. In this paper, we propose an autonomous moving strategy for the mobile sinks in data-gathering applications. In our solution, a mobile sink approaches the nodes with high residual energy to force them to forward data for other nodes and tries to avoid passing by the nodes with low energy. We performed simulation experiments to compare our solution with other three data-gathering schemes. The simulation results show that our strategy cannot only extend network lifetime notably but also provides scalability and topology adaptability.

Bluetooth Low Energy (BLE) is drawing more and more attention due to its recent appearance in consumer electronic products. As a low-power wireless solution, BLE provides attractive energy performance that makes it particularly suitable for portable, battery-driven electronic devices. Although there are some prior arts focusing on BLE energy performance, it still lacks a thorough study on the important aspect of device discovery. Such energy cost, introduced by intermittent scanning or connection setup, could seriously affect the battery endurance ability of the devices. In this paper, we present quantitative analysis on the neighbor discovery energy for BLE. The modeling results that built upon measurement of CC2541 Mini-Development Kit have been validated quite accurate via extensive experiments. In addition, several interesting conclusions are found while investigating the achieved energy model, which may provide precious guidelines to the design of energy-efficient applications for BLE.

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