text: Devices equipped with accelerometer sensors such as today's mobile devices can make use of motion to exchange information. A typical example for shared motion is shaking of two devices which are held together in one hand. Deriving a shared secret (key) from shared motion, e.g. for device pairing, is an obvious application for this. Only the keys need to be exchanged between the peers and neither the motion data nor the features extracted from it. This makes the pairing fast and easy. For this, each device generates an information signal (key) independently of each other and, in order to pair, they should be identical. The key is essentially derived by quantizing certain well discriminative features extracted from the accelerometer data after an implicit synchronization. In this paper, we aim at finding a small set of effective features which enable a significantly simpler quantization procedure than the prior art. Our tentative results with authentic accelerometer data show that this is possible with a competent accuracy (76 ) and key strength (entropy approximately 15 bits).
  @cite_0: Ubiquitous computing is associated with a vision of everything being connected to everything. However, for successful applications to emerge, it will not be the quantity but the quality and usefulness of connections that will matter. Our concern is how qualitative relations and more selective connections can be established between smart artefacts, and how users can retain control over artefact interconnection. We propose context proximity for selective artefact communication, using the context of artefacts for matchmaking. We further suggest to empower users with simple but effective means to impose the same context on a number of artefacts. To prove our point we have implemented Smart-Its Friends, small embedded devices that become connected when a user holds them together and shakes them.
  @cite_1: Hard restrictions in computing power and energy consumption favour symmetric key methods to encrypt the communication in wireless body area networks which in term impose questions on effective and user-friendly unobtrusive ways for key distribution. In this paper, we present a novel approach to establish a secure connection between two devices by shaking them together. Instead of distributing or exchanging a key, the devices independently generate a key from the measured acceleration data by appropriate signal processing methods. Exhaustive practical experiments based on acceleration data gathered from real hardware prototypes have shown that in about 80 of the cases, a common key can be successfully generated. The average entropy of these generated keys exceed 13bits.
  @cite_2: Small, mobile devices without user interfaces, such as Bluetooth headsets, often need to communicate securely over wireless networks. Active attacks can only be prevented by authenticating wireless communication, which is problematic when devices do not have any a priori information about each other. We introduce a new method for device-to-device authentication by shaking devices together. This paper describes two protocols for combining cryptographic authentication techniques with known methods of accelerometer data analysis to the effect of generating authenticated, secret keys. The protocols differ in their design, one being more conservative from a security point of view, while the other allows more dynamic interactions. Three experiments are used to optimize and validate our proposed authentication method.
  @cite_8: As the proliferation of pervasive and ubiquitous computing devices continues, users will carry more devices. Without the ability for these devices to unobtrusively interact with one another, the user’s attention will be spent on coordinating, rather than using, these devices. We present a method to determine if two devices are carried by the same person, by analyzing walking data recorded by low-cost MEMS accelerometers using the coherence function, a measure of linear correlation in the frequency domain. We also show that these low-cost sensors perform similarly to more expensive accelerometers for the frequency range of human motion, 0 to 10Hz. We also present results from a large test group illustrating the algorithm’s robustness and its ability to withstand real world time delays, crucial for wireless technologies like Bluetooth and 802.11. We present results that show that our technique is 100 accurate using a sliding window of 8 seconds of data when the devices are carried in the same location on the body, is tolerant to inter-device communication latencies, and requires little communication bandwidth. In addition we present results for when devices are carried on different parts of the body.
summary: Device pairing by shaking was first presented in Smart-Its Friends'' @cite_0 . The drawback of this technique was lacking of authentication of the involved devices in the interaction. Another closely related work included accelerometer based analysis to determine whether the devices are carried by the same person or not @cite_8 . The feature extraction algorithms used in @cite_2 includes coherence measure, which was originally introduced in @cite_8 , and quantized FFT coefficients. In @cite_1 , a similar approach in which acceleration signal was used for key generation was presented with a difference in using time domain acceleration features contrary to @cite_2 where only frequency domain features were used. They claim that the same key would rather likely be generated for different shaking processes if the key generation was based on a frequency-based technique. They concluded that the frequency domain is not suitable for key generation.