This paper presents a wake-up transceiver system based on narrowband power line communications (NB-PLC). An energy-pattern based sequence detection algorithm is proposed for the recognition of a wake-up sequence by the receiver. The method is characterized by low computational complexity and does not require channel parameter estimation and adaption. Furthermore, a blind packet repetition technique is introduced that accomplishes periodic impulsive noise avoidance. A realistic and straightforward narrowband power line channel model is presented and the proposed wake-up protocol is verified against it in terms of Monte-Carlo simulations. It is demonstrated, that the method leads to significant improvements compared to state of the art low-complexity PLC systems in typical NB-PLC channels

This paper presents a novel sequence detection algorithm well-suited for data transmission and wake-up receiver addressing in FSK-based narrowband power line communication applications, where the communication channel is characterized by non-white noise, interferers and frequency-selective signal attenuation. The algorithm is discussed in detail, verified by Monte Carlo simulations and compared to state of the art solutions. Furthermore, it is shown that, in an equivalent noise scenario, the proposed algorithm offers a performance comparable to the state of the art technologies with a significantly lower algorithmic complexity

Cyber security is an important issue in modern smart grid technology. As the smart grid is literally a network of networks, manifold potential vulnerabilities appear that allow privacy- and security-attacks within the whole energy chain, e.g. generation, transmission, distribution and consumption. Therefore, in order to achieve full customer acceptance and to ensure the stability of the current supply, all components of a smart grid communication network have to be highly secure and meet challenging privacy requirements. Scores of scientif c and governmental efforts exist to make smart grid technology secure with respect to potential cyber-attacks. Nevertheless, standardization is mandatory to ensure sophisticated security mechanisms through the whole network. This paper focuses on the work of smart grid security aspects on the low voltage segment within the German power grid. Evolving security standards are discussed against the background of potential vulnerabilities and it is emphasized, where additional effort is necessary to ensure privacy and security in the smart grid

Wireless communication systems used for home automation applications in Europe suffer from the recurring standardization problem. Devices from different manufacturers often use different and incompatible protocol waveforms for communication. In this paper, we present the development and the implementation of a feature-based automatic classification algorithm for communication standards that are commonly used in the European 868 MHz short range device (SRD) frequency band, that can help to overcome this problem. The received signal is initially preprocessed and then fed to a feature extraction unit that calculates statistical parameters from the signal. Those features are then used by a classifier that is based on a simple binary decision tree. The performance of the algorithm is verified via Monte-Carlo simulation and compared to the bit error rates of the considered communication standards as a function of the signal-to-noise ratio. Finally, the algorithm is implemente d in a software defined radio (SDR) platform and the measurement results are compared to the simulation-based success rates

Automatic recognition of communication standards is a crucial task, if universal interoperability between different communication systems is claimed. A gateway performing the recognition task can be used to interconnect heterogeneous systems with different and incompatible protocol waveforms. This paper proposes an algorithm for the automatic recognition of various communication standards used for wireless smart home networks in the European 868 MHz Short-Range Device frequency band. Relevant statistical key features are extracted from the received signal and the recognition is based on threshold comparison. The algorithm is appropriate for the implementation in low-complexity embedded systems and works over a wide range of signal-to-noise ratios (SNR)