Geoffrey Ottoy

AES Data Encryption in a ZigBee Network: Software or Hardware?

This paper describes the experiments which have been conducted to determine the optimal implementation concept for AES (Advanced Encryption Standard) data encryption in a ZigBee network [1,2]. Two concepts have been considered. The first one is a AES128-CBC hardware co-processor embedded on a Spartan 3A FPGA. The second configuration implements the same cryptographic algorithm on the processor which controls the ZigBee nodes. The ZigBee modules in the network contain an 8-bit microcontroller which takes care of the ZigBee protocol stack –and the encryption calculations in the second case. Both approaches are examined and compared. In this paper we show that –in general– a software implementation is feasible in a ZigBee network, though a low-power hardware cryptographic co-processor could prove to be useful in some cases.

A low-power MEMS microphone array for wireless acoustic sensors

In this article we present the design of a low-power MEMS microphone array for wireless sensors. The array is used as part of a device that performs acoustic Angle-Of-Arrival (AOA) measurements. The power consumption of this design is lower than that of comparable designs. Each of the 4 analog channels (each with a microphone and amplifier) can be turned on or off (standby) separately. The power consumption is 1.8 mW per channel, and about 0.13 μW in standby. For a single AOA detection cycle (array activation, audio sampling, AOA computation), the energy consumption is 6.02 mJ. When consecutive AOA detections are performed, the energy cost per detection converges to 3.20 mJ. The AOA accuracy corresponds with the expectations. With a mean error of 4°, this is lower than that of comparable designs.

On the choice of the appropriate AES data encryption method for ZigBee nodes

This paper describes the experiments that have been conducted to determine the optimal implementation method for AES Advanced Encryption Standard data encryption in a ZigBee network in terms of energy consumption. Four possible scenarios have been considered. The first one is a freely available AES-cryptographic algorithm on the processor which controls the ZigBee nodes. The second also involves the processor but is based on a proprietary, highly optimized algorithm. The other methods are based on hardware implementations. Whereas the third option is based on a cryptographic block, embedded in the ZigBee node controller, the last solution utilizes an AES128-CBC-MAC hardware co-processor embedded on a Spartan 3A FPGA. The ZigBee modules in the network contain an 8-bit microcontroller which takes care of the ZigBee protocol stack-and the encryption calculations in all but the last case. All approaches are examined and compared. We show that the usage of a microcontroller with an on-board AES processor is the optimal design choice for a new hardware platform. An optimized software library gives the best results when extending an existing platform. This paper gives developers an idea of the amount of energy they can gain/lose by choosing one of the four solutions. Copyright

A modular test platform for evaluation of security protocols in NFC applications

In this paper we present the advantages and possibilities of a modular test platform for the evaluation of security protocols in NFC applications. Furthermore, we also depict some practical implementation results of this modular system. The scope of the platform is to provide a highly modular system. Adding or removing certain functionality can be done without the need of rebuilding the entire system. Security measures in hardware as well as software can be tested and evaluated with this platform. It can serve as a basis for a broad range of security related applications, NFC being our domain of interest, but even so in other domains.

Modeling acoustic localization accuracy for scalable energy consumption in wireless sensor swarms

Sensor swarms can be a cost-effectieve and more privacy-friendly alternative for location based service systems in building automation and health-care. To increase the battery lifetime of such swarm networks, the energy consumption should be scaled to the required localization accuracy. In this paper we described the first steps in developing an energy model that couples localization accuracy to energy-related sensor parameters such as sample frequency and ADC resolution. The goal is to use the model for the localization of undetermined environmental sounds, by means of wireless acoustic sensors. We show that for TDOA-based localization, the signal sample rate can be under the Nyquist frequency, provided that enough frequency components remain present in the undersampled signal. The resulting localization error is comparable with that of similar localization systems.

Flexible design of a modular simultaneous exponentiation core for embedded platforms

In this paper we present a flexible hardware design for performing Simultaneous Exponentiations on embedded platforms. Simultaneous Exponentiations are often used in anonymous credentials protocols. The hardware is designed with VHDL and fit for use in embedded systems. The kernel of the design is a pipelined Montgomery multiplier. The length of the operands and the number of stages can be chosen before synthesis. We show the effect of the operand length and number of stages on the maximum attainable frequency as well as on the FPGA resources being used. Next to scalability of the hardware, we support different operand lengths at run-time. The design uses generic VHDL without any device-specific primitives, ensuring portability to other platforms. As a test-case we effectively integrated the hardware in a MicroBlaze embedded platform. With this platform we show that simultaneous exponentiations with our hardware are performed 70 times faster than with an all-software implementation.

Robust AOA based acoustic source localization method with unreliable measurements

Abstract The performance degradation problem of the angle of arrival (AOA)-based acoustic localization methods in the presence of unreliable bearing measurements (outliers) is addressed in this paper. Two typical M-estimators based on Tukey and Huber functions are applied to tackle the problem. Both functions are solved by the iterative reweighted nonlinear least squares (IRNLS) method. Considering the Huber function is convex in nature, it is specifically utilized to mitigate the influence of large residuals on pseudolinear estimator (PLE) by convex optimization. To make the IRNLS method more feasible to use, an approximate relationship between the outlier probability and the bound parameter is provided. The robustness and effectiveness of the proposed methods are clearly demonstrated through a series of simulation results in the presence of various unreliable measurements.

Saving energy in WSNs for acoustic surveillance applications while maintaining QoS

Wireless acuusuc sensor networks (wasns) are a promising technology for performing acoustic surveillance because of their flexibility and low cost. However, their commercialization is nowadays limited due to their high energy consumption, which is mainly a result of the high data rates required to stream audio data between sensor nodes. In order to improve energy efficiency in WASNs, we explore the benefits of introducing capabilities to reconfigure and collaborate. More specifically we consider an application for localizing and recording sound events in a warehouse environment. For studying this, we introduce an energy consumption model for a WASN node, which includes the cost of sensing, processing and wireless communications. Presented results show that the centralized features allow important energy savings, while maintaining a sufficient Quality of Service. For the considered setup, our simulations show that a centralized approach saves 22% of energy. Furthermore, by using a smart activation of nodes, the energy consumed by all the nodes can be balanced, resulting in a longer network lifetime.

An Improved 2D Triangulation Algorithm for Use With Linear Arrays

When localization is required to offer location-based services in user-centered applications, then (acoustic) angle-of-arrival (AOA) can be an energy efficient and user friendly option. Previous research has always assumed that a priori knowledge can be used to select the correct angles out of a set of possible values as an input to the AOA localization algorithm. In this paper, we propose an algorithm that is able to perform this selection automatically with a high chance of selecting the correct values. Even for an increasing standard deviation on the measured angles, the localization accuracy of the proposed algorithm is able to compete with an algorithm that does the selection based on a priori knowledge.

Measuring the NFC Peer-to-Peer Data Rate

NFC is a relatively new short-rangewireless technology. For bidirectional communication between two NFC devices, the NFC Forum specifies the Peer-to- Peer (P2P) standard. Several challenges remain for exchanging data between a mobile Android device and an embedded device. First of all, the current versions of Android implement the NFC P2P specification only partially. A second challenge is the implementation of the NFC P2P protocol stack on the embedded platform. For the developers to be able to make an educated choice on whether to use NFC (P2P) or not, knowledge of the data rate is essential as well. In this article we provide an overview of these challenges. We also create a representative setup with NFC P2P stacks according to the specification on both the mobile and the embedded device. With this setup we are able to measure the NFC P2P data rate and compare it to NFC connection handover to WiFi.