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The main topic of this paper is low-cost public key cryptography in wireless sensor nodes. Security in embedded systems, for example, in sensor nodes based on field programmable gate array (FPGA), demands low cost but still efficient solutions. Sensor nodes are key elements in the Internet of Things paradigm, and their security is a crucial requirement for critical applications in sectors such as military, health, and industry. To address these security requirements under the restrictions imposed by the available computing resources of sensor nodes, this paper presents a low-area FPGA-prototyped hardware accelerator for scalar multiplication, the most costly operation in elliptic curve cryptography (ECC). This cryptoengine is provided as an enabler of robust cryptography for security services in the IoT, such as confidentiality and authentication. The compact property in the proposed hardware design is achieved by implementing a novel digit-by-digit computing approach applied at the finite field and curve level algorithms, in addition to hardware reusing, the use of embedded memory blocks in modern FPGAs, and a simpler control logic. Our hardware design targets elliptic curves defined over binary fields generated by trinomials, uses fewer area resources than other FPGA approaches, and is faster than software counterparts. Our ECC hardware accelerator was validated under a hardware/software codesign of the Diffie-Hellman key exchange protocol (ECDH) deployed in the IoT MicroZed FPGA board. For a scalar multiplication in the sect233 curve, our design requires 1170 FPGA slices and completes the computation in 128820 clock cycles (at 135.31 MHz), with an efficiency of 0.209 kbps/slice. In the codesign, the ECDH protocol is executed in 4.1 ms, 17 times faster than a MIRACL software implementation running on the embedded processor Cortex A9 in the MicroZed. The FPGA-based accelerator for binary ECC presented in this work is the one with the least amount of hardware resources compared to other FPGA designs in the literature.

Channel estimation in wireless communication systems is usually accomplished by inserting, along with the information, a series of known symbols, whose analysis is used to define the parameters of the filters that remove the distortion of the data. Nevertheless, a part of the available bandwidth has to be destined to these symbols. Until now, no alternative solution has demonstrated to be fully satisfying for commercial use, but one technique that looks promising is superimposed training (ST). This work describes a hybrid software-hardware FPGA implementation of a recent algorithm that belongs to the ST family, known as Datadependent Superimposed Training (DDST), which does not need extra bandwidth for its training sequences (TS) as it adds them arithmetically to the data. DDST also adds a third sequence known as data-dependent sequence, that destroys the interference caused by the data over the TS. As DDST's computational burden is too high for the commercial processors used in mobile systems, a System on a Programmable Chip (SOPC) approach is used in order to solve the problem.

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This paper presents a general and hybrid (centralized and distributed) approach for the activation of processing elements (PEs) inside of a processor array using the polytope model. The proposed approach is suitable of being implemented on reconfigurable systems since by changing some mathematical expressions, the proposed control approach is able to provide activation patterns for different algorithms based on the polytope model. We have taken the Cholesky decomposition as example for developing our hybrid control towards a generalization of this scheme.