Connected devices are getting attention because of the lack of security mechanisms in current Internet-of-Thing (IoT) products. The security can be enhanced by using standardized and proven-secure block ciphers as advanced encryption standard (AES) for data encryption and authentication. However, these security functions take a large amount of processing power and power/energy consumption. In this paper, we present our hardware optimization strategies for AES for high-speed ultralow-power ultralow-energy IoT applications with multiple levels of security. Our design supports multiple security levels through different key sizes, power and energy optimization for both datapath and key expansion. The estimated power results show that our implementation may achieve an energy per bit comparable with the lightweight standardized algorithm PRESENT of less than 1 pJ/b at 10 MHz at 0.6 V with throughput of 28 Mb/s in ST FDSOI 28-nm technology. In terms of security evaluation, our proposed datapath, 32-b key out of 128 b cannot be revealed by correlation power analysis attack using less than 20 000 traces.

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Technological advances have made wireless sensor nodes cheap and reliable enough to be brought into various application domains. These nodes are powered by battery, thus they have a limited lifespan which is a major drawback for their acceptance. This paper addresses a power consumption control problem of wireless nodes equipped with batteries. Dynamic power management is used to dynamically re-configure the set of sensor nodes in order to provide given service and performance levels with a minimum number of active nodes and/or a minimum load on such components. The power control formulation is based on model predictive control with constraints and binary optimization variables, leading to a mixed integer quadratic programming problem. Simulations are performed to demonstrate the efficiency of the proposed control method.

Recommended by Michael HubnerWith forecasted hundreds of processing elements (PEs), future embedded systems will be able to handle multiple applications with very diverse running constraints. Systems will integrate distributed decision capabilities. In order to control the power and temperature, dynamic voltage frequency scalings (DVFSs) are applied at PE level. At system level, it implies to dynamically manage the different voltage/frequency couples of each tile to obtain a global optimization. This paper introduces a scalable multiobjective approach based on game theory, which adjusts at run-time the frequency of each PE. It aims at reducing the tile temperature while maintaining the synchronization between application tasks. Results show that the proposed run-time algorithm requires an average of 20 calculation cycles to find the solution for a 100-processor platform and reaches equivalent performances when comparing with an offline method. Temperature reductions of about 23% were achieved on a demonstrative test-case.

Technological advances have made wireless sensor nodes cheap and reliable enough to be brought into various application domains. The limited energy capacity of sensor nodes is the key factor that restricts their lifespan. In this paper, a Predictive Control strategy for Dynamic Power Management of a set of wireless sensor nodes is proposed. The control formulation is based on Model Predictive Control with constraints and binary optimization variables, leading to a Mixed Integer Quadratic Programming problem. The control algorithm proposed guarantees services and performances levels with a minimum number of active nodes and/or a minimum load on such components. The strategy is evaluated on a real testbench with wireless sensor nodes equipped with batteries and harvesting systems. Experimental results show the effectiveness of the control method proposed.

For the last 25 years, occupancy grids have been intensively used as a well-understood framework for many robotic applications, such as path planning or obstacle avoidance. They offer a unifying framework for multiple heterogeneous sensor integration using a probabilistic representation of sensor data. This integration is computed through Bayesian techniques or evidence combination approaches, both requiring high computation workload using real number representation. In critical application domains, it is challenging to fuse data coming out of several sensors in real-time using constrained embedded platforms. In this paper, we propose a revised theoretical formulation of multi-sensor fusion using only integer arithmetic. We apply this novel framework to compute occupancy grid by only using integer numbers to represent probabilities. Compared to the state-of-the-art solutions, our fusion framework enables implementation on platforms with no floating-point support. Our experiments demonstrate that fusion of real automotive data from a 4-scans LIDAR can be integrated into a microcontroller without a floating-point unit. Our approach opens the perspective for microcontroller or even for hardware block based on ASIC or FPGA to support occupancy grid applications with real-time performance.

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