Abstract INSPEX is anINtegrated Smart sPatial EXplorationsystem. It relies on a family of sensors, like automated vehicles do, to provide enough information to a digital system for it to make reliable inferences about the location of obstacles and other impediments in its environment. Unlike the automated vehicle case, INSPEX is minaturised, because it is intended for lightweight applications and for portable use by humans, for example, visually impaired persons navigating outdoors (among many similar use cases). The complexity of this hardware‐focused system merited the introduction of formal methods during its (essentially conventionally structured) development. The aim was to improve the dependability of parts of the implemented system and to estimate system characteristics via modelling and calculation that could not be obtained experimentally within the scope of the project. The paper overviews the experience of the very much human‐in‐the‐loop use of formal techniques in the INSPEX Project and focuses particularly on the human issues that impacted the cooperation between the conventional techniques and formal methods.

In energy-harvesting Internet of Things (EH-IoT) wireless networks, maintaining energy neutral operation (ENO) is crucial for their perpetual operation and maintenance-free property. Guaranteeing this ENO condition and optimal power-performance trade-off under transient harvested energy and wireless channel quality is particularly challenging. This paper proposes a multi-agent actor-critic reinforcement learning for modulating both the transmitter duty-cycle and output power based on the state-of-buffer (SoB) and the state-of-charge (SoC) information as a state. Thanks to these buffers, differently from the state-of-the-art, our solution does not require any model of the wireless transceiver nor any direct measurement of both harvested energy and wireless channel quality for adapting to these uncertainties. Simulation results of a solar powered EH-IoT node using real-life outdoor solar irradiance data show that the proposed method achieves better performance without system failures throughout a year compared to the state-of-the-art that suffers some system downtime. Our approach also predicts almost no system fails during five years of operation.

The INSPEX project aims to miniaturize state-of-the-art obstacle detection technology comprising heterogeneous sensors and advanced processing, so that it can be used for wearable devices. The project focuses on enhancing the white cane used by some visually impaired and blind people. Due to high demand for reliability and performance, the project is a good candidate for the use of formal methods. In this paper, we report lessons we have learned from formal modelling exercises related to the pre-processing of sensor information in INSPEX.

In this paper some local stability results for an autonomous Long Short-Term Memory neural network model with respect to the origin are provided. In particular, it is shown through linearization that the local asymptotic stability conditions with respect to the origin only depend on one of the weight matrices. Simulations indicate that these local stability conditions greatly influence the behavior of the autonomous four-dimensional neural network in the region where each variable's values vary between minus one and one. Finally, some sufficient stability conditions for the nonlinear model are formulated as a convex program involving linear matrix inequalities.

The INSPEX H2020 project main objective is to integrate automotive-equivalent spatial exploration and obstacle detection functionalities into a portable/wearable multi-sensor, miniaturised, low power device. The INSPEX system will detect and localise in real-time static and mobile obstacles under various environmental conditions in 3D. Potential applications range from safer human navigation in reduced visibility, small robot/drone obstacle avoidance systems to navigation for the visually/mobility impaired, this latter being the primary use-case considered in the project.

Nowadays, power consumption is one of the main limitations of electronic systems. In this context, novel and emerging devices provide new opportunities to extend the trend toward low-power design. In this survey article, we present a transversal survey on energy-efficient techniques ranging from devices to architectures. The actual trends of device research, with fully depleted planar devices, tri-gate geometries, and gate-all-around structures, allows us to reach an increasingly higher level of performance while reducing the associated power. In addition, beyond the simple device property enhancements, emerging devices also lead to innovations at the circuit and architectural levels. In particular, devices whose properties can be tuned through additional terminals enable a fine and dynamic control of device threshold. They also enable designers to realize logic gates and to implement power-related techniques in a compact way unreachable to standard technologies. These innovations reduce power consumption at the gate level and unlock new means of actuation in architectural solutions like adaptive voltage and frequency scaling.

The emerging SOI technologies provide an increased body bias range compared to traditional bulk technologies, opening new opportunities. From the power management perspective, a new degree of freedom is added to the supply voltage and clock frequency variation, increasing the complexity of the power optimization problem. In this paper, a method is proposed to manage the power consumed in an FD-SOI circuit through supply and body bias voltages, and clock frequency variation. Results for a Digital Signal Processor in STMicroelectronics 28nm FD-SOI technology show that the power reduction ratio can reach 17%.

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 services 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.

Dynamic Power Management and Dynamic Voltage and Frequency Scaling have been investigated during the last decades to reduce the power consumption of electronic circuits and systems. This paper proposes a new implementation of dynamic frequency scaling to manage the power consumption based on the occupancy level of a FIFO in a communication link between two components. Based on control theory, the method is simple and application independent. The PI controller proposed here was first tested in the MAT LAB environment and then designed using VHDL. Simulations in the MATLAB environment and in ModelSim have allowed the validation of the control technique proposed. Being synthesized in technology FD-SOI 28nm using RC synthesis tool from Cadence, the control method presents only 10.8% of Silicon area overhead with 1.6% of power consumption reduction. This preliminary result is appealing because it has been obtained without voltage scaling that will improve the power gain even more.

This paper provides an overview of the architecture for self-organizing, co-operative and robust Building Automation Systems (BAS) proposed by the EC funded FP7 SCUBA1project. We describe the current situation in monitoring and control systems and outline the typical stakeholders involved in the case of building automation systems. We derive seven typical use cases which will be demonstrated and evaluated on pilot sites. From these use cases the project designed an architecture relying on six main modules that realize the design, commissioning and operation of self-organizing, co-operative, robust BAS.

There is a strong push towards smart buildings that aim to achieve comfort, safety and energy efficiency, through building automation systems (BAS) that incorporate multiple subsystems such as heating and air-conditioning, lighting, access control etc. The design, commissioning and operation of BAS is already challenging when handling an individual subsystem; however when introducing co-operation between systems the complexity increases dramatically. Balancing the contradictory requirements of comfort, safety and energy efficiency and coping with the dynamics of constantly changing environmental conditions, usage patterns, user needs etc. is a demanding task. This paper outlines an approach to the systematic engineering of cooperating, adaptive building automation systems, which aims to formalize the engineering approach in the form of an integrated tool chain that supports the building stakeholders to produce site-specific robust and reliable building automation.

Building Automation usually involves a large number of systems that should cooperate in order to improve e.g. the user comfort, security, but also decrease the overall energy consumption. One aim of the EU funded SCUBA project is to improve the coordination among devices and systems installed in a building. This paper deals with the extension of a framework dedicated to Building Automation and built on top of the LINC resource-based middleware. Two particular tools developed in SCUBA and that make use of the middleware are also presented together with the encapsulation of the ontology-based Building Automation System model named BASOnt.

All mobile applications require high performances with very long battery life. The speed and power consumption trade-off clearly appears as a prominent challenge to optimize the overall energy efficiency. In Multiprocessor System-On-Chip architectures, the trade-off is usually achieved by dynamically adapting the supply voltage and the operating frequency of a processor cluster or of each processor at fine grain. This requires monitoring accurately, on-chip and at runtime, the supply voltage and temperature across the die. Within this context, this paper introduces a method to estimate, from on-chip measurements, using embedded statistical tests, the supply voltage and temperature of small die area using low-cost digital sensors featuring a set of ring oscillators solely. The results obtained, considering a 32nm process, demonstrate the efficiency of the proposed method. Indeed, voltage and temperature measurement errors are kept, in average, below 5mV and 7°C, respectively.

Power management is a hot-topic in complex System-on-Chip (SoC) designs. In the context of advanced technologies, Dynamic Voltage-Frequency Scaling (DVFS) techniques are widely proposed to improve efficiency. Nowadays, these mechanisms are composed of independent actuators controlling the applied voltage and clock frequency. A predefined sequence has to be used to switch from one state to another in order to avoid timing faults but increasing the energy cost. The timing of the sequence depends on the dynamic response of actuators. In this work, an external controller is designed in order to couple both actuators to manage the voltage and frequency transient periods, increasing power efficiency. The proposed controller has been implemented to couple a Vdd-hopping mechanism with a Frequency-Lock Loop circuit.

Building Automation Systems (BAS) aim at providing control of indoor environment while for instance decreasing energy consumption. Most of these BAS exist and work in stand-alone configuration but the global objective for the whole building could be properly reached if the various BAS were able to cooperate. Thus, an abstraction layer is required to overcome this heterogeneity drawback. The objective of this paper is to describe a resource-based middleware that provides coordination capabilities and ensure the main properties required by the Building Automation (BA) domain. Then a dedicated framework is presented together with its exemplification scenario.

The Impulse Radio - Ultra Wideband (IR-UWB) technology, which benefits from unprecedented ranging capabilities through precise Time of Arrival (TOA) estimation, is now widely considered as a credible solution for Location and Tracking (LT) applications in Wireless Sensor Networks (WSN). In this context, low-complexity receiver architectures such as Energy Detectors (ED) have been favored for the past years. Recent methods have also been proposed to determine ED thresholds for optimal leading-edge detection under simplified channel assumptions. Nevertheless, more realistic indoor multipath profiles still make threshold-based TOA estimation challenging with such receivers. In this paper, we present two new Bayesian TOA estimators that rely on the overall energy profile available at the output of the ED, as well as on realistic channel statistics (typically that of the IEEE 802.15.4a standard). Simulation results are provided for performance comparison with two popular threshold-based estimators, showing the robustness of the new methods at practical Signal to Noise Ratio (SNR) values.

Popular linearized estimators such as the Extended Kalman Filter (EKF) or the one-step linearized Least Squares (LS) estimator, which have been used for years to solve positioning and tracking problems in location-enabled wireless networks, require that available observations are locally linearized (as functions of estimated variables) around the predicted state or initial optimization guess. In most of practical cases however, using measured radiolocation metrics as direct observations (e.g. Time of Arrival (TOA) or Time Differential of Arrival (TDOA)) might lead to poor linearization conditions and hence alter estimation precision and robustness accordingly. In this paper, we present an optimization-based method that enables to adaptively build quasi-linear observations as combinations of raw measurements. The main idea is to choose the coefficients of the linear combinations that minimize the Mean Square Error (MSE) of location estimators, while taking into account both bias errors due to linearization and measurement noise effects.

A few years ago, IEEE 802.15.4a channel models t were initially introduced to assess the performance of Low Data Rate (LDR) Impulse Radio - Ultra Wideband (IR-UWB) peer-to-peer communications in a variety of indoor and outdoor environments, including Line of Sight (LOS) and Non Line of Sight (NLOS) propagation configurations. However, recent r wireless applications such as indoor target tracking or environ- i ment characterization necessitate simulating realistic location-dependent channel profile variability under node mobility. As an alternative to computationally demanding and/or proprietary deterministic prediction tools (e.g. Ray-Tracing), we propose in this paper a self-contained UWB channel model that preserves the statistical features of static IEEE 802.15.4a models as well as physical consistency (e.g. vanishing or appearing paths) over large-scale trajectories, while accounting for the spatial correlation of multipath components.

Reliable range-based tracking using ultra-wideband (UWB) radio signals is traditionally problematic in indoor environments. It is indeed widely admitted that non line-of-sight (NLOS) channel configurations introduce significant biases on measured temporal metrics such as times of arrival (TOA), and hence, alter position estimates accordingly. In order to enhance the tracking performance, we propose a specific extended Kalman filter (EKF) formulation, which enables to estimate as state variables the NLOS biases affecting the measured distances between the mobile station (MS) and the fixed base stations (BSs). The described solution is mainly based on the modeling of the deterministic angular-dependent bias variation experienced with MS mobility. It is shown that this approach is robust to harmful situations where all the links between the MS and the BSs simultaneously suffer from NLOS. Simulation results are provided for performance assessment in a few canonical scenarios.

A quaternion based feedback is developed for attitude stabilization of rigid body. The control design takes into account the input bounds and is based on cascaded saturation approach. The global stability is guaranteed. A simulation study of the proposed scheme is illustrated for the four-rotor helicopter.