Andrey Somov

Enabling smart cities through a cognitive management framework for the internet of things

The Internet of Things (IoT) is expected to substantially support sustainable development of future smart cities. This article identifies the main issues that may prevent IoT from playing this crucial role, such as the heterogeneity among connected objects and the unreliable nature of associated services. To solve these issues, a cognitive management framework for IoT is proposed, in which dynamically changing real-world objects are represented in a virtualized environment, and where cognition and proximity are used to select the most relevant objects for the purpose of an application in an intelligent and autonomic way. Part of the framework is instantiated in terms of building blocks and demonstrated through a smart city scenario that horizontally spans several application domains. This preliminary proof of concept reveals the high potential that self-reconfigurable IoT can achieve in the context of smart cities.

Energy-Aware gas sensing using wireless sensor networks

Wireless Sensor Networks (WSN) have recently been applied in various monitoring applications including hazardous gases detection. However, being a major power consumer of a sensor node, off-the-shelf gas sensors significantly constrain its lifetime. In this paper we present a WSN for hazardous gases detection with a special focus on the power consumption of the sensor node. The sensor node is designed on the basis of a planar catalytic sensor with improved power consumption characteristics. The power supply of the node is divided into digital and analogue parts. This is done to guarantee digital remote control of the device even when the analogue power source has already been depleted by the sensing circuit. In addition, we propose a differential gas measurement approach along with specific heating pulses for the sensor to secure substantial energy saving. The resulting average power consumption is 1.45 and 2.64 mW for the gas sensor and the sensor node respectively. With our techniques, the sensor node lifetime improves from 187 days up to 641 days.

Virtualization and Cognitive Management of Real World Objects in the Internet of Things

This paper presents a framework for the virtualization of real world objects and the cognitive management of their virtual counterparts. The framework consists of three levels of functionality and each level comprises cognitive entities that provide the means for self-management and learning, allowing for smart, flexible applications and objects. The presented framework enables the abstraction of the heterogeneity that derives from the vast amount of diverse objects/devices, while enhancing reliability and facilitates the consideration of the views of various users/stakeholders (owners of objects & communication means) for ensuring proper application provision, business integrity and, therefore, maximization of exploitation opportunities. The paper also presents a corresponding prototype that has been developed for the validation of the proposed approach, in a real-life fire detection scenario in a Smart Home.

Combustible gases and early fire detection: an autonomous system for wireless sensor networks

Fires or toxic gas leakages may have grave consequences like significant pecuniary loss or even lead to human victims. In this paper we present an autonomous wireless sensor system for early fire and gas leak detection. The system consists of two modules: a gas sensor module and a power management module. The operation of the gas sensor module is based on the pyrolysis product detection which makes it possible to detect fire before inflammation. In addition, the on board gas sensor can identify the type of leaking gas. A generic energy scavenging module, able to handle both alternating current and direct current based ambient energy sources, provides the power supply for the gas sensor module. The harvested energy is stored in two energy buffers of different kind, and is delivered to the sensor node in accordance to an efficient energy supply switching algorithm. At the end of the paper we demonstrate the experimental results on gas detection, energy consumption evaluation, and show how to ensure the system autonomous operation.

Circuit Design and Power Consumption Analysis of Wireless Gas Sensor Nodes: One-Sensor Versus Two-Sensor Approach

Wireless sensor networks (WSNs) have recently been applied in industrial monitoring applications including hazardous gases detection. As a major power consumer of a node, gas sensors may significantly constrain its lifetime. Hence, the sensing circuit must be carefully designed to optimize performance and retain accuracy. In this paper, we propose for the first time the principle of gas concentration measurement based on a single sensor in a voltage divider circuit, instead of the well-known Wheatstone bridge sensing circuit, which employs two sensors. We discuss the design of a real WSN node for gas sensing and evaluate it with respect to an identical platform that uses the Wheatstone bridge. The proposed approach ensures significant energy savings and helps to avoid zero offset issue. Besides, we employ a more efficient and secure sensor heating profile; it does not damage the sensor and does not make gas sensing dependent on environmental conditions. Experimental results show a 30% reduction in power with respect to the state-of-the-art.

Power consumption reduction in Wireless Sensor Networks through optimal synchronization

Operational lifetime of wireless sensor network (WSN) nodes is crucial in a variety of monitoring applications. As the radio chip is usually the most power-hungry component in small, low-cost WSN devices, battery lifetime can be extended by reducing the duty-cycle of the radio module. In fact, the wireless chip could be switched on just to run the tasks of the considered application, while it could be kept in sleep mode for all the rest of time. Of course, this approach is viable only if the monitoring tasks are scheduled periodically and if the devices are synchronized, namely if they have a common notion of time. Indeed, if nodes were unsynchronized, some of them might wake up when others are still sleeping and some connections could not be established. Since inter-node time synchronization can be maintained within known uncertainty boundaries only by repeatedly adjusting local clocks, synchronization activities can be also scheduled periodically. In this respect, this paper describes an analytical criterion to establish the value of the synchronization period minimizing the average power dissipated by a WSN node. The proposed analysis is validated by means of both simulation and experimental results.

Compact Low Power Wireless Gas Sensor Node With Thermo Compensation for Ubiquitous Deployment

Wireless sensor networks (WSNs) have recently been applied for industrial monitoring, including combustible and flammable gases monitoring. In this work, we present a wireless gas sensor node in which a widely used Wheatstone sensing circuit based on two sensors is exchanged with a single sensor circuit, as well as the associate gas measurement procedure. The core of the measurement procedure is the four-stage heating profile, which enables low power consumption of sensing circuit and thermo compensation adjustment. A thermo compensation algorithm is capable of avoiding the effect of the environmental temperature on the measurements by keeping stable zero-offset within ±1 mV and ensuring low absolute error within 0.1% vol. The thorough design of the sensor node allows it to fit into the 5.5 cm3 packaging, which ensures its true ubiquitous deployment in outdoor and industrial environment.

A methodology for power consumption evaluation of wireless sensor networks

Energy consumption is one of the most constraining requirements for the design and implementation of wireless sensor networks. Simulation tools allow one to significantly decrease the effort and time spent to choose the right solution. Existing simulators provide varying degrees of analysis for communication, application and energy domains. However, they do not provide enough flexibility to estimate the consumed power for a wide range of wireless sensor network (WSN) hardware (HW) platforms. In this paper we present a flexible and extensible simulation framework to estimate power consumption of sensor network applications for arbitrary HW platforms. This framework allows designers of sensor networks to estimate power consumption of the explored HW platform which permits the selection of an optimal HW solution and software (SW) implementation for the desired projects.

Plug4Green: A flexible energy-aware VM manager to fit data centre particularities

To maintain an energy footprint as low as possible, data centres manage their VMs according to conventional and established rules. Each data centre is however made unique due to its hardware and workload specificities. This prevents the ad hoc design of current VM managers from taking these particularities into account to provide additional energy savings. In this paper, we present Plug4Green, an energy-aware VM placement algorithm that can be easily specialized and extended to fit the specificities of the data centres. Plug4Green computes the placement of the VMs and state of the servers depending on a large number of constraints, extracted automatically from SLAs. The flexibility of Plug4Green is achieved by allowing the constraints to be formulated independently from each other but also from the power models. This flexibility is validated through the implementation of 23 SLA constraints and 2 objectives aiming at reducing either the power consumption or the greenhouse gas emissions. On a heterogeneous test bed, Plug4Green specialization to fit the hardware and the workload specificities allowed to reduce the energy consumption and the gas emission by up to 33% and 34%, respectively. Finally, simulations showed that Plug4Green is capable of computing an improved placement for 7500 VMs running on 1500 servers within a minute.

Wireless multi-sensor gas platform for environmental monitoring

Air quality control is a monitoring task of high priority in the scope of Smart City and Smart Home applications. In fact, a number of sensors are required to successfully detect the leaks of various gases and their different concentrations. In this work, we present an autonomous multisensory wireless platform which includes both analog and digital gas sensors for environmental monitoring. The platform includes four measurement circuits with sensors of different types: one for analog catalytic/semiconductor gas sensor, two for analog electrochemical gas sensors and one for a gas sensor with digital data transmission interface. The platform ensures low power consumption and can operate either as a separate monitoring sensor node or as a part of wireless sensor network.