Mobile Crowd-sensing Applications: Data Redundancies, Challenges, and Solutions

Conventional data collection methods that use Wireless Sensor Networks (WSNs) suffer from disadvantages such as deployment location limitation, geographical distance, as well as high construction and deployment costs of WSNs. Recently, various efforts have been promoting mobile crowd-sensing (such as a community with people using mobile devices) as a way to collect data based on existing resources. A Mobile Crowd-Sensing System can be considered as a Cyber-Physical System (CPS), because it allows people with mobile devices to collect and supply data to CPSs’ centers. In practical mobile crowd-sensing applications, due to limited budgets for the different expenditure categories in the system, it is necessary to minimize the collection of redundant information to save more resources for the investor. We study the problem of selecting participants in Mobile Crowd-Sensing Systems without redundant information such that the number of users is minimized and the number of records (events) reported by users is maximized, also known as the Participant-Report-Incident Redundant Avoidance (PRIRA) problem. We propose a new approximation algorithm, called the Maximum-Participant-Report Algorithm (MPRA) to solve the PRIRA problem. Through rigorous theoretical analysis and experimentation, we demonstrate that our proposed method performs well within reasonable bounds of computational complexity.

LOGO: an efficient local and global data collection mechanism for remote underwater monitoring

The oceans play an important role in our daily life and they form the lungs of our planet. Subsequently, the world ocean provides so many benefits for humans and the planet including oxygen production, climate regulation, transportation, recreation, food, medicine, economic, etc. However, the oceans suffer nowadays from several challenges ranging from pollution to climate change and destruction of underwater habitat. Hence, the use of remote sensing technologies, like sensor networks and IoT, is becoming essential in order to continuously monitor the wide underwater areas and oceans. Unfortunately, the limited battery power constitutes one of the major challenges and limitations of such technologies. In this paper, we propose an efficient LOcal and GlObal data collection mechanism, called LOGO, that aims to conserve the energy in remote sensing applications. LOGO is based on the cluster scheme and works on two network stages: local and global. The local stage is at the sensor node and aims to reduce its data transmission by eliminating on-period and in-period data redundancies. The global stage is at the autonomous underwater vehicle (AUV) level and aims to minimize the data redundancy among neighboring nodes based on a spatial-temporal node correlation and Kempe’s graph techniques. The simulation results on real underwater data confirm that LOGO mechanism is less energy consumption with high data accuracy than the existing techniques.

Numerical analysis of an infrared gas sensor utilizing an indium-tin-oxide-based plasmonic slot waveguide

Plasmonic waveguides have attracted much attention owing to the associated high field intensity at the metal–dielectric interface and their ability to confine the modes at the nanometer scale. At the same time, they suffer from relatively high propagation loss, which is due to the presence of metal. Several alternative materials have been introduced to replace noble metals, such as transparent conductive oxides (TCOs). A particularly popular TCO is indium tin oxide (ITO), which is compatible with standard microelectromechanical systems (MEMS) technology. In this work, the feasibility of ITO as an alternative plasmonic material is investigated for infrared absorption sensing applications: we numerically design and optimize an ITO-based plasmonic slot waveguide for a wavelength of 4.26 µm, which is the absorption line of CO2. Our optimization is based on a figure of merit (FOM), which is defined as the confinement factor divided by the imaginary part of the effective mode index (i.e., the intrinsic damping of the mode). The obtained optimal FOM is 3.2, which corresponds to 9 µm and 49 % for the propagation length (characterizing the intrinsic damping) and the confinement factor, respectively.