Performance Evaluation and Interference Characterization of Wireless Sensor Networks for Complex High-Node Density Scenarios

The uncontainable future development of smart regions, as a set of smart cities’ networks assembled, is directly associated with a growing demand of full interactive and connected ubiquitous smart environments. To achieve this global connection goal, large numbers of transceivers and multiple wireless systems will be involved to provide user services and applications anytime and anyplace, regardless the devices, networks, or systems they use. Adequate, efficient and effective radio wave propagation tools, methodologies, and analyses in complex indoor and outdoor environments are crucially required to prevent communication limitations such as coverage, capacity, speed, or channel interferences due to high-node density or channel restrictions. In this work, radio wave propagation characterization in an urban indoor and outdoor wireless sensor network environment has been assessed, at ISM 2.4 GHz and 5 GHz frequency bands. The selected scenario is an auditorium placed in an open free city area surrounded by inhomogeneous vegetation. User density within the scenario, in terms of inherent transceivers density, poses challenges in overall system operation, given by multiple node operation which increases overall interference levels. By means of an in-house developed 3D ray launching (3D-RL) algorithm with hybrid code operation, the impact of variable density wireless sensor network operation is presented, providing coverage/capacity estimations, interference estimation, device level performance and precise characterization of multipath propagation components in terms of received power levels and time domain characteristics. This analysis and the proposed simulation methodology, can lead in an adequate interference characterization extensible to a wide range of scenarios, considering conventional transceivers as well as wearables, which provide suitable information for the overall network performance in crowded indoor and outdoor complex heterogeneous environments.

[1]  Zhou Su,et al.  Interference Cooperation via Distributed Game in 5G Networks , 2019, IEEE Internet of Things Journal.

[2]  Moe Z. Win,et al.  Fundamental Limits of Wideband Localization— Part II: Cooperative Networks , 2010, IEEE Transactions on Information Theory.

[3]  Daesik Hong,et al.  Effect of Unpredictable Interference on MU-MIMO Systems in HetNet , 2018, IEEE Access.

[4]  Francisco Falcone,et al.  Convergence Analysis in Deterministic 3D Ray Launching Radio Channel Estimation in Complex Environments , 2014 .

[5]  John A. Stankovic,et al.  Empath: a continuous remote emotional health monitoring system for depressive illness , 2011, Wireless Health.

[6]  Aamir Mahmood,et al.  Real-Time Interference Identification via Supervised Learning: Embedding Coexistence Awareness in IoT Devices , 2018, IEEE Access.

[7]  George A. Bekey,et al.  Identification of human driver models in car following , 1974 .

[8]  John A. Stankovic,et al.  Research Directions for the Internet of Things , 2014, IEEE Internet of Things Journal.

[9]  George Koutitas,et al.  Multiple Human Effects in Body Area Networks , 2010, IEEE Antennas and Wireless Propagation Letters.

[10]  Dave Evans,et al.  How the Next Evolution of the Internet Is Changing Everything , 2011 .

[11]  Lajos Hanzo,et al.  Expectation–Maximization-Based Passive Localization Relying on Asynchronous Receivers: Centralized Versus Distributed Implementations , 2019, IEEE Transactions on Communications.

[12]  Daesik Hong,et al.  Theoretical Analysis of Interference Effect From Idle Cells in Ultra-Dense Small Cell Networks , 2018, IEEE Access.

[13]  Francisco Falcone,et al.  An accurate UTD extension to a ray-launching algorithm for the analysis of complex indoor radio environments , 2016 .

[14]  Erik Aguirre Gallego,et al.  Evaluation of electromagnetic dosimetry of wireless systems in complex indoor scenarios with human body interaction , 2012 .

[15]  Wei Ni,et al.  Effective Capacity Analysis in Ultra-Dense Wireless Networks With Random Interference , 2018, IEEE Access.

[16]  John C. Batchelor,et al.  Antennas and Propagation for Body-Centric Wireless Communications , 2012 .

[17]  Daesik Hong,et al.  Resource Split Full Duplex to Mitigate Inter-Cell Interference in Ultra-Dense Small Cell Networks , 2018, IEEE Access.

[18]  Ilenia Tinnirello,et al.  Impact of LoRa Imperfect Orthogonality: Analysis of Link-Level Performance , 2018, IEEE Communications Letters.

[19]  Haris Pervaiz,et al.  Radio Resource Management Scheme in NB-IoT Systems , 2018, IEEE Access.

[20]  Francisco Falcone,et al.  Intelligent Vehicle Communication: Deterministic Propagation Prediction in Transportation Systems , 2016, IEEE Vehicular Technology Magazine.

[21]  Per Lynggaard.  Using Machine Learning for Adaptive Interference Suppression in Wireless Sensor Networks , 2018, IEEE Sensors Journal.

[22]  W Wim Zeiler,et al.  Indoor air quality and thermal comfort strategies: the human-in-the-loop approach , 2011 .

[23]  Jianjun Jing,et al.  Distributed ABS-Slot Access in Dense Heterogeneous Networks: A Potential Game Approach With Generalized Interference Model , 2017, IEEE Access.

[24]  Marco Levorato,et al.  Content-Aware Cognitive Interference Control for Urban IoT Systems , 2018, IEEE Transactions on Cognitive Communications and Networking.

[25]  Harpreet S. Dhillon,et al.  Joint Uplink and Downlink Coverage Analysis of Cellular-based RF-powered IoT Network , 2017, IEEE Transactions on Green Communications and Networking.

[26]  P.S. Hall,et al.  Antennas and propagation for body centric wireless communications , 2012, IEEE/ACES International Conference on Wireless Communications and Applied Computational Electromagnetics, 2005..

[27]  Rahim Tafazolli,et al.  Channel Equalization and Interference Analysis for Uplink Narrowband Internet of Things (NB-IoT) , 2017, IEEE Communications Letters.

[28]  Mohsen Guizani,et al.  Smart Cities: A Survey on Data Management, Security, and Enabling Technologies , 2017, IEEE Communications Surveys & Tutorials.

[29]  Alagan Anpalagan,et al.  Planning of Ultra-Dense Wireless Networks , 2017, IEEE Network.

[30]  Antonio Pescapè,et al.  A sleep scheduling approach based on learning automata for WSN partial coverage , 2017, J. Netw. Comput. Appl..

[31]  Moe Z. Win,et al.  Cooperative Network Synchronization: Asymptotic Analysis , 2017, IEEE Transactions on Signal Processing.

[32]  Chenyang Lu,et al.  Real-World Empirical Studies on Multi-Channel Reliability and Spectrum Usage for Home-Area Sensor Networks , 2013, IEEE Transactions on Network and Service Management.

[33]  Francisco Falcone,et al.  Challenges in Wireless System Integration as Enablers for Indoor Context Aware Environments , 2017, Sensors.

[34]  Jianfeng Ma,et al.  Covert Wireless Communications in IoT Systems: Hiding Information in Interference , 2018, IEEE Wireless Communications.

[35]  Arumugam Nallanathan,et al.  Random Access Analysis for Massive IoT Networks Under a New Spatio-Temporal Model: A Stochastic Geometry Approach , 2017, IEEE Transactions on Communications.