Parameter assignment for improved connectivity and security in randomly deployed wireless sensor networks via hybrid omni/uni-directional antennas

Conguring a network system to operate at optimal levels of performance re-quires a comprehensive understanding of the eects of a variety of system parameterson crucial metrics like connectivity and resilience to network attacks. Traditionally,omni-directional antennas have been used for communication in wireless sensor net-works. In this thesis, a hybrid communication model is presented where-in, nodes ina network are capable of both omni-directional and uni-directional communication.The eect of such a model on performance in randomly deployed wireless sensor net-works is studied, specically looking at the eect of a variety of network parameterson network performance.The work in this thesis demonstrates that, when the hybrid communication modelis employed, the probability of 100% connectivity improves by almost 90% and thatof k-connectivity improves by almost 80% even at low node densities when comparedto the traditional omni-directional model. In terms of network security, it was foundthat the hybrid approach improves network resilience to the collision attack by almost85% and the cost of launching a successful network partition attack was increased byas high as 600%. The gains in connectivity and resilience were found to improve withincreasing node densities and decreasing antenna beamwidths

Array communications in wireless sensor networks

Imperial Users onl

Channel Characterization for Wireless Underground Sensor Networks

Wireless Underground Sensor Networks (WUSNs) are natural extensions of the established Wireless Sensor Network (WSN) phenomenon and consist of sensors buried underground which communicate through soil. WUSNs have the potential to impact a wide variety of applications including precision agriculture, environmental monitoring, border patrol, and infrastructure monitoring. The main difference between WUSNs and traditional wireless networks is the communication medium. However, a comprehensive wireless underground channel model for WUSNs has not been developed so far. In this thesis, the Soil Subsurface Wireless Communication (SSWC) channel model is developed based on an extensive empirical study in a large agriculture field. The results of the experiments provide important insights for the model, which have not been available in the wireless communication literature. The SSWC channel model captures the signal attenuation and bit error rate (BER) in underground settings based on five components: (1) The dielectric soil model estimates the soil permittivity based on soil parameters including soil moisture. (2) The direct wave model captures the attenuation of the line-of-sight signal between sender and receiver. (3) The reflected wave model considers the attenuation on the signal which is reflected at the soil surface before reaching the receiver. (4) The lateral wave model estimates the attenuation of a third front of waves that potentially reach the receiver. Due to the fact that a significant portion of the lateral waves’ propagation occurs over-the-air, this form of transmission is an excellent option to extend the communication range without increasing the power consumption. (5) The signal superposition model captures the phase shifting between the mentioned waves, the resulting attenuation, and the bit error rate. The SSWC model is validated through extensive underground experiments. To the best of our knowledge, this is the first channel model for the underground to underground communication in WUSNs with comprehensive set of features. The SSWC channel model is fundamental for the development of cross-layer communication solutions for WUSNs and for the development of underground to aboveground and aboveground to underground channel models for WUSNs

Power Beacon’s deployment optimization for wirelessly powering massive Internet of Things networks

Abstract. The fifth-generation (5G) and beyond wireless cellular networks promise the native support to, among other use cases, the so-called Internet of Things (IoT). Different from human-based cellular services, IoT networks implement a novel vision where ordinary machines possess the ability to autonomously sense, actuate, compute, and communicate throughout the Internet. However, as the number of connected devices grows larger, an urgent demand for energy-efficient communication technologies arises. A key challenge related to IoT devices is that their very small form factor allows them to carry just a tiny battery that might not be even possible to replace due to installation conditions, or too costly in terms of maintenance because of the massiveness of the network. This issue limits the lifetime of the network and compromises its reliability. Wireless energy transfer (WET) has emerged as a potential candidate to replenish sensors’ batteries or to sustain the operation of battery-free devices, as it provides a controllable source of energy over-the-air. Therefore, WET eliminates the need for regular maintenance, allows sensors’ form factor reduction, and reduces the battery disposal that contributes to the environment pollution. In this thesis, we review some WET-enabled scenarios and state-of-the-art techniques for implementing WET in IoT networks. In particular, we focus our attention on the deployment optimization of the so-called power beacons (PBs), which are the energy transmitters for charging a massive IoT deployment subject to a network-wide probabilistic energy outage constraint. We assume that IoT sensors’ positions are unknown at the PBs, and hence we maximize the average incident power on the worst network location. We propose a linear-time complexity algorithm for optimizing the PBs’ positions that outperforms benchmark methods in terms of minimum average incident power and computation time. Then, we also present some insights on the maximum coverage area under certain propagation conditions