Outage minimization of energy-harvesting wireless sensor network supported by UAV

Due to their adaptability, mobility, and capacity to offer an ideal channel, unmanned aerial vehicles (UAVs) have become a potential option for wireless power transfer and data collection in wireless sensor networks (WSNs). This paper examines energy-constrained WSNs, where data transfer to the data center is facilitated by UAV and sensors rely on radio frequency (RF) energy obtained by a Power Beacon (PB). However, due to energy limitations, sensors can only send data using the harvested energy. We consider a WSN in which the nodes are randomly distributed within a circular area, with the PB placed at the center of the WSN. To evaluate the system performance, we consider the dynamic nature of the wireless channel, which includes factors such as signal reflection, scattering, and diffraction. Through numerical analysis and simulations, the main aim is to identify the optimal system parameters that minimize the outage probability. This analysis provides valuable insights for designing more effective and reliable energy-harvesting WSNs with UAV as data collector. By leveraging UAV in WSNs, system performance can be improved, ensuring data transmission to destination nodes placed at a large distance from the WSN

ITR/SY: a distributed programming infrastructure for integrating smart sensors

Issued as final reportNational Science Foundation (U.S.

Localization in Wireless Sensor Networks

International audienceWith the proliferation of Wireless Sensor Networks (WSN) applications, knowing the node current location have become a crucial requirement. Location awareness enables various applications from object tracking to event monitoring, and also supports core network services such as: routing, topology control, coverage, boundary detection and clustering. Therefore, WSN localization have become an important area that attracted significant research interest. In the most common case, position related parameters are first extracted from the received measurements, and then used in a second step for estimating the position of the tracked node by means of a specific algorithm. From this perspective, this chapter is intended to provide an overview of the major localization techniques, in order to provide the reader with the necessary inputs to quickly understand the state-of-the-art and/or apply these techniques to localization problems such as robot networks. We first review the most common measurement techniques, and study their theoretical accuracy limits in terms of Cramer-Rao lower bounds. Secondly, we classify the main localization algorithms, taking those measurements as input in order to provide an estimated position of the tracked node(s)

Adaptive Sampling with Mobile Sensor Networks

Mobile sensor networks have unique advantages compared with wireless sensor networks. The mobility enables mobile sensors to flexibly reconfigure themselves to meet sensing requirements. In this dissertation, an adaptive sampling method for mobile sensor networks is presented. Based on the consideration of sensing resource constraints, computing abilities, and onboard energy limitations, the adaptive sampling method follows a down sampling scheme, which could reduce the total number of measurements, and lower sampling cost. Compressive sensing is a recently developed down sampling method, using a small number of randomly distributed measurements for signal reconstruction. However, original signals cannot be reconstructed using condensed measurements, as addressed by Shannon Sampling Theory. Measurements have to be processed under a sparse domain, and convex optimization methods should be applied to reconstruct original signals. Restricted isometry property would guarantee signals can be recovered with little information loss. While compressive sensing could effectively lower sampling cost, signal reconstruction is still a great research challenge. Compressive sensing always collects random measurements, whose information amount cannot be determined in prior. If each measurement is optimized as the most informative measurement, the reconstruction performance can perform much better. Based on the above consideration, this dissertation is focusing on an adaptive sampling approach, which could find the most informative measurements in unknown environments and reconstruct original signals. With mobile sensors, measurements are collect sequentially, giving the chance to uniquely optimize each of them. When mobile sensors are about to collect a new measurement from the surrounding environments, existing information is shared among networked sensors so that each sensor would have a global view of the entire environment. Shared information is analyzed under Haar Wavelet domain, under which most nature signals appear sparse, to infer a model of the environments. The most informative measurements can be determined by optimizing model parameters. As a result, all the measurements collected by the mobile sensor network are the most informative measurements given existing information, and a perfect reconstruction would be expected. To present the adaptive sampling method, a series of research issues will be addressed, including measurement evaluation and collection, mobile network establishment, data fusion, sensor motion, signal reconstruction, etc. Two dimensional scalar field will be reconstructed using the method proposed. Both single mobile sensors and mobile sensor networks will be deployed in the environment, and reconstruction performance of both will be compared.In addition, a particular mobile sensor, a quadrotor UAV is developed, so that the adaptive sampling method can be used in three dimensional scenarios

Robot Localization Obtained by Using Inertial Measurements, Computer Vision, and Wireless Ranging

Robots have long been used for completing tasks that are too difficult, dangerous, or distant to be accomplished by humans. In many cases, these robots are highly specialized platforms - often expensive and capable of completing every task related to a mission\u27s objective. An alternative approach is to use multiple platforms, each less capable in terms of number of tasks and thus significantly less complex and less costly. With advancements in embedded computing and wireless communications, multiple such platforms have been shown to work together to accomplish mission objectives. In the extreme, collections of very simple robots have demonstrated emergent behavior akin to that seen in nature (e.g., bee colonies) motivating the moniker of \u27\u27swarm robotics\u27\u27 - a group of robots working collaboratively to accomplish a task. The use of robotic swarms offers the potential to solve complex tasks more efficiently than a single robot by introducing robustness and flexibility to the system. This work investigates localization in heterogeneous and autonomous robotic swarms to improve their ability to carry out exploratory missions in unknown terrain. Collaboratively, these robots can, for example, conduct sensing and mapping of an environment while simultaneously evolving a communication network. For this application, among many others, it is required to determine an accurate knowledge of the robot\u27s pose (i.e., position and orientation). The act of determining the pose of the robot is known as localization. Some low cost robots can provide location estimates using inertial measurements (i.e., odometry), however this method alone is insufficient due to cumulative errors in sensing. Image tracking and wireless localization methods are implemented in this work to increase the accuracy of localization estimates. These localization methods complement each other: image tracking yields higher accuracy than wireless, however a line-of-sight (LOS) with the target is required; wireless localization can operate under LOS or non-LOS conditions, however has issues in multipath conditions. Together, these methods can be used to improve localization results under all sight conditions. The specific contributions of this work are: (1) a concept of \u27shared sensing\u27 in which extremely simple and inexpensive robots with unreliable localization estimates are used in a heterogeneous swarm of robots in a way that increases the accuracy of localization for the simple agents and simultaneously extends the sensing capabilities of the more complex robots, (2) a description, evaluation, and discussion of various means to estimate a robot\u27s pose, (3) a method for increasing reliability of RSSI measurements for wireless ranging/localization systems by averaging RSSI measurements over both time and space, (4) a process for developing an in-field model to be used for estimating the location of a robot by leveraging the existing wireless communication system

Network tomography application in mobile ad-hoc networks.

The memorability of mobile ad-hoc network (MANET) is the precondition of its management, performance optimization and network resources re-allocations. The traditional network interior measurement technique performs measurement on the nodes or links directly, and obtains the node or link performance through analyzing the measurement sample, which usually is used in the wired networks measurement based on the solid infrastructure. However, MANET is an infrastructure-free, multihop, and self-organized temporary network, comprised of a group of mobile nodes with wireless communication devices. Not only does its topology structure vary with time, but also the communication protocol used in its network layer or data link layer is diverse and non-standard. Specially, with the limitation of node energy and wireless bandwidth, the traditional interior network measurement technique is not suited for the measurement requirement of MANET. In order to solve the problem of interior links performance (such as packet loss rate and delay) measurement in MANET, this dissertation has adopted an external measurement based on network tomography (NT). Being a new measurement technology, NT collects the sample of path performance based on end-to-end measurement to infer the probability distribution of the network logical links performance parameters by using mathematical statistics theory, which neither need any cooperation from internal network, nor dependence from communication protocols, and has the merit of being deployed exibly. Thus from our literature review it can be concluded that Network Tomography technique is adaptable for ad-hoc network measurement. We have the following contribution in the eld of ad-hoc network performance: PLE Algorithm: We developed the PLE algorithm based on EM model, which statistically infer the link performance. Stitching Algorithm: Stitching algorithm is based on the isomorphic properties of a directed graph. The proposed algorithm concatenates the links, which are common over various steady state period and carry forward the ones, which are not. Hence in the process it gives the network performance analysis of the entire network over the observation period. EM routing: EM routing is based on the statistical inference calculated by our PLE algorithm. EM routing provides multiple performance metric such as link delay and hops of all the possible path in various time period in a wireless mesh network