An Efficient Algorithm in Computing Optimal Data Concentrator Unit Location in IEEE 802.15.4g AMI Networks

With a view to achieve several goals in the smart grid (SG) such as making the production and delivery of electricity more cost-effective as well as providing consumers with available information which assists them in controlling their cost, the advanced metering infrastructure (AMI) system has been playing a major role to realize such goals. The AMI network, as an essential infrastructure, typically creates a two-way communication network between electricity consumers and the electric service provider for collecting of the big data generated from consumer’s smart meters (SM). Specifically, there is a crucial element called a data concentrator unit (DCU) employed to collect the boundless data from smart meters before disseminating to meter data management system (MDMS) in the AMI systems. Hence, the location of DCU has significantly impacted the quality of service (QoS) of AMI network, in particular the average throughput and delay. This work aims at developing an efficient algorithm in determining the minimum number of DCUs and computing their optimum locations in which smart meters can communicate through good quality wireless links in the AMI network by employing the IEEE 802.15.4g with unslotted CSMA/CA channel access mechanism. Firstly, the optimization algorithm computes the DCU location based on a minimum hop count metric. Nevertheless, it is possible that multiple positions achieving the minimum hop count may be found; therefore, the additional performance metric, i.e. the average throughput and delay, will be utilized to select the ultimately optimal location. In this paper, the maximum throughput with the acceptable averaged delay constraint is proposed by considering the behavior of the AMI meters, which is almost stationary in the AMI network. In our experiment, the algorithm is demonstrated in different scenarios with different densities of SM, including urban, suburban, and rural areas. The simulation results illustrate that the smart meter density and the environment have substantially impacted on a decision for DCU location, and the proposed methodology is significantly effective. Furthermore, the QoS in urban area, i.e. a highly populated area for SM, of the AMI network is better than those in the suburban and rural areas, where the SM density is quite sparse, because multiple available hops and routes created by neighboring meters in the dense area can help improve the average throughput and delay with the minimum hop count

Object Localization and Tracking in 3D

The field of Computer Vision has repeatedly been recognized as an intellectual frontier whose boundaries of applicability are yet to be stipulated. The work attempts to demonstrate that vision can achieve an automatic localization and tracking of targets in a 3D space. Localization of targets has gained importance in the recent past due to the myriad of applications it plays a significant role in. It is analogous to detection of objects in a video sequence in the image processing domain. This work aims to localize a target based on range measurements obtained using a network of sensors scattered in the 3D continuum. To this end, the use of the biologically inspired particle swarm optimization(PSO) algorithm is motivated. In this context, a novel modification of PSO algorithm is proposed that leads to faster convergence, and eliminates the ip ambiguity encountered by coplanar sensors. The initial results over several simulation runs highlight the accuracy and speed of the proposed approach

Noise-sensing energy-harvesting wireless sensor network nodes

Noise pollution is becoming an increasing concern in many urban regions all over the world. An important step in fighting and mitigating noise pollution is its quantification. Wireless sensor networks (WSNs) can potentially help with these efforts, as they enable the simultaneous and continuous gathering of data over wide geographic regions. The need to replace batteries however makes the maintenance of such physically very large networks impractical. As an alternative to batteries, noise-sensing WSNs could also be powered by energy harvesting. While energy-harvesting WSNs have been demonstrated before, utilizing energy harvesting for powering noise-sensing WSNs still pose a significant challenge because of application’s unique requirements, such as a high power consumption profile for extended periods of time. In this thesis, we address four key areas of research necessary on to make energy-harvesting noise-sensing WSNs possible and, more importantly, practical to use in large-scale settings. The first key area that we address is that of new and emerging energy storage technologies, and how current algorithms and infrastructures must be modified to take advantage of them. The second key area is that of currently-accepted technical requirements, and their assessment on whether they would indeed lead to the attainment of long-term goals. The third key area is that of test methodologies for energy-harvesting designs, and how they should be modified to facilitate validation of results between researchers. The final key area is that of techniques and algorithms for future capabilities that energy-harvesting noise-sending WSNs will or can have, and how we should prepare for them, even though they may not yet exist. We provide research to support all four key areas in this work and provide concrete examples for each