Link Quality Estimation of Cross-Technology Communication: The Case with Physical-Level Emulation

Research on cross-technology communication ( CTC ) has made rapid progress in recent years. While the CTC links are complex and dynamic, how to estimate the quality of a CTC link remains an open and challenging problem. Through our observation and study, we find that none of the existing approaches can be applied to estimate the link quality of CTC. Built upon the physical-level emulation, transmission over a CTC link is jointly affected by two factors: the emulation error and the channel distortion. Furthermore, the channel distortion can be modeled and observed through the signal strength and the noise strength. We, in this article, propose a new link metric called C-LQI and a joint link model that simultaneously takes into account the emulation error and the channel distortion in the In-phase and Quadrature ( IQ ) domain. We accurately describe the superimposed impact on the received signal. We further design a lightweight link estimation approach including two different methods to estimate C-LQI and in turn the packet reception rate ( PRR ) over the CTC link. We implement C-LQI and compare it with two representative link estimation approaches. The results demonstrate that C-LQI reduces the relative estimation error by 49.8% and 51.5% compared with s-PRR and EWMA, respectively.

Adaptive Deep Rider LSTM-enabled Objective Functions for RPL routing in IoT applications

This paper presents a proposed Objective Function (OF) design using various routing metrics for improving the performance of IoT applications. The most important idea of the proposed design is the selection of the routing metrics with respect to the application requirements. The various metrics, such as Energy, Distance, Delay, Link quality, Trust (EDDLT) are used for improving the objective function design of the RPL in various IoT applications. Here, the Adaptive Deep rider LSTM is newly employed for the energy prediction where the Adaptive Deep Rider LSTM is devised by the combination of the adaptive theory with the Rider Adam Algorithm (RAA), and the Deep-Long Short Memory (Deep-LSTM). However, the evaluation of the proposed method is carried out energy dissipation, throughput, and delay by achieving a minimum energy dissipation of 0.549, maximum throughput of 1, and a minimum delay of 0.191, respectively.

Communications Technologies for Smart Grid Applications

Smart grid is a modern power grid infrastructure for improved efficiency, reliability, and safety, with smooth integration of renewable and alternative energy sources, through automated control and modern communications technologies. The smart grid offers several advantages over traditional power grids such as reduced operational costs and opening new markets to utility providers, direct communication with customer premises through advanced metering infrastructure, self-healing in case of power drops or outage, providing security against several types of attacks, and preserving power quality by increasing link quality. Typically, a heterogeneous set of networking technologies is found in the smart grid. In this chapter, smart grid communications technologies along with their advantages and disadvantages are explained. Moreover, research challenges and open research issues are provided.

An exploration of organizational readiness factors for Quality 4.0: an intercontinental study and future research directions

Purpose Quality 4.0 incorporates the role of automation and digitization and provides competitive advantage for organizations by enhancing customer experience and increase profitability. The purpose of this study is to critically examine the organizational readiness factors for the successful implementation of Quality 4.0 implementation and assess their importance.Design/methodology/approach This study applies a quantitative research methodology to examine readiness factors of Quality 4.0 in organizations by 147 senior management professionals in various organizations including manufacturing and service companies in America, Asia and Europe participated through an online survey.FindingsThe readiness factors for Quality 4.0 were critically ranked amongst manufacturing and service organizations by senior management professionals from three continents. Five significant reasons for non-adoption of Quality 4.0 were lack of resources, inability to link Quality 4.0 with the corporate strategy and objectives, lack of understanding of benefits, high initial investment and the current quality management strategy and methods are already delivering good results hence unsure of the need for Quality 4.0. The handling of big data in quality management was the most important factor for adopting Quality 4.0, irrespective of the size and nature of the organization. More accuracy and less errors and improved decision-making the factors of adopting Quality 4.0 in service sector were not significant for manufacturing sector. Small and medium-sized enterprises (SMEs) reported that costs and time savings over the long run were not so significant.Practical implications This study is focussed on the significance of pros and cons of adopting Quality 4.0 in organizations. Senior managers in both large and SMEs can benefit immensely from understanding before investing heavily towards implementing Quality 4.0. The importance of identified organizational readiness factors for the successful adoption of Quality 4.0 can be used as indicators to understand how ready an organization is to implement Quality 4.0. The top three readiness factors for the successful adoption of Quality 4.0 were identified as: top management commitment, leadership and organizational culture. Improved understanding of the readiness factors can be highly beneficial to senior quality professionals in both manufacturing and service companies in the journey towards successful implementation of Quality 4.0.Originality/value This is the first empirical study on assessing Quality 4.0 readiness factors at an intercontinental level and therefore serves as a foundation for many future studies. The study provides a theoretical foundation for the Quality 4.0 in terms of organizational readiness for successful adoption and overcoming implementation challenges. During the planning, implementation and progress review of Quality 4.0, review the readiness factors while planning and resourcing a Quality 4.0 implementation strategy to ensure effective performance.

IOT Technology Demonstration in ADNOC Onshore Fields. Real Time Monitoring at a Minimum Cost

In brownfields, controlling well integrity is critical in maintaining production and ensuring safety of the personnel and infrastructures. Equally important is optimizing and allocating production in wells by closely following wellhead upstream pressures (and temperatures). In the current situation, field crews have to move from well to well. This method is time consuming, exposes personnel to driving hazards and potentially dangerous areas. In addition, human reading of manual pressure gauges can result in large discrepancy in the reported values. Together with the low frequency of manual readings, this method does not allow for pro-active well intervention and can result in higher downtime in case of well tripping. Deploying remote monitoring with classical telemetry in fields with limited telecommunication infrastructure is costly and complex. Low Range Wide Area Network (LoRaWAN), a public wireless network technology developed in 2009, changes the situation. It enables low power compact battery sensors with up to 10 km radio range. This performance is sufficient to connect, in one go, most onshore wells without power nor connectivity. This paper describes a pilot project to evaluate the adequacy of this technology in ADNOC Onshore fields. The objective is to assess performance of LoRaWAN deployed Sensors along four metrics: deployment time, deployment cost, Base station radio coverage and data availability. The pilot uses a plug-in ATEX- certified Wireless Pressure and Temperature (P&T) sensors developed by the vendor SRETT, commercial LoRaWAN Base stations, and proprietary software to provide remote access to the data via cloud data storage and web based application. For this pilot, four Base stations were deployed in two giant oil fields collecting data from four well heads each equipped with two sensors (P&T). This combination allowed testing wireless link quality over eight radio paths, some with terrain obstacles between Sensors and Base stations. The complete system was fully tested and validated at the shop prior to field deployment. Performances during the deployment was evaluated, and Sensor behaviors were monitored over a three-month period. In the current environment, maintaining a high HSE standard on aging infrastructure must be made at a controlled cost. LoRaWAN IoT remote monitoring technology is cost effective and efficient to deploy. Once deployed, it will enable preventative safe detection of wells with potential issues, improved accuracy and understanding of production events and lead to a reduction of potential adverse situations thanks to an optimized intervention strategy.

Communication system improvement with control performance based on link quality in wireless sensor actuator networks

<span lang="EN-US">New communication and networking paradigms started with wireless sensor actuator networks (WSANs) to introduce new applications. One of these is the automatic gain control system (AGC). It will enable a high degree of the decentralized and mobile control. In this study, neural networks (NN) with fuzzy logic (one of the techniques of artificial intelligence (AI)) is used to enhance the control performance depending on the link quality. The NN and fuzzy inference system (FIS) with Mamdani’s method used to build a model reference, adaptive controller, for recompensing for delay time packets losses, and improving the reliability of WSAN. Between 88.62% and 99.99%, validation data is obtained for the medium and high conditions of operation with the proposed algorithm. Experimental and simulation results show a promising approach.</span>

Thermal entropy based hesitant fuzzy linguistic term set analysis in energy efficient opportunistic clustering

Limited energy resources and sensor nodes’ adaptability with the surrounding environment play a significant role in the sustainable Wireless Sensor Networks. This paper proposes a novel, dynamic, self-organizing opportunistic clustering using Hesitant Fuzzy Linguistic Term Analysis- based Multi-Criteria Decision Modeling methodology in order to overcome the CH decision making problems and network lifetime bottlenecks. The asynchronous sleep/awake cycle strategy could be exploited to make an opportunistic connection between sensor nodes using opportunistic connection random graph. Every node in the network observe the node gain degree, energy welfare, relative thermal entropy, link connectivity, expected optimal hop, link quality factor etc. to form the criteria for Hesitant Fuzzy Linguistic Term Set. It makes the node to evaluate its current state and make the decision about the required action (‘CH’, ‘CM’ or ‘relay’). The simulation results reveal that our proposed scheme leads to an improvement in network lifetime, packet delivery ratio and overall energy consumption against existing benchmarks.

Content Replication and Placement Schemes for Wireless Mesh Networks

<p>Recently, Wireless Mesh Networks (WMNs) have attracted much of interest from both academia and industry, due to their potential to provide an alternative broadband wireless Internet connectivity. However, due to different reasons such as multi-hop forwarding and the dynamic wireless link characteristics, the performance of current WMNs is rather low when clients are soliciting Web contents. Due to the evolution of advanced mobile computing devices; it is anticipated that the demand for bandwidth-onerous popular content (especially multimedia content) in WMNs will dramatically increase in the coming future. Content replication is a popular approach for outsourcing content on behalf of the origin content provider. This area has been well explored in the context of the wired Internet, but has received comparatively less attention from the research community when it comes to WMNs. There are a number of replica placement algorithms that are specifically designed for the Internet. But they do not consider the special features of wireless networks such as insufficient bandwidth, low server capacity, contention to access the wireless medium, etc. This thesis studies the technical challenges encountered when transforming the traditional model of multi-hop WMNs from an access network into a content network. We advance the thesis that support from packet relaying mesh routers to act as replica servers for popular content such as media streaming, results in significant performance improvement. Such support from infrastructure mesh routers benefits from knowledge of the underlying network topology (i.e., information about the physical connections between network nodes is available at mesh routers). The utilization of cross-layer information from lower layers opens the door to developing efficient replication schemes that account for the specific features of WMNs (e.g., contention between the nodes to access the wireless medium and traffic interference). Moreover, this can benefit from the underutilized resources (e.g., storage and bandwidth) at mesh routers. This utilization enables those infrastructure nodes to participate in content distribution and play the role of replica servers. In this thesis, our main contribution is the design of two lightweight, distributed, and scalable object replication schemes for WMNs. The first scheme follows a hierarchical approach, while the second scheme follows a flat one. The challenge is to replicate content as close as possible to the requesting clients and thus, reduce the access latency per object, while minimizing the number of replicas. The two schemes aim to address the questions of where and how many replicas should be placed in the WMN. In our schemes, we consider the underlying topology joint with link-quality metrics to improve the quality of experience. We show using simulation tests that the schemes significantly enhance the performance of a WMN in terms of reducing the access cost, bandwidth consumption and computation/communication cost.</p>