Energy Management in RFID-Sensor Networks: Taxonomy and Challenges

Ubiquitous Computing is foreseen to play an important role for data production and network connectivity in the coming decades. The Internet of Things (IoT) research which has the capability to encapsulate identification potential and sensing capabilities, strives towards the objective of developing seamless, interoperable and securely integrated systems which can be achieved by connecting the Internet with computing devices. This gives way for the evolution of wireless energy harvesting and power transmission using computing devices. Radio Frequency (RF) based Energy Management (EM) has become the backbone for providing energy to wireless integrated systems. The two main techniques for EM in RFID Sensor Networks (RSN) are Energy Harvesting (EH) and Energy Transfer (ET). These techniques enable the dynamic energy level maintenance and optimisation as well as ensuring reliable communication which adheres to the goal of increased network performance and lifetime. In this paper, we present an overview of RSN, its types of integration and relative applications. We then provide the state-of-the-art EM techniques and strategies for RSN from August 2009 till date, thereby reviewing the existing EH and ET mechanisms designed for RSN. The taxonomy on various challenges for EM in RSN has also been articulated for open research directives

Development of Portable Air Quality Index (AQI) and Emergency Vehicles Preemption Prototype Based on Internet of Mobile Things (IoMT)

The technological advancements of the Internet of Things (IoT) in the recent past have facilitated immense progress towards mitigation of environmental pollution through smart transportation systems and solutions. In particular, communication to the commuters about the traffic ahead or occurrences of congestion has been envisioned to play a major role in outsmarting traffic through mobile applications giving rise to the emergence of the Internet of Mobile Things (IoMT). However, the existing mobile applications that serve as traffic reporting solutions still face major issues such as fixed route suggestions, longer delays during busy hours or emergencies, inefficient prompting of road accidents and heavy traffic en route to a particular destination. This research aims at providing solutions for notifying the commuters with updates on the traffic based upon the Air Quality Index (AQI) of the routes towards the destination and also about the approach of emergency vehicles. The cross-platform mobile application in this way enables the user to opt for a route with good air quality so that the more congested routes are avoided thereby mitigating the air pollution induced by road traffic. The experimental testing and validation of the proposed methodology are applied for areas belonging to Greater Kuala Lumpur. The various timings divided according to peak and non-peak hours are experimentally tested for analyzing the parameters of traffic usage and pattern through the mobile application. The outcome of the experiments has showed that when traffic flow is modelled and governed through vehicular emissions and concentrations of air pollutants, nearly 75% of the congested traffic is reduced thereby, giving rise to pollution-free environment as well as mitigation of urban heat island (UHI) effect that is formed through vehicular heat generation and difference in temperatures. On the other hand, the approach of emergency vehicles also prompts the commuters to avoid panic. CCS Concepts • Hardware➝Emerging tools and methodologies. Keywords Air quality index; Air pollution; Road transportation; Internet of Things and traffic congestion. 1. INTRODUCTION In the past few decades, the population of vehicles has been on higher demand. This huge demand for vehicles results in heavy traffic congestion, accidents, pollution and costs millions of dollars for annual fuel consumption. Such drawbacks have led researchers to look for effective solutions to mitigate vehicular traffic congestion. The vehicular network environment is dynamic in nature due to the frequently changing topologies and network configurations. Though there are numerous existing Intelligent Transportation Systems (ITS) techniques comprising of Internet of Things (IoT) and Vehicular Adhoc Networks (VANETs), which enables the users to keep well-informed and well-updated about smarter ways to deal and handle utilization of transport networks, seldom do they provide guarantee for considering nonrecurring congestion as well as means for mitigation of traffic congestion induced air pollution and fuel consumption. Moreover, the long waiting hours of vehicles at signals and traffic jams leads to higher air pollution levels and heat generated from vehicular exhausts cause Urban Heat Island (UHI) effect. The developing countries like Malaysia, still face potential drawbacks such as increased air pollution levels, due to higher vehicle usage rate resulting in adverse health hazards such as respiratory diseases and asthma. In this research, the Air Quality Index (AQI) values obtained using the deployment of real-time AQI measuring Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Modeling Traffic Congestion Based on Air Quality for Greener Environment: An Empirical Study

The primary focus of this paper is to govern traffic congestion on urban road networks based upon a cumulative approach comprising of traffic flow modeling, vehicle emission modeling, and air quality modeling. Based upon the traffic conditions, a simulation model is proposed and further tested for performance metrics, which is relative to three main aspects, namely, the waiting time of the vehicles at the junctions/intersections/signals, the type of pollutant emitted by a vehicle, and the traveling time. The experimental analysis and validation are carried out for different case studies in Malaysia, such as Petaling Jaya, Shah Alam, Mont Kiara, and Jalan Tun Razak. Three different scenarios (morning, afternoon, and evening) are analyzed and tested to explore the traffic usage parameter. The results showed that when traffic is modeled and governed based upon traffic flow, vehicle emission, and air quality index (AQI), nearly 75% of traffic congestion is mitigated, hence making the atmosphere pollution free as well as avoiding Urban Heat Island (UHI) effect due to the heat generated from vehicles. The experimental results are tested, validated, and compared with existing solutions for performance analysis. The proposed model is aimed toward overcoming the major drawbacks of existing approaches, such as single-path suggestions, traffic delay during peak hours/emergencies, non-recurring congestion consideration, congestion avoidance instead of recovering from it, improper reporting of road accidents, and notifications about traffic jam ahead to the users and high vehicle usage rate

Energy management model for RFID sensor networks in internet of things (IoT) contexts / Shaik Shabana Anjum

The Internet of Things (IoT) technology has the capability to encapsulate the identification potential, sensing technology, artificial intelligence, and interconnection of nano - things, ultimately striving towards the objective of developing seamlessly interoperable and securely integrated systems. These integrated communication networks comprise of many interconnected units such as processor, memory, energy storage unit, radio, microcontroller and so on. The energy consumed by these units is very high during communication. Therefore, optimization of this energy consumption is a primary necessity to increase the lifetime of integrated systems. A crucial conduct norm for a sensor network is to avoid network failures and packet drop. One of the other essential requirements is to effectively manage the energy levels of the nodes according to the states of the operation required for an application. This research aims to propose an energy management model with the aim of allowing energy optimization of Radio Frequency (RF)-enabled Sensor Networks (RSN) through Energy Harvesting (EH) and Energy Transfer (ET) techniques. The main aim of this research is twofold – Firstly, to integrate the Wireless Sensor Network (WSN) nodes with Radio Frequency Identification (RFID) technology to enable energy optimization. The focus of this integration is to minimize the burden for the sensor nodes to rely completely on primary energy storage devices such as batteries and capacitors. Currently, these energy storage devices face the drawback of limited lifetime, node failure, energy scarcity, packet loss and poor network performance on the pretext of heavy sensing operations. Therefore, energy harvesting of sensor networks through RF signals is proposed in this research to address the drawback of frequent replacement of batteries, persistent recharge request, dead state of nodes and periodical eradication of batteries. Secondly, this research focuses on mathematical modeling of the RF sensor nodes within the proposed Energy Harvesting RSN (EHRSN) and Energy Transfer RSN (ETRSN) framework where the nodes are characterized using Semi Markov Decision Process (SMDP) and optimal policies are computed for numerically evaluating and analyzing the issue of higher energy consumption. The proposed EH and ET techniques are implemented through simulations and its performance evaluation is carried out in terms of parameters such as throughput, end-to-end delay, latency, network lifetime and residual energy levels. Furthermore, the proposed RSN energy model is validated using real hardware prototype where the results show nearly 80 % of additional energy saving achieved through EH and ET mechanisms of RF-enabled sensors. These proposed mechanisms which are implemented through event trigged approach and enhanced backscattering techniques are further tested and evaluated by comparing it with existing RSN systems in terms of performance and network latency. The proposed system is thereafter applied in IoT context of monitoring air quality levels using temperature, humidity, gas and dust sensors that are energized by RF signals. The empirical values recorded by these sensors that are configured and programmed according to the proposed energy model and energy management techniques are quantified, statistically analyzed and compared with existing systems in use, to validate the efficiency of the proposed system

An Optimal Management Modelling of Energy Harvesting and Transfer for IoT-based RF-enabled Sensor Networks

[EN] A crucial conduct norm for a sensor network is to avoid network failures and packet drop. One of the other essential requirements is to effectively manage the energy levels of the nodes according to the states of the operation required for an application. This paper focuses to propose an energy management model with the aim of allowing energy optimization of Radio Frequency(RF)-enabled Sensor Networks (RSN) during the process of Energy Harvesting (EH) and Energy Transfer (ET) through controlled optimization. Primarily, energy harvesting of sensor networks through RF signals is focussed in this research to address the drawback of frequent replacement of batteries, persistent recharge request, dead state of nodes and periodical eradication of batteries. Secondly, this paper focuses on mathematical modelling of the RF sensor nodes within the proposed Energy Harvesting RSN (EHRSN) and Energy Transfer RSN (ETRSN) framework of Energy Management RSN model (EMRSN) where the nodes are characterized as Semi Markov Decision Process (SMDP) and optimal policies are computed for numerically evaluating and analysing the issue of higher energy consumption. The most optimal state transitions are computed and mathematically formulated based upon stochastic dynamic programming to carry out the numerical analysis. It has been found that through controlled optimization, the sensor networks when energized through RF energy for EH process, the probability of 0.8 or more works best at the lower power level. On the other hand, for ET, the sensors tend to work more when the probability is either 0.8 or more at higher power levels. The results obtained are further employed to program the sensors accordingly in the Internet of Things (IoT) contexts during EH and ET processes to achieve maximum throughput, network lifetime and energy efficiencyThe authors would like to thank the University of Malaya Post-Doctoral Research Fellowship scheme for the required support provided to carry out this research. We would also like to extend our thankful regards to the anonymous reviewers for providing constructive suggestions that aided in improvisation of this manuscript.Anjum, SS.; Noor, R.; Ahmedy, I.; Anisi, MH.; Azzuhri, SR.; Kiah, MLM.; Lloret, J.... (2020). An Optimal Management Modelling of Energy Harvesting and Transfer for IoT-based RF-enabled Sensor Networks. Ad Hoc & Sensor Wireless Networks. 46(1-2):83-112. http://hdl.handle.net/10251/18934883112461-

Preliminary Exploratory Study: Are HEPs Ready for Communication & Multimedia Micro Credentials Adoption in Malaysia?

Micro-credentials are officially recognized records that demonstrate the completion of learning objectives during shorter, less time-consuming educational or training events. They concentrate on validating competency-based knowledge, outcomes, and/or skills using trustworthy assessments and open standards, which can improve graduates' chances of finding employment. An institution or organization may accept microcredential for credit or as an attestation for potential employers. Initially, micro-credentials were first established in online discussion forums and other social media platforms to differentiate average users from advanced users by awarding digital badges to the respondents who completed the necessary assessments and assignments, for the purposes of upskilling, as well as learning new skills. This exploratory research seeks to provide an overview of micro credential adoption among Higher Education Providers in Malaysi