A 3D-collaborative wireless network: towards resilient communication for rescuing flood victims

Every year, floods result in huge damage and devastation both to lives and properties all over the world. Much of this devastation and its prolonged effects result from a lack of collaboration among the rescue agents as a consequence of the lack of reliable and resilient communication platform in the disrupted and damaged environments. In order to counteract this issue, this paper aims to propose a three-dimensional (3D)- collaborative wireless network utilizing air, water and ground based communication infrastructures to support rescue missions in flood-affected areas. Through simulated Search and Rescue(SAR) activities, the effectiveness of the proposed network model is validated and its superiority over the traditional SAR is demonstrated, particularly in the harsh flood environments. The model of the 3D-Collaborative wireless network is expected to significantly assist the rescuing teams in accomplishing their task more effectively in the corresponding disaster areas

L-CAQ: Joint link-oriented channel-availability and channel-quality based channel selection for mobile cognitive radio networks

Channel availability probability (CAP) and channel quality (CQ) are two key metrics that can be used to efficiently design a channel selection strategy in cognitive radio networks. For static scenarios, i.e., where all the users are immobile, the CAP metric depends only on the primary users' activity whereas the CQ metric remains relatively constant. In contrast, for mobile scenarios, the values of both metrics fluctuate not only with time (time-variant) but also over different links between users (link-variant) due to the dynamic variation of primary- and secondary-users' relative positions. As an attempt to address this dynamic fluctuation, this paper proposes L-CAQ: a link-oriented channel-availability and channel-quality based channel selection strategy that aims to maximize the link throughput. The L-CAQ scheme considers accurate estimation of the aforementioned two channel selection metrics, which are governed by the mobility-induced non-stationary network topology, and endeavors to select a channel that jointly maximizes the CAP and CQ. The benefits of the proposed scheme are demonstrated through numerical simulation for mobile cognitive radio networks

Data-driven dynamic clustering framework for mitigating the adverse economic impact of Covid-19 lockdown practices

The COVID-19 disease has once again reiterated the impact of pandemics beyond a biomedical event with potential rapid, dramatic, sweeping disruptions to the management, and conduct of everyday life. Not only the rate and pattern of contagion that threaten our sense of healthy living but also the safety measures put in place for containing the spread of the virus may require social distancing. Three different measures to counteract this pandemic situation have emerged, namely: (i) vaccination, (ii) herd immunity development, and (iii) lockdown. As the first measure is not ready at this stage and the second measure is largely considered unreasonable on the account of the gigantic number of fatalities, a vast majority of countries have practiced the third option despite having a potentially immense adverse economic impact. To mitigate such an impact, this paper proposes a data-driven dynamic clustering framework for moderating the adverse economic impact of COVID-19 flare-up. Through an intelligent fusion of healthcare and simulated mobility data, we model lockdown as a clustering problem and design a dynamic clustering algorithm for localized lockdown by taking into account the pandemic, economic and mobility aspects. We then validate the proposed algorithms by conducting extensive simulations using the Malaysian context as a case study. The findings signify the promises of dynamic clustering for lockdown coverage reduction, reduced economic loss, and military unit deployment reduction, as well as assess potential impact of uncooperative civilians on the contamination rate. The outcome of this work is anticipated to pave a way for significantly reducing the severe economic impact of the COVID-19 spreading. Moreover, the idea can be exploited for potentially the next waves of corona virus-related diseases and other upcoming viral life-threatening calamities

IoT for energy efficient green highway lighting systems: Challenges and issues

The demand of highway lighting system is ubiquitous but its operation contributes to extensive financial cost and concerning environmental implications. For this reason, recent researches have investigated possible solutions to boost the efficiency of the existing lighting system. However, the ultimate guide for green energy enabled smart highway lighting system is still lacking in terms of quality and comprehensiveness. The purpose of this paper is to discuss divergent proceedings in the literature to establish procedures of designing and developing energy efficient green highway lighting system, taking into account performance and environmental impact perspectives. A complete taxonomy is presented to identify and organize the literature into several categories, including fundamental design principles with their advantages, disadvantages and research challenges. This paper also intends to give a possible framework to the readers to bridge the gaps among the existing studies. These findings are anticipated to inform researchers and policymaker on perceiving the benefits of the ameliorated energy efficiency in the highway lighting set-up. Furthermore, open issues identified in this paper will pave the way on achieving future highway lighting systems that are not only facilitating safe and seamless driving experience, but also energy-efficient for environmental sustainability

A connection probability model for communications networks under regional failures

Communications networks are often disrupted by human-initiated and natural disasters such as terrorist bombings, aerial bombardment, earthquakes, floods and volcanic eruptions. Estimating the connection probability of a communications network under a regional failure caused by such an event is essential to evaluating network performance. This paper proposes a probabilistic model called the Connection Probability of a Communications Network under Regional Failures. The proposed model estimates the connection probability of each link that connects a pair of nodes under a failure scenario; these probabilities are then used to compute the generalized connection probability metrics for the network of interest. The model is continuous over its failure region, which is considered to be circular with a certain radius. The maximum failure impact occurs at the epicenter and the impact gradually drops to zero beyond the failure circle. The model helps identify the failed network links and their degrees of failure. A case study involving the U.S. backbone communications network with various failure epicenters and radii demonstrates the applicability and utility of the proposed model

A 3D-collaborative wireless network: towards resilient communication for rescuing flood victims

Every year, floods result in huge damage and devastation both to lives and properties all over the world. Much of this devastation and its prolonged effects result from a lack of collaboration among the rescue agents as a consequence of the lack of reliable and resilient communication platform in the disrupted and damaged environments. In order to counteract this issue, this paper aims to propose a three-dimensional (3D)-collaborative wireless network utilizing air, water and ground based communication infrastructures to support rescue missions in flood-affected areas. Through simulated Search and Rescue(SAR) activities, the effectiveness of the proposed network model is validated and its superiority over the traditional SAR is demonstrated, particularly in the harsh flood environments. The model of the 3D-Collaborative wireless network is expected to significantly assist the rescuing teams in accomplishing their task more effectively in the corresponding disaster areas

L-CAQ: Joint link-oriented channel-availability and channel-quality based channel selection for mobile cognitive radio networks

Channel availability probability (CAP) and channel quality (CQ) are two key metrics that can be used to efficiently design a channel selection strategy in cognitive radio networks. For static scenarios, i.e., where all the users are immobile, the CAP metric depends only on the primary users' activity whereas the CQ metric remains relatively constant. In contrast, for mobile scenarios, the values of both metrics fluctuate not only with time (time-variant) but also over different links between users (link-variant) due to the dynamic variation of primary- and secondary-users' relative positions. As an attempt to address this dynamic fluctuation, this paper proposes L-CAQ: a link-oriented channel-availability and channel-quality based channel selection strategy that aims to maximize the link throughput. The L-CAQ scheme considers accurate estimation of the aforementioned two channel selection metrics, which are governed by the mobility-induced non-stationary network topology, and endeavors to select a channel that jointly maximizes the CAP and CQ. The benefits of the proposed scheme are demonstrated through numerical simulation for mobile cognitive radio networks

IoT based Hybrid Green Energy Driven Highway Lighting System

The worldwide concern to mitigate the soaring energy crisis introduces us to the small-scale renewable energy generation as a preferred enabling source for highway lighting. However, the extensive cost and performance inconsistency of the pure solar panel based solutions further motivate efforts in designing a hybrid energy solution for highway lighting in which Internet of Things (IoT) is envisioned to play a pivotal role in controlling multiple energy sources to provide an effective environment for such a small-scale application context. This paper proposes an IoT-enabled intra-network solution to organize the energy sources for improving the battery performance in a hybrid energy driven highway lighting system. More specifically, we consider the solar panel and Vertical Axis Wind Turbine (VAWT), which utilizes energy from the aerodynamic losses produced by vehicles in the highways, as two main sources for energy generation. This hybrid system allows for generating uninterrupted energy by solar during the day and by VAWT at all day and night times whenever a vehicle passes the lamppost. For maximum effectiveness, a micro-controller is employed in this system to sense the internal requirements for utmost performance. A test-bed prototype is developed to evaluate the performance of the proposed system over a pure solar based lighting system via a projected cost analysis. The result demonstrates withdrawal of solar dependency followed by a less energy requirement in the hybrid lighting system according to different busyness level of the highwa