Energy Efficient UAV-Assisted Emergency Communication with Reliable Connectivity and Collision Avoidance

Emergency communication is vital for search and rescue operations following natural disasters. Unmanned Aerial Vehicles (UAVs) can significantly assist emergency communication by agile positioning, maintaining connectivity during rapid motion, and relaying critical disaster-related information to Ground Control Stations (GCS). Designing effective routing protocols for relaying crucial data in UAV networks is challenging due to dynamic topology, rapid mobility, and limited UAV resources. This paper presents a novel energy-constrained routing mechanism that ensures connectivity, inter-UAV collision avoidance, and network restoration post-UAV fragmentation while adapting without a predefined UAV path. The proposed method employs improved Q learning to optimize the next-hop node selection. Considering these factors, the paper proposes a novel, Improved Q-learning-based Multi-hop Routing (IQMR) protocol. Simulation results validate IQMRs adaptability to changing system conditions and superiority over QMR, QTAR, and QFANET in energy efficiency and data throughput. IQMR achieves energy consumption efficiency improvements of 32.27%, 36.35%, and 36.35% over QMR, Q-FANET, and QTAR, along with significantly higher data throughput enhancements of 53.3%, 80.35%, and 93.36% over Q-FANET, QMR, and QTAR.Comment: 13 page

Multipoint Relay Selection based on Stability of Spatial Relation in Mobile Ad hoc Networks

Increasing stability is one of the main objectives in designing routing protocols for Mobile Ad-Hoc Network (MANETS). Various research schemes have been addressed to this challenge and to support it. In fact, some papers have considered modifications to MPRs selection mechanism in OLSR. In this paper, the author proposes a new mechanism to elect stable and sustainable nodes relay between all nodes in MANETs. In this mechanism, a mobility function is used as the main selection criterion based on the calculation of the spatial relation of a node relative to its neighbor. This mechanism is applied in OLSR protocol to choose stable and supportable MPRs nodes. This mechanism significantly finds more stable MPRs and it promises QoS metrics such as lost packets and delay. Simulation results reveals a significant performance gains and it motivates further examinations to develop the mechanism in order to improve the routing protocol requirements. Performances are evaluated based on Random Waypoint model and network simulator ns3

Structural health monitoring of bridges for improving transportation security

Structural health monitoring (SHM) is a promising technology for determining the condition of significant transportation structures objectively for efficient management and preservation of transportation assets. In addition to identifying, locating, and quantifying damage and deterioration due to effects of operation, aging, and natural hazards, the need for taking terrorism-related hazards into account has become evident after 9/11 terrorist attacks. Key transportation facilities like major bridges were identified by Department of Homeland Security (DHS) as possible terrorist targets since their loss or even temporary deficiency could lead to major impacts on economy and mobility. Several governmental, local, and private organizations have been working on identifying possible modes of threats, determining and sorting vulnerable structures, and establishing ways to prevent, detect and respond to such attacks. Authorities are also investigating ways to integrate current and future bridge management systems with security surveillance systems. Highway bridges are key links of the transportation system. This paper reviews security measures for bridges and discuss possible integration of structural health and security monitoring for improving security and safety of bridges and emergency management after a natural or man-made disaster

Towards a 6G embedding sustainability

From its conception, 6G is being designed with a particular focus on sustainability. The general philosophy of the H2020 Hexa-X project work on sustainability in 6G is based on two principles: to reduce direct negative life cycle impacts of 6G systems as much as possible (Sustainable 6G) and to analyze use cases that maximize positive environmental, social, and economic effects in other sectors of society (6G for Sustainability or its enablement effect). To apply this philosophy, Hexa-X is designing 6G with three sustainability objectives in mind: to enable the reduction of emissions in 6G-powered sectors of society, to reduce the total cost of ownership and to improve energy efficiency. This paper describes these objectives, their associated KPIs and quantitative targets, and the levers to reach them. Furthermore, to maximize the positive effects of 6G through the enablement effect, a link between 6G and the United Nations' Sustainable Development Goals (UN SDGs) framework is proposed and illustrated by Hexa-X use case families.Comment: IEEE ICC 2023 Second International Workshop on Green and Sustainable Networking (GreenNet), May 2023, Rome, Ital

Wildfire Monitoring Based on Energy Efficient Clustering Approach for FANETS

Forest fires are a significant threat to the ecological system’s stability. Several attempts have been made to detect forest fires using a variety of approaches, including optical fire sensors, and satellite-based technologies, all of which have been unsuccessful. In today’s world, research on flying ad hoc networks (FANETs) is a thriving field and can be used successfully. This paper describes a unique clustering approach that identifies the presence of a fire zone in a forest and transfers all sensed data to a base station as soon as feasible via wireless communication. The fire department takes the required steps to prevent the spread of the fire. It is proposed in this study that an efficient clustering approach be used to deal with routing and energy challenges to extend the lifetime of an unmanned aerial vehicle (UAV) in case of forest fires. Due to the restricted energy and high mobility, this directly impacts the flying duration and routing of FANET nodes. As a result, it is vital to enhance the lifetime of wireless sensor networks (WSNs) to maintain high system availability. Our proposed algorithm EE-SS regulates the energy usage of nodes while taking into account the features of a disaster region and other factors. For firefighting, sensor nodes are placed throughout the forest zone to collect essential data points for identifying forest fires and dividing them into distinct clusters. All of the sensor nodes in the cluster communicate their packets to the base station continually through the cluster head. When FANET nodes communicate with one another, their transmission range is constantly adjusted to meet their operating requirements. This paper examines the existing clustering techniques for forest fire detection approaches restricted to wireless sensor networks and their limitations. Our newly designed algorithm chooses the most optimum cluster heads (CHs) based on their fitness, reducing the routing overhead and increasing the system’s efficiency. Our proposed method results from simulations are compared with the existing approaches such as LEACH, LEACH-C, PSO-HAS, and SEED. The evaluation is carried out concerning overall energy usage, residual energy, the count of live nodes, the network lifetime, and the time it takes to build a cluster compared to other approaches. As a result, our proposed EE-SS algorithm outperforms all the considered state-of-art algorithms.publishedVersio

Quantifying restoration costs in the aftermath of an extreme event using system dynamics and dynamic mathematical modeling approaches

Extreme events such as earthquakes, hurricanes, and the like, lead to devastating effects that may render multiple supply chain critical infrastructure elements inoperable. The economic losses caused by extreme events continue well after the emergency response phase has ended and are a key factor in determining the best path for post-disaster restoration. It is essential to develop efficient restoration and disaster management strategies to ameliorate the losses from such events. This dissertation extends the existing knowledge base on disaster management and restoration through the creation of models and tools that identify the relationship between production losses and restoration costs. The first research contribution is a system dynamics inoperability model that determines inputs, outputs, and flows for roadway networks. This model can be used to identify the connectivity of road segments and better understand how inoperability contributes to economic consequences. The second contribution is an algorithm that integrates critical infrastructure data derived from bottom-up cost estimation technique as part of an object-oriented software tool that can be used to determine the impact of system disruptions. The third contribution is a dynamic mathematical model that establishes a framework to estimate post-disaster restoration costs from a whole system perspective. Engineering managers, city planners, and policy makers can use the methodologies developed in this research to develop effective disaster planning schemas and to prioritize post-disaster restoration operations --Abstract, page iv