Relief Networks Research Articles

Coverage Probability and Throughput Optimization in Integrated mmWave and Sub-6 GHz Multi-UAV-Assisted Disaster Relief Networks

Coverage Probability and Throughput Optimization in Integrated mmWave and Sub-6 GHz Multi-UAV-Assisted Disaster Relief Networks

A globalized robust optimization method for sustainable humanitarian relief network design with uncertain scenario probabilities

Disorganized treatment of casualties and irrational distribution of medical supplies pose significant challenges to relief activities, and recent disasters have brought attention to the need for optimizing emergency humanitarian relief networks. This paper proposes a scenario-based multi-objective globalized robust optimization (GRO) model to provide casualty transport schemes and medical supplies distribution strategies. Three optimization objectives in our proposed model are to minimize costs, carbon emissions, and inventory. A case study is based on the earthquake in Kermanshah, Iran. First, we investigate the sensitivity of the proposed model regarding the safe parameter and budget. Second, some comparative experimental results demonstrate that the GRO model exhibits great robustness in terms of obtain optimal solution. Compared with the nominal model, it can withstand some fluctuations caused by uncertainty and significantly reduce the conservatism of the robust model. Furthermore, simulation testing verifies the reliability of the GRO method though inspecting the probability that the simulated samples make the optimized strategies invalid. Finally, some management implications are obtained. This study highlights the importance of the effective relief activities that can balance life, social, economic, and environmental aspects to enable rational responses to emergencies.

Exploring the Functioning of Online Self-Organizations during Public Health Emergencies: Patterns and Mechanism

With the increasing use of social media, online self-organized relief has become a crucial aspect of crisis management during public health emergencies, leading to the emergence of online self-organizations. This study employed the BERT model to classify the replies of Weibo users and used K-means clustering to summarize the patterns of self-organized groups and communities. We then combined the findings from pattern discovery and documents from online relief networks to analyze the core components and mechanisms of online self-organizations. Our findings indicate the following: (1) The composition of online self-organized groups follows Pareto's law. (2) Online self-organized communities are mainly composed of sparse and small groups with loose connections, and bot accounts can automatically identify those in need and provide them with helpful information and resources. (3) The core components of the mechanism of online self-organized rescue groups include the initial gathering of groups, the formation of key groups, the generation of collective action, and the establishment of organizational norms. This study suggests that social media can establish an authentication mechanism for online self-organizations, and that authorities should encourage online interactive live streams about public health issues. However, it is important to note that self-organizations are not a panacea for all issues during public health emergencies.

Reliable design of a congested disaster relief network: A two-stage stochastic-robust optimization approach

The preparedness of humanitarian relief networks can be enhanced by pre-positioning resources in strategic locations and using them when disasters strike, a strategy that gives rise to a two-stage planning problem. This paper presents a novel two-stage stochastic-robust optimization approach for integrated planning of pre- and post-disaster positioning and allocation of relief resources, while taking into consideration the uncertainty about demand for relief services and disruptions in the relief facilities and the transportation network. The proposed approach enables planners to effectively use limited historical data and imperfect experts’ opinions to obtain robust solutions while avoiding the over-conservatism of classical robust optimization methods. The objective sought is to minimize the expected total time victims need to receive assistance, including both access time to facilities and waiting/service time in them. Congestion in relief facilities is accounted for by modeling them as queuing systems and penalizing waiting time. A decomposition method based on column-and-constraint generation is implemented to solve the problem, whereas the nonlinear terms corresponding to queuing in the second-stage problem are handled using a direct search procedure. Applicability of the proposed approach is demonstrated through a real case study and the numerical results are analyzed to draw managerial insights.

Food insecurity and the covid pandemic: uneven impacts for food bank systems in Europe.

Over the past few decades, large food banks that collect, warehouse, and redistribute food have become institutionalized across Europe. Although food banks gained increased visibility as important food relief mechanisms during the covid pandemic in 2020 and 2021, the crisis also highlighted their structural weaknesses and the fragility of the charity-based emergency food system. In particular, many European food banks faced higher costs, lower food stocks, uneven food donations, and lower numbers of volunteers and personnel as demand for food relief increased sharply. Also, many food banks lacked personal health and safety equipment for front-line staff and volunteers, many of whom were vulnerable or aged, thus forcing the closure of some charities due to health concerns. Yet, the impact of the pandemic was uneven across the continent as the covid pandemic strengthened some food banks while others were weakened. To explore these dynamics in detail, this paper utilizes in-depth interviews and surveys of key food bank operators in the Netherlands, Norway, and Greece to analyze how and why European food bank systems fared so differently from the pandemic. In short, the findings in this paper reveal how the Norwegian food bank system leveraged its position to increase fundraising and visibility, while the Netherlands food bank system suffered from long-term structural weaknesses, and the Greek food bank system was further embroiled in government tensions that threatened its existence. The preexisting structure of food bank systems, broader political economy, and historical context significantly impacted how food relief networks fared during the pandemic.

A Survey on Security Approaches on PPDR Systems Toward 5G and Beyond

The present survey seeks to contribute to the existing discussion about the changing of current Public Protection and Disaster Relief (PPDR) networks (TETRA, TETRAPOL, P25, and DMR) to commercial cellular networks (4G and 5G). The availability and robustness of these commercial networks are challenging current PPDR networks and creating opportunities for a successful transition of existing systems to commercial cellular networks. Specifically, 5G networks aim to support a massive number of heterogeneous devices while providing ultra-reliable low latency communications, higher mobility, and the capability of customised support of applications via network slicing, thus providing a natural contribution to mission critical systems. This survey details the essential mission requirements of security of PPDR systems, describes the state of the art of the most used technologies and discusses their evolution to 4G and 5G cellular networks. Furthermore, it analyses future research directions that may contribute to a successful transition of current systems to the newer cellular networks.

Disaster Relief Wireless Networks: Challenges and Solutions

Reliable and flexible emergency networks are of paramount essence for disaster relief during or after the event of disasters due to the destruction or lack of terrestrial communication infrastructures. Thanks to fast deployment and flexible mobilities, unmanned aerial vehicles (UAVs) emerge as a promising paradigm to efficiently establish emergency networks and perform immediate disaster relief tasks in affected areas. However, in such UAV-assisted disaster relief networks (UDRNs), the limited onboard batteries and computational capacities of UAVs hinder them from performing computation-intensive missions. Moreover, critical security vulnerabilities arise in data transmission among UAVs owing to the untrusted environment, open communication channels, and unreliable misbehavior tracing. To this end, this article investigates UDRNs based on blockchain and machine learning to achieve secure and efficient data transmission. Specifically, we first present a lightweight blockchain-enabled collaborative aerial-ground networking framework to safeguard data delivery under disasters, where a credit-based delegated proof-of-stake consensus protocol is further devised to enhance consensus efficiency while promoting UAVs' honest behaviors. In addition, by harnessing the idle computing power of ground vehicles (referred to as vehicular fog computing), a novel reinforcement learning-based algorithm is developed to intelligently offload UAVs' computation missions to the moving vehicles in the dynamic environment. Experimental results demonstrate that the proposed framework outperforms the existing approaches, in terms of consensus security, user utility, task latency, and energy consumption. Finally, future research directions in this emerging area are discussed.

Humanitarian relief network assessment using collaborative truck-and-drone system

The increasing number and severity of natural and man-made disasters worldwide has led to calls for more precise and effective humanitarian responses, and the use of humanitarian relief network assessment to reduce disaster uncertainty can play a vital role in the delivery of precise humanitarian operations. In this study, a collaborative truck-and-drone system was developed as a post-disaster assessment tool for use by humanitarian relief networks. The proposed system comprises a drone equipped with a camera that can launch from a truck to collect information from both nodes and links of a post-disaster transportation network. Following drone operation, the truck is used to retrieve and recharge the drone’s battery. To optimize this collaborative truck-and-drone system, we focused on the routing problem with the objective of maximizing the value of information collected from nodes and links within a predefined time limit, a problem made challenging by the need to determine the routes of the truck and drone in an integrated manner. To the best of our knowledge, this study was the first to consider the problem of collaborative truck-and-drone routing optimization with the goal of profit maximization. After formulating the proposed problem as a mixed-integer linear programming (MILP) model, we decomposed the problem structure into a path-based master problem and two sub-problems to allow the use of a column generation (CG) framework to tackle the problem. Numerical experiments were conducted to examine the proposed model and algorithm at various instance sizes that were generated by modifying an existing benchmark, with the results indicating that the proposed algorithm can obtain high-quality solutions with optimality gaps of less than 10% for all terminated instances within predefined time limit. A real-world instance—the Kartal district of Istanbul—was then used to demonstrate the practicality of the proposed model. Finally, the results of the numerical analysis were used to develop managerial insights for application by humanitarian relief agencies.

Delay Analysis in IoT Sensor Networks.

Internet of Things (IoT) devices, particularly those used for sensor networks, are often latency-sensitive devices. The topology of the sensor network largely depends on the overall system application. Various configurations include linear, star, hierarchical and mesh in 2D or 3D deployments. Other applications include underwater communication with high attenuation of radio waves, disaster relief networks, rural networking, environmental monitoring networks, and vehicular networks. These networks all share the same characteristics, including link latency, latency variation (jitter), and tail latency. Achieving a predictable performance is critical for many interactive and latency-sensitive applications. In this paper, a two-stage tandem queuing model is developed to estimate the average end-to-end latency and predict the latency variation in closed forms. This model also provides a feedback mechanism to investigate other major performance metrics, such as utilization, and the optimal number of computing units needed in a single cluster. The model is applied for two classes of networks, namely, Edge Sensor Networks (ESNs) and Data Center Networks (DCNs). While the proposed model is theoretically derived from a queuing-based model, the simulation results of various network topologies and under different traffic conditions prove the accuracy of our model.

An NGO disaster relief network for small and medium-scale natural hazards in China.

As China is one of the world’s most vulnerable countries to small and medium-scale natural hazards (SMNHs), its NGOs have had a great deal of experience in dealing with disaster relief, especially since the 2008 Wenchuan Earthquake; however, there have been few studies that have comprehensively examined China’s NGO disaster relief networks (NDRNs). One Foundation (OF), which is one of the most active public-fund raising foundations in China, has been engaged in developing a nationwide NDRN made up of 20 independently operating provincial networks that are focused on SMNHs. This paper explored the structures, operations and challenges in the representative North-Sichuan network, from which the following key factors for sustainable NDRNs were identified: (1) a combined hierarchal and networked organizational structure; and (2) a coordinated external and internal network operations mechanism that has a specific entry threshold to ensure healthy network expansion, periodic member NGO training to enhance network capacity, and reciprocal and cooperative government-NGO relationships. These findings provide a useful reference for the establishment and development of NDRNs in other countries.

Design and incentive decisions to increase cooperation in humanitarian relief networks

During humanitarian relief operations, designated facilities are established to assist the affected population and distribute relief goods. In settings where the authorities manage the operations, they instruct the population regarding which facility they should visit. However, in times of crises and uncertainty, these instructions are often not followed. In this work, we investigate how the authorities should invest in incentivizing the population to follow their instructions. These decisions need to be combined with those concerning the relief network design. The population’s behavior and level of cooperation are key factors in deciding on the incentive investments.We present a new mathematical model that incorporates decisions regarding which populations to incentivize to follow the local authorities’ instructions. Then, we develop properties that can help the authorities decide on the level of investment in incentives. A numerical study demonstrates that incentives can improve the system’s performance and enable an equitable supply allocation. Furthermore, an investment in a small number of communities is typically sufficient to significantly improve the system’s performance. We also demonstrate that incentives affect relief-network design decisions.

Learn to Coloring: Fast Response to Perturbation in UAV-Assisted Disaster Relief Networks

In this paper, we address the unmanned aerial vehicle (UAV)-assisted disaster relief networks, where UAVs can disseminate the emergency information to those terrestrial users in a multicast manner. Due to the limitation of spectrum resources, multiple UAVs have to reuse the same channel while the co-channel interference management is needed. The dynamic topology structure induced by mobility can be modeled as a dynamic graph in which the existence of an edge (interference relationship) between two vertices (multicast clusters) is dynamically changing. As such, the channel selection problem can be transformed into a dynamic graph coloring problem in which the graph structure evolves in continuous time slots and the previous coloring strategy becomes valid. In this regard, we propose a stochatic learning based algorithm which can converge rapidly. Simulation results demonstrate that our proposed method is fast in response to the perturbations in dynamic environments.

Pipe size sensitivity in pressure relief networks using genetic algorithms

This paper utilizes a stochastic optimization approach using genetic algorithms, for conducting rigorous pipe size sensitivity assessments onto the design of pressure relief networks. By sampling high performance candidates, only the finest options can survive. The pressure relief network system that was investigated in this work was previously reported in literature. The problem is constrained and involves minimizing a cost objective function that evaluates the overall network performance, in which the best pipe size combination should be selected for each segment within the network. The overall goal of this paper was to seek cost-effective designs for the pressure relief piping system by exploring different ranges of pipe diameters that are available for each segment in the network and comparing how the overall design of the system is affected, when the number of pipe size options to select from is varied.

The Unitarian’s Service Committee Marseille Office and the American networks to aid Spanish refugees. (1940-1943)

The Unitarian Service Committee was one of the most important US aid agencies involved in assisting refugees in the World War II context. In the article I analyse the origins of its action in Europe, focusing on a practically unknown aspect which as its intervention in favour of Spanish Republicans who had fled from Spain and the threat of Francoism in 1939. The Unitarian Service Committee (USC) began its operations in the spring of 1940 and an office of the Unitarian Service Committee would be established in Marseilles in 1941. From this office active work was focused mainly on medical help for the camp inmates in the south of France. The USC had an aid program dedicated exclusively to the Spanish refugees. This program was supported by funding from another American organization, the Joint Antifascist Refugee Committee closely linked to socialist and communist circles and whose chairman, Edward Barsky, was a former international Brigadier who had participated in the Spanish Civil War. I will analyse the links between these two organizations and their connections with international relief networks.

Social-Aware UAV-Assisted Mobile Crowd Sensing in Stochastic and Dynamic Environments for Disaster Relief Networks

In this paper, we consider a social-aware unmanned aerial vehicle (UAV) assisted mobile crowd sensing (MCS) system for disaster relief networks, and investigate how to recruit suitable UAVs to perform sensing tasks in stochastic and dynamic environments (both UAVs and tasks arrive stochastically). We formulate the task assignment problem into a dynamic matching problem, and propose a multiple-waitlist based task assignment (MWTA) algorithm to find the stable matching in time-varying environment. We prove that MWTA can achieve the dynamic stability containing the strategy-proofness, efficiency, and envy-freeness. Simulation results demonstrate the performance improvement of our proposed scheme compared with the traditional matching algorithm applied to deterministic matching model in stochastic setting.

Integrating location and network restoration decisions in relief networks under uncertainty

Prepositioning emergency relief items in emergency response facilities before an anticipated disaster is a common strategy to increase the effectiveness of relief distribution. In this paper, we assume that relief distribution activities are hampered due to damaged roads, which can be restored by repair teams using restoration equipment. We propose a two-stage stochastic programming model integrating facility location and network restoration decisions. Our integrated model decides on the location of restoration equipment prior to the disaster in addition to the facility location decisions. Moreover, decisions related to relief item distribution and network restoration are made jointly after the disaster for each disaster scenario. We capture uncertainty in the network availability by incorporating the repair times required to restore the damaged roads. To solve our integrated model efficiently, we develop a sample average approximation method with concentration sets motivated by Rosing and ReVelle’s (1997) Heuristic Concentration. These concentration sets are comprised of promising locations identified by information obtained from disaster scenarios. We limit our solution space in the first stage to concentration sets to reduce the problem size without sacrificing the solution quality significantly. Our computational results show significant improvement in unmet demand and cost measures by integrating location and network restoration models.

Route Selection Based on Connectivity-Delay-Trust in Public Safety Networks

Recently, the mission-critical push-to-talk (MCPTT) system has emerged as the new broadband technology for public safety networks. In this paper, we propose a route selection method based on the connectivity, delay, and trust in the MCPTT system to offer the best connectivity-delay-trust performance. To begin with, the connection probability between a pair of user equipment (UE) has been analyzed with the interference from other MCPTT groups. Then, the timeliness probability is studied to capture the level of confidence for the delay requirement. Moreover, the trust probability is calculated by a beta reputation system to prevent potential attacks from malicious nodes and ensure secure interactions among the forwarding UE. By combining three probabilistic results, we investigate the optimal path route selection method based on the Dijkstra's algorithm to find the path, which can meet the requirement of the MCPTT system. The simulation results validate our models and theoretical analysis. The results provide a useful insight into designing the MCPTT system for 5G public safety and disaster relief networks.

A grounded analysis of participation mechanisms of social rescue organisations in China following the Wenchuan earthquake

Since the Wenchuan earthquake on 12 May, 2008, social organisations involved in emergency management in China have been displaying an increasingly important role. The aim of this paper was to analyse the evolution of the participation of social rescue organisations in China in the past ten years. The research determines some related laws and associated problems. According to a grounded analysis of the characteristics, advantages, and evolution of the participation mechanism of blue sky rescue (BSR) and the rescue team of ramunion (RTR) in social rescues, suggestions are proposed to improve the participation mechanism of social rescue organisations from four aspects: system guarantee, organisational governance, communication and coordination, and social support.1 Moreover, the network management mechanism of social rescue organisations was also studied to improve disaster prevention and relief networks.

A grounded analysis of participation mechanisms of social rescue organisations in China following the Wenchuan earthquake

Since the Wenchuan earthquake on 12 May, 2008, social organisations involved in emergency management in China have been displaying an increasingly important role. The aim of this paper was to analyse the evolution of the participation of social rescue organisations in China in the past ten years. The research determines some related laws and associated problems. According to a grounded analysis of the characteristics, advantages, and evolution of the participation mechanism of blue sky rescue (BSR) and the rescue team of ramunion (RTR) in social rescues, suggestions are proposed to improve the participation mechanism of social rescue organisations from four aspects: system guarantee, organisational governance, communication and coordination, and social support.1 Moreover, the network management mechanism of social rescue organisations was also studied to improve disaster prevention and relief networks.

Robust location-allocation network design for earthquake preparedness

We develop robust models for earthquake preparedness by optimizing the number, location, and capacity of distribution centers (DCs). The goal is to minimize the total social costs, which include setup and initial supplies, as well as the deprivation costs associated with delayed access to supplies. The models incorporate various earthquake magnitude-specific uncertainties, such as facility damage, casualty by severity, and travel time. Examining the concept of social costs in light of an emerging concern in humanitarian logistics - the robustness of relief networks, we model two types of robustness: parameter uncertainty within a scenario and relative regret across scenarios. This unique approach reveals (1) the magnitude of social costs in the aftermath of an earthquake; (2) the hidden risks associated with inaccurate modeling of deprivation costs; and (3) the impact of budgetary constraints. We demonstrate the applicability of our approach via a case study featuring the Northridge region in California, which experienced two of the strongest earthquakes recorded in North America in 1971 and 1994.