ABSTRACT
The rapid outbreak of COVID-19 pandemic invoked scientists and researchers to prepare the world for future disasters. During the pandemic, global authorities on healthcare urged the importance of disinfection of objects and surfaces. To implement efficient and safe disinfection services during the pandemic, robots have been utilized for indoor assets. In this paper, we envision the use of drones for disinfection of outdoor assets in hospitals and other facilities. Such heterogeneous assets may have different service demands (e.g., service time, quantity of the disinfectant material etc.), whereas drones have typically limited capacity (i.e., travel time, disinfectant carrying capacity). To serve all the facility assets in an efficient manner, the drone to assets allocation and drone travel routes must be optimized. In this paper, we formulate the capacitated vehicle routing problem (CVRP) to find optimal route for each drone such that the total service time is minimized, while simultaneously the drones meet the demands of each asset allocated to it. The problem is solved using mixed integer programming (MIP). As CVRP is an NP-hard problem, we propose a lightweight heuristic to achieve sub-optimal performance while reducing the time complexity in solving the problem involving a large number of assets. © 2022 IEEE.
ABSTRACT
The COVID-19 pandemic forced upon the world, severe social distancing restrictions, which led to prolonged confinement across populations. The latter directly impacted actors along the supply chain in a variety of industrial sectors (for instance, raw material suppliers, manufacturers, distributors, and customers, among others). Some actors involved had to cease participation altogether due to closures. As a result, the supply chain requires restructuring and its reactivation requires careful consideration. In addition to the pandemic, poor air quality has brought about an environmental crisis in recent years. Primary polluters include greenhouse gas (GHG) emissions caused by manufacturers and distributors. Therefore, this research studies the problem of restructuring a particular multicommodity and hierarchized supply chain. Specifically for companies dealing with situations derived from a reduction in manufacturing capacity and service level in light of the pandemic. In this case, a company (leader) is faced with selecting customers that it will service in pursuit of maximizing profit, all while looking to minimize GHG emissions. The consolidated demand is nearshored once the leader company decides on the customers to be supplied. That is, an order is placed on a company with a lower hierarchy (follower). The follower, in turn, aims to minimize its own manufacturing costs without exceeding the pollution limits imposed by the government. However, its manufacturing plan inevitably pollutes and incurs different costs. In addition, the follower's decisions impact both leader's objective functions. We propose a bi-objective bi-level programming model to study this situation. To solve the problem in reasonable computational time, a heuristic algorithm that takes into account existing asynchrony between leader and follower companies is proposed to approximate the Pareto front. Computational experimentation reveals that the proposed algorithm provides good trade-off solutions, which can reduce GHG emissions by 67% on average without significantly affecting company revenue. Moreover, the algorithm is able to provide solutions for instances of up to 1000 nodes in a competitive computational timeframe. In addition, we discuss the advantages of computing GHG emissions proposed herein. Finally, useful managerial insights are discussed by performing a sensitivity analysis regarding the distribution company's minimum acceptable level of profit. © 2023 Elsevier Ltd
ABSTRACT
Wireless sensor networks (WSNs) are composed of a large number of spatially distributed sensor nodes to monitor and transmit information from the environment. However, the batteries used by these sensor nodes have limited energy and cannot be charged or replaced due to the harsh deployment environment. This energy limitation will seriously affect the lifetime of the network. Therefore, the purpose of this research is to reduce energy consumption and balance the load of sensor nodes by clustering routing protocols, so as to prolong the lifetime of the network. First, the coronavirus herd immune optimizer is improved and used to optimize the network clustering. Second, the cluster heads (CHs) are selected according to the energy and location factors in the clusters, and a reasonable CH replacement mechanism is designed to avoid the extra communication energy consumption caused by the frequent replacement of CHs. Finally, a multihop routing mechanism between the CHs and the base station is constructed by Q-learning. Simulation results show that the proposed work can improve the structure of clusters, enhance the load balance of nodes, reduce network energy consumption, and prolong the network lifetime. The appearance time of the first energy-depleted node is delayed by 25.8%, 85.9%, and 162.2% compared with IGWO, ACA-LEACH, and DEAL in the monitoring area of $300×300 m, respectively. In addition, the proposed protocol shows better adaptability in varying dynamic conditions. © 2001-2012 IEEE.
ABSTRACT
In recent years, some phenomena such as the COVID-19 pandemic have caused the autonomous vehicle (AV) to attract much attention in theoretical and applied research. This paper addresses the optimization problem of a heterogeneous fleet that consists of autonomous electric vehicles (AEVs) and conventional vehicles (CVs) in a Business-to-Consumer (B2C) distribution system. The absence of the driver in AEVs results in the necessity of studying two factors in modeling the problem, namely time windows in the routing plan and different compartments in the loading space of AEVs. The arrival and departure times of the AEV at the customer’s location must be pre-planned, because, the AEV is not able to decide what to do if the customer is late at this point. Also, due to increasing the security of the loads inside the AEVs and the lack of control of the driver during the delivery of the goods, each customer should only have access to his/her orders. Therefore, the compartmentation of the AEV’s loading area has been proposed in its conceptual model. We developed a mathematical model based on these properties and proposed a hybrid algorithm, including variable neighborhood search (VNS) via neighborhood structure of large neighborhood search (LNS), namely the VLNS algorithm. The numerical results shed light on the proficiency of the algorithm in terms of solution time and solution quality. In addition, employing AEVs in the mixed fleet is considered to be desirable based on the operational cost of the fleet. Author
ABSTRACT
The Internet of Things (IoT) has appreciably influenced the technology world in the context of interconnectivity, interoperability, and connectivity using smart objects, connected sensors, devices, data, and appliances. The IoT technology has mainly impacted the global economy, and it extends from industry to different application scenarios, like the healthcare system. This research designed anti-corona virus-Henry gas solubility optimization-based deep maxout network (ACV-HGSO based deep maxout network) for lung cancer detection with medical data in a smart IoT environment. The proposed algorithm ACV-HGSO is designed by incorporating anti-corona virus optimization (ACVO) and Henry gas solubility optimization (HGSO). The nodes simulated in the smart IoT framework can transfer the patient medical information to sink through optimal routing in such a way that the best path is selected using a multi-objective fractional artificial bee colony algorithm with the help of fitness measure. The routing process is deployed for transferring the medical data collected from the nodes to the sink, where detection of disease is done using the proposed method. The noise exists in medical data is removed and processed effectively for increasing the detection performance. The dimension-reduced features are more probable in reducing the complexity issues. The created approach achieves improved testing accuracy, sensitivity, and specificity as 0.910, 0.914, and 0.912, respectively. © 2022 John Wiley & Sons, Ltd.
ABSTRACT
The unprecedented road blockage mostly generates unnecessary hindrance, delay, and interruption during travel. Moreover, the emergence of the sudden third wave through the rapid spread of the omicron CoV-2 variant along with its new combinations with delta SARS-CoV-2 is leading to newer travel restrictions through various hotspots and containment zones contributing to enhanced travel interruption. While traveling, passengers in vehicles are unaware of road conditions during transit time due to blockage in a route. This causes a huge confusion and decision crisis at the edge of such blockage to find the most suitable alternative route. We have developed a software-based system including a mobile application that is capable of handling real-time constraints, transit service, and actual road conditions of a route. This system can be used to find and display alternative routes or maps without any confusion in case of sudden route blockage caused by mass gathering, accidents, or road construction in transit time. During this pandemic COVID-19, this system can also be used to avoid the localized hotspot for a safe and convenient journey. As this system is developed at the time of the pandemic, it is called an Automatic COVID-19 hotspot avoidance navigation system (ACHANS) that generates a unique optimal travel path while traveling to avoid road blockage/COVID-19 hotspot areas. The system works from the user perspective with coordination among the ACHANS Database, map routing server, ACHANS web applications, and ACHANS mobile applications. The process works by creating a buffer-centric radius generation, considering open and closed hotspot regions, and controlling clusters of hotspots. ACHANS database cum alternative roadways bypass system will be independently executed to avoid the localized hotspot for a safe and convenient journey. The proposed system is theoretically, experimentally, and statistically evaluated and verified for various traffic conditions where the performance dictates the efficacy of the scheme and can thereby establish travel and trade during the pandemic. © 2022, Ohmsha, Ltd. and Springer Japan KK, part of Springer Nature.
ABSTRACT
The COVID-19 pandemic calls for contactless deliveries. To prevent the further spread of the disease and ensure the timely delivery of supplies, this paper investigates a collaborative truck-drone routing problem for contactless parcel delivery (CRP-T&D), which allows multiple trucks and multiple drones to deliver parcels cooperatively in epidemic areas. We formulate a mixed-integer programming model that minimizes the delivery time, with the consideration of the energy consumption model of drones. To solve CRP-T&D, we develop an improved variable neighborhood descent (IVND) that combines the Metropolis acceptance criterion of Simulated Annealing (SA) and the tabu list of Tabu Search (TS). Meanwhile, the integration of K-means clustering and Nearest neighbor strategy is applied to generate the initial solution. To evaluate the performance of IVND, experiments are conducted by comparing IVND with VND, SA, TS, variants of VND, and large neighborhood search (LNS) on instances with different scales. Several critical factors are tested to verify the robustness of IVND. Moreover, the experimental results on a practical instance further demonstrate the superior performance of IVND. IEEE
ABSTRACT
The growth of the world's population, especially that of the elderly, along with the outbreak of infectious diseases such as COVID-19 have caused hospitals and healthcare centres to become full, and even economical treatments cost a lot. On that account, the conjunction of wireless body area networks (WBAN) and Internet of Things (IoT) for healthcare and medical diagnosis has become really important, and is accordingly one of the most popular and attractive areas of the Internet of Things (IoT). In such an IoT, a wireless body area network (WBAN) consists of a miniature sample of the Internet of Medical Things (IoMT) that can be either implanted in the human body or wearable. Nowadays, IoT has made healthcare evaluation possible. Instead of the patient being constantly hospitalized for treatment, the condition of the person is sent to the health centre by the IoMT over the Internet. IoT enables wireless communication between smart devices on one side and almost anything on the other. Since this network deals with medical and critical conditions, data must be sent to a physician or practitioner in the prescribed period;this indicates that routing is one of the most critical issues. Thus, routing is considered a very important challenge in WBANs. The present study describes thermal (temperature)-aware routing protocols in WBANs. Routing protocols in WBANs are divided into thermal (temperature)-aware, QoS-aware, security-aware, cluster-based, cross-layered, postured-based, cost-effect, link-aware, and opportunistic ones. In a WBAN, temperature rise in implant nodes can damage body tissues, which is dangerous for the patient. Accordingly, here, those algorithms were considered which are presented in thermal (temperature)-aware protocols. This paper first introduces IoT-based WBANs, their routing mechanism and challenges, after which it provides a detailed description of thermal (temperature)-aware algorithms. Finally, the advantages and disadvantages of these algorithms are presented. © 2022 The Authors. IET Communications published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.
ABSTRACT
The Internet of Things (IoT) has made it possible for health institutions to have remote diagnosis, reliable, preventive, and real-time decision-making. However, the anonymity and privacy of patients are not considered in IoT. Therefore, this article proposes a blockchain-based anonymous system, known as GarliMediChain, for providing anonymity and privacy during COVID-19 information sharing. In GarliMediChain, garlic routing and blockchain are integrated to provide low-latency communication, privacy, anonymity, trust, and security. Also, COVID-19 information is encrypted multiple times before transmitting to a series of nodes in the network. To ensure that COVID-19 information is successfully shared, a blockchain-based coalition system is proposed. The coalition system enables health institutions to share information while maximizing their payoffs. In addition, each institution uses the proposed fictitious play to study the strategies of others in order to update its belief by selecting the best responses from them. Furthermore, simulation results show that the proposed system is resistant to security-related attacks and is robust, efficient, and adaptive. From the results, the proposed proof-of-epidemiology-of-interest consensus protocol has 15.93%less computational cost than 26.30%of proof-of-work and 57.77%proof-of-authority consensus protocol, respectively. Nonetheless, the proposed GarliMediChain system promotes global collaborations by combining existing anonymity and trust solutions with the support of blockchain technology. IEEE
ABSTRACT
Collision-free or contact-free routing through connected networks has been actively studied in the industrial automation and manufacturing context. Contact-free routing of personnel through connected networks (e.g., factories, retail warehouses) may also be required in the COVID-19 context. In this context, we present an optimization framework for identifying routes through a connected network that eliminate or minimize contacts between randomly arriving agents needing to visit a subset of nodes in the network in minimal time. We simulate the agent arrival and network traversal process, and introduce stochasticity in travel speeds, node dwell times, and compliance with assigned routes. We present two optimization formulations for generating optimal routes-no-contact and minimal-contact-on a real-time basis for each agent arriving to the network given the route information of other agents already in the network. We generate results for the time-average number of contacts and normalized time spent in the network. © 2021 IEEE.
ABSTRACT
Virtual Private Networks (VPNs) have now taken an important place in computer and communication networks. A virtual private network is the extension of a private network that encompasses links through shared or public networks, such as the Internet. A VPN is a transmission network service for businesses with two or more remote locations. It offers a range of access speeds and options depending on the needs of each site. This service supports voice, data and video and is fully managed by the service provider, including routing equipment installed at the customer’s premises. According to its characteristics, VPN has widely deployed on ”COVID-19” offering extensive services to connect roaming employees to their corporate networks and have access to all the company information and applications. Hence, VPN focuses on two important issues such as security and Quality-of-Service. This latter has a direct relationship with network performance such as delay, bandwidth, throughput, and jitter. Traditionally, Internet Service Providers (ISPs) accommodate static point-to-point resource demand, named, Layer 1 VPN (L1VPN). The primary disadvantage of L1VPN is that the data plane connectivity does not guarantee control plane connectivity. Layer 2 VPN is designed to provide end-to-end layer 2 connection by transporting layer 2 frames between distributed sites. An L2VPN is suitable for supporting heterogeneous higher-level protocols. In this paper we propose an enhanced routing protocol based on Traffic Split Routing (TSR) and Shortest Path Routing (SPR) algorithms. Simulation results show that our proposed scheme outperforms the Shortest Path Routing (SPR) in term of network resources. Indeed, 72% of network links are used by the Enhanced Traffic Split Routing compared to Shortest Path Routing (SPR) which only used 44% of the network links © 2022,International Journal of Advanced Computer Science and Applications.All Rights Reserved