Local Storage Capacity Research Articles

Efficient and secure content-based image retrieval with deep neural networks in the mobile cloud computing

Smart devices offer a variety of more convenient forms to help us record our lives and generate a large amount of data in this process. Limited by the local storage capacity, many users outsource their image data directly to the cloud server. However, images stored in plaintext on the cloud server are very insecure, resulting in image privacy information can be easily leaked. Therefore, users will encrypt the images and outsource them to the cloud server, but the encrypted images cannot be retrieved. Therefore, we proposed a secure and efficient ciphertext image retrieval scheme based on image content retrieval (CBIR) and approximate homomorphic encryption (HE). First, we used approximate homomorphic encryption to encrypt images after resizing and uploaded the ciphertext images to the cloud for feature extraction of ciphertext. At the same time, the large images (size, dimension, and resolution) would generate data inflation after using homomorphic encryption. Therefore, the original images are encrypted using the chaotic image encryption scheme to reduce ciphertext size and computation costs. Second, we proposed two deepening network depth optimization strategies that address the problem of insufficient neural network depth. Finally, reducing the dimensionality of the ciphertext feature vector using locally sensitive hashing (LSH) can accelerate the retrieval of ciphertext images. Compared with other literature, our ciphertext image retrieval scheme can significantly reduce the rounds of user-server communication.

Caught off guard and beaten: The Ukraine war and food security in the Middle East.

The Ukraine war has led to a severe global food crisis due to complex supply disruptions and price increases of agricultural inputs. Countries of the Middle East have been directly affected because of their high dependence on food imports from Russia and Ukraine. Furthermore, this food crisis comes at times of high baseline vulnerability due to the compound impacts of COVID-19, repeated food shocks, and weakened states due to political-economic difficulties. This paper provides a detailed analysis of the food-related vulnerability of Middle Eastern countries in the wake of the Ukraine war. It contextualizes the varying impacts of this crisis in the region, and highlights country-level response strategies. The analysis shows a concerning and deepened crisis in the case of highly exposed and politically fragile countries with weakened food sectors; e.g., Lebanon, Sudan, and Yemen. Political-economic instabilities, limited domestic agriculture, and the lack of reliable grain reserves have aggravated the current food crisis in some countries. At the same time, indigenous short-term responses related to regional aid and cooperation have emerged, particularly in the Gulf countries, which have witnessed soaring revenues from higher energy prices. Alongside more regional frameworks for collaboration on food security, future action to mitigate such food crises should include the strengthening of local sustainable agriculture, storage capacities, and grain procurement strategies from international suppliers.

Mapping and assessing seagrass meadows changes and blue carbon under past, current, and future scenarios

Seagrasses store large amounts of blue carbon and mitigate climate change, but they have suffered strong regressions worldwide in recent decades. Blue carbon assessments may support their conservation. However, existing blue carbon maps are still scarce and focused on certain seagrass species, such as the iconic genus Posidonia, and intertidal and very shallow seagrasses (<10 m depth), while deep-water and opportunistic seagrasses have remained understudied. This study filled this gap by mapping and assessing blue carbon storage and sequestration by the seagrass Cymodocea nodosa in the Canarian archipelago using the local carbon storage capacity and high spatial resolution (20 m/pixel) seagrass distribution maps for the years 2000 and 2018. Particularly, we mapped and assessed the past, current and future capacity of C. nodosa to store blue carbon, according to four plausible future scenarios, and valued the economic implications of these scenarios. Our results showed that C. nodosa has suffered ca. 50 % area loss in the last two decades, and, if the current degradation rate continues, our estimations demonstrate that it could completely disappear in 2036 (“Collapse scenario”). The impact of these losses in 2050 would reach 1.43 MT of CO2 equivalent emitted with a cost of 126.3 million € (0.32 % of the current Canary GDP). If, however, this degradation is slow down, between 0.11 and 0.57 MT of CO2 equivalent would be emitted until 2050 (“Intermediate” and “Business-as-usual” scenarios, respectively), which corresponds to a social cost of 3.63 and 44.81 million €, respectively. If the current seagrass extension is maintained (“No Net Loss”), 0.75 MT of CO2 equivalent would be sequestered from now to 2050, which corresponds to a social cost saving of 73.59 million €. The reproducibility of our methodology across coastal ecosystems underpinned by marine vegetation provides a key tool for decision-making and conservation of these habitats.

Blockchain-Based Secure Data Storage Protocol for Sensors in the Industrial Internet of Things

The Industrial Internet of Things (IIoT) that introduces Internet of Things (IoT) technology into industrial environments is beneficial to construct smart factories. It utilizes various sensors to collect the data of industrial devices. These data are analyzed to improve the manufacturing efficiency and product quality. Cloud storage provides a solution for storing data outsourced, especially for sensors that have limited local storage and computational capacity. To ensure the privacy preserving of devices, the collected data should be stored in the formal ciphertext. Therefore, encrypted data sharing should be implemented to analyze the devices’ data. In this article, the cloud storage solution for sensors is considered. To achieve a secure and efficient data storage and sharing, a novel group signature scheme, which has less computation overhead and communication overhead, is designed to realize anonymous authentication first. And then, a novel blockchain-based cloud storage protocol for sensors in IIoT is constructed on basis of the proposed group signature scheme. Smart contract and proxy re-encryption are utilized in this protocol to realize secure data sharing with a less computational overhead. Furthermore, security proofs and performance evaluations demonstrate that this protocol is secure, privacy-preserving, and has at least 40% and 20% performance improvement in data storage and sharing phase, respectively.

An Interactive Web Application Helps Students Explore Water Balance Concepts

The concept of a water balance is a foundational topic in hydrology classrooms. While understanding and applying this concept is crucial to the introduction of more advanced topics, students often struggle to develop a thorough understanding of the relationships between components, assumptions, and limitations of a water balance. To aid students in developing a working understanding of a water balance, we developed a web application that runs a one dimensional Thornthwaite-type water balance at any of thousands of NOAA climate stations across the continental United States using the local soil-water storage capacity at the station location. Within the app, students can manipulate the soil-water storage capacity, latitude, temperature, and precipitation to better understand how it works and explore scenarios of land use, extreme weather, and climate change. The application is free and will run on any device that can open an internet browser window (laptops, chromebooks, smartphones, etc). Here we present the details of the model, functionality of the application, and link to several ready-made classroom activities. Finally, results from student surveys in two hydrology classrooms show that students may learn water balance concepts more effectively than traditional methods such as spreadsheet computations.

Dynamic discretization discovery for solving the Continuous Time Inventory Routing Problem with Out-and-Back Routes

In time dependent models, the objective is to find the optimal times (continuous) at which activities occur and resources are utilized. These models arise whenever a schedule of activities needs to be constructed. A common approach consists of discretizing the planning time and then restricting the decisions to those time points. However, this approach leads to very large formulations that are intractable in practice. In this work, we study the Continuous Time Inventory Routing Problem with Out-and-Back Routes (CIRP-OB). In this problem, a company manages the inventory of its customers, resupplying a single product from a single facility during a finite time horizon. The product is consumed at a constant rate (product per unit of time) by each customer. The customers have local storage capacity. The goal is to find the minimum cost delivery plan with out-and-back routes only that ensures that none of the customers run out of product during the planning period. We study the Dynamic Discovery Discretization algorithm (DDD) to solve the CIRP-OB by using partially constructed time-expanded networks. This method iteratively discovers time points needed in the network to find optimal continuous time solutions. We test this method by using randomly generated instances in which we find provable optimal solutions.

A Renewable Energy Community of DC Nanogrids for Providing Balancing Services

The massive expansion of Distributed Energy Resources and schedulable loads have forced a variation of generation, transmission, and final usage of electricity towards the paradigm of Smart Communities microgrids and of Renewable Energy Communities. In the paper, the use of multiple DC microgrids for residential applications, i.e., the nanogrids, in order to compose and create a renewable energy community, is hypothesized. The DC Bus Signaling distributed control strategy for the power management of each individual nanogrid is applied to satisfy the power flow requests sent from an aggregator. It is important to underline that this is an adaptive control strategy, i.e., it is used when the nanogrid provides a service to the aggregator and when not. In addition, the value of the DC bus voltage of each nanogrid is communicated to the aggregator. In this way, the aggregator is aware of the regulation capacity that each nanogrid can provide and which flexible resources are used to provide this capacity. The effectiveness of the proposed control strategy is demonstrated via numerical experiments. The energy community considered in the paper consists of five nanogrids, interfaced to a common ML-LV substation. The nanogrids, equipped with a photovoltaic plant and a set of lithium-ion batteries, participate in the balancing service depending on its local generation and storage capacity.

The urban water metabolism of Cape Town: Towards becoming a water sensitive city

To improve its resilience to increasing climatic uncertainty, the City of Cape Town (the City) aims to become a water sensitive city by 2040. To undertake this challenge, a means to measure progress is needed that quantifies the urban water systems at a scale that enables a whole-of-system approach to water management. Using an urban water metabolism framework, we (1) provide a first city-scale quantification of the urban water cycle integrating its natural and anthropogenic flows, and (2) assess alternative water sources (indicated in the New Water Programme) and whether they support the City towards becoming water sensitive. We employ a spatially explicit method with particular consideration to apply this analysis to other African or Global South cities. At the time of study, centralised potable water demand by the City amounted to 325 gigalitres per annum, 99% of which was supplied externally from surface storage, and the remaining ~1% internally from groundwater storage (Atlantis aquifer). Within the City’s boundary, runoff, wastewater effluent and groundwater represent significant internal resources which could, in theory, improve supply efficiency and internalisation as well as hydrological performance. For the practical use of alternative resources throughout the urban landscape, spatially explicit insight is required regarding the seasonality of runoff, local groundwater storage capacity and the quality of water as it is conveyed through the complex urban landscape. We suggest further research to develop metrics of urban water resilience and equity, both of which are important in a Global South context.&#x0D; Significance:&#x0D; &#x0D; This research provides the initial groundwork of quantifying the magnitude of the urban water cycle of the City of Cape Town at an annual timescale, in relation to becoming a water sensitive city. The urban water metabolism framework used in this study provides important insight to assess whole-of-system urban water dynamics and to benchmark progress towards becoming water sensitive. By quantifying the magnitude of flows into and out of the urban system, this research sheds light on the opportunities to improve circularity in the urban water cycle. The spatial approach adopted here provides a platform to interrogate the urban landscape and its role in the urban water cycle. By using data products that are available via national data sets or remote sensing, this approach can be applied to other African or Global South where data is characteristically scarce. Further work is required to establish metrics that can adequately describe urban water resilience and equity.&#x0D;

Dynamic Aggregation of Energy Storage Systems Into Virtual Power Plants Using Distributed Real-Time Clustering Algorithm

Energy storage systems are widely used for compensation of intermittent renewable energy sources and restoration of system frequency and voltage. In a conventional operation, all distributed energy storage systems are clustered into one fixed virtual power plant and their state of charges are maintained at a common value. In this article, it is proposed to dynamically cluster the energy storage systems into several virtual power plants based on the energy storage systems' power demands and capacities. This results in reduced network power losses. The proposed dynamic clustering algorithm enables to cluster agents (energy storage systems) based on their preselected feature states (local power demands and energy storage capacities). To determine the clusters, the distance of the agents' current feature states from the average estimates of the states is determined in all clusters. The algorithm also provides average estimates of designated auxiliary states that can be used for control purposes. Presented RTDS-based real-time implementation results verify that clustering energy storage systems (batteries) into dynamic virtual power plants can reduce the network power losses.

SecACS: Enabling lightweight secure auditable cloud storage with data dynamics

With the popularity of cloud computing and IoT, consistently increasing data brings us a great burden to store locally. Due to the local limited storage capacity and expensive maintenance cost, individuals and enterprises are preferred to outsource their data to the public commercial cloud (e.g., Google Drive and Dropbox). However, the commercial cloud can not be fully trusted because data on the cloud may be modified by various reasons (e.g., outside hackers’ attacks, hardware or software failures). To ensure data integrity, one of the most popular techniques is secure cloud storage (SCS). Nevertheless, traditional solutions of SCS with heavy cryptographic operations make data outsourcing computationally complicated. Moreover, most proposed approaches only focus on verifying data integrity while neglecting the functionality of data dynamics. To address these issues, we propose a Secure Auditable Cloud Storage (SecACS) scheme supporting data dynamics. SecACS requires less computation time by employing lightweight cryptographic operations. Compared to the previous solutions, the extensive experimental results demonstrate that SecACS achieves one order of magnitude faster in outsourcing data and twice faster in checking data integrity.

DEPLEST: A blockchain-based privacy-preserving distributed database toward user behaviors in social networks

Social networks record a significant amount of user behavior data every day. By analyzing this behavior data, companies or attackers use it for marketing or more questionable purposes. We propose a blockchain-based model to protect the privacy of users’ data in such big data environments. Traditional blockchain methods require too many resources for this task, so we propose a model that secures sensitive user information in a distributed blockchain and passes nonsensitive information through to the primary system in order to manage the blockchain size. Our DEPLEST algorithm performs these synchronization operations to keep local database storage and computational capacity within the limits of individual users’ devices. We also propose a consensus protocol for blockchain ledger maintenance that runs well on typical client systems and prove that this protocol has excellent Byzantine fault tolerance (BFT). Our experimental results show that DEPLEST meets the architectural and performance needs and that our consensus protocol outperforms the existing proof of work (PoW) and proof of stake (PoS) methods in this application.

Resources and potential of agri-food products with added value in Braničevo-Podunavlje region

The paper explores the agricultural resources of the Branicevo-Podunavlje region and opportunities of agrifood products development with added value. In particular, the aim is to evaluate the following resources and potentials of the Region: the workforce and its knowledge and skills, used agricultural land, on-farm value-adding activities, local traditional food products, agricultural buildings and storage capacities and knowledge as well as innovation transfer in agriculture. In the research was used spatial and sectoral analysis of agricultural resources and potential for achieving more reliable answers to key questions that arise in the context of the analysis of valueadded agri-food products in Branicevo-Podunavlje region. The research's results show that the use of comparative advantages and traditions, which the BP region has in the field of agricultural production presupposes the transformation of domestic agriculture and all forms of business entities in this activity. In the coming period, emphasis must be placed on the development of agri-food products with added value, which is focused on meeting the needs and wishes of consumers, with an emphasis on innovation, quality, high level of food hygiene and food safety standards.

Scalable multi-agent microgrid negotiations for a transactive energy market

Distributed energy resources are becoming increasingly common and forcing change in conventional energy markets with growing attention given to transactive energy networks that allow power trading between neighboring microgrids or distributed energy resources customers to supplement transactions with an electric utility. This study develops and evaluates a generalizable method for managing energy trading between microgrids in a grid-connected network through multi-agent techniques. The approach is demonstrated for a 3-node network and a 9-node network for a simulated year with hourly load and solar data for each unique microgrid agent. Results are compared against baseline networks without trading enabled to quantify a 3.6% and 5.4% reduction in the levelized cost of energy, respectively, with trading enabled for the 3-node and 9-node cases. Local energy storage capacities are varied to examine impact on the levelized cost of energy and trading behaviors. Results indicate that trading between microgrids reduces the levelized cost of energy for each individual node and the whole network, and that certain trends emerge between agents that allow some microgrids to operate at a lower cost than others.

Understanding the Impacts of Maritime Disruption Transportation to Hospital-Based Acute Health Care Supplies and Personnel in Coastal and Geographically Isolated Communities.

This study aimed to identify maritime transportation disruption impacts on available health care supplies and workers necessary to deliver hospital-based acute health care in geographically isolated communities post-disaster. Semi-structured interviews were conducted with 25 key informants knowledgeable about the hospital-based acute health care supply chain and workforce emergency management plans and procedures in 2 coastal communities in British Columbia. These locations were accessed primarily through maritime transportation, including one urban center and one smaller, more remote community. Interview transcriptions were thematically analyzed. Critical vulnerabilities to hospital-based acute health care delivery due to a maritime transportation disruption identified include lack of information about the existing supply chain, lack of formal plans and agreements, and limited local supply storage and workforce capacity. Measures to decrease vulnerability and enhance system capacity can be fostered to enhance acute health care system resilience for these and other geographically isolated communities. A maritime transportation disruption has the potential to impact the availability of hospital-based health care supplies and health care personnel necessary to deliver acute health care in coastal communities post-disaster. Multisector engagement is required to address complex interdependencies and competing priorities in emergency response. Additional research and public-private collaboration is necessary to quantify potential impacts of maritime transportation disruption on the acute health care system. (Disaster Med Public Health Preparedness. 2019;13:440-448).

Cooperative video caching scheme over software defined passive optical network

Localized video caching over passive optical network (PON), which means to cache videos on inner PON nodes (IPNs), i.e., optical line terminal (OLT), remote node (RN) and optical network units (ONUs), is now considered as an efficient scheme to provide better-quality video services for end users. However, given the limited local storage capacity of IPNs, the cooperation among IPNs is becoming critical for conducting localized video caching. In this paper, considering that caching videos on the RN will break the passive characteristic of PON, we study the problem of cooperative video caching (CVC) at the OLT-layer and the ONU-layer over GPON with a focus on maximizing the benefits introduced by localized video caching, under the finite local resources. We firstly design software defined GPON architectures to pave the way for efficiently implementing CVC, then we specifically study the CVC problem under two scenarios, one is that the cooperation can only be realized between the OLT and ONUs due to architecture constraint, the other is that the cooperation among all ONUs can also be enabled. In the first scenario, after proving the NP-hardness of the CVC problem, we derive a benefits upper bound for a special case with homogeneous required resources of videos of the problem, and then propose an efficient heuristic algorithm to solve the problem with general form. In the second scenario, given that the CVC problem is more complicated than that in the first scenario, we also design an effective heuristic algorithm to solve it. Simulation results validate the efficiency of the proposed methods.

Low-complexity prediction of frequency-rich biosignals for lossless compression in wearable technologies.

Wearable technologies that store, monitor and analyse a range of biosignals are an area of significant growth and interest for both industry and academia. The rate of data generation in these devices poses a considerable challenge with regards to the bandwidths of wireless transmission protocols, local storage capacities and the on-board power consumption requirements. This issue is particularly acute for frequency-rich biosignals containing significant higher frequency components that are un-served by conventional compression techniques. This paper proposes a low-complexity predictor, based on a low-order infinite impulse response bandpass filter, to accurately predict such biosignals for use in lossless compression. Experimental evaluation of the method demonstrates that it outperforms conventional predictors with an average 25 % reduction in predictor residual standard deviation. The predictor described here enables high-bandwidth wearable sensors that can be employed in systems with reduced power consumption for transmission, storage and compression leading to considerable improvements in user experience by reducing device mass and increasing battery life.

Joint optimization of regional water-power systems

Energy and water resources systems are tightly coupled; energy is needed to deliver water and water is needed to extract or produce energy. Growing pressure on these resources has raised concerns about their long-term management and highlights the need to develop integrated solutions. A method for joint optimization of water and electric power systems was developed in order to identify methodologies to assess the broader interactions between water and energy systems. The proposed method is to include water users and power producers into an economic optimization problem that minimizes the cost of power production and maximizes the benefits of water allocation, subject to constraints from the power and hydrological systems. The method was tested on the Iberian Peninsula using simplified models of the seven major river basins and the power market. The optimization problem was successfully solved using stochastic dual dynamic programming. The results showed that current water allocation to hydropower producers in basins with high irrigation productivity, and to irrigation users in basins with high hydropower productivity was sub-optimal. Optimal allocation was achieved by managing reservoirs in very distinct ways, according to the local inflow, storage capacity, hydropower productivity, and irrigation demand and productivity. This highlights the importance of appropriately representing the water users’ spatial distribution and marginal benefits and costs when allocating water resources optimally. The method can handle further spatial disaggregation and can be extended to include other aspects of the water-energy nexus.

Cell-specific accumulation patterns of gentamicin in the guinea pig cochlea

Intratympanic gentamicin therapy has become a popular treatment modality for Ménière's disease (MD) through controlled elimination of vertigo spells caused by the balance organ. However, the known ototoxic properties of aminoglycosides lead to cochlear damage. In order to gain more information about cellular preferences for aminoglycoside accumulation within the cochlea, gentamicin was immuno histochemically localized by light microscopy in male guinea pigs 1 and 7 days after intratympanic application (n = 8 ears/incubation time). Differences in the gentamicin-specific cellular storage capacities were quantified by determination of the local immuno staining intensities. Gentamicin was detected in every cochlear cell type, but with spatiotemporal variability. One day after application, an intense staining reaction was found in all cell types except the spiral ganglion cells and the stria vascularis. Six days later, gentamicin staining intensities were additionally reduced in the nerve fibers and the spiral ligament. Statistic analysis revealed strong cellular associations in respect to aminoglycoside accumulation. Furthermore, associations with recorded hearing losses were identified comparing the cellular gentamicin content in the organ of Corti, in the stria vascularis, in the spiral ganglion cells and in fibrocytes of the Limbus. In the lateral wall, clear differences in cellular gentamicin accumulation were found between type I fibrocytes of the spiral ligament compared with basal and intermediate cells of the stria vascularis. This finding was unexpected as these three cell types belong to a well-developed gap-junction system which normally enables unhampered cell communication. Cellular differences in local gentamicin storage capacities, transport processes and inherent diffusion barriers are discussed.

Efficient certificateless web-of-trust model for public-key authentication in MANET

Establishing trust among participating nodes in Mobile Ad-hoc NETworks (MANET) is a very challenging task due to the fact that these systems generally operate in highly dynamic and fully distributed environments. In particular, we note the absence of any online trusted authority. A PGP-like trust model is considered to be very suitable for the unique characteristics of MANETs since it does not require access to a trusted authority. Instead, the nodes themselves are allowed to establish trust by certifying each other in a self-organizing way. However, in large-scale networks, this model may impose high costs due to the expensive certificate chaining overheads which does not fit with Ad-hoc applications with high resource constrained nodes. In this paper, we propose a new certificateless PGP-like trust establishment scheme for MANET. In our approach, we adopt a different strategy by using self-certifying ID-based cryptography. The resulting model allows mobile nodes to determine public-keys of each other from only the nodes’ identities and their trust levels. The proposed approach reduces significantly the communication and computation costs and requires minimal local storage capacity even in large-scale networks. Furthermore, we define a trust metric which contributes in improving the reliability of a decision about the authenticity of a public-key. The performance and security analysis of our scheme demonstrate its efficiency compared to traditional PGP-like schemes.

Questioning the impact and sustainability of percolation tanks as aquifer recharge structures in semi-arid crystalline context

Since the beginning of the green revolution in the 1970s, large areas of India have been strongly dependent on groundwater for irrigation which has led to aquifer overexploitation in various parts of the country. To tackle this problem, Indian authorities rely on managed aquifer recharge (MAR). Perennial water resources in semi-arid regions of South India consist mostly of crystalline aquifers. Despite limited and only local storage capacity and high potential evaporation loss, MAR is promoted through percolation tanks. To assess the efficiency of MAR, a monitoring program and a water balance were developed for a percolation tank in Andhra Pradesh, India. The developed water balance shows that the tank has a limited local impact and only a few events contribute to tank replenishment. Enhanced infiltration (10,100 m3) is of the same order of magnitude as evaporation (8,000 m3). Potential benefits of the tank system depend on the observation scale and rainfall conditions. Generally, tanks have a local positive impact in their immediate surroundings. However, in the case of low rainfall, if there is no stream water flowing out of the watershed, the tank merely redistributes the water within the watershed. Inversely, during years with high precipitation, there may be a net benefit in terms of underground storage. Regarding the hydrological impact assessment of percolation tanks, it is necessary to take into account (1) the net “nonmanaged” water balance at watershed scale compared to “managed” water balance, (2) the inter-annual rainfall variations and (3) the impact for downstream users.