Service Requests Research Articles

‘We thought we would be welcomed with open arms’: Holocaust refugees in Dutch Caribbean internment

ABSTRACT The trajectories and experiences of more than a hundred Jewish refugees, who arrived in the Dutch colony of Suriname in December 1942, shed light on the complex connections and tensions between colonial hierarchies, Jewish heritage and antisemitism. Furthermore, De Jong's article highlights the often-overlooked role of the Dutch Caribbean during the Second World War, particularly in providing refuge to Holocaust survivors. The article first delves into the broader colonial context and the sociopolitical dynamics of wartime Suriname. The refugees, initially interned and subjected to restrictive regulations, struggled with their diminished status and the obligatory domestic chores, especially in this colonial setting. De Jong also touches on the complex interplay of racial hierarchies in the colony and antisemitism, shedding light on the refugees’ position within this new environment. Second, De Jong examines intra-Jewish tensions in Suriname through two case studies of conflict: the first surrounding a Purim celebration and the second concerning a request for a separate Ashkenazi service during the High Holidays. These conflicts reveal the challenges faced by the refugees and the established Jewish community in negotiating cultural and religious identities. The disputes were significant but eventually resolved, demonstrating the resilience and flexibility of both groups. The study provides a multifaceted understanding of the refugees’ experiences in Suriname by integrating traditional archival documents with personal narratives and oral histories. It underscores the Dutch Caribbean as a significant site of refuge and Jewish interaction during the Second World War, contributing to broader discussions on colonial spaces in Holocaust Studies

Mathematical modeling of the platform assignment problem in a ride-sourcing market with a third-party integrator

In the rapidly evolving ride-sourcing market, emerging third-party integrators offer passengers the convenience of accessing services from multiple platforms simultaneously. This innovative business model allows a third-party integrator to send ride service requests from passengers to multiple ride-sourcing platforms, ultimately selecting the most desirable one from the ones that respond. In this study, we address the integrator’s platform assignment problem in a ride-sourcing market, taking into account the stochastic nature of passenger waiting times influenced by factors such as the random locations of idle vehicles. Our findings reveal that a third-party integrator, using minimum waiting time as a criterion for platform assignment, can effectively reduce passengers’ generalized trip costs and increase both passenger demand and social welfare under specific moderate conditions. Furthermore, we demonstrate that when the waiting time on each platform follows the Weibull distribution with the scale parameter linearly dependent on the number of idle vehicles, the passenger waiting time with an integrator follows the same distribution as merging all platforms into one. Additionally, the probability of a platform being assigned to passengers equals the proportion of its idle vehicles across the entire integrated market under this distribution type. Moreover, we extend the proposed integrator platform assignment model to a scenario where the integrator makes assignments among selected options for passengers. Numerical examples demonstrate that when platforms have identical fares, platforms with smaller fleet sizes are more likely to benefit from integration, while those with larger fleet sizes may experience losses. We further demonstrate that platform competition for trip fares decreases passenger demand and social welfare if passengers cannot choose platform options. However, in cases where passengers have the freedom to select platform options, platform competition increases passenger demand and social welfare.

An edge server deployment method based on optimal benefit and genetic algorithm

With the speedy advancement and accelerated popularization of 5G networks, the provision and request of services through mobile smart terminals have become a hot topic in the development of mobile service computing. In this scenario, an efficient and reasonable edge server deployment solution can effectively reduce the deployment cost and communication latency of mobile smart terminals, while significantly improving investment efficiency and resource utilization. Focusing on the issue of edge server placement in mobile service computing environment, this paper proposes an edge server deployment method based on optimal benefit quantity and genetic algorithm. This method is firstly, based on a channel selection strategy for optimal communication impact benefits, it calculates the quantity of edge servers which can achieve optimal benefit. Then, the issue of edge server deployment is converted to a dual-objective optimization problem under three constraints to find the best locations to deploy edge servers, according to balancing the workload of edge servers and minimizing the communication delay among clients and edge servers. Finally, the genetic algorithm is utilized to iteratively optimize for finding the optimal resolution of edge server deployment. A series of experiments are performed on the Mobile Communication Base Station Data Set of Shanghai Telecom, and the experimental results verify that beneath the limit of the optimal benefit quantity of edge servers, the proposed method outperforms MIP, K-means, ESPHA, Top-K, and Random in terms of effectively reducing communication delays and balancing workloads.

The Markov Concept of the Energy Efficiency Assessment of the Edge Computing Infrastructure Peripheral Server Functioning over Time

The article is devoted to the research of the peripheral server energy consumption managing process defined based on the threshold policy by manipulating the values of the characteristic parameters of the arithmetic-logical complex of the latter. The research object is formalized by a Markov queue model with a single-threshold control scheme for the intensity of accepted requests service. A characteristic feature of the life cycle of a peripheral server is the non-stationary mode of operation in terms of energy consumption, due to the need to periodically poll the controlled sensor network and process the received data in real-time. To take into account this circumstance, the intensities of transitions in the heterogeneous birth-and-death Markov process of the created model are interpreted as non-random periodic functions of time. The resulting mathematical apparatus is summarized in the metric that allows us to estimate the target peripheral server’s energy consumption both at a specific moment and for a censored time interval (this distinguishes the obtained result from existing analogs).

Revving up VNDN: Efficient caching and forwarding by expanding content popularity perspective and mobility

Network caching in Vehicular Named Data Networks (VNDN) has the potential to support latency-sensitive services, though given the massive amount of content generated by vehicles in a VNDN, it is challenging to cache sufficiently diverse content across the network for an acceptable hit ratio. Redundant interest transmissions can negatively impact network overhead, wasting network resources when retrieving duplicate content. In the literature, a caching decision is typically based on ongoing popularity, computed from a vehicle’s experience with requests received for individual content. This leads to each vehicle caching the most popular content. Instead, we group content into different categories. All content in a popular category of vehicular service (e.g., tourist information category) is cached equally, not just the most popular content within a category (e.g., information relating to a particular museum). This increases content diversity in the cache. Further, we posit that a service requester from a particular location at a specific time would be interested in the same category of service/application as other users from the same location/time. Leveraging this observation, we consider both forthcoming spatial/temporal and ongoing popularity of vehicular services when ranking their popularity. We also address network overhead with a mobility model that captures the communication duration between vehicles. This parameter, which has been overlooked in existing literature, enables the selection of an appropriate forwarder for content requests. We use ndnSIM VNDN with a mobility trace of Luxembourg city for simulation. Compared to an approach where individual content-wise popularity is computed, our scheme shows more than 100% improvement in cache hit ratio where the number of contents in the network varied between 104 and 105. Simultaneously, we also enhanced the diversity of content caching, while considering the trade-off between cache hit ratio and network overhead.

PPVC: Towards a Personalized Local Differential Privacy-Preserving Scheme for V2G Charging Networks

The rapid development of electric vehicles provides users with convenience of life. When users enjoy the V2G charging service, privacy leakage of their charging location is a crucial security issue. Existing privacy-preserving algorithms for EV access to charging locations suffer from the problem of nondefendable background knowledge attacks and privacy attacks by untrustworthy third parties. We propose a personalized location privacy protection scheme (PPVC) based on differential privacy to meet users’ personalized EV charging requirements while protecting their privacy. First, by constructing a decision matrix, PPVC describes recommended routes’ utility and privacy effects. Then, a utility model is constructed based on the multiattribute theory. The user’s privacy preferences are integrated into the model to provide the route with the best utility. Finally, considering the privacy preference needs of users, the Euclidean distance share is used to assign appropriate privacy budgets to users and determine the generation range of false locations to generate the service request location with the highest utility. The experimental results show that the proposed personalized location privacy protection scheme can meet the service demands of users while reasonably protecting their privacy to provide higher service quality. Compared with existing solutions, PPVC improves the charging efficiency by up to 25%, and 8% at the same privacy protection level.

Optimizing QoS Metrics for Software-Defined Networking in Federated Learning

In the modern and complex realm of networking, the pursuit of ideal QoS metrics is a fundamental objective aimed at maximizing network efficiency and user experiences. Nonetheless, the accomplishment of this task is hindered by the diversity of networks, the unpredictability of network conditions, and the rapid growth of multimedia traffic. This manuscript presents an innovative method for enhancing the QoS in SDN by combining the load-balancing capabilities of FL and genetic algorithms. The proposed solution aims to improve the dispersed aggregation of multimedia traffic by prioritizing data privacy and ensuring secure network load distribution. By using federated learning, multiple clients can collectively participate in the training process of a global model without compromising the privacy of their sensitive information. This method safeguards user privacy while facilitating the aggregation of distributed multimedia traffic. In addition, genetic algorithms are used to optimize network load balancing, thereby ensuring the efficient use of network resources and mitigating the risk of individual node overload. As a result of extensive testing, this research has demonstrated significant improvements in QoS measurements compared to traditional methods. Our proposed technique outperforms existing techniques such as RR, weighted RR, server load, LBBSRT, and dynamic server approaches in terms of CPU and memory utilization, as well as server requests across three testing servers. This novel methodology has applications in multiple industries, including telecommunications, multimedia streaming, and cloud computing. The proposed method presents an innovative strategy for addressing the optimization of QoS metrics in SDN environments, while preserving data privacy and optimizing network resource usage.

Radiation Planning Assistant - A Web-based Tool to Support High-quality Radiotherapy in Clinics with Limited Resources.

Access to radiotherapy worldwide is limited. The Radiation Planning Assistant (RPA) is a fully automated, web-based tool that is being developed to offer fully automated radiotherapy treatment planning tools to clinics with limited resources. The goal is to help clinical teams scale their efforts, thus reaching more patients with cancer. The user connects to the RPA via a webpage, completes a Service Request (prescription and information about the radiotherapy targets), and uploads the patient's CT image set. The RPA offers two approaches to automated planning. In one-step planning, the system uses the Service Request and CT scan to automatically generate the necessary contours and treatment plan. In two-step planning, the user reviews and edits the automatically generated contours before the RPA continues to generate a volume-modulated arc therapy plan. The final plan is downloaded from the RPA website and imported into the user's local treatment planning system, where the dose is recalculated for the locally commissioned linac; if necessary, the plan is edited prior to approval for clinical use.

Aplikasi Pendokumentasian Layanan Balai Desa Muara Kintap Berbasis Web

The administrative and service division is in charge of handling and administering services that can be provided to the community at the Muara Kintap village hall. However, the public service data recording system is still manual by recording it in a book. This requires a lot of paper and of course requires a lot of budget and there is a risk of data loss, for example in the event of a fire, the book is tucked away, lost and so on. In this study, the authors built a system entitled Web-Based Muara Kintap Village Hall Service Documentation Application. This system aims to assist employees in recording community service data in a computerized manner, storing data safely using a database so that the data search process will also be easier. This system helps the public in submitting service requests easily online. The community can access the system, see what types of services are available and can submit requests for services by filling out form data through the system without having to come to the Muara Kintap village hall. The system development model uses the waterfall model. This system was built using a web-based software development approach using a MySQL database. The designs used are Entity Relationship Diagram (ERD) and Unified Modeling Language (UML). The programming language used is Hypertext Preprocessor (PHP) and the framework used is codeigniter 3. System testing using blackbox testing shows that all system functionality can run well and as expected by 100%.

A Privacy-Preserving Service Framework for Traveling Salesman Problem-Based Neural Combinatorial Optimization Network

The traveling salesman problem (TSP) is one of the classic combinatorial optimization problems, which can be widely used in intelligent transportation and logistics field. Neural network has shown great potential in combinatorial optimization tasks. However, it faces privacy leakage when a TSP neural combinatorial optimization network and user's data are directly outsourced to a cloud platform to provide and request service. In order to address the issue, this paper proposes a privacy-preserving service framework for a TSP-based neural combinatorial optimization network called PPSF. We first protect the service provider's model parameters and users' data by different split methods in multiple cloud servers, providing a secure outsourced mode for the participators in the PPSF framework. Then, in the secure outsourced mode, a series of secure computation protocols are designed for the cloud servers to support performing the secure computing in each service task. Moreover, it can also protect the process that the cloud servers respond to users after data processing and achieve private service result recovery. Finally, we prove that the proposed framework can realize privacy protection for TSP-based combinatorial optimization service and verify its utility and efficiency by experiments.

SD-SRF: An Intelligent Service Deployment Scheme for Serverless-operated Cloud-Edge Computing in 6G Networks

With the development of serverless computing, developers can implement and deploy business applications as a combination of stateless functions. Although originally proposed for cloud computing, serverless computing is gradually applied to cloud-edge systems for service deployment to provide users with high-quality, low-latency services. However, optimized service deployment in 6G networks is a very challenging issue because of the vast number of deployable devices in the network, and its permutations are highly exponential. In this paper, we propose optimized service deployment schemes for online and offline, respectively, to minimize the overall latency at a lower cost. (1) First, a SD-SRF algorithm based on the greedy algorithm is proposed to optimize the service deployment for a multi-layer edge network, which consists of two phases: SR and SF. (a) Services are deployed in the nearest ancestor devices in the routing tree of all such service requests with the least cost of deployment. (b) When the deployment cost of moving some service replicas to devices in the lower layer is less than the benefit, the service will fall. (2) However, the offline algorithm relies on the availability of prior information heavily, such as request arrival pattern and number, which is difficult to obtain. Therefore, this paper proposes a PPO-MSD algorithm for optimal deployment online, where a Markov decision process (MDP) is modeled. Extensive simulation results show that PPO-MSD outperforms existing algorithms in terms of overall delay and utility, and its performance is close to the optimal ones obtained by SD-SRF, with the SD-SRF and PPO-MSD algorithms reducing the delay on average by 32.40% and 9.91%.

AI/ML for beyond 5G systems: Concepts, technology enablers & solutions

5G brought an evolution on the network architecture employing the service-based paradigm, enabling flexibility in realizing customized services across different technology domains. Such paradigm gives rise to the adoption of analytics and Artificial Intelligence/Machine Learning (AI/ML) in mobile communications with the ease of collecting various measurements related to end-users and the network, which can be exposed towards consumers, including 3rd party applications. AI/ML may influence network planning and optimization considering the service life-cycle and introduce new operations provision, paving the way towards 6G. This article provides a survey on AI/ML considering the business, the fundamentals and algorithms across the radio, control, and management planes. It sheds light on the key technologies that assist the adoption of AI/ML in 3rd Generation Partnership Project (3GPP) networks considering service request, reporting, data collection and distribution and it overviews the main AI/ML algorithms characterizing them into user-centric and network-centric. Finally, it explores the main standardization and open source activities on AI/ML, highlighting the lessons learned and the further challenges that still need to be addressed to reap the benefits of AI/ML in automation for beyond 5G/6G mobile systems.

A-174 More Timely Patient Care Amidst Healthcare Staffing Shortages, Reducing Blood Specimen Tube Barcode Errors for Continuous Flow in an Automated Laboratory System

Abstract Background The Core Laboratory at Henry Ford Hospital utilizes total laboratory automation to support chemistry, hematology and coagulation analyzers, processing approximately 10 000 specimens daily. Testing and reporting delays can occur when the system encounters a specimen tube barcode error. Specimens will stop moving and causes approximately a 2-minute per error production pause while the error is resolved. This induces congestion for all other specimens in line behind the error. The seemingly trivial minutes of congestion leads to a domino effect, resulting in reduced line productivity, a temporary limit of testing capacity and prolonged TATs. The laboratory receives specimen tubes with labels generated from over 600 printers. The system encounters 200–250 errors daily associated with barcode read failures of various types. Errors account for an estimated loss of ∼320 h of staff productivity per month. Methods Automation line error files, manually recorded data from lab staff, observations of automation line errors occurring in real time, data from a printer maintenance blitz and barcode printer service requests were used in root cause analysis. Three progressive Kaizens events were organized in 2022 to induce rapid process change through focused chartering of events and actions. A probing study designed to induce controlled defects and stoppages helped recognize where congestion on the line was occurring. Simulation of barcode application errors allowed an understanding of the impact of label application. Staff and line productivity loss analysis predicted the potential opportunity of error reduction. Results Fifty-five percent of the barcode errors were caused by improper label application and 45% were poor print quality of the barcode on the label. Of the label application defects, 55% were caused by the label being placed upside-down, of which 34% were caused by the label format used by our Emergency Department. Print quality issues such as faded or pitted printing labels accounted for 45% of errors. The printer preventive maintenance blitz successfully reduced print quality errors by 30%. Converting to a more robust printer in two high-volume areas resulted in zero print quality errors over three months. Barcode label format changes were 100% effective in eliminating upside-down as a root cause. Conclusion Elimination of barcode line errors can recover 2.0 FTEs in three divisions per month, while increasing automation line productivity and a cost approximately$150 000 per year in work delays and unnecessary rework. The Kaizen approach to problem-solving allowed for rapid brainstorming amongst multiple teams. Altered label format allowed built-in engineering control by mistake-proofing the process and making label positioning irrelevant. Identification of areas where high volumes of labels were being printed and replacing those printers with more robust printers reduced service calls and eliminated printer causal factors since these printers require less hands-on maintenance. Finally, instituting preventative label printer maintenance and total quality management reduces or prevents errors associated with print quality. Sustaining mechanisms include continued real-time data documentation and communication of defects, collaboration with colleagues in Nursing for further root cause analysis of barcode application defects.

Intelligent intrusion detection framework for multi-clouds – IoT environment using swarm-based deep learning classifier

In the current era, a tremendous volume of data has been generated by using web technologies. The association between different devices and services have also been explored to wisely and widely use recent technologies. Due to the restriction in the available resources, the chance of security violation is increasing highly on the constrained devices. IoT backend with the multi-cloud infrastructure to extend the public services in terms of better scalability and reliability. Several users might access the multi-cloud resources that lead to data threats while handling user requests for IoT services. It poses a new challenge in proposing new functional elements and security schemes. This paper introduces an intelligent Intrusion Detection Framework (IDF) to detect network and application-based attacks. The proposed framework has three phases: data pre-processing, feature selection and classification. Initially, the collected datasets are pre-processed using Integer- Grading Normalization (I-GN) technique that ensures a fair-scaled data transformation process. Secondly, Opposition-based Learning- Rat Inspired Optimizer (OBL-RIO) is designed for the feature selection phase. The progressive nature of rats chooses the significant features. The fittest value ensures the stability of the features from OBL-RIO. Finally, a 2D-Array-based Convolutional Neural Network (2D-ACNN) is proposed as the binary class classifier. The input features are preserved in a 2D-array model to perform on the complex layers. It detects normal (or) abnormal traffic. The proposed framework is trained and tested on the Netflow-based datasets. The proposed framework yields 95.20% accuracy, 2.5% false positive rate and 97.24% detection rate.

REVIEW OF SOLUTIONS TO THE PROBLEM OF RESOURCE ALLOCATION IN FOG COMPUTING

Despite the high popularity of cloud computing, they are not recommended for use in applications and systems that are critical to network delays. Therefore, given the increasing volumes of data circulating over global networks, the concept of fog computing has become very relevant, as it allows to significantly offload cloud services, while minimizing latency and expanding computing capabilities of cloud technologies. As an intermediate layer between cloud and end user, fog computing improves network performance by taking on some of the load. The efficiency of fog computing as well as efficiency of any computer network, directly depends on the efficiency of resource allocation for service requests. Given this fact and taking into account the mobility, heterogeneity and dynamic nature of fog computing, there is an urgent challenge of organizing the distribution of resources in the fog to provide high quality user service. This review provides a full and comprehensive view of the available solutions to the problem of resource allocation in the fog. The article presents works classified with respect to quality of service metrics, policy types and classes of resource allocation algorithms. Approaches to resource management in fog computing for Internet of things, that is critical to delays. Analysis of results presented in the work overview with research and practical nature, made it possible to formulate the significance of solving this problem, to identify key challenges in the development of approaches to fog resource allocation and key metrics for their effeiciancy and performance evaluation. The conclusions obtained in the present study not only testify to the relevance and importance of the resource allocation problem in fog computing, but also allows to determine key factors that must be taken into account when solving it.

Evaluating MotorCycleApp: A Mobile Application Mechanic Finder for Repair and Maintenance Servicing

The remarkable surge in demand for motorcycle repair and maintenance services within the motorcycle industry can be attributed to the growing number of motorcycles in the Philippines. This paper presents the development of the MotorCyclepp mobile application, designed to facilitate the finding of motorcycle mechanics in Metro Cebu, Philippines. With this app, riders can effortlessly locate and choose from a list of available mechanics within their vicinity and send service requests accordingly. Simultaneously, mechanics can conveniently review the received requests and decide whether to accept or decline them. Once a request is accepted, the service begins, and upon completion, both parties can conclude their transactions and provide feedback messages. The Input-Process-Output (IPO) model was employed as the system’s methodology. The user interface (UI) design was created using Unified Modeling Language (UML), such as a use case diagram, sequence diagram, and class diagram. The evaluation procedures utilized the ISO 9126 model, which examines five quality characteristics: functionality, reliability, usability, efficiency, and portability, for data analysis. The results revealed that the app is unique and interesting but requires further improvements. The significance of this research to the body of literature and the industry will provide future researchers with a valuable reference for advancing mechanic finder app innovations. Keywords: finder, information technology, ISO 9126, mobile application.

Efficient Decentralized Multi-agent Learning in Asymmetric Bipartite Queueing Systems

New Algorithm Enables Efficient Decentralized Learning in Bipartite Queueing Systems Bipartite queueing systems, where agents with individual job queues request service from a pool of heterogeneous servers, are standard models for service applications like data networks and call centers. Traditionally, a central controller schedules agent requests with full knowledge of system parameters. However, emerging applications require decentralized operation without this central coordination or complete system information. This presents challenges as agents lack the global knowledge needed to efficiently route jobs. Recent research into efficient decentralized learning algorithms for such systems faces limitations in nonoptimal throughput, demanding computations, or degrading efficiency with exponential queue growth. In contrast, this paper introduces an algorithm that enables queues to efficiently learn decentralized scheduling policies while ensuring throughput optimality. The approach is computationally lightweight, achieving queue length bounds that scale polynomially rather than exponentially in system size. Experiments demonstrate faster convergence and robustness of our algorithm compared with prior decentralized algorithms.

Navigating the contradiction: balancing patient care and caregiver protection. From heroes to victims

The COVID emergency has accelerated professional and organizational transformations, prompting a re-signification of activity systems, routines, professional visions, concrete daily operations. An unforeseen scenario emerges, highlighting elements of uncertainty, fatigue, and discomfort, linked, on the one hand, to the challenge raised towards the professional identities of the various players, called to deal with the new organizational constraints, to reposition themselves in changing contexts. On the other hand, the challenge refers to the possibility to achieve a good balance between offering services aimed at the promotion and protection of health and, at the same time, to guarantee working safety and security conditions, in increasingly complex contexts in which tensions and contradictions coexist with reduction of resources and requests for more effective services.
 At risk is the possibility to cope with increasing situations of social conflicts (i.e. the no vax manifestations) and events such as those related to the aggressiveness of patients, the verbal and often physical aggression against the health professionals, exposed to the temptation to abandon work and devote oneself to something else. Tackling with patients taken in charge by the Services and with characteristics of aggressive behavior, decidedly above the sustainability thresholds (death threats; screams and insults; raids on the service; stalking; shadowing of operators, ... with situations of requesting emergency intervention by the police, which could only be limited to a light intervention in the absence of an explicit complaint against the person), generates understandable fears and dynamics of avoidance/expulsion. These are, more and more (even though not exclusively) at the basis of resignations and retirement from work. This is in evident contrast with the mission of the health service and therefore with the identification of personnel with the aim of taking care of every user with a need.
 Hence a situation of impasse (disenchantment/impotence/give up/avoidance), having to deal with balancing the threshold of the limit(boundary) and the limit(boundary) of the threshold, defining conditions of survival, of joint elaboration, of collective action agreed. The possibility of conceiving oneself as an emancipatory limit(boundary), avoiding fantasies of ‘expulsive killerage’ and ‘regulatory stiffening’, relies on a collective system alliance, capable of considering both realistic fears with respect to personal safety to be protected, and the elaboration of one's own defensive dynamics in the face of exposure to external aggression.
 At stake is the lacerating dilemma between the identification with a service that must take charge of the needs (whatever they may be) of a user/patient and, at the same time, with the need to protect one's own and others' (other patients) safety conditions to be able to fulfill the professional task to which one is called.

Operating performance of a Joule-Brayton pumped thermal energy storage system integrated with a concentrated solar power plant

The expected performance of an innovative Pumped Thermal Energy Storage (PTES) system based on a closed-loop Brayton-Joule cycle and integrated with a Concentrated Solar Power (CSP) plant are analysed in this study. The integrated PTES–CSP plant includes five machines (two compressors and three turbines), a central receiver tower system, three water coolers and three Thermal Energy Storage (TES) tanks, while argon and granite pebbles are chosen as working fluid and storage media, respectively. A sizing of the main components of the integrated plant has been firstly carried out for the design of an integrated PTES-CSP plant with a nominal net power of 5 MW and a nominal storage capacity of 6 equivalent hours of operation. Specific mathematical models have been developed in MATLAB-Simulink to simulate the PTES and CSP subsystem in different operating conditions, and to evaluate the thermocline profile evolution within the three storage tanks during/charging and discharging processes. A control strategy has finally been developed to determine the operating modes of the plant based on the grid service request, the solar availability, and the TES levels. The performance of the system during a summer and a winter day have been analysed considering the integration of the PTES subsystem in the Italian energy market for arbitrage. Results have demonstrated the technical feasibility of the hybridization of a PTES system with a CSP plant and the ability of the integrated system to participate to energy arbitrage, although a lower exergy roundtrip efficiency (about 54 %) has been observed with respect to the sole PTES system (about 60 %).

A novel hierarchical edge-based architecture for service oriented IoT

IoT (Internet of Things) is becoming increasingly prevalent in everyday life, with many commercialized IoT platforms available for use. In this paper, we propose a novel hierarchical IoT platform under a single administrating entity to provide scalability to an edge-based service-oriented IoT platform. To enable communication and resource sharing between edges while preserving the privacy benefit of the edge-based platform, a new type of service, called super service is introduced. And the scheduling problem of service requests to devices and a proposed solution are presented. To evaluate the proposed platform, a simulation framework is developed. Through a smart campus test-bed and extensive experiments with the simulation framework, the viability of the proposed platform is demonstrated.