Wireless sensor networks (WSNs) have attracted increasing attentions from both academic and industrial communities. It can be deployed in various kinds of environments to monitor and collect information. In this paper, we describe a WSN-based intelligent car parking system. In the system, low-cost wireless sensors are deployed into a car park field, with each parking lot equipped with one sensor node, which detects and monitors the occupation of the parking lot. The status of the parking field detected by sensor nodes is reported periodically to a database via the deployed wireless sensor network and its gateway. The database can be accessed by the upper layer management system to perform various management functions, such as finding vacant parking lots, auto-toll, security management, and statistic report. We have implemented a prototype of the system using crossbow motes. The system evaluation demonstrates the effectiveness of our design and implementation of the car parking system.

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Purpose The purpose of this paper is to develop an Internet of medical things (IoMT)-based geriatric care management system (I-GCMS), integrating IoMT and case-based reasoning (CBR) in order to deal with the global concerns of the increasing demand for elderly care service in nursing homes. Design/methodology/approach The I-GCMS is developed under the IoMT environment to collect real-time biometric data for total health monitoring. When the health of an elderly deteriorates, the CBR is used to revise and generate the customized care plan, and hence support and improve the geriatric care management (GCM) service in nursing homes. Findings A case study is conducted in a nursing home in Taiwan to evaluate the performance of the I-GCMS. Under the IoMT environment, the time saving in executing total health monitoring helps improve the daily operation effectiveness and efficiency. In addition, the proposed system helps leverage a proactive approach in modifying the content of a care plan in response to the change of health status of elderly. Originality/value Considering the needs for demanding and accurate healthcare services, this is the first time that IoMT and CBR technologies have been integrated in the field of GCM. This paper illustrates how to seamlessly connect various sensors to capture real-time biometric data to the I-GCMS platform for responsively supporting decision making in the care plan modification processes. With the aid of I-GCMS, the efficiency in executing the daily routine processes and the quality of healthcare services can be improved.

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Purpose -This paper aims to maintain the high service quality of the long-term care service providers by establishing a knowledge-based system so as to enhance the service quality of nursing homes and the performance of its nursing staff continually.Design/methodology/approach -An intelligent case-based knowledge management system (ICKMS) is developed with the integration of two artificial intelligence techniques, i.e. fuzzy logic and case-based reasoning (CBR). In the system, fuzzy logic is adopted to assess the performance through the analysis of the long-term care services provided, nurse performance and elderly satisfaction, whereas CBR is used to formulate a customized re-training program for quality improvement. A case study is conducted to validate the feasibility of the proposed system.Findings -The empirical findings indicate that the ICKMS helps in identification of those nursing staff who cannot meet the essential service standard. Through the customized re-training program, the performance of the nursing staff can be greatly enhanced, whereas the medical errors and complaints can be considerably reduced. Furthermore, the proposed methodology provides a cost-saving approach in the administrative work.The authors would like to thank the Research Office of the Hong Kong Polytechnic University for supporting the current project (Project Code: G-UA7Z). Quality improvement in nursing homes103 Practical implications -The findings and results of the study facilitate decision-making using the ICKMS for the long-term service providers to improve their performance and service quality by providing a customized re-training program to the nursing staff.Originality/value -This study contributes to establishing a knowledge-based system for the long-term service providers for maintaining the high service quality in the health-care industry.

With the increasing ageing population worldwide, providing effective nursing care planning in nursing homes is important in meeting the expectations of elderly patients and in streamlining the healthcare information process, hence maintaining high‐quality services. Instead of the traditional manual nursing care planning formulation based on expert experience and subjective judgement, this paper describes an adaptive decision support system, namely, the cloud‐based nursing care planning system, to enable decision making in formulating nursing care strategies. By integrating cloud computing technology and the case‐based reasoning (CBR) technique, medical records and documents pertaining to the elderly can be captured in real time, whereas appropriate treatment plans based on past similar treatment records can be formulated. However, the current case adaptation processes in CBR rely on domain experts to modify retrieved cases, which may not satisfy the needs of the elderly. Therefore, text mining is integrated in the case adaptation process of CBR for extracting up‐to‐date medical information from the Internet so that its efficiency can be improved. By conducting a pilot study in a nursing home, it was shown that the time for formulating applicable treatment plans for elderly patients can be reduced, and the service satisfaction level can be enhanced.

PurposeUnder the impact of Coronavirus disease 2019 (COVID-19), this paper contributes in the deployment of the Artificial Intelligence of Things (AIoT)-based system, namely AIoT-based Domestic Care Service Matching System (AIDCS), to the existing electronic health (eHealth) system so as to enhance the delivery of elderly-oriented domestic care services.Design/methodology/approachThe proposed AIDCS integrates IoT and Artificial Intelligence (AI) technologies to (1) capture real-time health data of the elderly at home and (2) provide the knowledge support for decision making in the domestic care appointment service in the community.FindingsA case study was conducted in a local domestic care centre which provided elderly oriented healthcare services to the elderly. By integrating IoT and AI into the service matching process of the mobile apps platform provided by the local domestic care centre, the results proved that customer satisfaction and the quality of the service delivery were improved by observing the key performance indicators of the transactions after the implementation of the AIDCS.Originality/valueFollowing the outbreak of COVID-19, this is a new attempt to overcome the limited research done on the integration of IoT and AI techniques in the domestic care service. This study not only inherits the ability of the existing eHealth system to automatically capture and monitor the health status of the elderly in real-time but also improves the overall quality of domestic care services in term of responsiveness, effectiveness and efficiency.

For companies to gain competitive advantage, an effective customer relationship management (CRM) approach is necessary. Based on customer purchase behaviour and ordering patterns, companies can be classified into different categories in terms of providing customised sales and promotions for customers. However, companies that lack an effective CRM strategy can only offer the same sales and marketing strategies to all customers. Furthermore, the traditional approach to managing customers is control via a centralised method, in which the information regarding customer segmentation is not shared among the customer network. Consequently, valuable customers may be neglected, resulting in the loss of customer loyalty and sales orders, and the weakening of trust in the customer-company relationship. This paper designs an integrated data analytic model (IDAM) in a peer-to-peer cloud, integrating RFM-based k-means clustering algorithm, analytical hierarchy processing and fuzzy logic to divide customers into different segments and hence formulate a customised sales strategy. A pilot study of IDAM is conducted in a trading company specialised in providing advanced manufacturing technology to demonstrate how IDAM can be applied to formulate an effective sales strategy to attract customers. Overall, this study explores the effective deployment of CRM into the peer-to-peer cloud so as to facilitate sales strategy formulation and trust between customers and companies in the network.