Platform Revenue Strategy Selection Considering Consumer Group Data Privacy Regulation

In the era of big data, consumer group privacy has become an important source of revenue for the digital platform. Considering the situation that the platform collects consumer group data privacy to generate business revenue, we explore how the service matching level and commission rate affect the platform revenue, social welfare, and seller benefits. Based on the theory of group privacy, the three-party equilibrium evolution is solved by constructing a sequential game model including platform, seller, and consumer alliance. It is found that when the service matching level of the platform is greater than the threshold value, there are two main situations: on the one hand, if using the data privacy of a consumer group is subject to market regulation, the platform will set a high commission rate and service matching level in order to maximize profit. However, social welfare and seller’s business benefit both reach a minimum in this case, and the three-party game cannot attain equilibrium. On the other hand, when the market governor relaxes the platform’s regulation on the use of consumer group privacy data and data revenue efficiency is high enough, the platform can maximize the revenue by increasing the service matching level and reducing the commission rate. The optimal commission rate depends on the data revenue efficiency of the platform. Moreover, when the platform sets the highest commission rate and the service matching level is at a medium level, a stable partial equilibrium among the three-party will be achieved. These conclusions can give some insights into platform’s business model choice decision.

WikiServe: Using Wikipedia to Match IoT based Services for Situation Response

A large number of smart devices (things) are being deployed with the swift development of Internet of Things (IOT). These devices, owned by different organizations, have a wide variety of services to offer over the web. During a natural disaster or emergency (i.e., a situation), for example, relevant IOT services can be found and put to use. However, appropriate service matching methods are required to find the relevant services. Organizations that manage situation responses and organizations that provide IOT services are likely to be independent of each other, and therefore it is difficult for them to adopt a common ontological model to facilitate the service matching. Moreover, there exists a large conceptual gap between the domain of discourse for situations and the domain of discourse for services, which cannot be adequately bridged by existing techniques. In this paper, we address these issues and propose a new method, WikiServe, to identify IOT services that are functionally relevant to a given situation. Using concepts (terms) from situation and service descriptions, WikiServe employs Wikipedia as a knowledge source to bridge the conceptual gap between situation and service descriptions and match functionally relevant IOT services for a situation. It uses situation terms to retrieve situation related articles from Wikipedia. Then it creates a ranked list of services for the situation using the weighted occurrences of service terms in weighted situation articles. WikiServe performs better than a commonly used baseline method in terms of Precision, Recall and F measure for service matching.

WikiServe: Using Wikipedia to Match IoT based Services for Situation Response

A large number of smart devices (things) are being deployed with the swift development of Inter- net of Things (IOT). These devices, owned by different organizations, have a wide variety of services to offer over the web. During a natural disaster or emergency (i.e., a situation), for example, relevant IOT services can be found and put to use. However, appropriate service matching methods are required to find the relevant services. Organizations that manage situation responses and organizations that provide IOT services are likely to be independent of each other, and therefore it is difficult for them to adopt a common ontological model to facilitate the service matching. Moreover, there exists a large conceptual gap between the domain of discourse for situations and the domain of discourse for services, which cannot be adequately bridged by existing techniques. In this paper, we address these issues and propose a new method, WikiServe, to identify IOT services that are functionally relevant to a given situation. Using concepts (terms) from situation and service descriptions, WikiServe employs Wikipedia as a knowledge source to bridge the conceptual gap between situation and service descriptions and match functionally relevant IOT services for a situation. It uses situation terms to retrieve situation related articles from Wikipedia. Then it creates a ranked list of services for the situation using the weighted occurrences of service terms in weighted situation articles. WikiServe performs better than a commonly used baseline method in terms of Precision, Recall and F measure for service matching.

Structural Balance of Social Internet of Things Networks with Ambiguous Relationships

In the social Internet of Things, social networks can be built among smart objects or between smart objects and people just like human beings. One of the factors that determines the effect and efficiency of service matching in SIoT is the structure of social networks. In this paper, we exploit the theory of structural balance in signed networks to optimize SIoT network structures to provide a friendly and stable environment as well as a solid foundation for service matching of social Internet of Things. Next, besides being friends or enemies, which are traditional relationships of structural balance or structural changes in signed networks, in our research, we introduce the ambiguous relationship which is the certain state between the hostile and the friendly status and we discuss the meaning and significance of ambiguous relationship in dynamical changes of structural balance in SIoT networks. Based on previous studies, we apply an enhanced objective function and a modified approach concerning the ambiguous relationship towards the dynamical change process. Experiments show that our approach is more effective and efficient than former studies in optimizing dynamical evolution of structural balance in signed networks of SIoT.

Mobile Internet of Things Dynamic Grid QoS Service Matching Mechanism and Simulation Analysis

With the development of the mobile Internet of Things, people have higher and higher requirements for services, and how to provide high-quality services that meet the needs of users has also become a hot issue of current research. This article is also in the context of the mobile Internet of Things, researching the solution to the service matching problem. In order to provide high-quality grid services, the grid system needs the support of quality of service (QoS). Based on the existing QoS matching strategy, a service matching strategy based on dynamic grid QoS is proposed. This paper combines semantic information, considers service quality QoS and user preferences, and improves the success rate of service matching through hierarchical matching, so as to provide users with high-quality services. At the same time, the classic Max-Min algorithm in grid task scheduling is analysed, and on this basis, the impact of QoS on task scheduling is fully considered, and combined with the concept of execution cost, a grid task based on QoS constraints (MOS) is proposed. Through simulation verification and analysis, the strategy in this paper not only achieved higher matching performance, but also improved the success rate of task execution, and the effect of system load balancing was also significantly improved.

Enhancing data-driven elderly appointment services in domestic care communities under COVID-19

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.

The Effect of Thickness-Based Dynamic Matching Mechanism on a Hyperledger Fabric-Based TimeBank System

In a community with an aging population, helping each other is a must society function. Lacking mutual trust makes the need for a fair and transparent service exchange platform on top of the public service administration’s list. We present an efficient blockchain-based TimeBank realization with a newly proposed dynamic service matching algorithm (DSMA) in this work. The Hyperledger Fabric (or Fabric in short), one of the well-known Consortium Blockchains, is chosen as our system realization platform. It provides the identity certification mechanism and has an extendable network structure. The performance of a DSMA is measured by the waiting time for a service to get a match, called the service-matching waiting time (SMWT). In our DSMA, the decision as to whether a service is to get a match or wait for a later chance depends dynamically on the total number of contemporarily available services (i.e., the thickness of the service market). To better the proposed TimeBank system’s service quality, a Dynamic Tuning Strategy (DTS) is designed to thicken the market size. Experimental results show that a thicker market makes on-chain nodes have more links, and in turn, they find a match easier (i.e., consume a shorter SMWT).