Crowdsourced Coverage as a Service: Two-Level Composition of Sensor Cloud Services

Neiat, Azadeh Ghari; Bouguettaya, Athman; Sellis, Timos; Mistry, Sajib

doi:10.1109/tkde.2017.2672738

Cited by 38 publications

(26 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…IoT users may crowdsource the functions of nearby IoT devices to fulfill their needs. Crowdsourcing enables a mobile ecosystem to share different services among IoT devices [3]. For example, a set of co-located smartphones may provide computing or storage for a nearby resourceconstrained smartwatch to render a map journey [4].…”

Section: Introductionmentioning

confidence: 99%

“…For example, a set of co-located smartphones may provide computing or storage for a nearby resourceconstrained smartwatch to render a map journey [4]. IoT devices can provide various types of crowdsourced services such as WiFi hotspots, wireless charging and environmental sensing [3], [5], [6]. This provides opportunities to build novel applications such as spatio-temporal targeted recommender systems [7] and shared IoT service markets [8].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Composing Energy Services in a Crowdsourced IoT Environment

Lakhdari

Bouguettaya

Mistry

et al. 2022

IEEE Trans. Serv. Comput.

Self Cite

View full text Add to dashboard Cite

We propose a novel framework for composing crowdsourced wireless energy services to satisfy users' energy requirements in a crowdsourced Internet of Things (IoT) environment. A new energy service model is designed to transform the harvested energy from IoT devices into crowdsourced services. We propose a new energy service composability model that considers the spatio-temporal aspects and the usage patterns of the IoT devices. A multiple local knapsack-based approach is developed to select an optimal set of partial energy services based on the deliverable energy capacity of IoT devices. We propose a heuristic-based composition approach using the temporal and energy capacity distributions of services. Experimental results demonstrate the effectiveness and efficiency of the proposed approach.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Composing Energy Services in a Crowdsourced IoT Environment

Lakhdari

Bouguettaya

Mistry

et al. 2022

IEEE Trans. Serv. Comput.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Therefore, selecting services with personalized QoS values for different users should be considered. In fact, personalized QoS-based approaches have been applied to Web service composition [3], Web service selection [4], QoS prediction [5], and so forth.…”

Section: Introductionmentioning

confidence: 99%

OPRC: An Online Personalized Reputation Calculation Model in Service-Oriented Computing Environments

Cai

Zhu

et al. 2019

IEEE Access

View full text Add to dashboard Cite

Cloud applications based on service-oriented architectures usually integrate many component services to implement specific application logic. In service-oriented computing environments, many Web services are provided for users to build service-oriented systems. Since the performance of the same Web service varies according to different users' perspectives, the users have to personally select the optimal Web services according to the quality-of-service (QoS) data observed by other similar users. However, users with a low reputation provide unreliable data, which has a negative impact on service selection. Moreover, the QoS data vary over time due to changes in user reputation; and therefore, how to calculate a personalized reputation for each user at runtime remains a substantial problem. To address this critical challenge, this paper proposes an online reputation calculation method, called the OPRC, to efficiently provide a personalized reputation for each user. Based on the users' observed QoS data, the OPRC employs MF and online learning techniques to calculate personalized reputations. To validate the approach, large-scale experiments are conducted, which contain two QoS attributes from 142 reliable users and 15 unreliable users. The results show that OPRC has high accuracy and effectiveness compared to other approaches.

show abstract

“…In such a network, many sensing devices which are deployed on the network edge are used for collecting data [ 2 , 4 , 29 , 30 , 31 , 32 , 33 ]. Then, sensing devices send data to cloud [ 28 , 34 ], where Data Centers (DCs) are located. Finally, users get requested data from DCs.…”

Section: Introductionmentioning

confidence: 99%

“…The conventional Cloud Computing model [ 1 , 25 , 28 , 34 ] and the Sensor-Cloud Network [ 28 , 34 ] have to face considerable challenges brought by the rapid development of ubiquitous sensing [ 42 , 43 , 44 ]. In a traditional model, all the sensed data collected by sensing devices have to be uploaded to DCs which are located in the cloud, then it is DCs’ job to reply every incoming interest packet with a data packet with a specified size and name same as in that interest packet.…”

Section: Introductionmentioning

confidence: 99%

Caching Joint Shortcut Routing to Improve Quality of Service for Information-Centric Networking

Huang

Liu

Zhang

et al. 2018

Sensors

View full text Add to dashboard Cite

Hundreds of thousands of ubiquitous sensing (US) devices have provided an enormous number of data for Information-Centric Networking (ICN), which is an emerging network architecture that has the potential to solve a great variety of issues faced by the traditional network. A Caching Joint Shortcut Routing (CJSR) scheme is proposed in this paper to improve the Quality of service (QoS) for ICN. The CJSR scheme mainly has two innovations which are different from other in-network caching schemes: (1) Two routing shortcuts are set up to reduce the length of routing paths. Because of some inconvenient transmission processes, the routing paths of previous schemes are prolonged, and users can only request data from Data Centers (DCs) until the data have been uploaded from Data Producers (DPs) to DCs. Hence, the first kind of shortcut is built from DPs to users directly. This shortcut could release the burden of whole network and reduce delay. Moreover, in the second shortcut routing method, a Content Router (CR) which could yield shorter length of uploading routing path from DPs to DCs is chosen, and then data packets are uploaded through this chosen CR. In this method, the uploading path shares some segments with the pre-caching path, thus the overall length of routing paths is reduced. (2) The second innovation of the CJSR scheme is that a cooperative pre-caching mechanism is proposed so that QoS could have a further increase. Besides being used in downloading routing, the pre-caching mechanism can also be used when data packets are uploaded towards DCs. Combining uploading and downloading pre-caching, the cooperative pre-caching mechanism exhibits high performance in different situations. Furthermore, to address the scarcity of storage size, an algorithm that could make use of storage from idle CRs is proposed. After comparing the proposed scheme with five existing schemes via simulations, experiments results reveal that the CJSR scheme could reduce the total number of processed interest packets by 54.8%, enhance the cache hits of each CR and reduce the number of total hop counts by 51.6% and cut down the length of routing path for users to obtain their interested data by 28.6–85.7% compared with the traditional NDN scheme. Moreover, the length of uploading routing path could be decreased by 8.3–33.3%.

show abstract

Crowdsourced Coverage as a Service: Two-Level Composition of Sensor Cloud Services

Cited by 38 publications

References 22 publications

Composing Energy Services in a Crowdsourced IoT Environment

Composing Energy Services in a Crowdsourced IoT Environment

OPRC: An Online Personalized Reputation Calculation Model in Service-Oriented Computing Environments

Caching Joint Shortcut Routing to Improve Quality of Service for Information-Centric Networking

Contact Info

Product

Resources

About