scispace - formally typeset
Search or ask a question
Journal ArticleDOI

Cloud-based crowd sensing: a framework for location-based crowd analyzer and advisor

01 Nov 2017-Vol. 263, Iss: 4, pp 042076
About: The article was published on 2017-11-01 and is currently open access. It has received 2 citations till now. The article focuses on the topics: Cloud computing.
Citations
More filters
Posted Content
TL;DR: In this paper, a framework for energy efficient computing is proposed that comprises a set of policies for controlling CPU cycles for the mode of local computing, time division between MPT and offloading, and mode selection.
Abstract: Achieving long battery lives or even self sustainability has been a long standing challenge for designing mobile devices. This paper presents a novel solution that seamlessly integrates two technologies, mobile cloud computing and microwave power transfer (MPT), to enable computation in passive low-complexity devices such as sensors and wearable computing devices. Specifically, considering a single-user system, a base station (BS) either transfers power to or offloads computation from a mobile to the cloud; the mobile uses harvested energy to compute given data either locally or by offloading. A framework for energy efficient computing is proposed that comprises a set of policies for controlling CPU cycles for the mode of local computing, time division between MPT and offloading for the other mode of offloading, and mode selection. Given the CPU-cycle statistics information and channel state information (CSI), the policies aim at maximizing the probability of successfully computing given data, called computing probability, under the energy harvesting and deadline constraints. The policy optimization is translated into the equivalent problems of minimizing the mobile energy consumption for local computing and maximizing the mobile energy savings for offloading which are solved using convex optimization theory. The structures of the resultant policies are characterized in closed form. Furthermore, given non-causal CSI, the said analytical framework is further developed to support computation load allocation over multiple channel realizations, which further increases computing probability. Last, simulation demonstrates the feasibility of wirelessly powered mobile cloud computing and the gain of its optimal control.

73 citations

Proceedings ArticleDOI
19 Jul 2019
TL;DR: A Heuristic Location Traceability (HLT) scheme is developed that considers the claims itself that humans may report their location information in the texts, images or other metadata and develops a novel algorithm to predict the location information with the help of these metadata.
Abstract: Online social media (e.g., Weibo, Twitter and Facebook) has emerged as a novel paradigm of sensing collecting observations (commonly called claims) from humans about the physical world in this big data era. These observations may contain location information directly based location-based sensor network (LBSN) or not, and maybe part of them, we can predict the information of location by inference through text, image or other metadata. Hence a challenging problem in social sensing lies in whether we can gain the location information. This problem is referred as location traceability problem. In Recent years, urban complex has been more and more popular and played an important role in the smart city. The location traceability problem, if well addressed, directly contributes to help police and property company to have an effective management in urban complex and improve their public image. However, two main challenges exist in the current location traceability solutions. The first one is the lack of location information without the help of LBSN scenario, where a great deal of sources are disseminating the information which is lack of location information, making the location traceability problem difficult. The second challenge is the large range of location information in urban complex scenario. However, the location information we need is the indoor location in the urban complex. In this paper, we developed a Heuristic Location Traceability (HLT) scheme to handle the above two challenges. Especially, the HLT scheme considers the claims itself that humans may report their location information in the texts, images or other metadata and develop a novel algorithm to predict the location information with the help of these metadata. Experimental results from a real world dataset show that our HLT scheme significantly outperforms other location traceability methods.
References
More filters
Journal ArticleDOI
TL;DR: Diverse strategies that are proposed in the literature to provide incentives for stimulating users to participate in mobile crowd sensing applications are surveyed and divided into three categories: entertainment, service, and money.
Abstract: Recent years have witnessed the fast proliferation of mobile devices (e.g., smartphones and wearable devices) in people's lives. In addition, these devices possess powerful computation and communication capabilities and are equipped with various built-in functional sensors. The large quantity and advanced functionalities of mobile devices have created a new interface between human beings and environments. Many mobile crowd sensing applications have thus been designed which recruit normal users to contribute their resources for sensing tasks. To guarantee good performance of such applications, it's essential to recruit sufficient participants. Thus, how to effectively and efficiently motivate normal users draws growing attention in the research community. This paper surveys diverse strategies that are proposed in the literature to provide incentives for stimulating users to participate in mobile crowd sensing applications. The incentives are divided into three categories: entertainment, service, and money. Entertainment means that sensing tasks are turned into playable games to attract participants. Incentives of service exchanging are inspired by the principle of mutual benefits. Monetary incentives give participants payments for their contributions. We describe literature works of each type comprehensively and summarize them in a compact form. Further challenges and promising future directions concerning incentive mechanism design are also discussed.

441 citations

Journal ArticleDOI
TL;DR: A novel solution that seamlessly integrates two technologies, mobile cloud computing and microwave power transfer, to enable computation in passive low-complexity devices such as sensors and wearable computing devices is presented.
Abstract: Achieving long battery lives or even self sustainability has been a long standing challenge for designing mobile devices. This paper presents a novel solution that seamlessly integrates two technologies, mobile cloud computing and microwave power transfer (MPT), to enable computation in passive low-complexity devices such as sensors and wearable computing devices. Specifically, considering a single-user system, a base station (BS) either transfers power to or offloads computation from a mobile to the cloud; the mobile uses harvested energy to compute given data either locally or by offloading. A framework for energy efficient computing is proposed that comprises a set of policies for controlling CPU cycles for the mode of local computing, time division between MPT and offloading for the other mode of offloading, and mode selection. Given the CPU-cycle statistics information and channel state information (CSI), the policies aim at maximizing the probability of successfully computing given data, called computing probability , under the energy harvesting and deadline constraints. The policy optimization is translated into the equivalent problems of minimizing the mobile energy consumption for local computing and maximizing the mobile energy savings for offloading which are solved using convex optimization theory. The structures of the resultant policies are characterized in closed form. Furthermore, given non-causal CSI, the said analytical framework is further developed to support computation load allocation over multiple channel realizations, which further increases the computing probability. Last, simulation demonstrates the feasibility of wirelessly powered mobile cloud computing and the gain of its optimal control.

418 citations

Journal ArticleDOI
TL;DR: A feasible and truthful incentive mechanism (TIM), to coordinate the resource auction between mobile devices as service users (buyers) and cloudlets as service providers (sellers) is proposed and extended to a more efficient design of auction (EDA).
Abstract: Mobile cloud computing offers an appealing paradigm to relieve the pressure of soaring data demands and augment energy efficiency for future green networks. Cloudlets can provide available resources to nearby mobile devices with lower access overhead and energy consumption. To stimulate service provisioning by cloudlets and improve resource utilization, a feasible and efficient incentive mechanism is required to charge mobile users and reward cloudlets. Although auction has been considered as a promising form for incentive, it is challenging to design an auction mechanism that holds certain desirable properties for the cloudlet scenario. Truthfulness and system efficiency are two crucial properties in addition to computational efficiency, individual rationality and budget balance. In this paper, we first propose a feasible and truthful incentive mechanism (TIM), to coordinate the resource auction between mobile devices as service users (buyers) and cloudlets as service providers (sellers). Further, TIM is extended to a more efficient design of auction (EDA). TIM guarantees strong truthfulness for both buyers and sellers, while EDA achieves a fairly high system efficiency but only satisfies strong truthfulness for sellers. We also show the difficulties for the buyers to manipulate the resource auction in EDA and the high expected utility with truthful bidding.

144 citations

Journal ArticleDOI
TL;DR: The proposed multi-resource allocation strategy enhances the quality of mobile cloud service, in terms of the system throughput (the number of admitted mobile applications) and the service latency, and outperforms greedy admission control over a broad range of environments.
Abstract: Mobile cloud computing utilizing cloudlet is an emerging technology to improve the quality of mobile services. In this paper, to better overcome the main bottlenecks of the computation capability of cloudlet and the wireless bandwidth between mobile devices and cloudlet, we consider the multi-resource allocation problem for the cloudlet environment with resource-intensive and latency-sensitive mobile applications. The proposed multi-resource allocation strategy enhances the quality of mobile cloud service, in terms of the system throughput (the number of admitted mobile applications) and the service latency. We formulate the resource allocation model as a semi-Markov decision process under the average cost criterion, and solve the optimization problem using linear programming technology. Through maximizing the long-term reward while meeting the system requirements of the request blocking probability and service time latency, an optimal resource allocation policy is calculated. From simulation result, it is indicated that the system adaptively adjusts the allocation policy about how much resource to allocate and whether to utilize the distant cloud according to the traffic of mobile service requests and the availability of the resource in the system. Our algorithm outperforms greedy admission control over a broad range of environments.

133 citations

Journal ArticleDOI
TL;DR: This article characterizes the unique features and challenges of MCSC and presents early efforts on MCSC to demonstrate the benefits of aggregating heterogeneous crowdsourced data.
Abstract: With the development of mobile sensing and mobile social networking techniques, mobile crowd sensing and computing (MCSC), which leverages heterogeneous crowdsourced data for large-scale sensing, has become a leading paradigm. Built on top of the participatory sensing vision, MCSC has two characteristic features: it leverages heterogeneous crowdsourced data from two data sources: participatory sensing and participatory social media; and it presents the fusion of human and machine intelligence in both the sensing and computing processes. This article characterizes the unique features and challenges of MCSC. We further present early efforts on MCSC to demonstrate the benefits of aggregating heterogeneous crowdsourced data.

92 citations