Sustainable and Efficient Data Collection from WSNs to Cloud
01 Apr 2019-Vol. 4, Iss: 2, pp 252-262
TL;DR: A time adaptive schedule algorithm (TASA) for data collection via multiple MSs is designed, with several provable properties, to reduce the delivery latency caused by unreasonable task allocation and optimize the energy consumption, which makes the sensor-cloud sustainable.
Abstract: The development of cloud computing pours great vitality into traditional wireless sensor networks (WSNs). The integration of WSNs and cloud computing has received a lot of attention from both academia and industry. However, collecting data from WSNs to cloud is not sustainable. Due to the weak communication ability of WSNs, uploading big sensed data to the cloud within the limited time becomes a bottleneck. Moreover, the limited power of sensor usually results in a short lifetime of WSNs. To solve these problems, we propose to use multiple mobile sinks (MSs) to help with data collection. We formulate a new problem which focuses on collecting data from WSNs to cloud within a limited time and this problem is proved to be NP-hard. To reduce the delivery latency caused by unreasonable task allocation, a time adaptive schedule algorithm (TASA) for data collection via multiple MSs is designed, with several provable properties. In TASA, a non-overlapping and adjustable trajectory is projected for each MS. In addition, a minimum cost spanning tree (MST) based routing method is designed to save the transmission cost. We conduct extensive simulations to evaluate the performance of the proposed algorithm. The results show that the TASA can collect the data from WSNs to Cloud within the limited latency and optimize the energy consumption, which makes the sensor-cloud sustainable.
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TL;DR: A secure data collection scheme for IoT-based healthcare system named SecureData, which applies a distributed database technique that includes a number of cloud data servers to guarantee patients’ personal data privacy at the cloud computing layer and the performance of SecureData is validated through simulations with FPGA.
Abstract: There are tremendous security concerns with patient health monitoring sensors in Internet of Things (IoT). The concerns are also realized by recent sophisticated security and privacy attacks, including data breaching, data integrity, and data collusion. Conventional solutions often offer security to patients’ health monitoring data during the communication. However, they often fail to deal with complicated attacks at the time of data conversion into cipher and after the cipher transmission. In this paper, we first study privacy and security concerns with healthcare data acquisition and then transmission. Then, we propose a secure data collection scheme for IoT-based healthcare system named SecureData with the aim to tackle security concerns similar to the above. SecureData scheme is composed of four layers: 1) IoT network sensors/devices; 2) Fog layers; 3) cloud computing layer; and 4) healthcare provider layer. We mainly contribute to the first three layers. For the first two layers, SecureData includes two techniques: 1) light-weight field programmable gate array (FPGA) hardware-based cipher algorithm and 2) secret cipher share algorithm. We study KATAN algorithm and we implement and optimize it on the FPGA hardware platform, while we use the idea of secret cipher sharing technique to protect patients’ data privacy. At the cloud computing layer, we apply a distributed database technique that includes a number of cloud data servers to guarantee patients’ personal data privacy at the cloud computing layer. The performance of SecureData is validated through simulations with FPGA in terms of hardware frequency rate, energy cost, and computation time of all the algorithms and the results show that SecureData can be efficient when applying for protecting security risks in IoT-based healthcare.
136 citations
Cites background from "Sustainable and Efficient Data Coll..."
...day health relies on this data collection [20], the protection of the data is greatly affected by cyber threats/attacks....
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TL;DR: A three-module framework, named “Ontology-Based Privacy-Preserving” (OBPP) is proposed to address the heterogeneity issue while keeping the privacy information of IoT devices, and can be widely applied to smart cities.
Abstract: IoT devices generate data over time, which is going to be shared with other parties to provide high-level services. Smart City is one of its applications which aims to manage cities automatically. Because of the large number of devices, three critical challenges come up: heterogeneity, privacy-preserving of generated data, and providing high-level services. The existing solutions cannot even solve two of the mentioned challenges simultaneously. In this paper, we propose a three-module framework, named “Ontology-Based Privacy-Preserving” (OBPP) to address these issues. The first module includes an ontology, a data storage model, to address the heterogeneity issue while keeping the privacy information of IoT devices. The second one contains semantic reasoning rules to find abnormal patterns while addressing the quality of provided services. The third module provides a privacy rules manager to address the privacy-preserving challenges of IoT devices achieved by dynamically changing privacy behaviors of the devices. Extensive simulations on a synthetic smart city dataset demonstrate the superior performance of our approach compared to the existing solutions while providing affordability and robustness against information leakages. Thus, it can be widely applied to smart cities.
135 citations
TL;DR: Results corroborate that the proposed mechanisms can efficiently stimulate mobile edge users to perform evaluation task and improve the accuracy of trust evaluation, and validate the validity of Quality-Aware Trustworthy Incentive Mechanism.
Abstract: Both academia and industry have directed tremendous interest toward the combination of Cyber Physical Systems and Cloud Computing, which enables a new breed of applications and services. However, due to the relative long distance between remote cloud and end nodes, Cloud Computing cannot provide effective and direct management for end nodes, which leads to security vulnerabilities. In this article, we first propose a novel trust evaluation mechanism using crowdsourcing and Intelligent Mobile Edge Computing. The mobile edge users with relatively strong computation and storage ability are exploited to provide direct management for end nodes. Through close access to end nodes, mobile edge users can obtain various information of the end nodes and determine whether the node is trustworthy. Then, two incentive mechanisms, i.e., Trustworthy Incentive and Quality-Aware Trustworthy Incentive Mechanisms, are proposed for motivating mobile edge users to conduct trust evaluation. The first one aims to motivate edge users to upload their real information about their capability and costs. The purpose of the second one is to motivate edge users to make trustworthy effort to conduct tasks and report results. Detailed theoretical analysis demonstrates the validity of Quality-Aware Trustworthy Incentive Mechanism from data trustfulness, effort trustfulness, and quality trustfulness, respectively. Extensive experiments are carried out to validate the proposed trust evaluation and incentive mechanisms. The results corroborate that the proposed mechanisms can efficiently stimulate mobile edge users to perform evaluation task and improve the accuracy of trust evaluation.
107 citations
Cites background from "Sustainable and Efficient Data Coll..."
...For distributed trust evaluation mechanisms, the relative long distance between underlying node networks and cloud makes it hard for cloud to obtain the fine-grained information of each end node [9, 29, 30]....
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TL;DR: A comprehensive taxonomy of sustainable cloud computing is proposed to investigate the existing techniques for sustainability that need careful attention and investigation as proposed by several academic and industry groups.
Abstract: The cloud-computing paradigm offers on-demand services over the Internet and supports a wide variety of applications. With the recent growth of Internet of Things (IoT)--based applications, the use of cloud services is increasing exponentially. The next generation of cloud computing must be energy efficient and sustainable to fulfill end-user requirements, which are changing dynamically. Presently, cloud providers are facing challenges to ensure the energy efficiency and sustainability of their services. The use of a large number of cloud datacenters increases cost as well as carbon footprints, which further affects the sustainability of cloud services. In this article, we propose a comprehensive taxonomy of sustainable cloud computing. The taxonomy is used to investigate the existing techniques for sustainability that need careful attention and investigation as proposed by several academic and industry groups. The current research on sustainable cloud computing is organized into several categories: application design, sustainability metrics, capacity planning, energy management, virtualization, thermal-aware scheduling, cooling management, renewable energy, and waste heat utilization. The existing techniques have been compared and categorized based on common characteristics and properties. A conceptual model for sustainable cloud computing has been presented along with a discussion on future research directions.
97 citations
Cites background from "Sustainable and Efficient Data Coll..."
...The evolution of energy management techniques (see Figure 23) and their comparison along with open research challenges [3, 25, 60, 61, 62, 66, 75, 76, 41, 60, 67, 125, 83, 74, 80] are presented in Table 13 of Appendix C....
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TL;DR: Theoretical analyses and experimental results indicate that the QTSAS protocol can greatly improve network performance compared with existing Quorum-based MAC protocols.
Abstract: Millions of sensors are deployed to monitor the smart grid. They consume huge amounts of energy in the communication infrastructure. Therefore, the establishment of an energy-efficient medium access control (MAC) protocol for sensor nodes is challenging and urgently needed. The Quorum-based MAC protocol independently and adaptively schedules nodes’ wake-up times and decreases idle listening and collisions, thereby increasing the network throughput and extending the network lifetime. A novel Quorum time slot adaptive condensing (QTSAC)-based MAC protocol is proposed for achieving delay minimization and energy efficiency for the wireless sensor networks (WSNs). Compared to previous protocols, the QTSAC-based MAC protocol has two main novelties: 1) It selects more Quorum time slots (QTSs) than previous protocols in the area that is far from the sink according to the energy consumption in WSNs to decrease the network latency and 2) It allocates QTSs only when data are transmitted to further decrease the network latency. Theoretical analyses and experimental results indicate that the QTSAS protocol can greatly improve network performance compared with existing Quorum-based MAC protocols. For intermediate-scale wireless sensor networks, the method that is proposed in this paper can enhance the energy efficiency by 24.64%–82.75%, prolong the network lifetime by 58%–27.31%, and lower the network latency by 3.59%–29.23%.
96 citations
Cites background from "Sustainable and Efficient Data Coll..."
...Moreover, because of the sheer number of human beings, a big data network [14], [15] is formed, which combines cloud computing [11], [16] with the Internet of Things (IoTs) [17], [18] and brings great convenience for smart energy management....
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References
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TL;DR: This paper study and solve the problem of personalized multi-keyword ranked search over encrypted data (PRSE) while preserving privacy in cloud computing with the help of semantic ontology WordNet, and proposes two PRSE schemes for different search intentions.
Abstract: In cloud computing, searchable encryption scheme over outsourced data is a hot research field. However, most existing works on encrypted search over outsourced cloud data follow the model of “one size fits all” and ignore personalized search intention. Moreover, most of them support only exact keyword search, which greatly affects data usability and user experience. So how to design a searchable encryption scheme that supports personalized search and improves user search experience remains a very challenging task. In this paper, for the first time, we study and solve the problem of personalized multi-keyword ranked search over encrypted data (PRSE) while preserving privacy in cloud computing. With the help of semantic ontology WordNet, we build a user interest model for individual user by analyzing the user’s search history, and adopt a scoring mechanism to express user interest smartly. To address the limitations of the model of “one size fit all” and keyword exact search, we propose two PRSE schemes for different search intentions. Extensive experiments on real-world dataset validate our analysis and show that our proposed solution is very efficient and effective.
665 citations
"Sustainable and Efficient Data Coll..." refers background in this paper
...oped to work as a strong backbone for WSNs [6], [7], [8]....
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632 citations
Additional excerpts
...applied to the sensor-cloud environment [37]....
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TL;DR: This article defines WSNs with MEs and provides a comprehensive taxonomy of their architectures, based on the role of the MEs, and provides an extensive survey of the related literature.
Abstract: Wireless sensor networks (WSNs) have emerged as an effective solution for a wide range of applications. Most of the traditional WSN architectures consist of static nodes which are densely deployed over a sensing area. Recently, several WSN architectures based on mobile elements (MEs) have been proposed. Most of them exploit mobility to address the problem of data collection in WSNs. In this article we first define WSNs with MEs and provide a comprehensive taxonomy of their architectures, based on the role of the MEs. Then we present an overview of the data collection process in such a scenario, and identify the corresponding issues and challenges. On the basis of these issues, we provide an extensive survey of the related literature. Finally, we compare the underlying approaches and solutions, with hints to open problems and future research directions.
540 citations
TL;DR: A new method of keyword transformation based on the uni-gram is developed, which will simultaneously improve the accuracy and creates the ability to handle other spelling mistakes and consider the keyword weight when selecting an adequate matching file set.
Abstract: Keyword-based search over encrypted outsourced data has become an important tool in the current cloud computing scenario. The majority of the existing techniques are focusing on multi-keyword exact match or single keyword fuzzy search. However, those existing techniques find less practical significance in real-world applications compared with the multi-keyword fuzzy search technique over encrypted data. The first attempt to construct such a multi-keyword fuzzy search scheme was reported by Wang et al. , who used locality-sensitive hashing functions and Bloom filtering to meet the goal of multi-keyword fuzzy search. Nevertheless, Wang’s scheme was only effective for a one letter mistake in keyword but was not effective for other common spelling mistakes. Moreover, Wang’s scheme was vulnerable to server out-of-order problems during the ranking process and did not consider the keyword weight. In this paper, based on Wang et al. ’s scheme, we propose an efficient multi-keyword fuzzy ranked search scheme based on Wang et al. ’s scheme that is able to address the aforementioned problems. First, we develop a new method of keyword transformation based on the uni-gram, which will simultaneously improve the accuracy and creates the ability to handle other spelling mistakes. In addition, keywords with the same root can be queried using the stemming algorithm. Furthermore, we consider the keyword weight when selecting an adequate matching file set. Experiments using real-world data show that our scheme is practically efficient and achieve high accuracy.
464 citations
"Sustainable and Efficient Data Coll..." refers background in this paper
...oped to work as a strong backbone for WSNs [6], [7], [8]....
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TL;DR: Simulation results show that the proposed LARP outperforms the existing routing protocols in terms of packet delivery ratio and normalized routing overhead, and are expected to be of greater value than other existing solutions in underwater environment.
Abstract: As the network communications technology developing, a new type of networks has appeared in the daily life which is named underwater sensor networks (UWSNs). UWSNs are a class of emerging networks that experience variable and high propagation delays and limited available bandwidth. There are comprehensive applications in this area such as oceanographic data collection, pollution monitoring, offshore exploration, assisted navigation and so on. Due to the different environment under the ocean, routing protocols in UWSNs should be re-designed to fit for the surroundings. In particular, routing protocols in UWSNs should ensure the reliability of message transmission, not just decrease the delay. In this paper, we propose a novel routing protocol named Location-Aware Routing Protocol (LARP) for UWSNs, where the location information of nodes is used to help the transmission of the message. Simulation results show that the proposed LARP outperforms the existing routing protocols in terms of packet delivery ratio and normalized routing overhead. We expect LARP to be of greater value than other existing solutions in underwater environment.
384 citations
"Sustainable and Efficient Data Coll..." refers background in this paper
...ditional WSNs, sensors transmit data to the static sink through multiple hops wireless communication [19], [20]....
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