Sensor Cloud Research Articles

Energy Efficient Opportunistic Sensing Management in Fog Cloud Environment

With so many people now wearing mobile devices with sensors (such as smartphones), utilizing the immense capabilities of these business mobility goods has become a prospective skill to significant behavioural and ecological sensors. A potential challenge for pervasive context assessment is opportunistic sensing, has been effectively used to a wide range of applications. The sensor cloud combines cloud technology with a wireless sensor, resulting in a scalable and cost-effective computing platform for real-time applications. Because the sensor's battery power is limited and the data centre’s servers consume a significant amount of energy to supply storage, a sensor cloud must be energy efficient. This study provides a Fog-based semantic for enabling these kinds of technologies quickly and successfully. The suggested structure is comprised of fundamental algorithms to help set up and coordinate the fog sensing jobs. It creates effective multihop routes for coordinating relevant devices and transporting acquired sensory data to fog sinks. It was claimed that energy-efficient sensor cloud approaches were categorized into different groups and that each technology was examined using numerous characteristics. The outcomes of a series of thorough test simulation in NS3 to define the practicality of the created console, as well as the proportion of each parameter utilized for each technology, are computed.

Energy-Efficient Cloud-Integrated Sensor Network Model Based on Data Forecasting Through ARIMA

An energy-efficient Model for Sensor-cloud is proposed based on data forecasting through an autoregressive integrated moving average (ARIMA). Generally, all the user requests are redirected to the wireless sensor network (WSN) through the cloud. In the traditional approach, user requests are generated every fifteen minutes, so the sensor must send data to the cloud every fifteen minutes. In the current approach, the sensors within the WSN communicate with the cloud every two hours. The data forecasting technique addresses most of the user requests using the ARIMA one-step ahead forecasting model in the cloud. This results in less frequency of data communication, thereby increasing the battery life of the sensor. The ARIMA-based forecasting model provides better accuracy because of fewer temperature data changes with respect to the current temperature, for the next two hours. The proposed method for the simulation in the sensor cloud system consumes significantly less energy than the traditional approach, and the error in forecasting becomes highly negligible.

Sensor Cloud Frameworks: State-of-the-Art, Taxonomy, and Research Issues

In recent times, Wireless Sensor Network (WSN) technology has received significant attention owing to its numerous applications in various mission critical services, such as healthcare, military monitoring, public safety systems, and forest monitoring. However, sensor node’s low computational power and inadequate advancements in battery design hinder resource-rigorous applications from fully exploiting WSN capabilities. The trend of shifting computations and storage to remote clouds offers the opportunity to integrate WSNs into the cloud to mitigate their limitations. The Sensor Cloud augments WSN capabilities of remote sensing and monitoring while exploiting resource-rich cloud infrastructure. To date, numerous solutions have been proposed to handle the integration concerns of WSNs with the cloud. However, a comprehensive study that covers various Sensor Cloud aspects in terms of architecture, network dynamics, heterogeneity, communication patterns, data management, and security, is still lacking. To fill this gap, state-of-the-art sensor cloud integration frameworks are analyzed in this article. Moreover, a detailed thematic taxonomy is presented to classify existing sensor cloud frameworks while focusing in service oriented and client server architectures. Related features and critical aspects of existing frameworks are inspected through a qualitative analysis based on the parameters selected from the literature and extracted from the taxonomy. Several research opportunities in this research domain are suggested to assist researchers in identifying and designing optimal resource sensor cloud integration frameworks to host emerging applications.

Using Internet of Things application for Monitoring Photo-Voltaic Panel Based on Ask Sensors Cloud

Internet of Things (IOT) devices and monitoring systems are essential for the accurate, reliable, and proper functioning of photovoltaic (PV) solar energy systems. The data acquisition and monitoring system ensure adequate operation and ascertain system malfunctions before they become significant problems. The IOT integrates ordinary items using modern technology in the physical scope. It is all feasible to utility voltage, current, temperature, and humidity sensors. The current research focuses on developing a cost-effective, accessible, and steadfast data monitoring system. A microcontroller-based data logger is stored in a cloud. The data can be downloaded directly from the website using the IoT technique because photovoltaic (PV) panels are built-in faraway locations and humans cannot reach them. The suggested system demonstrates the online utilization of solar PV power.

Secure Top-k query in edge-computing-assisted sensor-cloud systems

Sensor cloud provides a new way for the owners of different WSNs (Wireless Sensor Networks) to share their data to other parties (users/organizations). The edge-computing-assisted sensor-cloud systems (EC-assisted SCSs) can even supply real-time services for different parties. However, both the edge servers and the cloud servers in the systems are prone to be attacked in insecure network environments, since they are the bridges connecting both the end users and the WSNs in EC-assisted SCSs and attacking the servers can bring much more benefits for the attackers than just compromising some sensor nodes. In this paper, the problem of secure Top- k query in EC-assisted SCSs was studied, considering that non-trusted edge servers and non-trusted cloud servers exist in the systems, and a novel scheme was proposed to solve the problem. To achieve privacy preservation of the sensed data items and their scores, a secure key distribution protocol was designed, and some data-processing or integrity verification algorithms were also developed in the proposed scheme to preserve the data integrity of the query results. Thorough security analyses and extensive simulations show that the proposed scheme is efficient and secure, and it is perfectly fit for Top- k query in EC-assisted SCSs.

A Novel Machine Learning-Based Approach for Outlier Detection in Smart Healthcare Sensor Clouds

A smart healthcare sensor cloud is an amalgamation of the body sensor networks and the cloud that facilitates the early diagnosis of diseases and the real-time monitoring of patients. Sensitive data of the patients which are stored in the cloud must be free from outliers that may be caused by malfunctioned hardware or the intruders. This paper presents a machine learning-based scheme for outlier detection in smart healthcare sensor clouds. The proposed scheme is a hybrid of clustering and classification techniques in which a two-level framework is devised to identify the outliers precisely. At the first level, a density-based scheme is used for clustering while at the second level, a Gaussian distribution-based approach is used for classification. This scheme is implemented in Python and compared with a clustering-based approach (Mean Shift) and a classification-based approach (Support Vector Machine) on two different standard datasets. The proposed scheme is evaluated on various performance metrics. Results demonstrate the superiority of the proposed scheme over the existing ones.

A lightweight verifiable trust based data collection approach for sensor–cloud systems

A Lightweight Verifiable Trust based Data Collection (LVT-DC) approach is proposed to obtain credible data for mobile vehicles network, in which, a large number of IoT devices are deployed in smart city, and Mobile Vehicles (MVs) collect data from IoT devices and report data to cloud to construct various applications. First, some IoT devices are selected as core-IoT device whose data more than others. Then, Unmanned Aerial Vehicles (UAV) delivers a short verification code to each core-IoT devices and collects its data, and it embeds verification code into the data when the MVs collect it in order for cloud to check. Last, a verifiable trust inference method is proposed which includes two types of trust calculations. (a) Direct trust: MVs If the reported data cannot recover the verification code or is inconsistent with the UAV collection result, reduce the trust of the MVs, (b) Trust inference: If the data of a non-core-IoT devices reported by a MVs is inconsistent with the trusted MVs, then reduce its trust. After a large number of experimental results, the LVT-DC approach can quickly and accurately identify the credibility of the data collector and ensure credible data collection.

A Novel Machine Learning-Based Approach for Outlier Detection in Smart Healthcare Sensor Clouds

A smart healthcare sensor cloud is an amalgamation of the body sensor networks and the cloud that facilitates the early diagnosis of diseases and the real-time monitoring of patients. Sensitive data of the patients which are stored in the cloud must be free from outliers that may be caused by malfunctioned hardware or the intruders. This paper presents a machine learning-based scheme for outlier detection in smart healthcare sensor clouds. The proposed scheme is a hybrid of clustering and classification techniques in which a two-level framework is devised to identify the outliers precisely. At the first level, a density-based scheme is used for clustering while at the second level, a Gaussian distribution-based approach is used for classification. This scheme is implemented in Python and compared with a clustering-based approach (Mean Shift) and a classification-based approach (Support Vector Machine) on two different standard datasets. The proposed scheme is evaluated on various performance metrics. Results demonstrate the superiority of the proposed scheme over the existing ones.

Recommended System for Cluster Head Selection in a Remote Sensor Cloud Environment Using the Fuzzy-Based Multi-Criteria Decision-Making Technique

Clustering is an energy-efficient routing algorithm in a sensor cloud environment (SCE). The clustering sensor nodes communicate with the base station via a cluster head (CH), which can be selected based on the remaining energy, the base station distance, or the distance from the neighboring nodes. If the CH is selected based on the remaining energy and the base station is far away from the cluster head, then it is not an energy-efficient selection technique. The same applies to other criteria. For CH selection, a single criterion is not sufficient. Moreover, the traditional clustering algorithm head nodes keep changing in every round. Therefore, the traditional algorithm energy consumption is less, and nodes die faster. In this paper, the fuzzy multi-criteria decision-making (F-MCDM) technique is used for CH selection and a threshold value is fixed for the CH selection. The fuzzy analytical hierarchy process (AHP) and the fuzzy analytical network process (ANP) are used for CH selection. The performance evaluation results exhibit a 5% improvement compared to the fuzzy AHP clustering method and 10% improvement compared to the traditional method in terms of stability, energy consumption, throughput, and control overhead.

A Novel Machine Learning-Based Approach for Outlier Detection in Smart Healthcare Sensor Clouds

A smart healthcare sensor cloud is an amalgamation of the body sensor networks and the cloud that facilitates the early diagnosis of diseases and the real-time monitoring of patients. Sensitive data of the patients which are stored in the cloud must be free from outliers that may be caused by malfunctioned hardware or the intruders. This paper presents a machine learning-based scheme for outlier detection in smart healthcare sensor clouds. The proposed scheme is a hybrid of clustering and classification techniques in which a two-level framework is devised to identify the outliers precisely. At the first level, a density-based scheme is used for clustering while at the second level, a Gaussian distribution-based approach is used for classification. This scheme is implemented in Python and compared with a clustering-based approach (Mean Shift) and a classification-based approach (Support Vector Machine) on two different standard datasets. The proposed scheme is evaluated on various performance metrics. Results demonstrate the superiority of the proposed scheme over the existing ones.

Secure Healthcare Monitoring Sensor Cloud With Attribute-Based Elliptical Curve Cryptography

Sensor networks are integrated with cloud in many internet of things (IoT) applications for various benefits. Healthcare monitoring sensor cloud is one of the application that allows storing the patients' health data generated by their wearable sensors at cloud and facilitates the authorized doctors to monitor and advise them remotely. Patients' data at cloud must be secure. Existing security schemes (e.g., key policy attribute-based encryption [KP-ABE] and ciphertext policy attribute-based encryption [CP-ABE]) have higher computational overheads. In this paper, a security mechanism called attribute-based elliptical curve cryptography (ABECC) is proposed that guarantees data integrity, data confidentiality, and fine-grained access control. It also reduces the computational overheads. ABECC is implemented in .NET framework. Use of elliptical curve cryptography (ECC) in ABECC reduces the key length, thereby improving the encryption, decryption, and key generation time. It is observed that ABECC is 1.7 and 1.4 times faster than the existing approaches of KP-ABE and CP-ABE, respectively.

Fog computing based information classification in sensor cloud-agent approach

Sensor Cloud has emerged as a rising technology in removing the barriers of Wireless Sensor Network. It increases the lifetime of the sensor network by storing all the sensed information into cloud server instead of saving in node’s memory. Yet sensor cloud has some important issues like latency, information classification and accuracy which still need an attention to improve overall performance of the sensor cloud system. Fog computing is a problem solver for sensor cloud in removing latency and carrying out computational tasks in faster way. Fog computing is acting as a middleware between physical network and cloud, located at the edge of the network for fast computation and quick response. The proposed work utilizes the functionalities of sensor cloud and fog computing to classify and save the information in better way along with minimizing the latency issue. The Random forest classifier along with Genetic Algorithm is utilized for classifying the information and saving only required amount of information into cloud server with priority. Agent paradigm is included to save the energy of the physical sensor nodes and also to perform quick analyzing and classification of information at the fog server. The result shows that proposed work is working far better than conventional methods in terms of classification accuracy, latency, packet delivery ratio, energy consumption and network lifetime.

A Resource-constrained Edge IoT Device Data-deduplication Method with Dynamic Asymmetric Maximum

Smart vehicles use sophisticated sensors to capture real-time data. Due to the weak communication capabilities of wireless sensors, these data need to upload to the cloud for processing. Sensor clouds can resolve these drawbacks. However, there is a large amount of redundant data in the sensor cloud, occupying a large amount of storage space and network bandwidth. Deduplication can yield cost savings by storing one data copy. Chunking is essential because it can determine the performance of deduplication. Content-Defined Chunking (CDC) can effectively solve the problem of chunk boundaries shifted, but it occupies a lot of computing resources and has become a bottleneck in deduplication technology. This paper proposes a Dynamic Asymmetric Maximum algorithm (DAM), which uses the maximum value as the chunk boundaries and reducing the impact of the low-entropy string. It also uses the perfect hash algorithm to optimize the chunk search. Experiments show that our solution can effectively detect low-entropy strings in redundant data, save storage resources, and improve sensor clouds system throughput.

Agent-Based Improved Neuro-Fuzzy for Load Balancing in Sensor Cloud

Sensor cloud paradigm is a trending area for most of the applications. It collects the information from physical sensors and stores it in cloud servers, and it can be accessed anywhere. Energy optimization is one of the crucial issues in sensor cloud as sensed information are unprocessed and directly saved into cloud server thereby increasing energy consumption and delay which leads to unbalancing in the network. In this paper, agent-based improved neuro-fuzzy optimization is proposed to avoid transmission of redundant information into cloud along with load balancing among all nodes for equal energy consumption. The agents work on behalf of node, migrate to each node in the cluster, collect information, and submit to CH minimizing node energy consumption. Neuro-fuzzy along with weights is used to improve information accuracy and reducing energy consumption to improve overall network lifetime. Result shows that less energy is consumed along with minimum delay and information with great accuracy is saved into cloud server.

K-Anonymous Query Scheme on the Internet of Things: a Zero Trust Architecture

The paper investigates query-anonymity in Internet of things (IoT) formed by a sensor cloud, where the sensor nodes provide services of sensing and are subject to user queries of sensing data. Due to the heterogeneity and multi-carrier natures of the sensor cloud, user privacy could be impaired when the queries have to go through nodes of a third party. Thus, the paper firstly introduces a novel query k-anonymity scheme that countermeasures such a privacy threat. Based on the proposed k-anonymity scheme, the trade-offs between the achieved query-anonymity and various performance measures including, communication-cost, return-on-investment metric, path-length, and location anonymity metrics, are analyzed. By adopting a hybrid approach that takes into account the average and worst-case analysis, our evaluation results show that most of the obtained bounds on various performance anonymity trade-offs can be expressed precisely in terms of the offered level-of-anonymity k and network diameter d.

An Improved Dynamic Thermal Current Rating Model for PMU-Based Wide Area Measurement Framework for Reliability Analysis Utilizing Sensor Cloud System

Information technology expressively improves remote electricity measurement and monitoring. Integrating Dynamic Thermal Current Rating (DTCR) software packs with the exclusive phasor measurement-based Wide Area Measurement (WAM) framework, the remote Transmission Lines (TLs) current rating can be measured. WAM is used for data acquisition from different sensors, and also allows data transmissions and processing for which sensor cloud system (SCS) plays a vital role. DTCR with phasor-measurement based WAM framework is mainly used to analyze and determine the current ratings of overhead TLs using weather condition estimation or prediction methods. However, the recent study suggests that the accuracy of the DTCR has become an issue in the smart grid of Sarawak Energy Berhad (SEB). Hence, this article studies and discusses the relevant models and systems, and then proposes an improved thermal pi (π) model for the transmission line thermal model of DTCR software in WAM Framework. The performance of the improved π model will be distinguished from the existing thermal model. The weather factors that bring a substantial impact on the current rating is also considered, where the relevant data is monitored via different weather sensors. Besides, this study also focuses on calibrating the DTCR through phasor measurement in the WAM system, as well as the field measured data. All the data is collected from relevant sensors, and a detailed comparative analysis is provided based on the proposed model for the sake of improving the reliability of the system. The performance analysis of the thermal models is evaluated using Matlab software-based numerical analysis.

Quasi‐Identifier Recognition Algorithm for Privacy Preservation of Cloud Data Based on Risk Reidentification

Cloud computing plays an essential role as a source for outsourcing data to perform mining operations or other data processing, especially for data owners who do not have sufficient resources or experience to execute data mining techniques. However, the privacy of outsourced data is a serious concern. Most data owners are using anonymization‐based techniques to prevent identity and attribute disclosures to avoid privacy leakage before outsourced data for mining over the cloud. In addition, data collection and dissemination in a resource‐limited network such as sensor cloud require efficient methods to reduce privacy leakage. The main issue that caused identity disclosure is quasi‐identifier (QID) linking. But most researchers of anonymization methods ignore the identification of proper QIDs. This reduces the validity of the used anonymization methods and may thus lead to a failure of the anonymity process. This paper introduces a new quasi‐identifier recognition algorithm that reduces identity disclosure which resulted from QID linking. The proposed algorithm is comprised of two main stages: (1) attribute classification (or QID recognition) and (2) QID dimension identification. The algorithm works based on the reidentification of risk rate for all attributes and the dimension of QIDs where it determines the proper QIDs and their suitable dimensions. The proposed algorithm was tested on a real dataset. The results demonstrated that the proposed algorithm significantly reduces privacy leakage and maintains the data utility compared to recent related algorithms.

Sensor Cloud Intrusion Detection Based on Discrete Optimization Algorithm and Machine Learning

Sensor Cloud Intrusion Detection Based on Discrete Optimization Algorithm and Machine Learning

New Era of IoT Network Protection and PackagesThrough Prolonged Range

In this research mission, we motive to construct a protracted-variety (ProlongedRange)-primarily based net of factors (IoT) comfortable localization device and appliance primarily based totally on multisensory synthesiscomputation. The Prolonged range generation is used to layout a community security device and right now deal with the computing device, wherein the purpose is to broaden a community server host that collects and approaches function signals from the multisensingsignal series and evaluation processing module and at once detects region by using community nodes thru the sensors cloud, excessive-degree improvement platform, and the multisensory fusion computing workstations, which send the consequences to the crucial monitoring gadget thru the Wi-Fi gadgets of the Prolonged range network. The relaxed localization computing chip very final outcomes, as advanced on this mission, can be used in the domain names of electricity control, environmental manipulate, facts management, manufacturing unit monitoring, and renewable strength manipulate. The device of this task comprises prolonged range hosts, which get hold of signs from numerous nodes and are linked to multisensory fusion arithmetic device via a Wi-Fi network. To sum up, in this look at, we emphasize the usage of multisensory fusion computing generation to implement a relaxed localization gadget of a wireless sensor network technology (WSN), and we remember the use of the embedded system and prolonged range era to broaden a monitoring machine for manufacturing facility fire manage anti-theft, energy, facts, and protection primarily based on cozy localization. In this study, we pass domain names and integrate associated engineering automation, community safety technology, multisensory synthesis computation design, and the (ProlongedRange) localization method, and the study’sfindings are anticipated to make a contribution to the network protection of the protection organization and studies at theLora IoTlocalization method.

Fuzzy based Load Balancing in Sensor Cloud: Multi-Agent Approach

In recent years, sensor cloud showing its impact in the networking field and also it is being used for most of the applications. The major concern for sensor cloud is its lifetime because yet nodes are operated with limited energy, bandwidth and exhaust very soon affecting the whole network. To make reliable communication and optimizing the whole system for improving network lifetime, load balancing concept is included in sensor cloud. The fuzzy optimizer is responsible for finding the deserving cluster head and precise next hop node for information transmission. Fuzzy method is conducted at the sensor cloud server and the information is shared to sink node for physical network setup. Based on the importance of the information coming from physical network, it can be saved into priority or non priority servers using information classification technique leading to load balancing at server also. Agents are triggered into the network for information collection along with truthful information delivery ratio in a minimum time. The proposed method is compared with existing popular methods to check the load balancing capacity, and it is found that the proposed work is far better than existing methods with respect to response time, delay, energy consumption, minimum packets transmission and network lifetime.