Sensor Cloud Research Articles

Gaussian Distribution-Based Machine Learning Scheme for Anomaly Detection in Healthcare Sensor Cloud

Smart information systems are based on sensors that generate a huge amount of data. This data can be stored in cloud for further processing and efficient utilization. Anomalous data might be present within the sensor data due to various reasons (e.g., malicious activities by intruders, low quality sensors, and node deployment in harsh environments). Anomaly detection is crucial in some applications such as healthcare monitoring systems, forest fire information systems, and other internet of things (IoT) systems. This paper proposes a Gaussian distribution-based supervised machine learning scheme of anomaly detection (GDA) for healthcare monitoring sensor cloud, which is an integration of various body sensors of different patients and cloud. This work is implemented in Python. Use of Gaussian statistical model in the proposed scheme improves precision, throughput, and efficiency. GDA provides 98% efficiency with 3% and 4% improvements as compared to the other supervised learning-based anomaly detection schemes (e.g., support vector machine [SVM] and self-organizing map [SOM], respectively).

Score Prediction Algorithm Combining Deep Learning and Matrix Factorization in Sensor Cloud Systems

In this era of exponential growth in the scale of data, information overload has become an urgent problem, and the use of increasingly flexible sensor cloud systems (SCS) for data collection has become a mainstream trend. Recommendation algorithms can search massive data sets to uncover information that meets the needs of users based on their interests. To improve the accuracy of recommendation scoring, this article proposes a score prediction algorithm that combines deep learning and matrix factorization. To address the problem of sparse scoring data, our study employs a sensor cloud system to collect data information, preprocesses the collected information, and then uses a deep learning model combined with explicit and implicit feedback to generate recommendations. The proposed algorithm, MF-NeuRec, combines fusion matrix decomposition and the NeuRec model score prediction algorithm. The algorithm employs user-based and item-based NeuRec algorithms to extract the feature vectors of users and items under implicit feedback data. The obtained user and item feature vectors are integrated in a certain ratio through the use of matrix decomposition under the display feedback data. The user and item feature vectors obtained by the algorithm are merged and analyzed to predict how users will rate items. Experiments demonstrate that the algorithm can improve the accuracy of recommendations.

Priority Arbiter for Tiny OS The need of renown OS for WSN and IoT

In the present technical era, we are extremely dependent on technological applications such as internet, multimedia, social media, home automation, industrial automation, medical instrumentation, web technology so on and so forth. Moreover, as a backbone such applications are supporting the research related to science and technology in turn. There are certain technologies for example Wireless Sensor Network, IOTs, Artificial Intelligence, and Cloud Computing etc., working behind these applications as unseen hands. Nowadays in all these facilities, there is much more advancement and high demand for real-time applications to serve interactive services. Such necessities enforce the technologies to upgrade themselves to their next level. As such, in WSN, the existing Operating Systems also should upgrade interms of different concerns such as memory management, scheduling techniques, power supply scarcity issues and overall efficient utilization of available limited resources. In this regard, here is an attempt to improvise TinyOS, which is the popular OS for WSN and IOT. The survey on WSN applications reveals, what sort of improvisations are necessary to fulfill the requirements of varieties of applications. In that direction, for more efficient scheduling of tasks based on the situation, new technique is required. Being the best OS for low power devices of WSN and IOT, TinyOS hinders to support many application those need different type of scheduling than FCFS, which is the only scheduling technique for TinyOS. Hence, Integration of new Priority arbiter as a first step of main scheduler improvisation is the essence of this paper.

Towards Pricing for Sensor-Cloud

Motivated by complementing the ubiquitous wireless sensor networks (WSNs) and powerful cloud computing (CC), a lot of attention from both industry and academia has been drawn to Sensor-Cloud (SC). However, SC pricing is barely investigated. Towards pricing for SC, this paper 1) introduces five SC Pricing Models (SCPMs) first. Specifically, to charge a SC user, each SCPM considers one of the following factors respectively: i) the lease period of the SC user; ii) the required working time of SC; iii) the SC resources utilized by the SC user; iv) the volume of sensory data obtained by the SC user; v) the SC path that transmits sensory data from the WSN to the SC user. Further, this paper 2) performs analysis to discuss and exhibit the characteristics of the proposed SCPMs. With that, this paper 3) presents the case studies regarding the application of SCPMs. Eventually, this paper 4) conducts a review about the user behavior study. This paper aims to serve as a very favorable guidance for future research about pricing in SC.

A novel approach for energy- and memory-efficient data loss prevention to support Internet of Things networks

Internet of Things integrates various technologies, including wireless sensor networks, edge computing, and cloud computing, to support a wide range of applications such as environmental monitoring and disaster surveillance. In these types of applications, IoT devices operate using limited resources in terms of battery, communication bandwidth, processing, and memory capacities. In this context, load balancing, fault tolerance, and energy and memory efficiency are among the most important issues related to data dissemination in IoT networks. In order to successfully cope with the abovementioned issues, two main approaches—data-centric storage and distributed data storage—have been proposed in the literature. Both approaches suffer from data loss due to memory and/or energy depletion in the storage nodes. Even though several techniques have been proposed so far to overcome the abovementioned problems, the proposed solutions typically focus on one issue at a time. In this article, we propose a cross-layer optimization approach to increase memory and energy efficiency as well as support load balancing. The optimization problem is a mixed-integer nonlinear programming problem, and we solve it using a genetic algorithm. Moreover, we integrate the data-centric storage features into distributed data storage mechanisms and present a novel heuristic approach, denoted as Collaborative Memory and Energy Management, to solve the underlying optimization problem. We also propose analytical and simulation frameworks for performance evaluation. Our results show that the proposed method outperforms the existing approaches in various IoT scenarios.

CyanoTRACKER: A cloud-based integrated multi-platform architecture for global observation of cyanobacterial harmful algal blooms

Over the past decade, the global proliferation of cyanobacterial harmful algal blooms (CyanoHABs) have presented a major risk to the public and wildlife, and ecosystem and economic services provided by inland water resources. As a consequence, water resources, environmental, and healthcare agencies are in need of early information about the development of these blooms to mitigate or minimize their impact. Results from various components of a novel multi-cloud cyber-infrastructure referred to as “CyanoTRACKER” for initial detection and continuous monitoring of spatio-temporal growth of CyanoHABs is highlighted in this study. The novelty of the CyanoTRACKER framework is the collection and integration of combined community reports (social cloud), remote sensing data (sensor cloud) and digital image analytics (computation cloud) to detect and differentiate between regular algal blooms and CyanoHABs. Individual components of CyanoTRACKER include a reporting website, mobile application (App), remotely deployable solar powered automated hyperspectral sensor (CyanoSense), and a cloud-based satellite data processing and integration tool. All components of CyanoTRACKER provided important data related to CyanoHABs assessments for regional and global water bodies. Reports and data received via social cloud including the mobile App, Twitter, Facebook, and CyanoTRACKER website, helped in identifying the geographic locations of CyanoHABs affected water bodies. A significant increase (124.92%) in tweet numbers related to CyanoHABs was observed between 2011 (total relevant tweets = 2925) and 2015 (total relevant tweets = 6579) that reflected an increasing trend of the harmful phenomena across the globe as well as an increased awareness about CyanoHABs among Twitter users. The CyanoHABs affected water bodies extracted via the social cloud were categorized, and smaller water bodies were selected for the deployment of CyanoSense, and satellite data analysis was performed for larger water bodies. CyanoSense was able to differentiate between ordinary algae and CyanoHABs through the use of their characteristic absorption feature at 620 nm. The results and products from this infrastructure can be rapidly disseminated via the CyanoTRACKER website, social media, and direct communication with appropriate management agencies for issuing warnings and alerting lake managers, stakeholders and ordinary citizens to the dangers posed by these environmentally harmful phenomena.

Mobile Physiological Sensor Cloud System for Long-term Care

This world will soon enter its ageing and aged era. Many old people will be there on the earth. However, traditional long-term care (LTC) models, particularly for taking care of elderly, consume a huge amount of resources which have been invested and can be invested in current stage are terribly insufficient. To solve a part of this problem, in this study, we propose a Mobile Physiological Sensor Cloud System for Long-term Care (MPCLC), the function of which is collecting care-receiver's physiological data by using sensors. With the features of small in size, high convenience for use, and good immediacy of request response, it can partially solve the problems of LTC which are that the required labors are insufficient and the results of some traditional medical treatments are hard to track. After the data measured by using sensors are collected, the data are sent to the cloud for storage. Then the health reports will be calculated by the cloud, and delivered to care-givers and medical staffs for care reference under their requests. This can save the time that people go and come between hospitals and the places where care-receivers stay. One of the other key functions is to achieve the goal of preventing disease in advance.

Sensor Cloud Based Architecture with Efficient Data Computation and Security Implantation for Internet of Things Application

The growth of the information and communication technology has led the world to experience more sophisticated service that were once thought to be a fantasy. The emergence of the internet of things has further taken the world one step ahead by enabling the tangible things around to communicate. The IOT affords to offer more advanced services by the incorporation of the wireless sensors. The collection of the information and its delivery in the platform of internet of things is taken care by the enormous number of smart devices present in it and the numerous of smart devices present cause a very high amount of data flow. The issue arises in such platforms of internet of things in handling, regulating, storing and computing the huge amount of data flow. Move over the security of the data are also uncertain. To manage all these the paper delivers a sensor cloud based architecture with highly efficient computation and the security implantation for the application of internet of things. The proposed model improves the computation capability and the security of the data by using cloud service, the machine learning and the encryption scheme. The network simulator -3 evaluates the performance of the delivered model that provides a satisfying and sustaining results than the existing methods. The experiments were carried out on the grounds of the longevity of the network, overhead in the transmission the amount of energy used and as well as the packet drop ratio. The results attained on each ground were found be 25%, 30%, 38.76% and 50.7% better compared to the state of art approaches.

SASC: Secure and Authentication-Based Sensor Cloud Architecture for Intelligent Internet of Things.

Nowadays, the integration of Wireless Sensor Networks (WSN) and the Internet of Things (IoT) provides a great concern for the research community for enabling advanced services. An IoT network may comprise a large number of heterogeneous smart devices for gathering and forwarding huge data. Such diverse networks raise several research questions, such as processing, storage, and management of massive data. Furthermore, IoT devices have restricted constraints and expose to a variety of malicious network attacks. This paper presents a Secure Sensor Cloud Architecture (SASC) for IoT applications to improve network scalability with efficient data processing and security. The proposed architecture comprises two main phases. Firstly, network nodes are grouped using unsupervised machine learning and exploit weighted-based centroid vectors for the development of intelligent systems. Secondly, the proposed architecture makes the use of sensor-cloud infrastructure for boundless storage and consistent service delivery. Furthermore, the sensor-cloud infrastructure is protected against malicious nodes by using a mathematically unbreakable one-time pad (OTP) encryption scheme to provide data security. To evaluate the performance of the proposed architecture, different simulation experiments are conducted using Network Simulator (NS3). It has been observed through experimental results that the proposed architecture outperforms other state-of-the-art approaches in terms of network lifetime, packet drop ratio, energy consumption, and transmission overhead.

Task scheduling using edge computing system in smart city

SummaryA smart city will become an inevitable trend of social development. It includes many Cyber Physical Systems (CPSs) that can be endowed with different levels of intelligence. With the combination of CPS and cloud computing, smart sensor cloud system becomes popular in many areas where the physical nodes can be shared with multiple users, which can obtain high performance computing. In view of this idea, this paper studies the specific application of smart city‐online task scheduling. We treat each task as a physical node, and the scheduling of multiple computing tasks is to achieve sustainable services. However, when a physical sensor node receives commands from multiple tasks simultaneously, namely, resource conflict management problem. This conflicting resource management problem leads to the failures of services. To solve the resource conflict problem, we propose an extended Hungarian algorithm based on edge computing, which can obtain smaller delay and achieve sustainable service. Edge computing can be viewed as a buffer and controller between CPS and the cloud and can build a sustainable service system by handling malicious attacks. The experimental results show that our proposed method can effectively reduce resource conflicts, improve resource utilization, and make services more sustainable.

A Reliable Trust Computing Mechanism Based on Multisource Feedback and Fog Computing in Social Sensor Cloud

Social sensor cloud (SSC) is combined with social network, wireless sensor network, cloud computing, and fog computing, which is currently a new type of Internet of Things (IoT). In order to provide a convenient, open, and highly reliable SSC services, the devices of fog computing are distributed at the edge of cloud computing. The devices of fog computing can independently process and store data, and feedback more quickly in SSC. The sensing layer of SSC faces different types of physical attacks and communication attacks, such as message forgery, message tampering, reply attacks, hidden data attacks, etc., lead to the lack of trust between social sensors and cloud data centers in SSC. Therefore, the trust evaluation between the sensing layer and the network layer is necessary. However, computing the reliability of the social sensor data in cloud data centers will generate a large amount of trust computing overhead, communication overhead, and communication delay, which hinder the widespread application of SSC services. To combat this issue, a reliable trust computing mechanism (RTCM) based on multisource feedback and fog computing fusion is proposed. First, a new metric is designed for the trust of social sensor nodes, and multisource feedback trust value collection is performed at the sensing layer to improve the detection of malicious feedback nodes. Second, the trust feedback information of the sensing layer is collected by the devices of fog computing, and the recommendation trust calculation is performed, which reduces the communication delay and computing overhead. Third, a fusion algorithm is designed to aggregate different types of feedback trust values, which overcomes the limitation of trust weights in artificial weighting and subjective weighting in traditional trust mechanisms. Theoretical analyses and simulation results show that the proposed trust computing mechanism has better computational efficiency and higher reliability compared with existing methods.

A SURVEY ON SECURITY ISSUES IN SENSOR CLOUD ENVIRONMENT FOR AGRICULTURE IRRIGATION MANAGEMENT SYSTEM

Wireless sensor networks are used in various fields, such as military, healthcare, agriculture, environment monitoring, Monitoring and preparation of critical infrastructure. However, due to some of the restrictions of WSNs on storage, power consumption, communication, computation, scalable, security and privacy; people are looking forward cloud environment with large storage framework for the processing and storing of real time data. The cloud environment is providing large storage space and better services to the people in scalable manner. Sensor-cloud infrastructure is popular, providing an open, flexible and reconfigurable platform for many monitoring and control applications. In this work presenting a sensor-cloud infrastructure for storing data related to agriculture, Infrastructure that shows how to transfer data from WSNs to the cloud. In addition we addressed malicious attacks, issues and challenges in sensor cloud infrastructure. The idea of our proposed work is to collect all the samples for the soil. We choose the most favored plants around the India, then, select three plants, namely rice, sugarcane and wheat. After selecting the plants we collect data related to agriculture irrigation from different types of sensors such as soil moisture, temperature, humidity, and soil PH, CO2 and light sensors. Based on the data collected, our proposed system advice to farmers that usage of pesticides, fertilizers which are most suitable to their lands at the same time, we integrate to improve agriculture productivity in India.

A Cross Layer Design and Flower Pollination Optimization Algorithm for Secured Energy Efficient Framework in Wireless Sensor Network

One of the fast-growing, tremendous and highly demanding networks from various emerging applications like military, healthcare industry, airport and forests is wireless sensor network. These applications are incorporated into the cloud in order to utilize various energy resources, high performance computing and massive storage structure. Many challenges should be resolved in sensor cloud environment like security, continuous data transmission, power management and energy efficiency. Some of the earlier research works anticipated data compression method for energy conservation and encryption decryption algorithms for secured data transmission. Compressing different data from various sources is difficult which degrades the performance of the data transmission and encryption decryption has taken more computational time and complexity which raises the need for energy efficiently and secured data transmission schemes. Hence, this paper is motivated to solve the major issues in sensor cloud, energy efficiency, data security and maintenance and thereby improving the quality of service (QoS). A secured energy efficient framework (SEEF) is designed for secured data transmission and maintenance which comprises of various phases like a cross layer designed for power and traffic management, node and route investigation for security and dynamic optimal routing for improving the QoS using flower pollination algorithm. A smart duty cycle scheduling protocol (SDCS) is proposed to increase the lifetime of the network by avoiding data redundancy, transmission conflicts, traffic conflicts and energy reduction in the network. The end to end delay is reduced by reducing the interval between the packets and packet delivery ratio is improved by avoiding packet loss. SEEF is simulated in NS2 simulator and the results are verified. The performance shows that the throughput and packet delivery ratio is improved up to 0.57 MB/s and 96% respectively. The end to end delay and the energy consumed is reduced to 0.69 s and 0.076 J respectively. From the comparison it is identified that SEEF can increase the overall performance in sensor cloud.

Design Framework and Intelligent In-Vehicle Information System for Sensor-Cloud Platform and Applications

The sensor-cloud system (SCS) integrates sensors, sensor networks, and the cloud for managing sensors, collecting data, and decision-making. Smart transportation based on the sensor-cloud approach is constantly improving. The sensor cloud has promoted the industrialization of the Internet of Vehicles and has also brought it to a new research stage. SCS permits users to utilize its platform as a service, and providers offer several environments to users for the development of applications, which enhances the user driving experience. However, many users have concerns with the sensor-cloud platform (SCP) and the environment. Furthermore, to save sensor resources, we propose a novel design model and method for in-vehicle information systems (IVIS) in the framework of SCS, which can optimize the SCP user experience. Our contribution has three main aspects: first, we extract the new features presented by the IVIS under the sensor-cloud environment; second, we establish a mapping between the IVIS and user needs and innovatively propose the SCP-oriented experience element-level scheme; finally, an IVIS design method based on the experience element is proposed. Furthermore, this research establishes a mapping between user experience elements and intelligent IVIS design features in the SCS environment and proposes an innovative IVIS model.

Heuristic approach to minimise the energy consumption of sensors in cloud environment for wireless body area network applications

The wireless sensor networks are single user centric and end users who do not own sensors are unable to have access to any wireless network specific application. This sensor cloud is a new paradigm to manage the physical sensors which are deployed in any WSN application. Existing research work on sensor cloud is limited to guaranteeing minimal energy consumption. In this paper, a novel mathematical model based on virtual sensor grouping is proposed to minimise the cumulative energy consumption of sensors in cloud environment. Energy consumption of non-virtual sensor with virtual sensor group for two different applications is compared and the results are shown. Further the proposed sensor cloud infrastructure with power model is compared with traditional WSN with respect to energy efficiency, throughput and performance with quick synchronisation time for random run trials and results were found to be better than the conventional WSN.

Rapid retrieval of secured data from the sensor cloud using a relative record index and energy management of sensors

Rapid retrieval of secured data from the sensor cloud using a relative record index and energy management of sensors

Improving the Performance of MMPP/M/C Queue by Convex Optimization–A Real-World Application in Iron and Steel Industry

Now the information system of the iron and steel industry is a typical sensor cloud computing system. The system has accumulated a lot of relevant manufacturing data. Using these data can well solve the slab storage problem in the production process for the iron and steel industry. In this paper, we investigate a queueing system where customers arrive according to a Markov Modulated Poisson Process (MMPP). MMPP can describe how the arrival rate changes with the environment, which is more realistic. We develop an MMPP(3)/M/C queueing model to solve the congestion problem in the iron and steel industry. In the actual production process, the slab arrival rates vary with states, therefore MMPP is used to model the arrival process in this paper. Based on explicit performance measures, we develop a nonlinear optimization model of queueing system, and convert the model into a convex optimization problem. Through the convex optimization method, the MMPP(3)/M/C model, resulted from the practical system, can be analyzed by the M/M/C model approximately.

A Secure and Reliable Device Access Control Scheme for IoT Based Sensor Cloud Systems

Among other security concerns, the reliable device to device direct communication is an important research aspect in sensor cloud system application of Internet of things (IoT). The access control mechanism can ensure the reliability through secure communication among two IoT devices without mediation of intermediate agent. Mainly, it requires twofold strategy involving the authentication of each other and session key establishment. Quite recently, in 2019, Das <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> proposed a certificate based lightweight access control and key agreement scheme for IoT devices (LACKA-IoT) to ensure smooth and secure access control and claimed LACKA-IoT to withstand the several attacks. Specifically, it is claimed that LACKA-IoT can resist device impersonation and man in middle attacks. However, the proof in this article refutes their claim and it is shown here, that LACKA-IoT is insecure against both device impersonation and man in middle attacks. An adversary just by using public parameters and by listening the communication channel can impersonate any device. Moreover, the same can also launch successful man in middle attack using public parameters and listened messages from public channel. An improved protocol iLACKA-IoT is then proposed in the paper. The iLACKA-IoT provides resistance against various types of threats and provides the required level of security, for evidence both formal validation through random or real (ROR) model as well as the informal validation through discussion on attack resilience is provided. The iLACKA-IoT is not only better in security but also provides performance efficiency as compared with LACKA-IoT and related schemes.

CoC-SCS: Cooperative-Optimization Coverage Algorithm Based on Sensor Cloud Systems in Intelligent Computing

The coverage of traditional Wireless Sensor Networks (WSNs) is limited by node energy and data redundancy, which forces WSNs to be interrupted abnormally. Therefore, Cooperative-optimization Coverage Algorithm based on Sensor Cloud Systems in Intelligent Computing (CoC-SCS) is proposed. First, the algorithm determines the location information of the Focus Target Nodes (FTNs), and uses the Genetic Algorithm (GA) to give the node path planning. Second, the mutation parameters and the controllable threshold parameters are used in order to control the event domain nodes. Optimizing clustering makes the clustering of the nodes to be more uniform, so that it can improve the search ability of the global target nodes and even reduce the nodes energy consumption. Third, the adaptation function covers the continuity of the covered target positions and the monitoring range of the nodes. Optimization is also performed to achieve the goals of increasing network coverage and extending the network lifetime. Finally, simulation results show that the proposed CoC-SCS algorithm compared with other three algorithms in this paper has improved 11.27% and 16.35% on average in terms of network coverage, network lifetime, etc., and 13.95% in terms of network energy overhead, thereby we can further verify that CoC-SCS algorithm has strong stability and effectiveness.

Edge-based differential privacy computing for sensor–cloud systems

In sensor–cloud systems, with more personal data being hosted in cloud, privacy leakage is becoming one of the most serious concerns. Privacy computing is emerging as a paradigm to systematically enhance privacy protection. In other words, the new paradigm requests us to improve the computing model to provide a general privacy protection service. In this paper, we propose an edge-based model for data collection, in which the raw data from wireless sensor networks (WSNs) is differentially processed by algorithms on edge servers for privacy computing. A small quantity of the core data is stored on edge and local servers while the rest is transmitted to cloud for storage. In this way, the benefits are twofold. First, the data privacy is preserved since the original data cannot be retrieved even if the data stored in the cloud is leaked. Second, implemented by a differential storage method, compared to the state of the art, the edge-based model sends less data to the cloud and reduces the cost of communication and storage. Both theoretical analyses and extensive experiments validate our proposed method.