A Hybrid Approach for Enhancing Security in Internet of Things (IoT)

2019 ◽  
Author(s):  
M. Tanooj Kumar ◽  
Revanth Kumar Katragadda ◽  
Vishnu Srujan Kolli ◽  
Shaik Lahir Rahiman
Keyword(s):  
Internet Of Things ◽  
Hybrid Approach

scholarly journals Creating Personalized Recommendations in a Smart Community by Performing User Trajectory Analysis through Social Internet of Things Deployment

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2098 ◽  
Author(s):  
Guang Xing Lye ◽  
Wai Khuen Cheng ◽  
Teik Boon Tan ◽  
Chen Wei Hung ◽  
Yen-Lin Chen
Keyword(s):  
Internet Of Things ◽  
Service Discovery ◽  
Early Stage ◽  
Hybrid Approach ◽  
User Preferences ◽  
Personalized Recommendation ◽  
Heterogeneous Objects ◽  
High Satisfaction ◽  
The Social ◽  
Smart Community

Despite advancements in the Internet of Things (IoT) and social networks, developing an intelligent service discovery and composition framework in the Social IoT (SIoT) domain remains a challenge. In the IoT, a large number of things are connected together according to the different objectives of their owners. Due to this extensive connection of heterogeneous objects, generating a suitable recommendation for users becomes very difficult. The complexity of this problem exponentially increases when additional issues, such as user preferences, autonomous settings, and a chaotic IoT environment, must be considered. For the aforementioned reasons, this paper presents an SIoT architecture with a personalized recommendation framework to enhance service discovery and composition. The novel contribution of this study is the development of a unique personalized recommender engine that is based on the knowledge–desire–intention model and is suitable for service discovery in a smart community. Our algorithm provides service recommendations with high satisfaction by analyzing data concerning users’ beliefs and surroundings. Moreover, the algorithm eliminates the prevalent cold start problem in the early stage of recommendation generation. Several experiments and benchmarking on different datasets are conducted to investigate the performance of the proposed personalized recommender engine. The experimental precision and recall results indicate that the proposed approach can achieve up to an approximately 28% higher F-score than conventional approaches. In general, the proposed hybrid approach outperforms other methods.

scholarly journals Blockchains: Improve the Scalability and Efficiency of Conventional Blockchain by Providing a Lightweight Block Mining and Communication Algorithm

2020 ◽  
Vol 25 (6) ◽  
pp. 737-745
Author(s):  
Subba Rao Peram ◽  
Premamayudu Bulla
Keyword(s):  
Internet Of Things ◽  
Smart City ◽  
Smart Cities ◽  
Hybrid Approach ◽  
Security And Privacy ◽  
Public Key ◽  
The Internet ◽  
Byzantine Fault Tolerance ◽  
Complex Issue ◽  
The Internet Of Things

To provide secure and reliable services using the internet of things (IoT) in the smart cities/villages is a challenging and complex issue. A high throughput and resilient services are required to process vast data generated by the smart city/villages that felicitates to run the applications of smart city. To provide security and privacy a scalable blockchain (BC) mechanism is a necessity to integrate the scalable ledger and transactions limit in the BC. In this paper, we investigated the available solutions to improve its scalability and efficiency. However, most of the algorithms are not providing the better solution to achieve scalability for the smart city data. Here, proposed and implemented a hybrid approach to improve the scalability and rate of transactions on BC using practical Byzantine fault tolerance and decentralized public key algorithms. The proposed Normachain is compares our results with the existing model. The results show that the transaction rate got improved by 6.43% and supervision results got improved by 17.78%.

scholarly journals Contextualization using Context-Aware Publish and Subscribe (CAPS) based on IoT

2019 ◽  
Vol 9 (1) ◽  
pp. 1112-1119
Keyword(s):  
Internet Of Things ◽  
Data Processing ◽  
Hybrid Approach ◽  
Probabilistic Methods ◽  
Sensor Data ◽  
The Internet ◽  
Context Aware ◽  
Raw Data ◽  
Message Queue ◽  
Different Sources

The Internet of Things (IoT) activates massive data flow in the real world. Each computer can presently be linked to the internet and supply useful decision-making information. Virtually sensors are implemented in every aspect of life. From different sources of sensors can produce raw data. Due to the various data sources, the method of extracting information from the flow of data is mostly complicated, networks inadequate and criteria for real-time processing. In addition, an issue of context-aware data processing and architecture also present, despite the fact that they are essential criteria for stronger IoT structure. In order to meet this issue, we recommend a Context-aware Internet of Things Middleware (CAIM) architecture. This enables the incorporation of highly diverse IoT application context information by using light weigh protocol MQTT (Message Queue Telemetry Transport) for transmitting basic data streams from sensors to middleware and applications. In this paper, we propose a contextualization which means that obtain data from sensors of different sources. First have to create a context profile with the help of context type like user, activity, physical, and environment context. Then also is create a profile by using attributes. Finally, raw data can be change into contextualized data through CAPS (context-aware Publish-Subscribe) hybrid approach. This paper discusses the current context analysis strategies that use either rational models or probabilistic methods exclusively. The evaluation of identifying contextualization methods shows the shortcomings of IoT sensor data processing as well as offers alternative ways of identifying the context

scholarly journals QoS Enabled Layered Based Clustering for Reactive Flooding in the Internet of Things

Symmetry ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 634 ◽  
Author(s):  
Fawad Ali Khan ◽  
Rafidah Md Noor ◽  
Miss Laiha Mat Kiah ◽  
Noorzaily Mohd Noor ◽  
Saleh M. Altowaijri ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Internet Of Things ◽  
Research Work ◽  
Hybrid Approach ◽  
Computational Time ◽  
The Internet ◽  
Wide Range ◽  
Iot Devices ◽  
The Internet Of Things ◽  
Network Flooding

The Internet of Things has gained substantial attention over the last few years, because of connecting daily things in a wide range of application and domains. A large number of sensors require bandwidth and network resources to give-and-take queries among a heterogeneous IoT network. Network flooding is a key questioning strategy for successful exchange of queries. However, the risk of the original flooding is prone to unwanted and redundant network queries which may lead to heavy network traffic. Redundant, unwanted, and flooded queries are major causes of inefficient utilization of resources. IoT devices consume more energy and high computational time. More queries leads to consumption of more bandwidth, cost, and miserable QoS. Current existing approaches focused primarily on how to speed up the basic routing for IoT devices. However, solutions for flooding are not being addressed. In this paper, we propose a cluster-based flooding (CBF) as an interoperable solution for network and sensor layer devices which is also capable minimizing the energy consumption, cost, network flooding, identifying, and eliminating of redundant flooding queries using query control mechanisms. The proposed CBF divides the network into different clusters, local queries for information are proactively maintained by the intralayer cluster (IALC), while the interlayer cluster (IELC) is responsible for reactively obtain the routing queries to the destinations outside the cluster. CBF is a hybrid approach, having the potential to be more efficient against traditional schemes in term of query traffic generation. However, in the absence of appropriate redundant query detection and termination techniques, the CBF may generate more control traffic compared to the standard flooding techniques. In this research work, we used Cooja simulator to evaluate the performance of the proposed CBF. According to the simulation results the proposed technique has superiority in term of traffic delay, QoS/throughput, and energy consumption, under various performance metrics compared with traditional flooding and state of the art.

scholarly journals A hybrid approach to inferring the Internet of Things for complex activity recognition

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Qingjuan Li ◽  
Huansheng Ning ◽  
Tao Zhu ◽  
Shan Cui ◽  
Liming Chen
Keyword(s):  
Missing Data ◽  
Internet Of Things ◽  
Activity Recognition ◽  
Daily Activity ◽  
Hybrid Approach ◽  
The Internet ◽  
Markov Logic ◽  
Logic Network ◽  
Markov Logic Network ◽  
The Internet Of Things

AbstractWith the rapid development and large-scale uptake of the Internet of Things, smart home is evolving from a vision towards a realistically viable solution for assisted living. Activity recognition is one of the fundamental tasks in order to provide accurate and timely assistance and service. As daily living scenarios are full of similar activities, missing data, and noise, inferring complex activities using knowledge-driven reasoning algorithms suffers from several drawbacks, e.g., real-time raw sensor data segmentation, poor generalization, higher computational complexity, and scalability. To address these problems, this paper proposes a hybrid approach to complex daily activity recognition by merging the first-order logic and probability graphic modeling. Specifically, we develop a novel “Markov logic network” combining data-driven multi-feature and simplified rule-based modeling and inference, thus enabling and supporting the applicability and robustness of daily activity recognition. To evaluate the approach and associated methods, we design a testing scenario with a number of similar activity groups, missing data, or disturbance test datasets in a multi-modeling sensor scene. Initial results show our approach outperforms the traditional approach with a better accuracy in the situations of similar activities with missing data and noise disturbance. Experiments are also conducted to compare the Gibbs sampling and MC-SAT sampling algorithms for Markov logic network, and the results show that the Gibbs is better in our experimental settings.

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

2021 ◽  
Vol 1 (3) ◽  
Keyword(s):  
Internet Of Things ◽  
Hybrid Approach ◽  
Sensor Nodes ◽  
Third Party ◽  
Worst Case Analysis ◽  
User Privacy ◽  
Worst Case ◽  
Trade Offs ◽  
Network Diameter ◽  
Sensor Cloud

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.

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