Security in Wireless Sensor Network: A Current Look

Luqman, Mohammad; Faridi, Arman Rasool

doi:10.23919/indiacom54597.2022.9763130

Cited by 7 publications

(3 citation statements)

References 10 publications

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“…WSN middleware exists in many aspects of computer science, including artificial intelligence, computer systems and networks, security, database systems, human–computer interaction, vision and graphics, numerical analysis, programming languages, software engineering, bioinformatics, and the theory of computing [ 39 ]. WSNs are also employed in applications related to urban [ 40 ] and rural [ 41 ] projects, outdoor [ 42 ] and indoor [ 34 , 43 ] tracking, environmental monitoring [ 44 ], disaster monitoring [ 45 ], water quality [ 46 ], habitat monitoring [ 47 ], traffic monitoring [ 48 ], earthquake detection [ 49 ], volcano eruption [ 50 ], agriculture [ 51 ], weather forecasting [ 52 ], smart water management [ 53 ], smart cities [ 54 ], smart health [ 55 ], smart energy management [ 56 ], smart traffic management [ 57 ], smart retail [ 58 ], smart security [ 59 ], smart waste management [ 60 ], smart education [ 61 ], and smart homes [ 62 ].…”

Section: Related Workmentioning

confidence: 99%

Streamline Intelligent Crowd Monitoring with IoT Cloud Computing Middleware

Gazis,

Katsiri

2024

Sensors

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This article introduces a novel middleware that utilizes cost-effective, low-power computing devices like Raspberry Pi to analyze data from wireless sensor networks (WSNs). It is designed for indoor settings like historical buildings and museums, tracking visitors and identifying points of interest. It serves as an evacuation aid by monitoring occupancy and gauging the popularity of specific areas, subjects, or art exhibitions. The middleware employs a basic form of the MapReduce algorithm to gather WSN data and distribute it across available computer nodes. Data collected by RFID sensors on visitor badges is stored on mini-computers placed in exhibition rooms and then transmitted to a remote database after a preset time frame. Utilizing MapReduce for data analysis and a leader election algorithm for fault tolerance, this middleware showcases its viability through metrics, demonstrating applications like swift prototyping and accurate validation of findings. Despite using simpler hardware, its performance matches resource-intensive methods involving audiovisual and AI techniques. This design’s innovation lies in its fault-tolerant, distributed setup using budget-friendly, low-power devices rather than resource-heavy hardware or methods. Successfully tested at a historical building in Greece (M. Hatzidakis’ residence), it is tailored for indoor spaces. This paper compares its algorithmic application layer with other implementations, highlighting its technical strengths and advantages. Particularly relevant in the wake of the COVID-19 pandemic and general monitoring middleware for indoor locations, this middleware holds promise in tracking visitor counts and overall building occupancy.

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Section: Related Workmentioning

confidence: 99%

Streamline Intelligent Crowd Monitoring with IoT Cloud Computing Middleware

Gazis,

Katsiri

2024

Sensors

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“…It outperforms the cluster head election methods by increasing network lifetime and lowering energy usage by 28% and 34%, respectively. In [21], a layer-by-layer taxonomy of security attacks is explored, as well as possible mitigation strategies were comprehended about the assaults and their severity. By analyzing the above literatures, some of the problems are identified.…”

Section: Related Workmentioning

confidence: 99%

DERNNet: Dual Encoding Recurrent Neural Network Based Secure Optimal Routing in WSN

Venkatesh¹,

Asha²

2023

Computer Systems Science and Engineering

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A Wireless Sensor Network (WSN) is constructed with numerous sensors over geographical regions. The basic challenge experienced while designing WSN is in increasing the network lifetime and use of low energy. As sensor nodes are resource constrained in nature, novel techniques are essential to improve lifetime of nodes in WSN. Nodes energy is considered as an important resource for sensor node which are battery powered based. In WSN, energy is consumed mainly while data is being transferred among nodes in the network. Several research works are carried out focusing on preserving energy of nodes in the network and made network to live longer. Moreover, this network is threatened by attacks like vampire attack where the network is loaded by fake traffic. Here, Dual Encoding Recurrent Neural network (DERNNet) is proposed for classifying the vampire nodes s node in the network. Moreover, the Grey Wolf Optimization (GWO) algorithm helps for transferring the data by determining best solutions to optimally select the aggregation points; thereby maximizing battery/ lifetime of the network nodes. The proposed method is evaluated with three standard approaches namely Knowledge and Intrusion Detection based Secure Atom Search Routing (KIDSASR), Risk-aware Reputation-based Trust (RaRTrust) model and Activation Function-based Trusted Neighbor Selection (AF-TNS) in terms of various parameters. These existing methods may lead to wastage of energy due to vampire attack, which further reduce the lifetime and increase average energy consumed in the network. Hence, the proposed DERNNet method achieves 31.4% of routing overhead, 23% of end-to-end delay, 78.6% of energy efficiency, 94.8% of throughput, 28.2% of average latency, 92.4% of packet delivery ratio, 85.2% of network lifetime, and 94.3% of classification accuracy.

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“…Therefore, whether the information in question is delivered over the internet or kept in the cloud, maintaining the secrecy and preservation of data gathered by IoT devices is an important research problem that must be given priority 6 . Fog computing, which is more scalable than edge computing, has developed as a solution to these problems by handling the high processing demand and temporary storage 4,7,8 …”

Section: Introductionmentioning

confidence: 99%

Authentication of fog‐assisted IoT networks using Advanced Encryption credibility approach with modified Diffie–Hellman encryption

Luqman

Faridi

2023

Concurrency and Computation

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SummaryCloud storage in the network enables the users to access the data from anywhere irrespective of any devices, which prevents data loss and provides centralized management. The cloud server is a pooled, centrally located server resource that many users can access as needed. More efficient data storage and processing could be achieved through fog computing devices and, the fog node handles the task including the semantic transformation of the data before transmitting it to the back‐end cloud servers, managing the process of data from several edge devices simultaneously. The security of the cloud and fog nodes acts as a key component, and that must be ensured for effective computations. In this research, the security of the fog‐assisted Internet of Things (IoT) network is enhanced using the proposed advanced encryption credibility approach (Ad‐En credibility approach), which ensures the privacy and security of the networks by preventing malicious attempts of accessing the data. The proposed Ad‐En credibility approach is performed based on a modified Diffie‐Hellman algorithm that consists of different phases such as the setup phase, entry and identity phase, fog node entry validation phase, and authentication phase. These phases perform different functions and provide strong authentication mechanisms that secure the data with high confidentiality by detecting malicious attempts within a reduced time. The efficiency of the research is proved by measuring the computational time, memory usage, and detection rate. The proposed Ad‐En credibility approach consumed less time of 7 s and memory usage of 8.17 mb with a high detection rate of 85.34%.

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Security in Wireless Sensor Network: A Current Look

Cited by 7 publications

References 10 publications

Streamline Intelligent Crowd Monitoring with IoT Cloud Computing Middleware

Streamline Intelligent Crowd Monitoring with IoT Cloud Computing Middleware

DERNNet: Dual Encoding Recurrent Neural Network Based Secure Optimal Routing in WSN

Authentication of fog‐assisted IoT networks using Advanced Encryption credibility approach with modified Diffie–Hellman encryption

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