A Highly Accurate Machine Learning Approach for Developing Wireless Sensor Network Middleware

Despite the popularity of wireless sensor networks (WSNs) in a wide range of applications, security problems associated with them have not been completely resolved. Middleware is generally introduced as an intermediate layer between WSNs and the end user to resolve some limitations, but most of the existing middleware is unable to protect data from malicious and unknown attacks during transmission. We introduced an intelligent middleware based on an unsupervised learning technique called Generative Adversarial Networks (GANs) algorithm. GANs contain two networks: a generator (G) network and a discriminator (D) network. The G creates fake data similar to the real samples and combines it with real data from the sensors to confuse the attacker. Results illustrate that the proposed algorithm not only improves the accuracy of the data but also enhances its security by protecting data from adversaries. Data transmission from the WSN to the end user then becomes much more secure and accurate compared to conventional techniques

A Highly Accurate Deep Learning Based Approach For Developing Wireless Sensor Network Middleware

Despite the popularity of wireless sensor networks (WSNs) in a wide range of applications, the security problems associated with WSNs have not been completely resolved. Since these applications deal with the transfer of sensitive data, protection from various attacks and intrusions is essential. From the current literature, we observed that existing security algorithms are not suitable for large-scale WSNs due to limitations in energy consumption, throughput, and overhead. Middleware is generally introduced as an intermediate layer between WSNs and the end user to address security challenges. However, literature suggests that most existing middleware only cater to intrusions and malicious attacks at the application level rather than during data transmission. This results in loss of nodes during data transmission, increased energy consumption, and increased overhead. In this research, we introduce an intelligent middleware based on an unsupervised learning technique called the Generative Adversarial Networks (GANs) algorithm. GANs contain two networks: a generator (G) network and a discriminator (D) network. The G network generates fake data that is identical to the data from the sensor nodes; it combines fake and real data to confuse the adversary and stop them from differentiating between the two. This technique completely eliminates the need for fake sensor nodes, which consume more power and reduce both throughput and the lifetime of the network. The D network contains multiple layers that have the ability to differentiate between real and fake data. The output intended for this algorithm shows an actual interpretation of the data that is securely communicated through the WSN. The framework is implemented in Python with experiments performed using Keras. The results illustrate that the suggested algorithm not only improves the accuracy of the data but also enhances its security by protecting it from attacks. Data transmission from the WSN to the end user then becomes much more secure and accurate compared to conventional techniques. Simulation results show that the proposed technique provides higher throughput and increases successful data rates while keeping the energy consumption low

TERP: A Trusted and Energy Efficient Routing Protocol for Wireless Sensor Networks (WSNs)

Recently, Wireless Sensor Networks (WSNs) have got researchers attention due to its various useful and helpful applications in the real world with low cost sensors. The task of the sensors is to collect data from the environment and send it to the central node (sink node). However, the power is limited in these sensors and therefore it has a limited lifetime which is a big deal in WSNs. Another important issue in WSNs is the level of security. Since these sensor nodes exchange and transmit data among the network, the security of the data can be at risk. Hence, In this poster, we propose a novel trusted and energy efficient routing protocol (TERP), which is based on the Destination Sequenced Distance Vector Protocol (DSDV). TERP can avoid any malicious nodes (untrusted nodes) and thus increase the security level in the network, and decrease the power consumption level

Performance and Challenges of Service-Oriented Architecture for Wireless Sensor Networks

Wireless Sensor Networks (WSNs) have become essential components for a variety of environmental, surveillance, military, traffic control, and healthcare applications. These applications face critical challenges such as communication, security, power consumption, data aggregation, heterogeneities of sensor hardware, and Quality of Service (QoS) issues. Service-Oriented Architecture (SOA) is a software architecture that can be integrated with WSN applications to address those challenges. The SOA middleware bridges the gap between the high-level requirements of different applications and the hardware constraints of WSNs. This survey explores state-of-the-art approaches based on SOA and Service-Oriented Middleware (SOM) architecture that provide solutions for WSN challenges. The categories of this paper are based on approaches of SOA with and without middleware for WSNs. Additionally, features of SOA and middleware architectures for WSNs are compared to achieve more robust and efficient network performance. Design issues of SOA middleware for WSNs and its characteristics are also highlighted. The paper concludes with future research directions in SOM architecture to meet all requirements of emerging application of WSNs.https://doi.org/10.3390/s1703053

Performance and Challenges of Service-Oriented Architecture for Wireless Sensor Networks

Wireless Sensor Networks (WSNs) have become essential components for a variety of environmental, surveillance, military, traffic control, and healthcare applications. These applications face critical challenges such as communication, security, power consumption, data aggregation, heterogeneities of sensor hardware, and Quality of Service (QoS) issues. Service-Oriented Architecture (SOA) is a software architecture that can be integrated with WSN applications to address those challenges. The SOA middleware bridges the gap between the high-level requirements of different applications and the hardware constraints of WSNs. This survey explores state-of-the-art approaches based on SOA and Service-Oriented Middleware (SOM) architecture that provide solutions for WSN challenges. The categories of this paper are based on approaches of SOA with and without middleware for WSNs. Additionally, features of SOA and middleware architectures for WSNs are compared to achieve more robust and efficient network performance. Design issues of SOA middleware for WSNs and its characteristics are also highlighted. The paper concludes with future research directions in SOM architecture to meet all requirements of emerging application of WSNs

Intelligent and secure edge-enabled computing model for sustainable cities using green internet of things

Internet of Things (IoT) consists of a huge number of sensors along with physical things to gather and forward data intelligently. Green IoT applications based on Wireless Sensor Networks (WSNs) are developed in various domains, such as medical, engineering, industry, and smart cities to grow the production. To increase the performance of sustainable cities, communicating nodes are interconnected autonomously to observe the environment, where they need to be more energy-efficient. Edge computing operates in a distributed manner and improves the response time with the least latency through various edge servers. Although the integration of edge computing and Green IoT significantly improves the network performance in terms of computation and data storage, low powered sensors have constraints in terms of battery power, low transmission range, and security aspects. Therefore, adopting an emerging solution is needed to offer energy services with secure data delivery for sustainable cities. This paper presents an intelligent and secure edge-enabled computing (ISEC) model for sustainable cities using Green IoT, which aims to develop the communication strategy with decreasing the liability in terms of energy management and data security for data transportation. The proposed model generates optimal features using deep learning for data routing, which may help to train the sensors for predicting the finest routes toward edge servers. Moreover, the integration of distributed hashing with chaining strategy eases security solutions with efficient computing system. The experimental results reveal the improved performance of the proposed ISEC model against other solutions for energy consumption by 21 %, network throughput by 15 %, end-to-end delay by 12 %, route interruption by 36 %, and network overhead by 52 %