A Context Aware Framework for User Centered Services

In this paper, we introduce a context aware middleware framework that has been developed over the years to serve as an enabler for user centered services. Firstly, we will discuss about a sensory API mechanism developed to allow an abstraction of sensing elements to report information in a structured manner. We will then proceed to discuss how this sensed information is represented in an ontology, replicating a virtual model of the environment. This will facilitate reasoning capabilities, where entities that are inter-related can be resolved and used by the service. And finally we will describe how context specific to a user-centered service could be subscribed from the middleware. The context, once subscribed, will enable actions to be fired off when the particular context is met. The three core components when put together, will allow for services to react more specific to the users needs, based on the user’s ever changing context

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