Pervasive computing has made almost every device we see today to be communicated and function in collaboration with one another. Since the portable devices have become a part of our everyday life, people are more involved in a pervasive computing environment. They engage with many computational devices simultaneously without knowing the availability of their existence. The current world is being filled with more and more smart environments. These smart environments make them to be attracted towards the new technological emergence in the field of pervasive computing. Various researches are being carried out to improve the smart environment and their applications. Middleware plays a vital role in building the pervasive applications. The pervasive devices act based on the context of the situation, that is, they do their actions according to the environment of the application. They react to the situations smartly as they can take their own decisions based on the context developed for that specific application. Most of the pervasive applications were using its own middleware that is specific towards their need. As today, most of the applications are using their own middleware with their specific requirement, which leads us to unearth out their common features and their scope of using it. In this paper, a survey on the various hybrid aspects of the different context-aware middleware has been done. This middleware is classified based on service, context, and device aspects. Merits and demerits are identified from the existing smart environments, and future perspective of their development such as generic context-aware middleware need has been discussed.

Home automation systems are expanding increasingly popular because they can conveniently be employed to manage devices and appliances via voice or focused on physical activity utilizing sensor. From the various research, it shows that affording cost in bringing smartness to small organizations as well as normal users is challenging and there is a requirement for a better obvious and convenient method of connecting and managing equipment with mobile applications. The proposed system is created and built with the aim of enhancing control system performance and reliability. This technology may operate on any system and manage devices by connecting with home appliances and connected devices via a Wi-Fi device. The system involves a central processing module to manage devices via a home Wi-Fi connection that is linked to the internet for internet of things operations. It is recommended that an application be developed to connect, and configure new and current home appliances for control, which will lead to the connection and handling of other third-party devices via their software development kits. The suggested system enables additional features via a mobile application that allows the user to install new features created by the user to execute any activity with the system.

Mobile Edge Computing (MEC) is a distributed computing paradigm that delivers processing and data storage capabilities closer to the network edge, which is adjacent to mobile consumers and devices. MEC lowers latency, reduces data transmission times, and improves overall performance for mobile apps by relocating computing resources to the network’s edge. But, due to higher average load and longer elapsed time, modern end devices such as smartphones and tablets cause major load challenges in mobile computing networks. Furthermore, if smartphones cause unpredictable traffic patterns, it becomes impossible to model and forecast the nature of communication. Such confusing traffic figures are caused not just by bursty Internet traffic, but also by multitasking operating systems that allow users to swiftly switch between active apps. Mobility of users and end devices impose a difficult challenge to provide continuous services in mobile computing. In this paper, this issue is addressed using the Contextual Information Based Scheduling (CIBS) technique to optimally allocate resources and provide seamless service to the users. The proposed method is implemented with NS-3, an open-source network simulator that provides a comprehensive set of modules for Mobile Edge Computing (MEC) simulations, including mobility modelling support. The experimental results show that CIBS offers migration time of 97512ms, delay time of 372115ms, execution time of 1061328ms and downtime of 98715ms. The results are compared with the existing Mobility-Aware Joint Task Scheduling (MATS) approach. The obtained results show that CIBS outperforms MATS with regard to migration time, latency, execution time and downtime.

A Mobile Ad hoc Network (MANET) is an autonomous system comprising mobile nodes that self-organize and connect via wireless networks, without reliance on a predefined infrastructure. These nodes are inherently susceptible to jamming, a form of denial-of-service attack that renders mobile services unavailable in the affected area. In this work, we introduce an intelligent jammer, constructed based on the Received Signal Strength Index-based Transmission Power Control (RSSITPC) Algorithm. This algorithm leverages Received Signal-Strength Indicator (RSSI) data to ascertain the optimal transmission powers for neighboring nodes and dynamically adjust these powers. The design of the intelligent jammer system incorporates a circuit interface, power unit, power detector, and GSM scanner. It employs a DAC-centered RSSITPC Algorithm to differentiate the jamming signal from legitimate signals by comparing the voltage in the received signal. Following the design phase, a Jamming Attack (JA) analysis is conducted, utilizing metrics such as the Packet Send Ratio (PSR) and Packet Delivery Ratio (PDR). Subsequently, a Hybrid Cross-layer Rate Adaptation (CLRA) Scheme is implemented to enhance JA detection and improve Wireless Link Utilization. The Adaptive Neuro-Fuzzy Interference System (ANFIS) classifier is then used to categorize data as either attack or regular data. For regular data, the Control Channel Attack Prevention (CCAP) algorithm is applied as a preventive measure. The proposed system’s effectiveness is validated through comparative performance analysis with other widely used systems. Additionally, considerations are made for the adaptability of these methodologies to evolving intrusion techniques and changing network environments, as well as their scalability in larger, more complex networks.

The real world signals are all analog in nature. The digital signals and digital circuits offer greater advantages as compared to analog circuits in processing speed and efficient transmitting. So in order to convert the analog signals to digital efficiently an ldquoAnalog to digital converterrdquo is required. The System on Chip (SoC) forces the analog circuits to be integrated with digital circuits. To follow the scaling down of the SoC trend, analog to digital converter should be operated at low voltages. Thus the idea behind the paper is to design a 3-bit flash ADC using Threshold Inverter Quantization technique with 130 nm CMOS technology for high speed and low voltage applications. Threshold Inverter Quantization (TIQ) is a unique way to generate a comparator for a high speed CMOS flash ADC. To improve further, the fat tree encoder that is highly suitable for the ultrahigh speed flash ADCs. A fat tree encoder that has signal delay of ldquo0rdquo is used for better performance. The speed is improved by almost a factor of 2 when using the fat tree encoder. The fat tree encoder is an effective solution for the bottleneck problem in ultra-high speed ADCs. The proposed A/D converter is suitable for System on Chip (SoC) applications in wireless products and other ultra high speed applications.