Prithiviraj Ananth

Wireless sensor network (WSN) sensor nodes should have adequate energy. Reduced energy usage is essential to maximize the endurance of WSNs. Combining WSN with a more significant energy source, a cluster head (CH), is another effective strategy for extending WSN durability. A CH is dependent on the communication inside and between clusters. A CH’s energy level extends the cluster’s life for the complete WSN. Determining the energy required in WSNs while developing clustering algorithms is challenging. For maintaining energy efficiency in WSNs, this research offers K-medoids with sunflower-based clustering and a cross-layer-based optimal routing approach. An efficient fitness function generated from diverse objectives is used to choose the CH. After CH selection, sunflower optimization (SFO) indicates the best data transmission line to the sink node. The proposed protocol, SFO-CORP, increased the network lifetime by 19.6%, 13.63%, 11.13%, and 4.163% compared to the LEACH, EECRP, FEEC-IIR, and CL-IoT protocols, respectively. The experimental results showed that it performed better for packet delivery ratio, energy consumption, end-to-end delay, network lifetime, and computation efficiency.

Sharing and reusing data across apps and industries is critical for the Internet of Things to realize its full potential. However, various IoT systems exist, each with its networks, depictions, and interaction swatches. To address this problem, On the one extreme, the Fed4IoT project has established an IoT virtualization software that merges data from various sources. It makes the data that users need accessible in their choice operating system, but on the other hand. Data is converted into an impartial, standard transfer protocol to make this possible. The preferred format is the next generation service interfaces, and it is now being standardized by the European Telecommunications Standardization Institute Industrial Standards Group on Frame of reference Data Governance. The elements particular basis of interpretation data to next-generation service interfaces, passed over to the particular platform and transformed to the destination format, are known as Something Shields. Hand-building thing visors are possible, but it requires time and work, partially due to the variety of low-level data many sensors provide. As a result, it's necessary to aid the human developer and, ideally, completely automate the gathering, enriching, and exporting data to NGSI-LD. Automation has many potential answers, but it frequently necessitates a huge amount of manually tagged datasets, which is impractical in numerous internet of things scenarios. Knowledge infusion identifies with an application method that uses expert knowledge to match a schema or ontology obtained from information supplied a discourse marker or ontology, setting the framework for identifying gathering as much information and support transformation.

Despite the seemingly exponential growth of mobile and wireless communication, this same technology aims to offer uninterrupted access to different wireless systems like Radio Communication, Bluetooth, and Wi-Fi to achieve better network connection which in turn gives the best quality of service (QoS). Many analysts have established many handover decision systems (HDS) to enable assured continuous mobility between various radio access technologies. Unbroken mobility is one of the most significant problems considered in wireless communication networks. Each application needs a distinct QoS, so the network choice may shift appropriately. To achieve this objective and to choose the finest networks, it is important to select a best decision making algorithm that chooses the most effective network for every application that the user requires, dependent on QoS measures. Therefore, the main goal of the proposed system is to provide an enhanced vertical handover (VHO) decision making program by using a Multi-Criteria Fuzzy-Based algorithm to choose the best network. Enhanced Multi-Criteria algorithms and a Fuzzy-Based algorithm is implemented successfully for optimal network selection and also to minimize the probability of false handover. Furthermore, a double packet buffer is utilized to decrease the packet loss by 1.5% and to reduce the number of handovers up to 50% compared to the existing systems. In addition, the network setup has an optimized mobility management system to supervise the movement of the mobile nodes.

Mobile terminals typically can be connected to multiple wireless networks that offer different degrees of suitability for different classes of service. A genetic algorithm (GA) - based on a supervised approach - is used to assess the likelihood of a successful handover in heterogeneous networks to increase capacity and network performance. Traditional handoff schemes are prone to ping-pong and corner effects, and the development of an optimized handover scheme for a seamless, faster and less energy-consuming handover decision is a challenge. In order to ensure service continuity and to maintain the promising QoS, the handover decision should be made appropriately. The most general criteria for handover is the received signal strength, which is not sufficient in advanced networks. The GA scheme can effectively optimize the soft handoff decision by using the best-fit network for the mobile terminal (MT), taking into account the quality of service requirements (QoS), the network parameters, and the user preference for the cost of the various connection points for the MT is selected. The mathematical model-based approach to vertical handoff prediction involves a well-defined objective function taking into account signal strength, user equipment speed, load and cost per user bandwidth. The system performance can be improved according to the user settings by adjusting the weights in the defined objective function. Due to the high bandwidth and easy networking, the system consider the wireless access standards: UMTS, Wi-Fi, LTE and Wimax. The decision problem is formulated as several objective optimization problems and simulated using a monitored genetic algorithm. The simulation result shows that the number of handovers can be minimized when using optimized network parameter values.