RGIM: An Integrated Approach to Improve QoS in AODV, DSR and DSDV Routing Protocols for FANETS Using the Chain Mobility Model

Flying ad hoc networks (FANETs) are a collection of unmanned aerial vehicles that communicate without any predefined infrastructure. FANET, being one of the most researched topics nowadays, finds its scope in many complex applications like drones used for military applications, border surveillance systems and other systems like civil applications in traffic monitoring and disaster management. Quality of service (QoS) performance parameters for routing e.g. delay, packet delivery ratio, jitter and throughput in FANETs are quite difficult to improve. Mobility models play an important role in evaluating the performance of the routing protocols. In this paper, the integration of two selected mobility models, i.e. random waypoint and Gauss–Markov model, is implemented. As a result, the random Gauss integrated model is proposed for evaluating the performance of AODV (ad hoc on-demand distance vector), DSR (dynamic source routing) and DSDV (destination-Sequenced distance vector) routing protocols. The simulation is done with an NS2 simulator for various scenarios by varying the number of nodes and taking low- and high-node speeds of 50 and 500, respectively. The experimental results show that the proposed model improves the QoS performance parameters of AODV, DSR and DSDV protocol

Resources Efficient Dynamic Clustering Algorithm for Flying Ad-Hoc Network

Unmanned Aerial Vehicles, often known as UAVs, are connected in the form of a Flying Ad-hoc Network, or FANET, and placed to use in a variety of applications to carry out efficient remote monitoring. Their great mobility has an adverse effect on their energy consumption, which in turn has a detrimental effect on the network's stability and the effectiveness of communication. To manage the very dynamic flying behavior of UAVs and to keep the network stable, novel clustering algorithms have been implemented. In this context, a novel clustering technique is developed to meet the rapid mobility of UAVs and to offer safe inter-UAV distance, reliable communication, and an extended network lifespan. It also provides a detailed analysis of the similarities and distinctions between AODV, AOMDV, DSDV, and DumbAgent.The performance of these protocols is analyzed using the NS-2 simulator. The simulation results are shown in our study with AODV, AOMDV, DSDV, and DumbAgent. The results of the simulation make it abundantly evident that the AODV routing protocol outperforms the other routing protocols DSDV, AOMDV, and DumbAgent in terms of the number of packets lost, the amount of throughput achieved, the amount of routing overhead generated, and the amount of delay

Low-power neuromorphic sensor fusion for elderly care

Smart wearable systems have become a necessary part of our daily life with applications ranging from entertainment to healthcare. In the wearable healthcare domain, the development of wearable fall recognition bracelets based on embedded systems is getting considerable attention in the market. However, in embedded low-power scenarios, the sensor’s signal processing has propelled more challenges for the machine learning algorithm. Traditional machine learning method has a huge number of calculations on the data classification, and it is difficult to implement real-time signal processing in low-power embedded systems. In an embedded system, ensuring data classification in a low-power and real-time processing to fuse a variety of sensor signals is a huge challenge. This requires the introduction of neuromorphic computing with software and hardware co-design concept of the system. This thesis is aimed to review various neuromorphic computing algorithms, research hardware circuits feasibility, and then integrate captured sensor data to realise data classification applications. In addition, it has explored a human being benchmark dataset, which is following defined different levels to design the activities classification task. In this study, firstly the data classification algorithm is applied to human movement sensors to validate the neuromorphic computing on human activity recognition tasks. Secondly, a data fusion framework has been presented, it implements multiple-sensing signals to help neuromorphic computing achieve sensor fusion results and improve classification accuracy. Thirdly, an analog circuits module design to carry out a neural network algorithm to achieve low power and real-time processing hardware has been proposed. It shows a hardware/software co-design system to combine the above work. By adopting the multi-sensing signals on the embedded system, the designed software-based feature extraction method will help to fuse various sensors data as an input to help neuromorphic computing hardware. Finally, the results show that the classification accuracy of neuromorphic computing data fusion framework is higher than that of traditional machine learning and deep neural network, which can reach 98.9% accuracy. Moreover, this framework can flexibly combine acquisition hardware signals and is not limited to single sensor data, and can use multi-sensing information to help the algorithm obtain better stability