Cognitive Radio (CR) is an emerging technology to solve the issue of scarce spectrum resource utilization. The routing plays a significant role in CR Ad-hoc network and it has two major problems, those are spectrum load balance and energy efficiency. Hence, maintaining the spectrum load balance and energy efficiency becomes a difficult task in CR Ad-hoc Network. In this research, a new Multicast Ad-hoc On-Demand Distance Vector (MAODV) with Particle Swarm Optimization (PSO) algorithm was introduced for selecting best route of less energy consumption in the CR networks. The routing algorithm is implemented with an efficient particle encoding scheme and multi-objective fitness function. Moreover, the data packets are secured by using the RSA algorithm. The optimized lifetime attained from the desired CR ad-hoc networks by using this RSA technique. The multiple scenarios were simulated to compute and analyse the performances in terms of energy consumption, Packet Delivery Ratio (PDR), end to end delay, and throughput. The proposed method named as MAODV-PSO-RSA, which implemented in Network Simulator-2 (NS2). The main objective of the MAODV-PSO-RSA method is to improve the energy consumption during the routing process. The simulation results showed that MAODV-PSO-RSA method had improved 1-2.2 % of network performance compared to the existing methods such as Bio-Inspired Routing Protocol (BIRP) and Improved Frog Leap Inspired Protocol (IFLIP).
LTE, an acronym for Long Term Evolution is a standard developed by the 3 rd Generation Partnership Project (3GPP) for high peak data rates with a downlink speed of up to 150 megabits per second (Mbps) and an uplink speed of up to 50 Mbps. LTE is a way for cellular communications to operate at that high data rate. Routing is an important operation performed to route the data packets from the source node to destination node in any network. Hence there is a need for a protocol/algorithm to determine the best way to transfer the data. Routing protocols determine the best route to transfer data from one node to another. In this paper, a comparative study of the routing protocols for the application layer of LTE network is done. The protocols analyzed are Optimized Link State Routing (OLSR) and Routing Information Protocol (RIP). A study and comparison of the parameters are done based on the simulation results. The different performance metrics analyzed are Packet delivery ratio, Throughput, Average end-to-end delay and Jitter. The simulation results show that the best routing protocol w.r.t all the parameters analyzed is RIP.
Privacy protections for people filmed in public settings is a prerequisite to widespread camera use. For this reason, low-resolution videos are used from which specific people can be reliably obscured. Since the human region in low-resolution videos comprises of so few pixels and so little information, human detection is more challenging there than it is in high-resolution videos. With the current state of affairs, one of the most important challenges is tracking a target from lower resolution movies. Identification or monitoring of persons in low-resolution movies has become a common issue in many domains due to a lack of appropriate data. This study presents a novel people-detection algorithm that makes use of low-resolution film to overcome the aforementioned problem. In the first stage, a three-step procedure is executed, the video data gathered from low-resolution videos from various form of data is considered. The captured video is separated into frames and transformed from RGB to gray-scale. Local Binary Pattern (LBP) method is used in the second phase to accomplish background subtraction. Thirdly the feature extraction is performed in which histogram of optical flow (HOF)and some of the features are extracted in the form of eigen values. Finally these features are optimized using Modified Ant Colony Optimization (MACO) model to remove the unwanted features and select global features. Finally, classification operation is performed using Support Vector Machine (SVM) classifier to recognize the person from lower resolution videos. The results obtained using the implemented MACO-SVM obtains good results when compared with existing techniques with rate of accuracy 91.46% for soccer dataset, 90.8% for KTH dataset and 89.75% accuracy using VIRAT dataset.
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