An Automatic Zone Detection System for Safe Landing of UAVs

As the demand increases for the use Unmanned Aerial Vehicles (UAVs) to monitor natural disasters, protecting territories, spraying, vigilance in urban areas, etc., detecting safe landing zones becomes a new area that has gained interest. This paper presents an intelligent system for detecting regions to navigate a UAV when it requires an emergency landing due to technical causes. The proposed system explores the fact that safe regions in images have flat surfaces, which are extracted using the Gabor Transform. This results in images of different orientations. The proposed system then performs histogram operations on different Gabor-oriented images to select pixels that contribute to the highest peak, as Candidate Pixels (CP), for the respective Gabor-oriented images. Next, to group candidate pixels as one region, we explore Markov Chain Codes (MCCs), which estimate the probability of pixels being classified as candidates with neighboring pixels. This process results in Candidate Regions (CRs) detection. For each image of the respective Gabor orientation, including CRs, the proposed system finds a candidate region that has the highest area and considers it as a reference. We then estimate the degree of similarity between the reference CR with corresponding CRs in the respective Gabor-oriented images using a Chi square distance measure. Furthermore, the proposed system chooses the CR which gives the highest similarity to the reference CR to fuse with that reference, which results in the establishment of safe landing zones for the UAV. Experimental results on images from different situations for safe landing detection show that the proposed system outperforms the existing systems. Furthermore, experimental results on relative success rates for different emergency conditions of UAVs show that the proposed intelligent system is effective and useful compared to the existing UAV safe landing systems

Accelerating FPGA-surf feature detection module by memory access reduction

Feature detection is an important concept in the area of image processing to compute image abstractions of image information, which is used for image recognition and many other applications. One of the popular algorithm used is called the Speeded-Up Robust Features (SURF), which realized the scale space pyramid to detect the features. For this reason, prior researchers concentrate on applying parallelism onto the SURF multiple layers using technology such as Field Programmable Gate Array (FPGA). However, prior FPGA-SURF implementation does not emphasis on memory access limitation that can affect the overall performance of a system. This paper proposes a study on FPGA-SURF and memory access implementation in feature detection area. We conduct a profiling test and founds that the external memory access to fetch the integral image data in SURF highly affects the overall performance. We also found that the SURF algorithm memory access has redundant repeating pattern that can be reduced. Therefore, a controller design that stores repeating data (for the subsequent process) in an on-chip memory is proposed. This method reduces the external memory access and can increase the overall performance. The result shows that our proposed method improves the existing method (i.e. without the memory access reduction) by 1.23 times when the external memory latency is 20ns

The Multi-Objective Optimization Algorithm Based on Sperm Fertilization Procedure (MOSFP) Method for Solving Wireless Sensor Networks Optimization Problems in Smart Grid Applications

Prior studies in Wireless Sensor Network (WSN) optimization mostly concentrate on maximizing network coverage and minimizing network energy consumption. However, there are other factors that could affect the WSN Quality of Service (QoS). In this paper, four objective functions that affect WSN QoS, namely end-To-end delay, end-To-end latency, network throughput and energy efficiency are studied. Optimal value of packet payload size that is able to minimize the end-To-end delay and end-To-end latency, while also maximizing the network throughput and energy efficiency is sought. To do this, a smart grid application case study together with a WSN QoS model is used to find the optimal value of the packet payload size. Our proposed method, named Multi-Objective Optimization Algorithm Based on Sperm Fertilization Procedure (MOSFP), along with other three state-of-The-Art multi-objective optimization algorithms known as OMOPSO, NSGA-II and SPEA2, are utilized in this study. Different packet payload sizes are supplied to the algorithms and their optimal value is derived. From the experiments, the knee point and the intersection point of all the obtained Pareto fronts for all the algorithms show that the optimal packet payload size that manages the trade-offs between the four objective functions is equal to 45 bytes. The results also show that the performance of our proposed MOSFP method is highly competitive and found to have the best average value compared to the other three algorithms. Furthermore, the overall performance of MOSFP on four objective functions outperformed OMOPSO, NSGA-II and SPEA2 by 3%, 6% and 51%, respectively

Intersection and Complement Set (IACS) Method to Reduce Redundant Node in Mobile WSN Localization

The majority of the Wireless Sensor Network (WSN) localization methods utilize a large number of nodes to achieve high localization accuracy. However, there are many unnecessary data redundancies that contributes to high computation, communication, and energy cost between these nodes. Therefore, we propose the Intersection and Complement Set (IACS) method to reduce these redundant data by selecting the most significant neighbor nodes for the localization process. Through duplication cleaning and average filtering steps, the proposed IACS selects the normal nodes with unique intersection and complement sets in the first and second hop neighbors to localize the unknown node. If the intersection or complement sets of the normal nodes are duplicated, IACS only selects the node with the shortest distance to the blind node and nodes that have total elements larger than the average of the intersection or complement sets. The proposed IACS is tested in various simulation settings and compared with MSL* and LCC. The performance of all methods is investigated using the default settings and a different number of degree of irregularity, normal node density, maximum velocity of sensor node and number of samples. From the simulation, IACS successfully reduced 25% of computation cost, 25% of communication cost and 6% of energy consumption compared to MSL*, while 15% of computation cost, 13% of communication cost and 3% of energy consumption compared to LCC

High-Speed shortest path co-processor design

Shortest path algorithms are significant in graph theory and have been applied in many applications such as transportation and networking. Most of the shortest path calculation is performed on general purpose processor where instructions must be run to read the input, compute the result, and set the output which later on will slow down the overall performance. Therefore, the authors proposed a hardware approach which implements FPGA technology to find the shortest path between two nodes. The FPGA approach will demonstrate how parallelism can be used to significantly reduce calculation steps compared to sequential effort. In this paper, A-Star algorithm has been chosen for the shortest path calculation since it can achieve superior time running based on its heuristic behavior

Secure and Energy-Efficient Data Aggregation Method Based on an Access Control Model

Wireless sensor networks (WSNs) consist of a large number of sensor nodes that are distributed to capture the information about an area of interest. In WSN, many of the secure data aggregation works are conducted without addressing the authentication process. It is challenging to implement authentication while preserving the energy consumption in the network. The previous research that focus on these issues have several limitations, such as sharing the security key and the key length with a base station node, and not much attention is given to enhance the authentication of the Medium Access Control (MAC) server. This makes the data aggregation network are exposed to malicious activities. This paper presents a new protocol to address the security and energy issue in Wireless Sensor Network (WSN). This newly developed protocol is named Secure and Energy-Efficient Data Aggregation (SEEDA), which is the extension of SDAACA protocol. The proposed protocol aims to enhance authentication by generating a random value and random timestamp with a secret key. The base station node will verify the fake aggregated data when the packets are received using the generated key earlier. Furthermore, the attacks are detected and prevented by utilizing secure node authentication, data fragmentation algorithms, fully homomorphic encryption, and access control model. The secure node authentication algorithm prevents attacks from accessing the network. To avoid network delays, the base station node utilizes the distance information between the participating nodes. To ensure the reliability of our proposed method, we simulate two well-known attacks, called Sybil and sinkhole attacks. Several experimental scenarios are conducted to observe their effect. Evaluation metrics such as malicious activity detection rate, energy consumption, end-to-end delay, and resilience time are measured. The performance of the proposed protocol is compared with SDA, SDAT, SDALFA, EESSDA, SDAACA, and EESDA, which is a widely used protocol in the area of secure data aggregation. The simulation results show that the proposed SEEDA method outperforms the existing scheme with 98.84% malicious nodes detection rate, 3.04 joules for energy consumption, the maximum delay of 0.038 seconds, and the resilient time 0.054, 0.075 seconds when 8%,16% of malicious nodes affecting the network. © 2013 IEEE