Smart Home Security System Based on Zigbee

Wireless Sensor Networks (WSNs) are designed by hundreds or thousands of tiny, low cost and multifunctional sensor nodes. Each sensor node has very low battery life. Sensor nodes have finite storage capabilities, transmission and processing range and energy resources are also limited. There are many design issues in WSNs such as mobility, energy consumption, network topology, data aggregation, localization, production cost, security, network size and density etc. Routing protocols provide efficient working of the network, increase network lifetime, responsible for maintaining the routes in the network and perform reliable multi-hop communication under various conditions. LEACH (Low Energy Adaptive Clustering Protocol) is one of the hierarchical protocol in WSNs. LEACH uses TDMA MAC Protocol. During random data distribution, a number of TDMA slots are wasted. Because sensor nodes don't know either they have data to send or not, they continuously listen to the medium and this result in idle listening problem. This paper proposed OE-LEACH (An Optimized Energy Efficient LEACH Algorithm for WSNs) to enhance the performance of the LEACH Protocol, reduce time delay and energy consumption. Network Lifetime and throughput of WSN also increases. The Proposed method is simulated in MATLAB 2010a.

Objectives/Aim: The present paper deals with building a smart assistant with the aim of assisting the visually impaired people in mobility with confidence by realizing the nearby obstacles and also implement image processing techniques to recognize people. Methods: We use Raspberry Pi 4 which has increased computing performance and is interfaced to the picamera, GPS and GSM modules. Arduino pro mini with buzzer, ultrasonic sensors and vibration motors are used for obstacle detection. Certain libraries important for image processing are used such as OpenCV, Dlib, face detection (Haar cascades, HOG + Linear SVM, or CNNs), espeak for converting text to speech. Programming was implemented through Python and Arduino compiler. Findings: Analysis was carried out using the proposed system for the blind and visually impaired people who could move around comfortably with confidence and were also able to detect objects and recognize people. Hence the proposed system removes the use for the cumbersome white cane in exchange for small and handy modules which can be used in the form of wearables mounted on shoulders, hands and legs to detect the obstacles from multiple directions and provide for a comfortable wear. It was determined that the Raspberry pi 4 was incapable of running CNN detection for which ideally computer GPU was required, so HOG detection method was used instead. Application/Improvements: This project can be implemented mainly in the commercial field of helping visually impaired people with poor eyesight or being completely blind. Industrial applications can be devised and enhanced like robots and machineries along with Security, Identifying and Tracking. Keywords: Face Recognition; Obstacle Detection; Open Source Computer Vision (OpenCV); Deep Learning; Histogram of Oriented Gradients (HOG); Support Vector Machine (SVM); Convolutional Neural Network (CNN)

With the advent of ubiquitous computing society, many advanced technologies have enabled wireless sensor networks which consist of small sensor nodes. However, the sensor nodes have limited computing resources such as small size memory, low battery life, short transmission range, and low computational capabilities. Thus, decreasing energy consumption is one of the most significant issues in wireless sensor networks. In addition, numerous applications for wireless sensor networks are recently spreading to various fields (health-care, surveillance, location tracking, unmanned monitoring, nuclear reactor control, crop harvesting control, u-city, building automation etc.). For many of them, supporting security functionalities is an indispensable feature. Especially in case wireless sensor networks should provide a sufficient variety of security functions, sensor nodes are required to have more powerful performance and more energy demanding features. In other words, simultaneously providing security features and saving energy faces a trade-off problem. This paper presents a novel energy-efficient security architecture in an IEEE 802.15.4-based wireless sensor network called the Hybrid Adaptive Security (HAS) framework in order to resolve the trade off issue between security and energy. Moreover, we present a performance analysis based on the experimental results and a real implementation model in order to verify the proposed approach.

Cars can appear at any orientations in satellite images, but the widely used traditional histogram of oriented gradient (HOG) features is not rotation-invariant. Recently, a rotation-invariant HOG descriptor using Fourier analysis in polar coordinators has been proposed and has shown good performance; however, it is time and memory consuming. In this study, the authors improve this method to present an efficient rotation-invariant HOG descriptor for car detection in satellite images. The authors first convert spatial convolutions to multiplications in the frequency domain based on fast Fourier transform to speed up the descriptor computation. Then, they employ a backward search feature selection method based on support vector machine to reduce the descriptor dimensionality. This allows them to build the proposed efficient rotation-invariant HOG descriptor at low time and memory cost. The authors demonstrate that their method is more efficient and yields better detection performance in a public dataset for car detection task in satellite images.

Due to the limited battery power of sensor nodes, design of underwater wireless sensor networks (UWSNs) is very difficult. Information is not gained efficiently through sensor nodes in aquatic environment and energy consumption is also the major problem in UWSNs. We proposed logical sub-region with sink mobility for throughput maximization and energy consumption minimization in rectangular UWSNs (LSSR). Sensor nodes are randomly deployed in the network field. Two mobile sinks are moving strategically in the network field and gather data from their respective nodes and cover the maximum area of the network field. LSSR performs better in terms of network lifetime, throughput and residual energy as shown in the simulation results.

This paper presents a smart water management system using the microcontroller ZR16S08 as IoT solution, for water distribution support and losses prevention. The system operates through the smart monitoring of the water flow in pipes of the water distribution network, aiming to ensure quality of the water supply, knowing that water losses characterize one of the great problems in the world, as pipe holes may be open doors to water contaminants. As an alternative to circumvent this issue, a series of experiments were taken to create a network of sensors capable of monitoring water pipes in real time. Adopting criteria such as low consumption and low cost, the use the ZR16S08 microcontroller in the design of wireless sensor nodes that will be coupled in the water pipes was adopted. Complementing the system, a central processing unit, composed of a Raspberry Pi microcomputer, manages the traffic of the information collected by the sensor nodes and routes it to a web server. All data addressed by the central unit are available on-line by means of a supervisory platform. Considering the size of the sensor nodes, their power consumption, and regulatory issues, a link between the sensor nodes, operating at a frequency of 433 MHz, was defined. Preliminary results show the effectiveness of the proposed architecture for sensor nodes, allowing application for the monitoring of water and controlling losses.

As smart building becomes increasingly popular, it enables many interesting applications, such as smart lock, elderly monitoring and remote healthcare.This paper will introduce one sub-system in the smart building: smart power system.This smart power system consists of an Android application, a cloud server and a smart socket.In addition, human-device interaction methods in this system are studied.

Many applications in factory and process automation require robust wireless sensor networks (WSNs) for collecting sensor data with sampling and transmission rates up to 100 Hz from battery powered sensor nodes. To establish real-time communication in a star network topology, the LLDN mode of IEEE 802.15.4e amendment was released. We present a demonstration system which applies a recently introduced relaying method compatible to the LLDN mode, which uses dedicated relay nodes, thus increasing the reliability of the network while reducing the energy consumption of the sensor nodes. Furthermore, packet combining schemes on packets received erroneously from both, sensor node and relay node, are proposed, applied, and analyzed. Measurements with the demonstration system have shown that 96.4% of these erroneous transmissions could be recovered correctly.

The rise of IoT devices has led to a surge in data generation, necessitating efficient processing solutions. Traditional cloud-centric approaches face challenges like latency, bandwidth and privacy issues. Edge computing, a promising paradigm, enables data processing closer to the source, enhancing IoT-driven computer vision applications. This shift integrates edge computing frameworks with a study that proposed a novel drosophila food search-tuned convolutional neural network (DFS-CNN) and computer vision algorithms for real-time tasks like object detection and anomaly detection. We collected acquisition data from labeled Faces in the Wild (LFW) video and image dataset, the acquisition data were preprocessed using a bilateral filter for minimizing noise while maintaining sharp edges, and then filtered data were extracted using a histogram of oriented gradients (HOG) — the DFS-CNN model using the HOG feature set to detection the computer vision solution. The optimal DFS-CNN model was deployed on edge computing and is used in an IoT-based architecture to compute and transport data for real-time performance simulation using Tensor Flow Lite. The proposed method is compared to other traditional algorithms. The proposed DFS-CNN model detects objects in the LWT image with 96% accuracy. The proposed DFS-CNN model was used to address students’ inactivity status during the online exam, and that the outcome of the suggested method was tested with data latency, and real-time response, according to a comparative performance analysis.

Deployment of sensor network in hostile environment makes it mainly vulnerable to battery drainage attacks because it is impossible to recharge or replace the battery power of sensor nodes. Among different types of security threats, low power sensor nodes are immensely affected by the attacks which cause random drainage of the energy level of sensors, leading to death of the nodes. The most dangerous type of attack in this category is sleep deprivation, where target of the intruder is to maximize the power consumption of sensor nodes, so that their lifetime is minimized. Most of the existing works on sleep deprivation attack detection involve a lot of overhead, leading to poor throughput. The need of the day is to design a model for detecting intrusions accurately in an energy efficient manner. This paper proposes a hierarchical framework based on distributed collaborative mechanism for detecting sleep deprivation torture in wireless sensor network efficiently. Proposed model uses anomaly detection technique in two steps to reduce the probability of false intrusion.

We present a FPGA implementation of a pedestrian detector that can process VGA video at a frame rate of 30–40 frames per second in real time. The architecture implements an offline trained boosted detector in an attentional cascade. The cascade evaluates each frame using histogram of gradient (HOG) features in the LUV color space. Pedestrian targets are identified at 27 scales. Feature aggregation allows the design to operate on fewer candidate windows that would normally be required for a 640×480 frame. The design is implemented on a Xilinx Zynq FPGA using a FMC for video input. Video frames are buffered in off-chip DRAM and then streamed through a processing pipeline. The pipeline performs color space conversion, frame rescaling, HOG feature extraction, and candidate evaluation in a sliding window. The candidate evaluation is parallelized to evaluate two windows at a time. The entire pipeline is parallelized to evaluate multiple scales per frame.

A Wireless Sensor Network (WSN) is an emerging technology for a wide range of potential applications, including the environmental monitoring, medical, and target tracking systems. A WSN comprises a set of sensor nodes deployed in an area of interest. Sensor nodes are normally located in a dense and ad hoc manner, communicating each other in a multi-hop fashion in order to collect, process, and relay data. Most of WSNs are built on wireless, battery-powered sensor nodes. In such networks, it is hard to recharge or replace the energy-depleted nodes due to the desolate or harsh environment of the target area. Therefore, the efficient use of limited energy is a primary concern in designing protocols and usage strategies for WSNs. In this work an emergency scenario is considered and a WSN is used to monitoring the emergency area. To limit the power consumption, the WSN is activated only when the emergency occurs. Moreover an integrated protocol for routing and localization is developed to reduce communication, which is the most power-consuming operation in WSNs.

This paper presents the method to classify the speed limit traffic sign using multiple features, namely histogram of oriented gradient (HOG) and maximally stable extremal regions (MSER) features. The classification process is divided into the outer circular ring matching and the inner part matching. The HOG feature is employed to match the outer circular ring of the sign, while MSER feature is employed to extract the digit number in the inner part of the sign. Both features are extracted from the grayscale image. The algorithm detects the rotation angle of the sign by analyzing the blobs which is extracted using MSER. In the matching process, tested images are matched with the standard reference images by calculating the Euclidean distance. The experimental results show that the proposed method for matching the outer circular ring works properly to recognize the circular sign. Further, the digit number matching achieves the high classification rate of 93.67% for classifying the normal and rotated speed limit signs. The total execution time for classifying six types of speed limit sign is 10.75 ms.

To address the low accuracy problem of playground detection under complex background, the accurate playground localization based on multi-feature extraction and cascade classifier is proposed in this paper. It is difficult to utilize this information to separate objects from the complex background. Therefore, we adopt multi-feature extraction method to make the playgrounds more easily to be detected. The proposed localization method is partitioned into two modules: feature extraction and classification. First, multi feature extraction method combining histogram of oriented gradients (HOG) and Haar is utilized to extract features from raw images. HOG can authentically capture the shape information, which is extracted to characterize the local region. Haar can improve the image eigenvalue calculation effectively. Afterwards, cascade classifier based on AdaBoost algorithm is adopted to classify the extracted features. Finally we conduct the experiments with our proposed methodology on a publicly accessible remote sensing images from Google Earth. The results demonstrate that the proposed framework has a better effect with achieving high levels of recall, precision and F-score compared to the state-of-the-art alternatives, without sacrificing computational soundness. What is more, the results indicate that the proposed playground 1ocalisation method has strong robustness under different complex backgrounds with high detection rate.

Video processing applications have become increasingly difficult to implement on hardware, owing to the complex computer vision algorithms involved. This paper presents a real-time video processing architecture based on hardware/software co-design that improves execution speed and reduces the time to market of applications. We have implemented this framework for handwritten digit recognition on the Zybo Zynq-7000 ARM/FPGA SoC using Vivado High Level Synthesis (HLS) and Xillybus tools. Histogram of Oriented Gradients (HOG) feature extraction algorithm has been optimised for hardware execution and acceleration techniques have been applied on Vivado HLS to achieve a speed up of 38.89 for the HOG algorithm and recognition accuracy of 95.6%. Low precision arithmetic along with our approximations for costly functions, produced this significant gain in throughput by reducing 90% of the hardware resources required with just a marginal accuracy reduction by 1%. An overall performance improvement of 77% is obtained through hardware/software co-design over software execution. The framework identified digits seamlessly in a real-time video stream at 30 frames per second and enabled high frame rate video processing.