Problems faced by large-scale sensor network applications

A wireless sensor network is a wireless network composed of a large number of stationary or moving sensors in a self-organizing and multi-hop manner. Its purpose is to cooperatively sense, collect, process, and transmit monitoring information of the sensing objects within the geographic area covered by the network and report it to user. The sensor network is generally composed of sensor nodes, aggregation nodes and data servers.

More and more research and technology of sensor networks are aimed at specific industries and applications. Among them, many applications need to be deployed on a large scale to achieve high coverage, high precision perception and other purposes. For example, forest fire monitoring, enemy situation monitoring on the battlefield, etc., thousands of sensor nodes are deployed in the environment to complete monitoring tasks. However, compared with small-scale applications, in addition to the difference in the number of nodes in large-scale applications, there will be a series of problems such as difficult node management and unbalanced resource usage.

1 Problems faced by large-scale applications

After the development of sensor network theory and technology, although it has been widely used in various industries, most applications are still limited to a small scale. For example, in the application of volcanic activity monitoring [1], the designer arranged 16 sensor nodes in the crater to monitor volcanic temperature, vibration and other information; in the wild zebra monitoring system [2], 6 to 10 were deployed The suit is placed on the zebra's neck to monitor the movement and life habits of the zebra; in the Da Ya Island behavior monitoring system [3], 32 nodes are deployed to monitor the habits of the island's Shanghai birds. The problems of deployment, networking, and energy of a small number of sensor networks will not be too prominent, and their focus on solving and processing is different from that of large-scale applications. Therefore, to explore the problems and solutions in large-scale sensor networks, we must rely on the application system of large-scale sensor networks.

Many scientific research institutions and companies have already begun to experiment with large-scale sensor network applications. For example, the Green Field Thousand Transmission System [4] established by the Hong Kong University of Science and Technology in Tianmu Mountain, Zhejiang Province, has deployed thousands of nodes and has been continuously working for more than 1 year. . In order to reduce the occurrence of carbon monoxide poisoning accidents in winter, the carbon monoxide monitoring and alarm system deployed in Cui Gezhuang Township, Chaoyang District, Beijing has about 8,000 to 12,000 rooms in each village. Each room has a carbon monoxide monitoring sensor node. The node collects indoor carbon monoxide concentration information to the management node for monitoring and management. Existing application examples provide a strong basis for us to design and study large-scale sensor networks and systems. Through the study of large-scale sensor network systems, the three major problems facing large-scale applications are summarized.

1.1 Unbalanced traffic

In the sensor network, not every node's traffic is the same. This is mainly because the data aggregation service in the sensor network is the main business. In the process of data aggregation from the sensing node to the sink node, the node closer to the sink node needs to bear the more With more forwarding tasks, the data volume of nodes closer to the aggregation node is larger, resulting in a funnel flow effect. Unbalanced traffic easily exhausts the energy of nodes with large traffic prematurely, affecting the connectivity and working life of the entire network. When the size of the sensor node network increases, the traffic in the network will increase exponentially, and the average number of hops experienced by each packet will increase, making the intermediate nodes have to spend a lot of energy for data transfer, which reduces the energy Use efficiency; and due to the increase in the number of nodes, the cost of routing maintenance has also increased accordingly.

1.2 Unbalanced demand and function

In the Internet, data processing equipment and data transmission equipment often have clear boundaries, and because the use environment is mostly unrestricted, their performance and functions are easily matched. In contrast, in sensor networks, nodes are both information collection devices and intermediate routing functions. Almost every node needs to complete the functions of sensing, transmission, and calculation. However, the sensor node is often an embedded system, which causes a serious mismatch with the fact that its network resources, computing resources, and storage resources are limited. The sensor network's funnel flow characteristics make the network traffic and node load unbalanced, which further exacerbates this imbalance. In contrast, because the back-end system is not constrained by energy, its performance can be unrestricted and will not become a "bottleneck" of the system, and its completed function is mainly data storage or transparent transmission, which is relatively single. In a sensor network, a device with poor performance has a heavy workload, and a device with good performance has a low workload, resulting in a contradiction between demand and function imbalance.

1.3 The imbalance between theory and practice

Figure 1 Wireless channel model

Before the actual deployment of the wireless sensor network system, simulation and small-scale testing are often required. The theoretical model used differs greatly from the actual model. For example, the wireless channel model, the commonly used theoretical model is the disc model, that is, when the receiving device is located in a circle centered on the transmitting device and the communication distance is a radius, normal communication can be performed. However, in actual situations, due to obstructions and antenna patterns, the communication range cannot be a standard circle, but an irregular shape, as shown in Figure 1 [5]. In addition, the wireless channel is susceptible to interference from the surrounding environment, and its communication range is also time-varying, resulting in difficulty in evaluating the communication range. There is still no good model that can accurately describe the channel characteristics.

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