RFID与WSN集成网络节点部署优化研究
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摘要
伴随着时代的进步,人们认识到了科技的力量,在21世纪的今天,人们在科学方面的探索达到了前所未有的水平。传感网被认为是21世纪最具有影响力的改变世界的10大技术之一。按照感知技术的不同,传感网可以分为无线传感器网络技术(WSN)、射频识别技术(RFID)、紫蜂技术(Zigbee)及蓝牙技术(Bluetooth)等。而RFID与WSN集成技术拥有扩展的功能,在完成对物体识别、追踪它们位置的同时,可以感知辨识物体的环境信息,是将来传感网的一个发展趋势。
在应用RFID与WSN集成网络时,首先要解决节点的部署问题。节点部署是一种关键的低能耗技术,不但能够保证集成网络的连通质量和数据质量,提高整个网络的可靠性,而且还能够有效地降低集成网络的能耗。因此如何设计合理的节点部署对于延长节点寿命以及整个集成网络的生命周期具有重要的意义。在此背景下,本文研究RFID与WSN集成网络节点部署优化控制方法,具体内容如下:
RFID与WSN集成网络具有大规模、环境复杂、网络资源有限、随机部署、自组织等特点,根据电磁波在自由空间的传播规律,建立了集成RFID阅读器-无线传感器网络节点部署模型。
基于改进遗传优化策略,在复杂的传播环境,交叉覆盖及智能节点间不可避免的干扰等影响因素下,寻找智能节点的最佳位置。通过选取合适的参数,算法加快了寻找最优节点的速度,在保证覆盖率的前提下使干扰最小,这对于集成网络系统的大规模应用具有重要理论和实际意义。
基于惯性权重线性递减粒子群算法,提出了一种集成RFID阅读器-无线传感器网络节点部署优化策略。通过迭代,找到了智能节点的最优坐标位置,相比于粒子群算法,改进粒子群算法加快了寻找最优节点的速度,并能快速有效地收敛于最优解。仿真结果表明该集成网络节点部署优化控制方法在集成网络系统中是有效的。
Along with the progress of The Times, people recognized the power of science and technology, in the 21st century, people's exploration of science has reached unprecedented levels. Sensing network is considered to be the most influential one of 10 technologies which will change the world in the 21st century. According to the different perception, Sensing network can be divided into the wireless sensor network (WSN) technology, radio frequency identification (RFID), Zigbee technology (Zigbee), Bluetooth technology (Bluetooth) and so on. And the integration of RFID and WSN will maximize their effectiveness, give new perspectives to a broad range of useful applications, and bridge the gap between the real and the research world. This is because the resulting integrated technology will have extended capabilities, scalability, and portability as well as reduced unnecessary costs. It is a development tendency of sensing network in future.
In the application of integrated network, the first problem is to deploy the nodes. Nodes’deployment is a key technique to reduce the energy consumption. It can not only ensure the quality of integrated network’s connectivity and data, but also reduce the energy consumption of the integrated network effectively. So it is significant how to design the reasonable deployment of nodes. In this background, this paper studies the optimal deployment of nodes in the integration of RFID and WSN.
The integration of RFID and WSN is a network, which has large-scale, complex environment, limited resources, random deployment, self-organization and so on. According to the propagation law of electromagnetic wave in the free space, we establish the deployment model of integrating RFID readers with wireless sensor nodes.
Based on genetic optimization strategy, this paper presents an improved genetic algorithm to find the best position of smart nodes. It must satisfy a set of imperative constraints such as the complex propagation environments, the undesired mutual coverage and the unavoidable interference between smart nodes. By choosing proper parameters, the improved algorithm speeds up the search process. It can not only accelerate the speed of finding optimal deployment of nodes, but can also converge to the optimal solution effectively and rapidly. What is more, it can have a good coverage rate and can minimize the interference.
Based on the linearly decrease inertia weight, this paper presents an improved Particle Swarm Optimization approach for tackling this complex optimization problem. To validate this approach, computational results are presented for a typical test scenario. It can not only accelerate the speed of finding optimal nodes, but can also effectively and rapidly converge to the optimal solution. The simulation results show that the optimization control method of nodes deployment is effective in the integrated network system.
在应用RFID与WSN集成网络时,首先要解决节点的部署问题。节点部署是一种关键的低能耗技术,不但能够保证集成网络的连通质量和数据质量,提高整个网络的可靠性,而且还能够有效地降低集成网络的能耗。因此如何设计合理的节点部署对于延长节点寿命以及整个集成网络的生命周期具有重要的意义。在此背景下,本文研究RFID与WSN集成网络节点部署优化控制方法,具体内容如下:
RFID与WSN集成网络具有大规模、环境复杂、网络资源有限、随机部署、自组织等特点,根据电磁波在自由空间的传播规律,建立了集成RFID阅读器-无线传感器网络节点部署模型。
基于改进遗传优化策略,在复杂的传播环境,交叉覆盖及智能节点间不可避免的干扰等影响因素下,寻找智能节点的最佳位置。通过选取合适的参数,算法加快了寻找最优节点的速度,在保证覆盖率的前提下使干扰最小,这对于集成网络系统的大规模应用具有重要理论和实际意义。
基于惯性权重线性递减粒子群算法,提出了一种集成RFID阅读器-无线传感器网络节点部署优化策略。通过迭代,找到了智能节点的最优坐标位置,相比于粒子群算法,改进粒子群算法加快了寻找最优节点的速度,并能快速有效地收敛于最优解。仿真结果表明该集成网络节点部署优化控制方法在集成网络系统中是有效的。
Along with the progress of The Times, people recognized the power of science and technology, in the 21st century, people's exploration of science has reached unprecedented levels. Sensing network is considered to be the most influential one of 10 technologies which will change the world in the 21st century. According to the different perception, Sensing network can be divided into the wireless sensor network (WSN) technology, radio frequency identification (RFID), Zigbee technology (Zigbee), Bluetooth technology (Bluetooth) and so on. And the integration of RFID and WSN will maximize their effectiveness, give new perspectives to a broad range of useful applications, and bridge the gap between the real and the research world. This is because the resulting integrated technology will have extended capabilities, scalability, and portability as well as reduced unnecessary costs. It is a development tendency of sensing network in future.
In the application of integrated network, the first problem is to deploy the nodes. Nodes’deployment is a key technique to reduce the energy consumption. It can not only ensure the quality of integrated network’s connectivity and data, but also reduce the energy consumption of the integrated network effectively. So it is significant how to design the reasonable deployment of nodes. In this background, this paper studies the optimal deployment of nodes in the integration of RFID and WSN.
The integration of RFID and WSN is a network, which has large-scale, complex environment, limited resources, random deployment, self-organization and so on. According to the propagation law of electromagnetic wave in the free space, we establish the deployment model of integrating RFID readers with wireless sensor nodes.
Based on genetic optimization strategy, this paper presents an improved genetic algorithm to find the best position of smart nodes. It must satisfy a set of imperative constraints such as the complex propagation environments, the undesired mutual coverage and the unavoidable interference between smart nodes. By choosing proper parameters, the improved algorithm speeds up the search process. It can not only accelerate the speed of finding optimal deployment of nodes, but can also converge to the optimal solution effectively and rapidly. What is more, it can have a good coverage rate and can minimize the interference.
Based on the linearly decrease inertia weight, this paper presents an improved Particle Swarm Optimization approach for tackling this complex optimization problem. To validate this approach, computational results are presented for a typical test scenario. It can not only accelerate the speed of finding optimal nodes, but can also effectively and rapidly converge to the optimal solution. The simulation results show that the optimization control method of nodes deployment is effective in the integrated network system.
引文
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[2]刘福铭. RFID与无线传感器网络集成技术研究与开发[D]: [硕士论文].上海:上海交通大学, 2007
[3]王德胜.无线传感器网络节能策略研究[D]: [硕士论文].无锡:江南大学, 2008
[4]邹永祥,吴建平. RFID技术和无线传感器网络的融合—RFSN[J].科技信息, 2009, (14): 3-4
[5]陈瀚宁,朱云龙,胡琨元.基于多种群共生进化的RFID网络优化[J].解放军理工大学学报(自然科学版), 2008, 9(5): 413-416
[6]林祝亮,冯远静.基于粒子群算法的无线传感网络覆盖优化策略[J].计算机仿真, 2009, 26(4): 190-193
[7]李明,石为人.异构无线传感器网络中基于模拟退火算法的成本最优部署机制[J].传感技术学报, 2010, 23(6): 855-858
[8]袁浩.基于改进蜂群算法无线传感器感知节点部署优化[J].计算机应用研究, 2010, 27(7): 2704-2708
[9]董雷,刘凯. RFID技术在现代物流中的应用与实施研究[J].物流科技, 2010, 33(1): 58-60
[10]秦毅,彭力.基于RFID的超市物联网购物引导系统的设计与实现计算机研究与发展[J]. 2010, 47(z2): 350-354
[11]任守纲,徐焕良,黎安等.基于RFID/GIS物联网的肉品跟踪及追溯系统设计与实现[J].农业工程学报, 2010, 26(10): 229-235
[12] Kashif Ali, Waleed Alsalih, Hossam Hassanein. Set-Cover Approximation Algorithms for Load-aware Readers Placement in RFID Networks[C]. Communications (ICC), 2011 IEEE International Conference on. Japan: Kyoto, 2011. 1-6
[13]刘嵩岩,綦志强,苟鹏飞等.超高频RFID系统信号损失与覆盖模型研究[J].哈尔滨工程大学学报, 2008, 29(2): 162-166
[14]林俊龙,何晨.基于集合覆盖和禁忌搜索算法的WCDMA基站布局[J].上海交通大学学报, 2007, 41(6): 924-928
[15] Lee C Y, Kang H G. Cell planning with capacity expansion in mobile communication: A tabu search approach[J]. IEEE Transactions on Vehicular Technology, 2000, 49(5): 1678-1691
[16] Subratra R, Zomaya A Y. A comparison of three artificial life techniques for reporting cell planning in mobile computing[J]. IEEE Transactions on Parallel and Distributed Syetems,2003, 14(2): 142-153
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