Over the last fifteen years, wireless local area networks (WLANs) have been populated on large variety of pervasive devices hosting heterogeneous applications. Pervasive edge computing has accelerated more distributed network applications for these devices, eliminating the round-trip to help in achieving zero latency dream. However, These applications require significantly variable data rates for effective functioning, especially in pervasive computing. The static bandwidth of frequency channelization in current WLANs strictly restricts the maximum achievable data rate by a network station. This static behavior spawns two major drawbacks: under-utilization of scarce spectrum resources and less support to delay sensitive applications such as voice and video.To this point, if the computing is moved to the edge of WLANs to reduce the frequency of communications, the pervasive devices can be provided with better services during the communication and networking. Thus, we aim to distribute spectrum resources among pervasive devices based upon delay sensitivity of applications while simultaneously maintain the fair channel access semantics of medium access control (MAC) layer of WLANs. Henceforth, ultra-low latency, efficiency and reliability of spectrum resources can be assured.

In this paper, two novel algorithms have been proposed for adaptive channelization to offer rational distribution of spectrum resources among pervasive edge nodes based on their bandwidth requirement and assorted ambient conditions. The proposed algorithms have been implemented on a real test bed of commercially available universal software radio peripheral (USRP) devices. Thorough investigations have been carried out to enumerate the effect of dynamic bandwidth channelization on parameters such as medium utilization, achievable throughput, service delay, channel access fairness and bit error rate. The achieved empirical results demonstrate that we can optimally enhance the network wide throughput by almost 30% by using channels of adaptable bandwidths.

在过去的十五年中，无线局域网（WLAN）已在承载异构应用程序的各种普及型设备上得到了普及。普及的边缘计算为这些设备加速了更多的分布式网络应用程序，消除了往返过程，以帮助实现零延迟梦想。但是，这些应用需要显着变化的数据速率才能有效发挥功能，尤其是在普适计算中。当前WLAN中频率信道化的静态带宽严格限制了网络站可达到的最大数据速率。这种静态行为产生了两个主要缺点：频谱资源稀缺的利用不足，延迟语音和视频等敏感应用程序的支持较少。如果将计算移至WLAN的边缘以减少通信频率，则可以在通信和联网期间为普及型设备提供更好的服务。因此，我们旨在基于应用程序的延迟敏感性在普及型设备之间分配频谱资源，同时保持WLAN的媒体访问控制（MAC）层的公平信道访问语义。因此，可以确保频谱资源的超低等待时间，效率和可靠性。

本文提出了两种新的自适应信道化算法，根据带宽需求和各种环境条件，在普适边缘节点之间合理分配频谱资源。所提出的算法已在商用通用软件无线电外围设备（USRP）的真实测试床上实现。已经进行了彻底的研究以列举动态带宽信道化对诸如介质利用率，可达到的吞吐量，服务延迟，信道访问公平性和误码率等参数的影响。取得的经验结果表明，通过使用带宽可调的信道，我们可以将网络范围的吞吐量最佳地提高近30％。