A promising approach for overcoming poor crop yields in phosphorus (P)-deficient soils is to exploit the genetic variation among plants to grow under low P conditions. We examined the P requirements of three mungbean cultivars, T-77, MI-5 and E-72, using four P rates, 0, 30, 60 and 90 mg P kg -1 soil (designated P0, P1, P2 and P3, respectively). Nodulation was highest in T-77, and unlike the other cultivars, nodule numbers were not increased by P application. Similarly, growth of T-77 was the highest, and was not influenced by P rates. In contrast shoot yields of MI-5 and E-72 at P0 were only 76 and 65%, respectively, of the maximum obtained under P application. Nodule dry weight and the amount of N fixed (Ndfa) in each cultivar was enhanced by P application, with T-77 generally giving the lowest response, and accumulating the highest Ndfa. The data suggest a higher P requirement for N 2 fixation (especially for T-77) than for growth. All plants increased their P uptake as P rates increased, with T-77 accumulating the highest amount of P at each P level. Differences in the physiological P use efficiency, PPUE (g shoot mg -1 P) among genotypes were generally not significant, neither were there any consistent trends as P rates changed. The ability to absorb P therefore appeared to be more important than PPUE in enhancing growth. We conclude from our data that it is possible by selection to obtain plants capable of good growth and high N 2 fixation in soils of low P; cultivar T-77 is a good example.

The effect of different levels of soil phosphorus on nodulation, N z fixation and growth of supernodulating soybean mutant, nts 382, its parent, Bragg, and soybean cultivar, Chippewa, were compared in a greenhouse study. The P rates were, 0, 30, 60 and 90 mg kg -1 soil (P0, PI, P2 and P3, respectively).The reduced shoot growth of nts 382 (25% at P0 and 17% at P3) could not be attributed to P deficiency, as nts 382 achieved near maximum yield at a lower P rate (P1) than Bragg (Pz). The P response of Chippewa that produced the lowest shoot yield, was similar to that of Bragg. Root growth of nts 382 was more retarded than shoot growth (on average 50% that of Bragg), and yet absorbed a similar amount of total P as Bragg. Also, Chippewa that had the lowest root dry matter accumulated the highest P in the shoots. The data indicate that plant growth was influenced by differences in P use efficiency rather than differences in total P.In contrast to plant growth, nts 382 accumulated 26% more N than Bragg, and Chippewa accumulated a similar amount of N as Bragg. Thus again, Bragg used N most efficiently for growth. Soil N uptake was not governed by root biomass, and Chippewa absorbed more N from soil than both Bragg and nts 382 which absorbed similar amounts of soil N. Nodulation in nts 382 was significantly enhanced, being 528 and 892% greater than Bragg at P0 and P3, respectively, and nts 382 was the only cultivar whose nodulation was consistently enhanced by P addition. The percentage of N derived from fixation (% Ndfa) was not affected by supernodulation, but because of a greater accumulation of total N in nts 382, amount of N fixed (Ndfa) was on average 30% higher in nts 382 than Bragg. Also, while % Ndfa in either nts 382 or Bragg was not influenced by P addition, total N and consequently, Ndfa increased. However, for Chippewa which fixed the lowest N, both % Ndfa and Ndfa were increased by P addition. We conclude that although supernodulation had an adverse effect on shoot and root growth, these reductions could not be attributed to a reduced uptake of the two major plant nutrients, N and P, neither was there evidence that the mutation had caused a greater demand for these nutrients, other than for the greater requirement of P for nodulation.

As an emerging networking technology, cognitive radio networks (CRNs) have drawn immense attention in the wireless networking community. Since multimedia services have become widely popular among wireless communication services users, supporting those services over CRNs has become an interesting research topic in recent years. However, due to the random nature of the resource availability in CRNs, providing quality-of-service (QoS) guarantees for multimedia services is a challenging task. In this paper, we consider a secondary system operating over a time-slotted primary system and secondary users accessing the channels at the spectrum holes without interfering with primary users. As the capacity analysis is one of the basic steps to guarantee QoS, we analyze the constantrate voice capacity of multi-channel fully-connected CRNs under different generic channel access schemes with centralized and distributed control, respectively. The capacity is represented in terms of the number of simultaneous independent voice calls that the secondary system can support, providing stochastic delay guarantee. It is shown that the analytical results closely match with the simulation results, and the number of voice packets that can be simultaneously transmitted in a time-slot per channel has a significant impact on the capacity of the system. With proper medium access control, capacity analysis can help to develop a call admission control policy for QoS provisioning in CRNs.

Providing multimedia services over cognitive radio (CR) networks has become an interesting research topic during past few years. As multimedia applications require specific quality of service (QoS) guarantees, supporting multimedia applications to secondary users over a CR network is a challenging task due to the random nature of resource availability. In this paper, we consider a secondary system operating over a time-slotted primary system with multiple channels and secondary users accessing the channels at the spectrum holes without interfering with primary users. We derive the voice capacity of the CR system based on the theories of effective bandwidth (EB) and effective capacity (EC). The capacity is represented in terms of the number of simultaneous independent voice calls that the system can support, providing stochastic delay guarantee. It is shown that (i) the analytical results match well with simulation results and stays slightly lower than the simulation results due to the conservative nature of the EB and EC theories, and (ii) the mean duration of channel being unavailable to secondary users has a significant impact on the system capacity. With proper medium access control, this analysis can help to develop a call admission control policy for QoS provisioning in CR networks.

No abstract

No abstract

Abstract-Quality of service (QoS) support over cognitive radio networks (CRNs) is challenging due to the random spectrum availability. Elastic data traffic is a popular service whose service response time is an important QoS parameter. We analyze the mean response time of elastic data traffic service operating over a single channel time-slotted centralized CRN under three main service disciplines, namely, shortest processing time without preemption (SPTNP), shortest processing time with preemption, and shortest remaining processing time, in comparison with the processor sharing (PS) service discipline. It is shown that the SPTNP is a better choice over the PS service discipline when the traffic load is high, and that the preemption reduces the mean response time when the data file size (service time requirement) follows a heavy tailed distribution. The response time analysis can be used for call admission control to ensure service satisfaction.Index Terms-Cognitive radio network, elastic data traffic, quality-of-service, response time, shortest processor time, shortest remaining processor time, processor sharing.I. I The spectrum underutilization [2] is becoming a major setback for the development of next generation wireless networks. Among proposed solutions, the concept of cognitive radio networks (CRNs) [3][4] has become a popular choice due to its flexibility and adaptability to use in any available frequency band. It has been well accepted within the wireless communications research community to explore the underutilized portions of the radio spectrum using new-generation smart programmable radios, without harmfully interfering with the licensed primary users (PUs). Early research studies on CRNs mainly focused on physical layer aspects such as spectrum sensing [5] and power/rate controlling [6], and link layer aspects such as channel access coordination [7]. Further, the unpredictable nature of the opportunistic channels limits the research work on CRNs to best effort services without any strict quality of service (QoS) requirements. As the demand for wireless multimedia services keeps on increasing, supporting QoS-aware services over CRNs is essential.Research over CRNs. The impact of primary user activities on traffic congestion and the economic interaction between secondary user (SU) and primary network operators are studied in [12] and [13], respectively, when the SUs are data users. This type of services does not require strict QoS as in conversational or streaming services, but has a moderate service requirement in the form of response time. The response time of a service request (file) is defined as the time elapsed from the instant it is placed by a user to the instant that the service (file transmission) is completed. A. Related WorkMost of the resource allocation/scheduling works in CRNs mainly focus on throughput optimization/fairness, and they do not deal with any specific data file length distributions or response time as a performance metric. A performance analysis of elastic traff...

Traditional networking is hardware-based, having the control plane coupled with the data plane. Software-Defined Networking (SDN), which has a logically centralized control plane, has been introduced to increase the programmability and flexibility of networks. Knowledge-Defined Networking (KDN) is an advanced version of SDN that takes one step forward by decoupling the management plane from control logic and introducing a new plane, called a knowledge plane, decoupled from control logic for generating knowledge based on data collected from the network. KDN is the next-generation architecture for self-learning, self-organizing, and self-evolving networks with high automation and intelligence. Even though KDN was introduced about two decades ago, it had not gained much attention among researchers until recently. The reasons for delayed recognition could be due to the technology gap and difficulty in direct transformation from traditional networks to KDN. Communication networks around the globe have already begun to transform from SDNs into KDNs. Machine learning models are typically used to generate knowledge using the data collected from network devices and sensors, where the generated knowledge may be further composed to create knowledge ontologies that can be used in generating rules, where rules and/or knowledge can be provided to the control, management, and application planes for use in decision-making processes, for network monitoring and configuration, and for dynamic adjustment of network policies, respectively. Among the numerous advantages that KDN brings compared to SDN, enhanced automation and intelligence, higher flexibility, and improved security stand tall. However, KDN also has a set of challenges, such as reliance on large quantities of high-quality data, difficulty in integration with legacy networks, the high cost of upgrading to KDN, etc. In this survey, we first present an overview of the KDN architecture and then discuss each plane of the KDN in detail, such as sub-planes and interfaces, functions of each plane, existing standards and protocols, different models of the planes, etc., with respect to examples from the existing literature. Existing works are qualitatively reviewed and assessed by grouping them into categories and assessing the individual performance of the literature where possible. We further compare and contrast traditional networks and SDN against KDN. Finally, we discuss the benefits, challenges, design guidelines, and ongoing research of KDNs. Design guidelines and recommendations are provided so that identified challenges can be mitigated. Therefore, this survey is a comprehensive review of architecture, operation, applications, and existing works of knowledge-defined networks.