Inquiries regarding the use of the book should be directed to the rights and permissions department of INTECHOPEN LIMITED ([email protected]). This book presents the state of the art in mobile, wireless computing and future applications.
Introduction
The mobile ad hoc networks (MANETs) have evolved significantly over the past few decades where the difference with this type of network is the independence of a centralized infrastructure to organize the flow, for example a router or switch so if there is a device present in this network is disconnected or damaged, it can automatically adapt to a new topology and update the routing tables [1]. Finally, section 5 will demonstrate simulations of FANET behavior in different scenarios and routing protocols involving data transmission and video.
Challenges
Furthermore, maintaining the quality of service (QoS) even at the high speeds achieved by vehicles is one of the main challenges to be overcome in this field. Since the evaluation of routing protocols is one of the main research focuses in FANETs, it will be discussed in more detail in Section 4.
Applications
- Disaster monitoring
- Monitoring of agricultural areas
- Search and rescue operations
- Sensor networks
- Construction
- Product delivery
- Military service
In this way, new protocols were developed with a focus on mobile ad hoc networks, with FANETs being one of them, and just like beamforming, these protocols are still being tested but at a later stage.
Protocols
Proactive
And due to the size of the UAVs, it is possible to access places where a human would have difficulty [9]. In the DSDV protocol, the routing tables contain, in addition to the information of the network itself, a series of numbers that changes according to the network topology [3].
Reactive
OLSR is a specific protocol for ad hoc networks, and it has the main characteristic of choosing some of the network's nodes to act as relays, called multipoint relay (MPR) [24]. These numbers are sent to all the nodes of the network, keeping them always updated to avoid loops between the nodes [23].
Hybrid
The purpose of this mechanism is to avoid flooding in the network, which is caused by the excess of packets received by each node [25].
Performance evaluation
Simulation parameters
And it maintains routes if the network topology changes and communications are interrupted [24]. Several factors have a direct influence on this parameter, such as the routing protocols and the transmission speed of the network.
Results
It is a method used to measure the similarity between two images and determines the quality of the image received in transmission [30]. In the case of video transmission, this noise affects the fidelity of the video presentation at the destination.
Conclusions
This chapter presents query broadcast techniques used to reduce the cost of route discovery in ad hoc networks. On the other hand, a set of limited-broadcasting techniques allows for controlled packet flooding in a given ring, thereby reducing network congestion.
Route discovery in ad hoc networks
Flooding of query packets
- Reducing the flooding expenses
- Limiting the packet dropping
- Optimizing the path length
- Increasing reliability of the path
- Utilizing unicast and multicast modes
First, the precaution is to choose selective flooding, which prevents packet redundancy. The length of the path is another attribute that determines the stable route with the shortest length.
Unconfined broadcasting techniques
Confined broadcasting techniques
After discovering the route, the source node broadcasts the chase packets to stop further propagation of the query packets. Small enough portion of the network that depends on the predefined time-to-live (TTL) count.
Conclusion
Appropriate in proactive routing protocols where source node has link information of the entire network, which helps to prune the forwarding intermediate nodes. Appropriate in proactive routing protocols where source node has link information of the entire network, which helps to prune the forwarding intermediate nodes.
Challenges and issues in fifth generation wireless systems
- Access networks
- User equipment
- External IP networks
- Core network for mobile operators
In order to deal with this problem, it is necessary to first diagnose possible security threats to the future 5G, which will be given the right to enter the networks. In any case, the usual creation of UE fate recognition, combined with increased 5G systems measurement transfer capabilities, a huge variety of open working structures, and the truth that UE's future will be.
Importance of fifth generation
- Existing RATs evolution
- Hyper dense small-cell deployment
- Self-organizing network
- Machine type communication
- Millimeter-wave rats development
- Redesign of backhaul links
- Energy efficiency
- New spectrum allocation for 5G
- Bandwidth sharing
- Ran virtualization
In the following, all the meanings are complicated and highlight their function and importance for the achievement of the fifth generation. It is also called HetNet, which in turn significantly embellishes the spectral efficiency (b/s/Hz/m2) of the region.
Conclusion
Softwarization of mobile networks
The virtualization tools consist of the hypervisor and connector engine, and they are illustrated in Figure 1. Virtualization using the hypervisor is referred to as hypervisor-based virtualization, with the VMs as the computing resources or desktop emulators, and for the environment using docker engine is referred to as container-based virtualization, with the containers as the computing resources.
Energy efficient in future mobile networks
Network infrastructure
Network cloud implementation can be achieved by allowing virtual instances of functions to be hosted in data centers when needed. In terms of communication energy costs, we have energy consumed due to communication links (to and from each VM/container) and energy consumed due to the number of transmission (optical) drivers used to transmit data to the target BSs ).
Energy saving strategies in MEC
In virtualized computing platforms, energy consumption is related to computing and communication processes. With the advent of NFV, it is expected that the NFV framework [24, 46] can take advantage of virtualization technologies to significantly reduce energy consumption in large-scale network infrastructures.
Mobile datasets for network solutions
The location information can be used at different layers of the protocol stack to improve communication services in the network. In MANETs, mobility is limited to a given geographic area, and the nodes participating in the network are usually known in advance.
Network services in mobile networks 1 Mobility management
Context in mobile networks
Context can be defined locally or globally and can be set, managed, synchronized, combined and transferred. Context usually describes more complex conditions in the network and can be defined for a single node, a group of nodes, or all nodes in the network.
Localization in mobile networks
Additionally, geographic locations can be defined in the same or multiple spatial frames as the corresponding origin points. The time advance information used to synchronize multiple base station cells can be used for node localization.
Routing in mobile networks 1 Conventional routing protocols
Geographical routing protocols
Finally, choosing node D as the one closest to the goal corresponds to the basic greedy strategy. Such an area can be predicted from past target locations and node mobility information.
Geographical RRM
- Virtual cells
- Transmission channel allocation
- Geographical multiple access
- Numerical examples
- Discussion
The scaling factor is set to match the RWP model, i.e. the variance of the random mobility component to evenly distribute the nodes among the virtual cells. The node closest to the anchor point (ie, the virtual cell center) becomes the cluster head.
Conclusion
Në: Proceedings of the International Conference on Computing Communications and Networking Technologies (ICCCNT korrik 2014; Hefei, China. Proceedings of the International Conference on Computing Communications and Networking Technologies (ICCCNT korrik 2014; Hefei, Kinë.
Method for determining the location and required capacity of virtual reserved computing resources in case of an overload of the physical
Method for determining the location and required capacity of virtual-reserved computing resources in the event of an overload of the physical. Method for determining the size of the time interval of the constant configuration of computer resources.
Method for determining the size of the time interval of the constant configuration of computing resources
And to take into account the required performance of the virtual network function, the constraints on the value of the request processing time defined in Eq. According to the solution, each network function reserves a certain number of virtual network function resources based on an estimate of its largest resource requirements.
Method of local reconfiguration of network computing resources in case of failure or overload
The criterion (CRT in Figure 7) used to rank these nodes in a virtual network is the capacity of the virtual nodes. Let's give the objective function (26) in the form of a linear combination (with the weighted coefficients, b,c, ande) of the cost expressions.
Operating scheme of the resource management system
Analysis
Thus, before the operation starts, it is necessary to have statistics of the request arrival rate of the network function and the probability properties of the request service. According to the allocation method, the binding of each network function in the traditional network to the data center and the amount of resources to be reserved for the corresponding virtualized network function are determined.
Conclusions
A characteristic of mobile devices is the limited space for interaction and deployment of the graphical user interface. The usability of the mobile devices' applications is the main characteristic of user acceptance [2].
Mobile distributed user interfaces
- Distributed user interfaces
- Mobile applications
- Plasticity of user interfaces
- Mobile DUI
The user interface (UI) is the set of elements that allow the user to interact with computers. S ( I n ) ) is an n-tuple where each element is the state of the user interface I i that constitutes the DUI.
Examples
Tablet-SmartTV
Smartphone-smartwatch
Simultaneously: When the user works with the input elements found in the UIs of the tablet, the status changes are reflected in real time on the Smart TV. Simultaneously: When the user works with the input elements found in the UIs of the tablet, the status changes are reflected in real time on the Smart TV.
Tablets-smartphones
The user can run the app on a smartphone, smartwatch, or both devices using DUI. When the user decides to activate DUI and the app on the smartphone syncs with the app on the smartwatch.
Conclusions
In addition to adding elements to the editor, this state change is sent from the canvas to all devices in the array. In: Proceedings of the 15th International Conference on Human-Computer Interaction with Mobile Devices and Services.
Method of the correlation analysis between HOMA-IR and chosen state variables
2. We here provide our theoretical considerations on how DNL and adaptive thermogenesis (AT) can be assessed with mobile technology during the acute phase and at predicted stable equilibrium of energy metabolism. We calculate the absolute change of the Rw ratio ΔRw over the duration of the trial as in Eq.
Method of the analysis of the CC trial data with WFE-DNL-AT calculations
For demonstration purposes, we apply this method to analyze the published outcome of the CC trial [29]. The published data from the CC study were sparse, and the results of laboratory measurements were published only for baseline and for the endpoint.
Results of the correlation analysis between HOMA-IR and chosen state variables
Unknown values for the adaptive thermogenesis coefficientcAT and the unknown fraction of metabolized carbohydrate intake for de novo lipogenesiscDNL were assumed as time.
Results of the analysis of the CC trial data with WFE-DNL-AT calculations
The results of the WFE-DNL-AT calculations using the published CC trial data [29] are shown in Figures 1–6. The dashed lines labeled TRC model and TRF model are the results of the uncorrected WFE-DNL-AT model in the RC and RF arms of the CC study, respectively.
Discussion
Correlation results between HOMA-IR and fat mass, body weight, R-ratio and Rw-ratio are shown in Table 4. Modeling errors of fat mass, body weight and lean mass in grams are shown in Table 5. Adaptive thermogenesis / heat loss calculation is shown in Figure 6. The correlation results between HOMA-IR and fat mass, body weight, R-ratio and Rw-ratio are shown in Table 4. Modeling errors of fat mass, body weight and lean mass in grams are shown in Table 5.
Conclusion
WFE-DNL-AT is an extension of the WFE calculation to include de novo lipogenesis (DNL) and adaptive thermogenesis (AT). Here we provide our modeling of the adaptive thermogenesis that occurs with energy imbalance with metabolic changes to counteract the change in body composition.
Efficient and generic energy network efficient architecture
The total power consumption of the data center is related to the associated power consumed by each unit. The energy efficiency of the data center can be broadly defined as the number of useful calculations divided by the total energy used during a process [3, 5, 6].
Energy efficiency and target values of corresponding metrics for a data center
The total power of the data center is taken from the meters and presented as KW demand on the utility bill. Annual total KWh, It is the reciprocal of PUE (6) This parameter changes like this,.
CUE (carbon usage effectiveness)
This model is similar to the functional noise model in telecommunications, in that it establishes that the total electrical losses result from the sum of the electrical losses due to power, cooling, and lighting. The losses from the power supply are the sum of the losses from the distribution, from UPS and the other systems.
Measuring the green energy coefficient (GEC)
22] Green Grid Project; Harmonizing Global Metrics for Data Center Energy Efficiency, Global Taskforce Reaches Agreement on Data Center Productivity; 2011. 29]Green Grid Project; Harmonization of Global Metrics for Data Center Energy Efficiency, Global Taskforce Agrees on Measurement Protocols for GEC, ERF and CUE – Continues Discussion on Additional Energy Efficiency Metrics; 2012.
