Wireless Network Topics that have evolved in a continuous manner, which are both significant and interesting are listed below . Related to wireless networking, we recommend a few latest topics, encompassing explicit problem statements and possible solutions that could be more useful to solve these problems in an efficient way:
- Energy Efficiency in Wireless Sensor Networks (WSNs)
Problem Statement: As sensor nodes are energized using batteries in general, the energy constraints highly restrict the Wireless Sensor Networks. Less network durability could be resulted through energy ineffectiveness. For battery changing, regular maintenance is also required.
Possible Solution:
- Hierarchical Routing Protocols: For minimizing total energy usage, the protocols such as LEACH (Low-Energy Adaptive Clustering Hierarchy) must be applied. To aggregate and transfer data, these protocols arrange nodes into clusters, especially with a determined cluster head.
- Energy Harvesting: To energize the nodes, we plan to facilitate energy harvesting from ecological sources such as thermal, vibration, or solar energy by creating efficient technologies.
- Security in Internet of Things (IoT) Networks
Problem Statement: In terms of the diversity of devices and their resource limitations, the IoT networks are susceptible to different security hazards in a substantial manner. Applying strong security techniques is difficult due to this issue.
Possible Solution:
- Lightweight Encryption: Appropriate for IoT devices, the lightweight cryptographic algorithms have to be created. Without consuming device resources, they should offer sufficient security.
- Blockchain Technology: In IoT networks, accomplish data morality validation and safer, decentralized authentication by means of blockchain.
- Quality of Service (QoS) in Mobile Edge Computing (MEC)
Problem Statement: When considering the constrained resources at the edge and the dynamic character of mobile networks, it is intricate to offer reliable QoS (Quality of Service) in MEC platforms.
Possible Solution:
- Resource Allocation Algorithms: To assure QoS, computational resources have to be allocated in a dynamic way on the basis of actual-time requirements. For that, we aim to create adaptive resource allocation algorithms.
- Load Balancing Techniques: Among edge servers, share workloads in a uniform manner by applying load balancing policies. This is specifically for assuring seamless service distribution and obstructing barriers.
- Latency Reduction in 5G Networks
Problem Statement: For applications like remote surgery and autonomous vehicles which need actual-time communication, it is difficult to minimize latency in 5G networks.
Possible Solution:
- Network Slicing: Appropriate for various latency needs, virtual networks must be developed by utilizing network slicing. Through this process, assure that the required resources are obtained by critical applications.
- Edge Computing: To process data nearer to the end-users, the edge computing approaches have to be implemented. For data transmission, the necessary time could be minimized in a substantial manner.
- Spectrum Management in Cognitive Radio Networks
Problem Statement: To enhance the utilization of accessible spectrum and prevent interference, the effective spectrum handling is most significant in cognitive radio networks. In heavily populated regions, accomplishing this mission is crucial.
Possible Solution:
- Dynamic Spectrum Access: Excluding interference to authorized users, the unutilized spectrum bands must be detected and used by cognitive radios through dynamic spectrum access. For that, we intend to create algorithms.
- Machine Learning for Spectrum Sensing: For facilitating enhanced detection of accessible channels, the effectiveness and preciseness of spectrum sensing should be improved by applying machine learning methods.
- Interference Management in Dense Wireless Networks
Problem Statement: In dense wireless networks, the interference issue results in minimized network capability and ineffective functionality.
Possible Solution:
- Interference Coordination: Efficient methods such as Coordinated Multi-Point (CoMP) transmission must be applied, which can improve signal quality and minimize interference by combining several base stations.
- Adaptive Beamforming: For reducing interference with other users, the signal beams should be directed to target receivers by utilizing adaptive beamforming algorithms.
- Mobility Management in Vehicular Ad Hoc Networks (VANETs)
Problem Statement: Connection losses and regular topology variations could be resulted through the greater mobility of nodes in VANETs, and handling this mobility is also difficult. Communication preciseness can be impacted due to this problem.
Possible Solution:
- Predictive Routing Protocols: To forecast vehicle motions and provide interaction links early, we plan to create effective routing protocols. On network functionality, the effect of mobility has to be minimized through this approach.
- Geo-Networking: On the basis of vehicle positions, route data by utilizing geographic details. Across the highly secure and consistent paths, the flow of packets must be assured.
- Throughput Optimization in Massive MIMO Systems
Problem Statement: While handling power usage and computational intricacy in Massive MIMO frameworks, it is highly intricate to enhance throughput.
Possible Solution:
- Efficient Antenna Selection: For effective antenna selection, algorithms have to be created. Among computational costs and performance enhancements, the trade-off must be stabilized through these algorithms.
- Hybrid Beamforming: To refine the trade-offs among signal processing functionality and hardware intricacy, the hybrid analog-digital beamforming methods should be applied.
Wireless network Important Parameters in NS3 Simulation
Numerous major parameters are there for a wireless network, which are generally considered in NS3 simulation. Suitable for wireless network, we list out several parameters that are significant in NS3 simulation:
- Network Topology
- Node Configuration: It is important to consider mobility models, count of nodes, and node positions.
- Instance:
NodeContainer wifiStaNodes;
wifiStaNodes.Create(3);
NodeContainer wifiApNode;
wifiApNode.Create(1);
- Wireless Channel and Physical Layer Parameters
- Channel Type: Focus on the variety of wireless channel models (for instance: YansWifiChannel), which are generally utilized.
- Propagation Model: To simulate signal propagation, consider the ideal model (for instance: LogDistance, TwoRayGround, and FreeSpace).
- Instance:
YansWifiChannelHelper channel = YansWifiChannelHelper::Default();
YansWifiPhyHelper phy = YansWifiPhyHelper::Default();
phy.SetChannel(channel.Create());
- MAC Layer Parameters
- MAC Protocol: Specify the employed MAC protocol (for instance: CSMA, TDMA, and IEEE 802.11).
- Instance:
WifiMacHelper mac;
Ssid ssid = Ssid(“ns-3-ssid”);
mac.SetType(“ns3::StaWifiMac”, “Ssid”, SsidValue(ssid), “ActiveProbing”, BooleanValue(false));
- Network Traffic and Applications
- Traffic Type: Consider the traffic variety (for instance: TCP, UDP), which is currently produced.
- Traffic Pattern: Focus on traffic generation, and examine its pattern and rate (for instance: Poisson, CBR).
- Instance:
UdpEchoServerHelper echoServer(9);
ApplicationContainer serverApp = echoServer.Install(wifiApNode.Get(0));
serverApp.Start(Seconds(1.0));
serverApp.Stop(Seconds(10.0));
UdpEchoClientHelper echoClient(apInterface.GetAddress(0), 9);
echoClient.SetAttribute(“MaxPackets”, UintegerValue(1));
echoClient.SetAttribute(“Interval”, TimeValue(Seconds(1.0)));
echoClient.SetAttribute(“PacketSize”, UintegerValue(1024));
ApplicationContainer clientApp = echoClient.Install(wifiStaNodes.Get(0));
clientApp.Start(Seconds(2.0));
clientApp.Stop(Seconds(10.0));
- Mobility Model
- Mobility Patterns: Define the nodes’ motion patterns (for instance: ConstantVelocity, RandomWaypoint).
- Instance:
MobilityHelper mobility;
mobility.SetPositionAllocator(“ns3::GridPositionAllocator”,
“MinX”, DoubleValue(0.0),
“MinY”, DoubleValue(0.0),
“DeltaX”, DoubleValue(5.0),
“DeltaY”, DoubleValue(10.0),
“GridWidth”, UintegerValue(3),
“LayoutType”, StringValue(“RowFirst”));
mobility.SetMobilityModel(“ns3::RandomWalk2dMobilityModel”,
“Bounds”, RectangleValue(Rectangle(-50, 50, -50, 50)));
mobility.Install(wifiStaNodes);
- Routing Protocols
- Routing Protocol: For routing packets among nodes, consider the utilized protocols (for instance: OLSR, DSDV, and AODV).
- Instance:
InternetStackHelper stack;
stack.Install(wifiApNode);
stack.Install(wifiStaNodes);
Ipv4GlobalRoutingHelper::PopulateRoutingTables();
- Performance Metrics
- Throughput: Evaluate the efficient transmission rate of the data.
- Latency: To navigate from source to destination, the required time for a packet must be assessed.
- Packet Delivery Ratio (PDR): For the effectively delivered packets and those transmitted, this metric indicates the ratio.
- Energy Consumption: Specifically for the network nodes, evaluate the total energy usage.
- Simulation Parameters
- Simulation Time: For the simulation execution, specify the total time frame.
- Seed and Run Number: In simulations, utilize this number for initializing the randomness.
- Instance:
Simulator::Stop(Seconds(10.0));
Simulator::Run();
Simulator::Destroy();
Sample Code Structure
By integrating these parameters, a basic structure is offered by us for an NS3 simulation script:
#include “ns3/core-module.h”
#include “ns3/network-module.h”
#include “ns3/mobility-module.h”
#include “ns3/wifi-module.h”
#include “ns3/internet-module.h”
#include “ns3/applications-module.h”
using namespace ns3;
int main (int argc, char *argv[])
{
// Network Topology
NodeContainer wifiStaNodes;
wifiStaNodes.Create(3);
NodeContainer wifiApNode;
wifiApNode.Create(1);
// Wireless Channel and PHY Layer
YansWifiChannelHelper channel = YansWifiChannelHelper::Default();
YansWifiPhyHelper phy = YansWifiPhyHelper::Default();
phy.SetChannel(channel.Create());
// MAC Layer
WifiHelper wifi;
wifi.SetRemoteStationManager(“ns3::AarfWifiManager”);
WifiMacHelper mac;
Ssid ssid = Ssid(“ns-3-ssid”);
mac.SetType(“ns3::StaWifiMac”, “Ssid”, SsidValue(ssid), “ActiveProbing”, BooleanValue(false));
NetDeviceContainer staDevices;
staDevices = wifi.Install(phy, mac, wifiStaNodes);
mac.SetType(“ns3::ApWifiMac”, “Ssid”, SsidValue(ssid));
NetDeviceContainer apDevices;
apDevices = wifi.Install(phy, mac, wifiApNode);
// Mobility Model
MobilityHelper mobility;
mobility.SetPositionAllocator(“ns3::GridPositionAllocator”,
“MinX”, DoubleValue(0.0),
“MinY”, DoubleValue(0.0),
“DeltaX”, DoubleValue(5.0),
“DeltaY”, DoubleValue(10.0),
“GridWidth”, UintegerValue(3),
“LayoutType”, StringValue(“RowFirst”));
mobility.SetMobilityModel(“ns3::RandomWalk2dMobilityModel”,
“Bounds”, RectangleValue(Rectangle(-50, 50, -50, 50)));
mobility.Install(wifiStaNodes);
mobility.SetMobilityModel(“ns3::ConstantPositionMobilityModel”);
mobility.Install(wifiApNode);
// Internet Stack and Routing
InternetStackHelper stack;
stack.Install(wifiApNode);
stack.Install(wifiStaNodes);
Ipv4AddressHelper address;
address.SetBase(“10.1.3.0”, “255.255.255.0”);
Ipv4InterfaceContainer staInterfaces = address.Assign(staDevices);
Ipv4InterfaceContainer apInterface = address.Assign(apDevices);
Ipv4GlobalRoutingHelper::PopulateRoutingTables();
// Applications
UdpEchoServerHelper echoServer(9);
ApplicationContainer serverApp = echoServer.Install(wifiApNode.Get(0));
serverApp.Start(Seconds(1.0));
serverApp.Stop(Seconds(10.0));
UdpEchoClientHelper echoClient(apInterface.GetAddress(0), 9);
echoClient.SetAttribute(“MaxPackets”, UintegerValue(1));
echoClient.SetAttribute(“Interval”, TimeValue(Seconds(1.0)));
echoClient.SetAttribute(“PacketSize”, UintegerValue(1024));
ApplicationContainer clientApp = echoClient.Install(wifiStaNodes.Get(0));
clientApp.Start(Seconds(2.0));
clientApp.Stop(Seconds(10.0));
// Simulation Control
Simulator::Stop(Seconds(10.0));
Simulator::Run();
Simulator::Destroy();
return 0;
}
Emphasizing the field of wireless networking, we suggested several contemporary topics, including problem statements and possible solutions. Related to the wireless network, numerous significant parameters are specified by us, along with some sample codes.
Wireless Network Dissertation Ideas
Wireless Network Dissertation Ideas which scholars can approach for research are listed below, matlabsimulation.com will be your trusted partner for your research. Global guidance will be given by us we have all latest technologies to give prompt response.
- Intelligent Humidity Sensor for – Wireless Sensor Network Agricultural Application
- Detection of Forest Fires at Early Stages Using Wireless Sensor Network and Graphical User Interfacing
- On the Role of Wireless Sensor Networks in Subsurface Exploration: An Overview
- Smart Health Care Monitoring System using Light Fidelity and Wireless Biomedical Sensor Network
- Security Challenges in NDN Based Underwater Wireless Sensor Networks: An Overview
- Research Issues and Design Challenges for Wireless Sensor Network Based Real Time Applications
- An Accurate Methodology to Identify the Explosives Using Wireless Sensor Networks
- Survey on Key Management Schemes and Cluster based Routing Protocols in Wireless Sensor Network
- Energy Consumption Reduction in Wireless Sensor Network Based on Clustering
- Improved Energy Efficient Wireless Sensor Networks Using Multicast Particle Swarm Optimization
- An Authentic Key Management Scheme for Hierarchical Wireless Sensor Networks
- ODA: Optimal Deployment Algorithm for Wireless Sensor Network for Coverage Enhancement
- Survey on Key Management Schemes and Cluster based Routing Protocols in Wireless Sensor Network
- Symmetric Key Encryption for Secure Communication Using Wireless Hart in Wireless Sensor Networks (WSN)
- Survey on Algorithms for Energy Efficient Cluster Head Selection in Wireless Sensor Networks
- Enhanced Energy Efficient Cluster Head Selection Algorithm for Improvement in Network Lifetime for Wireless Sensor Network
- Cooperative Game Theory Based Approach for Target Coverage in Wireless Sensor Networks
- An Optimized Energy Aware Routing Algorithm Based on Multi-Objective in Wireless Sensor Networks
- IBEECP: Improved Biogeography Optimization Based Energy Efficient Clustering Protocol for Wireless Sensor Networks
- Intelligent Determination of Shortest Route for Troop Movement in Military Operations by Applying ISR in Wireless Sensor Networks
