DMLZERO

DMLZERO's Stars

• eds000n/routing

  implementation of some routing algorithms for wsn

  Language:Java11

• swiru95/sEden_Controller

  SDN Controller in OMNeT++/INET_Framework

  Language:C++72

• bounceur/CupCarbon

  IoT and WSN simulator

  Language:Java7149

• hitesh-Zaveri/Ileach

  Improved leach protocol for routing in WSN

  Language:MATLAB182

• shreyakupadhyay/SDN-Datacenter

  Making a software defined datacenter. Which includes various virtual networks with mutiple network functions deployed on it. This includes SDN network deployed on real hardware.

  Language:Python348

• YanHaoChen/Learning-SDN

  SDN 學習及實作範例。(因個人職涯關係，已不再維護，請見諒。)

  Language:Python386129

• tonydeng/sdn-handbook

  SDN手册

  Language:Shell1.2k262

• feiskyer/sdn-handbook

  SDN网络指南（SDN Handbook）

  Language:C1.5k417

• floodlight/floodlight

  Floodlight SDN OpenFlow Controller

  Language:Java758622

• kiyoshitaro/Mobile-Sink-

  Node Placement for Target Coverage and Network Connectivity in WSNs with Multiple Sinks

  Language:Java11

• gsolmaz/EventCoverage

  Simulation for WSNs with mobile sinks - Event coverage

  Language:Java1

• abhiramsingh0/Area_coverage_WSN

  Implementation of PHA algorithm in WSN

  Language:C++1

• OferLahav/WSN-algorithm

  New Coverage Algorithm

  Language:Java2

• Drvenkatesh/Gaussian-Distribution-based-Coverage-Hole-Detection-Algorithm-for-Wireless-Sensor-Networks

  11

• bbasso/Research

  My PhD research in reinforcement learning sami-markov vehicle routing problems

  Language:Matlab31

• SudiptaSingh/Q-Learning-based-smart-cab

  Problem Statement A smart city needs smart mobility, and to achieve this objective, the travel should be made convenient through sustainable transport solutions. Transportation system all over the world is facing unprecedented challenges in the current scenario of increased population, urbanization and motorization. Farewell to all difficulties as reinforcement learning along with deep learning can now make it simpler for consumers. In this paper we have applied reinforcement learning techniques for a self-driving agent in a simplified world to aid it in effectively reaching its destinations in the allotted time. We have first investigated the environment, the agent operates in, by constructing a very basic driving implementation. Once the agent is successful at operating within the environment, we can then identify each possible state the agent can be in when considering such things as traffic lights and oncoming traffic at each intersection. With states identified, we can implement a Q-Learning algorithm for the self-driving agent to guide the agent towards its destination within the allotted time. Finally, we can improve upon the Q-Learning algorithm to find the best configuration of learning and exploration factors to ensure the self-driving agent is reaching its destinations with consistently positive results. Our aim is also to find optimum values of parameters of the fitting function alpha, gamma and epsilon, so that the agent can work in an optimized way with the most optimum parameter values. Hence, a comparative analysis has also been conducted. Methodology used The solution to the smart cab objective is deep reinforcement learning in a simulated environment. The smart cab operates in an ideal, grid-like city (similar to New York City), with roads going in the North-South and East-West directions. Other vehicles will certainly be present on the road, but there will be no pedestrians to be concerned with. At each intersection there is a traffic light that either allows traffic in the North-South direction or the East-West direction. We have assumed that the smart cab is assigned a route plan based on the passengers' starting location and destination. The route is split at each intersection into waypoints, and the smart cab, at any instant, is at some intersection in the world. Therefore, the next waypoint to the destination, assuming the destination has not already been reached, is one intersection away in one direction (North, South, East, or West). The smart cab has only an egocentric view of the intersection it is at: It can determine the state of the traffic light for its direction of movement, and whether there is a vehicle at the intersection for each of the oncoming directions. For each action, the smart cab may either stay idle at the intersection, or drive to the next intersection to the left, right, or ahead of it. Finally, each trip has a time to reach the destination which decreases for each action taken (the passengers want to get there quickly). If the allotted time becomes zero before reaching the destination, the trip has failed. The smart cab will receive positive or negative rewards based on the action it has taken. Expectedly, the smart cab will receive a small positive reward when making a good action, and a varying amount of negative reward dependent on the severity of the traffic violation it would have committed. Based on the rewards and penalties the smart cab receives, the self-driving agent implementation should learn an optimal policy for driving on the city roads while obeying traffic rules, avoiding accidents, and reaching passengers' destinations in the allotted time. Environment: The smartcab operates in an ideal, grid-like city (similar to New York City), with roads going in the North-South and East-West directions. Other vehicles will certainly be present on the road, but there will be no pedestrians to be concerned with. At each intersection there is a traffic light that either allows traffic in the North-South direction or the East-West direction. U.S. Right-of-Way rules apply:  On a green light, a left turn is permitted if there is no oncoming traffic making a right turn or coming straight through the intersection.  On a red light, a right turn is permitted if no oncoming traffic is approaching from your left through the intersection. To understand how to correctly yield to oncoming traffic when turning left.

  Language:Jupyter Notebook192

• snack0verflow/smartTraffic

  Traffic management and priority routing using reinforcement learning. Smart India Hackathon top 10 national finalist.

  Language:Java1

• dugdmitry/adhoc_routing

  Reinforcement Learning based routing protocol implementation for wireless ad hoc networks.

  Language:Python427

• Dungyichao/Electric-Vehicle-Route-Planning-on-Google-Map-Reinforcement-Learning

  User can set up destination for any agent to navigate on Google Map and learn the best route for the agent based on its current condition and the traffic. Our result is 10% less energy consumption than the route provided by Google map

  Language:Python25065

• dennybritz/reinforcement-learning

  Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.

  Language:Jupyter Notebook21.1k6.1k

• MengGuo/ac_ltl_wsn

  Actor critic reinforcement learning + motion and task planning under LTL tasks + wireless sensor network routing

  Language:OpenEdge ABL136

• zinouzl/CH-Leach

  CH-Leach new Routing Protocol for WSN Based on Leach

  Language:MATLAB122

• tinagui/Spider-Monkey-Optimization-in-WSN

  Spider Monkey Optimization Routing Protocol (source code, algorithm description, publications and etc)

  61

• YathishJ/Wireless-Sensor-Network

  The goal of the project is to Design a routing algorithm that should provide the data integrity and delay differentiated service over the same WSN.

  Language:Matlab108

• bhanu1131/Routing-algorithm-in-WSN-Grid-based-approach-

  The algorithm shows how the sensed data from the sensor nodes is being routed to the sink efficiently.The energy of the sensor nodes is very less and the cost of transmitting data of 1Kb from the sensor nodes to a distance of 100 meters is same as performing 3 billion instructions in a general purpose register.Thus there is a requirement in minimising the number of transmissions and length of transmission and every node in the network must be covered . So a Grid is chosen of dimensions m*m (where m is maximum distance the node can communicate) and among all the nodes a centre head is chosen where all the nodes in the grid sends the data to the cluster head and all the cluster heads sends data to nearest cluster head to save the energy until it reaches the sink

  Language:Matlab208

• irakr/WSN-simulation

  Simulation of a routing protocol for Wireless Sensor Networks(WSN).

  Language:C++2313

• earthat/AODV-GUI-in-WSN

  This code is for a MATLAB GUI in which AODV routing protocol is implemented for WSN. The source nodes are changing each time with number of packets.

  Language:MATLAB4727

• mangapinheiro/Circular-Backbone-MAC

  Implementation of a MAC protocol for WSNs that uses self organization to create a circular high speed route on the network to reduce the transmission time between distant nodes

  Language:Java31

• Mohamadnet/MOPSO-WSN

  Routing in Wireless Sensor Network using Multiobjective Particle Swarm Optimization

  Language:Matlab4621

• hsvgbkhgbv/Mean-field-Fictitious-Play-in-Potential-Games

  Mean-field Fictitious Play in Potential Games

  Language:Python7