For the Internet of Things (IoT), Contiki – NG (Next Generation) is examined as a robust open-source operating system that is formulated for memory-restricted frameworks and has the capability to provide powerful assistance for networking and low-power functions. Typically, in study and educational scenarios, it is extensively employed for constructing and simulating IoT applications and networks. The Cooja simulator permits the users to simulate networks of Contiki-NG nodes, which is considered as a segment of Contiki-NG environment. It also enables the examining and advancement of IoT protocols, applications, and arrangements before implementing on realistic hardware.

The following are numerous project ideas for Contiki-NG and the Cooja simulator that extend different factors of IoT and networking study:

1. Smart City Applications

• Project Plan: By employing Contiki-NG in Cooja, aim to construct a simulation of an IoT-enabled smart city architecture. It is approachable to concentrate on incorporating different applications such as waste management models, traffic tracking, and smart street lighting. In order to combine and handle the IoT devices of the city, investigate the purpose of multi-hop wireless networks and energy-effective communication protocols.

2. Energy-efficient IoT Networks

• Project Plan: The energy-effective networking protocols for IoT devices has to be researched. Focus on formulating a project in Cooja in such a manner to simulate a network of battery-powered IoT devices, like sensors in a remote tracking application. It is appreciable to contrast the network lifetime and energy utilization of various routing and data transmission protocols such as CoAP (Constrained Application Protocol) and RPL (Routing Protocols for Low-Power and Lossy Networks).

3. IoT Security Protocols

• Project Plan: Within Contiki-NG, examine the deployment and performance of IoT safety protocols. Aim to develop a Cooja simulation, and by utilizing protocols such as DTLS (Datagram Transport Layer Security), it exhibits safer interaction among IoT devices. The influence of these safety criterions on resource utilization and model effectiveness has to be assessed in an efficient manner.

4. Wireless Sensor Network (WSN) for Environmental Monitoring

• Project Plan: Mainly, for actual-time ecological tracking like humidity, quality of air, and temperature, construct a WSN simulation in Cooja. Concentrating on data precision, network scalability, and actual-time data processing limitations, aim to examine in what way sensor nodes can effectively gather, process and transfer data to a central server.

5. IoT Protocols Comparison

• Project Plan: The scalability and effectiveness of IoT communication protocols such as CoAP and MQTT-SN (MQTT for sensor networks) in Contiki-NG has to be contrasted. By examining protocol efficacy, overhead, and consistency, focus on employing Cooja to simulate networks with differing intensities and congestion trends.

6. IPv6 over Low-Power Wireless Personal Area Networks (6LoWPAN)

• Project Plan: Concentrating on the limitations of IP connectivity in low-power, lossy networks specific to IoT applications, it is significant to simulate a 6LoWPAN network in Cooja. To assess the effectiveness of 6LoWPAN based on delay, energy utilization, and packet delivery ratio, focus on testing with network arrangements.

7. Interoperability in IoT Networks

• Project Plan: It is approachable to solve the limitation of interoperability across various IoT protocols and devices. A mixed-network simulation in Cooja has to be formulated that encompasses devices interacting over various protocols such as Bluetooth Low Energy, Wi-Fi, and Zigbee. Specifically, within Contiki-NG, focus on creating a gateway approach that facilitates continuous data exchange and connectivity among these devices.

8. Adaptive IoT Systems

• Project Plan: An IoT framework has to be developed in such a manner that must adjust to varying ecological situations or network conditions. To improve energy effectiveness, data exactness, or delay according to the actual-time exploration, this study could include the process of dynamically adapting data sampling levels, converting communication protocols, or redirecting data.

9. Machine Learning for IoT

• Project Plan: The incorporation of lightweight machine learning methods into Contiki-NG devices has to be investigated. Concentrating on trade-offs among computational complication and decision-making precision, simulate a network in Cooja where IoT devices utilize machine learning for works such as anomaly identification, ecological pattern detection, or predictive maintenance.

How to simulate Contiki NG Cooja projects?

Numerous steps are encompassed while simulating Contiki-NG projects in the Cooja simulator that range from establishing the Contiki-NG platform to arranging and executing simulations. For the Contiki-NG operating system, Cooja is considered as a multi-faceted network simulator, thereby permitting you to examine and correct wireless sensor network applications before implementing them on realistic hardware. We offer a stepwise instruction that assists you to begin simulation in effective manner:

Step 1: Install Contiki-NG and Cooja

1. Download Contiki-NG: Initially, you must download Contiki-NG from its GitHub repository. By utilizing Git with the below specified command, you can copy the repository.

git clone https://github.com/contiki-ng/contiki-ng.git

2. Install Java Development Kit (JDK): Generally, to execute, the Cooja needs Java. It is advisable to assure that you have the Java Development Kit (JDK) installed on your system. Based on your system, the version has to be selected. But, Java 8 or the novel version might be appropriate in general.
3. Navigate to the Cooja Directory: After Contiki-NG is copied to your local machine, Within the Contiki-NG folder, go to the tools/cooja directory.
4. Start Cooja: With the following command, execute Cooja.

ant run

5. Cooja is compiled and initiated by this command. It might take a certain period of time to compile, when you execute it for the first time.

Step 2: Create a New Simulation

1. New Simulation: As soon as the Cooja opens, it is appreciable to begin a novel simulation by means of clicking on File > New Simulation.
2. Configure the Simulation: A name has to be given for your simulation. Aim to arrange the simulation scenarios like beginning time and simulation momentum. If required, you can adapt these arrangements afterwards and set off the default choices.

Step 3: Add Nodes to the Simulation

1. Create or Import a Contiki-NG Application: You want a Contiki-NG application, before appending the nodes. Normally, you can develop your own applications or employ instance applications that are offered in the examples directory of Contiki-NG.
2. Add Nodes: By clicking on Motes > Add Motes > Create new mote type > sky mote, you can append nodes in Cooja. In your Contiki-NG application directory, go to the compiled application such as .c document for your mote kind.
3. Configure Nodes: It is appreciable to arrange the number of nodes you aim to append to your simulation and whenever necessary specify their preliminary locations.

Step 4: Configure Network Settings

1. Network Topology: Within the simulation platform, focus on configuring your motes. Generally, you can locate motes by utilizing any one of the autonomous placement tools of Cooja or in a manual way.
2. Radio Environment: By clicking on Simulation > Configure Radio Medium, adapt the radio medium scenarios in order to simulate various communication platforms.

Step 5: Run the Simulation

1. Start the Simulation: To execute your simulation, click on the Start button. Whenever essential, you can stop, pause, or modify the simulation.
2. Interact with Nodes: By opening the corresponding window of individual nodes, you can communicate with them. In this step, you can view records, write to their serial port, or correct them accordingly.

Step 6: Analyze the Results

• Use Tools for Analysis: To explore the activities of a network, Cooja offers tools like radio messages log, mote output console, and network graph. Typically, to collect perceptions regarding your simulation, it is beneficial to make use of these tools.
• Debug and Iterate: Make essential alterations or modifications to your simulation scenarios or application according to your exploration and repeat the simulation in order to examine variations.

Contiki NG Cooja Projects

We provide support for scholars working on a variety of Contiki NG Cooja projects and ideas, ranging from simple to complex simulations and algorithms. Feel free to reach out to us by phone or email to share your ideas. Additionally, we offer Google Meet discussions to ensure the well-being of scholars.

1. A Reference Implemenation for RPL Attacks Using Contiki-NG and COOJA
2. Medical Data Retrieval By Named Data Networking of Things Architecture in Contiki NG OS
3. Protocol Stack-Aware Comparison of Centroid Localization Algorithms Based on Anchor Density Using Cooja
4. Multi-Trace: Multi-level Data Trace Generation with the Cooja Simulator
5. Energy-Efficient Message Bundling with Delay and Synchronization Constraints in Wireless Sensor Networks
6. ECTS: Enhanced Centralized TSCH Scheduling with Packet Aggregation for Industrial IoT
7. DCS: Dilution-based Convergecast Scheduling in a TSCH network
8. The Contiki-NG open source operating system for next generation IoT devices
9. A Named Data Networking Stack for Contiki NG OS
10. Case Studies with the Contiki-NG Simulator to Design Strategies for Sensors’ Communication Optimization in an IoT-Fog Ecosystem
11. The Design and Implementation of an Information Centric Networking Architecture in Contiki NG OS
12. The revenge of asynchronous protocols: Wake-up Radio-based Multi-hop Multi-channel MAC protocol for WSN
13. Analysis of Wi-SUN FAN Network Formation Time
14. Cyber Secure Framework for Smart Agriculture: Robust and Tamper-Resistant Authentication Scheme for IoT Devices
15. CRA-RPL: A Novel Lightweight challenge-Response authentication-based technique for securing RPL against dropped DAO attacks
16. A Trust-Based Intrusion Detection System for RPL Networks: Detecting a Combination of Rank and Blackhole Attacks
17. Channel BlaQLisT: Channel Blacklist using Q-Learning for TSCH
18. VariBan: A Variable Bandwidth channel allocation algorithm for IEEE 802.15.4e-based networks
19. Evaluating the Impact of RPL Control Overhead on Network Performance
20. DETONAR-Light: An IoT Network Intrusion Detection Using DETONAR without a Sniffer Network