Contiki OS Research Articles

Performance Analysis of Tree-Based and Deep Learning Algorithms for Developing Distributed Secure Systems in IoT: A Comparative Study

Notably, IoT device utilization has experienced a substantial wave recently, and ensuring these devices' privacy and security has become a critical concern. ML-based security approaches are promising for IoT network protection against security concerns. This study provides a proximate analysis of tree-based and deep-learning algorithms for securing IoT domains. Specifically, we evaluate Decision Tree, RandomForest, XGBoost, Catboost, Extreme Tree, Light GMB, Adaptive Boosting, CNN, LSTM, MLP, GRU, and Autoencoder on four publicly available datasets - IoT23, CICID2017, EdgeIIoT, BotnetIoT and Contiki OS and Cooja simulation were used to generate a dataset featuring various RPL attacks. To assess the performance of a model, we measure its accuracy, precision, recall, and F1-score metrics. Our discoveries indicate that tree-based algorithms outperform deep learning algorithms regarding training time, memory usage, and interpretability while gaining comparable or even better detection accurateness. Conversely, deep-learning algorithms exhibit higher detection rates for rare or previously unseen attacks; their proficiency in detecting complex patterns and relationships within a given dataset has demonstrated remarkable efficacy in data analysis and classification tasks. We conclude that both tree-based and deep learning algorithms have their strengths and weaknesses, and in the IoT environment, one should base the choice of the algorithm on requirements and constraints. Our research shows hybrid approaches combining algorithm strengths can establish secure, distributed IoT systems

Machine Learning-based Intrusion Detection Technique for IoT: Simulation with Cooja

The Internet of Things (IoT) is one of the promising technologies of the future. It offers many attractive features that we depend on nowadays with less effort and faster in real-time. However, it is still vulnerable to various threats and attacks due to the obstacles of its heterogeneous ecosystem, adaptive protocols, and self-configurations. In this paper, three different 6LoWPAN attacks are implemented in the IoT via Contiki OS to generate the proposed dataset that reflects the 6LoWPAN features in IoT. For analyzed attacks, six scenarios have been implemented. Three of these are free of malicious nodes, and the others scenarios include malicious nodes. The typical scenarios are a benchmark for the malicious scenarios for comparison, extraction, and exploration of the features that are affected by attackers. These features are used as criteria input to train and test our proposed hybrid Intrusion Detection and Prevention System (IDPS) to detect and prevent 6LoWPAN attacks in the IoT ecosystem. The proposed hybrid IDPS has been trained and tested with improved accuracy on both KoU-6LoWPAN-IoT and Edge IIoT datasets. In the proposed hybrid IDPS for the detention phase, the Artificial Neural Network (ANN) classifier achieved the highest accuracy among the models in both the 2-class and N-class. Before the accuracy improved in our proposed dataset with the 4-class and 2-class mode, the ANN classifier achieved 95.65% and 99.95%, respectively, while after the accuracy optimization reached 99.84% and 99.97%, respectively. For the Edge IIoT dataset, before the accuracy improved with the 15-class and 2-class modes, the ANN classifier achieved 95.14% and 99.86%, respectively, while after the accuracy optimized up to 97.64% and 99.94%, respectively. Also, the decision tree-based models achieved lightweight models due to their lower computational complexity, so these have an appropriate edge computing deployment. Whereas other ML models reach heavyweight models and are required more computational complexity, these models have an appropriate deployment in cloud or fog computing in IoT networks.

GOLPMA: GROUP ORIENTED LIGHTWEIGHT PAYLOAD-BASED MUTUAL AUTHENTICATION FOR SECURED IoT COMMUNICATION

The rise of the Internet of Things (IoT) has increased the emphasis on protecting device communication. The Constrained Application Protocol (CoAP) extensively utilizes Datagram Transport Layer Security (DTLS) to provide end-to-end security; however, DTLS has a high connection overhead. This work presents a new Group Oriented Lightweight Payload-based Mutual Authentication (GOLPMA) mechanism that authenticates clients and servers before permitting communication using the Advanced Encryption Standard (AES). GOLPMA combines and improves essential parts of the CoAP architecture, resulting in a low-overhead, dependable, and computationally efficient alternative to DTLS. The payload-based mutual authentication technique only requires two round-trip message exchanges and restricts the payload of each message to 256 bits. GOLPMA surpasses typical DTLS/CoAP techniques based on throughput, latency, packet delivery ratio, average response time, mean jitter, authentication time and energy consumption in two separate network topologies with 15 and 30 nodes when examined using the Cooja simulator on Contiki OS. The results suggest that GOLPMA could be a safe, low-power replacement for DTLS in IoT applications

Congestion and Energy Aware Multipath Load Balancing Routing for LLNs

The Internet of Things (IoT) is presently in its golden era with its current technological evolution towards digital transformation. Low-power and Lossy Networks (LLNs) form the groundwork for IoT, where the IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) is designated by Internet Engineering Task Force as the benchmark protocol for routing. Although RPL, with its unique capabilities, has addressed many IoT routing requirements, Load balancing and Congestion control are the outliers. This paper builds on the RPL protocol and proposes a multipath Congestion and Energy Aware RPL (CEARPL) that alleviates the load balancing and congestion concerns associated with RPL and improves the network performance. For congestion avoidance, a Congestion and Energy Aware Objective Function (CEA-OF) is suggested during parent selection that considers multiple metrics like Child Count metric, Estimated Lifetime metric, and Queue Occupancy metric, to equally distribute the traffic in LLNs. The Queue Occupancy metric is used to detect congestion in the network, and a Multipath routing strategy is utilized to mitigate the congestion in the network. A comparison of the performance of CEA-RPL was made against the existing Objective Functions of RPL, OFO, and MRHOF, as well as COM-OF, utilizing Contiki OS 3.0's Cooja emulator. CEA-RPL projected superior results with power consumption lowering by 33%, end-to-end delay decreasing by 30%, queue loss ratio reducing by 49%, and packet receiving rate and network lifetime improving by 7% and 49%, on an average, respectively.

A new lightweight decentralized mitigation solution against Version Number Attacks for IoT Networks 

The present work describes a new technique to mitigate the version number attack (VNA), which is classified as one among the known denial of service (DDoS) damaging attacks targeting RPL-based (Routing Protocol for Low Power and Lossy Networks) IoTs networks. Through a VNA, the malicious behavior induces an increase in the control overhead and affects nodes’ ressources in terms of processing and memory, thereby the network availability is directly targeted. The lightweight proposed algorithm is run by each node where the main purpose is to halt the spread of a faked version number over the network and to recover victim nodes. The proposed solution has been implemented and simulated using Cooja under Contiki OS. Simulation results obviously show that our proposed technique promises significant improvements in various measured metrics while optimizing the node resources in terms of processing and memory usage. Compared to the network under attack, the control overhead has been shortened by 83% and the energy consumption has been reduced by 74%. In addition, the packet delivery ratio (PDR) has been improved to reach (99,6%), and the latency has been restored to attain the same value as in the normal case.   

An Analysis into Physical and Virtual Power Draw Characteristics of Embedded Wireless Sensor Network Devices under DoS and RPL-Based Attacks.

Currently, within the world, cybercrime is becoming increasingly rampant-often targeting civil infrastructure like power stations and other critical systems. A trend that is being noticed with these attacks is their increased use of embedded devices in denial-of-service (DoS) attacks. This creates a substantial risk to systems and infrastructures worldwide. Threats to embedded devices can be significant, and network stability and reliability can suffer, mainly through the risk of battery draining or complete system hang. This paper investigates such consequences through simulations of excessive loads, by staging attacks on embedded devices. Experimentation within Contiki OS focused on loads placed on physical and virtualised wireless sensor network (WSN) embedded devices by launching DoS attacks and by exploiting the Routing Protocol for Low Power and Lossy Networks (RPL). Results from these experiments were based on the metric of power draw, mainly the percentage increase over baseline and the pattern of it. The physical study relied on the output of the inline power analyser and the virtual study relied on the output of a Cooja plugin called PowerTracker. This involved experiments on both physical and virtual devices, and analysis of the power draws characteristics of WSN devices with a focus on embedded Linux platforms and Contiki OS. Experimental results provide evidence that peak power draining occurs with a malicious-node-to-sensor device ratio of 13-to-1. Results show a decline in power usage with a more expansive 16-sensor network after modelling and simulating a growing sensor network within the Cooja simulator.

Smart Healthcare Applications using Contiki and Cooja

The Internet of Things (IoT) is the most trending technology to influence the internet and communication technologies. Among several applications IoT, healthcare application plays a major role. Several emerging technologies related to IoT such as body sensor networks, advanced healthcare systems, cloud-based platforms for wireless transfer of data, storage and display of clinical data, etc. have contributed to the wide utilization of the technology in the field of medicines. A body sensor network scenario will be created with various body sensor nodes using 6LoWPAN and Rime protocol. Rime is a data collection protocol implemented using Contiki operating system.Contiki OS, a free open-source simulation tool, helps in analyzing the performance parameters of Wireless Personal Area Networks (WPAN). The Cooja simulator in Contiki OS simulates the network scenario and provides the means and tools to observe network behavior across many platforms and contexts. This paper presents work done on the analysis of the Rime protocol stack of the Wireless Sensor Networks(WSN). Various performance parameters of sensor nodes like light, temperature and blood glucose levels of body are measured using sensor nodes and also the position of neighboring nodes is measured and analyzed which helps to set up a more efficient Wireless Sensor Network. The proposed work focuses on the study of protocol standards in wireless mesh networks and the Performance are measured in case of mobility as well. Then by using Dijkstra’s algorithm, the shortest path to reach the record maintainer is measured. The proposed work focuses on the analysis of protocol standards used to monitor patient information in the healthcare management system.

MobiRPL: Adaptive, robust, and RSSI-based mobile routing in low power and lossy networks

This paper tackles the mobile routing issues in low-power and lossy networks (LLNs). The IPv6 standard routing protocol for LLNs, termed IPv6 routing protocol for low-power and lossy networks (RPL), has mostly been investigated in static LLNs and it has no explicit mechanism to support mobility. In addition, there is no mobile routing protocol that works well in mobile LLNs. Considering the importance of mobility support in many LLN applications, this work designs and implements MobiRPL, an adaptive, robust, and received signal strength indicator (RSSI)-based mobile routing scheme based on the RPL standard. To cope with network dynamics, the MobiRPL design focuses more on maintaining reliable routing topology than on minimizing energy consumption. This design choice significantly improves reliability while maintaining the acceptable energy consumption of mobile LLNs. We implement MobiRPL on Contiki OS, and evaluate its effectiveness extensively through Cooja simulation and testbed experiments. Our results from the testbed show that MobiRPL improves mobile nodes' packet delivery ratio by 11.3% compared to RPL and reduces the energy consumption of mobile nodes by 73.3% compared to the baseline scheme, i.e., the lightweight on-demand ad-hoc distance-vector routing protocol — next generation (LOADng).

Detections of IoT Attacks via Machine Learning-Based Approaches with Cooja

Once hardware becomes "intelligent", it is vulnerable to threats. Therefore, IoT ecosystems are susceptible to a variety of attacks and are considered challenging due to heterogeneity and dynamic ecosystem. In this study, we proposed a method for detecting IoT attacks that are based on ML-based approaches that release the final decision to detect IoT attacks. However, we have implemented three attacks as a sample in the IoT via Contiki OS to generate a real dataset of IoT-based features containing a mix of data from malicious nodes and normal nodes in the IoT network to be utilized in the ML-based models. As a result, the multiclass random decision forest ML-based model achieved 98.9% overall accuracy in detecting IoT attacks for the real novel dataset compared to the decision tree jungle, decision forest tree regression, and boosted decision tree regression, which achieved 87.7%, 93.2%, and 87.1%, respectively. Thus, the decision tree-based approach efficiently manipulates and analyzes the KoÜ-6LoWPAN-IoT dataset, generated via the Cooja simulator, to detect inconsistent behavior and classify malicious activities.

Quantifying data volatility for IoT forensics with examples from Contiki OS

Forensic investigations are often conducted under limited resource availability such as time, equipment, and people. As data acquisition is resource-demanding already, a higher emphasis needs to be put on prioritizing the investigative steps to optimize the probability of collecting the relevant evidence. Data volatility measures how quickly data disappears from a system and is an essential part of assessing the likelihood of collecting the most valuable evidence. An investigator can use a model for the volatility to estimate the probability of the existence of evidence. This work motivates and details a model for data volatility and exemplifies it for the Coffee File System used in Contiki OS, an operating system for IoT devices. We conducted experiments to test how well the model corresponds to the collected simulated data and cross-validate the model with observations from file system operations. The results revealed that an approximated model based on the known workings of the file system underestimated the volatility. While there are many sources describing volatility qualitatively, there is little research on quantitative volatility, and this paper is a stepping stone to understanding a quantitative approach to evidence volatility.

Geometric Series based effective RTO estimation Technique for CoCoA

Constrained Application Protocol (CoAP) is a standard data transfer protocol for Internet of Things. It has an in-built support for the basic congestion control mechanism that uses fixed Retransmission Time Out (RTO) for every transmission regardless of Round Trip Time (RTT), and performs Binary Exponential Backoff (BEB) when the packets get dropped. CoAP Simple Congestion Control/Advanced (CoCoA) is an enhanced congestion control mechanism over CoAP that adapts RTO based on RTT. It maintains Strong and Weak RTO estimators and uses a Variable Backoff Factor (VBF) instead of BEB when the packets get dropped. CoCoA uses an Exponential Weighted Moving Average (EWMA) to estimate the RTO for the next transmission. The weight used in EWMA is determined on the basis of whether the RTT estimated for the recent transmission was a Strong RTT or Weak RTT. However, the weights used to estimate the RTO are fixed (0.5 for Strong and 0.25 for Weak). These fixed weights lead to slow adaptation of RTO and affect the performance of the IoT applications. In this paper, we highlight the impact of having fixed weights while estimating the RTO in CoCoA. In particular, we show that the RTO in CoCoA fails to adapt quickly when the network conditions are lossless because it uses a fixed value for Strong RTO estimation (0.5). We propose a new algorithm called Geometric Series based effective RTO estimation Technique for CoCoA (GSRTC) to adapt the weight used in EWMA for estimating Strong RTO. GSRTC is integrated into CoCoA and validated against existing mechanisms using the Cooja simulator in Contiki OS and in a real testbed FIT/IoT-LAB. Our results show that GSRTC has lower Flow Completion Times (FCT), lesser retransmissions and better network throughput.

An Overview of Inter-Connectivity and Security in a Smart Hospital Building

Significant advancements in the past few years have resulted in improved Smart Building technologies for connectivity and protection. This article provides an overview of these developments. In addition, we provided a patient nurture scheme for clinic buildings as well as a layered Building Management System (BMS) architecture in this article. Then we conducted a comparison of our system to other comparable systems and highlighted the advantages of our method. Furthermore, we suggested several security measures based on the design and further study. In addition, this article includes a comparison of our system to other comparable systems. The advantages of our enterprise are then enumerated and discussed. Finally, we researched and utilized the Contiki OS and the Cooja emulator, as well as creating simulations, to get a more comprehensive and truthful picture of our system. The consequences of the data packet transfer are important since the protocol we employ has a very low proportion of packets lost, if not none at all With the Cooja simulator, we watch data in and into of the networks in real time. As a result, it seems to be a very helpful tool that provides, in addition to traffic, diagrams and other data that characterize our system. We want to mimic our whole system in the future using technologies like the Cooja emulator. We will then proceed with the installation of such a system if the outcomes are as anticipated. This article will serve as a starting point for making better choices in the future when developing smart structure systems.

Moving Object Detection over Wireless Visual Sensor Networks using Spectral Dual Mode Background Subtraction

Wireless Visual Sensor Networks (WVSN) play an essential role in tracking moving objects. WVSN's key drawbacks are storage, power, and bandwidth. Background subtraction is used in the early stages of target tracking to extract moving targets from video images. Many standard methods of subtracting backgrounds are no longer suitable for embedded devices because they use complex statistical models to manage small changes in lighting. This paper introduces a system based on the Partial Discrete Cosine Transform (PDCT), reducing the vast dimensions of processed data while retaining most of the important information, thereby reducing processing and transmission energy. It also uses a dual-mode single Gaussian model (SGM) for accurate detection of moving objects. The proposed system's performance is to be assessed using the standard CDnet 2014 benchmark dataset in terms of detection accuracy and time complexity. Furthermore, the suggested method is compared to previous WVSN background subtraction methods. Simulation results show that the proposed method consistently has 15% better accuracy and is up to 3 times faster than the state-of-the-art object detection methods for WVSN. Finally, we showed the practicality of the suggested method by simulating it in a sensor network environment using the Contiki OS Cooja Simulator and implementing it in a real testbed using Cortex M3 open nodes of IOT-LAB.

OF-FZ: An Optimized Objective Function for the Ipv6 Routing Protocol for LLNs

Internet of Things (IoT) has seen a lot of traction over the last few years and is expected to play a major role in controlling devices and communication. IoT network involves sensors with constrained memory, limited processing and power capabilities. The process of network formation largely decides the Quality of service in these networks. IPv6 Routing Protocol for LLNs (RPL) is specially designed by the IETF ROLL working group to cater to routing requirements in such networks. In RPL, an Objective function specifies a set of metrics or constraints which can be used as criteria for best parent and path selection, ensuring a faster route to the destination. In our work, we propose a novel objective function OF-FZ, which uses four metrics to make routing decisions. Fuzzy Logic is used to combine the metrics hop count (HC), ETX, delay and node residual energy (RE) to obtain a single decision metric termed as Quality Assurance (QA) score. During routing, a neighbor node having the highest QA score is chosen as the best parent. The proposed Objective Function leads to significant savings in energy consumption (EC) (9% lower), Packet Delivery Ratio (PDR) (3% higher), End-to-end Delay (8% lower) as compared to existing OFs. Controlling advertisement messages circulating in the network leads to a significant reduction in control overhead to the tune of 45%. Performance evaluation and deep dive of OF-FZ is done in three configurations: network scalability, increasing packet transmission rates and multi-sink scenarios using Contiki OS and COOJA network simulator.

SoftIoT: A resource-aware SDN/NFV-based IoT network

The proliferation of the Internet of things (IoT) devices and the corresponding high-volume sensing data pave the path for data-driven IoT services such as smart cities and smart farming. The first step towards achieving such services is to efficiently collect the data at collection centers, which requires an agile data collection strategy coping with dynamic environment changes. Network programmability (software-defined networking (SDN) or network function virtualization (NFV)) fits this need by enabling re-configurable IoT networks. However, existing SDN/NFV-based solutions in the IoT context fall back due to the lack of resource and overhead awareness and the compatibility with standard protocols. SoftIoT fills that gap by designing SDN/NFV-enabled IoT nodes and network architecture that consider resource and energy constraints a top priority. It optimizes the number of activated NFV nodes and assigns traffic sources to that VNFs over optimal routes of enough energy and link quality. We extensively evaluate the performance of SoftIoT in the Cooja simulator over the Contiki OS using real sensor traffic, which shows that the proposed solution consumes 40% and 60% less energy than existing works. SoftIoT also outperforms its counterparts over the packet delivery ratio, network lifetime, and overhead reduction while being compatible with standard protocols.

Coffee forensics — Reconstructing data in IoT devices running Contiki OS

The ability to examine evidence and reconstruct files from novel IoT operating systems, such as Contiki with its Coffee File System, is becoming vital in digital forensic investigations. Two main challenges for an investigator facing such devices are that (i) the forensic artifacts of the file system are not well documented, and (ii) there is a lack of available forensic tools. To meet these challenges, we use code review and an emulator to gain insight into the Coffee file system, including its functionality, and implement reconstruction of deleted and modified data from extracted flash memory in software. We have integrated this into a forensic tool, COFFOR, and analyzed the Coffee File System to reconstruct deleted and modified files. This paper presents an overview of the artifacts in the file system and implements methods for the chronological ordering of the deleted file versions, and discusses these methods’ limitations. Our results demonstrate that forensic acquisition and analysis of devices running the Contiki operating system can reveal live and deleted files, as well as file version history. In some cases, a complete, chronological ordering of the version history can be reconstructed.

Collision Avoidance Geographic P2P-RPL in Multi-Hop Indoor Wireless Networks

In home and building automation applications, wireless sensor devices need to be connected via unreliable wireless links within a few hundred milliseconds. Routing protocols in Low-power and Lossy Networks (LLNs) need to support reliable data transmission with an energy-efficient manner and short routing convergence time. IETF standardized the Point-to-Point RPL (P2P-RPL) routing protocol, in which P2P-RPL propagates the route discovery messages over the whole network. This leads to significant routing control packet overhead and a large amount of energy consumption. P2P-RPL uses the trickle algorithm to control the transmission rate of routing control packets. The non-deterministic message suppression nature of the trickle algorithm may generate a sub-optimal routing path. The listen-only period of the trickle algorithm may lead to a long network convergence time. In this paper, we propose Collision Avoidance Geographic P2P-RPL, which achieves energy-efficient P2P data delivery with a fast routing request procedure. The proposed algorithm uses the location information to limit the network search space for the desired route discovery to a smaller location-constrained forwarding zone. The Collision Avoidance Geographic P2P-RPL also dynamically selects the listen-only period of the trickle timer algorithm based on the transmission priority related to geographic position information. The location information of each node is obtained from the Impulse-Response Ultra-WideBand (IR-UWB)-based cooperative multi-hop self localization algorithm. We implement Collision Avoidance Geographic P2P-RPL on Contiki OS, an open-source operating system for LLNs and the Internet of Things. The performance results show that the Collision Avoidance Geographic P2P-RPL reduced the routing control packet overheads, energy consumption, and network convergence time significantly. The cooperative multi-hop self localization algorithm improved the practical implementation characteristics of the P2P-RPL protocol in real world environments. The collision avoidance algorithm using the dynamic trickle timer increased the operation efficiency of the P2P-RPL under various wireless channel conditions with a location-constrained routing space.

Mobile Wireless Sensor Networks: A Framework Based Solution for Physical Security Attacks

Mobile wireless sensor networks are made up of resource-constrained sensor nodes.The nodes in Mobile wireless sensor networks tend to move wirelessly from one location to another. In physically unattended and insecure areas, these networks are installed where they are vulnerable to numerous security threats, including physical security threats.Clone node attack is a physical attack in which the adversary physically compromises legitimate nodes and copies their data onto fake nodes to create clones of captured nodes. In static wireless sensor networks, comprehensive work isalready done to mitigate and detect the underlying attack. Mitigating and detecting this assault on mobile wireless sensor networks, however, remains a critical issue. To our knowledge, there is no framework-based method for bolstering multiple attacks onmobile wireless sensor networks.Consequently, this article proposes a framework-based approach that mitigates multiple attacks on mobile WSNs. To provide proof-of-concept for the framework, along with clone node attacks solution to jamming attacks is alsoincorporated in the framework. The implementation of the framework is done with the COOJA simulator and Contiki OS.

SAMP-RPL: secure and adaptive multipath RPL for enhanced security and reliability in heterogeneous IoT-connected low power and lossy networks

The Internet of things (IoT) is made up of huge sets of networked low-power devices. Regardless of the application specificities, those devices are generally tasked to provide highly important services that can be reached out through the internet. Communications with IoT networks cannot be achieved without the support of efficient underlying routing schemes. However, the constraints related to scarce resources, as well as the wireless and lossy nature of IoT networks may tremendously affect the communications reliability. Consequently, routing efficiency could be affected. Moreover, the exposure to internal and external cyber-attacks, especially those that threaten data and service availability, complicates even further routing and security missions in such a constrained context. In this paper, we propose an efficient solution that enhances both the communications reliability and security in the heterogeneous IoT-enabled low power and lossy networks. The solution relies on three variants of adaptive and secure multipath routing for IPv6 routing protocol for low-power and lossy networks (RPL). To the best of our knowledge, this is the first solution that deals with multipath routing security issue for RPL protocol, in IoT contexts. The proposed solution is evaluated with Cooja simulator of Contiki OS. The assessment results show obviously the effectiveness of the proposed solution for improved communication reliability and security, at low costs.

WITHDRAWN: Wireless sensor network and IoT based systems for healthcare application

Internet of Things (IoT) for healthcare applications is an emerging research field that has gained a lot of attention in the last few years. The paper presents the IoT remote healthcare monitoring system that provides the patient’s conditions through Web browser. For simulation purposes, we use Contiki OS with 6LoWPAN protocol stack, and Cooja, the built-in Contiki simulator. CoAP is selected as application level protocol for remote data access and representation.