Connected Internet Of Things Devices Research Articles

Software-Defined-Networking-Based One-versus-Rest Strategy for Detecting and Mitigating Distributed Denial-of-Service Attacks in Smart Home Internet of Things Devices.

The number of connected devices or Internet of Things (IoT) devices has rapidly increased. According to the latest available statistics, in 2023, there were approximately 17.2 billion connected IoT devices; this is expected to reach 25.4 billion IoT devices by 2030 and grow year over year for the foreseeable future. IoT devices share, collect, and exchange data via the internet, wireless networks, or other networks with one another. IoT interconnection technology improves and facilitates people's lives but, at the same time, poses a real threat to their security. Denial-of-Service (DoS) and Distributed Denial-of-Service (DDoS) attacks are considered the most common and threatening attacks that strike IoT devices' security. These are considered to be an increasing trend, and it will be a major challenge to reduce risk, especially in the future. In this context, this paper presents an improved framework (SDN-ML-IoT) that works as an Intrusion and Prevention Detection System (IDPS) that could help to detect DDoS attacks with more efficiency and mitigate them in real time. This SDN-ML-IoT uses a Machine Learning (ML) method in a Software-Defined Networking (SDN) environment in order to protect smart home IoT devices from DDoS attacks. We employed an ML method based on Random Forest (RF), Logistic Regression (LR), k-Nearest Neighbors (kNN), and Naive Bayes (NB) with a One-versus-Rest (OvR) strategy and then compared our work to other related works. Based on the performance metrics, such as confusion matrix, training time, prediction time, accuracy, and Area Under the Receiver Operating Characteristic curve (AUC-ROC), it was established that SDN-ML-IoT, when applied to RF, outperforms other ML algorithms, as well as similar approaches related to our work. It had an impressive accuracy of 99.99%, and it could mitigate DDoS attacks in less than 3 s. We conducted a comparative analysis of various models and algorithms used in the related works. The results indicated that our proposed approach outperforms others, showcasing its effectiveness in both detecting and mitigating DDoS attacks within SDNs. Based on these promising results, we have opted to deploy SDN-ML-IoT within the SDN. This implementation ensures the safeguarding of IoT devices in smart homes against DDoS attacks within the network traffic.

Implementation of a Cuckoo Search intelligent simulation system for node placement problem in Wireless Mesh Networks: Performance evaluation for tuning hyperparameters [formula omitted] and [formula omitted]

In recent years, Internet of Things (IoT) is the hottest topics in the research and implementation of wireless networks. Most of the research of IoT-based applications is related with the data collecting and processing using sensors and actuators. As the network environment for connecting IoT devices are considered WLAN or Bluetooth. The Wireless Mesh Networks (WMNs) are the most suitable networks for the Internet of Things (IoT) due to their numerous benefits. However, they have several issues related to wireless communications. One solution is the optimization of position and location of mesh routers. But finding the best location of mesh routers is an NP-hard problem. To deal with this issue, we propose and implement an intelligent simulation system based on the Cuckoo Search (CS) algorithm called WMN-CS. In the implemented system, the hyperparameters should be tuned to find better solutions. This paper evaluates the WMN-CS performance for various combination of hyperparameters by tuning host bird discovery rate (pa) and scale parameter (γ). The simulation results show that a better performance is achieved when the scale parameter (γ) is between 0.07 and 0.09 and the host bird recognition rate (pa) is between 0.900 and 0.925.

Performance evaluation of distributed multi-agent IoT monitoring based on intelligent reflecting surface

The advent of intelligent reflecting surface (IRS) technology has revolutionized the landscape of wireless communication systems, offering promising opportunities for enhancing the performance of Internet of Things (IoT) applications. This paper presents a comprehensive performance evaluation of multi-agent IoT monitoring systems leveraging IRS technology. We focus on three criteria for selecting IRS units and assess the impact on system performance. Specifically, we analyze the system performance by deriving an outage probability expression for each criterion. Our study begins by introducing the concept of IRS and its role in IoT monitoring. We then present three IRS unit selection criteria: optimal selection (OS), partial selection (PS), and random selection (RS). For each criterion, we mathematically model and analyze the system outage probability, shedding light on the reliability and connectivity of IoT devices. The outage probability expressions derived in this work offer valuable insights into the trade-offs associated with IRS unit selection criteria in the context of IoT monitoring. Additionally, our findings contribute to the optimization of multi-agent IoT monitoring systems, enabling improved communication performance and enhanced reliability.

A survey of the characteristics of SDN, NFV and information security in IoT and 5G networks

Background, The 5G technology has emerged as a trusted source to meet the increased demand of Internet of Things (IoT) devices, in addition to enabling Internet connectivity at high broadband speeds. Another important feature of 5G is the use of techniques such as Software Defined Networking (SDN) and Network Functions Virtualization (NVF), mechanisms responsible for performing network configurations through software, in addition to massive control and management of devices using the network configuration functions or device virtualization. Results, The concern with information security in the 5G network is increasing, as cybercriminals try to access important data that is transported over the network, since the demand for connected IoT devices will be greater, thus allowing for several possibilities of attacks. The understanding of possible threats and attacks is necessary, so that new measures are taken against cybercrimes presented in the 5G and IoT networks. Conclusions, This paper aims to elucidate some conceptions of what 5G technology is and the use of IoT in this network, contextualizing the SDN and NFV techniques to allow the configuration of the functionality and management of the network by software. In addition, concerns will be reported about possible information security attacks that may occur in 5G.

Detection of DoS Attacks in Smart City Networks With Feature Distance Maps: A Statistical Approach

Smart cities are highly digitalized cities, with a high volume of data stored digitally, as well as a large number of physical objects and Internet of Things (IoT) devices. It is no surprise that security concerns increase as connected IoT devices become more prevalent. There has long been a perception that Denial of Service (DoS) attacks give rise to a notable menace to the security of smart city networks. A statistical method based on feature distance maps is suggested in this work for the identification of DoS attacks in smart city networks. This study aims to build a DoS attack detection model that has a low computational complexity and a low rate of false positives. The performance of the model is arbitrated using smart city network traffic dataset. In order to make processing easier, the features are normalized using Min-Max normalization. A subset of features suitable for attack detection can be evaluated with Pearson Correlation Coefficient. A feature distance map method is proposed for extracting correlations between features between records. Manhattan distance measures are used to generate the feature distance maps. The feature distance maps enhance the statistical analysis process. Hellinger distance measure is used to generate the normal traffic profile by calculating the degree of similarity between the feature distance map of each record and the normal traffic profile mean. On the basis of the proposed threshold, the Hellinger distance is used to evaluate whether an unknown traffic data belongs to normal or attack. Compared with Mahalanobis distance-based DoS detection, the Hellinger distance-based attack detection takes less time to execute. Based on the theoretical and experimental results, the proposed DoS attack detection system has a low computational complexity and a low false positive rate. Furthermore, the proposed approach outperforms the existing state-of-the-art feature ranking based DoS attack detection systems.

Reliable cluster based data collection framework for IoT-big data healthcare applications

Internet of Things (IoT) devices installed in hospital direct data unceasingly; in this manner, energy usage augments with the number of broadcasts too. In this paper, Reliable Cluster based Data Collection Framework (RCDCF) for IoT-Big Data Healthcare Applications (HA) is developed. During clustering process, the connected IoT devices are grouped into clusters. In clustering technique, the available IoT devices are gathered into groups. The device with high battery capacity and processing ability is selected as a cluster head (CH). Each member of the cluster is allocated multiple slots by applying a general function pooled by the Fog node and the entire devices. To perceive and eliminate outliers from the sensor data, Density-based spatial clustering of applications with noise (DBSCAN) method is utilized. To forecast the objective and subjective behaviours of the equipments, a Random Forest Deep Neural Network (RF-DNN) based classification model is utilized. By experimental results, it has been shown that RCDCF achieves 19% and 20% reduced energy consumption at Cloud and Fog centers, respectively. Moreover, RCDCF has 2.1% and 1.3% increased correctness of data at Cloud and Fog data centers, respectively, when compared to the existing framework.

Toward Automatically Connecting IoT Devices with Vulnerabilities in the Wild

With the increasing number of Internet of Things (IoT) devices connected to the internet, the industry and research community have become increasingly concerned about their security impact. Adversaries or hackers often exploit public security flaws to compromise IoT devices and launch cyber attacks. However, despite this growing concern, little effort has been made to investigate the detection of IoT devices and their underlying risks. To address this gap, this article proposes to automatically establish relationships between IoT devices and their vulnerabilities in the wild. Specifically, we construct a deep neural network (DNN) to extract semantic information from IoT packets and generate fine-grained fingerprints of IoT devices. This enables us to annotate IoT devices in cyberspace, including their device type, vendor, and product information. We collect vulnerability reports from various security sources and extract IoT device information from these reports to automatically match vulnerabilities with the fingerprints of IoT devices. We implemented a prototype system and conducted extensive experiments to validate the effectiveness of our approach. The results show that our DNN model achieved a 98% precision rate and a 95% recall rate in IoT device fingerprinting. Furthermore, we collected and analyzed over 13,063 IoT-related vulnerability reports and our method automatically built 5,458 connections between IoT device fingerprints and their vulnerabilities. These findings shed light on the ongoing threat of cyber-attacks on IoT systems as both IoT devices and disclosed vulnerabilities are targets for malicious attackers.

Development a Software Defined Network (SDN) with Internet of Things (IoT) Security for Medical Issues

Because providing billions of objects with network connectivity, the Internet of Things (IoT) enables the collection and transfer of real-time data for intelligent applications. Therefore, IoT enables remote access and control of connected devices when there is a sufficient network infrastructure. Additionally, the introduction of software-defined networking (SDN) presents capabilities that allow internet providers and users to control and connect network equipment wirelessly, even as enabling a global perspective on the network, that has previously become a soaring interest area due to its extensive use for various applications and systems, including wireless sensor nodes, medical equipment, delicate home sensor systems, and some other connected IoT devices. In order to create worldwide connectivity between the Internet of Things (IoT) and based on the SDN architecture in the medical contexts, this paper's contribution is to outline some relevant directions. Additionally, we provide a model based on software defined network principles that depicts interactions between a group of people each of whom have a Nano network within their bodies and the medical services via the local network of a medical institution. For everybody electrical engineers to data engineers the requirement to integrate everything in a global setting is a significant problem. As a result, the cloud is useful for handling the instantaneous sharing of information. In terms of health care, the effort is also stated in terms of IoT architecture and services. IoT's current prospects for the healthcare sector are quite promising. Due to its capacity for sensing and measuring, it is also highly well-liked. From the smallest sensor to the massive amounts of data gathered, this revolution is completely changing how we view healthcare.

Internet of Things: Security and Privacy standards

Ensuring security in the interaction of the Internet of Things (IoT) has focused the attention of many researchers. At present, the issue of standards is a very complex and important one that directly affects both the development and implementation of the Internet of Things in everyday life. There are many standards and protocols that may not be consistent across different layers of the architecture. This paper provides an overview of the current International Internet of Things Security Standards, which are discussed in various aspects such as terminology, architecture models, security and integration scenarios, classification and protocols. A comparative analysis of standards and related research is conducted to help inform decisions about the security of IoT systems during their development and production. This research aims to overcome the difficulties encountered and develop practical recommendations for the selection of controls and security of connected IoT devices. The research methodology included identification of gaps in IoT standards, analysis of existing problems and proposed solutions.

Enabling Efficient IoT Device Connectivity and Dynamic Network Management through SDN: A Weighted Sum Method Approach

SDN-Enabled IoT Networks bring about a transformative shift in conventional network models by integrating the core principles of Software-Defined Networking (SDN) into the realm of the Internet of Things (IoT). This integration empowers the agile and effective management of IoT devices, facilitating smooth connectivity, optimized distribution of resources, and flexible network setups. Through the consolidation of control and the utilization of virtualization methods, SDN-Enabled IoT Networks amplify scalability, security, and real-time responsiveness. This addresses the obstacles presented by the extensive proliferation of IoT devices. This paradigm transition heralds a fresh era of interconnectedness, where SDN assumes a central role in harmonizing the intricate interplay of IoT devices and services. The rapid expansion of Internet of Things (IoT) devices has introduced unparalleled complexities in overseeing networks and establishing connections. This compels the need for inventive strategies to effectively manage the substantial surge of IoT devices and their ever-changing connectivity prerequisites. Software-Defined Networking (SDN) emerges as a promising approach to tackle these issues by enabling the flexible management of networks and allocation of resources. This study investigates the amalgamation of SDN within the realm of IoT, aiming to streamline device connections, optimize data transmission efficiency, and accommodate adaptable network setups. Introducing an innovative weighted sum technique for resource allocation optimization, this work lays the foundation for a comprehensive framework that bolsters IoT network performance and expandability. Four different SDN implementations are examined, including the Conventional IoT Network, SDN-enabled IoT utilizing Centralized Control, SDN-enabled IoT employing Distributed Control, SDN-enabled IoT with Hierarchical Control, and SDN-enabled IoT utilizing Hybrid Control. The assessment considers various aspects such as Enhanced Scalability, Enhanced Traffic Engineering, Heightened Security, Implementation Complexity, Difficulty of Migration, and Reliance on Vendors. The Conventional IoT Network secures a moderate 3rd position with a Preference Score of 0.56030, while the SDN-enabled IoT with Centralized Control holds the 5th rank at 0.49732, despite excelling in specific domains. The SDN-enabled IoT with Distributed Control achieves the top rank with a notable Preference Score of 0.79414 due to comprehensive performance, followed by the SDN-enabled IoT with Hierarchical Control securing the 2nd spot (Preference Score: 0.57022), and the SDN-enabled IoT with Hybrid Control taking the 4th position (Preference Score: 0.51300), particularly excelling in Traffic Engineering.

PPFchain: A novel framework privacy-preserving blockchain-based federated learning method for sensor networks

Internet of Things (IoT) has been widely used in many smart applications such as smart cities, smart agriculture, healthcare, industry, etc. In addition, the importance of IoT-based architectures has increased with the emergence of innovative technologies such as 5G networks and quantum computing. Electrochemical sensors (ECS) have recently been used in IoT-based architectures thanks to their easy integration into smart systems in many fields such as pharmaceutical, food, medical diagnosis, clinical, genetic analysis, wearable, forensic identification, and monitoring of environmental variables. However, Although IoT-based systems have ensured the availability of sensor data in traditional architecture, there are many challenges, such as low latency, availability, real-time data traceability, and security. In addition, new challenges regarding security and privacy have emerged in the system as the ever-growing smart connected IoT devices generate a significant amount of heterogeneous data. Given these challenges, in this paper, we propose PPFchain, a new federated learning-enabled blockchain-based framework to ensure the security and privacy of sensor-IoT-based architectures using sampled ECS data. PPFchain has a lightweight, low-cost, high-performance architecture in the IoT-based blockchain network. In the architecture, we used the federated model and cryptographic primitives for user and data privacy in off-chain fog nodes considering performance. Moreover, we compared with traditional blockchain models using various performance metrics for system performance in a distributed architecture, such as an event and storage-based smart contract. In addition, the results show that the PPFchain provides accuracy, efficiency, and enhanced security.

Combining Stochastic and Deterministic Modeling of IPFIX Records to Infer Connected IoT Devices in Residential ISP Networks

Residential Internet service providers (ISPs) today have limited device-level visibility into subscriber houses, primarily due to the network address translation (NAT) technology. The continuous growth of “unmanaged” consumer Internet of Things (IoT) devices combined with the rise of work-from-home makes home networks attractive targets to sophisticated cyber attackers. Volumetric attacks sourced from a distributed set of vulnerable IoT devices can impact ISPs by deteriorating the performance of their network, or even making them liable for being a carrier of malicious traffic. This article explains how ISPs can employ IP Flow Information eXport (IPFIX), a flow-level telemetry protocol available on their network, to infer connected IoT devices and ensure their cyber health without making changes to home networks. Our contributions are threefold: 1) we analyze more than nine million IPFIX records of 26 IoT devices collected from a residential testbed over three months and identify 28 flow features pertinent to their network activity that characterize the network behavior of IoT devices—we release our IPFIX records as open data to the public; 2) we train a multiclass classifier on stochastic attributes of IPFIX flows to infer the presence of certain IoT device types in a home network with an average accuracy of 96%. On top of the machine learning (ML) model, we develop a trust metric to track network activity of detected devices over time; and 3) finally, we develop deterministic models (DTs) of specific and shared cloud services consumed by IoTs, yielding an average accuracy of 92%. We show a combination of stochastic and DTs mitigates false positives in 75% of incidents at the expense of an average 7% reduction in true positives.

Improving the IoT and Cloud Computing integration using Hybrid Encryption

Wireless Sensor Network (WSN) is an essential technology in many Internet of Things (IoT) applications, and since sensor nodes suffer from limited resources, it has become possible to overcome storage capacity problems using cloud computing, the integration of Internet of Things (IoT) with cloud computing (CC) seeks to achieve new levels of efficiency in service delivery. Security and privacy are key factors that slow down the rapid and widespread adoption and deployment of both IoT and cloud computing. In the proposed model, an integrated IoT system with cloud computing was developed starting from the analysis, and design, to the implementation to connect IoT devices with the cloud, the security is achieved by using a hybrid encryption mechanism which provides the performance advantages of symmetric and asymmetric encryption algorithms. Where the Elliptic Curve Cryptography (ECC) algorithm is used for key generation and AES (Advanced Encryption Standard) algorithm is used for encryption and decryption of the sensors' data to provide a reliable computing environment. We have implemented the proposed system and showed the results of using CONTIKI COOJA 3.0 that connected with the cloud service provider, Evaluate a set of performance metrics such as power consumption, packet delivery ratio, and the algorithm execution time, in addition to verifying network immunity against the black hole attack.

The Effect of Using Social Engineering for Cybersecurity on the Internet of Things Environment

Although the Internet of Things (IoT) developers view human users as the weakest cybersecurity link and potentially a deciding factor in IoT security, IoT devices are exposed to a variety of security flaws and vulnerabilities, which most social engineers take advantage of to launch several data-collection attacks. For various purposes, this study looked at improving data security on MySQL databases and web pages by using TSL encryption protocols and the two algorithms (RSA & AES) that correlate with the Internet of Things environment and enable user authentication. The implementation was done on the proposed models and encryption methods used. This study aimed to protect the information that is shared on the Internet and connect IoT devices in a secure environment. The results revealed that the proposed study increased the level of protection with the help of complex conditions specified when registering the user and protecting it against attacks. As a result, one of the most important events that came true is the existence of cybersecurity. It is expected that this research will enhance the understanding of the IoT environment and exploit social engineering attacks to impose security on the IoT environment and preserve the rights of human users.

A Novel Deep Learning-Based Intrusion Detection System for IoT Networks

The impressive growth rate of the Internet of Things (IoT) has drawn the attention of cybercriminals more than ever. The growing number of cyber-attacks on IoT devices and intermediate communication media backs the claim. Attacks on IoT, if they remain undetected for an extended period, cause severe service interruption resulting in financial loss. It also imposes the threat of identity protection. Detecting intrusion on IoT devices in real-time is essential to make IoT-enabled services reliable, secure, and profitable. This paper presents a novel Deep Learning (DL)-based intrusion detection system for IoT devices. This intelligent system uses a four-layer deep Fully Connected (FC) network architecture to detect malicious traffic that may initiate attacks on connected IoT devices. The proposed system has been developed as a communication protocol-independent system to reduce deployment complexities. The proposed system demonstrates reliable performance for simulated and real intrusions during the experimental performance analysis. It detects the Blackhole, Distributed Denial of Service, Opportunistic Service, Sinkhole, and Workhole attacks with an average accuracy of 93.74%. The proposed intrusion detection system’s precision, recall, and F1-score are 93.71%, 93.82%, and 93.47%, respectively, on average. This innovative deep learning-based IDS maintains a 93.21% average detection rate which is satisfactory for improving the security of IoT networks.

Design of deep learning model for radio resource allocation in 5G for massive iot device

With recent advancements in Internet of Things (IoT) communications, the demand for fastest communication is an utmost concern of the IoT technology. The advent of 5G telecommunication networks enables to bridge the demand on satisfying the Quality-of-Service (QoS) concerns in IoT communication. The massive number of devices in IoT communication is henceforth do not lie under limited resource allocation by embedding the 5G telecommunication network. The existing deep learning approaches lack in the allocation of resource in the energy constrained environment. In this paper, we address the above limitation of allocation the resource to prominent IoT devices using Deep Belief Network. It is efficient in the utilization of hidden layers and the probabilistic correlation among the instances are constructed effectively. In this Deep Belief Network, the adoption of rules will imply the resource allocation with the devices of top priority and it reduces based on the priority. DBN learns the context of the 5G and connected IoT devices whereas the resources are provisioned effectively through the learning. The proposed strategy beats the current state-of-the-art techniques, whereas the DBN achieves 74 % of successful job completion that is effective compared to other existing strategies. The simulation is performed to test the efficacy of Deep Learning on allocation 5G resources to IoT model. The results show that the proposed model achieves higher level of allocating the resources than other methods.

ICache: An Intelligent Caching Scheme for Dynamic Network Environments in ICN-Based IoT Networks

Advanced network technologies and ubiquitous connected devices are boosting the development of the Internet of Things (IoT) at an unprecedented pace. However, as most of the connected IoT devices are battery powered, the energy consumption issue has become the bottleneck of the IoT’s development. Caching is a promising approach to reducing the energy consumption of the battery-powered devices since the requested data packets can be retrieved from intermediate nodes in the network, e.g., routers, instead of from the remote battery-powered IoT devices, which allows the IoT devices to spend more time in the sleep mode. To realize in-network caching and overcome the IP-based networks’ inefficiency support for IoT, building IoT over information-centric networking (ICN) is a promising approach advocated by researchers. However, existing works in this area assume the network environments are static, which hinders the development of existing approaches in the real dynamic network environments. In this article, we leverage the deep <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -networks (DQNs) to propose an intelligent caching scheme (named as iCache) that can automatically adjust the caching nodes’ caching parameters to make caching decisions for the dynamic network environments. Extensive evaluations were conducted and the results show that the proposed iCache outperforms the existing approaches in terms of the total energy consumption (e.g., more than 29% reduction compared to the caching transient data (CTD) caching scheme) and the average number of hops (e.g., more than 20% reduction compared to the CTD caching scheme).

Unsupervised Learning for Feature Selection: A Proposed Solution for Botnet Detection in 5G Networks

The world has seen exponential growth in deploying Internet of Things (IoT) devices. In recent years, connected IoT devices have surpassed the number of connected non-IoT devices. The number of IoT devices continues to grow and they are becoming a critical component of the national infrastructure. IoT devices' characteristics and inherent limitations make them attractive targets for hackers and cyber criminals. Botnet attack is one of the serious threats on the Internet today. This article proposes pattern-based feature selection methods as part of a machine learning (ML)-based botnet detection system. Specifically, two methods are proposed: the first is based on the most dominant pattern feature values and the second is based on maximal frequent itemset mining. The proposed feature selection method uses Gini impurity and an unsupervised clustering method to select the most influential features automatically. The evaluation results show that the proposed methods have improved the performance of the detection system. The developed system has a true positive rate of 100% and a false positive rate of 0% for best performing models. In addition, the proposed methods reduce the computational cost of the system as evidenced by the detection speed of the system.

IoT-based Fire Alarm: Design and Approach

The Internet of Things (IoT) describes the network of physical objects things that are embedded with sensors, software, and other technologies for the purpose of connecting and exchanging data with other devices and systems over the internet. These devices range from ordinary household objects to sophisticated industrial tools. With more than 7 billion connected IoT devices today, experts are expecting this number to grow to 10 billion by 2020 and 22 billion by 2025. Our main objective is to use this technology to build a working fire alarm that would not only work like a regular alarm but will also send a notification to cloud connected devices.

Improvement of the Secure Integration of IoT and Cloud Computing using Hybrid Encryption

Wireless Sensor Network (WSN) is an essential technology in many Internet of Things (IoT) applications, and since sensor nodes suffer from limited resources, it has become possible to overcome storage capacity problems using cloud computing, the integration of Internet of Things (IoT) with cloud computing (CC) seeks to achieve new levels of efficiency in service delivery. Security and privacy are key factors that slow down the rapid and widespread adoption and deployment of both IoT and cloud computing. In the proposed model, an integrated IoT system with cloud computing was developed starting from the analysis, and design, to the implementation to connect IoT devices with the cloud, the security is achieved by using a hybrid encryption mechanism which provides the performance advantages of symmetric and asymmetric encryption algorithms. Where the Elliptic Curve Cryptography (ECC) algorithm is used for key generation and AES (Advanced Encryption Standard) algorithm is used for encryption and decryption of the sensors' data to provide a reliable computing environment. We have implemented the proposed system and showed the results of using CONTIKI COOJA 3.0 that connected with the cloud service provider, Evaluate a set of performance metrics such as power consumption, packet delivery ratio, and the algorithm execution time, in addition to verifying network immunity against the black hole attack.