اینترنت اشیا

Open Access Article

1 - Scheduling of IoT Application Tasks in Fog Computing Environment Using Deep Reinforcement Learning
Pegah Gazori Dadmehr Rahbari Mohsen Nickray

20.1001.1.16823745.1399.18.2.9.1

With the advent and development of IoT applications in recent years, the number of smart devices and consequently the volume of data collected by them are rapidly increasing. On the other hand, most of the IoT applications require real-time data analysis and low latency More

With the advent and development of IoT applications in recent years, the number of smart devices and consequently the volume of data collected by them are rapidly increasing. On the other hand, most of the IoT applications require real-time data analysis and low latency in service delivery. Under these circumstances, sending the huge volume of various data to the cloud data centers for processing and analytical purposes is impractical and the fog computing paradigm seems a better choice. Because of limited computational resources in fog nodes, efficient utilization of them is of great importance. In this paper, the scheduling of IoT application tasks in the fog computing paradigm has been considered. The main goal of this study is to reduce the latency of service delivery, in which we have used the deep reinforcement learning approach to meet it. The proposed method of this paper is a combination of the Q-Learning algorithm, deep learning, experience replay, and target network techniques. According to experiment results, The DQLTS algorithm has improved the ASD metric by 76% in comparison to QLTS and 6.5% compared to the RS algorithm. Moreover, it has been reached to faster convergence time than QLTS. Manuscript profile

Open Access Article

2 - Optimal and Sub-optimal Transmitter-Receiver Design in Dense Wireless Sensor Networks and the Internet of Things
Farzad H. Panahi Fereidoun H. Panahi Zahra Askarizadeh Ardestani

20.1001.1.16823745.1400.19.3.4.5

With the rapid development of new technologies in the field of internet of things (IoT) and intelligent networks, researchers are more interested than ever in the concept of wireless sensor networks (WSNs). The emergence of these densely structured networks in recent ye More

With the rapid development of new technologies in the field of internet of things (IoT) and intelligent networks, researchers are more interested than ever in the concept of wireless sensor networks (WSNs). The emergence of these densely structured networks in recent years has raised the importance of the use of telecommunications technologies, such as ultra-wideband (UWB) technology with high reliability, industrial applications, and appropriate communication security. However, there are still numerous concerns about the extent of inter-network interference, particularly owing to undesired spectral discrete lines in this technology. As a result, it is necessary to provide an optimal solution to eliminate interference and control the power spectrum, and then design the optimal transmitter-receiver structures while considering high sensitivities to the synchronization problem in WSNs based on UWB technology. These goals are pursued in the present study by employing the optimal spectral strategy in the signal model, the structure of the transmitter sensor, and then constructing the optimal or sub-optimal receiver sensor structures, the results of which indicate improved communication performance in WSNs. Manuscript profile

Open Access Article

3 - Providing lightweight mutual group authentication of Internet of Things
reza sarabi miyanaji sam jabbehdari nasser modiri

20.1001.1.16823745.1400.19.3.5.6

The Internet of things is becoming the largest computing platform and we are seeing an increase in the number of devices in this environment. In addition, most Things in this infrastructure have the computational power and memory constraints. They cannot perform complex More

The Internet of things is becoming the largest computing platform and we are seeing an increase in the number of devices in this environment. In addition, most Things in this infrastructure have the computational power and memory constraints. They cannot perform complex computational operations. These limitations have been ignored in most traditional authentication methods. Meanwhile, in the new methods of authentication of this environment, not much attention has been paid to the issue of scalability. Therefore, the need for a lightweight, scalable authentication is felt. In this paper, a lightweight authentication protocol is presented in which things are placed in different groups. In each group, a group manager node is considered and as an agent, it performs authentication on behalf of other members. Therefore, Authentication is done in groups, which makes the proposed protocol highly scalable. The proposed method reduces the computational cost of nodes and servers and provides privacy through node anonymity. In addition, it has forward-looking privacy without the use of asynchronous encryption and key agreement. The AVISPA tool has been used to confirm the security of the proposed method. In our method, the computation time of the node and server in authentication has been decreased by 7.8% and 3.5%, respectively, compared with reviewing protocols. Manuscript profile

Open Access Article

4 - Detecting Human Activities Based on Motion Sensors in IOT Using Deep Learning
Abbas Mirzaei fatemeh faraji

20.1001.1.16823745.1400.19.4.10.3

Control of areas and locations and motion sensors in the Internet of Things requires continuous control to detect human activities in different situations, which is an important challenge, including manpower and human error. Permanent human control of IoT motion sensors More

Control of areas and locations and motion sensors in the Internet of Things requires continuous control to detect human activities in different situations, which is an important challenge, including manpower and human error. Permanent human control of IoT motion sensors also seems impossible. The IoT is more than just a simple connection between devices and systems. IoT information sensors and systems help companies get a better view of system performance. This study presents a method based on deep learning and a 30-layer DNN neural network for detecting human activity on the Fordham University Activity Diagnostic Data Set. The data set contains more than 1 million lines in six classes to detect IoT activity. The proposed model had almost 90% and an error rate of 0.22 in the evaluation criteria, which indicates the good performance of deep learning in activity recognition. Manuscript profile

Open Access Article

5 - Reliable and Energy Efficient Deployment Optimization of Internet of Things Applications in Cloud and Fog Infrastructure by Using Cuckoo Search Algorithm
Yaser Ramzanpoor میرسعید حسینی شیروانی

20.1001.1.16823745.1400.19.4.11.4

Deployment applications of internet of things (IoT) in fog infrastructure as cloud complementary leads effectively computing resource saving in cloud infrastructure. Recent research efforts are investigating on how to better exploit fog capabilities for execution and su More

Deployment applications of internet of things (IoT) in fog infrastructure as cloud complementary leads effectively computing resource saving in cloud infrastructure. Recent research efforts are investigating on how to better exploit fog capabilities for execution and supporting IoT applications. Also, the distribution of an application’s components on the possible minimum number of fog nodes for the sake of reduction in power consumption leads degradation of the service reliability level. In this paper, a hybrid meta-heuristic algorithm based on cuckoo search algorithm is presented for static deployment the components of IoT applications on fog infrastructure in the aim of trade-off between efficient power usage, reduction in the effect of one point of failure and boosting the application reliability against failure. The results of simulations show that the proposed approach in this paper reduces the power consumption of fog network and meets the quality of service requirement of IoT application with the high reliability level. Manuscript profile

Open Access Article

6 - Mutual Continuous Lightweight Authentication Based on Node Prioritization Using Traffic Rates for Internet of Things
reza sarabi miyanaji sam jabbehdari nasser modiri

20.1001.1.16823745.1401.20.2.5.1

Today, billions of devices are connected via the Internet of Things, often through insecure communications. Therefore, security and privacy issues of these devices are a major concern. Since devices in IoT are typically resource-constrained devices, the security solutio More

Today, billions of devices are connected via the Internet of Things, often through insecure communications. Therefore, security and privacy issues of these devices are a major concern. Since devices in IoT are typically resource-constrained devices, the security solutions of this environment in terms of processing and memory must be secure and lightweight. However, many existing security solutions are not particularly suitable for IoT due to high computation. So there is a need for a lightweight authentication protocol for IoT devices. In this paper, a mutual lightweight authentication protocol between nodes with limited resources and IoT servers is introduced that uses node prioritization based on traffic rates. This scheme is light due to the use of lightweight XOR and Hash operations. The proposed is resistant to cyber-attacks such as eavesdropping attack, and replay attack. The proposed is also secure using the AVISPA tool in the Dolev-Yao threat model. The security risks of this scheme are low compared to other lightweight methods. In addition, the proposal is compared with existing authentication schemes reduces the computational cost, protects privacy through anonymity of nodes, and provides forward secrecy. In our method, the execute time of authentication is reduced by 15% compared to the other methods. Manuscript profile

Open Access Article

7 - Improving IoT Botnet Anomaly Detection Based on Dynamic Feature Selection and Hybrid Processing
Boshra Pishgoo Ahmad akbari azirani

20.1001.1.16823745.1401.20.2.8.4

The complexity of real-world applications, especially in the field of the Internet of Things, has brought with it a variety of security risks. IoT Botnets are known as a type of complex security attacks that can be detected using machine learning tools. Detection of the More

The complexity of real-world applications, especially in the field of the Internet of Things, has brought with it a variety of security risks. IoT Botnets are known as a type of complex security attacks that can be detected using machine learning tools. Detection of these attacks, on the one hand, requires the discovery of their behavior patterns using batch processing with high accuracy, and on the other hand, must be operated in real time and adaptive like stream processing. This highlights the importance of using batch/stream hybrid processing techniques for botnet detection. Among the important challenges of these processes, we can mention the selection of appropriate features to build basic models and also the intelligent selection of basic models to combine and present the final result. In this paper, we present a solution based on a combination of stream and batch learning methods with the aim of botnet anomaly detection. This approach uses a dynamic feature selection method that is based on a genetic algorithm and is fully compatible with the nature of hybrid processing. The experimental results in a data set consisting of two known types of botnets indicate that on the one hand, the proposed approach increases the speed of hybrid processing and reduces the detection time of the botnets by reducing the number of features and removing inappropriate features, and on the other hand, increases accuracy by selecting appropriate models for combination. Manuscript profile

Open Access Article

8 - Proposing a Detection and Mitigation Approach for DDoS Attacks on SDN-Based IoT Networks
fatemeh MotieShirazi Seyedakbar Mostafavi

20.1001.1.16823745.1402.21.2.12.5

Internet of Things (IoT) is a network of objects on which objects can communicate with other objects. The Internet of Things is currently constantly under numerous attacks due to technical, legal and human problems. One of the most important of these attacks is the Deni More

Internet of Things (IoT) is a network of objects on which objects can communicate with other objects. The Internet of Things is currently constantly under numerous attacks due to technical, legal and human problems. One of the most important of these attacks is the Denial of Service (DoS) attack, in which normal network services are out of service and it is impossible for objects and users to access the server and other resources. Existing security solutions have not been able to effectively prevent interruption attacks in Internet of Things services. Software-oriented network (SDN) is a new architecture in the network based on the separation of the control and data plane of the network. Programmability and network management capability by SDN can be used in IoT services because some IoT devices send data periodically and in certain time intervals. SDN can help reduce or prevent the data flood caused by IoT if properly deployed in the data center. In this article, a method to detect DDoS attacks in Internet of Things based on SDN is presented and then an algorithm to reduce DDoS attacks is presented. The proposed method is based on the entropy criterion, which is one of the most important concepts in information theory and is calculated based on the characteristics of the flow. In this method, by using two new components on the controller to receive incoming packets and considering the time window and calculating entropy and flow rate, a possible attack is detected in the network, and then based on the statistics of the flow received from the switches, the certainty of the attack is determined. Compared to the existing methods, the proposed method has improved 12% in terms of attack detection time and 26% in terms of false positives/negatives. Manuscript profile

Open Access Article

9 - Friendship Selection Based on Social Features in Social Internet of Things
Mohammad Mahdian S.Mojtaba Matinkhah

The Social Internet of Things (SIoT) network is the result of the union of the Social Network and the Internet of Things network; wherein, each object tries to use the services provided by its friends. In this network, to find the right friend in order to use the right More

The Social Internet of Things (SIoT) network is the result of the union of the Social Network and the Internet of Things network; wherein, each object tries to use the services provided by its friends. In this network, to find the right friend in order to use the right service is demanding. Great number of objects' friends, in classical algorithms, causes increasing the computational time and load of network navigation to find the right service with the help of friendly objects. In this article, in order to reduce the computational load and network navigation, it is proposed, firstly, to select the appropriate object friend from a heuristic approach; secondly, to use an adapted binary cuckoo optimization algorithm (AB-COA) which tries to select the appropriate friendly object to receive the service according to the maximum response capacity of each friendly object, and finally, adopting the Adamic-Adar local index (AA) with the interest degree centrality criterion so that it represents the characteristics of the common neighbors of the objects are involved in the friend selection. Finally, by executing the proposed algorithm on the Web-Stanford dataset, an average of 4.8 steps was obtained for reaching a service in the network, indicating the superiority of this algorithm over other algorithms. Manuscript profile

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