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The Internet of Things (IoT) is a network of various interconnected objects capable of collecting and exchanging data without human interaction. These objects have limited processing power, storage space, memory, bandwidth and energy. Therefore, due to these limitations, data transmission and routing are challenging issues where data collection and analysis methods are essential. The Routing Protocol for Low-power and Lossy Networks (RPL) is one of the best alternatives to ensure routing in LoWPAN6 networks. However, RPL lacks scalability and basically designed for non-dynamic devices. Another drawback of the RPL protocol is the lack of load balancing support, leading to unfair distribution of traffic in the network that may decrease network efficiency. This study proposes a novel RPL-based routing protocol, QFS-RPL, using Q-learning algorithm policy and ideation from the Fisheye State Routing (FSR) protocol. The proposed QFS-RPL is as lightweight and agile as the standardized RPL and partially outperforms the mRPL protocol on mobile networks. This method supports multi-path routing, and at any given time in the network lifetime, all possible paths for sending data from any node to the sink are available. Therefore, QFS-RPL provides high resilience against errors, failures, and sudden network changes. To evaluate the performance of the proposed method, the Contiki operating system and Cooja simulator have been used in scenarios with mobile and stationary nodes and random network topologies. The results have been compared with RPL and mRPL. We have developed an algorithm for ease of data transfer in the IoT, which provides better performance than existing protocols, especially when dealing with a mobile network. The performance evaluation criteria considered for simulation are load balancing, energy consumption, number of table entries, Packet Delivery Ratio (PDR), End-to-End (E2E) latency, network throughput, convergence speed, and control packet overhead.

Optimization is necessary for solving and improving the solution of various complex problems. Every meta-heuristic algorithm can have a weak point, and multiple mechanisms and methods can be used to overcome these weaknesses. We can use hybrid meta-heuristic algorithms to arrive at an efficient algorithm. This paper presents a new and intelligent approach by hybridizing meta-heuristic algorithms and using different mechanisms simultaneously without significantly increasing the time complexity. For this purpose, two algorithms, Salp Swarm Optimization(SSO) and the African Vulture Optimization Algorithm (AVOA) have been hybridized. And to improve the optimization process of the Modified Choice Function and Learning Automata mechanisms. In addition, two other improving mechanisms, named Opposition-Based Learning (OBL) and β-hill climbing (BHC) technique, have been presented and integrated with the AVOA-SSA algorithm. Fifty-two standard benchmarks were used to test and evaluate the AVOA-SSA algorithm. Finally, an improved version of the Extreme Learning Machine(ELM) classifier has been used with real stock market data for stock market prediction. The obtained results indicate the excellent and acceptable performance of the AVOA-SSA algorithm in `solving optimization problems and has been able to achieve high-quality solutions.

As the Internet of Things networks expand globally, it is becoming increasingly important to protect against threats. one of the main reasons for the high number of false positives and low detection rates is the presence of redundant and irrelevant features. To address this problem, we propose a binary chimpanzee optimization algorithm for the feature selection process. This paper presents accurate network-based intrusion detection network, named parallel convolutional neural network long and short-term memory network branch, which has two branches. The input vector of the network is permuted in a 3-dimention space. This allows the model to extract highly discriminative features using a small number of layers. On the second branch, we used long and short-term memory network in parallel. The efficacy of the proposed deep model has been evaluated using three benchmark internet of things intrusion detection datasets, namely ToN-IoT, UNSW-NB15, and IoTID20 datasets. The experimental results demonstrated that the proposed binary chimpanzee optimization approach reduces about 60% of features, and the effectiveness of the proposed model was demonstrated by experimental results showing a high detection rate, high accuracy, and a relatively low false positive rate, which are measured as 99.54%, 99.56%, and 0.024 in the ToN-IoT and 99.79%, 99.78%, and 0.0032 in UNSW-NB15 and 100%, 100%, and zero in IoTID20 datasets, respectively.

With the increase in the number of processing cores on a chip, a highly efficient and scalable structure such as a network on a chip was proposed. Network-on-chip is a technology presented to solve the limitations of system-on-chip that includes a set of multiprocessors. In network-on-chip systems, delay in communication and reaching the ideal speed is one of the concerns of academic and industrial researchers today. Although network-on-chip offers a favorable solution to reduce the problem of the long delay of wires compared to traditional structures, communication delay is still a challenge. In this article, the various types of network router input ports architecture on the chip are reviewed and the operational comparison and evaluation of the technologies proposed in this field are mentioned.

Network on chip (NoC) is one of the communicative structures for multiple cores that has scalability. In designing the NoC micro-router architecture, the arbitration unit is very important due to its significant impact on performance, chip occupation level and NoC power consumption. In this paper, a router arbitration architecture is proposed with a combination of variable priority arbitration and Round Robin. In this architecture, arbitration examines the requests of other channels based on the Round Robin index after requesting the flit to exit the virtual channel in addition to checking the availability of the relevant virtual channel.The simulation results show that the architecture of the RR-SFVP arbitration unit compared to the standard RR method, is 13.7% smaller in area and has 5.7% less power consumption and 53.7% less critical path delay.

The Internet of Things (IoT) has rapidly grown recently, and mobile devices (MDs) have encountered widespread usage. All of these cause an increase in the demand for more powerful computing resources. Meanwhile, a new concept called mobile edge computing (MEC) was introduced as a promising technology to access powerful computing resources closer to the user side for a quick and effective response, especially for time-intensive applications. Task offloading has emerged as a solution to allocate resources among computing resources of smart devices or computational resources available in MEC. This study presents a new binary quantum approach based on an arithmetic optimization algorithm (BQAOA) for computational tasks offloading decisions on MDs with low complexity and guaranteed convergence. However, since task offloading is an NP-hard problem, there is a need to use methods that provide the optimal possible solution for various quality criteria, including response time and energy consumption. Indeed, this is where the advantages of arithmetic optimization algorithms (AOA) and quantum computing have been used to improve the performance of MDs. This paper introduces a 2-tier architecture from the user to the cloud computing server-side. Also, a Markov model is proposed to compute the average network bandwidth in the offloading problem. The proposed BQAOA is compared with the best state-of-the-art algorithms in heuristic and meta-heuristic fields in different scenarios. The simulation results showed 12.5%, 12%, and 26% improvement in energy consumption, makespan, and Energy SLA Violations (ESV) optimization parameters, respectively.