Mehdi Golsorkhtabaramiri

Unmanned Aerial Vehicles (UAVs) and Wireless sensor networks (WSNs) play an essential role in the Internet of Things (IoT) because of their easy usage and cost reduction. A UAV can used as an Air Base Station (ABS). One of the main challenges of an IoT/WSN is energy consumption, most of which relates to the transmutation part. Although many efforts have been made to propose several hybrid, dynamic, and static clustering protocols, most of these protocols apply to certain aspects of the network. So far, not many efforts have been made to present a hybrid protocol that can stay stable in various small and large-scale UAV-assisted IoT wireless networks. This paper presents a hybrid unequal hierarchy routing called an impressive clustering based on the fuzzy system (ICF). Clustering is performed at the beginning of each meta-round instead of each round to reduce overhead. This task scheduling is determined based on fuzzy systems. In ICF, a new clustering plan (CP) technique is presented that is determined based on the fuzzy system. CP is based on the scale of the network and distance of the network to the UAV whether routing should be single-hop or multi-hop. When the CP is single-hop inter-cluster routing, the cluster heads (CHs) closer to the ABS have a larger radius. In this case, CHs closer to ABS have more cluster members (CMs) to receive and data aggregation. When the CP is a multi-hop, the CHs closer to the ABS have a smaller radius. In this case, CHs closer to ABS have more energy to receive and relay data from far CHs to ABS. Choosing single-hop routing for small-scale networks helps reduce the control message's overhead. It also maintains the network's stability by selecting multi-hop routing in a large-scale network. Therefore, in different size networks, the protocol can work agreeably. Also, ICF uses the assistance to cluster heads (ACHs) technique which allows CHs to get help from some of its CMs to help share cluster load. The simulation results showed that the proposed method improves network stability, load-balancing, and network lifetime and performance.

Radio‐Frequency Identification and Wireless Sensor Networks (WSN) are two basic pillars in the Internet of Things (IOT); they are used to identify and track objects, and collect information from the environment in various applications of the IOT. One of the architectures used for combining these two technologies is the combination of readers with wireless sensor nodes. In the combination of Radio‐Frequency Identification and WSN, we face issues concerning reader collision, tag rereading and load imbalance. In the dense environments of this hybrid network, due to congestion and the possible mobility of nodes, controlling access to the shared channel is one of the basic challenges. In addition, owing to the energy limitations of these networks, data redundancy and tag re‐reading are critical issues. An effective anti‐collision protocol suitable for RFID‐WSN networks is proposed to avoid the above‐mentioned issues. The Energy‐saving and Anti‐collision Protocol (EAP) method is a CSMA‐based multi‐channel protocol that not only solves the issue of readback collisions, but also, through tagging, it prevents the rereading of the tags. To evaluate the performance of the EAP method, this protocol is compared with the most prominent reader anti‐collision protocols ever presented. The results indicate that due to the better throughput and higher energy efficiency that the proposed protocol has, when compared to other multi‐channel protocols, it will be a better option for use in the IOT platform with dense conditions.

Falling is one of the major health concerns, and its early detection is very important. The goal of this study is an early prediction of impending falls using wearable sensors data. The SisFall data set has been used along with two deep learning models (CNN and a combination model named Conv_Lstm). Also, a dynamic sampling method is offered to improve the accuracy of the models by increasing the equilibrium rate between the samples of the majority and minority classes. To fulfill the main idea of this paper, we present a future prediction strategy. Then, by defining a time variable ‘T’, the system replaces and labels the state of the next T s instead of considering the current state only. This leads to predicting falling states at the beginning moments of balance disturbance. The results of the experiments show that the Conv_Lstm model was able to predict the fall in 78% of cases and an average of 340 ms before the accident. Also, for the Sensitivity criterion, a value of 95.18% has been obtained. A post-processing module based on the median filter was implemented, which could increase the accuracy of predictions to 95%.

In the new industrial revolution known as Industry 4.0, radio frequency identification (RFID) systems are a key component of automatic detection. These systems have two main elements, namely Reader and Tag. In many Internet of Things (IoT) applications, the RFID system is used with lots of readers working together in a dense environment to read tags. The simultaneous operation of readers with a common sensory range increases the likelihood of reader‐to‐tag collision and reader‐to‐reader collision and reduces the number of successful reading and as a result, reduces network performance and average waiting time for each reader increased. Collisions happen when readers are in the interference range and start reading tags simultaneously, so it is necessary to use the right solution to control channel access in these systems. So far, various solutions have been proposed to control readers’ access to the communication channel. Some of them have not considered the existing standards for this type of system or have not been efficient enough to be used in the IoT. In this study, we propose a method that, by considering the distance between readers and the number of neighbourhoods, and the possibility of information sharing, allows readers to successfully read more tags with fewer collisions in a certain time frame. The results of the performance study in a real‐world environment showed that the suggested method outperformed similar methods in terms of network performance and has much better throughput, making it a superior choice for usage in IoT‐based RFID systems.

Due to the design of computer systems in the multi‐core and/or multi‐processor form, it is possible to use the maximum capacity of processors to run an application with the least time consumed through parallelisation. This is the responsibility of parallel compilers, which perform parallelisation in several steps by distributing iterations between different processors and executing them simultaneously to achieve lower runtime. The present paper focuses on the uniformisation of three‐level perfect nested loops as an important step in parallelisation and proposes a method called Towards Three‐Level Loop Parallelisation (TLP) that uses a combination of a Frog Leaping Algorithm and Fuzzy to achieve optimal results because in recent years, many algorithms have worked on volumetric data, that is, three‐dimensional spaces. Results of the implementation of the TLP algorithm in comparison with existing methods lead to a wide variety of optimal results at desired times, with minimum cone size resulting from the vectors. Besides, the maximum number of input dependence vectors is decomposed by this algorithm. These results can accelerate the process of generating parallel codes and facilitate their development for High‐Performance Computing purposes.

The growth of software techniques for implementing applications must go hand in hand with the growth of computer system hardware in the design of multi-core and multi-processor systems; otherwise, we cannot expect to be able to use maximum hardware capacities. One of the most important and challenging techniques for running applications is to run them in parallel with a focus on loop parallelism to reduce execution time. On the other hand, in recent years, many algorithms have been working on volumetric data, i.e., three-dimensional spaces; therefore, parallelization must be possible for all types of two-dimensional and three-dimensional loops. Uniformization is an important part of loop parallelism, and also the present paper’s focus. The proposed algorithm in the present paper performed uniformization with a combination of the frog leaping algorithm and the fuzzy system for two- and three-dimensional loops on a wide range of input dependence vectors and achieved a considerable variety of results in the desired time. The results of this study can be used to facilitate the development of parallel codes.