Search

The problem of radio frequency identification network planning (RNP) is one of the biggest challenges in the field of RFID research. In order to improve the service quality of RFID systems, in addition to finding the optimal number and location of readers, the interference between readers and tag coverage should be considered. In order to solve the problem of RFID network planning, this paper proposes an improved multi-objective brainstorming optimization algorithm (IMBSO) based on decomposition: Employ the golden sine operator to improve the convergence speed of the algorithm, and the Cauchy mutation operator is introduced to enhance the ability of the algorithm to jump out of the local optimum. The improved algorithm achieves a balance between exploration and development. This paper also combines the decomposition-based method with the IMBSO algorithm to establish an optimization model to optimize the reader position: the multi-objective optimization problem is transformed into multiple sub-problems by means of decomposition, all objective functions are optimized at the same time, which improves the search efficiency. The method proposed in this paper was tested on the RFID model and data set. Under the same experimental conditions, it was compared with multi-objective particle swarm optimization algorithm, multi-objective firefly algorithm and multi-objective brainstorming algorithm. The experimental results show that the algorithm proposed in this paper can be successfully applied to the problem of radio frequency identification network planning by achieving smaller interference and fewer readers while ensuring greater coverage. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicle (UAV) Internet of things have been widely used in military and civilian fields such as rescue, disaster relief, urban planning. Positioning service is the core technology for UAVs to perform various tasks. However, the UAV may be attacked by external conditions, resulting in its inability to obtain self-location information during mission. For the positioning problem of UAV signal interference, this paper proposes a cooperative positioning of UAV based on optimization algorithm. In order to solve the difficulty of UAV positioning, we propose the following solutions. Firstly, we construct different numbers of beacon nodes by using the flight information of UAVs in different cycles. Secondly, the unknown number of the positioning to be solved of the UAV is reduced to improve the accuracy and speed of the subsequent optimization algorithm. Thirdly, A multi-objective optimization model is established of the UAV motion parameters under inequality constraints. And we utilize a penalty function to convert the optimization model into a minimal value solution problem under no constraints. Finally, the positioning results of each UAV are obtained by the optimization algorithm. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) hold the promise of delivering new intelligent, cost-effective, and collaborative applications with the potential to have a great impact on our daily life. WSNs are often employed for detecting and tracking a wide range of entities involved in realistic scenarios where security is of vital importance. While selecting energy-efficient Cluster Heads (CHs) is the primary focus of the majority of clustering approaches currently in use in WSNs, researchers have not given adequate consideration to the security aspects of CHs when developing a CH selection strategy. Estimating the trust between the nodes not only makes the WSN secure, but also improves communication between nodes and makes the WSN more reliable. In this paper, we develop a secure and energy-aware clustering approach (SEACDSC) for WSNs by adapting sand cat swarm optimization algorithm (SCSO). SEACDSC incorporates a novel mechanism for determining secure and energy-efficient CHs among the WSN nodes. In particular, we propose a Discrete SCSO method, a variant of the traditional SCSO, to facilitate the secure and efficacious selection of CHs. The fitness function is designed by considering nodes' remaining energy and trust values for choosing CH efficiently. Furthermore, the exponential weighted moving average (EWMA) is used for dynamically updating the predefined threshold values following the network state. As demonstrated by the simulation results, SEACDSC outperforms the existing BAT-Based, MG-LEACH, Enhanced-LEACH, Improved-Leach, and RCH-LEACH techniques in terms of network stability, number of alive nodes, energy efficiency, reliability, average trust value of CHs and network lifetime. [ABSTRACT FROM AUTHOR]

Conventional wireless sensors have difficulty solving the problem of energy limitation, especially in sensor networks in hard-to-reach extreme areas. In order to solve the problem that it is difficult to charge wireless sensors in the field using conventional energy sources, the energy harvesting wireless senor is designed to use renewable energy sources for power supply. Considering the uncertainty and unknown nature of renewable energy generation, and the need for effective energy management of the sensor. In this paper, an Node Power Control Optimization (NPCO) power allocation algorithm is proposed to adjust the power allocation problem of wireless sensor nodes within each time slot. In addition, to address the unknown and random nature of energy arrival, this paper proposes a CLSTM model based on deep learning to predict the energy arrival. The continuous autonomous energy management of wireless sensor nodes is achieved by combining the CLSTM prediction results using the NPCO algorithm. The algorithm is applicable to continuous states and is able to show good performance in the verification of real solar data. The algorithm achieves better performance in terms of long-term average net bit rate compared to the current DDPG algorithm, AC algorithm, and Lyapunov optimization algorithm. [ABSTRACT FROM AUTHOR]

The design of underwater wireless sensor networks (UWSNs) faces many challenges, including power consumption, storage, battery life, and transmission bandwidth. UWSNs usually either use node clustering or multi-hop routing as their energy-efficient optimization algorithms. The cluster optimization technique will organize the sensor nodes into a cluster network, with each cluster led by a cluster head (CH). In contrast, the multi-hop optimization algorithm will create a multi-hop network by sending data to the base station (BS) while switching between different sensor nodes. However, the overburdens of CH nodes impact the performance of the cluster optimization method, whereas the overburdens of nodes close to the BS impact the performance of the multi-hop optimization algorithm. Therefore, clustering and routing procedures can be considered as a simultaneous NP-hard problem that metaheuristic algorithms can address. With this motivation, this paper proposes an energy-efficient clustering and multi-hop routing protocol using the metaheuristic-based algorithm to increase energy efficiency in UWSNs and lengthen the network life. However, the existing metaheuristic-based methods use two separate algorithms for clustering and multi-hop routing, increasing computational complexity, different initialization, and difficulty in hyperparameters' tuning. In order to address the mentioned shortcomings, this paper proposes a novel hierarchical structure called hierarchical chimp optimization (HChOA) for both clustering and multi-hop routing processes. The proposed HChOA is validated using various metrics after being simulated using an extended set of experiments. Results are compared to those from LEACH, TEEN, MPSO, PSO, and IPSO-GWO to validate the impact of the HChOA. According to the findings, the HChOA performed better than other lifespan and energy usage benchmarks. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) currently have numerous applications, especially in tracking and observing non-human activities. Sensor nodes in WSNs are known to have limited lifespans due to continuous sensing, which causes the battery to drain quickly. Therefore, Energy consumption is a significant research issue in WSN-assisted applications. Energy conservation now places a high priority on exact clustering and the choice of the best route from the sensor nodes to the sink. This research paper proposes a fuzzy with adaptive sailfish optimizer (ASFO) for cluster head selection and improved elephant herd optimization approach to find the most efficient shortest path route to preserve energy efficiency in WSNs. The suggested hybrid approach was implemented in MATLAB and achieved results are compared to those of four widely-used techniques, such as improved artificial bee colony optimization-based clustering (IABC-C), genetic algorithms (GA), particle swarm optimization (PSO), and hierarchical clustering-based CH election (HCCHE) approach. The Fuzzy with ASFO technique improves the Quality of Service (QoS) of performance metrics such as energy usage, packet loss ratio, end-to-end delay, packet delivery ratio, network lifetime, and buffer occupancy. The results show that the suggested Fuzzy with SFO has a better packet delivery ratio (99.8%), packet latency (1.12 s), throughput (98 bps), energy usage (10.90 mJ), network lifetime (5400 cycles), and packet loss ratio (0.6%) than the existing methods (PSO, GA, IABC-C, and HCCHE algorithms). [ABSTRACT FROM AUTHOR]

Sixth-generation wireless systems not only have more demanding communication requirements, they are also expected to have high-precision sensing capabilities and sufficient computing power. Integrated sensing, communication, and computation (ISCC) can meet the above system requirements and save spectrum resources. In this paper, we build a resource allocation and offloading decision problem in an ISCC scenario that makes considerations for user mobility and partial offloading policies. The established problem minimizes the average task cost when given constraints such as the typical sensing failure rate and task completion delay. We use Lyapunov optimization theory to transform the proposed problem and propose a two-level optimization algorithm based on matching theory to offer a solution for the transformed problem. The inner layer obtains the task offloading ratio through theoretical derivation, and the outer layer determines the base station access and channel assignment based on the inner layer results. The simulation results show that the average task cost can be effectively reduced while also guaranteeing high-quality sensing performance. [ABSTRACT FROM AUTHOR]

For many wireless sensor networks applications, a powerful localization algorithm is required to determine the exact positions of sensor nodes. In this paper, a new localization algorithm is presented which combines the distance vector hop (DV-Hop) algorithm with the shuffled shepherd optimization algorithm to determine the position for each unknown node within 3-D space. Studying the localization problem in 3-D space is more realistic, but more challenging due to the enlarged search space. Comparison of our proposed algorithm with its alternatives in literature shows that it offers improved accuracy and more stable performance with respect to changes in nodes density and communication range. [ABSTRACT FROM AUTHOR]

The emergence of the fifth generation (5G) mobile communication network highly promoted the enhancement of broadband wireless communication. One of the famous physical transmission technologies in regard to wireless communication is orthogonal frequency division multiplexing (OFDM) and the requirements of cognitive radio (CR) met by this OFDM. Spectrum sensing (SS) is a key enabling function in CR to improve utilization spectrum and eases the spectrum resources. Among more other modulation methods, OFDM is broadly utilized in various next generation and current wireless communications systems. This paper enables SS in OFDM based CR using proposed gannet optimization algorithm (GOA). Firstly, simulation is undergone and signal is received from OFDM based CR network. Here, generation of test statistics, such as signal energy, Eigen statistics, matched filter and wavelets done to ensure efficient CR communication without interference. Furthermore, fusion center undergoes fusion process, at which weights determined by proposed GOA and decision is finally processed. GOA diving patterns used for exploring the optimal region with in the search space and then enable exploitation phase to ensure better solution to compute weight vector. This research is evaluated by various performance metrics, such as mean square error and also bit error rate with values of 0.007 and 0.023, correspondingly for SNR of 20 dB with Nakagami fading channel. [ABSTRACT FROM AUTHOR]

In this paper, the spectrum sharing problem in cognitive radio is studied as a maximization optimization problem. First, two different optimization models are represented to maximize utilization and fairness in spectrum sharing. Then, an improved Teaching–Learning-Based-Optimization (iTLBO) algorithm is proposed and applied to solve the spectrum sharing optimization problem. Evaluated by 10 benchmark functions, the proposed iTLBO algorithm shows best optimization performance than the other optimization algorithms on most benchmark functions. Finally, the simulation results validate that the proposed spectrum sharing scheme improves both the spectrum utilization and fairness compared to other existing methods. [ABSTRACT FROM AUTHOR]

Cloud computing represents an evolved form of cluster, client server, and grid computing, enabling users to seamlessly access resources over the internet. The quality and reliability of the cloud computing services are depends on the specific tasks undertaken by the users. Task Scheduling emerges as a pivotal factor in enhancing the efficiency and reliability of a cloud environment, aiming to optimize resource utilization. Furthermore, efficient task scheduling holds a prime importance in achieving superior performance, minimizing response time, reducing energy consumption and maximizing throughput. Assigning work to essential resources is a challenging process to achieve better performance. However, this paper plans to propose a novel workload prioritization and optimal task scheduling in the cloud with two steps. At first, the ranks are allotted to the tasks with Analytical Hierarchy Process based ranking process that uses a k-means clustering strategy to group the workloads. Then, the tasks are scheduled under the consideration of constraints like makespan, utilization cost, and migration cost and risk probability; based on priority. Accordingly, the task scheduling is done optimally by the proposed hybrid optimization Blue Updated Jellyfish Search Optimization that combines algorithms like Blue Monkey Optimization and Jelly fish Search Optimization algorithms. The performance of the proposed scheduling process is validated and proved over the conventional methods. [ABSTRACT FROM AUTHOR]

A wireless sensor network (WSN) is made up of many sensor nodes with insufficient energy, storage, and processing capabilities. Data gathering and transmission to the base station are two of the main responsibilities of the sensor nodes (BS). As a result, the network lifespan becomes the key factor in the successful design of data collection strategies in WSN. In this study, we provide the Enriched energy optimized LEACH (EE-OLEACH) protocol for data transfer. Through a combination of efficient optimum clustering and an optimal route selection mechanism, it provides a means for energy-efficient routing in WSN. The Homogeneous Hunter-Wolf optimization (HHWO) is used for clustering, and a cluster head is chosen for each cluster to reduce energy loss among sensor nodes and maximize efficiency in their use of available resources. Nodes with the highest residual energy may receive the most energy-efficient routing. In order to send the data to BS, the nodes with the greatest residual energy are chosen. The pheromone-profound Ant optimization (PPAO) method was then used to reduce energy consumption throughout the path-selection process. It contributes to a higher packet delivery ratio while reducing power consumption. According to the experimental findings, the proposed EE-OLEACH performs better than the current Protocol in terms of packet delivery ratio, end-to-end latency, and energy usage. In this paper, we compare the performance of the existing hierarchical routing protocols under varying conditions (packet size, starting energy level, etc.) and demonstrate how the optimal CH selection based on a suggested algorithm improves both network lifetime and energy consumption. The Simulation results shows that the EE-OLEACH enhances energy efficiency by 30%, delay by 35%, node survived by 45%, network lifetime by 56%, packet delivery ratio by 47% and throughput by 38% when compared with other existing protocols. The results clearly show that the suggested EE-OLEACH extends the lifespan of the network and reduce the energy consumption. [ABSTRACT FROM AUTHOR]