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In recent years, airport runways have become a more critical bottleneck in airports, and it is very unusual to use only one runway to solve the Aircraft Landing Problem (ALP). The ALP includes the aircraft's landing scheduling and assigning them to runways. In addition to certain limited time frames for aircraft while landing, to prevent accidents, the distance between aircraft should be restricted during the flight and in the landing phase. In this paper, to solve the problem in multi-runway mode, a solution is proposed that has considered all the limitations to create a trade-off between the runways to reduce the traffic in the runways. The present study considers the balance between bands and offers a new method of improving the Imperialist Competitive Algorithm (ICA) while reducing the cost due to the early and late landing of the aircraft. In other words, a novel approach for addressing the ALP with multiple criteria, employing a delay and early landing cost optimization technique and runway balance strategy, as well as using multi-runway, which will reflect the current realities of the aviation industry and provide a more accurate and relevant analysis, has been presented. Thirty-two benchmark instances were selected and compared with four famous algorithms: Particle Swarm Optimization (PSO), Immunoglobulin-Based Artificial Immune System (IAIS), Grey Wolf Optimizer (GWO), and flower pollination algorithm (FPA) as the results on a small-scale indicate, the ICA method performs superior outcomes, except for one case. Regarding the two objectives, the presented method, compared to other methods, managed to reduce the cost by 3.5% (on a small-scale). Furthermore, on a large scale (500 aircraft), improved ICA has been able to reduce the cost of the early or late arrival of the aircraft by 35%. [ABSTRACT FROM AUTHOR]

Considering the expansion of the Internet of Things (IoT) and the volume of data and user requests, Mobile Edge Computing (MEC) is considered a novel and efficient solution that puts decentralized servers at the network’s edge. This has the effect of lowering bandwidth demand and transmission latency. Optimal edge server placement and allocation, as the first stage of MEC, can improve end-user service quality, edge computing system utility, and cost and energy consumption. The majority of previous edge server placement studies have employed only one objective or developed a fitness function by the weighted sum method for optimization. Usually, using a single optimization objective without considering other objectives cannot yield the desired results for a problem with a multi-objective design. On the other hand, assigning weights to objectives can lead to losing optimal points in non-convex problems and selecting improper weights. Therefore, in this paper, we propose a multi-objective solution for the positioning and allocation of edge servers for MEC services based on the NSGA-II algorithm. In this regard, we identify two workload variance and latency reduction objectives with extensive evaluations. The experimental evaluation of the results using real-world data reveals that solutions based on the NSGA-II yield superior convergence and diversity of Pareto front points compared to Multi-Objective Particle Swarm Optimization (MOPSO), Multi-Objective Biogeography Based Optimization (MOBBO), and Adaptive Weighted Sum Method (AWSM). Additionally, it effectively mitigates workload variance on servers and exhibits an average latency reduction of 8.79% in comparison to the adaptive weighted-sum approach, 9.19% in comparison to MOPSO, and 0.28% in comparison to MOBBO. [ABSTRACT FROM AUTHOR]

Highlights • Proposing robust and lightweight humanoid robot odometric learning method (LHOL). • Exploiting robot kinematics, IMU, and the actuators' internal pressure load data. • Experimentally validating LHOL method on the ARC humanoid robot platform. • Achieving accurate localization results for open-loop and closed-loop walk engines. Abstract Odometry as one of the inevitable parts of robot behavior control plays an essential role in the localization of humanoid robots. Calculating odometry on the humanoid robots is usually based on one or combination of the vision, laser scanner, magnetic sensor, and pressure sensors. Vision or laser scanner-based approaches require high computational power for analyzing visual information. Hence, these kinds of approaches are not suitable for all kind of the small humanoid robots. On the other hand, it is known that magnetic sensors have instability problems in different environments. Furthermore, calculating accurate dead reckoning (pure odometry) is very difficult because of the complex mechanical system of humanoid robots, the presence of many sources of uncertainty, and inaccuracy in motion executions such as foot slippage. Therefore, this paper presents a robust learning method to localize a humanoid robot named Lightweight Humanoid robot Odometric Learning method (LHOL). This method does not employ any vision, magnetic, additional pressure sensors, and laser scanners, and, therefore, eliminates dependencies to these sensors for the first time. The method's core learning is based on the artificial neural network (ANN) which uses kinematic computations, IMU (roll and pitch data), and the robot's actuators' internal present load data as input data. The proposed LHOL method proves high accuracy on a novel fully 3D printed kid-sized humanoid robot platform (ARC) with both open-loop and closed-loop walk engines at differently covered floors. [ABSTRACT FROM AUTHOR]

Recently, advances in hardware such as CMOS camera nodes have led to the development of Visual Sensor Networks (VSNs) that process sensed data and transmit the useful information to the base station for completing subsequent tasks. Today, object detection and sending useful information to the base station for object recognition is emerged as an important challenging issue in VSNs. Our investigations show that the face’s information is adequate for completing object recognition. According to literature, many approaches have been proposed for object detection and sending useful information to the base station to be completed subsequent tasks like object recognition. However, in most of them, lack of preprocessing methods in camera nodes causes network to be faced with large volume of data. For example when there is more than one object within the each camera node filed-of-view, conventional works deliver empty spaces among objects to the base station. Also, most of them send whole information about each object to the base station, while sending only face’s information of each object is adequate for completing object recognition. Therefore, in this paper, a preprocessing method in camera nodes named Preprocessing-based Multi-Face Detection (PMFD) is proposed. Our method works based on the extracting bounding box of each object’s face, using Boosting-based face detection algorithm, and sending only the faces’ information to the base station. The simulation results show that PMFD method has acceptable preprocessing time complexity and injects low volume of traffic into the network. Consequently, PMFD method prolongs the network lifetime in comparison with state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) have been studied for various applications, especially for facilitating relief operations in disasters such as earthquake and fire. In this paper, we propose a new geographic cross-layer routing method in WSNs called geographic cross-layer routing adapted for disaster (GCRAD) relief operations. GCRAD integrates MAC and routing using a handshake mechanism for relay selection and considers node's queue status, distance to the sink, and the number of potential relay nodes as the relay selection criteria. GCRAD eliminates the ineffective transmissions and shortens the handshake mechanism by affecting these criteria in a single step (all of them concurrently) and also, decreases the collision probability as much as possible. Through ns2-based simulations, we show that GCRAD significantly achieves lower end-to-end delay, lower energy consumption, and acceptable delivery ratio in comparison with the state-of-the-art geographic cross-layer routing methods, especially in high traffics, which is the main characteristic of disasters. [ABSTRACT FROM AUTHOR]

One of the important quality of services of Wireless Sensor Networks is the coverage, which focuses on providing near optimum coverage rate without decreasing network lifetime. Many distributed solutions have been proposed for this issue most of which try to select a minimum number of nodes as active while keeping others in sleep mode to preserve energy and extend network lifetime. However, these methods suffer from lacking a mathematical basis for their nodes selection approach. In this paper, we propose a distributed method for tackling this challenge by exploiting Game Theory as the mathematical basis for selecting active nodes named Game Theory based node Scheduling for Coverage control (GTSC). In GTSC, nodes compete each other to become active through exploiting their coverage redundancy, activation cost, the number of active neighbors, and uncovered region. The comparison of simulation results with the results of a well-known method and a state-of-the-art one shows that the proposed method outperforms both of them in terms of prolonging coverage, network lifetime, and energy efficiency, besides the redundancy rate reduction. [ABSTRACT FROM AUTHOR]

High-resolution images with wide field of view are important in realizing many applications of wireless multimedia sensor networks. Previous works that generally use multi-tier topology and provide such images by increasing the capabilities of camera sensor nodes lead to an increase in network cost. On the other hand, the resulting energy consumption is a considerable issue that has not been seriously considered in previous works. In this paper, high-resolution images with wide field of view are generated without increasing the total cost of network and with minimum energy dissipation. This is achieved by using image stitching in WMSNs, designing a two-tier network topology with new structure, and proposing a camera selection algorithm. In the proposed two-tier structure, low cost camera sensor nodes are used only in the lower-tier and sensor nodes without camera are considered in the upper-tier which decreases total network cost as much as possible. Also, since a simplified image stitching method is implemented and a new algorithm for selecting active nodes is utilized, energy dissipation in the network is decreased by applying the proposed methods. The results of simulations supported the preceding statements. [ABSTRACT FROM AUTHOR]

Owing to advances in information technology, online communications between people living in different parts of the world have considerably increased. The subsequent emergence of social networks helped this kind of communications to be further organized. One of the most important issues considered when analyzing these kinds of networks is community detection, in which a majority of studies tend to detect disjoint communities through analyzing linkages of networks. What this paper aims to achieve is to obtain overlapping communities in which the members have the same topics of interest, and where the strengths of connections between them are the consequence of their communications’ content analysis. Consequently, we have hereby proposed a generic framework for overlapping community detection in social networks with special focus on rating-based social networks. This framework considers the information shared by the users (ratings), as well as their topics of interest, for the sake of finding meaningful communities. This will lead us to topical communities in which members are interested in the same topics, and the strengths of their relationships are directly based on the rate of their viewpoints’ unity. Quantitative evaluations also reveal that the framework presented in this study achieves favorable results which are quite superior to the results of 3 other relevant frameworks in the literature. [ABSTRACT FROM AUTHOR]

Detecting communities in complex networks is one of the most important issues considered when analyzing these kinds of networks. A majority of studies in the field of community detection tend to detect communities through analyzing linkages of the networks. What this paper aims to achieve is to reach to a trade-off between similarity of nodes' attributes and density of connections in finding communities of social networks with node attributes. Since the community detection problem can be modeled as a seriously non-linear discrete optimization problem, we have hereby proposed a multi-objective discrete Biogeography Based Optimization (BBO) algorithm to find communities in social networks with node attributes. This algorithm uses the Pareto-based approach for community detection. Also, we introduced a new metric, SimAtt, to measure the similarity of node attributes in a community of a network and used it along with Modularity, which considers the linkage structure of a network to detect communities, as the two objective functions of the proposed method to be maximized. In the proposed method, a two phase sorting strategy is introduced which uses the non-dominated sorting and Crowding-distance to sort the generated solution of a population in each iteration. Moreover, this paper introduces a method for mutation probability approximation and uses a chaotic mechanism to dynamically tune the mutation probability in each iteration. Additionally, two novel strategies are introduced for mutation in unweighted and weighted networks. Since the final output of the proposed method is a set of non-dominated (Pareto-optimal) solutions, a metric named alpha_SAM is proposed to determine the best compromise solution among these non-dominated ones. Quantitative evaluations based on extensive experiments on 14 real-life data sets reveals that the method presented in this study achieves favorable results which are quite superior to other relevant algorithms in the literature. [ABSTRACT FROM AUTHOR]

• Improving the tradeoff between energy consumption and transmission delay in IoT. • Exploiting artificial bee colony to select the efficient cluster-heads in IoT. • Considering energy, neighbors and distance as the criteria for cluster-head selection. • Providing an artificial bee colony-based mechanism for clustering devices efficiently. • Considering distance and data volume as the criteria for clustering IoT devices. Wireless communication on the Internet of Things (IoT) requires context-aware data transmission protocols. Developing an energy-efficient clustering mechanism is the primary challenge in data transmission over IoT. The existing approaches struggle with the short lifetime of IoT, imbalance load distribution, and high transmission delay. This paper proposes a novel cluster-head selection and clustering mechanism on IoT. It is composed of two main phases. The first phase selects the near-optimal cluster-heads using Artificial Bee Colony (ABC) algorithm. Performance criteria include the residual energy of the devices, the number of neighbors, Euclidean distance between devices and the sink, and Euclidean distance between each device and its neighbors. The principal objective of the second phase is to group devices into some clusters based on Euclidean distance between each cluster-head and its members, and the data volume generated by clusters. Simulation results verify that our mechanism improves energy consumption, lifetime, and transmission delay. Image, graphical abstract [ABSTRACT FROM AUTHOR]