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Sustainable freight transportation is one of the essential concepts in the smart city. Under this concept, many people connected with mobile devices produce location data and potential opportunities for transporting small objects in a more environmentally friendly and sustainable way. Crowdshipping, which utilises public people as transportation, is one of the terminal solutions in the last mile delivery scenario. Nevertheless, precisely assigning the delivery to the right crowd willing to accept the job is challenging because the solution space is too large to perform a full search. This article proposes a trajectory feature extraction algorithm and a task‐to‐crowd matching (T2CM) algorithm for coping with the job‐to‐crowd assignment problem. A simulation based on the real‐world dataset is conducted on three different scenarios to justify the outcome from our proposed method to the job assignment results. [ABSTRACT FROM AUTHOR]

The fundamental utility of the Large-Scale Visual Sensor Networks (LVSNs) is to monitor specified events and to transmit the detected information back to the sink for achieving the data aggregation purpose. However, the events of interest are usually not uniformly distributed but frequently detected in certain regions in real-world applications. It implies that when the events frequently picked up by the sensors in the same region, the transmission load of LVSNs is unbalanced and potentially cause the energy hole problem. To overcome this kind of problem for network lifetime, a Comprehensive Visual Data Gathering Network Architecture (CDNA), which is the first comparatively integrated architecture for LVSNs is designed in this paper. In CDNA, a novel α-hull based event location algorithm, which is oriented from the geometric model of α-hull, is designed for accurately and efficiently detect the location of the event. In addition, the Chi-Square distribution event-driven gradient deployment method is proposed to reduce the unbalanced energy consumption for alleviating energy hole problem. Moreover, an energy hole repairing method containing an efficient data gathering tree and a movement algorithm is proposed to ensure the efficiency of transmitting and solving the energy hole problem. Simulations are made for examining the performance of the proposed architecture. The simulation results indicate that the performance of CDNA is better than the previous algorithms in the realistic LVSN environment, such as the significant improvement of the network lifetime. [ABSTRACT FROM AUTHOR]

The deployment problem (DP) of a wireless sensor network (WSN) is no doubt a critical issue because the strategies it takes will not only strongly impact the overall performance but also the power consumed by the sensors in such a system. This means that a good solution to the deployment problem (DP) cannot just “enhance its performance,” it can also “save the energy” to prolong the lifetime of a wireless sensor network (WSN). However, finding an optimum solution for most DPs with limited computation resources is still a challenging research problem, especially for the NP-hard optimization problems. Compared to exhaustive search and deterministic algorithms, metaheuristics provides an alternative way to solve these optimization problems, by looking for a near optimal solution using limited computation resources within a reasonable time. Aimed at providing not only a systematic survey of metaheuristics for solving the deployment problem but also a roadmap for researchers working on WSN and metaheuristics to further develop and improve a wireless sensor network system, this paper begins with an overview of WSN and the deployment problem, followed by discussions on metaheuristics and how to use them to solve the DP. Then, a comprehensive comparison between metaheuristics for the DP is given. Finally, open issues and future trends of this field are addressed. [ABSTRACT FROM AUTHOR]

Simple Summary: Biomedical ontology matching is a large-scale multi-modal multi-objective optimization problem with sparse Pareto optimal solutions. To effectively address this challenging problem, this paper proposes an adaptive multi-modal multi-Objective Evolutionary Algorithm. First, a novel multi-objective optimization model is constructed to simultaneously optimize both the alignment's f-measure and its conservativity. Then, a problem-specific algorithm is presented, which uses the guiding matrix to adaptively guide the algorithm's convergence and diversity in both objective and decision spaces. The experimental results show that our approach is able to effectively solve the biomedical ontology matching problem and to provide more options for decision makers. To integrate massive amounts of heterogeneous biomedical data in biomedical ontologies and to provide more options for clinical diagnosis, this work proposes an adaptive Multi-modal Multi-Objective Evolutionary Algorithm (aMMOEA) to match two heterogeneous biomedical ontologies by finding the semantically identical concepts. In particular, we first propose two evaluation metrics on the alignment's quality, which calculate the alignment's statistical and its logical features, i.e., its f-measure and its conservativity. On this basis, we build a novel multi-objective optimization model for the biomedical ontology matching problem. By analyzing the essence of this problem, we point out that it is a large-scale Multi-modal Multi-objective Optimization Problem (MMOP) with sparse Pareto optimal solutions. Then, we propose a problem-specific aMMOEA to solve this problem, which uses the Guiding Matrix (GM) to adaptively guide the algorithm's convergence and diversity in both objective and decision spaces. The experiment uses Ontology Alignment Evaluation Initiative (OAEI)'s biomedical tracks to test aMMOEA's performance, and comparisons with two state-of-the-art MOEA-based matching techniques and OAEI's participants show that aMMOEA is able to effectively determine diverse solutions for decision makers. [ABSTRACT FROM AUTHOR]

In recent years, researchers have conducted various studies on applying wireless networking technology and mobile devices in education settings. However, research on behavioral patterns in learners' online asynchronous discussions with mobile devices is limited. The purposes of this study are to develop a mobile learning system, mobile interactive teaching feedback system (MITFS), linked to both mobile devices and the internet, to support learners with online asynchronous discussion, and combine content analysis and sequential analysis to compare and contrast the social knowledge construction behavioral patterns of problem-based asynchronous discussion in e-learning and m-learning environments. This study investigated four weeks of online discussions in an “Introduction to Computer Science” course involving forty first year university students. The control group (online asynchronous discussion without mobile devices) and the experimental group (online asynchronous discussion with mobile devices) in the group discussions were explored. By using content analysis and sequential analysis for the problem-based online asynchronous discussion of the behavioral patterns and differences between students in control and experimental group, the results showed that using mobile devices in online asynchronous discussion influenced students' learning performance. Some interesting results were found. Firstly, when the students used mobile devices in discussion situations, they could more engage in reflecting thinking, sharing more information, and further facilitating social knowledge construction among group members. Secondly, the experimental group performed better than the control group in terms of participation and diversity in knowledge construction behavioral patterns. Finally, based upon the findings, some implications are proposed for further research. [Copyright &y& Elsevier]

Abstract: In this paper, we present an enhanced parallel cat swarm optimization (EPCSO) method for solving numerical optimization problems. The parallel cat swarm optimization (PCSO) method is an optimization algorithm designed to solve numerical optimization problems under the conditions of a small population size and a few iteration numbers. The Taguchi method is widely used in the industry for optimizing the product and the process conditions. By adopting the Taguchi method into the tracing mode process of the PCSO method, we propose the EPCSO method with better accuracy and less computational time. In this paper, five test functions are used to evaluate the accuracy of the proposed EPCSO method. The experimental results show that the proposed EPCSO method gets higher accuracies than the existing PSO-based methods and requires less computational time than the PCSO method. We also apply the proposed method to solve the aircraft schedule recovery problem. The experimental results show that the proposed EPCSO method can provide the optimum recovered aircraft schedule in a very short time. The proposed EPCSO method gets the same recovery schedule having the same total delay time, the same delayed flight numbers and the same number of long delay flights as the . The optimal solutions can be found by the proposed EPCSO method in a very short time. [Copyright &y& Elsevier]

Long short-term memory (LSTM) models provide high predictive performance through their ability to recognize longer sequences of time series data. More recently, bidirectional deep learning models (BiLSTM) have extended the LSTM capabilities by training the input data twice in forward and backward directions. In this paper, BiLSTM short term traffic forecasting models have been developed and evaluated using data from a calibrated micro-simulation model for a congested freeway in Melbourne, Australia. The simulation model was extensively calibrated and validated to a high degree of accuracy using field data collected from 55 detectors on the freeway. The base year simulation model was then used to generate loop detector data including speed, flow and occupancy which were used to develop and compare a number of LSTM models for short-term traffic prediction up to 60 min into the future. The modelling results showed that BiLSTM outperformed other predictive models for multiple prediction horizons for base year conditions. The simulation model was then adapted for future year scenarios where the traffic demand was increased by 25–100 percent to reflect potential future increases in traffic demands. The results showed superior performance of BiLSTM for multiple prediction horizons for all traffic variables. [ABSTRACT FROM AUTHOR]

This paper presents the development and evaluation of short-term traffic prediction models using unidirectional and bidirectional deep learning long short-term memory (LSTM) neural networks. The unidirectional LSTM (Uni-LSTM) model provides high performance through its ability to recognize longer sequences of traffic time series data. In this work, Uni-LSTM is extended to bidirectional LSTM (BiLSTM) networks which train the input data twice through forward and backward directions. The paper presents a comparative evaluation of the two models for short-term speed and traffic flow prediction using a common dataset of field observations collected from multiple freeways in Australia. The results showed BiLSTM performed better for variable prediction horizons for both speed and flow. Stacked and mixed Uni-LSTM and BiLSTM models were also investigated for 15-minute prediction horizons resulting in improved accuracy when using 4-layer BiLSTM networks. The optimized 4-layer BiLSTM model was then calibrated and validated for multiple prediction horizons using data from three different freeways. The validation results showed a high degree of prediction accuracy exceeding 90% for speeds up to 60-minute prediction horizons. For flow, the model achieved accuracies above 90% for 5- and 10-minute prediction horizons and more than 80% accuracy for 15- and 30-minute prediction horizons. These findings extend the set of AI models available for road operators and provide them with confidence in applying robust models that have been tested and evaluated on different freeways in Australia. [ABSTRACT FROM AUTHOR]

• The relationship among the users, helpers and server, is modeled as a cooperative protection system architecture. • Community structure based trilateral Stackelberg game model is proposed for achieving the collaboration ability. • The optimization method based on proposed model is designed by the backward induction process. • The security and the performances of proposed model and method are analyzed under different situations. Location-based services are widely used in mobile applications, which not only bring convenience, but also cause serious privacy concerns. Based on the characteristics of social network, this work proposes a cooperative protection architecture to model the relationship among users, communities and location-based service. Furthermore, in order to construct K anonymity set, a novel community structure-based trilateral Stackelberg game model is developed for K -anonymity protection. In addition, an optimization method based on the proposed model is designed by the backward induction process. Finally, the security and the performance under different situations such as the anonymity parameter K and the community structure parameter overlapping weights are analyzed. The analysis results indicate that the proposed model and the optimization method are effective for privacy protection and can achieve high secure in location-based services. [ABSTRACT FROM AUTHOR]

• Maximizing network lifetime will be cope with by resolving energy hole problem and preserving energy. • The energy hole problem is relieved by gradient deployment algorithm. • Based on the Choquet integral and Entropy, entropy-driven abnormal event monitoring algorithm is proposed. • Entropy-driven aggregation tree-based routing algorithm is designed for energy consumption. Data aggregation is one of the essential and fundamental processes in Wireless Sensor Networks (WSNs). When and how to gather data from the sensors to the sink has a direct impact on the lifetime of the WSNs because the energy consumption is proportion to the frequency of data transmission. In general, sensors in a WSN are randomly distributed for creating a massive coverage WSN environment within a short period. Because the sensors nearby the sink are responsible for more data forwarding tasks, they normally have much shorter lifetime than those located away from the sink. Once all sensors nearby the sink run out of energy, the data collected from the terminal sensors can not reach the sink because all established connections to the sink are broken, which is called the termination of a WSN lifetime. This paper proposes a strategy with multiple algorithms for deploying sensors aiming at maximizing a WSN lifetime. The proposed Entropy-driven Data Aggregation with Gradient Distribution (EDAGD) deployment strategy contains three algorithms: (1) multihop tree-based data aggregation, (2) entropy-driven aggregation tree-based routing algorithm and (3) gradient deployment algorithm. The numerical and experimental results indicating that the proposed EDAGD method outperforms the conventional algorithm with the random deployment strategy. [ABSTRACT FROM AUTHOR]

The wireless sensor network (WSN) is a system containing the event detection and the data gathering abilities. The data gathering mechanism is the fundamental but important procedure in the WSN environment. The way of the data gathering majorly affects the efficiency of WSNs on retrieving data at the sink node. It is generally known that the clustering techniques are effective to reduce the energy consumption in the WSNs. However, the research on the packet collision and the transmission delay in the Cluster based routing algorithm still remains limited. The packet loss and the transmission delay will happen more often due to collision and as such it will have negative impact on the WSN performance. In addition, the transmission delay phenomenon in the WSN may cause the inefficient result in the data gathering process. Unfortunately, it is usually neglected in the existing literature. To overcome the drawback of transmission delay and collision, a cluster-based converge cast tree (CCCT) protocol is proposed in this paper. The core of this protocol is to construct a cross cube spanning tree topology control algorithm. The proposed protocol performance is analyzed theoretically, which demonstrate that the protocol is efficient in avoiding packet collision and reducing the transmission delay. Finally, the protocol is examined by the simulations. The simulation results indicate that the proposed CCCT structure and algorithms outperform the existing approaches significantly in the realistic WSN environment. [ABSTRACT FROM AUTHOR]

Data gathering is a fundamental task in Wireless Visual Sensor Networks (WVSNs). Features of directional antennas and the visual data make WVSNs more complex than the conventional Wireless Sensor Network (WSN). The virtual backbone is a technique, which is capable of constructing clusters. The version associating with the aggregation operation is also referred to as the virtual backbone tree. In most of the existing literature, the main focus is on the efficiency brought by the construction of clusters that the existing methods neglect local-balance problems in general. To fill up this gap, Directional Virtual Backbone based Data Aggregation Scheme (DVBDAS) for the WVSNs is proposed in this paper. In addition, a measurement called the energy consumption density is proposed for evaluating the adequacy of results in the cluster-based construction problems. Moreover, the directional virtual backbone construction scheme is proposed by considering the local-balanced factor. Furthermore, the associated network coding mechanism is utilized to construct DVBDAS. Finally, both the theoretical analysis of the proposed DVBDAS and the simulations are given for evaluating the performance. The experimental results prove that the proposed DVBDAS achieves higher performance in terms of both the energy preservation and the network lifetime extension than the existing methods. [ABSTRACT FROM AUTHOR]

In this paper, a soft computing method, a probability theory, and a graph theoretic approach are utilized to describe this phenomenon. Uncertain random spectra, namely, natural tenacity, are established in this paper for assessing the survivability of MWSNs. We propose an elaborate mathematical formulation that relates the survivability to the natural tenacity. In addition, then the probability of link connectivity is proposed in order to connect the natural tenacity to the connectivity of the network, which reflects the most important indicators of network survivability. The natural tenacity of MWSNs is explored by both analytical and numerical ways. Moreover, we also compare it with other survivability measures. Under the smooth Gauss-semi-Markov mobility model, the effectiveness of the measure is verified through numerical analysis. The result indicates that the natural tenacity has a strong analytical ability to measure the survivability of MWSNs objectively. [ABSTRACT FROM AUTHOR]

The wireless sensor network (WSN) is composed of a set of sensor nodes. It is deemed suitable for deploying with large-scale in the environment for variety of applications. Recent advances in WSN have led to many new protocols specifically for reducing the power consumption of sensor nodes. A new scheme for predetermining the optimized routing path is proposed based on the enhanced parallel cat swarm optimization (EPCSO) in this paper. This is the first leading precedent that the EPCSO is employed to provide the routing scheme for the WSN. The experimental result indicates that the EPCSO is capable of generating a set of the predetermined paths and of smelting the balanced path for every sensor node to forward the interested packages. In addition, a scheme for deploying the sensor nodes based on their payload and the distance to the sink node is presented to extend the life cycle of the WSN. A simulation is given and the results obtained by the EPCSO are compared with the AODV, the LD method based on ACO, and the LD method based on CSO. The simulation results indicate that our proposed method reduces more than 35% power consumption on average. [ABSTRACT FROM AUTHOR]

To address the heterogeneity problem of sensor data, it is necessary to conduct the Sensor Ontology Matching (SOM) process to find the mappings among diverse sensor data with the same semantics connotation. Currently, many Multi-Objective Evolutionary Algorithms (MOEAs) have been used to match the ontologies, which aim at finding a set of solutions called Pareto Set (PS) in the Pareto Front (PF) to represent a set of trade-off proposals for different Decision Makers (DMs). Being inspired by the success of MOEA with Inverse Model (IM-MOEA) in solving complicated optimization problems, in this work, an Improved IM-MOEA (I-IM-MOEA)-based matching technique is further proposed to enhance the algorithm's matching efficiency as well as the alignment's quality. To overcome the drawback of IM-MOEA that has poor performance on irregular PF, an adjusted selection mechanism is employed to avert the massive reduction in non-domination solutions on irregular PF, a dynamic Reference Vectors (RVs) is used to decrease the computational resources and boost the efficiency of the algorithm, and a local search strategy is introduced to promote the results' quality. The experiment employs the benchmark provided by Ontology Alignment Evaluation Initiative (OAEI) and three sensor ontologies to assess the performance of I-IM-MOEA, and the experimental results show that I-IM-MOEA is both effective and efficient. [ABSTRACT FROM AUTHOR]

Recent years have seen the wide application of Location-Based Services (LBSs) in our daily life. Although users can enjoy many conveniences from the LBSs, they may lose their trajectory privacy when their location data are collected. Therefore, it is urgent to protect the user's trajectory privacy while providing high quality services. Trajectory k-anonymity is one of the most important technologies to protect the user's trajectory privacy. However, the user's attributes are rarely considered when constructing the k-anonymity set. It results in that the user's trajectories are especially vulnerable. To solve the problem, in this paper, a Spatiotemporal Mobility (SM) measurement is defined for calculating the relationship between the user's attributes and the anonymity set. Furthermore, a trajectory graph is designed to model the relationship between trajectories. Based on the user's attributes and the trajectory graph, the SM based trajectory privacy-preserving algorithm (MTPPA) is proposed. The optimal k-anonymity set is obtained by the simulated annealing algorithm. The experimental results show that the privacy disclosure probability of the anonymity set obtained by MTPPA is about 40% lower than those obtained by the existing algorithms while the same quality of services can be provided. [ABSTRACT FROM AUTHOR]

This research presents an optimization problem associated with scheduling of unrelated parallel machines considering the simultaneous effects of start-time-related deterioration, position-related learning and sequence-related setup times, with the aim to achieve an optimized value for the mean weighted tardiness and power consumption minimization. A workaround is proposed and served as the converter for solving the Mixed Integer Programming (MIP) problem by the continuous-based metaheuristic algorithms. Knowing the NP-hard nature of the problem, three metaheuristic algorithms, namely, Genetic Algorithm (GA), Cat Swarm Optimization (CSO) and Interactive Artificial Bee Colony (IABC), are employed to obtain quality solutions within acceptable computation time. A unique elitism strategy is introduced in the conventional CSO to reduce the computational time of its seeking mode and to provide improved solution, with the new algorithm form called CSO-Elit. Furthermore, a compact solution expression form is proposed for the use of GA, CSO-Elit and IABC to reduce the number of constraints. With the newly proposed model, many essential constraints are automatically built into the solution representation. Accordingly, the efficiency of algorithms is compared with LINGO regarding the quality of solutions, computational time, and overall power consumption using two sets of problems in small and large scales. In solving the small-scale cases, no statistically meaningful difference was observed between LINGO, GA and CSO-Elit, while in large-scale instances CSO-Elit demonstrated higher performance. The contributions of this paper lie in modeling a complex parallel machines problem considering power consumption minimization and proposing a new form of CSO algorithm with elitism strategy to reduce the computational time. • A novel problem has been identified and explored in this paper. • The model includes mathematical modeling and optimization of an unrelated parallel machine scheduling problem. • Several restrictions which are practical and industrially relevant are considered in this paper. • A new metaheuristic approach by adopting an elitism strategy into a metaheuristic algorithm is proposed. [ABSTRACT FROM AUTHOR]