Soft Computing

Region of interest (ROI) discovery is among the most common functions in location-based social networking services (LBSNS). While former researches mainly utilize the accurate location coordinates history, the road context-based active region extraction algorithm (RAREA) proposed in this paper explores the method to extract those regions with road contexts. Furthermore, based on the active regions extracted by RAREA, the kNN consistency-based relationship recommendation algorithm (kNNC-RRA) is proposed as well. The kNNC-RRA compares the similarity degree of the active regions among the users to find the individuals with similar preferences to recommend the potential relationships. Experimental results illustrate that by analyzing the characteristics of those road contexts, ROIs are able to be discovered with high efficiency. And our work shows that both privacy protection and personalized services can be achieved in LBSNS.

The fundamental criteria for industrial manipulator applications are vibration free and smooth motion with minimum time. This paper investigates the trajectory tracking and vibration control of rotary flexible joint manipulator with parametric uncertainties. Firstly, the dynamic modeling via Euler Lagrange equation for a single link flexible joint manipulator is discussed. Secondly, for the execution of smooth motion between two points, bounded and continuous jerk trajectory is developed and implemented. In addition, the prospective strategy uses the concatenation of fifth-order polynomials to provide a smooth trajectory between two-way points. In the planned algorithm, user can independently define the position, velocity, acceleration and jerk values at both initial and final positions. The feature of user-defined parameters gives the versatility to the suggested algorithm for generating trajectories for diverse applications of robotic manipulators. Moreover, the planned scheme is easy to implement and computationally efficient. In the last, the performance of the presented scheme is examined by comparison with cubic splines and a linear segment with parabolic blends (LSPB) techniques. Generated trajectories were evaluated successfully by carrying multiple experiments on QUANSER’s flexible joint manipulator.

Massive Open Online Courses are gaining popularity with millions of students enrolled, thousands of courses available and hundreds of learning institutions involved. Due to the high number of students and the relatively small number of tutors, student assessment, especially for complex tasks, is a typical issue of such courses. Thus, peer assessment is becoming increasingly popular to solve such a problem and several approaches have been proposed so far to improve the reliability of its outcomes. Among the most promising, there is fuzzy ordinal peer assessment (FOPA) that adopts models coming from fuzzy set theory and group decision Making. In this paper we propose an extension of FOPA supporting multi-criteria assessment based on rubrics. Students are asked to rank a small number of peer submissions against specified criteria, then provided rankings are transformed in fuzzy preference relations, expanded to obtain missing values and aggregated to estimate final grades. Results obtained are promising if compared to other peer assessment techniques both in the reconstruction of the correct ranking and on the estimation of students’ grades.

Cloud storage brings strong conveniences for flexible data sharing. When sharing data with a large number of entities described with fuzzy identities, the data owners must leverage a suitable encryption scheme to meet the security and efficiency requirements. (hierarchical) Identity-based encryption is a promising candidate to ensure fuzzy-entity data sharing while meeting the security requirement, but encounters the efficiency difficulty in multireceiver settings. We introduce the notion of subtree-based encryption (SBE) to support multireceiver data sharing mechanism in large-scale enterprises. Users in SBE are organized in a tree structure. Superior users can generate the secret keys to their subordinates. Unlike HIBE merely allowing a single decryption path, SBE enables encryption for a subset of users. We define the security notion for SBE, namely Ciphertext Indistinguishability against Adaptively Chosen-Sub-Tree and Chosen-Ciphertext Attack (IND-CST-CCA2). We propose two secure SBE schemes (SBEs). We first propose a basic SBEs against Adaptively Chosen-Sub-Tree and Chosen-Plaintext Attack (IND-CST-CPA), in which we do not allow the attacker to get decryption results from other users in the security game. We then propose a CCA2-secure SBEs from the basic scheme without requiring any other cryptographic primitives. Our CCA2-secure scheme natively allows public ciphertext validity test, which is a useful property when a CCA2-secure SBEs is used to design advanced protocols and auditing mechanisms for fuzzy-entity data sharing.

As the technology makes progress towards the era of fifth generation (5G) communication networks, energy efficiency (EE) becomes an significant design criterion, because it guarantees sustainable evolution. In this regard, the massive multiple-input multiple-output (MIMO) technology, where the base stations are outfitted with enormous count of antennas so as to reach multiple orders of spectral and energy efficiency gains, will be a fundamental technology enabler for 5G. This paper plans to implement a massive MIMO model considering the spectral efficiency (SE) and EE. Here, the main goal is to generate the optimal solutions for beam-forming vectors and power allocation. The optimal solution is formed in such a way that both the SE and EE are maximum through resource efficiency metric model. The beam-forming vectors and power allocations are generated by two modified meta-heuristic algorithm to frame a valuable analysis. The first algorithm uses the modified grey wolf optimization (GWO) termed as improved random vector-based GWO (IRV-GWO), and the second algorithm uses the modified lion algorithm (LA) termed as improved random vector-based LA (IRV-LA). Both the algorithms have the ability to solve the complex optimization problems under different applications with respect to better convergence rate, which in turn performs well for pertaining better trade-off between the SE and EE in massive MIMO technology.

Hesitant fuzzy set (HFS) takes several possible values as the membership degree of an element to a set to express the decision makers’ hesitance when making decisions. Since its appearance, the HFS has been widely used in many fields, such as decision making, clustering analysis. Lukasiewicz implication operator, an indispensable part of implication operators, can grasp more nuances compared with the others. In this paper, we shall combine the Lukasiewicz implication operator with HFSs to realize a direct clustering analysis algorithm and a novel alternative sorting method in decision making under hesitant fuzzy environment. To do that, we first apply the Lukasiewicz implication operator to deal with HFEs by getting a hesitant fuzzy Lukasiewicz implication operator, and then construct a hesitant fuzzy triangle product and a hesitant fuzzy square product based on the new implication operator. After that, the hesitant fuzzy square product is applied to define the similarity degree between HFSs, and based on which, we develop a direct clustering algorithm for hesitant fuzzy information. Meanwhile, the hesitant fuzzy triangle product is used to induce a new alternative sorting method. Finally, two numerical examples are given to illustrate the effectiveness and practicability of our method and algorithm, one of which involves the evaluation analysis of the Arctic development risk.

A biomedical application of a novel metaheuristic optimizer is proposed in this paper by constructing an enhanced arithmetic optimization algorithm (AOA). The latter algorithm was constructed using the logarithmic spiral (Ls) search mechanism from the whale optimization algorithm and the greedy selection scheme from the differential evolution algorithm. The proposed algorithm (Ls-AOA) was tested against unimodal and multimodal benchmark functions and demonstrated better capability comparatively using other efficient metaheuristic algorithms reported in the literature. The constructed Ls-AOA algorithm was then proposed to design a proportional-integral-derivative (PID) controller employed in a functional electrical stimulation (FES) system for the first time. The initial statistical and convergence profile assessment showed better performance of the proposed algorithm. The comparative analyses for transient and frequency responses were performed for the PID-controlled FES system using the original AOA, sine–cosine and particle swarm optimization algorithms and the traditional Ziegler-Nichols tuning scheme. Similarly, the FES system tuned with the latter methods was also assessed for disturbance rejection and noise elimination. All the comparative analyses demonstrated that the proposed Ls-AOA has the greater capability for the challenging biomedical FES system.

Efficient clustering is a well-documented NP-hard optimization problem in wireless sensor networks (WSNs). Variety of computational intelligence techniques including evolutionary algorithms, reinforcement learning, artificial immune systems and recently, artificial bee colony (ABC) metaheuristic have been applied for efficient clustering in WSNs. Due to ease of use and adaptive nature, ABC arose much interest over other population-based metaheuristics for solving optimization problems in WSNs. However, its search equation contributes to its insufficiency due to comparably poor exploitation cycle and requirement of certain control parameters. Thus, we propose an improved artificial bee colony (iABC) metaheuristic with an improved solution search equation to improve exploitation capabilities of existing metaheuristic. Further, to enhance the global convergence of the proposed metaheuristic, an improved population sampling technique is introduced through Student’s t-distribution, which require only one control parameter to compute and store and therefore increase efficiency of proposed metaheuristic. The proposed metaheuristic maintain a good balance between exploration and exploitation search abilities with least memory requirements; moreover, the use of first-of-its-kind compact Student’s t-distribution makes it suitable for limited hardware requirements of WSNs. Additionally, an energy-efficient clustering protocol based on iABC metaheuristic is presented, which inherits the capabilities of the proposed metaheuristic to obtain optimal cluster heads along with an optimal base station location to improve energy efficiency in WSNs. Simulation results show that the proposed clustering protocol outperforms other well-known protocols on the basis of packet delivery, throughput, energy consumption, network lifetime and latency as performance metric.

Recent advancements in Wireless Sensor Networks (WSNs) have generated interest in the field of sensor tracking events. Environment monitoring is one of the most important applications where WSNs are used to guarantee animal conservation. WSNs are crucial in many real-time applications because of their topology, scale, and communication capabilities. Data privacy and integrity are the essential components that maximize network, security, trust and effectiveness. The fundamental problem with WSNs is the effective communication and efficient information to exchange among several sensor nodes for extending the lifespan of the WSNs. Moreover, along with these issues, it is necessary to measure an optimal path with the highest degree of reliability. In this paper, a new fuzzy and secured clustering algorithm is proposed for improving the energy efficiency and security. In the proposed method, clustering is proved to be the best strategy for energy management of WSN metrics because it uses trust-based fuzzy logic to find malicious nodes and selecting reliable path for data communication. The simulation results show that the proposed technique detects progress in relation to a variety of performance parameters. When compared to other various existing protocols, the proposed work reduces energy consumption while increasing network lifetime and security of communication.