Summary In a vehicular ad‐hoc network (VANET), each vehicle is equipped with an on‐board unit to communicate vehicle to vehicle or vehicle to fixed infrastructure. VANET technology is offered to provide many facilities to passengers and drivers, including safety, entertainment, mobile commerce, driver assistance, and emergency alarms. VANET has unique features such as high‐speed node mobility and network topology dynamics. These special features cause many problems such as increased transmission delays and packet loss. On the other hand, providing a good routing plan for VANET is a critical issue. Therefore, this article proposes a cluster‐based routing using in‐vehicle meta‐heuristic algorithms (CRMHA‐VANET) which has two phases. In the first stage, the vehicles are clustered and the most suitable cluster head (CH) is selected using the gray wolf optimization algorithm (GWO). In the next step, the next suitable CH is selected for data transmission in direct paths using the technique for order of preference by similarity to ideal solution (TOPSIS). The performance of the proposed method is analyzed through several criteria such as package delivery rate, end‐to‐end delay and throughput. CRMHA‐VANET results in a 10% to 25% improvement over all performance metrics, that is, packet delivery rate, latency, and throughput, over CRBP (clustering routing based on PSO [particle swarm optimization]), WCV (weight based clustering for VANET), and AODV‐CD methods.

Internet of things (IoT) is a ubiquitous network that helps the system to monitor and organize the world through processing, collecting, and analyzing the data produced by IoT objects. The accurate localization of IoT objects is indispensable for most IoT applications, especially healthcare monitoring. Utilizing GPS as the positioning system is not cost-efficient and does not apply to some environments (e.g., deep forests, oceans, inside the buildings, etc.). Hereupon, copious position estimation approaches are developed in the literature. Among range-free approaches, distance vector-Hop (DV-Hop) is the widely used algorithm due to its straightforward applicability and can estimate the position of unknown objects that are far-off the anchors. Due to its low accuracy, various techniques were proposed to increase the accuracy of basic DV-Hop. In the most recent approach, meta-heuristic algorithms were used, the results of which were promising. In the present paper, Tunicate Swarm Algorithm and Harris hawk optimization were initially hybridized. Afterthought, the resulting hybrid algorithm was enhanced by appending a new phase. Then, the proposed hybrid algorithm was intermingled with the DV-Hop algorithm. In the first set of experiments, the proposed hybrid algorithm was evaluated on 50 test functions using average, SD, box plot, and p-value criteria. In the second part, the proposed localization algorithm's efficiency was investigated in twenty-eight different manners using node localization error, average localization error, and localization error variance metrics. The effectiveness of the contributions was evident from the experimental results.

Abstract Different nature‐inspired meta‐heuristic algorithms have been proposed to solve optimization problems. One of these algorithms is called social spider optimization (SSO) algorithm. Spiders' natural behaviors have inspired them to find the bait position by detecting vibrations in their web. Although the SSO algorithm has good accuracy in achieving optimal solutions, it suffers from a low convergence rate. In this paper, we attempted to improve SSO by changing its motion and mating parameters. To provide a practical example of using the new proposed algorithm, we based it on multi‐objective opposition‐based SSO, named MOPSSO. We used this algorithm in a feature selection process for analyzing text psychology, which is a multi‐objective problem. Textual psychology analysis is used in various fields, including collecting and analyzing people's views on various products, topics, social and political events. After selecting features, in order to classify the text, we used a new hybrid method that hybrids fuzzy C‐MEANS data clustering technique, a decision tree (DT), and Naïve Bayes (NB). Experimental results show that the improved SSO algorithm performs better than SSO, social spider algorithm, and CMA‐ES algorithms. Additionally, the performance of the proposed hybrid classification method is better than those of NB and DT.

Abstract One of the useful knowledge representation tools, which can describe the joint probability distribution between some random variables with a graphical model and can be trained by a dataset, is the Bayesian network (BN). A BN is composed of a network structure and a conditional probability distribution table for each node. Discovering an optimal BN structure is an NP‐hard optimization problem that various meta‐heuristic algorithms are applied to solve this problem by researchers. The genetic algorithms, ant colony optimization, evolutionary programming, artificial bee colony, and bacterial foraging optimization are some of the meta‐heuristic methods to solve this problem using a dataset. Most of these methods are applying a scoring metric to generate the best network structure from a set of candidates. A Fixed Structure Learning Automata‐Based (FSLA‐B) algorithm is presented in this paper to solve the structure learning problem of BNs. There is a fixed structure learning automaton for each pair of vertices in the BN's graph structure in the proposed algorithm. The action of this automaton determines the presence and direction of an edge between the vertices. The proposed algorithm performs a guided search procedure using the FSLA and escapes from local optimums. Several datasets are utilised in this paper to evaluate the performance of the proposed algorithm. By performing various experiments, multiple meta‐heuristic algorithms are compared with the introduced new one. The obtained results represented that the proposed algorithm could produce competitive results and find the near‐optimal solution for the BN structure learning problem.

Abstract The particle swarm optimization (PSO) and the whale optimization algorithm (WOA) are two admired optimization methods that have drawn various researchers' attention. The PSO implements some particles' intelligent movements in a search space, and the WOA is originated based on the hunting mechanism of humpback whales. The PSO and WOA have different strategies for moving towards the optimum solution. Nevertheless, both algorithms' performances encounter several problems, such as premature convergence and falling in local optimums. Several approaches have been proposed to enhance meta‐heuristic algorithms' performance, such as applying the chaotic maps, adding mathematical or stochastic operators or local searches, and hybridizing the algorithms. In this article, a new hybrid algorithm denoted as chaotic‐based hybrid whale and PSO has been presented by improving the WOA, combining it with PSO, and using the chaotic maps. The hybrid algorithm has significantly more diverse movements than both of the mentioned algorithms. Therefore, it explores different regions of a problem's search space more precisely and avoids local optima. The roulette wheel selection operator has also been applied based on their fitness value to select the proposed algorithm's search agents and exploit promising regions of the search space. In the hybrid algorithm, the chaotic maps have been applied to initialize the whales' population, particles of the particle swarm, and adjust motion parameters to increase population diversity. The multi‐swarm version of the proposed algorithm with higher performance than the single‐swarm version and other methods has been introduced in this article too. The proposed algorithms have been evaluated using 23 mathematical benchmark functions, including unimodal, multimodal, and composite functions and four engineering optimization problems. The obtained results and statistical tests prove that the proposed algorithms provide competitive solutions for most of the experiments, compared to the state‐of‐the‐art and well‐known optimization meta‐heuristic methods in terms of convergence towards the global optimum, local optima avoidance, exploration, and exploitation.

Abstract WBANs are of the promising technologies, applied in the e‐healthcare systems, for extracting and transferring critical medical data from patient body. In WBANs, the privacy and integrity of the patient's private medical data are very important, as a result, the WBANs communications and even communication with other E‐Healthcare components should be authenticated and secured. This paper provides a complete survey and analysis of the various authentication schemes proposed in the literature to improve the WBANs security. Besides, it classifies the proposed authentication schemes based on the applied techniques for authentication and illustrates each scheme in detail. Furthermore, it highlights the advantages and limitations of the authentication schemes and presents a comprehensive comparison of their capabilities and features. Finally, the paper concludes with open issues and future research directions. Copyright © 2016 John Wiley & Sons, Ltd.

Denial-of-service DoS attacks are one of the major security challenges in the emerging cloud computing models. Currently, numerous types of DoS attacks are conducted against the various cloud services and resources, which target their availability, service level agreements, and performance. This paper presents an in-depth study of the various types of the DoS attacks proposed for the cloud computing environment and classifies them based on the cloud components or services, which they target. Besides, it provides a comprehensive analysis of the vulnerabilities utilized in these DoS attacks and investigates about the state-of-the-art solutions presented in the literature to prevent, detect, or deal with each kind of DoS attacks in the cloud. Finally, it presents open research issues. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract Freshness of certificate status information is very important in validating public key certificates. However, existing certificate validation schemes suffer from high inconsistency, which makes network vulnerable to various security attacks. In this paper, we propose a new publish/subscribe‐based certificate validation scheme that provides fresh certificate status information in the mobile ad hoc networks. This scheme increases the security of public key infrastructure‐based security systems and improves the scalability of certificate validation systems. Also, according to the simulation results, our solution effectively decreases the processing overheads on responder nodes and reduces the messaging overhead of certificate status validations in mobile ad hoc networks. Copyright © 2014 John Wiley & Sons, Ltd.