These days, a sizable number of meta-heuristic algorithms are utilized to address many problems with numerous variables and huge complexity. One of the most popular swarm intelligence-based meta-heuristic methods is the chimp optimization algorithm inspired by chimps’ individual intelligence and sexual motivation in their group hunting. This paper proposes a weighted chimp optimization algorithm to tackle two main issues in large-scale numerical optimization problems, such as low convergence speed and local optima trapping to solve high-dimensional problems. The main difference between the weighted and standard chimp optimization algorithms is that a position-weighted equation is offered to enhance convergence speed and avoid local optima. Moreover, the balance between exploration and exploitation is carried out in the proposed method that is crucial in the swarm intelligence-based algorithms. The presented weighted chimp optimization algorithm method is evaluated in different conditions to prove that it is the best. For this purpose, a classical set of 30 unimodal, multimodal, and fixed-dimension multimodal benchmark functions is applied to investigate the pros and cons of characteristics of the weighted chimp optimization algorithm. Besides, the proposed algorithm is tested on the IEEE congress of evolutionary computation benchmark test functions. In order to shed more light on probing the performance of the weighted chimp optimization algorithm in large-scale numerical optimization and real-world problems, it is examined by 13 high-dimensional and ten real-world optimization problems. The results show that the suggested algorithm outperforms in terms of convergence speed, the probability of getting stuck in local minimums, exploration, and exploitation compared to state-of-the-art methods in the literature. Source codes are publicly available at https://se.mathworks.com/matlabcentral/fileexchange/99344-a-weighted-chimp-optimization-algorithm.

Water is a vital resource for life, and for the economy. Nowadays, one of the most serious challenges to solve is to manage the water scarcity. Current water management ICT systems are supported by specific vendor equipment, without considering any interoperability standards. The lack of standardization among producer's water ICT equipment hinders proper monitoring and control systems, resulting in low efficiency in water distribution and consumption, system's maintenance and improvement, and failure identification. In this paper we propose a smart water management model integrating Internet of Things technologies for decoupling decision support systems and monitoring from business processes coordination and subsystem implementation. The proposed smart water management model makes specific vendor equipment interoperable and manageable in a water management domain in a homogeneous way.

Resource consumption in residential areas requires novel contributions in the field of consumer information management and collaborative mechanisms for the exchange of resources, in order to optimize the overall consumption of the community. We propose an authorization system to facilitate access to consumer information and resource trading, based on blockchain technology. Our proposal is oriented to the Smart communities, an evolution of Community Energy Management Systems, in which communities are involved in the monitoring and coordination of resource consumption. The proposed environment allows a more reliable management of monitoring and authorization functions, with secure data access and storage and delegation of controller functions among householders. We provide the definition of virtual assets for energy and water resource sharing as an auction, which encourages the optimization of global consumption and saves resources. The proposed solution is implemented and validated in application scenarios that demonstrate the suitability of the defined consensus mechanism, trustworthiness in the level of provided security for resource monitoring and delegation and reduction on resource consumption by the resource trading contribution.

Vehicle-to-grid (V2G) technology has become a promising concept for the near future smart grid eco-system. V2G improves smart grid resiliency by enabling two-way communication and electricity flows while reducing the greenhouse gases emission. V2G practicality and stability is strongly based on the exchanged data between electrical vehicles (EVs) and the grid server (GS). However, using communication protocols to exchange vital information leads grid to being vulnerable against various types of attack. To prevent the well-known attacks in V2G network, this paper proposes a privacy-aware authentication scheme that ensures data integrity, confidentiality, users’ identity and location privacy, mutual authentication, and physical security based on physical unclonable function (PUF). Furthermore, the performance analysis shows that the proposed scheme outperforms the state-of-the-art, since EVs only use lightweight cryptographic primitives for every protocol execution.

In this paper, we explore the communication performance of vehicle-to-vehicle (V2V) networks assisted by a reconfigurable intelligent surface (RIS) and a simultaneous transmitting and reflecting intelligent omni-surface (STAR-IOS) under non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) schemes. In particular, we consider that the RIS is close to the transmitter vehicle while the STAR-IOS is near the receiver vehicles. In addition, it is assumed that the STAR-IOS exploits the energy-splitting (ES) protocol for communication and the fading channels between the RIS and STAR-IOS follow composite Fisher-Snedecor $\mathcal {F}$ distribution. Under such assumptions, we first use the central limit theorem (CLT) to derive the PDF and the CDF of equivalent channels at receiver vehicles, and then, we derive the closed-form expression of outage probability (OP) under NOMA/OMA scenarios. Additionally, by exploiting Jensen's inequality, we propose an upper bound of the ergodic capacity (EC) and subsequently develop an analytical expression of the energy efficiency (EE) for both NOMA and OMA cases. Further, our analytical results, which are double-checked with the Monte-Carlo simulation, reveal that applying RIS/STAR-RIS in V2V communications significantly enhances the communication performance of intelligent transportation systems (ITS). Besides, the results indicate that considering the NOMA scheme provides better performance in terms of the OP, EC, and EE as compared with the OMA case for the considered V2V communication system.

The Internet of Things (IoT) has become one of the most important concepts in various aspects of our modern life in recent years. However, the most critical challenge for the world-wide use of the IoT is to address its security issues. One of the most important tasks to address the security challenges in the IoT is to detect intrusion in the network. Although the machine/deep learning-based solutions have been repeatedly used to detect network intrusion through recent years, there is still considerable potential to improve the accuracy and performance of the classifier (intrusion detector). In this paper, we develop a novel training algorithm to better tune the parameters of the used deep architecture. To specifically do so, we first introduce a novel neighborhood search-based particle swarm optimization (NSBPSO) algorithm to improve the exploitation/exploration of the PSO algorithm. Next, we use the advantage of NSBPSO to optimally train the deep architecture as our network intrusion detector in order to obtain better accuracy and performance. For evaluating the performance of the proposed classifier, we use two network intrusion detection datasets named UNSW-NB15 and Bot-IoT to rate the accuracy and performance of the proposed classifier.