Wireless sensor networks (WSNs) are networks of distributed autonomous devices that can sense or monitor physical or environmental conditions cooperatively. WSNs face many challenges, mainly caused by communication failures, storage and computational constraints and limited power supply. Paradigms of computational intelligence (CI) have been successfully used in recent years to address various challenges such as data aggregation and fusion, energy aware routing, task scheduling, security, optimal deployment and localization. CI provides adaptive mechanisms that exhibit intelligent behavior in complex and dynamic environments like WSNs. CI brings about flexibility, autonomous behavior, and robustness against topology changes, communication failures and scenario changes. However, WSN developers are usually not or not completely aware of the potential CI algorithms offer. On the other side, CI researchers are not familiar with all real problems and subtle requirements of WSNs. This mismatch makes collaboration and development difficult. This paper intends to close this gap and foster collaboration by offering a detailed introduction to WSNs and their properties. An extensive survey of CI applications to various problems in WSNs from various research areas and publication venues is presented in the paper. Besides, a discussion on advantages and disadvantages of CI algorithms over traditional WSN solutions is offered. In addition, a general evaluation of CI algorithms is presented, which will serve as a guide for using CI algorithms for WSNs.

During the past few years, Wireless Sensor Networks (WSNs) have become widely used due to their large amount of applications. The use of WSNs is an imperative necessity for future revolutionary areas like ecological fields or smart cities in which more than hundreds or thousands of sensor nodes are deployed. In those large scale WSNs, hierarchical approaches improve the performance of the network and increase its lifetime. Hierarchy inside a WSN consists in cutting the whole network into sub-networks called clusters which are led by Cluster Heads. In spite of the advantages of the clustering on large WSNs, it remains a non-deterministic polynomial hard problem which is not solved efficiently by traditional clustering. The recent researches conducted on Machine Learning, Computational Intelligence, and WSNs bring out the optimized clustering algorithms for WSNs. These kinds of clustering are based on environmental behaviors and outperform the traditional clustering algorithms. However, due to the diversity of WSN applications, the choice of an appropriate paradigm for a clustering solution remains a problem. In this paper, we conduct a wide review of proposed optimized clustering solutions nowadays. In order to evaluate them, we consider 10 parameters. Based on these parameters, we propose a comparison of these optimized clustering approaches. From the analysis, we observe that centralized clustering solutions based on the Swarm Intelligence paradigm are more adapted for applications with low energy consumption, high data delivery rate, or high scalability than algorithms based on the other presented paradigms. Moreover, when an application does not need a large amount of nodes within a field, the Fuzzy Logic based solution are suitable.

This work is a survey on the usage of machine learning techniques in wireless sensor networks (WSNs) and mobile ad-hoc networks (MANETs). Its focus lies on approaches for data routing. The goal of the work is two-fold: first, to classify and evaluate the most important existing and on-going research in the area and, second, to provide a guide for researchers wanting to apply machine learning techniques. For this, it also gives short description of the most appropriate algorithms and suggests for which scenarios they can be best used.

Simulation is one of the most powerful tools we have for evaluating the performance of Opportunistic Networks. In this survey, we focus on available tools and models, compare their performance and precision and experimentally show the scalability of different simulators. We also perform a gap analysis of state-of-the-art Opportunistic Network simulations and sketch out possible further development and lines of research. This survey is targeted at students starting work and research in this area while also serving as a valuable source of information for experienced researchers.

Despite a large number of applications in the field of health care, military, ecology or agriculture, the Wireless Underground Sensor Network (WUSN) faces the problem of wireless Underground Communication (WUC) which largely attenuate the signal on the ground. For the case of precision agriculture, the motes are buried and they have to check the good growth of plants by verifying data like the water content. However, due to soil composition, the wave signal is attenuated as it travels across the ground. Thus, before a real deployment of WUSN, the prediction of the path loss due to signal attenuation underground is an important asset for the good network functioning. In this paper, we proposed a WUSN path loss for precision agriculture called WUSN-PLM. To achieve it, the proposed model is based on an accurate prediction of the Complex Dielectric Constant (CDC). WUSN-PLM allows evaluating the path loss according to the different types of communication (Underground-to-Underground, Underground to Aboveground and Aboveground to Underground). On each communication type, WUSN-PLM takes into account reflective and refractive wave attenuation according to the sensor node burial depth. To evaluate WUSN-PLM, intensive measurements on real sensor nodes with two different pairs of transceivers have been conducted on the botanic garden of the University Cheikh Anta Diop in Senegal. The results show that the proposed model outperforms the existing path loss models in different communication types. The results show that our proposed approach can be used on real cheap sensor with 87.13% precision and 85% balanced accuracy.

The number of vehicles on the road increases daily, causing many fatal accidents and wasting much time for the average commuter every day due to congestion. Vehicular ad hoc networks (VANETs) were introduced to overcome these issues by enabling vehicle-to-vehicle communication and vehicle-to-infrastructure communication. The prime challenge in VANETs is the necessity of very low communication delays, especially for safety-related applications due to the high mobility nature of vehicles. The VANET architecture introduces a network component, the Road Side Unit (RSU), to meet the required delay limitations. Even though the RSU is a critical component in VANETs, as expected, the RSUs were not deployed throughout the world because of their high investment cost. As a solution, the idea of mobile RSU (mRSU) was introduced, and, ever since, several techniques of mRSU deployment strategies have been proposed. In this survey, we first analyze the importance of the RSU to the VANET architecture with real-world data incorporating the new 5G standard. Then, we investigate the research done in the areas of mRSU and exploit the pros and cons of each mRSU deployment strategy. Finally, we also discuss the future research directions of mRSU, and we explain the challenges connected to these future trends.