The paradigm of Internet of Things (IoT) is paving the way for a world, where many of our daily objects will be interconnected and will interact with their environment in order to collect information and automate certain tasks. Such a vision requires, among other things, seamless authentication, data privacy, security, robustness against attacks, easy deployment, and self-maintenance. Such features can be brought by blockchain, a technology born with a cryptocurrency called Bitcoin. In this paper, a thorough review on how to adapt blockchain to the specific needs of IoT in order to develop Blockchain-based IoT (BIoT) applications is presented. After describing the basics of blockchain, the most relevant BIoT applications are described with the objective of emphasizing how blockchain can impact traditional cloud-centered IoT applications. Then, the current challenges and possible optimizations are detailed regarding many aspects that affect the design, development, and deployment of a BIoT application. Finally, some recommendations are enumerated with the aim of guiding future BIoT researchers and developers on some of the issues that will have to be tackled before deploying the next generation of BIoT applications.

A Review on the Use of Blockchain for the Internet of Things

In recent years, wireless sensor networks (WSNs) have played a major role in applications such as tracking and monitoring in remote environments. Designing energy efficient protocols for routing of data events is a major challenge due to the dynamic topology and distributed nature of WSNs. Main aim of the paper is to discuss hierarchical routing protocols in order to improve the energy efficiency and network lifetime. This paper provides a discussion about hierarchical energy efficient routing protocols based on classical and swarm intelligence approach. The routing protocols belonging to both categories can be summarized according to energy efficiency, data aggregation, location awareness, QoS, scalability, load balancing, fault tolerance, query based and multipath. A systematic literature review has been conducted for hierarchical energy efficient routing protocols reported from 2012 to 2017. This survey provides a technical direction for researchers on how to develop routing protocols. Finally, research gaps in the reviewed protocols and the potential future aspects have been discussed.

Comprehensive review for energy efficient hierarchical routing protocols on wireless sensor networks

Communication protocols for wireless sensor networks: A survey and comparison

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.

https://www.mdpi.com/1424-8220/19/2/322/pdf

Optimized Clustering Algorithms for Large Wireless Sensor Networks: A Review.

Reconnaissance mission has a wide application in both civil and military fields, which provides intelligence and basis for the following decision-making to accomplish certain goals. Due to numerous advantages of UAV swarms such as strong flexibility, high efficiency, and low cost, conducting reconnaissance missions by UAV swarms has become a trend of future. However, the path planning problem of UAV swarms is a key challenge in the aspect of model construction, algorithm, selection and high computational complexity, especially when the mission is complicated. In this paper, various distributed particle swarm optimization (DPSO)-based path planning algorithms are proposed for UAV swarms conducting a reconnaissance mission, in which targets are gathered in the form of clusters and different tactic needs are taken into consideration. Three algorithms named the maximum density convergence DPSO algorithm (MDC-DPSO), the fast cross-over DPSO algorithm (FCO-DPSO), and the accurate coverage exploration DPSO algorithm (ACE-DPSO) are proposed correspond to the needs of fast convergence, random cross-over, and accurate search, respectively. Different fitness functions and search strategies are specifically designed considering the mobility and communication constraints of the UAV swarms. Besides, the jump-out mechanism and revisit mechanism are designed to save invalid search efforts and avoid falling into local optimum. The simulation results demonstrate that the proposed algorithms are effective in generating paths for UAV swarms conducting a reconnaissance mission, which can be easily applied to large scale swarms.

/pdf/reconnaissance-mission-conducted-by-uav-swarms-based-on-57m2kp7zoa.pdf

Reconnaissance Mission Conducted by UAV Swarms Based on Distributed PSO Path Planning Algorithms

Swarm Intelligence techniques have been widely used in the science and engineer domains such as Mobile Ad Hoc Networks (MANETs) and Wireless Sensor Networks (WSNs). It is a relatively novel and promising field, focusing on the adaptation of collective behaviors of various natural creatures like ants, fish, birds, and honey bees, and a large number of routing protocols for WSNs have been developed according to the inspiration from the foraging behaviors of these species. In this paper, we first discuss the general principle of swarm intelligence and survey the research efforts on these SI based protocols according to various promising meta-heuristics. In the second part of the paper, we present the properties of termite colony optimization and fission-fusion social structure based spider monkey optimization based clustering in WSNs. Last, we conclude the paper with a comparative analysis, pointing out the fundamental issues and potential future directions.

Survey on swarm intelligence based routing protocols for wireless sensor networks: An extensive study

With rapid development, wireless sensor networks (WSNs) have been focused on improving the performance consist of energy efficiency, communication effectiveness, and system throughput. Many novel mechanisms have been implemented by adapting the social behaviors of natural creatures, such as bats, birds, ants, fish and honeybees. These systems are known as nature inspired systems or swarm intelligence in in order to provide optimization strategies, handle large-scale networks and avoid resource constraints. Spider monkey optimization (SMO) is a recent addition to the family of swarm intelligence algorithms by structuring the social foraging behavior of spider monkeys. In this paper, we aim to study the mechanism of SMO in the field of WSNs, formulating the mathematical model of the behavior patterns which cluster-based Spider Monkey Optimization (SMO-C) approach is adapted. In addition, our proposed methodology based on the Spider Monkey's behavioral structure aims to improve the traditional routing protocols in term of low-energy consumption and system quality of the network.

A novel cluster-based routing protocol wireless sensor networks using Spider Monkey Optimization

Wireless Sensor Networks (WSNs) have arisen as one of the hottest and most promising research areas nowadays, where one most critical research challenge is to extend the network lifetime, as in most cases sensor nodes can hardly be replaced with new batteries or recharged regularly. As a result, WSNs are often divided into clusters to reduce communication cost and energy consumption for packet routing, and the selection of cluster head can thus have great impacts on the overall network lifetime citeyang2018researchRecently, monkey-inspired off optimization (MO), as one of the latest members to the Swarm Intelligence (SI) family, has claimed its dedication to design smart, cost efficient yet simple and deployable clustering routing protocols for WSNs. In this paper, we conduct a detailed investigation on cluster head selection for such MO based routing protocols. We then propose an effective cluster head selection scheme based on the state-of-the-art Spider Monkey Optimization formulation, named SMOCH, and apply a greedy selection process between existing node positions and newly generated positions. Our experiment results show that our cluster-head selection scheme can observably increase node lifetime to a larger number of rounds and also decrease the energy consumption for communications.

On Cluster Head Selection in Monkey-inspired Optimization based Routing Protocol for WSNs

Different turfs have different growth characteristics, engendering differences in the number of maintenance cycles and amounts of pesticides used; therefore, studying their subtle color and shape differences through image recognition is crucial. Our study proposes an improved least squares support vector machine (LS-SVM) pixel classification method for this purpose. The sensitivity to local color changes in the hue, saturation, and value color space is considered, and the Sobel operator is used to extract the homogeneity as pixel-level color features. The maximum local energy, gradient, and second-order moment matrix of image pixels are obtained as texture features using a Gabor filter. Seven shape features of different plant leaves are calculated, multiple extracted features are used as LS-SVM classifier inputs, and samples are selected and trained with a dynamic threshold. The trained classifier can be used for segmentation. The experiments showed that it could use the local information of the color images and the excellent generalization ability of LS-SVM to segment lawn plants effectively. Under different weather conditions, the penalty coefficient, C, and kernel parameters with optimal generalization were Bayesian optimized to obtain a segmentation rate exceeding 95%. This algorithm yields a higher classification rate for plants with less obvious differences in texture and shape and optimizes space and time complexities.

https://www.spiedigitallibrary.org/journals/journal-of-electronic-imaging/volume-28/issue-2/023034/Lawn-plant-identification-and-segmentation-based-on-least-squares-support/10.1117/1.JEI.28.2.023034.pdf

Lawn plant identification and segmentation based on least squares support vector machine and multifeature fusion

Cancer is a disease characterized largely by the accumulation of somatic mutations during the lifetime of a patient. Distinguishing driver mutations from passenger mutations had posed a challenge in modern cancer research. With the state of art of microarray technologies and clinical studies, a large numbers of candidate genes are extracted. Extracting informative genes out of them is essential. In our project we aim to find the cancer driver genes using somatic mutation data and protein protein interaction data. We developed a generative mixture model coupled with Bayesian parameter estimation to estimate background mutation rates and driver probabilities of each gene as well as the proportion of drivers among all sequenced genes. We choose suitable prior distributions for modelling both driver probabilities and background mutations of each gene. We apply our method to ovarian cancer data and numerically estimated the solution. Upon convergence, we are able to discover and identify some new candidate cancer driver genes.

Tina Gui

Papers

Survey on swarm intelligence based routing protocols for wireless sensor networks: An extensive study

A novel cluster-based routing protocol wireless sensor networks using Spider Monkey Optimization

On Cluster Head Selection in Monkey-inspired Optimization based Routing Protocol for WSNs

Lawn plant identification and segmentation based on least squares support vector machine and multifeature fusion

A generative Bayesian model to identify cancer driver genes