An energy-efficient cluster head selection in wireless sensor network using grey wolf optimization algorithm

Clustering is considered as one of the most prominent solutions to preserve theenergy in the wireless sensor networks. However, for optimal clustering, anenergy efficient cluster head selection is quite important. Improper selectionofcluster heads(CHs) consumes high energy compared to other sensor nodesdue to the transmission of data packets between the cluster members and thesink node. Thereby, it reduces the network lifetime and performance of thenetwork. In order to overcome the issues, we propose a novelcluster headselection approach usinggrey wolf optimization algorithm(GWO) namelyGWO-CH which considers the residual energy, intra-cluster and sink distance.In addition to that, we formulated an objective function and weight parametersfor anefficient cluster head selection and cluster formation. The proposedalgorithm is tested in different wireless sensor network scenarios by varyingthe number of sensor nodes and cluster heads. The observed results conveythat the proposed algorithm outperforms in terms of achieving better networkperformance compare to other algorithms

Living IoT: A Flying Wireless Platform on Live Insects

Sensor networks with devices capable of moving could enable applications ranging from precision irrigation to environmental sensing. Using mechanical drones to move sensors, however, severely limits operation time since flight time is limited by the energy density of current battery technology. We explore an alternative, biology-based solution: integrate sensing, computing and communication functionalities onto live flying insects to create a mobile IoT platform. Such an approach takes advantage of these tiny, highly efficient biological insects which are ubiquitous in many outdoor ecosystems, to essentially provide mobility for free. Doing so however requires addressing key technical challenges of power, size, weight and self-localization in order for the insects to perform location-dependent sensing operations as they carry our IoT payload through the environment. We develop and deploy our platform on bumblebees which includes backscatter communication, low-power self-localization hardware, sensors, and a power source. We show that our platform is capable of sensing, backscattering data at 1 kbps when the insects are back at the hive, and localizing itself up to distances of 80 m from the access points, all within a total weight budget of 102 mg.Comment: Co-primary authors: Vikram Iyer, Rajalakshmi Nandakumar, Anran Wang, In Proceedings of Mobicom. ACM, New York, NY, USA, 15 pages, 201

Balancing the trade-off between cost and reliability for wireless sensor networks: a multi-objective optimized deployment method

The deployment of the sensor nodes (SNs) always plays a decisive role in the system performance of wireless sensor networks (WSNs). In this work, we propose an optimal deployment method for practical heterogeneous WSNs which gives a deep insight into the trade-off between the reliability and deployment cost. Specifically, this work aims to provide the optimal deployment of SNs to maximize the coverage degree and connection degree, and meanwhile minimize the overall deployment cost. In addition, this work fully considers the heterogeneity of SNs (i.e. differentiated sensing range and deployment cost) and three-dimensional (3-D) deployment scenarios. This is a multi-objective optimization problem, non-convex, multimodal and NP-hard. To solve it, we develop a novel swarm-based multi-objective optimization algorithm, known as the competitive multi-objective marine predators algorithm (CMOMPA) whose performance is verified by comprehensive comparative experiments with ten other stateof-the-art multi-objective optimization algorithms. The computational results demonstrate that CMOMPA is superior to others in terms of convergence and accuracy and shows excellent performance on multimodal multiobjective optimization problems. Sufficient simulations are also conducted to evaluate the effectiveness of the CMOMPA based optimal SNs deployment method. The results show that the optimized deployment can balance the trade-off among deployment cost, sensing reliability and network reliability. The source code is available on https://github.com/iNet-WZU/CMOMPA.Comment: 25 page

A Review of Wireless Sensor Networks with Cognitive Radio Techniques and Applications

The advent of Wireless Sensor Networks (WSNs) has inspired various sciences and telecommunication with its applications, there is a growing demand for robust methodologies that can ensure extended lifetime. Sensor nodes are small equipment which may hold less electrical energy and preserve it until they reach the destination of the network. The main concern is supposed to carry out sensor routing process along with transferring information. Choosing the best route for transmission in a sensor node is necessary to reach the destination and conserve energy. Clustering in the network is considered to be an effective method for gathering of data and routing through the nodes in wireless sensor networks. The primary requirement is to extend network lifetime by minimizing the consumption of energy. Further integrating cognitive radio technique into sensor networks, that can make smart choices based on knowledge acquisition, reasoning, and information sharing may support the network's complete purposes amid the presence of several limitations and optimal targets. This examination focuses on routing and clustering using metaheuristic techniques and machine learning because these characteristics have a detrimental impact on cognitive radio wireless sensor node lifetime

Probability-based Distance Estimation Model for 3D DV-Hop Localization in WSNs

Localization is one of the pivotal issues in wireless sensor network applications. In 3D localization studies, most algorithms focus on enhancing the location prediction process, lacking theoretical derivation of the detection distance of an anchor node at the varying hops, engenders a localization performance bottleneck. To address this issue, we propose a probability-based average distance estimation (PADE) model that utilizes the probability distribution of node distances detected by an anchor node. The aim is to mathematically derive the average distances of nodes detected by an anchor node at different hops. First, we develop a probability-based maximum distance estimation (PMDE) model to calculate the upper bound of the distance detected by an anchor node. Then, we present the PADE model, which relies on the upper bound obtained of the distance by the PMDE model. Finally, the obtained average distance is used to construct a distance loss function, and it is embedded with the traditional distance loss function into a multi-objective genetic algorithm to predict the locations of unknown nodes. The experimental results demonstrate that the proposed method achieves state-of-the-art performance in random and multimodal distributed sensor networks. The average localization accuracy is improved by 3.49\%-12.66\% and 3.99%-22.34%, respectively

DDoS Attack Detection in WSN using Modified Invasive Weed Optimization with Extreme Learning Machine

Wireless sensor networks (WSN) are the wide-spread methodology for its distribution of the vast amount of devoted sensor nodes (SNs) that is employed for sensing the atmosphere and gather information. The gathered information was transmitted to the sink nodes via intermediate nodes. Meanwhile, the SN data are prone to the internet, and they are vulnerable to diverse security risks, involving distributed denial of service (DDoS) outbreaks that might interrupt network operation and compromises data integrity. In recent times, developed machine learning (ML) approaches can be applied for the discovery of DDoS attacks and accomplish security in WSN. To achieve this, this study presents a modified invasive weed optimization with extreme learning machine (MIWO-ELM) model for DDoS outbreak recognition in the WSN atmosphere. In the presented MIWO-ELM technique, an initial stage of data pre-processing is conducted. The ELM model can be applied for precise DDoS attack detection and classification process. At last, the MIWO method can be exploited for the parameter tuning of the ELM model which leads to improved performance of the classification. The experimental analysis of the MIWO-ELM method takes place using WSN dataset. The comprehensive simulation outputs show the remarkable performance of the MIWO-ELM method compared to other recent approaches

A Distributed Intelligent Sensing Approach for Environmental Monitoring Applications

Scientific reports from around the world present us with the undeniable fact that the global ecosystem is undergoing severe change. As this shift accelerates, it is ever more critical that we are able to quantify the local effects of such changes, and further, their implications, from our daily life to the biological processes that put food on our tables. In this thesis, we study the application of sensor network technology to the observation and estimation of highly local phenomena---specifically at a scale between ten to several hundred square meters. Embedding knowledge about the observed process directly into the sensor nodes' behavior via dedicated resource management or control algorithms allows us to deploy dense networks with low power requirements. Ecological systems are notoriously complex. In our work we must thus be highly experimental; it is our highest goal that we construct an approach to environmental monitoring that is not only realistic, but practical for real-world use. Our approach is centered on a commercially available sensor network product, aided by an off-the-shelf quadrotor with minimal customization. We validate our approach through a series of experiments performed from simulation all the way to reality, in deployments lasting days to several months. We motivate the need for local data via two case studies examining physical phenomena. First, employing novel modalities, we study the eclosion of a common agricultural pest. We present our efforts to acquire data that is more local than commonly employed methods, culminating in a six month deployment in a Swiss apple orchard. Next, we apply a environmental fluid dynamics model to enable the estimation of sensible heat flux using an inexpensive sensor. We integrate the sensor with a wireless sensor network and validate its capabilities in a short-term deployment. Acquiring meaningful data on a local scale requires that we advance the state of the art in multiple aspects. Static sensor networks present a classical tension between resolution, autonomy, and accuracy. We explore the performance of algorithms aimed at providing all three, showing explicitly what is required to implement these approaches for real-world applications in an autonomous deployment under uncontrolled conditions. Eventually, spatial resolution is limited by network density. Such limits may be overcome by the use of mobile sensors. We explore the use of an off-the-shelf quadrotor, equipped with environmental sensors, as an additional element in system of heterogeneous sensing nodes. Through a series of indoor and outdoor experiments, we quantify the contribution of a such a mobile sensor, and various strategies for planning its path