A bio-inspired adaptive model for search and selection in the Internet of Things environment

The Internet of Things (IoT) is a paradigm that can connect an enormous number of intelligent objects, share large amounts of data, and produce new services. However, it is a challenge to select the proper sensors for a given request due to the number of devices in use, the available resources, the restrictions on resource utilization, the nature of IoT networks, and the number of similar services. Previous studies have suggested how to best address this challenge, but suffer from low accuracy and high execution times. We propose a new distributed model to efficiently deal with heterogeneous sensors and select accurate ones in a dynamic IoT environment. The model’s server uses and manages multiple gateways to respond to the request requirements. First, sensors were grouped into three semantic categories and several semantic sensor network types in order to define the space of interest. Second, each type’s sensors were clustered using the Whale-based Sensor Clustering (WhaleCLUST) algorithm according to the context properties. Finally, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was improved to search and select the most adequate sensor matching users’ requirements. Experimental results from real data sets demonstrate that our proposal outperforms state-of-the-art approaches in terms of accuracy (96%), execution time, quality of clustering, and scalability of clustering.

Iterative Sensor Clustering and Mobile Sink Trajectory Optimization for Wireless Sensor Network with Nonuniform Density

Sensor clustering and trajectory optimization are a hot topic for last decade to improve energy efficiency of wireless sensor network (WSN). Most of existing studies assume that the sensor is uniformly deployed or all regions in the WSN coverage have the same level of interest. However, even in the same WSN, areas with high probability of disaster will have to form a “hotspot” with more sensors densely placed in order to be sensitive to environmental changes. The energy hole can be serious if sensor clustering and trajectory optimization are formulated without considering the hotspot. Therefore, we need to devise a sensor clustering and trajectory optimization algorithm considering the hotspots of WSN. In this paper, we propose an iterative algorithm to minimize the amount of energy consumed by components of WSN named ISCTO. The ISCTO algorithm consists of two phases. The first phase is a sensor clustering phase used to find the suitable number of clusters and cluster headers by considering the density of sensor and residual battery of sensors. The second phase is a trajectory optimization phase used to formulate suitable trajectory of multiple mobile sinks to minimize the amount of energy consumed by mobile sinks. The ISCTO algorithm performs two phases repeatedly until the amount of energy consumed by the WSN is not reduced. In addition, we show the performance of the proposed algorithm in terms of the total amount of energy consumed by sensors and mobile sinks.

A Robust Multi-Sensor Data Fusion Clustering Algorithm Based on Density Peaks

In this paper, a novel multi-sensor clustering algorithm, based on the density peaks clustering (DPC) algorithm, is proposed to address the multi-sensor data fusion (MSDF) problem. The MSDF problem is raised in the multi-sensor target detection (MSTD) context and corresponds to clustering observations of multiple sensors, without prior information on clutter. During the clustering process, the data points from the same sensor cannot be grouped into the same cluster, which is called the cannot link (CL) constraint; the size of each cluster should be within a certain range; and overlapping clusters (if any) must be divided into multiple clusters to satisfy the CL constraint. The simulation results confirm the validity and reliability of the proposed algorithm.

A Correlation-Change Based Feature Selection Method for IoT Equipment Anomaly Detection

Selecting the right features for further data analysis is important in the process of equipment anomaly detection, especially when the origin data source involves high dimensional data with a low value density. However, existing researches failed to capture the fact that the sensor data are usually correlated (e.g., duplicated deployed sensors), and the correlations would be broken when anomalies occur with happen to the monitored equipment. In this paper, we propose to capture such sensor data correlation changes to improve the performance of IoT (Internet of Things) equipment anomaly detection. In our feature selection method, we first cluster correlated sensors together to recognize the duplicated deployed sensors according to sensor data correlations, and we monitor the data correlation changes in real time to select the sensors with correlation changes as the representative features for anomaly detection. To that end, (1) we conducted curve alignment for the sensor clustering; (2) we discuss the appropriate window size for data correlation calculation; (3) and adopted MCFS (Multi-Cluster Feature Selection) into our method to adapt to the online feature selection scenario. According to the experiment evaluation derived from real IoT equipment, we prove that our method manages to reduce the false negative of IoT equipment anomaly detection of 30% with almost the same level of false positive.

Clustering Algorithm Based on the Direction of Overlapping Field of Views for Wireless Multimedia Sensor Networks

Wireless Multimedia Sensor network (WMSN) composed of multiple video cameras with possibly overlapping field of views. Node clustering for coordinating multimedia sensing and processing based on classical sensor clustering algorithms cannot enable wireless multimedia sensor nodes to sense areas that are uncorrelated to the areas covered by radio neighboring sensors. In this paper, a distributed clustering algorithm is proposed for WMSNs based on the coverage areas of the overlapped field of views (FoVs) and also on the direction of the FoV. A node may belong to multiple clusters, if its FoV intersects more than one cluster-head which affects efficiently in terms of energy conservation in sensing and processing. Simulation results show that our proposed algorithm has a more advantage in energy conservation, and in decreasing the number of singular nodes which impacts on the clustering efficiency and prolongs the network lifetime effectively.