Real-life Wireless Sensor Networks Research Articles

Distributed enhanced multi-objective evolutionary algorithm based on decomposition for cluster analysis in wireless sensor network

Conventional clustering algorithms do not recognize patterns and structures with contradicting objectives in large, distributed datasets. Distributed clustering leverages rapid processing capabilities to allow multiple nodes to work together. This paper proposes a Distributed clustering based on Multiobjective Evolutionary Algorithm by Decomposition (D-MOEA/d) to solve various multiobjective optimization problems in wireless sensor networks (WSNs). In MOEA/d, a multiobjective optimization problem decomposes into several scalar optimization subproblems, each focusing on a distinct objective. Each subproblem is expressed as a clustering problem that uses local data to perform distributed clustering. The proposed method has been extended to achieve improved accuracy in less time by using a smaller feature subset with less redundancy. The Distributed Enhanced MOEA/d (DE-MOEA/d) avoids local optima by achieving diversity in the population using fuzzy-based nearest neighbor selection, sparse population initialization, and evolved mutation operator. This integration improves the accuracy of the clustering process at WSN nodes, ensuring the attainment of well-balanced solutions across multiple optimization criteria in the distributed environment. Average Euclidean and total symmetrical deviations are the two cost functions used to minimize while clustering on the MOEA/d framework. Six real-life WSN datasets are used to assess the performance of the proposed technique: (1) the Delhi air pollution dataset, (2) the Canada weather station dataset, (3) the Thames River water quality dataset, (4) the Narragansett Bay water quality dataset, (5) the Cook Agricultural land dataset and 6) Gordon Soil dataset. The simulation results of both proposed algorithms are compared with Multiobjective distributed particle swarm optimization (DMOPSO) and Distributed K-means (DK-Means). The proposed algorithm DE-MOEA/d performs better in terms of the Silhouette index (SI), Dunn index (DI), Davies–Bouldin index (DBI), and Kruskal–Wallis (KW) statistical test.

Distributed clustering in peer to peer networks using multi-objective whale optimization

Multi-objective clustering algorithms have superiority over its single objective counterparts as they include additional knowledge on properties of data in the form of objectives to discover the underlying clusters present in various datasets. This paper proposes a distributed clustering algorithm using multi-objective whale optimization (DMOWOA) for peer to peer network. The algorithm minimizes two objective functions to perform clustering, namely : Total Euclidean Deviation and Total Symmetrical Deviation. Both objective values are shared using diffusion method of cooperation to obtain correct partitioning at each peer. A single solution from the non-dominated solutions is selected as final solution based on its minimum distance to origin in the normalized objective space. The proposed algorithm’s performance is evaluated on four synthetic and five real-life wireless sensor network datasets (Canada weather station dataset, Delhi air pollution content dataset, Intel laboratory dataset, Washington cook agronomy farm dataset and Thames river water quality dataset). The comparison is carried out with multi-objective distributed particle swarm optimization (DMOPSO), distributed K-Means (DK-Means) and other seven recently developed nature inspired multi-objective clustering techniques. The proposed algorithm in most of the cases outperforms the existing techniques in terms of statistical measures Minkowski Score, Dunn index and Silhouette index. The average rank of the proposed algorithm is also better in Kruskal–Wallis statistical test.

Multilinear Plus Sparse Based Tensor Completion for Long-Term Operating Large-Scale and Heterogeneous Sensor Networks

Data integrity and correctness are essential for many internet of things applications. This work addresses the data completion problem for long-term operating, large-scale and heterogeneous wireless sensor networks (WSNs), using tensor completion technique. In most previous works, the data models only involve single temporal and single spatial dimensions, ignoring the higher-order intrinsic structures. Besides, most existing schemes use low-rankness and sparsity in a fixed domain to describe the data correlation, but neither can fit well with all the temporal, spatial and attribute correlation. To address these issues, we first propose a higher-order tensor model for data from WSNs, namely multilinear plus sparse (MPS) model, which has multiple temporal, multiple spatial and one attribute modes. The core idea is to describe the temporal-spatial-attribute correlation by the sparsity in the multilinear transform domain. Appropriate sparsifying transforms can be flexibly determined according to the specific natures of each mode. For the temporal and spatial modes, analytical transforms are adopted considering computational efficiency and addressing continuity feature. For the attribute mode, we utilize MPS to propose a robust ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> -norm based principle component analysis (robust ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> -PCA) algorithm to adaptively learn the sparsifying transform for various heterogeneous networks. Finally, based on MPS and the learned transform, we develop an efficient tensor completion algorithm using alternating direction method of multipliers, namely MPS-TC. The experimental results on real-life WSN data verify that the proposed robust ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> -PCA algorithm can learn an attribute transform more robustly than the conventional PCA and ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -PCA algorithms; and the proposed MPS-TC algorithm outperforms the state-of-the-art tensor completion algorithms in terms of signal-to-noise ratio and root mean square error.

Addressing Mobility in RPL With Position Assisted Metrics

Mobility is still an open challenge in wireless sensor networks (WSNs). Energy efficient routing strategies designed for static WSNs, such as routing protocol for low-power and lossy networks (RPL), generally have a slow response to topology changes. Moreover, their high signalling cost to keep up-to-date routes in the presence of mobile nodes makes them inefficient in these scenarios. In this paper, we introduce Kalman positioning RPL (KP-RPL), a novel routing strategy for WSNs with both static and mobile nodes, based on RPL. The objective of KP-RPL is to provide robust and reliable routing, considering the positioning inaccuracies and node disconnections that arise in real-life WSNs. This considers the original RPL for the communication among static nodes and position-based routing for mobile nodes, which use a novel RPL metric that combines Kalman positioning and blacklisting. The simulation results show that the reliability and the robustness of the network in harsh conditions are enhanced compared with geographical routing. Moreover, KP-RPL reduces the density and the number of simultaneously active anchor nodes for positioning. As a result, the infrastructure cost is lower, and the network lifetime is extended.

Detection of Intelligent Intruders in Wireless Sensor Networks

Most of the existing research works on the intrusion detection problem in a wireless sensor network (WSN) assume linear or random mobility patterns in abstracting intruders’ models in traversing the WSN field. However, in real-life WSN applications, an intruder is usually an intelligent mobile robot with environment learning and detection avoidance capability (i.e., the capability to avoid surrounding sensors). Due to this, the literature results based on the linear or random mobility models may not be applied to the real-life WSN design and deployment for efficient and effective intrusion detection in practice. This motivates us to investigate the impact of intruder’s intelligence on the intrusion detection problem in a WSN for various applications. To be specific, we propose two intrusion algorithms, the pinball and flood-fill algorithms, to mimic the intelligent motion and behaviors of a mobile intruder in detecting and circumventing nearby sensors for detection avoidance while heading for its destination. The two proposed algorithms are integrated into a WSN framework for intrusion detection analysis in various circumstances. Monte Carlo simulations are conducted, and the results indicate that: (1) the performance of a WSN drastically changes as a result of the intruder’s intelligence in avoiding sensor detections and intrusion algorithms; (2) network parameters, including node density, sensing range and communication range, play a crucial part in the effectiveness of the intruder’s intrusion algorithms; and (3) it is imperative to integrate intruder’s intelligence in the WSN research for intruder detection problems under various application circumstances.

Evaluation of RPL-compliant routing solutions in real-life WSNs

Wireless Sensor Networks (WSN) applications continue to expand and already cover almost all our daily activities improving from security and environmental efficiency to gaming experience. The diverse applications running on top of WSNs have led to the design of an immense number of routing protocols. Few years ago, the IETF standardized the IPv6 routing protocol for low-power and lossy networks (RPL) which is based on routing metrics to build communication paths between a source and the destination node. While significant efforts have focused on the design of routing metrics that satisfy the various applications, limited work has been reported on validating their performance using real-life motes. In this paper, we focus on validating the simulation results (obtained through the JSim simulator) using TelosB motes for a set of routing metrics that have been proposed in our previous articles.

Energy-efficient routing in wireless sensor networks using power-source type identification

The real-life wireless sensor networks (WSNs) nowadays often include nodes that are powered by various power sources: mains; primary or secondary batteries; or energy harvesting systems, which generate power from the environment. Although information about the parameters of the available power sources for the WSN node is crucial for optimising the operation of the whole network, the contemporary WSN nodes can only estimate the level of their supply voltage at the current time. Therefore, in this paper we are suggesting a simple mechanism that identifies the type of WSN node's power source based on the measurements of the supply voltage. Based on the suggested power-source type identification (PSTID) mechanism, we introduce and propose the special routing protocol that is intended for WSNs containing nodes that have different power supply sources. The suggested routing protocol allows a significant increase in the lifetime of the whole network compared to the scenarios when no PSTID data is available. The proposed routing protocol is more universal then the existing routing protocols that take into account only the value of the supply voltage.

A holistic approach to wireless sensor network routing in underground tunnel environments

The traditional networking builds on layered protocol architecture to isolate the complexities in different layers. It has been realized that real-life wireless sensor networks (WSNs) must be considered holistically across different layers for optimum performance. We consider a special case of WSNs that is deployed in underground tunnels. Underground communications present unique signal propagation characteristics due to the geographic and geological features, which in turn impact the underground network deployment and multi-hop routing patterns. We propose an efficient routing algorithm, called BRIT (Bounce Routing in Tunnels), for underground WSNs, and evaluate BRIT against the bottom-line AODV in terms of network throughput, packet loss rate, stability and latency using simulations. The contributions of the paper include a hybrid signal propagation model in three-dimensional underground tunnels, an assortment of sensor deployment strategies in tunnels, an integrated routing metric (forwarding speed), and a route suppression mechanism.