FCS-MBFLEACH: Designing an Energy-Aware Fault Detection System for Mobile Wireless Sensor Networks

Wireless sensor networks (WSNs) include large-scale sensor nodes that are densely distributed over a geographical region that is completely randomized for monitoring, identifying, and analyzing physical events. The crucial challenge in wireless sensor networks is the very high dependence of the sensor nodes on limited battery power to exchange information wirelessly as well as the non-rechargeable battery of the wireless sensor nodes, which makes the management and monitoring of these nodes in terms of abnormal changes very difficult. These anomalies appear under faults, including hardware, software, anomalies, and attacks by raiders, all of which affect the comprehensiveness of the data collected by wireless sensor networks. Hence, a crucial contraption should be taken to detect the early faults in the network, despite the limitations of the sensor nodes. Machine learning methods include solutions that can be used to detect the sensor node faults in the network. The purpose of this study is to use several classification methods to compute the fault detection accuracy with different densities under two scenarios in regions of interest such as MB-FLEACH, one-class support vector machine (SVM), fuzzy one-class, or a combination of SVM and FCS-MBFLEACH methods. It should be noted that in the study so far, no super cluster head (SCH) selection has been performed to detect node faults in the network. The simulation outcomes demonstrate that the FCS-MBFLEACH method has the best performance in terms of the accuracy of fault detection, false-positive rate (FPR), average remaining energy, and network lifetime compared to other classification methods

Geometric Constraint Based Range Free Localization Scheme For Wireless Sensor Networks (WSNs)

Localization of the wireless sensor networks (WSNs) is an emerging area of research. The accurate localization is essential to support extended network lifetime, better covering, geographical routing, and congested free network. In this thesis, we proposed four distributed range-free localization schemes. The proposed schemes are based on the analytical geometry, where an arc is used as the geometric primitive shape. The simulation and experimental validation are performed to evaluate the performance of the proposed schemes. First, we have proposed a mobile beacon based range-free localization scheme (MBBRFLS). The proposed scheme resolved the two underlying problems of the constraint area based localization: (i) localization accuracy depends on the size of the constraint area, and (2) the localization using the constraint area averaging. In this scheme, the constraint area is used to derive the geometric property of an arc. The localization begins with an approximation of the arc parameters. Later, the approximated parameters are used to generate the chords. The perpendicular bisector of the chords estimate the candidate positions of the sensor node. The valid position of the sensor node is identified using the logarithmic path loss model. The performance of proposed scheme is compared with Ssu and Galstyan schemes. From the results, it is observed that the proposed scheme at varying DOI shows 20.7% and 11.6% less localization error than Ssu and Galstyan schemes respectively. Similarly, at the varying beacon broadcasting interval the proposed scheme shows 18.8% and 8.3% less localization error than Ssu and Galstyan schemes respectively. Besides, at the varying communication range, the proposed scheme shows 18% and 9.2% less localization error than Ssu and Galstyan schemes respectively. To further enhance the localization accuracy, we have proposed MBBRFLS using an optimized beacon points selection (OBPS). In MBBRFLS-OBPS, the optimized beacon points minimized the constraint area of the sensor node. Later, the reduced constraint area is used to differentiate the valid or invalid estimated positions of the sensor node. In this scheme, we have only considered the sagitta of a minor arc for generating the chords. Therefore, the complexity of geometric calculations in MBBRFLS-OBPS is lesser than MBBRFLS. For localization, the MBBRFLS-OBPS use the perpendicular bisector of the chords (corresponding to the sagitta of minor arc) and the approximated radius. The performance of the proposed MBBRFLS-OBPS is compared with Ssu, Galstyan, and Singh schemes. From the results, it is observed that the proposed scheme using CIRCLE, vii SPIRAL, HILBERT, and S-CURVE trajectories shows 74.68%, 78.3%, 73.9%, and 70.3% less localization error than Ssu, Galstyan, and Singh schemes respectively. Next, we have proposed MBBRFLS using an optimized residence area formation (ORAF). The proposed MBBRFLS-ORAF further improves the localization accuracy. In this scheme, we have used the adaptive mechanism corresponding to the different size of the constraint area. The adaptive mechanism defines the number of random points required for the different size of the constraint area. In this scheme, we have improved the approximation accuracy of the arc parameters even at the larger size of the constraint area. Therefore, the localization accuracy is improved. The previous scheme MBBRFLS-OBPS use the residence area of the two beacon points for approximation. Therefore, the larger size of the constraint area degrades the approximation accuracy. In the MBBRFLS-ORAF, we have considered the residence area of the three non-collinear beacon points, which further improves the localization accuracy. The performance of the proposed scheme is compared with Ssu, Lee, Xiao, and Singh schemes. From the results, it is observed that the proposed MBBRFLS-ORAF at varying communication range shows 73.2%, 48.7%, 33.2%, and 20.7% less localization error than Ssu, Lee, Xiao, and Singh schemes respectively. Similarly, at the different beacon broadcasting intervals the proposed MBBRFLS-ORAF shows 75%, 53%, 38%, and 25% less localization error than Ssu, Lee, Xiao, and Singh schemes respectively. Besides, at the varying DOI the proposed MBBRFLS-ORAF shows 76.3%, 56.8%, 52%, and 35% less localization error than Ssu, Lee, Xiao, and Singh schemes respectively. Finally, we have proposed a localization scheme for unpredictable radio environment (LSURE). In this work, we have focused on the radio propagation irregularity and its impact on the localization accuracy. The most of the geometric constraint-based localization schemes suffer from the radio propagation irregularity. To demonstrate its impact, we have designed an experimental testbed for the real indoor environment. In the experimental testbed, the three static anchor nodes assist a sensor node to perform its localization. The impact of radio propagation irregularity is represented on the constraint areas of the sensor node. The communication range (estimated distance) of the anchor node is derived using the logarithmic regression model of RSSI-distance relationship. The additional error in the estimated distances, and the different placement of the anchor nodes generates the different size of the constraint areas. To improve the localization accuracy, we have used the dynamic circle expansion technique. The performance of the proposed LSURE is compared with APIT and Weighted Centroid schemes using the various deployment scenarios of the anchor nodes. From the results, it is observed that the proposed LSURE at different deployment scenarios of anchor nodes shows 65.94% and 73.54% less localization error than APIT and Weighted Centroid schemes

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Optimizing a Centralized Control Topology of an IoT Network Based on Hilbert Space

An Internet of Things network (IoTN) is composed of many small devices or nodes located in homes and/or offices, to be operated through the Internet from anywhere, making these devices smarter and more efficient. For improving the efficiency of an IoTN, in this chapter an optimized fractal algorithm (OFA) was proposed for designing a centralized control topology of an IoTN, whose nodes are distributed according to the Hilbert space-filling fractal. We developed the OFA to find the best nodes where a smart home device can find the highly reliable link between its neighbors by a software-defined network (SDN) with a target coverage since OFA only considers reliable links among devices. Finally, through laboratory tests and computer simulations, we demonstrated the effectiveness of this proposal by using a large amount of IoT devices making them more efficient operating systems. The quality of service (QoS) is a challenge that guaranteed the level of service delivery to an IoTN, so that OFA takes less time to reach its destination after it is generated by its source, increasing the probability that the target node can recover the original packet before the lifetime expires

Optimized Load Centroid and Rabin Onion Secured Routing in Wireless Sensor Network for IoT

Advances in wireless communication have geared up extensive insights wherein the sensors can themselves communicate with other sensors that form significant parts of the Internet of Things (IoT). However, the large-scale acceptance of WSN for IoT is still surfacing threats and controversies that apprehend the security aspects. There are a lot of attacks that can manipulate the routein WSN for IoT. In this work, an Optimized Load Centroid and Rabin Onion Routing (OLC-ROR) method are designed to improve the throughput rate with minimum routing overhead and latency. The proposed method is based on a Centroid and Rabin Signature, a Digital Signature technique. First, the optimal route is identified by considering both the load and residual energy using Load Centroid function. Then onion routing is used for selecting secured route amongst the optimality. Besides, the node genuineness is checked by applying the Rabin Signature

Optimized Load Centroid and Rabin Onion Secured Routing in Wireless Sensor Network for IoT

Advances in wireless communication have geared up extensive insights wherein the sensors can themselves communicate with other sensors that form significant parts of the Internet of Things (IoT). However, the large-scale acceptance of WSN for IoT is still surfacing threats and controversies that apprehend the security aspects. There are a lot of attacks that can manipulate the routein WSN for IoT. In this work, an Optimized Load Centroid and Rabin Onion Routing (OLC-ROR) method are designed to improve the throughput rate with minimum routing overhead and latency. The proposed method is based on a Centroid and Rabin Signature, a Digital Signature technique. First, the optimal route is identified by considering both the load and residual energy using Load Centroid function. Then onion routing is used for selecting secured route amongst the optimality. Besides, the node genuineness is checked by applying the Rabin Signature

Coverage and Connectivity in Three-Dimensional Networks

Most wireless terrestrial networks are designed based on the assumption that the nodes are deployed on a two-dimensional (2D) plane. However, this 2D assumption is not valid in underwater, atmospheric, or space communications. In fact, recent interest in underwater acoustic ad hoc and sensor networks hints at the need to understand how to design networks in 3D. Unfortunately, the design of 3D networks is surprisingly more difficult than the design of 2D networks. For example, proofs of Kelvin's conjecture and Kepler's conjecture required centuries of research to achieve breakthroughs, whereas their 2D counterparts are trivial to solve. In this paper, we consider the coverage and connectivity issues of 3D networks, where the goal is to find a node placement strategy with 100% sensing coverage of a 3D space, while minimizing the number of nodes required for surveillance. Our results indicate that the use of the Voronoi tessellation of 3D space to create truncated octahedral cells results in the best strategy. In this truncated octahedron placement strategy, the transmission range must be at least 1.7889 times the sensing range in order to maintain connectivity among nodes. If the transmission range is between 1.4142 and 1.7889 times the sensing range, then a hexagonal prism placement strategy or a rhombic dodecahedron placement strategy should be used. Although the required number of nodes in the hexagonal prism and the rhombic dodecahedron placement strategies is the same, this number is 43.25% higher than the number of nodes required by the truncated octahedron placement strategy. We verify by simulation that our placement strategies indeed guarantee ubiquitous coverage. We believe that our approach and our results presented in this paper could be used for extending the processes of 2D network design to 3D networks.Comment: To appear in ACM Mobicom 200