Optimal Clustering in Wireless Sensor Networks for the Internet of Things Based on Memetic Algorithm: MemeWSN

In wireless sensor networks for the Internet of Things (WSN-IoT), the topology deviates very frequently because of the node mobility. The topology maintenance overhead is high in flat-based WSN-IoTs. WSN clustering is suggested to not only reduce the message overhead in WSN-IoT but also control the congestion and easy topology repairs. The partition of wireless mobile nodes (WMNs) into clusters is a multiobjective optimization problem in large-size WSN. Different evolutionary algorithms (EAs) are applied to divide the WSN-IoT into clusters but suffer from early convergence. In this paper, we propose WSN clustering based on the memetic algorithm (MemA) to decrease the probability of early convergence by utilizing local exploration techniques. Optimum clusters in WSN-IoT can be obtained using MemA to dynamically balance the load among clusters. The objective of this research is to find a cluster head set (CH-set) as early as possible once needed. The WMNs with high weight value are selected in lieu of new inhabitants in the subsequent generation. A crossover mechanism is applied to produce new-fangled chromosomes as soon as the two maternities have been nominated. The local search procedure is initiated to enhance the worth of individuals. The suggested method is matched with state-of-the-art methods like MobAC (Singh and Lohani, 2019), EPSO-C (Pathak, 2020), and PBC-CP (Vimalarani, et al. 2016). The proposed technique outperforms the state of the art clustering methods regarding control messages overhead, cluster count, reaffiliation rate, and cluster lifetime

Performance improvement of MEMS Electromagnetic Vibration Energy Harvester using optimized patterns of micromagnet array

The widespread utilization of wireless sensor networks for the Internet of Things is heavily restrained due to the lack of a sustainable power source as a replacement of batteries. Scavenging mechanical energy from ubiquitous vibrations through miniaturized electromagnetic transducers has become a potential solution to this powering issue. This work proposes the design and performance analysis of fully integrated MEMS Electromagnetic Vibration Energy Harvesters. Through analytical formulation and thorough finite element analysis, we present a systematic design study to optimize the magnet-coil interaction in a precise location within a small footprint. The compact device topology yielded an electromagnetic coupling as high as 62.9mWb/m with the optimized stripe-shaped micro-magnets and rectangular micro-coils. The nonlinear spring topology demonstrated six times improvement in the half-power bandwidth compared with its linear counterpart, at the cost of reduced power density. The proposed designs can be developed using standard MEMS fabrication methods leveraging the CMOS compatible integration at the system level for potential applications in the Internet of Things

Distributed localized contextual event reasoning under uncertainty

We focus on Internet of Things (IoT) environments where sensing and computing devices (nodes) are responsible to observe, reason, report and react to a specific phenomenon. Each node captures context from data streams and reasons on the presence of an event. We propose a distributed predictive analytics scheme for localized context reasoning under uncertainty. Such reasoning is achieved through a contextualized, knowledge-driven clustering process, where the clusters of nodes are formed according to their belief on the presence of the phenomenon. Each cluster enhances its localized opinion about the presence of an event through consensus realized under the principles of Fuzzy Logic (FL). The proposed FLdriven consensus process is further enhanced with semantics adopting Type-2 Fuzzy Sets to handle the uncertainty related to the identification of an event. We provide a comprehensive experimental evaluation and comparison assessment with other schemes over real data and report on the benefits stemmed from its adoption in IoT environments

Special Section on Sensors Applications Symposium

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Tunable, multi-modal, and multi-directional vibration energy harvester based on three-dimensional architected metastructures

Conventional vibration energy harvesters based on two-dimensional planar layouts have limited harvesting capacities due to narrow frequency bandwidth and because their vibratory motion is mainly restricted to one plane. Three-dimensional architected structures and advanced materials with multifunctional properties are being developed in a broad range of technological fields. Structural topologies exploiting compressive buckling deformation mechanisms however provide a versatile route to transform planar structures into sophisticated three-dimensional architectures and functional devices. Designed geometries and Kirigami cut patterns defined on planar precursors contribute to the controlled formation of diverse three-dimensional forms. In this work, we propose an energy harvesting system with tunable dynamic properties, where piezoelectric materials are integrated and strategically designed into three-dimensional compliant architected metastructures. This concept enables energy scavenging from vibrations not only in multiple directions but also across a broad frequency bandwidth, thus increasing the energy harvesting efficiency. The proposed system comprises a buckled ribbon with optional Kirigami cuts. This platform enables the induction of vibration modes across a wide range of resonance frequencies and in arbitrary directions, mechanically coupling with four cantilever piezoelectric beams to capture vibrations. The multi-modal and multi-directional harvesting performance of the proposed configurations has been demonstrated in comparison with planar systems. The results suggest this is a facile strategy for the realization of compliant and high-performance energy harvesting and advanced electronics systems based on mechanically assembled platforms

Thompson Sampling-Based Channel Selection through Density Estimation aided by Stochastic Geometry

We propose a sophisticated channel selection scheme based on multi-armed bandits and stochastic geometry analysis. In the proposed scheme, a typical user attempts to estimate the density of active interferers for every channel via the repeated observations of signal-to-interference power ratio (SIR), which demonstrates the randomness induced by randomized interference sources and fading effects. The purpose of this study involves enabling a typical user to identify the channel with the lowest density of active interferers while considering the communication quality during exploration. To resolve the trade-off between obtaining more observations on uncertain channels and using a channel that appears better, we employ a bandit algorithm called Thompson sampling (TS), which is known for its empirical effectiveness. We consider two ideas to enhance TS. First, noticing that the SIR distribution derived through stochastic geometry is useful for updating the posterior distribution of the density, we propose incorporating the SIR distribution into TS to estimate the density of active interferers. Second, TS requires sampling from the posterior distribution of the density for each channel, while it is significantly more complicated for the posterior distribution of the density to generate samples than well-known distribution. The results indicate that this type of sampling process is achieved via the Markov chain Monte Carlo method (MCMC). The simulation results indicate that the proposed method enables a typical user to determine the channel with the lowest density more efficiently than the TS without density estimation aided by stochastic geometry, and ε-greedy strategies

Hybrid schedule management in 6TiSCH networks : the coexistence of determinism and flexibility

With the emergence of the Internet of Things (IoT), Industry 4.0 and Cyber-Physical System (CPS) concepts, there is a tremendous change ongoing in industrial applications that is imposing increasingly diverse and demanding network dynamics and requirements with a wider and more fine-grained scale. The purpose of this article is to investigate how a Hybrid Schedule Management in 6TiSCH architecture can be used to achieve the coexistence of applications with heavily diverse networking requirements. We study the fundamental functionalities and also describe network scenarios where such a hybrid scheduling approach can be used. In addition, we present the details about the design and implementation of the first 6TiSCH Centralized Scheduling Framework based on CoMI. We also provide theoretical and experimental analysis where we study the cost of schedule management operations and illustrate the operation of the CoMI-based 6TiSCH Schedule Management

Energy Harvesting for Sensor Nodes in the Internet of Things

Wireless sensor networks have an extensive range of applications in the real world. From military uses saving lives, to environmental applications monitoring the fauna and weather conditions, but also by checking the health of patients and even by automating our homes. This work presents a solution to implement an energy harvesting sensor network. By using solar energy to power a sensor node we can extend its lifetime beyond the one powered only by batteries. Moreover, this solution attempts to be energy efficient and to achieve a communication scheme in order to create a sensor network where nodes read environmental data and transmit back to a sink node. The communication scheme was successful to synchronize two nodes and transmit packets between them without collisions and avoiding loss of data due to lack of energy. Furthermore, the duty cycling algorithm allowed the node to operate at its maximum performance level, making the best use of its energy available without depleting it