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Industrial applications and automation controls are closely connected with heterogeneous Wireless Sensor Network (WSN); thus, Industrial Internet-of Thing (IIoT) enhances the productivity, scalability, and flexibility of the operations. However, in many cases such as tracking a device or service localization becomes very crucial to state back the operations at times. Existing literature show a various localization solutions, but they face the drawbacks of high energy consumption, and non-coherence in heterogeneity. Besides, in a WSN-based IIoT, non-cooperativeness exists among nodes due to various system and environmental parameters, which make the system resource-exhaustive. In this paper, we introduce a novel localization algorithm that addresses the above-mentioned problems. We call our proposed algorithm RAnge-free Power efficient COoperative Localization (RAPCOL). Specifically, the use of co-operative beacon nodes to broadcast the information to Base Station and low energy consumption highlights the novelty of the work. RAPCOL uses weight metrics for selecting optimal cooperative beacon nodes to prolong the network lifetime. We also introduce an Improvised Particle Swarm Optimization (IPSO) that credits in the contribution in RAPCOL. We run a overall nine sets of experiments to analyze the localization accuracy, effect of beacon nodes, sensor nodes, network connectivity, and sensing field, error frequency, residual energy, time, and network lifetime. A comparative study with the existing localization models shows that RAPCOL is 30 % better than the existing models in terms of accuracy and resource consumption. We observe stable performance of RAPCOL with a differentiated effect of beacon nodes and sensor nodes. We also observe that our proposed IPSO 20 % better in fast convergence to the optimal solution. Though RAPCOL localization time is 12 % higher than other existing protocols, RAPCOL's accuracy and energy saving mechanism make it efficient for IIoTs. [ABSTRACT FROM AUTHOR]

Wind energy has gained tremendous attention recently due to its abundance potential and to obviate the adverse impacts of fossil fuel consumption. However, the efficient development and operation of wind energy systems require precise and accurate wind speed prediction. To fulfill this aim, machine learning algorithms have shown its prowess in accurately predicting the wind speed and this study proposes an interpretable stacked ensemble with ridge regressor utilizing ML algorithms such as catboost (CB), gradient boost (GB), multilayer perceptron (MLP), and random forest (RF) as a base model. The base models' estimations are integrated by applying a ridge regressor to enhance the accuracy of wind speed estimation. The dataset consists of 87 600 samples from which 80% samples are used for training. The proposed approach provides interpretability of the prediction results through Shapley additive explanations and feature importance analysis. The result of the proposed method is calculated by comparing the estimation outcomes with each model such as CB, GB, MLP, and RF in terms of error metrics such as root mean squared error (RMSE) and mean absolute error (MAE). The proposed model outperforms the others due to a reduction of error in RMSE by 19.89% and MAE by 24% respectively. [ABSTRACT FROM AUTHOR]

In real-time and mission-critical applications of wireless sensor networks (WSNs), such as military, the location of monitored objects must only be informed by sensor nodes to the base station, remaining anonymous to the rest of the network. However, preserving source location privacy is a challenging task due to the open nature of WSNs. Several methods have been proposed to address this issue, including anonymity-based, obfuscation-based, and policy-based methods. Anonymity-based methods are unsuitable for protecting position privacy, obfuscation-based methods are unable to protect identity, and policy-based methods are appropriate for all location privacy categories but challenging for end-users to understand and manage. These methods take into account different assumptions about attackers, energy consumption, and other factors in WSNs such as transmission delay and fixed battery and memory. Prior research has focused on one or more aspects of WSNs while ignoring others. In this work, we propose a Dynamic Routing Approach For enhancing Source Location Privacy in WSNs (SLP-DRA) to preserve source location privacy by dynamically selecting a path and injecting fake messages into the network. The dynamic selection of a route for transmitting real messages from the source sensor node to the base station, in the presence of fake packets, successfully confuses the attacker about the exact location of the monitored object in the network. The simulated results are compared using commonly used performance metrics of WSNs such as energy usage, transmission delay, security, and network lifetime. The comparative results show that SLP-DRA enhances the security level in preserving source location privacy compared to other techniques without compromising network lifetime, transmission delay, or energy consumption. We validate that SLP-DRA is robust against commonly used attacks, such as hop-by-hop and backtracking attacks, in finding the source location in WSNs. [ABSTRACT FROM AUTHOR]

In this paper, an Internet of Energy (IoE) solution for providing optimal charging and control of resources in the era of electric vehicles (EVs) with energy trading and security is considered. A blockchain based security solution is presented by considering the IoE as a distributed energy system with internet of things (IoT). However, by inheriting the security concerns of IoTs, IoE also faces the security challenges and is considered here. Hierarchical key generation is used for cryptographic keys, based on lattice constructions. Along with a consensus tailored with Practical Byzantine Fault Tolerance (PBFT), an additional smart contract is also applied for utilization of energy transfer. The presented solution provides transparency, reliability, availability and failure recovery inheriting from the blockchain aspects. To the best of our knowledge, the present study is the first attempt in this direction of IoE-EV interface solutions. The experimental results, comparative analysis and security validation affirm the superiority of the proposed solution over the existing approaches. [ABSTRACT FROM AUTHOR]