Cryptocurrency price prediction poses significant challenges due to the inherent volatility and nonlineardynamics of the market. This study introduces a hybrid stacked modeling framework that integrates machine learning (ML) and deep learning (DL) techniques, capitalizing on their complementary strengths-ML models are effective at capturing nonlinearfeature interactions in structured data, while DL architectures are adept at modeling temporal dependencies in sequential data. The proposed model leverages historical price data, technical indicators, macroeconomic variables, and sentiment metrics, with feature engineering applied to enhance predictive capability. Empirical evaluation was conducted through two experimental setups: (i) short-term, monthly segment analysis and (ii) long-term generalization via five-fold cross-validation. The hybrid model outperformed individual baseline models, achieving up to 18.3% lower RMSE and 6.7% higher directional accuracy. Additionally, it yielded superior risk-adjusted returns, with Sharpe Ratios reaching 0.094 on the Ethereum dataset. Beyond technical improvements, this research offers foresight into digital financial markets, providing a robust tool for investors, institutions, and policymakers navigating the evolving cryptocurrency landscape. The model supports more informed decision-making, enhances market oversight, and contributes to the development of adaptive regulatory frameworks for digital finance.

A data-efficient intrusion detection framework tailored for Wireless Sensor Networks (WSNs) is proposed by leveraging active learning and metaheuristic optimization techniques. This framework addresses three major limitations of traditional models: data imbalance, inefficient hyperparameter tuning, and the need for large labeled datasets. To handle class imbalance, adaptive synthetic sampling generates synthetic instances for minority classes, particularly enhancing learning in complex regions of the feature space. For hyperparameter optimization, the Sandpiper Optimization (SO) algorithm is employed to fine-tune the regularization parameter of Logistic Regression (LR), leading to improved generalization. The issue of limited labeled data is tackled using Active Learning Uncertainty (ALU) and Entropy-based Active Learning (ALE), which query the most informative samples from the unlabeled pool, maximizing learning with minimal annotation effort. Simulation results show that LRALU, LRALE, and LRSO outperform traditional models with improvements of 18.18%, 19.48%, and 9.09% in accuracy; 9.30%, 1.16%, and 9.30% in precision; 18.18%, 19.48%, and 9.09% in recall; 12.20%, 8.54%, and 7.32% in F1-score; and 14.63%, 12.20%, and 9.76% in ROC-AUC, respectively. Additionally, log loss is reduced by 6.45%, 6.45%, and 35.48% for LRALU, LRALE, and LRSO, respectively. These results demonstrate that integrating intelligent sampling, active learning, and nature-inspired optimization significantly enhances intrusion detection performance in WSNs, providing an annotation-efficient solution for practical deployment.

Robust and intelligent intrusion detection is vital for securing Internet of Things (IoT) ecosystems against evolving cyber threats. However, existing systems face challenges such as class imbalance, suboptimal model performance due to manual hyperparameter tuning, and the high cost of labeled data. These limitations are addressed using the TON IoT dataset. To resolve data imbalance, the proximity weighted random affine shadow sampling generates boundary-focused synthetic samples that preserve class distribution. Further, to tackle suboptimal performance, bayesian optimization is applied to LeNet, resulting in LeBayesNet, which discovers the optimal configuration for high-accuracy detection. Next, to mitigate the scarcity of labeled data, MargiLeNet leverages marginal-based active learning, annotating the most uncertain samples to enhance model learning efficiently. Experimental results show that LeBayesNet and MargiLeNet improve performance over existing models by 7.69% and 3.30% in accuracy, 7.69% and 3.30% in F1-score, 7.69% and 3.30% in precision, 8.89% and 4.44% in the recall, 7.69% and 6.59% in receiver operating characteristic-area under the curve, 4.88% and 7.32% in matthews correlation coefficient, and 10.34% and 11.49% in precision-recall area under the curve, respectively. Both models significantly reduce hamming loss to 75% and 37.5%, indicating better generalization in complex and imbalanced scenarios. These advancements demonstrate the potential of optimization and active learning techniques in building accurate and adaptive intrusion detection systems for modern IoT networks.

This paper proposes a DL based framework using Multilayer Perceptron (MLP) tailored for multiclass DoS attack detection in Internet of Things (IoTs). After comparative data preprocessing, class imbalance is effectively mitigated using the proximity weighted random affine shadow oversampling method, enhancing minority class representation. Moreover, feature selection based on variance threshold is employed to streamline the input space and accelerate training. To reduce dependence on large labeled datasets, the approach incorporates Diversity-Based Sampling (DBS), an active learning strategy that focuses labeling efforts on diverse, informative samples. Furthermore, the proposed model’s performance is refined through metaheuristic-driven hyperparameter tuning using the Grasshopper Optimization Algorithm (GOA). This integrated methodology ensures more efficient learning, better generalization, and improved detection across varied attack scenarios in IoT settings. A comparative analysis with traditional machine deep learning and baseline models reveals that the proposed MLP+DBS and MLP+DBS+GOA model configurations consistently deliver superior performance across all evaluation metrics. Specifically, the proposed models achieve improvements of 5.7% and 9% in accuracy, 3.5% and 8.3% in precision, 5.7% and 9% in recall, 3.4% and 8.6% in F1-score, 2.3% and 3.3% in receiver operating characteristic area under the curve, and 3.3% and 6.6% in precision recall-area under the curve, respectively. These results demonstrate that the proposed models significantly outperform the existing approaches. This paper underscores the effectiveness of combining active learning and optimization for robust intrusion detection in resource-constrained IoT settings. The proposed models show strong potential for real-time deployment in smart environments requiring proactive and reliable security solutions.

With the rapid increase of connected devices, securing IoT networks against sophisticated cyber threats has become a critical research priority. However, effective intrusion detection in IoT environments is hindered by several core challenges, including severe class imbalance in network traffic, limited availability of annotated data for supervised learning, and the sensitivity of deep learning models to hyperparameter configurations. To address these limitations, we propose a data-driven DL framework that combines data balancing, active learning, and hyperparameter optimization. We employ the proximity-weighted synthetic oversampling technique to mitigate class imbalance by generating weighted synthetic samples. To reduce labeling overhead, we propose an active learning-based, Entropy-based Convolutional Neural Network (EntroConvNet), an intrusion detector for selective annotation of the most uncertain samples. Additionally, a novel Random Search Optimized Convolutional Neural Network (RS-ConvNet) is proposed to maximize detection performance. Experimental results on the TON IoT dataset show that EntroConvNet outperforms the baseline models with improvements of 3.45% in accuracy, 3.57% in precision, 1.10% in recall, 2.30% in F1-score, 3.19% in Area Under the Receiver Operating Characteristics Curve (AUC-ROC), 6.67% in Cohen’s Kappa and Mathews Correlation Coefficient (MCC), and 16.67% reduction in log loss and Hamming loss. Furthermore, RS-ConvNet achieves superior gains of 4.60% in accuracy, 5.95% in precision, 1.10% in recall, 3.45% in F1-score, 2.13% in AUC-ROC, 8% in Cohen’s Kappa and MCC, and also reduces log loss and Hamming loss by 20% and 25%, respectively. These results validate the proposed framework’s ability to deliver accurate, and annotation-efficient intrusion detection systems in dynamic IoT network environments.

The Internet of Things (IoT) has transformed modern technology by interconnecting physical devices to enable intelligent automation and real-time data exchange. However, securing IoT environments remains a critical challenge due to device heterogeneity, resource limitations, and vulnerabilities in lightweight communication protocols. Traditional Intrusion Detection Systems (IDS) often struggle with issues such as imbalanced datasets, suboptimal classification accuracy, difficulty in tuning hyperparameters, and a scarcity of labeled data. To address these limitations, we propose a novel IDS framework that integrates multiple advanced techniques. Initially, categorical labels are transformed using Label Encoding to facilitate effective model training. To mitigate data imbalance, the Localized Random Affine Shadowsampling (LoRAS) technique is applied, enhancing minority class representation. A Monte-Carlo Active Learning approach implemented on the DaNet architecture, termed MALD, is introduced to improve data efficiency by selectively querying the most informative samples. Additionally, we propose Elephant Herding Optimization applied to DaNet named EHODA to autonomously tune hyperparameters and maximize classification performance. Experimental results demonstrate that the proposed MALD and EHODA models significantly outperform conventional and state-of-the-art methods, achieving up to 93 % in Accuracy, Precision, Recall, and F1-Score, along with superior values in AUC-ROC of 0.98 and PR-AUC of 0.93. These findings affirm the effectiveness of our proposed framework for robust and adaptive intrusion detection in IoT environments.

Intrusion detection in Internet of Things (IoT) environments is a critical yet challenging task due to the heterogeneous nature of devices and the complex attack landscape. Traditional machine and deep learning models often suffer from limitations such as poor class balance, irrelevant or redundant features, suboptimal classification accuracy, ineffective hyperparameter tuning, and scarcity of labeled data. To address these challenges, we propose an enhanced intrusion detection system model. Variance threshold is applied to select informative features, while the proximity weighted random affine shadow sampling technique is used to balance the dataset effectively. Capsule Network (CapsNet) is employed for robust classification due to its ability to capture spatial hierarchies in data. To further optimize CapsNet, we implement the Reptile Search Algorithm (RSA), resulting in the Reptile-Optimized Capsule Network (ROC-Net). ROC-Net is further enhanced using Margin-Based Active Learning (MBAL), forming Marginal Active-learning with Reptile-optimized Capsule Network (MARCO-Net), which efficiently annotates the most uncertain samples from the unlabeled pool. Experimental results show that the proposed ROC-Net and MARCO-Net significantly outperform traditional models, achieving improvements of 8.75 % and 12.5 % in accuracy, 13.75 % and 11.25 % in precision, 1.19 % and 9.52 % in recall,$\mathbf{7. 4 1 \%}$and$\mathbf{1 1. 1 1 \%}$in F1-score,$\mathbf{1 1. 9 4 \%}$and$\mathbf{2 0. 9 0 \%}$in Matthews Correlation Coefficient, and 13.64 % and 22.73 % in Cohen's Kappa. Additionally, there is a reduction of 36.84 % and 52.63 % in hamming loss, and 75.86 % and 82.70 % in log loss, respectively. These findings demonstrate the effectiveness of the proposed system for accurate and efficient intrusion detection in IoT environments.

The rapid expansion of Internet of Things (IoT) networks has made them increasingly vulnerable to diverse cyber threats, necessitating the development of efficient Intrusion Detection Systems (IDS). Traditional models for IDS often face challenges such as data imbalance, scarcity of labeled samples, and suboptimal performance due to manual hyperparameter tuning. To address these issues, we propose a comprehensive IDS framework comprising three key components. First, we mitigate data imbalance using the proximity weighted synthetic oversampling technique, which enhances class distribution, followed by the use of Pointer Network (PtrNet) for classification due to its ability to model variable-length sequential data. Second, to handle the scarcity of labeled data, we introduce an entropy-based active learning strategy on PtrNet, termed Entropy-based Active Learning Pointer Network (EAL-PNet). Finally, we optimize model performance through harris hawk optimization applied to PtrNet, resulting in Hawk-Pointer Attention Network (HPA-Net). Experimental results demonstrate that the proposed models significantly outperform traditional approaches. EAL-PNet achieves a performance improvement of 9.30% in accuracy, 8.14% in F1-score, 8.14% in precision, 9.30% in recall, 3.16% in Receiver Operating Characteristic - Area Under the Curve (ROC-AUC), 13.92% in Matthews Correlation Coefficient (MCC) and Cohen's Kappa, and 45.71% reduction in log loss. Similarly, HPA-Net shows a 10.47% gain in accuracy, 10.47% in F1-score, 9.30% in precision, 10.47% in recall, 2.11% in ROC-AUC, 15.19% in MCC and Cohen's Kappa, and 51.43% decrease in log loss. These findings validate the effectiveness of the proposed framework in enhancing intrusion detection for IoT environments.

The increasing complexity and interconnectivity of Internet of Things (IoT) ecosystems have heightened the need for robust and intelligent intrusion detection mechanisms. However, the development of effective detection models is impeded by challenges such as imbalanced data distributions, limited availability of labeled samples, and the difficulty of tuning deep learning architectures to accommodate diverse threat patterns. In response to these challenges, this paper introduces two novel DenseNet-based frameworks, DN-UBS and DN-GBO, for advanced IoT intrusion detection. The proposed approach begins by applying a variance threshold technique on RT-IoT2022 dataset, to eliminate low-variance features, followed by synthetic minority oversampling technique to alleviate class imbalance and enhance minority class representation. DN-UBS integrates an uncertainty-based sampling strategy to iteratively select the most ambiguous instances for annotation, reducing labeling effort while improving model discriminability. In contrast, DN-GBO incorporates a gradient-based hyperparameter optimization using the hyperband strategy, allowing for automatic adjustment of network depth, learning rate, and regularization parameters. The DN-GBO achieved superior detection performance with an improvement of 7% in accuracy, 4% in F1-score, 12.8% in precision, 6 % in recall, 3 % in Receiver Operating CharacteristicArea Under the Curve (ROC-AUC), and 17.6 % in Matthews Correlation Coefficient (MCC). Similarly, DN-UBS also delivered high efficacy with an improvement of 5.3 % in accuracy, 3 % in F1-score, 3% in precision, 4.6% in recall, 4% in ROC-AUC, and 10.1 % in MCC, while minimizing reliance on labeled data. These findings highlight the effectiveness of the proposed models in delivering scalable, adaptive, and data-efficient solutions for securing IoT infrastructures against intrusive threats.

ABSTRACT Hypertension continues to be a primary cause of global death, necessitating early and accurate forecasting for effective treatments. The existing methods have drawbacks such as class imbalance, poor modeling of sequential and spatial connections, high computation costs, and lack of interpretability, even though Deep Learning (DL) models offer possible solutions. To tackle these difficulties, we present VahigoNet, a novel blending DL model that incorporates vanilla recurrent neural networks (VRNN) for capturing temporal correlations, Google network for extracting hierarchical spatial features, and highway networks (HighwayNet) for adaptive feature refinements. To achieve strong generalization, we utilize the synthetic minority oversampling technique (SMOTE) for data balance. VahigoNet substantially outperforms baseline models, showing enhancements of 9.39% in accuracy, 10.27% in precision, 8.63% in recall, 9.39% in F1‐score, and 3.10% in area under the curve‐receiver operating characteristic. A 10‐fold cross validation method is utilized to assess the model's generalizability, markedly reducing overfitting and improving robustness. A paired t ‐test is performed to evaluate statistical significance, demonstrating that the enhancements are substantial and clinically relevant. Additionally, explainable artificial intelligence (AI) methodologies, including local Interpretable model‐agnostic explanations (LIME) and SHapley Additive exPlanations, are incorporated to provide both local and global perspectives on feature contributions. These explainability strategies enhance transparency, making VahigoNet a more interpretable and clinically reliable model for hypertension prediction. The results demonstrate that VahigoNet is an exceptionally efficient and transparent method, achieving a balance between predictive capability and practical relevance in medical diagnostics.

Abstract In this paper, a hybrid blockchain‐based authentication scheme is proposed that provides the mechanism to authenticate the randomly distributed sensor IoTs. These nodes are divided into three types: ordinary nodes, cluster heads and sink nodes. For authentication of these nodes in a Wireless Sensor IoTs (WSIoTs), a hybrid blockchain model is introduced. It consists of both private and public blockchains, which are used to authenticate ordinary nodes and cluster heads, respectively. Moreover, to handle the issue of cluster head failure due to inefficient energy consumption, Improved Heterogeneous Gateway‐based Energy‐Aware Multi‐hop Routing (I‐HMGEAR) protocol is proposed in combination with blockchain. It provides a mechanism to efficiently use the overall energy of the network. Besides, the processed data of subnetworks is stored on blockchain that causes the issue of increased monetary cost. To solve this issue, an external platform known as InterPlanetary File System (IPFS) is used, which distributively stores the data on different devices. The simulation results show that our proposed model outperforms existing clustering scheme in terms of network lifetime and data storage cost of the WSIoTs. Our proposed scheme increases the lifetime of the network as compared to existing trust management model, intrusion prevention and multi WSN authentication schemes by 17.5%, 24.2% and 19.6%, respectively.

Summary Wireless sensor networks (WSNs) have attained a great attraction of researchers in the recent years. In these networks, many structures are considered that have different properties. This article offers a unique approach, the inter‐core based reconnection strategy (ICRS), which is intended to improve the robustness of Scale‐Free Networks (SFNs) in the setting of wireless sensor networks (WSNs), with a special emphasis on the Internet of Health Things (IoHT) network. SFNs' vulnerabilities to malicious assaults while remaining resilient to random attacks. The proposed ICRS overcomes this issue by offering a novel reconnection approach that employs separate edges between network centers. Destructive assaults that have a significant impact on network connectivity, emphasizing the importance of a robust network that can resist a variety of attacks. ICRS is positioned as a solution that optimizes the network via reconnection techniques, changing it into an onion‐like structure with increased robustness. The simulation results depict that ICRS outperforms the existing algorithms in terms of robustness enhancement. The results show that ICRS performs 48%, 29%, 22%, and 16% better than Barabasi Albert (BA), Hill Climbing (HC), Simulated Annealing (SA), Random Edge Swap Mechanism (RESM), and Robustness Strategy (ROSE), respectively.

Abstract In this article, Internet of Things (IoTs) devices are used for sensing the data through which the device owners earn revenue. Interested users can purchase data from IoT device owners, according to their demands. However, users are not confident about the quality of data they are purchasing. Moreover, the users do not rely on the device owner and are not willing to initiate data trading. Currently, data trading systems have many drawbacks, as they involve a third party, security and reputation mechanisms. Therefore, in this article, IoTs and BlockChain (BC) are integrated to monetize IoT's data and provide trustful data trading. A BC based review system to monetize IoT's data trading is developed through Ethereum smart contracts. The review system encourages the owners to provide authentic data and solves the issues regarding data integrity, fake reviews and conflicts between entities. Reviews and ratings are stored in the BC database for providing a guarantee about the data quality to users. To maintain data integrity, we use an advanced encryption standard (AES)‐256 encryption technique to encrypt data. Moreover, an arbitrator entity is responsible to resolve conflicts between data owner and users. The incentive is provided to the users and arbitrators to increase user participation and honesty. Simulations are performed for the validation of our system. We examine the proposed model using three parameters: gas consumption, mining time and encryption time.

This article proposes an energy trading model basedon blockchain to manage and supervise the trading process. In the model, proof-of-energy reputation generation and proof-of-energy reputation consumption consensus mechanisms are proposed to solve the high computational cost and huge monetary investment issues created by the existing consensus mechanisms. Similarly, a mutual verifiable fairness mechanism based on time commitment is presented, which is introduced to prevent cheating attacks in the model. The proposed model’s performance is assessed using energy cost, peak-to-average ratio, and trust. The simulation results show that the energy cost of the proposed model decreases by 40%. The results for the load balancing depict that the values of peak-to-average ratio of the proposed model with 20% and 50% peak demand reduction are 6.88 and 3.50, which are lower than 9.17 of the benchmark model. Moreover, the proposed model’s results show satisfactory performance for privacy and security of the system.

A Smart Community (SC) is an essential part of the Internet of Energy (IoE), which helps to integrate Electric Vehicles (EVs) and distributed renewable energy sources in a smart grid. As a result of the potential privacy and security challenges in the distributed energy system, it is becoming a great problem to optimally schedule EVs’ charging with different energy consumption patterns and perform reliable energy trading in the SC. In this paper, a blockchain‐based privacy‐preserving energy trading system for 5G‐deployed SC is proposed. The proposed system is divided into two components: EVs and residential prosumers. In this system, a reputation‐based distributed matching algorithm for EVs and a Reward‐based Starvation Free Energy Allocation Policy (RSFEAP) for residential homes are presented. A short‐term load forecasting model for EVs’ charging using multiple linear regression is proposed to plan and manage the intermittent charging behavior of EVs. In the proposed system, identity‐based encryption and homomorphic encryption techniques are integrated to protect the privacy of transactions and users, respectively. The performance of the proposed system for EVs’ component is evaluated using convergence duration, forecasting accuracy, and executional and transactional costs as performance metrics. For the residential prosumers’ component, the performance is evaluated using reward index, type of transactions, energy contributed, average convergence time, and the number of iterations as performance metrics. The simulation results for EVs’ charging forecasting gives an accuracy of 99.25%. For the EVs matching algorithm, the proposed privacy‐preserving algorithm converges faster than the bichromatic mutual nearest neighbor algorithm. For RSFEAP, the number of iterations for 50 prosumers is 8, which is smaller than the benchmark. Its convergence duration is also 10 times less than the benchmark scheme. Moreover, security and privacy analyses are presented. Finally, we carry out security vulnerability analysis of smart contracts to ensure that the proposed smart contracts are secure and bug‐free against the common vulnerabilities’ attacks. The results show that the smart contracts are secure against both internal and external attacks.

In this paper, a blockchain based scheme is proposed to provide registration, mutual authentication and data sharing in wireless sensor network. The proposed model consists of three types of nodes: coordinators, cluster heads and sensor nodes. A consortium blockchain is deployed on coordinator nodes. The smart contracts execute on coordinators to record the identities of legitimate nodes. Moreover, they authenticate nodes and facilitate in data sharing. When a sensor node communicate and accesses data of any other sensor node, both nodes mutually authenticate each other. The smart contract of data sharing is used to provide a secure communication and data exchange between sensor nodes. Moreover, the data of all the nodes is stored on the decentralized storage called interplanetary file system. The simulation results show the response time of IPFS and message size during authentication and registration.

Abstract Electricity theft (ET) causes major revenue loss in power utilities. It reduces the quality of supply, raises production cost, causes legal consumers to pay the higher cost, and impacts the economy as a whole. In this article, we use the State Grid Corporation of China (SGCC) dataset, which contains electricity consumption data of 1035 days for two classes: normal and fraudulent. In this work, ET detection model is proposed that consists of four steps: interpolation, data balancing, feature extraction, and classification. First, missing values of the dataset are recovered using the interpolation method. Second, resampling technique is implemented. ET consumers are 9% in the SGCC dataset that make the model inefficient to correctly classify both classes (normal and theft). A hybrid resampling technique is proposed, named synthetic minority oversampling technique with near miss. Third, residual network extracts the latent features from the SGCC dataset. Fourth, three tree based classifiers, such as decision tree (DT), random forest (RF), and adaptive boosting (AdaBoost) are applied to train the encoded feature vectors for classification. Besides, search for good hyperparameters is a challenging task, which is usually done manually and takes a considerable amount of time. To resolve this problem, Bayesian optimizer is used to simplify the tuning process of DT, RF, and AdaBoost. Finally, the results indicate that RF outperforms DT and AdaBoost.

This paper presents a hybrid model, named as hybrid deep neural network, which combines convolutional neural network, particle swarm optimization, and gated recurrent unit, termed as convolutional neural network‐particle swarm optimization‐gated recurrent unit model. The major aims of the model are to perform accurate electricity theft detection and to overcome the issues in the existing models. The issues include overfitting and inability of the models to handle imbalanced data. For this purpose, the electricity consumption data of smart meters is taken from state grid corporation of China. An electric utility company gathers the data from the intelligent antenna‐based smart meters installed at the consumers’ end. The dataset contains real‐time data with missing values and outliers. Therefore, it is first preprocessed to get the refined data followed by feature engineering for selection and extraction of the finest features from the dataset using convolutional neural network. The classification of electricity consumers is performed by dividing them into honest and fraudulent classes using the proposed particle swarm optimization‐gated recurrent unit model. The proposed model is evaluated by performing simulations in terms of several performance measures that include accuracy, area under the curve, F1‐score, recall, and precision. The comparison between the proposed hybrid deep neural network and benchmark models is also performed. The benchmark models include gated recurrent unit, long short term memory, logistic regression, support vector machine, and genetic algorithm‐based gated recurrent unit. The results indicate that the proposed hybrid deep neural network model is more efficient in handling class imbalanced issues and performing electricity theft detection. The robustness, accuracy, and generalization of the model are also analyzed in the proposed work.

Supply chains are evolving into automated and highly complex networks and are becoming an important source of potential benefits in the modern world. However, it is challenging to track the provenance of data and maintain traceability throughout the network. The traditional supply chains are centralized and dependent on third party for trading. Centralized systems lack transparency, accountability and auditability. In our proposed solution, we have presented a blockchain-based reputation system in Agriculture and Food (Agri-Food) supply chain. It leverages the key features of blockchain and smart contract deployed over ethereum blockchain network. Although blockchain provides an immutable network for supply chain events, it still fails to solve the problem of trust among entities. Therefore, a reputation system is required that logs reviews of sellers and maintains the trust between trading entities.

In a multi-agent system (MAS), the trust of each agent has become hot research issues in the smart grids. The traditional trust systems that use access control and cryptography are not sufficient to handle the dynamic behavior of agents. Also, they are inefficient to solve the computational overhead of the cryptographic primitives. Based on these limitations, this paper proposes a blockchain-based trust management system for MAS. The proposed system consists of two layers: a lower layer that enables an agent to perform direct and indirect trust evaluations of other agents during interactions. Multi-source feedback from the interactions among different aggregators is feed to the blockchain. The upper layer is used to perform trust credibility of agents based on trust distortion, consistency and reliability. The credibility evaluation is used to determine the dynamic behavior of agents and also detect dishonest agents in the system. Trust model and security analysis of the proposed system are provided. Moreover, simulation results evaluate the effectiveness of the proposed trust system while the system is secure against bad-mouthing and on-off attacks.

Local energy generation and peer to peer (P2P) energy trading in the local market can reduce energy consumption cost, emission of harmful gases (as renewable energy sources (RESs) are used to generate energy at user's premises) and increase smart grid resilience. In this paper, to implement a hybrid P2P energy trading market, a blockchain-based solution is proposed. A blockchain-based system is fully decentralized and it allows the market members to interact with each other and trade energy without involving any third party. Smart contracts play a very important role in the blockchain-based energy trading market. They contain all the necessary rules for energy trading. We have proposed three smart contracts to implement the hybrid electricity trading market. The market members interact with main smart contract which requests P2P smart contract and prosumer to grid (P2G) smart contract for further processing. The main objectives of this paper are to propose a model to implement an efficient hybrid energy trading market while reducing cost and peak to average ratio (PAR) of electricity.

Modern power grids depend on the Advanced Metering Infrastructure (AMI) for consumption monitoring, energy management and billing. However, AMIs are vulnerable to electricity theft cyber attacks due to addition of communication layer. Electricity theft is one of the major Non-Technical Losses (NTLs) in the electricity distribution systems that has become a global concern, recently. Although the machine learning techniques are widely used for Electricity Theft Detection (ETD) in literature, some significant challenges need to be address. (i) The consumption data is usually unlabeled, there should be proper method to label the data. (ii) The fair consumers significantly outnumber the fraudulent consumers, which negatively impacts the performance of classification algorithm. (iii) The performance of classifier must be validated using proper performance evaluation measures. In this paper, an enhanced ETD model is proposed that is an optimized classifier Differential Evaluation Random Under Sampling Boosting (DE-RUSBoost) is used for classification. Proposed classifier DE-RUSBoost is optimized using a metaheuristic optimization algorithm named Differential Evaluation (DE). The proposed method is evaluated on a real-world dataset, i.e., State Grid Corporation of China (SGCC) datasets. DE-RUSBoost achieves the highest accuracy of 96% and low false detection rate of 0.004. The proposed method outperforms its counterparts in terms of accuracy and false detection rate.

Smart grid (SG) is an information technology-enhanced power grid, which provides a two-way communication network between energy producers and customers. Also, it includes renewable energy (RE), smart meters, and smart devices that help to manage energy demands and reduce energy generation costs. However, SG is facing inherent difficulties, such as security-based reliability issues and energy inadequacy. In addition, existing energy planning models like levelized cost of energy (LCOE) that evaluate the cost of RE do not measure the impact of reliability on energy cost. LCOE is a method used to compare the economic costs of RE and non-RE. However, the main problem of evaluating RE based on LCOE is that it does not consider the fill rate (FR) and service level (SL) effect. This paper proposes a direct communication-based LCOE (BLCOE) model as the least-cost solution that measures the impact of energy reliability on generation cost using FR and SL. The model also considers daily variations in the cost of solar modules and battery storage across sub-Sahara Africa (SSA). Furthermore, Quasi-Newton's method is employed to optimize the capacity of solar module and battery storage. Simulation results show the reduction of energy costs by approximately 95% for battery and 75% for the solar modules. The future BLCOE varies across SSA on an average of about 0.049 $/kWh as compared to 0.15 $/kWh of an existing LCOE used in the literature.

The high penetration of renewable energy resources into the distributed system and their intermittent behavior of the non-dispatchable generation causes issues of demand supply mismatch and serious security and privacy concerned in the system. It is believed that incorporating blockchain will reduce costs, enhance data security, and improve the system efficiency. However, privacy issues are not completely eliminated and can hinder the wide applications of blockchain. In the study, we present a Reputation Based Starvation Free Energy Allocation Policy (Reputation-SFEAP) in a decentralized and distributed blockchain-based energy trading; while keeping Aggregator as Common Communication Point. In addition, Identity-Based encryption (ID-Based encryption) technique is added that improves transactional information privacy. According to the research analysis, it is observed that the proposed system model has optimal and fair energy allocation algorithms, which prevent all the energy users from energy starvation and share the available energy accordingly. Moreover, the incorporated encryption system has greater security-privacy level, which protects passive attacker and disguises attacker from penetration.

In this study, we propose a privacy preservation and efficient distributed searching and matching of Electric Vehicles (EVs) charging demander with suppliers based on reputation. Partially homomorphic encryption-based on reputation computation using local communication is used in the implementation, while hiding EVs users' location. A private blockchain is incorporated in the system to verify and permit secure trading of energy among the EVs' demander and suppliers. The results of the simulation show that the proposed privacy preserved algorithm converges more faster as compared to Bichromatic Mutual Nearest Neighbor (BMNN) algorithm.

Electricity theft is the primary cause of electrical power loss that significantly affects the revenue loss and the quality of electrical power. Nevertheless, the existing methods for the detection of this criminal behavior of theft are diversified and complicated since the imbalanced nature of the dataset, and high dimensionality of time-series data make it challenging to extract meaningful information. This paper addresses these problems by developing a novel electricity theft detection model, integrating three algorithms in a pipeline. The proposed method first applies the synthetic minority oversampling technique (SMOTE) for balancing the dataset, secondly integration of kernel function and principal component analysis (KPCA) for the feature extraction from high dimensional time-series data, and support vector machine (SVM) for the classification. Besides, the performance of the proposed pipeline is measured using a comprehensive list of performance metrics. Extensive experiments are performed by using real electricity consumption data, and results show that the proposed method outperforms other methods in terms of theft detection.

In this paper, we deal with some major energy issues related to the charging of vehicles in vehicular network. The exponential increase of Electric Vehicles (EVs) has led to the more complex problems. In general, there are two major issues related to th EVs. First, its difficult to find a nearest charging station with required energy. Second, how much energy is needed to reach charging station from current location. In traditional systems, the energy trading between charging station and EVs is not secured due to centralized girds. To deal with this problem, a consortium blockchain based secure energy trading system is proposed. Blockchain is used for secure energy trading with moderate cost. The main purpose of the proposed system is resource reduction and find out the present state of charging stations. Simulations and results show that the proposed schemes outperform the conventional schemes in terms of minimizing the charging cost of battery and expenses of EVs.

An Intelligent Transportation System (ITS) aims to achieve efficiency of traffic by minimizing its problems, such as traffic congestion, road accidents, etc. It is not only limited to control traffic congestion but also enhances the safety and comfort of the commuters. For road traffic safety and efficient infrastructure usage, vehicles need to communicate with each other to disseminate information related to traffic. However, vehicles cannot directly communicate with each other and other infrastructure because of privacy and security concerns. In the proposed work, blockchain is implemented on Road Side Units (RSUs) that are used to provide reliable communication between vehicles. Furthermore, cloud and edge servers are used to tackle the storage issue. We proposed a Transactions Filtering Pattern Matching Scheme (TFPMS) to filter the transactions before sending them to the blockchain network. In this way, it saves storage space and reduces computational overhead of blockchain. Moreover, we are exploiting consortium blockchain to implement our proposed scheme. Simulations are performed based on the number of transactions and cost to achieve high-quality data sharing between vehicles, which result in a reduction in storage overhead as compared to the existing schemes.

A large amount of data is involved in an effective and timely exchange of traffic information between vehicles in Vehicular Ad-hoc Networks (VANETs), which ensures efficiency and reliability. VANETs assist in sharing traffic information effectively and timely to improve traffic efficiency and reliability. However, less storage capability and selfish behavior of the vehicles are important issues that need to be tackled. Moreover, traditional storage mechanisms require the involvement of third parties, which are insecure, untrustworthy, non-transparent, and unreliable. To overcome these issues, we proposed a blockchain-based data storage scheme for VANETs by exploiting the benefits of the Interplanetary File System (IPFS), which is deployed on Road Side Units (RSUs). Furthermore, RSUs are able to receive the aggregation packet comprising of the event information acquired from the vehicles. After receiving and verifying the aggregation packet, the RSU stores the event's information in IPFS and the reputation values of vehicles in blockchain. Moreover, we proposed an incentive mechanism in this work, in which monetary incentives are given to the repliers who agree with the vehicle regarding the event information. The incentives are given by the initiator after verifying the signatures of the repliers. All the transactions involved in the incentive process are stored in blockchain. The simulation results prove the efficiency of the proposed scheme in terms of transaction cost and storage savings in VANETs.

Advertisement sharing in vehicular network through vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication is a fascinating in-vehicle service for advertisers and the users due to multiple reasons. It enable advertisers to promote their product or services in the region of their interest. Also the users get to receive more relevant ads. Usually, users tend to contribute in dissemination of ads if their privacy is preserved and if some incentive is provided. Recent researches have focused on enabling both of the parameters for the users by developing fair incentive mechanism which preserves privacy by using Zero-Knowledge Proof of Knowledge (ZKPoK) (Ming et al., 2019). However, the anonymity provided by ZKPoK can introduce internal attacker scenarios in the network due to which authenticated users can disseminate fake ads in the network without payment. As the existing scheme uses certificate-less cryptography, due to which malicious users cannot be removed from the network. In order to resolve these challenges, we employed conditional anonymity and introduced Monitoring Authority (MA) in the system. In our proposed scheme, the pseudonyms are assigned to the vehicles while their real identities are stored in Certification Authority (CA) in encrypted form. The pseudonyms are updated after a pre-defined time threshold to prevent behavioural privacy leakage. We performed security and performance analysis to show the efficiency of our proposed system.

In today's world, Internet of Things (IoT) helps people in many fields by enabling smart city projects in health monitoring, smart parking, industrial optimization, home energy management, etc. Daily life objects are connected with the Internet to allow access to their owners to keep an eye on their surroundings. The IoT network is comprised of nodes that are smart enough to perform any function and provide benefits to the people. However, any fault in the network opens up the risk of leaking personal information. The aim is to develop a scale-free network, which controls the effects of malicious attacks and consequently improves the network robustness. In this paper, our prime focus is to mitigate the effect of malicious nodes by providing a robust strategy to maintain the network stability. In this regard, we propose a topology named as a Cooperation based Edge Swap (CES) for improving the network robustness in the scale-free network. The CES uses the edge/link selection mechanism by involving the cooperation using a Rayleigh fading to swap the network topology for improving the network robustness. The simulations' outcome depicts the performance of the CES in terms of improving the network robustness.

The significance of electricity cannot be overlooked in terms of advancements in economic and technological fields. In this study, Ensemble Empirical Mode Decomposition (EEMD) method in combination with the Ensemble Bi-Long Short Term Memory (EBiLSTM) and Support Vector Machine (SVM) is used. Non linear and non stationary IMFs are forecast using EBiLSTM forecasting algorithm as it performs efficiently in complex and non linear scenario. Whereas, linear IMFs are forecast using SVM as EBiLSTM take high computational time unlike SVM. The proposed technique EEMD-EBiLSTM-SVM gives good results.

The radically increasing amount and enormous types of data generated by vehicles have brought in the innovated application of data trading in the Internet of Vehicles (IoV). However, the trustless environment in IoV enabled data trading faces conflicting interests and disputes of trading parties. To build trust, we exploit consortium blockchain for secure data trading with information transparency. In addition, a hash list of traded data is maintained by roadside units accompanied by bloom filters for fast lookup, to avoid data duplication. The reliability and integrity of trading data are ensured by using the digital signature scheme based on elliptic curve bilinear pairing. For long term availability of traded data, an external distributed storage, i.e., InterPlanetary File System (IPFS) can provide reliable, high capacity storage resources. The experimental results verified that our proposed solution is efficient for data trading in IoV and reliable for long term availability of data storage.

In this paper, a Hybrid Deep Neural Network (HDNN) is proposed in this work, which is the combination of Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU) and Particle Swarm Optimization (PSO), termed as CNN-GRU-PSO HDNN. In this paper, real time electricity consumption data of consumers is used, which is taken from an easily available online source, named as State Grid Corporation of China (SGCC). The original dataset consists of actual values along with the erroneous and missing values. The pre-processing steps are performed initially to refine the data. After that, feature selection and extraction are performed using CNN, which reduce both the dimensionality and the redundancy present in the dataset. Furthermore, the classification of provided data into honest and fake consumers is done using GRU-PSO technique. The proposed HDNN model's performance is then compared with various benchmark techniques like Logistic Regression (LR), Support Vector Machine (SVM), Long Short Term Memory (LSTM) and GRU. The efficiency of the proposed model is validated using various performance parameters like Area Under the Curve (AUC), precision, accuracy, recall and F1-Score. The simulation results show that the proposed model outperforms the existing techniques in terms of ETD and class imbalanced issues. Moreover, the proposed model is also more robust and accurate than the existing methods.

In today's modern world, people are cultivating towards the Internet of Things (IoT) networks due to their various demands in health monitoring, smart homes, traffic management, and industrial optimization, etc., IoT networks comprise of sensor nodes that have multiple functionalities to fulfill the demands of individuals. With the advancement in technology, the need for IoT networks is increasing as the devices are getting smarter day by day. The scale-free topology is considered to be the best topology for IoT networks because it is more robust against the attacks. For a scale-free network, robustness optimization is essential. Therefore, in this paper, to enhance the robustness, we have optimized a scale-free network through proposed the Improved Scale-Free Network (ISFN) technique. In ISFN, the edges are swapped based on their degree and nodes distance operation. This technique does not change the degree of the nodes of original topology which makes the optimized topology remains scale-free. Through experiments, we have compared the ISFN with two existing techniques, i.e., ROSE and SA. The results prove that by increasing the number of nodes, ISFN outperforms these existing techniques.

Internet of Things (IoTs) is widely growing domain of the modern era. With the advancement in technologies, the use of IoTs devices also increases. However, security risks regarding service provisioning and data sharing also increases. There are many existing security approaches. However, these approaches are not suitable for IoTs devices due to their limited storage and computation resources. These secure approaches also require a specific hardware. With the invention of blockchain technologies, many security risks are eliminated. Blockchain also supports data sharing mechanism. In this paper, we proposed a secure service providing mechanism for IoTs using blockchain. We introduced cloud nodes for maintaining the validity states of edge service providers. The edge node reputation is considered as a service rating given by end users. Incentive is given to edge servers after validation of service codes. Incentive is in the form of cryptocurrency. Incentive and edge node reputation values are stored in cloud node and are updated with respect to time. Smart contract is proposed to check the validity state of the edge servers. Smart contract is also used for the comparison and verification of the service codes provided by edge servers. In our proposed system, we perform service authentication at both cloud and edge server layer. Moreover, Proof of Authority (PoA) is used as a consensus mechanism. PoA enhanced overall performance of our proposed system. By experimental analysis, it is shown that our proposed model is suitable for resource constrained devices.

The emergence of smart home appliances has generated a high volume of data on smart meters belonging to different customers. However, customers can not share their data in deregulated smart grids due to privacy concern. Although, these data are important for the service provider in order to provide an efficient service. To encourage the customers' participation, this paper proposes an access control mechanism by fairly compensating customers for their participation in data sharing via blockchain using the concept of differential privacy. We addressed the computational issues of existing ethereum blockchain by proposing a proof of authority consensus protocol through the Pagerank mechanism in order to derive the reputation scores. Experimental results show the efficiency of the proposed model to minimize privacy risk, and maximize aggregator's profit. In addition, gas consumption, as well as the cost of the computational resources, is reduced.

Buffer-aided cooperative relaying is often investigated either using decode and forward (DF) or amplify and forward (AF) relaying rules. However, it is seldom investigated using the hybrid decode-amplify-forward (HDAF) relaying rule. In this work, the performance of signal-to-noise ratio (SNR) based HDAF relaying rule is followed for buffer-aided cooperative relaying. Relay with the best possible corresponding channel is determined for reception or transmission. When source to relay hop is the most powerful, data is forwarded to chosen relay and its SNR is compared against the predefined SNR threshold at the relay. If it is greater than the threshold, the decoded data is saved in the corresponding buffer. Otherwise, the amplified data is saved in the respective buffer. When relay to destination link is the most powerful, data is forwarded to the destination. The famous Markov chain analytical model is used to illustrate the progression of the buffer state and to get the outage probability expression. Mathematical and simulation outcomes support our findings and prove that the outage probability performance of the proposed technique beats the existing SNR based buffer-aided relaying protocols based on DF and AF relaying rules by 2.43 dBs and 8.6 dBs, respectively.

Despite significant performance gains, buffer-aided cooperative communication incurs an increased latency. It is handled by prioritizing relay-to-destination links. The contribution of this paper is two-fold, firstly, we studied buffer threshold based decode-and-forward (DF) relaying for Rayleigh fading channels in Nakagami-m fading channels. Secondly, we present the outage analysis of buffer occupancy based amplify and forward (AF) relaying by introducing a modified threshold in terms of signal to noise ratio at relay and destination, which enables the conventional Markov chain (MC) based analysis of DF relaying to work for AF relaying with slight modifications. Using this approach, we evaluate the system using MC-based analysis for outage probability, latency and throughput. The results depict that the buffer threshold based relaying can significantly decrease the latency and increase the average throughput by negotiating the outage probability. Furthermore, extensive Monte-Carlo simulations are carried out to prove the theoretical outcomes.

In recent times, different routing protocols have been proposed in the Internet of Things enabled Underwater Wireless Sensor Networks (IoT-UWSNs) to explore the underwater environment for different purposes, i.e., scientific and military purposes. However, high Energy Consumption (EC), End to End (E2E) delay, low Packet Delivery Ratio (PDR) and minimum network lifetime make the energy efficient communication a challenging task in Underwater Wireless Sensor Network (UWSN). The high E2E delay, EC and reliable data delivery are the critical issues, which play an important role to enhance the network throughput. So, this paper presents two energy efficient routing protocols namely: Shortest Path-Collision avoidance Based Energy Efficient Routing (SP-CBE2R) protocol and Improved-Collision avoidance Based Energy Efficient Routing (Im-CBE2R) protocol. At this end, both routing protocols minimize the probability of void hole occurrence and in return minimizes the EC and E2E delay. In both routing protocols, courier nodes are positioned at different strategic locations to keep the greedy forwarding continuous. The proposed routing protocols are also analyzed by varying the Packet Size (PS) and Data Rate (DR). Additionally, various simulations have been performed to authenticate the proposed routing protocols. Simulation results show that the proposed routing protocols outperform the baseline routing protocols in counterparts.

Cloud servers provide services over the internet by using Virtual Machines (VMs). The power consumption of Physical Machines (PMs) needs to be considered, as VMs are running on physical machines. When a consumer sends request to the cloud, it takes time to respond because of distant location of cloud. Due to which delay and latency issue arises. Fog is introduced to overcome the peculiarities of cloud. In fog computing environment, the operational challenges for the research community are: reducing the energy consumption and load balancing. The energy consumption of the fog resources depends on the requests that are allocated to the set of VMs. This is a challenging task. In this paper, three layered architecture cloud, fog and consumer layer are proposed. The cloud and fog provide VMs to run the consumers' application quickly. The meta-heuristic algorithm that is: Genetic Algorithm (GA) is proposed and Binary Particle Swarm Optimization (BPSO) is implemented to balance the set of requests on VMs of cloud and fog. The proposed and implemented algorithm is compared with existing PSO and BAT algorithms to measure efficiency. The Closest Data Center (CDC), Optimize Response Time (ORT), Reconfigure Dynamically with Load (RDL) is implemented to optimize the Response Time (RT) and Processing Time (PT). These policies also decide which requests are allocated to which Data Center (DC). The proposed GA and implemented BPSO are use to minimize the computational cost and also decrease the RT and PT of DCs.

Management of increasing amount of the electricity information provided by the smart meters is becoming more valuable and a very challenging issue in modern era, especially in residential sector for maintaining the records of consumers' consumption patterns. It becomes the necessity of retailers and utilities to provide the consumers more effective demand response programs for handling the uncertainties of their consumption patterns. In order to deal with the unceratian behaviours of the consumers and their unprecedented high volume of data, this work introduces the deep neuro-fuzzy optimizer for effective load and cost optimization. Three premises parameters: energy consumption, price and time of the day and two consequents parameters: peak and cost reduction are used for the opti-mization process of the optimizer. The dataset is taken from the Pecan Street Incorporation site and Takagi Sugeno fuzzy inference system is used for the evaluation of the rules developed from the memebership functions of the parameters. Membership Functions (MFs) are chosen as Guassian MFs for continuously monitoring the consumers' behaviours. Performance of this proposed energy optimizer is validated through the simulations which shows the robustness of optimizer in cost optimization and energy efficiency.

Forecasting of building energy consumption plays a key role in the energy management of the modern power system. However, the noise and randomness in the electricity load data makes it difficult to forecast accurate electricity load. In this paper, a novel scheme namely Empirical Mode Decomposition based Extreme Learning Machine (EMD-ELM) is proposed to forecast the electricity load consumption of a building. Randomness in the electric load data is removed using EMD, whereas, ELM is used to forecast the day, week and month ahead electricity load. To illustrate the usefulness of EMD-ELM, the performance is compared with the renowned neural networks namely Convolution Neural Network (CNN), Long Short Term Memory (LSTM) and ELM. The simulation results clearly indicate that EMD-ELM outperforms CNN, LSTM and ELM in forecasting the day, week and month ahead electricity load consumption of a building.

In this paper, we proposed a home energy management (HEM) scheme for minimization in electricity bills and reduction in peak load. This can be achieved by scheduling the usage timings of appliances (APP) for shifting load from peak hours (PHs) to OFF-peak hours (OPHs). In this study we proposed a technique which is hybrid of two bio-inspired optimization techniques chicken swarm optimization (CSO) and bacterial foraging optimization (BFA). Simulation results shows that proposed hybrid technique reduces the cost and load peaks by shifting load from PHs to OPHs and reduction in load peaks.

Nowadays, different schemes and ways are proposed to meet the user's load requirement of energy towards the Demand Side (DS) in order to encapsulate the energy resources. However, this Load Demand (LD) increases day by day. This increase in LD is causing serious energy crises to the utility and DS. As the usage of energy increases with the increase in user's demand respectively, the peak is increased in these hours which affect the customer's in term of high-cost prices. This issue is tackled using some schemes and their proper integration. Two-way communication is done by the utility through Smart Grid (SG) between utility and customers. Customers that show some good behavior and helps the utility to control this LD, can perform a key role here. In this paper, our main focus is to control the Customer Side Management (CSM) by reducing the peak generation from on-peak hours. In our scenario, we focus on saving the cost expenditure of users by giving them comfort and shifting the load of appliances from high LD hours to low LD hours. In this study, we adopt the optimization algorithms, like Bacterial Foraging Optimization Algorithm (BFOA), Flower Pollination Algorithm (FPA) and proposed our Hybrid Bacterial Flower Pollination Algorithm (HBFPA) to optimize the solution of our problem using the famous electricity scheme named as Critical Peak Pricing(CPP) with three different Operational Time intervals (OTIs). Simulations and results show that our scheme reduces the cost and peak to the average ratio by proper shifting the appliances from highly load demanding hours to the low demanding hours with the negligibly small difference between the maximum and minimum 90% of confidence interval.

This paper presents a new algorithm to solve the problem of electricity cost reduction by hybridization of two meta-heuristic techniques, i.e. Elephant Herding Optimization (EHO) and Firefly Optimization (FF). A home energy management controller (HEMC), based on scheduling of different household electrical tasks is proposed to maintain balance between load and demand profile. The objective of this study is to determine lowest cost while considering user comfort maximization factor and peak to average ratio (PAR). The proposed algorithm, i.e., Hybrid Elephant and Firefly (HEF) optimization is analyzed comparatively to its separate implemented versions to evaluate the performance and behavior towards scheduling process. Moreover, three different pricing models are used to calculate the total power consumption rate. Simulation results show that our proposed hybrid optimization technique performs more efficiently to achieve lowest cost and maximum consumer satisfaction.

The integration of smart meter infrastructure helps in bidirectional coordination and is used to gather huge amount of data. It helps in forecasting the power demand and generation. This further helps the energy management units to plan and take the efficient decisions for flexible power demand. However, still there is a chance of fluctuation in consumers' power demand. It requires an efficient solution that can manage the real time scenario. In this work, a game theory based coalition system for energy management is proposed where each home is considered as a player and intensive as slack power. This slack energy will be distributed among the homes using Shapley value that evenly distribute the power according to demand. Experimental results show that 14.2kW extra power is saved and distributed among homes different home during the different spans of the day.

In demand side management (DSM) scheduling of appliances based on consumer-defined priorities is an important task of a home energy management controller (EMC). This paper presents a DSM technique that is capable of controlling loads within a smart home considering time-varying appliances priorities. An evolutionary priority algorithm (EPA) was developed based on three postulations that allow time-varying priorities to be quantified in time and device-based features. Based on the input data considering the appliance's power ratings, its time of use, and absolute user comfort values the EPA is able to generate an optimal energy consumption pattern which would give maximum comfort at a predetermined user budget. A cost per unit comfort (χ) index, which relates the consumer expenditure to the achievable comfort is also demonstrated. To test the applicability of the proposed EPA, two budget scenarios of 1.5/day and 2.0/day are performed. The simulation results revealed that the proposed EPA obtained an optimal comfort value for the high budget with increased (χ).

Since the development of Smart Grid (SG), Home Energy Management (HEM) systems are emerged widely into it and consumers have an opportunity to schedule their smart appliances efficiently in smart homes. In this research, meta-heuristic techniques Harmony Search Algorithm (HSA), Pigeon Inspired Optimization (PIO) and our proposed Harmony Pigeon Inspired Optimization (HPIO) are adopted to efficiently schedule smart appliances in smart home. The aim of using the above proposed techniques is to reduce Electricity Cost (EC) and Peak-to-Average Ratio (PAR). HEM is proposed to further evaluate the performance of evaluated techniques. In this work, single home and multiple homes which consist of 10 ,30 and 50 homes are considered equipped with multiple smart appliances. These appliances are divided into three sets, which are thermostatically and non-thermostatically controllable, and non-controllable appliances under Time-ofUse (ToU) pricing scheme. Simulations are carried out on these parameters and results shows that proposed technique HPIO performed better than HSA and PIO in terms of minimizing waiting time and PAR. We have considered User Comfort (UC) in terms of waiting time.

In this paper modified opportunistic routing (MORR) protocol is propose, which focuses on addressing routing issues for wireless sensor networks (WSNs) for prolonging network lifetime and load balancing by selecting forwarder nodes, which have maximum residual energy so that network lifetime is enhanced. Furthermore, throughout the selection of forwarder node, channel interference is also taken into account to offer consistent communication. Consequently proposed scheme minimizes the chances of collision and packet duplication at the network layer by controlling number of forwarders and transmitters. Simulation results show that our scheme outperforms in term of average number of transmissions per packet, packet delay, energy consumption and residual energy as compared to baseline solution.

Routing hole problem is one of the most important issues in the underwater wireless sensor networks (UWSNs). It aims to analyze the routing hole boundary to prevent the formation of routing hole such that network lifetime and all other parameters are also increased. In this paper, we have proposed the two techniques to transmit the data in the presence of routing hole. In our protocol, the nodes transmit the data on the basis of an energy gradation (EG) and depth adjustment (DA) of the void nodes. However, these techniques are used for avoidance of routing hole. The efficient load balanced routing (ELBAR) using DA performed better than other two schemes. Simulation results depict that our schemes perform the better role in terms of energy efficiency, lifetime, deviation of energy consumption, path length and euclidean stretch.

Home energy management systems are widely used to cope up with the increasing demand for energy. They help to reduce carbon pollutants generated by excessive burning of fuel and natural resources required for energy generation. They also save the budget needed for installing new power plants. Price based automatic demand response (DR) techniques incorporated in these systems shift appliances from high price hours to low price hours to reduce electricity bills and peak to average ratio (PAR). In this paper, electricity load of home is categorized into three types: base load, shift-able interruptible load and shiftable non-interruptible load. In literature many metaheuristic optimization techniques have been implemented for scheduling of appliances. In this work for the optimization of energy usage genetic algorithm (GA) and bat algorithm (BA) are implemented with time of use (TOU) pricing scheme to schedule appliances to reduce electricity bills, the peak to average ratio and appliance delay time. A new technique bat genetic algorithm (BGA) has been proposed. It is hybrid of GA and BA. It outperforms GA and BA in terms of cost reduction and peak to average ratio for single home scenario as well as multiple home scenario. Operation time internals (OTIs) 15 minutes, 30 minutes and 1 hour have been considered to check their effect on cost reduction, PAR and user comfort (UC).

With the emergence of automated environment, energy demand by consumer is increasing day by day. More than 80% of total electricity is being consumed in residential sector. In this paper, a heuristic optimization technique is proposed for the efficient utilization of energy sources to balance load between demand and supply sides. An optimization technique is proposed which is a hybrid of Enhanced differential evolution (EDE) algorithm and Gray wolf optimization (GWO). The proposed scheme is named as hybrid gray wolf differential evolution (HGWDE). It is applied for home energy management (HEM) with the objective function of cost minimization and reducing peak to average ratio (PAR). Load shifting is performed from on peak hours to off peak hours on basis of user preference and real time pricing (RTP) tariff defined by utility. However, there is a trade off between user comfort and above mentioned parameters. To validate the performance of proposed algorithm, simulations have been carried out in MATLAB. Results illustrate that PAR and electricity bill have been reduced to 53.02%, and 12.81% respectively.

In this paper, our goal is to minimize the electricity cost, electricity consumption at minimum user discomfort while considering the peak electricity consumption. Electricity consumption may not be the same in residential, commercial and industrial areas. It may vary from each and every area. It is a challenging task to maintain the balance between the conflicting objectives: electricity consumption and user comfort. To meet the rising electricity demand in residential area, scheduleable devices can be equally distributed to the available time slots on the basis of average power consumption. The main objective is to minimize the electricity usage during the electricity peak hours by distributing the electricity load during the off-peak hours. In this regard, Genetic Algorithm (GA), Pigeon Inspired Optimization (PIO) and our proposed hybridization of GA and PIO (HGP) in Demand Side Management(DSM) are applied for residential load management to optimize the fitness function. GA, PIO and HGP are evaluated on the basis of real time pricing scheme (RTP) for single home with three different operational time interval (OTI) and for multiple homes with a single OTI. Simulations results shows that GA, PIO and HGP are able to minimize electricity bill and electricity consumption while minimizing the user discomfort. The performance of HGP is better than GA, PIO with respect to PAR, electricity load and electricity cost for both single home and multiple homes scenario. The feasible region between electricity cost and electricity consumption is also represented. Moreover, the desired trade-off between electricity cost and user comfort is also achieved in both techniques.

Home Energy Management Systems (HEMS) have been widely used for energy management in smart homes. Energy management in a smart home is a challenging task, which require efficient scheduling of appliances. The main focus of HEMS is to schedule the operation of appliances in such a way that it gives us optimized performance in terms of Peak to Average Ratio (PAR), Electric Cost (EC) minimization, execution time and User Comfort (UC). The Time of Use (ToU) pricing scheme is used in this paper. We used Genetic Algorithm (GA), Biogeography-based optimization (BBO) and our proposed hybrid Genetic Biogeography-based Optimization (GBBO), techniques to schedule appliances in single home and for multiple homes. Simulations are carried out using eight different appliances. The results show that GA and GBBO execute better in case of PAR reduction and EC minimization. GBBO outperforms in terms of user comfort. We calculated the UC in terms of waiting time.

In this paper, we propose a home energy management (HEM) scheme in the residential area for electricity cost and peak to average ratio (PAR) reduction. Furthermore, reduction in imported electricity from the external grid is also the objective of this study. Our proposed scheme schedules smart appliances as well as electrical vehicles (EVs) charging\discharging optimally according to the consumer preferences. Each consumer has its own grid-connected microgrid for electricity generation; which consists of wind turbine, solar panel, micro gas turbine (MGT) and energy storage system (ESS). Furthermore, the scheduling problem is mathematically formulated and solved by mixed integer linear programming (MILP). We also provide the comparison of the optimal solutions, while considering EVs with and without discharging capabilities. Findings from simulations affirm our proposed scheme in terms of above-mentioned objectives.

Fog computing concept is introduced to reduce the load on cloud and provide similar services as cloud. However, fog covers small area rather than cloud by storing the data temporarily and sends data to cloud for permanent storage. In this paper, an integrated fog and cloud based environment for effective energy management of buildings is proposed. So, the load on cloud and fog should be balanced. Various load balancing algorithms are used to manage the load among virtual machines (VMs). In this scenario, algorithm used for load balancing among VMs is round robin (RR). Service broker policies considered in this paper are; dynamically reconfigure with load (DR) and the proposed policy. New dynamic service proximity (DSP) service broker policy is proposed for fog selection and results of DSP policy are compared with DR policy. Therefore, a tradeoff is observed between cost and response time.

In this paper, a new orchestration of Fog-2-Cloud based framework is presented for efficiently managing the resources in the residential buildings. It is a three layered framework having: cloud layer, fog layer and consumer layer. Cloud layer is responsible for the on-demand delivery of the resources. Effective resource management is done through the fog layer because it minimizes the latency and enhances the reliability of cloud facilities. Consumer layer is based on the residential users who fulfill their daily electricity demands through fog and cloud layers. Six regions are considered in the study, where, each region has a cluster of buildings varying between 80 to 150 and each building has 80 to 100 homes. Load requests of the consumers are considered fixed during every hour in the complete day. Two control parameters are considered: clusters of buildings and load requests, whereas, three performance parameters: request per hour, response time and processing time are also included. These parameters are optimized by the round robin algorithm, equally spread current execution algorithm and our proposed algorithm shortest job first. The simulation results show that our proposed technique has outperformed the previous techniques in terms of the aforementioned parameters. Tradeoff occurs in the processing time of the algorithms as compared to response time and request per hour.

In energy constraint networks, the utilization of limited node battery is very crucial to enhance the network lifespan. The imbalanced node battery dissipation greatly effects the performance of the network. In this paper, we propose QLearning based energy-efficient and balanced data gathering routing protocol (QL-EEBDG). The effectiveness of a forwarder node is computed based on; residual energy of the source node and group energies of the neighbour nodes. The consideration of energy parameters provides complete control on the forwarder node selection and ensures efficient energy consumptions in the network. Still, due to topology changes, void node occurs which is avoided through adjacent node technique (QL-EEBDG-ADN). This scheme finds an alternate route via neighbor nodes to provide continuous communication among the network nodes. Simulations are performed to validate the effectiveness of proposed schemes against existing scheme based on energy tax, network lifetime.

This paper explored cooperative relaying in the presence of energy constrained relays with data storage facility. The relays depend only on the source signal to harvest energy and forward signal to the destination. The relay is selected according to the instantaneous strength of the wireless link. The strongest link among all links on both sides i.e., source-relay and relaydestination links is selected for relay to receive or transmit data, respectively. Two protocols are used for energy harvesting and information transfer namely: ”power splitting based relaying” and ”time switching based relaying”. We evaluate the outage probability performance of the presented scheme using Monte Carlo simulations. The results show that TSR performs better than PSR protocol.

This piece of work presents a novel design for buffer-aided relaying to increase the diversity gain. In this design, we used random buffer access method and associate each buffer location with its own antenna resource. Thus, each buffer location acts as an independent entity known as the virtual relay. The relay selection is based on the instantaneous strength of wireless link and the status of buffers. Markov chain is used to illustrate the growth of buffer status and to derive the closed-form expressions for the outage probability and diversity gain. The proposed design achieves the diversity gain of KL as compared to current buffer-aided max-link and max-max schemes having the diversity gain of 2K and K, respectively. Moreover, the proposed design achieves less delay at low SNR and 1+KL at high SNR. Analytical results are validated via simulation results.

In this paper, a demand side management (DSM) scheme is used to make the energy utilization efficient. The DSM scheme encourages the consumer to change the energy utilization patterns which benefit the utility. In return, consumer gets some incentives from the utility. The objectives of the proposed DSM system include: electricity bill reduction, peak to average ratio (PAR) minimization, and maximization of consumer comfort. In the proposed system, the electrical devices are scheduled by using elephant herding optimization (EHO) and adaptive cuckoo search (ACS) algorithms. Moreover, a new algorithm named as hybrid elephant adaptive cuckoo (HEAC) is proposed which uses the features of both former algorithms. The HEAC shows better performance as compared to EHO and ACS which is evident from the simulation results. Different electricity tariffs are introduced by the utility to provide incentives to the consumers. Time of use (ToU) tariff is used to make the system effective and enables the consumers to act according to the environment. The coordination can play a very important role in cost reduction as well as in user comfort maximization. The coordination is incorporated among the electrical devices by using dynamic programming (DP). Simulation results show the effectiveness of the proposed scheme in terms of electricity utilization cost, PAR reduction, and consumer comfort maximization.

This paper presents a decisive threshold termed as the buffer-threshold to control the selection probability of source-relay (SR) or relay-destination (RD) links for the buffer aided cooperative wireless networks. The weights of the links are reassigned using buffer-threshold and a link with the maximum weight is activated. The proposed scheme is termed as Buffer-Threshold based relay selection scheme (BTRS). The relations of the outage probability (OP) and the average delay are calculated by the Markov Modelling of the buffers. Theoretical results are analyzed for different cases of the buffer-threshold and validated by the Monte-carlo simulations. For the performance evaluation BTRS is compared with the max link relay selection (MLRS) scheme and the max weight relay selection (MWRS) scheme and outperforms its counterparts in terms of the average delay.

The integration of Smart Grid (SG) with cloud computing promises to develop an improved energy management system for utilities and consumers. New applications and services are developed which create large amount of data to be processed on cloud. Fog computing as an extension of cloud computing which helps to mitigate load on cloud data centers. In this paper, a three layered model based on cloud and fog framework is proposed to reduce load of consumers and power generation system. End user layer contains clusters of buildings which are connected to fog server layer. Fog layer is an intermediate layer which connects the end user layer to cloud layer. Three load balancing algorithms Round Robin (RR), throttled and proposed Particle Swarm Optimization with Simulated Annealing (PSOSA) are used for resource allocation. The service broker policy considered in this paper is optimized response time. The findings demonstrate that PSO-SA performs better than RR and throttled in order to alleviate response time, processing time and cost of virtual machine, microgrid and data transfer.

Smart Grid (SG) is a modernized electric grid that enhances reliability, efficiency, sustainability, and economics of electricity services. Moreover, it plays a vital role in modern energy infrastructure. The SG core challenges are, how to efficiently utilize different kinds of front end smart devices, such as smart meters and power assets, and in what manner, to process an enormous volume of data received from these devices. Further, the cloud and fog computing is a technology that provides on demand computational resources. It is a good solution to overcome these hurdles, then it has numerous good characteristics such as cost saving, energy saving, scalability, flexibility, and agility. In this paper, a cloud-fog based model is proposed for resource management in SG. The key idea of our model is to figure-out a hierarchical structure of cloud-fog computing to provide different types of computing services for resource management in SG. In addition, for load balancing, three algorithms are used: throttled, round robin and particle swarm optimization. The comparative discussion of these algorithms are presented in this paper.

Summary This paper presents four routing protocols for Underwater Sensor Networks (USNs): Location Error–resilient Transmission Range adjustment–based protocol (LETR), Mobile Sink–based GEographic and Opportunistic Routing (MSGER), Mobile Sink–based LETR (MSLETR), and Modified MSLETR (MMS‐LETR). LETR considers transmission range levels for finding neighbor nodes. If a node fails to find any neighbor node within its defined maximum transmission range level, it recovers from communication void regions using depth adjustment technology. MSGER and MSLETR avoid depth and transmission range adjustment and overcome the problem of communication void regions using MSs, whereas MMS‐LETR takes into account noise attenuation at various depth levels, elimination of retransmissions using multi‐path communication and load balancing. The performance of our proposed protocols is evaluated through simulations using different parameters. The simulation results show that MSS‐LETR supersedes all counterpart schemes in terms of packet loss ratio. LETR significantly improves network performance in terms of energy consumption, packet loss ratio, fraction of void nodes, and the total amount of depth adjustment.

This work proposes a residential load management system using multiagent technology with the objective of cost and comfort management. Smart appliances in a residential unit are modelled as agents which are controlled using optimization algorithm. These agents cooperate and communicate with each other using agent communication language (ACL) to reduce electricity cost and high peaks without affecting end user comfort. In this regards, different types of agents including load agents, price agent, temperature agent, management agent and optimal stopping rule (OSR) agent are considered. To make agent behaviour intelligent and adaptive, OSR is used. Simulations are conducted to provide the overview of residential load management using multiagent system (MAS). Simulation results show the effectiveness of MAS in terms of electricity cost, user comfort and peak shaving.

With the advent of smart grid (SG) and the emergence of information and communication technology, smart meters, bidirectional communication, smart homes and storage systems the energy consumption patterns at the consumer premises have been revolutionized. Moreover, with the rise of renewable energy sources (RESs), storage systems and electric vehicles (EVs) a profound amelioration in the energy management systems has been observed. Home energy management systems (HEMSs) help to control, manage and optimize the energy in smart homes. In this paper, we present a HEMS using multi-agent system (MAS) for smart homes. The HEMS uses priority techniques with the integration of electrical supply system (ESS). Furthermore, a bioinspired technique, binary particle swarm optimization (BPSO), is used for the optimal scheduling of appliances in a smart home. Simulation results illustrate the effectiveness of the HEMS in terms of electricity cost, demand, user comfort and peak to average ratio (PAR).

Design of energy efficient underwater wireless sensor networks (UWSNs) routing protocol to prolong network lifetime is a challenging task because of limited battery life of sensor nodes. In this paper, we propose mobile energy efficient square routing protocol (MEES) to balance energy consumption of nodes in the network. Two mobile sinks are deployed at the farthest distance from each other. In order to cover the maximum network field, both mobile sinks move linearly on the predefine path in clockwise direction. Sensor nodes transmit data directly to the mobile sink whenever it comes in its transmission range. Simulation results validate that our propose scheme outperforms the compared schemes (SEEC, BEEC) in terms of network lifetime, throughput, and energy consumption.

In this paper, a routing protocol; Maximum Coverage in Square field region (MCS) for Underwater Wireless Sensor Networks (UWSNs) is introduced. The overall area of the network is divided into ten sub regions and two mobile sinks (MSs) are deployed. The data is transmitted to the MS directly and mobility pattern of MS is adjusted in such a way that it covers the whole area of the network. When MS and sensor nodes are in transmission range of each other then data is transmitted. Simulation results show that MCS outperforms MC and EBECRP in terms of packet acceptance ratio and throughput.

Modeling of Underwater Wireless Sensor Networks (UWSNs) with a goal of maximum network lifetime and throughput with minimum energy consumption is a quite difficult task because of limited battery power and harsh underwater environment. Balanced Energy Efficient Rectangular routing protocol (BEER) covers the maximum network area with the mobility of sinks and collects the data from sensor nodes in their transmission range using direct transmission. Sink movement maximizes the throughput and balanced the energy consumption. Simulation results verify that our scheme performs outstanding in terms of network lifetime, stability period and throughput with minimum energy consumption.

Underwater wireless sensor networks (UWSNs) arouse as a better alternative of underwater wired instruments for data gathering. Acoustic signals offer low bandwidth and UWSNs faces low reliability, high delay and high energy consumption issues. Moreover, energy holes creation decreases network performance in terms of energy and throughput. The design of routing protocols which considers these challenges can improve data gathering. In this paper, we propose forward layered multipath power control-one (FLMPC-One) and FLMPC-Two routing protocols to reduce energy utilization, achieve reliability and elude energy holes. Both FLMPC-One and FLMPC-Two are multicast routing protocols. In order to achieve reliability, both schemes direct multiple copies towards surface through different paths which posses low noises by establishing binary tree. Mostly, current forwarder takes decision of next forwarder selection and gets deceived by energy holes. Therefore, FLMPC-One and FLMPC-Two makes decision by including two and three hops neighbors, respectively to detect and elude energy hole. In this way, they conserve energy and reduce delay introduced by retransmission.

In this paper, we propose an algorithm to alleviate coverage hole problem using geographic routing strategy for wireless sensor networks (WSNs). In order to accomplish desired results, an optimal number of forwarder nodes is computed along with the selection of path that has minimum energy consumption. Moreover, at each hop residual energy of a sensor is calculated and knowledge up-to one hop neighbors of forwarder node that ensures the avoidance of energy hole problem. Simulations are conducted to validate that our claim of outperforming compared existing schemes in terms of packet delivery ratio (PDR) and energy dissipation of the network nodes.

In Wireless Sensor Networks (WSNs), there are two major factors which minimize the performance of the network. The one is the void hole which occurs in a particular region due to unavailability of forwarder nodes. The other one is the presence of energy hole near the sink due to death of the nodes. An optimum transmission strategy is the need in this case in order to maximize the network lifetime via hole alleviation. To this end, we provide a solution that is able to optimize the network performance through balance transmission strategy by equally dividing the network into number of sectors to balance the energy among the nodes. Two types of nodes are considered, the one with minimum energy level are normal nodes, the other one with maximum energy level are super nodes. Void hole is removed while selecting super node as a data forwarder in each region and energy hole is alleviated while selecting super node as a forwarder for data transmission in case when nodes near the sink exhaust their energy and die out.

Due to the limited battery power of sensor nodes, design of underwater wireless sensor networks (UWSNs) is very difficult. Information is not gained efficiently through sensor nodes in aquatic environment and energy consumption is also the major problem in UWSNs. We proposed logical sub-region with sink mobility for throughput maximization and energy consumption minimization in rectangular UWSNs (LSSR). Sensor nodes are randomly deployed in the network field. Two mobile sinks are moving strategically in the network field and gather data from their respective nodes and cover the maximum area of the network field. LSSR performs better in terms of network lifetime, throughput and residual energy as shown in the simulation results.

This paper presents an incremental cooperative communication scheme for a buffer-aided three node relay network consisting of a source, an Amplify and Forward (AF) relay and a destination. We consider the presence of direct link and propose a scheme which is based on the concept of packet diversity rather than link diversity. The scheme is designed in such a way that packets that experienced bad channel conditions in source-relay link experience good channel conditions in the relay-destination link and vice versa. Source multi-casts all packets to both relay and destination. If direct transmission is not successful, source needs assistance from the relay. Based on the quality of relay-destination link, a packet is picked from buffer at the relay to be transmitted to the destination. Further, direct and relayed signals are combined at the destination using Maximal Ratio Combining (MRC) technique. The closed-form expressions for outage and error probabilities are derived, further, the delay and the diversity orders for the proposed scheme are also investigated.

Relay selection in cooperative communication is an efficient approach to mitigate the spectral efficiency loss faced in cooperative diversity systems. In this paper, we propose a relay selection scheme for buffer-aided cooperative systems. It simultaneously considers the instantaneous link quality and the buffer status of relay nodes in the relay selection decision. The normalized and weighted sum of these parameters results into the overall score of each link, and a link with maximum score is selected. First, the concept of equivalent outage of a link corresponding to a buffer-aided relay is explained, and then Markov chain (MC) modeling is used for the evaluation of states of buffer. We provide the specific examples with fixed values of number of relays and buffer size. The system achieves the full diversity gain of 2K for the smaller buffer sizes.

Due to unique and unreliable characteristics of Underwater Wireless Sensor Networks (UWSN), providing scalable and energy efficient services are very challenging. Acoustic communication is used for the data transmission, which has limited bandwidth that causes the performance deficiencies. In this paper, we propose Singular division of Multipath Power Control (SMPC) routing protocol for UWSNs. We divided the network area into vertical sections of equal size and tree based routing strategy is established for the data transmission. Multiple copies of the same data packet are generated by the source nodes and send through its leaf nodes toward surface gateways. Then surface gateways deliver all these packets to the sink. Multiple copies are combined and original packet is generated by sink. Simulation results are conducted on the bases of different parameters, and results shows that SMPC significantly improve the network performance in sense of energy consumption and end to end delay.

Short-term load forecasting (STLF) models are very important for electric industry in the trade of energy. These models have many applications in the day-to-day operations of electric utilities such as energy generation planning, load switching, energy purchasing, infrastructure maintenance, and contract evaluation. A large variety of STLF models have been developed that trade off between forecast accuracy and convergence rate. This paper presents an accurate and fast converging STLF model for industrial applications in a smart grid. In order to improve the forecast accuracy, modifications are devised in two popular techniques: mutual information based feature selection; and enhanced differential evolution algorithm based error minimization. On the other hand, the convergence rate of the overall forecast strategy is enhanced by devising modifications in the heuristic algorithm and in the training process of the artificial neural network. Simulation results show that accuracy of the newly proposed forecast model is 99.5% with moderate execution time, i.e., we have decreased the average execution of the existing bilevel forecast strategy by 52.38%.

Many routing protocols are proposed regarding energy efficiency in underwater wireless sensor networks (UWSNs). We propose sparsity-aware energy efficient clustering (SEEC) protocol for UWSNs. SEEC specially search sparse regions of the network. We divide the network region into subregions of equal size and search sparse and dense regions of the network field with the help of sparsity search algorithm (SSA) and density search algorithm (DSA). SEEC improves network lifetime through sink mobility in sparse regions and clustering in dense regions of the network. SEEC also achieves network stability with optimal number of clusters formation in dense regions of the network where each dense region logically represents a static cluster. The division of the network region into subregions control routing hole problem in the UWSNs. SEEC minimizes network energy consumption with balanced scheme operations. Effectiveness of our proposed protocol is verified by simulation results.

In this paper we propose an ECG optimization model for a smart home based on DSM. The proposed model is an efficient SHEM strategy. The model is proposed keeping in view the minimization of energy consumption, energy consumption cost and energy generation cost. The model is based on efficient scheduling of appliances and an ECG optimization algorithm is proposed. We are using and optimizing energy from two energy sources namely lceg and lcd which are also known as macrogrid and microgrid respectively. The problem is solved as cost optimization problem using genetic algorithm and mathematically formulated using binary MNKP. The simulation results show that our ECG model efficiently reduces the cost of energy consumption, energy generation and energy consumption utilization.

Large propagation delay, high error rate, low band-width and limited energy in Underwater Sensor Networks (UWSNs) attract the attention of most researchers. In UWSNs, efficient utilization of energy is one of the major issue, as the replacement of energy sources in such environment is very expensive. In this paper, we have proposed a Cluster Depth Based Routing (cDBR) that is based on existing Depth Based Routing (DBR) protocol. In DBR, routing is based on the depth of the sensor nodes: the nodes having less depth are used as a forward nodes and consumes more energy as compared to the rest of nodes. As a result, nodes nearer to sink dies first because of more load. In cDBR, cluster based approach is used. In order to minimize the energy consumption, load among all the nodes are distributed equally. The energy consumption of each node is equally utilized as each node has equal probability to be selected as a Cluster Head (CH). This improves the stability period of network from DBR. In cDBR Cluster Heads (CHs) are used for forwarding packets that maximizes throughput of the network. We have compared our results with DBR and Energy Efficient DBR (EEDBR). The simulation result validates that cDBR achieves better stability period and high throughput comparatively to DBR and EEDBR.

A non-cooperative power control game (NCPCG) is designed for efficient power consumption in the wireless sensor and ad hoc networks by introducing a novel cost function. The proposed cost function in a game theoretic framework facilitates the nodes in increasing power efficiency by making independent decisions in a distributed environment. The Nash equilibrium is achieved among the nodes converging to the optimal value of the utility function for the entire network, beyond which even further increase in the power level reduces the signal-to-noise (SNR) ratio.

This paper presents cooperative routing scheme to improve data reliability. The proposed protocol achieves its objectives, however, at the cost of surplus energy consumption. Thus sink mobility is introduced to minimize the energy consumption cost of nodes as it directly collects data from the network nodes at minimized communication distance.

In Underwater Wireless Sensor Networks (UWSNs) nodes are equipped with limited battery power and battery replacement is expensive due to underwater harsh environment. Therefore, we propose EBECRP an energy Efficient and Balanced Energy consumption Cluster based Routing Protocol for UWSNs. In depth base routing protocols nodes near the sink (low depth nodes) die in no time because of high load. We avoid depth base routing in our proposed scheme and use mobile sinks to balance load on all nodes. We also use the concept of clustering to reduce multi hoping which results in more energy consumption. The selected Cluster Heads (CHs) collect data from one hope neighbor nodes to reduce global communication into locally compressed communication. Simulation results show that EBECRP achieves maximum stability period and network life time.

In this paper, we introduce a generic architecture for demand side management (DSM) and use combined model of time of use tariff and inclined block rates. The problem formulation is carried via multiple knapsack and its solution is obtained via ant colony optimization (ACO). Simulation results show that the designed model for energy management achieves our objectives, it is proven as a cost-effective solution to increase sustainability of smart grid. The ACO based energy management controller performs more efficiently than energy management controller without ACO based scheduling in terms of electricity bill reduction, peak to average ratio minimization and user comfort level maximization.

Under water sensor networks(UWSNs) have attracted significantly to explore natural and undersea resources and gathering scientific data in aqueous conditions. The adverse characteristics in UWSNs communication and high cost limit the sensor nodes to spare deployment, causing delay, low propagation, power efficiency and floating node mobility. This proposed protocol is developed to handle these problems in under water sensor networks, two static sinks and four courier nodes are used to perform routing. Sensor nodes select their appropriate nearby static courier node to forward their data towards destination. Courier nodes have maximum energy, as compare to sensor nodes causing to enhance the network life time and provide equal distribution of energy consumption resulting to provide maximum throughput and stability of the network. Network field is hundred by hundred and providing maximum rounds through which we can closely over view the network life time, energy efficiency and throughput. Simulation results show maximum packet delivery per round. Courier nodes have maximum energy so the maximum routing will be performed by the courier nodes and sensor nodes will only sense their data and forward it to courier nodes causing to minimize the destabilization period of the network. Simulation results provide maximum throughput, minimum dead versus alive nodes and equal energy consumption per round.

We propose a new routing protocol MobiSink (mobile sink) for underwater sensor networks (UWSNs). We deploy the sink mobility in four horizontal regions of the network. The mobile sink moves in its own region to collect data from the transmission range sensor nodes. The transmission range of a node is calculated after fixed interval of time for mobile sink. In MobiSink nodes also take help of transmission range neighbors to communicate with sink cooperatively, if sink is out of range. The mobility pattern of sink and cooperative routing achieved better results as compared with other depth based routing protocols. The MobiSink scheme is validated via simulation, which shows better performance compared with depth based routing (DBR) and energy efficient depth based routing (EEDBR) protocols in terms of network life time, throughput and energy consumption.

Cooperation among nodes is mandatory for smooth operation of Mobile Ad Hoc Networks (MANETs) in terms of data routing. A participating node may refuse to deplete its resources for the benefit of others because of not getting any direct advantage for its service. Nodes showing such behavior are called selfish or non-cooperative nodes. Non-cooperative nodes can severely affect the performance of MANETs. Non-cooperative behavior of nodes in the MANETs may lead to network partitioning. In this paper, we address the issue of non-cooperative behavior by incorporating the concept of fuzzy logic closely coupled with the concept of trust. Fuzzy-based analyzer is used to distinguish between the non-cooperative behavior nodes and trustworthy nodes. We propose a fuzzy-based scheme to detect and isolate non-cooperative nodes in MANETs. In the proposed scheme, every node in the network constantly monitors its one-hop neighbors for their actions. Every node computes the trust of the observed neighbors. These trust values are passed on to a fuzzy function which is mapped into different classes. The resulting classes show the trust levels of the observed nodes. On the basis of the calculated trust value, the non-cooperative nodes are detected and isolated from the active routes of the MANET. Proposed fuzzy-based scheme is robust enough in terms of detecting packet drop attack in the network. Results show that proposed scheme detects non-cooperative nodes effectively with low false positives rate. Moreover, proposed scheme increases the packet delivery ratio and throughput in the presence of non-cooperative nodes in the network.

In this paper, we overcome the problem of energy holes in UWSNs while considering the unique characteristics of underwater communication. In proposed scheme we consider UWSNs where nodes are manually deployed according to the defined deployment pattern to satisfy our application requirements in terms of energy saving. We used mixed routing technique i.e. direct transmission and hop-by-hop transmission for energy balancing in continuous monitoring applications for UWSNs. Sensor nodes forward the total data traffic (generated plus received) periodically to the sink with calculated load weights using variable communication ranges. The transmission ranges for 1-hop, 2-hop and direct transmission to the sink are used for data transmission to achieve load balancing for balanced energy consumption of all sensor nodes in the network. We prove that our scheme outperforms the existing selected schemes in terms of network lifetime and energy conservation. We select an optimal result from the simulation results for different possible combinations of transmissions.

Today, energy is the most valuable resource, new methods and techniques are being discovered to fulfill the demand of energy. However, energy demand growth causes a serious energy crisis, especially when demand is comparatively high and creates the peak load. This problem can be handled by integrating Demand Side Management (DSM) with traditional Smart Grid (SG) through two way communication between utility and customers. The main objective of DSM is peak load reduction where SG targets cost minimization and user comfort maximization. In this study, our emphasis is on cost minimization and load management by shifting the load from peak hours toward the off peak hours. In this underlying study, we adapt hybridization of two optimization approaches, Bacterial Foraging (BFA) and Genetic Algorithm (GA). Simulation results verify that the adapted approach reduces the total cost and peak average ratio by shifting the load on off peak hours with very little difference between minimum and maximum 95% confidence interval.

In this paper, an energy efficient, interference and route aware (EEIRA) protocol is proposed for underwater wireless sensor networks (UWSNs). The protocol combines the direct and relay forwarding mechanisms in transmitting the packets from source to destination. The relaying process involves selection of the best relay from a set of relay nodes. A relay node having the least distance from source to destination and the minimum number of neighbor nodes qualifies the criterion of the best relay. The direct transmission is used when the best relay is not within the transmission range of the source node. From top to bottom, the network is divided into three different zones, destination, relay and source zone, respectively. The relay zone has the greatest area to have maximum choices of selecting the best relay from the relay nodes residing in it. Nodes in all the three regions can sense the attribute and send the data to the sink. The destination nodes send data directly to sink. The relay nodes send the packets to the sink either directly or through the relaying process. The source nodes in the bottom send the data to sink via the best relay node or directly. Based on simulation results, the proposed protocol outperforms the depth based routing (DBR) scheme in terms of energy efficiency by selecting the best relay, reducing number of hops and following the shortest path to reduce channel losses.

This work analyzes major energy management solutions in terms of their practical implementation. Every EMS proposed has its own merits and demerits. Energy management solutions are based upon 4 major categories i.e., EMS by a sensor network, EMS by using optimization technique, EMS by merging sensory information with optimization algorithm to yield better energy efficiency and integrating small scale micro grids at demand side to optimize energy preservation. Load shifting to low peak or low priced hours in such a way that user comfort is not much compromised is an NP-hard problem. Nature inspired evolutionary algorithms proves their worth in solving such problems. In this work, we focus mainly on energy and cost optimization with respect to above mentioned all categories that provide energy management solutions. Extensive simulations are conducted regarding each catagory to investigate impact of each regarding cost and energy savings.

In this paper, we propose a reliable and interference-aware routing protocol for underwater wireless sensor networks (UWSNs). Proposed protocol follows end-to-end path from source node to sink and selects next forwarder node of a data packet on the basis, having already established a path to sink. In this way, the problem of encounters void hole in depth based routing protocol is eliminated. Furthermore, during the selection of forwarding node, channel interference is also considered as routing metric to provide reliable communication. Therefore, proposed scheme reduces the probability of collision at the network layer, by selecting a neighbor node as the next forwarder of the data packet from the source node to the destination where the chance of channel interference is minimum. Simulation results verify the effectiveness of the proposed scheme in term of energy consumption, end-to-end delay and packet delivery ratio especially in a sparse network.

This paper conducts a comprehensive survey on fuzzy logic applications in wireless and mobile communication area in general and of growing LTE communication networks in particular. The survey aims to highlight the principles of fuzzy logic applications in the area of channel estimation, channel equalization, handover management, QoS management. Furthermore, in LTE advanced heterogeneous networks fuzzy logic applications in the area of interference management are also spotlighted.

In Underwater Wireless Sensor Networks (UWSNs), reliability is one of the major concerns for large number of applications. The underwater environment is very harsh and noisy. Fading is common and unavoidable, therefore achieving reliable data transfer requires innovative routing solutions. This paper presents a energy efficient cooperative routing with varying Depth threshold (Dth) called Depth and Energy Aware Cooperative Routing Protocol for UWSNs (DEAC). DEAC utilizes the broadcast nature of sensor nodes by performing cooperative routing. Optimised value of Dth is selected for a source node and varied according to the number of alive neighbors of that source node. Potential destination node is selected from outside of Dth and a potential relay node is selected from inside. Destination and relay are selected on the basis of depth, residual energy and link quality between sensor nodes. Source node forwards a data packet to destination node from two ways, directly from source node to destination node and via relay to destination node. At destination, two data packets received from source node and relay node are combined using Maximum Ratio Combining Technique (MRC). Simulation results show that DEAC achieves better performance over some existing depth based routing protocols in terms of throughput, packet Acceptance ratio, packet drop and energy consumption.

Smart grid is one of the most advanced technologies which plays a key role in maintaining balance between demand and supply by implementing demand response (DR). Residential users basically effect the overall performance of traditional grid due to maximum requirement of their energy demand. Home energy management (HEM) benefit the end user by monitoring, managing and controlling their energy consumption. Appliance scheduling is integral part of HEM as it manages energy demand according to supply by automatically controlling the appliances or by shifting the load from peak to off peak hours. Recently different techniques based on artificial intelligence (AI) are used to meet these objectives. In this research work, we evaluate the performance of HEM which is designed on the basis of heuristic algorithms, wind driven optimization (WDO), ganetic algorithm (GA) and binary particle swarm optimisation (BPSO). Finally, simulations are conducted in MATLAB to validate the performance of scheduling techniques in terms of cost, reduced peak to average ratio (PAR) and equally distributed energy consumption pattern. The simulation results prove that WDO algorithm based HEM proves to perform efficiently than BPSO and GA.

The acoustic environment suffers from a number of impairments which effect transmitted data reliability and integrity leads toward low-quality routing. Integral part of cooperative routing is reliable data delivery with trade-off energy consumption is high, because of multiple transmissions. In order to overcome this problem and getting advantage of cooperation routing, we proposed a scheme Sink Mobility with Incremental Cooperative Routing (SMIC) which involves Mobile Sinks to reduce energy consumption and achieve reliable data transfer. In this paper, selection parameter for relay and destination node is node's depth, residual energy and link quality (Signal-to-Noise Ratio) to achieve quality routing. Energy efficiency is achieved by optimized mobility pattern of Mobile Sinks (MSs) and using Amplify and Forward (AF) incremental cooperative routing which helps in efficient utilization of resources by using them, when needed. The proposed work is validated via simulations which show the relatively improved performance of our proposed protocol in terms of the selected performance metrics.

Energy is one of the most precious resource in Wireless Sensor Networks (WSNs) which is mainly consumed in communication, sensing and processing. Performance evaluation of WSN routing protocols primarily rely on simulation-based studies. Most of these studies only assume oversimplified First Order Radio Model (FORM) and ignore node's energy consumed in sensing and processing. This paper presents an Advanced first order Energy Consumption Model (A-ECM) for terrestrial WSNs. Unlike FORM, A-ECM factors in essential energy guzzlers of wireless transmission and reception (i.e., coding rate, bit rate and decoding) besides sensing and processing. The performance of A-ECM is validated through simulations which demonstrate the effectiveness of A-ECM for realistic scenarios.

In this work, we propose a novel quality link metric; Bandwidth adjusted Inverse ETX (BIETX) for Static Wireless Multi-hop Networks (SWMhNs). The proposed metric considers two path selection parameters into account i.e., packet delivery ratio and link capacity. For computing packet delivery ratios in BIETX, the mechanism of Expected Transmission Count (ETX) is adopted. On the other hand, we take two methods of computing link capacity in BIETX. These methods are based upon the size of pair probes; equal size and different size. We also enhance Optimized Link State Routing (OLSR) protocol while using BIETX. A comparative analysis of proposed metric with equal size and different size pair probe; BIETX-1 and BIETX-2, with two existing quality metrics (ETX and Expected Transmission Time (ETT)) in SWMhNs is also a part of this work. From simulation results, we conclude that BIETX-2 outperforms rest of the metrics because of low routing load in ad-hoc probes, and low routing latencies due to enhancements of routing update frequencies and window size in OLSR.

Underwater Wireless Sensor Networks (UWSNs) are getting growing interest because of wide-range of applications. Most applications of these networks demand reliable data delivery over longer period in an efficient and timely manner. However, resource-constrained nature of these networks makes routing in a harsh and unpredictable underwater environment challenging. Most existing schemes either employ static or mobile sink for data collection. However, in former sensors near the sink deplete out their energy more quickly which limits network lifetime. Mobile sink based schemes are not suitable for delay-sensitive large-scale applications. Unlike prior work, this paper presents a novel Advanced AUV-aided Energy Efficient Routing Protocol (AAEERP) for reliable data delivery. To prolong network lifetime, AAEERP employs an autonomous underwater vehicle to collect data from gateways. To minimize energy consumption, we use a shortest path tree algorithm while associating sensor nodes with the gateways and devise a criterion to limit the association count of nodes. Moreover, the role of gateways is rotated to balance the energy consumption. To prevent data loss, AAEERP allows dynamic data collection time to AUV depending up the count of member sensors for each gateway. The performance of the AAEERP is validated through simulations. Simulation results demonstrate the effectiveness of AAEERP in terms of various performance metrics.

In this paper, we present need-based clustering (NbC) with dynamic sink mobility (DYN-NbC) scheme for wireless sensor networks (WSNs). Our proposed scheme increases the stability period, network lifetime, and throughput of the WSN. The scheme incorporates dynamic sink mobility in a way that mobile sink (MS) moves from dense (in terms of number of nodes) regions towards sparse regions. Intelligently moving the sink to high density regions ensure maximum collection of data. As, more number of nodes (sensors) are able to send data directly to MS, therefore, significant amount of energy is saved in each particular round. However, there is a certain limitation to this approach. Nodes which are far from sink have to wait much for their turn. So, there are chances of buffer (node storage) overflow that is not desirable. To overcome this issue our scheme includes. Clustering (communication via CHs) becomes the part for those regions which are away from MS. Simulation results show that DYN-NbC outperforms the other two protocols D-LEACH and LEACH in terms of stability period, network lifetime, and network throughput.

Wireless Sensor Networks (WSNs) are expected to have wide applicability in the near future. In this paper, we propose (LEACH)2: Linearly Enhanced Approach for cluster handling improving the conventional protocol LEACH (Low Energy Adaptive Clustering Hierarchy) in WSNs. We divide the network area into four regions and study the network performance in the presence of one, two and three sinks. Simulation results show that our technique with three sinks performs better than other conventional routing techniques. (LEACH)2offers increased stability period, network lifetime and throughput than LEACH.

Orientation sensing is not a new concept. It is being used since ages however, with emergence of new technologies such as Wireless Body Area Sensor Networks (WBASNs), it gives new challenges. Commencement of smart phones that have built in orientation sensors are replacing expensive and complex Inertial Measurement Units (IMUs) designed for a specific purpose. Orientation sensing in WBASN have numerous applications. In e-health applications, rehabilitation investigation of backbone injuries can be measured by continues readings of posture. For that, gyroscopes and accelerometers are key sensors that play vital role. For machines such as robots and air crafts, such data fusion is in practice. However, considering human body movements yet there is a need to find an accurate fusion algorithm that meets all demands with low complexity. In this work, we discussed and compared two algorithms considering Wireless Body Area Sensor Fusion (WBASF) i.e. Kalman and Complementary data fusion techniques. According to our findings, Kalman Filter may have given very good results regarding machines however, Complementary filter proved itself better in performance, complexity and required computational power in WBASNs.

Human Activity Recognition (AR) using kinematic sensors is one of the widely used researched area based on Smartphone. Development in sensor networks technology provided birth to the applications that can give intelligent and amicable services based on the AR of people. Although, this technology supports analyzing different activities pattern, empowering applications to identify the activities performed user independently is still a fundamental concern. For improvement quality of life and personal safety, care giving process can be enhanced by introducing the AR, automatic fall detection, and prevention systems. Modern smartphones have different built in sensors like accelerometer, magnetometer, proximity, and gyroscope which can be used for AR as well as fall detection. In this paper, we present an AR and fall detection system which used built in sensors with alarm notification service. We use Signal Magnitude Vector (SMV) algorithm to analyze the fall like events. To overcome the false alarm activation problem, system uses different threshold values to determine the daily life activities like walking, standing, and sitting, that could be wrongly detected as a fall. For assessment, a trial setup is done to acquire sensor's information of diverse positions.

Wireless Sensor Network (WSN) is an emerging field for researchers in the current decade. For obtaining longevity of network lifetime, and reducing energy consumption, energy efficient routing protocol play a vital role. In this paper, we present a scalable and energy efficient routing protocol, A New Linear Cluster Handling (LCH) Technique Towards Energy Efficiency in Linear WSNs with multiple static sinks in a linearly enhanced field of 1000m×2m2. The whole network field is divided into four equal sub-regions. For efficient data gathering, we place three static sinks i.e. Two at the both corners and one at the centre of the field. A proactive routing protocol Distributed Energy Efficient Clustering with Linear Cluster Handling (DEEC-LCH)is implemented in the network field. Furthermore, a reactive protocol Threshold Sensitive Energy Efficient with Linear Cluster Handling (TEEN-LCH) is also implemented for the same scenario with three static sinks. Simulation shows improved results for our proposed protocols as compared to simple DEEC and TEEN, interim of network lifetime, throughput and energy consumption.

The state of the art of old-age grids into smart grids provides residents the opportunity to schedule their appliances to consume the energy optimally that leads to potentially balance the demand side as well as the supply side more effectively and minimizes the power Peak-to-Average Ratio (PAR), which ultimately provides benefit to the residents in the form of reduction cost and expense. The Energy Management System (EMS) in the home receives the market and system signals and controls the loads, Heating, Ventilation and Air Conditioning systems (HVAC), storages and local generation units according to the user preferences. This survey encompasses novel home energy management techniques including different shift able and non-shift able load scheduling methods and peak shaving strategies. Several Pricing strategies have been suggested for smart grid such as, Real-Time Pricing (RTP), Time of Use (ToU), Inclining Block Rates (IBR), Critical Peak Pricing (CPP), etc. Moreover, this paper discusses the HEM architecture and reveals that the different energy management techniques intelligently schedule the appliances in order to satisfy the maximum resident's comfort level and consume the energy optimally.

Two way communication of information and electricity in smart grid provides the promising solution to all the major problems of existing grid especially the classical problem of peak load management. Optimization of generation, transmission and distribution in smart grid is major concern of researchers. Demand Side Management (DSM) allows the active participation of users in order to shape the load curve according to utility requirements and Economic Dispatch (ED) ensures the power supply at minimum cost. Optimization of DSM and ED has great importance to realize the smart grid. Researchers have applied many techniques of Artificial Intelligence (AI) in order to optimize these applications like Genetic Particle Swarm Optimization (PSO). This paper presents a comprehensive review of PSO applications for HEMS and ED. Various cases of ED and Home Energy Management System (HEMS) in smart grid for DSM applications are presented to show the diverse applications of PSO. In second part of paper a HEMS with interrupting loads has also been implemented using PSO.

Smart Grid is supposed to play an important role in future energy management. In smart grid, Demand Side Management (DSM) is one of the main areas which is under focus of researchers in order to solve the classical problem of peak demand management. In this paper, we have proposed a Habit Based DSM (HBDSM) model for peak load shaving. Proposed method is based on individuals habit which is modeled using Markov Chain. The main focus of the work is to minimize the cost by optimizing battery consumption using Equal Interval Search algorithm in order to minimize energy consumption from the grid and so as to shave the demand curve. Simulation results prove the effectiveness of the proposed model.

Smart Grid (SG) is a next-generation electrical power system that use two way communication in the generation, consumption and delivery of the electrical energy. One of the key feature of SG is Demand Response (DR). In DR a pricing signal is provided to the customer via smart meters, and customer modifies their demand in response to price signals. However, most of the load scheduling schemes used day-ahead or Time of Use pricing scheme, these schemes are deviating from Real-time Pricing (RTP) scheme. In this paper, an RTP based scheduling scheme is proposed using Optimal Stopping Rule (OSR). AnOSR gives the best operating time of the device to reduce electricity bills. The cost minimization problem is formulated as an unconstrained optimization problem. Moreover, waiting time cost is also formulated as sub problem to reduce waiting time of the device. Waiting time is considered as a function of cost. After that, an algorithm is proposed to solve this optimization problem for various types of loads. Simulation results verify that proposed algorithm has low computational complexity and reduce electricity bill with less waiting time.

Wireless Body Area Networks (WBANs) are getting growing interest because of their suitability for wide range of medical and non-medical applications. These applications demand WBAN to stay functional for a longer time which requires energy-efficient operation. In this paper, we propose a new routing protocol for Balanced Energy Consumption (BEC) in WBANs. In BEC, relay nodes are selected based on a cost function. The nodes send their data to their nearest relay nodes to route it to the sink. The nodes closer to the sink send their data directly to it. Furthermore, the nodes send only critical data when their energy becomes less than a specific threshold. In order to distribute the load uniformly, relay nodes are rotated in each round based on a cost function. Simulation results show that BEC achieves 49% increased network lifetime than OINL (On Increasing Network Lifetime) algorithm.

The unique characteristics of Underwater Wireless Sensor Networks (UWSNs) attracted the research community to explore different aspects of these networks. Routing is one of the most important and challenging function in UWSNs, for efficient data communication and longevity of sensor node's battery timing. Sensor nodes have energy constraint because replacing the batteries of sensor nodes is an expensive and tough task in harsh aqueous environment. Also interference is a major performance influencing factor. Providing solutions for interference-free communication are also essential. In this paper, we propose three energy-efficient and interference-aware routing protocols named as Inverse Energy Efficient Depth-Based Routing protocol (IEEDBR), Interference-Aware Energy Efficient Depth-Based Routing protocol (IA-EEDBR) and Interference-Aware Inverse Energy Efficient Depth-Based Routing protocol (IA-IEEDBR). Unlike EEDBR, IEEDBR protocol uses depth and minimum residual energy information for selecting data forwarder. While IA-EEDBR takes minimum number of neighbors for forwarder selection. IA-IEEDBR considers depth, minimum residual energy along with minimum number of neighbors for selection of forwarder. Our proposed schemes are validated through simulation and the results demonstrate better performance in terms of improved network lifetime, maximized throughput and reduced path loss.

Wireless sensor networks and, particularly wireless body area networks (WBANs) are the key building blocks of upcoming generation networks. Modern health care system is one of the most popular WBAN application and a hot area of research in subject to present work. In recent years, research has focused on channel modeling, energy conservation and design of efficient medium access control (MAC) schemes. Less attention has been paid to the path-loss performance analysis. In this work, we propose LAEEBA (Link-aware and Energy Efficient scheme for Body Area Networks) which is a reliable, path loss efficient and high throughput routing protocol for WBANs. The characteristics of single-hop and multi-hop communication schemes have been utilized to reduce path-loss effects and increase network lifetime. A cost function is proposed to select the forwarder node on the basis of has high residual energy and minimum distance to sink. Residual energy parameter balances the energy consumption among the sensor nodes while distance parameter ensures successful packet delivery to sink. Simulation results show that LAEEBA protocol maximizes the network stability period and nodes stay alive for longer time, which contributes to sufficient decrease in the path-losses occurring in the links connecting sensors on a human body and hence transferring of data with much less losses. Results show better performance of our proposed protocol as compared to its given variants.

Energy conservation is one of the most important factors in Wireless Sensor Networks (WSNs) for reliability since nodes have limited resources of energy. There is a need to design such routing protocols, which efficiently use available energy and prolong network lifetime and stability period. We implement sink mobility in Clusterless Stable Election Protocol (CL-SEP) and proposed Tunnel Routing Protocol (TRP) for WSNs, which is two level heterogeneous. From the simulation results, it is seen that the proposed protocol outperforms the conventional SEP in stability period, network lifetime and throughput. It efficiently utilizes the available energy of the network by using Moving Sink (MS) and prolongs network lifetime and stability period.

In this paper, we consider an Energy Consumption Scheduler (ECS) unit inside a smart meter. The function of ECS is to flatten the peaks in the load curve of residential type users. The ECS dually communicates with the user as well as power utility in smart grid real time communication environment. Each user has a specific power capacity limit for their appliances operation. The ECS keeps check and balance condition whenever a user exceeds its power capacity limit. Also the most urgent appliance operations are given priority based on their internalpreemption state. By adopting ECS unit, the total peaks in the load curve are reduced up to 33.3%. Also the user's payment to the power utility becomes less due to efficiently managing their appliance operations. Additionally, the cost of per unit energy generation is also reduced by avoiding the peak power plants. Matlab/Simulink is used as a simulation tool for the realization of this model.

Wireless Body Area sensor Networks (WBANs) enable innovative health care monitoring. Limited energy source of a sensor node limits WBANs for long time monitoring of health care. Efficient energy utilization is therefore one of the research challenges inWBANs. In this research work we analysed energy utilization of popular routing techniques. We formulate a mathematical framework to identify energy utilization in transmission, receive and over-hearing processes. Simulation results show that how distance, packet size, and over-hearing effect different routing techniques from energy consumption perspective. From the analysis, we produced useful results which are helpful in: identifying overloaded nodes in the network which may cause creation of energy holes, and in designing new routing techniques for specific WBANs application.

To overcome energy hole problem unequal clustering structures have been proposed for the aim of network stability. Energy hole avoidance remains as a challenging problem. Hexagons are an ideal shape for uniform random deployment of nodes in Wireless Sensor Networks (WSN), because clusters areas are seamlessly divided by the hexagons. In addition, regular hexagons have largest coverage area and covers networks area without overlapping. In this paper, we design simple but effective scheme HEX for dividing network area into hexagonal cluster/ cell where sensors are distributed uniformly throughout the cell and introduced concept of physical clustering. Our results show that proposed technique yields the network stability period, lifetime and throughput by saving overall power consumption of a cluster. Furthermore, confidence interval and packets dropped across the link is also calculated using uniform random model.

IEEE 802.15.4 standard is gaining attention of researchers day by day due to its wide application arena. Till now numerous studies have conducted and yet lot more is in progress for understanding and rounding off sharp edges of this standard. This study briefly describes the standard i.e. IEEE 802.15.4 and explicitly non-beacon enabled slotted CSMA/CA. In slotted or non-beacon enabled CSMA/CA there are further two flavors. One deals with transmission of an extra control packet (ACK frame) on successfully reception of data packet however, other flavor do not transmit any control packet representing successful transmission. In later part of this paper, extensive simulations are conducted in comparison with these two flavors. Our studies imply to use non-ACK mode in low scalable environment ensuring high probability of getting channel access, low delay and better good put.

In this paper, we propose a reliable, energy efficient and high throughput routing protocol for Wireless Body Area Networks (WBANs). In Forwarding Data Energy Efficiently with Load Balancing in Wireless Body Area Networks (FEEL), a forwarder node is incorporated which reduces the transmission distance between sender and receiver to save energy of other nodes. Nodes consume energy in an efficient manner resulting in longer stability period. Nodes measuring electrocardiography (ECG) and glucose level send their data directly to the sink in order to have minimum delay. Simulation results show that FEEL protocol achieves improved stability period and throughput. As a result it helps in continuous monitoring of patients in WBANs.

Wireless Body Area Networks (WBANs) are playing promising role in healthcare field by allowing remote monitoring of patients. In such networks, designing a energy efficient routing topology is of main concern. For this purpose, we propose CEMob, Critical data transmission in Emergency with Mobility support in WBANs, as a routing layer protocol. CEMob avoid continuous transmission of information, thereby, preserving nodes' energy. Simulation results of CEMob are compared with that of contemporary routing protocols i.e. ATTEMPT and REATTEMPT. Comparison justify that CEMob has reduced energy consumption and is more efficient than the compared protocols.

In order to increase the network lifetime, scalable and energy-aware routing protocols are very essential for Wireless Sensor Networks (WSNs). In this paper, we propose a heterogeneity-aware Multi-hop Centralized Energy Efficient Clustering (MCEEC) protocol for routing in WSNs. Operation of MCEEC is based upon the advanced central control algorithm, in which Base Station (BS) is responsible for the selection of Cluster-Heads (CHs) which are selected on the bases of wireless sensors' (nodes') residual energy, average energy of the network, and average of the relative distance between nodes and BS. We adopt multi-hop inter-cluster communication for MCEEC. The advanced heterogeneous network model of the proposed protocol divides the network area into three equally spaced rectangular regions such that nodes of the same energy level are deployed in the respective region. Furthermore, nodes can only associate with their own region's CHs. In MCEEC, deployment of nodes in the network area is in descending order of energy level w.r.t BS's position. Simulation results show that MCEEC yields maximum scalability, network lifetime, stability period and throughput as compared to the selected routing protocols.

In this paper, we propose an Application-aware Threshold-based Centralized Energy Efficient Clustering (ATCEEC) protocol for routing in Wireless Sensor Networks (WSNs). The proposed protocol assumes that each wireless sensor (node) is capable of sensing two types of environmental dynamics; temperature and humidity. Operation of ATCEEC is based on an advanced central control algorithm, where, Base Station (BS) is responsible for the selection of Cluster Heads (CHs). This selection is carried out on the bases of nodes' residual energy, average energy of the network, and relative distance between nodes and BS. ATCEEC achieves significant stability, extended network lifetime and better control over the network operation. Our hybrid protocol is suitable for both proactive and reactive networks. Simulation results show that ATCEEC yields maximum network lifetime and stability period as compared to the selected protocols.

Frequent topological changes due to high mobility is one of the main issues in Vehicular Ad-hoc NETworks (VANETs). In this paper, we model transmission probabilities of 802.11p for VANETs and effect of these probabilities on average transmission time. To evaluate the effect of these probabilities of VANETs in routing protocols, we select Dynamic Source Routing (DSR), Fish-eye State Routing (FSR) and Optimized Link State Routing (OLSR). Framework of these protocols with respect to their packet cost is also presented in this work. A novel contribution of this work is enhancement of chosen protocols to obtain efficient behavior. Extensive simulation work is done to prove and compare the efficiency in terms of high throughput of enhanced versions with default versions of protocols in NS-2. For this comparison, we choose three performance metrics; throughput, End-to-End Delay (E2ED) and Normalized Routing Load (NRL) in different mobilities and scalabilities. Finally, we deduce that enhanced DSR (DSR-mod) outperforms other protocols by achieving 16% more packet delivery for all scalabilities and 28% more throughput in selected mobilities than original version of DSR (DSR-orig).

In this paper, we evaluate and analyze the impact of different network loads and varying no. of nodes on distance vector and link state routing algorithms. We select three well known proactive protocols; Destination Sequenced Distance Vector (DSDV) operates on distance vector routing, while Fish-eye State Routing (FSR) and Optimized Link State Routing (OLSR) protocols are based on link state routing. Further, we evaluate and compare the effects on the performance of protocols by changing the routing strategies of routing algorithms. We also enhance selected protocols to achieve high performance. We take throughput, End-to-End Delay (E2ED) and Normalized Routing Load (NRL) as performance metrics for evaluation and comparison of chosen protocols both with default and enhanced versions. Based upon extensive simulations in NS-2, we compare and discuss performance trade-offs of the protocols, i.e., how a protocol achieves high packet delivery by paying some cost in the form of increased E2ED and/or routing overhead. FSR due to scope routing technique performs well in high data rates, while, OLSR is more scalable in denser networks due to limited retransmissions through Multi-Point Relays (MPRs).

Energy efficient routing protocol for Wireless Sensor Networks (WSNs) is one of the most challenging task for researcher. Hierarchical routing protocols have been proved more energy efficient routing protocols, as compare to flat and location based routing protocols. Heterogeneity of nodes with respect to their energy level, has also added extra lifespan for sensor network. In this paper, we propose a Centralized Energy Efficient Clustering (CEEC) routing protocol. We design the CEEC for three level heterogeneous network. CEEC can also be implemented in multi-level heterogeneity of networks. For initial practical, we design and analyze CEEC for three level advance heterogeneous network. In CEEC, whole network area is divided into three equal regions, in which nodes with same energy are spread in same region.

Extensive energy is consumed by Transceiver communication operation [1]. Existing research on MAC layer focuses to maximize battery-powered sensor node's life. Bottleneck of MAC layer protocol design for WBAN is to achieve high reliability and energy minimization. Majority of MAC protocols designed for WBANs are based upon TDMA approach. However, a new protocol needs to be defined to achieve high energy efficiency, fairness and avoid extra energy consumption due to synchronization.

Recent advances in wireless communications, system on chip and low power sensor nodes allow realization of Wireless Body Area Networks (WBANs). WBANs comprise of tiny sensors, which collect information of a patient's vital signs and provide a real time feedback. In addition, WBANs also support many applications including ubiquitous healthcare, entertainment, gaming, military, etc. Ubiquitous healthcare is required by elderly people to facilitate them with instant monitoring anywhere they move around. In this paper, we provide a survey on different architectures used in WBANs for ubiquitous healthcare monitoring. Different standards and devices used in these architectures are also discussed in this paper. Finally, path loss in WBANs and its impact on communication is presented with the help of simulations performed for different models of In-Body communication and different factors (such as, attenuation, frequency, distance etc) influencing path loss in On-Body communications.

In this paper, we evaluate and compare the impact of link duration and path stability of routing protocols; Destination Sequence Distance vector (DSDV), Dynamic MANET On-Demand (DYMO) and Optimized Link State Routing (OLSR) at different number of connections and node density. In order to improve the efficiency of selected protocols; we enhance DYMO and OLSR. Simulation and comparison of both default and enhanced routing protocols is carried out under the performance parameters; Packet Delivery Ratio (PDR), Average End-to End Delay (AE2ED) and Normalized Routing Overhead (NRO). From the results, we observe that DYMO performs better than DSDV, MOD-OLSR and OLSR in terms of PDR, AE2ED, link duration and path stability at the cost of high value of NRO.node density.

One of the major issues in Wireless Body Area Sensor Networks (WBASNs) is efficient localization. There are various techniques for indoor and outdoor environments to locate a person. This study evaluating and compares performance of optimization schemes in indoor environments for optimal placement of wireless sensors, where patients can perform their daily activities. In indoor environments, the performance comparison between Distance Vector-Hop algorithm, Ring Overlapping Based on Comparison Received Signal Strength Indicator (ROCRSSI), Particle filtering and Kalman filtering based location tracking techniques, in terms of localization accuracy is estimated. Results show that particle filtering outperforms all. GPS and several techniques based on GSM location tracking schemes are proposed for outdoor environments. Hidden Markov GSM based location tracking scheme efficiently performs among all, in terms of location accuracy and computational overheads.

In this paper, a framework for experimental parameters in which Packet Delivery Ratio (PDR), effect of link duration over End-to-End Delay (E2ED) and Normalized Routing Overhead (NRO) in terms of control packets is analyzed and modeled for Mobile Ad-Hoc NETworks (MANETs) and Vehicular Ad-Hoc NETworks (VANETs) with the assumption that nodes (vehicles) are sparsely moving in two different road. Moreover, this paper contributes the performance comparison of one Proactive Routing Protocol; Destination Sequenced Distance vector (DSDV) and two reactive protocols; DYnamic Source Routing (DSR) and DYnamic MANET On-Demand (DYMO). A novel contribution of this work is enhancements in default versions of selected routing protocols. Three performance parameters; PDR, E2ED and NRO with varying scalabilities are measured to analyze the performance of selected routing protocols with their original and enhanced versions. From extensive simulations, it is observed that DSR outperforms among all three protocols at the cost of delay. NS-2 simulator is used for simulation with TwoRayGround propagation model to evaluate analytical results.

In this paper, we have modeled the routing overhead generated by three reactive routing protocols; Ad-hoc On-demand Distance Vector (AODV), Dynamic Source Routing (DSR) and DYnamic MANET On-deman (DYMO). Routing performed by reactive protocols consists of two phases; route discovery and route maintenance. Total cost paid by a protocol for efficient routing is sum of the cost paid in the form of energy consumed and time spent. These protocols majorly focus on the optimization performed by expanding ring search algorithm to control the flooding generated by the mechanism of blind flooding. So, we have modeled the energy consumed and time spent per packet both for route discovery and route maintenance. The proposed framework is evaluated in NS-2 to compare performance of the chosen routing protocols.

In this paper, we identify and analyze the requirements to design a new routing link metric for wireless multi- hop networks. Considering these requirements, when a link metric is proposed, then both the design and implementation of the link metric with a routing protocol become easy. Secondly, the underlying network issues can easily be tackled. Thirdly, an appreciable performance of the network is guaranteed. Along with the existing implementation of three link metrics Expected Transmission Count (ETX), Minimum Delay (MD), and Minimum Loss (ML), we implement inverse ETX; invETX with Optimized Link State Routing (OLSR) using NS-2.34. The simulation results show that how the computational burden of a metric degrades the performance of the respective protocol and how a metric has to trade-off between different performance parameters.

This paper presents how enhanced Quality of Service (QoS) in IEEE802.11e is achieved by providing traffics with different priorities performing the access to the wireless medium. Particularly, the EDCF set of parameters defines the priorities of the admission control mechanism during the Contention-based Period (CP). This can subsequently be declined to a variation of network dynamicity. Reliability analysis of different traffic classes (video, voice and data), without considering both network topology and node's mobility constraints, is not well appropriate. Being based upon scenarios, our proposed approach reveals how the behaviour of the service differentiation scheme is greatly affected according to the nodes' mobility (position and velocity). Thus, three levels (Low, Medium, and High) of node's speed are discerned. Depending on the Access Aategories (AC's) in QoS Stations (QSAT's), the Wireless LAN has been implemented on various static and dynamic scenarios using NS-2. Performance of EDCF based on the main MAC-layer metrics, such as throughput, End-2-End delay and jitter, is deeply investigated.