Despite the rapid expansion in the insurance industry, many issues remain unresolved and may require immediate action. As the insurance sector continues to evolve with the development of new technologies, it faces more challenges, especially related to data security and fraud. The fraud-prevention data and tactics presently used by insurance firms are outdated and ineffective. Additionally, insurance firms have traditionally handled the settlement of all consumer claims through lengthy manual processes. These manual processes need to be changed to provide opportunities for insurance businesses to grow. In the case of vehicles, the information obtained from an automobile data recorder can be used as evidence. Data from automated vehicles are critical because they can help the police, law enforcement agencies, and insurance companies to reconstruct the events leading up to a collision. Insurance companies require the forensic analysis of accident videos, which is a time-consuming process and involves a large amount of storage. Due to hardware limitations and associated costs, the current standalone (and often dedicated) computing infrastructures used for this purpose are quite limited. Previous research focused on simple video analysis tasks within cloud computing and blockchain technology. The requirements for a large-scale auto-insurance system are quite high and need more thorough investigation. In this paper, a review of the contribution of recent approaches to storing accidental data in cloud computing using blockchain is provided. We focused on the latest cloud and blockchain studies related to auto-insurance along with the related issues and challenges. Some useful solutions and recommendations are provided to address the identified issues and challenges in the cloud-based and blockchain-based auto-insurance sector.

Abstract Various devices are linked together through a variety of networks in order to exchange data in the Internet of Things (IoT). Life has changed dramatically as a result of the IoT's quickly developing applications, making it more fanciful and akin to living in a virtual environment. IoT security may be a major concern because it allows access to anything from anywhere at any time. Different mechanisms are used, including encryption, to safeguard the data transmitted by various IoT devices. A straightforward piece of content can be transformed into an intangible one via encryption. Two frequently used methods of encryption are symmetric key and asymmetric key cryptography. Asymmetric cryptography uses two separate keys for encoding and unscrambling messages as opposed to symmetric key cryptography, which uses just one key for both encoding and decoding. To protect IoT data, several public key cryptography techniques are utilized. However, numerous studies have demonstrated that it is incredibly challenging to safeguard data while utilizing the fewest resources from hackers and other cybercriminals. This study presents a concise overview of many existing encryption techniques and highlights their shortcomings. Conjointly presents an improved Rivest–Shamir–Adleman (RSA) algorithm to dodge the limits of diverse factorization assaults by upgrading complications of the factorization handle by utilizing five prime numbers and a fake open key instead of the original key and imposter modulus in its place of the product of five prime numbers. The proposed algorithm generates keys a little more slowly than the other two RSA algorithms (Rivest et al., 1978) and (Ivy et al., 2012) due to the usage of additional prime numbers and the difficulty of factorization preparation. For encryption and unscrambling, the suggested calculation takes less time than RSA (Ivy et al., 2012), but it costs more than RSA (Rivest et al., 1978). Since the suggested calculation employs five prime numbers instead of RSA's two prime numbers, it takes longer to encrypt data and decrypt it than RSA (Rivest et al., 1978). But since security comes first, the longer encryption and decryption time is acceptable.

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 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 as all fields of life like material production, health care, educational sector, etc., depend upon it to render consistent and high-quality services, increase productivity and business continuity. To this end, energy operators have experienced a continuous increasing trend in the electricity demand for the past few decades. This may cause many issues like load shedding, increased electricity bills, imbalance between supply and demand, etc. Therefore, forecasting of electricity demand using efficient techniques is crucial for the energy operators to decide about optimal unit commitment and to make electricity dispatch plans. It also helps to avoid wastage as well as the shortage of energy. In this study, a novel forecasting model, known as ELS-net is proposed, which is a combination of an Ensemble Empirical Mode Decomposition (EEMD) method, multi-model Ensemble Bi Long Short-Term Memory (EBiLSTM) forecasting technique and Support Vector Machine (SVM). In the proposed model, EEMD is used to distinguish between linear and non-linear intrinsic mode functions (IMFs), EBiLSTM is used to forecast the non-linear IMFs and SVM is employed to forecast the linear IMFs. Using separate forecasting techniques for linear and non-linear IMFs decreases the computational complexity of the model. Moreover, SVM requires low computational time as compared to EBiLSTM for linear IMFs. Simulations are performed to examine the effectiveness of the proposed model using two different datasets: New South Wales (NSW) and Victoria (VIC). For performance evaluation, Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) are used as performance metrics. From the simulation results, it is obvious that the proposed ELS-net model outperforms the start-of-the-art techniques, such as EMD-BILSTM-SVM, EMD-PSO-GA-SVR, BiLSTM, MLP and SVM in terms of forecasting accuracy and minimum execution time.

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.

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.

In Vehicular Ad hoc Networks (VANETs), a large amount of data is shared between vehicles and Road Side Units (RSUs) in real-time. VANETs improve traffic efficiency and reliability by timely sharing road events and traffic information. However, there is a need to tackle the issues of both less data storage capability and selfish behavior of the vehicles. The conventional data storage mechanisms involve a third party for data management and are non-transparent, unreliable, untrustworthy, and insecure. Therefore, a blockchain based data storage system is presented in this paper to overcome the aforementioned issues. The proposed system exploits benefits of an Interplanetary File System (IPFS). Due to the resource constraints of vehicles, the blockchain is implemented on the RSUs. The RSUs get the aggregated packets sent by the vehicles. The packets contain the events’ information that occur in the vehicles’ surroundings. After verifying a packet, RSUs store the information related to the event in IPFS and reputation value of the sender vehicle in the blockchain. The reputation value of a vehicle is calculated based upon the correctness of an event it signs or initiates. Moreover, an incentive mechanism is also proposed to provide monetary incentives to the replier vehicles who respond to the events’ information. The incentives are provided by the initiators after verification of the repliers’ signatures. The initiator is a vehicle who initializes the event. The transactions performed during the incentive process are stored in the blockchain. Finally, Oyente tool is used to analyze the security of the proposed smart contract. A comparison of the proposed scheme with the logistic regression scheme is also presented.

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.

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.

This study proposes an efficient energy management method to systematically manage the energy consumption in the residential area to alleviate the peak to average ratio and mitigate electricity cost along with user comfort maximization. We developed an efficient energy management scheme using mixed integer linear programming (MILP), which schedules smart appliances and charging/discharging of electric vehicles (EVs) optimally in order to mitigate energy costs. In the proposed model, consumer is able to generate its own energy from microgrid consisting of solar panels and wind turbines. We also consider an energy storage system (ESS) for efficient energy utilization. This work also performs energy forecasting using wind speed and solar radiation prediction for efficient energy management. Moreover, we perform extensive simulations to validate our developed MILP based scheme and results affirm the effectiveness and productiveness of our proposed energy efficient technique.

Multi-microgrid (MMG) system is a new method that concurrently incorporates different types of distributed energy resources, energy storage systems and demand responses to provide reliable and independent electricity for the community. However, MMG system faces the problems of management, real-time economic operations and controls. Therefore, this study proposes an energy management system (EMS) that turns an infinite number of MMGs into a coherence and efficient system, where each MMG can achieve its goals and perspectives. The proposed EMS employs a cooperative game to achieve efficient coordination and operations of the MMG system and also ensures a fair energy cost allocation among members in the coalition. This study considers the energy cost allocation problem when the number of members in the coalition grows exponentially. The energy cost allocation problem is solved using a column generation algorithm. The proposed model includes energy storage systems, demand loads, real-time electricity prices and renewable energy. The estimate of the daily operating cost of the MMG using a proposed deep convolutional neural network (CNN) is analyzed in this study. An optimal scheduling policy to optimize the total daily operating cost of MMG is also proposed. Besides, other existing optimal scheduling policies, such as approximate dynamic programming (ADP), model prediction control (MPC), and greedy policy are considered for the comparison. To evaluate the effectiveness of the proposed model, the real-time electricity prices of the electric reliability council of Texas are used. Simulation results show that each MMG can achieve energy cost savings through a coalition of MMG. Moreover, the proposed optimal policy method achieves MG's daily operating cost reduction up to 87.86% as compared to 79.52% for the MPC method, 73.94% for the greedy policy method and 79.42% for ADP method.

The emergence of the Demand Response (DR) program optimizes the energy consumption pattern of customers and improves the efficacy of energy supply. The pricing infra-structure of the DR program is dynamic (time-based). It has rather complex features including marginal costs, demand and seasonal parameters. There is variation in DR price rate. Sometime prices go high (peak load) if the demand of electricity is more than the generation capacity. The main objective of DR is to encourage the consumer to shift the peak load and gets incentives in terms of cost reduction. However, prices remain the same for all the users even if they shift the peak load or not. In this work, Game Theory (GT)-based Time-of-Use (ToU) pricing model is presented to define the rates for on-peak and shoulder-peak hours. The price is defined for each user according to the utilize load. At first, the proposed model is examined using the ToU pricing scheme. Afterward, it is evaluated using existing day-ahead real-time pricing scheme. Moreover, shifting load from on-peak hours to off-peak hours may cause rebound peak in off-peak hours. To avert this issue, we analysis the impact of Salp Swam Algorithm (SSA) and Rainfall Algorithm (RFA) on user electricity bill and PAR after scheduling. The experimental results show the effectiveness of the proposed GT-based ToU pricing scheme. Furthermore, the RFA outperformed SSA.

For the better management of energy, a coordination based energy management system as-a-service on fog is presented. An efficient system model is introduced to handle a social networking problem in order to maintain the balance between the produced and the required energy. This social network problem is formulated as a game theory based coalition method. With the increase in number of electricity consumers, the computational complexity of energy management system is becoming a threat for system efficiency in real-time environment. To deal with this dilemma, the service providers shift their computational and storage units on cloud and fog. The fog is an intermittent layer between the cloud and the end user which helps to make the system faster as compared to the cloud. In this scenario, a building with multiple apartments is considered. Where, each apartment is taken as a player and the surplus power as pay-off. The surplus energy will be distributed among the energy deficient apartments using Shapley value that unevenly distributes the power according to the demand. The experimental results show that 13 kW extra power is saved and distributed among energy deficient apartments during different times of a day.

An increase in the world's population results in high energy demand, which is mostly fulfilled by consuming fossil fuels (FFs). By nature, FFs are scarce, depleted, and non-eco-friendly. Renewable energy sources (RESs) photovoltaics (PVs) and wind turbines (WTs) are emerging alternatives to the FFs. The integration of an energy storage system with these sources provides promising and economical results to satisfy the user's load in a stand-alone environment. Due to the intermittent nature of RESs, their optimal sizing is a vital challenge when considering cost and reliability parameters. In this paper, three meta-heuristic algorithms: teaching-learning based optimization (TLBO), enhanced differential evolution (EDE), and the salp swarm algorithm (SSA), along with two hybrid schemes (TLBO + EDE and TLBO + SSA) called enhanced evolutionary sizing algorithms (EESAs) are proposed for solving the unit sizing problem of hybrid RESs in a stand-alone environment. The objective of this work is to minimize the user's total annual cost (TAC). The reliability is considered via the maximum allowable loss of power supply probability (LPSPmax) concept. The simulation results reveal that EESAs provide better results in terms of TAC minimization as compared to other algorithms at four LPSPmax values of 0%, 0.5%, 1%, and 3%, respectively, for a PV-WT-battery hybrid system. Further, the PV-WT-battery hybrid system is found as the most economical scenario when it is compared to PV-battery and WT-battery systems.

The increasing load demand in residential area and irregular electricity load profile encouraged us to propose an efficient Home Energy Management System (HEMS) for optimal scheduling of home appliances. We propose a multi-objective optimization based solution that shifts the electricity load from On-peak to Off-peak hours according to the defined objective load curve for electricity. It aims to manage the trade-off between conflicting objectives: electricity bill, waiting time of appliances and electricity load shifting according to the defined electricity load pattern. The defined electricity load pattern helps in balancing the load during On-peak and Off-peak hours. Moreover, for real-time rescheduling, concept of coordination among home appliances is presented. This helps the scheduler to optimally decide the ON/OFF status of appliances to reduce the waiting time of the appliance. Whereas, electricity consumers have stochastic nature, for which, nature-inspired optimization techniques provide optimal solution. For optimal scheduling, we proposed two optimization techniques: binary multi-objective bird swarm optimization and a hybrid of bird swarm and cuckoo search algorithms to obtain the Pareto front. Moreover, dynamic programming is used to enable coordination among the appliances so that real-time scheduling can be performed by the scheduler on user's demand. To validate the performance of the proposed nature-based optimization techniques, we compare the results of proposed schemes with existing techniques such as multi-objective binary particle swarm optimization and multi-objective cuckoo search algorithms. Simulation results validate the performance of proposed techniques in terms of electricity cost reduction, peak to average ratio and waiting time minimization. Also, test functions for convex, non-convex and discontinuous Pareto front are implemented to prove the efficacy of proposed techniques.

The communication between the Internet of Things (IoTs) devices is not secure and reliable. A large number of security risks are involved. The existing security mechanisms are not easy to manage because they require extra resources and thus, increase the overall cost of the system. The IoT devices are resource-limited devices and they do not perform many computation. The services requested from the IoT devices may be malicious and may have severe consequences when they are not being tackled by any efficient security mechanism. The reliability of the services is an important aspect of the IoT network. Blockchain provides a solution that is secure and effective in terms of reliability and cost. We have proposed a system model through which we protect the IoT devices from unreliable services. These services are provided by cloud service providers. The proposed system allows IoT devices to know about the ratings of the service providers before requesting the service. Smart contracts are introduced to store ratings of the service providers into the blockchain. IoT devices invoke the smart contract to acquire ratings of the service providers by providing service codes. Performance analysis and the experimental results show that the proposed model protects the IoT devices from unreliable services in a reasonable time and affordable cost.

Nowadays, constrained battery life expectancy is an important issue for reliable data delivery in an Underwater Wireless Sensor Network (UWSN). Conventional transmission methodologies increase the transmission overhead, i.e., the collision of packets, which influence the data transmission. Replacement of the sensors' battery in brutal underwater environment is a difficult task. Therefore, to maintain a strategic distance from the unexpected failure of the network and to increase the life expectancy of the network, energy efficient routing protocols are required. At this end, in this paper, a proactive routing protocol with three different network types is proposed to solve the aforementioned issues. The proposed protocol adaptively changes its communication strategy depending on the type of the network, i.e., dense network, partially dense network and sparse network. This adaptive strategy helps the routing protocols to continue their transmission by avoiding the void holes. In the proposed protocol named Proactive routing Approach with Energy efficient Path Selection (PA-EPS-Case I), vertical inter-transmission layering concept is introduced (using shortest and fastest path) in the dense and partially dense region. In addition, cluster formation concept is also appended to make transmission successful in the sparse regions. The Packet Delivery Ratio (PDR) is improved by the proposed protocol with minimum End to End (E2E) delay and packet drop ratio. Scalability of the proposed routing protocols is also analyzed by varying the number of nodes from 100-500. A comparative analysis is performed with two cutting edge routing protocols namely: Weighting Depth and Forwarding Area Division Depth Based Routing (WDFAD-DBR) and Cluster-based WDFAD-DBR (C-DBR). Simulation results demonstrate that proposed protocol achieved 12.64% higher PDR with 20% decrease in E2E delay than C-DBR. Furthermore, the proposed routing protocol outperformed C-DBR in terms of packet drop ratio up to 14.29% with an increase of EC up to 30%.

In this paper, we propose a home energy management system that employs load shifting strategy of demand side management to optimize the energy consumption patterns of a smart home. It aims to manage the load demand in an efficient way to minimize electricity cost and peak to average ratio while maintaining user comfort through coordination among home appliances. In order to meet the load demand of electricity consumers, we schedule the load in day-ahead and real-time basis. We propose a fitness criterion for proposed hybrid technique, which helps in balancing the load during ON-peak and OFF-peak hours. Moreover, for realtime rescheduling, we present the concept of coordination among home appliances. This helps the scheduler to optimally decide the ON/OFF status of appliances in order to reduce the waiting time of appliance. For this purpose, we formulate our real-time rescheduling problem as knapsack problem and solve it through dynamic programming. This paper also evaluates the behavior of the proposed technique for three pricing schemes including: time of use, real-time pricing, and critical peak pricing. Simulation results illustrate the significance of the proposed optimization technique with 95% confidence interval.

In this article, a three layered architecture is proposed for smart buildings. A fog based infrastructure is designed and deployed on the edge of network, where fog processes the private data collected through the smart meters and stores the public data on cloud. Further, end user has facility to schedule and control the home appliances by using a centralized energy management system. Moreover, the electricity and network resources utilization charges can be calculated. We analyze the performance of cloud based centralized system, considering the fog computing as an intermittent layer between system user layer and cloud layer and without considering fog computing. Simulation results prove that fog layer enhances the efficient utilization of network resources and also reduces the bottleneck on the cloud computing.