• Energy Research

Vehicular Ad hoc Network (VANET) is a modern concept that enables network nodes to communicate and disseminate information. VANET is a heterogeneous network, due to which the VANET environment exposes to have various security and privacy challenges. In the future, the automobile industry will progress towards assembling electric vehicles containing energy storage batteries employing these resources to travel as an alternative to gasoline/petroleum. These vehicles may have the capability to share their energy resources upon the request of vehicles having limited energy resources. In this article, we have proposed a trust management-based secure energy sharing mechanism, named vTrust, which computes the trust degree of nodes to authenticate nodes. The proposed mechanism is a multi-leveled centralized approach utilizing both the infrastructure and vehicles to sustain a secure environment. The proposed vTrust can aggregate and propagate the degree of trust to enhance scalability. The node that requests to obtain the energy resources may have to maintain a specified level of trust threshold for earning resources. We have also evaluated the performance of the proposed mechanism against several existing approaches and determine that the proposed mechanism can efficiently manage a secure environment during resource sharing by maintaining average malicious nodes detection of 91.3% and average successful energy sharing rate of 89.5%, which is significantly higher in comparison to the existing approaches.

Vehicular Ad-hoc Network (VANET) is a modern concept of transportation that was formulated by extending Mobile Ad-hoc Networks (MANETs). VANET presents diverse opportunities to modernize transportation to enhance safety, security, and privacy. Direct communication raises various limitations, most importantly, the overhead ratio. The most prominent solution proposed is to divide these nodes into clusters. In this paper, we propose a clustering mechanism that provides security and maintains quality after the cluster formulation based on the pre-defined Quality-of-Service (QoS) parameters. To address potential attacks in the VANET environment, the proposed mechanism uses blockchain to encrypt the trust parameters’ computation. A particular trust degree of a vehicle is evaluated by the base station, encrypted with the blockchain approach, and transmitted toward roadside units (RSUs) for further utilization. The system’s performance is evaluated and compared with the existing approaches. The results show a significant improvement in terms of security and clustering quality.

In recent times, the field of wireless sensor networks (WSNs) has attained a growing popularity in observing the environment due to its dynamic factors. Sensor data are gathered and forwarded to the base station (BS) through a wireless transmission medium. The data from the BS is further distributed to end-users using the Internet for their post analysis and operations. However, all sensors except the BS have limited constraints in terms of memory, energy and computational resources that degrade the network performance concerning the network lifetime and trustworthy routing. Therefore, improving energy efficiency with reliable and secure transmissions is a valuable debate among researchers for critical applications based on low-powered sensor nodes. In addition, security plays a significant cause to achieve responsible communications among sensors due to their unfixed and variable infrastructures. Keeping in view the above-mentioned issues, this paper presents an energy-aware graph clustering and intelligent routing (EGCIR) using a supervised system for WSNs to balance the energy consumption and load distribution. Moreover, a secure and efficient key distribution in a hierarchy-based mechanism is adopted by the proposed solution to improve the network efficacy in terms of routes and links integrity. The experimental results demonstrated that the EGCIR protocol enhances the network throughput by an average of 14%, packet drop ratio by an average of 50%, energy consumption by an average of 13%, data latency by an average of 30.2% and data breaches by an average of 37.5% than other state-of-the-art protocols.

Abstract The future Internet, known as Information-Centric Networking (ICN), is a realistic solution to content delivery between the content request generators (subscribers) and the server (publisher) in the Internet of Things (IoT) environment due to caching contents by in-network nodes. However, significant redundant copies of contents can be cached in this kind of network which, besides numerous advantages, introduces some undesirable features, such as security issues, content redundancy, access control, and cache overflow among others. ICN has different modules, such as mobility, routing, and caching, which are utmost important for the IoT network due to the nature of energy-constrained IoT devices. While numerous attempts are presently being made to institutionalize this emerging paradigm, careful considerations are needed to caching module at the early stage of this architecture. This is important instead of holding up until the innovation gets used and experienced. In this article, we first list some of the important features and limitations of the ICN-based IoT caching and then propose an ICN caching strategy that fits well in the energy efficient and secure IoT environment. The proposed strategy is simulated and compared with the ProbCache mechanism with regards to energy consumption and bandwidth utilization. Preliminary experimental analyses demonstrate that the proposed strategy produces better results than the ProbCache as long as the cache size of network nodes is increased.

Today’s buildings are responsible for about 40% of total energy consumption and 30–40% of carbon emissions, which are key concerns for the sustainable development of any society. The excessive usage of grid energy raises sustainability issues in the face of global changes, such as climate change, population, economic growths, etc. Traditionally, the power systems that deliver this commodity are fuel operated and lead towards high carbon emissions and global warming. To overcome these issues, the recent concept of the nearly zero energy building (nZEB) has attracted numerous researchers and industry for the construction and management of the new generation buildings. In this regard, this paper proposes various demand side management (DSM) programs using the genetic algorithm (GA), teaching learning-based optimization (TLBO), the enhanced differential evolution (EDE) algorithm and the proposed enhanced differential teaching learning algorithm (EDTLA) to manage energy and comfort, while taking the human preferences into consideration. Power consumption patterns of shiftable home appliances are modified in response to the real-time price signal in order to get monetary benefits. To further improve the cost and user discomfort objectives along with reduced carbon emission, renewable energy sources (RESs) are also integrated into the microgrid (MG). The proposed model is implemented in a smart residential complex of multiple homes under a real-time pricing environment. We figure out two feasible regions: one for electricity cost and the other for user discomfort. The proposed model aims to deal with the stochastic nature of RESs while introducing the battery storage system (BSS). The main objectives of this paper include: (1) integration of RESs; (2) minimization of the electricity bill (cost) and discomfort; and (3) minimizing the peak to average ratio (PAR) and carbon emission. Additionally, we also analyze the tradeoff between two conflicting objectives, like electricity cost and user discomfort. Simulation results validate both the implemented and proposed techniques.

Traditional power grid and its demand-side management (DSM) techniques are centralized and mainly focus on industrial consumers. The ignorance of residential and commercial sectors in DSM activities degrades the overall performance of a conventional grid. Therefore, the concept of DSM and demand response (DR) via residential sector makes the smart grid (SG) superior over the traditional grid. In this context, this paper proposes an optimized home energy management system (OHEMS) that not only facilitates the integration of renewable energy source (RES) and energy storage system (ESS) but also incorporates the residential sector into DSM activities. The proposed OHEMS minimizes the electricity bill by scheduling the household appliances and ESS in response to the dynamic pricing of electricity market. First, the constrained optimization problem is mathematically formulated by using multiple knapsack problems, and then solved by using the heuristic algorithms; genetic algorithm (GA), binary particle swarm optimization (BPSO), wind driven optimization (WDO), bacterial foraging optimization (BFO) and hybrid GA-PSO (HGPO) algorithms. The performance of the proposed scheme and heuristic algorithms is evaluated via MATLAB simulations. Results illustrate that the integration of RES and ESS reduces the electricity bill and peak-to-average ratio (PAR) by 19.94% and 21.55% respectively. Moreover, the HGPO algorithm based home energy management system outperforms the other heuristic algorithms, and further reduces the bill by 25.12% and PAR by 24.88%.

Smart grid is an emerging technology which is considered to be an ultimate solution to meet the increasing power demand challenges. Modern communication technologies have enabled the successful implementation of smart grid (SG), which aims at provision of demand side management mechanisms (DSM), such as demand response (DR). In this paper, we propose a hybrid technique named as teacher learning genetic optimization (TLGO) by combining genetic algorithm (GA) with teacher learning based optimization (TLBO) algorithm for residential load scheduling, assuming that electric prices are announced on a day-ahead basis. User discomfort is one of the key aspects which must be addressed along with cost minimization. The major focus of this work is to minimize consumer electricity bill at minimum user discomfort. Load scheduling is formulated as an optimization problem and an optimal schedule is achieved by solving the minimization problem. We also investigated the effect of power-flexible appliances on consumers’ bill. Furthermore, a relationship among power consumption, cost and user discomfort is also demonstrated by feasible region. Simulation results validate that our proposed technique performs better in terms of cost reduction and user discomfort minimization, and is able to obtain the desired trade-off between consumer electricity bill and user discomfort.