Renewable Energy Sources (RESs) are gaining considerable attention to reduce human dependence on fossil fuels and minimize harmful gases in our surroundings. Existing literature on energy trading focused on providing renewable energy to smart homes, smart buildings, and smart offices to fulfill their daily energy demands obtained from RESs. Besides, Electric Vehicles (EVs) use either power grid energy or a battery exchange mechanism to recharge their low EV batteries. The continuous use of power grids to recharge low EV batteries causes a significant load on power grids. Due to this, power grids are inadequate to fulfill the ever-increasing demands of EVs in the future. In this context, we propose a Blockchain Enabled Energy Trading (BEET) framework oriented EV charging. A system architecture of the BEET framework is presented to describe the functioning of each layer and its associated entities. We formulate an optimization problem that maximizes the revenue in the energy trading process using a knapsack optimization. Smart contracts are designed on the consortium blockchain network to sell and buy renewable energy to aggregators and from producers, respectively. Moreover, an EV charging mechanism is designed to intelligently allocate renewable energy to consumers at a low price. A comparative analysis is performed with state-of-the-art works in terms of charging price, revenue, throughput, and latency. The results indicate that the BEET framework outperforms compared to state-of-the-art works to address the renewable energy demand problem to realize E-mobility. It is clarified that the data considered in the experimental analysis were obtained from statistical simulations in realistic E-Mobility environment settings.

Smart fire detection and insurance systems have gained considerable attention from researchers and industries. At the same time, automatic requests for fire brigade services to cure fire and instant claims settlement to defend insurance fraud are lacking in literatures. We propose a trusted fire brigade service and insurance claim (FLAME) framework using blockchain for enterprises to provide immediate fire brigade services and prevent insurance frauds. A system model is presented to explain architecture, and overall functionality of FLAME using blockchain. Further, a sensing network and connectivity model is proposed to detect true fire and send an emergency service request to monitoring station. Smart contracts are designed to automate fire brigade service and insurance claim processes. A prototype of the FLAME is implemented on hyperledger besu blockchain using Istanbul Byzantine Fault Tolerance 2.0 consensus protocol. Simulation results show that latency and throughput of the FLAME are better compared to state-of-the-art models.

Edge computing has received significant attention from academia and industries and has emerged as a promising solution for enhancing the information processing capability at the edge for next generation 6G networks. The technical design of 6G edge networks in terms of offloading the computationally extensive task is very critical because of the overgrowth in data volume primarily due to the explosion of smart IoT devices, and the ever-reducing size of these energy-constrained devices in IoT systems. Toward harnessing the benefits of deep recurrent neural network based on Long Short Term Memory (LSTM) in the design of next-generation edge networks, this paper presents a framework DECENT- Deep learning Enabled green Computation for Edge centric Next generation 6G networks. The data offloading problem is modeled as a Markov decision process considering joint optimization of energy consumption, computation latency, and offloading rate for network utility in 6G environment. The algorithm learns faster from previous long-term offloading experiences and solves the optimization problem with better convergence speed. Simulation results of the proposed framework DECENT shows that it maximizes the network utility by overcoming the challenges as compared to the state-of-the-art techniques.

Abstract With the evolution of cybersecurity countermeasures, the threat landscape has also evolved, especially in malware from traditional file-based malware to sophisticated and multifarious fileless malware. Fileless malware does not use traditional executables to carry-out its activities. So, it does not use the file system, thereby evading signature-based detection system. The fileless malware attack is catastrophic for any enterprise because of its persistence, and power to evade any anti-virus solutions. The malware leverages the power of operating systems, trusted tools to accomplish its malicious intent. To analyze such malware, security professionals use forensic tools to trace the attacker, whereas the attacker might use anti-forensics tools to erase their traces. This survey makes a comprehensive analysis of fileless malware and their detection techniques that are available in the literature. We present a process model to handle fileless malware attacks in the incident response process. In the end, the specific research gaps present in the proposed process model are identified, and associated challenges are highlighted.

Geocasting in vehicular communication has witnessed significant attention due to the benefits of location oriented information dissemination in vehicular traffic environments. Various measures have been applied to enhance geocasting performance including dynamic relay area selection, junction nodes incorporation, caching integration, and geospatial distribution of nodes. However, the literature lacks toward geocasting under malicious relay vehicles leading to cybersecurity concern in vehicular traffic environments. In this context, this paper presents cybersecurity measures for geocasting in vehicular traffic environments focusing on security oriented vehicular connectivity. Specifically, a vehicular intrusion prevention technique is developed to measure the connectivity between the cache agent (CA) and cache user (CU) vehicles. The connectivity between static transport vehicles and CA/CU is measured via vehicular intrusion detection approach. The performance of the proposed vehicular cybersecurity measure is evaluated in realistic traffic environments. The comparative performance evaluation attests the benefits of security oriented geocasting in vehicular traffic environments.

In sensor-enabled Internet of Things (IoT), nodes are deployed in an open and remote environment, therefore, are vulnerable to a variety of attacks. Recently, trust-based schemes have played a pivotal role in addressing nodes' misbehavior attacks in IoT. However, the existing trust-based schemes apply network wide dissemination of the control packets that consume excessive energy in the quest of trust evaluation, which ultimately weakens the network lifetime. In this context, this paper presents an energy efficient trust evaluation (EETE) scheme that makes use of hierarchical trust evaluation model to alleviate the malicious effects of illegitimate sensor nodes and restricts network wide dissemination of trust requests to reduce the energy consumption in clustered-sensor enabled IoT. The proposed EETE scheme incorporates three dilemma game models to reduce additional needless transmissions while balancing the trust throughout the network. Specially: 1) a cluster formation game that promotes the nodes to be cluster head (CH) or cluster member to avoid the extraneous cluster; 2) an optimal cluster formation dilemma game to affirm the minimum number of trust recommendations for maintaining the balance of the trust in a cluster; and 3) an activity-based trust dilemma game to compute the Nash equilibrium that represents the best strategy for a CH to launch its anomaly detection technique which helps in mitigation of malicious activity. Simulation results show that the proposed EETE scheme outperforms the current trust evaluation schemes in terms of detection rate, energy efficiency and trust evaluation time for clustered-sensor enabled IoT.

Recently, virtualization in wireless sensor networks (WSNs) has witnessed significant attention due to the growing service domain for Internet of Things (IoT). Related literature on virtualization in WSNs explored resource optimization without considering communication failure in WSNs environments. The failure of a communication link in WSNs impacts many virtual networks running IoT services. In this context, this paper proposes a framework for optimizing fault tolerance (FT) in virtualization in WSNs, focusing on heterogeneous networks for service-oriented IoT applications. An optimization problem is formulated considering FT and communication delay as two conflicting objectives. An adapted nondominated sorting-based genetic algorithm (A-NSGA) is developed to solve the optimization problem. The major components of A-NSGA include chromosome representation, FT and delay computation, crossover and mutation, and nondominance-based sorting. Analytical and simulation-based comparative performance evaluation has been carried out. From the analysis of results, it is evident that the framework effectively optimizes FT for virtualization in WSNs.

Location-oriented services in vehicular cyber-physical system (VCPS) have witnessed significant attention due to their potentiality to address traffic safety and efficiency related issues. The multichannel communication aids these services by tuning their overall performance in vehicular environments. Related literature on multichannel communication focuses on interference as channel quality measure. However, uncertain mobility and density of vehicles significantly affect channel quality apart from interference. The static quantification of channel quality is not suitable due to the dynamic characteristics of the channel quality parameters. In this context, this paper proposes fuzzy-based channel selection framework for location-oriented services in multichannel VCPS environments. A system model is presented for deriving channel access delay (CAD) using Markov chain model. The channel quality is estimated using CAD and signal-to-interference ratio (SIR). The fuzzy logic-based channel selection framework is developed considering fuzzification and defuzzification of CAD and SIR. The comparative performance evaluation attests the benefit of the framework as compared to the state-of-the-art techniques in VCPS.