An Enhanced Trust Mechanism with Consensus-Based False Information Filtering Algorithm against Bad-Mouthing Attacks and False-Praise Attacks in WSNs

To defend against insider attacks in wireless sensor networks (WSNs), trust mechanisms (TMs) using the notion of trust in human society have been proposed and are still actively researched. In the WSN with a trust mechanism (TM), each sensor node evaluates the trustworthiness of its neighbor sensors based on their behaviors, for example packet forwarding, and collaborates only with trustworthy neighbors while removing untrustworthy neighbor from its neighbor list. The reputation system (RS) is an advanced type of trust mechanism that evaluates the trustworthiness of a node by additionally considering neighbor nodes’ observations or evaluations about it. However, intelligent inside attackers in WSNs can discover the security vulnerabilities of trust mechanisms by examining the operations of TM (or RS), because the software modules of the TM (or RS) are installed and operating in their local storage and memory, and thus, they can avoid detection by the trust mechanisms. Bad-mouthing attacks and false-praise attacks are well-known examples of such intelligent insider attacks. We observed that existing trust mechanisms do not have effective countermeasures to defend against such attacks. In this paper, we propose an enhanced trust mechanism with a consensus-based false information filtering algorithm (TM-CFIFA) that can effectively defend against bad-mouthing attacks and false-praise attacks. According to our experiment results, compared with an existing representative RS model, our TM-CFIFA shortened the detection time of a packet drop attacker, which is supported by a false-praise attacker by at least 83%, and also extended the lifetime of a victim sensor node that is under bad-mouthing attacks by at least 15.8%.

Hybrid QPSO-NNIA2 Algorithm for Multi-Objective Optimization Problem

To improve the convergence and distribution of a multi-objective optimization algorithm, a hybrid multi-objective optimization algorithm, based on the quantum particle swarm optimization (QPSO) algorithm and adaptive ranks clone and neighbor list-based immune algorithm (NNIA2), is proposed. The contribution of this work is threefold. First, the vicinity distance was used instead of the crowding distance to update the archived optimal solutions in the QPSO algorithm. The archived optimal solutions are updated and maintained by using the dynamic vicinity distance based m-nearest neighbor list in the QPSO algorithm. Secondly, an adaptive dynamic threshold of unfitness function for constraint handling is introduced in the process. It is related to the evolution algebra and the feasible solution. Thirdly, a new metric called the distribution metric is proposed to depict the diversity and distribution of the Pareto optimal. In order to verify the validity and feasibility of the QPSO-NNIA2 algorithm, we compare it with the QPSO, NNIA2, NSGA-II, MOEA/D, and SPEA2 algorithms in solving unconstrained and constrained multi-objective problems. The simulation results show that the QPSO-NNIA2 algorithm achieves superior convergence and superior performance by three metrics compared to other algorithms.

Implementation and analysis of stability improvement in VANET using different scenarios

In circumstances where hubs are portable or when hubs frequently turn now and again, the neighborhood topology infrequently stays static. Henceforth, it is important that every hub communicates its refreshed area data to the greater part of its neighbors. These area refresh parcels are normally alluded to as reference points. In most geographic directing conventions (e.g., GPSR), guides are communicated occasionally to maintain an exact neighbor list at every hub. Position refreshes are expensive from numerous points of view. Each refresh devours hub vitality; remote data transfer capacity, and expands the danger of parcel crash at the medium access control (MAC) layer. Parcel impacts cause bundle misfortune which thusly influences the directing execution because of diminished precision in deciding the right nearby topology.

Fade Duration Based Neighbor Cell List Optimization for Handover in Femtocell Networks

In a dense femtocell network, beyond co-tier and cross-tier interference mitigation, handover femtocell- femtocell and macrocell-femtocell is a major challenge. In order to perform successful handover, avoiding the scanning of a large neighbor list and shortening the handover period is required to identify the optimal neighbor. In this paper, a neighbor cell list optimization method based on fade duration along with an algorithm for open and hybrid femtocell networks is proposed. The proposed method considers fade duration outage probability (FDOP), distance between femtocell access points, and the operating frequency as benchmarks for optimization of the neighbor list selection process. FDOP determines a duration beyond which a connection is considered in an outage state. The simulation results based on this proposed method also show an improvement over previously proposed methods that create neighboring lists based on received signal and signal-to-noise ratio. Fade duration based optimization provides a much better prediction of traffic performance.