Laxmi Shrivastava

Vehicular Ad-Hoc Network is one of the most growing research fields and has become a promising topic for researchers. Day by day, vehicle density increases, causing very serious issues like road accidents and traffic jams. These issues can be minimized by a periodic exchange of awareness messages among the vehicles. Traffic density increases transmission of awareness messages, which causes channel congestion and degrades safety services, so congestion control schemes must ensure that the channel load should be below a particular threshold, boost the quality of services and minimize information loss and delay in the network. This paper proposes a machine learning-based risk-aware congestion control (MLB-RACC) scheme which is an efficient congestion control scheme based on the modified [ ] -means machine learning algorithms. MLB-RACC technique processes the input data and helps to improve the quality of services in the automotive industry. It works in three phases: detecting congestion, clustering of vehicles and controlling the broadcasting of awareness messages for decreasing the channel load. MLB-RACC scheme works by grouping the nearest vehicles and the number of groups or clusters is decided on the basis of transmission range/transmission power. The group member selects adaptive transmission rate by looking at the channel busy ratio (CBR). In this technique, message generation rate is controlled at two levels: first is at the clustering level and second is during the checking of CBR. The scheme is implemented through MATLAB, NS2 and the SUMO platform and provides evidence for the minimization of information loss by analyzing the throughput, packet loss and end-to-end delay in comparison to ordinary decentralized congestion control (DCC) technique.

In this world of Unmanned Aerial Vehicle (UAV), Flying Ad-hoc Network (FANET) is the growing field, which is based on unpredictable and dynamic topology, high mobility of nodes and 3-D movement. FANET is used in wide range of civil and commercial applications. The applicability of FANET encourages the difficulties in the development of network connection. It requires special purpose protocols for routing the UAV in any rigorous condition and environment. Adhoc On-demand Distance Vector (AODV) is a well established reactive routing protocol, which is well affected in large and multi-hop networks. AODV protocol does not include the path accumulation and multi-path routing features, as the other reactive protocol like Dynamic Source Routing (DSR) protocol supports. In this work, we used Hybrid ANT Colony Optimization (HACO) Routing Algorithm for AODV protocol improvement. After including this algorithm, AODV protocol supports path accumulation and multi-path routing features. We build a prototype of this work in OMNet++ Simu-lator and performs different simulation for various test conditions. Then, We compares the traditional AODV protocol with this improved version of AODV protocol(AODV-HACO) under different performance parameters. We find out 6.56 % improvement in traditional AODV to get Path accumulation and Multi-Path routing for FANET.

In the newly emerged electric supply industry, the profit maximizing tendency of market participants has developed the problem of transmission congestion as the most crucial issue. This paper proposes a multiobjective salp swarm algorithm (MOSSA) approach for transmission congestion management (CM), implementing demand side management activities. For this, demand response (DR) and distributed generation (DG) have been employed. For willingly reducing the demand, demand response has been called by providing appropriate financial incentives that supports in releasing the congestion over critical lines. Distributed generation implementing wind plant as renewable independent power producer (RIPP) has also been included in order to reduce the load curtailment of responsive customers to manage transmission congestion. The proposed incentive-based demand response and distributed generation approach of CM, has been framed with various strategies employing different thermal limits over transmission lines and has resulted into significant reduction in congestion and in-turn improvement of transmission reliability margin. Diversity has been obtained in multiobjective optimization by taking two and three objective functions, respectively (minimization of overall operational cost, CO₂ emission, and line loading). The by-products of the proposed algorithm for multiobjective optimization are minimized demand reduction, optimum size, and location of DG. To examine the proposed approach, it has been implemented on IEEE 30-bus system and a bigger power system IEEE 118-bus system; as well as the proposed technique of MOSSA has been compared and found better than reported methods and two other meta heuristic algorithms (multiobjective modified sperm swarm optimization and multiobjective adoptive rat swarm optimization).