Mobile Ad hoc Network (MANET) is an infrastructure-less network of mobile nodes which used in military, rescue operations, disaster relief, and rural areas for communications. To process the data packets, MANET nodes consume a lot of energy during real time data transmission. The conventional protocol selects the random route path in the network. Due to overhead in the random route path the power consumptions may exhaust the energy of nodes which causes the communication delay or may path failure. To address these issues an optimized energy efficient routing protocol could be used which prevents excessive consumption of node energy and enhances the network lifespan. In our research work, a novel nature-inspired Energy Efficient Optimized Sleep Scheduling (EEOSS) protocol is proposed to enhance the effectiveness and increase lifetime of ad hoc network. EEOSS protocol is the hybridization of ad hoc on-demand multi-path distance vector (AOMDV), Ant colony optimization (ACO), Particle swarm optimization (PSO) and sleep scheduling (SS) algorithms. AOMDV selects the multiple paths for data transmission whereas, ACO and PSO are used to identify the optimum route path based on number of nodes, packet size and node speed. To save the energy of nodes the SS algorithm puts nodes into sleep state when nodes are not participating actively in the network. The suggested protocol is compared experimentally with other existing routing protocols and measures performance based on throughput, energy consumption, end-to-end delay (E2ED), and network lifetime. The experiments are simulated on NS 2.35 and the results find that the EEOSS protocol has 12

In modern society numerous digital devices play a very significant role in day-to-day life. Digital devices are well connected and easily accessible through multiple sensors and Internet of Things (IoT) devices. Due to the rapid growth of digital devices, large amount of data traffics are being generated, which induces network congestion. To deal with large amount of data traffic a programmable Software Defined IoT (SD-IoT) infrastructure is utilized. For efficient and sustainable network, the data must be transmitted through optimal path in such a way to as to minimize energy consumption. Here, the network is partitioned into clusters to find an optimal path. Finding an optimal path from a set of possible paths is an NP-complete problem. To solve this problem, we propose to find a set of optimal border nodes of each cluster with other clusters in the network, so as to reduce the number of possible paths between clusters. The set of optimal border nodes will be selected in such a way so that they have maximum energy and minimum distances. This paper proposes an intelligent approach to find the set of optimal border nodes using Lion Swarm Optimization algorithm (LSOA). Once a set of optimal border nodes are obtained, an optimal path can be generated using a routing mechanism. The performance of the proposed work is analyzed in terms of packet delivery ratio, average latency, network lifetime and energy consumptions. The results show that the border nodes selected using LSOA finds better routes as compared to the border nodes selected using other state-of-the-art metaheuristics algorithm thereby, increases suitability of the network by energy conservation.

Managing conventional networks has become challenging due to rapid network growth and emergence of new technologies like big-data and cloud computing. To overcome such issues the new paradigm concept is suggested known as Software Defined Network (SDN). To comply with quality of services (QoS) of networks and maximize the reliability of network the load balancing issue must be considered. In a traditional network, load balance is predicated on the network’s current data flow. On the other hand, SDN controllers can establish better load balance in wider perspective of network. This paper surveys the load balance strategies in SDN network based on deterministic and non-deterministic approaches. Furthermore, the major problems of some algorithms have been addressed so that investigators might use improved load balancing approaches in future.

Software Defined Networking (SDN) is new paradigms type of networking that is a programmable network and overcomes the challenges like, network scalability and management of the traditional networks. While programmable network is growing, the security challenges are increasing for different applications. Software Defined Networking manages control planes and data planes separately, where the controller at control planes decides the path at run time and data plane forwards the packets based on controller's decisions. The applications of software are increasing day by day including in communication traffic in SDN, and therefore different applications of different industries and societies faces various challenges related to it. This paper examines the software defined programmable network and view the challenges due to security issues of programmable networking. Finally, this paper discussed with its security impacts and gives concluding remarks.

Software Defined Network (SDN) is a programmable network which separates the control logic-plane and hardware data-plane. The SDN centrally manages different Internet of Things (IoT) enabled smart devices like, actuators and sensors connected in the networks. Smart city infrastructure is an application of IoT network which purpose is to manage the city network without human interventions. To collect the real time data, such smart devices generate large amount of data and increasing the traffic in network. To maintain the quality of services (QoS) of smart city IoT networks, the SDN needs to deploy the multi-controllers. But the communication performance reduces due to unbalance load distribution on controllers. To balance the traffic load of controller an intelligent cluster based Grey Wolf Optimization Affinity Propagation (GWOAP) Algorithm is proposed when deploying the multiple controllers in SDN-IoT enabled smart city networks. The proposed algorithm is simulated and the experimental results able to calculates the minimum overall communication cost in comparison with Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Affinity Propagation (AP). The proposed GWOAP better balance the IoT enabled smart switches among clusters and node equalization is balanced for each controller in deployed topology. By using the proposed methodology, the traffic load of IoT enabled devices in smart city networks intelligently better balance among controllers.

Software Defined Networks (SDNs) is modern network paradigm that separately manages the control logic-plane and hardware data-plane. The programmable network SDN centrally manages different smart devices connected in the networks. These smart devices are facilitated by Internet of Things (IoT) and collect real time data for network devices. One of the emerging applications of IoT network is Smart City which purpose is to manage the city without human interventions. The rapid growths of IoT devices like actuators and sensors in smart city are generating large amount of data and increasing the traffic in network. To maintain the quality of services (QoS) of smart city IoT networks, the SDN characteristic is one way to provide better services to network users. Traditional IoT network having the challenges like, access delays, security and reliability issues. The SDN enabled IoT networks are capable to overcome these challenges and improves the network performance. One single controller is not sufficient to manage all IoT devices in smart city networks. So, multiple controllers are required to handle Software Defined-IoT Networks (SDN-IoT). Different metaheuristic algorithms are implemented to overcome controller placements problems (CPP) but as the number of controllers increases the performance of approaches degrades due to premature conversions. To better balance the exploration and exploitation features an intelligent Hybrid Differential Evolution and Whale Optimization (DEWO) Algorithm is proposed that optimizes the controller placements in SDN-IoT enabled smart city networks. The algorithm searches the optimal locations of controllers and balance the switch loads in respect of minimum latency. The algorithm also minimizes the link failure and evaluate the minimum end to end delay. The proposed hybrid algorithm is evaluated up to 40 controllers with other state of art method and able to achieve better results in comparison with other metaheuristic algorithms.

As everyone knows that language has always been a barrier in the path of communication for the people speaking different languages. A person going to some other country can learn a new language or carry a dictionary to communicate but, specially-abled people such as deaf or profoundly deaf person can only use sign language for communication. In a world where people barely understand this language it does not helps a lot in curbing the issue. So, the purpose of this proposed system is to develop a translation tool which can reduce the communication gap by converting the real-time gesture-based signs to text and finally to speech. This paper will first at discuss the design to recognize the hand gesture as it is one of the fastest way to communicate. And further the discussion will be about recognizing the digit and perform operations in addition to recognizing the English alphabets to form words. The paper reviewed the current study status of application aiming to recognize the hand gestures, symbols and movements to convert it into numbers and alphabets, and further into words and then sentences. According to the research the application will work as a medium in between an aphonic person and a normal person or vice versa. This paper shows the status of the application, customized hand gestures, the methods, analyzing the strength and week points and lists all the challenging problems in current research of hand gestures used for aphonic people school etc.

In the current era, each and every individual get benefited with available information on Internet that is used in decision-making by perceiving others' attitude, opinions, sentiments, and emotions. As we know sentiment analysis is one of the fields of natural language processing (NLP) that evaluates opinion of users and their sentiments, based on these sentiments and opinions, an individual or group can review their product and services. In this paper, we compared our rule-based model (RBM) sentiment lexicon features precision, recall, and F1 score to other known and established sentiment lexicons on Cornell movie review dataset and found the result is better and more accurate.