Smart manufacturing and data analytics can help in manufacturing sector, where the current scenarios transmission and data analysis from across the plant create manufacturing intelligence; it can be used to have a good impact across all side of operations. Predictive analysis helps that in a lot of ways in smart manufacturing. In this work, authors have tried to take values of a smart manufacturing company and have done predictive analysis to take out the energy consumption based on historical data.

Predictive Data Analysis for Energy Management of a Smart Factory Leading to Sustainability

Ad hoc networks are characterized by multihop wireless connectivity, frequently changing network topology and the need for efficient dynamic routing protocols. We compare the performance of three routing protocols for mobile ad hoc networks: Dynamic Source Routing (DSR), Ad Hoc On-Demand Distance Vector Routing (AODV), location-aided routing (LAR1).Our evaluation is based on energy consumption in mobile ad hoc networks. The performance differentials are analyzed using varying network load, mobility, and network size. We simulate protocols with GLOMOSIM simulator. Based on the observations, we make recommendations about when the performance of either protocol can be best. Keywords—Ad hoc Network, energy consumption, Glomosim, routing protocols.

Performance Evaluation of Routing Protocols for High Density Ad Hoc Networks Based on Energy Consumption by GlomoSim Simulator

Wireless Sensor Network (WSN) is generally used for constructing an efficient network with minimum infrastructure. With this note, the WSN can be considered to construct smart communication in the existing city to facilitate the inhabitants. Nowadays in India, so many cities are going to be transformed into smart cities. In those cities, the maximum electronic gadgets will be IoT enabled. These IoT enabled gadgets should be connected through a robust network. The WSN can be an alternative for constructing a robust network between the electronic devices and the local server (Sink Node). The WSN can also sense many external environmental factors to facilitate the user, such as traffic status, rainfall, heat or smoke, vibration, and pollution detection. One of the major important challenges for the construction of an effective Wireless Sensor Network is to use the existing infrastructure of a city. In this paper, the effective Wireless Sensor Network has been constructed using the existing infrastructure and this is the major reason to choose the existing roads to deploy the WSN nodes. One can easily use the existing lamp posts of those roads to deploy the WSN nodes. In this paper, the modified Ant Colony Optimization (ACO) technique has been used to construct efficient WSN. The ACO is the probabilistic technique which is used to solve computational problems of choosing a minimized path in a graph. Therefore, this technique can be useful in the construction of cost-effective WSN. After the construction of the WSN network, the total number of nodes is calculated to cover the area efficiently. In order to find the energy used for the network, the distance between each node as well as the distance between sink node and WSN nodes are determined and, essentially, everyone can easily measure the lifespan of the WSN. The research work has been compared with some existing research works and remarkable improvement in lifetime enhancement (20.77% and 84.1% with respect to two existing literature) has been observed.

Construction of Effective Wireless Sensor Network for Smart Communication Using Modified Ant Colony Optimization Technique

Vehicular Ad-hoc Networks (VANETs) combine intelligent vehicles on highways aim to solve many transportation problems. The performance of VANETs is affected by many parameters due to highly dynamic structure. We assessed the performance of VANETs over different highway’s scenarios and investigated that under which circumstances the performance will be better and vice versa. We adopted our experiments in infrastructure environment, where the road side units (RSUs) are connected with cloud server. The RSU periodically gathers spatial-temporal information and upload it to cloud, which could help the drivers to predict the status of road before journey. The experiments carried on two types of highway’s scenarios: varying vehicles densities and simulation time. The simulation result shows that selected performance metrics (throughput, E2E delay and packet loss) greatly affect in both scenarios. The simulation time within the interval 200 to 500 is an optimal choice during simulation experiments. The throughput and packet loss increases with increase in vehicle density. The end-to-end delay has an inverse relation with vehicle density. The highway scenarios are generated by SUMO and the actual simulation is done by NS2.

Performance Analysis of Vehicular Adhoc Network Using Different Highway Traffic Scenarios in Cloud Computing

Vehicular ad hoc networks (VANETs) are wireless communication networks which support cooperative driving among vehicles on the road. The specific characteristics of VANETs favor the development of attractive and challenging services and applications which rely on message exchanging among vehicles. These communication capabilities depend directly on the existence of nearby vehicles able to exchange information. Therefore, higher vehicle densities favor the communication among vehicles. However, vehicular communications are also strongly affected by the topology of the map (i.e., wireless signal could be attenuated due to the distance between the sender and receiver, and obstacles usually block signal transmission). In this paper, we study the influence of the roadmap topology and the number of vehicles when accounting for the vehicular communications capabilities, especially in urban scenarios. Additionally, we consider the use of two parameters: the SJ Ratio (SJR) and the Total Distance (TD), as the topology-related factors that better correlate with communications performance. Finally, we propose the use of a new density metric based on the number of vehicles, the complexity of the roadmap, and its maximum capacity. Hence, researchers will be able to accurately characterize the different urban scenarios and better validate their proposals related to cooperative Intelligent Transportation Systems based on vehicular communications.

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On the Study of Vehicle Density in Intelligent Transportation Systems

The wireless sensor networks sensor nodes are operated through battery power; for every particular activity, amount of energy is used. The node lifetime also depends on battery life. The longer the battery life leads to a longer life of the network. So, energy utilization is crucial. In this paper, we have used location-based routing protocol and multipath routing protocol for analyzing the consumption of energy. The principal objective of this work is to analyze the performance of different categories of routing protocols in terms of energy utilization. So that appropriate routing protocol may be adapted for future communication. The total energy is consumed for various activities, like routing protocols message exchanges, MAC layer operations as well as other network layers. Any kind of message exchanges required a certain amount of power. We have analyzed power consumption for individual nodes. The AOMDV and LAR routing protocols are used to evaluate their performance in terms of energy consumption. This is evident from the result that the AOMDV routing protocol utilizes lesser battery power as compared to protocol.

Location-Based and Multipath Routing Performance Analysis for Energy Consumption in Wireless Sensor Networks

The process of controlling various operating equipment, machinery, etc., automatically can be termed as automation. This is an efficient method to use in every field to reduce manpower, energy usage, and also for improving the quality, accuracy, precision, and efficiency of any system. The home automation system helps in automating various aspects of life. The invention of advanced technologies and computing methodology helps in automating various activities in the home, often termed as a smart home. Through this automated process, the user can easily closely monitor various activities at home. The computing technologies like the Internet of Things, evolutionary algorithm, data science, and machine learning (ML) algorithms help to analyze, predict, and smartly monitor the home activities. The integration of ML and artificial intelligence (AI) is increasing day by day in managing a smart home. Home automation can help in managing energy consumption, intruder detection, fire detection, gas leakage, and any abnormal activities in and surrounding the home. For example, our home kitchens can be upgraded with the integration of AI and ML to improve the functioning of pantries, fridges, stoves, cooking, and energy efficiency. Smart fridges, cooking technology, and stoves are already emerging in the home automation industry. In this chapter, we have discussed various projects, uses, and applications of AI and ML techniques in various domains.

Advance machine learning and artificial intelligence applications in service robot

Energy is a very crucial parameter in Mobile Ad Hoc Networks since mobile nodes are operated with this scarce resource. If nodes battery is drained, then ongoing transmission in the network is disrupted and discontinued. Mobile nodes can communicate with each other directly or multi-hop fashion. A mobile node uses battery power while working in various modes, i.e., transmitting, receiving, idle, and sleep. Routing protocols, MAC layer, and other network layers exchanges various control packets for executing their task. Overall, performance of the network indirectly depends on the battery power. So, wisely utilizing battery of individual nodes may significantly increase the network performance and lifetime. We have studied the energy utilization of individual node in the network. We have used AODV routing protocol for the analysis of individual node energy utilization in different mode of their operation. Finally, we observed that in receiving mode nodes average energy consumption is higher as compared to transmit mode operation.

Analysis of Energy Consumption in Dynamic Mobile Ad Hoc Networks

Ad hoc networks provides communication without the help of any fixed infrastructure. Due to dynamic topology changes, mobility of nodes and lack of central coordination routing in MANET is a challenging task. In MANET, achieving higher QoS is a hard job, various researchers are working to boost the performance. In this paper, we have done rigorous experiments on existing protocols such as AODV, DSDV, DSR, and AOMDV to expose the performance and QoS using NS2. The parameters that have been considered for experimental analysis are the throughput of the network, delay incurred, and packet delivery ratio (PDR).

An Investigation on Existing Protocols in MANET

In the wireless sensor networks, energy consumption is a very crucial issue because they work through the limited battery power of individual nodes. Here, we have analyzed various energy models on the performance. Different routing protocols are used on various node densities. In WSN, if nodes battery limit once exhausted the ongoing message exchanges for any kind of work get completely discontinued. A sensor node in WSN uses battery power when they are sending, receiving, and listening ongoing communication in transmitting, receiving, idle, and sleep mode. The total energy is consumed for different activities, like routing protocols message exchanges, MAC layer operations as well as other network layers. Any kind of message exchanges required a certain amount of power. Therefore, battery life is a major concern in the network. The maximum power consumption is observed when nodes receive and transmit packets from one node to another. So, proper utilization of battery of individual nodes is always desirable in this network. The individual node battery level determines the overall network performance and lifetime. In this paper, we have used various energy models. The AODV and DSR routing protocols are used for evaluating their performance in terms of energy consumption. Our simulation results show that AODV routing protocol used less battery power as compared to DSR in all energy models.

Indrani Das

Papers

Location-Based and Multipath Routing Performance Analysis for Energy Consumption in Wireless Sensor Networks

Advance machine learning and artificial intelligence applications in service robot

Analysis of Energy Consumption in Dynamic Mobile Ad Hoc Networks

An Investigation on Existing Protocols in MANET

Analysis of Energy Consumption of Energy Models in Wireless Sensor Networks