Akshay Malhotra

Design & Analysis of Miniaturized Asymmetric Coplanar Strip Fed Antenna for Multi-Band WLAN/WiMAX Applications

A novel, compact asymmetric coplanar strip (ACS)-fed multi-band antenna for Bluetooth/WLAN/WiMAX applications is proposed and discussed in this paper. The proposed antenna is composed of a simple monopole structure with a mirror-L shaped branch and two rectangular radiating strips. It has a very small size of 13.75 × 26 mm 2 including the ground plane. The mirror-L shaped branch excites a resonant mode at 2.5 GHz, and on the other side, ACS-fed monopole structure with two rectangular strips (one horizontal and one vertical) excite the resonant modes at 3.3 GHz and 5.75 GHz respectively. By properly selecting the lengths and positions of these radiating branches, multiband operation with wider impedance bandwidth can be achieved. The measured and simulated results show that the antenna has impedance bandwidth of 200 MHz (2.40-2.60 GHz), and 2800 MHz (3.2-6.0 GHz), and it can cover the 2.4 GHz Bluetooth, 2.4/5.2/5.8 GHz WLAN and 3.5/5.5 GHz WiMAX bands. The resonances achieved with this technique can be tuned independently, and the equations governing the resonances are given and confirmed by parametric studies. The proposed technique is further validated by designing another antenna working at 1.8/1.9 GHz PCS, 3.5/5.5 GHz WiMAX, 5.2/5.8 GHz WLAN bands.

Fuzzy-based fault-tolerant low-energy adaptive clustering hierarchy routing protocol for wireless sensor network

Wireless sensor networks employ a number of sensor nodes motes that have limitations of battery-power, sensing, computation, and wireless communication capabilities. The design of an efficient routing protocol to be used by these motes has a very significant impact on the overall lifetime of the network. Clustering motes and structuring them hierarchically provides better data aggregation and scalability while also considering the energy constraints. We present an enhanced version of Low Energy Adaptive Clustering Hierarchy LEACH routing protocol called Fuzzy Based Fault Tolerant-LEACH FTLEACH, which considers remaining battery-power, centrality of cluster head and node density during routing. The routing process involves clustering of motes, assigning of cluster head, replica cluster head and identifying faulty motes. Our results show that the enhanced approach provides better performance than the existing LEACH protocol in terms of first node fails, and half node alive and it exhibits better stability, and lower delay and maximises network lifetime.

Coordinated Inter Cell Interference Avoidance Techniques and Performance Parameters for Cross Layer Interference in LTE-A Network

The aim of the paper is to discuss and analyze the recent solutions based on coordinated Inter Cell Interference (ICI) techniques premeditated by researchers. The most thriving need of improved spectral density and user data throughput demands dense deployment of base stations and simultaneous resource allocation in same geographical area. This approach eases the problem of spectrum scarcity but increases ICI drastically. The higher generations of wireless network demand support of real time applications like video streaming, interactive online gaming, on demand data streaming and many more. To support these applications, wide bandwidth is essential. The 4th generation (4G) based on Long Term Evolution (LTE), advance releases (Rel 12 and 13) of LTE (LTE-A) and proposed 5th generation(5G) wireless networks focuses on the key parameter of higher system spectral efficiency to support simultaneous users with high peak bit rate. Inter Channel Interference (ICI) is the result of dense channel assignment in multi-layered network which causes drastic degradation in channel throughput. Being the most urgent need, enhanced ICI coordination (eICIC) methods encourage researchers to meet new technical challenges. This paper emphasis on frequency- time coordination based ICI avoidance methods for downlink transmission i.e., cross layer interference between femtocell base stations (HeNB) to Macrocell base station (MeNB). We present other interference coordination methods for understanding of multifaceted views of researchers. At the end of the paper performance parameter matrix for ICIC methods is described.

Congestion-Aware Opportunistic Routing Protocol in Wireless Sensor Networks

It is expected that 50 billion devices in the world will be connected on IOT by 2025. The importance of wireless sensor networks cannot be overstated in this scenario. Network becomes more beneficial to an application when it can be used to its full potential, which is difficult to achieve because of limitations of resources (processor, memory, and energy). There are many existing routing mechanisms which deal with these issues by reducing number of transmissions between sensor nodes by choosing appropriate path toward base station. In this paper, we propose a routing protocol to select the optimized route by using opportunistic theory and by incorporating appropriate sleep scheduling mechanisms into it. This protocol focuses on reduction of congestion in the network and thus increases an individual node’s life, the entire network lifetime, and reduces partitioning in the network.

Internet of Emotions: Emotion Management Using Affective Computing

The many advantages of increase in Human Machine Interaction are obvious but it has also led to issues such as emotional imbalance, depression, reduction in interpersonal communication etc. Internet of Emotions can be broadly categorized as internet based technologies which aim to mitigate these problems and facilitate better Human to Human interaction in real world. IoE can be defined as an ecosystem where emotion packets travel via internet to manage user’s real time experience. We propose a system which will detect emotional state of the user, categorize it and actuate outer net elements to manage the emotion of the user. Detailed algorithm is given which includes use of the passive sensors, smartphone, big data analytics and machine learning. The framework is further explained with example of stress management. The proposed system based on affective computing will play a vital role in development of products and platforms which emphasises user involvement.

A packet priority intimation-based data transmission for congestion free traffic management in wireless sensor networks

Abstract Congestion in Wireless Sensor Networks (WSN) reduces network performance by lowering throughput and increasing delay in transmitting packets. Many WSN applications rely on timely transmission of information to the destination. In such networks congestion control mechanisms play an important role to protract network lifetime, improve fairness and Quality of Service(QOS) in terms of throughput, packet delivery ratio and end-to-end delay. This paper proposes packet priority intimation-based (PPI) congestion control mechanism. We introduce a PPI bit in each packet to reflect its importance. Main objective of this mechanism is to send higher priority packets with minimum delay. The proposed PPI mechanism is deployed in the existing AODV routing protocol to convert the existing routing protocol to a congestion aware routing protocol. The performance of the proposed approach is simulated in Network Simulator (NS2) software and the efficiency is verified. The simulation results are compared with that of an existing approach.

Enhancing portfolio returns by identifying high growth companies in Indian stock market using artificial intelligence

The Indian Stock market, with over 5000 listed companies, where sorting of companies requires analysis of numerous financial parameters and ratios, is an example of a database from which single sub set or group of companies need to be found out for investing. With growing popularity of artificial intelligence in stock trading, it has been applied heavily into stock market for picking stocks. We apply K-means clustering algorithm, which uses unsupervised learning for clustering a dataset, to the listed Indian companies in an attempt to isolate businesses showing exceptionally high growth. The results show that, it is possible to achieve such grouping in an efficient and timely manner by K-means clustering. Testing of results is done by comparing absolute price returns and risk-adjusted returns of the obtained cluster and that of the popular and relevant market indices and top performing funds. The returns obtained from the cluster obtained using K-means clustering algorithm are found to outperform the indices and mutual funds by a comprehensive margin. These results emphasizes the role played by growth in stock performance in emerging markets along with subsequent conclusions arrived at therein, have important implications in the field of stock selection and portfolio management.

Identification of persisting trend in the Indian stock markets using hurst exponent

Efficient market hypothesis states that the current share prices reflect all relevant information about the underlying company. According to this widely accepted hypothesis, it is impossible for investors to consistently outperform the indices in the long run. Furthermore, the Random Walk Hypothesis, which is consistent with the Efficient Market Hypothesis, states that stock prices evolve in a random manner and price changes are independent of each other. This implies an absence of any persisting trends in the stock market that could possibly be taken advantage of with market timing strategies. We apply the Rescaled Range methodology for calculating the Hurst Exponent to Indian stock market data from 1996 to 2015 to determine the persistence of any trends, and hence to check for the predictability of the Indian markets on different time scales. Our results show that Indian stock markets do not conform to EMH, especially on consideration of larger time scales. The results have large implications on the nature of investment strategies and investor behavior with respect to the Indian stock market.