Sumedha Sirsikar

Bio: Sumedha Sirsikar is an academic researcher from Maharashtra Institute of Technology. The author has contributed to research in topics: Wireless sensor network & Key distribution in wireless sensor networks. The author has an hindex of 6, co-authored 28 publications receiving 152 citations. Previous affiliations of Sumedha Sirsikar include Savitribai Phule Pune University & Massachusetts Institute of Technology.

Minimization of average energy consumption to prolong lifetime of Wireless Sensor Network

Abstract: Wireless Sensor Network (WSN) is a network of sensor nodes that can sense the environment and send the information through wireless links to a sink. Wireless sensor nodes possess limited processing capability, storage and energy resources. The existence of sensor network depends on the life of sensor nodes i.e. ultimately on the energy consumption during its operation. Thus, in WSN, the efficient use of energy resources is very much necessary. Clustering is one of the approaches for energy saving in WSN. A cluster is a group of sensor nodes with one central entity named Cluster Head (CH). In this article, a new way of clustering for CH selection and cluster formation in WSNs is proposed; in which sensor network is divided into zones as per geographic locations of nodes. Clusters are formed within the zones by taking into account the residual energy of nodes and node distance. All cluster members send the sensed data to their respective CH. Hence, CH consumes more energy as it processes the collected data before forwarding it to Base Station (BS). Dropping of cluster head's residual energy below threshold value initiates cluster reformation. Performance analysis and simulation results are given with variations in number of nodes and transmission range. The obtained results show good performance of our algorithm in terms of reduced energy consumption, increased network lifetime and scalability of the network.

Skin Detection in YCbCr Color Space

Abstract: Skin detection is the process of finding skin-colored pixels and regions in an image or a video. This process is typically used as a preprocessing step to find regions that potentially have human faces and limbs in images. Several computer vision approaches have been developed for skin detection. Skin detectors typically transform a given pixel into an appropriate color space and then use a skin classifier to label the pixel whether it is a skin or a non-skin pixel. In this paper, an efficient method for skin color segmentation on color photos is implemented. This

Review on various routing protocols for heterogeneous wireless sensor network

Abstract: In the last decade, tremendous growth had been observed in the use of wireless sensor networks (WSNs). Precisely WSNs are well-known in real time applications using homogeneous sensor nodes. As sensor nodes are battery powered, it will become dead after consumption of the battery which decides the lifetime of WSNs. The battery of sensor nodes is neither replaced nor recharged. Hence it is essential to introduce the techniques to prolong the lifetime of the WSNs. Heterogeneous nodes in wireless sensor network is an effective way to increase network lifetime. This involves the need for energy efficiency in heterogeneous WSNs. Limitation on energy is a major concern, and hence it can be managed efficiently using clustering. In homogeneous clustering, each sensor node has equal initial energy but in heterogeneous clustering. There are two or more types of sensor nodes that have different initial energy. This paper describes the various heterogeneous WSN protocols that carry out a survey of the recent clustering protocols for heterogeneous WSNs. Heterogeneous routing protocols for WSNs are categorized based on some predefined performance estimation metrics such as network lifetime, number of heterogeneity level, cluster head selection, energy efficiency and stability.

How Can Heterogeneous Internet of Things Build Our Future: A Survey

Abstract: Heterogeneous Internet of Things (HetIoT) is an emerging research field that has strong potential to transform both our understanding of fundamental computer science principles and our future living. HetIoT is being employed in increasing number of areas, such as smart home, smart city, intelligent transportation, environmental monitoring, security systems, and advanced manufacturing. Therefore, relaying on strong application fields, HetIoT will be filled in our life and provide a variety of convenient services for our future. The network architectures of IoT are intrinsically heterogeneous, including wireless sensor network, wireless fidelity network, wireless mesh network, mobile communication network, and vehicular network. In each network unit, smart devices utilize appropriate communication methods to integrate digital information and physical objects, which provide users with new exciting applications and services. However, the complexity of application requirements, the heterogeneity of network architectures and communication technologies impose many challenges in developing robust HetIoT applications. This paper proposes a four-layer HetIoT architecture consisting of sensing, networking, cloud computing, and applications. Then, the state of the art in HetIoT research and applications have been discussed. This paper also suggests several potential solutions to address the challenges facing future HetIoT, including self-organizing, big data transmission, privacy protection, data integration and processing in large-scale HetIoT.

Optimized Clustering Algorithms for Large Wireless Sensor Networks: A Review.

Abstract: During the past few years, Wireless Sensor Networks (WSNs) have become widely used due to their large amount of applications. The use of WSNs is an imperative necessity for future revolutionary areas like ecological fields or smart cities in which more than hundreds or thousands of sensor nodes are deployed. In those large scale WSNs, hierarchical approaches improve the performance of the network and increase its lifetime. Hierarchy inside a WSN consists in cutting the whole network into sub-networks called clusters which are led by Cluster Heads. In spite of the advantages of the clustering on large WSNs, it remains a non-deterministic polynomial hard problem which is not solved efficiently by traditional clustering. The recent researches conducted on Machine Learning, Computational Intelligence, and WSNs bring out the optimized clustering algorithms for WSNs. These kinds of clustering are based on environmental behaviors and outperform the traditional clustering algorithms. However, due to the diversity of WSN applications, the choice of an appropriate paradigm for a clustering solution remains a problem. In this paper, we conduct a wide review of proposed optimized clustering solutions nowadays. In order to evaluate them, we consider 10 parameters. Based on these parameters, we propose a comparison of these optimized clustering approaches. From the analysis, we observe that centralized clustering solutions based on the Swarm Intelligence paradigm are more adapted for applications with low energy consumption, high data delivery rate, or high scalability than algorithms based on the other presented paradigms. Moreover, when an application does not need a large amount of nodes within a field, the Fuzzy Logic based solution are suitable.

Hierarchical routing protocols for wireless sensor network: a compressive survey

Abstract: Wireless sensor networks (WSNs) are one of the key enabling technologies for the internet of things (IoT). WSNs play a major role in data communications in applications such as home, health care, environmental monitoring, smart grids, and transportation. WSNs are used in IoT applications and should be secured and energy efficient in order to provide highly reliable data communications. Because of the constraints of energy, memory and computational power of the WSN nodes, clustering algorithms are considered as energy efficient approaches for resource-constrained WSNs. In this paper, we present a survey of the state-of-the-art routing techniques in WSNs. We first present the most relevant previous work in routing protocols surveys then highlight our contribution. Next, we outline the background, robustness criteria, and constraints of WSNs. This is followed by a survey of different WSN routing techniques. Routing techniques are generally classified as flat, hierarchical, and location-based routing. This survey focuses on the deep analysis of WSN hierarchical routing protocols. We further classify hierarchical protocols based on their routing techniques. We carefully choose the most relevant state-of-the-art protocols in order to compare and highlight the advantages, disadvantage and performance issues of each routing technique. Finally, we conclude this survey by presenting a comprehensive survey of the recent improvements of low-energy adaptive clustering hierarchy routing protocols and a comparison of the different versions presented in the literature.