Hari Prabhat Gupta

Sleep scheduling for partial coverage in heterogeneous wireless sensor networks

An important issue of research in a dense and random deployment of a wireless sensor network (WSN) is to maintain the coverage of the field and yet minimise the energy consumed by the sensors. Some monitoring applications may require only partial coverage of the field in which case redundant sensors can be identified and scheduled to sleep to minimise the energy consumption. In this paper, we address the problem of determining if a sensor is redundant while the desired partial coverage requirement is met. We assume that the WSN is heterogeneous in which all the sensors may not have the same sensing and/or communication radii. In such a heterogeneous WSN, we use a probabilistic approach to determine the degree of redundancy in the coverage. Given a partial coverage requirement for the field, we propose a scheduling protocol to identify the redundant sensors and put them to sleep. The proposed protocol is fully distributed and does not use any geographical information. We demonstrate the utility of the proposed approach in determining the redundancy in coverage with numerical and simulation results. Our simulations show that the proposed protocol effectively maintains the desired coverage and prolongs the network lifetime.

A Continuous Hand Gestures Recognition Technique for Human-Machine Interaction Using Accelerometer and Gyroscope Sensors

Recent advances in smart devices have sustained them as a better alternative for the design of human-machine interaction (HMI), because they are equipped with accelerometer sensor, gyroscope sensor, and an advanced operating system. This paper presents a continuous hand gestures recognition technique that is capable of continuous recognition of hand gestures using three-axis accelerometer and gyroscope sensors in a smart device. To reduce the influence of unstableness of a hand making the gesture and compress the data, a gesture coding algorithm is developed. An automatic gesture spotting algorithm is developed to detect the start and end points of meaningful gesture segments. Finally, a gesture is recognized by comparing the gesture code with gesture database using dynamic time warping algorithm. In addition, a prototype system is developed to recognize the continuous hand gestures-based HMI. With the smartphone, the user is able to perform the predefined gestures and control smart appliances using the Samsung AllShare protocol.

Geographic Routing in Clustered Wireless Sensor Networks Among Obstacles

An important issue of research in wireless sensor networks (WSNs) is to dynamically organize the sensors into a wireless network and route the sensory data from sensors to a sink. Clustering in WSNs is an effective technique for prolonging the network lifetime. In most of the traditional routing in clustered WSNs assumes that there is no obstacle in a field of interest. Although it is not a realistic assumption, it eliminates the effects of obstacles in routing the sensory data. In this paper, we first propose a clustering technique in WSNs named energy-efficient homogeneous clustering that periodically selects the cluster heads according to a hybrid of their residual energy and a secondary parameter, such as the utility of the sensor to its neighbors. In this way, the selected cluster heads have equal number of neighbors and residual energy. We then present a route optimization technique in clustered WSNs among obstacles using Dijkstras shortest path algorithm. We demonstrate that our work reduces the average hop count, packet delay, and energy-consumption of WSNs.

Demand-Based Coverage and Connectivity-Preserving Routing in Wireless Sensor Networks

An important issue of research in wireless sensor networks (WSNs) with dense and random deployment of sensors is to minimize the energy consumption while ensuring the desired coverage of the field of interest and connectivity of the network. In this paper, we present a demand-based coverage and connectivity-preserving routing protocol to provide desired coverage and connectivity requirements in WSNs. The protocol reduces the energy consumption by assigning the minimum required sensing range to the sensors and using a scheduling protocol to periodically turn off the communication radios of the sensors in a coordinated manner and a local route optimization with a power control technique. The proposed protocol is fully distributed and does not use any geographical information. Our simulations show that the proposed protocol effectively maintains the desired coverage and connectivity of the network and prolongs the network lifetime.

Analysis of the redundancy in coverage of a heterogeneous wireless sensor network

A heterogeneous wireless sensor network (WSN) consists of sensors with unequal ranges of sensing and/or communication. In a dense WSN, a part of the region covered by a sensor may also be covered redundantly by a neighbouring sensor. In this paper, we analyse the redundancy in the coverage of a heterogeneous WSN and define the redundancy degree of a sensor. We follow a probabilistic approach to derive the expected redundancy degree of a sensor with a given number of sensors of each type in the neighbourhood. We demonstrate the accuracy of the analysis, and study the impact of the number of sensors of different types on the expected redundancy degree with numerical and simulation results. We also demonstrate an application of the redundancy analysis in the design of a heterogeneous WSN. We propose an algorithm to determine the minimum number of sensors of different types required to satisfy the desired coverage ratio and simultaneously minimise the cost of the network.

Analysis of Stochastic

Coverage and connectivity are important metrics used to evaluate the quality of service of wireless sensor networks (WSNs) monitoring a field of interest (FoI). Most of the literature assumes that the sensors are deployed directly in the FoI. In this paper we assume that the sensors are stochastically deployed outside the FoI. For such WSNs, we derive probabilistic expressions for k-coverage and connectivity using exact geometry. We validate our analysis and demonstrate its utility to estimate the minimum number of sensors required for a desired level of coverage and connectivity. We also demonstrate an on-campus traffic monitoring system to count the number of vehicles, detect the direction of vehicle, and to identify the vehicle (two-wheeler or four-wheeler) using sensors along both sides of the road.

k

We propose a robust watermarking framework with a blind extraction process for HEVC encoded video, where a readable watermark is embedded invisibly in intra predicted blocks of the HEVC encoded video.Our watermarking framework enforces security by exploring the spatio-temporal characteristics of the compressed video and a random key for selection of embedding regions.We also analyze the strengths of different compressed domain features of HEVC encoded video for implementing the embedding algorithm.Experimental results demonstrate that the proposed work restrict the increase in video bit rate and degradation of perceptual quality.The proposed framework can also survive filtering, compressions, and noise additions maintaining good quality and robustness. Digital watermarking is an efficient and promising means for copyright protection of multimedia objects. Digital videos are stored and transmitted in a compressed format, which has drawn a great deal of attention in compressed domain watermarking for video. The embedding and extraction of watermark bits in compressed domain, therefore does not require complete decoding and re-encoding of the compressed video. There have been several compression standards for video. High Efficiency Video Coding (HEVC), a successor to H.264 Advanced Video Coding (AVC), is the latest standard for video compression with high compression efficiency. In this paper, we propose a robust watermarking framework with a blind extraction process for HEVC encoded video. A readable watermark is embedded invisibly in 4 × 4 intra predicted blocks of the HEVC encoded video. Our watermarking framework enforces security by exploring the spatio-temporal characteristics of the compressed video and a random key for selection of embedding regions. We also analyze the strengths of different compressed domain features of HEVC encoded video for implementing the embedding algorithm. Experimental results demonstrate that the proposed work restrict the increase in video bit rate and degradation of perceptual quality. The proposed framework can also survive filtering, compressions, and noise additions maintaining good quality and robustness.

-Coverage and Connectivity in Sensor Networks With Boundary Deployment

Coverage and connectivity are important factors that determine the quality of service of three-dimensional wireless sensor networks (3D WSNs) monitoring a field of interest (FoI). Most of the literature on the analysis of coverage and connectivity in 3D WSNs assumes the use of omni-directional sensors with spherical sensing regions. In this paper, we assume that the sensors are deployed uniformly at random in a FoI. We also consider a case when the sensors have only directional sensing capability and may have heterogeneity in terms of the sensing range, communication range, and/or probability of being alive. For such 3D heterogeneous directional WSNs, we derive probabilistic expressions for k -coverage and m -connectivity that are useful to optimize the cost of random deployment. We validate our analysis and demonstrate its benefits with numerical results. We also illustrate the application of this work for optimal design of a 3D heterogeneous directional WSN.

A robust watermarking framework for High Efficiency Video Coding (HEVC) - Encoded video with blind extraction process

k-coverage is an important issue in many applications of m-connected wireless networks, such as localization of objects, mobile robotics, and tourism industry, where k ≥ 1 and m ≥ 1. From the cost perspective, it is often very important to maintain the desired level of coverage and connectivity with a minimum number of nodes. In this paper, we address the problem of optimal deployment in term of the number of nodes required to achieve k-coverage in m-connected wireless networks under different values of k, m, coverage range, communication range, and area of the irregular field of interest (FoI). We divide the FoI into regular patterns and estimate the optimal distance between nodes and pattern for k-coverage in m-connected wireless networks. Next, we estimate the minimum number of nodes and their locations required for the coverage and connectivity. We validate the analysis and demonstrate the impact of k, m, coverage and communication ranges, and the FoI on the number of nodes using numerical, simulation, and prototype results.

Analysis of stochastic coverage and connectivity in three-dimensional heterogeneous directional wireless sensor networks

An important problem in a 3-D wireless sensor network with dense and random deployment of sensors is the minimization of the number of sensors required to cover a field of interest (FoI). Some monitoring applications may require the FoI to be k-covered, i.e., k ≥ 1, while redundant sensors must be scheduled to sleep to minimize energy consumption. In this paper, we address the problem of determining the probability of a sensor being redundant for the k-coverage of the FoI. We assume that the network is heterogeneous, in which all the sensors may not have the same sensing and/or communication radii. We use a probabilistic approach to estimate the volume of the sensing sphere of an arbitrary sensor that is redundantly covered by its neighbors. We prove a result to determine if a sensor is redundant for k-coverage, which is only based on information about the number of neighbors and their type. We propose a distributed protocol to schedule the redundant sensors to sleep, which requires no geographical information. Results demonstrate that the scheduling protocol reduces the number of active sensors and, thereby, prolongs the network lifetime.