Ravindara Bhatt

A two-tier strategy for priority based critical event surveillance with wireless multimedia sensors

Surveillance plays a vital role in protecting infrastructure facilities of a country and improving detection of cross-border activities. Compared to traditional surveillance systems, wireless multimedia sensor networks (WMSN) provide distinct advantages. In this paper we consider the problem of critical event surveillance in a region of interest with the help of WMSN. The challenge here is deployment cost, energy-efficient routing and preservation of coverage and connectivity of the network. To keep the deployment cost minimum, we propose a two-tier strategy consisting of (a) densely deployed low cost audio tier nodes and (b) sparsely placed high cost video tier nodes to monitor critical events occurring in a given area. The audio nodes perform the preliminary event detection task, whereas, the base station activates the rotatable-video nodes on a demand basis. Depending upon the cost of potential damage, an event is assigned a priority, and based upon that priority an event is assigned either energy efficient or a delay tolerant path along the audio and video tiers. We also propose two integer linear programming formulations MEAT and MEVT for minimization of energy consumption in audio and video tiers separately. We then present two approaches, namely Greedy and DCSEG, and compare them with a popular existing approach under various scenarios. Simulation results show considerable reduction in the number of active audio and video sensor nodes per event which leads to low deployment cost and reduction of average energy consumption in the network.

Redeployment strategies for Wireless Sensor Networks under random node failures and budget constraints

Wireless Sensor Network (WSN) may suffer from energy-hole problem due to exhaustion of battery and/ or deliberate destruction of some network nodes. This hole is of great concern when WSN is used in surveillance applications as Quality-of-Service (QoS) parameters may not be satisfied in some parts of the network. Detection and repair of these holes through redeployment of nodes is an important research challenge in WSN. In this paper, two types of node failures, namely self and intentional, are modeled with the help of random and Gaussian distribution respectively. The QoS parameters coverage and connectivity are computed using iso-sensing technique and neighbor node information respectively. Based on these parameters we perform redeployment within the budget constraints. The redeployment site is selected based on the maximum gain and a tour is planned using Travelling Salesman Problem (TSP) approach. The sensor redeployment scheme is integrated with network monitoring which results in an efficient network. Simulation results obtained with different network configurations validate the effectiveness of the proposed scheme.

Cost Modelling and Studies with Different Deployment Strategies for Wireless Multimedia Sensor Network Over Flat and Elevated Terrains

Wireless multimedia sensor networks (WMSNs) is widely used for surveillance application. These multimedia (audio and video) nodes are distributed according to different deployment strategies in a multi-tier heterogeneous architecture environment. In this paper we have modelled the deployment cost of WMSN considering the sensor type (audio or video), sensor configuration such as remaining energy of battery, deployment point, and terrain characteristics for surveillance applications. Using our proposed cost models we have studied the effects of different deployment strategies of WMSN over flat and elevated terrains. Our cost models helps in minimizing the cost of deployment while maintaining Quality-of-Service i.e., the coverage and connectivity of the audio and video sensors separately. We have formulated an integer linear program and proposed a heuristic solution to minimize the placement costs subject to network coverage requirements using our first cost model. Our second cost model is used to propose a scheme that will ensure connectivity of the network. We have done simulations with three network deployment strategies, namely deterministic, random and hybrid and show that the hybrid deployment of sensor nodes yields a balance of performance and cost as compared to the other two. Our study provides guidelines for the network architect to select a particular deployment strategy under performance and cost requirements.

Utilizing graph sampling and Connected Dominating Set for backbone construction in Wireless Multimedia Sensor Networks

Wireless Multimedia Sensor Networks consists of a large number of multimedia nodes which are randomly placed in a dense environment. A flat network topology leads to high energy consumption in the network. Therefore for cost effective data collection, a backbone framework is required for such multimedia networks. In this paper, we propose a centralized backbone framework utilizing graph sampling and Connecting Dominating Set strategy in sequence. Connected Dominating Set based backbone construction techniques is beneficial as compared to other existing approaches. However in case of a large and dense network this backbone construction is intensive. In such dense environment scenario graph sampling reduces the multimedia nodes while maintaining the graph properties and a backbone framework is constructed over these sampled nodes. Results show the effectiveness of our approach.

ADJACENCY MATRIX BASED ENERGY EFFICIENT SCHEDULING USING S-MAC PROTOCOL IN WIRELESS SENSOR NETWORKS

Communication is the main motive in any Networks whether it is Wireless Sensor Network, Ad-Hoc networks, Mobile Networks, Wired Networks, Local Area Network, Metropolitan Area Network, Wireless Area Network etc, hence it must be energy efficient. The main parameters for energy efficient communication are maximizing network lifetime, saving energy at the different nodes, sending the packets in minimum time delay, higher throughput etc. This paper focuses mainly on the energy efficient communication with the help of Adjacency Matrix in the Wireless Sensor Networks. The energy efficient scheduling can be done by putting the idle node in to sleep node so energy at the idle node can be saved. The proposed model in this paper first forms the adjacency matrix and broadcasts the information about the total number of existing nodes with depths to the other nodes in the same cluster from controller node. When every node receives the node information about the other nodes for same cluster they communicate based on the shortest depths and schedules the idle node in to sleep mode for a specific time threshold so energy at the idle nodes can be saved.

The big data system, components, tools, and technologies: a survey

The traditional databases are not capable of handling unstructured data and high volumes of real-time datasets. Diverse datasets are unstructured lead to big data, and it is laborious to store, manage, process, analyze, visualize, and extract the useful insights from these datasets using traditional database approaches. However, many technical aspects exist in refining large heterogeneous datasets in the trend of big data. This paper aims to present a generalized view of complete big data system which includes several stages and key components of each stage in processing the big data. In particular, we compare and contrast various distributed file systems and MapReduce-supported NoSQL databases concerning certain parameters in data management process. Further, we present distinct distributed/cloud-based machine learning (ML) tools that play a key role to design, develop and deploy data models. The paper investigates case studies on distributed ML tools such as Mahout, Spark MLlib, and FlinkML. Further, we classify analytics based on the type of data, domain, and application. We distinguish various visualization tools pertaining three parameters: functionality, analysis capabilities, and supported development environment. Furthermore, we systematically investigate big data tools and technologies (Hadoop 3.0, Spark 2.3) including distributed/cloud-based stream processing tools in a comparative approach. Moreover, we discuss functionalities of several SQL Query tools on Hadoop based on 10 parameters. Finally, We present some critical points relevant to research directions and opportunities according to the current trend of big data. Investigating infrastructure tools for big data with recent developments provides a better understanding that how different tools and technologies apply to solve real-life applications.

Cost models for 3-D deployment of Wireless Multimedia Sensor Networks

Three-Dimensional (3-D) Wireless Multimedia Sensor Networks (WMSNs) are extensively used for monitoring and surveillance applications. In a three-dimensional scenario the audio, video, and scalar sensors are placed in a 3-D space with the help of available deployment techniques. In this paper, we propose cost models to evaluate various deployment strategies with respect to their deployment costs. We compare deterministic and random deployment strategies in a 3-D space. Several researchers investigate cost models over the two-dimensional plane. Such cost models may not be realistic and fail to represent the real life scenario. Our proposed cost models can be used by network designer under the constraints of deployment budget and Quality-of-Service requirements. We observe that the deterministic deployment strategy is costly as compared to random deployment of sensor nodes. Further, the directional nature of video sensor node limits the coverage area in 3-D space.

A Stochastic Process Based Framework of Redeployment Model for Wireless Sensor Network

In this paper we propose a redeployment scheme of Wireless Sensor Network (WSN) nodes based on a stochastic process. WSN suffers from formation of hole in the network due to its asymmetric deployment, unbalanced energy consumption and intentional destruction of nodes in the network. The dynamic increase in Quality-of-Service (QoS) parameters such as coverage and connectivity also may lead to formation of holes which in turn degrades the performance of the network. Therefore, in order to maintain a desired QoS, a sensor redeployment scheme is important in WSN. During its lifecycle, a sensor node experiences active, sleep, diagnose, vulnerable, repair, and fail states. We analyze the Markov process and obtain the steady state probabilities for all the states. The availability of the nodes is presented with the help of SHARPE tool. Our work utilizes Discrete-Time Markov Chain and a Semi-Markov Process to illustrate the probabilities of WSN node in various states. The required redeployment nodes are then computed based on a stochastic analysis of the system and subject to the QoS requirements of the network.