Tamarapalli Venkatesh

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.

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 in three-dimensional heterogeneous directional wireless sensor networks

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.

Sleep Scheduling Protocol for

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.

k

Coverage is an important metric to measure the quality of service of a wireless sensor network monitoring a field of interest (FoI). From an energy perspective, it is often very important to maintain the desired coverage ratio with a minimum number of sensors. The literature on determining the critical sensor density (CSD) for the desired coverage ratio assumes that the FoI is unbounded or toroidal in shape. Although it is not a realistic assumption, it eliminates the border effects in analysis. Since the entire sensing area of the sensors near the boundary may not be useful for the coverage, the CSD estimated without the border effects is lower than the actual value. In this paper, we assume that the sensors are deployed uniformly at random in a convex polygon-shaped FoI and consider the border effects to derive the expected sensing area of a sensor used in the coverage. Next, we estimate the CSD required for the desired coverage ratio. We validate the analysis and demonstrate the impact of border effects on CSD using numerical results. Results show that our approach estimates the CSD better than another one that does not consider the exact geometry of the FoI.

-Coverage of Three-Dimensional Heterogeneous WSNs

Caching video objects closer to the users in delivery of on-demand video services in IPTV networks reduces the load on the network and improves the latency in video delivery. Partial caching of the video objects is attractive due to the space constraints of the cache and also due to the fact that some parts of the video might be more popular than the others. However, fixed segment-based caching of videos does not take into account the changing popularity of the segments and the changes in the viewing patterns of the users. In this work, we propose a partial caching strategy that considers the changes in the popularity of the segments over time and the access patterns of the users to compute the utility of the objects in the cache. We also propose to partition the cache to avoid the eviction of the popular objects (those not accessed frequently) by the unpopular ones which are accessed with higher frequency. We measured the popularity distribution and ageing of popularity from two online datasets and use the parameters in simulations. Our simulation results show that the proposed caching scheme improves the byte hit ratio when compared to the LRU caching scheme both for static and dynamic object pools and ageing of popularity.

Critical Sensor Density for Partial Coverage under Border Effects in Wireless Sensor Networks

Coverage is an important metric used to measure the quality of service of wireless sensor networks monitoring a field of interest (FoI). Existing literature on the coverage problem assumes that the sensors are deployed directly in the FoI. These results cannot be applied in some applications like canal water surface monitoring, because the sensors cannot be deployed on the water surface. In this paper, we analyze the coverage problem in applications where, the sensors are deployed uniformly at random outside the FoI near the boundary. We derive the expected value of the effective sensing area useful for k-coverage of the FoI using exact geometry. We demonstrate the utility of the analysis in estimation of the minimum number of sensors required for a desired level of coverage. With numerical results we show the impact of various parameters on the number of sensors.

A dynamic popularity-based partial caching scheme for video on demand service in IPTV networks

Coverage is an important metric used to measure the quality of service of three-dimensional wireless sensor networks (3D WSNs) monitoring a field of interest (FoI). While it is important to ensure the desired level of coverage, it is also necessary to do so with a minimum number of sensors from a network cost perspective. In this paper, we assume that the sensors are deployed at random uniformly in a three-dimensional FoI and all the sensors may not have the same sensing range and/or probability of being alive. In such a 3D heterogeneous WSN, we derive the critical condition for the desired level of coverage of the FoI using a probabilistic model. We validate our analysis and demonstrate its benefits. We also demonstrate an application of the analysis to the design of a minimum-cost 3D heterogeneous WSN.

Analysis of stochastic k-coverage in wireless sensor networks with boundary deployment

Missing values in traffic matrix (TM) is a well known fact which needs to be interpolated with least error as TM is a key input to various network operations and management tasks. Compressive sensing deals with the reconstruction of missing observations by taking advantage of the presence of low-rank structure in TMs. Matrix decomposition techniques, more specifically singular value decomposition (SVD) and its variants such as sparsity regularized matrix factorization (SRMF) and sparsity regularized SVD (SRSVD), has attracted considerable attention in the field of compressive sensing of traffic matrices. However, SVD suffers from two limitations stemmed in its assumptions, which involves an assumption of continuous random variables and lack of interpretability of decomposed matrices. In the present work, in order to address the above-identified limitations we develop a simple yet powerful compressive sensing framework with two key components: i) Temporally Local Interpolation (TLI) and ii) CUR decomposition. We utilize a publicly available real traffic matrix obtained from Abilene network. Results show that i) our preprocessing technique, TLI, outperforms existing baseline approximation in terms of exhibiting least error in reconstruction of missing values with loss rates ranging from 1% to 98%. ii) The proposed framework can reconstruct up to 98% of the pure random missing data with an error of 29.8%, which is found to be comparatively better than SVD-based approaches. iii) When augmented with k-Nearest Neighbors (KNN), the proposed framework can reconstruct up to 98% of the pure random missing data with an error of 28.9%, which is comparatively better than (SRMF + KNN) and (SRSVDB + KNN). iv) The proposed framework is also found to be computationally efficient in terms of low computational time as it takes less than 0.7 seconds (using Matlab on a 3.20 GHz Windows machine), which is the least computational time taken as compared to SRMF (3.02 seconds), NMF (1.01 seconds), SRSVD (1.00 second) and SRSVD base (0.83 seconds).

Critical sensor density for fault-tolerant coverage in 3D heterogeneous wireless sensor networks

Principal Component Analysis (PCA) has been employed for structural analysis of network traffic flows in order to capture the periodic, anomalous and noisy components of traffic flows. PCA suffers from fundamental limitation stemmed from the assumption that the variables in question are continuous random variables. Analysis of data for discrete random variables has been traditionally performed by employing Correspondence Analysis. In this work, we present a novel idea of structural analysis of network traffic flows using Correspondence Analysis (CA). Apart from overcoming several limitations of PCA, CA has an edge over PCA in terms of visualization of large traffic matrices.