A Slotted Aloha joint MAC-cum-Routing protocol for data gathering sensor network applications

In this paper we have proposed and implemented a joint Medium Access Control (MAC) -cum- Routing scheme for environment data gathering sensor networks. The design principle uses node 'battery lifetime' maximization to be traded against a network that is capable of tolerating: A known percentage of combined packet losses due to packet collisions, network synchronization mismatch and channel impairments Significant end-to-end delay of an order of few seconds We have achieved this with a loosely synchronized network of sensor nodes that implement Slotted-Aloha MAC state machine together with route information. The scheme has given encouraging results in terms of energy savings compared to other popular implementations. The overall packet loss is about 12%. The battery life time increase compared to B-MAC varies from a minimum of 30% to about 90% depending on the duty cycle.

Sensor network deployment for agronomical data gathering in semi-arid regions

We share our experience in planning, designing and deploying a wireless sensor network of one square kilometre area. Environmental data such as soil moisture, temperature, barometric pressure, and relative humidity are collected in this area situated in the semi-arid region of Karnataka, India. It is a hope that information derived from this data will benefit the marginal farmer towards improving his farming practices. Soon after establishing the need for such a project, we begin by showing the big picture of such a data gathering network, the software architecture we have used, the range measurements needed for determining the sensor density, and the packaging issues that seem to play a crucial role in field deployments. Our field deployment experiences include designing with intermittent grid power, enhancing software tools to aid quicker and effective deployment, and flash memory corruption. The first results on data gathering look encouraging.

Localized data gathering paradigms for small and marginal farm lands in Semi-Arid region – Issues and Concerns

In this work we explore the application of wireless sensor technologies for the benefit of small and marginal farmers in semi-arid regions. The focus in this paper is to discuss the merits and demerits of data gathering & relay paradigms that collect localized data over a wide area. The data gathered includes soil moisture, temperature, pressure, rain data and humidity. The challenge to technology intervention comes mainly due to two reasons: (a) Farmers in general are interested in crop yield specific to their piece of land. This is because soil texture can vary rapidly over small regions. (b) Due to a high run-off, the soil moisture retention can vary from region to region depending on the topology of the farm. Both these reasons alter the needs drastically. Additionally, small and marginal farms can be sandwiched between rich farm lands. The village has very little access to grid power. Power cuts can extend up to 12 hours in a day and upto 3 or 4 days during some months in the year. In this paper, we discuss 3 technology paradigms for data relaying. These include Wi-Fi (Wireless Fidelity), GPRS (General Packet Radio Service) and DTN (Delay and Disruption Tolerant Network) technologies. We detail the merits and demerits of each of these solutions and provide our final recommendations. The project site is a village called Chennakesavapura in the state of Karnataka, India.

Worst-Case Asymmetric Distributed Source Coding

We consider the asymmetric distributed source coding problem, where the recipient interactively communicates with N correlated informants to gather their data. We are mainly interested in minimizing the worst-case number of informant bits required for successful data-gathering at recipient, but we are also concerned with minimizing the number of rounds as well as the number of recipient bits. We provide two algorithms, one that optimally minimizes the number of informant bits and other that trades-off the number of informant bits to efficiently reduce the number of rounds and number of recipient bits.

Correlation, Coding, and Cooperation in Wireless Sensor Networks

We consider a single-hop data-gathering sensor network, consisting of a set of sensor nodes that transmit data periodically to a base-station. We are interested in maximizing the lifetime of this network. With our definition of network lifetime and the assumption that the radio transmission energy consumption forms the most significant portion of the total energy consumption at a sensor node, we attempt to enhance the network lifetime by reducing the transmission energy budget of sensor nodes by exploiting three system-level opportunities. We pose the problem of maximizing lifetime as a max-min optimization problem subject to the constraint of successful data collection and limited energy supply at each node. This turns out to be an extremely difficult optimization to solve. To reduce the complexity of this problem, we allow the sensor nodes and the base-station to interactively communicate with each other and employ instantaneous decoding at the base-station. The chief contribution of the paper is to show that the computational complexity of our problem is determined by the complex interplay of various system-level opportunities and challenges.

Enhancing sensor network lifetime using interactive communication

We are concerned with maximizing the lifetime of a data-gathering wireless sensor network consisting of set of nodes directly communicating with a base-station. We model this scenario as the m-message interactive communication between multiple correlated informants (sensor nodes) and a recipient (base-station). With this framework, we show that m-message interactive communication can indeed enhance network lifetime. Both worst-case and average-case performances are considered.

Computationally efficient optical tomographic reconstructions through waveletizing the normalized quadratic perturbation equation - art. no.61640R

In this paper, we present a wavelet - based approach to solve the non-linear perturbation equation encountered in optical tomography. A particularly suitable data gathering geometry is used to gather a data set consisting of differential changes in intensity owing to the presence of the inhomogeneous regions. With this scheme, the unknown image, the data, as well as the weight matrix are all represented by wavelet expansions, thus yielding the representation of the original non - linear perturbation equation in the wavelet domain. The advantage in use of the non-linear perturbation equation is that there is no need to recompute the derivatives during the entire reconstruction process. Once the derivatives are computed, they are transformed into the wavelet domain. The purpose of going to the wavelet domain, is that, it has an inherent localization and de-noising property. The use of approximation coefficients, without the detail coefficients, is ideally suited for diffuse optical tomographic reconstructions, as the diffusion equation removes most of the high frequency information and the reconstruction appears low-pass filtered. We demonstrate through numerical simulations, that through solving merely the approximation coefficients one can reconstruct an image which has the same information content as the reconstruction from a non-waveletized procedure. In addition we demonstrate a better noise tolerance and much reduced computation time for reconstructions from this approach.

Lessons from the Sparse Sensor Network Deployment in Rural India

We discuss the key issues in the deployment of sparse sensor networks. The network monitors several environment parameters and is deployed in a semi-arid region for the benefit of small and marginal farmers. We begin by discussing the problems of an existing unreliable 1 sq km sparse network deployed in a village. The proposed solutions are implemented in a new cluster. The new cluster is a reliable 5 sq km network. Our contributions are two fold. Firstly, we describe a. novel methodology to deploy a sparse reliable data gathering sensor network and evaluate the ``safe distance'' or ``reliable'' distance between nodes using propagation models. Secondly, we address the problem of transporting data from rural aggregation servers to urban data centres. This paper tracks our steps in deploying a sensor network in a village,in India, trying to provide better diagnosis for better crop management. Keywords - Rural, Agriculture, CTRS, Sparse.

Ultrasound-modulated optical tomography: direct recovery of elasticity distribution from experimentally measured intensity autocorrelation

Based on an ultrasound-modulated optical tomography experiment, a direct, quantitative recovery of Young's modulus (E) is achieved from the modulation depth (M) in the intensity autocorrelation. The number of detector locations is limited to two in orthogonal directions, reducing the complexity of the data gathering step whilst ensuring against an impoverishment of the measurement, by employing ultrasound frequency as a parameter to vary during data collection. The M and E are related via two partial differential equations. The first one connects M to the amplitude of vibration of the scattering centers in the focal volume and the other, this amplitude to E. A (composite) sensitivity matrix is arrived at mapping the variation of M with that of E and used in a (barely regularized) Gauss-Newton algorithm to iteratively recover E. The reconstruction results showing the variation of E are presented. (C) 2015 Optical Society of America