We address the question of compressing, into a fixed amount of storage space, a periodically sampled sequence of scalar values. The length of the sequence is, a-priori, unknown. The compression logic targets the minimization of the L∞ reconstruction error. Because of the limited storage, all relevant compression processing is performed in–place. The proposed scheme involves a synthesis of an early (“batch”) phase, and a subsequent “incremental” (online) phase. The technique is suitable for wireless sensor networks with limited storage and with unpredictable, and possibly rare, opportunities to communicate their data. We demonstrate that our scheme outperforms legacy sub-sampling techniques and that its reconstruction error gracefully degrades as the length of the data sequence increases. We show how a couple kilobytes of storage is sufficient to represent long sequences of sampled data, making it suitable for microcontroller-based wireless sensor nodes.

We consider the problem of lossy compression of time series data collected by wireless sensor nodes, such that it produces a limited volume of compressed data for a given amount of raw data. The lossy compression is performed in a manner that minimizes the resulting L∞reconstruction error. Producing a bounded volume of compressed data is desirable in circumstances where we wish to know, or limit, the rate at which the compressed data are communicated, e.g., for periodic communication scheduling of fixed payload transmissions. The work is also geared to understanding the impact of, and accommodating for, storage limitations of Wireless Sensor Network (WSN) nodes. The proposed scheme belongs to the class of piecewise linear approximations (PLAs) and its performance is compared to other PLA schemes proposed for WSNs. The evaluation is carried out using existing public data sets.

In this article, we present the design of an Internet-of-Things (IoT)-based dynamic irrigation scheduling system (AgriSens) for efficient water management of irrigated crop fields. The AgriSens provides real time, automatic, dynamic as well as remote manual irrigation treatment for different growth phases of a crop's life cycle using IoT. A low-cost water-level sensor is designed to measure the level of water present in a field. We propose an algorithm for automatic dynamic-cum-manual irrigation based on farmer requirements. The AgriSens has a farmer-friendly user interface, which provides field information to the farmers in a multimodal manner - visual display, cell phone, and Web portal. It achieves significant results with respect to different performance metrics, such as data validation, packet delivery ratio, energy consumption, and failure rate in various climatic conditions and with dynamic irrigation treatments. Experimental results show that the AgriSens helps improve the crop productivity by at most 10.21% over the traditional manual irrigation method, expands the network's lifetime 2.5 times more than the existing system yet achieving a reliability of 94% even after 500 h of operation.

The proposed work relates to the development ofBig-Sensor-Cloud Infrastructure(BSCI) that immensely enhances the usability and management of the physical sensor devices. Traditional Wireless Sensor Networks (WSNs) are manufactured in a proprietary, vendor-specific design. Thus, the renderability of WSNs is almost infeasible to people/organizations that do not own a network of their own. Thus, in the existing system, WSN-based applications are inaccessible to the naive-users or common people who do not own physical sensor devices. Recently, sensor-cloud infrastructure has been viewed as a substitute for traditional WSNs. However, with the increasing growth in the velocity, variety, and variability of data, the management becomes a serious concern and difficulty. Thus, existing systems are not able to capture, analyze, and control the present data efficiently, in real-time. BSCI is a distributed framework for “Big” sensor-data storage, processing, virtualization, leveraging, and efficient remote management. The methods of the proposed BSCI are persuasive as they are equipped with the ability to handle “Big” data with enormous heterogeneous data volumes (in zettabyte) generated with tremendous velocity. The framework interfaces between the physical and cyber worlds, thereby acquiring real-time data from the physical WSNs into the cloud platform. This data are processed and delivered to the end-users as a simple service – Sensors-as-a-Service (Se-aaS). BSCI completely maintains and manages the data and the metadata internally within its database. Multiple organizations with heterogeneous demand can be successfully served with Se-aaS through BSCI. From a user-perspective, BSCI is highly convenient as the users are completely abstracted from the underlying complex processing logic. This allows the naive users to envision the typical hardware sensor devices as simple accessible services like electricity, and water.

This work proposes an Activity-Aware Data Rate Tuning (A2D) scheme for Wireless Body Area Network (WBAN), while considering the criticality of the physiological sensed data. We consider different physical activities of the patients and thereafter, compute their health criticality. Further, on the basis of the health criticality value, the data rate of these physiological sensors are tuned. Depending on the physical activity of a patient, the value sensed by the physiological sensors may change. Consequently, when a healthy person runs, a particular sensor value may be significantly high, even if it is normal, however, the same data reading may be critical for a person who is sitting or standing. Thus, a WBAN is required to be activity-aware in order to measure the correct criticality values. We implemented in a real hardware platform system to show the effectiveness of the proposed scheme. Experimental results show that the proposed scheme is capable of tuning the data rate of different physiological sensors, based on human activity and critical conditions, while ensuring more than 90% of packet delivery ratio in intra-BAN communication and 93% in inter-BAN communication.

In many developing countries, agriculture is one of the primary livelihoods of common people. Agriculture requires various types of technologies for improving crop yields. The attack of animals in the agricultural land and the theft of crops by humans cause heavy loss in cultivation. In this work, we propose a hardware prototype using Wireless Sensor Network (WSN) for intruder detection in an agricultural field. The proposed system is named Agricultural Intrusion Detection (AID). AID helps to generate alarms in the farmer's house and at the same time transmits a text message to the farmer's cell phone when an intruder enters into the field. In order to implement the proposed scheme, we design and deploy Advanced Virtual RISC (AVR) micro-controller-based wireless sensor boards over an outdoor environment and evaluate the performance.

This paper addresses the optimization and performability of survivability strategies used in the next generation of high-capacity optical transport networks based on wavelength division multiplexing (WDM) technology. The framework of survivability presented is based on the self-healing mechanism used in SONET/SDH. The key issue addressed is how to determine the number of redundant components and wavelength units necessary to achieve a guaranteed level of survivability. A multi-modal failure probability model is developed and used in the reliability model of optical components. The multi-modal reliability analysis is used in conjunction with a reliability optimization technique to determine the optimum number of redundant components and wavelengths necessary in a SONET/WDM network to achieve a given degree of survivability.