Sanku Kumar Roy

We consider piece-wise linear lossy compression schemes for wireless sensor network applications, involving streaming sensor data, as small-size wireless packet payloads. Comparisons of existing schemes can mislead if no due care is paid to the exact cost, that we call representation cost , which counts how many bits are required to represent the sequence of the produced line segments. We rectify the situation in the presented work by presenting a fair means to evaluate the representation cost of competing schemes. Schemes that produce a small number of line segments can result in poor compression ratio when one considers the representation cost involved. Additionally, we outline an extremely competitive scheme which, at the cost of more computation, satisfies a given size for the compressed data output, while minimizing L _∞ error. It improves the compression ratio, reducing the representation cost by up to 140% over previous schemes while also practically achieving a lower maximum reconstruction error than the reconstruction error bound requested. We address several technicalities for fairly mapping, and evaluating, existing schemes that, when originally proposed, were indifferent to data representation concerns. We evaluate the compression schemes using a number of datasets, composed of different sensor types, compressed under a range of reconstruction error bounds and payload size restrictions, including some data that are challenging for any piece-wise linear compression scheme.

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 $L_{\infty }$ 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 subsampling 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.

The rapid growth of UAV applications necessitates a robust communication and networking architecture capable of addressing the diverse requirements of various applications concurrently, rather than relying on application-specific solutions. This paper proposes a generic and reliable multi-UAV communication and networking architecture designed to support the varying demands of heterogeneous applications, including short-range and long-range communication, star and mesh topologies, different data rates, and multiple wireless standards. Our architecture accommodates both adhoc and infrastructure networks, ensuring seamless connectivity throughout the network. Additionally, we present the design of a multi-protocol UAV gateway that enables interoperability among various communication protocols. Furthermore, we introduce a data processing and service layer framework with a graphical user interface of a ground control station that facilitates remote control and monitoring from any location at any time. We practically implemented the proposed architecture and evaluated its performance using different metrics, demonstrating its effectiveness.

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.

In this paper, we present the design of a new sensor node, named Multipurpose EnerGy-efficient Adaptable low-cost sensor Node (MEGAN), with all the desired features such as reconfigurability, flexibility, energy efficiency, and low-cost required to build the Internet of Things (IoT). Apart from the ability to interface a maximum of 32 different sensors and actuators, MEGAN allows a user to choose the desired communication module, depending on the required range of communication. We design a power management circuit to extend the lifetime of the resource-constrained sensor node. Additionally, it has an integrated recharging circuit on board, which can use the energy harvested from any unregulated energy source. MEGAN combats a major drawback of application-specific sensor nodes, because of the integration of switches and a programming port. The flexibility of MEGAN, with respect to the integration of any sensor or actuator, makes it a multipurpose adaptable sensor node. The analysis of the lifetime, received signal strength indicator, packet delivery ratio, adaptability, and reliability of MEGAN under different operating conditions establish the energy efficiency and superiority of its hardware design.

The proposed work relates to the development of Big-Sensor-Cloud Infrastructure (BSCI) that enhances the usability and management of the physical sensor devices. Traditional sensor networks are manufactured in a proprietary, vendor-specific design, and cannot handle application switching dynamically at runtime due to the presence of monolithic kernel. Thus, the applications are inaccessible to the common people who do not own physical sensor devices. Recently, sensor-cloud infrastructure has been viewed as a substitute for traditional sensor networks. 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 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). Multiple organizations with heterogeneous demand can be successfully served with Se-aaS. From a user-perspective, BSCI allows the naive users to envision the typical sensor devices as simple accessible services like electricity, and water.

In this paper, we propose a software-defined wireless sensor network architecture ( Soft-WSN )—an effort to support application-aware service provisioning in Internet of Things (IoT). Detailed architecture of the proposed system is presented involving the application, control, and infrastructure layers to enable software-defined networking (SDN) in IoT. We design a software-defined controller, which includes two management policies—device management and network management. Device management facilitates users to control their devices in the network. To enable device control mechanisms, we investigate three scheduling issues in a sensor node—sensing task, sensing delay, and active sleep. On the other hand, the topology of the network is controlled by the network management policies, which can be modified in run time to deal with dynamic requirements of IoT. Furthermore, the proposed scheme is implemented in a real hardware platform without changing the underlying sensor networking concepts, so that existing sensor devices can be seamlessly integrated. Therefore, in contrast to the existing SDN solutions for WSNs, the proposed system, Soft-WSN , focuses on both device management and topology management to meet run-time application-specific requirements of IoT, while enhancing flexibility and simplicity of WSN management. Experimental results on a real hardware-based test bed indicate that the proposed scheme is beneficial to meet real-time application-specific requirements of IoT, while ensuring significant improvements on network performance over the traditional approaches.

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.