Chayan Sarkar

DIAT: A Scalable Distributed Architecture for IoT

The advent of Internet of Things (IoT) has boosted the growth in number of devices around us and kindled the possibility of umpteen number of applications. One of the major challenges in the realization of IoT applications is interoperability among various IoT devices and deployments. Thus, the need for a new architecture-comprising smart control and actuation-has been identified by many researchers. In this paper, we propose a Distributed Internet-like Architecture for Things (DIAT), which will overcome most of the obstacles in the process of large-scale expansion of IoT. It specifically addresses heterogeneity of IoT devices, and enables seamless addition of new devices across applications. In addition, we propose an usage control policy model to support security and privacy in a distributed environment. We propose a layered architecture that provides various levels of abstraction to tackle the issues such as scalability, heterogeneity, security, and interoperability. The proposed architecture is coupled with cognitive capabilities that helps in intelligent decision-making and enables automated service creation. Using a comprehensive use-case, comprising elements from multiple-application domains, we illustrate the usability of the proposed architecture.

A scalable distributed architecture towards unifying IoT applications

The advent of Internet of Things (IoT) has kindled the possibility of umpteen number of applications. One of the major challenges in the realization of IoT applications is interoperability among various IoT entities. Thus, the need for a new architecture - comprising of smart control and actuation - has been identified by many researchers. In this article, we propose a distributed, interoperable architecture for IoT, which will overcome most of the obstacles in the process of large scale expansion of IoT. It specifically addresses heterogeneity of IoT devices, and enables seamless addition of new devices across applications. We propose a layered architecture that provides various levels of abstraction to tackle the issues such as, scalability, heterogeneity and interoperability. Using a comprehensive study of a use-cases, comprising elements from multiple-application domains, we illustrate the usability of the proposed architecture.

A unified semantic knowledge base for IoT

In the Internet of Things (IoT), interoperability among heterogeneous entities is an important issue. Semantic modeling is a key catalyst to support interoperability. In this work, we present a unified semantic knowledge base for IoT that uses ontologies as the building blocks. Most of the current ontologies for IoT mainly focus on resources, services and location information. We build upon the current state-of-the-art ontologies to provide contextual information and set of policies to execute services. Our knowledge base consists of several ontologies viz, resource, location, context & domain, policy and service ontologies. This helps in building a unified knowledge representation for IoT entities. In our knowledge base, we specifically model dynamic environments in which IoT entities operate. Our knowledge base also facilitates service-composition, discovery and modeling for IoT in dynamic environments.

No-sense: Sense with dormant sensors

The lifetime of a wireless sensor network mainly depends on battery capacity and energy consumption at each node for operations such as, sensing, processing and communication. Popular approaches to save energy have been to intelligently duty cycle and restrict the frequency of these operations, rendering lower quality data at the sink. In this article, we propose Virtual Sensing Framework (VSF), which reduces the frequency of the above mentioned operations at each node while not compromising on the sensing interval, and hence resulting in higher quality data at the sink. VSF creates virtual sensors at the sink to exploit the spatio-temporal correlations among sensed data. Using an adaptive model at every sensing iteration, the virtual sensors can predict multiple consecutive sensor data for dormant physical sensors with the help of only a few active physical sensors. We show that even when the sensed data represents different parameters (e.g., light, temperature), our proposed technique works well. Applying our technique on the real-world data sets, we attain substantial reduction in energy consumption per node while maintaining high accuracy of the sensed data. To achieve higher energy reduction, VSF has to be used in conjunction with various layers and protocols of the communication stack. Thus, it has the potential to open up new research insights to make the best use of statistical properties of collected sensor data in a network.

VSF: An Energy-Efficient Sensing Framework Using Virtual Sensors

In this paper, we describe virtual sensing framework (VSF), which reduces sensing and data transmission activities of nodes in a sensor network without compromising on either the sensing interval or data quality. VSF creates virtual sensors (VSs) at the sink to exploit the temporal and spatial correlations amongst sensed data. Using an adaptive model at every sensing iteration, the VSs can predict multiple consecutive sensed data for all the nodes with the help of sensed data from a few active nodes. We show that even when the sensed data represent different physical parameters (e.g., temperature and humidity), our proposed technique still works making it independent of physical parameter sensed. Applying our technique can substantially reduce data communication among the nodes leading to reduced energy consumption per node yet maintaining high accuracy of the sensed data. In particular, using VSF on the temperature data from IntelLab and GreenOrb data set, we have reduced the total data traffic within the network up to 98% and 79%, respectively. Corresponding average root mean squared error of the predicted data per node is as low as 0.36 °C and 0.71 °C, respectively. This paper is expected to support deployment of many sensors as part of Internet of Things in large scales.

A distributed model for approximate service provisioning in internet of things

With the advent of Internet of Things (IoT), many newer possibilities of service provisioning are opening up. Truly Google like searching for objects in our daily life is not ruled out in near future. Towards this aim, we have proposed Approximate service provisioning in our previous works [18, 19]. In this paper we propose a model for such a service. We discuss briefly the notion and concept of approximate services first and then we elaborate on the ideas of achieving such a paradigm. Further, since we know that there could be enormous number of devices in the future and in particular in IoT, a centralized solution seems impractical. Thus, we throw some light on the use of distributed approximate service models in this article.

Murphy loves CI: Unfolding and improving constructive interference in WSNs

Constructive Interference (CI) phenomenon has been exploited by Glossy, a mechanism for low-latency and reliable network flooding and time synchronization for wireless sensor networks. Recently, CI has also been used for other applications such as data collection and multicasting in static and mobile WSNs. These applications base their working on the high reliability promised by Glossy regardless of the physical conditions of deployment, number of nodes in the network, and unreliable wireless channels that may be detrimental for CI. There are several works that study the working of CI, but they present inconsistent views. We study CI from a receivers viewpoint, list factors that affect CI and also specify how and why they affect. We validate our arguments with results from extensive and rigorous experimentation in real-world settings. This paper presents comprehensive insights into CI phenomenon. With this understanding, we improve the performance of CI through an energy-efficient and distributed algorithm. We cause destructive interference on a designated byte to provide negative feedback. We leverage this to adapt transmission powers. Compared to Glossy, we achieve 25% lesser packet losses while using only half of its transmission power.

Sleeping Beauty: Efficient Communication for Node Scheduling

Typical Wireless Sensor Networks (WSN) deployments use more nodes than needed to accurately sense the phenomena of interest. This redundancy can be leveraged by switching-on only a subset of nodes at any time instant (node-scheduling) and putting the remaining nodes sleep. This effectively extends the network lifetime. In addition to sensing coverage, node-scheduling schemes must also ensure that (i) the network stays connected, and (ii) the time needed to wake-up the complete protocol stack after sleeping is minimized. We present Sleeping Beauty, a highly-efficient data collection protocol that aids node-scheduling schemes in both aspects. Sleeping Beauty uses a slotted and tightly synchronized communication primitive, where a node keeps its radio off for most of the time, except in the slots when it needs to participate for successful communication. Further, an efficient neighbor-discovery mechanism is included that provides partial, but sufficient topology information (potential parents) to avoid network partitions. Furthermore, Sleeping Beauty employs a novel, yet simple clock-offset estimation technique that maintains highly-accurate time synchronization over long radio-off periods (i.e., less than 500 us deviation even after 45, min of sleeping). This minimizes time wasted in resynchronizing the network in between data collection rounds. Through experiments on two different testbeds, we verified that Sleeping Beauty decreases the duty cycle up to a factor of 3 compared to state-of-the-art techniques, while achieving similar delivery ratios.

Energy Consumption and Latency in BLE Devices under Mutual Interference: An Experimental Study

Bluetooth is a widely used technology for short range communications. Limited device density and frequency hopping based communication usually eliminates the chances of mutual interference among independent Bluetooth Pico nets. However, with the advent of Internet of Things, there is a sharp increase in Bluetooth-equipped devices, especially in wearable devices. This gives raise to multiple collocated Pico nets, thus increasing the mutual interference leading to performance degradation of Bluetooth communication. In this work, we study the latency and energy consumption by Bluetooth low energy (BLE) devices under the influence of mutual interference, which has not been studied in the literature. Based on the experimental results involving 32 BLE devices, we investigate the influence of mutual interference and develop models for energy consumption and latency. These models can be utilized in future BLE enabled devices.

Where is PELE?: pervasive localization using wearable and handheld devices

Smartphones or in general handhelds commonly used for indoor localization purposes are not a viable option in places where people do not carry them all the time - for example, home and office. Alternatively, wearable devices can partially solve this problem but have many limitations with respect to power supply, processing capability, and availability of sensors. These issues prevent the adoption of many common handheld localization solutions. In this work, we present PErvasive Localization Engine (PELE), a distributed localization system that uses wearable and handheld jointly to address the above drawbacks. Using only magnetometer, accelerometer, and Bluetooth radio, localization is performed by means of a particle filter. In addition, a dynamic handoff mechanism is presented, which uses the wearable only when it is necessary, thus reducing energy consumption on the wearable without affecting the desired localization accuracy. Evaluating the system with ten participants, we achieve a localization accuracy of 90.31 % in an indoor environment spanning about 320 m2.