The constructive interference (CI) phenomenon has been exploited by a number of protocols for providing energy-efficient, low-latency, and reliable data collection and dissemination services in wireless sensor networks. These protocols consider CI to provide highly reliable packet delivery. This has attracted attention to understand the working of CI; however, the existing works present inconsistent views. Furthermore, these works do not study in the real-world settings where the physical conditions of deployment and unreliable wireless channels also impact the performance of CI. Therefore, we study the phenomenon of CI, considering a receiver's viewpoint and analyze the parameters that affect CI. We validate our arguments with results from extensive and rigorous experimentation in real-world settings. This paper presents comprehensive insights into the CI phenomenon. With the understanding, we develop the destructive interference-based power adaptation (DIPA), an energy-efficient and distributed algorithm, that adapts transmission power to improve the performance of CI. Since CI-based protocols cannot have an explicit acknowledgment packet, we make use of destructive interference on a designated byte to provide a feedback. We leverage this feedback to adapt transmission powers. We compared CI with and without DIPA in two real-life testbeds. On one testbed, we achieve around 25% lower packet losses while using only half of its transmission power for 64-B packets. On the other testbed, we achieve 25% lower packet losses while consuming only 47% of its transmission power for 128-B packets. Existing CI-based protocols can easily incorporate DIPA into them to achieve lower packet losses and higher energy efficiencies.

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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.@en

For the Internet of Things (IoT) applications that send a few bytes of sensor information infrequently, several long-range IoT technologies have been conceived. Narrowband IoT (NB-IoT) is one of them that stands out due to its extended coverage, high penetrability, and high reliability features. Among the primary goals set for the standard, low device power consumption is significant as the devices are typically deployed with batteries. In this paper, we characterize an NB-IoT device with respect to its energy consumption in different coverage classes for various scenarios. Based on the measurements, we estimate the lifetime of the device. We find that the lifetime of the device is significantly affected by the coverage class of the device. In order to augment the battery, we investigate the possibility of using ambient energy sources in the context of a smart home. For this we analyze real-world data sets, and find that the harvesting technologies can increase the average lifetime of the device. However, due to the spatio-temporal variations in the amount of energy that can be harvested, there are several challenges to be dealt with. We list the keys challenges of using energy-harvesting in NB-IoT, and those inherent to NB-IoT.@en

In smartcard-based travel payment systems, passengers have to place the smartcard near the journey registration devices once each for check-in and check-out to authenticate their travel. This is an annoying process when if the journey involves multiple stops. In this paper, we describe a working system of secure energy-efficient automatic ticketing (SEAT) for public transport, which transforms traditional check-in/check-out system into Be-in/Be-out system. In SEAT, a Bluetooth low energy (BLE) enabled smartphone communicates with registration devices to track the journey for pricing without any human intervention. SEAT is vigilant toward the energy consumption of the BLE device under various conditions, and security and privacy threats to the overall ecosystem. We develop models for energy consumption and latency for BLE devices under the influence of mutual interference based on experiments with 32 BLE devices. We utilize these models to develop an energy-efficient protocol for SEAT that is secure and privacy preserving. Our experiments show that a BLE module consumes only 18.3 J daily under the proposed system model, which is less than 0.1% of the total capacity of a typical smartphone battery.@en

Daylighting is the exploitation of solar energy in the form of natural lighting indoors and it plays an integral role in minimizing the energy footprint of a building. Smart daylighting enables us to design buildings that provide comfort and energy savings simultaneously. In this work, we propose a dynamic facade system for buildings that aims to maximize user comfort, while also maximizing energy savings by harvesting solar energy optimally. The solar panels on the facade can harvest the highest amount of energy when they are positioned perpendicular to the sun’s rays. However, this may result in unsatisfactory lighting conditions inside the room and we approach the trade-off as a multi-objective optimization problem. We present a preliminary exploration of the concept of smart skins for buildings, contributing to the creation of future sustainable buildings with zero energy footprint.

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Recently occupancy-based lighting control has shown a great promise towards achieving better energy efficiency. Generally, traditional systems use additional sensors to detect user presence with higher precision to better the control
algorithms. In this work, we show how a smartphone-based indoor localization can be utilized for this purpose without deploying additional sensors. Apart from automatic controlling of the lights, our system provides a sophisticated manual control, where each light unit can be controlled by just pointing the phone towards it. Based on our experiments, we show that up to 67% more energy saving is possible using our systems compared to traditional methods.@en

Computers were invented to automate the labour-intensive computing process. The advancement of semiconductor technology has reduced the form-factor and cost of computers, and increased their usability. This has gradually introduced computers in various control and automation systems. The further rise of miniatuarized computing devices paves the way for autonomous monitoring using embedded devices. In the last two decades, we observed a huge surge of such monitoring and control systems. These systems are generally termed as the wireless sensor and actuator networks (WSAN). In a WSAN, a number of sensor nodes monitors a deployment area where data collection by humans is either difficult or costly. These devices collaboratively report their sensor readings to a centralized node called the sink. The sink is connected with the Internet, thus delivers the data to the outside world. This way the deployment region can be monitored remotely. Similarly, some actuators can also be controlled remotely through the sink. In the last decade, the concept of the Internet of Things (IoT) has evolved where any device can be reached by any other device/system/human being from anywhere and anytime. Thus, WSANs can be seen as a precursor of IoT. However, the vision of IoT is not limited to mere remote connectivity. Unlike traditional WSAN, where devices are deployed in remote/critical locations for specific purposes, IoT devices would be integrated into our daily surroundings assisting us in every aspect of life. As the embedded devices are resource constrained, energy and computational efficiency is a major challenge for both WSAN and IoT devices. However, the problem escalates as the IoT devices are expected to perform a number of tasks as opposed to a specific task as performed by classical WSANs. Moreover, the goal of IoT is to take humans out of the control loop or reduce the human intervention as much as possible. This requires devices to exchange data and cooperate among themselves. Thus, IoT devices need to act smartly fulfilling various requirements within its resource constraints. Every existing and upcoming device and network would be part of the IoT ecosystem. As the number of devices is expected to grow multifold, managing these devices will be a challenge. Especially since these devices are under the control of various entities/organizations. Not to mention that the manufacturers of various devices and their specifications would also vary significantly. To accomplish the vision of IoT these devices need to be able to cooperate and collaborate among themselves even if they are managed differently. This thesis brings forward the concept of virtualization in IoT to tackle the challenges of a global IoT ecosystem. The first challenge that we tackle is how to virtualize the IoT. We propose a reference architectural model for IoT called DIAT. The reference architecture follows a layered design principle where each layer groups a number of similar functionalities together. This enables easy development of existing and new functionalities of each layer independently. To validate the feasibility and usability of such an architectural model, we developed a system based on a practical IoT-application scenario. To this extent, we developed a controller (iLTC) that operates the heating and lighting systems in an office environment such that these devices operate energy efficiently. At the same time the system ensures a comfortable surroundings for the occupants while eliminating any direct involvement from the occupants. As WSANs are an integral part of the IoT ecosystem, next, we revisited some of the classic problems of WSANs in the wake of virtualizing the IoT. As energy efficiency is one of the biggest issues in WSANs, we propose a solution to reduce the overall traffic in a network without affecting the quality of data/monitoring. We achieved this by virtualizing the WSAN, which leads to higher cooperation among the devices and a higher operational optimization. We developed the virtual sensing framework (VSF) that exploits the inherent correlation among the sensor nodes to predict sensor readings (virtual sensing). The basic idea is that if a number of nodes are highly correlated, sensor readings from only one of them is sufficient to predict the readings for rest of them. Due to virtualization, such a cooperation among the nodes is possible. This reduces the amount of data transfer within the network, which leads to energy-efficient network operation. Further, we developed an efficient data collection protocol, called Sleeping Beauty that complements the virtualized sensor network. Based on a centralized schedule, nodes deliver their sensor readings to the sink reliably and efficiently. The accomplishment of a centralized schedule depends on network-wide time synchronization. As the hardware clock of an embedded device drifts significantly within a short time span, we developed a simple self-rectification mechanism such that the overhead of synchronizing the network periodically can be reduced significantly. This technique can be used by any protocol that requires time synchronization other than Sleeping Beauty. Timely data collection is another desired aspect of IoT as opposed to classical WSANs where latency is generally compromised in order to achieve a higher energy efficiency. We developed a communication mechanism, called Rapid that not only delivers the sensor readings in a fixed time bound, it also reduces the energy consumption. Rapid forms a number of clusters on-the-fly, where the cluster-heads collect data from the cluster-members and send an aggregated packet to the sink. By exploiting the capture effect, Rapid achieves parallelization for intra-cluster communications. Further, it exploits the constructive interference based fast flooding to deliver the aggregated data, which eliminates hop-by-hop flow scheduling. These two factors reduces the overall end-to-end delay of all the flows. The proposition of this thesis is that by means of virtualization, traditional WSANs can be easily integrated into the grand vision of IoT. We proposed a reference architecture, validated by means of a case study, and developed several amendments to classical WSAN data collection, making it consume less energy and achieve lower latency. We are convinced that virtualization can be applied effectively to other (WSAN) functionality as well. The future of IoT is looking bright.@en

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.@en

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 receiver's 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.@en

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.@en

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.@en

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.@en

Automatic control of HVAC and artificial lights has been one of the popular methods for achieving energy-efficient buildings. The operating set-points are decided based on predefined values to ensure comfort level to most of the occupants based on prior studies. However, a person can feel comfortable beyond the traditional set-point ranges used in the energy management systems of buildings. In this work, we develop a smartphone application that learns individual preferences about thermal and visual comfort with minimal user intervention. These functions provide the flexibility to operate the controllers in an aggressively lower energy consuming state while maintaining the comfort level of the occupants. Using a HVAC energy consumption model, we show that individual comfort preference based set-point can attain lesser energy consumption as compared to fixed set-point.@en

In data gathering wireless sensor network applications, data correlation among the sensor nodes have been utilized to extend network lifetimes. It has been shown that the data correlation also exists between nodes that are far away, contrary to the assumption that correlation decreases as a function of distance. Therefore, it is possible to group the nodes based on the correlation among their data regardless of their location. Given that data from one active node per group is sufficient to reconstruct the sensed data for the remaining sensor nodes, most of the nodes can be kept in low-power sleep mode. However, only few active nodes will usually create a disconnected network, and hence failing the purpose of the deployment. In this paper we formalize this problem, referred to as Sleep-route, of selecting the minimum number of connected active nodes that are sufficient to predict the sensed data for remaining sleeping nodes with high accuracy. We prove that the problem is NP-hard. Thus, we develop a greedy algorithm, Sleep-route heuristic that provides near-optimal solutions. Using Contiki-based simulations, we show that our scheme can extend network lifetime up to 42% as compared to the state-of-the-art solutions.@en