Cooling systems consume more than 40% of overall buildings' energy consumption. These cooling systems provide comfortable and productive environments in building environments. The increase in global warming leads to the ever-increasing demand for cooling systems. There are two types of cooling systems — ducted-centralized and ductless-split. Optimizing energy consumption by ducted-centralized cooling systems has been the focus of researchers in the literature. However, very few have considered optimizing the energy consumed by the ductless-split. The solutions for ducted-centralized cooling systems control dampers inside ducts. In ductless-split cooling systems, there are no ducts. Hence, it is not possible to use a solution proposed for ducted-centralized to ductless-split. The solutions available for ductless-split cooling systems perform scheduling based on mathematical/machine learning models. In either case, since the solutions are static, they cannot adapt to the complex dynamics of the environment. In this paper, we propose OCSRL, a model-based reinforcement learning approach to optimize the energy consumption of ductless-split cooling systems. We evaluate the proposed OCSRL in both real-world experimentation and simulation. We compare OCSRL with the greedy and mathematical model-based approach Cooling-System Linear-Scheduling Algorithm (CLA) and All ACs ON (AA). In terms of the learning rate of the algorithm, energy consumption, temperature maintained, and execution time. OCSRL cuts the learning time by 97% compared to CLA in the simulation. Our proposed OCSRL cuts the learning time by 99% compared to CLA in real-world experiments. After learning, it immediately starts optimizing energy consumption. The proposed OCSRL saves up to 15% of energy compared to CLA in simulation and real-world experimentation while maintaining the desired temperature. OCSRL saves up to 50% of energy compared to AA. The proposed OCSRL reduces the execution time of ductless-split cooling systems in both simulation and experimentation by up to 9% and 65% compared to CLA and AA, respectively. This leads to a reduction in refrigerant consumption.

The IoT solutions are typically customized for particular sensors. Further, these solutions demand compute, storage, and network resources, which have different scales of scalability. An increase in the demand for IoT-based applications makes it difficult for developers to build extensible and scalable IoT applications. The core idea of the paper is to propose an architecture that can be scaled up and down depending on the needs. In our architecture, each service in the cloud can be modified independently and adapted for a different type and number of IoT sensors. We implement the proposed architecture on Amazon Web Services (AWS), a cloud offering from Amazon. We measure the effectiveness of scaling up and down in terms of the number of IoT devices and mobile clients that process data from the IoT devices.

Video conferencing platforms have been appropriated during the COVID-19 pandemic for different purposes, including classroom teaching. However, the platforms are not designed for many of these objectives. When users, like educationists, select a platform, it is unclear which platform will perform better given the same network and hardware resources to meet the required Quality of Experience (QoE). Similarly, when developers design a new video conferencing platform, they do not have clear guidelines for making design choices given the QoE requirements.

We address the problem of connecting IoT nodes to the Internet via challenged networks, which are characterized by minimal or intermittent network association. Examples of such networks can be found in forests with distant access points or in railway stations with overburdened access points. In such places, it is difficult for a node to establish and maintain an association with the access point. There exist several solutions that demonstrate the feasibility of data transfer using low bit-rate broadcast of discovery frames that do not necessitate association. We categorize them into a representative protocol called ViFi. However, the low bit-rate broadcast traffic hampers the performance of heavilyutilized networks by consuming significant airtime and resulting in a severe drop in goodput. We come up with a novel adaptive version of ViFi, Adaptive ViFi, that adapts to network conditions and intelligently selects a channel and an interval for the data transmission. In principle, our proposed solution is agnostic to WiFi standard, client, or AP in use. We develop an end-to-end working prototype on Raspberry Pi and hostapd-based access points. With a set of extensive experiments, we find that Adaptive ViFi gracefully addresses the limitations of ViFi in a heavily utilized network, at the same time it achieves up to 10.7x higher goodput than the ViFi. It co-exists well with other WiFi-based non-IoT clients with minimal impact on their goodput (4%). It scales well with a delivery rate of 77% and 50% with 150 and 300 nodes, respectively.

Abstract Decoy Routing (DR), a promising approach to censorship circumvention, uses routers (rather than end hosts) as proxy servers. Users of censored networks, who wish to use DR, send specially crafted packets, nominally addressed to an uncensored website. Once safely out of the censored network, the packets encounter a special router (the Decoy Router) which identifies them using a secret handshake, and proxies them to their true destination (a censored site). However, DR has implementation problems: it is infeasible to reprogram routers for the complex operations required. Existing DR solutions fall back on using commodity servers as a Decoy Router. But as servers are not efficient at routing, most web applications show poor performance when accessed over DR. A further concern is that the Decoy Router has to inspect all flows in order to identify the ones that need DR. This may itself be a breach of privacy for other users (who neither require DR nor want to be monitored). In this paper, we present a novel DR system, Siege- Breaker (SB), which solves the aforementioned problems using an SDN-based architecture. Previous proposals involve a single unit which performs all major operations (inspecting all flows, identifying the DR requests and proxying them). In contrast, SB distributes the tasks for DR among three independent modules. (1) The SDN controller identifies DR requests via a covert, privacy preserving scheme, and does not need to inspect all flows. (2) The reconfigurable SDN switch intercepts packets, and forwards them to a secret proxy efficiently. (3) The secret proxy server proxies the client’s traffic to the censored site. Our modular, lightweight design achieves performance comparable to direct TCP downloads, for both in-lab setups, and Internet based tests involving commercial SDN switches.

This paper aims to build a novel classification model that can distinguish the typical motion activities that a traveler would perform in a metro-journey. Following motion activities are focused in this work: waiting in a queue, traveling in a metro train, climbing-up, climbing-down, walking and stationary. We aim to build a classifier which can work on data sampled from smartphone sensors at a low frequency (4Hz). However, it is non-trivial to do so as the mentioned activities are not easily separable in data sampled at low frequency. Current works focus on data sampled at high frequency (40Hz). Also, they don't consider metro-journey specific activities such as queue. Our proposed model focuses on all the metro-journey specific activities while using data sampled at low frequency (4Hz). Experimental evaluation (datasets collected in Delhi Metro-rail network) indicate superior performance of our classifier (mean accuracy 92%) over the related work (mean accuracy 70%).

We address the problem of automating the process of network troubleshooting for a large-scale WiFi network. Specifically, we target identifying the causes of unnecessary active scans in WiFi networks, that are known to degrade the WiFi performance. We collect 340 hours worth of data with several thousands of episodes of active scans to train various machine learning models. Data is collected with 27 devices across vendors in varied network setups under a controlled setting. We study unsupervised and supervised machine learning techniques to conclude that a multilayer perceptron is the best model to detect the causes of active scanning. Further, we perform an in-vivo model validation in an uncontrolled real-world WiFi network. We also compare our models with a static rule-based approach. Our model improves the mean F1-score accuracy of cause detection from 0.78 to 0.99. Our proposed mechanism has the potential of being incorporated in the existing WiFi controllers, such as that of Cisco and Aruba.

Internet-of-Things (IoT) nodes in dense WiFi deployments struggle to maintain their associations with the access points (APs) and efficiently transmit their data. In the process, they waste their energy and airtime, which affects their battery-life and throughput. We present Elixir, a protocol for WiFi-based IoT nodes that enables them to transmit data without the need of a WiFi association. Elixir is implemented in the user space, which makes it easy to port the implementation to any Linux-based client and AP. We demonstrate its end-to-end operation on a Raspberry Pi module in good [signal strengths '40 to '50 dBm and no losses] and bad network conditions [signal strengths below '80 dBm and losses present]. We measure benefits of Elixir in terms of (a) the ability to transmit when the WiFi association cannot be maintained [in bad network condition], (b) lesser number of frames transmitted [50x - good network condition, 100x ? bad network condition], (c) reduced airtime consumption [10x - bad network condition], and (d) reduced battery expenditure [18% for both network conditions].

Real-world deployments of WiFi-based indoor localization in large public venues are few and far between as most state-of-the-art solutions require either client or infrastructure-side changes. Hence, even though high location accuracy is possible with these solutions, they are not practical due to cost and/or client adoption reasons. Majority of the public venues use commercial controller-managed WLAN solutions, that neither allow client changes nor infrastructure changes. In fact, for such venues we have observed highly heterogeneous devices with very low adoption rates for client-side apps.

A large number of web applications are written using server-side scripting languages. Although web browsers allow clients to run these applications, it is often cumbersome to depend on desktops for the services provided by the applications. Given the popularity and convenience of mobile devices, there is a clear need to have native mobile apps driving the applications along with web browsers. In this paper, we present a solution to re-engineer web applications developed using server-side scripting languages, into native mobile apps. The solution takes source code of the web application along with its test suite as input and produces corresponding cross-platform mobile apps. The entire re-engineering process is fully automatic requiring no manual intervention at any stage. Our solution is generic enough not only to handle popular server-side scripting languages, but also to output mobile apps that support diverse popular platforms including Android, iOS, and Windows Mobile. To showcase the capability and generality of our solution, we have developed a prototype tool KIRKE to handle applications developed using JSP, PHP, and ASP.NET. We present three case studies based on real-life codebases to evaluate the correctness, coverage, usability, and performance of our solution. The results indicate that KIRKE is capable of generating a mobile app that preserves the functionality of original web application and uses resources more efficiently when compared to the web application running on a mobile browser.

IEEE 802.11 WLAN (Wireless or WiFi LAN) clients discover neighboring APs (Access Points) by active or passive scanning. Such an active scan of WLAN injects probe frames in the network. Network conditions like packet losses, roaming, etc. result in increased active scanning and hence, an excessive increase of the probe traffic. Of the several causes inherent to WLANs like interference, we find an excessive probe traffic also has a potential of hampering goodput of a WiFi network. We confirm this behavior in a controlled home environment as well as in an uncontrolled enterprise environment. Our analysis of 36 hours of wireless traffic collected over a period of 5 months with approximately 45 million wireless frames reveals that goodput of a WLAN drops exponentially with increase in the probe traffic. Therefore, realtime detection of increase in probe traffic and knowledge of a threshold for acceptable probing is crucial for WLAN's performance. In this paper, we formulate a metric to measure the increase in probe traffic in realtime and evaluate its functioning empirically. The metric not only reflects increase in probe traffic correctly, it is even simple enough to allow its realtime measurement.

Cardio-Vascular Diseases (CVD) are one of the major healthcare problems across the world, causing deaths for nearly 17 million people every year. With more than 80% of all CVD cases occurring in developing countries, it is a big challenge that needs immediate attention. Specifically for India, World Health Organization (WHO) and many other organizations have predicted rapid growth of CVD patients in near future. It is known that CVD can be prevented or deferred, if detected in its earlier stages and by subsequently adapting to appropriate preventive methods. Cost and availability of lab equipments -- for early diagnosis of CVD -- act as deterrents in controlling the spread of CVD cases in India, particularly in the rural parts.

Location-aware mobile applications are steadily gaining popularity across the world. However, lack of Global Positioning System (GPS) and absence of Wi-Fi infrastructure prevent users with non-Smart phones (majority of population in developing countries) from using location-aware applications as their phones do not have access to their current location. Existing GSM based approaches such as Cell ID-based works on non-Smart phones but they require access to a comprehensive database of Cell IDs. Such a database either does not exist or is very limited in developing countries. In this paper, we propose a novel GSM-based approach of using Cell Broadcast Service (CBS) messages for getting current location on the phone. Proposed approach does not depend on a comprehensive database and can run on programmable low end phones. We demonstrate the effectiveness of our approach on data collected in New Delhi, India across two different operators and propose two space-time history based algorithms to improve upon the localization accuracy of our baseline CBS approach. The proposed algorithms provide up to 35% improvement in accuracy over the baseline method. Further, we compare accuracy of our CBS-based approach with that of Cell ID-based approach and also, present a multimodal approach that uses combination of both CBS and Cell ID (wherever available) to improve the localization accuracy.

No abstract available.

We present Sprinkler, a reliable data dissemination service for wireless embedded devices which are constrained in energy, processing speed, and memory. Sprinkler embeds a virtual grid over the network whereby it can locally compute a connected dominating set of the devices to avoid redundant transmissions and a transmission schedule to avoid collisions. Sprinkler transmits O(1) times the optimum number of packets in O(1) of the optimum latency; its time complexity is O(1). Sprinkler is tolerant to fail-stop and state corruption faults. Thus, Sprinkler is suitable for resource-constrained wireless embedded devices. We evaluate the performance of Sprinkler in terms of the number of packet transmissions and the latency, both in an outdoor and indoor environment. The outdoor evaluation is based on data from project ExScal, which deployed 203 extreme scale stargazer (XSS). Our indoor evaluation is based on an implementation in the Kansei testbed, which houses 210 XSSs whose transmission power is controllable to even low ranges. We compare Sprinkler with the existing reliable data dissemination services, analytically or using simulations also. Our evaluations show that Sprinkler is not only energy efficient as compared to existing schemes, but also has less latency. Further, the energy consumption of nodes and the latency grows linearly as a function of newly added nodes as the network grows larger.

The Kansei testbed at the Ohio State University is designed to facilitate research on networked sensing applications at scale. Kansei embodies a unique combination of characteristics as a result of its design focus on sensing and scaling: (i) Heterogeneous hardware infrastructure with dedicated node resources for local computation, storage, data exfiltration and back-channel communication, to support complex experimentation, (ii) Time accurate hybrid simulation engine for simulating substantially larger arrays using testbed hardware resources, (iii) High fidelity sensor data generation and real-time data and event injection, (iv) Software components and associated job control language to support complex multi-tier experiments utilizing real hardware resources and data generation and simulation engines. In this paper, we present the elements of Kansei testbed architecture, including its hardware and software platforms as well as its hybrid simulation and sensor data generation engines.

Project ExScal (for extreme scale) fielded a 1000+ node wireless sensor network and a 200+ node peer-to-peer ad hoc network of 802.11 devices in a 13km by 300m remote area in Florida, USA during December 2004. In comparison with previous deployments, the ExScal application is relatively complex and its networks are the largest ones of either type fielded to date. In this paper, we overview the key requirements of ExScal, the corresponding design of the hardware/software platform and application, and some results of our experiments.

We present Sprinkler, a reliable data dissemination service for wireless embedded devices which are constrained in energy, processing speed, and memory. Sprinkler embeds a virtual grid over the network whereby it can locally compute a connected dominating set of the devices to avoid redundant transmissions, and a transmission schedule to avoid collisions. Sprinkler transmits O(1) times the optimum number of packets in O(1) of the optimum latency; its time complexity is O(1). Thus, Sprinkler is suitable for resource-constrained wireless embedded devices. We evaluate the performance of Sprinkler in terms of the number of packet transmissions and the latency, both in an outdoor and an indoor environment. Our indoor evaluation is based on an implementation in the Kansei testbed that houses 210 XSSs whose transmission power is controllable to even low ranges. We compare Sprinkler with the existing reliable data dissemination services, analytically or using simulations also. Our evaluations show that Sprinkler is not only energy efficient as compared to existing schemes but also have less latency. Further, the energy consumption of nodes and the latency grows linearly as a function of newly added nodes as network grows larger