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Introduction to the Special Issue on BuildSys’17

Democratizing Authority in the Built Environment

Operating systems and applications in the built environment have relied upon central authorization and management mechanisms that restrict their scalability, especially with respect to administrative overhead. We propose a new set of primitives encompassing syndication, security, and service execution that unifies the management of applications and... (more)

Design and Analysis of a Query Processor for Brick

Brick is a recently proposed metadata schema and ontology for describing building components and the relationships between them. It represents... (more)

AutoCalib: Automatic Traffic Camera Calibration at Scale

Emerging smart cities are typically equipped with thousands of outdoor cameras. However, these cameras are usually not calibrated, i.e., information such as their precise mounting height and orientation is not available. Calibrating these cameras allows measurement of real-world distances from the video, thereby enabling a wide range of novel... (more)

Flux: A Platform for Dynamically Reconfigurable Mobile Crowd-Sensing

Flux is a platform for dynamically reconfigurable crowd-sensing using mobile devices like smartphones and tablets, programmed under a notion of region-based sensing. Each region is defined by a set of physical constraints that determine the sensing scope, e.g., based on device position or other environmental variables, plus a set of periodic tasks... (more)

A Scalable Room Occupancy Prediction with Transferable Time Series Decomposition of CO2 Sensor Data

Human occupancy counting is crucial for both space utilisation and building energy optimisation. In... (more)

A Scalable System for Apportionment and Tracking of Energy Footprints in Commercial Buildings

We propose a system that tracks each occupant’s personal share of energy use, or “energy footprint,” inside commercial... (more)

Smart Home Occupant Identification via Sensor Fusion Across On-Object Devices

Occupant identification proves crucial in many smart home applications such as automated home control and activity recognition. Previous solutions are... (more)

Optimal Discrete Net-Load Balancing in Smart Grids with High PV Penetration

Mitigating supply-demand mismatch is critical for smooth power grid operation. Traditionally, load curtailment techniques such as demand response have... (more)

Mechanisms and Policies for Controlling Distributed Solar Capacity

The rapid expansion of intermittent grid-tied solar capacity is making the job of balancing electricity’s real-time supply and demand... (more)

Inverted HVAC: Greenifying Older Buildings, One Room at a Time

Emerging countries predominantly rely on room-level air conditioning units (window ACs, space heaters, ceiling fans) for thermal comfort. These distributed units have manual, decentralized control leading to suboptimal energy usage for two reasons: excessive setpoints by individuals and inability to interleave different conditioning units for... (more)

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About TOSN

TOSN publishes original research papers (approximately 30-40 printed pages each in ACM Transaction style) and tutorial and survey papers (approximately 30-50 printed pages each). We also accept short technical notes that focus on industrial technologies and practical experience.
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Forthcoming Articles
Optimal Rate Control for Energy Harvesting Systems with Random Data and Energy Arrivals

Due to the random and dynamic energy harvesting process, it is challenging to conduct optimal rate control in Energy Harvesting Communication Systems (EHCS). Existing works mainly focus on two cases: 1) The traffic load is infinite (as long as there is energy, there is data to transmit), where the objective is to optimize the rate control policy subject to the dynamic energy arrivals, thus maximizing the average system throughput; 2) The traffic load is finite, where the objective is to optimize the rate control policy, thus minimizing the time by which all the packets are delivered. In this work, we focus on the optimal rate control of EHCS from another important and practical perspective, where the data and energy arrivals are both random. Given any deadline of T, our goal is to maximize the total throughput in [0; T]. Specifically, two scenarios are considered: 1) Energy is ready before the transmission; 2) Energy arrives randomly during the transmission. In both scenarios, we assume that the data arrive randomly during the transmission. For the first scenario, we develop a novel Stepwise Searching Algorithm (SSA) based on the cumulative curve methodology, which is shown to achieve the optimal solution and the

Resource-efficient and Automated Image-based Indoor Localization

Image-based indoor localization has aroused much interest recently, because it requires no infrastructure support. Previous approaches on image-based localization, due to their computation and storage requirements, often process queries at server. This does not scale well, incurs round-trip delay, and requires constant network connectivity. Many also require users to manually confirm the shortlisted matched landmarks, which is inconvenient, slow, and prone to selection error. To overcome these limitations, we propose HAIL a highly automated (in terms of image selection or confirmation) image-based localization algorithm distributed in mobile devices. HAIL achieves resource efficiency (in terms of storage and processing) by keeping only those distinguishing visual features for each landmark, and employing a data structure to search for the features efficiently. It further utilizes the motion sensors and map constraint to enhance the localization accuracy without user operation. We have implemented HAIL on Android platform and conducted extensive experiments in a food plaza and a premium shopping mall. Our results show that it achieves much higher localization accuracy (reducing the localization error by more than 20%) and computation efficiency (by more than 40% in time) as compared with the state-of-the-art approaches.

Participant Incentive Mechanism towards Quality-Oriented Sensing: Understanding and Application

The ubiquity of ever-more-capable mobile devices, especially smartphones, brings forth participatory sensing, a novel way to collect and interpret information from the environment using mobile devices. It can achieve unprecedented quantity of data. However, it is arduous to guarantee quality of data since everyone can contribute data without scrutinization. It is an important issue in quality-oriented participatory sensing. In this paper, we proposed a reputation-based incentive mechanism, RIM, to realize the idea. In our incentive mechanism, we identify the people who collected accurate data and regarded them as reputable users. Then, the reputable users are granted a higher chance to obtain rewards so that other people will try to become reputable as well. We analyze our incentive mechanism by formalization and premise implications. For a feasibility study of participatory sensing and verification of the implications, we deploy a participatory sensing application focusing on monitoring environmental noise in a specific location as a case study and conduct a simulation based on the case study to further evaluate the proposed incentive mechanism. The results from the case study and simulation present that RIM can remarkably increase the quality of collected data in participatory sensing, meanwhile corroborating our theoretical implications

Known and Unknown Facts of LoRa: Experiences from a Large Scale Measurement Study

LoRa is one of the LPWAN technologies designed for IoT has gained significant momentum amongst both industrial and research communities. Patented by Semtech, LoRa makes use of chirp spread spectrum modulation to deliver data. LoRa promises long battery life, far-reaching communication distances, and a high node density at the cost of data rate. In this paper we conduct a series of experiments to verify the claims made by Semtech on LoRa technology. Our results show that LoRa is capable of communicating over 10 km under line-of-sight environment however, under non-line-of-sight environments, LoRa performance is severely affected by obstructions such as buildings and vegetations. Moreover, the promised of prolonged battery life requires extreme tuning of parameters. Lastly, a LoRa gateway supports up to 6,000 nodes with PRR requirement of >70%. This study also explores the relationship between LoRa transmission parameters and proposes an algorithm to determine optimal settings in terms of coverage and power consumption under non-line-of-sight environment. It further investigates the impact of LoRaWAN on energy consumption and network capacity along with implementation of LoRa medium access mechanism and possible gains brought forth by implementing such a mechanism.

W3W: Energy Management of Hybrid Energy Supplied Sensors for Internet of Things

The usage of hybrid energy supplied sensors in the Internet of Things has enabled longer lifetime of sensors and expanded scope of applications. These sensors can combine advantages of environmental energy harvesting techniques and wireless energy harvesting techniques. However, how to coordinate them is still a challenge and has not been studied extensively. In this paper, we present a system based on mobile crowd wireless charging to manage energy of hybrid energy supplied sensors. When environmental energy is insufficient, the system will utilize smart devices carried by mobile users as chargers to provide wireless energy. We construct and study a W3W problem in the system: \underline{w}hen to leverage mobile crowd wireless charging to support rechargeable sensors, \underline{w}here to perform wireless energy transfer, and \underline{w}hom to allocate and incentivize as chargers to maximize useful energy value over all sensors subject to a budget. In order to control the actual quality of wireless energy charging, we propose a design principle named task completion trustfulness. We consider offline and online conditions and design corresponding algorithms with incentive allocations. Extensive simulations are conducted to demonstrate the effectiveness of our algorithms, which also validates our theoretical results.

Leveraging Fog Analytics for Context-Aware Sensing in Cooperative Wireless Sensor Networks

In this paper, we present a fog computing technique for real-time activity recognition and localization on-board wearable Internet of Things(IoT) devices. Our technique makes joint use of two light-weight analytic methods - Iterative Edge Mining(IEM) and Cooperative Activity Sequence-based Map Matching(CASMM). IEM is a decision-tree classifier that uses acceleration data to estimate the activity state. The sequence of activities generated by IEM is analyzed by the CASMM method for identifying the location. The CASMM method uses cooperation between devices to improve accuracy of classification, and then performs map-matching to identify the location. We evaluate the performance of our approach for activity recognition and localization of animals. The evaluation is performed using real-world acceleration data of cows collected during a pilot study in Dairygold-sponsored farm in Kilworth, Ireland. The analysis shows that our approach can achieve a localization accuracy of upto 99%. In addition, we exploit the location-awareness of devices and present an event-driven communication approach to transmit data from the IoT devices to the cloud. The delay-tolerant communication facilitates context aware sensing and significantly improves energy profile of the devices. Furthermore, an array-based implementation of IEM is discussed and resource assessment is performed to verify its suitability for device-based implementation.

ECT: Exploiting Cross-Technology Transmission for Reducing Packet Delivery Delay in IoT Networks

Recent advances in cross-technology communication have significantly improved the spectrum efficiency in the same ISM band among heterogeneous wireless devices (e.g., WiFi and ZigBee). However, further performance improvement in the whole network is hampered because the cross-technology network layer is missing. As the first cross-technology network layer design, our work, named ECT, opens a promising direction for significantly reducing the packet delivery delay via collaborative and concurrent cross-technology communication between WiFi and ZigBee devices. Specifically, ECT can dynamically change the nodes priorities and reduce the delivery delay from high priority nodes under unreliable links. The key idea of ECT is to leverage the concurrent transmission of important data and raw data from ZigBee nodes to the WiFi AP. We extensively evaluate ECT under different network settings and results show that our ECTs packet delivery delay is more than 29 times lower than the current state-of-the-art solution.

Rulers on Our Arms: Waving to Measure Object Size through Contactless Sensing

This paper proposes a system, Aware, which turns our wearable or mobile device into a ruler. It can estimate the size of objects that are too tall to reach or too far to touch. Such a design will enable a rich set of applications which count on the size information of surrounding environments/objects. Aware purely utilizes the motion sensors on the device for object size measures. It can also integrate with the crowdsourcing feature for both performance improvement and result sharing. We propose a series of key techniques to address three major challenges in the Aware design: 1) user's angle of line-of-sights to the object is used in the size measure but motion sensors track only the angle of arm's waving, 2) motion sensors are noisy that require novel and effective data processing techniques, otherwise the errors could easily overwhelm the final result, and 3) in the crowdsourcing mode, Aware needs to identify vicinal objects of similar sizes and effectively fuse the measured sizes that correspond to the same object. We consolidate above designs and implement Aware on Android platforms. Extensive experiments with four users show that Aware can achieve accurate measurement performance for the objects of various sizes.

A Novel Authenticated Key Agreement Protocol with Dynamic Credential for Wireless Sensor Networks

Public key cryptographic primitive (such as the famous Diffie-Hellman key agreement, or public key encryption) has recently been used as a standard building block in authenticated key agreement (AKA) constructions for wireless sensor networks (WSNs) to provide perfect forward secrecy (PFS), where some expensive cryptographic operation, i.e. exponentiation calculation, is involved. However, realizing such complex computation on resource constrained wireless sensors is inefficient, and even impossible for certain device. In this work, we introduce a new AKA scheme with PFS for WSNs without using any public key cryptographic primitive. In order to achieve PFS, we rely on a new a dynamic one-time authentication credential which is regularly updated in each session. In particular, each authentication credential is wisely associated with at most one session key that enables us to fulfill the security goal of PFS. Furthermore, our scheme can provide impersonation attack detection function which could allow principles to identify whether they have been previously impersonated by some attacker. We highlight that our scheme can be very efficiently implemented on sensors, since only hash function and XOR operation are required.

A General Framework for Spectrum Sensing Using Dedicated Spectrum Sensor Networks

Efficient spectrum sensing is essential for the success of Dynamic Spectrum Assignment (DSA) in Cognitive Radio Networks (CRNs). In conventional spectrum sensing schemes, secondary users (SUs) have to intelligently schedule their sensing and accessing cycles so that the spectrum opportunities are optimally exploited while the primary users are not harmed. In this paper, we propose a new sensing service model in which a dedicated spectrum sensor network (SSN) is adopted for the spectrum sensing tasks. We will describe the general framework for this SSN-enabled CRN and present the major challenges in such architecture. We will also study one of these challenges and formulate it as a boundary detection problem with notable and unknown erroneous inputs. A novel cooperative boundary detection scheme is designed which explores the recent advances in machine learning and computational geometry. We prove that our cooperative boundary detection can asymptotically approach the optimal solution. Real test-bed as well as simulation experiments show that compared with the traditional schemes, cooperative boundary detection can significantly reduce the spectrum sensing overhead and improve the effectiveness of DSA.

Routing-Aware and Malicious Node Detection in a Concealed Data Aggregation for WSNs

A WSN needs to be not only energy efficient, but also secure. Various attacks may make data aggregation unsecure. We investigate the reliable and secure end-to-end data aggregation problem considering selective forwarding attacks and modification attacks in homogeneous WSNs, and propose two data aggregation approaches. Our approaches, namely, Sign-Share and Sham-Share, use secret sharing and signatures to allow aggregators to aggregate the data without understanding the contents of messages and the base station to verify the aggregated data and retrieve the raw data from the aggregated data. To best of our knowledge, this is the first lightweight en-routing malicious node detection in concealed data aggregation. We have performed extensive simulation to compare our approaches and the two state-of-the-art approaches PIP and RCDA-HOMO. The simulation results show both Sign-Share and Sham-Share consume reasonable amount of time in processing the data and aggregating the data. The simulation results show that our first approach achieved average network lifetime of 102.33\% over PIP, and average aggregation energy consumption of 74.93%. Also, it achieved average aggregations processing time and sensor's data processing time of 95.4%, 90.34% over PIP and 98.7%,92.07% over RCDA-HOMO respectively while it achieved average network delay of 71.95% over PIP

Network Management of Multi-Cluster RT-WiFi Networks

Applying wireless technologies in cyber-physical systems (CPS) has received significant attention in recent years. In our previous work, a high-speed and flexible real-time wireless protocol called RT-WiFi has been designed to support a wide range of CPSs. To serve the CPS applications with communication nodes geographically distributed over a large area, multi-cluster RT-WiFi networks with multiple access points (AP) need to be deployed. Although effective scheduling algorithms have been designed to schedule tasks in RT-WiFi networks with a single AP, uncoordinated packet transmissions from multi-cluster RT-WiFi networks may suffer from co-channel interferences that cause performance degradation. The multi-cluster RT-WiFi network management problem is to resolve the co-channel interference through channel assignment for clusters and phasing assignment for communication tasks. In this paper, we first derive a conjunctive normal form encoding of the problem, and design a TScheduler that searches feasible solutions through the SAT solver. A novel LRTree Scheduler is further designed to solve the problem in chain graphs while keeping the number of used channels small and the network management overhead low. A testbed of multi-cluster RTWiFi network is deployed to validate the design of the multi-cluster RT-WiFi network and evaluate the performance of the proposed scheduling algorithms.

Radiation Constrained Fair Charging for Wireless Power Transfer

Recently wireless power transfer technology (WPT) attracts considerable attention and becomes a promis- ing technology to prolong the lifetime of wireless sensor networks (WSNs) by providing perpetual energy to sensors. However, electromagnetic radiation (EMR) incurred by WPT is largely overlooked by most existing literatures. In this paper, we first propose and study the radiation constrained fair charging problem for WP- T, i.e., maximizing the minimum utility of sensors by adjusting the power of wireless chargers with no EMR intensity at any location in the field exceeding a given threshold Rt. To address this problem, we first adopt an area discretization method to transform it from nonlinear to linear. Then, we propose four algorithms to deal with the reformulated problem, i.e., 1/3 and 1/4 Approximation Algorithms, Primal-Dual algorithm and area division algorithm. In particular, the area division algorithm is not only fully distributed but also provably achieves an approximation ratio of (1  ?). Further, we conduct extensive simulations and build a field testbed to verify our theoretical findings. Our simulation results show that the approximation ratios of the proposed algorithms hold; the Primal-Dual and area division algorithms have comparable performance of the optimal results and outperform baseline algorithms obviously.

LaPS: LiDAR-assisted Placement of Wireless Sensor Networks in Forests

The deployment of a wireless sensor network (WSN) is crucial to its reliability and performance. Yet, node placement is typically determined in-field via effort-demanding trial-and-error procedures, because existing approaches over-simplify the radio environment; this especially holds for forests, the focus of this paper, where trees greatly affect communication. We present LaPS, an approach exploiting remote sensing to identify the best node placement automatically and prior to deployment. Airborne Light Detection and Ranging (LiDAR) data acquired for the target forest are automatically processed to estimate its properties (e.g., tree position and diameter) that, once incorporated into a specialized path loss model, enable per-link estimates of the radio signal attenuation induced by trees. Finally, a genetic algorithm explores placement options by evolving towards a (sub-)optimal solution while satisfying the user's spatial and network requirements, whose formulation is very flexible and broadly applicable. Our experiments, focused on a real forest, confirm that LaPS yields topologies of significantly higher quality w.r.t. approaches using a regular placement or a standard path loss model. Further, the ability to quickly explore the impact that changes in user requirements have on topology is invaluable to improve the operation of WSNs and reduce the effort of their in-field deployment.

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