Video analytics plays a crucial role in the development of smart estates and cities. Applications such as garbage dumping detection, lift monitoring, safety surveillance, etc. rely on video analytics, and require fast response time. Traditional cloud-based systems are ill-suited for these applications due to their limitations in handling large volume of video data with low latency. In contrast, the IoT-Edge-Cloud paradigm is better suited for such applications, but it presents challenges such as system heterogeneity, and resource allocation and orchestration. There is a need for an efficient platform for distributed video stream processing where resource orchestration and scalability aspects are tailored to smart estate applications.In this paper, we present ViEdge, an edge-based platform for video analytics applications in smart estates. It is highly adaptable and scalable, making it ideal for various deployments in such environments. Our implementation of ViEdge utilizes Kubernetes (K8s) for resource management and orchestration, and Apache Storm for distributed video stream processing. To study ViEdge’s customization capabilities, we evaluated its performance on a heterogeneous edge testbed. We observed increased latency in Apache Storm when integrated with Kubernetes, affecting overall application performance. However, by developing a heuristic-based scheduler, we demonstrate that ViEdge effectively reduces end-to-end latency and enhances frame processing rates.

Urban air pollution is a major problem in many cities globally. Prolonged exposure to particulate matter in the air is harmful to health. Government agencies and industries use expensive EPA-rated Air Quality Monitoring Systems (AQMS) with specialized and expensive instruments. However, the air quality data provided by agencies is based on delayed averages and is region-based. The AirQ platform is a smart and cost-effective solution for air quality monitoring. The AirQ device is portable, low-cost and provides real-time and location-specific air quality data. It supports wireless data communication technologies including WiFi, 4G, Bluetooth, LoRa, etc. We have developed an ontology-based backend to support data management and the implementation of smart data analytics algorithms. The real-time air quality data and alerts are provided by the AirQ Dashboard and Smartphone App. The AirQ platform can be used to build large-scale and high-density air quality monitoring networks for Smart Cities, with devices deployed at many locations such as schools, childcare centers, hospitals, community centers, bus stops, parks, etc.

Conventional IoT infrastructure deployments require complex configurations and management at the sensor devices, gateway and the backend levels. We have developed a smart ontology-driven IoT platform which can significantly improve the scalability and efficiency of the deployment and management of large-scale IoT infrastructures. The platform provides a unified ontology management layer across the sensor devices, IoT gateway and IoT backend. It also provides a virtualization-based IoT gateway which supports the interoperability of sensor devices with heterogeneous network protocols.

Participatory sensing (PS), an emerging sensing paradigm through organizing people and their mobile devices as sensors, is regarded as a promising solution to solve large-scale urban issues. PS has been implemented in many applications such as transportation, environmental monitoring, public health, etc. These applications are of high relevance for smart cities. Smart cities rely on the infrastructures established by government and industrial agencies to sense urban dynamics. PS can complement these infrastructures by involving “human sensors” and obtaining insights into people's activities. In this paper, the key features and challenges of PS are surveyed. A prototype PS platform is designed for efficient collection and management of smartphone sensing and survey data. A transport trip quality measurement system (TQMS) has been developed using this platform. Our experience to organize PS activities through a novel collaboration driven incentive mechanism is elucidated. A comprehensive real-world PS case study on TQMS including platform promotion, project collaboration, participant recruitment, activity organization has been successfully completed. This paper is concluded by discussing the potential future applications of the PS platform.

Hand Hygiene Duration and Technique The Trip Quality Measurement System (TQMS) is a prototype mobile data participatory system for the efficient collection and management of mobility and survey data. A transport trip quality measurement application has been developed using this system. The target users of this application are the commuters and passengers who travel in buses, taxis, metro trains, etc. This application measures the quality of the users' transport trips based on various factors such as bumpiness of ride, wait/travel time, distance, etc. The system consists of a smartphone client (iOS and Android) app for collecting mobility and survey data from the users, and a data management and analytics framework to manage, process and analyze the participatory data to derive useful insights on the mobility pattern of users and the transport trip quality experienced by the users.

This paper presents our implementation experience in building an energy efficient HVAC system for cooling and air conditioning. The system exercises the "low exergy" theory and leverages high temperature water (18°C) cooling for better energy efficiency. In order to achieve this, the system decomposes the cooling and dehumidification functionalities, and employs decentralized air control for on-demand dehumidification and ventilation. The system comprises two control modules, namely, radiant cooling module and distributed ventilation module, cooperating with each other to provide the HVAC control. Abundant sensors and embedded control devices are customized and instrumented, and we develop a wireless sensor network to support control data exchange among those devices. Our experimental evaluation demonstrates that the system achieves accurate control targets and promptly responses to environment dynamics. The wireless sensor network effectively supports the system needs with long system lifespan. Compared with traditional HVAC systems, our system is of much higher energy efficiency, as measured by the standard Coefficient of Performance (COP) metric.

This paper exploits smartphones to design a transportation activity survey system that investigates when, where and how people travel in an urban area. In such a system, the essential requirement is collecting and processing big data which will raise two critical issues, energy-conservation and scalability. To address the former issue, the GPS sleeping interval of a smart-phone is controlled by the back-end servers adaptively based on the real-time moving speed and transportation modes. To address the latter issue, we consider MapReduce to design the back-end Cloud, where intelligent learning and classification algorithms are implemented to detect the stops and transportation modes and provide smartphones with an appropriate GPS sleeping interval based on the GPS statistics on the back-end Cloud. The unique feature of our system is to integrate participatory sensing and Cloud-enabled processing system closely which incorporates knowledge extracted from the Cloud (i.e., transportation modes) into sensing control of smartphones. In this way, sensing control could be optimized through the knowledge behind crowdsourced data. Our system has been deployed in Singapore to support the Land Transport Authority's transportation activity survey over 1 year. Extensive experimental results indicate that our system can reduce the energy consumption of smartphones efficiently and process concurrent data arrival from a huge number of users.

The ability to link two physical smart spaces in real-time, and apply the knowledge acquired from designing and managing the small densely instrumented space to the larger and less densely monitored space opens up a whole host of possibilities in terms of how architects and engineers approach building design, building environment modeling, energy resource optimization, and building control. We leverage and extend the EcoSense framework to link smart spaces. This provides the ability to `scale' environmental profiles derived from optimized simulation models and verified in a small physical building testbed to a much larger building space with relatively fewer sensors. We have implemented a prototype linkage framework connecting two real physical building testbeds to validate this concept.

The UrbanMobSense is a mobility sensing system designed to collect the travel patterns of people for transportation planning purposes. This system makes use of smartphones with various built-in sensors to capture human mobility automatically in a non-intrusive manner. To improve the user experience of smartphone users, the system is designed to be user-centric to address energy conservation and privacy preservation issues. The UrbanMobSense has been deployed in Singapore to support the Land Transport Authority (LTA)'s household travel survey.

Transportation activity surveys investigate when, where and how people travel in urban areas to provide information necessary for urban transportation planning. In Singapore, the Land Transport Authority (LTA) carries out such a survey amongst households every four years. The survey is conducted through conventional questionnaires and travel diaries. However, the conventional surveys are problematic and error-prone. We are developing a smartphone-based transportation activity survey system to replace the traditional household surveys, which can potentially be used by LTA in future.

With rapid development of sensor technologies and wireless network infrastructure, research and development of traffic related applications, such as real time traffic map and on-demand travel route recommendation have attracted much more attentions than ever before. Both archived and real-time data involved in these applications could potentially be very big, depending on the number of deployed sensors. Emerging Cloud infrastructure can elastically handle such big data and conveniently providing nearly unlimited computing and storage resources to hosted applications, to carry out analysis not only for long-term planning and decision making, but also analytics for near real-time decision support. In this paper, we propose Smart Traffic Cloud, a software infrastructure to enable traffic data acquisition, and manage, analyze and present the results in a flexible, scalable and secure manner using a Cloud platform. The proposed infrastructure handles distributed and parallel data management and analysis using ontology database and the popular Map-Reduce framework. We have prototyped the infrastructure in a commercial Cloud platform and we developed a real-time traffic condition map using data collected from commuters' mobile phones.

Accurate vehicular localization is important for various cooperative vehicle safety (CVS) applications such as collision avoidance, turning assistant, etc. In this paper, we propose a cooperative vehicular distance measurement technique based on the sharing of GPS pseudorange measurements and a weighted least squares method. The classic double difference pseudorange solution, which was originally designed for high-end survey level GPS systems, is adapted to low-end navigation level GPS receivers for its wide availability in ground vehicles. The Carrier to Noise Ratio (CNR) of raw pseudorange measurements are taken into account for noise mitigation. We present a Dedicated Short Range Communications (DSRC) based mechanism to implement the exchange of pseudorange information among neighboring vehicles. As demonstrated in field tests, our proposed technique increases the accuracy of the distance measurement significantly compared with the distance obtained from the GPS fixes.

Abstract Broadcast is a fundamental operation in wireless ad hoc and sensor networks. This paper proposes a simple and efficient broadcast protocol, called Broadcast based on Layered Diffusion (BLD), which is a stateless broadcast protocol with very low message overheads and very low computation requirements. In BLD, nodes do not need to exchange neighborhood information for building a broadcast backbone, but make their rebroadcast decisions locally. The design idea of BLD is to emulate the triangular tessellation for complete area coverage of the network field. It does not require nodes' location information for such tessellation emulation but only exploits the hop count information for each node to make local rebroadcast decision and timing. Simulation results show the effectiveness and efficiency of the BLD protocol and its robustness to localization errors and transmission errors. Copyright © 2009 John Wiley & Sons, Ltd.

With recent advances in technology, various wearable sensors have been developed for the monitoring of human physiological parameters. A Body Sensor Network (BSN) consisting of such physiological and biomedical sensor nodes placed on, near or within a human body can be used for real-time health monitoring. In this paper, we describe an on-going effort to develop a system consisting of interconnected BSNs for real-time health monitoring of soldiers. We discuss the background and an application scenario for this project. We describe the preliminary prototype of the system and present a blast source localization application.

Global climate change can be mitigated with innovative solutions in the ambient intelligence domain that utilize distributed and heterogeneous sensor networks for high resolution sensing of resource utilization (e.g. energy, water, etc.) via intelligent coordination among sensor nodes. We propose the EcoSense framework as a holistic solution that enables dynamic control and reconfiguration of the sensor nodes in an ambient environment based on our resource consumption patterns. EcoSense introduces service and context layers to existing sensor network stacks to enable interoperability between heterogeneous sensor node platforms. A prototype of the framework for an eco-aware smart space has been developed as a pervasive infrastructure for monitoring and minimizing resource consumption at individual levels and also for building eco-aware homes and workspaces.

Localization is an important research problem in WSNs (wireless sensor networks) and many WSN localization algorithms have been proposed in the literature. For a single sensor node whose anchors' positions are known, the location estimation error lower bound can be computed by using the CRLB (Cramer-Rao Lower Bound). However, it is still unclear to the research community what the localization error lower bound is from a network point of view. In this paper, we derive a lower bound of the expected localization error for a network whose sensor nodes and anchors are randomly distributed according to a Poisson point process. We show that the lower bound of the expected localization error for a network is a function of the anchor density and the variance of anchor-to-sensor distance measurements.

In this paper, we present an overview of a system that we are developing for the continuous monitoring of water distribution systems in Singapore. We discuss the design and implementation of the system that enables real-time detection of pipe burst events.

In energy-limited wireless sensor networks, network clustering and sensor scheduling are two efficient techniques for minimizing node energy consumption and maximizing network coverage lifetime. When integrating the two techniques, the challenges are how to select cluster heads and active nodes. In this paper, we propose a coverage-aware clustering protocol. In the proposed protocol, we define a cost metric that favors those nodes being more energy-redundantly covered as better candidates for cluster heads and select active nodes in a way that tries to emulate the most efficient tessellation for area coverage. Our simulation results show that the network coverage lifetime can be significantly improved compared with an existing protocol.

Based on our proposed semantics-based service-oriented model, we are building a large-scale sensor grid infrastructure that can seamlessly integrate heterogeneous sensor resources from different projects distributed across a wide geographical area. This model has several important features. First, it connects heterogeneous sensor resources via the Internet to automatically collect and aggregate sensor data in real-time. Second, the varied types and formats of sensor data are linked to the ontology of the respective sensor resource and the data is converted into a standard ontology-based (OWL) format in order to bind data semantics with the data for ease of access and processing. Third, the sensor data are stored in a large-scale distributed data archive whose schema is also derived from the common concepts found in the ontologies of various sensor nodes. Fourth, the sensor grid integrates computational resources for the compute-intensive processing of sensor data. Fifth, the sensor data can be conveniently accessed and shared via the Web through mash-ups, blogs and Web-services such as Google Earth and Microsoft's SensorMap. We are developing techniques and tools to publish, query, process, visualize, archive and search the vast amount of sensor data efficiently.

Localization is one of the most important research issues in wireless sensor networks (WSNs). Hop-count based localization has been proposed as a cost-effective alternative to the expensive hardware-based localization schemes. In this paper, we propose a new method to estimate distances between anchors and nodes based on hop-count information. Localization is then achieved using the estimated distances. Simulation results show that the performance of our proposed algorithm is much better than that of the DV-hop algorithm in terms of the node-to- anchor distance estimation and localization accuracy, and the improvement is proportional to the node density.

The middleware for a cyber-physical system is crucial as it tightly integrates computation with physical processes to achieve better reliability, distributed coordination, higher precision and efficiency, and better autonomous control. In this work, we design and develop the cyber-physical middleware framework for a large-scale water distribution system monitoring effort.

At present, the sensor node platforms from different vendors cannot easily interoperate with each other due to their heterogeneous programming environments, communication stacks and data management protocols. In this work, we develop a sensor network virtualization framework that will enable the seamless interoperability and scalability of heterogeneous sensor node platforms.

The rapid developments in wireless communications, embedded systems and MEMS sensor technologies have boosted the research and development on large-scale wireless sensor networks (WSNs). WSNs have very broad application areas such as security surveillance, environmental monitoring and healthcare monitoring. The paper presents a novel simulation framework called WSN middleware service modules simulation platform (WISDOM). The WISDOM simulator is written in Java and it uses the recursive porous agent simulation toolkit (Repast)O as the simulation engine to perform discrete event-driven simulations. WISDOM was used in WSN research (Wang et al.,2007) to simulate and verify middleware services for routing, sensing activity scheduling, group formation and management, target detection and tracking, and collaborative classification and fusion in a wireless sensor network. The versatility and performance of WISDOM for middleware service protocol development and evaluation have proven to be valuable.

In recent years, wireless sensor networks have received great interest with rapid developments in both hardware and software aspects. However, there is always a problem with limited energy supply for the tiny sensor node. In wireless imaging sensor network in which imaging devices are utilized, the bottleneck of energy consumption efficiency is even more serious. The main activities of such imaging networks are related to image data transmission over wireless channel. Therefore it is necessary to seek a good balance between the image quality and energy conservation. We propose an energy-efficient control scheme based on recent dynamic range coding techniques. The simulation results are presented with emphasis on prolonging the sensor network lifetime.

In this paper, we present the idea that large-scale ambient intelligence takes the vision of anytime-anywhere to anytime-anywhere-anything (A3). Based on this vision, we argue that a mix of computing, communication and interface technologies remains limited in providing seamless access to services if the data and services from various autonomously operating entities remain non-sharable. Thus, we propose a sensor grid infrastructure that forms the key resource sharing backbone and provides secure access to valuable sensor, computational, data, and storage resources for supporting large-scale ambient intelligence.

Target tracking is one of the very important applications of WSNs (Wireless Sensor Networks). Traditionally, Kalman filter [1] and its derivatives [2, 3] are some of the most popular algorithms in solving the signal tracking problem. In a WSNs tracking application, the target motion/state update dynamics might be linear or nonlinear depending on the specific scenario. The observation model might vary across the sampling interval. This paper compares the effectiveness, limitations and other related implementation issues in applying Kalman filter and extended Kalman filter to tackle target tracking problem in WSNs.

10.1109/CCNC.2007.125

When there are multiple queries posed to the resource-constrained wireless sensor network, it is critical to process them efficiently. In this paper, we propose a two-tier multiple query optimization (TTMQO) scheme. The first tier, called base station optimization, adopts a cost-based approach to rewrite a set of queries into an optimized set that shares the commonality and eliminates the redundancy among the queries in the original set. The optimized queries are then injected into the wireless sensor network. In the second tier, called in-network optimization, our scheme efficiently delivers query results by taking advantage of the broadcast nature of the radio channel and sharing the sensor readings among similar queries over time and space at a finer granularity. Our experimental results indicate that our proposed TTMQO scheme offers significant improvements over the traditional single query optimization technique.

Our goal is to design a light-weight but effective scheme to support multiple data acquisition and aggregation queries in a wireless sensor network, in order to minimize the number of radio transmissions. Apart from being much more powerful than sensor nodes, the base station is also the interface of a wireless sensor network. Thus, we use the base station as a filter to reduce duplicate data accesses from the sensor network, and as a screen to hide the query dynamics as much as possible. We design a two-tier optimization scheme, base station optimization and in-network optimization.

Coverage is an important performance metric for many applications such as surveillance in Wireless Sensor Networks (WSNs). Coverage control is used to select as few active nodes as possible from all deployed sensor nodes such that sufficient coverage of the monitored area can be guaranteed, while reducing the energy consumption of each individual sensor node to prolong the network lifetime. This paper presents a distributed and localized technique called the Layered Diffusion based Coverage Control (LDCC). The LDCC protocol does not require information on the node location coordinates when selecting active nodes. Instead, it exploits the hop count information, which is easily obtained in a WSN, to select active sensor nodes. Furthermore, the LDCC protocol is very simple and does not require any sophisticated computation such as distance or covered area computation. Our simulation results show that the LDCC protocol achieves high coverage ratio while incurring very low message overhead compared with other existing protocols.

With the rapid advances in technologies such as MEMS sensors, low-power embedded processing and wireless networking, sensor networks are becoming more powerful in terms of data acquisition and processing capabilities. Sensor networks can now be deployed in the physical world for various important applications such as environmental monitoring, weather monitoring and modeling, military surveillance, healthcare monitoring, tracking of goods and manufacturing processes, smart homes and offices, etc.

Sensor networks are widely used in many applications for collecting information from the physical environment. In these applications, it is usually necessary to track the relationships between sensor data readings within a time window to detect events of interest. However, it is difficult to detect such events by using the common aggregate or selection queries. We address the problem of processing window self-join in order to detect events of interest. Self-joins are useful in tracking correlations between different sensor readings, which can indicate an event of interest. We propose the Two-Phase Self-Join (TPSJ) scheme to efficiently evaluate self-join queries for event detection in sensor networks. Our TPSJ scheme takes advantage of the properties of the events and carries out data filtering during in-network processing. We discuss TPSJ execution with one window and we extend it for continuous event monitoring. Our experimental evaluation results indicate that the TPSJ scheme is effective in reducing the amount of radio transmissions during event detection.

The performance of the instruction memory hierarchy is of crucial importance in embedded systems. In this paper, we propose a reconfigurable instruction memory hierarchy for embedded systems whose architectural parameters can be customized for specific applications. The proposed instruction memory hierarchy consists of an instruction cache and a scratchpad memory (SPM). We propose an algorithm to manage this instruction memory hierarchy and optimize its performance. Given a fixed amount of reconfigurable on-chip storage resources and an application, our algorithm determines the sizes of the SPM and the instruction cache to best suit the application. It analyzes the application, partitions the available storage resources into SPM and cache, and assigns instructions to them. Our algorithm aims to reduce the instruction fetch miss rate, improve the system performance, and reduce the energy consumption. We have implemented this reconfigurable instruction memory hierarchy on the Altera Nios II FPGA platform. Our experimental results using five benchmarks from the MediaBench and the MiBench suites show that our proposed architecture provides significant performance improvements and energy reduction.

Wireless sensor networks have emerged as an exciting technology for a wide range of important applications that acquire and process information from the physical world. Grid computing has evolved as a standards-based approach for coordinated resource sharing. Sensor grids combine these two promising technologies by extending the grid computing paradigm to the sharing of sensor resources in wireless sensor networks. There are several issues and challenges in the design of sensor grids. In this paper, we propose a sensor grid architecture, called the scalable proxy-based architecture for sensor grid (SPRING), to address these design issues. We also developed a sensor grid testbed to study the design issues of sensor grids and to improve our sensor grid architecture design

Structured peer-to-peer systems can be organized hierarchically as two-level overlay networks. The top-level overlay consists of groups of nodes, where each group is identified by a group identifier. In each group, one or more nodes are designated as supernodes and act as gateways to the nodes at the second level. A collision occurs during join operations, when two or more groups with the same group identifier are created at the top-level overlay. Collisions increase the lookup path length and the stabilization overhead, and reduce the scalability of hierarchical peer-to-peer systems. We propose a new scheme to detect and resolve collisions, and we study the impact of the collision problem on the performance of peer-to-peer systems. Our simulation results show the effectiveness of our scheme in reducing collisions and maintaining the size of the top-level overlay close to the ideal size.

Cache coherence enforcement and memory latency reduction and hiding are very important and challenging problems in the design of large-scale distributed shared-memory (DSM) multiprocessors. We propose an integrated framework to solve these problems through a compiler-directed cache coherence scheme called the Cache Coherence with Data Prefetching (CCDP) scheme. The CCDP scheme enforces cache coherence by prefetching the potentially stale references in a parallel program. It also prefetches the nonstale references to hide their memory latencies. To optimize the performance of the CCDP scheme, some prefetch hardware support is provided to efficiently handle these two forms of data prefetching operations. We also developed the compiler techniques utilized by the CCDP scheme for stale reference detection, prefetch target analysis and prefetch scheduling. We evaluated the performance of the CCDP scheme via execution-driven simulations of several applications from the SPEC CFP95 and the Perfect benchmark suites. The simulation results show that the CCDP scheme provides significant performance improvements for the applications studied.