Garnet: a middleware architecture for distributing data streams originating in wireless sensor networks

We present an architectural framework, Garnet, which provides a data stream centric abstraction to encourage the manipulation and exploitation of data generated in sensor networks. By providing middleware services to allow mutually-unaware applications to manipulate sensor behaviour, a scalable, extensible platform is provided. We focus on sensor networks with transmit and receive capabilities as this combination poses greater challenges for managing and distributing sensed data. Our approach allows simple and sophisticated sensors to coexist, and allows data consumers to be mutually unaware of each other This also promotes the use of middleware services to mediate among consumers with potentially conflicting demands for shared data. Garnet has been implemented in Java, and we report on our progress to date and outline some likely scenarios where the use of our distributed architecture and accompanying middleware support enhances the task of sharing data in sensor network environments

Medians and Beyond: New Aggregation Techniques for Sensor Networks

Wireless sensor networks offer the potential to span and monitor large geographical areas inexpensively. Sensors, however, have significant power constraint (battery life), making communication very expensive. Another important issue in the context of sensor-based information systems is that individual sensor readings are inherently unreliable. In order to address these two aspects, sensor database systems like TinyDB and Cougar enable in-network data aggregation to reduce the communication cost and improve reliability. The existing data aggregation techniques, however, are limited to relatively simple types of queries such as SUM, COUNT, AVG, and MIN/MAX. In this paper we propose a data aggregation scheme that significantly extends the class of queries that can be answered using sensor networks. These queries include (approximate) quantiles, such as the median, the most frequent data values, such as the consensus value, a histogram of the data distribution, as well as range queries. In our scheme, each sensor aggregates the data it has received from other sensors into a fixed (user specified) size message. We provide strict theoretical guarantees on the approximation quality of the queries in terms of the message size. We evaluate the performance of our aggregation scheme by simulation and demonstrate its accuracy, scalability and low resource utilization for highly variable input data sets

Progressive Processing of Continuous Range Queries in Hierarchical Wireless Sensor Networks

In this paper, we study the problem of processing continuous range queries in a hierarchical wireless sensor network. Contrasted with the traditional approach of building networks in a "flat" structure using sensor devices of the same capability, the hierarchical approach deploys devices of higher capability in a higher tier, i.e., a tier closer to the server. While query processing in flat sensor networks has been widely studied, the study on query processing in hierarchical sensor networks has been inadequate. In wireless sensor networks, the main costs that should be considered are the energy for sending data and the storage for storing queries. There is a trade-off between these two costs. Based on this, we first propose a progressive processing method that effectively processes a large number of continuous range queries in hierarchical sensor networks. The proposed method uses the query merging technique proposed by Xiang et al. as the basis and additionally considers the trade-off between the two costs. More specifically, it works toward reducing the storage cost at lower-tier nodes by merging more queries, and toward reducing the energy cost at higher-tier nodes by merging fewer queries (thereby reducing "false alarms"). We then present how to build a hierarchical sensor network that is optimal with respect to the weighted sum of the two costs. It allows for a cost-based systematic control of the trade-off based on the relative importance between the storage and energy in a given network environment and application. Experimental results show that the proposed method achieves a near-optimal control between the storage and energy and reduces the cost by 0.989~84.995 times compared with the cost achieved using the flat (i.e., non-hierarchical) setup as in the work by Xiang et al.Comment: 41 pages, 20 figure

Enabling stream processing for people-centric IoT based on the fog computing paradigm

The world of machine-to-machine (M2M) communication is gradually moving from vertical single purpose solutions to multi-purpose and collaborative applications interacting across industry verticals, organizations and people - A world of Internet of Things (IoT). The dominant approach for delivering IoT applications relies on the development of cloud-based IoT platforms that collect all the data generated by the sensing elements and centrally process the information to create real business value. In this paper, we present a system that follows the Fog Computing paradigm where the sensor resources, as well as the intermediate layers between embedded devices and cloud computing datacenters, participate by providing computational, storage, and control. We discuss the design aspects of our system and present a pilot deployment for the evaluating the performance in a real-world environment. Our findings indicate that Fog Computing can address the ever-increasing amount of data that is inherent in an IoT world by effective communication among all elements of the architecture

The Meeting of Acquaintances: A Cost-efficient Authentication Scheme for Light-weight Objects with Transient Trust Level and Plurality Approach

Wireless sensor networks consist of a large number of distributed sensor nodes so that potential risks are becoming more and more unpredictable. The new entrants pose the potential risks when they move into the secure zone. To build a door wall that provides safe and secured for the system, many recent research works applied the initial authentication process. However, the majority of the previous articles only focused on the Central Authority (CA) since this leads to an increase in the computation cost and energy consumption for the specific cases on the Internet of Things (IoT). Hence, in this article, we will lessen the importance of these third parties through proposing an enhanced authentication mechanism that includes key management and evaluation based on the past interactions to assist the objects joining a secured area without any nearby CA. We refer to a mobility dataset from CRAWDAD collected at the University Politehnica of Bucharest and rebuild into a new random dataset larger than the old one. The new one is an input for a simulated authenticating algorithm to observe the communication cost and resource usage of devices. Our proposal helps the authenticating flexible, being strict with unknown devices into the secured zone. The threshold of maximum friends can modify based on the optimization of the symmetric-key algorithm to diminish communication costs (our experimental results compare to previous schemes less than 2000 bits) and raise flexibility in resource-constrained environments.Comment: 27 page

Energy-aware routing protocols in wireless sensor networks

Saving energy and increasing network lifetime are significant challenges in the field of Wireless Sensor Networks (WSNs). Energy-aware routing protocols have been introduced for WSNs to overcome limitations of WSN including limited power resources and difficulties renewing or recharging sensor nodes batteries. Furthermore, the potentially inhospitable environments of sensor locations, in some applications, such as the bottom of the ocean, or inside tornados also have to be considered. ZigBee is one of the latest communication standards designed for WSNs based on the IEEE 802.15.4 standard. The ZigBee standard supports two routing protocols, the Ad hoc On-demand Distance Vector (AODV), and the cluster-tree routing protocols. These protocols are implemented to establish the network, form clusters, and transfer data between the nodes. The AODV and the cluster-tree routing protocols are two of the most efficient routing protocols in terms of reducing the control message overhead, reducing the bandwidth usage in the network, and reducing the power consumption of wireless sensor nodes compared to other routing protocols. However, neither of these protocols considers the energy level or the energy consumption rate of the wireless sensor nodes during the establishment or routing processes. (Continues...)

SysMART Indoor Services: A System of Smart and Connected Supermarkets

Smart gadgets are being embedded almost in every aspect of our lives. From smart cities to smart watches, modern industries are increasingly supporting the Internet of Things (IoT). SysMART aims at making supermarkets smart, productive, and with a touch of modern lifestyle. While similar implementations to improve the shopping experience exists, they tend mainly to replace the shopping activity at the store with online shopping. Although online shopping reduces time and effort, it deprives customers from enjoying the experience. SysMART relies on cutting-edge devices and technology to simplify and reduce the time required during grocery shopping inside the supermarket. In addition, the system monitors and maintains perishable products in good condition suitable for human consumption. SysMART is built using state-of-the-art technologies that support rapid prototyping and precision data acquisition. The selected development environment is LabVIEW with its world-class interfacing libraries. The paper comprises a detailed system description, development strategy, interface design, software engineering, and a thorough analysis and evaluation.Comment: 7 pages, 11 figur