Energy-efficient query management scheme for a wireless sensor database system

Minimizing the communication overhead to reduce the energy consumption is an essential consideration in sensor network applications, and existing research has mostly concentrated on data aggregation and in-network processing. However, effective query management to optimize the query aggregation plan at the gateway side is also a significant approach to energy saving in practice. In this paper, we present a multiquery management framework to support historical and continuous queries, where the key idea is to reduce common tasks in a collection of queries through merging and aggregation, according to query region, attribute, time duration, and frequency, by executing the common subqueries only once. In this framework, we propose a query management scheme to support query partitioning, region aggregation and approximate processing, time partitioning and aggregation rules, multirate queries, and historical database. In order to validate the performance of our algorithm, a heuristic routing protocol is also described. The performance simulation results show that the overall energy consumption for forwarding and answering a collection of queries can be significantly reduced by applying our query management scheme. The advantages and disadvantages of the proposed scheme are discussed, together with open research issues

BALANCING PRIVACY, PRECISION AND PERFORMANCE IN DISTRIBUTED SYSTEMS

Privacy, Precision, and Performance (3Ps) are three fundamental design objectives in distributed systems. However, these properties tend to compete with one another and are not considered absolute properties or functions. They must be defined and justified in terms of a system, its resources, stakeholder concerns, and the security threat model. To date, distributed systems research has only considered the trade-offs of balancing privacy, precision, and performance in a pairwise fashion. However, this dissertation formally explores the space of trade-offs among all 3Ps by examining three representative classes of distributed systems, namely Wireless Sensor Networks (WSNs), cloud systems, and Data Stream Management Systems (DSMSs). These representative systems support large part of the modern and mission-critical distributed systems. WSNs are real-time systems characterized by unreliable network interconnections and highly constrained computational and power resources. The dissertation proposes a privacy-preserving in-network aggregation protocol for WSNs demonstrating that the 3Ps could be navigated by adopting the appropriate algorithms and cryptographic techniques that are not prohibitively expensive. Next, the dissertation highlights the privacy and precision issues that arise in cloud databases due to the eventual consistency models of the cloud. To address these issues, consistency enforcement techniques across cloud servers are proposed and the trade-offs between 3Ps are discussed to help guide cloud database users on how to balance these properties. Lastly, the 3Ps properties are examined in DSMSs which are characterized by high volumes of unbounded input data streams and strict real-time processing constraints. Within this system, the 3Ps are balanced through a proposed simple and efficient technique that applies access control policies over shared operator networks to achieve privacy and precision without sacrificing the systems performance. Despite that in this dissertation, it was shown that, with the right set of protocols and algorithms, the desirable 3P properties can co-exist in a balanced way in well-established distributed systems, this dissertation is promoting the use of the new 3Ps-by-design concept. This concept is meant to encourage distributed systems designers to proactively consider the interplay among the 3Ps from the initial stages of the systems design lifecycle rather than identifying them as add-on properties to systems

Rate-distortion Balanced Data Compression for Wireless Sensor Networks

This paper presents a data compression algorithm with error bound guarantee for wireless sensor networks (WSNs) using compressing neural networks. The proposed algorithm minimizes data congestion and reduces energy consumption by exploring spatio-temporal correlations among data samples. The adaptive rate-distortion feature balances the compressed data size (data rate) with the required error bound guarantee (distortion level). This compression relieves the strain on energy and bandwidth resources while collecting WSN data within tolerable error margins, thereby increasing the scale of WSNs. The algorithm is evaluated using real-world datasets and compared with conventional methods for temporal and spatial data compression. The experimental validation reveals that the proposed algorithm outperforms several existing WSN data compression methods in terms of compression efficiency and signal reconstruction. Moreover, an energy analysis shows that compressing the data can reduce the energy expenditure, and hence expand the service lifespan by several folds.Comment: arXiv admin note: text overlap with arXiv:1408.294

Data Management and Wireless Transport for Large Scale Sensor Networks

Today many large scale sensor networks have emerged, which span many different sensing applications. Each of these sensor networks often consists of millions of sensors collecting data and supports thousands of users with diverse data needs. Between users and wireless sensors there are often a group of powerful servers that collect and process data from sensors and answer users\u27 requests. To build such a large scale sensor network, we have to answer two fundamental research problems: i) what data to transmit from sensors to servers? ii) how to transmit the data over wireless links? Wireless sensors often can not transmit all collected data due to energy and bandwidth constraints. Therefore sensors need to decide what data to transmit to best satisfy users\u27 data requests. Sensor network users can often tolerate some data errors, thus sensors may transmit data in lower fidelity but still satisfy users\u27 requests. There are generally two types of requests-raw data requests and meta-data requests. To answer users\u27 raw data requests, we propose a model-driven data collection approach, PRESTO. PRESTO splits intelligence between sensors and servers, i.e., resource-rich servers perform expensive model training and resource-poor sensors perform simple model evaluation. PRESTO can significantly reduce data to be transmitted without sacrificing service quality. To answer users\u27 meta-data request, we propose a utility-driven multi-user data sharing approach, MUDS. MUDS uses utility function to unify diverse meta-data metrics. Sensors estimate utility value of each data packet and sends packets with highest utility first to improve overall system utility. After deciding what data to transmit from sensors to servers, the next question is how to transmit these data over wireless links. Wireless transport often suffers low bandwidth and unstable connectivity. In order to improve wireless transport, I propose a clean-slate re-design of wireless transport, Hop. Hop uses reliable perhop block transfer as a building block and builds all other components including hidden-terminal avoidance, congestion avoidance, and end-to-end reliability on top of it. Hop is built based on three key ideas: a) hop-by-hop transfer adapts to the lossy and highly variable nature of wireless channel significantly better than end-to-end transfer, b) the use of blocks as the unit of control is more efficient over wireless links than the use of packets, and c) the duplicated functionality in different layers in the network stack should be removed to simplify the protocol and avoid complex interaction

Resilient networking in wireless sensor networks

This report deals with security in wireless sensor networks (WSNs), especially in network layer. Multiple secure routing protocols have been proposed in the literature. However, they often use the cryptography to secure routing functionalities. The cryptography alone is not enough to defend against multiple attacks due to the node compromise. Therefore, we need more algorithmic solutions. In this report, we focus on the behavior of routing protocols to determine which properties make them more resilient to attacks. Our aim is to find some answers to the following questions. Are there any existing protocols, not designed initially for security, but which already contain some inherently resilient properties against attacks under which some portion of the network nodes is compromised? If yes, which specific behaviors are making these protocols more resilient? We propose in this report an overview of security strategies for WSNs in general, including existing attacks and defensive measures. In this report we focus at the network layer in particular, and an analysis of the behavior of four particular routing protocols is provided to determine their inherent resiliency to insider attacks. The protocols considered are: Dynamic Source Routing (DSR), Gradient-Based Routing (GBR), Greedy Forwarding (GF) and Random Walk Routing (RWR)

Unified Role Assignment Framework For Wireless Sensor Networks

Wireless sensor networks are made possible by the continuing improvements in embedded sensor, VLSI, and wireless radio technologies. Currently, one of the important challenges in sensor networks is the design of a systematic network management framework that allows localized and collaborative resource control uniformly across all application services such as sensing, monitoring, tracking, data aggregation, and routing. The research in wireless sensor networks is currently oriented toward a cross-layer network abstraction that supports appropriate fine or course grained resource controls for energy efficiency. In that regard, we have designed a unified role-based service paradigm for wireless sensor networks. We pursue this by first developing a Role-based Hierarchical Self-Organization (RBSHO) protocol that organizes a connected dominating set (CDS) of nodes called dominators. This is done by hierarchically selecting nodes that possess cumulatively high energy, connectivity, and sensing capabilities in their local neighborhood. The RBHSO protocol then assigns specific tasks such as sensing, coordination, and routing to appropriate dominators that end up playing a certain role in the network. Roles, though abstract and implicit, expose role-specific resource controls by way of role assignment and scheduling. Based on this concept, we have designed a Unified Role-Assignment Framework (URAF) to model application services as roles played by local in-network sensor nodes with sensor capabilities used as rules for role identification. The URAF abstracts domain specific role attributes by three models: the role energy model, the role execution time model, and the role service utility model. The framework then generalizes resource management for services by providing abstractions for controlling the composition of a service in terms of roles, its assignment, reassignment, and scheduling. To the best of our knowledge, a generic role-based framework that provides a simple and unified network management solution for wireless sensor networks has not been proposed previously

Cooperative models for synchronization, scheduling and transmission in large scale sensor networks: An overview

[[abstract]]What is the difference between classical remote sensing and sensor networks? What kind of data models that one can assume in the context of sensor networks? Can the sensors in the network concurrelty contribute to the sensing objective, without creating network conflicts ? It is becoming apparent that methodologies designed to resolve network resource allocation conflicts in the communications among open systems have several bottlenecks when applied to sustain networkign among concurrent sensing nodes. Can we structure the network activities so that they are always directly beneficial to the sensing task? The goal of this paper is to articulate these questions and indicate how some resource allocation conflicts can be removed embracing colaborative networking approaches among the sensors. © 2005 IEEE.[[fileno]]2030137030009[[department]]電機工程學