Multi-dimensional data indexing and range query processing via Voronoi diagram for internet of things

In a typical Internet of Things (IoT) deployment such as smart cities and Industry 4.0, the amount of sensory data collected from physical world is significant and wide-ranging. Processing large amount of real-time data from the diverse IoT devices is challenging. For example, in IoT environment, wireless sensor networks (WSN) are typically used for the monitoring and collecting of data in some geographic area. Spatial range queries with location constraints to facilitate data indexing are traditionally employed in such applications, which allows the querying and managing the data based on SQL structure. One particular challenge is to minimize communication cost and storage requirements in multi-dimensional data indexing approaches. In this paper, we present an energy- and time-efficient multidimensional data indexing scheme, which is designed to answer range query. Specifically, we propose data indexing methods which utilize hierarchical indexing structures, using binary space partitioning (BSP), such as kd-tree, quad-tree, k-means clustering, and Voronoi-based methods to provide more efficient routing with less latency. Simulation results demonstrate that the Voronoi Diagram-based algorithm minimizes the average energy consumption and query response time

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

Efficient and Error-bounded Spatiotemporal Quantile Monitoring in Edge Computing Environments

Underlying many types of data analytics, a spatiotemporal quantile monitoring (SQM) query continuously returns the quantiles of a dataset observed in a spatiotemporal range. In this paper, we study SQM in an Internet of Things (IoT) based edge computing environment, where concurrent SQM queries share the same infrastructure asynchronously. To minimize query latency while providing result accuracy guarantees, we design a processing framework that virtualizes edge-resident data sketches for quantile computing. In the framework, a coordinator edge node manages edge sketches and synchronizes edge sketch processing and query executions. The co-ordinator also controls the processed data fractions of edge sketches, which helps to achieve the optimal latency with error-bounded results for each single query. To support concurrent queries, we employ a grid to decompose queries into subqueries and process them efficiently using shared edge sketches. We also devise a relaxation algorithm to converge to optimal latencies for those subqueries whose result errors are still bounded. We evaluate our proposals using two high-speed streaming datasets in a simulated IoT setting with edge nodes. The results show that our proposals achieve efficient, scalable, and error-bounded SQM

In-network data acquisition and replication in mobile sensor networks

This paper assumes a set of n mobile sensors that move in the Euclidean plane as a swarm. Our objectives are to explore a given geographic region by detecting and aggregating spatio-temporal events of interest and to store these events in the network until the user requests them. Such a setting finds applications in mobile environments where the user (i.e., the sink) is infrequently within communication range from the field deployment. Our framework, coined SenseSwarm, dynamically partitions the sensing devices into perimeter and core nodes. Data acquisition is scheduled at the perimeter, in order to minimize energy consumption, while storage and replication takes place at the core nodes which are physically and logically shielded to threats and obstacles. To efficiently identify the nodes laying on the perimeter of the swarm we devise the Perimeter Algorithm (PA), an efficient distributed algorithm with a low communication complexity. For storage and fault-tolerance we devise the Data Replication Algorithm (DRA), a voting-based replication scheme that enables the exact retrieval of values from the network in cases of failures. We also extend DRA with a spatio-temporal in-network aggregation scheme based on minimum bounding rectangles to form the Hierarchical-DRA (HDRA) algorithm, which enables the approximate retrieval of events from the network. Our trace-driven experimentation shows that our framework can offer significant energy reductions while maintaining high data availability rates. In particular, we found that when failures across all nodes are less than 60%, our framework can recover over 80% of detected values exactly

Hierarchical Spatial Gossip for Multiresolution Representations in Sensor Networks

In this paper we propose a lightweight algorithm for constructing multi-resolution data representations for sensor networks. At each sensor node u, we compute, O(logn) aggregates about exponentially enlarging neighborhoods centered at u. The ith aggregate is the aggregated data from nodes approximately within 2 i hops of u. We present a scheme, named the hierarchical spatial gossip algorithm, to extract and construct these aggregates, for all sensors simultaneously, with a total communication cost of O(npolylogn). The hierarchical gossip algorithm adopts atomic communication steps with each node choosing to exchange information with a node distance d away with probability ∼ 1/d 3. The attractiveness of the algorithm attributes to its simplicity, low communication cost, distributed nature and robustness to node failures and link failures. We show in addition that computing multi-resolution aggregates precisely (i.e., each aggregate uses all and only the nodes within 2 i hops) requires a communication cost of Ω(n √ n), which does not scale well with network size. An approximate range in aggregate computation like that introduced by the gossip mechanism is therefore necessary in a scalable efficient algorithm. Besides the natural applications of multi-resolution data summaries in data validation and information mining, we also demonstrate the application of the pre-computed multi-resolution data summaries in answering range queries efficiently

Combined Coverage Area Reporting and Geographical Routing in Wireless Sensor-Actuator Networks for Cooperating with Unmanned Aerial Vehicles

In wireless sensor network (WSN) applications with multiple gateways, it is key to route location dependent subscriptions efficiently at two levels in the system. At the gateway level, data sinks must not waste the energy of the WSN by injecting subscriptions that are not relevant for the nodes in their coverage area and at WSN level, energy-efficient delivery of subscriptions to target areas is required. In this paper, we propose a mechanism in which (1) the WSN provides an accurate and up-to-date coverage area description to gateways and (2) the wireless sensor network re-uses the collected coverage area information to enable efficient geographical routing of location dependent subscriptions and other messages. The latter has a focus on routing of messages injected from sink nodes to nodes in the region of interest. Our proposed mechanisms are evaluated in simulation