A Query Processing Framework for Efficient Network Resource Utilization in Shared Sensor Networks

Abstract

Shared Sensor Network (SSN) refers to a scenario where the same sensing and communication resources are shared and queried by multiple Internet applications. Due to the burgeoning growth in Internet applications, multiple application queries can exhibit overlapping in their functional requirements, such as the region of interest, sensing attributes, and sensing time duration. This overlapping results in redundant sensing tasks generation leading to the increased overall network traffic and energy consumption. Existing approaches operate on data sharing among various tasks to minimize the upstream traffic. However, no existing work attempts to prevent the redundant task generation to reduce the downstream traffic. Moreover, the allocation of suitable sensor nodes to meet the Quality of Service (QoS) requirements of the queries is still an open issue. This article proposes an end-to-end query processing framework (named, QueryPM) that first, calculates the functional requirements similarity among queries to prevent the redundant task generation. Then, it takes the QoS and functional requirements into account while allocating the tasks on the sensor nodes. Extensive simulations on the proposed approach show that downstream traffic, upstream traffic, and energy consumption reduced to 60%, 20--40%, and 40%, respectively, as compared to state-of-the-art mechanisms.

Enhancing the Quality of Service by GBSO Splay Tree Routing Framework in Wireless Sensor Network

“…Thus it can perform an accurate and timely data transmission compared to several conventional techniques. The proposed method gives the highest PDR when compared with QPF [24], CCLBR [25], CSMA [26] and MIMO [27] due to its optimum balance of the transmission energy of the nodes, the optimum routing method. The above graph shows the relationship between the routing algorithm's average delay and the number of nodes for WSNs compared with DEBR [28], QEBSR [29] and EENC [30].…”

“…Where, rand is a chance value between 0 and 1. 𝐹𝐹 𝑑𝑑𝑗𝑗 is the force of 𝑗𝑗 𝑡𝑡ℎ search agent on 𝑖𝑖 𝑡𝑡ℎ search agent and is calculated by (24).…”

The Construction of a Shared Resource Base for Teaching Chinese Culture under the Architecture of Disciplinary Knowledge Mapping

“…Accordingly, a specific and vivid presentation of the "data structure" course in online e-learning is suitable for the knowledge needs of users. In this regard, the use of visualization technology to display knowledge maps can describe the knowledge resources and their structural relationships so that users have a clearer understanding of the domain knowledge of the vein and direction [12][13][14][15]. However, few e-learning platforms currently use knowledge graphs for data organization.…”

Virtual Grid-Based Routing for Query-Driven Wireless Sensor Networks

“…Sensor nodes have sensing, computing, and communicating capabilities in order to cater to the needs of applications and provide the required sensing information to the sink [1][2][3]. Sensor nodes are strategically positioned to detect and monitor various environmental parameters, including but not limited to humidity, pressure, light levels, and more [4][5][6]. On the basis of mobility, sinks can be characterized as either static or mobile.…”

“…These CHs subsequently relay the data to the sink. This data transmission from the sensor node to the sink can be query-driven [5,15], event-driven [16], periodic [17], and hybrid. In the context of query-driven data transmission, the sink initiates the process by sending queries.…”