Energy-Efficient Methods for Highly Correlated Spatio-Temporal Environments in Wireless Sensor Network Communications

Abstract

Continuous-monitoring (CM) of natural phenomenon is one of the major streams of applications in wireless sensor networks (WSNs), where aggregation and clustering techniques are beneficial as correlation dominates in both spatial and temporal aspects of sensed phenomenon. Conversely, in Event Driven Reporting (EDR), the efficient transmission of sensitive data related to some predefined alarm cases is of major importance. As such, reporting latency is a more important performance parameter. However, in some applications, the transmission of both CM and EDR data is encouraged or even required. For either CM or EDR applications, system performance can be greatly improved when both the number of packets to be transmitted as well as the packet size is reduced. This is especially true for highly dense sensor networks where many nodes detect the same values for the sensed phenomenon. Building on this, this paper focuses on studying and proposing compression techniques to improve the system performance in terms of energy consumption and reporting latency in both CM and EDR applications. Furthermore, we extend our analysis to hybrid networks where CM and EDR are required simultaneously. Specifically, this paper presents a simple aggregation technique named smart aggregation (SAG) for the CM applications and an event driven scheme named compression cluster scheme in spatial correlated region (CC_SCR). The proposed SAG exploits both spatial and temporal correlations where CC_SCR exploits the spatial correlation of such networks by data compression. Rationalizing the developments is attained by simulations that compare energy efficiency of the proposed SAG with k-hop M. A. Azim et al. 68 aggregation and CM based event driven reporting (CMEDR) schemes. Results of CC_SCR show that the technique may reduce the energy consumption drastically. In some specific cases the reduction becomes more than 10 times compared to a classical clustering scheme. Two different strategies for the transmission of event reports through the CM infrastructure are incorporated: PER and NPER protocols. Both strategies take advantage of the cluster-based architecture which assigns a TDMA schedule for the CM data transmission while using NP/CSMA for the transmission of the event information. Consequently, no extra energy is consumed for separate event clusters. As such, the number of packets to be transmitted is greatly reduced.

Towards a commodity solution for the internet of things

“…TiNA 4 exploits the temporal correlation in a sequence of sensor readings to suppress values and thus reduces data communication. This concept has been used to investigate sampling and compression at the software [15] and hardware level [16].…”

Recent trend in wireless sensor network and its applications: a survey

“…The new mote has been developed and discussed regarding the better battery life, radio performances and networking algorithms (Kling et al, 2005).WSN reliability was analysed using the fault tolerant method (Venkatesan et al, 2013); the impulse noise effect on wireless network relay has been simulated and studied (Ghadimi et al, 2012); even the WSN performance under the effect of collision and interference was studied (Onsy et al, 2014). A better technique has been proposed for continuous monitoring of natural phenomenon and event-driven reporting (Azim et al, 2014). Politi et al (2007) gave the details of the design and implementation of a hardware platform for WSN applications.…”

A framework for multimodal wireless sensor networks

“…These nodes communicate wirelessly with each other and work collaboratively to monitor physical variables (e.g., temperature, humidity, pressure, among others) in an area of interest, and perform some event tracking. Typically, these nodes periodically transmit data to a sink node that it is usually in charge of performing some pre-analysis and in-network data processing [1]. Then, the sink node sends those data to users, through a border router or gateway via Internet, as shown in Figure 1.1, for further analysis and visualization.…”

“…First, a Time-driven (proactive) reporting or Continuous Monitoring (CMnt) [3,4,5] for supervising an area, usually under normal situations (e.g., a water quality monitoring process), where Sensor Nodes (SNs) continually monitor and report their sensed data to a sink node in a periodic fashion over time. Second, an Event-driven (reactive) reporting (EDR) [1] is required for event detection and tracking, usually under emergency situations (e.g., a fire event). In this mode, SNs can immediately react upon the occurrence of a predefined event, detecting abrupt changes in the value of a specific interest physical variable, and reporting to a sink node [6,7,8].…”

“…In this mode, SNs can immediately react upon the occurrence of a predefined event, detecting abrupt changes in the value of a specific interest physical variable, and reporting to a sink node [6,7,8]. Those WSNs that support applications based on switching mechanisms between EDR and CMnt modes or vice versa, we named as MultiModal Wireless Sensor Networks (M2WSNs) 1 .…”