Conference Paper

Wireless Sensor Networks Powered by Ambient Energy Harvesting (WSN-HEAP) - Survey and Challenges

Networking Protocols Dept., Inst. for Infocomm Res., Singapore, Singapore
DOI: 10.1109/WIRELESSVITAE.2009.5172411 Conference: Wireless Communication, Vehicular Technology, Information Theory and Aerospace & Electronic Systems Technology, 2009. Wireless VITAE 2009. 1st International Conference on
Source: IEEE Xplore

ABSTRACT Wireless sensor networks (WSNs) research has pre-dominantly assumed the use of a portable and limited energy source, viz. batteries, to power sensors. Without energy, a sensor is essentially useless and cannot contribute to the utility of the network as a whole. Consequently, substantial research efforts have been spent on designing energy-efficient networking protocols to maximize the lifetime of WSNs. However, there are emerging WSN applications where sensors are required to operate for much longer durations (like years or even decades) after they are deployed. Examples include in-situ environmental/habitat monitoring and structural health monitoring of critical infrastructures and buildings, where batteries are hard (or impossible) to replace/recharge. Lately, an alternative to powering WSNs is being actively studied, which is to convert the ambient energy from the environment into electricity to power the sensor nodes. While renewable energy technology is not new (e.g., solar and wind) the systems in use are far too large for WSNs. Those small enough for use in wireless sensors are most likely able to provide only enough energy to power sensors sporadically and not continuously. Sensor nodes need to exploit the sporadic availability of energy to quickly sense and transmit the data. This paper surveys related research and discusses the challenges of designing networking protocols for such WSNs powered by ambient energy harvesting.

  • [Show abstract] [Hide abstract]
    ABSTRACT: Rare catastrophic events, like earthquakes, can cause substantial damage in a short span of time. Data on the level of stress sustained by buildings and other critical infrastructure acquired during the event can significantly help in post-disaster recovery and assessment of buildings' structural integrity. While installing sensors to acquire such data is not difficult, ensuring that there is power to drive the sensors at the critical moment of the event is a challenge. In this paper, we propose an event-driven energy-harvesting (EDEH) wireless sensor network (WSN) in which the sensors are powered by the energy harvested from the consequence of the event, e.g. buildings shaking during an earthquake. The scarce amount of energy harvested during the short event occurrence time poses great challenges for the medium access control (MAC) design, which is the focus of our research. Furthermore, when all sensors harvest energy from the event, they become active simultaneously leading to serious channel contention problems. As such, we first examine the amount of harvestable energy and then show analytically that our MAC protocol is able to provide higher packet delivery ratio than conventional wireless technology, e.g. IEEE802.15.4.
    2013 IEEE 38th Conference on Local Computer Networks (LCN 2013); 10/2013
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: With the parallel developments of both sensor and wireless communication technologies in recent years, wireless sensor networks (WSN) has become a so popular study area in computer science and preferred to use on different areas and applications such as military, health, industrial controls, fire brigade, greenhouse and so on. Water distribution systems are one of these. WSN based monitoring systems implemented on water distribution systems are used world-wide. Its main advantages are real-time monitoring and detecting leakages to prevent resource loss. But WSN have some challenges like power consumption (finite energy sources), storage area, coverage, security. All WSN systems have to face these problems. The aim of this work is to solve energy problem in WSN used in water distribution monitoring systems by generating energy from the water flow in the pipes. 1. Introduction Nowadays, Wireless sensor networks (WSN) are composed of many small and cheap sensors that can communicate in various ranges wirelessly. As the WSN have the advantage of monitoring the environment remotely with low costs, many applications based on WSN are used at different parts of life such as military, health, process control, wildlife monitoring, fire brigades, energy monitoring. On the other hand WSN have some restrictions like power consumption, communication range, security, self-organizing, routing. In this paper, we proposed a system based on wireless sensor networks which aims to detect and localize leakages in water transmission pipelines. While implementing this system, we have solved the energy problem of the sensors used in the system by harvesting energy from the flow of water in the pipes by a specific application developed. The monitoring system includes a set of sensors which perform the tasks such as measuring the water pressure and water quality parameters. Data collected by sensors will be sent to the data management center and the center uses data to monitor the system and to decide what to do.
  • [Show abstract] [Hide abstract]
    ABSTRACT: In rechargeable wireless sensor networks (r-WSNs), higher data transmitted efficiency is required because sensor have to operate in a very low duty cycle owing to sporadic availability of energy. In r-WSNs, Data collected by many sensors is based on common phenomena, and hence there is a high probability that this data has some redundancy. In this work, we address the problem of jointly optimizing data aggregation and routing so that the network workload can be maximized. Establish the relationship model between data aggregation rate and throughput, so that the balanced was set up between the data aggregation rate and maximum network data traffic. Through the use of optimal candidate sample allocation, the algorithm can coverage efficiently and can make the maximum data aggregation rate flow to the network while maximizing network workload. Simulations are carried out to show that the proposed algorithm can significantly improve workload.
    Wireless Personal Communications 11/2014; 79(1):773-788. DOI:10.1007/s11277-014-1886-9 · 0.98 Impact Factor

Full-text (2 Sources)

Available from
May 31, 2014