- About this Journal ·
- Abstracting and Indexing ·
- Advance Access ·
- Aims and Scope ·
- Annual Issues ·
- Article Processing Charges ·
- Articles in Press ·
- Author Guidelines ·
- Bibliographic Information ·
- Citations to this Journal ·
- Contact Information ·
- Editorial Board ·
- Editorial Workflow ·
- Free eTOC Alerts ·
- Publication Ethics ·
- Reviewers Acknowledgment ·
- Submit a Manuscript ·
- Subscription Information ·
- Table of Contents
International Journal of Distributed Sensor Networks
Volume 2012 (2012), Article ID 934240, 12 pages
Remaining Energy-Level-Based Transmission Power Control for Energy-Harvesting WSNs
Institute of Computer Application Technology, Hangzhou Dianzi University, Hangzhou 310018, China
Received 24 November 2011; Accepted 18 March 2012
Academic Editor: Jiming Chen
Copyright © 2012 Guojun Dai et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The purpose of this paper is to introduce a transmission power control scheme based on the remaining energy level and the energy-harvesting status of individual sensor nodes to extend the overall lifetime of wireless sensor networks (WSNs) and balance the energy usage. Ambient energy harvesting has been introduced as a promising technique to solve the energy constraint problem of WSNs. However, considering the tiny equipment and the inherent low and unbalanced harvesting capability due to environmental issues, there is still a long distance from perfectly solving the problem. In this paper, a wind and solar power joint-harvested WSN system has been demonstrated, which uses ultracapacitor as energy storage. By analyzing the power recharging, leakage, and energy consumption rate, a novel energy-level-based transmission power control scheme (EL-TPC) is produced. In EL-TPC scheme, the transmission power is classified into various levels according to the remaining energy level. By adapting the nodes' operation pattern, hierarchical network architecture can be formed, which prioritizes the use of high energy level, fast charging nodes to save the energy of uncharged nodes. The simulation and demonstration results show that EL-TPC scheme can significantly balance the energy consumption and extend the entire network lifetime.
Energy constraint has been one of the most important design issues of wireless sensor networks (WSNs) since last decade. For lifetime maintaining, many mechanisms have been produced either to minimize the energy usage or to expand the energy storage. Generally, these schemes can be classified into two main categories: energy management and energy harvesting .
It has been extensively studied that most of the energy is consumed by the radio transceiver, which indicates that an effective way is to reduce the time period when the onboard radio is on and minimize the amount of packet delivery. Therefore, sleep-wakeup duty cycle is introduced to allow sensor nodes to sleep when there are no operation requirements. In order to reduce the entire traffic load and achieve long sleep duration, energy-efficient MAC protocols , routing protocols , data aggregation schemes , and transmission power control schemes have been proposed as well as some cross-layer design .
However, these protocols still face some primary problems, such as the trade-off between scheme complexity and node simplicity, the trade-off between idle listening, overhearing, and control packet overhead in MAC protocols, the trade-off between energy-efficient routes and long latency in routing protocols, and also the trade-off between average number of hops and transmission power level as well as energy consumption balance problem. It is unavoidable that the sensor nodes that closer to the sink node have heavier traffic load than those farther away due to more routing relay. The sensor nodes that take more local computation, such as schedule assignment and broadcast, data aggregation, will also consume more energy. After a certain period of system operation, these nodes in critical positions may lose effect and result in the entire network invalid.
On the other hand, energy harvesting has now been introduced as a promising solution of energy constraint which allows the batteries of sensor nodes to be recharged using ambient energy resources, such as solar power, wind power, or even vibration power. In these designs, ambient energy can be converted to electrical energy and used directly or stored in the means of energy storages, for example, batteries and ultracapacitor. However, using rechargeable sensor nodes also suffers from many problems, such as the technical challenges in tiny ambient energy collection device production and implementation. The extremely low recharging speed due to typical ultra-low-power ambient energy and the unbalanced recharging speed also generate problems. The lifetime of energy storage component is also a design challenge under the situation of frequently recharging and consuming.
The purpose of this paper is to solve the problem of the unbalanced energy consumption and harvesting speed in ambient powered WSNs, which applies a transmission power control scheme to enhance or reduce the communication distance of sensor nodes based on the energy level situation. By applying such a scheme, the sensor nodes with higher remaining energy resource or higher energy harvesting capability will take more responses to the network data packet delivery, while the other nodes will be in an idle state for longer time.
Our previous work has been presented in ; a building surface was mounted, wind power was collected, and wireless sensor network system has been demonstrated, which aims to monitors the usage pattern of air conditioners (ACs), the outdoor temperature. The idea of energy-level-based transmission power control scheme (EL-TPC) was firstly introduced in  as well. It achieves energy saving and balancing by modifying the transmission power level on different nodes according to the remaining energy level, power recharging, and leakage speed of the nodes. This paper is extended from ; more clear descriptions and more details of EL-TPC will be provided. Theoretical analyses are made, and the corresponding network simulations are also extended to highlight the outstanding performance of EL-TPC. Solar energy-harvesting sensor nodes are also introduced in the demonstration system.
The remaining of this paper is organized as follows. In Section 2, the related works of energy harvesting and the corresponding management schemes are introduced and compared. Our previous work of EL-TPC is also briefly introduced. The demonstration WSNs architecture and hardware component design are described in Section 3 with the measured experiment results of the device parameters. The transmission power control scheme EL-TPC is introduced in Section 4 in details. In the following Section 5, the network simulation and real BSMSN system demonstration are set up and carried out; the performances are evaluated. Finally, the conclusion is given in Section 6.
2. Related Works
Although the energy-harvesting WSNs are not mature in techniques, they indeed point out a sufficient way of the development of future WSNs. Some energy management schemes have also been proposed to schedule the energy usage pattern based on this background. Transmission power control has been carried out to coordinate MAC protocols previously. These methods as well as our original EL-TPC scheme are summarized as follows.
2.1. Existing Energy-Harvesting WSNs Systems
Many examples of energy-harvesting WSNs systems have been demonstrated since 2005, such as Trio , Fleck , Prometheus , AmbiMax , Everlast , and TwinStar . In these systems, the resource of the ambient energy can either be solar power, wind power, or even mechanical vibration. Once the energy is collected by the sensor node, it will be stored either in NiMH batteries, Lithium batteries, ultracapacitors, or even directly supply the sensor nodes.
As a clean and safe ambient energy resource, solar power harvesting has been studied for a long time. However, as the power collecting board is relatively small which should fit the size of sensor nodes, the recharging rate of solar batteries is very slow. Another limitation of using solar power is that it cannot work during the night time or rainy days, which makes the network dependent on the weather. Wind power has also been well studied. Typically, it is not very stable when it is strongly constrained by the climate and the location of sensor nodes. Unless there is a constant wind resource, the performance is even worse than the solar power.
Compared with rechargeable batteries, the advantage of using ultracapacitor in WSNs is significant with the feature of fast recharging, unlimited recharging times, and invulnerable . However, the leakage causes a big problem that the energy cannot be stored for long.
2.2. Energy Management Schemes in Energy-Harvesting WSNs
After designing and demonstrating a prototyping energy-harvesting WSN, how to use and store the harvested energy consists of more research topics. The efficiency of the management schemes significantly determines the performance of energy-harvesting WSNs. Recently, many energy management schemes have been introduced for better usage of harvested energy.
A solar energy-harvesting system has been introduced in  as well as a corresponding energy-neutral mode, which enables the nodes to use as much energy as it gains from the environment and results in the energy input and output balance in any individual node. In , a joint energy management and resource allocation scheme is proposed to schedule the energy usage by adapting the sampling rate, while in , the authors propose a novel cost metric for the randomized minimum path recovery time (R-MPRT) routing protocol which can achieve better performance than normal routing protocols considered in energy-harvesting WSNs.
These schemes can help to improve the usage efficiency of harvested energy in a certain level and particular WSNs. However, due to the deployment area and environmental situations, the energy-harvesting speed is unbalanced, which brings more challenges in management scheme design.
2.3. Transmission Power Control Protocols
Transmission power control (TPC) techniques have been proposed as a coordination of routing or MAC protocols, which enables nodes to have dynamic transmission range and thus results in multiple coverage areas [16, 17]. In , two TPC schemes are proposed to coordinate BMAC, which achieves up to 57% energy saving in their simulation. An experimental-based TPC research has been represented and evaluated in , which shows that the real performance of TPC schemes is not as well as simulated, but their dynamic TPC scheme can perform up to 37% energy saving in 10% duty cycle.
A traditional trade-off in TPC schemes is whether it is better to make larger transmission range which can reduce the number of hops in the routes or will it be more energy efficient to use smaller transmission power which reduces the energy consumption of each transmission and reception. Another problem of dynamic transmission power is that it is harmful to energy balance, which consumes the energy in the nodes with larger transmission power much faster than those using low power.
2.4. Previous Work of EL-TPC
As mentioned in previous subsections, both energy harvesting and energy consumption are unbalanced. Independently, they are both harmful in WSNs. But if they can be combined, the drawbacks can be converted to optimizing features. This also generates our original idea of EL-TPC . By joint consideration of the remaining energy level, the energy-harvesting speed, sensor nodes are grouped, and different transmission power levels are assigned to each group. According to the given transmission power level, a specified radio connection topology is provided different from the location-based topology. The detailed description will be provided in the following sections.
3. System Architecture and Hardware Model
To verify all the proposed work, network simulation and real system implementation are both useful methods. In our earlier work, a project called Cyber IVY has been carried out which mainly supplies building surveillance functions. A building surface mounted sensor network (BSMSN) system has been implemented and published in . However, at that stage, the energy resource of the sensor nodes was normal rechargeable batteries; simple broadcasting-based communication was applied without any channel and energy management. In the original work of EL-TPC , Cyber IVY has moved to a new stage by using wind powered sensor nodes, which is called building surface wind-power collected wireless sensor network (BSWPWSN) system. Recently, solar powered sensor nodes are also used as a sufficient support. The detailed design issues and system features are discussed as follows.
3.1. Application Study
The application potential of Cyber IVY is still being explored, which will shortly combine air pollution and noise monitoring in near future. The sensor boards with more functions are being developed. At the recent stage, the recent system aims to monitor the usage pattern of air conditioners (ACs), the outdoor temperature, and day light lamination information. The main purpose of doing these monitoring tasks is to provide some sensible information for environment protection and electrical energy saving. It is reported that the air conditioners consume huge amount of electrical energy during summer and winter time, especially in public buildings such as laboratory building in the universities or shopping malls. As in public buildings, privacy will not be a problem. By monitoring the AC usage pattern, it is easy to evaluate the total amount of energy cost. With the coordination of outdoor and indoor temperature information, unusual usage of AC will also be obtained and can help in further energy saving, which has been demonstrated and presented in .
In our plan, the demonstrated BSMSN is a prototype of a certain set of simple designed, multifunctional, self-powered, long lifetime WSNs. Scientific problems can be formed, and design challenges can be detected along with the progress. The possible solutions will also be proposed, developed, implemented, and evaluated.
3.2. Hardware Architecture and Network Topology
HDU mote (Figure 1) node is used in BSMSN with its Chipcon CC2420 transceiver component. A large choice of onboard sensors is also provided, such as CO2 density, temperature, humidity, and light lamination. CC2420 can work on 31 different power levels and 14 orthogonal radio channels. Depending on the communication requirements, the transmission range can be adjusted by changing the power level.
In BSMSN, sensor nodes are densely deployed on the surface of external unit of AC, which covers the outside of the fan. This results in an advantage that the fan of the AC external unit can provide relatively stable wind power output to provide energy supply to sensor nodes when they are working. Figure 2 represents an example case of typical building surface and our sensor network deployment.
The transmission power of CC2420 transceiver can be adjusted with 31 levels, from level 0 to level 30. In our measurement results, if a node is operated with full-power state, all the nodes within a 150-meter range will be its one hop neighbor if the nodes are deployed in a pain floor. As the distance between two AC external units is relatively short in terms of 5–10 meters, a large number of ACs will be covered by a certain one as shown in Figure 3(a). In a typical hop-based packet collision avoidance MAC scheme, to guarantee a certain transmission to be successful, all the nodes other than the transmitter and receiver within such a large range have to be silent. In this case, very few nodes can be scheduled to transmit simultaneously, which causes low-efficiency problem. In another case shown in Figure 3(c), the transmission range is reduced to an unacceptable level in which no data delivery can be successful. In common knowledge or experience, the case shown in Figure 3(b) is much more reasonable than the other. The rest of this section will determine how to find a proper transmission range in our specific scenario.
3.3. Energy-Harvesting Component Design
In BSMSN system, wind power is the source of energy harvesting. As shown in Figure 4, the harvesting component is composed of four small fans facing the same direction. When the air conditioner is switched on, these small fans will follow the rotation of the fun of external unit. As the wind speed and strength of different area of the fan are not fixed, by trying various distances between four small fans and measuring the rotation speed of them, an approximately fastest rotation speed has been achieved. The simple generator behind the small fans will convert the wind power to electrical power.
Due to the feature of fast recharging and being invulnerable, Maxwell PC10 ultracapacitor is chosen to be the only power storage and resource of our sensor nodes. The rated voltage of PC10 is 2.5 volt, and the capacitance is 10 F. It can be recharged when the fans are rotating and supply sufficient energy for the nodes to maintain sensing and communication functions at the main time.
In our later work carried out in summer, solar power is also collected for sensor node recharging and operation. As shown in Figure 5, solar panel is used for energy harvesting, while the other components are the same as that of wind powered nodes. The energy collected is also stored in ultracapacitors, and the harvesting speed will be shown in Figure 8.
In our demonstration, the solar-powered sensor nodes are deployed between buildings as communication relays as well as environmental information sensing. To enhance the capability of solar energy harvesting and provide sufficient amount of energy supply in heavy traffic load situation, reflecting mirror is used for extra sunlight collection, which is shown in Figure 6. By this approach, the energy-harvesting speed is increased by approximately 50% in the demonstration.
3.4. Energy Recharging and Consumption Status
To know the status of energy recharging and consumption and the unbalanced situation which directs the design of the proposed EL-TPC scheme is very important. A set of experiments and measurements on related components and parameters have been carried out under various conditions.
As a natural feature of ultra-capacitor, leakage not only reduces the speed of charging, but also restricts the maximum duration of energy storage. The electrical energy will be lost even when the connected devices are powered off. The leakage in terms of voltage along with time is shown in Figure 7, in which Maxwell PC10 ultra-capacitor is used. The ultra-capacitor is firstly fully charged to 2.6 V. After approximately 45 hours, the voltage drops to an invalid level when the sensor node can no longer sufficiently work.
From Figure 7(a), it can also obtain that the leakage problem is more serious when the remaining energy level is higher. In Figure 7(b), the voltage reduction against time between 2.5 V to 2.3 V is illustrated, which shows that the stored energy is lost very fast in such high-voltage situation.
The curve of ultra-capacitor recharging is presented in Figure 8 under wind and solar power under various setups. It shows that with voltage increasing, the recharging speed will be slightly reduced. This is because is proportional to according to equation . As the average recharging speed is a constant value, the increasing of voltage will be reduced when increases. In our special hardware devices and scenarios, as the measurement is carried out at noon in summer time, the solar power harvesting speed is faster than that of wind power, especially with direct sunlight combined with concave mirror reflection. However, solar power collection is not stable as the sunlight lamination varies significantly during the day. During the nights and rainy days, it will even lose effect. The wind power collection in our system is much more stable. When the mounted AC is powered on, the fan in the external unit will supply constant wind power to our prototyping sensor node.
The energy consumption status is shown in Figure 9. In these measurements, the sensor node keeps the transceiver on all the time and broadcasts a “hello” message every second. The power level of the sensor node is set to be levels 4, 10, and 30, which can provide a radio-effective coverage for approximately 12 meters, 30 meters, and 100 meters, respectively. After 10.5, 8, and 4 minutes, the power level will be too low for the nodes to work, which is around 1.5 volts. The reason for choosing these three levels in the experiment is particularly due to the real application scenario as shown in the latter section, which specifies the coverage distance of the nodes. In different deployment environment and scenario, this can be varied.
Above all, it can be obtained that the sensor nodes consume energy a bit faster than the recharging speed. However, by considering a reasonable duty cycle which enables the sensor node to switch the radio transceiver off most of the time, it can achieve a balanced state in theory. The leakage is ignorable in most cases. But if a node resumes high energy level, it is a good choice to keep high-duty cycle. Otherwise, the energy will be lost rapidly. The remaining of this paper will focus on how to use transmission power control to make a negotiation considering all the above issues.
4. EL-TPC Scheme Description
According to the energy-harvesting design of sensor nodes, it is believed that the sensor node will no longer permanently lose effect due to power used up. The power-up nodes will go to a “long sleep” state when only recharging takes place.
In this section, a transmission power control scheme called EL-TPC will be introduced in detail on the purpose of balancing energy consumption to reduce the probability of the existence of “long sleep” state. In other words, the scheme will focus on how to keep most of the nodes in an “on” state to provide sufficient continuous service of WSNs. As mentioned in Section 2, the idea of EL-TPC was originally raised according to the unbalance of both energy consumption and harvesting. The fundamental goal is to make the nodes with more remaining energy and better harvesting capability to take more responses. With the development of our work, the scheme has been modified and is now wider applicable than that presented in .
4.1. Assumptions and System Parameters
There are many specified features of BSMSN including the system design and network topology. A list of assumptions and system parameters is summarized as follows.
Relatively Uniform Deployment
In BSMSN, the sensor nodes are mounted to external unit of ACs or other positions which have certain distance between each other. An example network topology is shown in Figure 2 where sensor nodes form a natural grid topology with a fixed distance between two neighboring nodes.
Time Perfect Synchronized
As synchronization is out of the scope of our EL-TPC scheme, in our experiment and simulation, sensor nodes are deployed after it is initialized and synchronized with the sink node. During the demonstration period, perfect synchronization is kept.
Packet Congestion Ignorable
There are 14 orthogonal radio channels available in CC2420 transceiver, and the transmission duration is relatively short in typical low sampling rate. By using a random back-off before each transmission, it is unlikely to have two nodes transmitting simultaneously and using the same channel.
Direct Connection Feasibility
In the demonstration of BSMSN system, the maximum diameter of the coverage area is approximately 100 meters. Therefore, with power level 30, any two nodes can achieve direct connection.
Limited Energy Storage Capability
The leakage of the ultra-capacitor makes the stored energy unable to provide the sensor nodes long time operation. It is necessary to increase the consumption rate when there is much energy left.
4.2. Multiple Transmission Range Choice
As mentioned in Section 2, a traditional trade-off in transmission power control schemes is between the low energy consumption rate in large quantity nodes and high energy consumption rate in low amount of nodes. It also limits the potential of TPC scheme in energy constraint WSNs as it may suffer from obvious energy unbalance.
In EL-TPC, this situation can be sorted out according to unbalanced energy-harvesting capability. Higher power and larger coverage can be applied in the nodes with more remaining energy and better harvesting capability, while the nodes short in energy can reduce the power level for energy saving. For example, three transmission power levels are determined in BSMSN system according to the measurement results as follows.
High Level (HL)
Nodes use extremely high transmission power, (e.g., level 30 or a bit below in CC2420), which enables it to directly communicate with the sink node. In our demonstration and simulation, only the nodes with 2 volts or higher voltage and have sufficient power supply will work in this level.
Low Level (LL)
Nodes without energy recharging reduce the power to a relatively low level by which it can only communicate with no more than one neighboring node in any direction. These nodes take very few sensing tasks and no communication relay functions. In the demonstration, the power level used is level 4 in CC2420.
Middle Level (ML)
Nodes use medium power level, for example, level 10, which cover a hop distance approximately 20–30 meters and have two or three neighboring nodes on the main four directions (up, down, left, and right) within the transmission range. Any energy-harvesting node with a voltage lower than 2 volts will stay in this level.
In traditional TPC schemes, the main goal is to find out an optimal or near-optimal transmission power level which will be applied to all the sensor nodes. But in our EL-TPC, the nodes in the same network may use different power levels. Therefore, an interesting phenomenon occurs that the communication is not totally of dual direction. Node A may hear node B, while node B may not hear node A if the transmission power in node A is lower than in node B. To ensure that all the packets can be successfully forwarded to the sink node, handshake process is very important in scheme initialization and updating.
4.3. System Setup and Data Packet Delivery Strategies
Instead of routing and medium access control (MAC) protocols, a data packet delivery control mechanism has been proposed to coordinate the EL-TPC scheme. There are three packet delivery strategies described as follows, which inherently form the routes and schedule the channel access.
The first strategy is direct or semidirect connection with the sink node. Once a node fulfills the requirements of high level (HL), it will form a cluster by broadcasting an “invitation” packet to all the nodes in its coverage, which is the entire network. When ML and LL nodes receive this “invitation,” they will return an acknowledgement addressed to this HL node after a random back-off. After receiving the ACK packet, the HL node will then reply with a “handshake” packet to the specific nodes to inform that they are cluster members.
However, as this ACK packet is transmitted in a lower power level, some of them may not be heard by the HL node. As shown in Figure 10, an HL node will form a cluster according to the connection status instead of geographic distances. During the data packet transmission, the sensed packet of the HL node will directly be received by the sink node and that from the cluster members will use the HL node as an intermediate, which forms semidirect connections to the sink node.
The second strategy is a multihop fashion. During the network initialization stage, if a node has not received any “handshake” packet, it will define itself as a noncluster member. Then, an “access request” packet will be sent to the nodes in its coverage by broadcasting. If there is any middle level (ML) neighbor who is a cluster member receives this packet, it will reply with an “access acceptance” packet. Then, a three-hop route is formed as shown in Figure 11, and this node will define itself as a cluster-access node.
If all the ML neighbors receive this “access request” are non-cluster members, there will be no “access acceptance” packet returned. After a timeout period, this node will broadcast a “routing request” packet. If there is any ML neighbor as a cluster-access node, a “routing acceptance” packet will be returned and a multihop route will be formed.
If there is no “routing acceptance” response returned after timeout, this node has to switch its radio transceiver off but keep ordinary sensing functions. When the voltage of its onboard ultra-capacitor is recharged to above 2 volts, it will become an HL node and forms its cluster. The stored history data will then be directly transmitted to the sink node. Otherwise, it will wait until the next updating starts.
According to the above procedure, a maximum length of 4-hop route can be formed. In our small scope demonstration network, this is enough as it is very unlikely to leave any ML node to “off” state. But with the expansion of the network size, this thing will be changed. By allowing all the ML nodes to take the routing request process, longer routes will be available.
The third strategy is periodically waking up and updating the schedule information if there are still remaining nodes left in a low level (LL) transmission power state. In these nodes, the energy-harvesting component is not working, which indicates that the fans of the AC external unit are not rotating. Therefore, there is no necessity of AC operation monitoring; only other environmental information such as outdoor temperature will be sensed. To save energy, the LL node turns off its radio transceiver most of the time. It will periodically wake up and broadcast a “hello” packet every 10 minutes, which tells the neighboring nodes that it has no valid connection to the sink node. If there are any neighbors that become a cluster head, a “cluster invitation” packet will be returned addressing to this LL node, and a handshake procedure starts.
Above all, there are three main functions corresponding to the three power levels. Direct connection with the sink and cluster head functions are assigned to HL nodes, while ML nodes can either be a cluster member or a routing rely node. The LL nodes have no routing functions; it can either be a cluster member or wait to be a cluster member. Figure 12 represents the status of the entire procedure.
5. Simulation Setup and Performance Evaluation
In order to evaluate EL-TPC scheme and examine the parameter choice, a network simulation is carried out. In our simulator, the procedure is executed strictly following the scheme description. Except the given assumptions and assigned parameters in Section 4, a list of parameter setups are shown as follows.
Uniform grid topology as shown in Figure 10 and pseudorandom topology are both used in our simulation. In grid topology, the distance between each sensor nodes is 8 meters. In the pseudorandom topology, 80 sensor nodes are randomly deployed in a 50 m × 80 m area with a minimum distance of 5 meters between each other, which fits the real building situation and avoids redundancy in node deployment.
Energy level choice is essentially important in EL-TPC. Level 30 and level 4 in CC2420 are used for HL and LL nodes, respectively. In ML nodes, level 7 is mainly examined, which enables the nodes to cover 2 neighbors along the four main directions in the grid topology. An expanded energy level of 10 is also tested in some scenarios.
In our proposed BSMSN, the sampling rate is determined by the real monitoring requirements. Periodic sensing is set in each node with 20 seconds per packet for HL and ML nodes, and 100 seconds per packet for LL nodes. The reason is that the LL nodes only sense the environmental data, but the ML and HL nodes also sense the AC working status. To examine the performance of EL-TPC, we also show the results with 1/4, 1/2, 2, and 4 times of the traffic load.
AC Working Status
The feature of the sensing object also affects the performance of the proposed scheme. The on/off state of AC is not only the monitored event but also determines whether the sensor node can get sufficient energy supply. In our survey results, 80% of air conditioners are in an “on” state during the day time, while 20% of which are in an “off” state.
The most important result is represented in Figures 13 and 14, which shows the situation of remaining energy level in the nodes against time. Figure 13 clarifies that the nodes achieve a dynamic balance in energy consumption and harvesting along with the time under the given conditions by showing four nodes with different starting energy level. After approximately 10–30-hour operation, the average remaining energy in all the sensor nodes becomes very similar, and all of them keep a dynamic balance. After that duration, most of the nodes are in ML state or become LL state during the night. There are always certain HL nodes which can form clusters and achieve direct or semi-direct connections to the sink node. In grid and pseudorandom topologies, the scheme works relatively well. Figure 13(c) shows that by varying the ML nodes’ energy level which changes the coverage of ML nodes, the energy performance is still stable.
Figure 14 provides the change of the remaining energy level of a randomly picked node along with time under different traffic loads. It shows that the average remaining energy level changes according the traffic load due the different transceiver energy consumption rate. If the traffic load is light enough, such as 25% of normal load, the energy level will increase continuously. With the increasing traffic load, the variation of the remaining energy level also increases, which means the node changes transmission power level frequently. It will finally break the balance as the traffic load is unaffordable for the node.
A natural feature of our proposed EL-TPC scheme is that there always exists certain isolated node which can neither be a cluster member nor achieve multihop relay. However, a node will not keep isolated for too long as the status of itself and the neighbors may change, which has been shown in Figures 13 and 14. In Figure 15, it is shown that the network also keeps a dynamic balance as the entire number of isolated nodes will not be too large if the harvesting power resource keeps working. With the probability of “on” state of the AC increases, the amount of isolated node at any time decreases until zero. In our scenario, the system works stably if more than 50% of the air conditioners are switched on.
A demonstration of BSMSN system has also been built up and kept working for 3-day continuos monitoring. In this real implementation, uniform topology shown in Figure 2 is applied, and the nodes are synchronized before deployment. During the experiment, the system is shown to keep stable data packet output and displayed in the PC where the sink node is plugged on. The voltages of some sample nodes are real time monitored by adding the voltage information to the data packets.
Figure 16 represents the remaining energy level in terms of voltage against time for real implemented BSMSN system. By varying the traffic load and the average number of used air conditioner, the system works stably in which no nodes lose effect during the three days.
This paper has introduced a transmission power control scheme based on the remaining energy level and the energy-harvesting status of sensor nodes to maintain the lifetime of WSNs called EL-TPC. By achieving dynamic energy balance in sensor nodes and the entire WSNs, EL-TPC scheme has been proved to work excellently in energy-harvesting sensor networks, which keeps the WSNs robust and recoverable. The main contribution is that the unbalanced energy capability is used to solve the problem of unbalanced energy consumption. By applying EL-TPC scheme, the WSNs will no longer lose effect and keep high-efficiency operation. The simulation results compared the performance in different scenarios and showed the advantages and limitations of EL-TPC.
A demonstration of system call BSMSN has also been implemented, in which wind power is the only energy-harvesting resource, while energy is stored in and supplied by ultra-capacitors. It proves the idea of EL-TPC to be effective and sufficient during 3-day continuos working. This system can be used for air conditioner usage pattern surveillance and outdoor temperature monitoring at the recent stage, but it is scalable. More sensing functions can be extended to the system, and it will fit the requirement of more complex application environment deployment.
This work is supported by the Natural Science Foundation of Zhejiang Province under Grant no.Z1080979, Zhejiang Provincial Key Innovation Team on Sensor Networks under Grant no. 2009R50046-9, no. 2009R50046-1, no. 2009R50046-8, and no. 2009R50046-5, the Preresearch Scheme of National Basic Research Program of China (973 Program) under Grant no. 2010CB334707, the National Natural Science Foundation of China under Grant no. 60903167, and the Natural Science Foundation of Zhejiang Province no. Y1110831.
- I. F. Akyildiz and M. C. Vuran, Wireless Sensor Networks, Wiley, Hoboken, NJ, USA, 2010.
- I. Demirkol, C. Ersoy, and F. Alagöz, “MAC protocols for wireless sensor networks: a survey,” IEEE Communications Magazine, vol. 44, no. 4, pp. 115–121, 2006.
- J. N. Al-Karaki and A. E. Kamal, “Routing techniques in wireless sensor networks: a survey,” IEEE Wireless Communications, vol. 11, no. 6, pp. 6–28, 2004.
- J. N. Al-Karaki, R. Ul-Mustafa, and A. E. Kamal, “Data aggregation and routing in wireless sensor networks: optimal and heuristic algorithms,” Computer Networks, vol. 53, no. 7, pp. 945–960, 2009.
- M. L. Sichitiu, “Cross-layer scheduling for power efficiency in wireless sensor networks,” in Proceedings of the IEEE 23rd Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM '04), pp. 1740–1750, Hong Kong, China, March 2004.
- J. Qiu, B. Lin, P. Liu, S. Zhang, and G. Dai, “Energy level based transmission power control scheme for energy harvesting WSNs,” in Proceedings of the IEEE Global Communications Conference, pp. 1–6, Houston, Tex, USA, December 2011.
- P. Dutta, J. Hui, J. Jeong et al., “Trio: enabling sustainable and scalable outdoor wireless sensor network deployments,” in Proceedings of the 5th International Conference on Information Processing in Sensor Networks (IPSN '06), pp. 407–415, Nashville, Tenn, USA, April 2006.
- P. Corke, P. Valencia, P. Sikka, T. Wark, and L. Overs, “Long-duration solar-powered wireless sensor networks,” in Proceedings of the 4th Workshop on Embedded Networked Sensors (EmNets '07), pp. 33–37, Cork, Ireland, June 2007.
- X. Jiang, J. Polastre, and D. Culler, “Perpetual environmentally powered sensor networks,” in Proceedings of the 4th International Symposium on Information Processing in Sensor Networks (IPSN '05), pp. 463–468, Los Angeles, Calif, USA, April 2005.
- C. Park and P. H. Chou, “AmbiMax: autonomous energy harvesting platform for multi-supply wireless sensor nodes,” in Proceedings of the 3rd Annual IEEE Communications Society on Sensor and Ad hoc Communications and Networks (SECON '06), pp. 168–177, September 2006.
- F. Simjee and P. H. Chou, “Everlast: Long-life, supercapacitor-operated wireless sensor node,” in Proceedings of the 11th ACM/IEEE International Symposium on Low Power Electronics and Design (ISLPED '06), pp. 197–202, Tegernsee Germany, October 2006.
- T. Zhu, Z. Zhong, Y. Gu, T. He, and Z. L. Zhang, “Leakage-aware energy synchronization for wireless sensor networks,” in Proceedings of the 7th ACM International Conference on Mobile Systems, Applications, and Services (MobiSys '09), pp. 319–332, Kraków, Poland, June 2009.
- A. Kansal, J. Hsu, S. Zahedi, and M. B. Srivastave, “Power management in energy harvesting sensor networks,” ACM Transactions on Embedded Computing Systems, vol. 6, no. 4, 2007.
- R. S. Liu, P. Sinha, and C. E. Koksal, “Joint energy management and resource allocation in rechargeable sensor networks,” in Proceedings of the IEEE (INFOCOM '10), Columbus, Ohio, USA, March 2010.
- D. Hasenfratz, A. Meier, C. Moser, J. J. Chen, and L. Thiele, “Analysis, comparison, and optimization of routing protocols for energy harvesting wireless sensor networks,” in Proceedings of the IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing (SUTC '10), IEEE International Workshop on Ubiquitous and Mobile Computing (UMC '10), pp. 19–26, June 2010.
- M. Cao, V. Raghunathan, S. Hanly, V. Sharma, and P. R. Kumar, “Power control and transmission scheduling for network utility maximization in wireless networks,” in Proceedings of the 46th IEEE Conference on Decision and Control (CDC '07), pp. 5215–5221, December 2007.
- R. L. Cruz and A. V. Santhanam, “Optimal routing, link scheduling and power control in multi-hop wireless networks,” in Proceedings of the 22nd Annual Joint Conference on the IEEE Computer and Communications Societies, vol. 1, pp. 702–711, April 2003.
- L. H. A. Correia, D. F. Macedo, A. L. dos Santos, A. A. F. Loureiro, and J. M. S. Nogueira, “Transmission power control techniques for wireless sensor networks,” Computer Networks, vol. 51, no. 17, pp. 4765–4779, 2007.
- J. Jeong, D. Culler, and J. H. Oh, “Empirical analysis of transmission power control algorithms for wireless sensor networks,” in Proceedings of the 4th International Conference on Networked Sensing Systems (INSS '07), pp. 27–34, Braunschweig, Germany, June 2007.
- L. Peng, S. Zhang, J. Qiu, and G. Dai, “Building surface mounted wireless sensor network for air conditioner energy auditing,” in Proceedings of the IEEE Conference on Computer Communications Workshops (INFOCOM '11), pp. 738–743, Shanghai, China, April 2011.