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Information Fusion Techniques Applied to Shared Sensor and Actuator Networks Albert Y. Zomaya School of Information Technologies The University of Sydney Sydney, Australia
Claudio M. de Farias, Luci Pirmez, Flávia Coimbra Delicato, Igor L. Dos Santos PPGI- iNCE/DCC-IM Universidade Federal do Rio de Janeiro Rio de Janeiro, Brazil Abstract — This work presents an adaptation (and enhancement) of a well-known fusion technique in order to deal with multiple applications simultaneously in a context of Shared Sensor and Actuator Networks (SSAN). SSAN allow the sensing infrastructure to be shared among multiple applications that can potentially belong to different users instead of assuming an applicationspecific network design. We also present simulation and tests conducted with the proposed solution on real nodes to validate our proposal. Keywords - Information fusion, Shared Sensor and Actuator Networks, wireless sensor and actuator networks.
I.
INTRODUCTION
While wireless sensor networks (WSNs) have been traditionally tasked with single applications, recent years have witnessed the emergence of Shared Sensor and Actuator Networks (SSAN) as integrated cyber-physical systems infrastructure for a multitude of applications. As the number of applications increase, also increases the amount of messages being transmitted. Sensor nodes rely on batteries often nonrechargeable, and the replacement of depleted batteries is not always feasible or desirable. Therefore, one of the greatest challenges in these networks is to save energy in order to extend the operational lifetime of these networks. Among the techniques employed to extend network lifetime [4], information fusion is one of the most widely used. According to several works [1] [8], sensor nodes consume less power in processing than in communication tasks; thus applying information fusion to minimize transmissions can be very effective in extending the overall system lifetime. The major problem with traditional fusion techniques when applied to SSANs is that they consider the universe (states and data intervals) of a single application. If the environment has a set of applications simultaneously running (as it is in a SSAN scenario) and they use the same information (such as temperature), each application will apply fusion for each set of data instead of fusing this information for all applications. Another drawback of current information fusion techniques when applied to this new scenario of SSAN is the potential loss of accuracy. In order to tackle such drawbacks and deal with SSAN scenarios, this work presents an enhancement of a wellknown fusion technique. Adapting fusion techniques to deal with multiple applications will reduce the number of transmitted messages on SSANs, once that each sensor will send a single message independently of the number of applications. We will also present simulation and tests on real nodes of the proposed solutions to validate our proposal. This
paper is organized as follows. Section 2 presents some related work. Section 3 describes the proposed enhanced fusion method. In Section 4, we describe the tests performed and present the results achieved. Finally,in Section 5 we draw our conclusions. II.
RELATED WORK
The SSAN approach has gained momentum only recently. In Efstratiou et al. [4] an extension to the traditional concept of WSANs (which aims at supporting a single application and a single user) is proposed. The proposal is based on the decoupling of the sensing and communication infrastructure from application ownerships. A framework is created which allows WSAN infrastructures to be shared among multiple applications potentially owned by different users. By achieving this level of decoupling, the WSAN infrastructures can be viewed as an accessible resource, which can be dynamically repurposed and re-programmed by different authorities, in order to support multiple applications. Most of the literature in the field of SSANs deals with the resource allocation problem. Works in [2,3] presented the first feasible solutions to the problem of sharing requirements of different applications within a SSAN scenario. Such works present solutions on how to deal with multiple requirements simultaneously by applying different weights to applications, and also describe an approach where data is not to be linked to a specific application, but instead it is available to all applications. This decoupling of the sensing infrastructure from the application can significantly reduce energy consumption, as pointed out by the results presented in the papers. Although relevant, Information Fusion Techniques (IFT) have not been discussed in the SSAN scenario so far. In the context of single application WSANs, there is a well-known wide range of IFTs being successfully applied. In Nakamura et al. [1] and Khalegui et al. [8], surveys on the current state-ofthe-art of IFT are presented. The surveys describe known methods, algorithms, architectures, and models for Information Fusion, discussing their applicability in the WSANs context. However, the described approaches do not consider multiple application requirements simultaneously. To the best of our knowledge, there is no other work that addresses the IFT problem in the SSAN context. III.
ENHANCED INFORMATION FUSION TECHNIQUES
As we mentioned, IFTs are widely used in traditional WSANs, but are not tailored to the new SSANs scenario. To
address this issue, we propose an extension in the Moving Average Filter (MAF) [11]. To describe our enhancements in the IFT, we will use two well-known WSAN applications as examples: the “Heating, Ventilation and Air-Conditioning” (HVAC) and Fire Detection applications. Although a SSAN running such applications may encompass many sensor types, for the sake of simplicity we will consider only temperature sensors. The HVAC application collects data from the temperature sensor(s). If the temperature value is higher (or lower) than a given threshold (in our example higher than 25Celsius degrees and lower than 20 degrees) the application sends a command to the air-conditioner to adjust the room temperature accordingly. The air-conditioner will turn itself off in case of finding temperatures over 35 degrees, because if the room temperature reaches this threshold, the air-conditioner is not being successful in restoring the temperature to a pleasant value, what could be due to a mechanical fault or some other problem such as the occurrence of a fire.This is a common behavior on smart air-conditioners and it avoids that, during a fire, electrical problems with the air-conditioner increase the fire. The Fire Detection application also periodically collects data from the temperature sensor(s). In this case, if the value from the temperature sensor is above 45 degrees, the application must send an alert about fire. These are simple applications aiming to outline our ideas about IFTs applied to SSAN. We have named our proposed IFT as an “enhanced” version of the Moving Average Filter. A. Traditional Moving Average Filter The Moving Average Filter [11] is a widely used IFT in the digital signal processing field that is computationally simple and capable to reduce signal noise, making it very interesting for the WSAN scenario. This filter computes the arithmetic mean of a number of input measurements to produce each point of the output signal. Given an input digital signal z = (z(1), z(2), ...), the output signal x = (x(1), x(2), ...)is estimated by equation 1, where M is the window size. ( )
∑
(
),
. (1)
The window size is the most important parameter to this equation since M is used to detect any changes in the signal level.The greater the value of M is, the cleaner is the output signal. Although simple to understand and use, Moving Average Filter deals only with measurements and does not deal with higher semantic levels, such as decisions. B. Enhanced Moving Average Filter In the Moving Average Filter technique, all the sensed data values are similarly weighted. However, in an environment with multiple applications, some sensing values could be more important to a given application than to another. For instance, a fire detection application is more important than a HVAC application and higher temperature values are more important to a fire detection application than to a HVAC application. A temperature value of 50 degrees has little significance to HVAC application, since the operational interval of this application does not consider such high temperature values, but it is a good indicative of a fire. If we consider all the sensed values equally, this could lead each application to present a
biased result. In the Enhanced Moving Average Filter (EMAF), we need to evaluate a given measurement considering a target application. In order to make this approach possible, we have modified the traditional equation of this technique, producing Equation 2 described below. ( )
∑
μ (
),
,μ
0, N = ∑
μ (2)
In Equation 2, M is the window size, z = {z(1),z(2), z(3)…} are input data, x = {x(1), x(2), x(3) …} are the data estimated by the method, μ is the weight given to a value based on its importanceto an application, and N is the sum of all weights. An application expert is responsible for choosing the weights. The Exponentially Weighted Moving Average (EWMA) [10] is a variant of the Moving Average Filter, which weights the data of each sensor to better evaluate a given environment. Our approach is different from the EWMA [10] since instead of assigning weights to the data of different sensors, we assume that all the sensors are capable of monitoring the same kind of information (temperature for instance), but the relevance of this information will change according to the requirements of each specific application that will use the data. IV.
EXPERIMENTS USING THE ENHANCED IFT
We performed tests both in a simulated environment and in a real sensor platform to evaluate the proposed enhanced IFT (EMAF).The designed SSAN consists of several sensor nodes, an actuator and a Sink Node. A sensor node can play the role of: (i) Collector nodes (CN), responsible for collecting data and sending it to the fusion node; (ii) Fusion Node (FN), responsible for executing the fusion methods and sending the result to an actuator and to a sink node; (iii) Actuators, responsible for taking physical actions on the environment. The node that plays the role of Fusion node was previously chosen. One sensor cannot be a Fusion node and a Collector Node simultaneously. For all the performed experiments, either using real or simulated nodes, the network was composed of MICAz sensors endowed with TinyOS 2.1.1 development environment [9]. The experiments on real nodes were conducted in a closed environment (laboratory). The simulated scenarios were performed with the TOSSIM simulator. It is important to notice that we used only standard TinyOS routing protocols. All the simulated experiments had the duration of 1 hour and each test was repeated 30 times, with a confidence interval of 95%. A. Scenario Our experimental scenario is based on common and important equipment from the smart grid scenario: the transmission tower. A transmission tower is a tall structure, commonly a steel lattice tower, used to support an overhead power line. In the smart grid context, the transmission tower is also responsible for storing energy using local batteries. An overhead power line is an electric power transmission line suspended by towers. Since most of the insulation is provided by air, the overhead power lines are generally the lowest-cost option for transmitting electric energy in large scale. We chose two applications for testing our enhanced IFT: the “overhead power line monitoring” and the “battery monitoring”. The first application has great importance because in the last few years the loads of the power lines have been
increasing. The knowledge about the power line temperature is necessary for making decisions about its loading. Due to the increasing load of the power lines, more electric energy turns into heat. This heating could damage the lines causing operational failures, which could lead to blackouts. The usual and safe line temperature is around 40°C - 65°C [5,7]. In the battery monitoring application, batteries are used as a solution to solve the problem of unexpected peaks in the electricity demand. Overheating could damage the battery; therefore the temperature must be monitored. High temperature values could also be an evidence of energy waste [6] (electricity turning into heat). The usual battery temperature is 40°C – 144°C. Our scenario consists of four CNs monitoring a battery and four monitoring a transmission line. The eight CNs send their data to a FN that performs the IFT. All CNs are in radio range of the FN. The FN sends its data to a Sink Node, which is responsible for sending information to external networks, and control messages to an Actuator Node, which takes the appropriate actions to guarantee the integrity of the monitored equipment. The battery is located inside the transmission tower. CNs monitoring the battery are mounted on the battery surface. CNs monitoring the transmission line are mounted along the line of the transmission tower, 2m apart from each other. It is important to mention that all the experiments adopted the same scenario in terms of: topology (nodes were static throughout the simulation); number of nodes; and routing protocol. To analyze the IFT efficiency, we defined a metric called “behavior’s change detection rate”, which measures the percentage of times that an IFT is able to correctly detect a relevant change on the applications’ behavior. In the context of the overhead power line monitoring application, a change on the application’s behavior occurs whenever temperature values are above 65°C, which could damage the power line and cause a transmission failure. In the context of the battery monitoring application, a change on the application’s behavior occurs when temperatures are above 144°C, which could damage it. B. Efficiency of MAF and EMAF A comparison between MAF and EMAF was conducted, allowing assessing the efficiency of both approaches in terms of “behavior’s change detection rate”. It is important to notice that when we evaluate MAF, we apply such IFT to each application individually, since the advantage of our approach is to enable dealing with multiple applications simultaneously. We also evaluated energy consumption, memory usage and delay. CNs collected temperature samples at every 15 seconds and transmitted them to the FN. FN performed the IFTs once per minute and sent the resulting fused data to the Sink Node as well as a control message to the Actuator Nodes. The initial air temperature, battery temperature and the overhead power line temperature were respectively set as 25°C, 44°C and 44°C. Battery and overhead power line temperatures changed according to the thermal models presented in [7]. To evaluate the impact of dealing with multiple applications simultaneously, we set time slots called T1, T2, T3 and T4. Each time slot represents a given application state. T1 represents ideal condition for both applications, which denotes situations in that both applications do not detect changes in
their behaviors; T2 represents an increase in the overhead power line temperature implying that the overhead power line application detects a change on its behavior. T3 represents an increase in the battery temperature; thus the battery monitoring application detects a change on the application’s behavior. T4 represents the occurrence of a change on the application’s behavior on both applications. We expect that: in T1, no application alerts change on its behavior; in T2, only the overhead power line monitoring application alerts a change; in T3, the battery monitoring application alerts a change; and in T4, both applications alert changes. Table I shows the achieved results. The first line represents how effective EMAF is in detecting changes on the application’s behavior. The second line represents how effective the battery monitoring application is in detecting a change on the application’s behavior. The third line represents how effectively the overhead power line monitoring application detects a change on the application’s behavior. The fourth line represents situations where traditional methods deal with multiple applications simultaneously. The columns represent the time slots. When both applications are in an ideal condition (T1), the results obtained by EMAF were better than those obtained by the traditional IFT in terms of behavior’s change detection. In the case where only the overhead power line monitoring application alerts a change (T2), it was observed that the results obtained by our enhanced IFT were also better than those obtained by the traditional IFT in terms of behavior’s change detection rate. In T2, since the parameter range of the overhead power line monitoring application is within the battery monitor application range, we had excellent results. When only the battery monitoring application alerts a change (T3), we observe from Table I that the results obtained by EMAF were again better than those obtained by MAF in terms of behavior’s change detection rate. However, our enhanced IFT had results worse in T3 than those found in T1 and T2. This happens as a consequence of the fact that the overhead power line monitoring application, which did not present change in T3, had a temperature range not included in the behavior’s change range of the battery monitoring application. The higher values brought by the battery monitoring application biased the final result. TABLE I.
IFT COMPARISON IN TERMS OF BEHAVIOR’S CHANGE
DETECTION RATE
T1
T2
T3
T4
EMAF
89,3%± 4,7
90,4%± 2,3
74,5%± 5,5
90,8%±3,6
Battery
100%
95,2%± 2,4
94,3%± 2,1
94,2%±0,3
Overhead power line
100%
93,8%±1,4%
93,4%± 1,5
94%± 1,2
MAF
72,1%±4,9
77,4%±2,9
44%± 6,1
78%± 2,1
When both applications alert a change (T4), the EMAF results were better than those obtained by the traditional IFT in
terms of behavior’s change detection rate. Our improved results are a consequence of the fact that the states of both applications represented changes. Due to this fact there was almost no misinterpretation of the data by EMAF. On the other hand, it was observed that MAF did not achieve good results, since such method cannot handle distinct data ranges properly. In T4, since both applications were in a state of change, our results were very good. The same scenario simulated using TOSSIM was implemented on a real sensor platform, in order to perform a fair comparison of achievedresults andto validate the performed simulations. Regarding the behavior’s change detection rate, the simulated experiment performed slightly better than the experiment with real nodes. Table II presents the difference between both environments. Each row represents one of the defined time slots; each column represents the difference between simulated and real results. Again, such difference can be explained by the presence of interference on the real nodes experiment that induced some packet loss that could not be matched by the noise simulation component of TOSSIM. TABLE II.
REAL X SIMULATED ENVIRONMENTS
Time slot
Difference between real and simulated environmentin terms of behavior’s change detection rate.
T1
1,5% ±0,9%
T2
2,3% ±0,7%
T3
2,4% ±1,4%
T4
1,9,0% ±1,1%
In terms of delay, both experiments behaved similarly. The delay in the simulated experiment was slightly higher than the real experiment, which was, on average, 14,562 and 12,739 ms, respectively. The experiment on real nodes consumed less energy than the simulated experiment. On average, each CN consumed 788964 J and the FN spent 719849 J, while in the simulated environment the obtained values were 801792 mJ to the CN and 7431552 mJ to the FN. Our Fusion system was deployed on MICAz sensor platform (4Kbytes of RAM and 128Kbytes of ROM). Components required for the role of CN consume 116 bytes (2.8%) of RAM and 1054 bytes (0.8%) of program memory; and for the role of FN consume 99 bytes (2.4%) of RAM and 2956 bytes (2.3%) of memory footprint. The external flash memory is completely available for the file system. Hence, it leaves the majority of the storage resources available for usage by the node operating system and applications. There are two kinds of control messages in our SSAN design: the message sent by the CNs to the FN, with 3 bytes length, and the message sent by the FN to the sink node, with 2 bytes length. Moreover, there are application data messages with 2 bytes length sent by the CNs to the FN.
V.
CONCLUSIONS
This paper presented EMAF, an enhanced fusion method (an extension of Moving Average Filter) for Shared Sensor and Actuator Networks. EMAF enhances the traditional fusion technique to work in the Shared Sensor and Actuator Networks environment, through exploring application similarities on data thresholds. We concluded that parameters range and the weight given to each application (a priority given to each application) are extremely important to lead to better fusion result. Since applications have different degrees of importance, it is logical to assume that its data have different degrees of importance. If an application with little importance but a large parameter range is considered equally, it will lead to an error. If we consider the source of the data, and weight it according to its importance, the fusion will give a result closer to reality. As future work, we intend to analyze and extend different fusion methods found in the literature to the Shared Sensor and Actuator scenario. Moreover, we intend to investigate how to combine different fusion methods and find combinations that have the best result in terms of behavior’s change detection rate. A framework able to decide which fusion method to apply given a certain amount of data is definitely a future work. Through some strategy, such as computational Intelligence, finding the most appropriate fusion method is useful in environments where applications change quickly, such as a SSAN scenario. REFERENCES [1]
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