Abstract
With the adoption of increasing number of occupancy sensor in building premises, there is a growing concern about the inclusion of the smarter features for catering up sophisticated demands of information processing in Internet-of-Things (IoT). Although, there are various commercially available occupancy sensors, but there is a bigger deal of trade-off between the existing offered featured and actual demands of the user that is quite dynamic. Therefore, we reviewed the most potential research work carried out towards incorporating various features of occupancy sensor in present times in order to investigate the degree of effectiveness in existing research contribution with respect to problems, techniques, advantages, and limitation. This is the first reported review manuscript in occupancy sensing that offers a quick view of existing research trends as well as brief of potential research gap with respect to open-end problems that are yet to be solved in future studies.
Keywords
1 Introduction
Occupation sensing is one of the developing technologies that are used within a building or any specific infrastructure in order to sense the inhabitants within the premises. There are different forms of sensors with different capabilities that are used for this purpose [1]. Both similar as well as heterogeneous forms of sensors are used for sensing the environmental parameters of the subject in order to perform the specific level of controlling [2]. Usage of visual sensors, Pyro-electric Infrared sensor (PIR), CO2 sensors, etc. are used for counting, motion sensing, and capturing concentration readings respectively [3]. Majority of the utilization of sensors can be seen at present in bigger infrastructure, e.g., shopping malls, hospitals, bigger corporate office, etc. But all of them use very simple forms of sensors as well as sensing technologies that don’t demand much dependency. However, with the proliferation of Internet-of-Things (IoT), there is a growing need for performing more number of smarter sensing that is capable of sustaining under variable conditions [4]. Unfortunately, for the occupancy sensing in order to grow in this line, there are some definitive challenges, e.g., (i) although the sensor comes at the lower price but the software or its internal algorithm will become much expensive with the rise of sophisticated user demand. To cater up such demands, the conventional microcontroller will not be eligible enough to maintain the low cost of operation. At the same time, none of the existing commercial products or the research-based techniques have ever spoken about the reduced computational complexity that finally leads to degradation of accuracy as well as expensive maintenance, (ii) there is a higher cost involved in automating the complete process of sensing as well as incorporating learning capabilities in occupancy sensing, (iii) in order to make the sensing more smarter, the existing research is also found to include more intelligent learning techniques, e.g., neural network, support vector machine, fuzzy logic, etc. However, all of them are still in very nascent stages of development, and such smarter tools are yet to be come out in commercial market, (iv) although products before launching undergoes series of test, but they are relatively more comfortable as feature offered by existing occupancy sensors are not much (not even more than 2–3 features). However, it is potentially challenging task to subject the algorithm (under research) for appropriate testing and hence many of the existing algorithms at present is not found to be benchmarked or are found to be compliant under any globally recognized standard, (v) with more research direction on small-scale integration of such sensors, the number of constraints will only keep on increasing where maintaining a well balance between sensing accuracy and all other parameters (e.g. energy efficiency, algorithm complexity) is near to impossible challenge). Therefore, this is the first of the kind of review work towards occupancy sensor where the effectiveness of existing research work has been discussed. The proposed manuscript has briefly discussed all the existing research techniques associated with the occupancy sensing. Section 1.1 discusses the existing literature that offers information about the study background followed by the discussion of research problems in Sect. 1.2 and proposed solution in Sect. 1.3. Section 2 discusses existing research work and its contribution with respect to advantages and limitation followed by the discussion of research trend in Sect. 3 and research gap highlighting all the potential unsolved and unaddressed problems connected to occupancy sensing in Sect. 4. Finally, the conclusive remarks are provided in Sect. 5.
1.1 Background
At present, there is no significant presence of any review manuscript related to the occupancy sensing mechanism. Hence, we only discuss some manuscripts which offer good amount of information about a functional operation associated with occupancy sensor. The work carried out by Pritoni et al. [5] have discussed about the contribution of occupancy sensor with respect to energy preservation. Similarly, the base discussion of energy-related utilization and the respective corresponding approaches were discussed by Rafsanjani et al. [6]. Kjaergaard et al. [7] have presented a discussion of the fundamental methods involved in occupancy sensing. The work carried out by Kleiminger et al. [8] has discussed a unique mechanism of occupancy sensing on the basis of the electricity bill. We find that such similar technique has been used by many researchers. Zhang et al. [9] have presented a detailed report about the occupancy sensing technologies that offers comprehensive information about the various components as well as the cumulative technology involved in the design process. All these studies provided significant information about the potential features of different technologies involved in the construction of different applications for improving the performance of the occupancy sensing system.
1.2 Research Problem
The significant research problems are as follows:
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Existing research work for implementation of occupancy sensing is highly scattered that renders impediment towards clear visualization of a research problem in the specified area of radar.
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There is few research work towards reviewing the existing system of occupancy sensing for which reason it is difficult to have the true picture of progress in the area of sensing.
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Existing implementation doesn’t offer much comparative analysis with other equivalent research-based approaches that don’t provide much information about the effectiveness of the techniques.
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There are many problems associated with occupancy sensing that are yet to be addressed in existing or upcoming research work, which is quite unclear in the existing system.
Therefore, the problem statement of the proposed study can be stated as “To understand the research effectiveness pertaining to elite outcomes in occupancy sensing and extract a true picture of research trend and potential research outcome.”
1.3 Proposed Solution
The core aim of this manuscript is to offer a snapshot of study effectiveness of approaches associated with occupancy sensing presented during the years 2010–2017. The study also aimed for exploring different forms of problems that have been solved using different techniques. The technique also discusses about the advantages as well as limitations associated with the existing problems. Finally, the present manuscript discusses about the existing research trend as well as research gap, and hence this acts as a significant contribution to the proposed study. This solution can be used by the future researchers in order to understand the effectiveness of the contribution of the existing researcher towards occupancy sensing. The next section discusses about existing research contribution towards occupancy sensing.
2 Existing Research on OS
At present, there are various forms of research contribution towards enhancing the applicability of the occupancy sensing. A closer look into the existing approaches shows that there are evolutions of multiple techniques for facilitating sensing of an object with the aid of WiFi data [10, 31], patterns of usage based on battery [21], illumination factor [14, 15], etc. There are also various forms of research-based technique in order to facilitate the process where the dominance of methodology is quite higher for an experimental approach. There are also certain degree of soft-computational-based approach e.g. usage of Markov approach [12, 13], fuzzy logic [18], stochastic approach [38], clustering [39], evidence theory [40], etc. It was also noticed that existing approaches of problem identification is mainly governed by application side and therefore existing approaches using experimental methods are more application specific and doesn’t work effectively if the working environment is altered. Some of the problems addressed are occupation estimation [10, 12, 31, 35], energy efficiency [15, 22, 29, 34, 38], infrastructure monitoring and management [11, 16, 21, 28, 33, 37] etc.
Figure 1 highlights the frequently used scheme of Occupancy Sensing, e.g., (i) exploring the presence of an object; (ii) localizing the presence of an object; (iii) counting the number of objects; (iv) monitoring the activity of a user; (v) identification of the precise user, and (vi) tracking the user synching with its mobility. Existing research approaches are evident to apply CO2 as one of the physiological factors to perform detection. It also makes use of infrared sensors (PIR Sensor), ultrasonic sensors, etc. Different forms of thermal sensors are also reported to capture the thermal imageries for proper detection of the subject. Adoption of acoustic-based, as well as electromagnetic-based approach, was also seen in existing methods of detection. At the same time, there are adoptions of the different form of experimental test-bed, where different types of wireless sensors or transceivers were used for capturing the presence of any object within the monitoring area. However, the bigger challenge lies in the usage of different forms of hardware for experimenting, as well as different software for performing the simulation. The brief outline of existing research contribution is as shown in Table 1.
3 Research Trend
We carry out an explicit investigation of existing research trends from all the major research manuscript publishers e.g. IEEE, ACM, InderScience, Elsevier, Springer, etc. Only the manuscripts published between 2010 to till date is considered for this analysis.
Figure 2 shows that there have been approximately 100 or less number of journals published towards addressing occupancy sensing, occupancy estimation, and occupancy prediction problems. The trend on occupation sensing and estimation is more than prediction problems. This figure is evidence to claim that there is more scope of significant research work towards sense. Figure 3 showcases the different forms of sensors used in recent times which shows that majority of the existing studies have used sensor fusion and Image sensors instead of electromagnetic sensors for capturing the information about the subject’s occupancy. Although, there are various studies towards the adoption of other forms of the sensor, but they are less relatively repeated or chosen in different forms of other experiments. A closer look will also show that adoption of image sensors is quite higher, but it is not witnessed in any significant journals. Similarly, adoption of the ultrasonic sensor has gained slight momentum as well as PIR sensors, but utilization of them are quite less reported in the existing system. We also find that there are 161 manuscripts dedicated for addressing energy problems and eight research manuscripts for counting/estimation problems. Therefore, the existing system is found to emphasize more on occupancy sensing problem with more adoption of sensor fusion. The inclination of problems is more on energy efficiency and not much on accuracy.
4 Research Gap
Existing research work towards occupancy sensing has made a lot of progress and technological advancement that has caused them to make fast entries into the global commercial market. At present, various commercial brands offer occupancy sensors at a very competitive price. However, all the existing commercial products are also closely associated with challenges, e.g., cross-platform interoperability, easy commissioning, reduced error rates, etc.
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Deviation in research focus: There is splitted research work where the research-based community has not mentioned the criticality of research problems. For an example, by observing research trend, it can be just understood that energy efficiency is a critical problem that has attracted the researchers in this, but on the other hand there are various applications where accuracy is highly demanded (may be at the cost of energy also), e.g., healthcare application, elderly people monitoring system, etc. Even the work carried out in addressing energy problems has not received more than 40% of energy saving.
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Lack of focus on complexity: Majority of the existing studies have used recursive principle as well as a rule-based mechanism based on which the occupancy sensors are operated. Unfortunately, such schemes potentially suffer from a reduction in computational complexity that is even not computed. There is not a single study which has reported the efficiency of an algorithm.
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Less Adoption of Doppler Radar: It is now proven from the research trend that there are less number of studies that use ultrasonic sensors, which is known for using the Doppler shift methods for tracking the objects. Utilization of such autonomous sensors is gaining pace at a very slow rate. The second generation of waves that are reported to be used in IoT application suffers from lack of cost-effectiveness problems as well as privacy problems.
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Non-inclusion of granularity in research contribution: Existing research work is too much scattered on addressing the problems using different forms of research methodology, where experimental methods are more dominant. Usage of experimental methods gives good reliability measure, but at the same time, it also makes the research either much application specific or too narrow. Because of such methodology adoption, the current occupancy sensor is quite capable of identifying the specific physiological factor but fails if the patterns of physiological factors differ from usual patterns. Hence, they are incapable for sensing the complex biological signal pattern.
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Lack of capability of performing classification: Usage of existing occupancy sensing mechanism performs frequency modulation in order to control the translational motion of the subject. Such forms of the signals are represented using time and frequency in order to generate a spectrum. The stationary spectrums are quite easier to be studied and are also addressed in the majority of existing studies. However, dynamic spectrums are quite difficult to be categorized or classified that poses a bigger impediment towards occupancy prediction, which is next line of research in IoT.
5 Conclusion
This manuscript presents a discussion of existing research approaches in occupancy sensing where the prime motive is to understand the effectiveness of research techniques. After reviewing the existing research contribution, we found that majority of them have similar kinds of problems, e.g., no benchmarking, no focus on complexity analysis, less balance between accuracy and other features, etc. As a research trend, we find that there is less work towards usage of ultrasonic sensors and more experimental work has been carried out towards occupancy sensing/estimation problems. Therefore, our research work will continue in the direction of overcoming the research gap with respect to incorporating of granularity in occupancy sensing framework and further work for classification-based problems that are yet to be addressed in the existing system. Also, the occupancy sensing is often made by HD-cameras and HPC-powered image processing/machine learning algorithms.
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Mane, P.K., Narasimha Rao, K. (2019). Review of Research Progress, Trends and Gap in Occupancy Sensing for Sophisticated Sensory Operation. In: Silhavy, R. (eds) Cybernetics and Algorithms in Intelligent Systems . CSOC2018 2018. Advances in Intelligent Systems and Computing, vol 765. Springer, Cham. https://doi.org/10.1007/978-3-319-91192-2_22
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