Efficient low cost supervisory system for Internet of Things enabled ...

ICC2017: WT04-5thIEEE International Workshop on Smart Communication Protocols and Algorithms (SCPA 2017)

Efficient Low Cost Supervisory System for Internet of Things Enabled Smart Home Md.Sarwar Kamal∗ , Sazia Parvin , Kashif Saleem¶ , Hussam Al-Hamadi§ , Amjad Gawanmeh§

∗ Depart.

of Computer Science and Engineering, East West University, Dhaka, Bangladesh, [email protected] University of New South Wales, Canberra, Australia, [email protected] ¶ Center of Excellence in Information Assurance, King Saud University, KSA, [email protected] § Department of Electrical and Computer Engineering, Khalifa University, Abu Dhabi, UAE {hussam.alhamadi,amjad.gawanmeh}@kustar.ac.ae

Abstract—Internet of Things (IoT) is a hot and debated subject in current digitalized era. IoT enables internet connectivity for all kinds of devices and physical objects. The current world is approaching towards virtualization of different types of systems that enables performing activities without direct physical interaction. The combination of high-speed internet and intelligent devices makes it easier to manage multiple jobs smoothly without the limitation of distances. The outstanding advantages of these promising technologies require the deployment and utilization of proper methods to handle the difficulties arise with these new applications. in the real world. This paper propose an efficient low cost supervisory system for smart home automation that can be managed using IoT. The proposed system is based on Apriori algorithm and will help to monitor and control all the home appliances and electronic devices through a supervisory system in a most efficient and reliable manner. Both the consumers and the suppliers will get the opportunity to manage the power distribution by monitoring the electricity consumption.

I. I NTRODUCTION Internet of things (IoT) develops a new era of technological world with a new innovation of intelligence computing [1], [2]. IoT can be termed as the connection among various kinds of digital and analog devices like smart phones, personal computer, personal digital assistance, and tablets to the internet, that create in very new approach of communication among things and people and consequently among also things [3]. The pivotal aim of IoT is to monitor and connect the physical objects/devices around us in a more smooth and smart way [4]. IoT offers to modify the shape of living by ensuring cost effective living including safety, security, and entertainment. Rapid development of technology makes the human daily life more comfortable with the help of recently designed smart systems and devices [5], [6]. Due to the rapid improvements in internet and the development of smart embedded systems, people are getting more customized in using the internet to avail numerous services around the globe [7], [8]. Daily life utilities are mostly enabled with internet protocol (IP) [9], and in the near future, many of these will smart devices that are communicating over IoT [10]. The pivotal aim of IoT is to monitor and connect the physical objects/devices around us in a smart manner [4]. IoT offers to modify the shape of living by ensuring cost effective living including safety, security, and entertainment. This technology can be applied in multiple daily life disciplines in order to enhance the life [8].

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Smart homes are augmenting more interest among all people irrespective of rich and poor throughout the world [11], [12]. The research community is putting lot of efforts in enhancing the IoT from every aspect to make it more and more efficient and reliable [9], [13]–[17]. In this scenario, an intelligent algorithm might help to handle IoT based devices to accomplish the activities of daily routine in an optimal manner [8], [11]. In this work, an algorithm approach has been proposed for home automation and metering system to increase the efficiency and reliability of power consumption control and supervisory. Initially all data is collected from the sensors and organized with the help of Apriori algorithm [18], [19] that is used to perform dynamic analysis. Based on the algorithm criteria, the common rules are generated to control the connected devices. The pivotal benefit of these rules is to train the system for upcoming events. A web portal is designed and developed in a manner to control IoT embedded appliance and other electronic devices at home from any part of the world in a reliable manner. Consequently, the electricity consumption can be determined and the automated billing can also be generated with the help of the web portal. At this stage, frequent used devices in a home can be categorized according to the number of hours the device is being used. The Section II reviews the related state of the art literature available yet. Section III presents and explains the proposed system architecture, Section IV illustrates Apriori algorithmic followed by the operation of the system that is discussed in Section V. Finally, the paper is concluded in Section VI. II. R ELATED W ORK In [20], the authors have developed a system using Representational State Transfer (REST) based Web services that does not require a dedicated server PC for monitoring and controlling environment. The authors implemented server application within Arduino with the ability to configure and enable the communication and to extract the messages in JavaScript Object Notation (JSON) format. The authors have utilized JAVA programming language based the Android Software Development Kit (SDK) to develop the Smartphone application that provides the functionalities to the user as, remote connection, then the control on devices, monitoring the devices, and schedule management. The application have the


option for user under settings to enter the network details to connect with the micro Web-server in their application. The application is synchronized with the Web-server in order to retrieve the data from actuators and sensors. In this work, no comparison of results to justify the system functionality and performance are presented by the authors. The authors in [2] proposed Internet of Things based low cost ubiquitous sensing system to monitor regular domestic conditions. The system provides basic operations as remote management and control of domestic devices to the user. The system framework and the functionality depends on the combination of information system for data aggregation, pervasive distributed sensing units, and reasoning and context awareness. The authors utilized XBee-S2 modules built by Digi equipped with sensors and is connected with a coordinator using wireless communication. The coordinator is then attached to the router runs an open source embedded Linux (OpenWRT). In this system, still internet protocol (IP) v6 is working until the router that works as gateway for packet transformation from ZigBee network based devices to IPv6 format, which means that the gateway is connected to internet not the devices. In another work, the authors in [21] considered IoT-based home automation using low-cost android phones. The authors propose two types of home automation using Bluetooth and Ethernet. Nonetheless, data sources in this designing is not clear. As a consequence, a deadlock situation arises when multiple phones try to access common web portal. For experimentation, the authors utilized micro-controller based Arduino board and a customized Android based mobile phone with customized application. The flexible standalone, low cost smart home system is presented in [22]. This system is based on Arduino platform that communicates over the internet with a customized app installed on Android mobile. The Arduino device is enabled with sensors and functionalities such as intrusion detection sensors, humidity sensors, temperature sensors, smoke/gas sensors, power plugs, and light switches to be monitored and controlled by the mobile app. The heart of the system is the micro web-server programmed and run on Arduino Mega 2560. The authors have tested and have analyzed the efficiency of the application and its connectivity over the internet. The system generates alerts based on the given thresholds only in the form of emails. In [23], the authors emphasis more on customer-centric technique to make the system more scalable and based on this technique have presented an architecture for the IoT based last-meter smart grid. Their architecture provides four main advantages first of which is seamless integration of smart grid with smart home applications. Secondly, the collection and analyzation of the sensed data through communication protocols. Furthermore, the system can be only be accessed by the authorized persons. Fourth one is to simplify interaction with nontechnical users, the system allows mapping of sensor and actuator to the layer of abstraction. This system is deployed and is tested to provides monitoring and managing power consumption in real time. The experimentation depends

on ZigBee network that connects and communicate with IoT server via gateway that translates packets from ZigBee to IP gateway. One of the recent study [24] has designed a smart home approach with moderate security and low development cost under IoT. However, this process only considers sequential approach that cannot handle congestion generated by large number of devices. Power utilization and conservation in homes in considered in another research [3] IoT device are merged in a common line. In this development, they applied image processing system to identify human functionalities. But image processing consumes longer time to select desired devices. In the same analysis, another research is designed based on WiFi and GSM technology. The problem of this work is to collect data from sensors and control according to the desired devices [25]. Though there are quite a few types of research are carried out elsewhere, still, there is the scope for more improvement of home automation system using IoT. III. S UPERVISORY S YSTEM FOR S MART H OME The supervisory system is shown in Figure 1. The supervisory system enables the user to select the way he would like to operate the smart home. It will enable the user to control and monitor several home appliances such as light, fan, TV, air cooler, washing machine etc. can be controlled. To accomplish the operations, a supervisory system has been designed. In database part of the web site, Apriori algorithm is implemented under database quarry language. Apriori algorithm will be used in order to validate the most frequent data recorded on the database. Then the user can access the supervisory system through a dedicated webpage, such as (http://ha.dcoders.net/), where he/she will send a command through a Wi-Fi router/Internet. Then, the command will receive by the microcontroller via Wi-Fi module. Afterwards, according to the command send by the user the electronic devices can be controlled. From Fig. 1, it can also define the system consists of two-way communication. The first is to control the devices using this dedicated supervisory system, as previously depicted. The supervisory system enables the user to obtain information about the power consumption by the devices, in addition, the meter reading can be obtained directly. That means the devices will send the power consumption rating to the supervisory system with the help of a dedicated hardware that can be implanted using a simple microcontroller. IV. P ROPOSED A LGORITHM A. Apriori Classifier Apriori is a classic algorithm for learning and generating association rules from large data set. Association rules are defined as IF-THEN statements that help to discover the relationships between unrelated data in relational database and other nature of data and properties. The Apriori algorithm is considered as a moderately depth preference selection approach that handles frequent item sets, which are then combined with Association Rues (AR). The association rules


Wi-Fi (Internet)

Smart Home IoT Enabled

Arduino ino Controller l

Supervisory System Processed data From algorithm

Fig. 1. Overview of Supervisory System Architecture for Smart Home.

correlate the different types of data items with preferable properties or attributes [1]. For example, let B = {B1 , B2 , . . . Bn } represents a set of binary attributes called item set, and T = {T1 , T2 , . . . , Tn }is a set of transactions called the database. Each transaction in T has a unique transaction ID and contains a sub-set of items from B. Thus, a rule is defined as implication of the form X → Y where, X, Y ⊆ B and X ∩ Y = ∅ . The set of items X and Y are called antecedent and consequent of the rules respectively. An association rule can simply defined by simple notation: X → Y [S, C]

(1)

Where X and Y are two different item sets, S is support and C is confidence, here the support count refers to the frequencies of the rules within the data classification. Support count is performed on different data level such as high and low value. High value means that rule involves a highest impact on database and low value indicates less importance in the association rules generation. support(X → Y [S, C]) = P (P ∩ Q)

(2)

Meanwhile, the confidence C defines the percentage of transactions in the rule generation. We define confidence in percentage using the following expression:

conf idence(X → Y ) = P

X Y

support(X, Y ) = support(Y )

(3)

B. Apriori Algorithm for controlling electronic device In our daily life we use different house hold electronic devices. These electronic devices are used based on a fixed time (Day or night) or season (winter or summer).We consider a database D which contains 1000 electronic device users. In our experiment we consider five electronic devices such Light (L), Fan (F), Room Heater (H) and Air Cooler (AC). We convert the using hours of these electric devices into binary flags (1/0) on different cases such as morning, night, winter and summer. Database DD records the power usage of different electronic devices for 1000 users and their usage per hour is scanned. In Winter season, minimum hours are

Algorithm 1 Apriori Algorithm for Minimum Support procedure Apriori_Algorithm(Ek , Fk ) Input: Ek : Candidate item set of size k //set of electronic device. Output: Fk : frequent item set of size k //electronic device in candidate item set. Initialize: • F1 = {f requentitems}; // initialize set of frequencies items •m=1 REPEAT • Em = candidategeneratesf romF m //generates a new candidate REPEAT • ∀t ∈ T // transaction in T, database subgroups of electronic device • do begin Em+1 = subset(Em , t) // this function generate candidates in transaction ∀e · e ∈ Em ∧ e ∈ Em+1 // for all candidates • do begin e.count + + //determine support • end Fm+1 = e|e ∈ Em ∧ e ∈ Em+1 ∧ e.count ≥ minSup // new set • end m=m+1 UNTIL Fm = ∅, UNTIL m > k, OUTPUT Ek+1 Fk+1 END end procedure

considered depending on the usage, for instance, light and room heater account for 10 hours, and an average less than 1 hour for fan and air condition. Algorithm 1 shows the proposed Apriori algorithm for processing the database. TABLE I M ASTER DATA FILE FOR CLASSIFICATION WITH DIFFERENT ELECTRONIC DEVICES IN WINTER SEASON . User ID U1 U2 U3 U4 ... U500 U501 ... U999 U1000

L>=10hr 1 0 0 1 0 0 1 0 1 0

H>=10hr 0 1 1 0 1 0 0 1 0 0

F=10hr}

826

{H}>=10hr}

789

{H}>=10hr}

789

{F=10hr,F= 10hr}, {AC = 10hr, H >= 10hr}, {L >= 10hr, AC = 10hr, AC = 10hr)( H >= 10hr) → (AC = 10hr( AC = 10hr) R2 : (L >= 10hr) → (H >= 10hr ( AC = 10hr)( H >= 10hr) → (AC = 10hr ( AC = 10hr) RW3 : (L >= 10hr) → (H >= 10hr ( AC = 10hr)( H >= 10hr) → (AC = 10hr ( AC = 10hr) RS3 : (L >= 10hr) → (F >= 10hr( AC