International Journal of Interactive Mobile Technologies (iJIM) – eISSN: 1865-7923 – Vol 17 No 11 (2023) Paper—The Impact of a Collaborative IoT Framework for Smart Cities and Environmental Monitoring The Impact of a Collaborative IoT Framework for Smart Cities and Environmental Monitoring https://doi.org/10.3991/ijim.v17i11.36277 Suzan Husein1(), Darun Mudhafar2, Saravana Balaji B2 1 Salahaddin University, Erbil, Iraq 2 Lebanese French University, Erbil, Iraq suzan.husein@su.edu.krd Abstract—This paper proposed an architecture that is SenSquare, it is capa- ble to carry various data sources which is coming through an open IOT program and throng sensor campaign, as well as displaying an integrated access for the utilizers. The goals of smart cities are to progress the quality of citizen in life through leveraging details on process of the urban scales that removed by diverse data source gathered at city broad deployment. In this paper checking the whole surfaces of a compound system, traversing through problems of various quality, this paper contract with the diverse data ranking, the Mobile Crowd sensing (MCS) administration to data ecological, acting information and integration, the services of IOT installation and redeployments. This study explained the sug- gested resolutions in cooperation with a functions and current ways to sitting un- lock defies. Eventually, it pretends the ability of the SenSquare by jointly the desktop client and mobile. Keywords—IoT, CBM, MCS, DS, SenSquare, CCU, EPA 1 Introduction The technological improvements in technology of wireless communication and elec- tronics of consumer drove the smart device to work for ubiquitous calculating and spread sensor in the applications of the smart cities. Because of that the urbanization of world keep goings to develop with the predictable overall populations multiply the 2050, there’s a universal direction on the way of smart cities [1]. The smart city is one of the most important solutions that enable us to go on with daily life during the pan- demic and beyond [2]. That’s fundamentally devote for the rate lowering to devices that creates them further obtainable to anyone, and the raising amount of the services which these appliances get to utilizers in a common actuality [3]. Usually, every internet of thing’s manufacturer supplies their own cloud, they pos- sess services, creating IoT essential island, or so-known “Intranet of Things”. Internet of things used in solving education problems, and the most important applications of the Internet of things in education and its importance for students and teachers [4]. In- ternet of Things (IoT) is one such technology that is sweeping the economic, business and service landscapes with its disruption mechanisms that influence areas such as 68 http://www.i-jim.org https://doi.org/10.3991/ijim.v17i11.36277 mailto:example@example.org Paper—The Impact of a Collaborative IoT Framework for Smart Cities and Environmental Monitoring smart homes, hospitals, cities, as well as transportation and energy utilization with many exciting and novel applications [5]. Controlling that determination is a complex function to networking with people and programming proficiency of computer, almost unbearable to people excluding the abilities. These directions altogether with impressive development of the technologies of IoT for environmental controlling, drove to connotation of cooperative of IoT “C-IoT”. Its commonly introduced like a pattern that are fractures the mentioned silos that are pre- senting nowadays in perpendicular markets through allowing interoperability and tele- communication over humans, governmental existence and projects [6]. Actually, this connotation until now poorly explored in the literature and presenting actions usually concentrate on few or one of their sides without occupation in to the huge picture’ ac- count. The major connotation over that action is based is the ability of ending utilizer to become an important section of data collecting instated of just service consumers. The throng sensor is allowing notion to this pattern, according to this, utilizers could effectively review data which could preferably be utilized either through them or through the others, besides a raising locative covering as well as without the requiring to deploying controlling networks sensors immediately in the area. Furthermore, like most sensors that are previously obtainable in the modern smart devices, each utilizer could share in the data collecting or without own devoted sensors. These solutions are known like a Mobile crowd sensing (MCS), it has the significant of becoming effective in cost, when as well as supplying the true data time. As stated at Figure 1 explain a various building prevents of the smart city, containing smart transmission, smart water, smart power, smart civilian services, smart water, smart structure, smart unwanted col- lection, and smart houses. iJIM ‒ Vol. 17, No. 11, 2023 69 Paper—The Impact of a Collaborative IoT Framework for Smart Cities and Environmental Monitoring Fig. 1. Different Applications of Smart City 2 Related work On of technologies of the opener enabling to smart cities supplication is the IoT that mostly indicates to interconnect the smart topics through technology of internet extend- ing and the IoT closely circulate in different cities. Internet of Things (IoT) sample expected to include a massive effect on humans’ life. Studies display that the enormous raises in devices number for every person, notable a skyrocket 50 billion from 2020 that interprets bigger than 6 appliances for every person, on the average [8]. Till now, its un necessary characteristic to IoT’ future, like the utilizers must become able to modify their included services through creating sense on various obtainable data cur- rents to some extent than existence stuck beside what inventive through the creator. That’s essentially fact when think about and consider cooperating script which seek individual to action together like a mobilize. Understanding the environments and re- vealing prospect hazards to citizens as well as nature always has been a critical stage of interesting in science of environmental. Particularly, the area which matches beside this study fall in the group community based monitoring (CBM). This pattern defined like “an operation where focused natives, the government organization, Academia, industry, collection of community, and regional institutions participate to monitor, path and reply to the problems of general community environmental attention’’ [9]. The CBM envi- ronmental occurs and diffused in many schemas and it’s classified on the upper of the both abilities and the consciousness that given to entrant [10]. 70 http://www.i-jim.org Paper—The Impact of a Collaborative IoT Framework for Smart Cities and Environmental Monitoring The written works on human cooperative labours taken over CBM and IoT conno- tation is enormous, truly the CBM could also become active in various appearance. Indicating to the “advisory CBM” for more details, whenever residents are taking part in gathering data as well as gauges without existence of necessarily implicated in mon- itoring the outcomes neither of resolutions taken over them. It named as “collaborative CBM” a pattern in that involved are yet the essential exporter of the information, as well as they could gain access in to results and could take resolutions on the future guidance. Cooperative CBM acts additional compound structure of the utilizer pool, such as, it could contain residents, stakeholders, consumers and producers, as well as could become moreover classified [11]. When completely distributed, Internet of things (IoT) would enable a massive diversity of the modern services to become obtainable in the society [12]. Manufacturing smarter cities posing several technological defiance of the IoT, cloud, big data [13], between another technology, as well as supplies chances to modern ap- plications (perpendicular) like the smart energy administration [14]. System of Context aware gain various kinds of the information to adapt and logic their behaviour unes- corted by explicit utilizer intrusion [14]. Recently, beside the coming of internet of things and many applications in the smart cities, the management of aware context to an increasing extent utilized within field of huge analytics data [15]. Irrespective of hoe the data sensor and another information are succeeding, the context requires to become completely understood as well as neatly considered of the IoT founded applications for warranty an efficient, dynamic as well as scalable process of resolution making. Fur- thermore, cooperative CBM acts a fundamental significance to the contributor, so it seeks minimum outright incentives for reaching a satisfying covering. As well as, power granted for both the harmful and the inexpert utilizer may be serious to the data truthfulness [16], an instance of one of these campaigns awarded through Louisiana Bucket band [16], the health of ecological with the justice community gathering paper for participants and the initiatives related pollution of petrochemical by observers. Nu- merous IoT founded infrastructure, trade and not, depend on this connotation. And this is the status of Xively [17], AllJoyn [18], ThingWorx [19], and Cumulocity [15], this resolution is strong during whole the existences managing performed inside them, as well as they conduct like islands of IoT that include small or without interoperability beside others. This could drive to increase the data with unavailability while coopera- tion with monitoring of environmental, during interesting of data to the mutual ad- vantage. Further, and it connect the utilizer with a particular producer that may not supply whole the appliance requires, thus the possibility limiting of this infrastructure. Actually, several integrate architectures suggested in the scientific written works, trying to control this issue and trying to act a stratum to combine diverse networks of IoT for one, distributed and scalable, IoT universal system. And one of the maximum remark- able attempt is awarded at [19], that the writers visualize a singular, Internet such as, IoT’ architecture, acting careful 3 layered modules, that things are absentminded, inter- preted semantically and virtualized. iJIM ‒ Vol. 17, No. 11, 2023 71 Paper—The Impact of a Collaborative IoT Framework for Smart Cities and Environmental Monitoring 3 SenSquare architecture This part focused on donating transported through SenSquare, the practical data pro- gram at which experience and algorithms acted and executed inside this paper. The SenSquare are suggested lately like a singular planning to the ecosystem depending of the data on Crowd sourced that able of supplying aggregated services to the mutual advantage [19]. Various IoT founded data programs are recently establishment like the systems of isolated, disabled to share or communicate information together, thus con- frontation data multitude, generated through various sources usually to the identical objective. For controlling this problem, the SenSquare is recently advanced inside the area of an integrated architecture, capable to steward data awarding through various actualities and making uniform the path that data id acted. If we uniformed the coming data from governmental sources and official, the open sources of data and sources of Crowd sensed, to that we would outline the variances and Section IV-A’ details. Gen- erally, the structured data in the “Data Streams” (DS), that instantiated beside the in- formation of the static meta on the kind of data row supplied and updated periodically through data source beside modern measurements. Every modern measurement needful arrives beside a data of spatial (coordinates GPS) as well as temporary data (timestamp). Source, practically if the end utilizer sharing to the measurements through means of the approach of a Crowd sensed, may be abide to an operation that founds many re- warding related sort to providing measurements. If the source is not supplying data typically on a constant average, actually, every modern data upgrade provided is con- nected beside a answer, that includes a stating configuration where or when to extend the following upgrade. This decision possessed according to a group of principles that are established through governmental stakeholders or industrial, which are concerned in Crowd sensed data. This mechanism’s algorithm is characterized in [19]. 3.1 Data sources The major SenSquare’ contribution is awarded through its general approach to overcome with various sources. Particularly it merges outcomes beside a broad assortment of various features that extension through a high or low reliability as well as high or low update average. This section summarized source that considered in recent state of system implementation. It known as the Reliable Resources these are coming through official institutions or governmental, to that environmental sensing which the major business concentrate. 3.2 System architecture As it has been explained previously, the SenSquare’ architecture is built like a sys- tem of client-server centralized, that there’s no link of communication is potential be- tween the clients, the entity of centric such a CCU (Central Coordination Unit), that is divided to various modules it consecrated to several functions [8]. Every module indi- cated to a CDB that is unit gathering the raw of data on whole the related measurements, 72 http://www.i-jim.org Paper—The Impact of a Collaborative IoT Framework for Smart Cities and Environmental Monitoring whole the streams of data, the commands, and data on the utilizers and the services of instantiated. The CDB’ structure summarized and displayed at Figure.2 besides the re- lational scheme. Fig. 2. SenSquare Architecture, outlining whole the variant facets that clients could contribute to data gathering 4 Batch classification Awarding data homogeneity is not a simple function. This section describing an ex- tra phase that required to be possessed when gathering data are coming through Unre- liable Resources that it has been considered: the ThingSpeak7 with SparkFun8. Thing- Speak is an open source data program started in 2010 through the ioBridge that supplies an individual cloud to every utilizer that it is potential to storage measurements and displaying a simple API and tool of an analysis to a simple entrance to this information. The SparkFun Electronics Ltd. was established in 2003 as well as discovered their ma- jor activity like a microcontroller seller and manufacturer for unlocking source hard- ware. However, it hosts unlocking source data cloud beside functionalities comparable with the ThingSpeak’s despite of the negligible variations. As demonstrated at [5] the cohabitation of both programs is related since the utilizer’s distribution subscribing to this programs is driven geographically, i.e. The ThingSpeak utilizers are more repeated significantly in Asia and Europe, while the SparkFun is more common in US. Particu- larly, it has ordered in “data channels” that include one data streams or more relative of a various data class each. Whole the data streams are upgraded at identical time with a singular operation. This data is not labelled uniquely with a class of data and infor- mation on the kind of data extended is not secured to become reliable. In fact the fields of data are totally labelled over the utilizers’ decision, so they may outcome often not even relative within data itself. At the SenSquare it has been introduced the “Data Class” entity that such a four letter uniquely tag which identify the kind of data together beside its section of measure, like it displayed in Figure 3. This tag, jointly with the iJIM ‒ Vol. 17, No. 11, 2023 73 Paper—The Impact of a Collaborative IoT Framework for Smart Cities and Environmental Monitoring geolocation, and is consider compulsory to the aim of service composition and interop- erability, that are the concepts of fundamental that describe the ecosystem. Whereas the geolocation not able be infer while not specified, the data class could be recovered through the stream name, the description, and the tags, and the name allocated to the channel of data. Within the project’ scope it has been designed a ranking algorithm capable to infer the class of data of a constant percentage of the streams of data. This algorithm utilizes an approach of supervised learning, As stated at [15] tested on a group of 2000 streams of data, stochastically removed through whole the geolocated streams that belonging to both the programs. For performing the test it has been manually cat- egorized whole the streams discovering a group C of 33 various classes of data. It re- quired to remove 800 stream of data, for one following reasons or more: (i) the classi- fication considered very difficult also to the true people, such as cases during fields named at a progressive path as field1, field2 so on as well as the description of channel and the tags become absent, (ii) the stream or the data label has been considered sense- less, as in movement, alarm, utilizer or during whole measurements report of a default worth as 0, a exemplary behaviour of the trial examples, (iii) A class has been consid- ered not relevant to the aims of this project, such as, the streams reporting measure- ments of the CPU usage. Consequently, as stated a [1] it has finished with 1200 constant and manually commented on the data streams that they built up their algorithm. It has been chosen to implement on their dataset DS an exemplary 10- fold over validation [9] that has been exceedingly acceptable in literature upon the years for the aim of clas- sification of text in various fields of application [7], because it keep away from loss of important features while dividing a training dataset and group of test sets. As a conse- quence, for every iteration, identifying 120 streams like the trial group Tst as well as the rest contained in the coaching set Train. Algorithm actions like follows: to every class c ∈ C, initializing a clear Dc dictionary that has been loaded with the c(Train), i.e. the whole streams’ name in the coaching group belonging to this class. After that, to every name of stream s ∈ Tst, it has been calculated its resemblance with every class, choosing distance (s; c) = min {wd(s, t) | t Ꞓ Dc)}, this is, the smallest value of editing distance among s as well as whole the dictionary’s terms to this class. To the aim of edit distance calculating, it has been utilized the Damerau-Levenshtein distance [6]. It has been also noticed that many streams are very small names, e.g. the temperature is often specified with “t” or “t1” only. Because of some reason requiring to print the correct distance calculation, i.e. separating the distance of Damerau- Levenshtein that is through absolute definition, from the biggest worth among the two operands’ length like: 𝑤𝑤𝑤𝑤(𝑠𝑠, 𝑡𝑡) = (𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑠𝑠ℎ𝑡𝑡𝐷𝐷𝑡𝑡𝐷𝐷(𝑠𝑠, 𝑡𝑡) )/(max ��𝑙𝑙𝐷𝐷𝐷𝐷𝑙𝑙𝑡𝑡ℎ(𝑠𝑠), 𝑙𝑙𝐷𝐷𝐷𝐷𝑙𝑙𝑡𝑡ℎ(𝑡𝑡)��) ∈ [0, 1] (1) However, because of the extreme utilize of robust stream names abbreviations, uti- lize stemming are not chosen. Thereafter, appointed the s flow into class c0 like the distance (s; c0) = (min {distance (s; c) | c ∈ C}), it’s, a class to that a smallest correct range through s a smallest between whole classes. For achieving the metrics, indicated to macro rated (F_measure (MF_Meas)) computed as follow: 74 http://www.i-jim.org Paper—The Impact of a Collaborative IoT Framework for Smart Cities and Environmental Monitoring 𝑀𝑀𝑀𝑀 − 𝑀𝑀𝐷𝐷𝐷𝐷𝑠𝑠 = (2. 𝑀𝑀𝑀𝑀𝐷𝐷𝐷𝐷𝑀𝑀 . 𝑀𝑀𝑀𝑀𝐷𝐷𝑀𝑀 )/(𝑀𝑀𝑀𝑀𝐷𝐷𝐷𝐷𝑀𝑀 + 𝑀𝑀𝑀𝑀𝐷𝐷𝑀𝑀) (2) That the M Prec and M Rec are, the averages weighted of the accuracy respectively, and the calling back calculated on upper of matrix of confusion to every class. The weighted rate is calculated through the selecting the current number of monitoring re- ferring to every class like a discriminant’ normalization. As stated at [8] Table 1 sum- maries the outcomes that gained through the cross-validation. Like it could be con- cluded through the table, the used algorithm achieved a good baseline outcome. Table 1. Evaluation Metrics from the Data Cross-Validation MPrec MRec MF-Meas Mean 0.8883 0.8953 0.8918 Std Deviation 0.0317 0.0275 0.029 Table 2. Symbols Used in the Algorithm Train Training set T st Test set D i Dictionary for the class i. C Set of all classes. Name(f) Function extracting the name of the stream t. C real Real class to which stream t belongs, as annotated manually. Cassigned(t) Class to which our algorithm assigned the stream t as belonging to. Wd(s,t) Our normalized edit distance between the string s and t. Distance(s,c) Minimum edit distance between the string s and all the string in Dc. T Pc True positives with respect to class c. FPc False positives with respect to class c. FNc False negatives with respect to class c. MPrec Weighted average precision of the algorithm among all classes. MRec Weighted average recall of the algorithm among all classes. Mf-Meas Harmonic mean of MPrec and MRec. 5 Implementation and testing The data which makes gathering between the utilizers are raw information typically on many places defined. From the default, an entrant must continuously become ex- tended beside the measurements access which her or his personal appliances are creat- ing. Mostly, this measurement is focusing on the interest environment to such utilizer, e.g. a place that she or he living. And that is the state of the weather, to that, like is said, that the access to environmental data created could become a worthy source of earning. Some arrival are extended in shape of a interface servicing known as PSI (Personal Service Instance), a straightforward view of pure data linked for the identical appliance. With the top considerations mentioned, beside the respect to the crowd sensing pattern, iJIM ‒ Vol. 17, No. 11, 2023 75 Paper—The Impact of a Collaborative IoT Framework for Smart Cities and Environmental Monitoring the used program goals at extending whole utilizers beside a universal access for sensed data. For this reasons, performed a process with the help of that utilizers could overall currents raw data and form services, which could become exploited through the other utilizers too. And could straightforwardly think on whole the famous information which can be obtained from integrating measurement of raw sensing like the humidity and temperature, and it’s the state of humidex or the (Heat Index) that is a calculation of derived calculated over the temperature’ worth and the moisture and it is generally in- dicated like the “temperature of human-perceived”. One more example is awarded through the Dew Point that the matched to the largest temperature on that water vapor in air would intensify and by the liquid dew. That is dependent again at the humidity values and the temperature of air and could calculated over some measurements. Furthermore, the derived quantities’ definition could be pro- vided to the custom ones. Such as, beside the scope automation of house, an entrant may be attentive in automatically opening the windows when the temperature of envi- ronmental receive a worth upon a confirmed threshold. As well as, continuously, some participant may desire to join the temperature value with many other because of the confirmed requirements, such as she or he may become sensitive to pollen, so, should there be a top concentricity of pollen of airborne, the entrant desire more utilize the conditioning of air. And this way is the straightforward aggregation instance of data like a service of tradition which the utilizer could produce that, consequently, outcomes in an energy combination storing and secure health. At the suggested planning, the “air- ing for pollen intolerant” service deliberated to become produced only once and could become an example through various entrant in several locations, extended that the true sensors are obtainable in some places. 5.1 Web interface The purpose of this section is to display a real application to the program service, sophisticated like a (RESTful) Web implementation. Client interface’ Screenshots are shown in Figure 3. And at the Figure 3(a) displays the interface beside that utilizer could form the CST through entering a title, choosing a result kind along with after an aided creator to create the term. In this particular state, the last utilizer wants for producing a (CST) beside that she/ he could watch its circumstances benefit environmental for deciding when an exercise like jogging is not appropriate because of a harmful amalgamation to large moisture with large concentricity from the air (PM10). Like it‘s displayed within the photo, outputting phrasing is (100 -(PM10 *0.84)) – ((Humidity * 0.63)). Beside mention of the table with data class used in this section, the Humidity is weighted like a worth among (0 & 100), whereas (PM10), that attitudes in order to concentrate rough particulate issue (beside the diameter among (2.5 & 10 μm)) of the atmosphere, it’s weighted in the (μg/m3). Present day produced (CST), produced like Jogging, it’s deliberate to restoration a favourable digit if that's the case is regarded as passable, adverse or else, Figure 5(b) displays activities of a utilizers trying for instantiating the CST beside the (C deployment) inside outskirt on cities like Italy, as well as "Bologna.". 76 http://www.i-jim.org Paper—The Impact of a Collaborative IoT Framework for Smart Cities and Environmental Monitoring 5.2 Mobile application and widget The Android mobile application has been described in this section that called (Habitatest) that is made up of activity of the android for combining all services, as well as widgets to show the services of utilizers. As shown at Figure.3 (a) displaying application of Habitatest major screen, which the utilizer is Capable to choose the services to monitoring. Selection could be creating any from putting in the ID string or from QR code scanning that could become recovered by the web service. The utilizer could announce every services number of interest to become watched by the Habitatest implementation and an upgrade frequency. (a) (b) (c) (d) Fig. 3. Web interface Screenshots to service production and making example. The Figure 3(a) displays the customized assistance production; Figure 3(b) acts the intended sensor se- lected a service to be instantiated; Figure 3(c) such a list of the possible customised ser- vices that can be invoked, and Figure 3(d) displays the operating a service, showing the coveted worth Capable to choose the services to monitoring. Selection could be creating any from putting in the ID string or from QR code scanning that could become recovered by the web service. The utilizer could announce every services number of interest to become watched by the Habitatest implementation and an upgrade frequency. Thereafter pos- sessing this stride, the utilizer is followed to activity as it displaying in Figure 5(b), by that the utilizer can choose whole data is interested into. She or he could as well as integrate jointly data of the identical class over four variant methods. The utilizer could iJIM ‒ Vol. 17, No. 11, 2023 77 Paper—The Impact of a Collaborative IoT Framework for Smart Cities and Environmental Monitoring integrate the data if it is through the mean extractor of the examples of the demand data class, their total, the largest or the smallest worth. The opinion beyond this select is the truth which the utilizer could choice various services offering the identical kind of data, as well as just an overall worth is awarded from of them. The instance might be speci- fied from the sensors amount of temperature constituted near of the users’ habitation, which, instead of choosing other or the one, but wish to watch the maximum tempera- ture in the region. Thus, the utilizer choice entire group in the image's displaying at Figure 4(a), drops with drags whole sensor array of temperature over every other as it displayed in Figure 4(b), however selects for getting highest from them like it displayed at Figure 4(c). If a utilizer is not enthusiastic in every data types, they could only drag them off the display to extract them through watching range. Thereafter appealing clas- ses of data selection, probably, which gathering, the personal with local service inside implementation are produced. The service isn’t stored on the (CCU), it’s just the user that generates it are available. Then utilizer could select to immediately watch the ser- vice by (Habitatest) application, or through utilizing a widgets that extended, like at Figure 5(a) with Figure 5(b). Thereafter choosing minimum (the Figure 5(a)) with the maximum version of (Figure 5(b)), utilizers could bring updates immediately through their home screen. If utilizer chosen rather than a class of data to be watched, then widget would award potential for the utilizer for transferring by one data kind for other by left with right arrow. A “Habitatest” application works then at the distance, collect- ing whole data chosen through utilizer with combining them with each other locally go after the given directions, supposing that the utilizer chosen to work so. As well as the utilizer contains chance to select "data class" to bring historian measurements, as it displayed at the Figure 4(d). (a) (b) 78 http://www.i-jim.org Paper—The Impact of a Collaborative IoT Framework for Smart Cities and Environmental Monitoring (c) (d) Fig. 4. The (Habitatest) application. Figure 4(a) displays the screen composition for services; Figure 4(b) such a dedicated of identical merge groups of data, alongside in Figure 4(c) that the amalgamation way; eventually, Figure 4(d) acts the charts on the wanted ser- vice (a) (b) Fig. 5. The widgets extended from the (Habitatest) APP. In Figure 5(a) displaying one straight- forward, whereas Figure 5(b) shows extra information to the utilizer, like as the s valid- ity of service area, the date, as well as geolocation information 6 Discussion Above we recount several technologies and architectures which have been suggested in the related word for finding application in the whole IoT system and the advisory that known as SenSquare. In this section, will discussing them and trying to select these platforms that are more useful. The above figures describes the big data management process at smart cities. And characterize the connected challenges within the utilizing of big data and the potential solution to control these provocation so the smart resolu- iJIM ‒ Vol. 17, No. 11, 2023 79 Paper—The Impact of a Collaborative IoT Framework for Smart Cities and Environmental Monitoring tions could be picked in time. These smart resolutions favourably impact the smart cit- ies functioning. As well as, this section describes the complete process in detail. In the smart cities, the data through various sources for example sensors, RFID, social media, and antenna, is gathered, recovered, and saved in cloud [15, 16] that supports in de- creasing the storage’ cost of and procedures [13]. Moreover, it is significant to guaran- tee the security and the privacy of big data, as well as, the techniques and the tools utilized for gathering the big data groups are usually deprived of security of the ade- quate and privacy weighted [11]. Shortage of sufficient compliance policies, presenting applications and technologies, usually breach privacy and security of the data unknow- ingly and knowingly [14,15]. For achieving the ultimate advantages without coming to terms data authenticity, it’s necessary to identify the recorded utilizers that could filter the data for drawing the worthy information out of it. The data that are filtered requires to become organised like any geographic situation and patterns’ homogeneity [17]. Different computational appliances mounted over the city extend suitable information linked to the weather and traffic. some information supports in picking decisions in timely and stores money and time many of functional appliances for instance the energy harvesting wireless techno- logical appliances, safety instruments, and IoT sensing appliances could more progress the quality of people’ life in smart cities. Different ingredients of the smart city like smart people, smart economy, smart environment, smart living, smart mobility, and smart governance [18, 19], constantly communicate with every other through infor- mation of feedforward intelligent and feedbacks producing the whole city effective and competitive. The continued interactions among machines and humans extend chances to control the challenges through defining modern methodologies and technologies. 7 Conclusion This program possesses the ability to gain the function of environmental watching to the Smart City with the considerably fine granularity, associating the connotations of the open data sources and crowd sensing, thus devices exploiting owned through end utilizers for producing services of worthy. It has been suggested an open architecture to extensions in many roads and until now authorizing the cohabitation of varied data collecting ways. For giving the heterogeneous data singular interpretability, it has de- signed a general data structure. Actually it has been displayed rather than half of inhab- itants are interested of participate at the campaign of crowd sensing in no rewarding. However explained potential on correctly distributing bigger of 80% data streams re- ceiving through uncertain martials utilizing a straightforward approach of (NLP) that could be mutual beside others to progress the accuracy and more. Eventually, this paper presented the IoT service installation implementation, the ability that displays custom- izable monitoring services and modular, to that also displayed 2 clients: the web imple- mentation and the mobile application. For completely exploit the potential of IoT, the data must be obtainable to variant services, so the utilizers could customize them and dressmaker to their behaviours. That’s why the future development of this system or of the other systems depending on the identical concept require to pay their attempts 80 http://www.i-jim.org Paper—The Impact of a Collaborative IoT Framework for Smart Cities and Environmental Monitoring across both integration and collaboration. Their value observing that several utilizers could create sensible information that doesn’t planned to be share. That’s why picking of description, to advance progress, a potential to utilizer personal information collect- ing in a location of decentralized beside different data stream by “SenSquare”, at some privacy way, interested like sensitive data safety, is protected. Moreover, data with a lot of volume increases the issue of the reliability of data that we purposed to deal them to future actions. 8 References [1] Montori, F.; Bedogni, L. and Bononi, L. (2017): “A Collaborative Internet of Things Archi- tecture for Smart Cities and Environmental Monitoring”, IEEE Internet of Things Journal, Vol.3,No.18, pp. 2327-4662. [2] Alrikabi, H. T. S., & Ali Jasim, N. (2021). Design and Implementation of Smart City Appli- cations Based on the Internet of Things. 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[18] Atzoria, L., Ierab, A., Morabitoc, G., (2010): "The Internet of Things: A survey", Computer Networks, Vo.l.54, No.15, pp. 2787-2805. https://doi.org/10.1016/j.comnet.2010.05.010 [19] Baldau C.; Dustdar, f.; Rosenberg, M. (2007): “A survey on context-aware systems”, Intl. Journal Ad Hoc and Ubiquitous Computing, Vol.2, No.4, pp.13-21. https://doi.org/10.1504/ IJAHUC.2007.014070 9 Authors Suzan Tahsein Husein is an assistant lecturer in the Department of Software and Informatics Engineering at the Salahaddin University, she became a faculty member in 2021, and she completed her undergraduate studies in 2009 and her MSc studies in 2020, Currently interested in ITS, IoT, VANET, AI, and Computer vision (email: su- zan.husein@su.edu.krd. ORCID: https://orcid.org/0000-0001-5225-0798). Darun Mudhafar Hamad joined Lebanese French University on December 2021 as teaching staff and as the director of quality assurance of Health Science college. She completed her Bachelor Degree in Computer science / IT at the Salahaddin University- Science College. She obtained his Master at the Lebanese French University college of engineering, computer science. She has served as teaching staff at Gasha institute for 4 years (email: daroon.Muthafar@lfu.edu.krd). Dr. Saravana Balaji B working as Assistant Professor, Department of Information Technology, College of Engineering & Computer Science, Lebanese French Univer- sity, Erbil, Kurdistan, Iraq. He has completed undergraduate, postgraduate and doctor- ate engineering degree in Computer Science and Engineering. So far, he has pub-lished more than 40 research articles in various reputed international journals and conferences (ORCID: https://orcid.org/0000-0001-9077-1658, email: saravanabalaji.b@gmail. com). Article submitted 2022-10-20. Resubmitted 2023-02-15. Final acceptance 2023-03-12. Final version pub- lished as submitted by the authors. 82 http://www.i-jim.org https://doi.org/10.1109/RTSI.2016.7740616 https://doi.org/10.1109/RTSI.2016.7740616 https://doi.org/10.1109/JIOT.2014.2387155 https://doi.org/10.1002/ett.2704 https://doi.org/10.1109/JIOT.2014.2306328 https://doi.org/10.1109/JIOT.2014.2306328 https://doi.org/10.1016/j.comnet.2010.05.010 https://doi.org/10.1504/IJAHUC.2007.014070 https://doi.org/10.1504/IJAHUC.2007.014070 https://orcid.org/0000-0001-5225-0798 https://orcid.org/0000-0001-9077-1658