Table of Contents

    Topic review

    Location Aware Schemes in IoT

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    Submitted by: Abdul Saboor

    Definition

    The rapid development in wireless technologies is positioning the Internet of Things (IoT) as an essential part of our daily lives. Localization is one of the most attractive applications related to IoT. In the past few years, localization has been gaining attention because of its applicability in safety, health monitoring, environment monitoring, and security.

    1. Introduction

    The Internet is a necessity for billions of people worldwide who need it to complete their daily tasks [1][2]. Furthermore, it provides various entertainment applications such as movies, music, and gaming. One estimate states that more than 58% of the world’s population has access to the Internet to perform such daily tasks. The popularity and growth of the Internet exponentially increased (roughly 1170%) from 2000 to 2020 [3]. It is transforming the world into a global village where people can connect and communicate worldwide using the Internet.
    The Internet allows different devices and appliances to connect and communicate, which led to a new domain called the Internet of Things (IoT) [4][5][6][7][8]. The IoT architecture consists of three layers: the physical layer, the network layer, and the application layer, as shown in Figure 1. The physical layer consists of various sensors attached to the subject. These sensors collect the data/information from the subject. Generally, the nature of data depends on the IoT application requirements. For example, localization-based applications require monitoring and collection of the locality information of the subject. Likewise, telehealth applications need to monitor the vitals of patients, and agricultural applications measure the temperature. The physical layer forwards the data to the network layer.
    Figure 1. IoT architecture.
    The network layer is the middle layer between the application and sensor layer in IoT architecture. The network layer aims to transmit the data/information from the sensors to the application layer. The medium of data transmission (wireless or wired) varies based on the application and requirements. Furthermore, the network layer tries to reduce the network’s data traffic and overheads using optimization techniques. The application layer is the top layer that controls the services provided to the applications. This layer offers an interface to the user to control and manage the IoT devices. Furthermore, it provides services to the application depending on the nature of the application.
    IoT is an extension of the Internet that envisions connecting all daily devices to the Internet for communications through interactions or sensing devices. These sensing devices are connected to form a network, termed a wireless sensor network (WSN) [9]. The IoT consisting of WSNs is essential for transforming the world into a smart world [10]. According to the Cisco Annual Internet Report [11], the number of IoT devices will rise from 6.1 billion to 14.7 billion by the end of 2023. Among those 14.7 billion devices, more than 48% of them will assist users in performing daily tasks, such as home automation, security, and tracking applications. Therefore, they act as building blocks in smart cities, smart homes, smart transportation, smart healthcare, smart grids, and smart industry [12][13][14][15][16].
    However, there exist numerous challenges in the development of such IoT applications. For example, health-related applications require rapid and reliable data transmission. Monitoring applications in smart environments require energy-efficient and robust protocols [17]. Similarly, there are challenges associated with cost, connectivity, and hardware limitations [18]. However, localization is one key challenge that needs to be addressed in the majority of smart applications. It is the process of acquiring an object or user’s location through intelligent devices (sensors) in an indoor or outdoor environment. It is a critical requirement in most smart applications [19][20]. An exponential increase in smartphones, wristwatches, and other intelligent wireless IoT devices is motivating researchers to develop efficient localization schemes. As a result, we are witnessing a significant rise in localization schemes intended to operate in healthcare, agriculture, environmental work, and habitat monitoring [21][22][23].

    2. Review of Location Aware Schemes in IoT

    A comparison of the state-of-the-art studies is given in Table 1. From the table, it is clear that most of the existing surveys target a particular localization domain, i.e., outdoor or indoor.
    Table 1. Comparison between this paper and published surveys.

    References

    Indoor

    Localization

    Outdoor

    Localization

    Smartphone

    Localization

    Security

    Energy

    Efficiency

    Accuracy

    Target

    Recovery

    Target

    Prediction

    [24]

         

       

    [25]

         

       

    [26]

         

       

    [27]

       

     

    [28]

           

       

    [29]

       

     

       

    [30]

     

       

       

    [31]

       

     

    [32]

         

       

    [33]

     

     

       

    [34]

     

       

    [35]

     

       

       

    [36]

       

     

    [37]

     

     

     

    Ours

    The main focuses of already published surveys were accuracy, energy efficiency, target prediction, and security. They lack some critical KPIs, such as recovery, prediction, security, and localization with smart gadgets, i.e., smartphones. Only 28% of publications covered prediction; 35% covered security and localization with smartphones. Simultaneously, no published survey covered the target recovery KPI, which is an important indicator that affects the overall performance of the IoT-based localization scheme.
    Furthermore, these surveys lacked detail and generic discussion in terms of protocols and techniques for localization schemes. Therefore, there is a need for a cross-domain survey that puts forward an in-depth discussion on the IoT-based localization scheme. A total of 40 papers were selected using the PRISMA approach. The key ideas of the selected papers are presented in Table 2. In addition to that, the selected studies were analyzed based on publishing details and target applications.
    Table 2. Key ideas of selected papers.

    Name

    Overview

    Delaney et al. [38]

    This paper presents an energy efficient routing protocol using NHs model for tree structured WSN. Apart from energy efficiency, the proposed solution has the ability to present good results in a lossy network environment.

    Alaybeyoglu et al. [39]

    This paper presents an efficient tracking scheme for high speed targets. Additionally, the proposed scheme helps in reducing the target miss ratio during the whole tracking lifecycle.

    Mirsadeghi et al. [40]

    This paper presents an energy efficient prediction based target tracking scheme for WSN. The node closest to the object or with the highest energy is selected as a CH to prolong the network lifetime.

    Patil et al. [41]

    This paper presents an energy efficient WSHAN to improve the efficiency of target tracking target recovery.

    Rouhani et al. [42]

    This paper presents a solution to resolve the boundary target tracking issues using static clustering. The proposed solution is energy efficient, reasonably accurate and reliable in terms of target tracking.

    Wahdan et al. [43]

    This paper presents a hybrid solution of static networking clustering and dynamic CH. The dynamic CH uniformly utilize the energy of member SNs to prolong the network lifetime and prediction.

    Zhou et al. [44]

    This paper presents a fusion of MMA and PPHD for multi-target tracking in an urban area. Additionally, K-mean clustering is used to calculate the number of targets at any given time. The proposed scheme results in the tracking of dynamically changing unknown numbers of targets in urban areas.

    Amudha et al. [45]

    This paper presents a multi camera based scheme for target tracking. In this scheme, the camera near the mobile target is activated while all other cameras remain in a sleep state to conserve energy. In contrast, all the cameras are activated when a target is lost to improve the tracking.

    Bhowmik et al. [46]

    This paper presents an algorithm is to improve the overall coverage and target tracking. In addition to that, the proposed algorithm uses the FSM based RSSI tracking algorithm to make it more energy efficient.

    Jinan et al. [47]

    This paper presents a multi-model framework based on the PUESRF and JPDA. It results in improving the accuracy and precision of data that makes target tracking consistent.

    Darabkh et al. [48]

    This paper presents an adaptive CH algorithm with an aim to achieve a better target tracking by efficiently electing CH and cluster members. The proposed algorithm is energy efficiency and improves the network scalability.

    Khakpour et al. [49]

    This paper presents a fusion of DCTT and PCTT against vehicular tracking in a Vehicular Ad-hoc Network. To improve the target prediction, The DCTT performs in a distributed manner while PCTT is used for a centralized prediction algorithm.

    Joshi et al. [50]

    This paper presents a static cluster based target tracking for the prediction that is independent of wireless network architecture (homogenous or heterogeneous). The proposed scheme uses a linear prediction technique to calculate direction and speed to improve the target prediction.

    Xiao et al. [51]

    This paper presents a novel ASMT using Bayesian estimation to efficiently track multiple targets. The ASMT provides high accuracy, precision based multi-target tracking, less computation and solves the data association problem in WSN very efficiently by using location state and velocity state.

    Silva et al. [52]

    This paper presents an energy efficient scheme with the ability to detect and highlight the fake node positioning and bogus data flooding.

    Oracevic et al. [53]

    This paper presents a SRPTT algorithm to prevent the rouge SN from faking its location or flooding the bogus packets in a WSN. The SRPTT maintains a balance between security and mobile target tracking by employing a reputation concept.

    Alshamaa et al. [54]

    This paper presents a novel zoning based localization technique for indoor target tracking. The proposed technique develops a belief function by combining fingerprint based target observation and evidence associated with sensor mobility to improve the accuracy of target tracking.

    Chen et al. [55]

    This paper presents an adaptive extended kalman filter to remove and update the noise covariance. The proposed solution results in improving the accuracy and reliability of target tracking.

    Panag et al. [56]

    This paper presents a DHSCA to uniformly utilize sensors during the tracking. The proposed algorithm simplifies the set-up phase time of the network resulting in reducing the overhead of the network.

    Zhang et al. [57]

    This paper presents a dynamic clustering-based adaptive filtering scheme for target tracking in a WSN. The proposed scheme consists of two stages hierarchal data aggregation technique, which results in accurate and energy efficient target tracking.

    Qian et al. [58]

    This paper presents an AUKF algorithm to enhance the robustness and accuracy of the recovery mechanism. The AUKF fine-tunes the noise covariance matrix to increase the accuracy and robustness of the recovery mechanism. The vigorous scheduling of static and mobile SNs improves the tracking probability with less energy consumption.

    Zhang et al. [59]

    This paper presents an algorithm based on a hybrid sensor network to estimate the target region via static sensors. Additionally, a movement algorithm is presented for nodes to select the location. The proposed solution results in conserving the energy by reducing the target tracking sensors.

    Li et al. [60]

    This paper presents a sensor selection technique based on POMDP to reduce the sensor selection lagging. It results in improving the target tracking accuracy and reliability.

    Darabkh et al. [61]

    This paper presents an error and Energy-aware cluster head selection algorithm to improve the target localization. The proposed algorithm improves energy consumption and simplifies the selection of cluster members. Additionally, it reduces the packets overhead by minimizing the transmission of control messages.

    Liu et al. [62]

    This paper presents energy efficient scheme with low prediction accuracy. Apart from energy efficiency, it reduces the target miss rate probability.

    Luo et al. [63]

    This paper presents a scheme to improve the target tracking for an indoor environment using a CLTA.

    Yu et al. [64]

    This paper presents a mobile node-based target tracking scheme to enhance the target tracking accuracy and transmission reliability.

    Vallas et al. [65]

    This paper presents a Gaussian filter-based multi-sigma point filter to reduce the curse of dimensionality in high dimension systems. Furthermore, it improves the efficiency of tracking the multiple targets in a WSN.

    Ghodousi et al. [66]

    This paper presents an energy efficient tracking scheme using ARIMA and UKF. The ARIMA, after observing target in equal interval, predicts its future location while UKF estimates the target location. The proposed scheme preserves the energy of SNs and improves the network lifetime.

    Liang et al. [67]

    This paper presents a trust-based distributed KF scheme for secure and reliable target tracking.

    Khan et al. [68]

    This paper presents a dynamic clustering-based verifiable multi iteration scheme to improve target tracking. The proposed scheme improves the accuracy and reliability of tracking.

    Liu et al. [69]

    This paper presents an object localization scheme to provide better localization results on the sequences undergoing shape deformation and illumination changes.

    Nguyen et al. [70]

    This paper presents a solution to improve the accuracy of target tracking in harsh radio environments. The proposed scheme is efficient in both indoor and outdoor environments.

    Ullah et al. [71]

    This paper presents an underwater target tracking scheme intending to achieve energy efficiency and tracking accuracy.

    Alberto et al. [72]

    This paper presents a multi-model tracking system by unifying fingerprint-based tracking with neural networks. The proposed system also employs a Gaussian outliers filter with neural networks to further improve the tracking accuracy.

    Liu et al. [73]

    This paper presents a scheme for tracking multiple targets in a harsh environment accurately and precisely.

    Liu et al. [74]

    This paper presents an AFS for accurate and efficient target tracking. The proposed scheme is robust and fault-tolerant with a low target loss rate. Moreover, PSO is used to fine-tune and improve the overall tracking performance.

    Mahmoudreza et al. [75]

    This paper presents a solution to tackle the multiple target tracking problems with accurate data association. It results in the prevention of false alarms.

    Li et al. [76]

    This paper presents a hybrid solution to provide accurate and reliable localization in harsh manufacturing workshops.

    Reisinger et al. [77]

    This paper presents an IMM tracking scheme unified with UKF to track the targets efficiently.

    2.1. Distribution Based on Publishing Year

    We aim to highlight the recent trends in target localization. Therefore, the papers from the last seven years (2014 and onward) are considered. The yearly distribution of the selected publications is presented in Figure 2. From the figure, it is depicted that there was less interest in localization in earlier years. However, it started rising from 2017. The last three years of research (2017–2019) comprised 67% of publications selected for this review. 2020 was just beginning when the papers were shortlisted. However, based on the trend, more contributions are expected in this domain than in past years.
    Figure 2. Distribution based on publication year.

    2.2. Distribution Based on Publication Venue

    This section aims to highlight the publication venue distribution. Our study includes various publication venues, such as IEEE, Elsevier, MDPI, and SAGE. The distribution of publications concerning the venues is presented in Figure 3. It was found that IEEE and Elsevier support most publications in the domain of target tracking—57.5% and 20%, respectively. Therefore, these two venues are recommended for localization in IoT.
    Figure 3. Distribution based on publishing venue.

    2.3. Distribution Based on Publication Type

    We have only considered conference proceedings and journal articles for the review. The distribution percentages of those publication types are presented in the form of a pie chart. From the Figure 4, it is clear that our results are mainly backed up by journal articles (67%).
    Figure 4. Distribution based on publication type.

    2.4. Analysis Based on Localization KPIs

    There are various localization KPIs, such as energy efficiency, localization accuracy, target prediction, target recovery, and security. Every paper is trying to address single or multiple KPIs. Table 3 lists the selected publications along with the target KPIs. The lifetime of sensors is a major concern in WSNs, as battery replacement is a tiring and time-consuming job. Additionally, low-battery or abandoned sensors can halt the performance of the overall network in emergencies.
    Table 3. KPIs Addressed in Selected Papers.

    Name

    Energy Efficiency

    Localization Accuracy

    Target Prediction

    Target Recovery

    Security

    Delaney et al. [38]

           

    Alaybeyoglu et al. [39]

       

    Mirsadeghi et al. [40]

       

    Patil et al. [41]

     

    Rouhani et al. [42]

         

    Wahdan et al. [43]

       

    Zhouet al. [44]

     

       

    Amudha et al. [45]

     

     

    Bhowmik et al. [46]

         

    Jinan et al. [47]

     

       

    Darabkh et al. [48]

       

    Khakpour et al. [49]

     

       

    Joshi et al. [50]

     

       

    Xiao et al. [51]

     

         

    Silva et al. [52]

     

     

    Oracevic et al. [53]

     

     

    Alshamaa et al. [54]

     

         

    Chen et al. [55]

     

       

    Panag et al. [56]

           

    Zhang et al. [57]

         

    Qian et al. [58]

       

     

    Zhang et al. [59]

         

    Li et al. [60]

     

       

    Darabkh et al. [61]

         

    Liu et al. [62]

       

    Luo et al. [63]

     

         

    Yu et al. [64]

     

       

    Vallas et al. [65]

     

       

    Ghodousi et al. [66]

       

    Liang et al. [67]

     

       

    Khan et al. [68]

       

    Liu et al. [69]

     

       

    Nguyen et al. [70]

     

         

    Ullah et al. [71]

         

    Alberto et al. [72]

     

         

    Liu et al. [73]

     

         

    Liu et al. [74]

     

     

    Mahmoudreza et al. [75]

     

       

    Li et al. [76]

     

         

    Reisinger et al. [77]

     

    Furthermore, accurate predictions and identification of targets are desired in location-aware schemes. Therefore, energy efficiency, tracking accuracy, and target prediction are the most researched KPIs in target tracking of WSNs, as shown in Figure 5. In contrast, target recovery and security were explored in only 11% of the papers selected. These are also important aspects of localization that need the researcher’s attention in the future. In addition to the above analysis, a general overview of all the papers, including the proposed approach, network structure, number of targets, and performance parameters, is presented in Table 4.
    Figure 5. Distribution based on target challenges.
    Table 4. Overview of selected papers.

    Ref.

    Proposed Approach

    Network Structure

    Number of Targets

    Performance Parameters

    Tool

    [38]

    ETX-NH

    Tree

    Single

    PDR: 96%

    TOSSIM

    [39]

    PF-DLSTA

    Tree

    Single

    N/A

    NS2

    [40]

    Low Power Target Prediction Mechanism

    Dynamic Cluster

    Single

    MR: 0.69%

    N/A

    [41]

    WSHAN

    Dynamic Cluster

    Single

    EE: 37%

    MATLAB

    [42]

    BCTT

    Static Cluster

    Single

    EE: 48%

    Omnet++

    [43]

    SCDCH

    Static Cluster

    Single

    N/A

    MATLAB

    [44]

    PPHD-MMA

    Dynamic Cluster

    Multiple

    N/A

    N/A

    [45]

    VGTR

    Dynamic Cluster

    Single

    TMR: 99% reduction

    MATLAB

    [46]

    DCTC with Fuzzy Sensing

    Tree

    Single

    N/A

    TinyOS and nesC

    [47]

    JPDA, PUESRF

    Dynamic Cluster

    Multiple

    N/A

    N/A

    [48]

    IPAH

    Dynamic Cluster

    Single

    EE: 40% improved,

    LE: 52% improved

    MATLAB

    [49]

    DCTT, PCTT

    Static Cluster

    Single

    N/A

    NS2 + TOSSIM

    [50]

    Prediction based object tracking algorithm

    Static Cluster

    Single

    PA: 99%

    NS2

    [51]

    ASMT

    Static Cluster

    Multiple

    FR: >14%

    N/A

    [52]

    GTPM

    Dynamic Cluster

    Single

    N/A

    NS2

    [53]

    SRPTT

    Static Cluster

    Single

    N/A

    Java Simulator

    [54]

    Extended observation model, 2nd mobility model

    Static Cluster

    Single

    N/A

    N/A

    [55]

    AEKF

    Static Cluster

    Single

    RMSE: 32.53%

    N/A

    [56]

    DHSCA

    Static Cluster

    Single

    N/A

    Fortran PowerStation 4.0

    [57]

    ACDF

    Dynamic Cluster

    Single

    N/A

    N/A

    [58]

    AUKF

    Static Cluster

    Single

    N/A

    MATLAB

    [59]

    HNTA

    Hybrid Cluster

    Multiple

    N/A

    N/A

    [60]

    Adaptive sensor selection algorithm with POMDP

    Dynamic Cluster

    Multiple

    N/A

    N/A

    [61]

    EEA-IAH

    Dynamic Cluster

    Single

    N/A

    MATLAB

    [62]

    LPPT

    Static Cluster

    Single

    Reduce MR: 36.34%,

    EE: 5.2 times

    Omnet++

    [63]

    CLTA

    Dynamic Cluster

    Single

    LE: 0.65 m

    MATLAB

    [64]

    FTS

    Tree

    Single

    LE: >50 improvement

    MATLAB

    [65]

    DMGIF

    Dynamic Cluster

    Multiple

    N/A

    N/A

    [66]

    ARIMA, AUKF

    Dynamic Cluster

    Single

    N/A

    Opnet + MATLAB

    [67]

    Trust-based distributed Kalman filtering.

    Dynamic Cluster

    Single

    N/A

    N/A

    [68]

    Dynamic cooperative multilateral sensing

    Dynamic Cluster

    Single

    LE: 19% improved

    MATLAB

    [69]

    ELM compressive sensing

    Dynamic Cluster

    Single

    N/A

    MATLAB

    [70]

    LEMon, LEMon-M

    Static Cluster

    Single

    Outdoor and Indoor LE:

    10 m and 2 m improved

    N/A

    [71]

    Distance and angle-based localization

    Dynamic Cluster

    Single

    LE: 90% improved,

    ABL: 104.9 m

    N/A

    [72]

    SWiBluX

    Dynamic Cluster

    Single

    LE: 45% improved

    N/A

    [73]

    TS PM-PHD

    Dynamic Cluster

    Multiple

    N/A

    N/A

    [74]

    AFS for MC-SSN

    Tree

    Single

    LE: <0.2%

    N/A

    [75]

    AIE-MCMCDA

    Dynamic Cluster

    Multiple

    LE: 0.39–4.12%

    N/A

    [76]

    CS-BnB, BnB-AMCL

    Dynamic Cluster

    Single

    LE: 0.005 m/0.111 deg

    4WS4WDr

    [77]

    IMM, UKF

    Dynamic

    Multiple

    EE: 4 times

    N/A

    The entry is from 10.3390/s21093228

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