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Delay Constrained in Iot Using Fuzzy Logic Sets : a Survey

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Internet of Things (IoT) referred as a pervasive network architecture which provides services to the physical world by processing and analyzing data. In this modern era IoT has been shown much significance and rapidly developing by connecting heterogeneous devices with various technologies. This paper focus on studying the issues of Delay Constraints in IoT with Fuzzy logic Set. Also the technical survey proposes a move toward IoT route selection based on delay using fuzzy logic applications. In this process, fuzzy logic is used to convert in math terms the rough information uttered by a locate of linguistic rules. The routing is estimated using Transmission Count, Number of Intermediate Nodes and total Energy Consumed. The survey performed is connected with the arithmetic data analysis and found that high reliability with 100% of data packet delivery, less delay for data delivery and high range of QoS is obtained. This survey shows the packet delivery ratio (PDR) is attain in the network life time increases with efficient power consumption.

Keywords— Internet of things (IoT); Quality of service (QoS), Fuzzy logic, Packet Delivery Ratio (PDR).


To studied the problem of determining an optimal relay node placement strategy such that a certain performance objective is met. It showed that the problem is NPHard, and proposed a process, which, as can be concluded from numerical experiments, gives solutions of reasonably good quality, using extremely reasonable computation time. In the worst case, the algorithm can do as bad as using all but one potential relay locations instead of just one optimum relay [1]. In efficient deployment of these networks will require as much hands-off configuration and management as possible, as

the size of these networks increase beyond trial dimensions. Incorporating intelligence in a low cost device is an important requirement if the limited resources of these networks are to be used effectively [2]. Some services may tolerate longer delays than others and deal with the order with which packets are transmitted. Space priorities control the allocation of buffer space to arriving packets at an input or output port queue of a network switch. This type of priority mechanism exploits the fact that certain packets generated by traffic sources are less important than others and may, therefore, be discarded without significantly affecting the QoS constraints [3]. The major purpose is to provide the reader the occasion of considerate what has been done and what still remains to be addressed, as well as which are the enabling factors of this evolutionary process and what are its weaknesses and risk factors [4]. The attacker can generate routing loops by altering and false routing information [5] blocks the transmission of network and enlarge the network path by sending lot of error messages hence it increase point to point delay etc.

For scheduling services with different features and QoS constraints, this paper proposes a hybrid scheduling strategy, in which delay-sensitive services are granted preemptive priority and non-delay-sensitive services are granted non-preemptive priority. Theoretical analysis verified that preemptive scheme results in longer waiting length of queue [6].The end to end delay or waiting time affects the battery life of the large number of tiny nodes equipped with processor, memory and short range wireless communication. For these kinds of systems who support wide range of IoT applications it is a strong requirement to reduce end-to-end delay and losses during message transmissions. The improvement of messaging order in term of delay time for two classes of priority experimented in term of energy and prolonged lifetime of the network can be seen in the evaluation results of the implemented system [7]. Delay is a QoS metric that is measured to analyze the improvement of the proposed network. This metric increase when number of hops increases, more waiting time of a packet, etc. Traditionally the multi-casting packets have certain waiting time for the reason of minimizing collision, but this leads to higher delay [8].These problems are exacerbated due to the unattended operation of the organization, the require for a extensive life span, the directness of the systems, and the reality of the corporeal world. The ambition is for this compilation of solutions to generate a robust system in spite of noisy, faulty, and nondeterministic underlying physical world realities [9]. Multi-service it providing more than one distinct application or service. This involves not only manifold transfer types inside the network, but also the talent of a solitary network to support all applications without QoS compromise. There are two request classes: throughput and delay broadminded elastic traffic of and the bandwidth and delay sensitive inelastic traffic which can be additional discriminate by data-related applications with diverse QoS requirements. Therefore, a forbidden, best advance to provide dissimilar complex traffics, each with its possess application QoS needs is required [10]. QoS in IoT is still not clear because the definition of service in IoT is not exactly the same, in which a service can be defined as the simple acquisition and processing of information and the decision making process in identification, communication, and so on.

The traditional QoS attributes such as throughput, delay, or jitter are evidently inappropriate in IoT [11]. In most of the business to choose the best path from the candidate path, and taking into account the real-time requirements and link unstable due to frequent outages, select the propagation delay to meet the requirements of the delayed threshold, delay jitter, the minimum as the optimal path, the path to ensure the reliability and stability of the transmission. The possibility for node failure, the use of alternate path to ensure that in the event of a node failure, and the network can continue forwarding packets to provide a link interruption caused by the tolerance of unpredictable node failure [12].

QoS Architecture

There are a range of thought and achievement ideas used in important and scheming IoT architectures such as (i) important the architecture base the service mechanism for providing the services in IoT using applicable protocols and access networks for implementing the IoT systems – service oriented architectures (ii) keeping the context or situations of the application systems as the base for designing the architecture – context aware architectures (iii) the design of architectures built around the middleware (software components) as the base to implement the IoT systems – middleware architectures. The architecture of IoT at high level can be defined with multiple layers for ease of implementation, maintenance and support. The basic functions of any IoT systems would remain same and would depend on the application domain; enabling technologies used and required quality factors. Service quality/Quality of Service of an IoT system should be embedded in each and every component of IoT system either in for form of software, hardware and interaction and integration implementations [13].

A service in IoT can be defined by the combinations of capabilities of ‘functionalities, interoperability, interactions, communication abilities, related data and ability of using the related data’ of device(s) for implementing the IoT system to meet the requirements of specific application(s) and end user system(s). An effective service oriented IoT system should have the abilities to search and discover services, should have clear categories of services and should be able to make service compositions. There are different service categories, search and discovery methods defined in some of the research papers are some of the research issues in the services oriented architectures in IoT. The Quality of Service as a nonfunctional component is the ‘capability of providing satisfactory service’ by different service providers and systems. Due to heterogeneous nature of IoT, the overall QoS in IoT is the capability of providing service by various service providers like–sensing service, network service, cloud service and services by various enabling technologies and components of IoT. The applicability of a rest of QoS restraint depends on the correct IoT demand field in federation with enable technologies and service providers (for example the QoS parameters applicable for RFID may not be applicable for WSN).


Fadi M. Al-Turjman, Ashraf E. Al-Fagih et al presents an optimized delay-tolerant approach for integrated sensor networks. This is a novel scheme for data routing and courier nodes’ selection in RSNs. DIRSN’s formulation minimizes delay across the network without violating the main dense-deployment and load-balancing requirements. In accumulation, DIRSN builds on SIWR’s narrative structural design to locate the greatest set of couriers that guarantee to provide connectivity. Our collective approach is compare to three types of RSN integration architectures and the results show that our architecture and courier selection approaches perform substantially better than other architectures in terms of minimizing delay, cost, packet loss, and in handling extensive traffic demands [14].

Wasan Twayej, and H. S. Al-Raweshidy et al presents a Multilevel Clustering Multiple Sink (MLCMS) with IPv6 protocol over Low Wireless Personal Area Networks (6LoWPAN) is promoted using a sophisticated mathematical equation for electing cluster heads (CH) for each level, so as to prolong network lifetime. Secondly, improved N.P that prolongs the life time of the organization and maximizes the reduction of delay is achieved throughout an adaptive sleep mode format. The sensor field is separated into quarters with diverse stage of cluster heads (CHs) and two optimal locations sinks. The presentation of the MLCMS protocol is evaluated and compared with the multi-hop low-energy adaptive clustering hierarchy (M-LEACH) protocol [15].

Ajay Vikram Singh, Vandana Juyal , Ravish Saggar et al presents The function of Artificial Neural Network is to calculate and learn, trust value that can be shared among network devices. Our algorithm lowers the need of nodes resources like energy consumption, computation time and space overheads. The proposed algorithm enhances the routing performance in DTN. The earlier work claiming better efficiency generally ends up consuming network’s resources. On the contrary our proposed algorithm provides in-built security, without any additional overhead. To the best of our knowledge the proposed work is the first of its kind, providing ingrained security feature to the DTN. This work gives vantage point to the researchers in the field over other schemes proposed in the past [16].

M. Jain et al present various performance measures for multiserver queuing system with queue dependent heterogeneous servers by using recursive method. The incorporation of additional servers in a sequent manner proves beneficial to reduce the backlog of the system in particular when traffic load is high. The optimal threshold parameters for turning on the servers are determined by constructing a cost relationship among the cost factors and using a heuristic approach. The cost analysis provided may be helpful in establishing the tradeoff between the costs associated with the servers and waiting times [17].

Guillermo Gast´on Lorente, Bart Lemmens, Matthias Carlier et al presents a multicast forwarding algorithm for IPv6 based WSNs, which addresses some of the shortcomings of the currently available solutions. In particular, our mechanism allows sources of multicast traffic to be located inside the network and support dynamic group registrations, at the expense of slightly higher memory consumption. Moreover, the proposed protocol is configurable in order to trade off energy consumption, latency, and reliability. Our experiments show that the proposed threshold for BMRF Mixed mode succeeds in getting the best of Link Layer broadcast and Link Layer unicast. For random topologies, the mixed mode only yields gain for channel check rates higher than 8 Hz [18].


A. Hybrid scheduling strategy

A hybrid scheduling strategy, in which delay-sensitive services are granted preemptive priority and non-delay-sensitive services are granted non-preemptive priority. Theoretical analysis verified that preemptive scheme results in longer waiting length of queue. There are distinct differences on data type, packet length and Qos constraints among various IOT services, so a scheduling method at the router and gateway is needed to guarantee the effective packets transmission. Current researches on packet scheduling by queuing states analysis focus on such indices as queue length and preemption probability [6].

Quality of Service (QoS) scheduling algorithm

In detailed letters are private into high priority (HP) and best effort (BE) and the corresponding Quality of Service (QoS) scheduling algorithm is planned. Doing so will enable the IoT network to differ emergency messages from non-mission critical messages. In adding, network-layer routing algorithms are also in use into indication in declaration scheduling, aspire to supply an additional most excellent possible resolution by applying certain scale of cross-layer design methodology. Here sensor nodes are separated in IoT subgroups. Each subgroup has a broker distribute for all nodes and maintain two queues for HP and BE messages correspondingly. QoS consciousness is instigating in IoT subgroups by conveying traffic priorities and preparation them with proposed [7].

Routing Protocol for Low-Power and Lossy Networks (RPL)

Routing Protocol for Low-Power and Lossy Networks (RPL) as a standard routing for LLNs. RPL is based on Directed Acyclic Graph (DAG) whereby all paths are oriented toward and terminating at root node(s) called DAG root. In reasonable, as the immensity of LLN deployments augment, a all the identical Non-Storing mode network will incur a high level of communication overhead, and a homogeneous Storing mode network will require too much memory resources. The Non-storing modes require modest recollection for store routing states. Thus, it provides a benefit for the devices with incomplete processing and storage capabilities. However, the Non-Storing modes require a source routing header (SRH) to be friendly to all packets. With this accessory, the packet sizes not only increase, but also become variable depending on the path length. Considering a multihop IoT network, hop-by-hop re-composition at every hop contributes additional latency. The intermediate nodes are forced to store packets for an undetermined time, thus, giving impact on critical resources such as memory and battery algorithm making them energy efficient as well [8].

Finite capacity queuing system

The proposed model can be used to evaluate the performance of smart devices to meet various QoS constraints under varying input parameterization. In the proposed system, it is assumed that the arriving traffic is classified into low priority (normal traffic) and high priority (emergency traffic). The inter-arrival times and service times for each arriving class of traffic are distributed according to exponential distribution. We proposed a cost-effective analytical model for a finite capacity queuing system with pre-emptive resume service priority and push-out buffer management scheme. The investigative replicate can be worn to forecast the presentation of elegant devices under a range of transfer situation that convene the QoS constraints [17].

BMRF Algorithm

Conventional Bi-directional Multicast RPL Forwarding (BMRF) that is used for multicast forwarding is enhanced in this paper. The Enhanced BMRF (EBMRF) algorithm is proposed with the aim of suppressing duplicate packets. Duplicate packets occur due to the forwarding of packets to multiple nodes. In this algorithm the packets received from upward nodes are accepted only by the parent nodes. Then these parent nodes multicast packets to their child nodes. Since packets from downward node is considered to be unicast (i.e.) performed by using Multi-fuzzy model in this paper. So, BMRF is enhanced by additionally including a unique ID in each multicast packet. Therefore, the unnecessary duplicate packets are suppressed by identifying the packet ID. If the packet is received from downward node, then it is considered to be unicast packet, which is required to reach root node. If the packet received from the parent node, it is defined as multicast packet and hence it performs EBMRF [18].

Fuzzy sets

The main contribution of this work, compared to the proposals available in the literature and considered in the study, includes an optimization of the RPL protocol to reduce the delay and to increase the reliability and QoS, guaranteeing also an increase in the network life-time. In this proposal, it is not considered the variation in the number of sensor nodes and reduces the delay process. In this work, the variation in the number of sensor nodes was not considered since, during the study, it was observed that there was no variation of the results altering the numeral of strategy throughout the use of the planned objective functions associated to route classifier based on fuzzy system. This is necessary because the route selection process of the proposed move toward uses only aspect connected to energy, location in the steering tree and numeral of transmission estimation.

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Delay Constrained in IoT Using Fuzzy Logic Sets : A Survey. (2019, March 27). GradesFixer. Retrieved January 25, 2022, from
“Delay Constrained in IoT Using Fuzzy Logic Sets : A Survey.” GradesFixer, 27 Mar. 2019,
Delay Constrained in IoT Using Fuzzy Logic Sets : A Survey. [online]. Available at: <> [Accessed 25 Jan. 2022].
Delay Constrained in IoT Using Fuzzy Logic Sets : A Survey [Internet]. GradesFixer. 2019 Mar 27 [cited 2022 Jan 25]. Available from:
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