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In the internet of things (IoT), there are resource-constrained and immense heterogeneous electronic gadgets worldwide. Till now, no single IoT application layer messaging protocol is the best, nor axiomatic for every requirement. This paper exhaustively summarizes information on the messaging protocols from the available previous research sources online. Our goal is to encapsulate a simple guideline so that users can choose an optimal messaging protocol quickly according to development requirements and specifications. For this purpose, we have reviewed the literature on six enabling and evolving application layer messaging protocols used for IoT systems namely, message queuing telemetry transport (MQTT), advanced message queuing protocol (AMQP), the constrained application protocol (CoAP), extensible messaging and presence protocol (XMPP), data distribution service (DDS), and simple text-oriented messaging protocol (STOMP) in terms of some interrelated metrics. Additionally, we represented a critical analysis of the application layer messaging protocols. This study will be helpful to readers with valuable insights and guide research scholars and developers in choosing optimal application layer messaging protocols based on development specifications and requirements. [ABSTRACT FROM AUTHOR]

The proliferation of Internet of Things (IoT) devices in terrestrial environments necessitates efficient data transmission and management protocols to optimize network performance. This research paper presents a comprehensive study on the implementation of IoT networks in a terrestrial setting, focusing on the integration of LZW (Lempel-Ziv-Welch) compression with three prominent IoT protocols: CoAP (Constrained Application Protocol), M2M (machine-to-machine), and MQTT (Message Queuing Telemetry Transport). We did a thorough test of these protocols using the NetSim simulator. We looked at how well they worked in terms of throughput, en-toend delay, and routing overhead, all of which are important for loT network efficiency. The point of our study is to find out what the pros and cons of these protocols are when they are combined with LZW compression in a terrestrial loT setting. The results of our analysis reveal significant variations in the performance of CoAP, M2M, and MQTT when subjected to data compression. Throughput efficiency, which directly impacts data transmission rates, is scrutinized alongside end-to-end delay, a critical factor in ensuring timely data delivery. Additionally, we explore the implications of protocol choice on routing overhead, a crucial metric for network resource utilization. This research contributes to the ongoing efforts to optimize IoT networks in terrestrial settings, offering valuable guidance to network architects and developers seeking to strike the right balance between protocol selection and data compression for improved IoT performance. [ABSTRACT FROM AUTHOR]

In the of the Internet of Things (IoT) realm, congestion is considered a serious issue affecting network throughput due to the requirement of multiple nodes for message exchange. With free-space optical communications, which can help send a message wirelessly, congestion control mechanisms nowadays depend upon the carrier sense of multiple access with collision avoidance (CSMA/CA), using the so-called Backoff Algorithm (BA). These algorithms, including Binary Exponential Backoff (BEB), Enhanced Fibonacci Backoff (EFB), Estimation-Based Backoff (EBA), and Backoff Interval Isolation (BII), have been introduced to facilitate congestion control mechanisms. Implementing such algorithms, however, might not deliver the greatest performance for Constrained Application Protocol (CoAP) which typically operates under limited resources. Therefore, the present study aims to introduce a new backoff algorithm, Triangular Number Sequence Backoff (TNSB), and to compare its performance with that of the aforementioned algorithms under the Transmission Control Protocol (TCP) and the Cooja network simulator. Statistical analysis involves ANOVA (F-test) and post hoc multiple comparison tests. The study shows that its performance is not significantly different from the others at the low congestion level. At the middle and the high congestion levels, it yields the highest throughput with the shortest settling time, while the packet loss rate and the response time are satisfactory.

In the of the Internet of Things (IoT) realm, congestion is considered a serious issue affecting network throughput due to the requirement of multiple nodes for message exchange. With free-space optical communications, which can help send a message wirelessly, congestion control mechanisms nowadays depend upon the carrier sense of multiple access with collision avoidance (CSMA/CA), using the so-called Backoff Algorithm (BA). These algorithms, including Binary Exponential Backoff (BEB), Enhanced Fibonacci Backoff (EFB), Estimation-Based Backoff (EBA), and Backoff Interval Isolation (BII), have been introduced to facilitate congestion control mechanisms. Implementing such algorithms, however, might not deliver the greatest performance for Constrained Application Protocol (CoAP) which typically operates under limited resources. Therefore, the present study aims to introduce a new backoff algorithm, Triangular Number Sequence Backoff (TNSB), and to compare its performance with that of the aforementioned algorithms under the Transmission Control Protocol (TCP) and the Cooja network simulator. Statistical analysis involves ANOVA (F-test) and post hoc multiple comparison tests. The study shows that its performance is not significantly different from the others at the low congestion level. At the middle and the high congestion levels, it yields the highest throughput with the shortest settling time, while the packet loss rate and the response time are satisfactory. [ABSTRACT FROM AUTHOR]

The constrained application protocol (CoAP) is an application layer protocol in IoT, with underlying support for congestion control mechanism. It minimizes the frequent retransmissions, but does not optimize the throughput or adapt dynamic conditions. However, designing an efficient congestion control mechanism over the IoT poses new challenges because of its resource constraint nature. In this context, this article presents a new Intelligent congestion control algorithm (iCoCoA) for constraint devices, motivated by the success of the deep reinforcement learning in various applications. The iCoCoA learns from the various network features to decide the best Retransmission Timeout to mitigate the congestion in the dynamic environments. It also optimizes the throughput, energy, and unnecessary frequent retransmissions compared with the existing models. iCoCoA is developed and tested on the Cooja simulator and compared it with the standard protocols such as CoAP, CoCoA, and CoCoA+ in continuous and burst traffic conditions. The proposed iCoCoA mitigates congestion, outperforms 4–15% in throughput, 3–10% better packet delivery ratio, and 7–16% energy-efficiency with reduced number of retransmissions. [ABSTRACT FROM AUTHOR]

Cloud computing is a massive amount of dynamic ad distributed resources that are delivered on request to clients over the Internet. Typical centralized cloud computing models may have difficulty dealing with challenges caused by IoT applications, such as network failure, latency, and capacity constraints. One of the introduced methods to solve these challenges is fog computing which makes the cloud closer to IoT devices. A system for dynamic congestion management brokerage is presented in this paper. With this proposed system, the IoT quality of service (QoS) requirements as defined by the service-level agreement (SLA) can be met as the massive amount of cloud requests come from the fog broker layer. In addition, a forwarding policy is introduced which helps the cloud service broker to select and forward the high-priority requests to the appropriate cloud resources from fog brokers and cloud users. This proposed idea is influenced by the weighted fair queuing (WFQ) Cisco queuing mechanism to simplify the management and control of the congestion that may possibly take place at the cloud service broker side. The system proposed in this paper is evaluated using iFogSim and CloudSim tools, and the results demonstrate that it improves IoT (QoS) compliance, while also avoiding cloud SLA violations. [ABSTRACT FROM AUTHOR]

In Internet of Things (IoT) networks, congestion is growing with the increasing number of devices, and a large amount of collected data must be transferred. Congestion control is one of the most significant challenges for such networks. The Constrained Application Protocol (CoAP) has been adopted for the IoT to satisfy the demand for smart applications. However, CoAP uses a basic congestion control algorithm that operates only when congestion occurs. Thus, the basic CoAP and most similar loss-based congestion control schemes have remaining issues for burst data transfer in dynamic network environments. This paper proposes a novel rate-based congestion control scheme using fuzzy control for CoAP, called FuzzyCoAP. We use the round-trip time gradient and bottleneck bandwidth gradient as inputs for FuzzyCoAP to infer the degree of congestion. FuzzyCoAP uses this indicator to predict early congestion and adjusts the sending rate to avoid congestion. FuzzyCoAP uses the congestion degree to update the variable RTO for retransmissions. On the other hand, FuzzyCoAP dynamically checks for the available bandwidth to gain high performance for burst data transfer. Various simulation experiments have demonstrated the feasibility of the FuzzyCoAP in different traffic scenarios. We compared the proposed scheme with representative loss-based CoAP schemes, that is, the basic CoAP. The simulation results proved that FuzzyCoAP provides high performance in terms of delay, throughput, loss rate, and retransmissions compared with the basic CoAP.

IoT is emerging as a massive web of heterogeneous networks estimated to interconnect over 41 billion devices by 2025, generating around 79 zettabytes of data. The heterogeneous network shall bring in a plethora of digital services leveraging cloud and communication technologies to drive smart city applications. As users access these services remotely in a ubiquitous environment over public channels, it becomes imperative to secure their communication. Both entity and message authentication emerge as a critical security primitive to thwart unauthorized access and prevent the falsification of messages. While researchers have given due attention to achieving mutual authentication between the subscriber (remote user) and gateway node (broker), the mutual authentication between the gateway node and an IoT sensor node is left to be desired. It could be done at the peril of a rogue or a shadow IoT device unauthorizedly joining an IoT-based network. Some of the widely used IoT-specific application layer protocols like constrained application protocol (COAP) and message queue telemetry transport (MQTT) protocol are not inherently equipped with adequate security safeguards. They, therefore, rely on underlying transport layer security protocols, which are highly computationally intensive. To address this issue, this paper proposes a three-factor authentication framework suitable for IoT-driven critical applications based upon identity, password and a digital signature scheme. The framework employs publish-subscribe pattern leveraging elliptical curve cryptography (ECC) and computationally low hash chains. The formal and informal security analysis shows that the framework is resistant to different types of cryptographic attacks. Furthermore, the automated validation performed with the Scyther tool verifies that there are no cryptographic attacks found on any of the claims stated in the proposed framework. Finally, a comparison of the framework security features, computational, and communication overheads is carried out with other existing protocols. [ABSTRACT FROM AUTHOR]

Congestion management in the Internet of Things (IoT) is one of the most challenging tasks in improving the quality of service (QoS) of a network. This is largely because modern wireless networks can consist of an immense number of connections. Consequently, limited network resources can be consumed simultaneously. This eventually causes congestion that has adverse impacts on both throughput and transmission delay. This is particularly true in a network whose transmissions are regulated by the Constrained Application Protocol (CoAP), which has been widely adopted in the IoT network. CoAP has a mechanism that allows connection-oriented communication by means of acknowledgment messages (ACKs) and retransmission timeouts (RTOs). However, during congestion, a client node is unable to efficiently specify the RTO, resulting in unnecessary retransmission. This overhead in turn causes even more extensive congestion in the network. Therefore, this research proposes a novel scheme for optimally setting the initial RTO and adjusting the RTO backoff that considers current network utilization. The scheme consists of three main components: 1) a multidimensional congestion estimator that determines congestion conditions in various aspects, 2) precise initial RTO estimation by means of a relative strength indicator and trend analysis, and 3) a flexible and congestion-aware backoff strategy based on an adaptive-boundary backoff factor evaluated by using a fuzzy logic system (FLS). The simulation results presented here reveal that the proposed scheme outperforms state-of-the-art methods in terms of the carried load, delay and percentage of retransmission.

The Internet Engineering Task Force (IETF) standardized the Constrained Application Protocol (CoAP) for Internet of Things (IoT) devices to meet the demands of IoT applications. Due to the constrained IoT environment, CoAP was designed based on UDP as a lightweight protocol with simple congestion control, which leverages the basic binary exponential backoff. However, the basic congestion control of CoAP is unable to effectively perform reliable bursty data transfer in IoT networks. Recent studies have indicated that CoAP and its modifications still suffer from critical performance problems regarding congestion control, throughput, and delay. The current congestion control of the CoAP does not support bursty data transfer. In contrast to the current schemes that focus on a loss-based mechanism and a retransmission time-out (RTO) calculation, we propose a new rate control scheme, RCOAP, for reliable bursty data transfer in IoT networks. RCOAP uses the concept of regulating the transmission rate of CoAP sources. The key features of RCOAP are 1) estimating the initial sending rate by probing the bottleneck bandwidth, 2) adjusting the sending rate according to the dynamic network condition, and 3) distinguishing between losses due to congestion and losses due to wireless errors for the purpose of maintaining high throughput. Simulation results indicate that RCOAP is suitable for bursty data transfer. RCOAP shows a throughput increase of approximately 135% compared to the basic CoAP, CoCoA, and CoCoA+ under the same conditions while maintaining a low delay, loss rate, and a low number of retransmission attempts.

Cloud computing is a massive amount of dynamic ad distributed resources that are delivered on request to clients over the Internet. Typical centralized cloud computing models may have difficulty dealing with challenges caused by IoT applications, such as network failure, latency, and capacity constraints. One of the introduced methods to solve these challenges is fog computing which makes the cloud closer to IoT devices. A system for dynamic congestion management brokerage is presented in this paper. With this proposed system, the IoT quality of service (QoS) requirements as defined by the service-level agreement (SLA) can be met as the massive amount of cloud requests come from the fog broker layer. In addition, a forwarding policy is introduced which helps the cloud service broker to select and forward the high-priority requests to the appropriate cloud resources from fog brokers and cloud users. This proposed idea is influenced by the weighted fair queuing (WFQ) Cisco queuing mechanism to simplify the management and control of the congestion that may possibly take place at the cloud service broker side. The system proposed in this paper is evaluated using iFogSim and CloudSim tools, and the results demonstrate that it improves IoT (QoS) compliance, while also avoiding cloud SLA violations.

Internet of Things (IoT) refers to uniquely identifiable entities. Its vision is the world of connected objects. Due to its connected nature the data produced by IoT is being used for different purposes. Since IoT generates huge amount of data, we need some scalable storage to store and compute the data sensed from the sensors. To overcome this issue, we need the integration of cloud and IoT, so that the data might be stored and computed in a scalable environment. Harmonization of IoT in Cloud might be a novel solution in this regard. IoT devices will interact with each other using Constrained Application Protocol (CoAP). In this paper, we have implemented harmonizing IoT in Cloud. We have used CoAP to get things connected to each other through the Internet. For the implementation we have used two sensors, fire detector and the sensor attached with the door which is responsible for opening it. Thus our implementation will be storing and retrieving the sensed data from the cloud. We have also compared our implementation with different parameters. The comparison shows that our implementation significantly improves the performance compared to the existing system. [ABSTRACT FROM AUTHOR]

In this study, the authors have modelled the performance of a scalable proxy virtualisation framework to support an appropriate level of quality of service (QoS) for delay sensitive applications in cloud‐based large‐scale sensor networks. The proposed model leverages the functionalities of a constrained application protocol (CoAP) to expose a standard interface for transparently decoupling of the applications from servers and ensuring the implementation of the custom QoS metrics through virtualisation techniques. The model, combined with the prediction‐based dynamic resource allocation approach, enables the computation of the number of requests that can be supported for a given service rate and required QoS. It also features the evaluation of different performance metrics, particularly, the probability distribution of the response time and the ability to identify the optimal balance between the resource cost and the service performance. The numerical results obtained from the proposed analytical model can be used to help design and optimise industrial cloud‐based sensing systems. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) applications are expected to have access to sensitive data. On the other hand, IoT devices are not powerful enough to implement complex security solutions. But even if this was not a problem, there are so many other security challenges related to the IoT that a security breach should not be considered an exceptional case. In this paper, we propose an information tracking mechanism that can be used as an audit tool in case of a security incidence. To achieve this goal, we leverage software-defined networking (SDN). SDN is a promising technology that receives increasing attention, since it enables network "programmability" and intelligent packet forwarding. In this paper, we consider the case of an SDN deployment in the network of a single provider that interconnects various IoT devices. Furthermore, specialized network attachments points are used for parsing IoT specific protocols and perform the necessary actions. Moreover, in order to make our solution even more realistic and to facilitate deployment, we base our constructions and design solely on existing SDN standards. [ABSTRACT FROM AUTHOR]

The ecosystem for an Internet of Things (IoT) generally comprises endpoint clients, network devices, and cloud servers. Thus, data transfers within the network present multiple security concerns. The recent boom in IoT applications has accelerated the need for a network infrastructure that provides timely and safe information exchange services. A shortcoming of many existing networks is the use of static key authentication. To enable the use of automatic key update mechanisms in IoT devices and enhance security in lightweight machine-to-machine (M2M) communications, we propose a key update mechanism, namely, double OTP (D-OTP), which combines both one-time password (OTP) and one-time pad to achieve an IoT ecosystem with theoretically unbreakable security. The proposed D-OTP was implemented into the Constrained Application Protocol (CoAP) through the commonly used libcoap library. The experimental results revealed that an additional 8.93% latency overhead was required to obtain an unbreakable guarantee of data transfers in 100 CoAP communication sessions.

One of the noticeable characteristics of the Internet-of-Things (IoT) devices is that they are resource-constrained, which makes them incompatible with the standard Internet protocols, e.g., HTTP. Nevertheless, IoT offers the Constrained Application Protocol (CoAP) as an alternative protocol for IoT devices where it is considered to be lightweight in regard to power consumption, network traffic, and so on. The main challenges associated with CoAP, however, still remain unsolved where most of the state-of-the-art approaches group servers manually and return the exact matches only. To address these issues, we propose a novel Semantically Enhanced CoAP Gateway (SECoG) for complex service mashups. SECoG enables users to create a simple or complex CoAP service mashup via a single HTTP request. Furthermore, a prototype implementation of SECoG was deployed on a real-world testbed to evaluate the proposed approach in terms of the reliability, execution time, network traffic, and accuracy. [ABSTRACT FROM AUTHOR]

The Internet Engineering Task Force (IETF) has developed and promoted several standards intended to facilitate Internet of things communication among constrained devices in low‐power low‐rate networks (LLNs). Specifically, IETF introduces a series of protocols that deal with different layers of the stack ranging from IPv6 adaptation to routing in LLN. Among them, the constrained application protocol (CoAP) is a session protocol that is used to carry sensor and actuator traffic. Since CoAP transport relies on the user datagram protocol, and in order to provide reliability, it introduces a mode operation known as confirmable where messages are considered delivered once they have been acknowledged. One drawback of this approach, however, is the latency that results from the retransmission of packets in lossy networks. In this study, the authors present, model, compare and evaluate a forward error correction mechanism that enables CoAP to improve its reliability while reducing latency. [ABSTRACT FROM AUTHOR]

This paper presents a virtual private storage framework (VPSF) using Internet of Things (IoT) local networks. The VPSF uses the extra storage space of sensor devices in an IoT local network to store users’ private data, while guaranteeing expected network lifetime, by partitioning the storage space of a sensor device into data and system volumes and, if necessary, logically integrating the extra data volumes of the multiple sensor devices to virtually build a single storage space. When user data need to be stored, the VPSF gateway divides the original data into several blocks and selects the sensor devices in which the blocks will be stored based on their residual energy. The blocks are transmitted to the selected devices using the modified speedy block-wise transfer (BlockS) option of the constrained application protocol (CoAP), which reduces communication overhead by retransmitting lost blocks without a retransmission request message. To verify the feasibility of the VPSF, an experimental implementation was conducted using the open-source software libcoap. The results demonstrate that the VPSF is an energy-efficient solution for virtual private storage because it averages the residual energy amounts for sensor devices within an IoT local network and reduces their communication overhead.

Ambient Intelligence aims at simplifying the interaction of a user with her surrounding context, minimizing the effort needed to increase comfort and assistance. Nevertheless, especially in built and structured environments, current technologies and market solutions are often far from providing the required levels of automation, coordination and adaptivity. Devices should interact autonomously, should reach opportunistic decisions and take actions accordingly. In particular, user activities and profiles should be among the manifold implicit factors to be taken into account in that process. Home and Building Automation (HBA) is one of the most relevant scenarios suffering from the poorness of the allowed system intelligence, automation and adaptivity. Devices are logically associated through static profiles defined at system deployment stage. The large majority of solutions are proprietary and centralized, and require manual configuration. This paper proposes a novel semantic-based framework complying with the emerging Social Internet of Things paradigm. Infrastructured spaces can be intended as populated by device agents organized in social networks, interacting autonomously and sharing information, cooperating and orchestrating resources. A service-oriented architecture allows collaborative dissemination, discovery and composition of service/resource descriptions. Semantic Web languages are adopted as knowledge representation layer and mobile-oriented implementations of non-monotonic inferences for semantic matchmaking are used to give decision capabilities to software agents. Finally, the Linked Data Platform (LDP) over the Constrained Application Protocol (CoAP) provides the knowledge organization and sharing infrastructure underpinning social object interactions. The framework has been implemented and tested in a home automation prototype integrating several communication protocols and off-the-shelf devices. Experiments advocate the effectiveness of the approach. [ABSTRACT FROM AUTHOR]

The Constrained Application Protocol (CoAP) is a simple and lightweight machine-to-machine (M2M) protocol for constrained devices for use in lossy networks which offers a small memory capacity and limited processing. Designed and developed by the Internet Engineering Task Force (IETF), it functions as an application layer protocol and benefits from reliable delivery and simple congestion control. It is implemented for request/response message exchanges over the User Datagram Protocol (UDP) to support the Internet of Things (IoT). CoAP also provides a basic congestion control mechanism. In dealing with its own congestion, it relies on a fixed interval retransmission timeout (RTO) and binary exponential backoff (BEB). However, the default CoAP congestion control is considered to be unable to effectively perform group communication and observe resources, and it cannot handle rapid, frequent requests. This results in buffer overflow and packet loss. To overcome these problems, we proposed a new congestion control mechanism for CoAP Observe Group Communication, namely Congestion Control Random Early Detection (CoCo-RED), consisting of (1) determining and calculating an RTO timer, (2) a Revised Random Early Detection (RevRED) algorithm which has recently been developed and primarily based on the buffer management of TCP congestion control, and (3) a Fibonacci Pre-Increment Backoff (FPB) algorithm which waits for backoff time prior to retransmission. All the aforementioned algorithms were therefore implemented instead of the default CoAP mechanism. In this study, evaluations were carried out regarding the efficiency of the developed CoCo-RED using a Cooja simulator. The congestion control mechanism can quickly handle the changing behaviors of network communication, and thus it prevents the buffer overflow that leads to congestions. The results of our experiments indicate that CoCo-RED can control congestion more effectively than the default CoAP in every condition.

In this paper, we present a stepwise adjustment of the constrained application protocol observing period (SACOP) for Internet of Things (IoT) applications using wireless sensor networks (WSNs), which dynamically adjust observing periods depending on the Rx queue status of the constrained application protocol (CoAP) client. The operation of SACOP consists of the following two consecutive phases: overflow alert and observing period adjustment. In the former phase, a client sends a buffer overflow alert when the queue of the client reaches the predefined queue threshold. In the latter phase, the servers change their own observing period level depending on whether or not they receive a buffer overflow alert message. Therefore, SACOP can significantly reduce the number of dropped messages caused by buffer overflow. A simulation showed that SACOP achieved a higher network performance than the legacy approach with regard to the number of dropped messages. [ABSTRACT FROM AUTHOR]

Recent technological advancements allowed widening the applicability scope of the RFID (Radio Frequency Identification) technology from item identification to sensor-enabled computation platforms. This feature, added to the native radio energy-harvesting capability and the extremely low power consumption, has attracted the interest of research and industrial communities and pushed them to include the RFID technology into a global network of interconnected objects, as envisaged by the Internet of Things paradigm. In the last few years, standardization bodies have made significant efforts to design lightweight approaches, such as CoAP (Constrained Application Protocol), to efficiently manage resource-constrained nodes by using traditional web interfaces; nevertheless, RFID integration is not addressed yet. In this paper, we propose a CoAP-compliant solution where RFID tags, behaving as virtual CoAP servers, are directly accessible by remote CoAP clients via a reader, which acts as a CoAP proxy. A real testbed, addressing key aspects, such as tag addressing, discovery and management of CoAP requests via RFID operations, is deployed to validate the feasibility of the proposal. Experimental results show rapid response times: less than 60 ms are requested for resource retrieval, while from 80 to 360 ms for sending data to the RFID device, depending on the tag memory dimension. [ABSTRACT FROM AUTHOR]