Low-power Wireless Personal Area Networks Research Articles

Enhanced Hybrid Congestion Mitigation Strategy for ‘6LoWPAN-RPL based patient-centric IoHT’

The Internet of Healthcare Things (IoHT) is rapidly evolving, providing new opportunities to enhance healthcare delivery. However, the resource limitation of connected medical sensing devices leads to congestion, resulting in reduced network performance, delayed data transmission, and loss of critical medical information, which can have significant consequences in healthcare. To address this issue, this paper proposes an Enhanced Hybrid Congestion Mitigation Strategy (EHCMS) for IPv6 over low-power wireless personal area networks (6LoWPAN) and routing protocol for low-Power and lossy networks (RPL) based patient-centric IoHT (PC-IoHT). The EHCMS combines several techniques, including traffic management, network topology optimization, and load balancing, to enhance network performance and reduce congestion. The proposed framework is a hybrid strategy that utilizes resource- and traffic-control mechanisms to alleviate congestion in the 6LoWPAN-RPL-based patient-centric IoHT network. For the resource-control-based approach, a congestion-aware composite objective function is designed using a few congestion-specific routing metrics and formulated as a multi-attribute decision-making problem solved using Grey relational analysis (GRA). In addition, a non-linear multi-criteria optimization problem-based transmission rate adaptation mechanism is contrived as a traffic-control scheme for congestion mitigation. The effectiveness of the proposed EHCMS is evaluated using simulations on the Cooja simulator in the Contiki-3.0 OS and compared with existing congestion-alleviating strategies. The results demonstrate that the proposed framework can significantly reduce congestion in the IoHT network, perform better than existing works, and improve the quality of service. This research paper contributes to the field of IoHT by proposing an effective congestion mitigation strategy that enhances the reliability and performance of the PC-IoHT network, ultimately improving the quality of healthcare delivery.

Enabling Seamless Connectivity: Networking Innovations in Wireless Sensor Networks for Industrial Application.

The wide-ranging applications of the Internet of Things (IoT) show that it has the potential to revolutionise industry, improve daily life, and overcome global challenges. This study aims to evaluate the performance scalability of mature industrial wireless sensor networks (IWSNs). A new classification approach for IoT in the industrial sector is proposed based on multiple factors and we introduce the integration of 6LoWPAN (IPv6 over low-power wireless personal area networks), message queuing telemetry transport for sensor networks (MQTT-SN), and ContikiMAC protocols for sensor nodes in an industrial IoT system to improve energy-efficient connectivity. The Contiki COOJA WSN simulator was applied to model and simulate the performance of the protocols in two static and moving scenarios and evaluate the proposed novelty detection system (NDS) for network intrusions in order to identify certain events in real time for realistic dataset analysis. The simulation results show that our method is an essential measure in determining the number of transmissions required to achieve a certain reliability target in an IWSNs. Despite the growing demand for low-power operation, deterministic communication, and end-to-end reliability, our methodology of an innovative sensor design using selective surface activation induced by laser (SSAIL) technology was developed and deployed in the FTMC premises to demonstrate its long-term functionality and reliability. The proposed framework was experimentally validated and tested through simulations to demonstrate the applicability and suitability of the proposed approach. The energy efficiency in the optimised WSN was increased by 50%, battery life was extended by 350%, duplicated packets were reduced by 80%, data collisions were reduced by 80%, and it was shown that the proposed methodology and tools could be used effectively in the development of telemetry node networks in new industrial projects in order to detect events and breaches in IoT networks accurately. The energy consumption of the developed sensor nodes was measured. Overall, this study performed a comprehensive assessment of the challenges of industrial processes, such as the reliability and stability of telemetry channels, the energy efficiency of autonomous nodes, and the minimisation of duplicate information transmission in IWSNs.

Secured lightweight authentication for 6LoWPANs in machine-to-machine communications

The development of machine-to-machine (M2M) technologies is becoming increasingly important in the rapidly growing domain of wireless sensor networks (WSNs) and the Internet of Things (IoT). Adopting IPv6 over 6LoWPANs (Low-Power Wireless Personal Area Networks) is instrumental in communicating across diverse domains within WSNs, albeit with its challenges. Particularly, resource limitations and security vulnerabilities remain significant concerns. 6LoWPAN-based M2M protocols that rely on authentication and key establishment schemes (AKE) often fall short due to inadequate security issues and excessive resource requirements. This paper addresses these challenges by introducing a secure and resource-efficient framework—Lightweight AKE for 6LoWPAN Nodes (LAKE-6LN). LAKE-6LN capitalizes on the clustering architecture's merits and contrasts conventional router-centric approaches. To ensure lightweight and efficient operation, it uses hash functions, XOR functions, and symmetric encryption techniques. Pseudo-identity, sequence tracking numbers, and secure parameters ensure privacy and protection against attacks, including traceability, perfect forward secrecy, ephemeral secret leakage, and secure the session key. An informal analysis of LAKE-6LN's security confirms that compliance with all essential security properties has been achieved. In addition, the framework's logical robustness and security analysis are rigorously verified using BAN logic, AVISPA, and Scyther tools. LAKE-6LN has demonstrated superior performance over related schemes, demonstrating a reduction in storage costs (by 33.33 % to 85.71 %), computational overhead (by 14.28 % to 95.97 %), communication overhead (by 16.12 % to 51.85 %), and energy consumption (by 22.04 % to 99.40 %). In our comparative analysis, LAKE-6LN demonstrates its resilience against various security threats, demonstrating its potential to secure 6LoWPAN networks in M2M.

A Proposed Healthcare Architecture using Cloud Computing in WSN Environment with a case study

Internet of Things (IoT) considers a future that can connect something/someone/ any service with appropriate information communication technology that it brings innovation in the fields of home, smart home, medical system, article surveillance, and logistics. Because of this inclination, this paper develops a trailblazer: Internet of Things-aware, intelligent architecture, that can be used to monitor and track patients and other related computing devices personnel, and within hospitals premises. In line with the IoT vision, we propose IoT based healthcare architecture dependent on different but complementary technologies, especially the WSN, RFID, and smart mobile, all interacting via a Constrained Application Protocol (CoAP) over low-power wireless personal area network (6LoWPAN) framework. The framework possesses the capability to gather data instantaneously in natural status and in physical conditions of patients after which it gets processed for analysis, thereby providing services to the user. The proposed model also focuses on the security aspect in the network of the healthcare system. The security model proposes IoT based medical services which comprises three security services: protection, detection and reaction services

Evaluation of 6LoWPAN Generic Header Compression in the Context of a RPL Network.

The Internet of Things (IoT) facilitates the integration of diverse devices, leading to the formation of networks such as Low-power Wireless Personal Area Networks (LoWPANs). These networks have inherent constraints that make header and payload compression an attractive solution to optimise communication. In this work, we evaluate the performance of Generic Header Compression (6LoWPAN-GHC), defined in RFC 7400, for IEEE 802.15.4-based networks running the IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL). Through simulation and real-device experiments, we study the impact of 6LoWPAN-GHC on energy consumption and delays and investigate for which scenarios 6LoWPAN-GHC is beneficial. We show that all RPL control packets are compressible by 6LoWPAN-GHC, which reduces their transmission delay and as such their vulnerability to interference. However, for the devices under study transmitting at 250 kbit/s, the energy gain obtained from sending a compressed packet is outweighed by the energy needed to compress it. The use of 6LoWPAN-GHC causes an energy increase of between 2% and 26%, depending on the RPL packet type. When the range is more important than the bandwidth and a sub-GHz band is used at 10 kbit/s, an energy gain of 11% to 29% can be obtained, depending on the type of RPL control packet.

Deep-Reinforcement-Learning-Aided Loss-Tolerant Congestion Control for 6LoWPAN Networks

The IPv6 over Low-power Wireless Personal Area Network (6LoWPAN) protocol stack is a promising solution to connect Wireless Sensor Networks (WSNs) with the Internet for realizing a ubiquitous network interconnection of all things. However, 6LoWPAN networks face a critical challenge to control congestion caused by the burst of data traffic from wireless sensors. Packet loss will occur when buffer overflows. This paper focuses on the loss-tolerant congestion control problem in 6LoWPAN networks, which has not been addressed in existing works. We formulate the congestion control problem as a non-cooperative Markov game framework and conceive a novel congestion control method, namely Deep reinforcement learning aided Loss-tolerant Congestion Control (DLCC), to alleviate congestion while maintaining a tolerable packet loss imposed by buffer overflow. The proposed DLCC employs Deep Reinforcement Learning (DRL) to solve the curse of state dimensionality, while packet loss constraints are handled by utilizing Lagrange multipliers to integrate the reward with loss constraints. By dynamically updating Lagrange multipliers in an online learning procedure, DLCC finds the optimal congestion control policy. Our simulation results show that DLCC maintains the packet loss rate below the tolerable threshold at the presence of congestion. In contrast to existing hybrid congestion control algorithms, the proposed DLCC algorithm is more energy-efficient and provides higher throughput, lower average delay and better fairness.

CRA-RPL: A Novel Lightweight challenge-Response authentication-based technique for securing RPL against dropped DAO attacks

IPv6 over Low-Power Wireless Personal Area Networks (6LoWPAN) is one of the most prominent networking technologies currently fueling the drastic growth of the Internet of Things (IoT) market. As 6LoWPAN runs on resource-constrained devices like ultra-low powered micro-controllers and radio transceivers, therefore use of traditional routing protocols is not recommended. To solve the problem of achieving energy-efficient routing in 6LoWPAN, Routing Protocol for Low-power and Lossy Networks (RPL) is specified by IETF. Although RPL gives many benefits to 6LoWPAN, but the research fraternity has raised many concerns regarding its security. One such security issue is the Dropped Destination Advertisement Object (DDAO) attack. In a DDAO attack, an attacker exploits the standard DAO forwarding technique of RPL to perform the attack without getting noticed. Using multiple experiments, we have observed that the key network performance parameters are severely affected by the DDAO attack. In this view, this paper proposes a novel lightweight Challenge-Response Authentication-based technique for securing RPL against DDAO attacks. The key idea of CRA-RPL is to use a modified version of control messages by incorporating challenge-response pair for authenticating DAO-ACK messages. CRA-RPL is implemented on a widely used Contiki-NG embedded operating system and validated on Cooja Simulator. Performance of CRA-RPL is compared with ContikiRPL (i.e., standard RPL implementation). The experimental findings indicate that CRA-RPL effectively identifies and counteracts DDAO attacks in static and mobile environments without devastatingly affecting the resource-constrained nodes. In a DDAO attack, an attacker exploits the standard DAO forwarding technique of RPL to perform the attack without getting noticed. Using multiple experiments, we have observed that the key network performance parameters are severely affected by the DDAO attack. In this view, this paper proposes a novel lightweight Challenge-Response Authentication-based technique for securing RPL against DDAO attacks. The key idea of CRA-RPL is to use a modified version of control messages by incorporating challenge-response pair for authenticating DAO-ACK messages. CRA-RPL is implemented on a widely used Contiki-NG embedded operating system and validated on Cooja Simulator. Performance of CRA-RPL is compared with ContikiRPL (i.e., standard RPL implementation). The experimental findings indicate that CRA-RPL effectively identifies and counteracts DDAO attacks in static and mobile environments without devastatingly affecting the resource-constrained nodes.

Multi-Sensor Based healthcare monitoring system by LoWPAN-based architecture

The development of the Internet of Things (IoT) has recently revived interest in IPv6 over Low-Power Wireless Personal Area Networks (6LoWPAN). For the enormous IP-based detection technologies in the future IoT, 6LoWPAN mobile compatibility is still in its infancy. A significant 6LoWPAN use of IoT, the hospital's wireless technology, which continuously monitors patients' vital signs, regardless of whether they are moving. To track accurate patient positions, communication between patient nodes and the hospital network needs to be maintained through sensor network. In addition, it must offer automatic switching and improve the power consumption of devices. In this research, a hospital structure based on 6LoWPAN with a Media Access Control (MAC) design for patient data was offered. Our preliminary numerical results indicate a decrease in the expenses associated with transfers on the mobile router, which generally represents a bottleneck in such a network. Biological ECG signals are routed via low pan before transmission via routing algorithms to the gateway network.

Blackhole attacks in internet of things networks: a review

The internet of things (IoT) is one of data revolution area and is the following extraordinary mechanical jump after the internet. In terms of IoT, it is expected that electronic gadgets that are used on a regular basis would be connected to the current of the internet. IPv6 over low-power wireless personal area networks (6LoWPAN) is a one of IPv6 header pressure technology, and accordingly, it is vulnerable to attack. The IoT is a combination of devices with restricted resource assets like memory, battery power, and computational capability. To solve this, RPL or routing protocol for low power Lossy network is deploy by utilizing a distance vector scheme. One of denial of service (Dos) attack to RPL network is blackhole attack in which the assailant endeavors to become a parent by drawing in a critical volume of traffic to it and drop all packets. In this paper, we discuss research on numerous attacks and current protection methods, focusing on the blackhole attack. There is also discussion of challenge, open research issues and future perspectives in RPL security. Furthermore, research on blackhole attacks and specific detection technique proposed in the literature is also been presented.

Design and Implementation of a 6LoWPAN-Based Lightweight Wireless Embedded Internet Platform for IoT Applications

<p>The Internet of Things (IoT) plays an important role in the revolution of the Internet with the rise of Industry 4.0. It is closely related to low-power embedded devices, which have not been entirely IP-enabled until now. The 6LoWPAN (acronym of IPv6 over low-power wireless personal area networks) is an IPv6 protocol for Wireless Embedded Internet. It can help enable even the minimum embedded systems with limited processing capabilities to participate in the IoT. This study investigates a reliable way of future IoT networking by designing and implementing a 6LoWPAN-based lightweight Wireless Embedded Internet platform, named Lowvy (abbreviated from low-power wireless connectivity). Lowvy is characterized by low power consumption, constrained memory, small size, a low-cost IPv6-enabled wireless embedded device, and a constrained application protocol (CoAP). We did different application experiments to test the performance of the platform in terms of received signal strength indicator (RSSI), power consumption, sensor integration, and internet integration application with 6LoWPAN. Comparing Lowvy with other wireless embedded IoT platforms, Lowvy is less expensive and more compact. Moreover, Lowvy has a powerful performance in terms of signal strength for a longer wireless distance transmission of about 25 to 30 meters and has a lower power consumption of 41.60 milliwatts.</p> <p> </p>

QSec-RPL: Detection of version number attacks in RPL based mobile IoT using Q-Learning

Internet of Things (IoT) has revolutionized the networking by connecting the real world entities to the Internet. IoT connects the communication devices and has an incredible impact on perspective analytics on the massive volume of data produced every day. An attacker may exploit vulnerabilities of IoT entities and compromise users’ security and privacy. Development of solutions to address security and privacy issues of IoT is in premature stage and considered as challenge. This challenge becomes more critical when the devices in the network are resource-constrained in terms of energy, processing and memory. The IPv6 over Low-Power Wireless Personal Area Networks (6LoWPAN) has emerged in recent years as an adaptation layer to carry IPv6 packets over IEEE 802.15.4. Many IoT applications use Routing Protocol for Low Power and Lossy Networks (RPL) as a network layer protocol developed for routing in 6LoWPAN. Security is challenging in resource constrained environment where encryption may not be a viable solution. Version number attack is one of the most common network layer attacks against RPL based 6LoWPAN. The RPL specification does not address the integrity of the version number and therefore leaves version number mechanism as a weak point in terms of security. This paper investigates the impact of version number attack in RPL networks while considering mobility of the sensor nodes. We propose a solution that utilizes Q-Learning strategy to detect the malicious nodes that are performing version number attack. The proposed approach detects malicious nodes with reasonable accuracy while imposing significantly less overhead on the nodes of low power and lossy networks. There are other approaches too like Message Authentication Codes (MAC) based on symmetric keys but these techniques have memory and communication overhead. So we propose different approach Q-Learning to detect the attacker nodes.

Fragmentation Attacks and Countermeasures on 6LoWPAN Internet of Things Networks: Survey and Simulation

The Internet of things is a popular concept in the current digital revolution. Nowadays, devices worldwide can be connected to the Internet, enhancing their communication, capabilities, and intelligence. Low-Power Wireless Personal Area Network (6LoWPAN) was specifically designed to build wireless networks for IoT resource-constrained devices. However, 6LoWPAN is susceptible to several security attacks. The fragmentation mechanism, in particular, is vulnerable to various attacks due to the lack of fragment authentication and verification procedures in the adaptation layer. This article provides a survey of fragmentation attacks and available countermeasures. Furthermore, the buffer reservation attack, one of the most harmful fragmentation attacks that may cause DoS, is studied and simulated in detail. A countermeasure for this attack is also implemented based on a reputation-scoring scheme. Experiments showed the harmful effects of the buffer reservation attack and the effectiveness of the implemented reputation-scoring countermeasure.

Performance evaluation of mobile RPL-based IoT networks under version number attack

The Internet of Things (IoT) has a vital role in communication and has many cross-platform applications which generate a massive volume of data. IoT interconnects various devices from small to big without the direct intervention of humans. The resource-constrained environment poses a significant problem in IoT applications, and it is challenging to develop secure applications. The Internet community endeavours to cope with such challenges by developing different internet protocols. IETF ROLL working group standardized a mechanism called IPv6 over Low-Power Wireless Personal Area Networks (6LoWPAN) to carry IPv6 packets over IEEE 802.15.4. 6LoWPAN which supports the constrained environment uses the Routing Protocol for Low Power and Lossy Networks (RPL) as a routing protocol. It is essential to secure such applications since the malicious attacker can breach the privacy and security of humans through a small device. Traditional security mechanisms are not prominent in a resource-constrained context. Version attack is one of the most common attacks in RPL based 6LoWPAN. The network becomes unstable due to the version attack, which results in a Denial of Service attack. The integrity of the version number is not provided by RPL specifications, leading to threats for IoT applications. The impact of a version number attack on an RPL-based network is demonstrated in this study. The implications on the constrained network when the nodes are mobile is the main objective of this paper. In many IoT applications nodes move and it is vital to address the impact of mobility in a constrained environment. This paper investigates the network’s performance in terms of packet delivery, delay, and power consumption in RPL based IoT when there is version attack. Version attacks must be prevented as quickly as possible since they have the potential to significantly disrupt mobile networks. The main contribution of this research is a performance metric-based analysis of mobile RPL-based IoT networks under attack.

A Study on Impact of DIS flooding Attack on RPL-based 6LowPAN Network

RPL is the default routing protocol for IPv6-based Low-Power Wireless Personal Area Networks (6LoWPAN). In an RPL-based 6LoWPAN, wireless sensor nodes or IoT devices connect to the network via DODAG Information Solicitation (DIS) packets with a predetermined time interval. A compromised or malicious node can use the abovementioned technique to broadcast unauthorised DIS messages to neighboring nodes to conduct routing attacks, such as DIS flooding attacks. In this paper, we conduct a simulation-based analysis of the impact of the DIS flooding attack on RPL-based 6LoWPAN under four distinct scenarios, each with two test cases. Scenario 1 is the reference topology, devoid of attacker nodes or test cases. From scenario 2 to scenario 4, the number of DIS attacker nodes increases from one to three to five. In each scenario, in test case A, all attacker nodes are deployed outside the sink node's radio range; in test case B, all attacker nodes are deployed within the sink node's radio range. Our study shows that increasing the number of attacker nodes and deployment location significantly negatively impacts PDR, E2ED, and power consumption. In the case of five DIS attackers nodes deployed within the sink node's radio range, the experimental result decreased PDR by 4.5%, E2ED increased by 62.98%, and the average power consumption increased by 126.7%.

A mechanism to detecting flooding attacks in quantum enabled cloud-based lowpower and lossy networks

RPL (Routing Protocol) is the Quantum enabled Cloud-based Low-Power Wireless Personal Area Networks (6LoWPAN) routing protocol. Destination Oriented Directed Acyclic Graph (DODAG) information Solicitation (DIS) messages are received by a node in the RPL protocol to access the network. This function may be abused by a malicious node to transmit illegitimate DIS messages to neighbouring nodes to launch a DIS flooding attack. It is found that the DIS flooding attack raises the overhead of the network control packet, which greatly degrades the efficiency of the network. This further raises the network resource demand of the nodes. A defence scheme called DIS-mitigation in RPL is proposed to resolve this problem. The proposed system greatly mitigates the impact of the DIS flooding attack on the output of the network and also finds the nodes that produce the DIS flood attack effectively. The efficacy of the suggested scheme is related to the traditional RPL protocol and the current stable RPL mechanism. In both static and dynamic network conditions, the experimental findings demonstrate that the proposed model identifies and mitigates DIS flooding attacks rapidly and effectively, without introducing any major overhead to the nodes.

A Systematic Literature Review on Machine and Deep Learning Approaches for Detecting Attacks in RPL-Based 6LoWPAN of Internet of Things.

The IETF Routing Over Low power and Lossy network (ROLL) working group defined IPv6 Routing Protocol for Low Power and Lossy Network (RPL) to facilitate efficient routing in IPv6 over Low-Power Wireless Personal Area Networks (6LoWPAN). Limited resources of 6LoWPAN nodes make it challenging to secure the environment, leaving it vulnerable to threats and security attacks. Machine Learning (ML) and Deep Learning (DL) approaches have shown promise as effective and efficient mechanisms for detecting anomalous behaviors in RPL-based 6LoWPAN. Therefore, this paper systematically reviews and critically analyzes the research landscape on ML, DL, and combined ML-DL approaches applied to detect attacks in RPL networks. In addition, this study examined existing datasets designed explicitly for the RPL network. This work collects relevant studies from five major databases: Google Scholar, Springer Link, Scopus, Science Direct, and IEEE Xplore® digital library. Furthermore, 15,543 studies, retrieved from January 2016 to mid-2021, were refined according to the assigned inclusion criteria and designed research questions resulting in 49 studies. Finally, a conclusive discussion highlights the issues and challenges in the existing studies and proposes several future research directions.

6LoWPAN Stack Model Configuration for IoT Streaming Data Transmission over CoAP

Abstract: Different protocols have been developed for the Internet of things (IoT), such as the constrained application protocol (CoAP) for the application layer of the IPv6 over low-power wireless personal area networks (6LoWPAN) stack model. Data transmitted over 6LoWPAN are limited by the throughput and the frame size defined by IEEE 805.14.5 standards. Choosing the best configuration for data transmission involves a trade off between the application requirements, the constrained network configuration, the constrained device specifications and IoT application protocols. This paper provides an analysis of message size and structure recommendations for the 6LoWPAN stack model for different network topologies using CoAP. CoAP is a promising application protocol for the 6LoWPAN stack model because it can effectively manage the transmission required functionality in small header UDP packets compared to TCP packets. However, a data model is also required to realize an effective IoT model. While fragmentation and reassembly are supported by CoAP, they should be avoided for this type of model. As for any conceptual model, a high configuration between layers is mandatory. Additionally, the proposed message format is useful for semantic web of things application development and for WSN design and management.

Mitigation of black hole attacks in 6LoWPAN RPL-based Wireless sensor network for cyber physical systems

A cyber–physical system (CPS) classically comprises the physical components, computational units, the controller, and the communication network. The Wireless sensor networks (WSNs) is a key component in the CPS and connects the distributed sensors for computation and communication. As the usage of such networks increases, the attack surface also increases and mechanisms for mitigating security threats must be developed. The nodes in the WSN are low-powered devices that cannot host traditional security systems. Moreover, specialized architectures of such networks mean that new attacks specifically targeting such architectures would be discovered. The proposed work introduces a security mechanism for black hole attack in Ripple Routing Protocol (RPL) networks, utilizing features such as IPv6 over Low-Power Wireless Personal Area Networks (6LoWPAN) network discovery. The state-of-the-art mechanisms existing today for such threats are either heavyweight intrusion detection systems or require nodes working in promiscuous mode. The promiscuity of nodes can be a security concern in itself, whereas large intrusion detection systems require huge processing and network overheads. The proposed work does not rely on either but utilizes a distributed timer-based mechanism to perform malicious node detection. The work has been evaluated using the Cooja simulator, and it has been seen that it can detect black holes with high accuracy resulting in a decrease in packet loss. The true positive rate can reach up to 100%.

ASIS Edge Computing Model to Determine the Communication Protocols for IoT Based Irrigation

Internet of Things (IoT) provide a promising Smart irrigation facilitator for continual monitoring and control of environmental parameters, thereby leading to a huge volume of data to be efficiently collected, transferred, processed and stored. The deployment of cloud-based infrastructure with on-field connectedness, allowing information exchange among IoT nodes, and the usage of energy scavenging (e.g., solar power) in feeding them, become necessary, since agricultural fields are in lack of wired energy supply and, often, a reliable (Internet) network coverage. Therefore, these issues can be addressed through the integration of Edge computing in IoT scenarios. An efficient strategy is required to select the best communication technology with a motive of increasing the network performance between the IoT devices, Edge device and cloud. Application Specific Infrastructure Selection (ASIS) is an edge computing model developed to select the appropriate communication protocols according to the infrastructure requirements of three different real time scenarios namely: Assembly line automation, Smart parking system and Automatic irrigation system are deployed to get the most suitable application specific protocol from ZigBee, LoRa (Long Range) and LoWPAN (Low-Power Wireless Personal Area Network) to implement in real-time basis. ASIS model is proposed as a network resource manager that is capable of sensing, acting, signal processing, and/or communication abilities to perform a protocol selection according to their physical and technological limitations. Further Edge based ASIS model is developed to enhance the network performance even better when compared with cloud-based model. Automatic irrigation system is extended in the Edge based ASIS model. The overall ASIS system is evaluated by means of network parameters such as network usage, network delay and power consumption. The ASIS model and Edge based ASIS model is deployed in iFogSim simulator that compares each protocol used in the above IoT scenarios. Finally, the scenario of Automatic irrigation system is modeled using Edge based ASIS model where ZigBee with edge performs better compared with cloud-based model. Experimental results show that ASIS based Edge implementation lessen the overall network parameters in contrast to non-edge deployment in automatic irrigation scenario.

Assessment of Routing Attacks and Mitigation Techniques with RPL Control Messages: A Survey

Routing Protocol for Low-Power and Lossy Networks (RPL) is a standard routing protocol for the Low Power and Lossy Networks (LLNs). It is a part of the IPv6 over Low-Power Wireless Personal Area Network (6LoWPAN) protocol stack. Features such as energy-efficient mechanisms and availability of the secure modes of operations make RPL suitable for the constrained Internet of Things (IoT) devices. Hence, the majority of IoT applications rely on RPL for data communication. However, routing security in RPL-based IoT networks is a significant concern, motivating us to study and analyze routing attacks and suggested countermeasures against them. To this end, we provide a comprehensive survey on the state-of-the-art security threats and their corresponding countermeasures in RPL-based IoT networks. Based on our study, we propose a novel classification scheme that uses a mapping between RPL attacks and their countermeasure techniques to the RPL control messages used to develop these techniques. Furthermore, we provide an in-depth statistical analysis that includes analysis of routing attacks through the RPL control messages, distribution of various mitigation techniques as per the method used, RPL control messages involved in the mitigation techniques, and details of the tools used by multiple researchers. In the end, we highlight some open challenges and future research opportunities on this topic. We believe that this survey will be beneficial to researchers and practitioners working in the area of RPL security.