Intrusion Prevention Scheme Against Rank Attacks for Software-Defined Low Power IoT Networks

The burgeoning demands of the Internet of Things (IoT) applications such as video/audio streaming in surveillance, disaster recovery, multimedia, and healthcare paves the need to provide better Quality of Service (QoS) delivery. The growth in the amount of data generated by multimedia applications increases congestion in the network. Software-Defined Networking (SDN) is an emerging approach that centrally controls the network and solves the congestion problem in the network. SDN has many advantages that help in network management, traffic shaping, and routing in the IoT network to manage the data generated from a diverse range of applications. In this paper, we propose a congestion technique using Hierarchical Token Bucket (HTB) to manage the bandwidth in the Software-Defined IoT (SDIoT) network. Further, we propose a routing scheme to compute optimal routing shortest paths using Dijkstra’s algorithm by selecting the min-cost path based on the priorities of traffic flows. The results illustrate that the proposed approach achieves a reduction in end-to-end delay by 38%, 44% and higher average throughput by 29%, 43% in comparison with the benchmark schemes - SDN with HTB and the Delay Minimization method, respectively.

Energy efficient thwarting rank attack from RPL based IoT networks: A review

The Internet of Things (IoT) is an emerging technology that plays a vital role in interconnecting various objects into a network to provide desired services within its resource constrained characteristics. In IoT, the Routing Protocol for Low power and Lossy network (RPL) is the standardized proactive routing protocol that achieves satisfying resource consumption, but it does not consider the node's routing behavior for forwarding data packets. The malicious intruders exploit these loopholes for launching various forms of routing attacks. Different security mechanisms have been introduced for detecting these attacks singly. However, the launch of multiple attacks such as Rank attack and Sybil attacks simultaneously in the IoT network is one of the devastating and destructive situations. This problem can be solved by establishing secure routing with trustworthy nodes. The trustworthiness of the nodes is determined using trust evaluation methods, where the parameters considered are based on the factors that influence in detecting the attacks. In this work, Providing Routing Security using the Technique of Collective Trust (PROTECT) mechanism is introduced, and it aims to provide a secure RPL routing by simultaneously detecting both Rank and Sybil attacks in the network. The advantage of the proposed scheme is highlighted by comparing its performance with the performance of the Sec-Trust protocol in terms of detection accuracy, energy consumption, and throughput.

Edge computing has played a vital role in improving the performance of the Internet of Things (IoT) network. It provides computation locally at the edge of the network, placed near the field of data generation. It has significantly reduced some of IoT’s significant challenges, such as scalability, availability, reliability, and resiliency. In this domain, researchers are further incorporating advanced technology such as Software-Defined Network (SDN) to improve the Edge-based IoT network’s overall performance. Moreover, Edge-based IoT network’s working depends on the type of protocol used for data delivery. Various data transmission protocols can be used to transmit a packet from source to destination. This paper aims to determine the performance of the two widely used protocols, namely IPv6 over low-power wireless personal area networks (6LoWPAN) and OpenFlow for SDN enabled Edge-based IoT network. The experimentation was carried out in a Mininet-WiFi emulator. The results indicate that SDN based OpenFlow protocol can improve the system performance in terms of average round trip time (RTT) of a packet, packet loss ratio, and jitter compared to the conventional 6LoWPAN protocol.KeywordsInternet of ThingsSoftware-defined network6LoWPANOpenFlowEdge computation

Maintaining adequate energy in low-powered Internet of Things (IoT) nodes is crucial for the development of several applications like smart homes, autonomous industries, etc. These IoT nodes exploit adaptive duty cycling techniques for the efficient utilization of energy resources. However, such adaptive duty cycling of IoT nodes results in their asynchronous operations thereby inducing energy holes in the network. These energy holes lead to information loss and poor quality of services of IoT networks. In this regard, energy harvesting using Mobile Energy Transmitters (MET) can improve the lifetime of an IoT network. In this work, we are introducing a metric named Age of Charging (AoC) metric to quantify the repetitive charging of power deficit IoT nodes. Energy-efficient scheduling of MET is proposed to minimize the expected average AoC such that the energy harvested by IoT nodes is maximized. In this regard, the optimization problem is first remodeled into a Markov decision process. Subsequently, a deep reinforcement learning algorithm is developed based upon the twin delayed deep deterministic policy gradient scheme for energy-efficient scheduling of MET in asynchronous IoT networks. The simulation results indicate that the proposed algorithm outperforms the conventional Deep Q-networks and soft-actor-critic algorithms. These results motivate the usage of MET-aided energy harvesting in self-sustaining IoT networks.

Software Defined Networks (SDN) have been utilized in applications of the Internet of Things (IoT), termed as software-defined IoT network, because of the popularity and capability of mobile devices being used for networking in relatively large areas. In a software-defined IoT system, the sensing data are asynchronously harvested by the mobile sensing nodes, and are also asynchronously uploaded to the gateways of a software-defined network. Thus, all the sensing data are asynchronously transmitted from the gateways to the data servers in the pattern of multipoints-to-point (M2P) data transmissions. Even if the sensing data are generated from the same sensing event, the controller of SDN has no knowledge about this relationship. Thus, such asynchronous M2P data transmissions from the same sensing event at the gateways will generate many redundant requests to their controller by OpenFlow protocol of SDN. In this paper, we investigate the redundant requests caused by the asynchronous M2P data transmissions in the software-defined IoT network. We model the relationship between the sensing events and the uploading gateways by utilizing their spatial locations and the distribution of mobile sensor nodes. To reduce the loads on the controller for the asynchronous M2P data transmissions, we propose an One-Request Scheme for Software-Defined IoT Networks (ORSIN), to batch the updating the forwarding rules of the multiple data transmissions from the same event by the first one request from a gateway. Our extensional simulations verify the effectiveness of our proposed approach.

Optimizing beamforming in IoT-WET: A symbiotic-based approach under imperfect channel state information

Internet of Things (IoT) deployments mostly relies on the establishment of Low-Power and Lossy Networks (LLNs) among a large number of constraint devices. The Internet Engineering Task Force (IETF) provides an effective IPv6-based LLN routing protocol, namely the IPv6 Routing Protocol for Low Power and Lossy Network (RPL). RPL provides adequate protection against external security attacks but stays vulnerable to internal routing attacks such as a rank attack. Malicious RPL nodes can carry out a rank attack in different forms and cause serious network performance degradation. An experimental study of the impact of the decreased rank attack on the overall network performance is presented in this paper. In also besides, it is important to understand the main influencing factors in this context. In this study, several some many network scenarios were considered with varying network sizes, attacker properties, and topological setups. The experimental results indicate a noticeable adverse effect of the rank attack on the average PDR, delay, ETX, and beacon interval. However, such impact was varied according to network size, attacker position, attacker neighbor count, number of attack-affected nodes, and overall hops increase. The results give a practical reference to the overall performance of RPL networks under rank attacks.

The Routing Protocol for Low Power and Lossy Networks (RPL) is experiencing a wave of security threats, due to the need of its utility in the Internet of Things (IoT). RPL is so far considered as one of the most utilized routing protocols in new IoT applications, which is one of the reasons the latter may lead to confronting challenges later on. In other words, security is thus the primary interest in RPL, regarding the nature of objects and their different constraints that are regularly regarded as security vulnerabilities for cyber-attacks. To continue, it is evident to claim that it is mandatory to determine and analyze all the kinds of attacks that target this protocol, such as the rank attack which is best known for its strongest threats that lead to degraded topology performance and energy consumption of nodes in the network. This study presents an overview of RPL, a proposed taxonomy of all the recent published works about rank attacks as well as the different mitigation methods, and the damage caused on network parameters. To conclude, a comparison between several attacks has been adopted using the Friedman test, and a discussion has been presented with some other recent research concerning security against rank attack.

A fuzzy based Co-Operative Blackmailing Attack detection scheme for Edge Computing nodes in MANET-IOT environment

The concept of Security Aware Routing is not widely adopted in current networks. However, the new IoT-centric core networks give possibilities to re-open that field of research. We consider routing to be an addition to existing network security methods, especially in IoT domain. Security-aware routing incorporates the security and safety metrics to the traditional set of metrics (bandwidth, network delay, hop count, path cost, load etc.). The paper shows a new approach in which, based on the Software Defined Networks (SDN) we estimate trust relationships between nodes and flows and use them to create SDN paths, based on the Cognitive Packet Network (CPN) principle. The Random Neural Networks (RNN) supported with cognitive packets are used for making routing decisions. The proposed solution was designed and is being implemented within the SerIoT project to demonstrate secure networks for the Internet of Things (IoT).

Internet of Things (IoT) networks are being designed to connect billions of sensors/devices for an application-specific environment to provide services and carry out special tasks. It enables the virtual connection of the real-world physical devices with Internet cyberspace. While, there is an exponentially growing trend in the use of IoT technology for diverse applications such as smart agriculture, smart industries, smart hospitals, smart homes, etc., there are pressing design issues which affect the IoT network performance in the long run, for example, replacement of batteries for increasing the lifetime of devices to maintain the network longevity. To tackle this challenge, various wireless powered-based energy transfer paradigms were proposed over time. Backscatter communication (BackCom) is likewise one of the energy-efficient solutions proposed, which can satisfy the stringent green communication aspects of IoT networks. To achieve massive connectivity in BackCom-enabled IoT network, selecting a suitable multiple access scheme is also a crucial part. Most multiple access schemes proposed for BackCom systems are based on frequency or timeslots sharing techniques. Multiple access techniques proposed for BackCom IoT system can reap the gain of non-orthogonal multiple access (NOMA) principle. In NOMA, multiple end nodes (ENs) of sensors network is served in the same resource block, thereby, increasing the spectrum efficiency of BackCom IoT system. In this chapter, we discuss the prospects of backscatter technique with application of NOMA for green communication in IoT network.

Threats, Detection and Mitigation of Rank Attack: A Survey

Objectives: The key objectives are to enhance the performance of Internet of Things (IoT) networks to attain a high packet delivery ratio (PDR), efficient residual energy (RE) usage, minimum energy consumption, and decrease packet loss during data transmission. Performance metrics are used to discover and choose high, top-notch, and long-living paths, increasing network lifetime and optimizing resource management by preventing the participation of non-essential nodes and inefficient paths in the network routing process. Methods: To devise an energy-efficient technique and refine it with decision-making for selecting an optimal path, which employs a composite metric path quality index (PQI) to enhance path quality, comprising RE for priority of nodes with maximum remaining energy, link quality score (LQS), packet delivery ratio (PDR) and hop count (HC) to minimize the path length must be integrated into the routing process to make data transmission more efficient. Hence, intermediate nodes ensure path quality, determine if packet flow is good or bad, eliminate unnecessary nodes, and optimize the whole routing by deciding on a path. The proposed technique simulates IoT network scenarios in which nodes dynamically compute these metrics and select optimal paths for transmitting data to the sink node. Findings: The proposed technique achieves a much higher PDR and an energy-proficient routing mechanism by balancing the quality of the optimal path, the residual energy, and the number of hops, which can not only prolong the network lifetime but also ensures that resources are being used efficiently, in turn all leading to a better network performance than existing methods. Simulation results show that the proposed technique significantly improves the PDR over the existing methods and reduces overall energy consumption. In this work, the validated results show that metrics PQI, RE, LQS, and HC collectively improve data delivery and ensure higher survivability of the network by optimizing the path choosing and eliminating the unnecessary nodes from the source to the target. The proposed technique simulation showed a 32.15% increase in network RE and a 36.44% improvement in PDR. Novelty: The technique presented in this paper introduces a routing approach designed with the unique integration of RE, LQS, PDR, and HC, deriving PQI metric, which will improve the path selection in IoT networks. Unlike existing methods, this method considers energy efficiency, path quality instead of link quality, and path length together to avoid over-utilizing energy-draining nodes while favoring stable, efficient paths. The approach fills a gap in existing IoT environments by focusing on energy-wise efficiency and path reliability and reducing the number of nodes unnecessarily concerned with the dynamic optimal path selection. As IoT deployments increase, so does the need for energy-efficient data transmission, and the proposed technique supports the sustainability of IoT networks. Keywords: Energy Efficiency, Path Quality Indicator, Residual Energy, Hop Count, Quality of Service

The blockchain network architecture is a promising technology for constructing highly secure Internet of Things (IoT) networks. IoT networks typically consist of many sensors and actuators. Blockchain network technology can be applied to secure control robots in smart factories or for reliable drone delivery in smart cities. The distributed ledger and data block validation across blockchain networks guarantee the ultimate data security. However, the current blockchain technology is restricted in terms of its overall deployment across IoT networks. A general permissionless blockchain technology typically targets high-performance network nodes with sufficient computing power and memory space. A blockchain node with less computing power and memory, such as an IoT sensor or actuator, cannot employ blockchain technology as a fully functional node. A lightweight blockchain provides practical blockchain availability to IoT networks. We propose an operational advance to develop a lightweight blockchain for IoT networks. The opportunistic block validation optimizes the block validation process. It measures the network vulnerability based on the node reputation, block validation degree, number of fraudulent messages, and network stability. The reinforcement learning mechanism employed in the opportunistic block validation determines whether block validation is to be performed. Two separately developed reinforcement learning methods can increase the processing performance while maintain the transaction data integrity. In addition, the proposed blockchain technology is easily implementable because it adopts a Hyperledger development environment. Directly embedding the proposed blockchain middleware platform in small computing devices proves the practicability of the proposed block validation mechanism.