Software-defined Wireless Sensor Networks Research Articles

UAV-enabled software defined data collection from an adaptive WSN

AbstractUnmanned aerial vehicle (UAV)-based data gathering from wireless sensor networks is one of the recent research topics that has currently attracted research interest. One of the challenges for the UAV-aided WSN data collection efforts is to design an energy-efficient UAV/drone communication with arbitrarily dispersed ground sensors by improving the ground network structure. This paper aims to develop a technique titled UAV Fuzzy Travel Path' that supports UAV smooth path design and enables ground network topology shifting. A comprehensive UAV-based data collection model is proposed to enable dynamic orchestration/re-orchestration of wireless ground sensors to jointly improve network performance and UAV path fluidity. This provides a more flexible ground network framework that can be restructured based on network demands and UAV optimal paths, effectively allowing for a software-defined network concept. The main contribution of this work is the implementation of the software-defined wireless sensor network on the ground network that adaptably supports the movement of the UAV and enhances the communication network’s energy efficiency with a proposed latency analytical analysis via network orchestration/re-orchestration phases. The main significance of this research is in offering a flexible span for UAV path design than being fixed in one strict route for data gathering purposes. Four various simulation tools are employed for modelling and performance evaluation, namely MATLAB, CupCarbon, Contiki-Cooja and Mission Planner. The proposed software-defined ground network system demonstrates encouraging results in terms of network performance metrics including energy consumption of UAV versus ground sensor nodes energy usage, packet delivery rate, and the communication time of the ground orchestrated or/and re-orchestrated network.

A Topology Control Algorithm Taking into Account Energy and Quality of Transmission for Software-Defined Wireless Sensor Network

The efficient use of energy in wireless sensor networks is critical for extending node lifetime. The network topology is one of the factors that have a significant impact on the energy usage at the nodes and the quality of transmission (QoT) in the network. We propose a topology control algorithm for softwaredefined wireless sensor networks (SDWSNs) in this paper. Our method is to formulate topology control algorithm as a nonlinear programming (NP) problem with the objective to optimizing two metrics, maximum communication range, and desired degree. This NP problem is solved at the SDWSN controller by employing the genetic algorithm (GA) to determine the best topology. The simulation results show that the proposed algorithm outperforms the Max Power algorithm in terms of average node degree and energy expansion ratio.

An Energy-Efficient Routing Protocol with Reinforcement Learning in Software-Defined Wireless Sensor Networks.

The enormous increase in heterogeneous wireless devices operating in real-time applications for Internet of Things (IoT) applications presents new challenges, including heterogeneity, reliability, and scalability. To address these issues effectively, a novel architecture has been introduced, combining Software-Defined Wireless Sensor Networks (SDWSN) with the IoT, known as the SDWSN-IoT. However, wireless IoT devices deployed in such systems face limitations in the energy supply, unpredicted network changes, and the quality of service requirements. Such challenges necessitate the careful design of the underlying routing protocol, as failure to address them often results in constantly disconnected networks with poor network performance. In this paper, we present an intelligent, energy-efficient multi-objective routing protocol based on the Reinforcement Learning (RL) algorithm with Dynamic Objective Selection (DOS-RL). The primary goal of applying the proposed DOS-RL routing scheme is to optimize energy consumption in IoT networks, a paramount concern given the limited energy reserves of wireless IoT devices and the adaptability to network changes to facilitate a seamless adaption to sudden network changes, mitigating disruptions and optimizing the overall network performance. The algorithm considers correlated objectives with informative-shaped rewards to accelerate the learning process. Through the diverse simulations, we demonstrated improved energy efficiency and fast adaptation to unexpected network changes by enhancing the packet delivery ratio and reducing data delivery latency when compared to traditional routing protocols such as the Open Shortest Path First (OSPF) and the multi-objective Q-routing for Software-Defined Networks (SDN-Q).

Energy efficient and coverage aware grey wolf optimizer based clustering process for Software-defined wireless sensor networks

Clustering has been adopted as a critical technique for reducing energy consumption in wireless sensor networks (WSNs). Furthermore, the selected group of Cluster Heads (CHs) should enhance the network’s coverage while minimizing any impact on its energy efficiency. Although distributed CHs election processes have improved the network’s lifespan, they have struggled to provide adequate coverage. Moreover, they usually use impractical assumptions like unlimited transmission range and location cognition. On the other hand, centralized meta-heuristic-based CHs selection algorithms have shown remarkable improvement in clustering problems using centralized decisions. However, software-defined wireless sensor networks (SDWSN) have gained a prevalent use in recent years. SDWSN consists of stripping control from nodes and centralizing it in an external controller. This paper introduces an energy-efficient and coverage-aware Grey wolf optimizer (GWO) based clustering process for SDWSN. Based on a novel fitness function, our proposed algorithm aims to maximize coverage, decrease blind spots, and preserve energy in the network. Our algorithm is widely evaluated on various homogeneous and heterogeneous network scenarios, with varied network sizes and base station (BS) locations, versus prominent clustering algorithms. The obtained results testify that our proposed algorithm shows better performance in energy efficiency and CHs distribution. Our algorithm improved coverage up to a mean of 31.42%, 27.72%, 18.69%, 21.12%, 13.81%, 4.14%, 3.8% and 8.31% compared to CREEP, DEEC, I-LEACH, HEESR, BRE-LEACH, PSO-ECHS, EPSO-CEO, and GWO-C, respectively, in all simulation cases.

Sailfish optimization-based controller selection (SFO-CS) for energy-aware multi-hop routing in software defined wireless sensor network (SDWSN)

Sailfish optimization-based controller selection (SFO-CS) for energy-aware multi-hop routing in software defined wireless sensor network (SDWSN)

Improved Accurate Localization using PSO and the Weighted Dijkstra Algorithm in Software Defined Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are crucial in various fields, including monitoring the environment, surveillance, and healthcare. They rely on localization services for accurate data transfer and optimal network performance. Traditional WSN techniques struggle to adapt to dynamic environmental changes beyond the intended task scope. A synergy between Software-Defined Networking (SDN) and WSN has been suggested to address this issue. This research paper presents proposed approach for machine learning-based localization in Software Defined Wireless Sensor Networks (SDWSNs) using Particle Swarm Optimization (PSO) technique and the Weighted Dijkstra algorithm. PSO technique is used for clustering, the weighted Dijkstra algorithm (WDA) for finding the shortest path and sending data packets, and machine learning algorithms like AdaBoost and Naïve Bayes for data classification. The effectiveness of the proposed approach is measured using energy consumption, throughput, network lifespan, and packet delivery ratio, outperforming existing models like OEERP, LEACH, DRINA, and BCDCA. The machine learning algorithms' performance is also evaluated, with Naïve Bayes achieving the highest accuracy of 78.24% and AdaBoost 98.90%.

A Novel SDWSN-Based Testbed for IoT Smart Applications

Wireless sensor network (WSN) environment monitoring and smart city applications present challenges for maintaining network connectivity when, for example, dynamic events occur. Such applications can benefit from recent technologies such as software-defined networks (SDNs) and network virtualization to support network flexibility and offer validation for a physical network. This paper aims to present a testbed-based, software-defined wireless sensor network (SDWSN) for IoT applications with a focus on promoting the approach of virtual network testing and analysis prior to physical network implementation to monitor and repair any network failures. Herein, physical network implementation employing hardware boards such as Texas Instruments CC2538 (TI CC2538) and TI CC1352R sensor nodes is presented and designed based on virtual WSN- based clustering for stationary and dynamic networks use cases. The key performance indicators such as evaluating node (such as a gateway node to the Internet) connection capability based on packet drop and energy consumption virtually and physically are discussed. According to the test findings, the proposed software-defined physical network benefited from “prior-to-implementation” analysis via virtualization, as the performance of both virtual and physical networks is comparable.

Application-Aware Scheduling for IEEE 802.15.4e Time-Slotted Channel Hopping Using Software-Defined Wireless Sensor Network Slicing.

Given the improvements to network flexibility and programmability, software-defined wireless sensor networks (SDWSNs) have been paired with IEEE 802.15.4e time-slotted channel hopping (TSCH) to increase network efficiency through slicing. Nonetheless, ensuring the quality of service (QoS) level in a scalable SDWSN remains a significant difficulty. To solve this issue, we introduce the application-aware (AA) scheduling approach, which isolates different traffic types and adapts to QoS requirements dynamically. To the best of our knowledge, this approach is the first to support network scalability using shared timeslots without the use of additional hardware while maintaining the application's QoS level. The AA approach is deeply evaluated compared with both the application traffic isolation (ATI) approach and the application's QoS requirements using the IT-SDN framework and by varying the number of nodes up to 225. The evaluation process took into account up to four applications with varying QoS requirements in terms of delivery rate and delay. In comparison with the ATI approach, the proposed approach enhanced the delivery rate by up to 28% and decreased the delay by up to 57%. Furthermore, even with four applications running concurrently, the AA approach proved capable of meeting a 92% delivery rate requirement for up to 225 nodes and a 900 ms delay requirement for up to 144 nodes.

Energy-Efficient Topology Control Mechanism for IoT-Oriented Software-Defined WSNs

In time-varying software-defined wireless sensor networks (SDWSNs) for Internet-of-Things (IoT) applications, the topology may change due to the interference or abnormal events, thus leading to network performance degradation. In this paper, an energy-efficient topology control (TC) mechanism applied for IoT-oriented SDWSNs is proposed to maximize the network energy efficiency (EE) during the dynamic topology maintenance. First, a hierarchical SDWSN architecture consisting of the cluster-based sensing network and the programmable relay network is presented. Second, two TC algorithms based on the link EE are proposed to apply in the cluster and relay sub-networks of SDWSN, respectively. In the cluster sub-network, the proposed distributed TC algorithm enables the link interference mitigation by employing power control and rate allocation in each cluster. In the relay sub-network, the proposed centralized TC algorithm first utilizes a specified model to construct the original topology. During the dynamic topology maintenance, the proposed centralized TC algorithm is realized by the value-iteration learning method based on a Markov decision process (MDP) model, upon which the state-transition probability (STP) of the relay sub-network is obtained, where the relay-network state is composed of the link, the queue, and the residual energy ratio states for all nodes in the relay sub-network. Finally, simulation results show that both two TC algorithms can improve the corresponding sub-network EE of time-varying SDWSN.

Securing Topology Control in SDWSNs Using Identity-Based Cryptography

In software-defined wireless sensor networks (SDWSNs), topology control is a fundamental procedure to maintain the global network topology. However, the open wireless channels of SDWSNs make it possible for an attacker to eavesdrop, replay, or modify the topology messages, thus posing a great threat to the network operations. The security of SDWSN topology control has not received enough attention yet. Identity-based cryptography (IBC) may be fitter for SDWSNs due to its capability of generating the public key from the node identity directly, compared with traditional cryptography. In particular, identity-based combined encryption and signature cryptography (IBCES) could encrypt and sign the messages using the same identity. As such, to secure the confidentiality, integrity, and authentication of topology information, we put forward a secure topology control mechanism based on IBCES. First, we use an identity-based encryption authenticated key agreement scheme to implement the authentication of neighbor nodes and hop-to-hop verification via secure neighbor discovery and topology discovery processes. Then through the node admission and key establishment process, the end-to-end secure channels are established between the nodes, sinks, and Controller. Finally, secure topology collection and management processes supporting flat and hierarchical network structures are designed to guarantee the security of topology information. Theoretical analysis shows that our methods could satisfy the security needs of SDWSN topology control and resist several security attacks. The experimental results indicate that our mechanisms are suitable for SDWSNs.

Energy-efficient and delay-guaranteed routing algorithm for software-defined wireless sensor networks: A cooperative deep reinforcement learning approach

Wireless sensor networks (WSNs) have undergone rapid development but still suffer from energy shortage issues. Meanwhile, existing energy-efficient routing approaches lack a cross-layer cooperation mechanism and cannot offer intelligent decision-making capabilities for resource-constrained nodes. In this paper, we design an intelligent routing algorithm with adaptive duty cycling for application in software-defined WSNs (SDWSNs) to maximize the network lifetime and data transmission reliability subject to a long-term data queue stability constraint. In particular, we introduce Lyapunov optimization to decouple the multistage routing optimization problem into per-frame deterministic problems with delay guarantee. To optimize the routing algorithm and adaptively adjust the duty-cycling mechanism, we propose a cooperative deep reinforcement learning (CDRL) model. Moreover, we develop a centralized training and distributed execution framework to decouple the model training and inference processes, which provides the capability of intelligent routing decision-making for resource-constrained nodes with low computational complexity. In addition, we design a new hybrid routing metric and a unique feature construction and extraction scheme to capture fine-grained information from our hybrid routing metric. The proposed CDRL algorithm adopts a cooperative and iterative model optimization strategy to achieve highly reliable data transmission and optimal long-term network performance. Simulation results demonstrate the effectiveness of the proposed algorithm in terms of network lifetime, packet delivery ratio, and statistical delay performance under various scenarios compared to the EBR-RL, EA-AGG, and QLRP algorithms.

A Method for Detecting LDoS Attacks in SDWSN Based on Compressed Hilbert–Huang Transform and Convolutional Neural Networks

Currently, Low-Rate Denial of Service (LDoS) attacks are one of the main threats faced by Software-Defined Wireless Sensor Networks (SDWSNs). This type of attack uses a lot of low-rate requests to occupy network resources and hard to detect. An efficient detection method has been proposed for LDoS attacks with the features of small signals. The non-smooth small signals generated by LDoS attacks are analyzed employing the time–frequency analysis method based on Hilbert–Huang Transform (HHT). In this paper, redundant and similar Intrinsic Mode Functions (IMFs) are removed from standard HHT to save computational resources and to eliminate modal mixing. The compressed HHT transformed one-dimensional dataflow features into two-dimensional temporal–spectral features, which are further input into a Convolutional Neural Network (CNN) to detect LDoS attacks. To evaluate the detection performance of the method, various LDoS attacks are simulated in the Network Simulator-3 (NS-3) experimental environment. The experimental results show that the method has 99.8% detection accuracy for complex and diverse LDoS attacks.

A review of localization algorithms based on software defined networking approach in wireless sensor network

Wireless Sensor Network (WSN) localization is an important challenge, because A strong relationship between aggregation sense data and the location of data's origin. Software Defined Networks (SDN) technique is support localization algorithm and take into account all network's limitations and constraints. By making use of global network knowledge provided by SDN controller, the results of previous studies show that considerable improvement in network performance can be achieved. This paper explores the recently proposed localization algorithms and discusses the simulation results for each method used in Software Defined Wireless Sensor Networks (SDWSN) to find the best way to localized nodes with the highest accuracy and lowest energy consumption. This paper also present Software defined networking paradigm and WSNS challenges which solved by SDWSNs.

An unsupervised and hierarchical intrusion detection system for software-defined wireless sensor networks

Wireless sensor networks are considered as the foundation of the Internet of Things. Inherent problems in wireless sensor networks such as power consumption, lack of flexibility, and disability in development and programming have led to serious challenges in these networks. Software-defined networking (SDN) is flexible with development and programming capabilities that decouple the control and data planes. The combination of wireless sensor networks and software-defined networks has created the idea of software-defined wireless sensor networks (SDWSNs). Security is considered as one of the most fundamental issues in any network. Due to their combinatorial nature, the software-defined wireless sensor networks faced a variety of security challenges for both wireless sensor networks and software-defined networks. This paper proposes a novel architecture with an unsupervised intrusion detection algorithm using a hierarchical approach to improve the security of integrated software-defined wireless sensor networks. In the proposed architecture, the sensors are not fully dependent on the SDWSN controller; instead, they run the appropriate intrusion detection algorithm module locally at the layer. The data analysis results in different zones, produced by clustering based on entropy and cumulative point similarity as criteria, are sent to the SDWSN controller, and decisions are made after the final check of data normality or abnormality. To examine the effectiveness of the proposed architecture and algorithm, the sensors were simulated on Cooja, WSN-DS and NSL-KDD standardized datasets. The results show that the proposed method is able to detect the abnormal traffic up to 97%.

Secure and Energy-Efficient Network Topology Obfuscation for Software-Defined WSNs

Network topology obfuscation (NTO) is generally considered as a promising proactive mechanism to mitigate traffic analysis attacks. The main challenge is to strike a balance among energy consumption, reliable routing, and security levels due to resource constraints in sensor nodes. Furthermore, software-defined wireless sensor networks (WSNs) are more vulnerable to traffic analysis attacks due to the uncovered pattern of control traffic between the controller and the nodes. In this article, a new energy-aware NTO mechanism is proposed, which maximizes the attack costs and is efficient and practical to be deployed. Specifically, first, a route obfuscation method is proposed by utilizing ranking-based route mutation, based on four different critical criteria: 1) route overlapping; 2) energy consumption; 3) link costs; and 4) node reliability. Then, a sink node obfuscation method is introduced by selecting several fake sink nodes that are indistinguishable from actual sink nodes, according to the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -anonymity model. As a result, the most suitable routes and sink nodes can be selected, and a highest obfuscation level can be reached without sacrificing energy efficiency. Finally, extensive simulation results demonstrate that the proposed methods can strongly mitigate traffic analysis attacks and achieve effective NTO for software-defined WSNs. In addition, the proposed methods can reduce the success rate of the attacks while achieving lower energy consumption and higher network lifetime.

A Novel Energy Efficient Harvesting Technique for SDWSN using RF Transmitters with MISO Beamforming

Software Defined Wireless Sensor Networks (SDWSN) is emerged to overcome the additional energy consumption in WSN. Even then the sensor nodes in the SDWSN suffer from scarce battery resources. Generally, the Radio Frequency (RF) transmitters are deployed around the base station in the SDWSN to overcome the high energy consumption problem. To enhance harvesting energy and coverage of nodes in the network, a new energy harvesting technique using RF transmitters with Multiple Input and Single Output (MISO) beamforming is proposed. In this method, multiple antenna RF transmitters and single antenna sensor nodes are deployed. The optimization problem subject to Signal to Noise Ratio (SNR) and energy harvesting constraints is formulated for hybrid beamforming design to reduce the transmit power in the network. The optimization problem based on convex Second Order Cone Programming (SOCP) is derived to get the optimal solution for hybrid beamforming design. The beamforming technique is used to steer the beam in the desired direction and null to the other direction improves the energy harvesting. The simulation results show that the proposed technique provides better average harvesting energy, average transmit power, average residual energy and throughput than the existing RF transmitter based energy harvesting methods.

Selective forwarding attack detection and network recovery mechanism based on cloud-edge cooperation in software-defined wireless sensor network

A software-defined wireless sensor network can dynamically configure the nodes in a network according to the demand of the application layer. In practical applications, such as environmental monitoring, the nodes in a wireless sensor network(WSN) are deployed in the field environment on a large scale, and the data rely on multihop transmission to reach the sink node. The data extremely easy to selective forwarding attacks during data transmission. Therefore, this study analyzes the models of selective forwarding attacks and proposes an abnormal node detection method, which includes a node behavior measurement scheme and trust-value evaluation mechanism. In addition, the application of a software-defined network (SDN) presents increasing network delay. Hence, herein a network recovery mechanism was designed based on cloud-edge cooperation to ensure the rapid recovery of the network after identifying the abnormal nodes. Moreover, experiments were conducted using simulation software and actual hardware. We verified the effectiveness of the proposed scheme. The experimental results revealed that the proposed method can effectively identify abnormal nodes, reduce the packet dropping ratio and shorten the network recovery delay by 77.2%. The research in this paper solves the security problem of SDWSN.

Implementation of Artificial Intelligence in a Software-Defined Wireless Sensor Network

Incorporating a Software-Defined Network (SDN) paradigm into a Wireless Sensors Network makes it possible to realise Software-Defined Wireless Sensor Networks (SDWSNs), also known as Software-Defined Wireless Sensor Networks (WSN). The reason for this is to investigate and discover answers to the issues that are brought about by WSN. Both artificial intelligence (AI) and machine learning play a key role in our culture and are assisting in the development of systems that are able to manage themselves on their own. Our culture is influenced significantly by both of these. WSNs have found use in a wide variety of industrial applications, including some in which the network's performance and dependability are of the utmost importance to the accomplishment of the overall project. Using a wide array of cutting-edge AI approaches can considerably improve both the usability and dependability of these apps. This can be accomplished by enhancing the functionality of the implementations. Looking further into application of Artificial Intelligence SDN techniques might result in advances in network management, security, or routing in SDWSN. If these issues are addressed, the network may become more reliable. In this paper, we examine the application of machine learning algorithms in software-defined networks (SDN) and analyse the prospect of utilising these AI in software-defined wireless sensor networks (SDWSN) to overcome the challenges presented by WSN and increase the performance as well as the dependability of the system.

Securing Software-Defined WSNs Communication via Trust Management

Software-defined wireless sensor networks (SDWSNs) can be functionally affected by malicious sensor nodes that perform arbitrary actions, e.g., message dropping or flooding. The malicious nodes can degrade the availability of the network due to in-band communications and the inherent lack of secure channels in SDWSNs. In this article, we design a hierarchical trust management scheme for SDWSNs (namely, TSW) to detect potential threats inside SDWSNs while promoting node cooperation and supporting decision making in the forwarding process. TSW evaluates the trustworthiness of involved nodes and enables the detection of malicious behavior at various levels of the SDWSN architecture. We develop sensitive trust computational models to detect several malicious attacks. Furthermore, we propose separate trust scores and parameters for control and data traffic, respectively, to enhance the detection performance against attacks directed at the crucial traffic of the control plane. Furthermore, we develop an acknowledgment-based trust recording mechanism by exploiting some built-in SDN control messages. To ensure the resilience and honesty of the trust scores, a weighted averaging approach is adopted, and a reliability trust metric is defined. Through extensive analyses and numerical simulations, we demonstrate that TSW is efficient in detecting malicious nodes that launch several communications and trust management threats, such as black-hole, selective forwarding, denial of service, bad mouthing, and ON–OFF attacks.

Control Message Quenching-Based Communication Protocol for Energy Management in SDWSN

The rise of the Internet of Things has facilitated the growth of Wireless Sensor Networks (WSNs). WSN has many challenges concerning network and management. It is discovered that Software-Defined Wireless Sensor Networks (SDWSNs) could simplify WSN configuration, routing, and management. However, for obtaining a thorough view of the underlying topology, SDWSN causes many control messages to be exchanged. This consequently depletes the energy of nodes. Wherefore, solving the energy challenges of SDWSN is imperative, before they could be successfully implemented. Realizing this, we propose an architecture that adopts the control message quenching technique in the data layer devices. Based on the proposed architecture, we propose a communication protocol, Control Message Quenching-based SDWSN (CMQ-SDWSN) that facilitates a minimal exchange of control messages between the infrastructure layer and the control layer by placing entry loggers in the data layer. Furthermore, we formulated an Integer Linear Programming (ILP) problem in the control layer which enables better partitioning and distribution of rules. The simulation results show that the proposed architecture and the communication protocol reduce the control message overhead by 33.66% on average and minimize the energy consumption of nodes by 33.51% on average when compared to its counterparts.