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Software-Defined Networking for Sensor Networks and Internet of Things

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (10 August 2019) | Viewed by 45847

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Prof. Dr. Joel J. P. C. Rodrigues

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1. Post-Graduation Program on Electrical Engineering, Federal University of Piauí (UFPI), Teresina, Brazil
2. Senior Researcher, Instituto de Telecomunicações, Covilhã, Portugal
Interests: vehicular communications; Internet of Things; eHealth technologies
Special Issues, Collections and Topics in MDPI journals

Dr. Deepak Gupta

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Department of Computer Science and Engineering, Maharaja Agrasen institute of Technology (GGSIPU), Delhi 110086, India
Interests: software engineering; software usability; human computer interaction; algorithm computing; soft computing; neural networks; testing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, Sensor Networks (SN) and Internet of Things (IoT) seem to have become an emerging technology, which is gaining popularity among researchers. Software-defined networking (SDN) is an emerging network design and management paradigm that offers a flexible way to reduce the complexity of network management and configuration. Sensor Networks are composed of resource-constrained devices, with the purpose of gathering information from the environment. These devices can sense, process, and communicate, increasing the amount of information available and enhancing our perception of the world. SDN-based wireless sensor networks (SDWSNs) consist of a set of software-defined sensor nodes equipped with different types of sensors. In SDWSN, sensor nodes are able to conduct different sensing tasks according to the programs injected into them, and the functionalities of these nodes can also be dynamically configured by injecting different application-specific programs. SDWSNs adopt the characteristics of SDN and can provide energy-efficient solutions for various problems, such as topology management, sleep scheduling, routing, localization, etc.

IoT is considered a bridging platform that connects the physical world and the cyber space, so that innovative applications and services with high efficiency and productivity can be obtained. However, in-depth research efforts on systems, networks, and architectures of IoT for efficient large-scale deployments are still required to fill the gaps between satisfying quality of service requirements and cost-effective implementations and operations. The different planes of networking devices can be separated with the application of SDN. This helps in achieving exceptional flexibility in programmability and enormous potentials for optimization of network resource usage. Thus, SDN is an emerging technology that addresses these gaps by enabling new ways of IoT communications and services through evolving networking devices and systems with adaptive and scalable functionalities.

The aim of this Special Issue is to invite researchers to submit original manuscripts that cover and explore these gaps. This Special Issue solicits novel papers on a broad range of topics, including, but not limited to: Cutting edge technologies, novel studies, and innovative developments that can realize and elevate the effectiveness and advantages of the emerging SDN-assisted sensor network and IoT techniques, and related areas.

All submitted papers to this Special Issue are to focus on state-of-the-art research in various aspects of smarter Sensor networks and IoT, supported with SDN, from academic and industry viewpoints. Topics of interests include, but are not limited to:

Architecture, networking protocols, QoS, and cross-layer optimization design;
Communications for SDN–IoT;
Field trials and deployments of SDN–IoT;
IoT cloud platform based on SDN;
Interworking of SDN with sensor networks;
SDN architecture integration with IoT;
SDN-based mobile networks over IoT;
Security and performance of SDN for IoT;
Traffic engineering and flow recovery in SDN–IoT;
Wireless enabled intelligent transportation systems;
Energy-efficient solutions for various problems, such as topology management, sleep scheduling, routing, and localization.

Prof. Dr. Joel Rodrigues
Dr. Deepak Gupta
Guest Editors

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Published Papers (8 papers)

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Research

19 pages, 306 KiB

Open AccessArticle

Cuckoo-PC: An Evolutionary Synchronization-Aware Placement of SDN Controllers for Optimizing the Network Performance in WSNs

by Shirin Tahmasebi, Mohadeseh Safi, Somayeh Zolfi, Mohammad Reza Maghsoudi, Hamid Reza Faragardi and Hossein Fotouhi

Sensors 2020, 20(11), 3231; https://doi.org/10.3390/s20113231 - 6 Jun 2020

Cited by 20 | Viewed by 3903

Abstract

Due to reliability and performance considerations, employing multiple software-defined networking (SDN) controllers is known as a promising technique in Wireless Sensor Networks (WSNs). Nevertheless, employing multiple controllers increases the inter-controller synchronization overhead. Therefore, optimal placement of SDN controllers to optimize the performance of a WSN, subject to the maximum number of controllers, determined based on the synchronization overhead, is a challenging research problem. In this paper, we first formulate this research problem as an optimization problem, then to address the optimization problem, we propose the Cuckoo Placement of Controllers (Cuckoo-PC) algorithm. Cuckoo-PC works based on the Cuckoo optimization algorithm which is a meta-heuristic algorithm inspired by nature. This algorithm seeks to find the global optimum by imitating brood parasitism of some cuckoo species. To evaluate the performance of Cuckoo-PC, we compare it against a couple of state-of-the-art methods, namely Simulated Annealing (SA) and Quantum Annealing (QA). The experiments demonstrate that Cuckoo-PC outperforms both SA and QA in terms of the network performance by lowering the average distance between sensors and controllers up to 13% and 9%, respectively. Comparing our method against Integer Linear Programming (ILP) reveals that Cuckoo-PC achieves approximately similar results (less than 1% deviation) in a noticeably shorter time. Full article

(This article belongs to the Special Issue Software-Defined Networking for Sensor Networks and Internet of Things)

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24 pages, 4435 KiB

Open AccessArticle

Blockchain and Random Subspace Learning-Based IDS for SDN-Enabled Industrial IoT Security

by Abdelouahid Derhab, Mohamed Guerroumi, Abdu Gumaei, Leandros Maglaras, Mohamed Amine Ferrag, Mithun Mukherjee and Farrukh Aslam Khan

Sensors 2019, 19(14), 3119; https://doi.org/10.3390/s19143119 - 15 Jul 2019

Cited by 143 | Viewed by 9689

Abstract

The industrial control systems are facing an increasing number of sophisticated cyber attacks that can have very dangerous consequences on humans and their environments. In order to deal with these issues, novel technologies and approaches should be adopted. In this paper, we focus on the security of commands in industrial IoT against forged commands and misrouting of commands. To this end, we propose a security architecture that integrates the Blockchain and the Software-defined network (SDN) technologies. The proposed security architecture is composed of: (a) an intrusion detection system, namely RSL-KNN, which combines the Random Subspace Learning (RSL) and K-Nearest Neighbor (KNN) to defend against the forged commands, which target the industrial control process, and (b) a Blockchain-based Integrity Checking System (BICS), which can prevent the misrouting attack, which tampers with the OpenFlow rules of the SDN-enabled industrial IoT systems. We test the proposed security solution on an Industrial Control System Cyber attack Dataset and on an experimental platform combining software-defined networking and blockchain technologies. The evaluation results demonstrate the effectiveness and efficiency of the proposed security solution. Full article

(This article belongs to the Special Issue Software-Defined Networking for Sensor Networks and Internet of Things)

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19 pages, 1342 KiB

Open AccessArticle

Slice Management for Quality of Service Differentiation in Wireless Network Slicing

by Namwon An, Yonggang Kim, Juman Park, Dae-Hoon Kwon and Hyuk Lim

Sensors 2019, 19(12), 2745; https://doi.org/10.3390/s19122745 - 19 Jun 2019

Cited by 33 | Viewed by 4344

Abstract

Network slicing is a technology that virtualizes a single infrastructure into multiple logical networks (called slices) where resources or virtualized functions can be flexibly configured by demands of applications to satisfy their quality of service (QoS) requirements. Generally, to provide the guaranteed QoS in applications, resources of slices are isolated. In wired networks, this resource isolation is enabled by allocating dedicated data bandwidths to slices. However, in wireless networks, resource isolation may be challenging because the interference between links affects the actual bandwidths of slices and degrades their QoS. In this paper, we propose a slice management scheme that mitigates the interference imposed on each slice according to their priorities by determining routes of flows with a different routing policy. Traffic flows in the slice with the highest priority are routed into shortest paths. In each lower-priority slice, the routing of traffic flows is conducted while minimizing a weighted summation of interference to other slices. Since higher-priority slices have higher interference weights, they receive lower interference from other slices. As a result, the QoS of slices is differentiated according to their priorities while the interference imposed on slices is reduced. We compared the proposed slice management scheme with a naïve slice management (NSM) method that differentiates QoS among slices by priority queuing. We conducted some simulations and the simulation results show that our proposed management scheme not only differentiates the QoS of slices according to their priorities but also enhances the average throughput and delay performance of slices remarkably compared to that of the NSM method. The simulations were conducted in grid network topologies with 16 and 100 nodes and a random network topology with 200 nodes. Simulation results indicate that the proposed slice management increased the average throughput of slices up to 6%, 13%, and 7% and reduced the average delay of slices up to 14%, 15%, and 11% in comparison with the NSM method. Full article

(This article belongs to the Special Issue Software-Defined Networking for Sensor Networks and Internet of Things)

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20 pages, 3697 KiB

Open AccessArticle

Entry Aggregation and Early Match Using Hidden Markov Model of Flow Table in SDN

by Cheng Wang and Hee Yong Youn

Sensors 2019, 19(10), 2341; https://doi.org/10.3390/s19102341 - 21 May 2019

Cited by 23 | Viewed by 4473

Abstract

The usage of multiple flow tables (MFT) has significantly extended the flexibility and applicability of software-defined networking (SDN). However, the size of MFT is usually limited due to the use of expensive ternary content addressable memory (TCAM). Moreover, the pipeline mechanism of MFT causes long flow processing time. In this paper a novel approach called Agg-ExTable is proposed to efficiently manage the MFT. Here the flow entries in MFT are periodically aggregated by applying pruning and the Quine–Mccluskey algorithm. Utilizing the memory space saved by the aggregation, a front-end ExTable is constructed, keeping popular flow entries for early match. Popular entries are decided by the Hidden Markov model based on the match frequency and match probability. Computer simulation reveals that the proposed scheme is able to save about 45% of space of MFT, and efficiently decrease the flow processing time compared to the existing schemes. Full article

(This article belongs to the Special Issue Software-Defined Networking for Sensor Networks and Internet of Things)

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19 pages, 3041 KiB

Open AccessArticle

A Hybrid Method for Mobile Agent Moving Trajectory Scheduling using ACO and PSO in WSNs

by Yu Gao, Jin Wang, Wenbing Wu, Arun Kumar Sangaiah and Se-Jung Lim

Sensors 2019, 19(3), 575; https://doi.org/10.3390/s19030575 - 30 Jan 2019

Cited by 47 | Viewed by 4303

Abstract

Wireless Sensor Networks (WSNs) are usually troubled with constrained energy and complicated network topology which can be mitigated by introducing a mobile agent node. Due to the numerous nodes present especially in large scale networks, it is time-consuming for the collector to traverse all nodes, and significant latency exists within the network. Therefore, the moving path of the collector should be well scheduled to achieve a shorter length for efficient data gathering. Much attention has been paid to mobile agent moving trajectory panning, but the result has limitations in terms of energy consumption and network latency. In this paper, we adopt a hybrid method called HM-ACOPSO which combines ant colony optimization (ACO) and particle swarm optimization (PSO) to schedule an efficient moving path for the mobile agent. In HM-ACOPSO, the sensor field is divided into clusters, and the mobile agent traverses the cluster heads (CHs) in a sequence ordered by ACO. The anchor node of each CHs is selected in the range of communication by the mobile agent using PSO based on the traverse sequence. The communication range adjusts dynamically, and the anchor nodes merge in a duplicated covering area for further performance improvement. Numerous simulation results prove that the presented method outperforms some similar works in terms of energy consumption and data gathering efficiency. Full article

(This article belongs to the Special Issue Software-Defined Networking for Sensor Networks and Internet of Things)

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17 pages, 3248 KiB

Open AccessArticle

Dynamic Load Balancing of Software-Defined Networking Based on Genetic-Ant Colony Optimization

by Hai Xue, Kyung Tae Kim and Hee Yong Youn

Sensors 2019, 19(2), 311; https://doi.org/10.3390/s19020311 - 14 Jan 2019

Cited by 60 | Viewed by 8394

Abstract

Load Balancing (LB) is one of the most important tasks required to maximize network performance, scalability and robustness. Nowadays, with the emergence of Software-Defined Networking (SDN), LB for SDN has become a very important issue. SDN decouples the control plane from the data forwarding plane to implement centralized control of the whole network. LB assigns the network traffic to the resources in such a way that no one resource is overloaded and therefore the overall performance is maximized. The Ant Colony Optimization (ACO) algorithm has been recognized to be effective for LB of SDN among several existing optimization algorithms. The convergence latency and searching optimal solution are the key criteria of ACO. In this paper, a novel dynamic LB scheme that integrates genetic algorithm (GA) with ACO for further enhancing the performance of SDN is proposed. It capitalizes the merit of fast global search of GA and efficient search of an optimal solution of ACO. Computer simulation results show that the proposed scheme substantially improves the Round Robin and ACO algorithm in terms of the rate of searching optimal path, round trip time, and packet loss rate. Full article

(This article belongs to the Special Issue Software-Defined Networking for Sensor Networks and Internet of Things)

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21 pages, 1436 KiB

Open AccessArticle

An Optimization Routing Algorithm Based on Segment Routing in Software-Defined Networks

by Xiaolan Hou, Muqing Wu and Min Zhao

Sensors 2019, 19(1), 49; https://doi.org/10.3390/s19010049 - 22 Dec 2018

Cited by 20 | Viewed by 5287

Abstract

Software-defined networks (SDNs) are improving the controllability and flexibility of networks as an innovative network architecture paradigm. Segment routing (SR) exploits an end-to-end logical path and is composed of a sequence of segments as an effective routing strategy. Each segment is represented by a middle point. The combination of SR and SDN can meet the differentiated business needs of users and can quickly deploy applications. In this paper, we propose two routing algorithms based on SR in SDN. The algorithms aim to save the cost of the path, alleviate the congestion of networks, and formulate the selection strategy by comprehensively evaluating the value of paths. The simulation results show that compared with existing algorithms, the two proposed algorithms can effectively reduce the consumption of paths and better balance the load of the network. Furthermore, the proposed algorithms take into account the preferences of users, actualize differentiated business networks, and achieve a larger comprehensive evaluation value of the path compared with other algorithms. Full article

(This article belongs to the Special Issue Software-Defined Networking for Sensor Networks and Internet of Things)

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19 pages, 976 KiB

Open AccessArticle

Interdomain I/O Optimization in Virtualized Sensor Networks

by Congfeng Jiang, Tiantian Fan, Yeliang Qiu, Hongyuan Wu, Jilin Zhang, Neal N. Xiong and Jian Wan

Sensors 2018, 18(12), 4395; https://doi.org/10.3390/s18124395 - 12 Dec 2018

Cited by 18 | Viewed by 3778

Abstract

In virtualized sensor networks, virtual machines (VMs) share the same hardware for sensing service consolidation and saving power. For those VMs that reside in the same hardware, frequent interdomain data transfers are invoked for data analytics, and sensor collaboration and actuation. Traditional ways of interdomain communications are based on virtual network interfaces of bilateral VMs for data sending and receiving. Since these network communications use TCP/IP (Transmission Control Protocol/Internet Protocol) stacks, they result in lengthy communication paths and frequent kernel interactions, which deteriorate the I/O (Input/Output) performance of involved VMs. In this paper, we propose an optimized interdomain communication approach based on shared memory to improve the interdomain communication performance of multiple VMs residing in the same sensor hardware. In our approach, the sending data are shared in memory pages maintained by the hypervisor, and the data are not transferred through the virtual network interface via a TCP/IP stack. To avoid security trapping, the shared data are mapped in the user space of each VM involved in the communication, therefore reducing tedious system calls and frequent kernel context switches. In implementation, the shared memory is created by a customized shared-device kernel module that has bidirectional event channels between both communicating VMs. For performance optimization, we use state flags in a circular buffer to reduce wait-and-notify operations and system calls during communications. Experimental results show that our proposed approach can provide five times higher throughput and 2.5 times less latency than traditional TCP/IP communication via a virtual network interface. Full article

(This article belongs to the Special Issue Software-Defined Networking for Sensor Networks and Internet of Things)

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