Optimized Link Utilization Based Approach for Traffic Engineering in SDWSN

Wireless Sensors Networks (WSNs) have been gaining significant attention in both the industries and the academia in recent years. However, WSN are vulnerable to growing security threats and attacks which are inept for the current traditional security mechanisms to cope with or mitigate such security challenges. As a solution, Software Defined Network (SDN) has emerged and been merged with WSN to form what is known as Software Defined Wireless Sensor Network (SDWSN). SDWSN is introduced to bring flexibility, programmability and innovation to the WSN network. However, SDWSN is not exempted from security challenges. SDWSN is faced with multitude of security challenges inherited from both SDN and WSN which have become a bottle-neck to its operations and applicability. Therefore, this paper presents security challenges in both WSN and SDN which are transferable to SDWSN. We discussed the inheritable security challenges from the perspective of SDN and WSN by outlining several possible threats, attacks and some countermeasures. This is important to bring together these challenges in order to attract or stimulate more research and development activities aim at realizing a more secure and dependable SDWSN.

Wireless Sensor Network (WSN), which are enablers of the Internet of Things (IoT) technology, are typically used en-masse in widely physically distributed applications to monitor the dynamic conditions of the environment. They collect raw sensor data that is processed centralised. With the current traditional techniques of state-of-art WSN programmed for specific tasks, it is hard to react to any dynamic change in the conditions of the environment beyond the scope of the intended task. To solve this problem, a synergy between Software-Defined Networking (SDN) and WSN has been proposed. This paper aims to present the current status of Software-Defined Wireless Sensor Network (SDWSN) proposals and introduce the readers to the emerging research topic that combines Machine Learning (ML) and SDWSN concepts, also called ML-SDWSNs. ML-SDWSN grants an intelligent, centralised and resource-aware architecture to achieve improved network performance and solve the challenges currently found in the practical implementation of SDWSNs. This survey provides helpful information and insights to the scientific and industrial communities, and professional organisations interested in SDWSN, mainly the current state-of-art, ML techniques, and open issues.

The Software-Defined Wireless Sensor Networking (SDWSN) paradigm aims to solve inherent issues present in Wireless Sensor Networks (WSN), such as resource constraints, by adopting a Software-Defined Networking (SDN) approach to the management of these WSNs. The security aspect of SDWSN has received little attention due to a focus on the architecture. As this paradigm is a combination of both WSN and SDN, some solutions from both paradigms can be adapted to consider SDWSN. One of the main problems with implementing security within WSN, lies within its inherent issues, such as resource constraints. However, due to the centralization of control brought about by the SDN paradigm, most of these issues are alleviated, leaving room for WSN security implementations. In order to investigate the use of WSN cryptography within SDWSN, cryptography methods have been implemented within a SDWSN network in order to verify whether the SDWSN paradigm does allow for resource intense WSN security implementations.

The paper analyses the performance of a software defined wireless sensor network (SD-WSN) to a more conventional ad-hoc wireless sensor network (WSN). Recently SD-WSN has increased in popularity due to the demand of managed and service oriented sensor networks. In this analysis, we perform several simulation experiments comparing convergence time, packet reception ratio, and data delivery time for the SDN-WISE SD-WSN system and an ad-hoc WSN based the RPL routing protocol. The experimental results confirm the expected overhead required in a SD-WSN compared to an ad-hoc approach and exposes some design flaws in the SDN-WISE architecture.

Software-Defined Wireless Sensor Network: A Comprehensive Survey

This1 article introduces Software Defined Network (SDN) technology into the Wireless Sensor Network (WSN) field, the working mechanism of OPENFLOW, and the software defined wireless sensor network (SDN-WISE). The network architecture is improved, and the data plane and control plane of the WSN are decoupled. In-depth research on data processing and forwarding in the network is intensified. Based on the SDN-WISE architecture, a rule-action forwarding strategy derived from OPENFLOW is derived from the WSN. The forwarding table is stored at the starting point of the communication link. The route nodes do not need to check the table to forward data, which effectively reduces the network energy consumption. The network is simulated and tested through experiments. The simulation structure shows that the algorithm has certain advantages[1, 2].

This paper draws attention to techniques available for the minimization of control overhead in software-defined wireless sensor networks (SDWSNs). Software-defined networking (SDN) promises improved management flexibility and control for inherently resource-constrained and heterogeneous wireless sensor network (WSN) implementations. However, due to the in-band traffic channel available for data and control traffic in SDN-based WSNs, overhead control traffic has been viewed as a bottleneck affecting network performance and controller responsiveness. A discussion on the need for control message quenching (CMQ) and the various categories of CMQ implementations in SDWSN is made in this paper. Furthermore, a CMQ algorithm based on reducing duplicate flow request packets is discussed and demonstrated for implementation in an SDN-based WSN framework. Results show a significant reduction in control overhead traffic and noticeable improvement in energy efficiency. However, trade-offs in terms of packet delivery rate and packet delay are also observed as a result of the CMQ algorithm. A discussion on future work necessary to optimize CMQ algorithms in order to minimize the associated trade-offs is also made.

Energy efficiency is one of the key requirements in Wireless Sensor Networks (WSNs). In order to optimize energy usage at sensor nodes, this paper proposes a new WSN architecture that relies on concepts of Software Defined Networking (SDN). Since SDN is a relatively new technology, originally envisioned for wired networks, it can't be expected to get immediately adopted in WSN domain, regardless of potential benefits. For this reason, we consider scenario where SDN nodes coexist with traditional sensor nodes, and propose a new routing algorithm for SDN controller that prolongs the WSN lifetime even when small percentage of SDN nodes is deployed.

The emerging software defined networking enables the separation of control plane and data plane and saves the resource consumption of the network. Breakthrough in this area has opened up a new dimension to the design of software defined method in wireless sensor networks (WSNs). However, the limited routing strategy in software defined WSNs (SDWSNs) imposes a great challenge in achieving the minimum traffic load. In this paper, we propose a flow splitting optimization (FSO) algorithm for solving the problem of traffic load minimization (TLM) in SDWSNs by considering the selection of optimal relay sensor node and the transmission of optimal splitting flow. To this end, we first establish the model of different packet types and describe the TLM problem. We then formulate the TLM problem into an optimization problem which is constrained by the load of sensor nodes and the packet similarity between different sensor nodes. Afterwards, we present a Levenberg–Marquardt algorithm for solving the optimization problem of traffic load. We also provide the convergence analysis of the Levenberg–Marquardt algorithm. Finally, we implement the FSO algorithm in the NS-2 simulator and give extensive simulation results to verify the efficiency of FSO algorithm in SDWSNs.

The software‐defined wireless sensor networks (SDWSNs) have been proposed recently to solve the energy limitation of sensor nodes and extend the lifetime of the wireless sensor networks by fast node reconstruction and dynamical resource allocation. In this study, the authors investigate an energy‐efficient resource allocation algorithm in SDWSNs, in which radio resource allocation could be handled at central controllers with powerful storage and computation capacity. In this algorithm, the authors formulate an optimisation problem to minimise the energy consumption, under the individual constraint of quality of service. Then, the initial optimisation problem is transformed using semidefinite relaxation, to achieve centralised adaptive bandwidth and power allocation (CABPA). Additionally, two special cases are derived to reveal the performance of the CABPA. Furthermore, an OpenFlow‐based scheme is proposed for information exchanging and updating to realise the centralised resource allocation. Meanwhile, a distributed scheme with limited information about the whole network is developed to serve as a performance benchmark for the CABPA in the SDWSN. Finally, the simulation results reveal that the proposed CABPA performs better than the other algorithms, and it balances the power and bandwidth utilisation.

Compared to conventional Wireless Sensor Networks (WSNs), Software-Defined WSNs (SD-WSNs) introduce the software defined networking (SDN) paradigm to offer more adaptivity, flexibility and scalability. Research on reliable and trusted routing for SD-WSNs is a fundamental but very challenging task due to their unstable wireless connectivity, low security assurance and diverse inner adversaries. Hence, we propose a trusted link-separation multipath selection method for SD-WSNs in adversarial environments by jointly considering routing efficiency and security. Specifically, we introduce a Bayesian model to evaluate the nodes’ trustworthiness based on their communication behaviors. After that, we formulate the trusted link-separation multipath selection as a multi-objective optimization problem. Then, we generate k trusted link-separation paths by solving the optimization problem using a greedy algorithm. Comparative experiments using six different topologies, including three random networks (Waxman, ER random graphs, and WS small-world topologies) and three real ones (Cernet, Bandcon, and DFN), validate the superior performance of the proposed method compared with the delay-first and the trust-first baseline routing schemes.KeywordsSoftware-Defined Wireless Sensor NetworksTrusted routingLink-separation multipath selectionMulti-objective optimizationMalicious node detection.

Software defined wireless sensor network (SDWSN) is a new network architecture for sensor networks. It provides an information monitoring and management technology solution for wireless sensor network, and has become a research hot spot in recent years. Despite the advantages of SDWSN, there are still some unavoidable problems such as over concentrated load and imbalanced energy consumption with the increase of network scale. To deal with the shortages of load and energy balance in SDWSN, we propose a new hierarchical subnet space based network balance algorithm. The proposed algorithm innovatively optimizes the network structure and forwarding rules of SDWSN. Fog computing is also applied to reduce packet transmission requirement, which helps to further balance load and reduce energy consumption. In our optimized network structure, the whole network is divided into several subnets and sub-controllers are selected for each subnet. Sensor data packets are collected and compressed in the sub-controller and sent to the center controller along the path consisted of sun-controllers. Simulation results show that our proposed algorithm performs better than other existed algorithms in load balancing and energy saving.

Software-Defined Network enabled Vehicle to Vehicle secured data transmission protocol in VANETs

Wireless Sensor Networks (WSNs) have a wide range of applications scenarios in computer vision, from pedestrian detection to robotic visual navigation. In response to the growing visual data services in WSNs, we propose a proactive caching strategy based on Stacked Sparse Autoencoder (SSAE) to predict content popularity (PCDS2AW). Firstly, based on Software Defined Network (SDN) and Network Function Virtualization (NFV) technologies, a distributed deep learning network SSAE is constructed in the sink nodes and control nodes of the WSN network. Then, the SSAE network structure parameters and network model parameters are optimized through training. The proactive cache strategy implementation procedure is divided into four steps. (1) The SDN controller is responsible for dynamically collecting user request data package information in the WSNs network. (2) The SSAEs predicts the packet popularity based on the SDN controller obtaining user request data. (3) The SDN controller generates a corresponding proactive cache strategy according to the popularity prediction result. (4) Implement the proactive caching strategy at the WSNs cache node. In the simulation, we compare the influence of spatiotemporal data on the SSAE network structure. Compared with the classic caching strategy Hash + LRU, Betw + LRU, and classic prediction algorithms SVM and BPNN, the proposed PCDS2AW proactive caching strategy can significantly improve WSN performance.

The ever-growing increase in modern and ubiquitous applications of wireless sensor networks (WSNs) is causing energy scarcity, which is a serious threat to the lifetime of the network. Wireless power transfer emerges as a promising solution to replenish the sensor nodes. In wireless power transfer, energy is transferred to sensor nodes through dedicated energy transmitters. In addition, software-defined WSNs (SDWSNs) have been recently realized to fully explore and efficiently utilize the resources of WSNs. In this paper, we present an energy efficient SDWSN with wireless power transfer. We propose a mechanism to place energy transmitters and determine minimum number of energy transmitters. For placement of energy transmitters, a tradeoff between maximum energy charged in the network and fair distribution of energy is studied. We present this mechanism by defining a utility function to maximize both total energy charged and fairness. For minimum number of energy transmitters, an optimization problem is formulated and solved while satisfying the constraint on minimum energy charged by each sensor node. We also propose an energy-efficient scheduling scheme for energy transmitters for the given tasks of energy charging. The focus is to minimize the energy consumption of energy transmitters while keeping sensor nodes sufficiently charged. Finally, this paper is supported by extensive simulation results, which illustrate the performance of energy-efficient SDWSNs with wireless power transfer in terms of energy charged, fairness, number of energy transmitters, number of tasks, and energy consumption.