Software-defined Wireless Sensor Networks Research Articles

Centralized Energy Prediction in Wireless Sensor Networks Leveraged by Software-Defined Networking

Resource Constraints in Wireless Sensor Networks are a key factor in protocols and application design. Furthermore, energy consumption plays an important role in protocols decisions, such as routing metrics. In Software-Defined Networking (SDN)-based networks, the controller is in charge of all control and routing decisions. Using energy as a metric requires such information from the nodes, which would increase packets traffic, impacting the network performance. Previous works have used energy prediction techniques to reduce the number of packets exchanged in traditional distributed routing protocols. We applied this technique in Software-Defined Wireless Sensor Networks (SDWSN). For this, we implemented an energy prediction algorithm for SDWSN using Markov chain. We evaluated its performance executing the prediction on every node and on the SDN controller. Then, we compared their results with the case without prediction. Our results showed that by running the Markov chain on the controller we obtain better prediction and network performance than when running the predictions on every node. Furthermore, we reduced the energy consumption for topologies up to 49 nodes for the case without prediction.

Architectural Design, Improvement, and Challenges of Distributed Software-Defined Wireless Sensor Networks

Architectural Design, Improvement, and Challenges of Distributed Software-Defined Wireless Sensor Networks

Confronting DDoS Attacks in Software-Defined Wireless Sensor Networks based on Evidence Theory

DDoS attacks aim at making the authorized users unable to access the network resources. In the present paper, an evidence theory based security method has been proposed to confront DDoS attacks in software-defined wireless sensor networks. The security model, as a security unit, is placed on the control plane of the software-defined wireless sensor network aiming at detecting the suspicious traffic. The main purpose of this paper is detection of the DDoS attack using the central controller of the software-defined network and entropy approach as an effective light-weight and quick solution in the early stages of the detection and, also, Dempster-Shafer theory in order to do a more exact detection with longer time. Evaluation of the attacks including integration of data from the evidence obtained using Dempster-Shafer and entropy modules has been done with the purpose of increasing the rate of detection of the DDoS attack, maximizing the true positive, decreasing the false negative, and confronting the attack. The results of the paper show that providing a security unit on the control plane in a software-defined wireless sensor network is an efficient method for detecting and evaluating the probability of DDoS attacks and increasing the rate of detection of an attacker.

Repeated game theory-based reducer selection strategy for energy management in SDWSN

The sensor-generated data by Internet of Things are considered to be the most common source of big data. A wide range of applications are relying on these data for analytics. While a considerable amount of data is sufficient for the application users to get valuable insights, sending vast amount of data to the cloud seems inappropriate and it only increases the communication cost in the network. It is well-known that an increase in communication cost increases energy depletion in the network. Since sensor nodes have a restricted power supply, it is necessary to harness the energy of nodes to prolong the network lifetime. In this paper, a solution for energy management of sensor nodes is proposed by integrating the software defined framework with the sensor network, software defined wireless sensor networks (SDWSN), that aids in processing the data inside the network before transferring it to the sink node. To this context, a game model has been formulated for selecting the appropriate nodes as reducers which will execute the reducer function. The software defined network (SDN) controller, geographically placed outside of the wireless sensor network, is responsible for selecting the reducers and dynamically load reducing function on them. Based on the selection, a routing protocol, routing via respective reducer (RVRR), that forwards data packets via in-network processing path and control packets via common path has been proposed. This remarkably reduces the communication cost, thereby prolonging the lifetime of the deployed network. The RVRR algorithm is implemented in NS-3 simulator to evaluate the performance of proposed work in SDWSN environment.

Opt-ACM: An Optimized load balancing based Admission Control Mechanism for Software Defined Hybrid Wireless based IoT (SDHW-IoT) network

The wide network applications of Internet of Things (IoT) urge to integrate the different wireless networking technologies. However, the future generation technologies like Software Defined Networking (SDN) can be useful to alleviate the associated challenges such as security, installation issues, coverage, etc. The growing number of IoT users produce massive volume of traffic causing heavy network load conditions, resulting in the state of network congestion. The network congestion may cause severe performance degradation issues such as frequent packet drops, longer delays, low throughput, etc. Thus, a promising solution is needed to reduce and/or prevent the network congestion. This paper presents an Optimized load balancing based Admission Control Mechanism (Opt-ACM) for effective network flow management resulting in the reduced network congestion. In addition to this, the paper highlights the challenges of the existing solutions, and discusses a Software Defined Hybrid Wireless based IoT (SDHW-IoT) network architecture consisting of Software Defined Wireless Sensor Network (SDWSN) and Software Defined Wireless Mesh Network (SDWMN). To validate the efficiency of Opt-ACM, a Mixed-Integer Linear Programming (MILP) based optimization problem is formulated and tested using a well-known mathematical optimization solver called Gurobi. Additionally, Opt-ACM is also emulated in Mininet-Wifi with varying network scenarios against some traditional (OLSR and OSPF) and state-of-the-art (FACOR and EASDN) approaches. Opt-ACM achieves an overall efficiency of 9.47% and 12.32% over other approaches for Packet Delivery Ratio (PDR) and Packet Loss Ratio (PLR) respectively. Similarly, an average improved efficiency of 26.77% and 33.10% is achieved with respect to the Average Delay (AD) and Average Jitter (AJ) metrics respectively.

Cluster-based flow control in hybrid software-defined wireless sensor networks

Software-defined networking (SDN) is a cornerstone of next-generation networks and has already led to numerous advantages for data-center networks and wide-area networks. However, SDN is not widely adopted in constrained networks, such as Wireless Sensor Networks (WSN), due to excessive control overhead, lossy medium, and in-band control channels. Therefore, a key challenge to enable Software-Defined Wireless Sensor Networks (SD-WSN) is to reduce the number of control messages required to configure the data plane. In this paper, we propose a cluster-based flow control approach in hybrid SDNs. Our approach is hybrid in the sense that it takes advantage of distributed legacy routing and centralized SDN routing. In addition, it makes a trade-off between the granularity of flow control and the communication overhead induced by the SDN controller. The approach partitions a network into clusters with minimum number of border nodes. Instead of handling the individual flows of each node, the SDN controller only manages incoming and outgoing traffic flows of clusters through border nodes, while the flows inside each cluster are controlled by a distributed legacy WSN routing algorithm. Our proof-of-concept implementations in both software and hardware show that our approach is efficient with respect to reducing the number of nodes that must be managed and the number of control messages. In comparison to benchmark solutions with and without clustering, our solution reduces communication costs for flow configuration in an SD-WSN at least by 27% and at most by 88% respectively, without degrading packet delay nor delivery rate.

Optimization of energy-efficient dynamic task assignment for wireless sensor networks based on particle swarm algorithm

This paper provides an in-depth analysis of the optimization of energy-efficient dynamic task allocation in wireless sensor networks through an improved particle swarm optimization algorithm, and introduces the idea of software-defined networking into wireless sensor network to propose a software-defined wireless sensor network non-uniform cluster routing protocol. The protocol decouples the data layer from the control layer, and the base station performs the cluster head election, network clustering, and routing control operations. The base station optimizes the cluster head election process by electing cluster head nodes using an improved particle cluster algorithm. Based on the elected cluster head nodes, the base station calculates their corresponding contention radius and plans the data transmission path. The results of the calculation are sent to the corresponding nodes for cluster creation and data transmission. The simulation results fully show that the use of this protocol can achieve the purpose of significantly extending the service life of the network. This paper comprehensively analyses the whole process of mobile charging of UAVs under improved conditions and proposes a path planning algorithm. The multi-level weighted charging path planning proposed in this paper considers both fairness and timeliness. Finally, the paper verifies the effectiveness of the algorithm.

Optimizing the Lifetime of Software Defined Wireless Sensor Network via Reinforcement Learning

Reinforcement learning (RL) is an unsupervised learning technique used in many real-time applications. The essence of RL is a decision-making problem. In RL, the agent constantly interacts with the environment and selects the next action according to previous feedback in terms of reward. In this paper, RL trains Software-Defined Wireless Sensor Networks (SDWSNs) controller to optimize the routing paths. We combine RL and SDN, where RL is applied to the SDN controller to generate the routing tables. We also propose four different reward functions for optimization of the network performance. RL-based SDWSN improves network performance by 23% to 30% in terms of lifetime compared with RL-based routing techniques. RL-based SDWSN performs well because it can intelligently learn the routing path at the controller. In addition, it has a faster network convergence rate than RL-based WSN.

Predicting the energy consumption in software defined wireless sensor networks: a probabilistic Markov model approach

The smart world is connecting all universe more than ever thought possible, benefiting from the significant advances of the Internet of Things (IoT) applications using wireless sensor networks (WSN) as the core technology. A challenging issue in the IoT paradigm is the heterogeneity in different parts of the network. The network developers need to use resources belonging to different platforms for their applications, and the software defined network (SDN) approach is a mainly considered solution. In this paper, a software defined wireless sensor network (SDWSN) with an energy predictor model (SDWSN-EPM) based on the Markov probabilistic model is proposed to reduce the energy consumption and the network latency. The energy consumption rate (ECR) of the sensor nodes is modeled using the Markov model and the states of the sensor nodes. The ECR is used by the SDN controller to predict the residual energy level of the nodes and consequently, the energy consumption of the network. The cumulative distribution functions (CDF) of the delay, power consumption, and the network lifetime in both SDWSN and SDWSN-EPM schemes are compared. The results confirm that the SDWSN-EPM model significantly improves the performance of the sensor networks.

Energy-Efficient Relay-Selection-Based Dynamic Routing Algorithm for IoT-Oriented Software-Defined WSNs

In this article, a dynamic routing algorithm based on energy-efficient relay selection (RS), referred to as DRA-EERS, is proposed to adapt to the higher dynamics in time-varying software-defined wireless sensor networks (SDWSNs) for the Internet-of-Things (IoT) applications. First, the time-varying features of SDWSNs are investigated from which the state-transition probability (STP) of the node is calculated based on a Markov chain. Second, a dynamic link weight is designed for DRA-EERS by incorporating both the link reward and the link cost, where the link reward is related to the link energy efficiency (EE) and the node STP, while the link cost is affected by the locations of nodes. Moreover, one adjustable coefficient is used to balance the link reward and the link cost. Finally, the energy-efficient routing problem can be formulated as an optimization problem, and DRA-EERS is performed to find the best relay according to the energy-efficient RS criteria derived from the designed link weight. The simulation results demonstrate that the path EE obtained by DRA-EERS through an available coefficient adjustment outperforms that by Dijkstra’s shortest path algorithm. Again, a tradeoff between the EE and the throughput can be achieved by adjusting the coefficient of the link weight, i.e., increasing the impact of the link reward to improve the EE, and otherwise, to improve the throughput.

Optimizations for Energy Efficiency in Software-Defined Wireless Sensor Networks.

Software-defined wireless sensor networking (SDWSN) is an emerging networking architecture which is envisioned to become the main enabler for the internet of things (IoT). In this architecture, the sensors plane is managed by a control plane. With this separation, the network management is facilitated, and performance is improved in dynamic environments. One of the main issues a sensor environment is facing is the limited lifetime of network devices influenced by high levels of energy consumption. The current work proposes a system design which aims to improve the energy efficiency in an SDWSN by combining the concepts of content awareness and adaptive data broadcast. The purpose is to increase the sensors’ lifespan by reducing the number of generated data packets in the resource-constrained sensors plane of the network. The system has a distributed management approach, with content awareness being implemented at the individual programmable sensor level and the adaptive data broadcast being performed in the control plane. Several simulations were run on historical weather and the results show a significant decrease in network traffic. Compared to similar work in this area which focuses on improving energy efficiency with complex algorithms for routing, clustering, or caching, the current proposal employs simple computing procedures on each network device with a high impact on the overall network performance.

Novel Cluster Rotating and Routing Strategy for software defined Wireless Sensor Networks

The biggest problems faced by the software defined wireless sensor network are energy conservation and load balancing techniques which impose a high level of constraint. In general clustering is used in a network in order to decrease the use of energy thereby enhancing lifetime of the network. Hot spot problems are a common issue caused due to drainage of battery when there are more multi-hop networks that are close to the base station. In order to overcome this restriction, we have proposed the use of multilayer clustering architecture that is used to choose the intra and inter-cluster communication, rotation of cluster head and forwarding node. Using the routing table, the proposed methodology will be able to efficiently handle the rotation of the forwarder node. The rotation takes place based on the residual energy’s threshold levels and also exploits the non-forwarder node, backup forwarder node, forwarder node and decision maker node to enhance the routing strategy of the WSN. Testing and evaluation of the proposed work is done using C programming language and results show that this methodology has better results than EADUC and TLPER as far as hop count, communication and energy consumption are taken into consideration in the cluster formation.

Software-Defi Wireless Sensor Networks: A Systematic Review, Architecture and Challenges

Software-defined networking (SDN) paradigm leads to innovation and simplicity in network configuration and management. Wireless sensor networks (WSNs) are becoming an essential component of the Internet of Things (IoTs). The combination of these two models gives rise to a new paradigm: Software-defined wireless sensor networks (SDWSNs). This paper provides a systematic review on SDWSNs. The study also discusses open challenges of further research on SDWSNs.

On the Lifetime of Asynchronous Software-Defined Wireless Sensor Networks

In this article, we consider a software-defined wireless sensor network (WSN) architecture which conserves energy by applying asynchronous duty cycling. In asynchronous sensor networks, the overhearing adversely impacts the energy consumption of the nodes. Using a mathematical model, we compute the maximum lifetime of the network and accordingly propose a multichannel operation and transmit power control to reduce the effect of overhearing in asynchronous networks. Our comprehensive test results confirm that for the network with load-aware nonuniform sensor node deployment, the network lifetime is much higher compared to a uniform deployment. We also show that the use of data aggregation software-defined WSNs (SDWSNs) can improve the network lifetime as compared to the data gathering networks.

A Specific Structure of Protocol to Increase the System Lifetime of Wireless Sensor Network

In present time there are many research are going on in the field of wireless sensor network which is defined by software that is known as Software defined wireless sensor network that is used for after the distributing of nodes in the network. . In this research work we propose an algorithm for the energy consumption of software defined wireless sensor network. This algorithm is used for to control of selected nodes in the network that are provide the many task dynamically, to make the system network functional manner . The control nodes in the networks are selected as a Nondeterministic Problem to reduce the energy and transmission distance in the system. The LEACH protocol is very useful protocol ti increase the system lifetime of the network. In this paper we consider the low energy adaptive protocol that combines the ideas of Media Access Control protocol and routing algorithm.

An Effective Edge-Assisted Data Collection Approach for Critical Events in the SDWSN-Based Agricultural Internet of Things

In the traditional agricultural wireless sensor networks (WSNs), there is a large amount of redundant data and high latency on critical events (CEs) for data collection systems, which increases the time and energy consumption. In order to overcome these problems, an effective edge computing (EC) enabled data collection approach for CE in smart agriculture is proposed. First, the key features data types (KFDTs) are extracted from the historical dataset to keep the main information on CEs. Next, the KFDTs are selected as the collection data type of the software-defined wireless sensor network (SDWSN). Then, the event types are decided by searching the minimum average variance between the sensing data of active nodes and the average value of the key feature data obtained by EC. Furthermore, the sensing nodes are driven to sense the event-related data with a consideration of latency constraints by the SDWSN servers. A real-world testbed was set up in a smart greenhouse for experimental verification of the proposed approach. The results showed that the proposed approach could reduce the number of needed sensors, sensing time, collection data volume, communication time, and provide the low latency agricultural data collection system. Thus, the proposed approach can improve the efficiency of CE sensing in smart agriculture.

Smart-Green-Mult (SGM): overhear from topological kingpins in software defined wireless sensor networks

Wireless sensor networks (WSNs) are an important component of the Internet of Things (IoT). Each multicast group in a WSN consists of a set of multicast members. In a sparse network, multicast members belonging to the same group may not be very closely spaced. On the other hand, in a dense environment, more than one multicast member of a group may highly reside within downlink regions of the same router which may or may not be a multicast member. In that case, a multicast message can be delivered to more than one multicast member in a shot. This characteristic improves the status of a node from an ordinary router to a topological kingpin. If a good number of topological kingpins are included in a multicast tree, then a great amount of energy is saved increasing network throughput. The present article proposes one such energy efficient multicast scheme: Smart–Green–Mult (SGM), based on Software Defined Wireless Sensor Network (SD-WSN) framework. The network is divided into several zones. The shape of each zone is either circular or elliptical or polygonal. Each zone is under control of an Software Defined Network controller. SDN controller of each zone is aware of the topology of the zone and can compute energy efficient paths from any source to any destination inside or outside the zone. Overall this is a multicast protocol in SD-WSN.

Scheduled Optimal SDWSN Using Wireless Transfer of Power

Energy scarcity is one of the biggest threats faced by today’s society because of the increasing demand for Wireless Sensor Networks. Wireless power transfer is one of the merging solutions that promise to address this energy scarcity and replenish the sensor nodes. In this method, dedicated energy transmitters are used to transfer energy to the sensor nodes. Moreover, Software-defined Wireless Sensor Networks (SDWSNs) have been proposed recently and are defined such that they are used to utilize the WSN resources to its maximum potential. The proposed methodology proposes a Wireless power transfer using SDWSN. We have designed a method to find the minimum energy transmitters by placing the energy transmitters in the node. A trade-off is made between fair distribution of energy and maximum energy charged in the network to place the energy transmitters. The proposed mechanism is defines a utility function in order to increase fairness and total energy charged. The goal is to decrease the energy consumed by the transmitters while retaining the charge of the sensor nodes. The simulation results illustrate the optimum efficiency of the proposed methodology with respect to energy consumption, number of tasks, number of energy transmitters, fairness and energy charged.

A Hybrid Intrusion Detection System for SDWSN using Random Forest (RF) Machine Learning Approach

It is indeed an established fact which network security systems had certain technical problems that mostly tends to lead to security risks. Nowadays, Attackers could still continue to abuse the security vulnerabilities as well as shatter the systems and networks, and is quite pricey and even sometimes extremely difficult to resolve all layout and computing faults. The above appears to suggest that methodologies relying on preventive measures seem to be no longer secure and perhaps tracking of intrusion is necessary as a last line of defense. A Hybrid in Software Defined Wireless Sensor Network (SDWSN) the Intrusion Detection System is designed for this paper which really incorporates the benefits of Salp Swarm Optimization (SSO) algorithm as well as the classification of Machine Learning method it is based upon Random Forest (RF). We propose SSO optimization procedures to guarantee that the ideal features for the intrusion detector are chosen and in addition for improving the Random Forest (RF) classifier detection efficiency. To assess / calculate the reliability of the proposed approach here we make use of the generic NSL KDD dataset. Therefore, our proposed hybrid IDS-SSO-RF classifier further analyzes these detected abnormal activities. The known and unknown attacks are also identified. Hybrid framework also shown by the experimental results can reliably detect anomaly behavior and obtains better results in terms in terms of delay, delivery ratio, drop overhead, energy consumption and throughput.

Internet of Things (IoT) for Next-Generation Smart Systems: A Review of Current Challenges, Future Trends and Prospects for Emerging 5G-IoT Scenarios

The Internet of Things (IoT)-centric concepts like augmented reality, high-resolution video streaming, self-driven cars, smart environment, e-health care, etc. have a ubiquitous presence now. These applications require higher data-rates, large bandwidth, increased capacity, low latency and high throughput. In light of these emerging concepts, IoT has revolutionized the world by providing seamless connectivity between heterogeneous networks (HetNets). The eventual aim of IoT is to introduce the plug and play technology providing the end-user, ease of operation, remotely access control and configurability. This paper presents the IoT technology from a bird’s eye view covering its statistical/architectural trends, use cases, challenges and future prospects. The paper also presents a detailed and extensive overview of the emerging 5G-IoT scenario. Fifth Generation (5G) cellular networks provide key enabling technologies for ubiquitous deployment of the IoT technology. These include carrier aggregation, multiple-input multiple-output (MIMO), massive-MIMO (M-MIMO), coordinated multipoint processing (CoMP), device-to-device (D2D) communications, centralized radio access network (CRAN), software-defined wireless sensor networking (SD-WSN), network function virtualization (NFV) and cognitive radios (CRs). This paper presents an exhaustive review for these key enabling technologies and also discusses the new emerging use cases of 5G-IoT driven by the advances in artificial intelligence, machine and deep learning, ongoing 5G initiatives, quality of service (QoS) requirements in 5G and its standardization issues. Finally, the paper discusses challenges in the implementation of 5G-IoT due to high data-rates requiring both cloud-based platforms and IoT devices based edge computing.