Software-defined Sensor Networks Research Articles

An Extended Software-Defined Approach for Reprogramming Low-end IoT Devices

The usage of the Software-Defined Sensor Networks (SD-WSNs) as support for applications of the Internet of Things (IoT) has been the focus of recent research to reduce the complexity of configuration and management. This simplification allows greater flexibility for the configuration of these networks in operating time. However, although more flexible than traditional approaches, the existing SD-WSN architectures found in the literature have limitations mainly with regard to the reprogramming of devices in operating time. In this article, we propose a novel approach called Extended Software-Defined Wireless Sensor Network (ESD-WSN), in which the concept of the SDNs is extended to the application layer allowing the reprogramming of low-end devices in operation time without the need for firmware update. The proposed architecture was validated by simulation and an experimental study using very constrained low-end devices. The simulation/experimental results showed the effectiveness, flexibility, and efficiency of the proposal.

A novel method of mobility-based clustering protocol in software defined sensor network

The distributed clustering method is widely used to extend network lifetime in traditional wireless sensor networks. However, it is difficult to achieve the idea performance of the whole network, such as transmission rate, energy consumption, and control overhead, neglecting the global stability of the network. To tackle this problem, a centralized mobility-based clustering (CMBC) protocol is proposed in the software defined sensor network. The method of CMBC mainly consists of two aspects: first, CMBC clusters the nodes with the connection time between the mobile nodes (i.e., noncluster head nodes, non-CH) and the cluster head (CH) and establishes stable topological structures between the non-CHs and the CH; second, when emergencies occur, the centralization management control center sends the configuration files to replace the CH. Compared to the distributed network of MBC, WCRA and IMP-MECA protocol, the proposed method can be applied in scenarios with high-speed mobile nodes to improve the network performance in terms of transmission successful rate, average power consumption, and average control overhead.

A reliable and cost-efficient code dissemination scheme for smart sensing devices with mobile vehicles in smart cities

The smart cities constitute sensing devices that collect a large amount of data. The sensor-based devices are widely using software-defined technology. The adaptation of software-defined devices to the new applications and environment requires a regular update in the programs. However, it is challenging to update the code of a large number of smart sensors spread widely in the smart cities in a fast and cost-efficient way. The mobile vehicles can be used to disseminate the code across these sensing devices. In this paper, a Reliable and Cost-efficient Code mule Selection Scheme (RCCSS) is proposed for code dissemination for software-defined sensor networks. The duty cycle aware broadcast scheme is designed with Ranking-based Ant System (RAS) to provide the maximum coverage of code dissemination. The taxi trajectory dataset is used in experimental evaluation of the proposed code dissemination scheme. The experiment results validate the effectiveness of proposed models over the existing code dissemination techniques.

A Load Balancing Routing Mechanism Based on SDWSN in Smart City

In the wireless sensor network infrastructure of smart cities, whether the network traffic is balanced will directly affect the service quality of the network. Because of the traditional WSN (wireless sensor network) architecture, load balancing technology is difficult to meet the requirements of adaptability and high flexibility. This paper proposes a load balancing mechanism based on SDWSN (software defined wireless sensor network). This mechanism utilizes the advantages of a centralized control SDN (software defined network) and flexible traffic scheduling. The OpenFlow protocol is used to monitor the running status and link load information of the network in real time. According to the bandwidth requirement of the data flow, the improved load balanced routing is obtained by an Elman neural network. The simulation results show that the improved SDSNLB (software-defined sensor network load balancing) routing algorithm has better performance than LEACH (Low Energy Adaptive Clustering Hierarchy) protocol in balancing node traffic and improving throughput.

Localisation algorithm with node selection under power constraint in software‐defined sensor networks

In this study, the authors propose an improved localisation algorithm in the software-defined sensor networks (SDSNs). This algorithm includes a node-selection strategy under the whole network power constraint, based on the software-defined networking (SDN) technique for providing the centralised control of the network. The analogous Cramer-Rao lower bound (A-CRLB) value is derived for each participating node, which represents a fundamental bound on the variance of the position estimator and is used to evaluate the contribution of each node to localisation accuracy. On the basis of A-CRLB values, the most helpful nodes for localisation are selected to maximise the sum of the nodes' contributory values to the localisation accuracy. With the global network knowledge provided by the SDN controller in the SDSN, the node-selection strategy is formulated into a 0-1 programming problem on the premise of power satisfaction of each node. Furthermore, the proposed node-selection -based localisation algorithm is applied to both noncooperative and cooperative localisation scenarios. Simulation results show that the proposed algorithms provide efficient and effective localisation schemes in SDSNs, and can improve the performance in terms of both the selection convergence speed and the localisation accuracy.

Building SDN-Based Agricultural Vehicular Sensor Networks Based on Extended Open vSwitch

Software-defined vehicular sensor networks in agriculture, such as autonomous vehicle navigation based on wireless multi-sensor networks, can lead to more efficient precision agriculture. In SDN-based vehicle sensor networks, the data plane is simplified and becomes more efficient by introducing a centralized controller. However, in a wireless environment, the main controller node may leave the sensor network due to the dynamic topology change or the unstable wireless signal, leaving the rest of network devices without control, e.g., a sensor node as a switch may forward packets according to stale rules until the controller updates the flow table entries. To solve this problem, this paper proposes a novel SDN-based vehicular sensor networks architecture which can minimize the performance penalty of controller connection loss. We achieve this by designing a connection state detection and self-learning mechanism. We build prototypes based on extended Open vSwitch and Ryu. The experimental results show that the recovery time from controller connection loss is under 100 ms and it keeps rule updating in real time with a stable throughput. This architecture enhances the survivability and stability of SDN-based vehicular sensor networks in precision agriculture.

Energy Minimization in Multi-Task Software-Defined Sensor Networks

After a decade of extensive research on application-specific wireless sensor networks (WSNs), the recent development of information and communication technologies makes it practical to realize the software-defined sensor networks (SDSNs), which are able to adapt to various application requirements and to fully explore the resources of WSNs. A sensor node in SDSN is able to conduct multiple tasks with different sensing targets simultaneously. A given sensing task usually involves multiple sensors to achieve a certain quality-of-sensing, e.g., coverage ratio. It is significant to design an energy-efficient sensor scheduling and management strategy with guaranteed quality-of-sensing for all tasks. To this end, three issues are investigated in this paper: 1) the subset of sensor nodes that shall be activated, i.e., sensor activation, 2) the task that each sensor node shall be assigned, i.e., task mapping, and 3) the sampling rate on a sensor for a target, i.e., sensing scheduling. They are jointly considered and formulated as a mixed-integer with quadratic constraints programming (MIQP) problem, which is then reformulated into a mixed-integer linear programming (MILP) formulation with low computation complexity via linearization. To deal with dynamic events such as sensor node participation and departure, during SDSN operations, an efficient online algorithm using local optimization is developed. Simulation results show that our proposed online algorithm approaches the globally optimized network energy efficiency with much lower rescheduling time and control overhead.