A robust congestion control scheme for cluster wireless multimedia sensor networks with propagation delay and external interference

This paper proposes a novel WOA-based robust control scheme with two kinds of propagation latencies and external disturbance implemented in Software-Defined Wireless Networks (SDWNs) to maximize overall throughput and enhance the stability of the global network. Firstly, an adjustment model developed using the Additive-Increase Multiplicative-Decrease (AIMD) adjustment scheme with propagation latency in device-to-device paths and a closed-loop congestion control model with propagation latency in device–controller pairs are proposed, and the effect of channel competition from neighboring forwarding devices is analyzed. Subsequently, a robust congestion control model with two kinds of propagation latencies and external disturbance is established. Then, a new WOA-based scheduling strategy that considers each individual whale as a specific scheduling plan to allocate appropriate sending rates at the source side is presented to maximize the global network throughput. Afterward, the sufficient conditions are derived using Lyapunov–Krasovskii functionals and formulated using Linear Matrix Inequalities (LMIs). Finally, a numerical simulation is conducted to verify the effectiveness of this proposed scheme.

Congestion control in Cluster Wireless Sensor Networks (CWSNs) has drawn widespread attention and research interests. The increasing number of nodes and scale of networks cause more complex congestion control and management. Active Queue Management (AQM) is one of the major congestion control approaches in CWSNs, and Random Early Detection (RED) algorithm is commonly used to achieve high utilization in AQM. However, traditional RED algorithm depends exclusively on source-side control, which is insufficient to maintain efficiency and state stability. Specifically, when congestion occurs, deficiency of feedback will hinder the instability of the system. In this paper, we adopt the Additive-Increase Multiplicative-Decrease (AIMD) adjustment scheme and propose an improved RED algorithm by using neighbor feedback and scheduling scheme. The congestion control model is presented, which is a linear system with a non-linear feedback, and modeled by Lur’e type system. In the context of delayed Lur’e dynamical network, we adopt the concept of cluster synchronization and show that the congestion controlled system is able to achieve cluster synchronization. Sufficient conditions are derived by applying Lyapunov-Krasovskii functionals. Numerical examples are investigated to validate the effectiveness of the congestion control algorithm and the stability of the network.

Recent trend to steer Wireless Sensor Networks towards a broad outdoor environment allows to monitor harsh areas and prevent a disaster situation. To guarantee an extensive work of the system and smooth cooperation among involved devices a new clustering approach needs to be found. In this paper, we present a new algorithm for an Event-oriented Focal Weight-based clustering for Wireless Sensor Network which will be able to monitor various parameters (physical, biological, and chemical) of the outdoor environment and signal the current situation further to the network. Shown results demonstrate the applicability of the algorithm for the large amount of nodes with various power levels. The use of a dual-band transceiver and advanced processing elements allow to accommodate new features in the nodes and guarantee a high performance with the low power consumption which is crucial for the outdoor system with no human attendance.

Wireless sensor networks (WSNs) has acquired more attention because of applications in various fields. The sensors have limited power sources as they cannot be replaced because of hostile nature. In this study, Genetic Algorithm (GA) based approaches for routing and clustering in WSNs have been proposed. The significant purpose of the process is to enhance lifetime of sensor and Quality of Service (QoS). The results illustrate that proposed algorithms such as LECR-GA or NLCR-GA works better than the existing algorithm with respect to several performance metrics such as number of packets, energy consumption acknowledged by the base station.

This paper considers wireless sensor (hyper) networks by single-valued neutrosophic (hyper)graphs. It tries to extend the notion of single-valued neutrosophic graphs to single-valued neutrosophic hypergraphs and it is derived single-valued neutrosophic graphs from single-valued neutrosophic hypergraphs via positive equivalence relation. We use single-valued neutrosophic hypergraphs and positive equivalence relation to create the sensor clusters and access to cluster heads. Finally, the concept of (extended) derivable single-valued neutrosophic graph is considered as the energy clustering of wireless sensor networks and is applied this concept as a tool in wireless sensor (hyper) networks.

Integration of heterogeneous communication systems is the major objective of Future Internet. Heterogeneous systems such as wired and wireless assert the concept of Internet of Things (IoT). Communication standard protocols based global network of identical addressable interconnected resources conferred to IoT. In this article, it is described how wireless sensor networks (WSN) are one of the keys enabling the IoT. Clustering in WSNs plays an important role since it allows altering the network topology and helps in balancing of traffic loads. Load balancing improves the lifespan and scalability of the WSNs. Here, a heuristic approach, coverage aware load balanced clustering, has been developed in order to increase the lifespan of the WSNs. Moreover, simulation results are demonstrated for a sample network and the results show that the network lifespan has increased and simultaneously energy consumption is reduced compared to the other existing techniques.

Clustering has become a crucial operation in Wireless Sensor Networks (WSNs). Affinity Propagation (AP) is a relatively new clustering technique that has been shown to possess several advantages over long-standing algorithms such as K-means, particularly in terms of quality of clustering and multi-criteria support. However, the original AP algorithm is computationally intensive making it unsuitable for clustering in WSNs. A hierarchical decentralized variation of AP (Hi-WAP) has been recently proposed to reduce the processing cost of AP while minimizing the potentially negative effect of distribution (due to the lack of a global view) on clustering quality. In this paper, we explore the suitability of Hi-WAP for clustering in WSNs. We employ the level of distortion and the processing time as evaluation metrics. We propose an extension to Hi-WAP, termed LAP; Location-aware Affinity Propagation, where clustering is performed while being cognizant of nodes’ locations. Simulation results reveal that LAP, in general, outperforms Hi-WAP. We further study the optimization of LAP parameter values with the objective of minimizing processing time while maintaining a desirable low level of distortion.

Large scale Wireless Sensor Networks (WSNs) often utilize multiple mobile sink nodes to improve the network lifetime and scalability. However, most of the studies conducted in this context, consider unlimited buffer capacity for the sink nodes. But, this model cannot truly describe the behavior of WSNs and causes congestion in the sink nodes. To solve this problem, in this paper, we use limited buffer capacity for each mobile sink node in WSNs and present a two-level Fuzzy Logic Controller (FLC)-based dynamic clustering scheme and congestion prevention. In this scheme, sink nodes try to predict current load based on their loads in previous rounds by using ARIMA method and based on it, the first FLC selects the nearest uncongested sink node from multiple available mobile sink nodes. Then, the second FLC applies the output of the first FLC to select appropriate nodes as cluster heads to mitigate the energy consumption in the network. Extensive simulation results indicate the effectiveness of the proposed fuzzy logic-based solution in reducing congestion in the mobile sink nodes and improving load balancing in them which these result in the network lifetime improvement and decreasing the number of retransmissions.

We present the design, development, and deployment of an inexpensive, power-efficient, clustered, and scalable wireless sensor network (WSN) testbed. The testbed operates in a harsh environment in which neither GPS nor Internet connectivity are available. We use this testbed to collect real-time data during football games and other major events at Bobby Dodd stadium at Georgia Tech. The sensing devices in the testbed are synchronized without GPS or beacons, yet achieve sufficient accuracy to support modal analysis and detect if the stands are experiencing torsion. We have also developed a cognitive radio backhaul link to establish communication between the WSN in the stadium and a server in our lab. We present in detail the architecture, hardware components, and embedded software of the structural health monitoring platform. We demonstrate that crowd behavior, such as rhythmic stomping, can be detected during a game.

A problem of stabilization of cluster unit in wireless sensor and actor network is discussed in this paper. Through modeling WSAN (wireless sensor and actor network) as a new multiple input single output model, this problem is transferred to stabilization of multi-sensor and single controller system with long but bounded multiple random time-delay and packet-loss. Using theories of jump linear system, mode-independent and mode-dependent controllers satisfying mean square stability are successfully designed. Finally, numerical examples and simulations have been given to show validity of the developed theory.

Congestion control in wireless sensor networks based on bird flocking behavior

Clustering in wireless sensor networks has been one of the most effective mechanisms for addressing the issues of network lifetime, scalability and bandwidth reusability. In this paper, application of a relatively new meta-heuristic called as quantum inspired genetic algorithm has been proposed for dealing with the problem of multi-hop energy balanced unequal clustering and prolonging the network lifetime. Quantum inspired genetic algorithm conflates the concepts of evolutionary computation with the principles of quantum computing. The quantum computing principles impart huge parallel processing capabilities to the genetic algorithm due to which these algorithms provide better quality solutions in lesser amount of time. Quantum inspired algorithms are capable of providing competitive solutions even with a very small population size which results in reduced computational cost as population size is a factor in complexity calculation for evolutionary and swarm algorithms. The effectiveness of proposed approach has been evaluated by comparing it with traditional genetic algorithm based clustering for wireless sensor networks. Simulation results clearly indicate that quantum inspired genetic algorithm is capable of performing clustering in more efficient way and increasing the network lifetime as compared to traditional genetic algorithm.

Wireless Sensor Networks (WSN) has been attracting growing interests for developing a new generation of large-scale embedded computing systems. However, the communication paradigms in wireless sensor networks differ from the ones associated to traditional wireless networks, triggering the need for new communication protocols and energy-consuming model. The NDSA (A Novel Data-sending Algorithm) Based on Cluster for wireless sensor networks aims at the design of a scalable data-sending model for supporting large-scale embedded computing applications with critical requirements. In this paper, we assume intra-cluster data taking on Gaussian distribution. According to the desired accuracy given by system, NDSA can automatically adjust the number of data-sending nodes. Experimental results show that NDSA can lengthen the life of network significantly better than other similar algorithms.

Congestion in Wireless Sensor Network (WSN) occurs when traffic load allocated to any sensor node is beyond its capacity. It is necessary to control the congestion traffic to support traditional quality of service (such as packet loss ratios, packet delay, wasting energy and throughput) especially, multimedia application of Wireless Multimedia Sensor Network (WMSN). Congestion control in WSN has been shown to be very effective with respect to extending lifespan of system. Hence to avoid the congestion in WSN, we proposed an efficient protocol called Priority-based Congestion Control Protocol (PCCP) which prevents an upstream congestion in WSN. The PCCP creates priority table based an importance of each node, and then sends this information to all the nodes within the network. Secondly, the major function of PCCP is to measure congestion level in the network using ratio of packet inter-arrival time along over packet service time. PCCP uses to control upstream congestion along with congestion degree and packet table. PCCP is hop-by-hop upstream congestion control protocol for WSN. PCCP provides work for packet-based computation to optimize congestion control. It can work under both single path & multipath routing. PCCP ensures guarantee of packet loss as well as delay, resulting each node can avoid unfairness and achieve flexible throughput. In this paper, we have proposed that PCCP provides better QoS by controlling network recourse management and network traffic to consuming efficient energy.

In this paper, an active queue management (AQM) controller is designed for the problem of congestion control. In TCP networks, the packet-dropping probability function is considered as a control input. Thus, a TCP/AQM controller is modeled as a time-varying delay system with a saturated input. The objective of the work described here is to design robust controller capable of achieving the desired queue size and guaranteeing asymptotic stability of the operating point. To achieve this aim, we have proposed an observer-based controller. For the particular TCP mode, applying the Lyapunov-Krasovskii function defined, the system can be stabilize with the obtained observer and the state feedback control law by using linear matrix inequality (LMI). Simulation results show that the derived control strategy is validated.