Topology Control Protocol Research Articles

Joint topology control and routing in a UAV swarm for crowd surveillance

Aerial surveillance using unmanned aerial vehicles (UAVs) provides an on-demand and cost-effective solution to smart-city monitoring needs, owing to their three-dimensional positioning adjustment and autonomy. The optimal deployment of a UAV swarm, also known as a flying ad hoc network (FANET), to achieve on-demand coverage of mobile ground targets (MGTs) is challenging in terms of controlling UAV mobility to maximize coverage while maintaining quality of service. Data routing from UAVs to a base station (BS) without awareness of the updated topology causes link breakages, excessive retransmissions, high congestion, and energy holes. Therefore, we propose a joint topology control and routing (JTCR) protocol comprising three modules to perform crowd surveillance. The first JTCR module provides virtual force-based mobility control (VFMC), which controls the mobility of UAVs to track MGTs, ensuring stable bi-connectivity. The second module provides energy-efficient mobility-aware fuzzy clustering that clusters the FANET to aggregate the sensed data to each cluster head (CH) by utilizing the UAV mobility provided by the VFMC. The third module provides topology-aware Q-routing, which routes the aggregated data from CH UAVs to the BS by selecting an optimal path in terms of delay, path stability, and energy consumption. According to our performance study, the proposed JTCR outperforms existing routing protocols in terms of tracking-coverage rate, connectivity rate, the number of retransmissions, packet delivery ratio, end-to-end delay, and energy consumption. This is mainly enabled by the realistic mobility control of the UAV swarm at the reasonable cost of control overhead.

Topology Control and Medium Access Control (MAC) Protocol for Wireless Sensor Networks (WSNs) in Cyber-Physical System

The system reachability set is calculated by covering all possible behaviours of the system through a finite number of simulation steps to ensure that the system trajectory stays within a set safety region. In this paper, the theory of the game method is applied to the design of the controller, a very small controller is designed, and good control results are obtained by simulation. The system gradually shows a divergent trend and cannot achieve stable control. A multihop channel reservation Medium Access Control (MAC) protocol based on a parallel mechanism is proposed. The multihop channel reservation mechanism is proposed based on periodic node sleep, and the node uses the reservation frame to make the reservation of the channel and transmits the data according to the reservation information; the parallel mechanism is used to make the reservation information and data transmission simultaneously.

Self-organizing topology for energy-efficient ad-hoc communication networks of mobile devices

When physical communication network infrastructures fail, infrastructure-less communication networks such as mobile ad-hoc networks (MANET), can provide an alternative. This, however, requires MANETs to be adaptable to dynamic contexts characterized by the changing density and mobility of devices and availability of energy sources. To address this challenge, this paper proposes a decentralized context-adaptive topology control protocol. The protocol consists of three algorithms and uses preferential attachment based on the energy availability of devices to form a loop-free scale-free adaptive topology for an ad-hoc communication network. The proposed protocol has a number of advantages. First, it is adaptive to the environment, hence applicable in scenarios where the number of participating mobile devices and their availability of energy resources is always changing. Second, it is energy-efficient through changes in the topology. This means it can be flexibly combined with different routing protocols. Third, the protocol requires no changes on the hardware level. This means it can be implemented on all current phones, without any recalls or investments in hardware changes. The evaluation of the protocol in a simulated environment confirms the feasibility of creating and maintaining a self-adaptive ad-hoc communication network, consisting of multitudes of mobile devices for reliable communication in a dynamic context.

Machine Learning-Based Models for Real-time Traffic Flow Prediction in Vehicular Networks

In recent years, ML-based models are gaining enormous attention from both the automotive industry and academia to support IoVs. Through the accurate prediction of traffic/road conditions, various safety and infotainment applications can efficiently utilize the network entities and enhance the quality of service. Topology control and mobility management protocols in IoVs, among others, would achieve higher efficiency through the support of real-time traffic flow forecasting. However, the current research trend on improving prediction accuracy refrains from answering the essential question of whether ML-based prediction schemes are suitable for real-time traffic prediction. To answer this question, a thorough extensive study to evaluate the efficiency of prediction- based traffic flow schemes is required. In this article, we investigate the effectiveness of various ML-based prediction models by considering both the prediction accuracy and computational time cost. Accordingly, we present rigorous quantitative analysis to identify the important factors that may restrict the use of ML-based prediction models to support real-time services in the IoV environment.

Optimizing topologies in wireless sensor networks: A comparative analysis between the Grey Wolves and the Chicken Swarm Optimization algorithms

Finding the suitable solution in constrained environments, such as wireless sensor networks (WSNs), is in the application area of meta-heuristic algorithms. The constrained resources of such networks include: low surplus energy, limited computational power, and small communication bandwidth. Despite this fact, not all meta-heuristic algorithms guarantee suitable energy-efficient routing due to their complexity and the need for specific parameters’ tuning. This had motivated our attempts to benchmark the efficacy of topology control protocols through the application of two meta-heuristic algorithms; the Grey Wolves Optimizer (GWO) and the Chicken Swarm Optimization (CSO) algorithms. In addition to the performance analysis that has been proven through the simulations, the assessment had covered a comparative analysis of the behavior of each algorithms’ operators. The performance indicator was finding the smallest number of active nodes for the network’s operation. These nodes should have high residual energy for the purpose of extending the network’s operation time. The proposed solution penalizes any topology that trades-off the coverage characteristic of the network. Through a number of quantitative experiments, the results showed the dominance of CSO based algorithm as active nodes’ reduction had reached up to 12% of the total numbers of network’s nodes.

Coordination of Congestion and Awareness Control in Vehicular Networks

Vehicular networks need to guarantee the communication reliability levels necessary to satisfy the application requirements, while ensuring a stable network operation even under dense deployments. To this aim, congestion and awareness control protocols dynamically adapt the same communication parameters based on context conditions. If the two protocols operate independently, negative interactions or conflicts can arise. This situation can occur if for example congestion control requires decreasing the transmission power to reduce the channel load, but this reduction negatively influences the vehicles’ awareness range. To address these interactions or conflicts, this paper proposes and evaluates a methodology to coordinate congestion and awareness control protocols. A key advantage of the proposed methodology is that it does not require the integration of the interacting protocols, nor does it require changing their original design. The obtained results demonstrate the effectiveness of the proposed coordination methodology. In addition, the proposed methodology can be extended to the coordination of multiple protocols operating over the same communication parameters. This is here demonstrated considering the coordination of congestion, awareness and topology control protocols.

Distributed Topology Control for Energy-Efficient and Reliable Wireless Communications

A dense wireless ad hoc network with a high average node degree offers a strong connectivity for packet routing, but at the same time increases the probability of interference between nodes and results in rapid depletion of node energy. To remedy this issue, topology control mechanisms aim at optimizing transmission power of nodes in a wireless ad hoc network to maintain the connectivity of the network, reduce the energy wastage, and increase the network throughput. In this paper, we propose a partially localized topology control algorithm, namely articulation points based topology control (APTC), which effectively saves power consumption in a wireless ad hoc network with a low communication overhead. Unlike the existing topology control protocols, APTC designates articulation points to be initiators and builds a tree of minimum spanning trees to achieve power saving while maintaining network connectivity. The simulation results demonstrate the superiority of APTC over the existing topology control algorithms in terms of power consumption and communication overhead.

Learning automaton based topology control protocol for extending wireless sensor networks lifetime

Energy efficiency is one of the critical challenges in wireless sensor networks because the nodes in such networks have limited resources. Therefore, they should be managed efficiently in order to exploit the network's functionality for a longer period of time. Topology control mechanisms can help the nodes to leverage their resources efficiently. Several topology control protocols for WSNs have been proposed to decrease energy consumption of the nodes and increase the network capacity. Leveraging a lower transmission range can help the nodes to mitigate their energy consumptions. In this paper, we propose a topology control protocol based on learning automaton, which is named LBLATC. The learning automaton of every sensor node chooses the proper transmission range of the node using the reinforcement signal which is produced by the learning automaton of neighbor sensor nodes. The simulation runs carried out to verify the performance of the proposed protocol. It acts on average 15% better than current state-of-art in term of selecting a proper transmission range.

Mobility prediction in mobile ad hoc networks using eye of coverage approach

Mobility prediction plays an important role in efficient planning and management of the bandwidth resources available in wireless networks. Mobility prediction allows estimating the stability of the paths in a Mobile Ad Hoc Networks. The prediction of mobility is explained as that prediction for the next movement of a mobile user in which the user travels between the nodes or the access points in the wireless network. This predicted path or its movement can further help in increasing the efficiency of that of the wireless network, by means of allocating resources effectively to its access point with the highest probability (which is the next point in attachment) as opposed to mindlessly allocating the excessive resources in the neighbourhood of the mobile nodes. Identification of such stable paths improves routing process by reducing the overhead and connection interruptions. As of now the mobility prediction and mobility aware topology control protocols are assumed to know its current mobility; by the way future positions are predicted along with future distances between neighbouring nodes. We introduce a novel approach for mobility prediction in MANET that accurately predicts the mobility pattern and their future location. Our proposed scheme can provide accurate prediction.

RTCP: a redundancy aware topology control protocol for wireless sensor networks

Topology control-based sleep-scheduling aims at exploiting node redundancy to save energy and extend the network lifetime, by putting as many nodes as possible in sleep mode, while maintaining a connected network. In this paper, we propose a redundancy aware topology control protocol (RTCP) for a wireless sensor network which exploits the sensor redundancy in the same region. This is achieved by dividing the network into groups so that a connected backbone can be maintained by keeping a necessary set of working nodes and turning off the redundant ones. RTCP allows applications to parameterise the desired connectivity degree. It identifies node redundancy, in terms of communication; it groups redundant nodes together according to their redundancy degrees and threshold of connectivity level. Finally, it schedules nodes in groups for active or sleep mode. The simulation results illustrate that RTCP outperforms some other existing algorithms, in terms of energy conservation, network lifetime and connectivity guarantee.

The Effects of Using Some Different Types of Learning Automata on Presentation of Adaptive Neighbor-Based and Energy Efficient Topology Control Protocols in Wireless Sensor Networks

The high number of nodes and dynamic and periodic topological changes, as well as constraints in the physical size of nodes, energy resources, and power of processing are some characteristics of sensor networks that make them different from other networks. One method to overcome these constraints is topology control with the aim of reducing energy consumption and increasing the network’s capacity, which has the most influence on the network’s efficiency, especially in terms of energy consumption and lifetime. In consideration of learning Automata’s abilities, such as low computational load and adaptability to changes via low environmental feedbacks, in this paper, neighbor-based topology control protocols based on learning Automata have been proposed somehow that all nodes are equipped with Automata. The nodes try to adapt their selected actions with required conditions for creating a connected and energy efficient network by selecting the best radio range for themselves. This approach finally forms a proper topology, and in this way it lowers the network’s energy consumption in its lifetime. The exclusive characteristic of these methods is the high number of transmission ranges that each node can select as transmission radius. In the first proposed protocol, a P-model environment is used for learning phase, but in the second proposed protocol, a Q-model environment is applied. Simulation results show favorite functionality of proposed protocols in comparison with some other similar protocols from topology control point of view, as well as high improvement of achieved results for the Q-model environment.

Neighbor discovery and routing schemes for mobile ad-hoc networks with beamwidth adaptive smart antennas

The use of smart directional antennas in ad-hoc networks has recently received much attention since this technology offers a way to optimize power consumption, increase spatial reuse and reduce co-channel interference. Neighbor discovery is an important first step for these self-configured networks because it makes it possible to identify the active nodes surrounding a given node and this information is essential for other higher layer protocols like medium access control, routing and topology control protocols. In this article, we present two algorithms that aim to take full advantage of the capabilities of smart antennas. The first one aims to increase the number of discovered nodes by considering the nodes that are located in the second-hop neighborhood of a node. The second algorithm seeks to reduce the number of hops in a route between source and destination nodes by using the extended range that can be achieved when the nodes are equipped with directional antennas. The results of the simulations have shown a reduction in the end-to-end delay and an increase in the network throughput when the scheme is compared with other reactive routing protocols.

Topology Control for Self-Adaptation in Wireless Sensor Networks with Temporary Connection Impairment

In this work, the problem of topology control for self-adaptation in stationary Wireless Sensor Networks (WSNs) is revisited, specifically for the case of networks with a subset of nodes having temporary connection impairment between them. This study focuses on misbehaviors arising due to the presence of\enskip “dumb” nodes [Misra et al. 2014; Roy et al. 2014a, 2014b, 2014c; Kar and Misra 2015], which can sense its surroundings but cannot communicate with its neighbors due to shrinkage in its communication range by the environmental effects attributed to change in temperature, rainfall, and fog. However, a dumb node is expected to behave normally on the onset of favorable environmental conditions. Therefore, the presence of such dumb nodes in the network gives rise to impaired connectivity between a subset of nodes and, consequently, results in change in topology. Such phenomena are dynamic in nature and are thus distinct from the phenomena attributed to traditional isolation problems considered in stationary WSNs. Activation of all the sensor nodes simultaneously is not necessarily energy efficient and cost-effective. In order to maintain self-adaptivity of the network, two algorithms, named Connectivity Re-establishment in the presence of Dumb nodes ( CoRD ) and Connectivity Re-establishment in the presence of Dumb nodes Without Applying Constraints ( CoRDWAC ), are designed. The performance of these algorithms is evaluated through simulation-based experiments. Further, it is also observed that the performance of CoRD is better than the existing topology control protocols—LETC and A1—with respect to the number of nodes activated, overhead, and energy consumption.

Collaborative Topology Control for Many-to-One Communications in Wireless Sensor Networks

Topology control is relevant in wireless sensor network because of two reasons, namely minimal sensor coverage and power constraints. The former condition is typically satisfied by high-density deployment, whereas the latter mainly concerns with the control protocol design that is adaptable. Controlling communication topology is at the center of the efforts to optimize network performance while improving energy conservation. A dense topology often results in high interference and lower spatial reuse thus reduced capacity, while sparse topology is susceptible to network partitioning and sub-optimal path selection from the routing layer. Topology control has been extensively studied in both flat and hierarchical network by mean of power adjustment and clustering, respectively. Despite a common goal of making the topology less complex both techniques differ in their approach. While the focus of clustering is to form a connected backbone which consists of a minimum subset of nodes, i.e., dominating set, power adjustment focuses on minimizing energy consumption. Combining both approaches remains a relatively lesser explored area. We proposed a hybrid framework called collaborative topology control protocol, which combines dominating set-based clustering and transmission power adjustment. The protocol operates in two stages. During the first stage, a parameterized minimum virtual connected dominating set algorithm is executed to obtain clusters of various desirable properties. In the second stage, each cluster-head executes a distributed power adjustment algorithm. The simulation results show that the proposed topology control framework is capable of versatile performance in terms of transmission range/energy cost, the number of neighbors, edges, and hop distance. Moreover, the topology construction process uses the locally available information only with minimal communication overhead.

Topology Control in Mobile Wireless Sensor Networks

Topology control is a set of strategies and actions those aim to improve energy efficiency, lifetime and reliability of the networks. One of the most effective methodology is topology reduction, which is a part of the overall topology control process. Topology control process is usually considered under two phases: construction and maintenance. This hard work would even be harder for mobile ad-hoc sensor networks, those consist of devices with limited capabilities and continual independent movement. In this work, we made our efforts to find out effects of mobility of the nodes in an ad-hoc wireless sensor network on network parameters, by using different pre-defined topology control protocols. Thus, we made simulations using an experimental simulator, called Atarraya. Furthermore, we measured the performance of each protocol and compared the outcomes in order to find the best topology control strategy. Lastly, possible solutions to the uncovered problems are proposed.

Erratum to: MRL-SCSO: Multi-agent Reinforcement Learning-Based Self-Configuration and Self-Optimization Protocol for Unattended Wireless Sensor Networks

Resource-constrained nodes in unattended wireless sensor network (UWSN) operate in a hostile environment with less human intervention. Achieving the optimal quality of service (QoS) in terms of packet delivery ratio, delay, energy, and throughput is crucial. In this paper, we propose a topology control and data dissemination protocol that uses multi-agent reinforcement learning (MRL) and energy-aware convex-hull algorithm, for effective self-configuration and self-optimization (SCSO) in UWSN, called MRL-SCSO. MRL-SCSO maintains a reliable topology in which the effective active neighbor nodes are selected using MRL. The network boundary is determined using convex-hull algorithm to maintain the connectivity and coverage of the network. The boundary nodes transmit data under high traffic load conditions. The performance of MRL-SCSO is evaluated for various nodes count and under different load conditions by using the Contiki’s Cooja simulator. The results showed that MRL-SCSO stabilizes the performance and improves QoS.

Multicast Connections in Wireless Sensor Networks with Topology Control

The article explores the quality of multicast trees constructed by heuristic routing algorithms in wireless sensor networks where topology control protocols operate. Network topology planning and performance analysis are crucial challenges for wire and wireless network designers. They are also involved in the research on routing algorithms, and protocols for these networks. In addition, it is worth to emphasize that the generation of realistic network topologies makes it possible to construct and study routing algorithms, protocols and traffic characteristics for WSN networks.

A distributed lightweight Redundancy aware Topology Control Protocol for wireless sensor networks

WSN consists of a large number of sensor nodes randomly deployed, and, in many cases, it is impossible to replace sensors when a node failure occurs. Thus, applications tend to deploy more nodes than necessary to cope with possible node failures and to increase the network lifetime, which leads to create some sensing and communication redundancy. However, sensors in the same region, may collect and forward the same information, which will waste more energy. In this paper, we propose a distributed Lightweight Redundancy aware Topology Control Protocol (LRTCP) for wireless sensor networks. It exploits the sensor redundancy in the same region by dividing the network into groups so that a connected backbone can be maintained by keeping a minimum of working nodes and turning off the redundant ones. LRTCP identifies equivalent nodes in terms of communication based on their redundancy degrees with respect of some eligibility rules. Simulation results indicate that, compared with existing distributed topology control algorithms, LRTCP improves network capacity and energy efficiency.

A distributed joint topology control and forwarding protocol in MANETs using game theory

In mobile ad-hoc networks MANETs, each node tries to maximise its utility in the network and saves its limited resources including energy and bandwidth due to different ownerships and selfish behav...

A distributed joint topology control and forwarding protocol in MANETs using game theory

In mobile ad-hoc networks MANETs, each node tries to maximise its utility in the network and saves its limited resources including energy and bandwidth due to different ownerships and selfish behaviour, which can cause nodes not to cooperate in network tasks such as topology control and packet forwarding and it affects performance of all tasks of the network. Thus, the induced topology lacks favourable specifications including connectivity, optimised energy consumption. Topology control and packet forwarding mutually affect each other. In addition, retrieving complete information about MANETs is challenging because of mobility and high signalling cost. In this paper, the issue of nodes selfishness in topology control and packet forwarding has been addressed by game theory. It is assumed that nodes have local information. Simulation results show that the proposed model decreases the signalling cost of the topology control, increases the network life and dramatically decreases the transmission power variance.