A Co-evolutionary Algorithm-based Enhanced Grey Wolf Optimizer for the Routing of Wireless Sensor Networks

Wireless networks are frequently installed in arduous environments, heightening the importance of their consistent operation. To achieve this, effective strategies must be implemented to extend the lifespan of nodes. Energy-conserving routing protocols have emerged as the most prevalent methodology, as they strive to elongate the network\u27s lifetime while guaranteeing reliable data routing with minimal latency. In this paper, a plethora of studies have been done with the purpose of improving network routing, such as the integration of clustering techniques, heterogeneity, and swarm intelligence-inspired approaches. A comparative investigation was conducted on a variety of swarm-based protocols, including a new coevolutionary binary grey wolf optimizer (Co-BGWO), a BGWO, a binary whale optimization, and a binary Salp swarm algorithm. The objective was to optimize cluster heads (CHs) positions and their number during the initial stage of both two-level and three-level heterogeneous networks. The study concluded that these newly developed protocols are more reliable, stable, and energy-efficient than the standard SEP and EDEEC heterogeneous protocols. Specifically, in 150 m2 area of interest, the Co-BGWO and BGWO protocols of two levels were found the most efficient, with over than 33% increase in remaining energy percentage compared to SEP, and over 24% more than EDEEC in three-level networks

A Review of Wireless Sensor Networks with Cognitive Radio Techniques and Applications

The advent of Wireless Sensor Networks (WSNs) has inspired various sciences and telecommunication with its applications, there is a growing demand for robust methodologies that can ensure extended lifetime. Sensor nodes are small equipment which may hold less electrical energy and preserve it until they reach the destination of the network. The main concern is supposed to carry out sensor routing process along with transferring information. Choosing the best route for transmission in a sensor node is necessary to reach the destination and conserve energy. Clustering in the network is considered to be an effective method for gathering of data and routing through the nodes in wireless sensor networks. The primary requirement is to extend network lifetime by minimizing the consumption of energy. Further integrating cognitive radio technique into sensor networks, that can make smart choices based on knowledge acquisition, reasoning, and information sharing may support the network's complete purposes amid the presence of several limitations and optimal targets. This examination focuses on routing and clustering using metaheuristic techniques and machine learning because these characteristics have a detrimental impact on cognitive radio wireless sensor node lifetime

Performance enhancement of wireless communication systems through QoS optimisation

Providing quality of service (QoS) in a communication network is essential but challenging, especially when the complexities of wireless and mobile networks are added. The issues of how to achieve the intended performances, such as reliability and efficiency, at the minimal resource cost for wireless communications and networking have not been fully addressed. In this dissertation, we have investigated different data transmission schemes in different wireless communication systems such as wireless sensor network, device-to-device communications and vehicular networks. We have focused on cooperative communications through relaying and proposed a method to maximise the QoS performance by finding optimum transmission schemes. Furthermore, the performance trade-offs that we have identified show that both cooperative and non-cooperative transmission schemes could have advantages as well as disadvantages in offering QoS. In the analytical approach, we have derived the closed-form expressions of the outage probability, throughput and energy efficiency for different transmission schemes in wireless and mobile networks, in addition to applying other QoS metrics such as packet delivery ratio, packet loss rate and average end-to-end delay. We have shown that multi-hop relaying through cooperative communications can outperform non-cooperative transmission schemes in many cases. Furthermore, we have also analysed the optimum required transmission power for different transmission ranges to obtain the maximum energy efficiency or maximum achievable data rate with the minimum outage probability and bit error rate in cellular network. The proposed analytical and modelling approaches are used in wireless sensor networks, device-to-device communications and vehicular networks. The results generated have suggested an adaptive transmission strategy where the system can decide when and how each of transmission schemes should be adopted to achieve the best performance in varied conditions. In addition, the system can also choose proper transmitting power levels under the changing transmission distance to increase and maintain the network reliability and system efficiency accordingly. Consequently, these functions will lead to the optimized QoS in a given network

Practical packet combining for use with cooperative and non-cooperative ARQ schemes in wireless sensor networks

Although it is envisaged that advances in technology will follow a "Moores Law" trend for many years to come, one of the aims of Wireless Sensor Networks (WSNs) is to reduce the size of the nodes as much as possible. The issue of limited resources on current devices may therefore not improve much with future designs as a result. There is a pressing need, therefore, for simple, efficient protocols and algorithms that can maximise the use of available resources in an energy efficient manner. In this thesis an improved packet combining scheme useful on low power, resource-constrained sensor networks is developed. The algorithm is applicable in areas where currently only more complex combining approaches are used. These include cooperative communications and hybrid-ARQ schemes which have been shown to be of major benefit for wireless communications. Using the packet combining scheme developed in this thesis more than an 85% reduction in energy costs are possible over previous, similar approaches. Both simulated and practical experiments are developed in which the algorithm is shown to offer up to approximately 2.5 dB reduction in the required Signal-to-Noise ratio (SNR) for a particular Packet Error Rate (PER). This is a welcome result as complex schemes, such as maximal-ratio combining, are not implementable on many of the resource constrained devices under consideration. A motivational side study on the transitional region is also carried out in this thesis. This region has been shown to be somewhat of a problem for WSNs. It is characterised by variable packet reception rate caused by a combination of fading and manufacturing variances in the radio receivers. Experiments are carried out to determine whether or not a spread-spectrum architecture has any effect on the size of this region, as has been suggested in previous work. It is shown that, for the particular setup tested, the transitional region still has significant extent even when employing a spread-spectrum architecture. This result further motivates the need for the packet combining scheme developed as it is precisely in zones such as the transitional region that packet combining will be of most benefit

Guided self-organisation in open distributed systems

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Distributed Robotic Vision for Calibration, Localisation, and Mapping

This dissertation explores distributed algorithms for calibration, localisation, and mapping in the context of a multi-robot network equipped with cameras and onboard processing, comparing against centralised alternatives where all data is transmitted to a singular external node on which processing occurs. With the rise of large-scale camera networks, and as low-cost on-board processing becomes increasingly feasible in robotics networks, distributed algorithms are becoming important for robustness and scalability. Standard solutions to multi-camera computer vision require the data from all nodes to be processed at a central node which represents a significant single point of failure and incurs infeasible communication costs. Distributed solutions solve these issues by spreading the work over the entire network, operating only on local calculations and direct communication with nearby neighbours. This research considers a framework for a distributed robotic vision platform for calibration, localisation, mapping tasks where three main stages are identified: an initialisation stage where calibration and localisation are performed in a distributed manner, a local tracking stage where visual odometry is performed without inter-robot communication, and a global mapping stage where global alignment and optimisation strategies are applied. In consideration of this framework, this research investigates how algorithms can be developed to produce fundamentally distributed solutions, designed to minimise computational complexity whilst maintaining excellent performance, and designed to operate effectively in the long term. Therefore, three primary objectives are sought aligning with these three stages

Topics in Distributed Algorithms: On Wireless Networks, Distributed Storage and Streaming

Distributed algorithms are executed on a set of computational instances. Werefer to these instances as nodes. Nodes are runningconcurrently and are independent from each other. Furthermore, they have their own instructions and information. In this context, the challenges are to show thatthe algorithm is correct, regardless of computational, or communication delaysand to show bounds on the usage of communication.We are especially interested the behaviour after transient faults and underthe existence of Byzantine nodes.This thesis discusses fundamental communication models for distributed algorithms. These models are implementing abstract communication methods. First, we address medium access control for a wireless medium with guaranteeson the communication delay. We discuss time division multiple access(TDMA) protocols for ad-hoc networks and we introduce an algorithm that creates aTDMA schedule without using external references for localisation, or time. We justify our algorithm by experimental results.The second topic is the emulation of shared memory on message passingnetworks. Both, shared memory and message passing are basic interprocessorcommunication models for distributed algorithms. We are providing a way ofemulating shared memory on top of an existing message passing network underthe presence of data corruption and stop-failed nodes. Additionally, we ensurethe privacy of the data that is stored in the shared memory. The third topic looks into streaming algorithms and optimisation. We study the problem of sorting a stream ofvehicles on a highway with severallanes so that each vehicle reaches its target lane. We look into optimality interms of minimising the number of move operations, as well as, minimising the length of the output stream. We present an exact algorithm for the case oftwo lanes and show that NP-Hardness for a increasing number of lanes