Study on Different Topology Manipulation Algorithms in Wireless Sensor Network

Wireless sensor network (WSN) comprises of spatially distributed autonomous sensors to screen physical or environmental conditions and to agreeably go their information through the network to a principle area. One of the critical necessities of a WSN is the efficiency of vitality, which expands the life time of the network. At the same time there are some different variables like Load Balancing, congestion control, coverage, Energy Efficiency, mobility and so on. A few methods have been proposed via scientists to accomplish these objectives that can help in giving a decent topology control. In the piece, a few systems which are accessible by utilizing improvement and transformative strategies that give a multi target arrangement are examined. In this paper, we compare different algorithms' execution in view of a few parameters intended for every target and the outcomes are analyzed. DOI: 10.17762/ijritcc2321-8169.15029

Task Allocation and Collaborative Localisation in Multi-Robot Systems

To utilise multiple robots, it is fundamental to know what they should do, called task allocation, and to know where the robots are, called localisation. The order that tasks are completed in is often important, and makes task allocation difficult to solve (40 tasks have 1047 different ways of completing them). Algorithms in literature range from fast methods that provide reasonable allocations, to slower methods that can provide optimal allocations. These algorithms work well for systems with identical robots, but do not utilise robot differences for superior allocations when robots are non-identical. They also can not be applied to robots that can use different tools, where they must consider which tools to use for each task. Robot localisation is performed using sensors which are often assumed to always be available. This is not the case in GPS-denied environments such as tunnels, or on long-range missions where replacement sensors are not readily available. A promising method to overcome this is collaborative localisation, where robots observe one another to improve their location estimates. There has been little research on what robot properties make collaborative localisation most effective, or how to tune systems to make it as accurate as possible. Most task allocation algorithms do not consider localisation as part of the allocation process. If task allocation algorithms limited inter-robot distance, collaborative localisation can be performed during task completion. Such an algorithm could equally be used to ensure robots are within communication distance, and to quickly detect when a robot fails. While some algorithms for this exist in literature, they provide a weak guarantee of inter-robot distance, which is undesirable when applied to real robots. The aim of this thesis is to improve upon task allocation algorithms by increasing task allocation speed and efficiency, and supporting robot tool changes. Collaborative localisation parameters are analysed, and a task allocation algorithm that enables collaborative localisation on real robots is developed. This thesis includes a compendium of journal articles written by the author. The four articles forming the main body of the thesis discuss the multi-robot task allocation and localisation research during the author’s candidature. Two appendices are included, representing conference articles written by the author that directly relate to the thesis.Thesis (Ph.D.) -- University of Adelaide, School of Mechanical Engineering, 201

Reliable cost-optimal deployment of wireless sensor networks

Wireless Sensor Networks (WSNs) technology is currently considered one of the key technologies for realizing the Internet of Things (IoT). Many of the important WSNs applications are critical in nature such that the failure of the WSN to carry out its required tasks can have serious detrimental effects. Consequently, guaranteeing that the WSN functions satisfactorily during its intended mission time, i.e. the WSN is reliable, is one of the fundamental requirements of the network deployment strategy. Achieving this requirement at a minimum deployment cost is particularly important for critical applications in which deployed SNs are equipped with expensive hardware. However, WSN reliability, defined in the traditional sense, especially in conjunction with minimizing the deployment cost, has not been considered as a deployment requirement in existing WSN deployment algorithms to the best of our knowledge. Addressing this major limitation is the central focus of this dissertation. We define the reliable cost-optimal WSN deployment as the one that has minimum deployment cost with a reliability level that meets or exceeds a minimum level specified by the targeted application. We coin the problem of finding such deployments, for a given set of application-specific parameters, the Minimum-Cost Reliability-Constrained Sensor Node Deployment Problem (MCRC-SDP). To accomplish the aim of the dissertation, we propose a novel WSN reliability metric which adopts a more accurate SN model than the model used in the existing metrics. The proposed reliability metric is used to formulate the MCRC-SDP as a constrained combinatorial optimization problem which we prove to be NP-Complete. Two heuristic WSN deployment optimization algorithms are then developed to find high quality solutions for the MCRC-SDP. Finally, we investigate the practical realization of the techniques that we developed as solutions of the MCRC-SDP. For this purpose, we discuss why existing WSN Topology Control Protocols (TCPs) are not suitable for managing such reliable cost-optimal deployments. Accordingly, we propose a practical TCP that is suitable for managing the sleep/active cycles of the redundant SNs in such deployments. Experimental results suggest that the proposed TCP\u27s overhead and network Time To Repair (TTR) are relatively low which demonstrates the applicability of our proposed deployment solution in practice

Stochastic Programming Models For Electric Vehicles’ Operation: Network Design And Routing Strategies

Logistic and transportation (L&T) activities become a significant contributor to social and economic advances throughout the modern world Road L&T activities are responsible for a large percentage of CO2 emissions, with more than 24% of the total emission, which mostly caused by fossil fuel vehicles. Researchers, governments, and automotive companies put extensive effort to incorporate new solutions and innovations into the L&T system. As a result, Electric Vehicles (EVs) are introduced and universally accepted as one of the solutions to environmental issues. Subsequently, L&T companies are encouraged to adopt fleets of EVs. Integrating the EVs into the logistic and transportation systems introduces new challenges from strategic, planning, and operational perspectives. At the strategical level, one of the main challenges to be addressed to expand the EV charging infrastructures is the location of charging stations. Due to the longer charging time in EVs compared to the conventional vehicles, the parking locations can be considered as the candidate locations for installing charging stations. Another essential factor that should be considered in designing the Electric Vehicle Charging Station (EVCS) network is the size or capacity of charging stations. EV drivers\u27 arrival times in a community vary depending on various factors such as the purpose of the trip, time of the day, and day of the week. So, the capacity of stations and the number of chargers significantly affect the accessibility and utilization of charging stations. Also, the EVCSs can be equipped by distinct types of chargers, which are different in terms of installation cost, charging time, and charging price. City planners and EVCS owners can make low-risk and high-utilization investment decisions by considering EV users charging pattern and their willingness to pay for different charger types. At the operational level, managing a fleet of electric vehicles can offer several incentives to the L&T companies. EVs can be equipped with autonomous driving technologies to facilitate online decision making, on-board computation, and connectivity. Energy-efficient routing decisions for a fleet of autonomous electric vehicles (AEV) can significantly improve the asset utilization and vehicles’ battery life. However, employing AEVs also comes with new challenges. Two of the main operational challenges for AEVs in transport applications is their limited range and the availability of charging stations. Effective routing strategies for an AEV fleet require solving the vehicle routing problem (VRP) while considering additional constraints related to the limited range and number of charging stations. In this dissertation, we develop models and algorithms to address the challenges in integrating the EVs into the logistic and transportation systems

QoS multicast routing protocol oriented to cognitive network using competitive coevolutionary algorithm

The human intervention in the network management and maintenance should be reduced to alleviate the ever-increasing spatial and temporal complexity. By mimicking the cognitive behaviors of human being, the cognitive network improves the scalability, self-adaptation, self-organization, and self-protection in the network. To implement the cognitive network, the cognitive behaviors for the network nodes need to be carefully designed. Quality of service (QoS) multicast is an important network problem. Therefore, it is appealing to develop an effective QoS multicast routing protocol oriented to cognitive network. In this paper, we design the cognitive behaviors summarized in the cognitive science for the network nodes. Based on the cognitive behaviors, we propose a QoS multicast routing protocol oriented to cognitive network, named as CogMRT. It is a distributed protocol where each node only maintains local information. The routing search is in a hop by hop way. Inspired by the small-world phenomenon, the cognitive behaviors help to accumulate the experiential route information. Since the QoS multicast routing is a typical combinatorial optimization problem and it is proved to be NP-Complete, we have applied the competitive coevolutionary algorithm (CCA) for the multicast tree construction. The CCA adopts novel encoding method and genetic operations which leverage the characteristics of the problem. We implement and evaluate CogMRT and other two promising alternative protocols in NS2 platform. The results show that CogMRT has remarkable advantages over the counterpart traditional protocols by exploiting the cognitive favors

A Grey Wolf Optimization-Based Clustering Approach for Energy Efficiency in Wireless Sensor Networks

In the realm of Wireless Sensor Networks, the longevity of a sensor node's battery is pivotal, especially since these nodes are often deployed in locations where battery replacement is not feasible. Heterogeneous networks introduce additional challenges due to varying buffer capacities among nodes, necessitating timely data transmission to prevent loss from buffer overflows. Despite numerous attempts to address these issues, previous solutions have been deficient in significant respects. Our innovative strategy employs Grey Wolf Optimization for Cluster Head selection within heterogeneous networks, aiming to concurrently optimise energy efficiency and buffer capacity. We conducted comprehensive simulations using Network Simulator 2, with results analysed in MATLAB, focusing on metrics such as energy depletion rates, remaining energy, node-to-node distance, node count, packet delivery, and average energy in the cluster head selection process. Our approach was benchmarked against leading protocols like LEACH and PEGASIS, considering five key performance indicators: energy usage, network lifespan, the survival rate of nodes over time, data throughput, and remaining network energy. The simulations demonstrate that our Grey Wolf Optimisation method outperforms conventional protocols, showing a 9% reduction in energy usage, a 12% increase in node longevity, a 9.8% improvement in data packet delivery, and a 12.2% boost in data throughput