Propose effective routing method for mobile sink in wireless sensor network

Wireless sensor network is one of the popular technologies used for maximizing the lifetime of network and to enhance the data collection process and energy efficiency by mobility. So, this work was proposed and focused on sink mobility which plays a key role in data collection process. The main challenge task was to discover the route in the active network. We have proposed an opportunistic algorithm in this paper with mobile sink to discover the ideal path starting the source to destination node. The proposed system has focused on a sensor field to sense and to report on building during fires where the sensors could be destroyed. The proposed system was evaluated through simulation and compared with existing algorithms (Genetic algorithm, multi-layer perceptron neural network). The performance which showed data delivery can be increased by up to 95%

Two Timescale Convergent Q-learning for Sleep--Scheduling in Wireless Sensor Networks

In this paper, we consider an intrusion detection application for Wireless Sensor Networks (WSNs). We study the problem of scheduling the sleep times of the individual sensors to maximize the network lifetime while keeping the tracking error to a minimum. We formulate this problem as a partially-observable Markov decision process (POMDP) with continuous state-action spaces, in a manner similar to (Fuemmeler and Veeravalli [2008]). However, unlike their formulation, we consider infinite horizon discounted and average cost objectives as performance criteria. For each criterion, we propose a convergent on-policy Q-learning algorithm that operates on two timescales, while employing function approximation to handle the curse of dimensionality associated with the underlying POMDP. Our proposed algorithm incorporates a policy gradient update using a one-simulation simultaneous perturbation stochastic approximation (SPSA) estimate on the faster timescale, while the Q-value parameter (arising from a linear function approximation for the Q-values) is updated in an on-policy temporal difference (TD) algorithm-like fashion on the slower timescale. The feature selection scheme employed in each of our algorithms manages the energy and tracking components in a manner that assists the search for the optimal sleep-scheduling policy. For the sake of comparison, in both discounted and average settings, we also develop a function approximation analogue of the Q-learning algorithm. This algorithm, unlike the two-timescale variant, does not possess theoretical convergence guarantees. Finally, we also adapt our algorithms to include a stochastic iterative estimation scheme for the intruder's mobility model. Our simulation results on a 2-dimensional network setting suggest that our algorithms result in better tracking accuracy at the cost of only a few additional sensors, in comparison to a recent prior work

Application priority framework for fixed mobile converged communication networks

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The current prospects in wired and wireless access networks, it is becoming increasingly important to address potential convergence in order to offer integrated broadband services. These systems will need to offer higher data transmission capacities and long battery life, which is the catalyst for an everincreasing variety of air interface technologies targeting local area to wide area connectivity. Current integrated industrial networks do not offer application aware context delivery and enhanced services for optimised networks. Application aware services provide value-added functionality to business applications by capturing, integrating, and consolidating intelligence about users and their endpoint devices from various points in the network. This thesis mainly intends to resolve the issues related to ubiquitous application aware service, fair allocation of radio access, reduced energy consumption and improved capacity. A technique that measures and evaluates the data rate demand to reduce application response time and queuing delay for multi radio interfaces is proposed. The technique overcomes the challenges of network integration, requiring no user intervention, saving battery life and selecting the radio access connection for the application requested by the end user. This study is split in two parts. The first contribution identifies some constraints of the services towards the application layer in terms of e.g. data rate and signal strength. The objectives are achieved by application controlled handover (ACH) mechanism in order to maintain acceptable data rate for real-time application services. It also looks into the impact of the radio link on the application and identifies elements and parameters like wireless link quality and handover that will influence the application type. It also identifies some enhanced traditional mechanisms such as distance controlled multihop and mesh topology required in order to support energy efficient multimedia applications. The second contribution unfolds an intelligent application priority assignment mechanism (IAPAM) for medical applications using wireless sensor networks. IAPAM proposes and evaluates a technique based on prioritising multiple virtual queues for the critical nature of medical data to improve instant transmission. Various mobility patterns (directed, controlled and random waypoint) has been investigated and compared by simulating IAPAM enabled mobile BWSN. The following topics have been studied, modelled, simulated and discussed in this thesis: 1. Application Controlled Handover (ACH) for multi radios over fibre 2. Power Controlled Scheme for mesh multi radios over fibre using ACH 3. IAPAM for Biomedical Wireless Sensor Networks (BWSN) and impact of mobility over IAPAM enabled BWSN. Extensive simulation studies are performed to analyze and to evaluate the proposed techniques. Simulation results demonstrate significant improvements in multi radios over fibre performance in terms of application response delay and power consumption by upto 75% and 15 % respectively, reduction in traffic loss by upto 53% and reduction in delay for real time application by more than 25% in some cases

Unmanned Aerial Vehicle (UAV)-Enabled Wireless Communications and Networking

The emerging massive density of human-held and machine-type nodes implies larger traffic deviatiolns in the future than we are facing today. In the future, the network will be characterized by a high degree of flexibility, allowing it to adapt smoothly, autonomously, and efficiently to the quickly changing traffic demands both in time and space. This flexibility cannot be achieved when the network’s infrastructure remains static. To this end, the topic of UAVs (unmanned aerial vehicles) have enabled wireless communications, and networking has received increased attention. As mentioned above, the network must serve a massive density of nodes that can be either human-held (user devices) or machine-type nodes (sensors). If we wish to properly serve these nodes and optimize their data, a proper wireless connection is fundamental. This can be achieved by using UAV-enabled communication and networks. This Special Issue addresses the many existing issues that still exist to allow UAV-enabled wireless communications and networking to be properly rolled out

A survey of machine learning techniques applied to self organizing cellular networks

In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future

Prediction and optimization techniques for performance enhancement of vehicular ad-hoc networks

Imperial Users onl