A Survey on Energy Efficient Network Coding for Multi-hop Routing in Wireless Sensor Networks

AbstractNetwork coding consists of intelligently aggregating data packets by means of binary or linear combinations. Recently, network coding has been proposed as a complementary solution for energy efficient multi-hop routing in Wireless Sensor Networks (WSNs). This is because network coding, through the aggregation of packets, considerably reduces the number of transmissions throughout the network. Although numerous network coding techniques for energy efficient routing have been developed in the literature, not much is known about a single survey article reporting on such energy efficient network coding within multi-hop WSNs. As a result, this paper addresses this gap by first classifying and discussing the recent developed energy efficient network coding techniques. The paper then identifies and explains open research opportunities based on analysis of merits of such techniques. This survey aims at providing the reader with a brief and concise idea on the current state-of-art research on network coding mainly focusing on its applications for energy efficient WSNs

Survey on Data-Centric based Routing Protocols for Wireless Sensor Networks

The great concern for energy that grew with the technological advances in the field of networks and especially in sensor network has triggered various approaches and protocols that relate to sensor networks. In this context, the routing protocols were of great interest. The aim of the present paper is to discuss routing protocols for sensor networks. This paper will focus mainly on the discussion of the data-centric approach (COUGAR, rumor, SPIN, flooding and Gossiping), while shedding light on the other approaches occasionally. The functions of the nodes will be discussed as well. The methodology selected for this paper is based on a close description and discussion of the protocol. As a conclusion, open research questions and limitations are proposed to the reader at the end of this paper

Improving Temporal Coverage of an Energy-Efficient Data Extraction Algorithm for Environmental Monitoring Using Wireless Sensor Networks

Collecting raw data from a wireless sensor network for environmental monitoring applications can be a difficult task due to the high energy consumption involved. This is especially difficult when the application requires specialized sensors that have very high energy consumption, e.g. hydrological sensors for monitoring marine environments. This paper introduces a technique for reducing energy consumption by minimizing sensor sampling operations. In addition, we illustrate how a randomized algorithm can be used to improve temporal coverage such that the time between the occurrence of an event and its detection can be minimized. We evaluate our approach using real data collected from a sensor network deployment on the Great Barrier Reef

Deep learning for internet of underwater things and ocean data analytics

The Internet of Underwater Things (IoUT) is an emerging technological ecosystem developed for connecting objects in maritime and underwater environments. IoUT technologies are empowered by an extreme number of deployed sensors and actuators. In this thesis, multiple IoUT sensory data are augmented with machine intelligence for forecasting purposes

Efficient energy management in ultra-dense wireless networks

The increase in demand for more network capacity has led to the evolution of wireless networks from being largely Heterogeneous (Het-Nets) to the now existing Ultra-dense (UDNs). In UDNs, small cells are densely deployed with the goal of shortening the physical distance between the base stations (BSs) and the UEs, so as to support more user equipment (UEs) at peak times while ensuring high data rates. Compared to Het-Nets, Ultra-dense networks (UDNs) have many advantages. These include, more network capacity, higher flexibility to routine configurations, and more suitability to achieve load-balancing, hence, fewer blind spots as well as lower call blocking probability. It should be noted that, in practice, due to the high density of deployed small cells in Ultra-Dense Networks, a number of issues, or rather concerns, come with this evolution from Het-Nets. Among these issues include problems with efficient radio resource management, user cell association, inter- and intra-cell interference management and, last but not least, efficient energy consumption. Some of these issues which impact the overall network efficiency are largely due to the use of obsolete algorithms, especially those whose resource allocation is based solely on received signal power (RSSP). In this paper, the focus is solely on the efficient energy management dilemma and how to optimally reduce the overall network energy consumption. Through an extensive literature review, a detailed report into the growing concern of efficient energy management in UDNs is provided in Chapter 2. The literature review report highlights the classification as well as the evolution of some of the Mobile Wireless Technologies and Mobile Wireless Networks in general. The literature review report provides reasons as to why the energy consumption issue has become a very serious concern in UltraDense networks as well as the various techniques and measures taken to mitigate this. It is shown that, due to the increasing Mobile Wireless Systems’ carbon footprint which carries serious negative environmental impact, and the general need to lower operating costs by the network operators, the management of energy consumption increases in priority. By using the architecture of a Fourth Generation Long Term Evolution (4G-LTE) UltraDense Network, the report further shows that more than 65% of the overall energy consumption is by the access network and base stations in particular. This phenomenon explains why most attention in energy efficiency management in UDNs is largely centred on reducing the energy consumption of the deployed base stations more than any other network components like the data servers or backhauling features used. Furthermore, the report also provides detailed information on the methods/techniques, their classification, implementation, as well as a critical analysis of the said implementations in literature. This study proposes a sub-optimal algorithm and Distributed Cell Resource Allocation with a Base Station On/Off scheme that aims at reducing the overall base station power consumption in UDNs, while ensuring that the overall Quality of Service (QoS) for each User Equipment (UE) as specified in its service class is met. The modeling of the system model used and hence formulation of the Network Energy Efficiency (NEE) optimization problem is done viii using stochastic geometry. The network model comprises both evolved Node B (eNB) type macro and small cells operating on different frequency bands as well as taking into account factors that impact NEE such as UE mobility, UE spatial distribution and small cells spatial distribution. The channel model takes into account signal interference from all base stations, path loss, fading, log normal shadowing, modulation and coding schemes used on each UE’s communication channels when computing throughout. The power consumption model used takes into account both static (site cooling, circuit power) and active (transmission or load based) base station power consumption. The formulation of the NEE optimization problem takes into consideration the user’s Quality-of-service (QoS), inter-cell interference, as well as each user’s spectral efficiency and coverage/success probability. The formulated NEE optimization problem is of type Nondeterministic Polynomial time (NP)-hard, due to the user-cell association. The proposed solution to the formulated optimization problem makes use of constraint relaxation to transform the NP-hard problem into a more solvable, convex and linear optimization one. This, combined with Lagrangian dual decomposition, is used to create a distributed solution. After cellassociation and resource allocation phases, the proposed solution in order to further reduce power consumption performs Cell On/Off. Then, by using the computer simulation tools/environments, the “Distributed Resource Allocation with Cell On/Off” scheme’s performance, in comparison to four other resource allocation schemes, is analysed and evaluated given a number of different network scenarios. Finally, the statistical and mathematical results generated through the simulations indicate that the proposed scheme is the closest in NEE performance to the Exhaustive Search algorithm, and hence superior to the other sub-optimal algorithms it is compared to

Radio network management in cognitive LTE-Femtocell Systems

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London.There is a strong uptake of femtocell deployment as small cell application platforms in the upcoming LTE networks. In such two-tier networks of LTEfemtocell base stations, a large portion of the assigned spectrum is used sporadically leading to underutilisation of valuable frequency resources. Novel spectrum access techniques are necessary to solve these current spectrum inefficiency problems. Therefore, spectrum management solutions should have the features to improve spectrum access in both temporal and spatial manner. Cognitive Radio (CR) with the Dynamic Spectrum Access (DSA) is considered to be the key technology in this research in order to increase the spectrum efficiency. This is an effective solution to allow a group of Secondary Users (SUs) to share the radio spectrum initially allocated to the Primary User (PUs) at no interference. The core aim of this thesis is to develop new cognitive LTE-femtocell systems that offer a 4G vision, to facilitate the radio network management in order to increase the network capacity and further improve spectrum access probabilities. In this thesis, a new spectrum management model for cognitive radio networks is considered to enable a seamless integration of multi-access technology with existing networks. This involves the design of efficient resource allocation algorithms that are able to respond to the rapid changes in the dynamic wireless environment and primary users activities. Throughout this thesis a variety of network upgraded functions are developed using application simulation scenarios. Therefore, the proposed algorithms, mechanisms, methods, and system models are not restricted in the considered networks, but rather have a wider applicability to be used in other technologies. This thesis mainly investigates three aspects of research issues relating to the efficient management of cognitive networks: First, novel spectrum resource management modules are proposed to maximise the spectrum access by rapidly detecting the available transmission opportunities. Secondly, a developed pilot power controlling algorithm is introduced to minimise the power consumption by considering mobile position and application requirements. Also, there is investigation on the impact of deploying different numbers of femtocell base stations in LTE domain to identify the optimum cell size for future networks. Finally, a novel call admission control mechanism for mobility management is proposed to support seamless handover between LTE and femtocell domains. This is performed by assigning high speed mobile users to the LTE system to avoid unnecessary handovers. The proposed solutions were examined by simulation and numerical analysis to show the strength of cognitive femtocell deployment for the required applications. The results show that the new system design based on cognitive radio configuration enable an efficient resource management in terms of spectrum allocation, adaptive pilot power control, and mobile handover. The proposed framework and algorithms offer a novel spectrum management for self organised LTE-femtocell architecture. Eventually, this research shows that certain architectures fulfilling spectrum management requirements are implementable in practice and display good performance in dynamic wireless environments which recommends the consideration of CR systems in LTE and femtocell networks