Energy efficient data transmission using multiobjective improved remora optimization algorithm for wireless sensor network with mobile sink

A wireless sensor network (WSN) is a collection of nodes fitted with small sensors and transceiver elements. Energy consumption, data loss, and transmission delays are the major drawback of creating mobile sinks. For instance, battery life and data latency might result in node isolation, which breaks the link between nodes in the network. These issues have been avoided by means of mobile data sinks, which move between nodes with connection issues. Therefore, energy aware multiobjective improved remora optimization algorithm and multiobjective ant colony optimization (EA-MIROA-MACO) is proposed in this research to improve the WSN’s energy efficiency by eliminating node isolation issue. MIRO is utilized to pick the optimal cluster heads (CHs), while multiobjective ant colony optimization (MACO) is employed to find the path through the CHs. The EA-MIROA-MACO aims to optimize energy consumption in nodes and enhance data transmission within a WSN. The analysis of EA-MIROA-MACO’s performance is conducted by considering the number of alive along with dead nodes, average residual energy, and network lifespan. The EA-MIROA-MACO is compared with traditional approaches such as mobile sink and fuzzy based relay node routing (MSFBRR) protocol as well as hybrid neural network (HNN). The EA-MIROA-MACO demonstrates a higher number of alive nodes, specifically 192, over the MSFBRR and HNN for 2,000 rounds

A Combined Dual Leader and Relay Node Selection for Markov Cluster Based WSN Routing Protocol

The major challenge in Wireless Sensor Networks (WSNs) is to increase the node’s lifespan and decrease energy utilization. To avoid this issue, many Clustering Routing Protocols (CRPs) have been developed, where Cluster Head (CH) in each cluster accumulates the data from each other node and transfers it to the sink through Relay Nodes (RNs). But both CHs and RNs dissipate more energy to aggregate and transfer data. As a result, it is vital to choose the appropriate CHs and RNs concurrently to reduce energy utilization. Hence, this article proposes a Weighted Markov Clustering with Dual Leader and Relay node Selection based CRP (WMCL-DLRS-CRP) in WSNs. This protocol aims to lessen energy dissipation during inter- and intra-cluster communication. Initially, a Markov Clustering (MCL) algorithm is applied by the sink to create nodes into clusters based on a threshold distance. Then, a dual leader selection scheme is proposed to elect dual CHs in each cluster according to the node weighting factor that considers the node’s remaining energy, the distance between CHs and sink, the distance among all nodes, and abundance. Also, an RN selection scheme is proposed to choose the appropriate RNs based on a new Predicted Transmission Rate (PTR) factor. Moreover, the elected RNs transfer the data from the CHs to the sink, resulting in a tradeoff between the node’s energy utilization and lifetime. At last, extensive simulations illustrate that the WMCL-DLRS-CRP achieves better network performance compared to the existing protocols

Efficient Clustering Protocol Based on Stochastic Matrix & MCL and Data Routing for Mobile Wireless Sensors Network

In this paper, we have already presented a new approach for data routing dedicated to mobile Wireless Sensors Network (WSN) based on clustering. The proposed method is based on stochastic matrix and on the Markov Chain Cluster (MCL) algorithm to organize a large number of mobile sensors into clusters without defining the required clusters number in advance. It is based on mobile sensors connectivity to determinethe optimal number of clusters and to form compact and well separated clusters. Our proposed approach is a distributed method using nodes locations, degrees and theirs residual energies during the cluster head election. Simulation results showed that the proposed approach reduced the loss packets rate by 80%, the energy consumption by 30% and improved the data delivery rate by 70% compared to LEACH-M protocol. Moreover, it outperforms the E-MBC protocol and reduced the average energy consumption and loss packets rate by 60%; as well as it improved the success packets delivery rate by 40%

Structure and topology of transcriptional regulatory networks and their applications in bio-inspired networking

Biological networks carry out vital functions necessary for sustenance despite environmental adversities. Transcriptional Regulatory Network (TRN) is one such biological network that is formed due to the interaction between proteins, called Transcription Factors (TFs), and segments of DNA, called genes. TRNs are known to exhibit functional robustness in the face of perturbation or mutation: a property that is proven to be a result of its underlying network topology. In this thesis, we first propose a three-tier topological characterization of TRN to analyze the interplay between the significant graph-theoretic properties of TRNs such as scale-free out-degree distribution, low graph density, small world property and the abundance of subgraphs called motifs. Specifically, we pinpoint the role of a certain three-node motif, called Feed Forward Loop (FFL) motif in topological robustness as well as information spread in TRNs. With the understanding of the TRN topology, we explore its potential use in design of fault-tolerant communication topologies. To this end, we first propose an edge rewiring mechanism that remedies the vulnerability of TRNs to the failure of well-connected nodes, called hubs, while preserving its other significant graph-theoretic properties. We apply the rewired TRN topologies in the design of wireless sensor networks that are less vulnerable to targeted node failure. Similarly, we apply the TRN topology to address the issues of robustness and energy-efficiency in the following networking paradigms: robust yet energy-efficient delay tolerant network for post disaster scenarios, energy-efficient data-collection framework for smart city applications and a data transfer framework deployed over a fog computing platform for collaborative sensing --Abstract, page iii

Biologically inspired, self organizing communication networks.

PhDThe problem of energy-efficient, reliable, accurate and self-organized target tracking in Wireless Sensor Networks (WSNs) is considered for sensor nodes with limited physical resources and abrupt manoeuvring mobile targets. A biologically inspired, adaptive multi-sensor scheme is proposed for collaborative Single Target Tracking (STT) and Multi-Target Tracking (MTT). Behavioural data obtained while tracking the targets including the targets’ previous locations is recorded as metadata to compute the target sampling interval, target importance and local monitoring interval so that tracking continuity and energy-efficiency are improved. The subsequent sensor groups that track the targets are selected proactively according to the information associated with the predicted target location probability such that the overall tracking performance is optimized or nearly-optimized. One sensor node from each of the selected groups is elected as a main node for management operations so that energy efficiency and load balancing are improved. A decision algorithm is proposed to allow the “conflict” nodes that are located in the sensing areas of more than one target at the same time to decide their preferred target according to the target importance and the distance to the target. A tracking recovery mechanism is developed to provide the tracking reliability in the event of target loss. The problem of task mapping and scheduling in WSNs is also considered. A Biological Independent Task Allocation (BITA) algorithm and a Biological Task Mapping and Scheduling (BTMS) algorithm are developed to execute an application using a group of sensor nodes. BITA, BTMS and the functional specialization of the sensor groups in target tracking are all inspired from biological behaviours of differentiation in zygote formation. Simulation results show that compared with other well-known schemes, the proposed tracking, task mapping and scheduling schemes can provide a significant improvement in energy-efficiency and computational time, whilst maintaining acceptable accuracy and seamless tracking, even with abrupt manoeuvring targets.Queen Mary university of London full Scholarshi

Network coding for reliable wireless sensor networks

Wireless sensor networks are used in many applications and are now a key element in the increasingly growing Internet of Things. These networks are composed of small nodes including wireless communication modules, and in most of the cases are able to autonomously con gure themselves into networks, to ensure sensed data delivery. As more and more sensor nodes and networks join the Internet of Things, collaboration between geographically distributed systems are expected. Peer to peer overlay networks can assist in the federation of these systems, for them to collaborate. Since participating peers/proxies contribute to storage and processing, there is no burden on speci c servers and bandwidth bottlenecks are avoided. Network coding can be used to improve the performance of wireless sensor networks. The idea is for data from multiple links to be combined at intermediate encoding nodes, before further transmission. This technique proved to have a lot of potential in a wide range of applications. In the particular case of sensor networks, network coding based protocols and algorithms try to achieve a balance between low packet error rate and energy consumption. For network coding based constrained networks to be federated using peer to peer overlays, it is necessary to enable the storage of encoding vectors and coded data by such distributed storage systems. Packets can arrive to the overlay through any gateway/proxy (peers in the overlay), and lost packets can be recovered by the overlay (or client) using original and coded data that has been stored. The decoding process requires a decoding service at the overlay network. Such architecture, which is the focus of this thesis, will allow constrained networks to reduce packet error rate in an energy e cient way, while bene ting from an e ective distributed storage solution for their federation. This will serve as a basis for the proposal of mathematical models and algorithms that determine the most e ective routing trees, for packet forwarding toward sink/gateway nodes, and best amount and placement of encoding nodes.As redes de sensores sem fios são usadas em muitas aplicações e são hoje consideradas um elemento-chave para o desenvolvimento da Internet das Coisas. Compostas por nós de pequena dimensão que incorporam módulos de comunicação sem fios, grande parte destas redes possuem a capacidade de se configurarem de forma autónoma, formando sistemas em rede para garantir a entrega dos dados recolhidos. (…

Energy efficient in cluster head and relay node selection for wireless sensor networks

Wireless Sensor Networks (WSNs) are defined as networks of nodes that work in a cooperative way to sense and control the surrounding environment. However, nodes contain limited energy which is the key limiting factor of the sensor network operation. In WSN architecture, the nodes are typically grouped into clusters where one node from each cluster is selected as the Cluster Head (CH) and relays utilisation to minimise energy consumption. Currently, the selection of CH based on a different combination of input variables. Example of these variables includes residual energy, communication cost, node density, mobility, cluster size and many others. Improper selection of sensor node (i.e. weak signal strength) as CH can cause an increase in energy consumption. Additionally, a direct transmission in dual-hop communication between sensor nodes (e.g. CH) with the base station (BS) uses high energy consumption. A proper selection of the relay node can assist in communication while minimising energy consumption. Therefore, the research aim is to prolong the network lifetime (i.e. reduce energy consumption) by improving the selection of CHs and relay nodes through a new combination of input variables and distance threshold approach. In CH selection, the Received Signal Strength Indicator (RSSI) scheme, residual energy, and centrality variable were proposed. Fuzzy logic was utilized in selecting the appropriate CHs based on these variables in the MATLAB. In relay node selection, the selection is based on the distance threshold according to the nearest distance with the BS. The selection of the optimal number of relay nodes is performed using K-Optimal and K-Means techniques. This ensures that all CHs are connected to at least one corresponding relay node (i.e. a 2-tier network) to execute the routing process and send the data to BS. To evaluate the proposal, the performance of Multi-Tier Protocol (MAP) and Stable Election Protocol (SEP) was compared based on 100, 200, and 800 nodes with 1 J and random energy. The simulation results showed that our proposed approach, refer to as Energy Efficient Cluster Heads and Relay Nodes (EECR) selection approach, extended the network lifetime of the wireless sensor network by 43% and 33% longer than SEP and MAP, respectively. This thesis concluded that with effective combinations of variables for CHs and relay nodes selection in static environment for data routing, EECR can effectively improve the energy efficiency of WSNs

Planning the deployment of fault-tolerant wireless sensor networks

Since Wireless Sensor Networks (WSNs) are subject to failures, fault-tolerance becomes an important requirement for many WSN applications. Fault-tolerance can be enabled in different areas of WSN design and operation, including the Medium Access Control (MAC) layer and the initial topology design. To be robust to failures, a MAC protocol must be able to adapt to traffic fluctuations and topology dynamics. We design ER-MAC that can switch from energy-efficient operation in normal monitoring to reliable and fast delivery for emergency monitoring, and vice versa. It also can prioritise high priority packets and guarantee fair packet deliveries from all sensor nodes. Topology design supports fault-tolerance by ensuring that there are alternative acceptable routes to data sinks when failures occur. We provide solutions for four topology planning problems: Additional Relay Placement (ARP), Additional Backup Placement (ABP), Multiple Sink Placement (MSP), and Multiple Sink and Relay Placement (MSRP). Our solutions use a local search technique based on Greedy Randomized Adaptive Search Procedures (GRASP). GRASP-ARP deploys relays for (k,l)-sink-connectivity, where each sensor node must have k vertex-disjoint paths of length ≤ l. To count how many disjoint paths a node has, we propose Counting-Paths. GRASP-ABP deploys fewer relays than GRASP-ARP by focusing only on the most important nodes – those whose failure has the worst effect. To identify such nodes, we define Length-constrained Connectivity and Rerouting Centrality (l-CRC). Greedy-MSP and GRASP-MSP place minimal cost sinks to ensure that each sensor node in the network is double-covered, i.e. has two length-bounded paths to two sinks. Greedy-MSRP and GRASP-MSRP deploy sinks and relays with minimal cost to make the network double-covered and non-critical, i.e. all sensor nodes must have length-bounded alternative paths to sinks when an arbitrary sensor node fails. We then evaluate the fault-tolerance of each topology in data gathering simulations using ER-MAC

Security risk assessment in cloud computing domains

Cyber security is one of the primary concerns persistent across any computing platform. While addressing the apprehensions about security risks, an infinite amount of resources cannot be invested in mitigation measures since organizations operate under budgetary constraints. Therefore the task of performing security risk assessment is imperative to designing optimal mitigation measures, as it provides insight about the strengths and weaknesses of different assets affiliated to a computing platform. The objective of the research presented in this dissertation is to improve upon existing risk assessment frameworks and guidelines associated to different key assets of Cloud computing domains - infrastructure, applications, and users. The dissertation presents various informal approaches of performing security risk assessment which will help to identify the security risks confronted by the aforementioned assets, and utilize the results to carry out the required cost-benefit tradeoff analyses. This will be beneficial to organizations by aiding them in better comprehending the security risks their assets are exposed to and thereafter secure them by designing cost-optimal mitigation measures --Abstract, page iv