A short review on sleep scheduling mechanism in wireless sensor networks

Sleep scheduling, also known as duty cycling, which turn- s sensor nodes on and off in the necessary time, is a common train of thought to save energy. Sleep scheduling has become a significant mech- anism to prolong the lifetime of WSNs and many related methods have been proposed in recent years, which have diverse emphases and appli- cation areas. This paper classifies those methods in different taxonomies and provides a deep insight into them

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Design of energy efficient protocols-based optimisation algorithms for IoT networks

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe increased globalisation of information and communication technologies has transformed the world into the internet of things (IoT), which is accomplished within the resources of wireless sensor networks (WSNs). Therefore, the future IoT networks will consist of high density of connected nodes that suffer from resource limitation, especially the energy one, and distribute randomly in a harsh and large-scale areas. Accordingly, the contributions in this thesis are focused on the development of energy efficient design protocols based on optimisation algorithms, with consideration of the resource limitations, adaptability, scalability, node density and random distribution of node density in the geographical area. One MAC protocol and two routing protocols, with both a static and mobile sink, are proposed. The first proposed protocol is an energy efficient hybrid MAC protocol with dynamic sleep/wake-up extension to the IEEE 802.15.4 MAC, namely, HSW-802.15.4. The model automates the network by enabling it to work exibly in low and high-density networks with a lower number of collisions. A frame structure that offers an enhanced exploitation for the TDMA time slots (TDMAslots) is provided. To implement these enhanced slots exploitation, this hybrid protocol rst schedules the TDMAsslots, and then allocates each slot to a group of devices. A three-dimensional Markov chain is developed to display the proposed model in a theoretical manner. Simulation results show an enhancement in the energy conservation by 40% - 60% in comparison to the IEEE 802.15.4 MAC protocol. Secondly, an efficient centralised clustering-based whale optimisation algorithm (CC- WOA) is suggested, which employs the concept of software de ned network (SDN) in its mechanism. The cluster formulation process in this algorithm considers the random di- versi cation of node density in the geographical area and involves both sensor resource restrictions and the node density in the tness function. The results offer an efficient con- servation of energy in comparison to other protocols. Another clustering algorithm, called centralised load balancing clustering algorithm (C-LBCA), is also developed that uses par- ticle swarm optimisation (PSO) and presents robust load-balancing for data gathering in IoT. However, in large scale networks, the nodes, especially the cluster heads (CHs), suffer from a higher energy exhaustion. Hence, in this thesis, a centralised load balanced and scheduling protocol is proposed utilising optimisation algorithms for large scale IoT net- works, named, optimised mobile sink based load balancing (OMS-LB). This model connects the impact of the Optimal Path for the MS (MSOpath) determination and the adjustable set of data aggregation points (SDG) with the cluster formulation process to de ne an op- timised routing protocol suitable for large scale networks. Simulation results display an improvement in the network lifespan of up to 54% over the other approaches

QIBMRMN: Design of a Q-Learning based Iterative sleep-scheduling & hybrid Bioinspired Multipath Routing model for Multimedia Networks

Multimedia networks utilize low-power scalar nodes to modify wakeup cycles of high-performance multimedia nodes, which assists in optimizing the power-to-performance ratios. A wide variety of machine learning models are proposed by researchers to perform this task, and most of them are either highly complex, or showcase low-levels of efficiency when applied to large-scale networks. To overcome these issues, this text proposes design of a Q-learning based iterative sleep-scheduling and fuses these schedules with an efficient hybrid bioinspired multipath routing model for large-scale multimedia network sets. The proposed model initially uses an iterative Q-Learning technique that analyzes energy consumption patterns of nodes, and incrementally modifies their sleep schedules. These sleep schedules are used by scalar nodes to efficiently wakeup multimedia nodes during adhoc communication requests. These communication requests are processed by a combination of Grey Wolf Optimizer (GWO) & Genetic Algorithm (GA) models, which assist in the identification of optimal paths. These paths are estimated via combined analysis of temporal throughput & packet delivery performance, with node-to-node distance & residual energy metrics. The GWO Model uses instantaneous node & network parameters, while the GA Model analyzes temporal metrics in order to identify optimal routing paths. Both these path sets are fused together via the Q-Learning mechanism, which assists in Iterative Adhoc Path Correction (IAPC), thereby improving the energy efficiency, while reducing communication delay via multipath analysis. Due to a fusion of these models, the proposed Q-Learning based Iterative sleep-scheduling & hybrid Bioinspired Multipath Routing model for Multimedia Networks (QIBMRMN) is able to reduce communication delay by 2.6%, reduce energy consumed during these communications by 14.0%, while improving throughput by 19.6% & packet delivery performance by 8.3% when compared with standard multimedia routing techniques

Enhancing SDN WISE with Slicing Over TSCH

[EN] IWSNs (Industrial Wireless Sensor Networks) have become the next step in the evolution of WSN (Wireless Sensor Networks) due to the nature and demands of modern industry. With this type of network, flexible and scalable architectures can be created that simultaneously support traffic sources with different characteristics. Due to the great diversity of application scenarios, there is a need to implement additional capabilities that can guarantee an adequate level of reliability and that can adapt to the dynamic behavior of the applications in use. The use of SDNs (Software Defined Networks) extends the possibilities of control over the network and enables its deployment at an industrial level. The signaling traffic exchanged between nodes and controller is heavy and must occupy the same channel as the data traffic. This difficulty can be overcome with the segmentation of the traffic into flows, and correct scheduling at the MAC (Medium Access Control) level, known as slices. This article proposes the integration in the SDN controller of a traffic manager, a routing process in charge of assigning different routes according to the different flows, as well as the introduction of the Time Slotted Channel Hopping (TSCH) Scheduler. In addition, the TSCH (Time Slotted Channel Hopping) is incorporated in the SDN-WISE framework (Software Defined Networking solution for Wireless Sensor Networks), and this protocol has been modified to send the TSCH schedule. These elements are jointly responsible for scheduling and segmenting the traffic that will be sent to the nodes through a single packet from the controller and its performance has been evaluated through simulation and a testbed. The results obtained show how flexibility, adaptability, and determinism increase thanks to the joint use of the routing process and the TSCH Scheduler, which makes it possible to create a slicing by flows, which have different quality of service requirements. This in turn helps guarantee their QoS characteristics, increase the PDR (Packet Delivery Ratio) for the flow with the highest priority, maintain the DMR (Deadline Miss Ratio), and increase the network lifetime.This work has been supported by the MCyU (Spanish Ministry of Science and Universities) under the project ATLAS (PGC2018-094151-B-I00), which is partially funded by AEI, FEDER and EU and has been possible thanks to the collaboration of the Instituto Tecnologico de Informatica (ITI) of Valencia.Orozco-Santos, F.; Sempere Paya, VM.; Albero Albero, T.; Silvestre-Blanes, J. (2021). Enhancing SDN WISE with Slicing Over TSCH. Sensors. 21(4):1-29. https://doi.org/10.3390/s21041075S12921

A survey on fault diagnosis in wireless sensor networks

Wireless sensor networks (WSNs) often consist of hundreds of sensor nodes that may be deployed in relatively harsh and complex environments. In views of hardware cost, sensor nodes always adopt relatively cheap chips, which makes these nodes become error-prone or faulty in the course of their operation. Natural factors and electromagnetic interference could also influence the performance of the WSNs. When sensor nodes become faulty, they may have died which means they cannot communicate with other members in the wireless network, they may be still alive but produce incorrect data, they may be unstable jumping between normal state and faulty state. To improve data quality, shorten response time, strengthen network security, and prolong network lifespan, many studies have focused on fault diagnosis. This survey paper classifies fault diagnosis methods in recent five years into three categories based on decision centers and key attributes of employed algorithms: centralized approaches, distributed approaches, and hybrid approaches. As all these studies have specific goals and limitations, this paper tries to compare them, lists their merits and limits, and propose potential research directions based on established methods and theories

A Centralised Routing Protocol with a Scheduled Mobile Sink-Based AI for Large Scale I-IoT

Extensive efforts have been undertaken to enhance thecentralisedmonitoring-basedsoftwaredefinednetwork(SDN) concept of the large-scale Intelligent-Internet of Things (I-IoT). Furthermore, the number of IoT devices in vast environments is increasing and a scalable routing protocol has therefore become essential. However, due to associated resource restrictions, only very small functions can be configured using IoT nodes, principally those related to the power supply. One solution for increasing network scalability and prolonging the life of the network is to use the mobile sink (MS). However, determining the optimal set of data gathering points (SDG), optimal path, scheduling the entire network with the MS in an energy efficient manner and prolonging the life of the network present huge challenges, particularly in large-scale networks. This paper therefore proposes an energy efficient routing protocol based on artificial intelligence (AI), i.e., particle swarm optimisation (PSO) and genetic algorithm (GA), for large scale I-IoT networks under the SDN and cloud architecture. The basic premise is to exploit cloud resources such as storage and data-centre units by using a centralised SDN controller-based AI to calculate: a load-balanced table of clusters (CT), an optimal SDG, and an optimal path for the MS (MSOpath). Moreover, the proposed new routing technique will prevent significant energy dissipation by the cluster head (CH) and by all nodes in general by scheduling the whole network. Consequently, the SDN controller essentially balances energy consumption by the network during the routing construction process as it considers both the SDG and the movement of the MS. Simulation results demonstrate the effectiveness of the suggested model by improving the network lifespan up to 54%, volume of data aggregated by the MS up to 93% and reducing the delay of the MSOpath by 61% in comparison to other approaches

A distributed delay-efficient data aggregation scheduling for duty-cycled WSNs

With the growing interest in wireless sensor networks (WSNs), minimizing network delay and maximizing sensor (node) lifetime are important challenges. Since the sensor battery is one of the most precious resources in a WSN, efficient utilization of the energy to prolong the network lifetime has been the focus of much of the research on WSNs. For that reason, many previous research efforts have tried to achieve tradeoffs in terms of network delay and energy cost for such data aggregation tasks. Recently, duty-cycling technique, i.e., periodically switching ON and OFF communication and sensing capabilities, has been considered to significantly reduce the active time of sensor nodes and thus extend network lifetime. However, this technique causes challenges for data aggregation. In this paper, we present a distributed approach, named distributed delay efficient data aggregation scheduling (DEDAS-D) to solve the aggregation-scheduling problem in duty-cycled WSNs. The analysis indicates that our solution is a better approach to solve this problem. We conduct extensive simulations to corroborate our analysis and show that DEDAS-D outperforms other distributed schemes and achieves an asymptotic performance compared with centralized scheme in terms of data aggregation delay.N/