85 research outputs found
A Novel Approach for Enhancing Routing in Wireless Sensor Networks using ACO Algorithm
Wireless Sensors Network (WSN) is an emergent technology that aims to offer innovative capacities. In the last decade, the use of these networks increased in various fields like military, science, and health due to their fast and inexpressive deployment and installation. However, the limited sensor battery lifetime poses many technical challenges and affects essential services like routing. This issue is a hot topic of search, many researchers have proposed various routing protocols aimed at reducing the energy consumption in WSNs. The focus of this work is to investigate the effectiveness of integrating ACO algorithm with routing protocols in WSNs. Moreover, it presents a novel approach inspired by ant colony optimization (ACO) to be deployed as a new routing protocol that addresses key challenges in wireless sensor networks. The proposed protocol can significantly minimize nodes energy consumption, enhance the network lifetime, reduce latency, and expect performance in various scenarios
Enhanced PEGASIS using Dynamic Programming for Data Gathering in Wireless Sensor Network
A number of routing protocol algorithms such as Low-Energy Adaptive Clustering Hierarchy (LEACH) and Power-Efficient Gathering in Sensor Information Systems (PEGASIS) have been proposed to overcome the problem of energy consumption in Wireless Sensor Network (WSN) technology. PEGASIS is a development of the LEACH protocol, where within PEGASIS all nodes are active during data transfer rounds thus limiting the lifetime of the WSN. This study aims to propose improvements from the previous PEGASIS version by giving the name Enhanced PEGASIS using Dynamic Programming (EPDP). EPDP uses the Dominating Set (DS) concept in selecting a subset of nodes to be activated and using dynamic programming based optimization in forming chains from each node. There are 2 topology nodes that we use, namely random and static. Then for the Base Station (BS), it will also be divided into several scenarios, namely the BS is placed outside the network, in the corner of the network, and in the middle of the network. Whereas to determine the performance between EPDP, PEGASIS and LEACH, an analysis of the number of die nodes, number of alive nodes, and remaining of energy were analyzed. From the experiment result, it was found that the EPDP protocol had better performance compared to the LEACH and PEGASIS protocols in terms of number of die nodes, number of alive nodes, and remaining of energy. Whereas the best BS placement is in the middle of the network and uses static node distribution topologies to save more energy
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On-demand fuzzy clustering and ant-colony optimisation based mobile data collection in wireless sensor network
In a wireless sensor network (WSN), sensor nodes collect data from the environment and transfer this data to an end user through multi-hop communication. This results in high energy dissipation of the devices. Thus, balancing of energy consumption is a major concern in such kind of network. Appropriate cluster head (CH) selection may provide to be an efficient way to reduce the energy dissipation and prolonging the network lifetime in WSN. This paper has adopted the concept of fuzzy if-then rules to choose the cluster head based on certain fuzzy descriptors. To optimise the fuzzy membership functions, Particle Swarm Optimisation (PSO) has been used to improve their ranges. Moreover, recent study has confirmed that the introduction of a mobile collector in a network which collects data through short-range communications also aids in high energy conservation. In this work, the network is divided into clusters and a mobile collector starts from the static sink or base station and moves through each of these clusters and collect data from the chosen cluster heads in a single-hop fashion. Mobility based on Ant-Colony Optimisation (ACO) has already proven to be an efficient method which is utilised in this work. Additionally, instead of performing clustering in every round, CH is selected on demand. The performance of the proposed algorithm has been compared with some existing clustering algorithms. Simulation results show that the proposed protocol is more energy-efficient and provides better packet delivery ratio as compared to the existing protocols for data collection obtained through Matlab Simulations
DESIGN OF MOBILE DATA COLLECTOR BASED CLUSTERING ROUTING PROTOCOL FOR WIRELESS SENSOR NETWORKS
Wireless Sensor Networks (WSNs) consisting of hundreds or even thousands of
nodes, canbe used for a multitude of applications such as warfare intelligence or to
monitor the environment. A typical WSN node has a limited and usually an
irreplaceable power source and the efficient use of the available power is of utmost
importance to ensure maximum lifetime of eachWSNapplication. Each of the nodes
needs to transmit and communicate sensed data to an aggregation point for use by
higher layer systems. Data and message transmission among nodes collectively
consume the largest amount of energy available in WSNs. The network routing
protocols ensure that every message reaches thedestination and has a direct impact on
the amount of transmissions to deliver messages successfully. To this end, the
transmission protocol within the WSNs should be scalable, adaptable and optimized
to consume the least possible amount of energy to suite different network
architectures and application domains. The inclusion of mobile nodes in the WSNs
deployment proves to be detrimental to protocol performance in terms of nodes
energy efficiency and reliable message delivery. This thesis which proposes a novel
Mobile Data Collector based clustering routing protocol for WSNs is designed that
combines cluster based hierarchical architecture and utilizes three-tier multi-hop
routing strategy between cluster heads to base station by the help of Mobile Data
Collector (MDC) for inter-cluster communication. In addition, a Mobile Data
Collector based routing protocol is compared with Low Energy Adaptive Clustering
Hierarchy and A Novel Application Specific Network Protocol for Wireless Sensor
Networks routing protocol. The protocol is designed with the following in mind:
minimize the energy consumption of sensor nodes, resolve communication holes
issues, maintain data reliability, finally reach tradeoff between energy efficiency and
latency in terms of End-to-End, and channel access delays. Simulation results have
shown that the Mobile Data Collector based clustering routing protocol for WSNs
could be easily implemented in environmental applications where energy efficiency of
sensor nodes, network lifetime and data reliability are major concerns
Framework for Cross Layer Energy Optimization in Wireless Sensor Networks
Cross-layer routing technique interacts among the various layers of the OSI model and exchanges information among them. It enhances the usage of network resources and achieves significant performance improvements in Quality of Service (QoS) parameters. The Low Energy Adaptive Clustering Hierarchy Protocol (LEACH) routing algorithm consumes higher energy due to communication overhead and thus, a hierarchical model-based routing protocol named Cross-Layer Energy Efficient Scalable-Low Energy Adaptive Clustering Hierarchy Protocol (CLEES-LEACH) is proposed. This increases scalability using the Carrier Sense Multiple Access/Collision Avoidance (CSMA/CA) protocol between the intermediary node and cluster head, with the overhead of latency. A Linear Programming model is used, which further makes use of scheduling to overcome latency. Energy efficiency and latency are addressed with the proposed cross-layer routing algorithm CLEESLEACH. The cross-layer design establishes Physical, Media Access Control (MAC), and Network layer interactions in the proposed algorithm. The present LEACH algorithm also increases the network overhead as there is no mechanism for communication among the network layer and consumes high energy. In the proposed algorithm CLEES-LEACH, latency is reduced to 25% and throughput is maximized to 20% compared to existing Energy-Efficient Distributed Schedule Based protocol (EEDS) and Integer Linear Programming (ILP) protocols. The energy consumption is also reduced to 20 % and the scalability is increased to 10 % compared to the existing LEACH and CL-LEACH
Energy-aware routing protocols in wireless sensor networks
Saving energy and increasing network lifetime are significant challenges in
the field of Wireless Sensor Networks (WSNs). Energy-aware routing protocols
have been introduced for WSNs to overcome limitations of WSN including limited
power resources and difficulties renewing or recharging sensor nodes batteries.
Furthermore, the potentially inhospitable environments of sensor locations, in some
applications, such as the bottom of the ocean, or inside tornados also have to be
considered. ZigBee is one of the latest communication standards designed for
WSNs based on the IEEE 802.15.4 standard. The ZigBee standard supports two
routing protocols, the Ad hoc On-demand Distance Vector (AODV), and the
cluster-tree routing protocols. These protocols are implemented to establish the
network, form clusters, and transfer data between the nodes. The AODV and the
cluster-tree routing protocols are two of the most efficient routing protocols in terms
of reducing the control message overhead, reducing the bandwidth usage in the
network, and reducing the power consumption of wireless sensor nodes compared to
other routing protocols. However, neither of these protocols considers the energy
level or the energy consumption rate of the wireless sensor nodes during the
establishment or routing processes. (Continues...)
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
Joint transceiver design and power optimization for wireless sensor networks in underground mines
Avec les grands dĂ©veloppements des technologies de communication sans fil, les rĂ©seaux de capteurs sans fil (WSN) ont attirĂ© beaucoup dâattention dans le monde entier au cours de la derniĂšre dĂ©cennie. Les rĂ©seaux de capteurs sans fil sont maintenant utilisĂ©s pour a surveillance sanitaire, la gestion des catastrophes, la dĂ©fense, les tĂ©lĂ©communications, etc. De tels rĂ©seaux sont utilisĂ©s dans de nombreuses applications industrielles et commerciales comme la surveillance des processus industriels et de lâenvironnement, etc. Un rĂ©seau WSN est une collection de transducteurs spĂ©cialisĂ©s connus sous le nom de noeuds de capteurs avec une liaison de communication distribuĂ©e de maniĂšre alĂ©atoire dans tous les emplacements pour surveiller les paramĂštres. Chaque noeud de capteur est Ă©quipĂ© dâun transducteur, dâun processeur de signal, dâune unitĂ© dâalimentation et dâun Ă©metteur-rĂ©cepteur. Les WSN sont maintenant largement utilisĂ©s dans lâindustrie miniĂšre souterraine pour surveiller certains paramĂštres environnementaux, comme la quantitĂ© de gaz, dâeau, la tempĂ©rature, lâhumiditĂ©, le niveau dâoxygĂšne, de poussiĂšre, etc. Dans le cas de la surveillance de lâenvironnement, un WSN peut ĂȘtre remplacĂ© de maniĂšre Ă©quivalente par un rĂ©seau Ă relais Ă entrĂ©es et sorties multiples (MIMO). Les rĂ©seaux de relais multisauts ont attirĂ© un intĂ©rĂȘt de recherche important ces derniers temps grĂące Ă leur capacitĂ© Ă augmenter la portĂ©e de la couverture. La liaison de communication rĂ©seau dâune source vers une destination est mise en oeuvre en utilisant un schĂ©ma dâamplification/transmission (AF) ou de dĂ©codage/transfert (DF). Le relais AF reçoit des informations du relais prĂ©cĂ©dent et amplifie simplement le signal reçu, puis il le transmet au relais suivant. Dâautre part, le relais DF dĂ©code dâabord le signal reçu, puis il le transmet au relais suivant au deuxiĂšme Ă©tage sâil peut parfaitement dĂ©coder le signal entrant. En raison de la simplicitĂ© analytique, dans cette thĂšse, nous considĂ©rons le schĂ©ma de relais AF et les rĂ©sultats de ce travail peuvent Ă©galement ĂȘtre dĂ©veloppĂ©s pour le relais DF. La conception dâun Ă©metteur/rĂ©cepteur pour le relais MIMO multisauts est trĂšs difficile. Car Ă lâĂ©tape de relais L, il y a 2L canaux possibles. Donc, pour un rĂ©seau Ă grande Ă©chelle, il nâest pas Ă©conomique dâenvoyer un signal par tous les liens possibles. Au lieu de cela, nous pouvons trouver le meilleur chemin de la source Ă la destination qui donne le rapport signal sur bruit (SNR) de bout en bout le plus Ă©levĂ©. Nous pouvons minimiser la fonction objectif dâerreur quadratique moyenne (MSE) ou de taux dâerreur binaire (BER) en envoyant le signal utilisant le chemin sĂ©lectionnĂ©. Lâensemble de relais dans le chemin reste actif et le reste des relais sâĂ©teint, ce qui permet dâĂ©conomiser de lâĂ©nergie afin dâamĂ©liorer la durĂ©e de vie du rĂ©seau. Le meilleur chemin de transmission de signal a Ă©tĂ© Ă©tudiĂ© dans la littĂ©rature pour un relais MIMO Ă deux bonds mais est plus complexe pour un ...With the great developments in wireless communication technologies, Wireless Sensor Networks (WSNs) have gained attention worldwide in the past decade and are now being used in health monitoring, disaster management, defense, telecommunications, etc. Such networks are used in many industrial and consumer applications such as industrial process and environment monitoring, among others. A WSN network is a collection of specialized transducers known as sensor nodes with a communication link distributed randomly in any locations to monitor environmental parameters such as water level, and temperature. Each sensor node is equipped with a transducer, a signal processor, a power unit, and a transceiver. WSNs are now being widely used in the underground mining industry to monitor environmental parameters, including the amount of gas, water, temperature, humidity, oxygen level, dust, etc. The WSN for environment monitoring can be equivalently replaced by a multiple-input multiple-output (MIMO) relay network. Multi-hop relay networks have attracted significant research interest in recent years for their capability in increasing the coverage range. The network communication link from a source to a destination is implemented using the amplify-and-forward (AF) or decode-and-forward (DF) schemes. The AF relay receives information from the previous relay and simply amplifies the received signal and then forwards it to the next relay. On the other hand, the DF relay first decodes the received signal and then forwards it to the next relay in the second stage if it can perfectly decode the incoming signal. For analytical simplicity, in this thesis, we consider the AF relaying scheme and the results of this work can also be developed for the DF relay. The transceiver design for multi-hop MIMO relay is very challenging. This is because at the L-th relay stage, there are 2L possible channels. So, for a large scale network, it is not economical to send the signal through all possible links. Instead, we can find the best path from source-to-destination that gives the highest end-to-end signal-to-noise ratio (SNR). We can minimize the mean square error (MSE) or bit error rate (BER) objective function by sending the signal using the selected path. The set of relay in the path remains active and the rest of the relays are turned off which can save power to enhance network life-time. The best path signal transmission has been carried out in the literature for 2-hop MIMO relay and for multiple relaying it becomes very complex. In the first part of this thesis, we propose an optimal best path finding algorithm at perfect channel state information (CSI). We consider a parallel multi-hop multiple-input multiple-output (MIMO) AF relay system where a linear minimum mean-squared error (MMSE) receiver is used at the destination. We simplify the parallel network into equivalent series multi-hop MIMO relay link using best relaying, where the best relay ..
A Survey on RF Energy Harvesting-RFEH- in WSNs
International audienceLately, WSNs have attracted lot of attention due to their ubiquitous nature and their varied utilization in IOT, Cyber Physical Systems, and other emerging fields. The restricted energy related to wireless sensor networks is a considerable bottleneck of these networks. To surpass this serious limitation, the design and development of high performance and efïŹcient energy harvesting systems for WSN environments are being inspected. We present a comprehensive taxonomy of the different energy harvesting sources that can be adopted by wireless sensor networks. We discuss also many freshly suggested energy prediction models that have the ability to boost the energy harvested in wireless sensor networks. To finish, we identify some of the challenges that still need to be addressed to develop cost-effective, efïŹcient, and reliable energy harvesting systems for the WSN environment
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