This paper proposes a clustering algorithm - Ba-lanced Minimum Radius Clustering (BMRC) - for use in large scale, distributed Wireless Sensor Networks (WSN). Cluster balancing is an intractable problem to solve in a distributed manner, and distribution is important, by reason of both avoiding specialised node vulnerability and minimising message overhead.The BMRC algorithm described here distributes several of the cluster balancing functions to the cluster-heads. In proposing this algorithm, several tentative claims have been made for it, namely that it is suitable for arbitrary number of cluster heads; that its pecifies a way to elect cluster heads and use them to create the local models; that it accomplishes optimal balanced clusters in distributed manner; that it is scalable and it uses the number-of-hops as a clustering parameter; that it is energy efficient. These claims were studied and verified by simulation.
Information extraction from sensor networks using the Watershed transform alg...M H
Wireless sensor networks are an effective tool to provide fine resolution monitoring of the physical environment. Sensors generate continuous streams of data, which leads to several computational challenges. As sensor nodes become increasingly active devices, with more processing and communication resources, various methods of distributed data processing and sharing become feasible. The challenge is to extract information from the gathered sensory data with a specified level of accuracy in a timely and power-efficient approach. This paper presents a new solution to distributed information extraction that makes use of the morphological Watershed algorithm. The Watershed algorithm dynamically groups sensor nodes into homogeneous network segments with respect to their topological relationships and their sensing-states. This setting allows network programmers to manipulate groups of spatially distributed data streams instead of individual nodes. This is achieved by using network segments as programming abstractions on which various query processes can be executed. Aiming at this purpose, we present a reformulation of the global Watershed algorithm. The modified Watershed algorithm is fully asynchronous, where sensor nodes can autonomously process their local data in parallel and in collaboration with neighbouring nodes. Experimental evaluation shows that the presented solution is able to considerably reduce query resolution cost without scarifying the quality of the returned results. When compared to similar purpose schemes, such as “Logical Neighborhood”, the proposed approach reduces the total query resolution overhead by up to 57.5%, reduces the number of nodes involved in query resolution by up to 59%, and reduces the setup convergence time by up to 65.1%.
Adaptive Routing in Wireless Sensor Networks: QoS Optimisation for Enhanced A...M H
One of the key challenges for research in wireless sensor networks is the development of routing protocols that provide application-specific service guarantees. This paper presents a new cluster-based Route Optimisation and Load-balancing protocol, called ROL, that uses various quality of service (QoS) metrics to meet application requirements. ROL combines several application requirements, specifically it attempts to provide an inclusive solution to prolong network life, provide timely message delivery and improve network robustness. It uses a combination of routing metrics that can be configured according to the priorities of user-level applications to improve overall network performance. To this end, an optimisation tool for balancing the communication resources for the constraints and priorities of user applications has been developed and Nutrient-flow-based Distributed Clustering (NDC), an algorithm for load balancing is proposed. NDC works seamlessly with any clustering algorithm to equalise, as far as possible, the diameter and the membership of clusters. This paper presents simulation results to show that ROL/NDC gives a higher network lifetime than other similar schemes, such Mires++. In simulation, ROL/NDC maintains a maximum of 7\% variation from the optimal cluster population, reduces the total number of set-up messages by up to 60%, reduces the end-to-end delay by up to 56%, and enhances the data delivery ratio by up to 0.98% compared to Mires++.
An Overview of Information Extraction from Mobile Wireless Sensor NetworksM H
Information Extraction (IE) is a key research area within the field of Wireless Sensor Networks (WSNs). It has been characterised in a variety of ways, ranging from the description of its purposes, to reasonably abstract models of its processes and components. There has been only a handful of papers addressing IE over mobile WSNs directly, these dealt with individual mobility related problems as the need arises. This paper is presented as a tutorial that takes the reader from the point of identifying data about a dynamic (mobile) real world problem, relating the data back to the world from which it was collected, and finally discovering what is in the data. It covers the entire process with special emphasis on how to exploit mobility in maximising information return from a mobile WSN. We present some challenges introduced by mobility on the IE process as well as its effects on the quality of the extracted information. Finally, we identify future research directions facing the development of efficient IE approaches for WSNs in the presence of mobility.
Interpolation Techniques for Building a Continuous Map from Discrete Wireless...M H
Wireless sensor networks (WSNs) typically gather data at a discrete number of locations. However, it is desirable to be able to design applications and reason about the data in more abstract forms than in points of data. By bestowing the ability to predict inter-node values upon the network, it is proposed that it will become possible to build applications that are unaware of the concrete reality of sparse data. This interpolation capability is realised as a service of the network. In this paper, the ‘map’ style of presentation has been identified as a suitable sense data visualisation format. Although map generation is essentially a problem of interpolation between points, a new WSN service, called the map generation service, which is based on a Shepard interpolation method, is presented. A modified Shepard method that aims to deal with the special characteristics of WSNs is proposed. It requires small storage, can be localised and integrates the information about the application domain to further reduce the map generation cost and improve the mapping accuracy. Flood management application is considered to demonstrate how MGS-generated maps can be used in various applications. Empirical analysis has shown that the map generation service is an accurate, a flexible and an efficient method.
Simulation Issues in Wireless Sensor Networks: A SurveyM H
This paper presents a survey of simulation tools and systems for wireless sensor networks. Wireless sensor network modelling and simulation methodologies are presented for each system alongside judgments concerning their relative ease of use and accuracy. Finally, we propose a mixed-mode simulation methodology that integrates a simulated environment with real wireless sensor network testbed hardware in order to improve both the accuracy and scalability of results when evaluating different prototype designs and systems.
Map as a Service: A Framework for Visualising and Maximising Information Retu...M H
This paper presents a distributed information extraction and visualisation service, called the mapping service, for maximising information return from large-scale wireless sensor networks. Such a service would greatly simplify the production of higher-level, information-rich, representations suitable for informing other network services and the delivery of field information visualisations. The mapping service utilises a blend of inductive and deductive models to map sense data accurately using externally available knowledge. It utilises the special characteristics of the application domain to render visualisations in a map format that are a precise reflection of the concrete reality. This service is suitable for visualising an arbitrary number of sense modalities. It is capable of visualising from multiple independent types of the sense data to overcome the limitations of generating visualisations from a single type of sense modality. Furthermore, the mapping service responds dynamically to changes in the environmental conditions, which may affect the visualisation performance by continuously updating the application domain model in a distributed manner. Finally, a distributed self-adaptation function is proposed with the goal of saving more power and generating more accurate data visualisation. We conduct comprehensive experimentation to evaluate the performance of our mapping service and show that it achieves low communication overhead, produces maps of high fidelity, and further minimises the mapping predictive error dynamically through integrating the application domain model in the mapping service.
Information Extraction from Wireless Sensor Networks: System and ApproachesM H
Recent advances in wireless communication have made it possible to develop low-cost, and low power Wireless Sensor Networks (WSN). The WSN can be used for several application areas (e.g., habitat monitoring, forest fire detection, and health care). WSN Information Extraction (IE) techniques can be classified into four categories depending on the factors that drive data acquisition: event-driven, time-driven, query-based, and hybrid. This paper presents a survey of the state-of-the-art IE techniques in WSNs. The benefits and shortcomings of different IE approaches are presented as motivation for future work into automatic hybridization and adaptation of IE mechanisms.
An Integrated Inductive-Deductive Framework for Data Mapping in Wireless Sens...M H
Wireless sensor networks (WSNs) havean intrinsic interdependency with the environments inwhich they operate. The part of the world with whichan application is concerned is defined as that applica-tion’sdomain.Thispaperadvocatesthatanapplicationdomain of a WSN can serve as a supplement to analysis,interpretation,andvisualisationmethodsandtools.Webelieve it is critical to elevate the capabilities of thedata mapping services proposed in [1] to make use of the special characteristics of an application domain. Inthis paper, we propose an adaptive Multi-DimensionalApplication Domain-driven (M-DAD) mapping frame-work that is suitable for mapping an arbitrary num-ber of sense modalities and is capable of utilising therelations between different modalities as well as otherparameters of the application domain to improve themapping performance. M-DAD starts with an initialuser defined model that is maintained and updatedthroughout the network lifetime. The experimentalresults demonstrate that M-DAD mapping frameworkperforms as well or better than mapping services with-out its extended capabilities.
Information extraction from sensor networks using the Watershed transform alg...M H
Wireless sensor networks are an effective tool to provide fine resolution monitoring of the physical environment. Sensors generate continuous streams of data, which leads to several computational challenges. As sensor nodes become increasingly active devices, with more processing and communication resources, various methods of distributed data processing and sharing become feasible. The challenge is to extract information from the gathered sensory data with a specified level of accuracy in a timely and power-efficient approach. This paper presents a new solution to distributed information extraction that makes use of the morphological Watershed algorithm. The Watershed algorithm dynamically groups sensor nodes into homogeneous network segments with respect to their topological relationships and their sensing-states. This setting allows network programmers to manipulate groups of spatially distributed data streams instead of individual nodes. This is achieved by using network segments as programming abstractions on which various query processes can be executed. Aiming at this purpose, we present a reformulation of the global Watershed algorithm. The modified Watershed algorithm is fully asynchronous, where sensor nodes can autonomously process their local data in parallel and in collaboration with neighbouring nodes. Experimental evaluation shows that the presented solution is able to considerably reduce query resolution cost without scarifying the quality of the returned results. When compared to similar purpose schemes, such as “Logical Neighborhood”, the proposed approach reduces the total query resolution overhead by up to 57.5%, reduces the number of nodes involved in query resolution by up to 59%, and reduces the setup convergence time by up to 65.1%.
Adaptive Routing in Wireless Sensor Networks: QoS Optimisation for Enhanced A...M H
One of the key challenges for research in wireless sensor networks is the development of routing protocols that provide application-specific service guarantees. This paper presents a new cluster-based Route Optimisation and Load-balancing protocol, called ROL, that uses various quality of service (QoS) metrics to meet application requirements. ROL combines several application requirements, specifically it attempts to provide an inclusive solution to prolong network life, provide timely message delivery and improve network robustness. It uses a combination of routing metrics that can be configured according to the priorities of user-level applications to improve overall network performance. To this end, an optimisation tool for balancing the communication resources for the constraints and priorities of user applications has been developed and Nutrient-flow-based Distributed Clustering (NDC), an algorithm for load balancing is proposed. NDC works seamlessly with any clustering algorithm to equalise, as far as possible, the diameter and the membership of clusters. This paper presents simulation results to show that ROL/NDC gives a higher network lifetime than other similar schemes, such Mires++. In simulation, ROL/NDC maintains a maximum of 7\% variation from the optimal cluster population, reduces the total number of set-up messages by up to 60%, reduces the end-to-end delay by up to 56%, and enhances the data delivery ratio by up to 0.98% compared to Mires++.
An Overview of Information Extraction from Mobile Wireless Sensor NetworksM H
Information Extraction (IE) is a key research area within the field of Wireless Sensor Networks (WSNs). It has been characterised in a variety of ways, ranging from the description of its purposes, to reasonably abstract models of its processes and components. There has been only a handful of papers addressing IE over mobile WSNs directly, these dealt with individual mobility related problems as the need arises. This paper is presented as a tutorial that takes the reader from the point of identifying data about a dynamic (mobile) real world problem, relating the data back to the world from which it was collected, and finally discovering what is in the data. It covers the entire process with special emphasis on how to exploit mobility in maximising information return from a mobile WSN. We present some challenges introduced by mobility on the IE process as well as its effects on the quality of the extracted information. Finally, we identify future research directions facing the development of efficient IE approaches for WSNs in the presence of mobility.
Interpolation Techniques for Building a Continuous Map from Discrete Wireless...M H
Wireless sensor networks (WSNs) typically gather data at a discrete number of locations. However, it is desirable to be able to design applications and reason about the data in more abstract forms than in points of data. By bestowing the ability to predict inter-node values upon the network, it is proposed that it will become possible to build applications that are unaware of the concrete reality of sparse data. This interpolation capability is realised as a service of the network. In this paper, the ‘map’ style of presentation has been identified as a suitable sense data visualisation format. Although map generation is essentially a problem of interpolation between points, a new WSN service, called the map generation service, which is based on a Shepard interpolation method, is presented. A modified Shepard method that aims to deal with the special characteristics of WSNs is proposed. It requires small storage, can be localised and integrates the information about the application domain to further reduce the map generation cost and improve the mapping accuracy. Flood management application is considered to demonstrate how MGS-generated maps can be used in various applications. Empirical analysis has shown that the map generation service is an accurate, a flexible and an efficient method.
Simulation Issues in Wireless Sensor Networks: A SurveyM H
This paper presents a survey of simulation tools and systems for wireless sensor networks. Wireless sensor network modelling and simulation methodologies are presented for each system alongside judgments concerning their relative ease of use and accuracy. Finally, we propose a mixed-mode simulation methodology that integrates a simulated environment with real wireless sensor network testbed hardware in order to improve both the accuracy and scalability of results when evaluating different prototype designs and systems.
Map as a Service: A Framework for Visualising and Maximising Information Retu...M H
This paper presents a distributed information extraction and visualisation service, called the mapping service, for maximising information return from large-scale wireless sensor networks. Such a service would greatly simplify the production of higher-level, information-rich, representations suitable for informing other network services and the delivery of field information visualisations. The mapping service utilises a blend of inductive and deductive models to map sense data accurately using externally available knowledge. It utilises the special characteristics of the application domain to render visualisations in a map format that are a precise reflection of the concrete reality. This service is suitable for visualising an arbitrary number of sense modalities. It is capable of visualising from multiple independent types of the sense data to overcome the limitations of generating visualisations from a single type of sense modality. Furthermore, the mapping service responds dynamically to changes in the environmental conditions, which may affect the visualisation performance by continuously updating the application domain model in a distributed manner. Finally, a distributed self-adaptation function is proposed with the goal of saving more power and generating more accurate data visualisation. We conduct comprehensive experimentation to evaluate the performance of our mapping service and show that it achieves low communication overhead, produces maps of high fidelity, and further minimises the mapping predictive error dynamically through integrating the application domain model in the mapping service.
Information Extraction from Wireless Sensor Networks: System and ApproachesM H
Recent advances in wireless communication have made it possible to develop low-cost, and low power Wireless Sensor Networks (WSN). The WSN can be used for several application areas (e.g., habitat monitoring, forest fire detection, and health care). WSN Information Extraction (IE) techniques can be classified into four categories depending on the factors that drive data acquisition: event-driven, time-driven, query-based, and hybrid. This paper presents a survey of the state-of-the-art IE techniques in WSNs. The benefits and shortcomings of different IE approaches are presented as motivation for future work into automatic hybridization and adaptation of IE mechanisms.
An Integrated Inductive-Deductive Framework for Data Mapping in Wireless Sens...M H
Wireless sensor networks (WSNs) havean intrinsic interdependency with the environments inwhich they operate. The part of the world with whichan application is concerned is defined as that applica-tion’sdomain.Thispaperadvocatesthatanapplicationdomain of a WSN can serve as a supplement to analysis,interpretation,andvisualisationmethodsandtools.Webelieve it is critical to elevate the capabilities of thedata mapping services proposed in [1] to make use of the special characteristics of an application domain. Inthis paper, we propose an adaptive Multi-DimensionalApplication Domain-driven (M-DAD) mapping frame-work that is suitable for mapping an arbitrary num-ber of sense modalities and is capable of utilising therelations between different modalities as well as otherparameters of the application domain to improve themapping performance. M-DAD starts with an initialuser defined model that is maintained and updatedthroughout the network lifetime. The experimentalresults demonstrate that M-DAD mapping frameworkperforms as well or better than mapping services with-out its extended capabilities.
An Overview and Classification of Approaches to Information Extraction in Wir...M H
Recent advances in wireless communication have made it possible to develop low-cost, and low power Wireless Sensor Networks (WSN). The WSN can be used for several application areas (e.g., habitat monitoring, forest fire detection, and health care). WSN Information Extraction (IE) techniques can be classified into four categories depending on the factors that drive data acquisition: event-driven, time-driven, query-based, and hybrid. This paper presents a survey of the state-of-the-art IE techniques in WSNs. The benefits and shortcomings of different IE approaches are presented as motivation for future work into automatic hybridisation and adaptation of IE mechanisms.
The novel applications of sensor networks impose some requirements in wireless sensor network design. With the energy efficiency and lifetime awareness, the throughput and network delayalso required to support emerging applications of sensor networks. In this paper, we propose
throughput and network delay aware intra-cluster routing protocol. We introduce the back-up links in the intra-cluster communication path. The link throughput, communication delay, packet loss ratio, interference, residual energy and node distance are the considered factors in finding efficient path of data communication among the sensor nodes within the cluster. The
simulation result shows the higher throughput and lower average packet delay rate for the proposed routing protocol than the existing benchmarks. The proposed routing protocol also shows energy efficiency and lifetime awareness with better connectivity rate.
A Review on Geographical Location Based Energy Efficient Direction Restricted...IJRES Journal
Delay Tolerant Network (DTNs) is a wireless network that experiences frequent connectivity and due to mobility of nodes long duration partitions occurred during transmission of data. DTN has the main feature that there is not full path present from source to destination. In Delay Tolerant Network (DTN), traditional routing protocol for mobile Ad-hoc protocol to be ineffective to extend of message transmission between different nodes. Delay tolerant networks (DTNs) are used in many applications like in deep space communications, under water Acoustic Network, Sparsely Populated Areas Networks Etc. In such network a routing with minimum energy congumption is major issue. In this paper, we try to explore a routing issue in DTN. First energy requirement and routing with their corresponding countermeasures in DTN are explained. Moving nodes in DTN keep the updating of network as well energy at every stage. By using the geographical concept the location of each node is maintained by updating in topology. There are many routing protocols are available for routing purpose in DTN.
Review on Clustering and Data Aggregation in Wireless Sensor NetworkEditor IJCATR
Wireless Sensor Network is a collection of various sensor nodes with sensing and communication capabilities. Clustering is the
process of grouping the set of objects so that the objects in the same group are similar to each other and different to objects in the other
group. The main goal of Data Aggregation is to collect and aggregate the data by maintaining the energy efficiency so that the network
lifetime can be increased. In this paper, I have presented a comprehensive review of various clustering routing protocols for WSN, their
advantages and limitation of clustering in WSN. A brief survey of Data Aggregation Algorithm is also outlined in this paper. Finally, I
summarize and conclude the paper with some future directions
Improving the scalability by contact information compression in routingijitjournal
The existence of reduced scalability and delivery leads to the development of scalable routing by contact
information compression. The previous work dealt with the result of consistent analysis in the performance
of DTN hierarchical routing (DHR). It increases as the source to destination distance increases with
decreases in the routing performance. This paper focuses on improving the scalability and delivery through
contact information compression algorithm and also addresses the problem of power awareness routing to
increase the lifetime of the overall network. Thus implementing the contact information compression (CIC)
algorithm the estimated shortest path (ESP) is detected dynamically. The scalability and release are more
improved during multipath multicasting, which delivers the information to a collection of target
concurrently in a single transmission from the source.
There are number of cluster based routing algorithms in mobile ad hoc networks. Since ad hoc networks are not accompanied by fixed access points, efficient routing is a must for such networks. Clustering approach is applied in mobile ad hoc network because clusters are more easily manageable and are more viable. It consists of segregating the given network into several reasonable clusters by using a clustering algorithm. By performing clustering we elect a worthy node from the cluster as the cluster head in such a way that we strive to reduce the management overheads and thus increasing the efficiency of routing. As for the fact that nodes in mobile ad hoc network have frequent host change and frequent topology change routing plays an important role for maintenance and backup mechanism to stabilize network performance. This paper aims to review the previous research papers and provide a survey on the various cluster based routing protocols in mobile ad hoc network. This paper presents analytical study of cluster based routing algorithms from literature. Index Terms— Ad- hoc networks, Cluster head, Clustering, Protocol, Route selection.
Congestion in Wireless Sensor Networks has negative impact on the Quality of Service.
Congestion effects the performance metrics, namely throughput and per-packet energy
consumption, network lifetime and packet delivery ratio. Reducing congestion allows better
utilization of the network resources and thus enhances the Quality of Service metrics of the
network. Traffic Aware Dynamic Routing to Alleviate Congestion in Wireless Sensor Networks
reduces congestion by considering one hop neighbor routing in the network. This paper
proposed an algorithm for Quality of Service Based Traffic-Aware Data forwarding for
congestion control in wireless sensor networks based on two hop neighbor information. On
detection of congestion, the algorithm forwards data packets around the congestion areas by
spreading the excessive packets through multiple paths. The path with light load or under
loaded nodes is efficiently utilized whenever congestion occurs. The main aspect of the
algorithm is to build path to the destination using two independent potential fields depth and
queue length. Queue length field solves the traffic-aware problem. Depth field creates a
backbone to forward packets to the sink. Both fields are combined to yield a hybrid potential
field to make dynamic decision for data forwarding. Network Simulator used for simulating the
algorithm is NS2. The proposed algorithm performs better.
Data Aggregation Routing Protocols in Wireless Sensor Networks : A TaxonomyIJCNCJournal
Routing in Wireless Sensor Network (WSN) aims to interconnect sensor nodes via single or multi-hop
paths. The routes are established to forward data packets from sensor nodes to the sink. Establishing a
single path to report each data packet results in increasing energy consumption in WSN, hence, data
aggregation routing is used to combine data packets and consequently reduce the number of transmissions.
This reduces the routing overhead by eliminating redundant and meaningless data. There are two models
for data aggregation routing in WSN: mobile agent and client/server. This paper describes data
aggregation routing and classifies then the routing protocols according to the network architecture and
routing models. The key issues of the data aggregation routing models (client/server and mobile agent) are
highlighted and discussed.
Designing an opportunistic routing scheme for adaptive clustering in mobile a...eSAT Publishing House
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Mobile Data Gathering with Load Balanced Clustering and Dual Data Uploading i...1crore projects
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An Approach to Data Extraction and Visualisation for Wireless Sensor NetworksM H
Ever since descartes introduced planar coordinate systems, visual representations of data have become a widely accepted way of describing scientific phenomena. Modern advances in measurement and instrumentation have required increasingly sophisticated visual representations, to ensure that scientists can quickly and accurately interpret increasingly complex data. Most recently, wireless sensor networks (WSNs) have emerged as a technology which is capable of collecting a vast amount of data over space and time. The sheer volume of the data makes it difficult to be interpreted by humans into meaningful insights. This presents a number of challenges for developers of visualisation techniques which seek to ``map'' the data sensed by a network. Visualisation techniques helps to turn large amounts of raw data into credible visual information such as graphs, charts, or maps, that can assist in understanding of the meaning of that data. In this paper we propose a map as a suitable data visualisation and extraction tool. We aim to develop an in-network distributed information extraction and visualisation service. Such a service would greatly simplify the production of higher-level information-rich representations suitable for informing other network services and the delivery of field information visualisation.
ON THE PERFORMANCE OF INTRUSION DETECTION SYSTEMS WITH HIDDEN MULTILAYER NEUR...IJCNCJournal
Deep learning applications, especially multilayer neural network models, result in network intrusion detection with high accuracy. This study proposes a model that combines a multilayer neural network with Dense Sparse Dense (DSD) multi-stage training to simultaneously improve the criteria related to the performance of intrusion detection systems on a comprehensive dataset UNSW-NB15. We conduct experiments on many neural network models such as Recurrent Neural Network (RNN), Long-Short Term Memory (LSTM), Gated Recurrent Unit (GRU), etc. to evaluate the combined efficiency with each model through many criteria such as accuracy, detection rate, false alarm rate, precision, and F1-Score.
A QoI Based Energy Efficient Clustering for Dense Wireless Sensor Networkijassn
In a wireless sensor network Quality of Information (QoI), Energy Efficiency, Redundant data avoidance,
congestion control are the important metrics that affect the performance of wireless sensor network. As
many approaches were proposed to increase the performance of a wireless sensor network among them
clustering is one of the efficient approaches in sensor network. Many clustering algorithms concentrate
mainly on power Optimization like FSCH, LEACH, and EELBCRP. There is necessity of the above
metrics in wireless sensor network where nodes are densely deployed in a given network area. As the nodes
are deployed densely there is maximum possibility of nodes appear in the sensing region of other nodes. So
there exists an option that nodes have to send the information that is already reached the base station by its
own cluster members or by members of other clusters. This mechanism will affect the QoI, Energy factor
and congestion control of the wireless sensor networks. Even though clustering uses TDMA (Time Division
Multiple Access) for avoiding congestion control for intra clustering data transmission, but it may fail in
some critical situation. This paper proposed a energy efficient clustering which avoid data redundancy in a
dense sensor network until the network becomes sparse and hence uses the TDMA efficiently during high
density of the nodes.
ENERGY-EFFICIENT MULTI-HOP ROUTING WITH UNEQUAL CLUSTERING APPROACH FOR WIREL...IJCNCJournal
The development of an energy-efficient routing protocol, capable of extending the life of the network, is one of the main constraints of wireless sensor networks (WSN). Research studies on WSN routing prove that clustering offers an effective approach to prolong the lifetime of a WSN, particularly when it is combined with multi-hop communication that can reduces energy costs by minimizing the distance between transmitter and receiver. Most clustering algorithms using multi-hop in data transmission encounter the hotspot problem. In this work, an Energy-efficient Multi-hop routing with Unequal Clustering approach (EMUC) is proposed, to create clusters of different sizes, which depend on the distance between the sensor node and the base station. Equilibrate the energy dissipation between the cluster heads is the purpose of this approach by adopting multi-hop communication to relay data to the base station. The implementation of multi-hop mode to transmit data to the base station reduces the energy cost of transmission over long distances. The effectiveness of this approach is validated through performed simulations, which prove that EMUC balances energy consumption between sensor nodes, mitigates the hotspots problem, saves more energy and significantly extends the network lifetime.
A wireless network consists of a set of wireless nodes forming the network. The bandwidth allocation scheme used in wireless networks should automatically adapt to the network’s environments, where issues such as mobility are highly variable. This paper proposes a method to distribute the bandwidth for wireless network nodes depending on dynamic methodology;this methodology uses intelligent clustering techniques that depend on the student’s distribution at the university campus, rather than the classical allocation methods. We propose a clustering-based approach to solve the dynamic bandwidth allocation problem in wireless networks, enabling wireless nodes to adapt their bandwidth allocation according to the changing number of expected users over time. The proposed solution allows the optimal online bandwidth allocation based on the data extracted from the lectures timetable, and fed to the wireless network control nodes, allowing them to adapt to their environment. The environment data is processed and clustered using the KMeans clustering algorithm to identify potential peak times for every wireless node. The proposed solution feasibility is tested by applying the approach to a case study, at the Arab American University campus wireless network.
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An Overview and Classification of Approaches to Information Extraction in Wir...M H
Recent advances in wireless communication have made it possible to develop low-cost, and low power Wireless Sensor Networks (WSN). The WSN can be used for several application areas (e.g., habitat monitoring, forest fire detection, and health care). WSN Information Extraction (IE) techniques can be classified into four categories depending on the factors that drive data acquisition: event-driven, time-driven, query-based, and hybrid. This paper presents a survey of the state-of-the-art IE techniques in WSNs. The benefits and shortcomings of different IE approaches are presented as motivation for future work into automatic hybridisation and adaptation of IE mechanisms.
The novel applications of sensor networks impose some requirements in wireless sensor network design. With the energy efficiency and lifetime awareness, the throughput and network delayalso required to support emerging applications of sensor networks. In this paper, we propose
throughput and network delay aware intra-cluster routing protocol. We introduce the back-up links in the intra-cluster communication path. The link throughput, communication delay, packet loss ratio, interference, residual energy and node distance are the considered factors in finding efficient path of data communication among the sensor nodes within the cluster. The
simulation result shows the higher throughput and lower average packet delay rate for the proposed routing protocol than the existing benchmarks. The proposed routing protocol also shows energy efficiency and lifetime awareness with better connectivity rate.
A Review on Geographical Location Based Energy Efficient Direction Restricted...IJRES Journal
Delay Tolerant Network (DTNs) is a wireless network that experiences frequent connectivity and due to mobility of nodes long duration partitions occurred during transmission of data. DTN has the main feature that there is not full path present from source to destination. In Delay Tolerant Network (DTN), traditional routing protocol for mobile Ad-hoc protocol to be ineffective to extend of message transmission between different nodes. Delay tolerant networks (DTNs) are used in many applications like in deep space communications, under water Acoustic Network, Sparsely Populated Areas Networks Etc. In such network a routing with minimum energy congumption is major issue. In this paper, we try to explore a routing issue in DTN. First energy requirement and routing with their corresponding countermeasures in DTN are explained. Moving nodes in DTN keep the updating of network as well energy at every stage. By using the geographical concept the location of each node is maintained by updating in topology. There are many routing protocols are available for routing purpose in DTN.
Review on Clustering and Data Aggregation in Wireless Sensor NetworkEditor IJCATR
Wireless Sensor Network is a collection of various sensor nodes with sensing and communication capabilities. Clustering is the
process of grouping the set of objects so that the objects in the same group are similar to each other and different to objects in the other
group. The main goal of Data Aggregation is to collect and aggregate the data by maintaining the energy efficiency so that the network
lifetime can be increased. In this paper, I have presented a comprehensive review of various clustering routing protocols for WSN, their
advantages and limitation of clustering in WSN. A brief survey of Data Aggregation Algorithm is also outlined in this paper. Finally, I
summarize and conclude the paper with some future directions
Improving the scalability by contact information compression in routingijitjournal
The existence of reduced scalability and delivery leads to the development of scalable routing by contact
information compression. The previous work dealt with the result of consistent analysis in the performance
of DTN hierarchical routing (DHR). It increases as the source to destination distance increases with
decreases in the routing performance. This paper focuses on improving the scalability and delivery through
contact information compression algorithm and also addresses the problem of power awareness routing to
increase the lifetime of the overall network. Thus implementing the contact information compression (CIC)
algorithm the estimated shortest path (ESP) is detected dynamically. The scalability and release are more
improved during multipath multicasting, which delivers the information to a collection of target
concurrently in a single transmission from the source.
There are number of cluster based routing algorithms in mobile ad hoc networks. Since ad hoc networks are not accompanied by fixed access points, efficient routing is a must for such networks. Clustering approach is applied in mobile ad hoc network because clusters are more easily manageable and are more viable. It consists of segregating the given network into several reasonable clusters by using a clustering algorithm. By performing clustering we elect a worthy node from the cluster as the cluster head in such a way that we strive to reduce the management overheads and thus increasing the efficiency of routing. As for the fact that nodes in mobile ad hoc network have frequent host change and frequent topology change routing plays an important role for maintenance and backup mechanism to stabilize network performance. This paper aims to review the previous research papers and provide a survey on the various cluster based routing protocols in mobile ad hoc network. This paper presents analytical study of cluster based routing algorithms from literature. Index Terms— Ad- hoc networks, Cluster head, Clustering, Protocol, Route selection.
Congestion in Wireless Sensor Networks has negative impact on the Quality of Service.
Congestion effects the performance metrics, namely throughput and per-packet energy
consumption, network lifetime and packet delivery ratio. Reducing congestion allows better
utilization of the network resources and thus enhances the Quality of Service metrics of the
network. Traffic Aware Dynamic Routing to Alleviate Congestion in Wireless Sensor Networks
reduces congestion by considering one hop neighbor routing in the network. This paper
proposed an algorithm for Quality of Service Based Traffic-Aware Data forwarding for
congestion control in wireless sensor networks based on two hop neighbor information. On
detection of congestion, the algorithm forwards data packets around the congestion areas by
spreading the excessive packets through multiple paths. The path with light load or under
loaded nodes is efficiently utilized whenever congestion occurs. The main aspect of the
algorithm is to build path to the destination using two independent potential fields depth and
queue length. Queue length field solves the traffic-aware problem. Depth field creates a
backbone to forward packets to the sink. Both fields are combined to yield a hybrid potential
field to make dynamic decision for data forwarding. Network Simulator used for simulating the
algorithm is NS2. The proposed algorithm performs better.
Data Aggregation Routing Protocols in Wireless Sensor Networks : A TaxonomyIJCNCJournal
Routing in Wireless Sensor Network (WSN) aims to interconnect sensor nodes via single or multi-hop
paths. The routes are established to forward data packets from sensor nodes to the sink. Establishing a
single path to report each data packet results in increasing energy consumption in WSN, hence, data
aggregation routing is used to combine data packets and consequently reduce the number of transmissions.
This reduces the routing overhead by eliminating redundant and meaningless data. There are two models
for data aggregation routing in WSN: mobile agent and client/server. This paper describes data
aggregation routing and classifies then the routing protocols according to the network architecture and
routing models. The key issues of the data aggregation routing models (client/server and mobile agent) are
highlighted and discussed.
Designing an opportunistic routing scheme for adaptive clustering in mobile a...eSAT Publishing House
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Mobile Data Gathering with Load Balanced Clustering and Dual Data Uploading i...1crore projects
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An Approach to Data Extraction and Visualisation for Wireless Sensor NetworksM H
Ever since descartes introduced planar coordinate systems, visual representations of data have become a widely accepted way of describing scientific phenomena. Modern advances in measurement and instrumentation have required increasingly sophisticated visual representations, to ensure that scientists can quickly and accurately interpret increasingly complex data. Most recently, wireless sensor networks (WSNs) have emerged as a technology which is capable of collecting a vast amount of data over space and time. The sheer volume of the data makes it difficult to be interpreted by humans into meaningful insights. This presents a number of challenges for developers of visualisation techniques which seek to ``map'' the data sensed by a network. Visualisation techniques helps to turn large amounts of raw data into credible visual information such as graphs, charts, or maps, that can assist in understanding of the meaning of that data. In this paper we propose a map as a suitable data visualisation and extraction tool. We aim to develop an in-network distributed information extraction and visualisation service. Such a service would greatly simplify the production of higher-level information-rich representations suitable for informing other network services and the delivery of field information visualisation.
ON THE PERFORMANCE OF INTRUSION DETECTION SYSTEMS WITH HIDDEN MULTILAYER NEUR...IJCNCJournal
Deep learning applications, especially multilayer neural network models, result in network intrusion detection with high accuracy. This study proposes a model that combines a multilayer neural network with Dense Sparse Dense (DSD) multi-stage training to simultaneously improve the criteria related to the performance of intrusion detection systems on a comprehensive dataset UNSW-NB15. We conduct experiments on many neural network models such as Recurrent Neural Network (RNN), Long-Short Term Memory (LSTM), Gated Recurrent Unit (GRU), etc. to evaluate the combined efficiency with each model through many criteria such as accuracy, detection rate, false alarm rate, precision, and F1-Score.
A QoI Based Energy Efficient Clustering for Dense Wireless Sensor Networkijassn
In a wireless sensor network Quality of Information (QoI), Energy Efficiency, Redundant data avoidance,
congestion control are the important metrics that affect the performance of wireless sensor network. As
many approaches were proposed to increase the performance of a wireless sensor network among them
clustering is one of the efficient approaches in sensor network. Many clustering algorithms concentrate
mainly on power Optimization like FSCH, LEACH, and EELBCRP. There is necessity of the above
metrics in wireless sensor network where nodes are densely deployed in a given network area. As the nodes
are deployed densely there is maximum possibility of nodes appear in the sensing region of other nodes. So
there exists an option that nodes have to send the information that is already reached the base station by its
own cluster members or by members of other clusters. This mechanism will affect the QoI, Energy factor
and congestion control of the wireless sensor networks. Even though clustering uses TDMA (Time Division
Multiple Access) for avoiding congestion control for intra clustering data transmission, but it may fail in
some critical situation. This paper proposed a energy efficient clustering which avoid data redundancy in a
dense sensor network until the network becomes sparse and hence uses the TDMA efficiently during high
density of the nodes.
ENERGY-EFFICIENT MULTI-HOP ROUTING WITH UNEQUAL CLUSTERING APPROACH FOR WIREL...IJCNCJournal
The development of an energy-efficient routing protocol, capable of extending the life of the network, is one of the main constraints of wireless sensor networks (WSN). Research studies on WSN routing prove that clustering offers an effective approach to prolong the lifetime of a WSN, particularly when it is combined with multi-hop communication that can reduces energy costs by minimizing the distance between transmitter and receiver. Most clustering algorithms using multi-hop in data transmission encounter the hotspot problem. In this work, an Energy-efficient Multi-hop routing with Unequal Clustering approach (EMUC) is proposed, to create clusters of different sizes, which depend on the distance between the sensor node and the base station. Equilibrate the energy dissipation between the cluster heads is the purpose of this approach by adopting multi-hop communication to relay data to the base station. The implementation of multi-hop mode to transmit data to the base station reduces the energy cost of transmission over long distances. The effectiveness of this approach is validated through performed simulations, which prove that EMUC balances energy consumption between sensor nodes, mitigates the hotspots problem, saves more energy and significantly extends the network lifetime.
A wireless network consists of a set of wireless nodes forming the network. The bandwidth allocation scheme used in wireless networks should automatically adapt to the network’s environments, where issues such as mobility are highly variable. This paper proposes a method to distribute the bandwidth for wireless network nodes depending on dynamic methodology;this methodology uses intelligent clustering techniques that depend on the student’s distribution at the university campus, rather than the classical allocation methods. We propose a clustering-based approach to solve the dynamic bandwidth allocation problem in wireless networks, enabling wireless nodes to adapt their bandwidth allocation according to the changing number of expected users over time. The proposed solution allows the optimal online bandwidth allocation based on the data extracted from the lectures timetable, and fed to the wireless network control nodes, allowing them to adapt to their environment. The environment data is processed and clustered using the KMeans clustering algorithm to identify potential peak times for every wireless node. The proposed solution feasibility is tested by applying the approach to a case study, at the Arab American University campus wireless network.
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Energy Aware Talented Clustering with Compressive Sensing (TCCS) for Wireless...IJCNCJournal
Wireless sensor networks (WSNs) are networks of sensor nodes that interact wirelessly to gather information about the surrounding environment. Nodes are often low-powered and dispersed in an ad hoc, decentralized manner. Although WSNs have gained in popularity, they still have several serious shortcomings, like limited battery life and bandwidth. In this paper, the cluster head (CH) selection, the Compressive Sensing (CS) theory, the Connection-based Decentralized Clustering (CDC), the relay node selection, and the Multi Objective Genetic Algorithm (MOGA)are all taken into account The initial stage provided a theoretical revision to the concepts of network construction, compressive sensing, and MOGA, which impacted the improvement of network lifetime. In the second stage developed a novel model such as Energy Aware Talented Clustering with Compressive Sensing (TCCS) for the sensor network. This approach considers increasing longevity but also raises the network's overall quality of service (QoS). In the analysis, the TCCS model is applied to both the centralized and distributed networks and compared with the existing methods. When compared to the previous methods, the simulation results show that the proposed work performs better in terms of the calculation of maximum packet delivery ratio of 93.93 percent, minimum energy consumption of 8.04J, maximum energy efficiency of 91.04 percent, maximum network throughput of 465.51kbps, minimum packet loss of 282 packets, and minimum delay of 63.82 msec.
ENERGY AWARE TALENTED CLUSTERING WITH COMPRESSIVE SENSING (TCCS) FOR WIRELESS...IJCNCJournal
Wireless sensor networks (WSNs) are networks of sensor nodes that interact wirelessly to gather
information about the surrounding environment. Nodes are often low-powered and dispersed in an ad hoc,
decentralized manner. Although WSNs have gained in popularity, they still have several serious
shortcomings, like limited battery life and bandwidth. In this paper, the cluster head (CH) selection, the
Compressive Sensing (CS) theory, the Connection-based Decentralized Clustering (CDC), the relay node
selection, and the Multi Objective Genetic Algorithm (MOGA)are all taken into account The initial stage
provided a theoretical revision to the concepts of network construction, compressive sensing, and MOGA,
which impacted the improvement of network lifetime. In the second stage developed a novel model such as
Energy Aware Talented Clustering with Compressive Sensing (TCCS) for the sensor network. This
approach considers increasing longevity but also raises the network's overall quality of service (QoS). In
the analysis, the TCCS model is applied to both the centralized and distributed networks and compared
with the existing methods. When compared to the previous methods, the simulation results show that the
proposed work performs better in terms of the calculation of maximum packet delivery ratio of 93.93
percent, minimum energy consumption of 8.04J, maximum energy efficiency of 91.04 percent, maximum
network throughput of 465.51kbps, minimum packet loss of 282 packets, and minimum delay of 63.82 msec.
CFMS: A CLUSTER-BASED CONVERGECAST FRAMEWORK FOR DENSE MULTI-SINK WIRELESS SE...ijwmn
Convergecast is one of the most challenging tasks in Wireless Sensor Networks (WSNs). Indeed, this data
collection process must be conducted while copying with packet collisions, nodes’ congestion or data
redundancy. These issues always result in energy waste which is detrimental to network efficiency and
lifetime. This paper is aimed to address these problems in large-scale multi-sink WSNs. Inspired by our
previous work MSCP, we designed a lightweight protocol stack that seamlessly combines clustering, pathvector routing, sinks’ duty cycling, data aggregation and transmission scheduling in order to minimise
message overhead and packet losses. Simulation results show that this solution can mitigate delay while
significantly increasing packet delivery and network lifetime.
CFMS: A CLUSTER-BASED CONVERGECAST FRAMEWORK FOR DENSE MULTI-SINK WIRELESS SE...ijwmn
Convergecast is one of the most challenging tasks in Wireless Sensor Networks (WSNs). Indeed, this data
collection process must be conducted while copying with packet collisions, nodes’ congestion or data
redundancy. These issues always result in energy waste which is detrimental to network efficiency and
lifetime. This paper is aimed to address these problems in large-scale multi-sink WSNs. Inspired by our
previous work MSCP, we designed a lightweight protocol stack that seamlessly combines clustering, pathvector routing, sinks’ duty cycling, data aggregation and transmission scheduling in order to minimise
message overhead and packet losses. Simulation results show that this solution can mitigate delay while
significantly increasing packet delivery and network lifetime.
CFMS: A Cluster-based Convergecast Framework for Dense Multi-Sink Wireless Se...ijwmn
Convergecast is one of the most challenging tasks in Wireless Sensor Networks (WSNs). Indeed, this data
collection process must be conducted while copying with packet collisions, nodes’ congestion or data
redundancy. These issues always result in energy waste which is detrimental to network efficiency and
lifetime. This paper is aimed to address these problems in large-scale multi-sink WSNs. Inspired by our
previous work MSCP, we designed a lightweight protocol stack that seamlessly combines clustering, pathvector routing, sinks’ duty cycling, data aggregation and transmission scheduling in order to minimise
message overhead and packet losses. Simulation results show that this solution can mitigate delay while
significantly increasing packet delivery and network lifetime.
A Survey on Clustering Techniques for Wireless Sensor Network IJORCS
Wireless sensor networks have been used in various fields like battle feilds, surveillance, schools, colleges, etc. It has been used in our day-to-day life. Its growth increases day by day. Sensor node normally senses the physical event from the environment such as temperature, sound, vibration, pressure etc. Sensor nodes are connected with each other through wireless medium such as infrared or radio waves it depends on applications. Each node has its internal memory to store the information regarding the event packets. In this paper we will come to know the various algorithms in clustering techniques for wireless sensor networks and discuss them. Clustering is a key technique used to extend the lifetime of a sensor network by reducing energy consumption .It can also increase network scalability. Sensor nodes are considered to be homogeneous since the researches in the feild of WSNs have been evolved but in reality homogeneous sensor networks hardly exist. Here we will discuss some of the impact of heterogeneous sensor networks on WSN and various clustering algorithms used in HWSN.
ENERGY EFFICIENT HIERARCHICAL CLUSTER HEAD ELECTION USING EXPONENTIAL DECAY F...ijwmn
In the recent years, wireless sensor network (WSN) have witnessed increased interest in information gathering in applications such as combat field reconnaissance, security surveillance, environmental monitoring, patient health monitoring and so on. Thus, there is a need for scalable and energy-efficient routing, data gathering and aggregation protocols in these WSN environments. Various hierarchical
clustering Protocols have been proposed by authors for WSN to improve system stability, lifetime, and energy efficiency. Clustering involves grouping nodes into disjoint and non-overlapping clusters. In this paper we motivate the need for clustering. Secondly, we present general classification of published clustering schemes. Thirdly, we review some existing clustering algorithms proposed for WSNs; highlighting their objectives, features, and so on. Finally, we develop an Average Energy (AvE) prediction algorithm using exponential decay function y=Ae-ax+B. We then combine this function with the
probabilistic distributed LEACH of algorithm to determine suitable CHs. The combined algorithm was implemented on MATLAB simulator and tested for homogenous network. The result gathered from the simulation shows that the extended algorithm in homogenous network mode is able to achieve 39%
stability, 11% Average energy Dissipation per round and 40% Lifespan better than LEACH-Homo. This paper proposes a new direction in improving energy efficiency of WSN routing protocol, which is desirable in some critical WSN applications. .
Energy Consumption Reduction in Wireless Sensor Network Based on ClusteringIJCNCJournal
ABSTRACT
One of the important issues in the routing protocol design in Wireless Sensor Networks (WSNs) is minimizing energy consumption and maximizing network lift time. Nowadays networks and information systems are one of the main parts of modern life that without them, people cannot live. On the hand, the impairment of these networks leads to great and incalculable costs. In this paper, a new method based on clustering has presented that problem of energy consumption is solved. The proposed algorithm is that energy-based clustering can create clusters of the same energy level and distribute energy efficiency across the WNS nodes. This proposed clustering protocol classify network nodes based on energy and neighbourhood criteria and attempts to better balance energy in clusters and ultimately increase network lifetime and maintain network coverage. Results are shown that the proposed algorithm is on average 40% better than LEACH algorithm and 14% better than IBLEACH algorithm.
KEYWORDS
Wireless Sensor Network, Clustering, LEACH Algorithm, IBLEACH Algorithm
Abstract Link : http://aircconline.com/abstract/ijcnc/v11n2/11219cnc03.html
Full Details : http://aircconline.com/ijcnc/V11N2/11219cnc03.pdf
A Professional QoS Provisioning in the Intra Cluster Packet Level Resource Al...GiselleginaGloria
Wireless mesh networking has transpired as a gifted technology for potential broadband wireless access. In a communication network, wireless mesh network plays a vital role in transmission and are structured in a mesh topology. The coordination of mesh routers and mesh clients forms the wireless mesh networks which are routed through the gateways. Wireless mesh networks uses IEEE 802.11 standards and has its wide applications broadband home networking and enterprise networking deployment such as Microsoft wireless mesh and MIT etc. A professional Qos provisioning in intra cluster packet level resource allocation for WMN approach takes power allocation, sub carrier allocation and packet scheduling. This approach combines the merits of a Karush-Kuhn-Tucker (KKT) algorithm and a genetic algorithm (GA) based approach. The KKT algorithm uses uniform power allocation over all the subcarriers, based on the optimal allocation criterion. The genetic algorithm is used to generate useful solutions to optimization and search problems and it is also used for search problems. By combining the intrinsic worth of both the approaches, it facilitates effective QOS provisioning at the packet level. It is concluded that, this approach achieves a preferred stability between system implementation and computational convolution.
Congestion Control Clustering a Review PaperEditor IJCATR
Wireless Sensor Networks consists of sensor nodes which are scattered in the environment, gather data and transmit it to a
base station for processing. Energy conservation in the Wireless Sensor Networks (WSN) is a very important task because of their
limited battery power. The related works so far have been done have tried to solve the problem keeping in the mind the constraints of
WSNs. In this paper, a priority based application specific congestion control clustering (PASCCC) protocol has been studied, which
often integrates the range of motion and heterogeneity of the nodes to detect congestion in a very network. Moreover a comparison of
the various clustering techniques has been done. From the survey it has been found that none of the protocol is efficient for energy
conservation. Hence the paper ends with future scope to overcome these issues.
An Improved Energy Efficient Wireless Sensor Networks Through Clustering In C...Editor IJCATR
One of the major reason for performance degradation in Wireless sensor network is the overhead due to control packet and
packet delivery degradation. Clustering in cross layer network operation is an efficient way manage control packet overhead and which
ultimately improve the lifetime of a network. All these overheads are crucial in a scalable networks. But the clustering always suffer
from the cluster head failure which need to be solved effectively in a large network. As the focus is to improve the average lifetime of
sensor network the cluster head is selected based on the battery life of nodes. The cross-layer operation model optimize the overheads
in multiple layer and ultimately the use of clustering will reduce the major overheads identified and their by the energy consumption
and throughput of wireless sensor network is improved. The proposed model operates on two layers of network ie., Network Layer
and Transport Layer and Clustering is applied in the network layer . The simulation result shows that the integration of two layers
reduces the energy consumption and increases the throughput of the wireless sensor networks.
An Improved Energy Efficient Wireless Sensor Networks Through Clustering In C...Editor IJCATR
One of the major reason for performance degradation in Wireless sensor network is the overhead due to control packet and
packet delivery degradation. Clustering in cross layer network operation is an efficient way manage control packet overhead and which
ultimately improve the lifetime of a network. All these overheads are crucial in a scalable networks. But the clustering always suffer
from the cluster head failure which need to be solved effectively in a large network. As the focus is to improve the average lifetime of
sensor network the cluster head is selected based on the battery life of nodes. The cross-layer operation model optimize the overheads
in multiple layer and ultimately the use of clustering will reduce the major overheads identified and their by the energy consumption
and throughput of wireless sensor network is improved. The proposed model operates on two layers of network ie., Network Layer
and Transport Layer and Clustering is applied in the network layer . The simulation result shows that the integration of two layers
reduces the energy consumption and increases the throughput of the wireless sensor networks.
Similar to Algorithmic Construction of Optimal and Load Balanced Clusters in Wireless Sensor Networks (20)
Building Programming Abstractions for Wireless Sensor Networks Using Watershe...M H
The availability and quality of information extracted from Wireless Sensor Networks (WSNs) revolutionised a wide range of application areas. The success of any WSN application is, nonetheless, determined by the ability to retrieve information with the required level of accuracy, within specified time constraints, and with minimum resource utilisation. This paper presents a new approach to localised information extraction that utilises the Watershed segmentation algorithm to dynamically group nodes into segments, which can be used as programming abstractions upon which different query operations can be performed. Watershed results in a set of well delimited areas, such that the number of necessary operations (communication and computation) to answer a query are minimised. This paper presents a fully asynchronous Watershed implementation, where nodes can compute their local data in parallel and independently from one another. The preliminary experimental results demonstrate that the proposed approach is able to significantly reduce the query processing cost and time without involving any loss of efficiency.
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Experimental Applications of Mapping Services in Wireless Sensor NetworksM H
Wireless sensor networks typically gather data at a number of locations. However, it is desirable to be able to design applications and reason about the data in more abstract forms than points of data. This paper examines one way in which this can be done. By bestowing the ability to predict inter-node values upon the network, it is proposed that it will become possible to build applications that are unaware of the concrete reality of sparse data. This interpolation capability is realised as a service of the network. We present an implementation of this service and discuss its merits and shortcomings. Additionally, we present an initial application of the service in the form of isopleth generation. That is, the delineation of contours of constant parameter value. Finally, we discuss the improvements required to create more sophisticated applications and services and examine the benefits these improvements would bring.
CSP as a Domain-Specific Language Embedded in Python and JythonM H
Recently, much discussion has taken place within the Python programming community on how best to support concurrent programming. This paper describes a new Python library, python-csp, which implements synchronous, message-passing concurrency based on Hoare’s Communicating Sequential Processes. Although other CSP libraries have been written for Python, python-csp has a number of novel features. The library is implemented both as an object hierarchy and as a domain-specific language, meaning that programmers can compose processes and guards using infix operators, similar to the original CSP syntax. The language design is intended to be idiomatic Python and is therefore quite different to other CSP libraries. python-csp targets the CPython interpreter and has variants which reify CSP process as Python threads and operating system processes. An equivalent library targets the Jython interpreter, where CSP processes are reified as Java threads. jython-csp also has Java wrappers which allow the library to be used from pure Java programs. We describe these aspects of python-csp, together with performance benchmarks and a formal analysis of channel synchronisation and choice, using the model checker SPIN.
Modelling Clustering of Wireless Sensor Networks with Synchronised Hyperedge ...M H
This paper proposes Synchronised Hyperedge Replacement (SHR) as a suitable modelling framework for Wireless Sensor Networks (WSNs). SHR facilitates explicit modelling of WSNs applications environmental conditions (that significantly affect applications performance) while providing a sufficiently high level of abstraction for the specification of the underling coordination mechanisms. Because it is an intractable problem to solve in distributed manner, and distribution is important, we propose a new Nutrient-flow-based Distributed Clustering (NDC) algorithm to be used as a working example. The key contribution of this work is to demonstrate that SHR is sufficiently expressive to describe WSNs algorithms and their behaviour at a suitable level of abstraction to allow onward analysis.
Pennies from Heaven: a retrospective on the use of wireless sensor networks f...M H
Wireless sensor networks are finding many applications in terrestrial sensing. It seems natural to propose their use for planetary exploration. A previous study (the Mars daisy) has put forward a scenario using thousands of millimeter scale wireless sensor nodes to undertake a complete survey of an area of a planet. This paper revisits that scenario, in the light of some of the discussions surrounding its presentation. The practicality of some of the ideas put forward is examined again, and an updated design sketched out. It is concluded that the updated design could be produced using currently available technology.
Enhancing Student Learning Experience and Satisfaction Using Virtual Learning...M H
The paper presents a project that aims to enhance students experiences and satisfaction through the use of a Virtual Learning Environment. Particularly, it aims at developing a blended learning community to support diverse student population, including students with special learning needs. This project focuses on the teaching/learning aspects of students experiences and satisfaction. Other aspects are geared towards use by student support staff and those whose main responsibility is technical or system administration support. Various methods were used to measure the success of the project and its implementation. Evaluation results show a significant increase in student satisfaction and enhanced progression rate.
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1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
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Algorithmic Construction of Optimal and Load Balanced Clusters in Wireless Sensor Networks
1. Algorithmic Construction of Optimal and Load
Balanced Clusters in Wireless Sensor Networks
Mohammad Hammoudeh∗ , Saeed Abuzour∗ , Sarah Mount† , Omar Aldabbas‡ , Mai Alfawair §
∗ Department of Computing, Manchester Metropolitan University, Manchester, UK, m.hammoudeh@mmu.ac.uk
† School of Computing and IT, University of Wolverhampton, Wolverhampton, UK, s.mount@wlv.ac.uk
‡ Al-Balqa Applied University, Faculty of Engineering, Jordan, Al-Salt, o.aldabbas@bau.edu.jo
§ Al-Zaytoonah Private University of Jordan, Faculty of Science and IT, Jordan, m.alfawair@alzaytoonah.edu.jo
Abstract—This paper proposes a clustering algorithm - Ba- communication efficiency. Nodes in the upper tier are called
lanced Minimum Radius Clustering (BMRC) - for use in large cluster heads and act as a routing backbone, while nodes in the
scale, distributed Wireless Sensor Networks (WSN). Cluster lower tier perform the sensing tasks. A sink-based single tier
balancing is an intractable problem to solve in a distributed
manner, and distribution is important, by reason of both avoiding network can lead to congestion at the gateway especially in
specialised node vulnerability and minimising message overhead. dense sensor networks. This can cause communication delays
The BMRC algorithm described here distributes several of the and inadequate tracking of the sensed events. Moreover, some
cluster balancing functions to the cluster-heads. In proposing this of the routing algorithms for such network architecture are
algorithm, several tentative claims have been made for it, namely commonly not scalable, e.g. [10]. To overcome these problems,
that it is suitable for arbitrary number of cluster heads; that it
specifies a way to elect cluster heads and use them to create the network clustering has been proposed as a possible solution.
local models; that it accomplishes optimal balanced clusters in In this paper a clustering algorithm that satisfies the following
distributed manner; that it is scalable and it uses the number-of- requirements is proposed:
hops as a clustering parameter; that it is energy efficient. These
claims were studied and verified by simulation. 1) Scalability: Clustering approaches are particularly temp-
Index Terms—Wireless Sensor Networks, Clustering, Load- ting to large-scale high-density sensor network applica-
Balancing. tions.
2) Load-balancing: One of the main challenges in WSNs
I. I NTRODUCTION systems design is balancing the resource usage of indivi-
dual nodes while maintaining the desired global network
Routing has proved to be a key issue in the area of
behaviour. Clustering algorithms must be able to orga-
WSNs research. Based on the literature, e.g. [1], it is well-
nise nodes in such a way that communication and pro-
understood that network lifetime, scalability, robustness, and
cessing load is minimised as well as energy consumption
the performance of the WSNs applications often depends
is distributed equalley among nodes to achieve longer
profoundly on efficient and reliable communication. However,
network life. Distributing workload amongst nodes will
it is difficult to attain both scalable and robust communication
also help to prevent energy depletion in one part of the
in WSNs. As clustering approaches are particularly tempting
network.
to large-scale high-density sensor network applications, clus-
3) Clustering: Clustering must be kept simple and decentra-
tering is sought as a solution to provide the requirements
lised. Each node should be able to independently make
stated above. In WSNs, clustering is the process of logical
its decisions based on the available local information.
grouping a set of nodes into disjoint and homogeneous groups,
A distributed implementation of clustering algorithms is
called clusters, based on a shared property such as nodes
expected to create minimal communication overload on
geographical location. Nodes within the same cluster are more
the sensor nodes. Clusters setup should be efficient in
closely related to one another than nodes assigned to different
terms of processing and communication. Furthermore,
clusters. Clustering has become an increasingly important task
the clustering algorithm should assist achieving load-
in modern WSNs domains. It is an effective technique to
balancing requirements through fair distribution of sen-
achieve scalability, self-organisation, reduce control messages,
sor nodes among various clusters. Also, the total number
power saving, bandwidth reusability, channel access, routing,
of clusters in the sensor network should be sustained
enhanced resource allocation, and others. Therefore, clustering
equal or around the optimal number of clusters defined
is another very important optimisation problem in WSNs.
by [11] which is 5% of the total number of nodes in the
Many protocols use heuristics to elect cluster-heads. These
network.
heuristics are based on the minimisation of both transmission
distances and the number of cluster heads. In this paper we study a set of clustering algorithms in the
Cluster-based routing is one of the most popular routing literature, particularly the algorithm presented in [12]. Based
schemes in WSNs [2], [3], [4], [5], [6], [7], [8], [9]. It is a on the findings in the literature we propose a new distributed
two or more tier routing scheme known for its scalability and clustering algorithm that solve some of the problems described
2. minimised. In [12], the maximum distance between any pair of
nodes in a cluster C is called the diameter of the cluster. The
algorithm is based on the theorem which states that for any
cluster P with the maximum diameter d, there is a cluster P
with maximum diameter d such that P is linearly separable
and d ≤ d. Since the linearly separable principle does not al-
Figure 1. An example showing that the clustering algorithm in [12] does not ways hold in balanced problems, this algorithm is not suitable
necessarily generate energy efficient clusters. According to the definition of for the optimal balanced clustering. Particularly, the definition
diameter in [12], node two is moved to Cluster 1 to achieve load balancing as
shown in Figure (b). However, it is clear that this moving step will not result
of the metric diameter is not appropriate to WSNs clustering as
in better energy balanced clusters since the distance between node 2 and the it considers the distance between any pair of nodes in a cluster
cluster-head in Cluster 1 is large. instead of the distance between the node and its cluster-head.
For instance two nodes in a cluster could be independent and
not related and thus the distance between them has no effect on
above. This algorithms aim to achieve optimal cluster balan- the cluster performance. Another drawback of this algorithm is
cing using two clustering parameters, the radius and the hope that every node should have knowledge about all other nodes
count. in the cluster, this knowledge is difficult to acquire, and causes
The following Section describes a number of clustering communication overhead. Moreover, this algorithm does not
techniques for WSNs. Section III describes the BMRC cluste- consider the transmission range of nodes when moving them
ring algorithm proposed by the authors and Section IV gives from one cluster to another. Finally, using the diameter instead
an experimental evaluation of the technique, carried out in of the distance between the nodes and their cluster-heads could
simulation. Section V concludes. result in energy inefficient clustering as shown in the example
in Figure 1.
II. R ELATED W ORK
In this section, we briefly review the main clustering proto- III. BALANCED M INIMUM R ADIUS C LUSTERING
cols used in this paper, e.g. for comparison, and we refer the Balanced Minimum Radius Clustering (BMRC) is a cluste-
interested reader to [13], [14], [15], [16], [17] and references ring algorithm that generates optimally balanced clusters based
there in for a comprehensive survey of the recent clustering on unbalanced clusters. The distributed balanced clustering
algorithms. consists of four different steps: (1) Local clustering; (2)
Low Energy Adaptive Clustering Hierarchy (LEACH) [11] Determination of a local model; (3) Determination of a global
is one of the most promising routing algorithms for sensor model which is based on all local models; (4) Finally, updating
networks. However, LEACH has been based on a number of of all local models.
assumptions which in the authors’ opinion limit its effecti- In BMRC, the nodes are clustered locally, then the respec-
veness in a number of applications. LEACH is well-suited tive cluster-head extracts a suitable representative information
for applications where constant monitoring is needed and about its cluster. These representatives are sent to the sink
data collection occurs periodically to a centralised location. node, which combines all local representatives to generate a
It increases network lifetime in two ways. First, the load is balancing plan. This approach is efficient, because the local
distributed to all nodes but not all at the same time. Second, clustering can be carried out quickly and independently from
there is lossless aggregation of data by the cluster-heads. The each other. Furthermore, it achieves lower transmission cost,
protocol is powerful and simple since nodes do not require as the number of transmitted representatives is much smaller
global knowledge or location information to create clusters. than the cardinality of the complete data set. Based on the
Despite the significant overall energy savings, however, the small number of representatives, the global cluster balancing
unrealistic assumptions made by the protocol raise a number can be done very efficiently.
of issues. These assumptions are listed in [18]. The proposed distributed balanced-clustering algorithm is
MuMHR [18] is an improvement over LEACH. MuMHR carried out on two different levels, i.e. the local level and
provides solutions to some of the limitations of LEACH. the global. On the local level, all sites carry out clustering
The main objective of this protocol is to provide substan- independently from each other using MuMHR algorithm. After
tially energy-efficient and robust communication. Similar to having completed the local unbalanced clustering, a local
LEACH, MuMHR does not generate optimally balanced clus- model is determined. Our proposed local models consist of
ters. This algorithm is studied in details in subsection III-A. a set of representatives for each locally found cluster. Each
Balanced Clustering algorithm proposed in [12] studies the representative is a concrete description for nodes residing on
theoretical aspects of the clustering problem in WSNs with the corresponding local cluster. BMRC builds initial network
application to energy optimisation. The algorithm considers clusters-based algorithm. The resulting clusters are then mo-
the clustering problem with the energy expenditure as an im- dified by BMRC to form a load balanced clusters that are
perative optimisation parameter. The authors define an optimal energy efficient. Next the local model is transferred to sink
clustering algorithm such that each cluster is balanced and node, where the local models are merged in order to form
the total distance between sensor nodes in the same cluster is a global model. The global model is created by analysing
3. the local representatives. This analysis is similar to a new
clustering phase with suitable global clustering parameters. A
global cluster-identifier is assigned to each local representative.
This resulting global balanced clustering is sent to all local
sites that start modifying their clusters to implement the global
model. This is a very difficult step as there might exist
dependencies between nodes located on different sites which Figure 2. A balancing scenario
are not taken into consideration by the creation of the local
models. In contrast to a central balanced clustering of the
complete network, the central balancing of the local clusters BMRC has many advantages including: (1) it is suitable for
can be carried out much faster. arbitrary number of cluster-heads; (2) it specifies a way to elect
In today’s WSNs, communication is orders of magnitude cluster-heads and use them to create the local models; (3) it
more expensive than local computation. The amount of energy accomplishes optimal balanced clusters in distributed manner;
needed to transmit a message to a destination at distance d (4) it is scalable and uses the number-of-hops as a clustering
from the source can be calculated by the following formula: parameter; (5) it is energy efficient; (6) it does not require
e = kdc where k and c are constants for a specific wireless global network knowledge.
system [12]. Therefore, minimising the distance helps to Figure 2 sketches a simple example that illustrates how
reduce the communication overhead and the energy dissipation BMRC works. The first step is to generate local clusters
thereafter. On the other hand, balancing the clusters is needed that are mostly unbalanced. These clusters are shown in Fi-
for evenly distributing the load among all cluster-head to gure 2 (a) where Cluster 1 has a single node and Cluster 2 has
avoid energy depletion at one area of the network. Therefore, three nodes. Next, the cluster-heads generate cluster represen-
BMRC is designed to utilise a combination of two clustering tatives and send it to the sink. For example, the representatives
parameters. for Cluster 1 is (H1, (3, 3),1,[1]) and for Cluster 2 is (H2,
1) Radius: is the maximum distance from a node to its (9,9),3, [2,4]). The elements of the tuple are: the cluster-head
cluster-head ID; cluster-head location; number of member nodes; and a
2) Number-of-hops: is the number of intermediate nodes list of nodes. When processing the local representatives, using
between a node and its cluster-head the diameter metric the sink determines a global model which
As communication is the most costly task in terms of energy, includes moving node 4 from Cluster 2 to Cluster 1. Upon
it must be used mostly carefully. To minimise the brid- receiving the global clustering model, the cluster-heads starts
ging distance between nodes and their respective cluster- implementing that model by moving node 4 to Cluster 1. The
head one needs to minimise the Radius clustering parameter. result is optimally balanced clusters as shown in Figure 2 (b).
Furthermore, forwarding messages at intermediate nodes to the In the next subsections we discuss BMRC clustering steps
next nodes involves turning the node transmitters on which in details.
increases the total amount of energy needed to transmit a
message from a source to destination. The number-of-hops A. Local clustering
metric were also chosen as a second clustering parameter
to reduce the total amount of energy consumption involved In this step, nodes are clustered locally using any multi-hop
in transmitting a message through multi-hops. We define clustering algorithm. In this work we use MuMHR [18] routing
diameter as a hybrid clustering parameter which is composed algorithm but any other multi-hop clustering algorithm can be
of both: Radius and the number-of-hops. Using the hybrid deployed. It is always desirable to use energy efficient cluste-
clustering parameters, diameter, the full BMRC algorithm is ring algorithms that produce cluster with node distribution as
written as shown in Algorithm 1. close as possible to the optimal clustering in order to make
the balancing step simpler. Furthermore, the deployed local
Algorithm 1 Balanced minimum Radius k-clustering create clustering algorithm is important because it determines the
local clusters using MuMHR. cluster-heads who generate the local cluster representatives.
MuMHR (Multi-hop, Multi-path, Hierarchal Routing) is a
1: if node is a cluster-head node then
wireless sensor networks cluster-based routing algorithm. It
2: calculate local model
is an improvement over LEACH [11]. It relaxes some of
3: send local model to sink
the unrealistic assumptions made by LEACH such as the
4: end if
single hop communication. The main objective of MuMHR
5: if node is sink then
protocol is to provide substantially energy-efficient and robust
6: then calculate global optimal balanced clustering model
communication. The energy efficiency is achieved by load
7: send global model to all cluster-heads
balancing at two levels: (1) at the network level, which
8: end if
involves traffic multiplexing over multiple paths; (2) at the
9: when cluster-head nodes receive the global model then
cluster level, introducing rotation of the cluster-heads every
10: implement the changes defined by the global model
given interval of time.
4. The operation of the proposed routing protocol can be Algorithm 2 Calculation of the global model at the sink
split into two phases: the setup phase and the data transfer 1: Input: Set of models Sn
phase. During the set-up phase cluster-heads are selected 2: Output: n disjoint clusters S1 , ..., Sn with minimum
and hierarchy is created. During the data transmission phase, max{diameter(S1 ), ..., diameter(Sn )}
sensing nodes transmit data to their cluster-head. The cluster- 3: Find the biggest cluster of two adjacent clusters C1 and
heads aggregates the received data before transmission to the C2
sink or immediately multiplex messages over multiple lines in 4: if (|C1 | < |C2 |) then then
time critical applications. 5: Swap C1 and C2
MuMHR reduces the energy expenditure by shortening 6: end if
the distance between the node and its cluster-head and by 7: while (|C2 | = |C1 |) do do
reducing the setup communication overhead. This is done 8: Find a point v ⊂ C1 such that diameter(C2 ∪ {v} −
through incorporating the number-of-hops metric together with diameter(C2 ) is minimised
the back-off waiting time. The back-off waiting time helps to 9: C1 ← C1 − {v}
decrease the number of set-up messages and aid the formation 10: C2 ← C2 ∪ {v}
of more geographically uniform clusters. During the back-off 11: end while
waiting time, sensor nodes receive advertisement messages and
only consider the message with the smallest number-of-hops
received during that time. D. Updating of the Local Clustering based on the Global
Although MuMHR aims at load balancing, the results Model
published in [18] shows that node distribution among clusters After having created a global clustering model, the sink
is not even. BMRC algorithm generates optimally balanced will transmit the complete global model to all cluster-heads.
clusters based on these unbalanced clusters. The cluster-heads collaborate to modify their cluster using the
global model such that each cluster has equal number of nodes
B. Determination of a local model while minimising the diameter parameter.
After having the nodes clustered locally, we need a small IV. E XPERIMENTAL E VALUATION
number of representatives which describe the local cluste-
In order to implement the BMRC algorithm and study its
ring result accurately. We have to find an optimum trade-
properties, a WNS simulator called Dingo [19] was used.
off between the following two mutually conflicting require-
Dingo is a development tool for WSNs. Dingo features a
ments: cluster representatives should be compact as much
customisable energy module which makes it more flexible than
as possible; and provide an accurate description of a local
the energy model used by NS-2 [20] and other simulators.
cluster. As the maximum cluster diameter and the list of cluster
Moreover, Dingo features a significant improvement in the
member nodes are computed during the clustering phase, it
simulation performance by giving the option to split the
might serve as good representatives. Unfortunately, the number
visualisation from the simulation. It forces thread switching
can become very high, especially in very dense networks.
to occur so that threads can not dominate the scheduler which
Therefore, we define a list, L, of nodes which contains all
makes the GUI more responsive.
nodes that are diameter/2 far from the cluster-head. The
Dingo provides tools for the simulation and deployment of
local model also contains the cluster-head ID and location,
high-level, Python code on real sensor networks. For example,
number of nodes that are members of that particular cluster,
Dingo-boom provides a two-way interface between MoteIV’s
and the list L. The cluster-head location is used by the sink
Boomerang class motes and Dingo. Dingo-top is another tool
to find adjacent clusters that possibly can exchange members
which is used to dump network topology data to a text
to achieve optimal cluster balancing based on the received
file and generate a graphical representation of that topology.
diameter parameters of local clusters.
Furthermore, Dingo has several features in the form of plugins.
These can be activated/deactivated on the plugin menu. Also,
C. Determination of the global model
Dingo has a "Topology" menu which can be used to change
To find a global clustering model, we use Algorithm 2. The the network topology of a simulation from a random topology
aim is to create optimally balanced clustering where nodes to/from a grid. Network topologies can be loaded and saved.
are evenly distributed over different clusters using only the For our experiments, we created a 50-node network, where
local model information available at the sink. The sink node the nodes are scattered randomly on 600 × 600 grid, such
will decide how the local clusters are going to be modified that no two nodes share the same location. The transmission
and disseminate this information to all cluster-heads. The range of each node is bound to 100m. The processing delay
sink decisions are a set of sensor node moving steps from for transmitting a message is randomly chosen between 0
one cluster to another based on the defined hybrid diameter and 5ms, simulating real-world characteristics of low-power
parameter. Each cluster could exchange nodes with one or radio transmission.
more adjacent clusters to arrive to an optimal energy efficient We compare the performance of BMRC with that of
node distribution. MuMHR and the balanced minimum diameter algorithm.
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