SCSBE: Secured Cluster and Sleep Based Energy-Efficient Sensory Data Collection with Mobile Sinks

Wireless sensor networks applications involve a position of inaccessible metropolitan vicinity en-closed by wireless sensor nodes (WSNs)-monitors environmental parameters like battle field surveillance, home applications like fire alarm, health monitoring, etc. Energy plays a vital role in Wireless sensor networks. So, we have to concentrate more on balanced energy consumption for maximizing the network lifetime. Minimizing the whole network overhead and vigor disbursement coupled with the multi-hop data reclamation process that ensuring balanced energy consumption among SNs which results in prolonged network lifetime. This can be achieved by forwarding the sensed data to their cluster heads and then filtering the data before sending it to their tryst nodes, which is located in proximity to MS’s trajectory. Sleep and awakening of nodes periodically helps to retain their energy for some more time. The events occurring in any part of the network should be identified by the nodes, while arrangements sleep and active among the nodes. (i.e.) the nodes should be scheduled to sleep, so that the outstanding nodes can take care of the whole network. The eXtensible Randomized Matrix Arithmetic Coding (XRMAC) Technique has been used to enhance the security among all the nodes in the network. Simulation results show that our Proposed Scheme can have better Lifetime, improved throughput, reduced delay compared to other existing methods.

Optimal Coverage Multi-Path Scheduling Scheme with Multiple Mobile Sinks for WSNs

Wireless Sensor Networks(WSNs)are usually formed with many tiny sensors which are randomly deployed within sensing field for target monitoring.These sensors can transmit their monitored data to the sink in a multi-hop communication manner.However,the‘hot spots’problem will be caused since nodes near sink will consume more energy during forwarding.Recently,mobile sink based technology provides an alternative solution for the long-distance communication and sensor nodes only need to use single hop communication to the mobile sink during data transmission.Even though it is difficult to consider many network metrics such as sensor position,residual energy and coverage rate etc.,it is still very important to schedule a reasonable moving trajectory for the mobile sink.In this paper,a novel trajectory scheduling method based on coverage rate for multiple mobile sinks(TSCR-M)is presented especially for large-scale WSNs.An improved particle swarm optimization(PSO)combined with mutation operator is introduced to search the parking positions with optimal coverage rate.Then the genetic algorithm(GA)is adopted to schedule the moving trajectory for multiple mobile sinks.Extensive simulations are performed to validate the performance of our proposed method.

A Novel Approach to Energy Optimization:Efficient Path Selection in Wireless Sensor Networks with Hybrid ANN

In pursuit of enhancing the Wireless Sensor Networks(WSNs)energy efficiency and operational lifespan,this paper delves into the domain of energy-efficient routing protocols.InWSNs,the limited energy resources of Sensor Nodes(SNs)are a big challenge for ensuring their efficient and reliable operation.WSN data gathering involves the utilization of a mobile sink(MS)to mitigate the energy consumption problem through periodic network traversal.The mobile sink(MS)strategy minimizes energy consumption and latency by visiting the fewest nodes or predetermined locations called rendezvous points(RPs)instead of all cluster heads(CHs).CHs subsequently transmit packets to neighboring RPs.The unique determination of this study is the shortest path to reach RPs.As the mobile sink(MS)concept has emerged as a promising solution to the energy consumption problem in WSNs,caused by multi-hop data collection with static sinks.In this study,we proposed two novel hybrid algorithms,namely“ Reduced k-means based on Artificial Neural Network”(RkM-ANN)and“Delay Bound Reduced kmeans with ANN”(DBRkM-ANN)for designing a fast,efficient,and most proficient MS path depending upon rendezvous points(RPs).The first algorithm optimizes the MS’s latency,while the second considers the designing of delay-bound paths,also defined as the number of paths with delay over bound for the MS.Both methods use a weight function and k-means clustering to choose RPs in a way that maximizes efficiency and guarantees network-wide coverage.In addition,a method of using MS scheduling for efficient data collection is provided.Extensive simulations and comparisons to several existing algorithms have shown the effectiveness of the suggested methodologies over a wide range of performance indicators.

An Efficient Path Planning Strategy in Mobile Sink Wireless Sensor Networks

Wireless sensor networks (WSNs) are considered the backbone ofthe Internet of Things (IoT), which enables sensor nodes (SNs) to achieveapplications similarly to human intelligence. However, integrating a WSNwith the IoT is challenging and causes issues that require careful exploration.Prolonging the lifetime of a network through appropriately utilising energyconsumption is among the essential challenges due to the limited resourcesof SNs. Thus, recent research has examined mobile sinks (MSs), which havebeen introduced to improve the overall efficiency of WSNs. MSs bear theburden of data collection instead of consuming energy at the routeing bySNs. In a network, some areas generate more data through SNs that containfrequent, urgent messages. These messages carry sensitive data that must bedelivered immediately to user applications. Collecting such messages via MSs,especially on a large scale, increases delays, which are not tolerable in some realapplications. This issue has not been studied much. Thus, the present studyutilises the advantages of the priority parameter to concentrate on these areasand proposes a new model named ‘energy efficient path planning of MS-basedarea priority’ (EEPP-BAP). This method involves non-urgent and urgentmessages. It is comprised of four procedures. Initially, after SNs are distributedrandomly in a wide monitoring field, the monitoring field is partitionedinto equal zones according to priority, either differently or equally. Next isclustering based on the cluster head (CH) selected to perform the particleswarm optimisation algorithm (PSO). Then, the MS moves first to the zoneswith higher priority and less distance to perform the brain storm optimisationalgorithm. Finally, for urgent messages from the other zones at which theMS continues, the proposed approach establishes a routeing technique usingmulti-hop communication based on the MS position and using PSO. The proposed solution is aimed at delivering urgent messages to MSs free of latencyand with minimal packet loss. The simulation results proved that the EEPPBAP method can improve network performance compared with other modelsbased on different parameters that have been used to construct the controlledmovement of MSs in large-scale environments involving urgent messages. Theproposed method increased the average lifetime of SNs to 206.6% on average,reduced the average end-to-end delay to 7.1%, and increased the averagepacket delivery ratio to 36.9%.

An Energy-Efcient Mobile-Sink Path-Finding Strategy for UAV WSNs

Data collection using a mobile sink in a Wireless Sensor Network(WSN)has received much attention in recent years owing to its potential to reduce the energy consumption of sensor nodes and thus enhancing the lifetime of the WSN.However,a critical issue of this approach is the latency of data to reach the base station.Although many data collection algorithms have been introduced in the literature to reduce delays in data delivery,their performances are affected by the ight trajectory taken by the mobile sink,which might not be optimized yet.This paper proposes a new path-nding strategy,called Energy-efciency Path-nding Strategy(EPS)in the Air-Ground Collaborative Wireless Sensor Network(AGCWSN).The proposed approach is able to greatly enhance the efciency of data collection.The performance of the proposed strategy is simulated and compared with the existing strategies over several parameters.The simulation results show that the mobile sink with EPS can collects data with lower data delivery delay as compared to other existing strategies.The number of data retransmissions between sensor nodes and mobile sink in EPS is also the lowest in EPS among several existing strategies.The data delivery delay is 66%and 120%lower than Rest Center Tractor Scanning(RCTS)and Non-stop Center Tractor Scanning(NCTS)in irregular and grid topology respectively.The data delivery delay is 62%lower than Two Row Scanning(TRS)in grid topology and 120%lower than RkM in irregular topology.The packet loss of EPS-2 is 1.3%lower than RkM.

Reactive data collection protocol using mobile sink in wireless sensor network

We suggest event collection protocol in a specific region where sensors are deployed to detect and collect events.In the traditional multi-hop routing in wireless sensor networks reporting events to a sink node or base-station will cause imbalanced energy consumption of static sensors.To solve this problem,we use mobile sink.In this paper,we study the design of efficiency routing protocol for supporting efficient data collecting in mobile sink wireless sensor networks(mWSNs).We suggest the following two main ideas.First,we use reactive protocol to cut off unnecessary delay.Mobile sink makes a path to access to sensor node.Second,we model mobile sink movement depending on data frequency,so we can reduce moving distance efficiently.We simulate this protocol and compare it with the traditional method.Simulation results show this protocol reduces distance significantly and is suitable for mWSNs with heavy traffic.

Genetics Based Compact Fuzzy System for Visual Sensor Network

As a component of Wireless Sensor Network(WSN),Visual-WSN(VWSN)utilizes cameras to obtain relevant data including visual recordings and static images.Data from the camera is sent to energy efficient sink to extract key-information out of it.VWSN applications range from health care monitoring to military surveillance.In a network with VWSN,there are multiple challenges to move high volume data from a source location to a target and the key challenges include energy,memory and I/O resources.In this case,Mobile Sinks(MS)can be employed for data collection which not only collects information from particular chosen nodes called Cluster Head(CH),it also collects data from nearby nodes as well.The innovation of our work is to intelligently decide on a particular node as CH whose selection criteria would directly have an impact on QoS parameters of the system.However,making an appropriate choice during CH selection is a daunting task as the dynamic and mobile nature of MSs has to be taken into account.We propose Genetic Machine Learning based Fuzzy system for clustering which has the potential to simulate human cognitive behavior to observe,learn and understand things from manual perspective.Proposed architecture is designed based on Mamdani’s fuzzy model.Following parameters are derived based on the model residual energy,node centrality,distance between the sink and current position,node centrality,node density,node history,and mobility of sink as input variables for decision making in CH selection.The inputs received have a direct impact on the Fuzzy logic rules mechanism which in turn affects the accuracy of VWSN.The proposed work creates a mechanism to learn the fuzzy rules using Genetic Algorithm(GA)and to optimize the fuzzy rules base in order to eliminate irrelevant and repetitive rules.Genetic algorithmbased machine learning optimizes the interpretability aspect of fuzzy system.Simulation results are obtained using MATLAB.The result shows that the classification accuracy increase along with minimizing fuzzy rules count and thus it can be inferred that the suggested methodology has a better protracted lifetime in contrast with Low Energy Adaptive Clustering Hierarchy(LEACH)and LEACHExpected Residual Energy(LEACH-ERE).

Reinforcement Learning to Improve QoS and Minimizing Delay in IoT

Machine Learning concepts have raised executions in all knowledge domains,including the Internet of Thing(IoT)and several business domains.Quality of Service(QoS)has become an important problem in IoT surrounding since there is a vast explosion of connecting sensors,information and usage.Sen-sor data gathering is an efficient solution to collect information from spatially dis-seminated IoT nodes.Reinforcement Learning Mechanism to improve the QoS(RLMQ)and use a Mobile Sink(MS)to minimize the delay in the wireless IoT s proposed in this paper.Here,we use machine learning concepts like Rein-forcement Learning(RL)to improve the QoS and energy efficiency in the Wire-less Sensor Network(WSN).The MS collects the data from the Cluster Head(CH),and the RL incentive values select CH.The incentives value is computed by the QoS parameters such as minimum energy utilization,minimum bandwidth utilization,minimum hop count,and minimum time delay.The MS is used to col-lect the data from CH,thus minimizing the network delay.The sleep and awake scheduling is used for minimizing the CH dead in the WSN.This work is simu-lated,and the results show that the RLMQ scheme performs better than the base-line protocol.Results prove that RLMQ increased the residual energy,throughput and minimized the network delay in the WSN.

Distributed Algorithms for Event Reporting in Mobile-Sink WSNs for Internet of Things

Wireless Sensor Networks（WSNs） have many applications, such as climate monitoring systems, fire detection, smart homes, and smart cities. It is expected that WSNs will be integrated into the Internet of Things（IoT）and participate in various tasks. WSNs play an important role monitoring and reporting environment information and collecting surrounding context. In this paper we consider a WSN deployed for an application such as environment monitoring, and a mobile sink which acts as the gateway between the Internet and the WSN. Data gathering is a challenging problem in WSNs and in the IoT because the information has to be available quickly and effectively without delays and redundancies. In this paper we propose several distributed algorithms for composite event detection and reporting to a mobile sink. Once data is collected by the sink, it can be shared using the IoT infrastructure. We analyze the performance of our algorithms using WSNet simulator, which is specially designed for event-based WSNs. We measure various metrics such as average residual energy, percentage of composite events processed successfully at the sink, and the average number of hops to reach the sink.

Non-interference topology scheme in wireless sensor networks

A novel topology scheme, cell with multiple mobile sinks method （CMMSM）, is proposed in this article for the collection of information and for the environment monitoring in wireless sensor networks. The system consists of many static sensors, scattered in a large scale sensing field and multiple mobile sinks, cruising among the clusters. Conservation of energy and simplification of protocol are important design considerations in this scheme. The noninterference topology scheme largely simplifies the full-distributed communication protocol with the ability of collision avoidance and random routing. The total number of cluster heads in such a topology was analyzed, and then an approximate evaluation of the total energy consumption in one round was carded out. Simulation results show that CMMSM can save considerable energy and obtain higher throughput than low-energy adaptive clustering hierarchy （LEACH） and geographical adaptive fidelity （GAF）.