Mobile Sink

Using a mobile sink to reduce the energy consumption of nodes and to prevent the formation of energy holes in wireless sensor networks(WSNs).Benefits are dependent on the path taken by the mobile sink, particularly in delay sensitive applications ,as all sensed data must be collected within a given time constraint. An approach proposed to address this challenge is to form a hybrid moving pattern in which a mobile sink node only visits rendezvous points (RPs), as opposed to all nodes. Sensor nodes that are not RPs forward their sensed data via multi-hopping to the nearest RP node. Fundamental problem becomes computing a tour that visits all the RPs within a given delay bound. Identifying the optimal tour, however is an NP hard problem .To address this problem Weighted Rendezvous Planning (WRP) method is proposed whereby each sensor node is assigned a weight corresponding to its hop distance from the tour and the number of data packets that it forwards to the closest RP.WRP enables a mobile sink to retrieve all sensed data within a given deadline while conserving the energy expenditure of sensor nodes. More specifically, WRP reduces energy consumption and also increases the network lifetime as compared with existing methods

Recent developments on Wireless Sensor Networks have made their application in a wide range
such as military sensing and tracking, health monitoring, traffic monitoring, video surveillance and so on.
Wireless sensor nodes are restricted to computational resources, and are always deployed in a harsh,
unattended or unfriendly environment. Therefore, network security becomes a tough task and it involves
the authorization of admittance to data in a network. The problem of authentication and pair wise key
establishment in sensor networks with mobile sink is still not solved in the mobile sink replication attacks.
In q-composite key pre distribution scheme, a large number of keys are compromised by capturing a
small fraction of sensor nodes by the attacker. The attacker can easily take a control of the entire network
by deploying a replicated mobile sinks. Those mobile sinks which are preloaded with compromised keys
are used authenticate and initiate data communication with sensor node. To determine the above problem
the system adduces the three-tier security framework for authentication and pair wise key establishment
between mobile sinks and sensor nodes. The previous system used the polynomial key pre distribution
scheme for the sensor networks which handles sink mobility and continuous data delivery to the
neighbouring nodes and sinks, but this scheme makes high computational cost and reduces the life time of
sensors. In order to overcome this problem a random pair wise key pre distribution scheme is suggested
and further it helps to improve the network resilience. In addition to this an Identity Based Encryption is
used to encrypt the data and Mutual authentication scheme is proposed for the identification and
isolation of replicated mobile sink from the network.

The isolated accessible Methane (CH4) Absorption Fueling System (MAFS) is matured based on the mechanics of Wireless Sensor Network (WSN) comprised of gas sensing capable motes complementing a MAFS-WSN. Discrete routing protocols have been designed earlier for data collection in both compatible and divergent networks. This research presents a novel Scheduling based Clustering (SBC) with Mobile Sink (MS) strategy (SBC-MS) which supplements data collection in MAFS-WSN. The SBC-MS strategy attempts to exploit the vital parameters of energy and distance in selecting the appropriate cluster head that well suits MAFS-WSN in reliable gas detection. The MSs are exploited to reduce the energy expenditure in data communication. Extensive experimentations have been carried out with the proposed SBC-MS to ensure the QOS of MAFS-WSN in terms of schedulability and reliability. The simulation results prove that SBC-MS outperforms the earlier clustering technique M-LEACH in terms of network lifetime, energy consumption, end-to-end delay and data rate.

A network that does not include any central controller and allows nodes to enter or leave the network at any time is known as a wireless sensor network. There are very small sized nodes deployed in these networks in large areas. The energy consumption of the nodes in these networks is a major concern since it directly affects the lifetime of the network. These dead node cause several problems. To enhance the network lifetime mobile sink is introduced which improve network performance. But mobile also leads to another challenge such as mobile sink scheduling and (re)routing. The complete network is divided into fixed size clusters and based on the energy and distance, cluster heads are chosen from the clusters. The remaining nodes of the cluster are considered to be individual. Several clustering algorithms have been designed by researchers which can be broadly categorized based on the manner in which clusters are formed and the kinds of parameters included during the selection of cluster heads. This research focuses on aggregating the data from cluster heads by deploying multiple mobile sinks. The bee colony algorithm is used to make the base station mobile. The simulation of the proposed technique is performed in MATLAB and results are analyzed in terms of various parameters.

Most applications of underwater wireless sensor networks (UWSNs) demand reliable data delivery over a longer period in an efficient and timely manner. However, the harsh and unpredictable underwater environment makes routing more challenging as compared to terrestrial WSNs. Most of the existing schemes deploy mobile sensors or a mobile sink (MS) to maximize data gathering. However, the relatively high deployment cost prevents their usage in most applications. Thus, this paper presents an autonomous underwater vehicle (AUV)-aided efficient data-gathering (AEDG) routing protocol for reliable data delivery in UWSNs. To prolong the network lifetime, AEDG employs an AUV for data collection from gateways and uses a shortest path tree (SPT) algorithm while associating sensor nodes with the gateways. The AEDG protocol also limits the number of associated nodes with the gateway nodes to minimize the network energy consumption and

Using a mobile sink to reduce the energy consumption of nodes and to prevent the formation of energy holes in wireless sensor networks (WSNs). Benefits are dependent on the path taken by the mobile sink, particularly in delay sensitive applications ,as all sensed data must be collected within a given time constraint. An approach proposed to address this challenge is to form a hybrid moving pattern in which a mobile sink node only visits rendezvous points (RPs), as opposed to all nodes. Sensor nodes that are not RPs forward their sensed data via multi-hopping to the nearest RP node. Fundamental problem becomes computing a tour that visits all the RPs within a given delay bound. Identifying the optimal tour, however is an NP hard problem. To address this problem Weighted Rendezvous Planning(WRP) method is proposed whereby each sensor node is assigned a weight corresponding to its hop distance from the tour and the number of data packets that it forwards to the closest RP.WRP enables a mobile sink to retrieve all sensed data within a given deadline while conserving the energy expenditure of sensor nodes. More specifically, WRP reduces energy consumption and also increases the network lifetime as compared with existing methods.

Recently, sink mobility has been shown to be highly beneficial in improving network lifetime in wireless sensor networks (WSNs). Numerous studies have exploited mobile sinks (MSs) to collect sensed data in order to improve energy efficiency and reduce WSN operational costs. However, there have been few studies on the effectiveness of MS operation on WSN closed operating cycles. Therefore, it is important to investigate how data is collected and how to plan the trajectory of the MS in order to gather data in time, reduce energy consumption, and improve WSN network lifetime. In this study, we combine two methods, the cluster-head election algorithm and the MS trajectory optimization algorithm, to propose the optimal MS movement strategy. This study aims to provide a closed operating cycle for WSNs, by which the energy consumption and running time of a WSN is minimized during the cluster election and data gathering periods. Furthermore, our flexible MS movement scenarios achieve both a long network lifetime and an optimal MS schedule. The simulation results demonstrate that our proposed algorithm achieves better performance than other well-known algorithms. This is an Open Access article distributed under the term of Korea Open Government License (KOGL) Type 4: Source Indication + Commercial Use Prohibition + Change Prohibition (http://www.kogl.or.kr/news/dataView.do?dataIdx=97).

Most applications of underwater wireless sensor networks (UWSNs) demand reliable data delivery over a longer period in an efficient and timely manner. However, the harsh and unpredictable underwater environment makes routing more challenging as compared to terrestrial WSNs. Most of the existing schemes deploy mobile sensors or a mobile sink (MS) to maximize data gathering. However, the relatively high deployment cost prevents their usage in most applications. Thus, this paper presents an autonomous underwater vehicle (AUV)-aided efficient data-gathering (AEDG) routing protocol for reliable data delivery in UWSNs. To prolong the network lifetime, AEDG employs an AUV for data collection from gateways and uses a shortest path tree (SPT) algorithm while associating sensor nodes with the gateways. The AEDG protocol also limits the number of associated nodes with the gateway nodes to minimize the network energy consumption and Sensors 2015, 15 29150 to prevent the gateways from overloading. Moreover, gateways are rotated with the passage of time to balance the energy consumption of the network. To prevent data loss, AEDG allows dynamic data collection at the AUV depending on the limited number of member nodes that are associated with each gateway. We also develop a sub-optimal elliptical trajectory of AUV by using a connected dominating set (CDS) to further facilitate network throughput maximization. The performance of the AEDG is validated via simulations, which demonstrate the effectiveness of AEDG in comparison to two existing UWSN routing protocols in terms of the selected performance metrics.

Localization of sensor nodes is important in many aspects in wireless sensor networks. The known location of sensor node helps in determining the event of interest. A mobile sink is introduced to track the event driven sensor nodes in the path of the event, thus conserving energy and time. We present a novel range based localization algorithm which helps the mobile sink to compute the location of the sensor
nodes efficiently. The data transfer from the mobile sink and the sensor nodes is used to estimate the sensor location. The sensor nodes do not need to spend energy on neighbouring interaction for localization. The localization mechanism has been implemented in TOSSIM. The simulation results show that our scheme performed better than other range-based schemes.

Mobile sinks are proposed as a possible solution
to the hotspot problem, which leads to the early death of the
nodes close to the sink, due to the concentration of data traffic
towards the sinks. However, the route updates employed by the
source nodes to determine the position of the sinks introduce an
overhead in terms of data reporting delays and energy consumptions.
On the other hand, decreased frequency of route updates
causes stale sink position information to be utilized for data
packets which results in suboptimal data dissemination paths.
In this paper, we investigate this inherent trade-off and provide
an insight on how to fine tune the route update parameters of a
mobile sink routing protocol. For this purpose, we define a system
employing a simple routing protocol and determine the optimal
operating points from the results obtained via simulations, under
varying degrees of route updates and sink mobility.

Most applications of underwater wireless sensor networks (UWSNs) demand reliable data delivery over a longer period in an efficient and timely manner. However, the harsh and unpredictable underwater environment makes routing more challenging as compared to terrestrial WSNs. Most of the existing schemes deploy mobile sensors or a mobile sink (MS) to maximize data gathering. However, the relatively high deployment cost prevents their usage in most applications. Thus, this paper presents an autonomous underwater vehicle (AUV)-aided efficient data-gathering (AEDG) routing protocol for reliable data delivery in UWSNs. To prolong the network lifetime, AEDG employs an AUV for data collection from gateways and uses a shortest path tree (SPT) algorithm while associating sensor nodes with the gateways. The AEDG protocol also limits the number of associated nodes with the gateway nodes to minimize the network energy consumption and

Localization of sensor nodes is important in many aspects in wireless sensor networks. The known location of sensor node helps in determining the event of interest. A mobile sink is introduced to track the event driven sensor nodes in the path of the event, thus conserving energy and time. We present a novel range based localization algorithm which helps the mobile sink to compute the location of the sensor nodes efficiently. The data transfer from the mobile sink and the sensor nodes is used to estimate the sensor location. The sensor nodes do not need to spend energy on neighbouring interaction for localization. The localization mechanism has been implemented in TOSSIM. The simulation results show that our scheme performed better than other range-based schemes.

A Wireless Sensor Network (WSN) contains many tiny sensor nodes which have minimum battery power, low-cost, with computing and communication competences with limited range for communication with multi model and embedded sensing capability. Environment, habitat and military surveillance monitoring are the most significant application of WSN. The lifetime of the network depends on energy power of these sensor nodes. To efficiently improve the lifetime of the network, a cluster- based routing protocol is an effective scheme. In that, the numbers of sensor nodes are split into several groups known as Clusters. Based on some parameters, each cluster node will elect a node from their cluster to be the head, called as cluster head (CH). All sensor nodes gather the information from their surroundings and send it to the corresponding CHs. Afterward the data will be send to the Base Station (Sink) by the CHs. Thus, selecting the suitable CH can decline significant extent of energy dissipation. In this paper, we present a fuzzy based approach which will elect the CH with respect to energy and distance. On the other hand, we also exhibited the working benefits of using mobile sink, which travels in the network area and collects the information from sensor nodes, for improving the lifetime of the network, using different data collection strategies, instead of static base station. The Rendezvous nodes will have the ability to transfer data when the mobile sink comes near. The proposed technique is analyzed using network simulator.

Today, science and technology is developing, particularly the internet of things (IoT), there is an increasing demand in the sensor field to serve the requirements of individuals within modern life. Wireless sensor networks (WSNs) was created to assist us to modernize our lives, saving labor, avoid dangers, and that bring high efficiency at work. There are many various routing protocols accustomed to increase the ability efficiency and network lifetime. However, network systems with one settled sink frequently endure from a hot spots issue since hubs close sinks take a lot of vitality to forward information amid the transmission method. In this paper, the authors proposed combining the colony optimization algorithm ant colony optimization (ACO) routing algorithm and mobile sink to deal with that drawback and extend the network life. The simulation results on MATLAB show that the proposed protocol has far better performance than studies within the same field.

Wireless Sensor Networks (WSNs) have been emerged i
n many important aspects in the real world, such as
industry, agriculture, and military applications. A
s the main challenge that WSNs facing is the energy
consumption, it is necessary to investigate the sui
tability of using mobile sinks for data collection
in these
networks. In this paper, therefore, a new data gath
ering technique with mobile elements referred to as
Intersection Point of Communication Ranges (IPCR) i
s proposed. The IPCR algorithm endeavours to
compute the optimal trajectory of the mobile sink f
or which the data collection latency is reduced.
Simulation results presented in this study showed t
hat the IPCR algorithm has achieved the optimal Tra
vel
Sales-Man Problem algorithm. In addition, the IPCR
algorithm outperformed the Connectivity Based Data
Collection (CBDC) algorithm in terms of data gather
ing latency and network throughput.

We study the rendezvous data collection problem for the mobile sink in wireless sensor networks. We introduce to jointly optimize trajectory planning for the mobile sink and workload balancing for the network. By doing so, the mobile sink is able to efficiently collect network-wide data within a given delay bound and the network can eliminate the energy bottleneck to dramatically prolong its lifetime. Such a joint optimization problem is shown to be NP-hard and we propose an approximation algorithm, named RPS-LB, to approach the optimal solution. In RPS-LB, according to observed properties of the median reference structure in the network, a series of Rendezvous Points (RPs) are selected to construct the trajectory for the mobile sink and the derived approximation ratio of RPS-LB guarantees that the formed trajectory is comparable with the optimal solution. The workload allocated to each RP is proven to be balanced mathematically. We then relax the assumption that mobile sink knows the location of each sensor node and present a localized, fully distributed version, RPS-LB-D, which largely improves the system applicability in practice. We verify the effectiveness of our proposals via extensive experiments.