Mobile Sink In Wireless Sensor Networks Research Articles

TBDD: Territory-Bound Data Delivery for Large-Scale Mobile Sink Wireless Sensor Networks

The hierarchical structure-based data dissemination is the most popular technique in mobile sink wireless sensor networks (MS-WSNs). An ingenious virtual structure design combined with a precise routing management strategy is significant to attaining efficient data dissemination in hierarchical approaches. This paper proposes a hierarchical protocol called TBDD that divides the network into multiple partitions called Regions, and spots the location of the mobile sink (MS) according to these partitions. TBDD dynamically assigns a defined role to each division by adopting the mobility of the sink. Thus, the protocol takes advantage of the sink’s movement and the Regions’ flexible role in balancing energy consumption throughout the network. A Region is designated as active if it contains the sink node or passive otherwise. By using the territory of the active region as a temporal location of the MS, the proposed protocol hides the local movements (i.e., moves inside the active region) of the sink from the rest of the network. In such a way, regardless of the exact position of the sink, sensed data flows from different network ends to the sink’s temporal location. Therefore, TBDD reduces the query request and response burden employed to get the position of the sink. Besides, TBDD implements a spanning tree to report the location information of the MS. Last; we applied an opportunistic routing technique that captures multiple network criteria to elect packet forwarder nodes. The proposed protocol is mathematically analyzed and experimentally evaluated and shows outstanding performance in terms of the number of hops, energy consumption, delay, network lifetime, and success ratio.

Rendezvous Based Adaptive Path Construction for Mobile Sink in WSNs Using Fuzzy Logic

In this paper, an adaptive path construction approach for Mobile Sink (MS) in wireless sensor networks (WSNs) for data gathering has been proposed. The path is constructed based on selecting Rendezvous Points (RPs) in the sensing field where the MS stops in order to collect the data. Compared with the most existing RP-based schemes, which rely on fixed RPs to construct the path where these points will stay fixed during the whole network lifetime, we propose an adaptive path construction where the locations of the RPs are dynamically updated using a Fuzzy Inference System (FIS). The proposed FIS, which is named Fuzzy_RPs, has three inputs and one output. The inputs are: the remaining energy of the sensor nodes, the transmission distance between the RPs and the sensor nodes, and the number of surrounding neighbors of each node. The output of FIS is a weight value for each sensor node generated based on the previous three parameters and, thus, each RP is updated to its new location accordingly. Simulation results have shown that the proposed approach extends the network lifetime compared with another existing approach that uses fixed RPs. For example, in terms of using the first dead node as a metric for the network lifetime, when the number of deployed sensor nodes changes from 150 to 300, an improvement that ranges from 48.3% to 83.76% has been achieved compared with another related approach that uses fixed RPs.

Introduction of a Method Using GIS to Generate Rendezvous Areas for Mobile Sinks in WSNs

This paper proposes an algorithm to change the concept of rendezvous points (RPs), which have been commonly used in mobile sink routing problem of wireless sensor networks (WSNs) to rendezvous areas (RAs) and to generate RAs by using geographic information system (GIS) tools. With the traditional RPs, a mobile sink must have visited the points, and possible deviations from the points would cause the mobile sink difficulty finding exact locations to collect data. However, with suggested RAs, the mobile sink is capable of receiving data from sensor nodes in an RA by visiting any points within the RA. It also reduces the traveling distance of the mobile sink, thereby prolonging its lifetime. With appropriate extensions with GIS tools, the algorithm can generate 3D RAs, which can be simulated in more realistic environments.

Agent-based Localization using Mobile Sink in Wireless Sensor Networks

Aims: This paper presents agent-based localization using the mobile sink in wireless sensor networks. This proposed scheme is less expensive than GPS. It has a longer lifetime and is more energy-efficient. The mobile sink has a large and easily replenishable energy reserve and is movable within the sensor network's deployment area. Objectives: In this work, a mobile sink is proposed that traverses inside the network's boundary and gathers information at a low energy cost. Methods: The use of a mobile sink in localization introduces a new way to reduce energy consumption in WSNs. The location of a mobile sink beacon signal is known to all the sensor nodes and is also communicated periodically to all the sensor nodes. The distance measurements of the three beacon signals broadcasted by the mobile sink moving in a predetermined path and time slot are considered in this scheme, which uses the trilateration method to compute the position of the node. For isolated nodes, location estimation is performed for non-GPS equipped nodes to derive from the network their locations by using the reference node beacon signals and performing multilateration. For nodes that receive only one beacon signal from the sink, position estimation is performed by considering the reference node beacon signal by iterative multilateration technique. Results: In this scheme, reference nodes are those nodes that are localized by the sink, and no GPS is included. The proposed algorithm is simulated using C language, and some of the performance parameters used for the evaluation of the scheme are localization time, localization error, data gathering time, and communication overhead. Conclusion: The proposed work is compared to the centralized algorithm and the proposed work has been found to outperform the existing networks.

New method for route efficient energy calculations with mobile-sink for wireless sensor networks

<p>Despite proposing a number of algorithms and protocols, especially those related to routing, for the purpose of reducing energy consumption in wireless sensor networks, which is one of the most important issues facing this type of network. In this research paper, energy consumption and cost are calculated taking into account energy consumption and the amount of data transferred to a thousand nodes through specific paths towards the mobile sink. The proposed model simulated by sending various amounts of data with specific path to know the energy consumption of each track and the network life time with 250, 500, and 1000 bits. Cost calculated using various weight for each track of these paths and the coefficient of movement time and path loss factor and others related to the transmission and receiving circuits. And finally, the results compared with a previous method it showed the efficiency of our method used and calculating 1000 nodes with various amount of bits to show the experimental results. Deep learning used to remember each and every path of each position or nearby to avoid calculation cost later.</p>

Lightweight route adjustment strategy for mobile sink wireless sensor networks

<span>As a result of numerous applications and low installation costs, wireless sensor networks (WSNs) have expanded excessively. The main concern in the WSN environment is to lower energy consumption amidst nodes while preserving an acceptable level of service quality. Using multi-mobile sinks to reduce the nodes' energy consumption have been considered as an efficient strategy. In such networks, the dynamic network topology created by the sinks mobility makes it a challenging task to deliver the data to the sinks. Thus, in order to provide efficient data dissemination, the sensor nodes will have to readjust the routes to the current position of the mobile sinks. The route re-adjustment process could result in a significant maximization in the communication cost, which boosts the total energy depletion. This paper proposes a lightweight routes re-adjustment strategy for mobile sink wireless sensor networks (LRAS-MS) aimed at minimizing communication cost and energy consumption by reducing route re-adjustment in a cluster-based WSN environment. The simulation results show a significant reduction in communication costs and extending the network lifetime while maintaining comparable low data delivery delay. </span>

Fuzzy Logic-Based Path Planning for Data Gathering Mobile Sinks in WSNs

Mobile sinks (MSs) are capable of collecting data along specified paths in wireless sensor networks (WSNs). They are deployed as a popular alternative for data loggers, to which all nodes have to send sensory data. If MSs' paths (or cycles) are not well determined, it might take a relatively long time for the MSs to make a round trip. Recent research works have proposed methods to determine rendezvous points (RPs) that the MSs must pass by to collect data, with the aim of reducing the data-collection time. Determination of the number of RPs is important, and it is challenging to make ensure that there are sufficient RPs widely located throughout a sensor network, forming a circle along which the MSs can spend limited time traveling. This research presents a method for designing paths and pinpointing RPs for MSs to collect data, as well as determining the next hop to relay data for each sensor node. Instead of reducing the MSs' travel time, the focus of this research is to preserve the energy of all sensor nodes in WSNs. Our method determines the maximum number of RPs such that the MSs can run through each RP's communication range (within a time constraint) without depleting their own energy. The method comprises three main steps. First, we calculate the number of RPs and design the MS path. Second, the exact data-collection points are determined. The last step is to specify the path along which sensory data are relayed to the MS. In our experiments, we simulate two WSNs of different sizes. The results show that our method outperforms the others by 70%-80% in terms of the sensor node uptime, power consumption, MS traveling time and the number of RPs.

One-Hop Data Collection by Four Quadrants Moving Model for Mobile Sink Wireless Sensor Networks

In wireless sensor networks, the energy of sensors close to the sink within one-hop is prone to be exhausted since these sensors need to forward the data from other sensors to the sink and the energy of each sensor is limited. Once the energy of these sensors close to the sink within one-hop is exhausted, the data thus could not be sent to the sink. To address this issue, the general solution is to ensure that each sensor could consume energy to forward the data from other sensors to by using the mobile sink because the location of the mobile sink is dynamic. In this situation, each sensor may be close to the sink within one-hop. The energy consumption among all sensors thus could be balanced and the network lifespan also could be prolonged. To reduce more energy consumption of sensors by forwarding data to the sink, the one-hop data collection by mobile sink was proposed to claim the sink moved to the source sensor within one-hop to collect the data in this paper. Hence, only the source sensors send the data to the sink within one-hop and other sensors could not forward the data to the sink to save energy. In the one-hop data collection by mobile sink, the rendezvous point moving model (RPMM) and the minimum moving model (MMM) were common proposed to be used. However, the time and energy consumption for collecting data in RPMM and MMM may not be minimized with the large number of source sensors. While the source sensors increase, the mobile sink needs more time and energy to collect data. To address these issues, we proposed a one-hop data collection by four quadrants moving model, FQMM, to collect data in this paper. The implementation tool, such as simulator, was used by Java language with Java SDK to evaluate performance under our proposal and the comparing proposal. For the numerical results, the maximal moving hop count, MHC, in FQMM was 30% less than the maximal MHC in RPMM. The maximal MHC in FQMM was 36% less than the maximal MHC in MMM. The minimum MHC in FQMM was 22% less than the minimum MHC in RPMM. The minimum MHC in FQMM was 28% less than the minimum MHC in MMM. The average MHC in FQMM was 25% less than the average MHC in RPMM. The average MHC in FQMM was 31% less than the average MHC in MMM. The total energy consumption in FQMM was 55% less than that in RPMM. The total energy consumption in FQMM was 59% less than that in MMM. Since the number of source sensors is often large in the real condition, FQMM could be applied for the regarding applications of WSN with mobile sink.

Seamless clustering multi-hop routing protocol based on improved artificial bee colony algorithm

An important issue in the mobile sink wireless sensor networks (MSWSNs) is sensor energy optimization. In order to alleviate the problem of unbalanced network load and high energy consumption in MSWSNs, we proposed a new data collection protocol in this paper: seamless clustering multi-hop routing protocol based on improved artificial bee colony algorithm (IABCP). Because of limited by the communication sensing range and intelligence of ordinary nodes, routing paths can only be constructed by crude methods. And the movement of the sink node will generate a large amount of energy consumption for locating the sink node. In order to solve this problem, we assign the task of routing table generation to the sink node, which will generate the routing table through the improved artificial bee colony algorithm. In addition, we adopt a new method to select cluster head (CH) nodes; node uses the average energy of the surrounding nodes and its own residual energy to calculate the claimed cluster head time. Moreover, we added a sub cluster head CH-β node. When the CH node reaches the number of replacement rounds, the CH-β node becomes CH directly. The simulation results show that our routing protocol is more robust compared with three other protocols.

An energy efficient and load balanced sink mobility for wireless sensor networks

The uniform energy dissipation of sensor nodes is a key objective to optimise network lifetime of wireless sensor networks (WSNs). In recent studies, a mobile sink implementation has been used instead of static sink and it offers better energy balanced network. This approach used four design constraints of sink mobility in WSN to achieve uniform energy dissipation of nodes in the network. These design constraints are: sink mobility pattern, node deployment technique, scheduling of node activities, and routing of data to the sink. In the literature, the existing approaches have utilised only subset of these design constraints. In our proposed approach all four design constraints have been used in an integrated manner and have been implemented in a three steps solution to achieve the energy load balanced sink mobility in the WSNs. The results have been compared using performance metrics such as coverage density, network lifetime, and energy balancing condition. These metrics are derived and further implemented through simulation. The simulation results confirm that the proposed solution outperforms the existing sink mobility patterns.

An energy efficient and load balanced sink mobility for wireless sensor networks

The uniform energy dissipation of sensor nodes is a key objective to optimise network lifetime of wireless sensor networks (WSNs). In recent studies, a mobile sink implementation has been used instead of static sink and it offers better energy balanced network. This approach used four design constraints of sink mobility in WSN to achieve uniform energy dissipation of nodes in the network. These design constraints are: sink mobility pattern, node deployment technique, scheduling of node activities, and routing of data to the sink. In the literature, the existing approaches have utilised only subset of these design constraints. In our proposed approach all four design constraints have been used in an integrated manner and have been implemented in a three steps solution to achieve the energy load balanced sink mobility in the WSNs. The results have been compared using performance metrics such as coverage density, network lifetime, and energy balancing condition. These metrics are derived and further implemented through simulation. The simulation results confirm that the proposed solution outperforms the existing sink mobility patterns.

DEDC: Joint Density-Aware and Energy-Limited Path Construction for Data Collection Using Mobile Sink in WSNs

Data collection is one of the most important issues in Wireless Sensor Networks (WSNs). Many data collection algorithms have been proposed for collecting data using a mobile sink. However, the appropriateness and the number of the selected anchors, which significantly impact the network lifetime of the given WSNs, still can be improved. This paper proposes a Joint Density-Aware and Energy-Limited Path Construction algorithm for Data Collection, called DEDC, aiming to select as more as possible appropriate anchors under the path length constraint for prolonging the network lifetime. Initially, the proposed DEDC determines the grid size according to the path length constraint, partitions the monitoring region into several grids and identifies the grids to be balance or unbalance grids. Based on the partitioned grids, the proposed DEDC constructs a regular path and then further adjusts the path segments for these unbalanced grids. The regular path construction and path adjustment aim to construct a path passing through as more as possible anchors for balancing the forwarding loads and prolonging the network lifetime. Performance evaluations reveal that the proposed DEDC outperforms existing data collection mechanisms in terms of energy consumption, network lifetime, and SD energy consumptions.

Weighted Rendezvous Planning on Q-Learning Based Adaptive Zone Partition with PSO Based Optimal Path Selection

Nowadays, wireless sensor network (WSN) has emerged as the most developed research area. Different research have been demonstrated for reducing the sensor nodes’ energy consumption with mobile sink in WSN. But, such approaches were dependent on the path selected by the mobile sink since all sensed data should be gathered within the given time constraint. Therefore, in this article, the issue of an optimal path selection is solved when multiple mobile sinks are considered in WSN. In the initial stage, Q-learning based Adaptive Zone Partition method is applied to split the network into smaller zones. In each zone, the location and residual energy of nodes are transmitted to the mobile sinks through Mobile Anchor. Moreover, Weighted Rendezvous Planning is proposed to assign a weight to every node according to its hop distance. The collected data packets are transmitted to the mobile sink node within the given delay bound by means of a designated set of rendezvous points (RP). Then, an optimal path from RP to mobile sink is selected utilizing the particle swarm optimization algorithm which is applied during routing process. Experimental results demonstrated the effectiveness of the proposed approach where the network lifetime is increased by the reduction of energy consumption in multihop transmission.

Adaptive Mobile Sink for Energy Efficient WSN Using Biogeography-Based Optimization

Early death of cluster heads (CHs) located near the sink due to excessive data relay load causes energy holes in wireless sensor networks (WSNs). A widely adopted solution to energy hole problem is to divide the deployment region into multiple sub regions and use mobile sink (MS) to aggregate data from each sub-region. However, inside a sub-region, CHs close to MS dissipate their energy quickly and die despite of the sink mobility. The authors map the problem of distributing data relay load optimally to multiple CHs and locating MS near these multiple CHs using metaheuristic algorithm biogeography-based optimization (BBO). Furthermore, there is a need of optimum routing of data to the MS inside each sub-region of a MS WSN. Proposed mobile sink distributed load routing algorithm (MSDR-BBO) selects the optimum routing CHs in MS WSN as per data transfer requirements of sensor nodes (SNs) and CHs. MSDR-BBO is validated using Matlab simulation as well as Netsim emulator, and it outperforms latest MS WSN algorithms like nested routing, SENMA, and CMS2TO by 4.6%, 11.7%, and 17.4%, respectively.

Mobile Sink-Based Path Optimization Strategy in Wireless Sensor Networks Using Artificial Bee Colony Algorithm

In traditional static wireless sensor networks (WSNs), the unbalanced communication overhead in different regions will result in premature death of some monitoring nodes. The introduction of mobile sink in WSNs can not only balance the node traffic load, but also obtain even energy consumption of nodes, thus effectively avoiding the “hot spot” problem and prolonging the network lifetime. However, the mobility of the sink will lead to frequent changes in the aspect of network topology, which can aggravate the overhead of the node's reorganization in hierarchical WSNs. Therefore, it is essential to obtain the optimal trajectory design of the mobile sink so as to improve the ability of data gathering. In this paper, a mobile sink-based path optimization strategy in WSNs using artificial bee colony algorithm is proposed. First, the problem of overall energy consumption in the network can be transformed into the minimization of the total hops between all subnodes and the rendezvous points of the mobile sink. The objective function and the constraint criterion should be established. Second, an improved artificial bee colony algorithm is proposed to solve the problem. On the one hand, the cumulative factor is introduced to the position update of the employed bee stage to speed up the convergence of the algorithm. On the other hand, the Cauchy mutation operator is presented to increase the diversity of the feasible solution and enhance the global search ability of the algorithm. The simulation results show that the proposed algorithm is better than the traditional methods in the aspects of energy efficiency and the real-time performance of data collection.

MS‐routing‐G i : routing technique to minimise energy consumption and packet loss in WSNs with mobile sink

Energy efficiency is a major factor in designing wireless sensor networks (WSNs). Clustering design and grid-based scheme are one of the techniques to save energy in WSNs. However, for a mobile sink in WSNs, packet loss rate that occurs due to the sink mobility is also a significant challenge. Here, an enhanced routing-Gi protocol for a mobile sink in WSNs has been proposed to not only enhance energy efficiency but also minimise the packet loss rate. Simulation results show that the proposed protocol is more efficient than the previous protocols in terms of the network lifespan, energy efficiency, and packet delivery ratio. Thus, the proposed approach in this work could further be adapted to investigate highly mobility of sink in WSNs.

Distributed trajectory design for data gathering using mobile sink in wireless sensor networks

Several studies have demonstrated the benefits of using a mobile sink (MS) to reduce energy consumption resulting from multi-hop data collection using a static sink in wireless sensor networks (WSNs). However, using MS may increase data delivery latency as it needs to visit each sensor node in the network to collect data. This is a critical issue in delay-sensitive applications where all sensed data must be gathered within a given time constraint. In this paper, we propose a distributed data gathering protocol utilizing MS for WSNs. The proposed protocol designs a trajectory for the MS, which minimizes energy consumption and delay. Our protocol operates in four main phases: data sensing, rendezvous point (RP) selection, trajectory design, and data gathering. In data sensing, a number of deployed sensor nodes keep sensing the target field for a specific period of time to capture events. Then, using a cluster-based RP selection algorithm, some sensor nodes are selected to become RPs based on local information. The selected RPs are then used to determine a trajectory for the MS. To do so, we propose three trajectory design algorithms that support different types of applications, namely reduced energy path (REP), reduced delay path (RDP), and delay bound path (DBP). The MS moves through the constructed path to accomplish its data gathering according to an effective scheduling technique that is introduced in this work. We validate the proposed protocol via extensive simulations over several metrics such as energy, delay, and time complexity.

Mobile sink discovery mechanism in wireless sensor networks with duty cycles

Wireless sensor network (WSN) is a multi-hop, self-organizing distributed network system composed of multiple micro-sensors through wireless communication. There are problems such as energy holes and transmission link interruption due to node failure for multi-hop mode of data transmission. Mobile sink (MS) supports data forwarding and collection to avoid multi-hop transmission, extending network life by saving network energy. In the work, we discussed transmission energy and delay problems of data collection by MS in WSN with duty cycle. Based on fixed MS moving speed and regular transmission performance, the network life cycle was maximized to propose an asynchronous path independent energy efficient algorithm irrelevant to the path. After that, the efficiency of the protocol was verified by experiments.

An Optimal Data Gathering Method for Mobile Sinks in WSNs

In Wireless Sensor Networks (WSN), energy is one of the most important resources since each node collects, processes and transmits data to its base station. Most of the traditional works in WSNs are consisted of static nodes and one base station. Recently, some mobile data gathering methods are proposed to prolong the operation time of sensor networks. One or more mobile collectors are used to gather sensed data from sensor nodes at short transmission ranges. This paper presents to find optimal visiting points and data gathering path for a mobile sink within clusters. With defining an optimal clustering and data gathering path, this method improves the data collection performance as well as the network lifetime extension of sensor networks. The network lifetime is increased to 20% when sensor nodes are divided into from 4 clusters to 15 clusters.

Adjustable Trajectory Design Based on Node Density for Mobile Sink in WSNs.

The design of movement trajectories for mobile sink plays an important role in data gathering for Wireless Sensor Networks (WSNs), as it affects the network coverage, and packet delivery ratio, as well as the network lifetime. In some scenarios, the whole network can be divided into subareas where the nodes are randomly deployed. The node densities of these subareas are quite different, which may result in a decreased packet delivery ratio and network lifetime if the movement trajectory of the mobile sink cannot adapt to these differences. To address these problems, we propose an adjustable trajectory design method based on node density for mobile sink in WSNs. The movement trajectory of the mobile sink in each subarea follows the Hilbert space-filling curve. Firstly, the trajectory is constructed based on network size. Secondly, the adjustable trajectory is established based on node density in specific subareas. Finally, the trajectories in each subarea are combined to acquire the whole network’s movement trajectory for the mobile sink. In addition, an adaptable power control scheme is designed to adjust nodes’ transmitting range dynamically according to the movement trajectory of the mobile sink in each subarea. The simulation results demonstrate that the proposed trajectories can adapt to network changes flexibly, thus outperform both in packet delivery ratio and in energy consumption the trajectories designed only based on the network size and the whole network node density.