Target Tracking in a Two-Tiered Hierarchical Sensor Network

In the wireless sensor network, an important problem is that how to improve the network lifetime. In recent years, the proposal of using relay nodes to maximize the network lifetime has been suggested in the literature. It is established that, in a two-tiered sensor network (TTSN), higher-power relay nodes which are responsible for receiving and transmitting data can be used as the cluster heads. However, how to extend the network lifetime in a TTSN in this proposal remains a challenge. In this paper, we propose a new approach termed as DSA, to minimize the energy consumption of maximum energy dissipation relay node, which can greatly extend the lifetime of the Two-Tiered Sensor Network. The Main idea is that dividing the relay nodes into several layers at first and then choosing a best transfer path for every relay node at each layer to extend the lifetime of TTSN. We design an algorithm to implement the idea. The advantage of this novel algorithm in terms of complexity and its capability in extending the lifetime of TTSN has been analyzed in detail. By simulations, we compare our method with the direct transmit energy model, minimum transmission energy model and broadcast incremental power algorithm. Simulation results demonstrate significant improvements have been achieved in our method in terms of lifetime of TTSN.

Prolonged network lifetime, scalability and efficient load balancing are essential for optimal performance of a wireless sensor network. Clustering provides an effective way of extending the lifetime of a sensor network. Clustering is the process that divides sensor networks into smaller localized group (called clusters) of members with a cluster head. Clustering protocols need to elect optimal number of clusters in hierarchically structured wireless sensor networks. Any clustering scheme that elects clusters uniformly (irrespective of the distance from Base Station) incurs excessive energy usage on clusters proximal and distant to Base Station. In single hop networks a gradual increment in the energy depletion rate is observed as the distance from the cluster head increases[17]. This work focuses on the analysis of wasteful energy consumption within a uniform cluster head election model (EPEM) and provides an analytical solution to reduce the overall consumption of energy usage amongst the clusters elected in a wireless sensor network. A circular model of sensor network is considered, where the sensor nodes are deployed around a centrally located Base Station. The sensor network is divided into several concentric rings centred at the Base Station. A model, Unequal Probability Election Model (UEPEM), which elects cluster heads non-uniformly is proposed. The probability of cluster head election depends on the distance from the Base Station. UEPEM reduces the overall energy usage by about 21% over EPEM. The performance of UEPEM improves as the number of rings is increased.

Wireless Sensor Network is comprised of sensor nodes with a limited energy supply in the form of built-in batteries. Efficient utilization of limited energy supply of sensor nodes is one of the key design issues in wireless sensor networks. Hence, energy efficiency needs to be enhanced to prolong the stability and lifetime of WSN. In this paper, we propose Energy-Efficient Clustering Protocol based on Energy Intervals (EECP-EI) for multi-level WSN. EECP-EI associates sensor nodes to clusters based on energy intervals. The cluster heads are selected based on average energy of certain energy interval and the total estimated energy of network at a particular round. The role of cluster head is rotated within the cluster to evenly distribute the energy among sensor nodes inside the cluster. The sensed data is aggregated at the cluster head to reduce the amount of data that needs to be communicated to the base station. The sensed data is communicated to the base station using hierarchy of cluster heads that are in ten-meter distance to the sending cluster head. When the cluster formation no longer remains optimal, every sensor node in the network transmits data directly to the base station. Simulations show that EECP-EI shows the improved stability period of 37.08%, 36.37%, and 19.39%, and the improved network lifetime of 6.58%, 13.13%, and 32.15% as compared to LEACH, DEEC, and DDEEC respectively.

ABSTRACTIn wireless sensor networks (WSNs), nodes typically operate with limited energy supplies, making efficient data gathering essential for prolonging network lifespan. One effective approach to reduce energy consumption is clustering. However, using a fixed sink to collect data can lead to energy depletion in specific nodes, causing bottlenecks. A mobile sink, on the other hand, can address this issue by enhancing network performance and reducing energy load on individual nodes. This paper introduces a hierarchical cluster‐based data aggregation method that employs fuzzy logic alongside a mobile sink to improve energy efficiency. The strategy has two main stages: clustering and data aggregation. In the clustering stage, the process is split into two steps: identifying cluster heads and organizing clusters. A fuzzy inference system assesses each node's potential as a cluster head based on factors such as remaining energy, node connectivity, and centrality. The nodes with the highest scores are selected as primary cluster heads, while those with slightly lower scores serve as backup cluster heads. Clusters are then formed around these chosen heads. In the data aggregation phase, cluster heads gather data from cluster members and forward it either to a mobile sink or directly to the base station (BS). Cluster heads located within a specified range (distance ≤ r) of the BS send data directly, while others route data via the mobile sink. This technique enhances data transmission efficiency and optimizes energy consumption, contributing to overall network improvement. The HDAMM approach demonstrated considerable advancements over earlier methods in terms of energy efficiency, delay reduction, packet delivery rate, and network longevity.

In the paper, we devise and evaluate a fully decentralized, light-weight, dynamic clustering algorithm for target tracking. Instead of assuming the same role for all the sensors, we envision a hierarchical sensor network that is composed of (a) a static backbone of sparsely placed high-capability sensors which assume the role of a cluster head (CH) upon triggered by certain signal events; and (b) moderately to densely populated low-end sensors whose function is to provide sensor information to CHs upon request. A cluster is formed and a CH becomes active, when the acoustic signal strength detected by the CH exceeds a pre-determined threshold. The active CH then broadcasts an information solicitation packet, asking sensors in its vicinity to join the cluster and provide their sensing information. We address and devise solution approaches (with the use of Voronoi diagram) to realize dynamic clustering: (I1) how CHs cooperate with one another to ensure that for the most of time only one CH (preferably the CH that is closest to the target) is active; (I2) when the active CH solicits for sensor information, instead of having all the sensors in its vicinity reply, only a sufficient number of sensors respond with non-redundant, essential information to determine the target location; and (I3) both packets with which sensors respond to their CHs and packets that CHs report to subscribers do not incur significant collision. Through both probabilistic analysis and ns-2 simulation, we show with the use of Voronoi diagram, the CH that is usually closest to the target is (implicitly) selected as the leader and that the proposed dynamic clustering algorithm effectively eliminates contention among sensors and renders more accurate estimates of target locations as a result of better quality data collected and less collision incurred.

We present in this paper an investigation on a special class of wireless sensor networks for monitoring critical infrastructures that may extend for hundreds of miles in distances. Such networks are fundamentally different from traditional sensor networks in that the sensor nodes in this class of networks are deployed along narrowly elongated geographical areas and form a chain-type topology. Based on careful analysis of existing sensor network architectures, we first demonstrate the needs to develop new architecture and networking protocols to match the unique topology of chain-type sensor networks. We then propose hierarchical network architecture that consists of clusters of sensor nodes to enable the chain-type sensor networks to be scalable to cover typically long range of infrastructure with tolerable delay in network-wide data collection. To maintain energy efficient operations and maximize the lifetime for such a chain-type sensor network, we devise a smart strategy for the deployment of cluster heads. Protocols for network initialization and seamless operations of the chain-type sensor networks are also developed to match the proposed hierarchical architecture and cluster head deployment strategy. Simulations have been carried out to verify the performance of the hierarchical architecture, the smart node deployment strategy, and the corresponding network initialization and operation protocols.

The improvement of sensor networks’ lifetime has been a major research challenge in recent years. This is because sensor nodes are battery powered and may be difficult to replace when deployed. Low energy adaptive clustering hierarchical (LEACH) routing protocol was proposed to prolong sensor nodes lifetime by dividing the network into clusters. In each cluster, a cluster head (CH) node receives and aggregates data from other nodes. However, CH nodes in LEACH are randomly elected which leads to a rapid loss of network energy. This energy loss occurs when the CH has a low energy level or when it is far from the BS. LEACH with two level cluster head (LEACH-TLCH) protocol deploys a secondary cluster head (2CH) to relieve the cluster head burden in these circumstances. However, in LEACH-TLCH the optimal distance of CH to base station (BS), and the choicest CH energy level for the 2CH to be deployed for achieving an optimal network lifetime was not considered. After a survey of related literature, we improved on LEACH-TLCH by investigating the conditions set to deploy the 2CH for an optimal network lifetime. Experiments were conducted to indicate how the 2CH impacts on the network at different CH energy levels and (or) CH distance to BS. This, is referred to as factor-based LEACH (FLEACH). Investigations in FLEACH show that as CHs gets farther from the BS, the use of a 2CH extends the network lifetime. Similarly, an increased lifetime also results as the CH energy decreases when the 2CH is deployed. We further propose FLEACH-E which uses a deterministic CH selection with the deployment of 2CH from the outset of network operation. Results show an improved performance over existing state-of-the-art homogeneous routing protocols.

A sensor node is that the significant part of a wireless sensor network. Sensor nodes have various roles in a network includes identifying data storage, data processing and routing method. Cluster is an organizational element for wireless sensor networks. The powerful environment of this network is very essential for them to be broken down into clusters to make easier responsibilities such as communication. Cluster heads are the group head of a cluster have greater data rate match the alternative cluster member. They frequently needed to associate activities within the cluster. These methods comprise but are not controlled to data aggregation and forming account of a cluster. Base station is at the upper level of organized wireless sensor network. It generates communication link among the sensor network and the end user. The data in a sensor network can be used for an enormous variety of applications. A detailed application is form use of network data over the internet retaining a personal digital assistant or desktop computer. This paper contributions mobility based reactive protocol named “Mobility of Sink based Data Collection Protocol (MSDCP)”. This protocol sensor with great energy and maximum quantity information are picked as cluster heads that gather data from the common nodes between the clusters. This data is placed unless mobile sink comes within the transmission area of cluster heads and request for the gathered data. One time the request is received from cluster head and it forward data to the mobile sink.

In smart Wireless Sensor Networks (WSNs), clustering is one of the most power efficient techniques which minimize the transmission ranges of sensor nodes. Instead of sending packets directly to the BS with high power levels, sensor nodes in the sensing filed can be organized in groups (i.e. clusters), where Cluster Heads (CHs) manage the transmission by aggregating the whole group of packets and retransmit them to the Base Station (BS). Therefore, the clustering technique solve the problem that nodes close to the BS consume a large amount of power due to the need to be active most of the time to retransmit packets to the BS (i.e. sink hole problem). In this paper, we propose a novel Raining Energy Cluster Head Selection (RECHS) protocol which is based on a simple Statistical Discrimination (SD) metric which detects collided packets before decoding as well as the Multi-Dimensional Slotted ALOHA (MDSA) MAC protocol which organizes the access to the communication channel. Hence, both reception and transmission power consumption will be minimized remarkably. In addition, in RECHS protocol, a simple Cluster Head (CH) selection algorithm is proposed which is based on the ratio between the remaining energy of every member (node) in a cluster and the initial (start-up) energy as well as the minimum distance between the CH and the sink node. In this paper, we will show how the proposed CH selection mechanism in RECHS protocol along with the SD technique and the MDSA MAC protocol overcomes some well-known protocols such as LEACH where a CH is selected in a distributed (i.e. random) fashion and the MAC protocol is TDMA. In particular, we will show how the performance of RECHS protocol overcomes LEACH protocol in terms of the percentage of alive nodes, the average number of successfully arrived packets to the BS (i.e. the throughput), and the average residual energy of the network per simulation round.

Most of the sensor nodes in a Wireless Sensor Network (WSN) have limited energy. In order to increase the network lifetime, some energy efficient algorithms were proposed earlier. It has been a challenge to design wireless sensor networks to enable applications for oceanographic data collection, pollution monitoring, offshore exploration, disaster prevention, assisted navigation and tactical surveillance applications. The main objective of this work is to find out the data routing method such that the battery life of sensor nodes can be prolonged. In this paper, A Two Cluster Head Energy efficient Wireless Sensor Network (TCHE-WSN) Algorithm was put forward. WSN consists of sensor nodes which sense the physical parameters such as temperature, humidity, pressure and light etc and send them to a fusion center namely Base Station (BS) from where one can get the value of physical parameters at any time. Requirement of monitoring the environment might be anywhere, like middle of the sea or under the earth where man cannot go often to recharge the batteries which supplies the sensing device, transceiver and memory unit in the sensor node. So the usage of the battery power must be judicious in WSN. The present study demonstrates the data routing method from node to BS (Base Station) using two cluster heads to prolong the battery life of sensor nodes. The two cluster head analogy reduces the overhead of single cluster head, avoids packet collision, and improves reliable data transmission with little compromise in energy consumption and hop count compare to single cluster head method.

The sensor network performs gathering, monitoring, and tracking of objects in the given area. The sensor nodes are normally distributed randomly in the network area for collecting the information. The major issues in Wireless Sensor Networks (WSN) are coverage, energy, and limited resources. Sensor Nodes’ (SN) performance depends on so many parameters but normally depends on Residual Energy (RE) and Distance from the base station. The Cluster Head (CH) cooperatively communicates with Base Station (BS) via routing protocols. The proposed Energy Efficient Multilevel Region Based (EEMRB) protocol performs the task by partitioning the entire network area into multiple levels and sub-levels. The sub-levels are partitioned to perform clusters to communicate the sensor via CH (s) using single/multi-hop communication to BS. The proposed protocol is compared with the Stable Election Protocol and shows improvement in network lifetime. Based on the proposed protocol data set, a Multivariate Polynomial Regression (MPR) Model is proposed to predict network lifetime. The model uses packet size and node density as network design parameters. The simulation results show that the size of the packet and network area play a major role in network lifetime. Therefore, the lifetime of the predicted model and EEMRB protocol are close to each other. This prediction model is suitable for the prediction of any network area's lifetime.

As it is well known, in Wireless Sensor Networks, the sensor nodes will be either mobile or static. When mobility is concerned, on the whole network performance could be degraded, since the sensor nodes are furnished with restricted battery power, restricted memory, less computational ability and lower range of communication. So, a mechanism which is effective is needed there for forwarding the data packets with efficient energy management and coverage. With that note, the principle target of this work is to propose systematic method of CH selection based on the factors such as low mobility, density of the nodes and their remaining energy. Moreover, an innovative method called Node-Grade Based Clustering (NGBC) is proposed in this paper so as to select the CHs, studying the node’s energy and position regarding to their Base Station (BS), which will act as a sink for collected information. The CHs are replaced in every round based on its duty cycle on sensor nodes and Threshold Energy Rate (TER). Since the BS evaluates the quantity of every round a CH (Cluster Head) can sustain, it minimizes the quantity of energy consumed and increases the WSN’s lifetime. The results of the simulation demonstrate that the proposed algorithm attains higher coverage, efficiency in energy and network lifetime. Furthermore, the performance results in the work which is proposed, are distinguished with the algorithms proposed previously such as LEACH and HEED using some evaluation metrics like packet delivery ratio, throughput, energy consumption and end-to-end delay to prove the efficiency of energy efficient NGBC.

Cluster-based routing scheme is able to prolong the network lifetime and save the energy consumption. In this field, many cluster-based routing protocols are proposed such as LEACH, TEEN, APTEEN, and so on. On the other hand, the chain-based routing protocols construct the chain such as PEGASIS and concentric-clustering routing scheme. The concentric-clustering routing scheme is the multiple-chain among sensor node to transmit the data to the base station or lower cluster head. However, a problem of this scheme is that the number of sensor nodes in the each level is unbalanced as assigning the level based on total number of level. Thus, the unbalanced energy consumption among sensor nodes is occurred. Also, as this scheme selects the next cluster head to be closely located at current cluster head, energy of cluster head dissipates quickly on the multipath among cluster heads. To overcome these problems, we propose the advanced concentric-clustering routing scheme adapted to large-scale sensor networks. Our scheme fixes the number of sensor node on level for the same number of sensor nodes in each level. In addition, we consider the residual energy as the criterion of cluster head election for balanced energy consumption as well as the distance from lower cluster head or base station to upper cluster head. As the simulation results, our scheme shows a better performance of energy consumption than other multiple-chaining schemes about 24.4%.

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> In this paper, we address the problem of target tracking in a collaborative acoustic sensor network. To cope with the inherent characteristics and constraints of wireless sensor networks, we present a novel target-tracking algorithm with power-aware concerns. The underlying tracking methodology is described as a multiple-sensor tracking/fusion technique based on particle filtering. As discussed in the most recent literature, particle filtering is defined as an emerging Monte Carlo state estimation technique with proven superior performance in many target-tracking applications. More specifically, in our proposed method, each activated sensor transmits the received acoustic intensity and the direction of arrival (DOA) of the target to the sensor fusion center (a dedicated computing and storage platform, such as a microserver). The fusion center uses each received DOA to generate a set of estimations based on the state partition technique, as described later in this paper. In addition, a set of sensor weights is calculated based on the acoustic intensity received by each activated sensor. Next, the weighted sum of the estimates is used to generate the proposal distribution in the particle filter for sensor fusion. This technique renders a more accurate proposal distribution and, hence, yields more precise and robust estimations of the target using fewer samples than those of the traditional bootstrap filter. In addition, since the majority of the signal processing efficiently resides on the fusion center, the computation load at the sensor nodes is limited, which is desirable for power-aware systems. Last, the performance of the new tracking algorithm in various tracking scenarios is thoroughly studied and compared with standard tracking methods. As shown in the theory and demonstrated by our experimental results, the state-partition-based centralized particle filter reliably outperforms the traditional method in all experiments. </para>

Target tracking is one of the most important applications for wireless sensor networks. In this paper, a scalable dynamical average consensus algorithm of target tracking is proposed. Kalman prediction is employed to help setting tracking cluster through nearest neighbor criterion. Selected sensors, called Tasking sensor nodes, are working locally for target estimate through observation or innovation based consensus distributed kalman filter. In the algorithm, Cluster head is not fusion center, but a ordinary node for choose tasking sensor of next step and forwards current state estimate and corresponding error covariance. Each tasking sensor only requires information from neighboring nodes that will decrease communication bandwidth of overall network. Simulation results show that, compared with central kalman filter algorithm, observation and innovation based consensus distributed kalman filter will get comparable accuracy while it will be more robust due to distributed manner.