Energy Dissipation Of Sensor Nodes Research Articles

Exploring the tradeoff between energy dissipation, delay, and the number of backbones for broadcasting in wireless sensor networks through goal programming

Broadcasting, which is an essential mode of operation in wireless sensor networks (WSNs), dissipates a non-negligible portion of the energy budget of a sensor node. Broadcasting is achieved by the dissemination of broadcast packets (originated at the BS) by a set of relay nodes, which constitute a backbone so that all sensor nodes receive broadcast packets. Utilization of multiple backbones is necessary to achieve balanced energy dissipation of sensor nodes in broadcasting. In this study, we propose two mixed integer programming (MIP) models (i.e., the flow-based model and the node-based model), which minimize the energy dissipation of the highest energy-consuming node for broadcasting by utilizing multiple backbones. Balanced energy minimization and delay minimization objectives are integrated through a goal programming (GP) framework built upon the foundation provided by the scalable node-based model. Performance evaluations based on the optimal solutions of our models reveal that maximum energy dissipation and delay in WSN broadcasting can be significantly reduced, simultaneously, by utilizing multiple backbones (e.g., with two backbones maximum energy dissipation and delay can be, concurrently, reduced by more than 2% and 22%, respectively, in comparison to the single backbone case, likewise, it is also possible to simultaneously reduce maximum energy dissipation and delay more than 8% and 13%, respectively, depending on the priorities assigned to the objectives). Nevertheless, employing more than two backbones does not provide any significant performance improvements.

SMAC-Based WSN Protocol-Current State of the Art, Challenges, and Future Directions

Wireless Sensor Networks (WSNS) have become an indispensable tool in this epoch of technological advancements, particularly for progress made in the Internet of things. Wireless sensor nodes are deployed to collect and transmit vital data from the environment to a base station for analysis. Nevertheless, the limited battery power of the sensor nodes is rapidly drained when they stay awake for an extended period. Research has shown that significant sources of energy dissipation of sensor nodes are idle listening, packet collision, control overhead, and overhearing. One optimal solution is employing a low duty cycle mac protocol, particularly the sensor mac (SMAC) protocol. It is essential to have a detailed knowledge of the challenges identified in SMAC and solutions suggested to mitigate these challenges and the future directions. In this paper, we review techniques in SMAC protocols implemented in WSNS. In particular, we provide highlights of recent developments in the schemes used in SMAC for mitigating the challenges in SMAC and present research gaps in SMAC protocol. Finally, we discuss open issues that need to be addressed to advance the design and implementation of SMAC in WSN applications.

An enhanced energy proficient clustering (EEPC) algorithm for relay selection in heterogeneous WSNs

The recent advancements in Wireless Sensor Networks (WSNs) have brought attention to the field of sensor tracking events. Habitat monitoring is considered as one of the most important applications of WSNs, which ensures wildlife conservation. Researchers have proposed various solutions to select the optimal path in the field of sensor tracking. However, energy dissipation of sensor nodes and fault tolerance during data transformation is still one of the major challenges of the WSN environment in dynamic scenarios. In this paper, an Enhanced Energy Proficient Clustering (EEPC) is proposed to reduce the energy consumption of the entire sensor nodes in the field of tracking events. The network is created with both fixed and mobile nodes. Initially, fixed nodes broadcast information, and mobile nodes select the cluster head from fixed nodes. The mobile nodes select their cluster head (CH) based on their associated placement and energy level. Mobile sensor nodes (SNs) transmit data to the CH. It introduces the concept of finding relay nodes, which are fixed nodes. The EEPC algorithm selects the relay nodes based on their velocity and location by calculating particle fitness value. The selected intermediate relay nodes transmit the collected information to the Base Station (BS) using the sensor data fusion technique. The link fault of nodes could be predicted based on the deviation value. The simulation results show that the proposed approach points out progress with reference to various performance metrics. The proposed work minimizes the energy depletion and enhances the network lifetime compared to other existing protocols.

Obstacle-Aware Connectivity Establishment in Wireless Sensor Networks

In wireless sensor networks (WSNs), the sensor nodes collect data from the target area and send it to the base station through a multi-hop connection. As a result, high energy dissipation of the sensor node may occur. Some times failure of nodes on large scale create partitions in the network. Also, the presence of obstacles is the reason for the coverage gap in networks. The introduction of mobile nodes has been measured to be a worthy approach to significantly decrease the energy dissipation of the static sensor nodes. This job is realized by considering the route through which the mobile collectors move to collect data from the sensor nodes. Another challenging job is to find the path of the mobile collector in an area with obstacles. In this work, an obstacle aware connectivity establishment (OACE) using mobile nodes in wireless sensor networks is proposed. The mobile robot collects data from each partition and carries forward to other partitions so data finally reaches to sink. To achieve this, a Delaunay triangulation based obstacle avoidance clustering has been performed. The performance of the proposed algorithm has been shown by varying the sensor nodes to a single mobile node and multiple mobile nodes. The number of partitions in the network also plays an important role in network performance. Simulation results show that the proposed method with multiple mobile collectors is more energy-efficient with less latency as compared to the single mobile collector for data collection.

Energy aware decision stump linear programming boosting node classification based data aggregation in WSN

Data collection is a considerable operation in Wireless sensor network (WSN) to minimize the energy dissipation of sensor nodes and thereby increasing the network lifetime. A lot of research works have been designed for data gathering in WSN. A multi-mobile agent itinerary planning based energy and fault aware data aggregation and zone-based energy-aware data collection routing protocol was introduced to obtain better energy usage and data delivery in WSN. However, data aggregation accuracy was not increased and energy consumption during data aggregation was not reduced. In order to overcome such limitations, an Energy Aware Decision Stump Linear Programming Boosting Node Classification based Data Aggregation (EADSLPBNC-DA) Model is proposed. Initially, residual energy for every sensor node is calculated for performing the node classification. After that, linear programming boosting classification (LPBC) model is applied in EADSLPBNC-DA Model which increased the margin between the training samples (i.e., sensor nodes) of different classes. The LPBC model constructs a strong classifier by combining number of weak decision stump result. Subsequently, strong classifier in EADSLPBNC-DA Model accurately classifies each input sensor node as higher energy or lower energy node which reduced the misclassification error. Then, the lesser energy sensor nodes in WSN transmit the data packets to the neighboring higher energy sensor nodes through calculating the distance by Manhattan distance formula. Finally, the sink node gathers the data packets from the higher energy sensor nodes. As a result, EADSLPBNC-DA Model attains energy efficient data collection in WSN. Experimental evaluation of EADSLPBNC-DA Model is carried out on factors such as energy consumption, delay, data aggregation accuracy, network lifetime and data aggregation time with respect to number of sensor nodes and number of data packets. From the simulation results, proposed EADSLPBNC-DA Model significantly reduces the energy consumption by 22%, delay by 35% and data aggregation time by 28% when compared to the existing techniques. In addition, the data aggregation accuracy and network lifetime gets increased by 13% and 10% respectively compared to state-of-the-art works.

Energy Efficient Target Tracking Method for Multi-Sensory scheduling in Wireless Sensor Networks

Data collection utilizing wireless sensors networks (WSNs) has been utilized for surveillance, monitoring environment, animal etc. Target tracking of maneuvering objects is an essential need of modern life. Nonetheless, because of diverse nature of sensor and complex environment, sensors measurement errors need to be minimized considering diverse motion states in process of tracking (sensing) operation. Enhancing network lifetime (i.e., reducing energy dissipation of sensor nodes) and improving tracking quality are major concern of target tracking using WSN. Form improving network energy efficiency, multi-sensory target tracking method has been modelled using Kalman Filter (KF) by existing target tracking method. The KF based model are affected due to presence of noise or missing data. For overcoming research issues this paper present an H-infinity filter (HF) to evaluate fusion for maneuvering target tracking in WSN. Further, to minimize the estimation errors and reduces/controlling the effects of outliers fuzzy H-infinity (FHF) filter for target tracking WSN is presented. Experiment outcome shows proposed HF and FHF fusion model attain better performance than existing KF based method for clustered based WSN in terms of positional and velocity root mean square error and energy dissipation.

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.

Similarity-aware data aggregation using fuzzy c-means approach for wireless sensor networks

For resource-constrained IoT systems, data collection is one of the fundamental operations to reduce the energy dissipation of sensor nodes and improve the network lifetime. However, an anomaly or deviation will exert a great influence on the quality of data collected, especially for a data aggregation scheme. By taking into account data-aware clustering and detection of anomalous events, a similarity-aware data aggregation using a fuzzy c-means approach for wireless sensor networks is proposed. Firstly, by using a fuzzy c-means approach, the clustering process can be performed to organize sensors into clusters based on data similarity. Next, an effective support degree function is defined for further outlier diagnosis. Afterwards, the appropriate weight of valid data can be obtained by taking advantage of the probability distribution characteristics of normal samples within a certain period. Finally, the aggregation result in the cluster can be estimated. Practical database-based simulations have confirmed that the proposed data aggregation method can achieve better performance than traditional methods in terms of data outlier detection accuracy and relative recovery error.

A new fuzzy logic based node localization mechanism for Wireless Sensor Networks

Being aware of the nodes positions is a key issue in order to locate precisely the sensor node, localization is very important information about sensor nodes in wireless sensor network (WSNs). Hence, the precision improvement is a significant issue that allows an effective data transmission between sensor network (SN) in order to save their energy and extend the network lifetime. In this work, we propose and implement a new mechanism for geographic routing. Therefore, the proposed mechanism is relied on a weighted centroid localization technique, where the positions of unknown nodes are calculated using fuzzy logic method. For this, we propose a fuzzy localization algorithm that uses flow measurement through wireless channel to compute the distance separating the anchor and the sensor nodes. Subsequently, our work is based on the centroid algorithm that calculates the position of unknown nodes using fuzzy Mamdani and Sugeno inference system for increasing the accuracy of estimated positions. Once the localization algorithm has detected the location of nodes with unknown position, the proposed mechanism selects effectively the next-elected CH to reduce the energy dissipation of sensor nodes, which leads to an extension of the network lifetime. The main advantages of the proposed mechanism are three folds: the first is to minimize the position error of nodes and reduces the error localization average. The second is to increase the number of packets transmitted to the next hop cluster head (CH) based on the localization algorithm. The third one is to, reduce the energy consumption of nodes and then extends the network lifetime using an efficient selection of next hop CH. The obtained simulation results show that the proposed mechanism outperforms the existing solutions in terms of energy consumption, execution time (localization time) and localization error, similarly for the number of the packets transmitted to the base station.

An efficient trajectory based routing scheme for delay-sensitive data in wireless sensor network

Exploiting sink mobility for balancing the energy dissipation of sensor nodes and preventing energy holes is a growing trend in the field of wireless sensor network (WSN). The mobile sink moves throughout the network and collects data in a single-hop fashion resulting in less energy consumption of the sensor nodes. Despite the advantage of this scheme, data forwarding to the mobile sink during an emergency situation is a major issue that should be dealt with. For efficient data delivery within the stipulated deadline, the sensor node which has sensed a delay-intolerant data needs to forward the data towards the mobile sink. In this paper, a Moore curve based trajectory of the mobile sink has been considered for efficient sink based data collection. This work is also motivated by anycast forwarding of delay sensitive data to the mobile sink before the sensed data lose its relevance, where the node follows a strict sleep-wake pattern. We have evaluated the effectiveness of the proposed trajectory based data collection scheme by simulation using MATLAB. The delay-intolerant data routing scheme (DRS) shows that the data reaches the mobile sink within the given deadline while restricting the energy dissipation of the sensor nodes to a minimum. This have been verified by extensive simulations which show that when compared to static sink, the usage of a mobile sink improves the network lifetime by almost 12%. This work has also been compared with an existing work (PMDD) to show its validity. The simulation results depict that DRS shows an improvement of about 25%–29% in terms of network lifetime.

Addressing Data Aggregation Using Polynomial Regression in WSNs

One of the scarcest resources in wireless sensor networks (WSNs) is energy. Thus, a major issue of WSN is to prolong the network lifetime. The energy dissipation of sensor nodes should be minimized within the network for prolong network lifetime. For the sake of this, data aggregation emerged as an important field of research in modern era. Intensive research has been carried out for providing novel energy efficient and secure data aggregation techniques. Unlike conventional data aggregation techniques, polynomial regression based data aggregation technique increases the network lifetime by transmitting compressed data packets of constant size. Data compression has been carried out by performing regression function on the sensed data. These techniques provide good approximation of the sensed value and are scalable as the data compression ratio is nearly constant. In this paper, we present a detailed review on different polynomial regression based data aggregation techniques and provide a comparison among them. Keywords: Data aggregation, Data correlation, Polynomial regression, Security, Wireless sensor networks.

Grid Based Cluster Head Selection Mechanism for Wireless sensor network

Wireless sensor network (WSN) consists of hundred to thousands sensor nodes to gathered the information from physical environment. Different clustering based algorithms have been proposed to improve network lifetime and energy efficiency. Practically it is not feasible to recharge the battery of sensor nodes when they are sensing the data. In such situation energy is crucial resource and it should be improved for life span of WSN. Cluster head (CH) has an important role in hierarchical energy efficient routing protocols because it receives data from nodes and sends towards base station (BS) or sink node. This paper presents a grid based cluster head selection (GBCHS) mechanism by dividing the network field into MXN uniform size partitions that aims to minimize the energy dissipation of sensor nodes and enhancing network lifetime. Simulation experiments have been performed in network simulator (NS2) that show our proposed GBCHS approach outperformed than standard clustering hierarchy LEACH protocol.

Modified Leach in Wireless Sensor Network

Wireless sensor network consists of large number of sensor nodes which is used to capture environmental conditions, process and transfer it to base station. These sensor nodes are sensitive to energy consumption and gets exhausted on working. So we need to emphasize on reducing energy dissipation of sensor nodes in order to improve network lifetime. Various routing protocols are developed to enhance network scalability and stability. The cluster based routing is an efficient way of reducing energy dissipation by limiting data transmission from nodes to base station. For this purpose we proposed a new routing method which improves the life span of network, its data transmitting capability with low energy consumption. It is an improvised version of LEACH protocol which conserves energy by load balancing .Multi hop routing is proposed for the transmission of data. Reduction of energy consumption is achieved with the help of TDMA scheduling. Time division multiple access (TDMA)-based MAC can potentially reduce the delay and provide real-time guarantees as well as save power by eliminating collisions. To keep network alive we had added a random node on replacement of dyeing node. The performance evaluation of protocol is based on simulation test performed in network simulator 2 (NS2) to prove the effectiveness of this proposed method in terms of network lifetime and energy efficiency. Keywords: Base station (BS), Clustering, Energy efficiency, Routing protocol, Wireless sensor network (WS

A MATHEMATICAL PROGRAMMING APPROACH TO THE MULTI-ROUND TOPOLOGY CONSTRUCTION PROBLEM IN WIRELESS SENSOR NETWORKS

One of the important issues in wireless sensor networks is how to save power consumption and extend the network lifetime. For this purpose, various network topology construction algorithms have been studied. However, most of them are based on a method repeating single round optimization. In other words, by considering the energy dissipation of sensor nodes only in the next round, a network topology in the next round is constructed in a round-by-round manner. The set of the topology constructions based on such a round-by-round view may be far from the optimal solution to maximize the network lifetime. To address this issue, we take the energy dissipation of sensor nodes over multiple rounds into account, and we consider the problem as a multi-dimensional knapsack problem, which enables us to find optimal network topologies until at least one sensor node exhausts its battery power. We also propose a solution method to maximize the network lifetime. The computational experiments show that the proposed approach provides efficient topology construction in the wireless sensor network in terms of network lifetime compared to the cluster-based approach.