A sleep scheduling based cooperative data transmission for wireless sensor network

Maximizing the network lifetime through balancing energy and lowering the delay is a primary design goal of wireless sensor networks (WSNs). In WSNs, the sensor nodes task is to acquire the data from the area or point of interest and transmit it to the sink node via relay nodes timely. Relay nodes must dissipate some energy to perform this task. So, the relay nodes and cluster heads face the challenge of energy balancing. Hence, choosing the optimal cluster heads and relay nodes is necessary to balance the energy among the sensor nodes. In this context, this paper presents a novel clustering algorithm to balance energy by considering different metrics of WSNs, including distance, energy, link lifetime, and delay characteristics—using a relay node, etc.In this paper, a Hybrid Heuristic Data Aggregation (HHDAP) Protocol is used by combining particle swarm optimization (PSO) and improved Ant colony optimization (ACO).Through this HHDAP algorithm, the relay nodes are chosen based on a sensor node's distance, energy, link lifetime, and delay metrics. This work is simulated using NS2, and the performance of proposed and existing works are evaluated. The results show the performance improvements of the proposed work over the existing algorithms in terms of different quality of service metrics.

In recent years there are many extensive applications of wireless sensor networks such as environment monitoring, surveillance, enemy tracking, etc. Since the available energy of sensor nodes are limited and hard to renew, energy supervision is critical for nodes in wireless sensor networks. In this paper, we propose a dynamic multi hop routing technique using residual energy based clustering algorithm to prolong node as well as network lifetime. Here, clusters are constructed using certain suitable parameters such as remaining energy of the nodes, its centrality, energy efficiency and cluster heads selection frequency Furthermore, after collecting all data within a cluster, the cluster heads gradually enhance their transmission range till finding a high residual energy based adjacent cluster heads to forward the data to the base station. In this way the proposed method generates an energy efficient routing path from each sensor to base station to send the data. Simulation results show that our approach effectively conserves energy for cluster heads as well as cluster members and prolong their life time effectively. This proposed method also reduces number of clusters and thus improve the nodes life time significantly.

The energy of sensor nodes in operation mode is primarily consumed by the wireless transceivers. Therefore, reducing the transmitted data can lead to significant energy savings. Compressive sensing is a technique that can reproduce an original signal using a smaller number of samples than required by the Nyquist theorem, by exploiting the sparsity of the signal in the represented domain. In Wireless Sensor Networks, compressed sampling is performed at the sensor node, and decompression is performed at the sink node. However, the limited computing and resource constraints in sensor nodes should be taken into consideration when applying the compressed sensing technique. This paper proposes using a non-linear system to generate chaos-based coefficient sequences applied in the sensor nodes of a landslide warning system. The experimental study demonstrated that the sensor node utilizing pseudo-random sampling is faster and less complex in comparison to the sensor node employing random sampling.

Wireless sensor networks consist of a large number of sensor nodes with limited battery power, and how to use energy of sensor nodes more efficiently to extend the lifetime of networks is a primary goal in the design of wireless sensor networks. Data aggregation can reduce the traffic in network by utilizing the abilities of local computation and storage of sensor nodes. In this paper, we propose a novel data aggregation scheme based on wavelet-entropy (DAWE in short) for wireless sensor networks. Firstly, we use multi-scale wavelet transforms to spread signals in multi-scale range, and then aggregate information using wavelet-entropy discriminance theorem in cluster members and cluster heads with LEACH clustering algorithm to reduce transmitting packets. Finally, we evaluate the performance of proposed data aggregation based on wavelet- entropy scheme by simulations.

This paper is a performance survey on the residual energy of nodes in a wireless sensor network (WSN). Wireless sensor nodes are limited in application due to constraints in energy of nodes’ battery and addressing the problem of energy management is essential to lengthen the life of a deployed network of wireless sensors. In the method used, Sensor nodes were equally distributed on the complete border of the network field. An exclusive cluster, six hierarchical, and eighteen hierarchical clusters were formed based on even partition of the border of the network field. The choice of Cluster Head (CH) within each cluster formed was based on selection of a sensor node with minimal transmission distance to the Base Station. A comparison of the various hierarchical scenarios was simulated in MATLAB. The mean residue energies (MREs) after the network lifetime are 3%, 52%, and 35% of the initial node energy for single, six and eighteen hierarchical clusters respectively. The residual energies of sensor nodes above the recorded MREs were taken on a class interval of 50’s of the node identity (nid) and respective averages of residual energies of sensor nodes (AEr) of each selected class interval were evaluated. Based on the result, certain categories of sensor nodes at a section of the network field indicated that the battery energy of the affected nodes was not maximally utilized thereby leading to enormous residual energy. It was concluded that the residual energy of nodes in a WSN could be optimized to extend the lifetime.

In recent years, wireless sensor networks become a new way to obtain information from an interesting area. There are many extensive applications of wireless sensor networks such as environment monitoring, surveillance, enemy tracking, etc. Since the available energy of sensor nodes are limited and hard to renew, energy supervision is critical for nodes and network lifetime in wireless sensor networks. In this paper, we propose a new method to improve ACPM and solve the problem of isolated nodes and to prolong their lifetime. After cluster construction, the isolated nodes gradually enhance their transmission range till finding an adjacent cluster to link. Furthermore, after collecting all data within a cluster, a cluster-head node will assign the most powerful member node in the cluster to forward the data to the base station. Simulation results show that our approach effectively conserves energy for isolated nodes.

In general, wireless sensor network works by a small battery-powered, or limited energy. Once the wireless sensor network is deployed, the energy of small sensor nodes can not be replaced. So, to improve energy efficiency and extend the survival time of the whole network is a crucial issue. This paper presents a new type of energy balancing algorithm for wireless sensor networks. The BCDCP-M algorithm draws on the main idea of the BCDCP routing protocol. When the base station divides the network, the new algorithm makes the number of cluster heads equal to the optimal number of cluster heads as far as possible. On cluster head election, not only the average energy of the sensor network, but also the remaining energy of the individual node must be taken into account. In data transmission, we use the multi-hop method to select the optimal path. The simulation results show that the survival time of the network in the new BCDCP-M algorithm is 19% longer than BCDCP.

In wireless sensor network (WSN), the energy of sensor nodes is limited. Designing efficient routing method for reducing energy consumption and extending the WSN’s lifetime is important. This paper proposes a novel energy‐efficient, static scenario‐oriented routing method of WSN based on edge computing named the NEER, in which WSN is divided into several areas according to the coverage of gateway (or base station), and each of the areas is regarded as an edge area network (EAN). Each edge area network is abstracted into a weighted undirected graph model combined with the residual energy of the sensor nodes. The base station (or a gateway) calculates the optimal energy consumption path for all sensor nodes within its coverage, and the nodes then perform data transmission through their suggested optimal paths. The proposed method is verified by the simulations, and the results show that the proposed method may consume about 37% less energy compared with the conventional WSN routing protocol and can also effectively extend the lifetime of WSN.

Background: Event detection and monitoring applications involve highly populated sensor nodes in Wireless Sensor Networks (WSNs). Dense deployment of nodes leads to correlated sensor observations in the spatial and temporal domain. Most of the previous works focused on constant sensing radii for spatially correlated sensor observations. However, in real time scenario, the sensor nodes may have variable sensing coverage areas, which comprise a Heterogeneous WSN. background: Spatial correlation model discussed in prior literature focused on Homogeneous sensing of sensor nodes. But, real time scenario the condition changes due to interferences obstructing sensing areas. Also, different manufacturers may provide different specifications for sensing areas, thus resulting into Heterogeneous sensing. Objective: To address this issue, we present an Enhanced Weighted Spatial Correlation Model for Heterogeneous sensor nodes in WSNs. Method: The mathematical framework considers the spatial coordinates of sensor nodes, the distances between the sensor nodes, and their sensing coverage. Furthermore, the correlation coefficient is calculated in terms of overlapping areas for randomly deployed nodes. Performance of the correlation model is evaluated and analyzed in terms of event distortion function. In addition to this, a macro and micro-zone concept is introduced, wherein sensor information is weighted for better event estimation at the sink node. Moreover, dynamic weighing of nodes like Inverse, Shepard’s and Gaussian distance weighing algorithms are simulated and analyzed for minimal event distortion. Over and above, the system performance is evaluated for different approaches considering reporting nodes with and without clustering of sensor nodes for macro and micro-zone concept. Simulation results for the Enhanced Weighted Spatial Correlation Model developed are obtained using MATLAB software. method: In order to evaluate the performance of the enhanced correlation model considering Macro and Micro-zone concept, simulations are carried out inMATLAB. Simulations are performed for ‚ trials and averaging of the values are finally used for analysis of results. Results: The comparative study shows an improved system performance in terms of minimal distortion obtained for non-clustered nodes; thereby reducing the computational complexity of cluster formation. Furthermore, the dynamic weighing algorithms outperform the existing fixed weighing algorithms for the correlation model with the lowest distortion function. Conclusion: Moreover, in the above algorithms, the event distortion gradually decreases and later becomes constant with the increase in the number of representative nodes. Hence, it illustrates that minimal distortion can be achieved by activating lesser number of representative nodes, thereby preserving the energy of other sensor nodes and increasing the lifetime of WSNs.

An energy-efficient cross-layer-based opportunistic routing protocol and partially informed sparse autoencoder for data transfer in wireless sensor network

Wireless sensor networks (WSNs) are used to collect large amounts of data over a wide area. However, the sensor nodes used in WSNs have a short lifespan because they are generally battery-operated. Research is actively underway to increase the lifespan of WSNs via techniques such as energy harvesting and wireless power transfer (WPT). This study proposes a scheme for clustering and adjusting the WSN data collection rate to alleviate the hotspot problem and improve network connectivity. The proposed scheme is targeted at WSNs in which the sensor nodes harvest solar energy. In addition, an unmanned aerial vehicle (UAV) traverses along a given path and delivers energy to sensor nodes using WPT, while collecting data as a sink node. In this scheme, the number of cluster heads is determined by considering the number of nodes at each hop distance and the maximum amount of data that can be transferred with the available energy. Further, sensor nodes limit the amount of data they collect to reduce the relay load on intermediate nodes. To achieve this, nodes consider the amount of data transferred by parent nodes, especially nodes in the hotspot, and the number of child nodes. The sink node traverses along a given path, and collects the data accumulated in the cluster head while supplying its remaining energy to the cluster head. The amount of energy it transfers is determined by considering the number of cluster heads. Consequently, nodes in the hotspot are prevented from becoming blacked out from a lack of energy, and the energy harvesting efficiency is increased. Simulation results indicate that the proposed scheme reduces the number of blackout nodes in the hotspot areas near cluster heads. Furthermore, the data collection and monitoring performance increases with the increase in network connectivity.

Cooperative communication is shown to be an efficient method of combating fading in wireless networks. By ”sharing” their antennas cooperating single-antenna nodes create a virtual multi-antenna system and therefore, benefit from spatial diversity. Network coding being a particular cooperative communication technique provides substantial gains in data rate, especially in networks with many sink nodes. By allowing intermediate nodes of a network to mix the incoming data streams, one can achieve the multicast capacity. Recently, Xiao and Skoglund showed that binary network coding (BNC) is not optimal for multi-user multi-relay networks in terms of diversity and proposed diversity network coding (DNC) scheme that outperforms BNC approach. Following this approach we examine potential diversity gains of using the DNC scheme for multi-source line networks. We show that the DNC technique outperforms both conventional time-orthogonal transmission and the BNC scheme. Further, the problem of optimal scheduling of the transmission is explored. We formulate the optimization problem and propose efficient solutions. Numerical results are presented to support theoretical findings.

Wireless sensor networks are prone to partitioning because of limited energy in sensor nodes. In this paper, a new cluster algorithm will be proposed in order to use the energy more effectively and prolong the lifetime of wireless sensor networks. Firstly, to reduce the energy consumption during the cluster building, the parameters such as node energy, distances between nodes and the numbers of neighbor nodes are used in the stages of cluster heads selection and clusters building to balance the load distribution. Also the conditions are set to induce the frequency of cluster rebuilding. And then the data transmission path between intra-cluster and inter-cluster are set. At last, to solve the problem of the delay bringing by multiple hops transmission, TDMA schedule is also reset. Simulation results show that the proposed algorithm can not only effectively reduce the frequency of network reconstruction, but also can balance energy consumption and prolong the lifetime of the networks

Wireless sensor networks (WSNs) are a new and most recent technology which is used in many critical applications. WSN is a network which is deployable anywhere in the environment. Due to this nature of WSN, an unattended network is more probable for vulnerable and unavoidable attacks. WSN is comprised of huge different types of nodes which are distributed within the network. Some of the nodes have sensing technique for detecting unusual behaviour in the network. Detection and prevention mechanisms are used for securing the network, with the consideration of energy efficiency. Biometric-based system is most efficient way of detecting an intruder which is one of the major techniques of pattern matching. Since WSN is more probable vulnerable to various harmful security attacks as it has the nature of broadcasting the transmission data and has very low capacity of computation, an Intrusion detection system (IDS) is used to detect such mobile attackers or intruders. Intrusion prevention system (IPS) uses a mechanism which enables the reduction of consumption of energy in sensor nodes of WSN.

According to the characteristics of piggery, such as complex environment, numerous control objects, complex wiring, a piggery environment control system based on wireless sensor networks was designed. This system consisted of the wireless network composed of various devices and sensors in piggery, the control center with ARM-LINUX and the remote control center. The energy of sensor nodes was offered by batteries in wireless sensor networks so that the power source was limited. In order to strengthen the node energy management, the LEACH algorithm was combined with AODV routing protocol, and a new optimization algorithm called LEACH-A was proposed in this paper. The wireless sensor networks execution process was divided into two phases, including cluster building and data transmission. In the two phases, the node residual energy was calculated by a weighted function and the cluster head role was changed by the size of remaining energy. NS-2 simulation results show that the proposed algorithm prolonged the lifetime of the nodes and the networks, and realized the energy optimization management.