A sleep scheduling algorithm of redundant nodes in power communication sensor networks based on relative local density

The rapid development of the agricultural Internet of Things (IoT) is inseparable from the support of wireless sensor networks (WSNs) in recent years. To further facilitate the adaptation of WSNs to agricultural applications, reducing the energy consumption of sensor nodes in agricultural environments has become a crucial problem. Sleep scheduling in randomly deployed WSNs is an effective method to reduce the energy consumption of sensor nodes, which can extend the network lifetime while ensuring network coverage. However, most of the existing sleep scheduling algorithms require frequent information exchange (such as broadcasting to find neighboring nodes and collecting nodes’ residual energy), which will inevitably lead to massive energy consumption. To address this problem, in this article, an unmanned aerial vehicle (UAV)-assisted sleep scheduling algorithm (UAVSS) is proposed, which avoids excessive information exchange among nodes and ensures sufficient network coverage with the least number of sensor nodes. The UAV traverses all the sensor nodes along the shortest path to help information exchange and gather sensing data. Furthermore, the UAV path is planned by using the latest monarch butterfly optimization (MBO) algorithm. Simulation results indicate that the proposed UAVSS can prolong the network lifetime while ensuring the required area coverage.

Target tracking is a typical application in wireless sensor networks. Both energy efficiency and tracking performance are important issues that need to be considered. They are a pair of contradictions most of the time. Saving energy often sacrifices tracking performance, while enhancing tracking performance needs to consume more energy. In this paper, an efficient sleep scheduling algorithm is put forward to tackle the above problem in energy harvesting sensor networks. At first, we modify the probability-based prediction and sleep scheduling (PPSS) algorithm to track the target and further use another sleep scheduling algorithm we proposed to wake tracking nodes when the target is likely to be missed (i.e., it is unsuccessful to wake next-moment tracking nodes). Secondly, a double-storage energy harvesting architecture is employed to increase residual energy of sensor nodes and to extend network lifetime. Simulation results reveal that the proposed sleep scheduling algorithm can improve tracking performance and prolong network lifetime compared with the PPSS algorithm and the proposed algorithm without energy harvesting.

Wireless Sensor Networks (WSNs) consist of a large number of small and low cost sensor nodes powered by small batteries and equipped with various sensing devices. Usually, for many applications, once a WSN is deployed, probably in an inhospitable terrain, it is expected to gather the required data for quite some time, say for years. Since each sensor node has limited energy, these nodes are usually put to sleep to conserve energy, and this helps to prolong the network lifetime. There are two major approaches to sleep scheduling of sensor nodes, viz. (i) random (ii) synchronized. Any sleep scheduling scheme has to ensure that data can always be routed from source to sink. In this paper, we propose a novel approach for sleep scheduling of sensor nodes using a tree and an energy aware routing protocol which is integrated with the proposed sleep scheduling scheme. The tree is rooted at the sink node. The internal nodes of the tree remain awake and the leaf nodes are made to sleep. This provides an assured path from any node to the sink node. The tree is periodically reconstructed considering the remaining energy of each node with a view to balance energy consumption of nodes, and remove any failed nodes from the tree. The proposed approach also considerably reduces average energy consumption rate of each node as we are able to put more number of nodes to sleep in comparison to other approaches. Additional fault-tolerance is provided by keeping two paths from each node towards the sink. Extensive simulation studies of the proposed routing protocol has been carried out using Castalia simulator, and its performance has been compared with that of a routing protocol, called GSP, which incorporates sleep scheduling using random approach. The simulation results show that the proposed approach has longer network lifetime in comparison to that provided by GSP, and the energy consumption of nodes is also balanced.

With rapid adoption of advanced wireless sensors in last decades, industrial wireless sensor networks (IWSNs) are increasingly deployed in the industries for various applications. Sleep scheduling is a common approach in IWSNs to overcome network lifetime problem due to energy-constrained sensor nodes. However, in real-environment, node’s transmit power varies in different directions due to non-isotropic nature of electromagnetic transmission, path-loss, noise, and temperature. Thus, radio irregularity results in link asymmetry, thereafter, affects the performance of sleep scheduling in IWSNs. In this paper, we evaluate the impacts of radio irregularity on sleeping probability and lifetime performances of well-known connected k-neighborhood (CKN)-based sleep scheduling algorithms in duty-cycled IWSNs. We derive the upper-limit of sleep probability with radio irregularity variables. From the extensive simulations, we show that radio irregularity increases the number of awake nodes in duty-cycled sensor networks, therefore, network lifetime decreases with increasing values of link asymmetry parameters. Finally, an adverse impact of radio irregularity is observed in higher k-value in CKN-based algorithm due to more awake nodes to satisfy the k-connectivity in presence of link asymmetry.

Sleep and wake-up scheduling of sensor nodes is an efficient solution to prolong the network lifetime. However, existing scheduling algorithms may significantly decrease the number of active nodes so that the network may be intermittently connected. In light of this, traditional geographic routing protocols are inappropriate to obtain low latency routes due to route discovery and data forwarding latency. Recently, Lu Su et al. have proposed an ODML routing scheme that finds low latency routes in intermittently-connected sensor networks by using RREQ and RREP messages [9]. Nevertheless, ODML does not consider the latency of RREP and has a high routing overhead. This paper proposes a novel Multicandidate Greedy Routing (MGR) scheme that makes the best effort to find minimum latency routes in wireless sensor networks. In MGR, each source node sends an RREQ to a set of first wake-up forwarder candidates and selects a route with minimum estimated delivery latency based on their replies. Simulation results demonstrate that our proposed scheme performs better than the distance-based greedy forwarding and ODML in terms of delivery latency and routing overhead.

Timely reliability modeling and evaluation of wireless sensor networks with adaptive N-policy sleep scheduling

We propose a phased waking three dimensional coverage scheme based on hibernation of redundant nodes for the limited energy of nodes and redundancy in wireless sensor networks. A large number of sensor nodes are randomly deployed in the monitoring region and the redundant nodes can hibernate which will be waken by phases after the on-duty nodes are exhausted. Each node¿s state can be determined at any moment until all of the nodes in the entire sensor network are exhausted. The simulation results show that our method improves the network performance. Besides, the phased waking strategy outperforms the one not by stages, and waking the nodes after hibernation achieves higher network performance than directly waking in turn with the same number of nodes deployed in the 3D monitoring region.

Optimizing the use of the limited energy resource in a mobile wireless sensor network (MWSN) is a key challenge for researchers to maintain longer network survival. This paper attempts to provide an energy-efficient data routing solution for large MWSNs. The aim of this work is to propose a cluster-based scheduling protocol for MWSN. The network is firstly divided into an optimal number of clusters according to sensors connectivity. Secondly, a sleep scheduling algorithm is proposed to save the energy consumption by turning off the overlapped nodes in the sensing field. This method is distributed among sensors in each cluster. It is based on the perimeter coverage level of mobile nodes to schedule their activities according to their weights. The weight is used to promote the energy efficiency criterian for all sensors in a cluster. The proposed approach ranges from sensors deployment, their organization to their operational mode. Experimental results demonstrate that the proposed cluster-based scheduling algorithm, based on the perimeter coverage of sensors, provides higher energy efficiency and longer lifetime coverage for MWSNs as compared to other protocols.

This paper presents an energy-aware, sleep scheduling algorithm called SSMTT to support multiple target tracking sensor networks. SSMTT leverages the awakening result of interfering targets to save the energy consumption on proactive wake-up communication. For the alarm message-miss problem introduced by multiple target tracking, we present a solution that involves scheduling the sensor nodes' sleep pattern. We compare SSMTT against three sleep scheduling algorithms for single target tracking: the legacy circle scheme, MCTA, and TDSS. Our experimental evaluations show that SSMTT achieves better energy efficiency than handling multiple targets separately through single target tracking algorithms.

The compaction and sintering processes of multi-component silicon powders, including single-action die compaction, uniaxial hot pressing and bidirectional hot compaction, were numerically performed by FEM (finite element method) modelling. The relationships of initial packing structure, compaction method, pressing pressure and sintering temperature between overall relative densities of multi-component silicon components were systematically discussed and studied. Meanwhile, various macro- and microscopic properties of silicon composite powders were carefully characterized, such as overall and local relative density, local stress distributions and void filling behaviour. The results show that fully compacted silicon composite targets with low internal stress can be obtained by using uniaxial hot pressing or bidirectional hot compaction method. And the physical experiments were carried out to verify the accuracy of the simulation results.

Energy harvesting sensor networks sparked extensive research interest in recent years. However, little attention has been paid to sleep scheduling for area surveillance in rechargeable sensor networks. In this paper, we attempt to settle the problem of keeping rechargeable Wireless Sensor Networks operating for a long time while satisfying certain desired area coverage and energy consumption balance among sensor nodes. The energy harvested by a node is still limited and unstable. So we put forward a distributed heuristic range-based sleep scheduling algorithm which integrates the residual energy of nodes with the recharging frequency constraint. It aims to find out a small set of nodes running in the active mode whose energy evaluation functions are higher than their neighbors within the r <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sub> -range. This algorithm is simple and does not need precise location information while guaranteeing area coverage. Simulation results show that the algorithm can prolong network lifetime compared to that of the network with nonrechargeable sensor nodes or with rechargeable nodes whereas lack of sleep scheduling corresponding to the uncertain energy supplements.

For the purpose of saving the resources of wireless sensor networks, it is necessary to fuse the existing data in wireless sensor networks. When using the current method to fuse data in wireless sensor networks, the fusing time is too long to remove the noise signal in wireless sensor networks effectively. In the process of fusing, data packet loss is easy to occur. There are some problems such as low fusion efficiency, poor denoising performance and high packet loss rate. A multi-path data hierarchical fusion method based on routing algorithm is proposed in this paper. The redundant node decision method based on mesh partitioning is combined with the grid algorithm and EECRS protocol to establish a routing protocol based on redundant node elimination, and the redundant data existing in the redundant nodes of the wireless sensor network is removed. Based on the statistical data of the data, the probability density function is deduced and analyzed, and the noise filtering matrix is constructed to optimize the measurement matrix and remove the noise existing in the wireless sensor network. The multi-path scheduling technology is used to synthesize the pheromone table and the data forwarding flow table, and the ant is used to actively monitor the link QOS real-time information in the path finding process to update the pheromone table and the flow table, and calculate the link idle rate according to the update result. According to the calculation result, the data transmission path is selected, and the multi-path data layer fusion is completed. The experimental results show that the proposed method has high fusion efficiency, good denoising performance and low packet loss rate.

Contiki’s Cooja is a very popular wireless sensor network (WSN) simulator, but it lacks support for modelling sensing coverage, focusing instead on network connectivity and protocol performance. However, in practice, it is the ability of a sensor network to provide a satisfactory level of coverage that defines its ultimate utility for end-users. We introduce WSN-Maintain, a Cooja-based tool for coverage and network lifetime evaluation in an in-building WSN. To extend the network lifetime, but still maintain the required quality of coverage, the tool finds coverage redundant nodes, puts them to sleep and automatically turns them on when active nodes fail and coverage quality decreases. WSN-Maintain together with Cooja allow us to evaluate different approaches to maintain coverage. As use cases to the tool, we implement two redundant node algorithms: greedy-maintain, a centralised algorithm, and local-maintain, a localised algorithm to configure the initial network and to turn on redundant nodes. Using data from five real deployments, we show that our tool with simple redundant node algorithms and reading correlation can improve energy efficiency by putting more nodes to sleep.

Target Coverage and Network Connectivity in Wireless Sensor Networks (WSNs) plays a momentous role in the field of monitoring environment, habitant observing, disaster recovery, surveillance, etc. Coverage and Network Connectivity collectively can be considered as a proportion of Quality of Service (QoS) in a sensor network. It implies the coverage of each point in the detecting field while in the meantime fulfilling the measure that each node is surrounded by the equivalent neighbourhood of at least one other node. Many primary issues influence the outline and performance of WSN. A Comprehensive survey is made based on the scheduling algorithm and set cover approach, in which we analyze various approaches and their algorithm complexity. Firstly, the scheduling algorithm intermittently provides the required data service with guaranteed efficiency. This methodology can efficiently reduce the entire number of active nodes in the network, resulting in maximizing the network lifetime. Secondly, the set cover approach plays a major role in solving target coverage problem which group sensors into each cover set thereby, target coverage was achieved. Typical issues and open research challenges are addressed by classifying the approaches available in literature into three broad categories namely, adaptable coverage radius, coverage deployment strategy, and sleep scheduling. This survey aims to provide a better perceptive of the existing open challenges and possible futuristic approaches for target coverage and network connectivity in wireless sensor networks.

The reliability of communication can be enhanced by increasing the network connectivity. Topology control and node sleep scheduling are used to reduce the energy consumption. This paper considers the problem of maintaining k-connectivity of WSN at minimum energy level while keeping only a subset of sensor nodes active to save energy. In our proposed scheme, each node is assumed to have multiple power levels and neighbor proximity not exact location information is adopted. Firstly the network partition is attained by power based clustering, and next nodes are divided into equivalent classes according to the role of data forwarding to different adjacent clusters. Then Node Scheduling and Power Adjustment (NSPA) algorithm selects a subset of nodes with different power levels to construct the local minimum energy graph while maintaining network connectivity. If the number of intra-cluster nodes which have adjacent clusters exceeds a certain threshold, k-NSPA is employed. Finally, a k-connected topology can be obtained. The simulation shows that our scheme can obtain the redundant nodes while maintaining network k-connected and it is more energy efficient compared with previous work.Keywords k-connectivitypowerclusteringequivalence