Enhancement of wireless sensor network lifetime by deploying heterogeneous nodes

In wireless sensor networks (WSNs), preserving energy requires utmost attention, as they are highly resource constrained. Clustering is commonly considered as one of the efficient energy conservation techniques. In a clustered WSN, due to the nature of operation, cluster heads (CHs) near the sink bear the major share of data forwarding compared with the CHs far away from the sink, resulting in an energy hole problem. First, we have analyzed the optimization of network lifetime by balancing the energy consumption among different CHs. To meet the requirement of optimization of network lifetime, we have devised a routing-aware optimal clustering strategy. Further, keeping the network model in mind, we identify Archimedes' spiral, based on which a deployment function is proposed for distributing a member node and a CH. The performance of the optimal clustering strategy is evaluated in terms of energy balance and network lifetime. Simulation results show that our scheme provides satisfactory network performance in terms of end-to-end delay and throughput. Finally, all the results are compared with two competing schemes that confirm our scheme's supremacy in terms of both design performance metrics as well as network performance metrics.

Energy is one of the crucial factors in wireless sensor network. In this paper, we consider energy fairness problem in heterogeneous wireless sensor network. Firstly, we propose a nodes' deployment model in which we deploy heterogeneous sensor nodes judiciously to conserve energy. This prevents the problem of occurrence of 'energy holes' and ensures prolonged network lifetime. Then, we use Game Theory to model the packet transmission among sensor nodes, and get the Nash equilibrium by properly designing the measurement function. Finally, the simulation results fully prove the effectiveness of the proposed scheme in this paper, which not only make the energy consumption of the network balanced but the network lifetime also improved.

A scheme for WSNs (wireless sensor networks) security is given by dividing sensing tetrahedron into clusters and using the symmetric polynomials in this paper.The sensing tetrahedron is divided into a number of small grids.All those sensor nodes, both ordinary sensor nodes and heterogeneous sensor nodes are distributed in the sensing tetrahedron.In a grid, all ordinary sensor nodes and heterogeneous sensor nodes establish their shared keys through using the symmetric polynomials.All the heterogeneous sensor nodes establish their shared keys through using the symmetric polynomials.At last, all sensor nodes establish their keys directly or indirectly in the whole sensing tetrahedron.Analysis and comparison demonstrate this scheme enhances the WSN security, has good network connectivity, saves node storage, reduces network computing load, and extends the network lifetime.

Task requirement aware pre-processing and Scheduling for IoT sensory environments

Wireless Sensor Networks (WSNs) have led to tremendous growth in the development of sensor technology and offer numerous applications, such as wildlife monitoring, environment, healthcare, military surveillance and security systems. In terms of performance, the evolutions of WSN routing protocols play a vital role in extending the lifetime of networking operations. Due to the limited resources of power in sensor nodes, the design and implementation of an energy-efficient routing protocol comprise a key challenge for researchers. Before, in the case of a static sink, nodes at farther areas from the sink transmit their sensed data at a longer transmission range and die early, while nearby nodes face lesser transmission costs and stay alive for a longer period. This unbalanced energy distribution creates energy holes in ares far from the sink in WSNs. Mobility-based sink routing protocols are proposed to minimize the sub-optimal energy consumption in WSNs, as the sink mobility covers the sensor field, which alleviates the overall load-balancing among sensor nodes. In order to overcome the energy hole issue while prolonging the network lifetime, it is important to determine the optimal mobility pattern. In this regard, we propose the Spiral Mobility based on Optimized Clustering (SMOC) routing protocol and the Multiple sink-based SMOC (M-SMOC) routing protocol for large-scale WSNs. Performance evaluations of the proposed protocols are compared with various existing routing protocols based on static sink, random mobility and grid mobility. Different numbers of performance parameters are considered for evaluation, such as network lifetime, network stability, packet drop ratio, packet delivery ratio, end-to-end delay, average energy consumption, network connection time and the impact of different heterogeneity levels. Experimental results show the benefits of the spiral mobility pattern and how it improves the network lifetime and stability period over the existing state-of-the-art routing protocols.

With the rapid development of Internet of Things (IoT) technology, the number of nodes in wireless sensor networks (WSNs) is explosively increasing, and the scale of network is increased gradually. Traditional single-layer non-clustering network is no longer suitable for current WSNs, which results in high maintenance cost and fast deterioration of network performance. By analyzing the impact of existing static and dynamic clustering schemes on network performance, it is concluded that additional factors need to be considered to improve the overall performance of the network, such as residual energy of nodes, number of neighbor nodes and load balancing. Therefore, an adaptive multi-layer clustering networking strategy based on capability weights is proposed. Based on the real-time changes of each cluster density, node load and residual energy, the node capacity weights are updated dynamically according to the actual network performance, then the cluster heads are renewed adaptively. By comparing the performance metrics in the experiments, proposed strategy can effectively reduce the load of key nodes such as cluster head, and improves the network performance metrics such as average transmission delay, average transmission hops and load balancing.

Recent developments in wireless sensor networks (WSNs) have led to significant advances in energy-efficient routing protocols through the integration of deep learning and bio-inspired optimization algorithms. WSNs face critical challenges due to limited battery capacity, computational constraints, and dynamic network topology. This review article provides a comprehensive comparative analysis of state-of-the-art approaches for energy optimization in WSNs, focusing on clustering and routing protocols. We examine three major publications that propose hybrid frameworks combining deep learning architectures (CapsNet, ResNet-50) with metaheuristic optimization algorithms (ETSA, SOPSO, ACO). The analysis categorizes these protocols based on network architecture, energy models, optimization strategies, and performance metrics. We compare their effectiveness in extending network lifetime, reducing energy consumption, improving packet delivery ratio, and minimizing transmission delay. The comparative study demonstrates that hybrid deep learning-swarm intelligence approaches achieve 18-38% improvement in network lifetime, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{1 2} \boldsymbol{-} \mathbf{1 4 \%}$</tex> reduction in energy consumption, and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{1 5} \boldsymbol{-} \mathbf{2 4 \%}$</tex> lower delay compared to traditional protocols. This article serves as a comprehensive reference for researchers and practitioners working on nextgeneration energy-efficient WSN deployments.

Wireless sensor networks (WSN) have emerged recently and gained widespread popularity. The applications of WSN involve remote medical monitoring system, disaster management, security surveillance, and emergency response system. Most of these applications are used in harsh environments where the sensor nodes are operated in an unattended manner. The tiny nodes used for sensing in WSN are battery operated and have limited resources in terms of energy, storage and power. One of the ways to efficiently utilize the energy is to use clustering. Clustering is the way to gather data from group of similar or different items. In this paper we propose an energy efficient scheme, ECMS (Energy-Efficient Clustering scheme for Multihop wireless Sensor networks) for both cluster heads and senor nodes. The proposed scheme works in two phases. In first phase efficient cluster formation is done by using the energy factor and computation of distance between nodes to base station. In second phase gateway nodes are incorporated and used instead of direct communication between cluster head nodes to avoid long distance communications and out of range cluster heads are also communicated using these gateway nodes. The simulation results prove the effectiveness of the proposed scheme towards less energy utilization and enhancement in network life time.

This paper focuses on enhancement of lifetime in an event driven wireless sensor network (WSN). The network lifetime is enhanced by optimal deployment of sensor nodes in a given region as premature shortening of life takes place leading to uneven consumption of energy. In the proposed scheme, the senor nodes are linearly deployed over the sub-regions and the inter-node distance between the sensor nodes depends on the statistics of events sensed by the sensor nodes in multi hop mode of communication. The performance is measured with respect to the residual energy at each sensor node and total number of days spent by sensor nodes. The lifetime of the sensor network is defined as the survival of some fraction of nodes. For an approximate Poisson distribution for occurrence of events in different sub-regions, the variation of energy consumption in sensor nodes is negligibly small. Further if some nodes are having finite residual energy even after failure of some nodes, then those nodes can continue their communication in single hop mode and hence the lifetime of the network is further extended.

Wireless sensor network performance metrics for building applications

Wireless sensor networks attract so much attention in current IoT-enabled industrial and domestic applications having either homogeneous or heterogeneous sensors deployed to acquire information of intent. WSNs are designed to operate using self-powered sensor nodes as their choice of application is geographic critical. Such nodes must support energy efficiency so that network longevity becomes high. Cluster head selection plays a crucial stage in a WSN architecture which mainly focuses on the minimization of network energy consumption. It groups sensor nodes in such a way that a sophisticated network cluster is formed to have enhanced life time besides a low power consumption. A popular clustering technique, known as LEACH and its variants, is found to be energy efficient compared to its counterparts. The authors propose a novel fully connected energy efficient clustering (FCEEC) mechanism using the electrostatic discharge algorithm to establish a fully connected network with shortest path routing from sensor nodes (SNs) to cluster head (CH) in a multihop environment. The proposed electrostatic discharge algorithm (ESDA) enhances network life time while attaining energy efficient full connectivity between sensor nodes. As a result of ESD, the dead node count is reduced significantly so that the network longevity is increased. In the end, simulation results exhibited improved performance metrics such as energy efficiency, dead node count, packet delivery, and network latency compared to certain conventional CH selection approach.

Wireless networks play a crucial role in the communication systems nowadays. Wireless networks are being increasingly used in the communication among devices of the most varied types and sizes. User mobility, affordability, flexibility and ease of use are few of many reasons for making them very appealing to new applications and more users everyday. A Wireless Sensor Network (WSN) is composed of sensor nodes spread over the field to sense the data. The sensed data must be gathered &amp; transmitted to Base Station (BS) for end user queries. The used sensor nodes being in- expensive having low computation power &amp; limited energy so are not as much reliable as their expensive macro sensor counter parts but their size and cost enable hundred to thousand of micro sensors to achieve high quality fault tolerant system. In an environment where in each round all sensor nodes have to send data to base station; it is required to effectively utilize energy of sensor nodes so as to increase the life- time of the system. The use of data aggregation &amp; fusion as proposed in LEACH increases system lifetime by a factor of 8 as compared to conventional routing protocols. In this work, our main focus is the static sensors are randomly selected and the base stations have their information all a priori. Basically, the sensors are in direct communication range of each other and can transmit to and receive from the base station. The nodes periodically sense the environment and have always data to send in each round of communication. The nodes fuse/ aggregate the data they receive from the others with their own data, and produce only one packet regardless of how many packets they receive. The problem is to find a routing scheme or an efficient protocol to deliver data packets collected from sensor nodes to the base station. It maximizes the lifetime of the sensor network under the system model given above. However, the definition of quality of service of the sensor network provides is not specified. Secondly, where the nodes are densely deployed, the quality of the system is affected as soon as a significant amount of nodes die, since adjacent nodes record identical or related data. In this case, the lifetime of the network is the time elapsed until half of the nodes or some specified portion of the nodes die. In general terms, the time in rounds where the last node depletes all of its energy defines the lifetime of the overall sensor network. Taking these different possible requirements under consideration, our work provides a proper timing of all deaths for all algorithms in detail as well as chooses the shortest possible path for communication with better memory management scheme and leaves the decision which one to choose to system designers.

A practical cross layer cooperative MAC framework for WSNs

Wireless sensor networks (WSN) play a key role in enabling wireless communication technology among several nodes to remotely communicate and exchange information. WSN consists of tiny sensor nodes equipped with battery, scattered in an area to gather information around an environment and send to data collection node known as sink or base station (BS). WSN have been widely used in various applications like agriculture, fire detection, health care and military and has become imperative necessity for future revolutionary area like UAV (unmanned aerial vehicles), IoT (Internet of things) and smart cities which employs large scale sensor nodes. However sensor nodes are limited to battery, memory, low computational power, resource and bandwidth. Continues sensing of events, makes node to drain its battery faster and goes dead fast. For resource constrained WSN, hierarchical cluster based approaches are considered as energy efficient and improves network performance for large scale WSN. Minimizing energy consumption and extending network lifetime are major challenging issues of WSN, clustering methods with optimized routing have offered solution to optimize energy utilization. To balance energy consumption and improve network lifetime many existing hierarchical clustering approaches have been proposed, however existing method does not consider rotation of cluster head (CH) and considers cluster head selection based on residual energy and distance parameter. In this paper we propose an improved energy efficient cluster tree (IEECT) based routing to improve energy efficiency of hierarchical cluster. IEECT considers modification of existing LEACH (Low energy adaptive clustering hierarchy) protocol to improved energy efficient LEACH by considering energy parameters like residual node energy and average network energy. IEECT accounts optimal number of cluster head (CH) and selection of CH is done using threshold value among sensor nodes. Proposed IEECT combines tree based routing and data aggregation scheme to maintain desirable quality of service. Simulation experiments are carried out by using network simulator. Performance of IEECT is evaluated in terms of PDR, delay, energy consumption, network lifetime and overhead.

To solve the energy hole problem in wireless sensor networks with double sinks, a double sink energy hole avoidance strategy is proposed. The main idea is that two data sinks are set at fixed positions on both sides of the rectangular network to collect nodes data in the corresponding area of the network. In the network, sensor nodes are organized in non-uniform clusters. Clusters close to sink have a smaller cluster radius, and clusters far from sink have a larger cluster radius. According to the results of threshold training, monitoring area of double sink is dynamically adjusted based on the difference of energy consumption and load of nodes in the double sink monitoring area, so that the energy consumption load of nodes in the double sink monitoring area tends to be the same, so as to avoid the premature occurrence of energy hole phenomenon in the area with large load, leading to network failure. The experimental results demonstrate that the strategy proposed in this paper can efficiently balance the energy dissipation of double sink and prolong the network energy utilization efficiency and network lifetime.