Lifetime Of Wireless Sensor Networks Research Articles

Synchronous Wireless Sensor and Sink Placement Method Using Dual-Population Co-evolutionary Constrained Multiobjective Optimization Algorithm

Optimal wireless sensor placement (OWSP) plays a pivotal role in structural health monitoring. This study proposes a method to determine the simultaneous placement of sensors and sinks that minimizes energy consumption and maximizes information effectiveness. Network connectivity and reliability are critical constraints that determine the lifetime of wireless sensor networks. In this study, OWSP was formulated as a constrained multi-objective optimization problem with mixed-integer programming. Accordingly, a dual-population constrained multi-objective optimization (DCCMO) algorithm, which includes new crossover and mutation operators, was developed. In DCCMO, weak cooperation between two offspring populations is exploited to improve the efficiency of the solution search. The performance of DCCMO was compared to that of five other state-of-the-art algorithms using numerical examples with varying network parameters. DCCMO not only successfully matches the constrained Pareto front but also balances energy consumption and information effectiveness while exhibiting greater diversity and faster convergence than all other tested algorithms.

A novel self‐adaptive hybrid slime mould naked mole‐rat algorithm for numerical optimization and energy‐efficient wireless sensor network

SummaryA wireless sensor network (WSN) is made up of widely spaced nodes inside the sensor field. Clustering is an effective data collection approach that lowers energy consumption by organizing nodes into groups. So, choosing the right cluster head (CH) and employing an effective routing protocol is required to prolong the lifetime of WSNs. This paper develops a novel self‐adaptive hybrid slime mould naked mole‐rat algorithm (SM‐NMRA) based on the hybridization of the slime mould algorithm (SMA) and naked mole‐rat algorithm (NMRA) to solve the above‐said problem of WSN. The new approach integrates SMA's wrap food abilities with NMRA to improve the working capabilities of the original NMRA. A new stagnation phase inspired by the grey wolf optimizer and cuckoo search algorithms has been integrated to handle the local optima stagnation problem. In addition, self‐adaptivity has been added for SM‐NMRA's parameters. Here, the performance of SM‐NMRA is evaluated for CEC 2005 and CEC 2014 numerical test suites. The statistical outcomes along with Freidman's test, Wilcoxon's rank‐sum test, and convergence profiles validate the superior performance of SM‐NMRA. The proposed clustering protocol for efficient CH selection in WSN using SM‐NMRA outperforms other state‐of‐the‐art techniques with improved network lifespan and reduced energy consumption.

Intelligent routing algorithm for wireless sensor networks dynamically guided by distributed neural networks

Using reinforcement learning to adjust the power balance of sensor nodes dynamically is an essential approach for extending the lifetime of wireless sensor networks (WSNs), which makes sensor nodes adapt to changing environments by calculating optimal decision solutions in real time. However, as WSN grows, error propagation cannot keep up with topological changes in the networks, which prevents the sensor nodes’ routing decisions from being timely optimized. In this paper, we propose the DNN-HR algorithm, which optimizes the reinforcement learning process of WSN in three ways. First, this paper constructs a two-level learning structure. Discrete nodes perform reinforcement learning during the interaction with their neighboring nodes. Simultaneously, the neural networks that fit the V values guide the underlying nodes’ learning. Second, the algorithm divides the sensor networks into two-level subspaces according to their geographic locations, and cluster heads are elected in each subspace to form weight incremental propagation trees. Third, each sensor node sends the weight increments it learned to its first-level cluster head. At the first-level cluster head, the weight increments sent by multiple nodes are received and accumulated, and then the results are sent to the second-level cluster head. The base station acquires the weight increments of all the second-level cluster heads and then broadcasts the updated neural network weights to all the WSN nodes. Experiments determine the appropriate heuristic parameters. Compared to other algorithms, the experiments show that our proposed scheme significantly improves the packet delivery rate, node survival rate, and energy standard deviation of nodes.

Hybrid Muddy Soil Fish Optimization-Based Energy Aware Routing in IoT-Assisted Wireless Sensor Networks

The seamless operation of interconnected smart devices in wireless sensor networks (WSN) and the Internet of Things (IoT) needs continuously accessible end-to-end routes. However, the sensor node (SN) relies on a limited power source and tends to cause disconnection in multi-hop routes because of a power shortage in the WSN, eventually leading to the inefficiency of the total IoT network. Furthermore, the density of available SNs affects the existence of feasible routes and the level of path multiplicity in the WSN. Thus, an effective routing model is predictable to extend the lifetime of WSN by adaptively choosing the better route for the data transfers between interconnected IoT devices. This study develops a Hybrid Muddy Soil Fish Optimization-based Energy Aware Routing Scheme (HMSFO-EARS) for IoT-assisted WSN. The presented HMSFO-EARS technique majorly focuses on the identification of optimal routes for data transmission in the IoT-assisted WSN. To accomplish this, the presented HMSFO-EARS technique involves the integration of the MSFO algorithm with the Adaptive β-Hill Climbing (ABHC) concept. Moreover, the presented HMSFO-EARS technique derives a fitness function for maximizing the lifespan and minimizing energy consumption. To demonstrate the enhanced performance of the HMSFO-EARS technique, a series of experiments was performed. The simulation results indicate the better performance of the HMSFO-EARS algorithm over other recent approaches with reduced energy consumption, less delay, high throughput, and extended network lifetime.

An Efficient LEACH Clustering Protocol to Enhance the QoS of WSN

The development of an energy-efficient routing protocol for wireless sensor networks has been a challenging task for academics. It is very different from the conventional routing protocols, which are based on I.P. addresses. These conventional routing protocols are not preferable for WSNs since conventional routing protocols depend heavily on the routing tables, which often require updates. Also, the WSN varies from thousands to ten thousand, making the task of managing routing tables not easy and economical in terms of hardware resources. There is always research going on to develop an efficient routing protocol in terms of energy for WSNs. LEACH protocol is one of them. Energy consumption in WSNs became a vital factor to be focused on in enforcing an efficient routing strategy. Numerous LEACH protocol variations propose improvements to the current Protocol. In our study, we looked at different LEACH protocol iterations and adopted a modified LEACH protocol to extend the lifetime of wireless sensor networks. We adopted various power levels for transmission between the cluster node, cluster head, and base station, as well as a novel method for choosing the cluster head. Our modified Protocol performs better when compared to parameters like network lifetime, dead nodes per round, Cluster heads formed, packets sent to the base station, etc.

A Metaheuristic Approach Towards Enhancement of Network Lifetime in Wireless Sensor Networks

A Metaheuristic Approach Towards Enhancement of Network Lifetime in Wireless Sensor Networks

An Energy Efficient Technique for Improved Network Lifetime in Wireless Sensor Network (WSN) Through Energy, Distance, and Density-Based Clustering

An Energy Efficient Technique for Improved Network Lifetime in Wireless Sensor Network (WSN) Through Energy, Distance, and Density-Based Clustering

Enhanced meta-heuristic optimization of resource efficiency in multi-relay underground wireless sensor networks.

Achieving a balanced energy and spectral resource utilization is an interesting key design to extend the lifetime of underground wireless sensor networks (UWSNs) where sensor nodes are equipped with small limited energy batteries and communicate through a challenging soil environment. In this article, we apply an improved meta-heuristic algorithm, based on the Salp Swarm Algorithm (SSA), for multi-relay UWSNs where cooperative relay nodes amplify and forward sensed data, received from the buried source nodes, to the aboveground base station. Hence, the optimal nodes transmission powers, maximizing the network resource efficiency, are obtained and used to select beneficial relay nodes. The algorithm enhances the standard SSA by considering the chaotic map for salps population initialization and the uniform crossover technique for salps positions updates. Simulation results show that the proposed algorithm significantly outperforms the SSA in resource efficiency optimization and network lifetime extension. The obtained gain increases when the number of cooperative relay nodes increases. Furthermore, simulations prove the efficiency of the proposed algorithm against other meta-heuristic algorithms.

Toward the Minimal Wait-for Delay for Rechargeable WSNs with Multiple Mobile Chargers

Nowadays, the flourish of the internet of things incurs a great demand for progressive technologies to prolong the lifetime of Wireless Sensor Networks. Exploiting a fleet of Mobile Chargers (MCs) to replenish the energy-critical sensor nodes provides a new dimension to maintain long-term network operations, but may suffer from high charging delay due to MC’s limited mobility. Most existing studies focus on the reduction of server-oriented delay, i.e., the overall time taken by MCs (servers) to carry out sensor charging and travel inside the sensing field. However, these solutions may not be robust enough as some energy-critical sensor nodes will run out of the stored energy before the charger’s arrival. In this article, we address this challenge by reducing the client-oriented delay—referred to as the wait-for delay —which is defined as the “arrival times” at the to-be-charged sensor nodes (clients). To this end, we first formulate a novel wait-for charging delay minimization problem under the multi-node energy charging scheme. We then prove the NP-hardness of the proposed problem. Inspired by empirical observations, we devise an efficient approximation algorithm with a provable approximation ratio for the problem. We have evaluated the proposed algorithm using real-life system settings. The experimental results suggest that the proposed algorithm certainly performs better than the existing benchmarks; it could reduce the wait-for delay by up to 87.4 percent.

Coverage and Lifetime Optimization by Self-Optimizing Sensor Networks.

We propose an approach to self-optimizing wireless sensor networks (WSNs) which are able to find, in a fully distributed way, a solution to a coverage and lifetime optimization problem. The proposed approach is based on three components: (a) a multi-agent, social-like interpreted system, where the modeling of agents, discrete space, and time is provided by a 2-dimensional second-order cellular automata, (b) the interaction between agents is described in terms of the spatial prisoner's dilemma game, and (c) a local evolutionary mechanism of competition between agents exists. Nodes of a WSN graph created for a given deployment of WSN in the monitored area are considered agents of a multi-agent system that collectively make decisions to turn on or turn off their batteries. Agents are controlled by cellular automata (CA)-based players participating in a variant of the spatial prisoner's dilemma iterated game. We propose for players participating in this game a local payoff function that incorporates issues of area coverage and sensors energy spending. Rewards obtained by agent players depend not only on their personal decisions but also on their neighbor's decisions. Agents act in such a way to maximize their own rewards, which results in achieving by them a solution corresponding to the Nash equilibrium point. We show that the system is self-optimizing, i.e., can optimize in a distributed way global criteria related to WSN and not known for agents, provide a balance between requested coverage and spending energy, and result in expanding the WSN lifetime. The solutions proposed by the multi-agent system fulfill the Pareto optimality principles, and the desired quality of solutions can be controlled by user-defined parameters. The proposed approach is validated by a number of experimental results.

Application of improved black hole algorithm in prolonging the lifetime of wireless sensor network

Sensor technology is developing rapidly and up to date. The lifetime of the Wireless Sensor Network (WSN) has also attracted many researchers, and the location of the Base Station (BS) plays a crucial role in prolonging the lifetime. The energy consumption in the WSN occurs during transmission of data and selection of cluster-head nodes. A reasonable location of the BS prolongs the lifetime of the WSN. This study proposes a Levy Flight Edge Regeneration Black Hole algorithm (LEBH) to speed up convergence and enhance optimization capabilities. The performance of LEBH and other heuristic algorithms are compared on CEC 2013. The result shows that the LEBH outperforms other heuristics in most cases. In this study, the energy consumption and WSN models are simulated. Subsequently, the proposed LEBH is combined with relocation technology to change the location of the BS to prolong the lifetime. Simulation results show LEBH-BS prolongs the lifetime of the WSN better than random-base and static-base stations and other heuristic algorithms in most cases.

EEEDCS: Enhanced energy efficient distributed compressive sensing based data collection for WSNs

Compressive sensing (CS) is an effective strategy for data collection and maintaining energy consumption balance in wireless sensor networks (WSN). CS usually exploits the space-time correlations of signal information and the compressed data acquired with this property from the remote field. This research proposes an efficient data-gathering method based on CS, which performs sequential sampling with the progressive reconstruction of sensor data through joint data dependencies. The proposed enhanced energy efficient distributed compressive sensing (EEEDCS) utilizes ℓ2-regularization with iterative re-weighted ℓ1-minimization(IRW-ℓ1) for an estimate of the current signal measurements and regularly updates the previous signal measurements and provides better reconstruction accuracy. Extensive experiments were performed to analyze the proposed method with different network topologies and correlation ranges. Experimental simulations are performed with varying topologies of the network as 49 nodes, 64 nodes, 81 nodes, and 100 nodes. Under 100 node topology, the proposed method saves energy by 8.95%, 14.65%, 20.71%, 22.93%, 25.98%, and 29.24% compared with the baseline models at 40% sampling rate. Also, the proposed EEEDCS method was evaluated with the Pacific Sea Surface Temperature dataset. The Proposed EEEDCS saves energy by 8.76%, 13.97%, 18.18%, 23.90%, 33.14%, and 39.57% compared with the baseline models at a 40% sampling rate. From the results, the proposed model accurately reconstructs the signal samples, and shows its effectiveness over the baseline models considered for comparison, consumes less energy for data collection, and extends the lifetime of sensors and WSNs.

Multilayer Joint Optimization of Packet Size and Adaptive Transmission Scheduling of Wireless Sensor Networks for Mechanical Vibration Monitoring

Limitations pertaining to the storage capacity and bandwidth of wireless sensor networks (WSNs) for mechanical vibration monitoring (MVM) pose difficulties in the realization of reliable and energy-efficient communication of big data. To prolong the lifetime of battery-limited WSNs, this paper proposes a multi-layer joint optimization of packet size and adaptive transmission scheduling (ATS) to jointly optimize the energy consumption of acquisition nodes from the storage and transmission layers. First, owing to the frequent storage and transmission of massive mechanical vibration data in WSNs, the packet size significantly affects the energy consumption during storage and transmission. The proposed packet size optimization based on the lowest energy consumption (PSO-LEC) model optimizes the energy consumption of acquisition nodes from three aspects: writing, reading, and transmission of data. Second, when transmitting massive vibration data frequently, the direct beacon frame conflict and indirect beacon frame conflict markedly induce data conflict and delay problems. Therefore, adaptive transmission scheduling based on the time division multiple access (TDMA/ATS) method is proposed to optimize the errors in data collision and transmission delay. Thus, the packet size and ATS are considered jointly to improve the network lifetime for MVM. Comprehensive experimental results indicate that the transmission reliability and energy-efficiency of the proposed scheme are significantly improved, and the network life is effectively prolonged for MVM.

Maximization of WSN Based IoT Systems Lifetime by Minimized Intra-cluster Transmission Distance Clustering Protocol

Interney of Things (IoT) enabled by Wireless Sensor Network (WSN) is the principal idea behind target tracking, environment survelance, and patients monitoring systems in which human attentions are very crucial for round the clock. Since the sensor nodes that constitute the IoT is power constrained, it is suffering energy related problems which further badly affect the lifetime of the core sensor network. A well-knows topology management and routing scheme called Clustering is widely used for WSNs in maximizing the network lifetime due to its intrinsic characteristics. Clustering solves the energy constrained issues of WSN by providing a local infrastructure like arrangement to manage the network and resources in suitable manner. Various clustering approaches have been proposed so far by scientific community to address energy issues of WSN. But these existing approaches fail to provide required clustering output to improve lifetime by balancing the energy consumption in efficient manner. In this work, we propose a Minimized Intra-cluster Transmission Distance Clustering Protocol (MITDCP) to improve lifetime of WSN by innovatively clustering and intelligently placing the Base Station (BS). Innovative clustering involves a FCM (Fuzzy C Means) with Cluster Balancing algorithm to create balanced clusters. Then the proposed work makes use of back off timer weighted with residual energy to select and rotate Cluster Head (CH). Simulations show that our proposed work has achieved significant improvement in lifetime of WSN beneath the IoT systems when compared with Improved Energy Efficiency Clustering Protocol (IEECP).

Modified squirrel search algorithm based data aggregation framework for improved network lifetime in wireless sensor network

WSNs (Wireless Sensor Networks) have various applications that include environments and traffic monitors, surveillance of battlefields, innovative agriculture because of their communication effectiveness. Individual sensor nodes in WSNs have restrictions in energy consumptions, packet deliveries, network longevities, and latencies. In previous work, an energy efficient data aggregation is achieved by developing an EADSLPBNC-DA framework for sensor network. Data aggregation performance is enhanced with minimum time through classifying the sensor nodes. But it has issue with data aggregation time, energy level of nodes will be changes periodically which is not concentrated in the previous work. The goal of this study is to address aforementioned difficulties and hence MSSA (Modified Squirrel Search Algorithm) which can improve data aggregations is proposed. Data aggregations based on CHs (cluster heads) improves energy efficiencies and network longevities. Aggregations of CHs related data is critical for minimizing sensor node energy consumptions in WSNs and their selections enhance WSNs lifespan. The MSSA-DA generates best fitness values using objective function which selects optimal sensor nodes as CHs to aggregate data optimally. The sensor nodes are chosen depending on their proximities, energies, and bandwidths. The suggested MSSA-DA system was simulated for assessing network lives, energy consumptions, accuracies, and data aggregation times. Experimental results suggested MSSA-DA model enhances data aggregation accuracies and network lives while consuming lesser energies and times.

A modified whale‐dragonfly algorithm and self‐adaptive cuckoo search‐based clustering strategy for augmenting network lifetime in wireless sensor networks

SummaryDesigning an energy efficient and durable wireless sensor networks (WSNs) is a key challenge as it personifies potential and reactive functionalities in harsh antagonistic environment at which wired system deployment is completely infeasible. Majority of the clustering mechanisms contributed to the literature concentrated on augmenting network lifetime and energy stability. However, energy consumption incurred by cluster heads (CHs) are high and thereby results in minimized network lifetime and frequent CHs selection. In this paper, a modified whale‐dragonfly optimization algorithm and self‐adaptive cuckoo search‐based clustering strategy (MWIDOA‐SACS) is proposed for sustaining energy stability and augment network lifetime. In specific, MWIDOA‐SACS is included for exploiting the fitness values that aids in determining two optimal nodes that are selected as optimal CH and cluster router (CR) nodes in the network. In MWIDOA, the search conduct of dragon flies is completely updated through whale optimization algorithm (WOA) for preventing load balancing at CHs. It minimized the overhead of CH by adopting CHs and CR for collecting information from cluster members and transmitting the aggregated data from CHs to the base station (BS). It included self‐adaptive cuckoo search (SACS) for achieving sink mobility using radius, energy stability, received signal strength, and throughput for achieving optimal data transmission process after partitioning the network into unequal clusters. Simulation experiments of the proposed MWIDOA‐SACS confirmed better performance in terms of total residual energy by 21.28% and network lifetime by 26.32%, compared to the competitive CH selection strategies.

Charging Protocol for Partially Rechargeable Mobile Sensor Networks.

Wireless sensor networks (WSNs) have wide applicability in services used in daily life. However, for such networks, limited energy is a critical issue. The efficiency of a deployed sensor network may be subject to energy supply. Wireless rechargeable sensor networks have recently been proposed and discussed. Most related studies have involved applying static rechargeable sensors to an entire rechargeable environment or having mobile chargers patrol the environment to charge sensors within it. For partially rechargeable environments, improving the recharge efficiency and extending the lifetime of WSNs are considerable challenges. Scientists have devoted attention to energy transmission technologies and mobile sensor network (MSN) applications. In this paper, we propose a flexible charging protocol in which energy can be transmitted from certain energy supply regions to other regions in an MSN. Mobile rechargeable sensors are deployed to monitor the environment. To share energy in a certain region, the sensors move to replenish their energy and transmit energy to sensors outside the energy supply region. The efficiency of the proposed protocol is also discussed in the context of various situations. The evaluation results suggest that the flexible protocol is more efficient than other charging protocols in several situations.

An Energy-Efficient Data Aggregation Clustering Algorithm for Wireless Sensor Networks Using Hybrid PSO

Extending the lifetime of wireless sensor networks (WSNs) and minimizing energy costs are the two most significant concerns for data transmission. Sensor nodes are powered by their own battery capacity, allowing them to perform critical tasks and interact with other nodes. The quantity of electricity saved from each sensor together in a WSN has been strongly linked to the network’s longevity. Clustering conserves the most power in wireless transmission, but the absence of a mechanism for selecting the most suitable cluster head (CH) node increases the complexity of data collection and the power usage of the sensor nodes. Additionally, the disparity in energy consumption can lead to the premature demise of nodes, reducing the network’s lifetime. Metaheuristics are used to solve non-deterministic polynomial (NP) lossy clustering problems. The primary purpose of this research is to enhance the energy efficiency and network endurance of WSNs. To address this issue, this work proposes a solution where hybrid particle swarm optimization (HPSO) is paired with improved low-energy adaptive clustering hierarchy (HPSO-ILEACH) for CH selection in cases of data aggregation in order to increase energy efficiency and maximize the network stability of the WSN. In this approach, HPSO determines the CH, the distance between the cluster’s member nodes, and the residual energy of the nodes. Then, ILEACH is used to minimize energy expenditure during the clustering process by adjusting the CH. Finally, the HPSO-ILEACH algorithm was successfully implemented for aggregating data and saving energy, and its performance was compared with three other algorithms: low energy-adaptive clustering hierarchy (LEACH), improved low energy adaptive clustering hierarchy (ILEACH), and enhanced PSO-LEACH (ESO-LEACH). The results of the simulation studies show that HPSO-ILEACH increased the network lifetime, with an average of 55% of nodes staying alive, while reducing energy consumption average by 28% compared to the other mentioned techniques.

Using advanced distributed energy efficient clustering increasing the network lifetime in wireless sensor networks

Using advanced distributed energy efficient clustering increasing the network lifetime in wireless sensor networks

Energy-Efficient Clustering and Routing Using ASFO and a Cross-Layer-Based Expedient Routing Protocol for Wireless Sensor Networks

Today's critical goals in sensor network research are extending the lifetime of wireless sensor networks (WSNs) and lowering power consumption. A WSN necessitates the use of energy-efficient communication networks. Clustering, storage, communication capacity, high configuration complexity, low communication speed, and limited computation are also some of the energy limitations of WSNs. Moreover, cluster head selection remains problematic for WSN energy minimization. Sensor nodes (SNs) are clustered in this work using the Adaptive Sailfish Optimization (ASFO) algorithm with K-medoids. The primary purpose of research is to optimize the selection of cluster heads through energy stabilization, distance reduction, and latency minimization between nodes. Because of these constraints, achieving optimal energy resource utilization is an essential problem in WSNs. An energy-efficient cross-layer-based expedient routing protocol (E-CERP) is used to determine the shortest route, dynamically minimizing network overhead. The proposed method is used to evaluate the packet delivery ratio (PDR), packet delay, throughput, power consumption, network lifetime, packet loss rate, and error estimation, and the results were superior to existing methods. PDR (100%), packet delay (0.05 s), throughput (0.99 Mbps), power consumption (1.97 mJ), network lifespan (5908 rounds), and PLR (0.5%) for 100 nodes are the performance results for quality-of-service parameters.