A Hybrid Approach for Energy Consumption and Improvement in Sensor Network Lifespan in Wireless Sensor Networks

In this paper, we propose an improved clustering algorithm for wireless sensor networks (WSNs) that aims to increase network lifetime and efficiency. We introduce an enhanced fuzzy spider monkey optimization technique and a hidden Markov model-based clustering algorithm for selecting cluster heads. Our approach considers factors such as network cluster head energy, cluster head density, and cluster head position. We also enhance the energy-efficient routing strategy for connecting cluster heads to the base station. Additionally, we introduce a polling control method to improve network performance while maintaining energy efficiency during steady transmission periods. Simulation results demonstrate a 1.2% improvement in network performance using our proposed model.

A Total Randomized SLP-Preserving Technique with Improved Privacy and Lifetime in WSNs for IoT and the Impact of Radio Range on SLP

Enhanced source location privacy and prolonged network lifetime are imperative for WSNs-the skin of IoT. To address these issues, a novel technique named source location privacy with enhanced privacy and network lifetime (SLP-E) is proposed. It employs a reverse random walk followed by a walk on annular rings, to create divergent routing paths in the network, and finally, min-hop routing together with the walk on dynamic rings to send the packets to the base station (BS). The existing random walk-based SLP approaches have either focused on enhancing only privacy at the cost of network lifetime (NLT) or have aimed at improving the amount of privacy without degrading the network lifetime performance. Unlike these schemes, the objectives of the proposed work are to simultaneously improve the safety period and network lifetime along with achieving uniform privacy. This combination of improvements has not been considered so far in a single SLP random walk-based scheme. Additionally, this study investigates for the first time the impact of the sensors' radio range on both privacy strength and network lifetime metrics in the context of SLP within WSNs. The performance measurements conducted using the proposed analytical models and the simulation results indicate an improvement in the safety period and network lifespan. The safety period in SLP-E increased by 26.5%, 97%, 123%, and 15.7% when compared with SLP-R, SRR, PRLPRW, and PSSLP techniques, respectively. Similarly, the network lifetime of SLP-E increased by 17.36%, 0.2%, 83.41%, and 13.42% when compared with SLP-R, SRR, PRLPRW, and PSSLP techniques, respectively. No matter where a source node is located within a network, the SLP-E provides uniform and improved privacy and network lifetime. Further, the simulation results demonstrate that the sensors' radio range has an impact on the safety period, capture ratio, and the network lifetime.

Maximize Lifetime of Wireless Rechargeable Sensor Networks with Mobile Energy-Limited Charging Device

Mobile charging devices (MCDs) have been regarded as a promising way to solve the energy shortage of wireless sensor networks. Due to ignoring some important factors, such as redundant sensor nodes, there is still room to improve network lifetimes. We propose a charging strategy for wireless sensor networks with one energy-limited MCD. To give the best support for sensor nodes which need charging the most, an algorithm is proposed to find the minimum sensor nodes which keep the coverage and connectivity of the network and have the least energy requirements. Then, the goal of maximizing network lifetime is changed into how to utilize the limited energy of the MCD to guarantee the minimum sensor nodes work as long as possible. If the MCD has enough energy for all sensor nodes, the charging algorithm is designed to minimize the outage time of the network and maximize charging efficiency. Otherwise, if the energy capacity is larger than the least energy requirement, the charging target minimizes the outage time of the minimum sensor node; otherwise the charging problem becomes maximizing the lifetime of minimum sensor nodes, which has lower complexity. The results of simulation experiments confirm that our scheme prolongs network lifetime and improves charging efficiency.

A Distributed Particle-Swarm-Optimization-Based Fuzzy Clustering Protocol for Wireless Sensor Networks

Clustering is considered to be one of the most effective ways for energy preservation and lifetime maximization in wireless sensor networks (WSNs) because the sensor nodes are equipped with limited energy. Thus, energy efficiency and energy balance have always been the main challenges faced by clustering approaches. To overcome these, a distributed particle swarm optimization-based fuzzy clustering protocol called DPFCP is proposed in this paper to reduce and balance energy consumption, to thereby extend the network lifetime as long as possible. To this end, in DPFCP cluster heads (CHs) are nominated by a Mamdani fuzzy logic system with descriptors' residual energy, node degree, distance to the base station (BS), and distance to the centroid. Moreover, a particle swarm optimization (PSO) algorithm is applied to optimize the fuzzy rules, instead of conventional manual design. Thus, the best nodes are ensured to be selected as CHs for energy reduction. Once the CHs are selected, distance to the CH, residual energy, and deviation in the CH's number of members are considered for the non-CH joining cluster in order to form energy-balanced clusters. Finally, an on-demand mechanism, instead of periodic re-clustering, is utilized to maintain clusters locally and globally based on local information, so as to further reduce computation and message overheads, thereby saving energy consumption. Compared with the existing relevant protocols, the performance of DPFCP was verified by extensive simulation experiments. The results show that, on average, DPFCP improves energy consumption by 38.20%, 15.85%, 21.15%, and 13.06% compared to LEACH, LEACH-SF, FLS-PSO, and KM-PSO, and increases network lifetime by 46.19%, 20.69%, 20.44%, and 10.99% compared to LEACH, LEACH-SF, FLS-PSO, and KM-PSO, respectively. Moreover, the standard deviation of the residual network was reduced by 61.88%, 55.36%, 54.02%, and 19.39% compared to LEACH, LEACH-SF, FLS-PSO, and KM-PSO. It is thus clear that the proposed DPFCP protocol efficiently balances energy consumption to improve the overall network performance and maximize the network lifetime.

A Software Framework for Intelligent Security Measures Regarding Sensor Data in the Context of Ambient Assisted Technology

Ambient assisted technology (AAT), which has the potential to enhance patient care and productivity and save costs, has emerged as a strategic goal for developing e-healthcare in the future. However, since the healthcare sensor must be interconnected with other systems at different network tiers, distant enemies have additional options to attack. Data and resources integrated into the AAT are vulnerable to security risks that might compromise privacy, integrity, and availability. The gadgets and network sensor devices are layered with clinical data since they save personal information such as patients' names, addresses, and medical histories. Considering the volume of data, it is difficult to ensure its confidentiality and security. As sensing devices are deployed over a wider region, protecting the privacy of the collected data becomes more difficult. The current study proposes a lightweight security mechanism to ensure the data's confidentiality and integrity of the data in ambient-assisted technology. In the current study, the data are encrypted by the master node with adequate residual energy, and the master node is responsible for encrypting the data using the data aggregation model using a node's key generated using an exclusive basis system and a Chinese remainder theorem. The integrity of the data is evaluated using the hash function at each intermediate node. The current study defines the design model's layered architecture and layer-wise services. The model is further analyzed using various evaluation metrics, such as energy consumption, network delay, network overhead, time in generating hash, tradeoff between encryption and decryption, and entropy metrics. The model is shown to adequately perform on all measures considered in the analysis.

An Innovative Clustering Hierarchical Protocol for Data Collection from Remote Wireless Sensor Networks Based Internet of Things Applications

Recently, unmanned aerial vehicles (UAVs) have emerged as a viable solution for data collection from remote Internet of Things (IoT) applications. However, the successful implementation in this regard necessitates the development of a reliable and energy-efficient routing protocol. This paper proposes a reliable and an energy-efficient UAV-assisted clustering hierarchical (EEUCH) protocol designed for remote wireless sensor networks (WSNs) based IoT applications. The proposed EEUCH routing protocol facilitates UAVs to collect data from ground sensor nodes (SNs) that are equipped with wake-up radios (WuRs) and deployed remotely from the base station (BS) in the field of interest (FoI). During each round of the EEUCH protocol, the UAVs arrive at the predefined hovering positions at the FoI, perform clear channel assignment, and broadcast wake-up calls (WuCs) to the SNs. Upon receiving the WuCs by the SNs' wake-up receivers, the SNs perform carrier sense multiple access/collision avoidance before sending joining requests to ensure reliability and cluster-memberships with the particular UAV whose WuC is received. The cluster-member SNs turn on their main radios (MRs) for data packet transmission. The UAV assigns time division multiple access (TDMA) slots to each of its cluster-member SNs whose joining request is received. Each SN must send the data packets in its assigned TDMA slot. When data packets are successfully received by the UAV, it sends acknowledgments to the SNs, after which the SNs turn off their MRs, completing a single round of the protocol. The proposed EEUCH routing protocol with WuR eliminates the issue of cluster overlapping, improves the overall performance, and increases network stability time by a factor of 8.7. It also improves energy efficiency by a factor of 12.55, resulting in a longer network lifespan compared to Low Energy Adaptive Clustering Hierarchy (LEACH) protocol. Moreover, EEUCH collects 5.05 times more data from the FoI than LEACH. These results are based on simulations in which the EEUCH protocol outperformed the existing six benchmark routing protocols proposed for homogeneous, two-tier, and three-tier heterogeneous WSNs.

Sensor Topology Optimization in Dense IoT Environments by Applying Neural Network Configuration

In dense IoT deployments of wireless sensor networks (WSNs), sensor placement, coverage, connectivity, and energy constraints determine the overall network lifetime. In large-size WSNs, it is difficult to maintain a trade-off among these conflicting constraints and, thus, scaling is difficult. In the related research literature, various solutions are proposed that attempt to address near-optimal behavior in polynomial time, the majority of which relies on heuristics. In this paper, we formulate a topology control and lifetime extension problem regarding sensor placement, under coverage and energy constraints, and solve it by applying and testing several neural network configurations. To do so, the neural network dynamically proposes and handles sensor placement coordinates in a 2D plane, having the ultimate goal to extend network lifetime. Simulation results show that our proposed algorithm improves network lifetime, while maintaining communication and energy constraints, for medium- and large-scale deployments.

Energy Aware Software Defined Network Model for Communication of Sensors Deployed in Precision Agriculture

A significant technological transformation has recently occurred in the agriculture sector. Precision agriculture is one among those transformations that largely focus on the acquisition of the sensor data, identifying the insights, and summarizing the information for better decision-making that would enhance the resource usage efficiency, crop yield, and substantial quality of the yield resulting in better profitability, and sustainability of agricultural output. For continuous crop monitoring, the farmlands are connected with various sensors that must be robust in data acquisition and processing. The legibility of such sensors is an exceptionally challenging task, which needs energy-efficient models for handling the lifetime of the sensors. In the current study, the energy-aware software-defined network for precisely selecting the cluster head for communication with the base station and the neighboring low-energy sensors. The cluster head is initially chosen according to energy consumption, data transmission consumption, proximity measures, and latency measures. In the subsequent rounds, the node indexes are updated to select the optimal cluster head. The cluster fitness is assessed in each round to retain the cluster in the subsequent rounds. The network model's performance is assessed against network lifetime, throughput, and network processing latency. The experimental findings presented here show that the model outperforms the alternatives presented in this study.

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.

Energy-Efficient LoRa Routing for Smart Grids

Energy-efficient routing protocols in Internet of Things (IoT) applications are always of colossal importance as they improve the network's longevity. The smart grid (SG) application of the IoT uses advanced metring infrastructure (AMI) to read and record power consumption periodically or on demand. The AMI sensor nodes in a smart grid network sense, process, and transmit information, which require energy, which is a limited resource and is an important parameter required to maintain the network for a longer duration. The present work discusses a novel energy-efficient routing criterion in an SG environment realised using LoRa nodes. Firstly, a modified LEACH protocol-cumulative low-energy adaptive clustering hierarchy (Cum_LEACH) is proposed for cluster head selection among the nodes. It uses the cumulative energy distribution of the nodes to select the cluster head. Furthermore, for test packet transmission, multiple optimal paths are created using the quadratic kernelised African-buffalo-optimisation-based LOADng (qAB_LOADng) algorithm. The best optimal path is selected from these multiple paths using a modified version of the MAX algorithm called the SMAx algorithm. This routing criterion showed an improved energy consumption profile of the nodes and the number of active nodes after running for 5000 iterations compared to standard routing protocols such as LEACH, SEP, and DEEC.

A Dual Cluster-Head Energy-Efficient Routing Algorithm Based on Canopy Optimization and K-Means for WSN

Wireless sensor networks (WSN) are widely used in various applications, such as environmental monitoring, healthcare, event detection, agriculture, disaster management, and so on. Due to their small size, sensors are limited power sources and are often deployed in special environments where frequent battery replacement is not feasible. Therefore, it is important to reduce the energy consumption of sensors and extend the network lifetime. An effective way to achieve this is clustering. This paper proposes a dual cluster-head energy-efficient algorithm (DCK-LEACH), which is based on K-means and Canopy optimization. Considering that the K-means algorithm is sensitive to the location of the initial clustering centers, this paper uses both the dynamic Canopy algorithm and K-means algorithm for clustering. For cluster-head election, this algorithm uses a hierarchy to minimize the cluster-head burden and balance the network load. The primary cluster-head is selected by two objectives: the node's residual energy and the distance from the node to the clustering center. The vice cluster-head is selected by the residual energy of the node, and the distance from the nodes to the base station. Simulator results show that DCK-LEACH significantly prolongs the energy-critical node lifetime and the network lifetime compared with existing protocols.

Game Theory-Based Energy-Efficient Clustering Algorithm for Wireless Sensor Networks

Energy efficiency is one of the critical challenges in wireless sensor networks (WSNs). WSNs collect and transmit data through sensor nodes. However, the energy carried by the sensor nodes is limited. The sensor nodes need to save energy as much as possible to prolong the network lifetime. This paper proposes a game theory-based energy-efficient clustering algorithm (GEC) for wireless sensor networks, where each sensor node is regarded as a player in the game. According to the length of idle listening time in the active state, the sensor node can adopt favorable strategies for itself, and then decide whether to sleep or not. In order to avoid the selfish behavior of sensor nodes, a penalty mechanism is introduced to force the sensor nodes to adopt cooperative strategies in future operations. The simulation results show that the use of game theory can effectively save the energy consumption of the sensor network and increase the amount of network data transmission, so as to achieve the purpose of prolonging the network lifetime.

Network Lifetime Improvement through Energy-Efficient Hybrid Routing Protocol for IoT Applications

The application of the Internet of Things (IoT) in wireless sensor networks (WSNs) poses serious challenges in preserving network longevity since the IoT necessitates a considerable amount of energy usage for sensing, processing, and data communication. As a result, there are several conventional algorithms that aim to enhance the performance of WSN networks by incorporating various optimization strategies. These algorithms primarily focus on the network layer by developing routing protocols to perform reliable communication in an energy-efficient manner, thus leading to an enhanced network life. For increasing the network lifetime in WSNs, clustering has been widely accepted as an important method that groups sensor nodes (SNs) into clusters. Additionally, numerous researchers have been focusing on devising various methods to increase the network lifetime. The prime factor that helps to maximize the network lifetime is the minimization of energy consumption. The authors of this paper propose a multi-objective optimization approach. It selects the optimal route for transmitting packets from source to sink or the base station (BS). The proposed model employs a two-step approach. The first step employs a trust model to select the cluster heads (CHs) that manage the data communication between the BS and nodes in the cluster. Further, a novel hybrid algorithm, combining a particle swarm optimization (PSO) algorithm and a genetic algorithm (GA), is proposed to determine the routes for data transmission. To validate the efficacy of the proposed hybrid algorithm, named PSOGA, simulations were conducted and the results were compared with the existing LEACH method and PSO, with a random route selection for five different cases. The obtained results establish the efficiency of the proposed approach, as it outperforms existing methods with increased energy efficiency, increased network throughput, high packet delivery rate, and high residual energy throughout the entire iterations.

A Reinforcement Learning Routing Protocol for UAV Aided Public Safety Networks

In Public Safety Networks (PSNs), the conservation of on-scene device energy is critical to ensure long term connectivity to first responders. Due to the limited transmit power, this connectivity can be ensured by enabling continuous cooperation among on-scene devices through multipath routing. In this paper, we present a Reinforcement Learning (RL) and Unmanned Aerial Vehicle- (UAV) aided multipath routing scheme for PSNs. The aim is to increase network lifetime by improving the Energy Efficiency (EE) of the PSN. First, network configurations are generated by using different clustering schemes. The RL is then applied to configure the routing topology that considers both the immediate energy cost and the total distance cost of the transmission path. The performance of these schemes are analyzed in terms of throughput, energy consumption, number of dead nodes, delay, packet delivery ratio, number of cluster head changes, number of control packets, and EE. The results showed an improvement of approximately 42% in EE of the clustering scheme when compared with non-clustering schemes. Furthermore, the impact of UAV trajectory and the number of UAVs are jointly analyzed by considering various trajectory scenarios around the disaster area. The EE can be further improved by 27% using Two UAVs on Opposite Axis of the building and moving in the Opposite directions (TUOAO) when compared to a single UAV scheme. The result showed that although the number of control packets in both the single and two UAV scenarios are comparable, the total number of CH changes are significantly different.

LeHE-MRP: leveraging health monitoring by enhancing throughput of multi-hop routeing protocol in WBANs

Health monitoring is one of the interesting and emerging technologies in the field of wireless body area networks (WBANs). In WBANs, routeing plays a major role in developing energy-efficient wireless network. In multi-hop routeing, data transmission consumes less energy as compared to single-hop routeing. In SIMPLE multi-hop routeing protocol, the selection of parent node/forwarder node is selected based on residual energy and distance. To further improve the performance of SIMPLE, we propose a new protocol, named it as LeHE-MRP. In LeHE-MRP, we calculate the cost function, based on residual energy, distance and number of packets to sink node. The node with maximum cost function (parent node/forwarder node) will be selected as data forwarder node to transfer data from other nodes to sink. We compare our LeHE-MRP with two existing state of the art algorithm, M-ATTEMPT and SIMPLE. Simulations show that our scheme improves network lifetime and throughput of a network.

A Joint Energy Replenishment and Data Collection Strategy in Heterogeneous Wireless Rechargeable Sensor Networks

In wireless rechargeable sensor networks, mobile vehicles (MVs) combining energy replenishment and data collection are studied extensively. To reduce data overflow, most recent work has utilized more vehicles to assist the MV to collect buffered data. However, the practical network environment and the limitations of the vehicle in the data collection are not considered. UAV-enabled data collection is immune to complex road environments in remote areas and has higher speed and less traveling cost, which can overcome the lack of the vehicle in data collection. In this paper, a novel framework joining the MV and UAV is proposed to prolong the network lifetime and reduce data overflow. The network lifetime is correlated with the charging order; therefore, we first propose a charging algorithm to find the optimal charging order. During the charging period of the MV, the charging time may be longer than the collecting time. An optimal selection strategy of neighboring clusters, which could send data to the MV, was found to reduce data overflow. Then, to further reduce data overflow, an algorithm is also proposed to schedule the UAV to assist the MV to collect buffered data. Finally, simulation results verified that the proposed algorithms can maximize network lifetime and minimize the data loss simultaneously.

Energy-Efficient Wireless Sensor Network with an Unequal Clustering Protocol Based on a Balanced Energy Method (EEUCB)

A hot spot problem is a problem where cluster nodes near to the base station (BS) tend to drain their energy much faster than other nodes due to the need to perform more communication. Unequal clustering methods such as unequal clustering routing (UDCH) and energy-efficient fuzzy logic for unequal clustering (EEFUC) have been proposed to address this problem. However, these methods only concentrate on utilizing residual energy and the distance of sensor nodes to the base station, while limited attention is given to enhancing the data transmission process. Therefore, this paper proposes an energy-efficient unequal clustering scheme based on a balanced energy method (EEUCB) that utilizes minimum and maximum distance to reduce energy wastage. Apart from that, the proposed EEUCB also utilizes the maximum capacity of node energy and double cluster head technique with a sleep-awake mechanism. Furthermore, EEUCB has devised a clustering rotation strategy based on two sub-phases, namely intra- and inter-clustering techniques, that considers the average energy threshold, average distance threshold, and BS layering node. The performance of the proposed EEUCB protocol is then compared with various prior techniques. From the result, it can be observed that the proposed EEUCB protocol shows lifetime improvements of 57.75%, 19.63%, 14.7%, and 13.06% against low-energy adaptive clustering hierarchy (LEACH), factor-based LEACH FLEACH, EEFUC, and UDCH, respectively.

A Novel Spread Spectrum and Clustering Mixed Approach with Network Coding for Enhanced Narrowband IoT (NB-IoT) Scalability

The Narrowband Internet of Things (NB-IoT) is a very promising licensed Internet of things (IoT) technology for accommodating massive device connections in 5G and beyond. To enable network scalability, this study proposes a two-layers novel mixed approach that aims not only to create an efficient spectrum sharing among the many NB-IoT devices but also provides an energy-efficient network. On one layer, the approach uses an Adaptive Frequency Hopping Spread Spectrum (AFHSS) technique that uses a lightweight and secure pseudo-random sequence to exploit the channel diversity, to mitigate inter-link and cross-technology interference. On the second layer, the approach consists of a clustering and network coding (data aggregation) approach based on an energy-signal strength mixed gradient. The second layer contributes to offload the BS, allows for energy-efficient network scalability, helps balance the energy consumption of the network, and enhances the overall network lifetime. The proposed mixed strategy algorithm is modelled and simulated using the Matrix Laboratory (MATLAB) Long Term Evolution (LTE) toolbox. The obtained results reveal that the proposed mixed approach enhances network scalability while improving energy efficiency, transmission reliability, and network lifetime when compared to the existing spread spectrum only, nodes clustering only, and mixed approach with no network coding approaches.

Towards Network Lifetime Enhancement of Resource Constrained IoT Devices in Heterogeneous Wireless Sensor Networks

The participating nodes in Wireless Sensor Networks (WSNs) are usually resource-constrained in terms of energy consumption, storage capacity, computational capability, and communication range. Energy is one of the major constraints which requires an efficient mechanism that takes into account the energy consumption of nodes to prolong the network lifetime. Particularly in the large scale heterogeneous WSNs, this challenge becomes more critical due to high data collection rate and increased number of transmissions. To this end, clustering is one of the most popular mechanisms which is being used to minimize the energy consumption of nodes and prolong the lifetime of the network. In this paper, therefore, we propose a robust clustering mechanism for energy optimization in heterogeneous WSNs. In the proposed scheme, nodes declare themselves as cluster head (CH) based on available resources such as residual energy, available storage and computational capability. The proposed scheme employs the multi criteria decision making technique named as Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) which allows the child nodes to select the optimal CH among several potential CH candidates. Moreover, we also propose mechanisms such as CH-acquaintanceship and CH-friendship in order to prolong the network lifetime. Simulation results show that our proposed scheme minimizes the control overhead, reduces the power consumption and enhances overall lifetime of the network by comparing with the most recent and relevant proposed protocol for WSNs.

BOLD: Bio-Inspired Optimized Leader Election for Multiple Drones

Over the past few years, unmanned aerial vehicles (UAV) or drones have been used for many applications. In certain applications like surveillance and emergency rescue operations, multiple drones work as a network to achieve the target in which any one of the drones will act as the master or coordinator to communicate, monitor, and control other drones. Hence, drones are energy-constrained; there is a need for effective coordination among them in terms of decision making and communication between drones and base stations during these critical situations. This paper focuses on providing an efficient approach for the election of the cluster head dynamically, which heads the other drones in the network. The main objective of the paper is to provide an effective solution to elect the cluster head among multi drones at different periods based on the various physical constraints of drones. The elected cluster head acts as the decision-maker and assigns tasks to other drones. In a case where the cluster head fails, then the next eligible drone is re-elected as the leader. Hence, an optimally distributed solution proposed is called Bio-Inspired Optimized Leader Election for Multiple Drones (BOLD), which is based on two AI-based optimization techniques. The simulation results of BOLD compared with the existing Particle Swarm Optimization-Cluster head election (PSO-C) in terms of network lifetime and energy consumption, and from the results, it has been proven that the lifetime of drones with the BOLD algorithm is 15% higher than the drones with PSO-C algorithm.

Energy-Efficient and Fast MAC Protocol in UAV-Aided Wireless Sensor Networks for Time-Critical Applications

Unmanned aerial vehicle (UAV)-aided wireless sensor networks (UWSNs) can be effectively used for time-critical sensing applications. UAVs can be used to collect the sensed data from sensors and transfer them to a base station. The real-time transfer of data is highly desired in the time-critical applications. However, the medium access control (MAC) protocols designed for UWSNs so far are primarily focused on the efficient use of UAVs to collect data in the sensing areas. In this paper, we propose an energy-efficient and fast MAC (EF-MAC) protocol in UWSNs for time-critical sensing applications. EF-MAC adopts carrier sense multiple access (CSMA) for the registration of sensor nodes with a UAV and time division multiple access (TDMA) with variable slot time for the transmission of collected data. The UAV is equipped with two transceivers to minimize both energy consumption and delay in air-to-ground communication. The energy consumption and delay are formally analyzed and the performance of EF-MAC is evaluated via extensive simulation. The simulation results show that the proposed EF-MAC outperforms the conventional MAC protocols in terms of energy efficiency and communication delay.

PDTR: Probabilistic and Deterministic Tree-based Routing for Wireless Sensor Networks

This paper proposed a “Probabilistic and Deterministic Tree-based Routing for WSNs (PDTR)”. The PDTR builds a tree from the leaves to the head (sink), according to the best elements in the initial probabilistic routing table, measured by the product of hops-count distribution, and transmission distance distribution, to select the best tree-paths. Each sender node forwards the received data to the next hop via the deterministic built tree. After that, when any node loses of its energy, PDTR updates the tree at that node. This update links probabilistically one of that node’s children to a new parent, according to the updated probabilistic routing table, measured by the product of the updated: Hops-count distribution, transmission distance distribution, and residual energy distribution at the loss of ℓe energy. By implementing the control parameters in each distribution, PDTR shows the impact of each distribution in the routing path. These control parameters are oriented by the user for different performances. The simulation results prove that selecting the initial best paths to root the packets via unicast, then improving the tree at the node with loss of energy by rooting the packets via anycast, leads to better performance in terms of energy consumption and network lifetime.

Integrating Software Engineering Processes in the Development of Efficient Intrusion Detection Systems in Wireless Sensor Networks

Applying Software Engineering (SE) processes is vital to critical and complex systems including security and networking systems. Nowadays, Wireless Sensor Networks (WSNs) and their applications are found in many military and civilian systems which make them attractive to security attackers. The increasing risks and system vulnerabilities of WSNs have encouraged researchers and developers to propose many security solutions including software-based Intrusion Detection Systems (IDSs). The main drawbacks of current IDSs are due to the lack of clear, structured software development processes. Unfortunately, a substantial gap has been observed between WSN and SE research communities. Integrating SE and WSNs is an emerging topic that will be expanded as technology evolves and spreads in all life aspects. Consequently, this paper highlighted the importance of Requirement Engineering, Software Design, and Testing when developing IDSs for WSNs. Three software IDS designs were proposed in this study: Scheduling, Broadcast, and Watchdog designs. The three designs were compared in terms of consumed energy and network lifetime. Although the same IDS approach was used, but, by highlighting the design phase and implementing different designs, the network lifetime was increased by 73.6% and the consumed energy was reduced by 20% in some of the designs. This is a clear indication of how following a proper SE process could affect the performance of the IDS in WSN. Moreover, conclusions were drawn in regard to applying software engineering processes to IDSs to deliver the required functionalities, with respect to operational constraints, with an improved performance, accuracy and reliability.

Successive Interference Cancellation Based Throughput Optimization for Multi-Hop Wireless Rechargeable Sensor Networks

Wireless Sensor Networks are constrained by low channel utilization and limited battery capacity, so they are widely regarded as the mainly performance bottlenecks. In this paper, in order to improve channel utilization and prolong network lifetime, we investigate the cooperation of multi-hop Wireless Rechargeable Sensor Networks (WRSNs) with Successive Interference Cancellation (SIC) technology. In WRSNs, since the flow rate of each node is unknown, the power of the nodes is not constant. However, SIC will not work if the signal power levels at receive node cannot be sorted. To solve this issue, we first construct a minimum energy routing and unify the transmit rate to determine the transmit power. We can also obtain the time scheduling scheme after determining the routing and power. Next, we formulate an optimization problem, with the objective of maximizing the mobile charger’s vacation time over the rechargeable cycle. Finally, we provide a near-optimal solution and prove its feasible performance. Simulation results present that SIC can achieve the better upper boundary on throughput (compared to inference avoidance increasing about 180–450%) and no extra transmit and receive energy consumption in the multi-hop WRSNs.

Energy-Efficient Cluster-Head Selection for Wireless Sensor Networks Using Sampling-Based Spider Monkey Optimization

Extending the lifetime and stability of wireless sensor networks (WSNs) through efficient energy consumption remains challenging. Though clustering has improved energy efficiency through cluster-head selection, its application is still complicated. In existing cluster-head selection methods, the locations where cluster-heads are desirable are first searched. Next, the nodes closest to these locations are selected as the cluster-heads. This location-based approach causes problems such as increased computation, poor selection accuracy, and the selection of duplicate nodes. To solve these problems, we propose the sampling-based spider monkey optimization (SMO) method. If the sampling population consists of nodes to select cluster-heads, the cluster-heads are selected among the nodes. Thus, the problems caused by different locations of nodes and cluster-heads are resolved. Consequently, we improve lifetime and stability of WSNs through sampling-based spider monkey optimization and energy-efficient cluster head selection (SSMOECHS). This study describes how the sampling method is used in basic SMO and how to select cluster-heads using sampling-based SMO. The experimental results are compared to similar protocols, namely low-energy adaptive clustering hierarchy centralized (LEACH-C), particle swarm optimization clustering protocol (PSO-C), and SMO based threshold-sensitive energy-efficient delay-aware routing protocol (SMOTECP), and the results are shown in both homogeneous and heterogeneous setups. In these setups, SSMOECHS improves network lifetime and stability periods by averages of 13.4%, 7.1%, 34.6%, and 1.8%, respectively.

An Improved Energy-Efficient Routing Protocol for Wireless Sensor Networks

Cluster-based hierarchical routing protocols play an essential role in decreasing the energy consumption of wireless sensor networks (WSNs). A low-energy adaptive clustering hierarchy (LEACH) has been proposed as an application-specific protocol architecture for WSNs. However, without considering the distribution of the cluster heads (CHs) in the rotation basis, the LEACH protocol will increase the energy consumption of the network. To improve the energy efficiency of the WSN, we propose a novel modified routing protocol in this paper. The newly proposed improved energy-efficient LEACH (IEE-LEACH) protocol considers the residual node energy and the average energy of the networks. To achieve satisfactory performance in terms of reducing the sensor energy consumption, the proposed IEE-LEACH accounts for the numbers of the optimal CHs and prohibits the nodes that are closer to the base station (BS) to join in the cluster formation. Furthermore, the proposed IEE-LEACH uses a new threshold for electing CHs among the sensor nodes, and employs single hop, multi-hop, and hybrid communications to further improve the energy efficiency of the networks. The simulation results demonstrate that, compared with some existing routing protocols, the proposed protocol substantially reduces the energy consumption of WSNs.

An Energy-Efficient Routing Protocol for Reliable Data Transmission in Wireless Body Area Networks

Wireless body area networks will inevitably bring tremendous convenience to human society in future development, and also enable people to benefit from ubiquitous technological services. However, one of the reasons hindering development is the limited energy of the network nodes. Therefore, the energy consumption in the selection of the next hop must be minimized in multi-hop routing. To solve this problem, this paper proposes an energy efficient routing protocol for reliable data transmission in a wireless body area network. The protocol takes multiple parameters of the network node into account, such as residual energy, transmission efficiency, available bandwidth, and the number of hops to the sink. We construct the maximum benefit function to select the next hop node by normalizing the node parameters, and dynamically select the node with the largest function value as the next hop node. Based on the above work, the proposed method can achieve efficient multi-hop routing transmission of data and improve the reliability of network data transmission. Compared with the priority-based energy-efficient routing algorithm (PERA) and modified new-attempt routing protocol (NEW-ATTEMPT), the simulation results show that the proposed routing protocol uses the maximum benefit function to select the next hop node dynamically, which not only improves the reliability of data transmission, but also significantly improves the energy utilization efficiency of the node and prolongs the network lifetime.

AEF: Adaptive En-Route Filtering to Extend Network Lifetime in Wireless Sensor Networks

Static sink-based wireless sensor networks (WSNs) suffer from an energy-hole problem. This incurs as the rate of energy consumption on sensor nodes around sinks and on critical paths is considerably faster. State-of-the-art en-routing filtering schemes save energy by countering false report injection attacks. In addition to their unique limitations, these schemes generally do not examine energy awareness in underlying routing. Mostly, these security methods are based on a fixed filtering capacity, unable to respond to changes in attack intensity. Therefore, these limitations cause network partition(s), exhibiting adverse effects on network lifetime. Extending network lifetime while preserving energy and security thus becomes an interesting challenge. In this article, we address the aforesaid shortcomings with the proposed adaptive en-route filtering (AEF) scheme. In energy-aware routing, the fitness function, which is used to select forwarding nodes, considers residual energy and other factors as opposed to distance only. In pre-deterministic key distribution, keys are distributed based on the consideration of having paths with a different number of verification nodes. This, consequently, permits us to have multiple paths with different security levels that can be exploited to counter different attack intensities. Taken together, the integration of the special fitness function with the new key distribution approach enables the AEF to adapt the underlying dynamic network conditions. The simulation experiments under different settings show significant improvements in network lifetime.

Optimal Number of Message Transmissions forProbabilistic Guarantee of Latency in the IoT

The Internet of Things (IoT) is now experiencing its first phase of industrialization. Industrial companies are completing proofs of concept and many of them plan to invest in automation, flexibility and quality of production in their plants. Their use of a wireless network is conditioned upon its ability to meet three Key Performance Indicators (KPIs), namely a maximum acceptable end-to-end latency L, a targeted end-to-end reliability R and a minimum network lifetime T. The IoT network has to guarantee that at least R% of messages generated by sensor nodes are delivered to the sink with a latency ≤L, whereas the network lifetime is at least equal to T. In this paper, we show how to provide the targeted end-to-end reliability R by means of retransmissions to cope with the unreliability of wireless links. We present two methods to compute the maximum number of transmissions per message required to achieve R. MFair is very easy to compute, whereas MOpt minimizes the total number of transmissions necessary for a message to reach the sink. MFair and MOpt are then integrated into a TSCH network with a load-based scheduler to evaluate the three KPIs on a generic data-gathering application. We first consider a toy example with eight nodes where the maximum number of transmissions MaxTrans is tuned per link and per flow. Finally, a network of 50 nodes, representative of real network deployments, is evaluated assuming MaxTrans is fixed. For both TSCH networks, we show that MOpt provides a better reliability and a longer lifetime than MFair, which provides a shorter average end-to-end latency. MOpt provides more predictable end-to-end performances than Kausa, a KPI-aware, state-of-the-art scheduler.

Energy Efficient Multipath Routing Algorithm for Wireless Multimedia Sensor Network

Wireless multimedia sensor networks (WMSNs) are capable of collecting multimedia events, such as traffic accidents and wildlife tracking, as well as scalar data. As a result, WMSNs are receiving a great deal of attention both from industry and academic communities. However, multimedia applications tend to generate high volume network traffic, which results in very high energy consumption. As energy is a prime resource in WMSN, an efficient routing algorithm that effectively deals with the dynamic topology of WMSN but also prolongs the lifetime of WMSN is required. To this end, we propose a routing algorithm that combines dynamic cluster formation, cluster head selection, and multipath routing formation for data communication to reduce energy consumption as well as routing overheads. The proposed algorithm uses a genetic algorithm (GA)-based meta-heuristic optimization to dynamically select the best path based on the cost function with the minimum distance and the least energy dissipation. We carried out an extensive performance analysis of the proposed algorithm and compared it with three other routing protocols. The results of the performance analysis showed that the proposed algorithm outperformed the three other routing protocols.

Wireless body area network for health monitoring

A Wireless Sensor Networks (WSNs) consists basically of a group of nodes, that communicate with each other through a wireless transmission, and does not need any existing infrastructure. The recent developments in technology and wireless communication, to be used in various applications, foster the development of Wireless Body Area Networks (WBANs). They are emerging as important networks in order to reduce the need for patients, and to help the elderly and chronically ill people to live an independent life. In this paper, we propose a routing protocol for wireless body area networks, to transfer data in the network with minimum energy consumption, and longer network lifetime through multi-hop communication. The proposed protocol has been verified by performing simulations, and the obtained results show that our routing protocol ensures a robust optimisation of the energy consumption which helps to increase the lifetime of the network and its stability.

An Energy-Efficient Two-Stage Cooperative Routing Scheme in Wireless Multi-Hop Networks

Cooperative routing is one of the most widely used technologies for improving the energy efficiency and energy balance of wireless multi-hop networks. However, the end-to-end energy cost and network lifetime are greatly restricted if the cooperative transmission model is not designed properly. The main aim of this paper is to explore a two-stage cooperative routing scheme to further improve the energy efficiency and prolong the network lifetime. A two-stage cooperative (TSC) transmission model is firstly designed in which the core helper is introduced to determine the helper set for cooperation. Then, the two-stage link cost is formulated where x, the weight of residual energy, is introduced to be adjusted for different design goals. By selecting the optimal helper set, the two-stage link cost of each link can be optimized. Finally, based on the designed TSC transmission model and the optimized two-stage link cost, a distributed two-stage cooperative routing (TSCR) scheme is further proposed to minimize the end-to-end cooperative routing cost. Simulation results evaluate the effect of x on the different performance metrics. When x equals 0, TSCR can achieve the shortest end-to-end transmission delay and highest energy efficiency, while a larger x can achieve a longer network lifetime. Furthermore, simulation results also show that the proposed TSCR scheme can effectively improve both the energy efficiency and network lifetime compared with the existing schemes.

An Improved Routing Schema with Special Clustering Using PSO Algorithm for Heterogeneous Wireless Sensor Network

Energy efficiency and energy balancing are crucial research issues as per routing protocol designing for self-organized wireless sensor networks (WSNs). Many literatures used the clustering algorithm to achieve energy efficiency and energy balancing, however, there are usually energy holes near the cluster heads (CHs) because of the heavy burden of forwarding. As the clustering problem in lossy WSNs is proved to be a NP-hard problem, many metaheuristic algorithms are utilized to solve the problem. In this paper, a special clustering method called Energy Centers Searching using Particle Swarm Optimization (EC-PSO) is presented to avoid these energy holes and search energy centers for CHs selection. During the first period, the CHs are elected using geometric method. After the energy of the network is heterogeneous, EC-PSO is adopted for clustering. Energy centers are searched using an improved PSO algorithm and nodes close to the energy center are elected as CHs. Additionally, a protection mechanism is also used to prevent low energy nodes from being the forwarder and a mobile data collector is introduced to gather the data. We conduct numerous simulations to illustrate that our presented EC-PSO outperforms than some similar works in terms of network lifetime enhancement and energy utilization ratio.

Improved Location Estimation in Wireless Sensor Networks Using a Vector-Based Swarm Optimized Connected Dominating Set

Location estimation in wireless sensor networks (WSNs) has received tremendous attention in recent times. Improved technology and efficient algorithms systematically empower WSNs with precise location identification. However, while algorithms are efficient in improving the location estimation error, the factor of the network lifetime has not been researched thoroughly. In addition, algorithms are not optimized in balancing the load among nodes, which reduces the overall network lifetime. In this paper, we have proposed an algorithm that balances the load of computation for location estimation among the anchor nodes. We have used vector-based swarm optimization on the connected dominating set (CDS), consisting of anchor nodes for that purpose. In this algorithm, major tasks are performed by the base station with a minimum number of messages exchanged by anchor nodes and unknown nodes. The simulation results showed that the proposed algorithm significantly improves the network lifetime and reduces the location estimation error. Furthermore, the proposed optimized CDS is capable of providing a global optimum solution with a minimum number of iterations.

CMTN-SP: A Novel Coverage-Control Algorithm for Moving-Target Nodes Based on Sensing Probability Model in Sensor Networks

The non-consecutive coverage problem for the target nodes in Sensor Networks could lead to the coverage blind area and a large amount of redundant data, which causes the bottleneck phenomenon for the communication link. A novel Coverage Control Algorithm for Moving Target Nodes Based on Sensing Probability Model (CMTN-SP) is proposed in this work. Firstly, according to the probability theory, we derive the calculation method for the expectation of the coverage quality with multiple joint nodes, which aims to reduce the coverage blind area and improving network coverage rate. Secondly, we employ the dynamic transferring mechanism of the nodes to re-optimize the deployment of the nodes, which alleviates the rapid exhaustion of the proper network energy. Finally, it is verified via the results of the simulation that the network coverage quality could not only be improved by the proposed algorithm, but the proposed algorithm could also effectively curb the rapid exhaustion of the node energy.

Energy-Efficient Medium Access Control Protocols for Cognitive Radio Sensor Networks: A Comparative Survey

The increase of application areas in wireless sensor networks demands novel solutions in terms of energy consumption and radio frequency management. Cognitive radio sensor networks (CRSNs) are key for ensuring efficient spectrum management, by making it possible to use the unused licensed frequency spectrum together with the unlicensed frequency spectrum. Sensor nodes powered by energy-constrained batteries necessarily require energy-efficient protocols at the routing and medium access control (MAC) layers. In CRSNs, energy efficiency is more important because the sensor nodes consume additional energy for spectrum sensing and management. To the best of authors' knowledge, there is no survey on "energy-efficient" MAC protocols for CRSNs in the literature, even though a conceptual review on MAC protocols for CRSNs was presented at a conference recently. In this paper, energy-efficient MAC protocols for CRSNs are extensively surveyed and qualitatively compared. Open issues, and research challenges in the design of MAC protocols for CRSNs, are also discussed.

Cooperative Data Collection Mechanism Using Multiple Mobile Sinks in Wireless Sensor Networks

Data collection problems have received much attention in recent years. Many data collection algorithms that constructed a path and adopted one or more mobile sinks to collect data along the paths have been proposed in wireless sensor networks (WSNs). However, the efficiency of the established paths still can be improved. This paper proposes a cooperative data collection algorithm (CDCA), which aims to prolong the network lifetime of the given WSNs. The CDCA initially partitions the n sensor nodes into k groups and assigns each mobile sink acting as the local mobile sink to collect data generated by the sensors of each group. Then the CDCA selects an appropriate set of data collection points in each group and establishes a separate path passing through all the data collection points in each group. Finally, a global path is constructed and the rendezvous time points and the speed of each mobile sink are arranged for collecting data from k local mobile sinks to the global mobile sink. Performance evaluations reveal that the proposed CDCA outperforms the related works in terms of rendezvous time, network lifetime, fairness index as well as efficiency index.

Bayesian Compressive Sensing Based Optimized Node Selection Scheme in Underwater Sensor Networks

Information acquisition in underwater sensor networks is usually limited by energy and bandwidth. Fortunately, the received signal can be represented sparsely on some basis. Therefore, a compressed sensing method can be used to collect the information by selecting a subset of the total sensor nodes. The conventional compressed sensing scheme is to select some sensor nodes randomly. The network lifetime and the correlation of sensor nodes are not considered. Therefore, it is significant to adjust the sensor node selection scheme according to these factors for the superior performance. In this paper, an optimized sensor node selection scheme is given based on Bayesian estimation theory. The advantage of Bayesian estimation is to give the closed-form expression of posterior density function and error covariance matrix. The proposed optimization problem first aims at minimizing the mean square error (MSE) of Bayesian estimation based on a given error covariance matrix. Then, the non-convex optimization problem is transformed as a convex semidefinite programming problem by relaxing the constraints. Finally, the residual energy of each sensor node is taken into account as a constraint in the optimization problem. Simulation results demonstrate that the proposed scheme has better performance than a conventional compressed sensing scheme.

Joint Energy Supply and Routing Path Selection for Rechargeable Wireless Sensor Networks

The topic of network lifetime has been attracting much research attention because of its importance in prolonging the standing operation of battery-restricted wireless sensor networks, and the rechargeable wireless sensor network has emerged as a promising solution. In this paper, we propose a joint energy supply and routing path selection algorithm to extend the network lifetime based on an initiative power supply. We develop a two-stage energy replenishment strategy to supplement the energy consumption of nodes as much as possible. Furthermore, the influence of charging factors on the selection of next-hop nodes in data routing is considered. The simulation results show that our algorithm effectively prolong the network lifetime, and different demands of network delay and energy consumption can be obtained by dynamically adjusting parameters.

An Energy Balanced and Lifetime Extended Routing Protocol for Underwater Sensor Networks

Energy limitation is an adverse problem in designing routing protocols for underwater sensor networks (UWSNs). To prolong the network lifetime with limited battery power, an energy balanced and efficient routing protocol, called energy balanced and lifetime extended routing protocol (EBLE), is proposed in this paper. The proposed EBLE not only balances traffic loads according to the residual energy, but also optimizes data transmissions by selecting low-cost paths. Two phases are operated in the EBLE data transmission process: (1) candidate forwarding set selection phase and (2) data transmission phase. In candidate forwarding set selection phase, nodes update candidate forwarding nodes by broadcasting the position and residual energy level information. The cost value of available nodes is calculated and stored in each sensor node. Then in data transmission phase, high residual energy and relatively low-cost paths are selected based on the cost function and residual energy level information. We also introduce detailed analysis of optimal energy consumption in UWSNs. Numerical simulation results on a variety of node distributions and data load distributions prove that EBLE outperforms other routing protocols (BTM, BEAR and direct transmission) in terms of network lifetime and energy efficiency.

An Energy Centric Cluster-Based Routing Protocol for Wireless Sensor Networks

Clustering is an effective way to prolong the lifetime of a wireless sensor network (WSN). The common approach is to elect cluster heads to take routing and controlling duty, and to periodically rotate each cluster head’s role to distribute energy consumption among nodes. However, a significant amount of energy dissipates due to control messages overhead, which results in a shorter network lifetime. This paper proposes an energy-centric cluster-based routing mechanism in WSNs. To begin with, cluster heads are elected based on the higher ranks of the nodes. The rank is defined by residual energy and average distance from the member nodes. With the role of data aggregation and data forwarding, a cluster head acts as a caretaker for cluster-head election in the next round, where the ranks’ information are piggybacked along with the local data sending during intra-cluster communication. This reduces the number of control messages for the cluster-head election as well as the cluster formation in detail. Simulation results show that our proposed protocol saves the energy consumption among nodes and achieves a significant improvement in the network lifetime.

An Effective Hybrid Routing Algorithm in WSN: Ant Colony Optimization in combination with Hop Count Minimization

Low cost, high reliability and easy maintenance are key criteria in the design of routing protocols for wireless sensor networks (WSNs). This paper investigates the existing ant colony optimization (ACO)-based WSN routing algorithms and the minimum hop count WSN routing algorithms by reviewing their strengths and weaknesses. We also consider the critical factors of WSNs, such as energy constraint of sensor nodes, network load balancing and dynamic network topology. Then we propose a hybrid routing algorithm that integrates ACO and a minimum hop count scheme. The proposed algorithm is able to find the optimal routing path with minimal total energy consumption and balanced energy consumption on each node. The algorithm has unique superiority in terms of searching for the optimal path, balancing the network load and the network topology maintenance. The WSN model and the proposed algorithm have been implemented using C++. Extensive simulation experimental results have shown that our algorithm outperforms several other WSN routing algorithms on such aspects that include the rate of convergence, the success rate in searching for global optimal solution, and the network lifetime.

A Trust-Based Secure Routing Scheme Using the Traceback Approach for Energy-Harvesting Wireless Sensor Networks

The Internet of things (IoT) is composed of billions of sensing devices that are subject to threats stemming from increasing reliance on communications technologies. A Trust-Based Secure Routing (TBSR) scheme using the traceback approach is proposed to improve the security of data routing and maximize the use of available energy in Energy-Harvesting Wireless Sensor Networks (EHWSNs). The main contributions of a TBSR are (a) the source nodes send data and notification to sinks through disjoint paths, separately; in such a mechanism, the data and notification can be verified independently to ensure their security. (b) Furthermore, the data and notification adopt a dynamic probability of marking and logging approach during the routing. Therefore, when attacked, the network will adopt the traceback approach to locate and clear malicious nodes to ensure security. The probability of marking is determined based on the level of battery remaining; when nodes harvest more energy, the probability of marking is higher, which can improve network security. Because if the probability of marking is higher, the number of marked nodes on the data packet routing path will be more, and the sink will be more likely to trace back the data packet routing path and find malicious nodes according to this notification. When data packets are routed again, they tend to bypass these malicious nodes, which make the success rate of routing higher and lead to improved network security. When the battery level is low, the probability of marking will be decreased, which is able to save energy. For logging, when the battery level is high, the network adopts a larger probability of marking and smaller probability of logging to transmit notification to the sink, which can reserve enough storage space to meet the storage demand for the period of the battery on low level; when the battery level is low, increasing the probability of logging can reduce energy consumption. After the level of battery remaining is high enough, nodes then send the notification which was logged before to the sink. Compared with past solutions, our results indicate that the performance of the TBSR scheme has been improved comprehensively; it can effectively increase the quantity of notification received by the sink by 20%, increase energy efficiency by 11%, reduce the maximum storage capacity needed by nodes by 33.3% and improve the success rate of routing by approximately 16.30%.

Mutual-Information-Based Incremental Relaying Communications for Wireless Biomedical Implant Systems

Network lifetime maximization of wireless biomedical implant systems is one of the major research challenges of wireless body area networks (WBANs). In this paper, a mutual information (MI)-based incremental relaying communication protocol is presented where several on-body relay nodes and one coordinator are attached to the clothes of a patient. Firstly, a comprehensive analysis of a system model is investigated in terms of channel path loss, energy consumption, and the outage probability from the network perspective. Secondly, only when the MI value becomes smaller than the predetermined threshold is data transmission allowed. The communication path selection can be either from the implanted sensor to the on-body relay then forwards to the coordinator or from the implanted sensor to the coordinator directly, depending on the communication distance. Moreover, mathematical models of quality of service (QoS) metrics are derived along with the related subjective functions. The results show that the MI-based incremental relaying technique achieves better performance in comparison to our previous proposed protocol techniques regarding several selected performance metrics. The outcome of this paper can be applied to intra-body continuous physiological signal monitoring, artificial biofeedback-oriented WBANs, and telemedicine system design.

A Game Theoretic Approach for Balancing Energy Consumption in Clustered Wireless Sensor Networks

Clustering is an effective topology control method in wireless sensor networks (WSNs), since it can enhance the network lifetime and scalability. To prolong the network lifetime in clustered WSNs, an efficient cluster head (CH) optimization policy is essential to distribute the energy among sensor nodes. Recently, game theory has been introduced to model clustering. Each sensor node is considered as a rational and selfish player which will play a clustering game with an equilibrium strategy. Then it decides whether to act as the CH according to this strategy for a tradeoff between providing required services and energy conservation. However, how to get the equilibrium strategy while maximizing the payoff of sensor nodes has rarely been addressed to date. In this paper, we present a game theoretic approach for balancing energy consumption in clustered WSNs. With our novel payoff function, realistic sensor behaviors can be captured well. The energy heterogeneity of nodes is considered by incorporating a penalty mechanism in the payoff function, so the nodes with more energy will compete for CHs more actively. We have obtained the Nash equilibrium (NE) strategy of the clustering game through convex optimization. Specifically, each sensor node can achieve its own maximal payoff when it makes the decision according to this strategy. Through plenty of simulations, our proposed game theoretic clustering is proved to have a good energy balancing performance and consequently the network lifetime is greatly enhanced.

An Energy Scaled and Expanded Vector-Based Forwarding Scheme for Industrial Underwater Acoustic Sensor Networks with Sink Mobility

Industrial Underwater Acoustic Sensor Networks (IUASNs) come with intrinsic challenges like long propagation delay, small bandwidth, large energy consumption, three-dimensional deployment, and high deployment and battery replacement cost. Any routing strategy proposed for IUASN must take into account these constraints. The vector based forwarding schemes in literature forward data packets to sink using holding time and location information of the sender, forwarder, and sink nodes. Holding time suppresses data broadcasts; however, it fails to keep energy and delay fairness in the network. To achieve this, we propose an Energy Scaled and Expanded Vector-Based Forwarding (ESEVBF) scheme. ESEVBF uses the residual energy of the node to scale and vector pipeline distance ratio to expand the holding time. Resulting scaled and expanded holding time of all forwarding nodes has a significant difference to avoid multiple forwarding, which reduces energy consumption and energy balancing in the network. If a node has a minimum holding time among its neighbors, it shrinks the holding time and quickly forwards the data packets upstream. The performance of ESEVBF is analyzed through in network scenario with and without node mobility to ensure its effectiveness. Simulation results show that ESEVBF has low energy consumption, reduces forwarded data copies, and less end-to-end delay.

An Eccentricity Based Data Routing Protocol with Uniform Node Distribution in 3D WSN

Due to nonuniform node distribution, the energy consumption of nodes are imbalanced in clustering-based wireless sensor networks (WSNs). It might have more impact when nodes are deployed in a three-dimensional (3D) environment. In this regard, we propose the eccentricity based data routing (EDR) protocol in a 3D WSN with uniform node distribution. It includes network partitions called 3D subspaces/clusters of equal member nodes, an energy-efficient routing centroid (RC) nodes election and data routing algorithm. The RC nodes election conducts in a quasi-static nature until a certain period unlike the periodic cluster heads election of typical clustering-based routing. It not only reduces the energy consumption of nodes during the election phase, but also in intra-communication. At the same time, the routing algorithm selects a forwarding node in such a way that balances the energy consumption among RC nodes and reduces the number of hops towards the sink. The simulation results validate and ensure the performance supremacy of the EDR protocol compared to existing protocols in terms of various metrics such as steady state and network lifetime in particular. Meanwhile, the results show the EDR is more robust in uniform node distribution compared to nonuniform.

An Energy-Efficient Mobile Sink-Based Unequal Clustering Mechanism for WSNs

Network lifetime and energy efficiency are crucial performance metrics used to evaluate wireless sensor networks (WSNs). Decreasing and balancing the energy consumption of nodes can be employed to increase network lifetime. In cluster-based WSNs, one objective of applying clustering is to decrease the energy consumption of the network. In fact, the clustering technique will be considered effective if the energy consumed by sensor nodes decreases after applying clustering, however, this aim will not be achieved if the cluster size is not properly chosen. Therefore, in this paper, the energy consumption of nodes, before clustering, is considered to determine the optimal cluster size. A two-stage Genetic Algorithm (GA) is employed to determine the optimal interval of cluster size and derive the exact value from the interval. Furthermore, the energy hole is an inherent problem which leads to a remarkable decrease in the network's lifespan. This problem stems from the asynchronous energy depletion of nodes located in different layers of the network. For this reason, we propose Circular Motion of Mobile-Sink with Varied Velocity Algorithm (CM2SV2) to balance the energy consumption ratio of cluster heads (CH). According to the results, these strategies could largely increase the network's lifetime by decreasing the energy consumption of sensors and balancing the energy consumption among CHs.

Lifetime Maximization via Hole Alleviation in IoT Enabling Heterogeneous Wireless Sensor Networks

In Internet of Things (IoT) enabled Wireless Sensor Networks (WSNs), there are two major factors which degrade the performance of the network. One is the void hole which occurs in a particular region due to unavailability of forwarder nodes. The other is the presence of energy hole which occurs due to imbalanced data traffic load on intermediate nodes. Therefore, an optimum transmission strategy is required to maximize the network lifespan via hole alleviation. In this regard, we propose a heterogeneous network solution that is capable to balance energy dissipation among network nodes. In addition, the divide and conquer approach is exploited to evenly distribute number of transmissions over various network areas. An efficient forwarder node selection is performed to alleviate coverage and energy holes. Linear optimization is performed to validate the effectiveness of our proposed work in term of energy minimization. Furthermore, simulations are conducted to show that our claims are well grounded. Results show the superiority of our work as compared to the baseline scheme in terms of energy consumption and network lifetime.

Unequal Probability Marking Approach to Enhance Security of Traceback Scheme in Tree-Based WSNs

Fog (from core to edge) computing is a newly emerging computing platform, which utilizes a large number of network devices at the edge of a network to provide ubiquitous computing, thus having great development potential. However, the issue of security poses an important challenge for fog computing. In particular, the Internet of Things (IoT) that constitutes the fog computing platform is crucial for preserving the security of a huge number of wireless sensors, which are vulnerable to attack. In this paper, a new unequal probability marking approach is proposed to enhance the security performance of logging and migration traceback (LM) schemes in tree-based wireless sensor networks (WSNs). The main contribution of this paper is to overcome the deficiency of the LM scheme that has a higher network lifetime and large storage space. In the unequal probability marking logging and migration (UPLM) scheme of this paper, different marking probabilities are adopted for different nodes according to their distances to the sink. A large marking probability is assigned to nodes in remote areas (areas at a long distance from the sink), while a small marking probability is applied to nodes in nearby area (areas at a short distance from the sink). This reduces the consumption of storage and energy in addition to enhancing the security performance, lifetime, and storage capacity. Marking information will be migrated to nodes at a longer distance from the sink for increasing the amount of stored marking information, thus enhancing the security performance in the process of migration. The experimental simulation shows that for general tree-based WSNs, the UPLM scheme proposed in this paper can store 1.12–1.28 times the amount of stored marking information that the equal probability marking approach achieves, and has 1.15–1.26 times the storage utilization efficiency compared with other schemes.