Dynamic Wireless Sensor Networks Research Articles

Fuzzy Logic-Based Geographic Routing Protocol for Dynamic Wireless Sensor Networks.

The geographic routing protocol only requires the location information of local nodes for routing decisions, and is considered very efficient in multi-hop wireless sensor networks. However, in dynamic wireless sensor networks, it increases the routing overhead while obtaining the location information of destination nodes by using a location server algorithm. In addition, the routing void problem and location inaccuracy problem also occur in geographic routing. To solve these problems, a novel fuzzy logic-based geographic routing protocol (FLGR) is proposed. The selection criteria and parameters for the assessment of the next forwarding node are also proposed. In FLGR protocol, the next forward node can be selected based on the fuzzy location region of the destination node. Finally, the feasibility of the FLGR forwarding mode is verified and the performance of FLGR protocol is analyzed via simulation. Simulation results show that the proposed FLGR forwarding mode can effectively avoid the routing void problem. Compared with existing protocols, the FLGR protocol has lower routing overhead, and a higher packet delivery rate in a sparse network.

DynaPro: Dynamic Wireless Sensor Network Data Protection Algorithm in IoT via Differential Privacy

With the advent of the era of intelligent Internet of Things (IoT), more and more personal information are collected in the process of deploying IoT in various domains. This brings the problem of data protection in IoT. To address this problem, we propose a data protection algorithm, called DynaPro, based on differential privacy for dynamic wireless sensor networks (WSN), which is the sensor layer of IoT. DynaPro has addressed two issues in data protection of wireless sensor networks. The first issue is the dynamic topology of the network. In order to solve this problem, DynaPro has adopted methods like time window, hierarchical sampling, and graph similarity to process the snapshots of the dynamic network topology. The second issue is addition of noise in differential privacy. To address this issue, DynaPro uses Hierarchical Random Graph (HRG) as middleware to apply differential privacy protection. Instead of adding noise to network data and topology directly, DynaPro adds noise to HRG. In this way, DynaPro can hide the network topology but retain the necessary structural information to support data analysis. Theoretical analysis and experimental results show that DynaPro can preserve the important network features of the original network topology under the premise of the differential privacy protection model.

A novel PSO algorithm for dynamic wireless sensor network multiobjective optimization problem

AbstractAll real‐life multiobjective optimization problems (MOPs) are considered dynamic. It is occurring due to fluctuations in environmental or the global market instabilities that lead to the quick fluctuations of prices. Wireless sensor network optimization problem, by nature, is considered one of the dynamic MOP (DMOP) and needs to be solved by a special method to save time and effort. Thus, this paper proposed a novel particle swarm optimization (PSO) for dynamic wireless sensor network MOP to accelerate data transfer in networks and reduce energy losses. Generally, in DMOPs, the optimization period is broken into several equal subperiods. In each subperiod, there is a dynamic parameter that changes. In the proposed approach, PSO is used to handle DMOPs without any changing of its structure and has fast convergence properties. However, a new mechanism to choose the personal and global preferred particles is introduced, which based on the distance between the nondominated solutions obtained so far and the particles positions by using Euclidean metric. In addition, two types of archives are used to maintain the nondominated solutions. The first one is used to store the nondominated solutions obtained by each particle, whereas the second type is used to store the nondominated solutions achieved by all particles. The novel methodology performance is proved by applying it on three benchmark problems that were chosen from the literature and one design problem from the engineering domain. The simulation results mentioned that the proposed algorithm is active and efficacious in solving DMOPs.

Energy Efficient Cluster Based Routing Protocol for Dynamic and Static Nodes in Wireless Sensor Network

Power consumption is considered one of the most significant challenges in the wireless network sensors (WSNs). In this paper, an investigation of the power consumption is done by making a comparison between static and dynamic WSNs. We have compared the results of the static network with the results of the dynamic network. Static and dynamic wireless Sensor networks have the same architecture (Homogenous) and proposed protocol. Depending on the suggested protocol, the simulation results show that the energy consumption in the static wireless sensor network was less than the dynamic wireless sensor network. However, moving the sensors in the dynamic WSN present real improvement in delivering packets to the base station. In the proposed routing protocol, transmitting data process is done in a hierarchal way. Cheap sensors are introduced and deploy them intensively to improve the QoS in the network. The final results and the conclusion are reported.

Cross-layer multipath routing scheme for wireless multimedia sensor network

Multimedia transmission in wireless multimedia sensor network requires restricted quality of services (QoS) conditions. Where the resource-constrained nature of wireless multimedia sensor network (WMSNs) adds more challenges. Such a situation demands a routing strategy that can ensure QoS transmission and exploit the resources efficiently. At present, the single-path routing techniques cannot guarantee the end to end QoS requirements of video transmission causing the video quality degradation. Besides, the traditional network layers stack is not suitable for dynamic wireless sensor network environment, where important information is required from nonadjacent layers to enhance the routing decision. This paper proposes the design of cross-layer multipath routing (CLMR) scheme in order to ensure QoS and minimize energy consumption. CLMR is designed to determine the suitable multipath and to send the multimedia packets according to their importance. The cross-layer design between application, network and physical layers is adapted to obtain optimal routing decision. The simulation results show that the CLMR has a significant improvement in performance as compared to other similar existing protocols, where the obtained average delay with high delivery is less than 150 ms, while the energy consumption is reduced, and the PSNR values for the tested clips are more than 30 db.

Attack Resistant Agent Based Dynamic Key Management in Dynamic Wireless Sensor Networks

The adaptation of Wireless Sensor Network (WSN) and its utility in real world applications like healthcare is growing faster. With its growth, it also brought vulnerabilities that cause security attacks. It is more so with the emerging technology known as Internet of Things (IoT) which combines physical and digital worlds. At least, as far as WSN is concerned, it is inevitable to have end to end security to protect communications that are sensitive and confidential.Many security schemes came into existence as found in the literature. However, most of them are inefficient for dynamic key management in dynamic WSN. Recently we proposed a scheme named as Agent Based Dynamic Key Management (ABDKM). In this paper we enhance and evaluate it against various attacks such as black hole and rushing.The enhanced scheme is named as an Attack Resilient-Agent Based Dynamic Key Management (AR-ABDKM). The scheme is implemented using NS2. The simulation results showed the remarkable performance improvement of the proposed scheme besides its attack resiliency.

Accurate per-link loss tomography in dynamic sensor networks

Wireless Sensor Networks (WSNs) have been successfully applied in many application areas. Understanding the wireless link performance is very helpful for both protocol designers and network managers to improve the network performance and prolong the network lifetime. Loss tomography is a popular approach to infer the per-link loss ratios from end-to-end delivery ratios. Previous studies, however, are usually targeted for networks with static or slowly changing routing paths. In this work, we propose Dophy, a Dynamic loss tomography approach specifically designed for dynamic WSNs where each node dynamically selects the forwarding nodes towards the sink. The key idea of Dophy is based on an observation that most existing protocols use retransmissions to achieve high data delivery ratio. Dophy employs arithmetic encoding to encode the number of retransmissions along the paths compactly. Dophy incorporates two mechanisms to optimize its performance. First, Dophy intelligently reduces the size of the symbol set by aggregating the number of retransmissions, reducing the encoding overhead significantly. Second, Dophy periodically updates the probability model to minimize the overall transmission overhead. We implement Dophy on the TinyOS platform and evaluate its performance extensively using large-scale simulations. Results show that Dophy achieves both high encoding efficiency and high estimation accuracy. Comparative studies show that Dophy significantly outperforms traditional loss tomography approaches in terms of accuracy.

A Slotted Sense Multiple Access Protocol for Timely and Reliable Data Transmission in Dynamic Wireless Sensor Networks

A wireless medium access control protocol that uses a topology-dependent slot schedule is vulnerable to link failures. This becomes severer in industrial fields due to node mobility and various obstructions unfriendly to wireless communication. The proposed protocol allocates a big sharable slot (SS) to each tree level, producing a topology-independent schedule that makes the protocol highly responsive to the changes of topology. Then, the nodes at the same level use carrier sense multiple access (CSMA)/CA for data transmission and channel hopping to cope with the varying condition of a channel. The use of CSMA/CA and channel hoping makes data transmission reliable against internal and external interferences. Another feature is that every node gets its SS using a slot generation function independently of other nodes. Simulations and experiments verify that the proposed protocol achieves good performance in terms of packet delivery ratio and energy efficiency against the variation of multipath fading, various external sources of interference, and node mobility.

CRPD: a novel clustering routing protocol for dynamic wireless sensor networks

A wireless sensor network (WSN) consists of a large number of static or mobile, low-cost, and low-power sensor nodes. And energy is one of the most important factors that should be considered. In this paper, we propose clustering-based routing protocol for dynamic networks (CRPD) to reduce energy consumption and improve energy efficiency through clustering and routing algorithms. The basic idea is to periodically update the network topology and select the node with larger degree and high residual energy as the cluster head to be responsible for data aggregation and transmission. With the nodes moving, joining, and choosing the optimal clustering radius, the energy load of the whole network can be evenly distributed to each sensor node, which can significantly prolong the network lifetime. Extensive simulations show that CRPD is more energy-efficient than the existing protocols.

Randomized and Efficient Time Synchronization in Dynamic Wireless Sensor Networks: A Gossip‐Consensus‐Based Approach

This paper proposes novel randomized gossip‐consensus‐based sync (RGCS) algorithms to realize efficient time calibration in dynamic wireless sensor networks (WSNs). First, the unreliable links are described by stochastic connections, reflecting the characteristic of changing connectivity gleaned from dynamic WSNs. Secondly, based on the mutual drift estimation, each pair of activated nodes fully adjusts clock rate and offset to achieve network‐wide time synchronization by drawing upon the gossip consensus approach. The converge‐to‐max criterion is introduced to achieve a much faster convergence speed. The theoretical results on the probabilistic synchronization performance of the RGCS are presented. Thirdly, a Revised‐RGCS is developed to counteract the negative impact of bounded delays, because the uncertain delays are always present in practice and would lead to a large deterioration of algorithm performances. Finally, extensive simulations are performed on the MATLAB and OMNeT++ platform for performance evaluation. Simulation results demonstrate that the proposed algorithms are not only efficient for synchronization issues required for dynamic topology changes but also give a better performance in terms of converging speed, collision rate, and the robustness of resisting delay, and outperform other existing protocols.

Distributed Sequential Estimation in Wireless Sensor Networks

This paper considers the problem of decentralized sequential estimation in dynamic wireless sensor networks. A coherent medium access control layer is considered, and optimal linear precoder and decoder matrices are designed to minimize the mean square error (MSE) in an online setting. Different from the state-of-the-art decentralized estimators, the proposed framework is flexible enough to handle time-varying parameters, channel gains, and power constraints. Although the general transceiver design problem is nonconvex, a fast block coordinate descent-based method is proposed that incurs very low complexity and yields near-optimal solutions. Motivated by the need to reduce the communication overhead incurred by the centralized schemes, two fully distributed transceiver design algorithms that make use of the constrained linear minimum MSE machinery are also advocated. The resulting approximate precoders are not only near optimal but can also be calculated locally at each sensor. Finally, the entire framework is generalized so as to allow tracking of parameters that follow a known state-space model. Extensive simulations are provided to demonstrate the efficacy of the proposed class of algorithms.

Compressed Sensing in Multi-Hop Large-Scale Wireless Sensor Networks Based on Routing Topology Tomography

Data acquisition from multi-hop large-scale outdoor wireless sensor network (WSN) deployments for environmental monitoring is full of challenges. This is because of the severe resource constraints on tiny battery-operated motes (e.g., bandwidth, memory, power, and computing capacity), the data acquisition volume from large-scale WSNs, and the highly dynamic wireless link conditions in outdoor harsh communication environments. We present a novel compressed sensing approach, which can recover the sensing data at the sink with high fidelity when a very few data packets need to be collected, leading to a significant reduction of the network transmissions and thus an extension of the WSN lifetime. Interplaying with the dynamic WSN routing topology, the proposed approach is both efficient and simple to implement on the resource-constrained motes without motes’ storing of any part of the random projection matrix, as opposed to other existing compressed sensing-based schemes. We further propose a systematic method via machine learning to find a suitable representation basis, for any given WSN deployment and data field, which is both sparse and incoherent with the random projection matrix in compressed sensing for data collection. We validate our approach and evaluate its performance using a real-world outdoor multi-hop WSN testbed deployment in situ . The results demonstrate that our approach significantly outperforms existing compressed sensing approaches by reducing data recovery errors by an order of magnitude for the entire WSN observation field while drastically reducing wireless communication costs at the same time.

A task scheduling algorithm based on Q-learning and shared value function for WSNs

In dynamic Wireless Sensor Networks (WSNs), each sensor node should be allowed to schedule tasks by itself based on current environmental changes. Task scheduling on each sensor node should be done online towards balancing the tradeoff between resources utilization and application performance. In order to solve the problem of frequent exchange of cooperative information in existing cooperative learning algorithms, a task scheduling algorithm based on Q-learning and shared value function for WSNs, QS is proposed. Specifically, the task model for target monitoring applications and the cooperative Q-learning model are both established, and some basic elements of reinforcement learning including the delayed rewards and the state space are also defined. Moreover, according to the characteristic of the value of the function change, QS designs the sending constraint and the expired constraint of state value to reduce the switching frequency of cooperative information while guaranteeing the cooperative learning effect. Experimental results on NS3 show that QS can perform task scheduling dynamically according to current environmental changes; compared with other cooperative learning algorithms, QS achieves better application performance with achievable energy consumption and also makes each sensor node complete its functionality job normally.

Improved artificial bee colony metaheuristic for energy-efficient clustering in wireless sensor networks

Energy-efficient clustering is a well known NP-hard optimization problem for complex and dynamic Wireless sensor networks (WSNs) environment. Swarm intelligence (SI) based metaheuristic like Ant colony optimization, Particle swarm optimization and more recently Artificial bee colony (ABC) has shown desirable properties of being adaptive to solve optimization problem of energy efficient clustering in WSNs. ABC arose much interest over other population-based metaheuristics for solving optimization problems in WSNs due to ease of implementation however, its search equation contributes to its insufficiency due to poor exploitation phase and storage of certain control parameters. Thus, we propose an improved Artificial bee colony (iABC) metaheuristic with an improved search equation to enhance its exploitation capabilities and in order to increase the global convergence of the proposed metaheuristic, an improved population sampling technique is introduced through Student’s-t distribution, which require only one control parameter to compute and store, hence increase efficiency of proposed metaheuristic. The proposed metaheuristic maintain a good balance between exploration and exploitation search abilities with least memory requirements, moreover the use of first of its kind compact Student’s-t distribution, make it suitable for limited hardware requirements of WSNs. Further, an energy efficient bee clustering protocol based on iABC metaheuristic is introduced, which inherit the capabilities of the proposed metaheuristic to obtain optimal cluster heads and improve energy efficiency in WSNs. Simulation results show that the proposed clustering protocol outperforms other well known SI based protocols on the basis of packet delivery, throughput, energy consumption and extend network lifetime.

Lifetime Enhancement of Dynamic Heterogeneous Wireless Sensor Networks with Energy-Harvesting Sensors

Lifetime enhancement has been the major constraint of developing wireless sensor networks (WSNs). Most of previous related works separately considered dynamics and heterogeneity of WSNs, and did not consider energy-harvesting (EH) sensors, which can absorb natural power (e.g., solar and wind power) to extend lifetime of sensor devices. Therefore, this work investigates the problem of extending the lifetime of dynamic heterogeneous WSNs with EH sensors to enhancing the total WSN lifetime. This problem can be characterized as finding the maximal number of covers each of which is a part of all sensors so that all targets can be monitored by these sensors. Since the case for static WSNs has been shown to be NP-complete, the concerned problem is also NP-complete. Hence, this work first models this problem mathematically, and then proposes a novel harmony search algorithm with multiple populations and local search (HSAML) for this problem with dynamics, heterogeneity, and EH sensors. By simulation, the network lifetime, stability, and executing time of the proposed algorithm are analyzed. From experimental results, the proposed HSAML performs better than the conventional algorithm in terms of average network lifetime for larger-scale problems (i.e., when the number of common and EH sensors is small). In addition, the results confirm that adding EH sensors really helps extend the total WSN lifetime.

Analysis of Dynamic Topology Wireless Sensor Networks for the Internet of Things (IOT)

Analysis of Dynamic Topology Wireless Sensor Networks for the Internet of Things (IOT)

Mobile entities in wireless sensor networks: comparative study and performance analysis

Traditionally, wireless sensor network (WSN) was static, but due to the necessity of today's applications, there has been a paradigm shift from a static WSN to dynamic mobile WSN. This dynamism can be realised by adding extra network elements called mobile entities (MEs) like mobile sinks (MSs), mobile cluster heads (MCHs), mobile relays (MRs), mobile sensor nodes (MSNs), mobile agents (MAs) and mobile chargers (MCs) to a static network. The full potential of MEs can be harnessed to yield maximum benefits in static WSN. In this work, first we have analysed the parameters that affect the lifetime when an ME is added to a static network. Second, we present a survey and performance analysis of each of the MEs discussed in literature. Finally, this paper concludes with the simulations and summaries of each of the MEs. This paper intends to spark new interest and development in mobile-assisted WSN.

Mobile entities in wireless sensor networks: comparative study and performance analysis

Traditionally, wireless sensor network (WSN) was static, but due to the necessity of today's applications, there has been a paradigm shift from a static WSN to dynamic mobile WSN. This dynamism can be realised by adding extra network elements called mobile entities (MEs) like mobile sinks (MSs), mobile cluster heads (MCHs), mobile relays (MRs), mobile sensor nodes (MSNs), mobile agents (MAs) and mobile chargers (MCs) to a static network. The full potential of MEs can be harnessed to yield maximum benefits in static WSN. In this work, first we have analysed the parameters that affect the lifetime when an ME is added to a static network. Second, we present a survey and performance analysis of each of the MEs discussed in literature. Finally, this paper concludes with the simulations and summaries of each of the MEs. This paper intends to spark new interest and development in mobile-assisted WSN.

The complexity of data aggregation in static and dynamic wireless sensor networks

The contribution of this paper is threefold. First, we give tight bounds for the complexity of the problem of data aggregation in static networks. In more details, we show that the problem remains NP-complete when the graph is of degree at most three. Second, we investigate the complexity of the same problem in a dynamic network, that is, a network whose topology can evolve through time. In the case of dynamic networks, we show that the problem is NP-complete even in the case where the graph is of degree at most two. Third, we give the first lower and upper bounds for the minimum data aggregation time in a dynamic graph. We also observe that even in a well-connected evolving graphs, the optimal solution cannot be found by a distributed algorithm or by a centralized algorithm that does not know the future.

Dynamic Power Control Clustering Wireless Sensor Networks based on Multi-Packet Reception.

Objectives: A new method of cluster head election based on Energy Evaluation Factor (EEF) using multi-packet reception technology is proposed in clustering the Wireless Sensor Networks (WSN) to improve the performance and makes the network efficient and increases lifespan of the network. Methods: First EEF is calculated initially for clustering the WSN, the transmission power ranges of the CH elected in each group is calculated and adjusted considering the count of Cluster Head (CH) in elected in the area of interest as second, and thirdly we determine the distance from the sink to CH and its residual energy to implement interference cancellation algorithm in sink using transmission power levels. Findings: Simulation results show that this power control strategy can reduce the energy consumption of sensor nodes and increase network throughput, which can greatly improve network lifetime. Improvements: The authors believe that their findings will be useful guidelines for moving forward with successive interference cancellation research in WSNs.