A method to enhance lifetime in data aggregation for multi-hop wireless sensor networks

Wireless Sensor Networks nowadays find wide variety of applications especially in real time. Innovative methods of energy efficient protocols and transmission reduction techniques keep improving to enhance the lifetime of the sensor nodes as they are powered by non-rechargeable batteries. Multi hop transmission and data aggregation are major techniques to reduce the power spent by the sensor node. In this paper, we propose a new ribbon structure for the existing multi hop WSN topologies with modified media access control mechanism called co-operative MAC. The ribbon structure is proposed to reap benefits of PEGASIS and APTEEN protocols. The low power consumption as in PEGASIS is maintained but the number of data packets transmitted is reduced by half. In the proposed scheme, only one of the two nodes along the parallel path involves in data transmission alternating roles in every cycle of aggregation. However, for values sensed above threshold, the inactive node interferes with normal cycle and gets its data transmitted to the sink node. This algorithm is compared with cluster based and chain based protocols and the simulation results show significant energy savings.     

Fuzzy-based unequal clustering protocol for underwater wireless sensor networks

Underwater wireless sensor network (UWSN) is a network made up of underwater sensor nodes, anchor nodes, surface sink nodes or surface stations, and the offshore sink node. Energy consumption, limited bandwidth, propagation delay, high bit error rate, stability, scalability, and network lifetime are the key challenges related to underwater wireless sensor networks. Clustering is used to mitigate these issues. In this work, fuzzy-based unequal clustering protocol (FBUCP) is proposed that does cluster head selection using fuzzy logic as it can deal with the uncertainties of the harsh atmosphere in the water. Cluster heads are selected using linguistic input variables like distance to the surface sink node, residual energy, and node density and linguistic output variables like cluster head advertisement radius and rank of underwater sensor nodes. Unequal clustering is used to have an unequal size of the cluster which deals with the problem of excess energy usage of the underwater sensor nodes near the surface sink node, called the hot spot problem. Data gathered by the cluster heads are transmitted to the surface sink node using neighboring cluster heads in the direction of the surface sink node. Dijkstra's shortest path algorithm is used for multi-hop and inter-cluster routing. The FBUCP is compared with the LEACH-UWSN, CDBR, and FBCA protocols for underwater wireless sensor networks. A comparative analysis shows that in first node dies, the FBUCP is up to 80% better, has 64.86% more network lifetime, has 91% more number of packets transmitted to the surface sink node, and is up to 58.81% more energy efficient than LEACH-UWSN, CDBR, and FBCA.     

A faulty node detection scheme for wireless sensor networks that use data aggregation for transport

This paper presents a faulty node detection approach for wireless sensor networks that aggregate measurement data on their way toward the sink (base station). The approach is based on the idea of commanding sensor nodes on the aggregation paths to temporarily stop including their readings in the received aggregated readings from their upstream neighbors. The scheme is dependent on the ability of the sink to detect faulty nodes through changes in the received aggregated readings at the sink using a Markov Chain Controller (MCC). The algorithm that is run in the sink uses the MCC to assign a state to each sensor node based on transitions that are triggered by receiving aggregated path readings, and accordingly deduces the nodes that may be faulty. The experimental results show at least 98% detection rate at the cost of reasonable detection delays and generated wireless network traffic. Copyright © 2016 John Wiley & Sons, Ltd.     

A Multi-Level Strategy for Energy Efficient Data Aggregation in Wireless Sensor Networks

In this paper, we have proposed energy efficient multi-level aggregation strategy which considers data sensing as continuous stochastic process. Our proposed strategy performs filtration of sensed data by removing the redundancy in the sensed data pattern of the sensor node using Brownian motion. Further, the filtered data at the sensor node undergoes entropy-based processing prior to the transmission to cluster head. The head node performs wavelet-based truncation of the received entropy in order to select higher information bearing packets before transmitting them to the sink. Overall, our innovative approach reduces the redundant packets transmissions yet maintaining the fidelity in the aggregated data. We have also optimized the number of samples that should be buffered in an aggregation period. In addition, the power consumption analysis for individual sensors and cluster heads is performed that considers the communicational and computational cost as well. Simulation of our proposed method reveals quality performance than existing data aggregation method based on wavelet entropy and entropy based data aggregation protocols respectively. The evaluation criteria includes—cluster head survival, aggregation cycles completed during simulation, energy consumption and network lifetime. The proposed scheme reflects high potential on practical implementation by improving the life prospects of the sensor network commendably.     

Deterministic binary matrix based compressive data aggregation in big data WSNs

In big data wireless sensor networks, the volume of data sharply increases at an unprecedented rate and the dense deployment of sensor nodes will lead to high spatial-temporal correlation and redundancy of sensors’ readings. Compressive data aggregation may be an indispensable way to eliminate the redundancy. However, the existing compressive data aggregation requires a large number of sensor nodes to take part in each measurement, which may cause heavy load in data transmission. To solve this problem, in this paper, we propose a new compressive data aggregation scheme based on compressive sensing. We apply the deterministic binary matrix based on low density parity check codes as measurement matrix. Each row of the measurement matrix represents a projection process. Owing to the sparsity characteristics of the matrix, only the nodes whose corresponding elements in the matrix are non-zero take part in each projection. Each projection can form an aggregation tree with minimum energy consumption. After all the measurements are collected, the sink node can recover original readings precisely. Simulation results show that our algorithm can efficiently reduce the number of the transmitted packets and the energy consumption of the whole network while reconstructing the original readings accurately.     

Energy conserving security mechanisms for wireless sensor networks

Since wireless sensor networks are emerging as innovative technologies for realizing a variety of functions through a number of compact sensor nodes, security must be justified and ensured prior to their deployment. An adversary may compromise sensor nodes, forcing them to generate undesired data, and propagation of these data packets through the network results in wasteful energy consumption. We develop a security mechanism to detect energy-consuming useless packets, assuming that a sensor node is able to generate multiple message authentication codes (MAC) using preshared secrets. The forwarding nodes along the path verify the validity of the packet by checking the authenticity of the attached MACs. This mechanism performs well when a malicious node does not have all the cryptographic keys. However, packets, generated by the malicious node having all the keys, would be considered as legitimate, and thus, the forwarding nodes become unable to detect and discard them. To deal with this problem, we devise another mechanism in which each forwarding node is capable of checking such suspicious nodes. We have quantified the security strength through analysis and simulations to show that the proposed mechanisms make the entire network energy conserving.     

A Centralized Mechanism for Preventing DDOS Attack in Wireless Sensor Networks

Wireless sensor networks face numerous limitations. Security and Privacy are the two most essential parameters that require consideration in wireless sensor networks for conveying responsive information amid basic applications. High density and limited communication range of sensor nodes, forwarding packets in sensor networks have caused the performance of during multi-hop data transmission. Hence communication with different devices these days are not secure, due to the absence of centralized monitoring and overprotective requirements. This paper is related to speak about Distributed Denial of Service which debilitates the ability of the network and the data being transmitted. The earlier system guarantees the WSN through a self arranged and confined procedure between the nodes in the sensor environment. Here, the authors present the Centralized Detect Eliminate and Control algorithm for authorization and centralized monitoring component to discover the node that has turned into a victim node and to get rid of the information communicated to the fatality node from the neighbour nodes. Overprotective of the communication between the nodes leads to dependability. The simulation results improve the malicious node detection rate and increase the various parameters like throughput and reduce the average delay. This leads to, the overall detection rate built, eventually enhancing the parameters of the network environment.

     

SmartMote: An Adaptive Update Mechanism for Wireless Ad-Hoc Sensor Network

This paper describes a novel update mechanism for large wireless ad-hoc sensor networks (WASNs). In wireless sensor networks, the nodes may have to be reprogrammed, especially for design-implement-test iterations. Manually reprogramming is a very cumbersome work, and may be infeasible if nodes of the network are unreachable. In addition, replacing the executed application on a node by transmitting the complete program image is inefficient for small changes in the code either. It consumes a lot of bandwidth and time. Therefore, an on-the-fly update mechanism is required. This paper exploits programmable packets to update sensor behaviors. To reduce the code transferred and power consumption, a group management architecture is developed. This architecture helps reduce power consumption and increase node number that control by Leader Node in WASNs. The proposed update mechanism, SmartMote, has been implemented on the Tmote-based Octopus II sensor node. Performance evaluation as well as measurement is conducted in the paper to illustrate the significance of the proposed mechanism.     

Data Aggregation for Increase Performance of Wireless Sensor Networks Using Learning Automata Approach

Wireless sensor networks, a new generation of networks, are composed of a large numbers of nodes and the communication between nodes takes place wirelessly. The main purpose of these networks is collecting information about the environment surrounding the network sensors. The sensors collect and send the required information. There are many challenges and research areas concerned in the literature, one of which is power consumption in network nodes. Nodes in these networks have limited energy sources and generally consume more energy in long communication distances and therefore run out of battery very fast. This results in inefficacy in the whole system. One of the proposed solutions is data aggregation in wireless networks which leads to improved performance. Therefore, in this study an approach based on learning automata is proposed to achieve data aggregation which leads to dynamic network at any hypothetical region. This approach specifies a cluster head in the network and nodes send their data to the cluster head and the cluster head sends the information to the main receiver. Also each node can change its sensing rate using learning automata. Simulation results show that the proposed method increases the lifetime of the network and more nodes will be alive.

     

Random Mobility and Heterogeneity-Aware Hybrid Synchronization for Wireless Sensor Network

Random mobility of a node in wireless sensor networks (WSNs) causes the frequent changes in the network dynamics with increased cost in terms of energy and bandwidth. During data collections and transmission, they need the additional efforts to synchronize and schedule the activities of nodes. A key challenge is to maintain the global clock scale for synchronization of nodes at different levels to minimize the energy consumption and clock skew. It is also difficult to schedule the activities for effective utilization of slots allocated for aggregated data transmission. The paper proposes the Random Mobility and Heterogeneity-aware Hybrid Synchronization Algorithm (MHS) for WSN. The proposed algorithm uses the cluster-tree for efficient synchronization of CH and nodes in the cluster and network, level-by-level. The network consists of three nodes with random mobility and are heterogeneous regarding energy with static sink. All the nodes and CH are synchronized with the notion of the global timescale provided by the sink as a root node. With the random mobility of the node, the network structure frequently changes causing an increase in energy consumption. To mitigate this problem, MHS aggregate data with the notion of a global timescale throughout the network. Also, the hierarchical structure along with pair-wise synchronization reduces the clock skews hence energy consumption. In the second phase of MHS, the aggregated data packets are passed through the scheduled and synchronized slots using TDMA as basic MAC layer protocol to reduce the collision of packets. The results are extended by using the hybrid approach of scheduling and synchronization algorithm on the base protocol. The comparative results show that MHS is energy and bandwidth efficient, with increased throughput and reduced delay as compared with state-of-the-art solutions.     

Probabilistic Data Aggregation in Information-based Clustered Sensor Network

Efficient data aggregation helps in achieving maximum performance for complex interactive and sensing applications. In our proposed work, we have considered a heterogeneous sensor network which is partitioned into clusters. Each cluster is further divided into smaller information-based groups (also called similar groups) by the local processing center (LPC). The LPC acts as a cluster head and computes the probabilistic similarity of sensors by exploiting resemblance in the pattern of their sensed data. It further schedules the active and sleep duration of sensor nodes, such that only single node from every group remains active to participate in the aggregation cycle. The LPC gathers multi-characteristic dataset from the active clustered nodes and processes them by using probabilistic aggregation model. The proposed model normalizes the multi-characteristic data for deriving relative weights of each of the characteristic attribute. Finally, the aggregated information is transmitted to the global processing center. Our protocol is evaluated with wide range of performance metrics which includes aggregation gain, information accuracy, aggregation miss ratio, network lifetime and energy consumption.     

An Energy Efficient Barrier Coverage Algorithm for Wireless Sensor Networks

Intrusion detection is one of the most important applications of wireless sensor networks. When mobile objects are entering into the boundary of a sensor field or are moving cross the sensor field, they should be detected by the scattered sensor nodes before they pierce through the field of sensor (barrier coverage). In this paper, we propose an energy efficient scheduling method based on learning automata, in which each node is equipped with a learning automaton, which helps the node to select best node to guarantee barrier coverage, at any given time. To apply our method, we used coverage graph of deployed networks and learning automata of each node operates based on nodes that located in adjacency of current node. Our algorithm tries to select minimum number of required nodes to monitor barriers in deployed network. To investigate the efficiency of the proposed barrier coverage algorithm several computer simulation experiments are conducted. Numerical results show the superiority of the proposed method over the existing methods in term of the network lifetime and our proposed algorithm can operate very close to optimal method.     

An intelligent and knowledge‐based overlapping clustering protocol for wireless sensor networks

Overlapping is one of the topics in wireless sensor networks that is considered by researchers in the last decades. An appropriate overlapping management system can prolong network lifetime and decrease network recovery time. This paper proposes an intelligent and knowledge‐based overlapping clustering protocol for wireless sensor networks, called IKOCP. This protocol uses some of the intelligent and knowledge‐based systems to construct a robust overlapping strategy for sensor networks. The overall network is partitioned to several regions by a proposed multicriteria decision‐making controller to monitor both small‐scale and large‐scale areas. Each region is managed by a sink, where the whole network is managed by a base station. The sensor nodes are categorized by various clusters using the low‐energy adaptive clustering hierarchy (LEACH)‐improved protocol in a way that the value of p is defined by a proposed support vector machine–based mechanism. A proposed fuzzy system determines that noncluster heads associate with several clusters in order to manage overlapping conditions over the network. Cluster heads are changed into clusters in a period by a suggested utility function. Since network lifetime should be prolonged and network traffic should be alleviated, a data aggregation mechanism is proposed to transmit only crucial data packets from cluster heads to sinks. Cluster heads apply a weighted criteria matrix to perform an inner‐cluster routing for transmitting data packets to sinks. Simulation results demonstrate that the proposed protocol surpasses the existing methods in terms of the number of alive nodes, network lifetime, average time to recover, dead time of first node, and dead time of last node.     

A Learning Automata Based Area Coverage Algorithm for Wireless Sensor Networks

One way to reduce energy consumption in wireless sensor networks is to reduce the number of active nodes in the network. When sensors are redundantly deployed, a subset of sensors should be selected to actively monitor the field （referred to as a ＂cover＂）, whereas the rest of the sensors should be put to sleep to conserve their batteries. In this paper, a learning automata based algorithm for energy-efficient monitoring in wireless sensor networks （EEMLA） is proposed. Each node in EEMLA algorithm is equipped with a learning automaton which decides for the node to be active or not at any time during the operation of the network. Using feedback received from neighboring nodes, each node gradually learns its proper state during the operation of the network. Experimental results have shown that the proposed monitoring algorithm in comparison to other existing methods such as Tian and LUC can better prolong the network lifetime.     

Stable routing algorithm for mobile ad hoc networks using mobile agent

Wireless ad hoc networks are growing important because of their mobility, versatility, and ability to work with fewer infrastructures. The mobile ad hoc network is an autonomous system consisting of mobile nodes connected with wireless links. Establishing a path between two nodes is a complex task in wireless networks. It is still more complex in the wireless mobile ad hoc network because every node is no longer as an end node and an intermediate node. In this paper, it focuses on design of connectionless routing protocol for the wireless ad hoc networks based on the mobile agent concept. The proposed model tries to discover the best path taking into consideration some concerns like bandwidth, reliability, and congestion of the link. The proposed model has been simulated and tested under various wireless ad hoc network environments with the help of a different number of nodes. The results demonstrate that the proposed model is more feasible for providing reliable paths between the source and destination with the minimum control message packets over the network. It has delivered more number of packets to the destination over the network. Copyright © 2012 John Wiley & Sons, Ltd.     

Opportunistic routing with in-network aggregation for asynchronous duty-cycled wireless sensor networks

We propose an opportunistic routing protocol for wireless sensor networks designed to work on top of an asynchronous duty-cycled MAC. Opportunistic routing can be very effective when used with asynchronous duty-cycled MAC because expected waiting time of senders—when they stay on active mode and transmit packet streams—is significantly reduced. If there are multiple sources, energy consumption can be reduced further through in-network aggregation. The idea proposed in this paper is to temporarily increase duty cycle ratio of nodes holding packets, in order to increase chance of in-network aggregation and thus reduce energy consumption and extend network lifetime. In the proposed protocol called opportunistic routing with in-network aggregation (ORIA), whenever a node generates a packet or receives a packet to forward, it waits for a certain amount of time before transmitting the packet. Meanwhile, the node increases its duty cycle ratio, hoping that it receives packets from other nodes and aggregate them into a single packet. Simulation results show that ORIA saves considerable amount of energy compared to general opportunistic routing protocols, as well as tree-based protocols.     

A secure wireless mission critical networking system for unmanned aerial vehicle communications

A privacy-preserving secure communication in ad hoc (without infrastructure) mission critical wireless networking system suitable for unmanned aerial vehicle communication systems is introduced and analyzed. It is expected that in a critical condition, few ad hoc (without infrastructure) mission critical wireless networking systems will work together. To make the simple and low cost privacy-preserving secure communication among the same network, each transmitting mobile node generates packets in such a way that its wanted receiving mobile nodes can read the message packets easily. On the other hand, the unwanted receiving mobile nodes from other networks cannot read those message packets. In addition, the unwanted receiving mobile nodes receive ‘jamming packets’ if they try to read them. This mechanism prevents the malicious receivers (readers from other networks) from reading the packets and obtaining information from this network. Results show that the throughput is very high and does not detect any jamming packets, if the receiving nodes of a network try to read packets transmitted by the nodes from the same networks.     

CS-POSA: A High Performance Scheduling Algorithm for WDM Star Networks

In this paper a new packet scheduling algorithm for WDM star networks is introduced. The protocol adopted is pre-transmission coordination-based and packet collisions have been eliminated due to predetermination of the timeslots each node transmits in a demand matrix. The requests of the transmitted packets are predicted through Markov chains in order to reduce the calculation time of the final scheduling matrix. This is accomplished by pipelining the schedule computation. The innovation that this algorithm introduces is to modify the service sequence of the node. The proposed algorithm is studied via extensive simulation results and it is proved that changing the sequence that nodes transmit, from the node with the largest number of requests to the node with the fewest requests, that there is an increase in the throughput of the network, with a minimum (almost zero) cost in mean time delay and in delay variance.     

大规模输电线路状态监测传感器网络的周期性低功耗通信技术方案

为了实现输电线路监测的功耗低、寿命长、绿色发展的目的，提出大规模输电线路状态监测传感器网络周期性低功耗通信技术方案。依据网络中传感器网络组网特征以及节点运行状态转换特点，设置睡眠定时器，以周期性运行方式使传感器网络通信节点在初始化、睡眠、激活状态间转换，通信节点在输电线路状态监测数据无传递需求时进入睡眠状态，节省通信功耗；传感器网络汇聚（sink）节点利用梯度创建上行路由，通过源路由的方式创建下行路由，以跳数和剩余能量为依据进行上、下行路由数据分组传递，降低节点功耗，延长通信运行时间。实验显示，大规模输电线路状态监测传感器网络应用该技术方案后，通信功耗明显降低，运行时间明显延长，且不会影响监测传感器网络的数据传输性能，延长了监测传感器网络的使用寿命。     

Data aggregation and dilution by modulus addressing in wireless sensor networks

DADMA is a distributed data aggregation and dilution technique for sensor networks where nodes aggregate/dilute sensed data by following the rules given in an SQL statement. Our test results show that DADMA reduces the number of transmitted packets 60% on the average.