An efficient and high-performance WSNs restoration algorithm for fault nodes based on FT in data aggregation scheduling

Energy efficient Data Aggregation Scheduling is essential in Ubiquitous sensor networks. Optimal communication distances between the nodes and the base station improves the network lifetime. Clustering improves the efficiency of the data aggregation problem. Many clustering techniques exist to find the optimal number of clusters in the network. The computation complexity of the methods to obtain optimal number of clusters is fair when genetic approaches are employed instead of the classical approaches. Since nodes in USN are dynamic, finding data aggregation schedules is difficult in a USN. Concurrent transmissions improve the throughput of the network, but the SINR perceived at the receiver should be greater than or equal to a certain threshold value for a successful transmission. Also the cluster heads and the member nodes dissipate energy. This power dissipation is more when there are more number of cluster heads. The noise and the power dissipation play a major role in decreasing the network life time. Hence in this paper we designed an efficient energy dissipation algorithm for data aggregation scheduling using the principles of Genetic algorithm and SINR. Since the battery power and bandwidth are the limited resources for the nodes in the USN our data aggregation scheduling algorithm gives equal priority for all of the following while electing a node as a cluster head. They are (a) Total power dissipation from all cluster heads in the USN, (b) The total power dissipation from the nodes at each cluster heads, (c) The SINR perceived at the cluster head should be more than a threshold value at each cluster head for the transmission to be successful, (d) The optimal schedule for the nodes in the USN so that all the nodes transmits their data finally to the base station quickly. With the application of the genetic methods our algorithm proved to be efficient when compared with the existing algorithms in obtaining maximum network life time with minimum number of clusters.

Efficient fault-tolerant collision-free data aggregation scheduling for wireless sensor networks

Data aggregation is a critical functionality in wireless sensor networks. This paper focuses on enhancing energy and delay efficiency for the data aggregation scheduling problem. In the existing data aggregation scheduling algorithms, a sensor node has to start its radio numerous times to receive all data from its children nodes in a period, and this will waste lots of extra energy and time due to the transceiver turning on and off. According to interference of multiple data transmission and competitor sets of the sensor nodes, we propose an energy and delay efficient algorithm to generate a collision-free schedule called contiguous data aggregation scheduling. Since the sensor nodes consume energy and time for state transitions(transceiver turns on and off), this algorithm assigns to sensor nodes of the same parent node consecutive time slots to reduce the frequency of state transitions. Theoretical analysis and simulation results are used to demonstrate the efficiency of our proposed algorithm. Specifically, compared to the existing data aggregation scheduling algorithms, our proposed algorithm achieves a good tradeoff between energy consumption and delay performance.

Data aggregation is an essential operation in wireless sensor network applications. This paper focuses on the data aggregation scheduling problem. Based on maximal independent sets, a distributed algorithm to generate a collision-free schedule for data aggregation in wireless sensor networks is proposed. The time latency of the aggregation schedule generated by the proposed algorithm is minimized using a greedy strategy. The latency bound of the schedule is 24D + 6 Delta + 16, where D is the network diameter and Delta is the maximum node degree. The previous data aggregation algorithm with least latency has the latency bound (Delta- Delta 1)R, where R is the network radius. Thus in our algorithm Delta contributes to an additive factor instead of a multiplicative factor, which is a significant improvement. To the best of our knowledge, the proposed algorithm is the first distributed algorithm for data aggregation scheduling. This paper also proposes an adaptive strategy for updating the schedule when nodes fail or new nodes join in a network. The analysis and simulation results show that the proposed algorithm outperforms other aggregation scheduling algorithms.

An Intelligent delay efficient data aggregation scheduling for distributed sensor networks

In order to minimise the delay of data aggregation scheduling, a task classification aware data aggregation scheduling algorithm is proposed. Through the multi-power and multi-channel approach of sensor nodes, maximum independent sets are used to construct network topology structure based on data aggregation backbone tree. According to the scheduling priority, the data aggregation scheduling within clusters is achieved by approximating the greedy algorithm. Besides, combined with sparse coefficient, sensing task type reduces the amount of data transmission, and then the level of cluster head nodes in the network is used to achieve data aggregation scheduling between clusters. Numerical results show that the proposed algorithm can reduce cluster heads data traffic and energy consumption, while shortening the data aggregation delay and enhancing the network survivability.

Data aggregation is used to reduce the average number of transmissions and to save energy by aggregating multiple packets into a single packet in Wireless Sensor Networks (WSNs). Data aggregation scheduling problems have been extensively studied to make sure collision-free packet delivery to a sink node, with the goal of minimizing the aggregation time. Almost all existing data aggregation schemes have a continuous energy consumption problem of RF transceiver because they assume that each node is always in an active state. Recently, the data aggregation scheduling in duty-cycled WSNs is considered to reduce energy consumption. In this paper, we propose a delay-aware data aggregation scheduling scheme to minimize latency in duty-cycled WSNs. In a data aggregation tree construction phase, our proposed scheme constructs Connected Dominating Set (CDS) tree on the shortest path in terms of delay according to an active time slot of each node. The CDS tree is used as virtual backbone for efficient data aggregation. In a scheduling phase, the proposed scheme ensures collision-free data aggregation to the sink with reduced latency by considering data receiving time in each node. The simulation results show that comparing to related work, our proposed scheme can reduce the average time for data aggregation to the sink by 54% and 50% with various node densities and duty cycle respectively.

To break through the limitation of battery-powered wireless sensor networks, a novel kind of network, named battery-free wireless sensor network (BF-WSN), is proposed. Battery-free sensor nodes in BF-WSNs harvest energy from power sources in their ambient environment, such as solar power, wind power and radio frequency (RF) signal power, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">etc.</i> , instead of batteries. Therefore, the energy consumption of battery-free sensor nodes are not limited by the battery capacity anymore. However, they still have limited energy harvesting rates and energy capacities. Data aggregation is a fundamental operation in sensor networks where the sensory data gathered by the relay nodes can be merged by in-network computation, such as taking the maximum, average, or sum, etc., of them. Due to the energy features of BF-WSNs, the data aggregation scheduling problem in BF-WSNs is more complicated and the previous aggregation scheduling algorithms designed for battery-powered WSNs are no longer applicable. This paper investigates the Minimum-Latency Aggregation Scheduling problem in BF-WSNs, which is proved to be NP-hard. Then, we propose the Data Aggregation Scheduling algorithm to solve the problem. Finally, the theoretical analysis and extensive simulation results are provided to verify the performance of the proposed algorithm.

In-network data aggregation is a fundamental traffic pattern in many applications of wireless sensor networks (WSNs).Data aggregation scheduling aims to find a collisionfree transmission schedule scheme for data aggregation while minimizing the total network latency.This paper focuses on the data aggregation scheduling problem in dutycycled WSNs (dc-WSNs), in which low-duty-cycle techniques are employed for energyconsuming operations.Based on greedy strategy, we propose two latency-efficient data aggregation scheduling algorithms, namely GAS-PAS and GAS-SAS for dc-WSNs.We theoretically derive the latency upper bounds of the proposed algorithms, and the results demonstrate that both GAS-PAS and GAS-SAS achieve constant approximation to the optimal latency.We also conduct extensive simulations to show that the proposed scheduling algorithms can improve data aggregation latency in dc-WSNs under various network settings, comparing with state-of-the-art algorithms in the literature.

Source location privacy (SLP) is an important property for the class of asset monitoring problems in wireless sensor networks (WSNs). SLP aims to prevent an attacker from finding a valuable asset when a WSN node is broadcasting information due to the detection of the asset. Most SLP techniques focus at the routing level, with typically high message overhead. The objective of this paper is to investigate the novel problem of developing a TDMA MAC schedule that can provide SLP. We make a number of important contributions: (i) we develop a novel formalisation of a class of eavesdropping attackers and provide novel formalisations of SLP-aware data aggregation schedules (DAS), (ii) we present a decision procedure to verify whether a DAS schedule is SLP-aware, that returns a counterexample if the schedule is not, similar to model checking, and (iii) we develop a 3-stage distributed algorithm that transforms an initial DAS algorithm into a corresponding SLP-aware schedule against a specific class of eavesdroppers. Our simulation results show that the resulting SLP-aware DAS protocol reduces the capture ratio by 50% at the expense of negligible message overhead.

Data aggregation scheduling for variable aggregation rate model has wide application and should take network lifetime and energy efficiency into consideration. In this paper, the time-slot scheduling problem for the variable aggregation rate model is presented, and a time-slot scheduling integrating consideration of minimizing the energy consumption named Makeup Integer based Data Aggregation Scheduling (MIDAS) is proposed. The proposed MIDAS scheme integrates two core phases, namely, data aggregation set construction and aggregation set based scheduling algorithm. The key idea of MIDAS is to minimize the number of receiving and sending data packets in hotspot and to reduce the number of aggregated packets in network for better scheduling performance in network lifetime. Furthermore, it is also essential to increase energy utilization efficiency of the nodes in the middle layer by exploiting the remaining energy of peripheral nodes. A series of experiments are simulated to demonstrate that the proposed scheme has significantly increased the network lifetime and the energy utilization efficiency under the different aggregation rates and different network scales. Comparing with the SDAS, the lifetime can be increased by as much as 25%. The energy utilization efficiency can be improved by as much as 30%.

Due date of data delivery is a key factor in timeliness-crucial wireless sensor networks (e.g. battlefield sensor networks, cyber-physical systems, and wireless multimedia sensor networks). Data aggregation is a well-known methodology to reduce transmission time. However, the decrease of transmission time does not definitely mean increasing the ratio of data delivery on time from the view of the whole network. In this paper, we studied how to minimize tardiness in data aggregation scheduling in consideration of due date for single-hop wireless sensor networks. Each data sensor has its own due date and data size. Tardiness penalty is induced if the finish time of data transmission is later than its due date for a data sensor. The scheduling problem is firstly formulated. A dynamic programming algorithm (DPS) is proposed for the problem which the data sizes of all data sensors are the same. A forward shift heuristic algorithm (FSH) and a variable neighborhood search heuristic algorithm (VSNH) are proposed to minimize the total transmission tardiness. Simulation experiments show that FSH outperforms the earliest deadline first and delay minimization aggregation algorithms. VSNH starting from the output of DPS is the best scheduling algorithm to solve the data aggregation scheduling problem with the objective of minimizing total transmission tardiness.

In-network data aggregation is one of the popular optimization methodologies in the realm of wireless sensor networks (WSNs). To enable effective implementation, a routing tree is formed and the node transmissions are carefully scheduled to meet flow constraints. Minimizing the data delivery latency has been the most common objective of the data aggregation scheduling optimization. Prior work on this optimization problem pursued heuristics to overcome the complexity of the problem and used an upper bound on latency as a metric to assess the quality of the solution. In this paper we argue that for small and medium sized networks it is computationally feasible to obtain the optimal solution. We formulate the data aggregation scheduling problem as a linear integer program. Two variants of the problem are considered. The first assumes that the routing tree is predefined, e.g., through a network layer protocol, and node transmissions are to be scheduled to minimize delay. For the second variant, the routing topology formation and node schedule are to be optimized in an integrated manner. The proposed solutions are compared to the existing heuristics via extensive simulation experiments.

Data aggregation is one of the most essential operations in wireless sensor networks (WSNs), in which data from all sensor nodes is collected at a sink node. A lot of studies have been conducted to assure collision-free data delivery to the sink node, with the goal of minimizing aggregation delay. The minimum delay data aggregation problem gets more complex when recent WSNs have adopted the duty cycle scheme to conserve energy and to extend the network lifetimes. The reason is that the duty cycle yields a notable increase of communication delay, beside a reduction of energy consumption, due to the periodic sleeping periods of sensor nodes. In this paper, we propose a novel data aggregation scheme that minimizes the data aggregation delay in duty-cycled WSNs. The proposed scheme takes the sleeping delay between sensor nodes into account to construct a connected dominating set (CDS) tree in the first phase. The CDS tree is used as a virtual backbone for efficient data aggregation scheduling in the second phase. The scheduling assigns the fastest available transmission time for every sensor node to deliver all data collision-free to the sink. The simulation results show that our proposed scheme reduces data aggregation delay by up to 72% compared to previous work. Thanks to data aggregation delay reduction, every sensor node has to work shorter and the network lifetime is prolonged.

Fault Tolerant Data Aggregation Scheduling with Local Information in Wireless Sensor Networks