In this paper, we consider precoder design for wireless data aggregation in sensor networks. The precoder optimization problem can be formulated as minimization of mean squared error under transmit power and block diagonal constraints. We include statistical correlation of data into the optimization problem, which is appeared in typical applications but is ignored in conventional designing methods. We propose precoder optimization algorithms based on projected gradient descent with projection onto the constraint sets. The proposed method can achieve better performance than the conventional methods that do not incorporate data correlation, especially when data are highly correlated. We also extend the proposed approach to the context of over-the-air computation.

The spread of the Internet of Things (IoT) has led to the generation of large amounts of data, requiring massive communication, computing, and storage resources. Cloud computing plays an important role in realizing most IoT applications classified as massive machine type communication and cyber-physical control applications in vertical domains. To handle the increasing amount of IoT data, it is important to reduce the traffic concentrated in the cloud by distributing the computing and storage resources to the network edge side and to suppress the latency of the IoT applications. In this paper, we first present a recent literature review on fog/edge computing and data aggregation as representative traffic reduction technologies for efficiently utilizing communication, computing, and storage resources in IoT systems, and then focus on data aggregation control minimizing the latency in an IoT gateway. We then present a unified modeling for statistical and nonstatistical data aggregation and analyze its latency. We analytically derive the Laplace-Stieltjes transform and average of the stationary distribution of the latency and approximate the average latency; we subsequently apply it to an adaptive aggregation number control for the time-variant data arrival. The transient traffic characteristics, that is, the absorption of traffic fluctuations realizing a stable optimal latency, were clarified through a simulation with a time-variant Poisson input and non-Poisson inputs, such as a Beta input, which is a typical IoT traffic model.

Data aggregation trees in wireless sensor networks (WSNs) are being used for gathering data for various purposes. Especially for the trees within buildings or civil structures, the total amount of energy consumption in a tree must be reduced to save energy. Therefore, the minimum energy-cost aggregation tree (MECAT) and MECAT with relay nodes (MECAT_RN) problems are being discussed to reduce energy consumption in data aggregation trees in WSNs. This paper proposes the tree node switching algorithm (TNSA) that improves on the previous algorithms for the MECAT and MECAT_RN problems in terms of energy efficiency. TNSA repeatedly switches nodes in a tree to reduce the number of packets sent in the tree. Packets are reduced by improving the accommodation efficiency of each packet, in which multiple sensor reports are accommodated. As a result of applying TNSA to MECATs and MECAT-RNs, energy consumption can be reduced significantly with a small burden.

In this paper, we consider the design problem of an unknown-input observer for distributed network systems under the existence of communication delays. In the proposed method, each node estimates all states and calculates inputs from its own estimate. It is assumed that the controller of each node is given by an observer-based controller. When calculating each node, the input values of the other nodes cannot be utilized. Therefore, each node calculates alternative inputs instead of the unknown inputs of the other nodes. The alternative inputs are generated by own estimate based on the feedback controller of the other nodes given by the assumption. Each node utilizes these values instead of the unknown inputs when calculating the estimation and delay compensation. The stability of the estimation error of the proposed observer is proven by a Lyapunov-Krasovskii functional. The stability condition is given by a linear matrix inequality (LMI). Finally, the result of a numerical simulation is shown to verify the effectiveness of the proposed method.

Sensor-data gathering using multi-hop connections in a wireless sensor network is being widely used, and a tree topology for data gathering is considered promising because it eases data aggregation. Therefore, many sensor-tree-creation algorithms have been proposed. The sensors in a tree, however, generally run on batteries, so long tree lifetime is one of the most important factors in collecting sensor data from a tree over a long period. It has been proven that creating the longest-lifetime tree is a non-deterministic-polynomial complete problem; thus, all previously proposed sensor-tree-creation algorithms are heuristic. To evaluate a heuristic algorithm, the time complexity of the algorithm is very important, as well as the quantitative evaluation of the lifetimes of the created trees and algorithm speed. This paper proposes an algorithm called assured switching with accurate graph optimization (ASAGAO) that can create a sensor tree with a much longer lifetime much faster than other sensor-tree-creation algorithms. In addition, it has much smaller time complexity.

This paper proposes a distributed delay-compensated observer for a wireless sensor network with delay. Each node of the sensor network aggregates data from the other nodes and sends the aggregated data to the neighbor nodes. In this communication, each node also compensates communication delays among the neighbor nodes. Therefore, all of the nodes can synchronize their sensor measurements using scalable and local communication in real-time. All of the nodes estimate the state variables of a system simultaneously. The observer in each node is similar to the delay-compensated observer with multi-sensor delays proposed by Watanabe et al. Convergence rates for the proposed observer can be arbitrarily designed regardless of the communication delays. The effectiveness of the proposed method is verified by a numerical simulation.

To reduce the server load and communication costs of machine-to-machine (M2M) systems, sensor data are aggregated in M2M gateways. Aggregation logic is typically programmed in the C language and embedded into the firmware. However, developing aggregation programs is difficult for M2M service providers because it requires gateway-specific knowledge and consideration of resource issues, especially RAM usage. In addition, modification of aggregation logic requires the application of firmware updates, which are risky. We propose a rule-based sensor data aggregation system, called the complex sensor data aggregator (CSDA), for M2M gateways. The functions comprising the data aggregation process are subdivided into the categories of filtering, statistical calculation, and concatenation. The proposed CSDA supports this aggregation process in three steps: the input, periodic data processing, and output steps. The behaviors of these steps are configured by an XML-based rule. The rule is stored in the data area of flash ROM and is updatable through the Internet without the need for a firmware update. In addition, in order to keep within the memory limit specified by the M2M gateway's manufacturer, the number of threads and the size of the working memory are static after startup, and the size of the working memory can be adjusted by configuring the sampling setting of a buffer for sensor data input. The proposed system is evaluated in an M2M gateway experimental environment. Results show that developing CSDA configurations is much easier than using C because the configuration decreases by 10%. In addition, the performance evaluation demonstrates the proposed system's ability to operate on M2M gateways.

Recently, many wireless sensor networks (WSNs) have employed mobile sensor nodes to collect a variety of data from mobile elements such as humans, animals and cars. In this letter, we propose an efficient mobile data aggregation scheme to improve the overall performance in gathering the data of the mobile nodes. We first propose a spatial mobile data aggregation scheme to aggregate the data of the mobile node spatially, which is then extended to a two-tier mobile data aggregation by supplementing a temporal mobile data aggregation scheme to aggregate the data of multiple mobile nodes temporally. Simulation results show that our scheme significantly reduces the energy consumption and gathering delay for data collection from mobile nodes in WSNs.

This paper proposes a reliable data aggregation scheduling that uses caching and re-transmission based on track topology. In the proposed scheme, a node detects packet losses by overhearing messages that includes error indications of the child nodes, from its neighbor nodes. If packet losses are detected, as a backup parent, the node retransmits the lost packet. A retransmission strategy is added into the adaptive timeout scheduling scheme, which adaptively configures both the timeout and the collection period according to the potential level of an event occurrence. The retransmission steps cause an additional delay and power consumption of the sensor nodes, but dramatically increase the data accuracy of the aggregation results. An extensive simulation under various workloads shows that the proposed scheme outperforms other schemes in terms of data accuracy and energy consumption.

Since wireless sensor networks (WSNs) are resources-constrained, it is very essential to gather data efficiently from the WSNs so that their life can be prolonged. Data aggregation can conserve a significant amount of energy by minimizing transmission cost in terms of the number of data packets. Many applications require privacy and integrity protection of the sampled data while they travel from the source sensor nodes to a data collecting device, say a query server. However, the existing schemes suffer from high communication cost, high computation cost and data propagation delay. To resolve the problems, in this paper, we propose a new and efficient integrity protecting sensitive data aggregation scheme for WSNs. Our scheme makes use of the additive property of complex numbers to achieve sensitive data aggregation with protecting data integrity. With simulation results, we show that our scheme is much more efficient in terms of both communication and computation overheads, integrity checking and data propagation delay than the existing schemes for protecting integrity and privacy preserving data aggregation in WSNs.

One of the most challenging issues in wireless sensor networks is extension of the overall network lifetime. Data aggregation is one promising solution because it reduces the amount of network traffic by eliminating redundant data. In order to aggregate data, each sensor node must temporarily store received data, which requires a specific amount of memory. Most sensor nodes use static random access memory (SRAM) or flash memory for storage. SRAM can be implemented in a one-chip sensor node at low cost; however, SRAM requires standby energy, which consumes a lot of power, especially because the sensor node spends most of its time sleeping, i.e. its radio circuits are quiescent. This study proposes two types of divided SRAM: equal-size divided SRAM and equal-ratio divided SRAM. Simulations show that both proposed SRAM types offer reduced power consumption in various situations.

This letter investigates the cluster lifetime of single-hop wireless sensor networks with cooperative Multi-Input Single-Output (MISO) scheme. The energy consumptions of both intra-cluster and out-cluster communications are considered. Moreover, uniform and linear data aggregations are discussed. It is found the optimal transmission scheme varies with the distance from the cluster to the base station. More interestingly and novelly, the effect of cluster size on the cluster lifetime has been clarified.

In-network data aggregation is one of the most important issues for achieving energy-efficiency in wireless sensor networks since sensor nodes in the surrounding region of an event may generate redundant sensed data. The redundant sensed data should be aggregated before being delivered to the sink to reduce energy consumption. Which node should be selected as a Data Aggregation Node (DAN) for achieving the best energy efficiency is a difficult issue. To address this issue, this letter proposes a scheme to select a DAN for achieving energy-efficiency in an event region. The proposed scheme uses an analytical model to select the sensor node that has the lowest total energy consumption for gathering data from sensor nodes and for forwarding aggregated data to a sink, as a DAN. Analysis and simulation results show that the proposed scheme is superior to other schemes.

A network of sensors can be used to obtain state based data from the area in which they are deployed. To reduce costs, the data sent via intermediate sensors to a sink are often aggregated. In this letter, we introduce Self-Construction of Aggregation Tree (SCAT) scheme which uses a novel data aggregation scheme utilizing the knowledge of the mobile node and the infrastructure (static node tree) in gathering the data from the mobile node. The static nodes can construct a near- optimal aggregation tree by themselves, using the knowledge of the mobile node, which is a process similar to forming the centralized aggregation tree.

The wireless sensor network is a resource-constrained self-organizing system that consists of a large number of tiny sensor nodes. Due to the low-cost and low-power nature of sensor nodes, sensor nodes are failure-prone when sensing and processing data. Most presented fault-tolerant research for wireless sensor networks focused on crash faults or power faults and less on Byzantine faults. Hence, in this paper, we propose a power-saving data aggregation algorithm for Byzantine faults to provide power savings and high success rates even in the environment with high fault rates. The algorithm utilizes the concept of Byzantine masking quorum systems to mask the erroneous values and to finally determine the correct value. Our simulation results demonstrate that when the fault rate of sensor nodes is up to 50%, our algorithm still has 48% success rate to obtain the correct value. Under the same condition, other fault-tolerant algorithms are almost failed.

In this paper, proactive data filtering (PDF) algorithm is proposed for data aggregation (or data fusion) in wireless sensor networks. The objective of the algorithm is to further reduce the energy consumption when sensor nodes perform data aggregation. In many applications, the sensor field will be overwhelmed by unnecessary and redundant sensory information when the sink node disseminates a query throughout the sensor field. In order to reduce the energy consumption, our scheme employs intelligent decision logic in the sensor node which delays or deactivates the transmission of its response. A performance evaluation shows that data aggregation with the PDF significantly improves energy-efficiency.

In this paper, the problem of Redundant Duplicated RREQ Network-wide Flooding (RDRNF), induced by multiple sensor nodes during route discovery in event-driven wireless sensor networks, is described. In order to reduce the number of signaling messages during the route discovery phase, a novel extension, named the Localized Route Discovery Extension (LRDE), to the on-demand ad hoc routing protocol, is proposed. The LRDE reduces energy consumption during route discovery. The heuristically and temporarily selected Path Set-up Coordinator (PSC) plays the role of a route request broker that alleviates redundant route request flooding. The LRDE also sets a route path be aggregation-compatible. The PSC can effectively perform data aggregation through the routing path constructed by the LRDE. The simulation results reveal that significant energy is conserved by reducing signaling overhead and performing data aggregation when LRDE is applied to on-demand routing protocols.

Incorporating sensor nodes with data aggregation capability to transmit less data flow in wireless sensor networks could reduce the total energy consumption. This calls for the efficient and effective data-centric routing algorithm to facilitate this advantage. In the first part of this paper, we model the data-centric routing problem by rigorous mixed integer and linear mathematical formulation, where the objective function is to minimize the total transmission cost subject to multicast tree constraints. With the advancement of sensor network technology, sensor nodes with configurable transmission radius capability could further reduce energy consumption. The second part of this paper considers the transmission radius assignment of each sensor node and the data-centric routing assignment jointly. The objective function is to minimize the total power consumption together with consideration of construction of a data aggregation tree and sensor node transmission radius assignment. The solution approach is based on Lagrangean relaxation in conjunction with the novel optimization-based heuristics. From the computational experiments, it is shown that the proposed algorithms calculate better solution than other existing heuristics with improvement ratio up to 169% and 59% with respect to fixed transmission radius and configurable transmission radius for network with 300 random generated nodes.