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In wireless sensor network (WSN), most of the devices function on batteries. These nodes or devices have inadequate amount of initial energy which are consumed at diverse rates, based on the power level and intended receiver. In sleep scheduling algorithms, most of the sensor nodes are turned to sleep state to preserve energy and improve the network lifetime (NL). In this paper, an energy-efficient dynamic cluster-based protocol is proposed for WSN especially for physics-based applications. Initially, the network is divided into small clusters using adaptive clustering. The clusters are managed by the cluster heads. The cluster heads are elected based on the novel dynamic threshold. Afterwards, general variable neighborhood search is used to obtain the energy-efficient paths for inter-cluster data aggregation which is used to communicate with the sink. The performance of the proposed method is compared with competitive energy-efficient routing protocols in terms of various factors such as stable period, NL, packets sent to base station and packets sent to cluster head. Extensive experiments prove that the proposed protocol provides higher NL than the existing protocols.

With advancement in multimedia applications, Wireless sensor networks (WSNs) are becoming popular due to their inherent characteristics and wide range of applications. However, WSNs contain very small sensor nodes, these nodes are battery constrained. Also, the batteries of these sensor nodes are not either replaceable or rechargeable. Therefore, many energy efficient protocols have been implemented so far to improve the network lifetime. However, the data aggregation at sink may suffer from data flooding issue, which reduces the network lifetime of WSNs. For handling this issue, in this paper, an effective data aggregation approach is designed. We have designed a priority-based data aggregation control protocol, which considers token bucket, Lempel–Ziv–Welch (LZW) compression and a hybrid of ant colony optimization and particle swarm optimization-based soft computing approach. Extensive experiments reveal that the presented protocol provides better network lifetime in contrast to the existing energy efficient protocols.

Statistics such as $k$th minimum value play a crucial role in our data-driven society, for example by informing decision-making. In this paper, we propose an efficient privacy-preserving protocol that allows a group of users who do not trust each other, for example in a peer-to-peer (P2P) network, to jointly calculate the $k$th minimum value. Specifically, in our proposed protocol each user’s data is converted to a binary bit string following a certain rule. Then, the bits at the same position are aggregated from the leftmost to the rightmost. As far as we know, this is the first published scheme to obtain $k$th minimum value in a P2P network without affecting users’ privacy. We also remark that the proposed protocol can be easily generalized to compute other statistics, such as maximum value, minimum value, and median value, while achieving high efficiency in a privacy-preserving P2P network. We then demonstrate that the proposed protocol achieves forward security and is resilient to a range of external and internal attacks.

Road side units (RSUs) can act as fog nodes to perform data aggregation at the edge of network, which can reduce communication overhead and improve the utilization of network resources. However, because the RSU is public infrastructure, this feature may bring data security and privacy risks in data aggregation. In this paper, we propose a secure multi-subinterval data aggregation scheme, named SMDA, with interval privacy preservation for vehicle sensing systems. Specifically, our scheme combines the $1\text{-}\text{R}$ encoding theory and proxy re-encryption to protect interval privacy, this can ensure that the interval information is only known by the data center, and the RSU can classify the encrypted data without knowing the plaintext of the data and interval information. Meanwhile, our scheme employs the Paillier homomorphic encryption to accomplish data aggregation at the RSU, and the Identity-based batch authentication technology to solve authentication and data integrity. Finally, the security analysis and performance evaluations illustrate the safety and efficiency of our scheme.

In the present era, Underwater Wireless Sensor Network (UWSN) is an emerging technology that involves a huge amount of sensor nodes to collect and monitor information from the underwater environment. However, the data transmission process is constrained due to the collision and energy consumption which can adversely affect the performance. Hence, there is an essential need to develop a suitable mechanism that addresses these challenges using a data aggregation-based routing mechanism in UWSN. In this paper, a Multi-Slot Scheduling with a Two-Layer Hexagonal based Integrated Aggregation model (MSS-TLHIA) is proposed that offers a prolonged lifetime with less energy consumption and collision avoidance. In this model, data aggregation is performed using the aggregator node selection process. Initially, the entire network is partitioned into several hexagonal grids using the golden ratio. This partitioning offers an improved coverage area for every node which are participating in the network. Once the network is partitioned into coverage areas called clusters, a Cluster Head (CH) is selected using the ranking-based fuzzy mechanism. Then, an aggregator node is selected in common for both the layers of the hexagonal grids. In order to prevent the energy drain of the aggregator node completely and to prolong their lifetime, the aggregator node is re-selected for every time slot. Furthermore, the occurrence of collision is avoided by the multi-slot scheduling process. Experimental results demonstrate that the performance of the proposed model is compared with other existing protocols and achieves better results in terms of network lifetime, energy consumption, collision rate, packet dropped rate, packet delivery, and data forwarding measures.

This paper presents a new method for sensory evaluation of industrial products with uncertain information. In this approach, sensory data provided by different evaluators are transformed into measures of consistency on fuzzy satisfaction degrees. Based on these measures of consistency, we obtain the aggregated information for all evaluators and all attributes and measure the dissimilarity between evaluators and between used evaluation attributes. The effectiveness of this method has been validated in the fabric hand evaluation for a number of samples of knitted cotton.

In WSN, extending the network life is still a major problem that needs to be tackled. Cross-layer protocols are used to get around these problems. In this paper, a new cross-layer design routing model is presented using a clustering-based technique. The proposed model is proceeding with the optimal cluster-based routing model via a new algorithm. Initially during the network generation, the node’s energy is predicted by the deep learning model termed as DNN model on the basis of the distance between node and sink as the input. Subsequently, during the clustering process, the cluster head is optimally selected via a new optimisation algorithm named Self-Improved Shuffle Shepherd Optimisation (SISSO) Algorithm. The cluster head selection is done by considering the constraints including Link quality, Distance, Overhead, Energy and Delay. Finally, a Modified Kernel Least Mean Square (MKLMS)-based data aggregation process is to eliminate the redundant data transmission. The performance of the SISSO method is proven superior over other conventional approaches with regard to the alive node and network lifetime. In the alive node analysis of supernode, the proposed SISSO model achieves the maximal number of alive supernodes at 2,000 rounds (i.e. 0.67) than other conventional methods.

In this paper, we consider a network of energy constrained sensors deployed over a region. Each sensor node in such a network is systematically gathering and transmitting sensed data to a base station (via clusterheads). This paper focuses on reducing the power consumption of wireless sensor networks. We first extend LEACH's stochastic clusterhead selection algorithm by an average energy-based (LEACH-AE) deterministic component to reduce energy consumption. And then an unequal clustering idea is introduced to further reduce energy consumption of clusterheads. Simulation results show that our modified scheme can extend the network life by up to 38% before the first node dies in the network. Through both theoretical analysis and numerical results, it is shown that the proposed algorithm achieves better performance than the existing clustering algorithms such as LEACH, DCHS, LEACH-C.

In the Wireless Sensor Network (WSN), the data prediction approach is needed to attain data effectively by diminishing node energy consumption. Hence, in this research, Water cycle Fire Fly Optimization and Deep Long Short-Term Memory (WCFO+ Deep LSTM) approach is employed for aggregation and reduction of data. The processes involved in the developed method are node simulation, cluster-based topology construction, routing tree construction, and data aggregation. Initially, IoT nodes are simulated in the network environment. The cluster-based topology construction is made using the WCFO algorithm. The WCFO is developed by the integration of the Firefly Optimization Algorithm (FOA) and Water cycle algorithm (WCA). The cluster-based topology is constructed by considering the objective function that includes the parameters, including distance, delay, link quality, and energy. After that, the routing process is performed using developed WCFO approach for constructing a routing tree and estimating the optimal path. Finally, the Deep LSTM is trained by the proposed WCFO algorithm, which is utilized for executing data reduction and data aggregation process with minimum energy consumption. The devised WCFO+ Deep LSTM approach achieved better performance in terms of prediction error, delay, energy, and Packet delivery ratio (PDR) with values 0.029, 0.001s, 0.161J and 99.054%, respectively.

The minimum latency data aggregation schedule is one of the fundamental problems in wireless sensor networks. Most existing works assumed that the transmission ranges of sensor nodes cannot be adjusted. However, sensors with adjustable transmission ranges have advantages in energy saving, reducing transmission interference and latency. In this paper, we study the minimum latency conflict-aware data aggregation scheduling problem with adjustable transmission radii: given locations of sensors along with a base station, all sensors could adjust their transmission radii and each sensor's interference radius is α times of its transmission radius, we try to find a data aggregation schedule in which the data from all sensors can be transmitted to the base station without conflicts, such that the latency is minimized. We first partition the set of all nodes into two parts: the major set and the minor set. Then, we design different scheduling strategies for the two sets, respectively. Finally, we propose an approximation algorithm for the problem and prove the performance ratio of the algorithm is bounded by a nearly constant. Our experimental results evaluate the efficiency of the proposed algorithm.

Smart grid represents the future of power grid system. Apart from its various advantages on real-time monitor and control, it also brings many new security challenges. For example, users’ consumption data needs to be collected without violating their privacy. Moreover, authenticity and integrity of the data need to be protected during transmission. To solve these problems, many data aggregation schemes for smart grid have been proposed in the past few years. However, most of these schemes have employed expensive cryptographic functions, such as homomorphic encryption, making them impractical for the real use. In this paper, we introduce a lightweight privacy preserving data aggregation scheme for smart grid. A simple and innovative method is designed for the data aggregation phase. The benefit is that our proposed scheme is not only more efficient than the existing schemes, but also it achieves unconditional security in this phase. In other words, even if the adversary has unlimited computational power, he/she is unable to find out the consumption data for any individual user.

Data aggregation is an important method to reduce the communication overhead in wireless sensor networks. Authors proposed a Improved Pattern-based Encrypted Data Aggregation Scheme for wireless sensor networks. Cluster head broadcasts the hash value of detected reading to its child and the inner-cluster nodes with different hash values transmit messages to cluster head. Messages were encrypted with symmetric key and the cluster heads completed the operations such as data integrity verification, identity authentication and data aggregation without decryption. Analysis and simulation show that the proposed scheme can resilient against various malicious attacks effectively, provides higher security and lower communication overhead than related schemes.