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Energy constraint is always a serious issue in wireless sensor networks, as the energy possessed by the sensors is limited and non‐renewable. Data aggregation at intermediate base stations increases the lifespan of the sensors, whereby the sensors' data are aggregated before being communicated to the central server. This paper proposes a query‐based aggregation within Monte Carlo simulator to explore the best and worst possible query orders to aggregate the sensors' data at the base stations. The proposed query‐based aggregation model can help the network administrator to envisage the best query orders in improving the performance of the base stations under uncertain query ordering. Furthermore, it aims to examine the feasibility of the proposed model to engage simultaneous transmissions at the base station and also to derive a best‐fit mathematical model to study the behavior of data aggregation with uncertain querying order.

The paper considers small and medium‐sized wireless sensor networks comprised of randomly deployed sensors in a square arena. It formulates the query‐based data aggregation problem as an uncertain ordering problem within Monte Carlo simulator, generating several thousands of uncertain orders to schedule the responses of M sensors at the base station within the specified time interval. For each selected time interval, the model finds the best possible querying order to aggregate the data with reduced idle time and with improved throughput. Furthermore, it extends the model to include multiple sensing parameters and multiple aggregating channels, thereby enabling the administrator to plan the capacity of its WSN according to specific time intervals known in advance.

The experimental results within Monte Carlo simulator demonstrate that the query‐based aggregation scheme show a better trade‐off in maximizing the aggregating efficiency and also reducing the average idle‐time experienced by the individual sensor. The query‐based aggregation model was tested for a WSN containing 25 sensors with single sensing parameter, transmitting data to a base station; moreover, the simulation results show continuous improvement in best‐case performances from 56 percent to 96 percent in the time interval of 80 to 200 time units. Moreover, the query aggregation is extended to analyze the behavior of WSN with 50 sensors, sensing two environmental parameters and base station equipped with multiple channels, whereby it demonstrates a shorter aggregation time interval against single channel. The analysis of average waiting time of individual sensors in the generated uncertain querying order shows that the best‐case scenario within a specified time interval showed a gain of 10 percent to 20 percent over the worst‐case scenario, which reduces the total transmission time by around 50 percent.

The proposed query‐based data aggregation model can be utilized to predict the non‐deterministic real‐time behavior of the wireless sensor network in response to the flooded queries by the base station.

This paper employs a novel framework to analyze all possible ordering of sensor responses to be aggregated at the base station within the stipulated aggregating time interval.

The purpose of this paper is to present a novel delay-bounded and power-efficient routing for in-network data aggregation, called DPIDA, which aims to ensure a compromise between the energy consumed during the collection of data sensed by a set of source sensor nodes and their timely delivery to the sink node.

Based on the ant-colony-optimization metaheuristic, the proposal establishes a routing structure that maximizes the number of overlapping routes and minimizes the total transmission power while ensuring delay-bounded paths and a symmetric transmission power assignment to reliably deliver the sensed data.

The proposal was extensively compared to two other known protocols regarding different keys factors. Simulation results, including topology snapshots, show the ability of DPIDA to ensure the energy–latency tradeoff. They also show the superiority of DPIDA compared to the two considered protocols.

This paper presents a novel ant-based protocol that uses in-network data aggregation and transmission power-adjustment techniques to conserve the energy of nodes while ensuring delay-bounded paths and a reliable deliverance of data which is ensured by providing a symmetric transmission power assignment.

Wireless sensor networks are expected to be an integral part of any pervasive computing environment. This implies an ever‐increasing need for efficient energy and resource management of both the sensor nodes, as well as the overall sensor network, in order to meet the expected quality of data and service requirements. There have been numerous studies that have looked at the routing of data in sensor networks with the sole intention of reducing communication power consumption. However, there has been comparatively little prior art in the area of multi‐criteria based routing that exploit both the semantics of queries and the state of sensor nodes to improve network service longevity. In this paper, we look at routing in sensor networks from this perspective and propose an adaptive multi‐criteria routing protocol. Our algorithm offers automated reconfiguration of the routing tree as demanded by variations in the network state to meet application service requirements. Our experimental results show that our approach consistently outperforms, in terms of Network Lifetime and Coverage, the leading semantic‐based routing algorithm which reconfigures the routing tree at fixed periods.

In the last decade, XML has become the de facto standard for data exchange in the world wide web (WWW). The positive benefits of data exchangeability to support system and software heterogeneity on application level and easy WWW integration make XML an ideal data format for many other application and network scenarios like wireless sensor networks (WSNs). Moreover, the usage of XML encourages using standardized techniques like SOAP to adapt the service‐oriented paradigm to sensor network engineering. Nevertheless, integrating XML usage in WSN data management is limited by the low hardware resources that require efficient XML data management strategies suitable to bridge the general resource gap. The purpose of this paper is to present two separate strategies on integrating XML data management in WSNs.

The paper presents two separate strategies on integrating XML data management in WSNs that have been implemented and are running on today's sensor node platforms. The paper shows how XML data can be processed and how XPath queries can be evaluated dynamically. In an extended evaluation, the performance of both strategies concerning the memory and energy efficiency are compared and both solutions are shown to have application domains fully applicable on today's sensor node products.

This work shows that dynamic XML data management and query evaluation is possible on sensor nodes with strict limitations in terms of memory, processing power and energy supply.

The paper presents an optimized stream‐based XML compression technique and shows how XML queries can be evaluated on compressed XML bit streams using generic pushdown automata. To the best of the authors' knowledge, this is the first complete approach on integrating dynamic XML data management into WSNs.

This study aims to provide a secured data aggregation with reduced energy consumption in WSN. Data aggregation is the process of reducing communication overhead in wireless sensor networks (WSNs). Presently, securing data aggregation is an important research issue in WSNs due to two facts: sensor nodes deployed in the sensitive and open environment are easily targeted by adversaries, and the leakage of aggregated data causes damage in the networks, and these data cannot be retrieved in a short span of time. Most of the traditional cryptographic algorithms provide security for data aggregation, but they do not reduce energy consumption.

Nowadays, the homomorphic cryptosystem is used widely to provide security with low energy consumption, as the aggregation is performed on the ciphertext without decryption at the cluster head. In the present paper, the Paillier additive homomorphic cryptosystem and Boneh et al.’s aggregate signature method are used to encrypt and to verify aggregate data at the base station.

The combination of the two algorithms reduces computation time and energy consumption when compared with the state-of-the-art techniques.

The secured data aggregation is useful in health-related applications, military applications, etc.

The new combination of encryption and signature methods provides confidentiality and integrity. In addition, it consumes less computation time and energy consumption than existing methods.

The purpose of this paper is to secure health data collection and transmission (SHDCT). In this system, a native network consists of portable smart devices that interact with multiple gateways. It entails IoMT devices and wearables connecting to exchange sensitive data with a sensor node which performs the aggeration process and then communicates the data using a Fog server. If the aggregator sensor loses the connection from the Fog server, it will be unable to submit data directly to the Fog server. The node transmits encrypted information with a neighboring sensor and sends it to the Fog server integrated with federated learning, which encrypts data to the existing data. The fog server performs the operations on the measured data, and the values are stored in the local storage area and later it is updated to the cloud server.

SHDCT uses an Internet-of-things (IoT)-based monitoring network, making it possible for smart devices to connect and interact with each other. The main purpose of the monitoring network has been in the collection of biological data and additional information from mobile devices to the patients. The monitoring network is composed of three different types of smart devices that is at the heart of the IoT.

It has been addressed in this work how to design an architecture for safe data aggregation in heterogeneous IoT-federated learning-enabled wireless sensor networks (WSNs), which makes use of basic encoding and data aggregation methods to achieve this. The authors suggest that the small gateway node (SGN) captures all of the sensed data from the SD and uses a simple, lightweight encoding scheme and cryptographic techniques to convey the data to the gateway node (GWN). The GWN gets all of the medical data from SGN and ensures that the data is accurate and up to date. If the data obtained is trustworthy, then the medical data should be aggregated and sent to the Fog server for further processing. The Java programming language simulates and analyzes the proposed SHDCT model for deployment and message initiation. When comparing the SHDCT scheme to the SPPDA and electrohydrodynamic atomisation (EHDA) schemes, the results show that the SHDCT method performs significantly better. When compared with the SPPDA and EHDA schemes, the suggested SHDCT plan necessitates a lower communication cost. In comparison to EHDA and SPPDA, SHDCT achieves 4.72% and 13.59% less, respectively. When compared to other transmission techniques, SHDCT has a higher transmission ratio. When compared with EHDA and SPPDA, SHDCT achieves 8.47% and 24.41% higher transmission ratios, respectively. When compared with other ways it uses less electricity. When compared with EHDA and SPPDA, SHDCT achieves 5.85% and 18.86% greater residual energy, respectively.

In the health care sector, a series of interconnected medical devices collect data using IoT networks in the health care domain. Preventive, predictive, personalized and participatory care is becoming increasingly popular in the health care sector. Safe data collection and transfer to a centralized server is a challenging scenario. This study presents a mechanism for SHDCT. The mechanism consists of Smart healthcare IoT devices working on federated learning that link up with one another to exchange health data. Health data is sensitive and needs to be exchanged securely and efficiently. In the mechanism, the sensing devices send data to a SGN. This SGN uses a lightweight encoding scheme and performs cryptography techniques to communicate the data with the GWN. The GWN gets all the health data from the SGN and makes it possible to confirm that the data is validated. If the received data is reliable, then aggregate the medical data and transmit it to the Fog server for further process. The performance parameters are compared with the other systems in terms of communication costs, transmission ratio and energy use.

Programming Sensor Networks currently is a subtle task not because of enormous amount of code but due to inherent limitations of embedded hardware like the power, memory, network bandwidth and clock speed. In addition, there are very few programming abstractions and standards available which lead to close coupling between the application code and the embedded OS requiring understanding of low‐level primitives during implementation. A Middleware can provide glue code between the applications and the heterogeneity of devices by providing optimized set of services for autonomously managing the resources and functionality of wireless nodes in a distributed wireless sensor network. This paper presents an autonomous middleware framework for low power distributed wireless sensor networks that support adaptive sensor functionality, context aware communications, clustering, quality of service and faulttolerance. Finally an application on how to use the autonomous middleware is illustrated on the Envelope System Research Apparatus (ESRA).

Wireless sensor networks (WSNs) will profoundly influence the ubiquitous computing landscape. Their utility derives not from the computational capabilities of any single sensor node, but from the emergent capabilities of many communicating sensor nodes. Consequently, the details of communication within and across single hop neighborhoods is a fundamental component of most WSN applications. But these details are often complex, and popular embedded languages for WSNs provide only low‐level communication primitives. We propose that the absence of suitable communication abstractions contributes to the difficulty of developing large‐scale WSN applications. To address this issue, we present the design and implementation of a Remote Procedure Call (RPC) abstraction for nesC and TinyOS, the emerging standard for developing WSN applications. We present the key language extensions, operating system services, and automation tools that enable the proposed abstraction. We illustrate these contributions in the context of a representative case study, and analyze the overhead introduced when using our approach. We use these results to draw conclusions regarding the suitably of our work to resource‐constrained sensor nodes.

A primary task of wireless sensor networks is to measure environmental conditions. In most applications, a sink node is responsible for collecting data from the sensors through multihop communications. The communication pattern is called convergecast. However, radio congestion around the sink can easily become a bottleneck for the convergecast. The purpose of this paper is to consider both scheduling algorithms and routing structures to improve the throughput of convergecast.

The paper addresses the issue from two perspectives. First by considering the transition scheduling that reduces radio interference to perform convergecast efficiently. Second, by studying the effects of routing structures on convergecast. A routing algorithm, called disjoint‐strip routing, is proposed as an alternative to existing shortest‐path routing.

The paper shows that constructing a shortest‐length conflict‐free schedule is equivalent to finding a minimal vertex coloring. To solve the scheduling problem, a virtual‐node expansion is proposed to handle relay operations and then coloring algorithms are utilized. Regarding the routing structures, a disjoint‐strip algorithm is proposed to leverage possible parallel transmissions. Proposed algorithms are evaluated through simulations.

This paper separates the problem for optimizing data‐collection throughput into two stages: constructing a routing structure on a given deployment; and scheduling the activation time of each link. Determining routing topologies and communication schedules for optimal throughput are shown to be hard, so heuristics are applied in both stages. VNE is proposed, which makes traffic information visible to coloring algorithms. The advantage of VNE is verified through simulations. VNE can be applied to any coloring algorithm and any deterministic traffic pattern. It is shown that routing structures set a limit on the performance of scheduling algorithms. There are two possible ways in routing algorithms to improve convergecast throughput: first, by reducing the total number of transmissions during data collection; second, by transferring data in parallel. The shortest‐path routing addresses the first point while DS addresses the second one. As expected, when the deployments are even and balanced, minimizing the number of transmissions is more effective than parallelizing them. On the other hand, when the deployments are unbalanced and conflicts are not strict, parallel transmissions can improve the throughput.

The purpose of this paper is to examine the issue of “formalization” in business networks as an instrument of industrial policy. Formalization in business networks is not a debated topic but it can affect organizational and inter-organizational dynamics considerably. The aim of the paper is to understand if the introduction of a normative tool that enhances formalized networks can be effective to promote network aggregations among SMEs. Second, the aim is to understand if this formalization supports good-working networks, i.e., capable to introduce new products or to enter new markets/customers.

The paper refers to a review of the literature and mainly to an empirical research on formalized network contracts (NCs) that have been conducted in the latest two years. This research has used both secondary data, collected accessing to databases and reports given by institutions and the government, and primary data, collected in specific direct interviews. These interviews have been concerned both institutions such as Chamber of Commerce, Confindustria and the Ministry of Economic Development and the Small Business Association, and 15 cases of NCs in Italy.

The formalization has consequences both internally and externally to the NC. Internally it can act as an element to reduce ambiguity and building elements of “fragile trust,” in the absence of basic elements of “resilient trust” and in the presence of changes in the competitive environment. Externally, the formalization through the NC allows the policy maker to identify more clearly companies’ aggregations in order to let them being destination of specific industrial policies. However even if the formalization has in some cases enhanced new networks’ creation, in many cases formalization has generated positive results when companies had already experienced networking outside the frame of the NC.

Further research on formalization in networks should be developed following the “history” of formalized networks over time in order to understand how much formalization should be used as a long-term tool for industrial policy.

The paper can be useful both for companies that want to sign a NC and for the institutions developing industrial policies devoted to support companies’ aggregations in the form of NC.

The paper presents a new legal tool – the “NC” – introduced in 2009 by the Italian Government to enhance firms’ aggregation; second, the paper debates the topic of formalization in networks that is not much debated in literature; and finally, the paper also adopts an industrial economic approach and is among the few attempts to integrate industrial policies and industrial marketing and purchasing thinking.